From 667889785b58727bae073cdd68c3c5909d58e800 Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Fri, 13 Nov 2020 11:15:13 -0500
Subject: [PATCH] Updating pages

---
 _build/.doctrees/01_query.doctree             |   Bin 0 -> 152515 bytes
 _build/.doctrees/02_coords.doctree            |   Bin 0 -> 81016 bytes
 _build/.doctrees/03_motion.doctree            |   Bin 0 -> 567520 bytes
 _build/.doctrees/04_select.doctree            |   Bin 0 -> 522886 bytes
 _build/.doctrees/05_join.doctree              |   Bin 0 -> 154364 bytes
 _build/.doctrees/06_photo.doctree             |   Bin 0 -> 217232 bytes
 _build/.doctrees/07_plot.doctree              |   Bin 0 -> 845826 bytes
 .../AstronomicalData/01_query.doctree         |   Bin 0 -> 145284 bytes
 .../AstronomicalData/02_coords.doctree        |   Bin 0 -> 80803 bytes
 .../.doctrees/AstronomicalData/README.doctree |   Bin 0 -> 38005 bytes
 .../lato_latin-ext/1.44.1/LICENSE.doctree     |   Bin 0 -> 6506 bytes
 .../open-sans_all/1.44.1/LICENSE.doctree      |   Bin 0 -> 6505 bytes
 .../_build/jupyter_execute/01_query.doctree   |   Bin 0 -> 142856 bytes
 .../_build/jupyter_execute/02_coords.doctree  |   Bin 0 -> 80314 bytes
 _build/.doctrees/README.doctree               |   Bin 0 -> 38448 bytes
 _build/.doctrees/environment.pickle           |   Bin 0 -> 67164 bytes
 _build/.doctrees/glue_cache.json              |     1 +
 _build/.doctrees/index.doctree                |   Bin 0 -> 36882 bytes
 _build/.doctrees/last_resort.doctree          |   Bin 0 -> 4244 bytes
 _build/.doctrees/test_setup.doctree           |   Bin 0 -> 7132 bytes
 _build/html/.buildinfo                        |     4 +
 _build/html/01_query.html                     |  1247 ++
 _build/html/02_coords.html                    |  1874 +++
 _build/html/03_motion.html                    |  1364 ++
 _build/html/04_select.html                    |  1260 ++
 _build/html/05_join.html                      |  1070 ++
 _build/html/06_photo.html                     |  1184 ++
 _build/html/07_plot.html                      |  1070 ++
 _build/html/AstronomicalData/01_query.html    |  1403 ++
 _build/html/AstronomicalData/02_coords.html   |  1824 +++
 _build/html/AstronomicalData/README.html      |   337 +
 .../vendor/lato_latin-ext/1.44.1/LICENSE.html |   260 +
 .../vendor/open-sans_all/1.44.1/LICENSE.html  |   260 +
 .../_build/jupyter_execute/01_query.html      |  1384 ++
 .../_build/jupyter_execute/02_coords.html     |  1821 +++
 _build/html/README.html                       |   379 +
 _build/html/_images/03_motion_28_0.png        |   Bin 0 -> 9782 bytes
 _build/html/_images/03_motion_45_0.png        |   Bin 0 -> 115356 bytes
 _build/html/_images/03_motion_79_0.png        |   Bin 0 -> 15658 bytes
 _build/html/_images/03_motion_81_0.png        |   Bin 0 -> 81181 bytes
 _build/html/_images/03_motion_88_0.png        |   Bin 0 -> 81640 bytes
 _build/html/_images/03_motion_98_0.png        |   Bin 0 -> 15355 bytes
 _build/html/_images/04_select_11_0.png        |   Bin 0 -> 83720 bytes
 _build/html/_images/04_select_13_0.png        |   Bin 0 -> 82339 bytes
 _build/html/_images/04_select_25_0.png        |   Bin 0 -> 80696 bytes
 _build/html/_images/04_select_51_0.png        |   Bin 0 -> 36708 bytes
 _build/html/_images/04_select_57_0.png        |   Bin 0 -> 40126 bytes
 _build/html/_images/05_join_9_0.png           |   Bin 0 -> 32869 bytes
 _build/html/_images/06_photo_12_0.png         |   Bin 0 -> 20526 bytes
 _build/html/_images/06_photo_23_0.png         |   Bin 0 -> 24092 bytes
 _build/html/_images/06_photo_61_0.png         |   Bin 0 -> 32706 bytes
 _build/html/_images/06_photo_63_0.png         |   Bin 0 -> 10093 bytes
 _build/html/_images/07_plot_13_0.png          |   Bin 0 -> 10598 bytes
 _build/html/_images/07_plot_50_0.png          |   Bin 0 -> 153493 bytes
 _build/html/_images/07_plot_57_0.png          |   Bin 0 -> 63671 bytes
 _build/html/_images/07_plot_63_0.png          |   Bin 0 -> 35250 bytes
 _build/html/_images/07_plot_69_0.png          |   Bin 0 -> 143652 bytes
 _build/html/_images/07_plot_72_0.png          |   Bin 0 -> 151315 bytes
 ...-main.c949a650a448cc0ae9fd3441c0e17fb0.css |     1 +
 ...ables.06eb56fa6e07937060861dad626602ad.css |     7 +
 _build/html/_sources/01_query.ipynb           |  1675 +++
 _build/html/_sources/01_query.md              |  1073 ++
 _build/html/_sources/02_coords.ipynb          |  1972 +++
 _build/html/_sources/03_motion.ipynb          |  1896 +++
 _build/html/_sources/04_select.ipynb          |  1445 ++
 _build/html/_sources/05_join.ipynb            |  1301 ++
 _build/html/_sources/06_photo.ipynb           |  1372 ++
 _build/html/_sources/07_plot.ipynb            |  1174 ++
 .../_sources/AstronomicalData/01_query.ipynb  |  1642 +++
 .../_sources/AstronomicalData/02_coords.ipynb |  1970 +++
 .../html/_sources/AstronomicalData/README.md  |   172 +
 .../vendor/lato_latin-ext/1.44.1/LICENSE.md   |    20 +
 .../vendor/open-sans_all/1.44.1/LICENSE.md    |    20 +
 .../_build/jupyter_execute/01_query.ipynb     |  1640 +++
 .../_build/jupyter_execute/02_coords.ipynb    |  1966 +++
 _build/html/_sources/README.md                |   172 +
 _build/html/_sources/index.md                 |   169 +
 _build/html/_sources/last_resort.ipynb        |    72 +
 _build/html/_sources/test_setup.ipynb         |   136 +
 _build/html/_static/__init__.py               |     0
 _build/html/_static/basic.css                 |   855 ++
 _build/html/_static/clipboard.min.js          |     7 +
 _build/html/_static/copy-button.svg           |     1 +
 _build/html/_static/copybutton.css            |    67 +
 _build/html/_static/copybutton.js             |   153 +
 _build/html/_static/copybutton_funcs.js       |    47 +
 ...index.d431a4ee1c1efae0e38bdfebc22debff.css |     6 +
 _build/html/_static/doctools.js               |   315 +
 _build/html/_static/documentation_options.js  |    12 +
 _build/html/_static/file.png                  |   Bin 0 -> 286 bytes
 _build/html/_static/images/logo_binder.svg    |    19 +
 _build/html/_static/images/logo_colab.png     |   Bin 0 -> 7601 bytes
 .../html/_static/images/logo_jupyterhub.svg   |     1 +
 _build/html/_static/jquery-3.5.1.js           | 10872 ++++++++++++++++
 _build/html/_static/jquery.js                 |     2 +
 .../_static/js/index.30270b6e4c972e43c488.js  |    32 +
 _build/html/_static/language_data.js          |   297 +
 _build/html/_static/minus.png                 |   Bin 0 -> 90 bytes
 _build/html/_static/mystnb.css                |   184 +
 ...-main.c949a650a448cc0ae9fd3441c0e17fb0.css |     1 +
 ...ables.06eb56fa6e07937060861dad626602ad.css |     7 +
 _build/html/_static/plus.png                  |   Bin 0 -> 90 bytes
 _build/html/_static/pygments.css              |    74 +
 _build/html/_static/searchtools.js            |   514 +
 ...-theme.be0a4a0c39cd630af62a2fcf693f3f06.js |    17 +
 ...theme.bfb7730f9caf2ec0b46a44615585038c.css |     5 +
 _build/html/_static/sphinx-book-theme.css     |     1 +
 _build/html/_static/sphinx-thebe.css          |   120 +
 _build/html/_static/sphinx-thebe.js           |    96 +
 _build/html/_static/togglebutton.css          |    90 +
 _build/html/_static/togglebutton.js           |    76 +
 _build/html/_static/underscore-1.3.1.js       |   999 ++
 _build/html/_static/underscore.js             |    31 +
 .../vendor/fontawesome/5.13.0/LICENSE.txt     |    34 +
 .../vendor/fontawesome/5.13.0/css/all.min.css |     5 +
 .../5.13.0/webfonts/fa-brands-400.eot         |   Bin 0 -> 133034 bytes
 .../5.13.0/webfonts/fa-brands-400.svg         |  3570 +++++
 .../5.13.0/webfonts/fa-brands-400.ttf         |   Bin 0 -> 132728 bytes
 .../5.13.0/webfonts/fa-brands-400.woff        |   Bin 0 -> 89824 bytes
 .../5.13.0/webfonts/fa-brands-400.woff2       |   Bin 0 -> 76612 bytes
 .../5.13.0/webfonts/fa-regular-400.eot        |   Bin 0 -> 34390 bytes
 .../5.13.0/webfonts/fa-regular-400.svg        |   803 ++
 .../5.13.0/webfonts/fa-regular-400.ttf        |   Bin 0 -> 34092 bytes
 .../5.13.0/webfonts/fa-regular-400.woff       |   Bin 0 -> 16800 bytes
 .../5.13.0/webfonts/fa-regular-400.woff2      |   Bin 0 -> 13584 bytes
 .../5.13.0/webfonts/fa-solid-900.eot          |   Bin 0 -> 202902 bytes
 .../5.13.0/webfonts/fa-solid-900.svg          |  4938 +++++++
 .../5.13.0/webfonts/fa-solid-900.ttf          |   Bin 0 -> 202616 bytes
 .../5.13.0/webfonts/fa-solid-900.woff         |   Bin 0 -> 103300 bytes
 .../5.13.0/webfonts/fa-solid-900.woff2        |   Bin 0 -> 79444 bytes
 .../vendor/lato_latin-ext/1.44.1/LICENSE.md   |    20 +
 .../files/lato-latin-ext-100-italic.woff      |   Bin 0 -> 23416 bytes
 .../files/lato-latin-ext-100-italic.woff2     |   Bin 0 -> 18228 bytes
 .../1.44.1/files/lato-latin-ext-100.woff      |   Bin 0 -> 29264 bytes
 .../1.44.1/files/lato-latin-ext-100.woff2     |   Bin 0 -> 23300 bytes
 .../files/lato-latin-ext-300-italic.woff      |   Bin 0 -> 24056 bytes
 .../files/lato-latin-ext-300-italic.woff2     |   Bin 0 -> 18868 bytes
 .../1.44.1/files/lato-latin-ext-300.woff      |   Bin 0 -> 32196 bytes
 .../1.44.1/files/lato-latin-ext-300.woff2     |   Bin 0 -> 24836 bytes
 .../files/lato-latin-ext-400-italic.woff      |   Bin 0 -> 32220 bytes
 .../files/lato-latin-ext-400-italic.woff2     |   Bin 0 -> 26312 bytes
 .../1.44.1/files/lato-latin-ext-400.woff      |   Bin 0 -> 30924 bytes
 .../1.44.1/files/lato-latin-ext-400.woff2     |   Bin 0 -> 25320 bytes
 .../files/lato-latin-ext-700-italic.woff      |   Bin 0 -> 32564 bytes
 .../files/lato-latin-ext-700-italic.woff2     |   Bin 0 -> 26344 bytes
 .../1.44.1/files/lato-latin-ext-700.woff      |   Bin 0 -> 30356 bytes
 .../1.44.1/files/lato-latin-ext-700.woff2     |   Bin 0 -> 24712 bytes
 .../files/lato-latin-ext-900-italic.woff      |   Bin 0 -> 31260 bytes
 .../files/lato-latin-ext-900-italic.woff2     |   Bin 0 -> 25636 bytes
 .../1.44.1/files/lato-latin-ext-900.woff      |   Bin 0 -> 29700 bytes
 .../1.44.1/files/lato-latin-ext-900.woff2     |   Bin 0 -> 24344 bytes
 .../vendor/lato_latin-ext/1.44.1/index.css    |   120 +
 .../vendor/open-sans_all/1.44.1/LICENSE.md    |    20 +
 .../files/open-sans-all-400-italic.woff       |   Bin 0 -> 53024 bytes
 .../files/open-sans-all-400-italic.woff2      |   Bin 0 -> 41076 bytes
 .../1.44.1/files/open-sans-all-400.woff       |   Bin 0 -> 55268 bytes
 .../1.44.1/files/open-sans-all-400.woff2      |   Bin 0 -> 43236 bytes
 .../vendor/open-sans_all/1.44.1/index.css     |   120 +
 _build/html/_static/webpack-macros.html       |    28 +
 _build/html/genindex.html                     |   247 +
 _build/html/index.html                        |   445 +
 _build/html/last_resort.html                  |   313 +
 _build/html/objects.inv                       |     7 +
 _build/html/run_on_colab_small.png            |   Bin 0 -> 2937 bytes
 _build/html/search.html                       |   266 +
 _build/html/searchindex.js                    |     1 +
 _build/html/test_setup.html                   |   369 +
 _build/jupyter_execute/01_query.ipynb         |  1675 +++
 _build/jupyter_execute/01_query.py            |   601 +
 _build/jupyter_execute/02_coords.ipynb        |  1972 +++
 _build/jupyter_execute/02_coords.py           |   413 +
 _build/jupyter_execute/03_motion.ipynb        |  1914 +++
 _build/jupyter_execute/03_motion.py           |   776 ++
 _build/jupyter_execute/03_motion_28_0.png     |   Bin 0 -> 9782 bytes
 _build/jupyter_execute/03_motion_45_0.png     |   Bin 0 -> 115356 bytes
 _build/jupyter_execute/03_motion_79_0.png     |   Bin 0 -> 15658 bytes
 _build/jupyter_execute/03_motion_81_0.png     |   Bin 0 -> 81181 bytes
 _build/jupyter_execute/03_motion_88_0.png     |   Bin 0 -> 81640 bytes
 _build/jupyter_execute/03_motion_98_0.png     |   Bin 0 -> 15355 bytes
 _build/jupyter_execute/04_select.ipynb        |  1460 +++
 _build/jupyter_execute/04_select.py           |   611 +
 _build/jupyter_execute/04_select_11_0.png     |   Bin 0 -> 83720 bytes
 _build/jupyter_execute/04_select_13_0.png     |   Bin 0 -> 82339 bytes
 _build/jupyter_execute/04_select_25_0.png     |   Bin 0 -> 80696 bytes
 _build/jupyter_execute/04_select_51_0.png     |   Bin 0 -> 36708 bytes
 _build/jupyter_execute/04_select_57_0.png     |   Bin 0 -> 40126 bytes
 _build/jupyter_execute/05_join.ipynb          |  1304 ++
 _build/jupyter_execute/05_join.py             |   564 +
 _build/jupyter_execute/05_join_9_0.png        |   Bin 0 -> 32869 bytes
 _build/jupyter_execute/06_photo.ipynb         |  1384 ++
 _build/jupyter_execute/06_photo.py            |   569 +
 _build/jupyter_execute/06_photo_12_0.png      |   Bin 0 -> 20526 bytes
 _build/jupyter_execute/06_photo_23_0.png      |   Bin 0 -> 24092 bytes
 _build/jupyter_execute/06_photo_61_0.png      |   Bin 0 -> 32706 bytes
 _build/jupyter_execute/06_photo_63_0.png      |   Bin 0 -> 10093 bytes
 _build/jupyter_execute/07_plot.ipynb          |  1197 ++
 _build/jupyter_execute/07_plot.py             |   691 +
 _build/jupyter_execute/07_plot_13_0.png       |   Bin 0 -> 10598 bytes
 _build/jupyter_execute/07_plot_50_0.png       |   Bin 0 -> 153493 bytes
 _build/jupyter_execute/07_plot_57_0.png       |   Bin 0 -> 63671 bytes
 _build/jupyter_execute/07_plot_63_0.png       |   Bin 0 -> 35250 bytes
 _build/jupyter_execute/07_plot_69_0.png       |   Bin 0 -> 143652 bytes
 _build/jupyter_execute/07_plot_72_0.png       |   Bin 0 -> 151315 bytes
 .../AstronomicalData/01_query.ipynb           |  1642 +++
 .../AstronomicalData/01_query.py              |   605 +
 .../AstronomicalData/02_coords.ipynb          |  1970 +++
 .../AstronomicalData/02_coords.py             |   411 +
 .../_build/jupyter_execute/01_query.ipynb     |  1640 +++
 .../_build/jupyter_execute/01_query.py        |   603 +
 .../_build/jupyter_execute/02_coords.ipynb    |  1966 +++
 .../_build/jupyter_execute/02_coords.py       |   407 +
 _build/jupyter_execute/last_resort.ipynb      |    72 +
 _build/jupyter_execute/last_resort.py         |    22 +
 _build/jupyter_execute/test_setup.ipynb       |   136 +
 _build/jupyter_execute/test_setup.py          |    67 +
 _build/latex/03_motion_28_0.png               |   Bin 0 -> 9782 bytes
 _build/latex/03_motion_45_0.png               |   Bin 0 -> 115356 bytes
 _build/latex/03_motion_79_0.png               |   Bin 0 -> 15658 bytes
 _build/latex/03_motion_81_0.png               |   Bin 0 -> 81181 bytes
 _build/latex/03_motion_88_0.png               |   Bin 0 -> 81640 bytes
 _build/latex/03_motion_98_0.png               |   Bin 0 -> 15355 bytes
 _build/latex/04_select_11_0.png               |   Bin 0 -> 83720 bytes
 _build/latex/04_select_13_0.png               |   Bin 0 -> 82339 bytes
 _build/latex/04_select_25_0.png               |   Bin 0 -> 80696 bytes
 _build/latex/04_select_51_0.png               |   Bin 0 -> 36708 bytes
 _build/latex/04_select_57_0.png               |   Bin 0 -> 40126 bytes
 _build/latex/05_join_9_0.png                  |   Bin 0 -> 32869 bytes
 _build/latex/06_photo_12_0.png                |   Bin 0 -> 20526 bytes
 _build/latex/06_photo_23_0.png                |   Bin 0 -> 24092 bytes
 _build/latex/06_photo_61_0.png                |   Bin 0 -> 32706 bytes
 _build/latex/06_photo_63_0.png                |   Bin 0 -> 10093 bytes
 _build/latex/07_plot_13_0.png                 |   Bin 0 -> 10598 bytes
 _build/latex/07_plot_50_0.png                 |   Bin 0 -> 153493 bytes
 _build/latex/07_plot_57_0.png                 |   Bin 0 -> 63671 bytes
 _build/latex/07_plot_63_0.png                 |   Bin 0 -> 35250 bytes
 _build/latex/07_plot_69_0.png                 |   Bin 0 -> 143652 bytes
 _build/latex/07_plot_72_0.png                 |   Bin 0 -> 151315 bytes
 _build/latex/LICRcyr2utf8.xdy                 |   101 +
 _build/latex/LICRlatin2utf8.xdy               |   239 +
 _build/latex/LatinRules.xdy                   |   607 +
 _build/latex/Makefile                         |    64 +
 ...-main.c949a650a448cc0ae9fd3441c0e17fb0.css |     1 +
 ...ables.06eb56fa6e07937060861dad626602ad.css |     7 +
 _build/latex/book.aux                         |   240 +
 _build/latex/book.fdb_latexmk                 |   203 +
 _build/latex/book.fls                         |   428 +
 _build/latex/book.idx                         |     0
 _build/latex/book.ilg                         |     5 +
 _build/latex/book.ind                         |     0
 _build/latex/book.out                         |    98 +
 _build/latex/book.pdf                         |   Bin 0 -> 1383403 bytes
 _build/latex/book.tex                         |  6779 ++++++++++
 _build/latex/book.toc                         |    99 +
 _build/latex/footnotehyper-sphinx.sty         |   269 +
 _build/latex/index.html                       |     2 +
 _build/latex/latexmkjarc                      |    22 +
 _build/latex/latexmkrc                        |     9 +
 _build/latex/make.bat                         |    31 +
 _build/latex/python.ist                       |    16 +
 _build/latex/sphinx.sty                       |  1957 +++
 _build/latex/sphinx.xdy                       |   230 +
 _build/latex/sphinxcyrillic.sty               |    55 +
 _build/latex/sphinxhighlight.sty              |   105 +
 _build/latex/sphinxhowto.cls                  |   102 +
 _build/latex/sphinxmanual.cls                 |   128 +
 _build/latex/sphinxmessages.sty               |    21 +
 _build/latex/sphinxmulticell.sty              |   317 +
 _build/pdf/book.pdf                           |   Bin 0 -> 69517 bytes
 268 files changed, 99651 insertions(+)
 create mode 100644 _build/.doctrees/01_query.doctree
 create mode 100644 _build/.doctrees/02_coords.doctree
 create mode 100644 _build/.doctrees/03_motion.doctree
 create mode 100644 _build/.doctrees/04_select.doctree
 create mode 100644 _build/.doctrees/05_join.doctree
 create mode 100644 _build/.doctrees/06_photo.doctree
 create mode 100644 _build/.doctrees/07_plot.doctree
 create mode 100644 _build/.doctrees/AstronomicalData/01_query.doctree
 create mode 100644 _build/.doctrees/AstronomicalData/02_coords.doctree
 create mode 100644 _build/.doctrees/AstronomicalData/README.doctree
 create mode 100644 _build/.doctrees/AstronomicalData/_build/html/_static/vendor/lato_latin-ext/1.44.1/LICENSE.doctree
 create mode 100644 _build/.doctrees/AstronomicalData/_build/html/_static/vendor/open-sans_all/1.44.1/LICENSE.doctree
 create mode 100644 _build/.doctrees/AstronomicalData/_build/jupyter_execute/01_query.doctree
 create mode 100644 _build/.doctrees/AstronomicalData/_build/jupyter_execute/02_coords.doctree
 create mode 100644 _build/.doctrees/README.doctree
 create mode 100644 _build/.doctrees/environment.pickle
 create mode 100644 _build/.doctrees/glue_cache.json
 create mode 100644 _build/.doctrees/index.doctree
 create mode 100644 _build/.doctrees/last_resort.doctree
 create mode 100644 _build/.doctrees/test_setup.doctree
 create mode 100644 _build/html/.buildinfo
 create mode 100644 _build/html/01_query.html
 create mode 100644 _build/html/02_coords.html
 create mode 100644 _build/html/03_motion.html
 create mode 100644 _build/html/04_select.html
 create mode 100644 _build/html/05_join.html
 create mode 100644 _build/html/06_photo.html
 create mode 100644 _build/html/07_plot.html
 create mode 100644 _build/html/AstronomicalData/01_query.html
 create mode 100644 _build/html/AstronomicalData/02_coords.html
 create mode 100644 _build/html/AstronomicalData/README.html
 create mode 100644 _build/html/AstronomicalData/_build/html/_static/vendor/lato_latin-ext/1.44.1/LICENSE.html
 create mode 100644 _build/html/AstronomicalData/_build/html/_static/vendor/open-sans_all/1.44.1/LICENSE.html
 create mode 100644 _build/html/AstronomicalData/_build/jupyter_execute/01_query.html
 create mode 100644 _build/html/AstronomicalData/_build/jupyter_execute/02_coords.html
 create mode 100644 _build/html/README.html
 create mode 100644 _build/html/_images/03_motion_28_0.png
 create mode 100644 _build/html/_images/03_motion_45_0.png
 create mode 100644 _build/html/_images/03_motion_79_0.png
 create mode 100644 _build/html/_images/03_motion_81_0.png
 create mode 100644 _build/html/_images/03_motion_88_0.png
 create mode 100644 _build/html/_images/03_motion_98_0.png
 create mode 100644 _build/html/_images/04_select_11_0.png
 create mode 100644 _build/html/_images/04_select_13_0.png
 create mode 100644 _build/html/_images/04_select_25_0.png
 create mode 100644 _build/html/_images/04_select_51_0.png
 create mode 100644 _build/html/_images/04_select_57_0.png
 create mode 100644 _build/html/_images/05_join_9_0.png
 create mode 100644 _build/html/_images/06_photo_12_0.png
 create mode 100644 _build/html/_images/06_photo_23_0.png
 create mode 100644 _build/html/_images/06_photo_61_0.png
 create mode 100644 _build/html/_images/06_photo_63_0.png
 create mode 100644 _build/html/_images/07_plot_13_0.png
 create mode 100644 _build/html/_images/07_plot_50_0.png
 create mode 100644 _build/html/_images/07_plot_57_0.png
 create mode 100644 _build/html/_images/07_plot_63_0.png
 create mode 100644 _build/html/_images/07_plot_69_0.png
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diff --git a/_build/.doctrees/02_coords.doctree b/_build/.doctrees/02_coords.doctree
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new file mode 100644
index 0000000..9e26dfe
--- /dev/null
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diff --git a/_build/.doctrees/index.doctree b/_build/.doctrees/index.doctree
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diff --git a/_build/.doctrees/last_resort.doctree b/_build/.doctrees/last_resort.doctree
new file mode 100644
index 0000000000000000000000000000000000000000..965d01ae2918b8a211b014b902061bc86011bec7
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+<!DOCTYPE html>
+
+<html>
+  <head>
+    <meta charset="utf-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+    <title>Chapter 1 &#8212; Astronomical Data in Python</title>
+    
+  <link rel="stylesheet" href="_static/css/index.d431a4ee1c1efae0e38bdfebc22debff.css">
+
+    
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+    <script src="_static/copybutton.js"></script>
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+        const thebe_selector_output = ".output"
+    </script>
+    <script async="async" src="_static/sphinx-thebe.js"></script>
+    <link rel="index" title="Index" href="genindex.html" />
+    <link rel="search" title="Search" href="search.html" />
+    <link rel="next" title="Chapter 2" href="02_coords.html" />
+    <link rel="prev" title="Astronomical Data in Python" href="README.html" />
+
+    <meta name="viewport" content="width=device-width, initial-scale=1" />
+    <meta name="docsearch:language" content="en" />
+
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+  </head>
+  <body data-spy="scroll" data-target="#bd-toc-nav" data-offset="80">
+    
+
+    <div class="container-xl">
+      <div class="row">
+          
+<div class="col-12 col-md-3 bd-sidebar site-navigation show" id="site-navigation">
+    
+        <div class="navbar-brand-box">
+<a class="navbar-brand text-wrap" href="index.html">
+  
+  
+  <h1 class="site-logo" id="site-title">Astronomical Data in Python</h1>
+  
+</a>
+</div>
+
+<form class="bd-search d-flex align-items-center" action="search.html" method="get">
+  <i class="icon fas fa-search"></i>
+  <input type="search" class="form-control" name="q" id="search-input" placeholder="Search this book..." aria-label="Search this book..." autocomplete="off" >
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+  <ul class="nav sidenav_l1">
+ <li class="toctree-l1">
+  <a class="reference internal" href="README.html">
+   Astronomical Data in Python
+  </a>
+ </li>
+</ul>
+<ul class="current nav sidenav_l1">
+ <li class="toctree-l1 current active">
+  <a class="current reference internal" href="#">
+   Chapter 1
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="02_coords.html">
+   Chapter 2
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="03_motion.html">
+   Chapter 3
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="04_select.html">
+   Chapter 4
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="05_join.html">
+   Chapter 5
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="06_photo.html">
+   Chapter 6
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="07_plot.html">
+   Chapter 7
+  </a>
+ </li>
+</ul>
+
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+
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+  Powered by <a href="https://jupyterbook.org">Jupyter Book</a>
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+    
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+    <div class="col-12 col-md-3 bd-topbar-whitespace site-navigation show">
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+        <!-- ipynb file if we had a myst markdown file -->
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+                class="btn btn-secondary topbarbtn" title="Download source file" data-toggle="tooltip"
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+        <!-- Download PDF via print -->
+        <button type="button" id="download-print" class="btn btn-secondary topbarbtn" title="Print to PDF"
+            onClick="window.print()" data-toggle="tooltip" data-placement="left">.pdf</button>
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+        aria-label="Connect with source repository"><i class="fab fa-github"></i></button>
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+            href="https://github.com/AllenDowney/AstronomyDataPython"><button type="button" class="btn btn-secondary topbarbtn"
+                data-toggle="tooltip" data-placement="left" title="Source repository"><i
+                    class="fab fa-github"></i>repository</button></a>
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+</div>
+
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+                data-placement="bottom" onclick="toggleFullScreen()" title="Fullscreen mode"><i
+                    class="fas fa-expand"></i></button></a>
+
+        <!-- Launch buttons -->
+
+    </div>
+
+    <!-- Table of contents -->
+    <div class="d-none d-md-block col-md-2 bd-toc show">
+        
+        <div class="tocsection onthispage pt-5 pb-3">
+            <i class="fas fa-list"></i> Contents
+        </div>
+        <nav id="bd-toc-nav">
+            <ul class="nav section-nav flex-column">
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#data">
+   Data
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#prerequisites">
+   Prerequisites
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#outline">
+   Outline
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#query-language">
+   Query Language
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#installing-libraries">
+   Installing libraries
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#connecting-to-gaia">
+   Connecting to Gaia
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#databases-and-tables">
+   Databases and Tables
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#columns">
+   Columns
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#writing-queries">
+   Writing queries
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#asynchronous-queries">
+   Asynchronous queries
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#operators">
+   Operators
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#cleaning-up">
+   Cleaning up
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#formatting-queries">
+   Formatting queries
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#summary">
+   Summary
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#best-practices">
+   Best practices
+  </a>
+ </li>
+</ul>
+
+        </nav>
+        
+    </div>
+</div>
+    <div id="main-content" class="row">
+        <div class="col-12 col-md-9 pl-md-3 pr-md-0">
+        
+              <div>
+                
+  <div class="section" id="chapter-1">
+<h1>Chapter 1<a class="headerlink" href="#chapter-1" title="Permalink to this headline">¶</a></h1>
+<p><em>Astronomical Data in Python</em> is an introduction to tools and practices for working with astronomical data.  Topics covered include:</p>
+<ul class="simple">
+<li><p>Writing queries that select and download data from a database.</p></li>
+<li><p>Using data stored in an Astropy <code class="docutils literal notranslate"><span class="pre">Table</span></code> or Pandas <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code>.</p></li>
+<li><p>Working with coordinates and other quantities with units.</p></li>
+<li><p>Storing data in various formats.</p></li>
+<li><p>Performing database join operations that combine data from multiple tables.</p></li>
+<li><p>Visualizing data and preparing publication-quality figures.</p></li>
+</ul>
+<p>As a running example, we will replicate part of the analysis in a recent paper, “<a class="reference external" href="https://arxiv.org/abs/1805.00425">Off the beaten path: Gaia reveals GD-1 stars outside of the main stream</a>� by Adrian M. Price-Whelan and Ana Bonaca.</p>
+<p>As the abstract explains, “Using data from the Gaia second data release combined with Pan-STARRS photometry, we present a sample of highly-probable members of the longest cold stream in the Milky Way, GD-1.�</p>
+<p>GD-1 is a <a class="reference external" href="https://en.wikipedia.org/wiki/List_of_stellar_streams">stellar stream</a>, which is “an association of stars orbiting a galaxy that was once a globular cluster or dwarf galaxy that has now been torn apart and stretched out along its orbit by tidal forces.�</p>
+<p><a class="reference external" href="https://www.sciencemag.org/news/2018/10/streams-stars-reveal-galaxy-s-violent-history-and-perhaps-its-unseen-dark-matter">This article in <em>Science</em> magazine</a> explains some of the background, including the process that led to the paper and an discussion of the scientific implications:</p>
+<ul class="simple">
+<li><p>“The streams are particularly useful for … galactic archaeology — rewinding the cosmic clock to reconstruct the assembly of the Milky Way.�</p></li>
+<li><p>“They also are being used as exquisitely sensitive scales to measure the galaxy’s mass.�</p></li>
+<li><p>“… the streams are well-positioned to reveal the presence of dark matter … because the streams are so fragile, theorists say, collisions with marauding clumps of dark matter could leave telltale scars, potential clues to its nature.�</p></li>
+</ul>
+<div class="section" id="data">
+<h2>Data<a class="headerlink" href="#data" title="Permalink to this headline">¶</a></h2>
+<p>The datasets we will work with are:</p>
+<ul class="simple">
+<li><p><a class="reference external" href="https://en.wikipedia.org/wiki/Gaia_(spacecraft)">Gaia</a>, which is “a space observatory of the European Space Agency (ESA), launched in 2013 … designed for astrometry: measuring the positions, distances and motions of stars with unprecedented precision�, and</p></li>
+<li><p><a class="reference external" href="https://en.wikipedia.org/wiki/Pan-STARRS">Pan-STARRS</a>, The Panoramic Survey Telescope and Rapid Response System, which is a survey designed to monitor the sky for transient objects, producing a catalog with accurate astronometry and photometry of detected sources.</p></li>
+</ul>
+<p>Both of these datasets are very large, which can make them challenging to work with.  It might not be possible, or practical, to download the entire dataset.
+One of the goals of this workshop is to provide tools for working with large datasets.</p>
+</div>
+<div class="section" id="prerequisites">
+<h2>Prerequisites<a class="headerlink" href="#prerequisites" title="Permalink to this headline">¶</a></h2>
+<p>These notebooks are meant for people who are familiar with basic Python, but not necessarily the libraries we will use, like Astropy or Pandas.  If you are familiar with Python lists and dictionaries, and you know how to write a function that takes parameters and returns a value, you know enough Python to get started.</p>
+<p>We assume that you have some familiarity with operating systems, like the ability to use a command-line interface.  But we don’t assume you have any prior experience with databases.</p>
+<p>We assume that you are familiar with astronomy at the undergraduate level, but we will not assume specialized knowledge of the datasets or analysis methods we’ll use.</p>
+</div>
+<div class="section" id="outline">
+<h2>Outline<a class="headerlink" href="#outline" title="Permalink to this headline">¶</a></h2>
+<p>The first lesson demonstrates the steps for selecting and downloading data from the Gaia Database:</p>
+<ol class="simple">
+<li><p>First we’ll make a connection to the Gaia server,</p></li>
+<li><p>We will explore information about the database and the tables it contains,</p></li>
+<li><p>We will write a query and send it to the server, and finally</p></li>
+<li><p>We will download the response from the server.</p></li>
+</ol>
+<p>After completing this lesson, you should be able to</p>
+<ul class="simple">
+<li><p>Compose a basic query in ADQL.</p></li>
+<li><p>Use queries to explore a database and its tables.</p></li>
+<li><p>Use queries to download data.</p></li>
+<li><p>Develop, test, and debug a query incrementally.</p></li>
+</ul>
+</div>
+<div class="section" id="query-language">
+<h2>Query Language<a class="headerlink" href="#query-language" title="Permalink to this headline">¶</a></h2>
+<p>In order to select data from a database, you have to compose a query, which is like a program written in a “query language�.
+The query language we’ll use is ADQL, which stands for “Astronomical Data Query Language�.</p>
+<p>ADQL is a dialect of <a class="reference external" href="https://en.wikipedia.org/wiki/SQL">SQL</a> (Structured Query Language), which is by far the most commonly used query language.  Almost everything you will learn about ADQL also works in SQL.</p>
+<p><a class="reference external" href="http://www.ivoa.net/documents/ADQL/20180112/PR-ADQL-2.1-20180112.html">The reference manual for ADQL is here</a>.
+But you might find it easier to learn from <a class="reference external" href="https://www.gaia.ac.uk/data/gaia-data-release-1/adql-cookbook">this ADQL Cookbook</a>.</p>
+</div>
+<div class="section" id="installing-libraries">
+<h2>Installing libraries<a class="headerlink" href="#installing-libraries" title="Permalink to this headline">¶</a></h2>
+<p>The library we’ll use to get Gaia data is <a class="reference external" href="https://astroquery.readthedocs.io/en/latest/">Astroquery</a>.</p>
+<p>If you are running this notebook on Colab, you can run the following cell to install Astroquery and the other libraries we’ll use.</p>
+<p>If you are running this notebook on your own computer, you might have to install these libraries yourself.</p>
+<p>If you are using this notebook as part of a Carpentries workshop, you should have received setup instructions.</p>
+<p>TODO: Add a link to the instructions.</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span># If we&#39;re running on Colab, install libraries
+
+import sys
+IN_COLAB = &#39;google.colab&#39; in sys.modules
+
+if IN_COLAB:
+    !pip install astroquery astro-gala pyia
+</pre></div>
+</div>
+</div>
+<div class="section" id="connecting-to-gaia">
+<h2>Connecting to Gaia<a class="headerlink" href="#connecting-to-gaia" title="Permalink to this headline">¶</a></h2>
+<p>Astroquery provides <code class="docutils literal notranslate"><span class="pre">Gaia</span></code>, which is an <a class="reference external" href="https://astroquery.readthedocs.io/en/latest/gaia/gaia.html">object that represents a connection to the Gaia database</a>.</p>
+<p>We can connect to the Gaia database like this:</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">astroquery.gaia</span> <span class="kn">import</span> <span class="n">Gaia</span>
+</pre></div>
+</div>
+<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>Created TAP+ (v1.2.1) - Connection:
+	Host: gea.esac.esa.int
+	Use HTTPS: True
+	Port: 443
+	SSL Port: 443
+Created TAP+ (v1.2.1) - Connection:
+	Host: geadata.esac.esa.int
+	Use HTTPS: True
+	Port: 443
+	SSL Port: 443
+</pre></div>
+</div>
+<p>Running this import statement has the effect of creating a <a class="reference external" href="http://www.ivoa.net/documents/TAP/">TAP+</a> connection; TAP stands for “Table Access Protocol�.  It is a network protocol for sending queries to the database and getting back the results.  We’re not sure why it seems to create two connections.</p>
+</div>
+<div class="section" id="databases-and-tables">
+<h2>Databases and Tables<a class="headerlink" href="#databases-and-tables" title="Permalink to this headline">¶</a></h2>
+<p>What is a database, anyway?  Most generally, it can be any collection of data, but when we are talking about ADQL or SQL:</p>
+<ul class="simple">
+<li><p>A database is a collection of one or more named tables.</p></li>
+<li><p>Each table is a 2-D array with one or more named columns of data.</p></li>
+</ul>
+<p>We can use <code class="docutils literal notranslate"><span class="pre">Gaia.load_tables</span></code> to get the names of the tables in the Gaia database.  With the option <code class="docutils literal notranslate"><span class="pre">only_names=True</span></code>, it loads information about the tables, called the “metadata�, not the data itself.</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">tables</span> <span class="o">=</span> <span class="n">Gaia</span><span class="o">.</span><span class="n">load_tables</span><span class="p">(</span><span class="n">only_names</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>INFO: Retrieving tables... [astroquery.utils.tap.core]
+INFO: Parsing tables... [astroquery.utils.tap.core]
+INFO: Done. [astroquery.utils.tap.core]
+</pre></div>
+</div>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">for</span> <span class="n">table</span> <span class="ow">in</span> <span class="p">(</span><span class="n">tables</span><span class="p">):</span>
+    <span class="nb">print</span><span class="p">(</span><span class="n">table</span><span class="o">.</span><span class="n">get_qualified_name</span><span class="p">())</span>
+</pre></div>
+</div>
+<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>external.external.apassdr9
+external.external.gaiadr2_geometric_distance
+external.external.galex_ais
+external.external.ravedr5_com
+external.external.ravedr5_dr5
+external.external.ravedr5_gra
+external.external.ravedr5_on
+external.external.sdssdr13_photoprimary
+external.external.skymapperdr1_master
+external.external.tmass_xsc
+public.public.hipparcos
+public.public.hipparcos_newreduction
+public.public.hubble_sc
+public.public.igsl_source
+public.public.igsl_source_catalog_ids
+public.public.tycho2
+public.public.dual
+tap_config.tap_config.coord_sys
+tap_config.tap_config.properties
+tap_schema.tap_schema.columns
+tap_schema.tap_schema.key_columns
+tap_schema.tap_schema.keys
+tap_schema.tap_schema.schemas
+tap_schema.tap_schema.tables
+gaiadr1.gaiadr1.aux_qso_icrf2_match
+gaiadr1.gaiadr1.ext_phot_zero_point
+gaiadr1.gaiadr1.allwise_best_neighbour
+gaiadr1.gaiadr1.allwise_neighbourhood
+gaiadr1.gaiadr1.gsc23_best_neighbour
+gaiadr1.gaiadr1.gsc23_neighbourhood
+gaiadr1.gaiadr1.ppmxl_best_neighbour
+gaiadr1.gaiadr1.ppmxl_neighbourhood
+gaiadr1.gaiadr1.sdss_dr9_best_neighbour
+gaiadr1.gaiadr1.sdss_dr9_neighbourhood
+gaiadr1.gaiadr1.tmass_best_neighbour
+gaiadr1.gaiadr1.tmass_neighbourhood
+gaiadr1.gaiadr1.ucac4_best_neighbour
+gaiadr1.gaiadr1.ucac4_neighbourhood
+gaiadr1.gaiadr1.urat1_best_neighbour
+gaiadr1.gaiadr1.urat1_neighbourhood
+gaiadr1.gaiadr1.cepheid
+gaiadr1.gaiadr1.phot_variable_time_series_gfov
+gaiadr1.gaiadr1.phot_variable_time_series_gfov_statistical_parameters
+gaiadr1.gaiadr1.rrlyrae
+gaiadr1.gaiadr1.variable_summary
+gaiadr1.gaiadr1.allwise_original_valid
+gaiadr1.gaiadr1.gsc23_original_valid
+gaiadr1.gaiadr1.ppmxl_original_valid
+gaiadr1.gaiadr1.sdssdr9_original_valid
+gaiadr1.gaiadr1.tmass_original_valid
+gaiadr1.gaiadr1.ucac4_original_valid
+gaiadr1.gaiadr1.urat1_original_valid
+gaiadr1.gaiadr1.gaia_source
+gaiadr1.gaiadr1.tgas_source
+gaiadr2.gaiadr2.aux_allwise_agn_gdr2_cross_id
+gaiadr2.gaiadr2.aux_iers_gdr2_cross_id
+gaiadr2.gaiadr2.aux_sso_orbit_residuals
+gaiadr2.gaiadr2.aux_sso_orbits
+gaiadr2.gaiadr2.dr1_neighbourhood
+gaiadr2.gaiadr2.allwise_best_neighbour
+gaiadr2.gaiadr2.allwise_neighbourhood
+gaiadr2.gaiadr2.apassdr9_best_neighbour
+gaiadr2.gaiadr2.apassdr9_neighbourhood
+gaiadr2.gaiadr2.gsc23_best_neighbour
+gaiadr2.gaiadr2.gsc23_neighbourhood
+gaiadr2.gaiadr2.hipparcos2_best_neighbour
+gaiadr2.gaiadr2.hipparcos2_neighbourhood
+gaiadr2.gaiadr2.panstarrs1_best_neighbour
+gaiadr2.gaiadr2.panstarrs1_neighbourhood
+gaiadr2.gaiadr2.ppmxl_best_neighbour
+gaiadr2.gaiadr2.ppmxl_neighbourhood
+gaiadr2.gaiadr2.ravedr5_best_neighbour
+gaiadr2.gaiadr2.ravedr5_neighbourhood
+gaiadr2.gaiadr2.sdssdr9_best_neighbour
+gaiadr2.gaiadr2.sdssdr9_neighbourhood
+gaiadr2.gaiadr2.tmass_best_neighbour
+gaiadr2.gaiadr2.tmass_neighbourhood
+gaiadr2.gaiadr2.tycho2_best_neighbour
+gaiadr2.gaiadr2.tycho2_neighbourhood
+gaiadr2.gaiadr2.urat1_best_neighbour
+gaiadr2.gaiadr2.urat1_neighbourhood
+gaiadr2.gaiadr2.sso_observation
+gaiadr2.gaiadr2.sso_source
+gaiadr2.gaiadr2.vari_cepheid
+gaiadr2.gaiadr2.vari_classifier_class_definition
+gaiadr2.gaiadr2.vari_classifier_definition
+gaiadr2.gaiadr2.vari_classifier_result
+gaiadr2.gaiadr2.vari_long_period_variable
+gaiadr2.gaiadr2.vari_rotation_modulation
+gaiadr2.gaiadr2.vari_rrlyrae
+gaiadr2.gaiadr2.vari_short_timescale
+gaiadr2.gaiadr2.vari_time_series_statistics
+gaiadr2.gaiadr2.panstarrs1_original_valid
+gaiadr2.gaiadr2.gaia_source
+gaiadr2.gaiadr2.ruwe
+</pre></div>
+</div>
+<p>So that’s a lot of tables.  The ones we’ll use are:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">gaiadr2.gaia_source</span></code>, which contains Gaia data from <a class="reference external" href="https://www.cosmos.esa.int/web/gaia/data-release-2">data release 2</a>,</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">gaiadr2.panstarrs1_original_valid</span></code>, which contains the photometry data we’ll use from PanSTARRS, and</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">gaiadr2.panstarrs1_best_neighbour</span></code>, which we’ll use to cross-match each star observed by Gaia with the same star observed by PanSTARRS.</p></li>
+</ul>
+<p>We can use <code class="docutils literal notranslate"><span class="pre">load_table</span></code> (not <code class="docutils literal notranslate"><span class="pre">load_tables</span></code>) to get the metadata for a single table.  The name of this function is misleading, because it only downloads metadata.</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">meta</span> <span class="o">=</span> <span class="n">Gaia</span><span class="o">.</span><span class="n">load_table</span><span class="p">(</span><span class="s1">&#39;gaiadr2.gaia_source&#39;</span><span class="p">)</span>
+<span class="n">meta</span>
+</pre></div>
+</div>
+<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>Retrieving table &#39;gaiadr2.gaia_source&#39;
+Parsing table &#39;gaiadr2.gaia_source&#39;...
+Done.
+
+
+
+
+
+&lt;astroquery.utils.tap.model.taptable.TapTableMeta at 0x7f922376e0a0&gt;
+</pre></div>
+</div>
+<p>Jupyter shows that the result is an object of type <code class="docutils literal notranslate"><span class="pre">TapTableMeta</span></code>, but it does not display the contents.</p>
+<p>To see the metadata, we have to print the object.</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">meta</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>TAP Table name: gaiadr2.gaiadr2.gaia_source
+Description: This table has an entry for every Gaia observed source as listed in the
+Main Database accumulating catalogue version from which the catalogue
+release has been generated. It contains the basic source parameters,
+that is only final data (no epoch data) and no spectra (neither final
+nor epoch).
+Num. columns: 96
+</pre></div>
+</div>
+<p>Notice one gotcha: in the list of table names, this table appears as <code class="docutils literal notranslate"><span class="pre">gaiadr2.gaiadr2.gaia_source</span></code>, but when we load the metadata, we refer to it as <code class="docutils literal notranslate"><span class="pre">gaiadr2.gaia_source</span></code>.</p>
+<p><strong>Exercise:</strong> Go back and try</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">meta</span> <span class="o">=</span> <span class="n">Gaia</span><span class="o">.</span><span class="n">load_table</span><span class="p">(</span><span class="s1">&#39;gaiadr2.gaiadr2.gaia_source&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>What happens?  Is the error message helpful?  If you had not made this error deliberately, would you have been able to figure it out?</p>
+</div>
+<div class="section" id="columns">
+<h2>Columns<a class="headerlink" href="#columns" title="Permalink to this headline">¶</a></h2>
+<p>The following loop prints the names of the columns in the table.</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">for</span> <span class="n">column</span> <span class="ow">in</span> <span class="n">meta</span><span class="o">.</span><span class="n">columns</span><span class="p">:</span>
+    <span class="nb">print</span><span class="p">(</span><span class="n">column</span><span class="o">.</span><span class="n">name</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>solution_id
+designation
+source_id
+random_index
+ref_epoch
+ra
+ra_error
+dec
+dec_error
+parallax
+parallax_error
+parallax_over_error
+pmra
+pmra_error
+pmdec
+pmdec_error
+ra_dec_corr
+ra_parallax_corr
+ra_pmra_corr
+ra_pmdec_corr
+dec_parallax_corr
+dec_pmra_corr
+dec_pmdec_corr
+parallax_pmra_corr
+parallax_pmdec_corr
+pmra_pmdec_corr
+astrometric_n_obs_al
+astrometric_n_obs_ac
+astrometric_n_good_obs_al
+astrometric_n_bad_obs_al
+astrometric_gof_al
+astrometric_chi2_al
+astrometric_excess_noise
+astrometric_excess_noise_sig
+astrometric_params_solved
+astrometric_primary_flag
+astrometric_weight_al
+astrometric_pseudo_colour
+astrometric_pseudo_colour_error
+mean_varpi_factor_al
+astrometric_matched_observations
+visibility_periods_used
+astrometric_sigma5d_max
+frame_rotator_object_type
+matched_observations
+duplicated_source
+phot_g_n_obs
+phot_g_mean_flux
+phot_g_mean_flux_error
+phot_g_mean_flux_over_error
+phot_g_mean_mag
+phot_bp_n_obs
+phot_bp_mean_flux
+phot_bp_mean_flux_error
+phot_bp_mean_flux_over_error
+phot_bp_mean_mag
+phot_rp_n_obs
+phot_rp_mean_flux
+phot_rp_mean_flux_error
+phot_rp_mean_flux_over_error
+phot_rp_mean_mag
+phot_bp_rp_excess_factor
+phot_proc_mode
+bp_rp
+bp_g
+g_rp
+radial_velocity
+radial_velocity_error
+rv_nb_transits
+rv_template_teff
+rv_template_logg
+rv_template_fe_h
+phot_variable_flag
+l
+b
+ecl_lon
+ecl_lat
+priam_flags
+teff_val
+teff_percentile_lower
+teff_percentile_upper
+a_g_val
+a_g_percentile_lower
+a_g_percentile_upper
+e_bp_min_rp_val
+e_bp_min_rp_percentile_lower
+e_bp_min_rp_percentile_upper
+flame_flags
+radius_val
+radius_percentile_lower
+radius_percentile_upper
+lum_val
+lum_percentile_lower
+lum_percentile_upper
+datalink_url
+epoch_photometry_url
+</pre></div>
+</div>
+<p>You can probably guess what many of these columns are by looking at the names, but you should resist the temptation to guess.
+To find out what the columns mean, <a class="reference external" href="https://gea.esac.esa.int/archive/documentation/GDR2/Gaia_archive/chap_datamodel/sec_dm_main_tables/ssec_dm_gaia_source.html">read the documentation</a>.</p>
+<p>If you want to know what can go wrong when you don’t read the documentation, <a class="reference external" href="https://www.vox.com/future-perfect/2019/6/4/18650969/married-women-miserable-fake-paul-dolan-happiness">you might like this article</a>.</p>
+<p><strong>Exercise:</strong> One of the other tables we’ll use is <code class="docutils literal notranslate"><span class="pre">gaiadr2.gaiadr2.panstarrs1_original_valid</span></code>.  Use <code class="docutils literal notranslate"><span class="pre">load_table</span></code> to get the metadata for this table.  How many columns are there and what are their names?</p>
+<p>Hint: Remember the gotcha we mentioned earlier.</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Solution</span>
+
+<span class="n">meta2</span> <span class="o">=</span> <span class="n">Gaia</span><span class="o">.</span><span class="n">load_table</span><span class="p">(</span><span class="s1">&#39;gaiadr2.panstarrs1_original_valid&#39;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">meta2</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>Retrieving table &#39;gaiadr2.panstarrs1_original_valid&#39;
+Parsing table &#39;gaiadr2.panstarrs1_original_valid&#39;...
+Done.
+TAP Table name: gaiadr2.gaiadr2.panstarrs1_original_valid
+Description: The Panoramic Survey Telescope and Rapid Response System (Pan-STARRS) is
+a system for wide-field astronomical imaging developed and operated by
+the Institute for Astronomy at the University of Hawaii. Pan-STARRS1
+(PS1) is the first part of Pan-STARRS to be completed and is the basis
+for Data Release 1 (DR1). The PS1 survey used a 1.8 meter telescope and
+its 1.4 Gigapixel camera to image the sky in five broadband filters (g,
+r, i, z, y).
+
+The current table contains a filtered subsample of the 10 723 304 629
+entries listed in the original ObjectThin table.
+We used only ObjectThin and MeanObject tables to extract
+panstarrs1OriginalValid table, this means that objects detected only in
+stack images are not included here. The main reason for us to avoid the
+use of objects detected in stack images is that their astrometry is not
+as good as the mean objects astrometry: “The stack positions (raStack,
+decStack) have considerably larger systematic astrometric errors than
+the mean epoch positions (raMean, decMean).� The astrometry for the
+MeanObject positions uses Gaia DR1 as a reference catalog, while the
+stack positions use 2MASS as a reference catalog.
+
+In details, we filtered out all objects where:
+
+-   nDetections = 1
+
+-   no good quality data in Pan-STARRS, objInfoFlag 33554432 not set
+
+-   mean astrometry could not be measured, objInfoFlag 524288 set
+
+-   stack position used for mean astrometry, objInfoFlag 1048576 set
+
+-   error on all magnitudes equal to 0 or to -999;
+
+-   all magnitudes set to -999;
+
+-   error on RA or DEC greater than 1 arcsec.
+
+The number of objects in panstarrs1OriginalValid is 2 264 263 282.
+
+The panstarrs1OriginalValid table contains only a subset of the columns
+available in the combined ObjectThin and MeanObject tables. A
+description of the original ObjectThin and MeanObjects tables can be
+found at:
+https://outerspace.stsci.edu/display/PANSTARRS/PS1+Database+object+and+detection+tables
+
+Download:
+http://mastweb.stsci.edu/ps1casjobs/home.aspx
+Documentation:
+https://outerspace.stsci.edu/display/PANSTARRS
+http://pswww.ifa.hawaii.edu/pswww/
+References:
+The Pan-STARRS1 Surveys, Chambers, K.C., et al. 2016, arXiv:1612.05560
+Pan-STARRS Data Processing System, Magnier, E. A., et al. 2016,
+arXiv:1612.05240
+Pan-STARRS Pixel Processing: Detrending, Warping, Stacking, Waters, C.
+Z., et al. 2016, arXiv:1612.05245
+Pan-STARRS Pixel Analysis: Source Detection and Characterization,
+Magnier, E. A., et al. 2016, arXiv:1612.05244
+Pan-STARRS Photometric and Astrometric Calibration, Magnier, E. A., et
+al. 2016, arXiv:1612.05242
+The Pan-STARRS1 Database and Data Products, Flewelling, H. A., et al.
+2016, arXiv:1612.05243
+
+Catalogue curator:
+SSDC - ASI Space Science Data Center
+https://www.ssdc.asi.it/
+Num. columns: 26
+</pre></div>
+</div>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Solution</span>
+
+<span class="k">for</span> <span class="n">column</span> <span class="ow">in</span> <span class="n">meta2</span><span class="o">.</span><span class="n">columns</span><span class="p">:</span>
+    <span class="nb">print</span><span class="p">(</span><span class="n">column</span><span class="o">.</span><span class="n">name</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>obj_name
+obj_id
+ra
+dec
+ra_error
+dec_error
+epoch_mean
+g_mean_psf_mag
+g_mean_psf_mag_error
+g_flags
+r_mean_psf_mag
+r_mean_psf_mag_error
+r_flags
+i_mean_psf_mag
+i_mean_psf_mag_error
+i_flags
+z_mean_psf_mag
+z_mean_psf_mag_error
+z_flags
+y_mean_psf_mag
+y_mean_psf_mag_error
+y_flags
+n_detections
+zone_id
+obj_info_flag
+quality_flag
+</pre></div>
+</div>
+</div>
+<div class="section" id="writing-queries">
+<h2>Writing queries<a class="headerlink" href="#writing-queries" title="Permalink to this headline">¶</a></h2>
+<p>By now you might be wondering how we actually download the data.  With tables this big, you generally don’t.  Instead, you use queries to select only the data you want.</p>
+<p>A query is a string written in a query language like SQL; for the Gaia database, the query language is a dialect of SQL called ADQL.</p>
+<p>Here’s an example of an ADQL query.</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">query1</span> <span class="o">=</span> <span class="s2">&quot;&quot;&quot;SELECT </span>
+<span class="s2">TOP 10</span>
+<span class="s2">source_id, ref_epoch, ra, dec, parallax </span>
+<span class="s2">FROM gaiadr2.gaia_source&quot;&quot;&quot;</span>
+</pre></div>
+</div>
+<p><strong>Python note:</strong> We use a <a class="reference external" href="https://docs.python.org/3/tutorial/introduction.html#strings">triple-quoted string</a> here so we can include line breaks in the query, which makes it easier to read.</p>
+<p>The words in uppercase are ADQL keywords:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">SELECT</span></code> indicates that we are selecting data (as opposed to adding or modifying data).</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">TOP</span></code> indicates that we only want the first 10 rows of the table, which is useful for testing a query before asking for all of the data.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">FROM</span></code> specifies which table we want data from.</p></li>
+</ul>
+<p>The third line is a list of column names, indicating which columns we want.</p>
+<p>In this example, the keywords are capitalized and the column names are lowercase.  This is a common style, but it is not required.  ADQL and SQL are not case-sensitive.</p>
+<p>To run this query, we use the <code class="docutils literal notranslate"><span class="pre">Gaia</span></code> object, which represents our connection to the Gaia database, and invoke <code class="docutils literal notranslate"><span class="pre">launch_job</span></code>:</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">job1</span> <span class="o">=</span> <span class="n">Gaia</span><span class="o">.</span><span class="n">launch_job</span><span class="p">(</span><span class="n">query1</span><span class="p">)</span>
+<span class="n">job1</span>
+</pre></div>
+</div>
+<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>&lt;astroquery.utils.tap.model.job.Job at 0x7f9222e9cb20&gt;
+</pre></div>
+</div>
+<p>The result is an object that represents the job running on a Gaia server.</p>
+<p>If you print it, it displays metadata for the forthcoming table.</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">job1</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>&lt;Table length=10&gt;
+   name    dtype  unit                            description                            
+--------- ------- ---- ------------------------------------------------------------------
+source_id   int64      Unique source identifier (unique within a particular Data Release)
+ref_epoch float64   yr                                                    Reference epoch
+       ra float64  deg                                                    Right ascension
+      dec float64  deg                                                        Declination
+ parallax float64  mas                                                           Parallax
+Jobid: None
+Phase: COMPLETED
+Owner: None
+Output file: sync_20201005090721.xml.gz
+Results: None
+</pre></div>
+</div>
+<p>Don’t worry about <code class="docutils literal notranslate"><span class="pre">Results:</span> <span class="pre">None</span></code>.  That does not actually mean there are no results.</p>
+<p>However, <code class="docutils literal notranslate"><span class="pre">Phase:</span> <span class="pre">COMPLETED</span></code> indicates that the job is complete, so we can get the results like this:</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">results1</span> <span class="o">=</span> <span class="n">job1</span><span class="o">.</span><span class="n">get_results</span><span class="p">()</span>
+<span class="nb">type</span><span class="p">(</span><span class="n">results1</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>astropy.table.table.Table
+</pre></div>
+</div>
+<p><strong>Optional detail:</strong>  Why is <code class="docutils literal notranslate"><span class="pre">table</span></code> repeated three times?  The first is the name of the module, the second is the name of the submodule, and the third is the name of the class.  Most of the time we only care about the last one.  It’s like the Linnean name for gorilla, which is <em>Gorilla Gorilla Gorilla</em>.</p>
+<p>The result is an <a class="reference external" href="https://docs.astropy.org/en/stable/table/">Astropy Table</a>, which is similar to a table in an SQL database except:</p>
+<ul class="simple">
+<li><p>SQL databases are stored on disk drives, so they are persistent; that is, they “survive� even if you turn off the computer.  An Astropy <code class="docutils literal notranslate"><span class="pre">Table</span></code> is stored in memory; it disappears when you turn off the computer (or shut down this Jupyter notebook).</p></li>
+<li><p>SQL databases are designed to process queries.  An Astropy <code class="docutils literal notranslate"><span class="pre">Table</span></code> can perform some query-like operations, like selecting columns and rows.  But these operations use Python syntax, not SQL.</p></li>
+</ul>
+<p>Jupyter knows how to display the contents of a <code class="docutils literal notranslate"><span class="pre">Table</span></code>.</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">results1</span>
+</pre></div>
+</div>
+<p><i>Table length=10</i></p>
+<table id="table140265627585264" class="table-striped table-bordered table-condensed">
+<thead><tr><th>source_id</th><th>ref_epoch</th><th>ra</th><th>dec</th><th>parallax</th></tr></thead>
+<thead><tr><th></th><th>yr</th><th>deg</th><th>deg</th><th>mas</th></tr></thead>
+<thead><tr><th>int64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th></tr></thead>
+<tr><td>4530738361793769600</td><td>2015.5</td><td>281.56725362448725</td><td>20.40682117430378</td><td>0.9785380604519425</td></tr>
+<tr><td>4530752651135081216</td><td>2015.5</td><td>281.0861565355257</td><td>20.523350496351846</td><td>0.2674800612552977</td></tr>
+<tr><td>4530743343951405568</td><td>2015.5</td><td>281.37114418299177</td><td>20.474147574053124</td><td>-0.43911323550176806</td></tr>
+<tr><td>4530755060627162368</td><td>2015.5</td><td>281.2676236268299</td><td>20.558523922346158</td><td>1.1422630184554958</td></tr>
+<tr><td>4530746844341315968</td><td>2015.5</td><td>281.1370431749541</td><td>20.377852388898184</td><td>1.0092247424630945</td></tr>
+<tr><td>4530768456615026432</td><td>2015.5</td><td>281.8720921436347</td><td>20.31829694530366</td><td>-0.06900136127674149</td></tr>
+<tr><td>4530763513119137280</td><td>2015.5</td><td>281.9211808864116</td><td>20.20956829578524</td><td>0.1266016679823622</td></tr>
+<tr><td>4530736364618539264</td><td>2015.5</td><td>281.4913475613274</td><td>20.346579041327693</td><td>0.3894019486060072</td></tr>
+<tr><td>4530735952305177728</td><td>2015.5</td><td>281.4085549165704</td><td>20.311030903719928</td><td>0.2041189982608354</td></tr>
+<tr><td>4530751281056022656</td><td>2015.5</td><td>281.0585328377638</td><td>20.460309556214753</td><td>0.10294642821734962</td></tr>
+</table>
+<p>Each column has a name, units, and a data type.</p>
+<p>For example, the units of <code class="docutils literal notranslate"><span class="pre">ra</span></code> and <code class="docutils literal notranslate"><span class="pre">dec</span></code> are degrees, and their data type is <code class="docutils literal notranslate"><span class="pre">float64</span></code>, which is a 64-bit floating-point number, used to store measurements with a fraction part.</p>
+<p>This information comes from the Gaia database, and has been stored in the Astropy <code class="docutils literal notranslate"><span class="pre">Table</span></code> by Astroquery.</p>
+<p><strong>Exercise:</strong> Read <a class="reference external" href="https://gea.esac.esa.int/archive/documentation/GDR2/Gaia_archive/chap_datamodel/sec_dm_main_tables/ssec_dm_gaia_source.html">the documentation of this table</a> and choose a column that looks interesting to you.  Add the column name to the query and run it again.  What are the units of the column you selected?  What is its data type?</p>
+</div>
+<div class="section" id="asynchronous-queries">
+<h2>Asynchronous queries<a class="headerlink" href="#asynchronous-queries" title="Permalink to this headline">¶</a></h2>
+<p><code class="docutils literal notranslate"><span class="pre">launch_job</span></code> asks the server to run the job “synchronously�, which normally means it runs immediately.  But synchronous jobs are limited to 2000 rows.  For queries that return more rows, you should run “asynchronously�, which mean they might take longer to get started.</p>
+<p>If you are not sure how many rows a query will return, you can use the SQL command <code class="docutils literal notranslate"><span class="pre">COUNT</span></code> to find out how many rows are in the result without actually returning them.  We’ll see an example of this later.</p>
+<p>The results of an asynchronous query are stored in a file on the server, so you can start a query and come back later to get the results.</p>
+<p>For anonymous users, files are kept for three days.</p>
+<p>As an example, let’s try a query that’s similar to <code class="docutils literal notranslate"><span class="pre">query1</span></code>, with two changes:</p>
+<ul class="simple">
+<li><p>It selects the first 3000 rows, so it is bigger than we should run synchronously.</p></li>
+<li><p>It uses a new keyword, <code class="docutils literal notranslate"><span class="pre">WHERE</span></code>.</p></li>
+</ul>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">query2</span> <span class="o">=</span> <span class="s2">&quot;&quot;&quot;SELECT TOP 3000</span>
+<span class="s2">source_id, ref_epoch, ra, dec, parallax</span>
+<span class="s2">FROM gaiadr2.gaia_source</span>
+<span class="s2">WHERE parallax &lt; 1</span>
+<span class="s2">&quot;&quot;&quot;</span>
+</pre></div>
+</div>
+<p>A <code class="docutils literal notranslate"><span class="pre">WHERE</span></code> clause indicates which rows we want; in this case, the query selects only rows “where� <code class="docutils literal notranslate"><span class="pre">parallax</span></code> is less than 1.  This has the effect of selecting stars with relatively low parallax, which are farther away.  We’ll use this clause to exclude nearby stars that are unlikely to be part of GD-1.</p>
+<p><code class="docutils literal notranslate"><span class="pre">WHERE</span></code> is one of the most common clauses in ADQL/SQL, and one of the most useful, because it allows us to select only the rows we need from the database.</p>
+<p>We use <code class="docutils literal notranslate"><span class="pre">launch_job_async</span></code> to submit an asynchronous query.</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">job2</span> <span class="o">=</span> <span class="n">Gaia</span><span class="o">.</span><span class="n">launch_job_async</span><span class="p">(</span><span class="n">query2</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">job2</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>INFO: Query finished. [astroquery.utils.tap.core]
+&lt;Table length=3000&gt;
+   name    dtype  unit                            description                            
+--------- ------- ---- ------------------------------------------------------------------
+source_id   int64      Unique source identifier (unique within a particular Data Release)
+ref_epoch float64   yr                                                    Reference epoch
+       ra float64  deg                                                    Right ascension
+      dec float64  deg                                                        Declination
+ parallax float64  mas                                                           Parallax
+Jobid: 1601903242219O
+Phase: COMPLETED
+Owner: None
+Output file: async_20201005090722.vot
+Results: None
+</pre></div>
+</div>
+<p>And here are the results.</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">results2</span> <span class="o">=</span> <span class="n">job2</span><span class="o">.</span><span class="n">get_results</span><span class="p">()</span>
+<span class="n">results2</span>
+</pre></div>
+</div>
+<p><i>Table length=3000</i></p>
+<table id="table140265625141056" class="table-striped table-bordered table-condensed">
+<thead><tr><th>source_id</th><th>ref_epoch</th><th>ra</th><th>dec</th><th>parallax</th></tr></thead>
+<thead><tr><th></th><th>yr</th><th>deg</th><th>deg</th><th>mas</th></tr></thead>
+<thead><tr><th>int64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th></tr></thead>
+<tr><td>4530738361793769600</td><td>2015.5</td><td>281.56725362448725</td><td>20.40682117430378</td><td>0.9785380604519425</td></tr>
+<tr><td>4530752651135081216</td><td>2015.5</td><td>281.0861565355257</td><td>20.523350496351846</td><td>0.2674800612552977</td></tr>
+<tr><td>4530743343951405568</td><td>2015.5</td><td>281.37114418299177</td><td>20.474147574053124</td><td>-0.43911323550176806</td></tr>
+<tr><td>4530768456615026432</td><td>2015.5</td><td>281.8720921436347</td><td>20.31829694530366</td><td>-0.06900136127674149</td></tr>
+<tr><td>4530763513119137280</td><td>2015.5</td><td>281.9211808864116</td><td>20.20956829578524</td><td>0.1266016679823622</td></tr>
+<tr><td>4530736364618539264</td><td>2015.5</td><td>281.4913475613274</td><td>20.346579041327693</td><td>0.3894019486060072</td></tr>
+<tr><td>4530735952305177728</td><td>2015.5</td><td>281.4085549165704</td><td>20.311030903719928</td><td>0.2041189982608354</td></tr>
+<tr><td>4530751281056022656</td><td>2015.5</td><td>281.0585328377638</td><td>20.460309556214753</td><td>0.10294642821734962</td></tr>
+<tr><td>4530740938774409344</td><td>2015.5</td><td>281.3762569536416</td><td>20.436140058941206</td><td>0.9242670062090182</td></tr>
+<tr><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr>
+<tr><td>4467710915011802624</td><td>2015.5</td><td>269.9680969307347</td><td>1.1429085038160882</td><td>0.42361471245557913</td></tr>
+<tr><td>4467706551328679552</td><td>2015.5</td><td>270.033164589881</td><td>1.0565747323689927</td><td>0.922888231734588</td></tr>
+<tr><td>4467712255037300096</td><td>2015.5</td><td>270.7724717923047</td><td>0.6581664892880896</td><td>-2.669179465293931</td></tr>
+<tr><td>4467735001181761792</td><td>2015.5</td><td>270.3628606248308</td><td>0.8947079323599124</td><td>0.6117399163086398</td></tr>
+<tr><td>4467737101421916672</td><td>2015.5</td><td>270.5110834661444</td><td>0.9806225910160181</td><td>-0.39818224846127004</td></tr>
+<tr><td>4467707547757327488</td><td>2015.5</td><td>269.88746280594927</td><td>1.0212759940136962</td><td>0.7741412301054209</td></tr>
+<tr><td>4467732772094573056</td><td>2015.5</td><td>270.55997182760126</td><td>0.9037072088489417</td><td>-1.7920417800164183</td></tr>
+<tr><td>4467732355491087744</td><td>2015.5</td><td>270.6730790702491</td><td>0.9197224705139885</td><td>-0.3464446494840354</td></tr>
+<tr><td>4467717099766944512</td><td>2015.5</td><td>270.57667173120825</td><td>0.726277659009568</td><td>0.05443955111134051</td></tr>
+<tr><td>4467719058265781248</td><td>2015.5</td><td>270.7248052971514</td><td>0.8205551921782785</td><td>0.3733943917490343</td></tr>
+</table>
+<p>You might notice that some values of <code class="docutils literal notranslate"><span class="pre">parallax</span></code> are negative.  As <a class="reference external" href="https://www.cosmos.esa.int/web/gaia/archive-tips#negative%20parallax">this FAQ explains</a>, “Negative parallaxes are caused by errors in the observations.�  Negative parallaxes have “no physical meaning,� but they can be a “useful diagnostic on the quality of the astrometric solution.�</p>
+<p>Later we will see an example where we use <code class="docutils literal notranslate"><span class="pre">parallax</span></code> and <code class="docutils literal notranslate"><span class="pre">parallax_error</span></code> to identify stars where the distance estimate is likely to be inaccurate.</p>
+<p><strong>Exercise:</strong> The clauses in a query have to be in the right order.  Go back and change the order of the clauses in <code class="docutils literal notranslate"><span class="pre">query2</span></code> and run it again.</p>
+<p>The query should fail, but notice that you don’t get much useful debugging information.</p>
+<p>For this reason, developing and debugging ADQL queries can be really hard.  A few suggestions that might help:</p>
+<ul class="simple">
+<li><p>Whenever possible, start with a working query, either an example you find online or a query you have used in the past.</p></li>
+<li><p>Make small changes and test each change before you continue.</p></li>
+<li><p>While you are debugging, use <code class="docutils literal notranslate"><span class="pre">TOP</span></code> to limit the number of rows in the result.  That will make each attempt run faster, which reduces your testing time.</p></li>
+<li><p>Launching test queries synchronously might make them start faster, too.</p></li>
+</ul>
+</div>
+<div class="section" id="operators">
+<h2>Operators<a class="headerlink" href="#operators" title="Permalink to this headline">¶</a></h2>
+<p>In a <code class="docutils literal notranslate"><span class="pre">WHERE</span></code> clause, you can use any of the <a class="reference external" href="https://www.w3schools.com/sql/sql_operators.asp">SQL comparison operators</a>; here are the most common ones:</p>
+<table class="colwidths-auto table">
+<thead>
+<tr class="row-odd"><th class="head"><p>Symbol</p></th>
+<th class="text-align:left head"><p>Operation</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">&gt;</span></code></p></td>
+<td class="text-align:left"><p>greater than</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">&lt;</span></code></p></td>
+<td class="text-align:left"><p>less than</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">&gt;=</span></code></p></td>
+<td class="text-align:left"><p>greater than or equal</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">&lt;=</span></code></p></td>
+<td class="text-align:left"><p>less than or equal</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">=</span></code></p></td>
+<td class="text-align:left"><p>equal</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">!=</span></code> or <code class="docutils literal notranslate"><span class="pre">&lt;&gt;</span></code></p></td>
+<td class="text-align:left"><p>not equal</p></td>
+</tr>
+</tbody>
+</table>
+<p>Most of these are the same as Python, but some are not.  In particular, notice that the equality operator is <code class="docutils literal notranslate"><span class="pre">=</span></code>, not <code class="docutils literal notranslate"><span class="pre">==</span></code>.
+Be careful to keep your Python out of your ADQL!</p>
+<p>You can combine comparisons using the logical operators:</p>
+<ul class="simple">
+<li><p>AND: true if both comparisons are true</p></li>
+<li><p>OR: true if either or both comparisons are true</p></li>
+</ul>
+<p>Finally, you can use <code class="docutils literal notranslate"><span class="pre">NOT</span></code> to invert the result of a comparison.</p>
+<p><strong>Exercise:</strong> <a class="reference external" href="https://www.w3schools.com/sql/sql_operators.asp">Read about SQL operators here</a> and then modify the previous query to select rows where <code class="docutils literal notranslate"><span class="pre">bp_rp</span></code> is between <code class="docutils literal notranslate"><span class="pre">-0.75</span></code> and <code class="docutils literal notranslate"><span class="pre">2</span></code>.</p>
+<p>You can <a class="reference external" href="https://gea.esac.esa.int/archive/documentation/GDR2/Gaia_archive/chap_datamodel/sec_dm_main_tables/ssec_dm_gaia_source.html">read about this variable here</a>.</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Solution</span>
+
+<span class="c1"># This is what most people will probably do</span>
+
+<span class="n">query</span> <span class="o">=</span> <span class="s2">&quot;&quot;&quot;SELECT TOP 10</span>
+<span class="s2">source_id, ref_epoch, ra, dec, parallax</span>
+<span class="s2">FROM gaiadr2.gaia_source</span>
+<span class="s2">WHERE parallax &lt; 1 </span>
+<span class="s2">  AND bp_rp &gt; -0.75 AND bp_rp &lt; 2</span>
+<span class="s2">&quot;&quot;&quot;</span>
+</pre></div>
+</div>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Solution</span>
+
+<span class="c1"># But if someone notices the BETWEEN operator, </span>
+<span class="c1"># they might do this</span>
+
+<span class="n">query</span> <span class="o">=</span> <span class="s2">&quot;&quot;&quot;SELECT TOP 10</span>
+<span class="s2">source_id, ref_epoch, ra, dec, parallax</span>
+<span class="s2">FROM gaiadr2.gaia_source</span>
+<span class="s2">WHERE parallax &lt; 1 </span>
+<span class="s2">  AND bp_rp BETWEEN -0.75 AND 2</span>
+<span class="s2">&quot;&quot;&quot;</span>
+</pre></div>
+</div>
+<p>This <a class="reference external" href="https://sci.esa.int/web/gaia/-/60198-gaia-hertzsprung-russell-diagram">Hertzsprung-Russell diagram</a> shows the BP-RP color and luminosity of stars in the Gaia catalog.</p>
+<p>Selecting stars with <code class="docutils literal notranslate"><span class="pre">bp-rp</span></code> less than 2 excludes many <a class="reference external" href="https://xkcd.com/2360/">class M dwarf stars</a>, which are low temperature, low luminosity.  A star like that at GD-1’s distance would be hard to detect, so if it is detected, it it more likely to be in the foreground.</p>
+</div>
+<div class="section" id="cleaning-up">
+<h2>Cleaning up<a class="headerlink" href="#cleaning-up" title="Permalink to this headline">¶</a></h2>
+<p>Asynchronous jobs have a <code class="docutils literal notranslate"><span class="pre">jobid</span></code>.</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">job1</span><span class="o">.</span><span class="n">jobid</span><span class="p">,</span> <span class="n">job2</span><span class="o">.</span><span class="n">jobid</span>
+</pre></div>
+</div>
+<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>(None, &#39;1601903242219O&#39;)
+</pre></div>
+</div>
+<p>Which you can use to remove the job from the server.</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">Gaia</span><span class="o">.</span><span class="n">remove_jobs</span><span class="p">([</span><span class="n">job2</span><span class="o">.</span><span class="n">jobid</span><span class="p">])</span>
+</pre></div>
+</div>
+<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>Removed jobs: &#39;[&#39;1601903242219O&#39;]&#39;.
+</pre></div>
+</div>
+<p>If you don’t remove it job from the server, it will be removed eventually, so don’t feel too bad if you don’t clean up after yourself.</p>
+</div>
+<div class="section" id="formatting-queries">
+<h2>Formatting queries<a class="headerlink" href="#formatting-queries" title="Permalink to this headline">¶</a></h2>
+<p>So far the queries have been string “literals�, meaning that the entire string is part of the program.
+But writing queries yourself can be slow, repetitive, and error-prone.</p>
+<p>It is often a good idea to write Python code that assembles a query for you.  One useful tool for that is the <a class="reference external" href="https://www.w3schools.com/python/ref_string_format.asp">string <code class="docutils literal notranslate"><span class="pre">format</span></code> method</a>.</p>
+<p>As an example, we’ll divide the previous query into two parts; a list of column names and a “base� for the query that contains everything except the column names.</p>
+<p>Here’s the list of columns we’ll select.</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">columns</span> <span class="o">=</span> <span class="s1">&#39;source_id, ra, dec, pmra, pmdec, parallax, parallax_error, radial_velocity&#39;</span>
+</pre></div>
+</div>
+<p>And here’s the base; it’s a string that contains at least one format specifier in curly brackets (braces).</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">query3_base</span> <span class="o">=</span> <span class="s2">&quot;&quot;&quot;SELECT TOP 10 </span>
+<span class="si">{columns}</span><span class="s2"></span>
+<span class="s2">FROM gaiadr2.gaia_source</span>
+<span class="s2">WHERE parallax &lt; 1</span>
+<span class="s2">  AND bp_rp BETWEEN -0.75 AND 2</span>
+<span class="s2">&quot;&quot;&quot;</span>
+</pre></div>
+</div>
+<p>This base query contains one format specifier, <code class="docutils literal notranslate"><span class="pre">{columns}</span></code>, which is a placeholder for the list of column names we will provide.</p>
+<p>To assemble the query, we invoke <code class="docutils literal notranslate"><span class="pre">format</span></code> on the base string and provide a keyword argument that assigns a value to <code class="docutils literal notranslate"><span class="pre">columns</span></code>.</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">query3</span> <span class="o">=</span> <span class="n">query3_base</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="n">columns</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>The result is a string with line breaks.  If you display it, the line breaks appear as <code class="docutils literal notranslate"><span class="pre">\n</span></code>.</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">query3</span>
+</pre></div>
+</div>
+<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>&#39;SELECT TOP 10 \nsource_id, ra, dec, pmra, pmdec, parallax, parallax_error, radial_velocity\nFROM gaiadr2.gaia_source\nWHERE parallax &lt; 1\n  AND bp_rp BETWEEN -0.75 AND 2\n&#39;
+</pre></div>
+</div>
+<p>But if you print it, the line breaks appear as… line breaks.</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">query3</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>SELECT TOP 10 
+source_id, ra, dec, pmra, pmdec, parallax, parallax_error, radial_velocity
+FROM gaiadr2.gaia_source
+WHERE parallax &lt; 1
+  AND bp_rp BETWEEN -0.75 AND 2
+</pre></div>
+</div>
+<p>Notice that the format specifier has been replaced with the value of <code class="docutils literal notranslate"><span class="pre">columns</span></code>.</p>
+<p>Let’s run it and see if it works:</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">job3</span> <span class="o">=</span> <span class="n">Gaia</span><span class="o">.</span><span class="n">launch_job</span><span class="p">(</span><span class="n">query3</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">job3</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>&lt;Table length=10&gt;
+      name       dtype    unit                              description                             n_bad
+--------------- ------- -------- ------------------------------------------------------------------ -----
+      source_id   int64          Unique source identifier (unique within a particular Data Release)     0
+             ra float64      deg                                                    Right ascension     0
+            dec float64      deg                                                        Declination     0
+           pmra float64 mas / yr                         Proper motion in right ascension direction     0
+          pmdec float64 mas / yr                             Proper motion in declination direction     0
+       parallax float64      mas                                                           Parallax     0
+ parallax_error float64      mas                                         Standard error of parallax     0
+radial_velocity float64   km / s                                                    Radial velocity    10
+Jobid: None
+Phase: COMPLETED
+Owner: None
+Output file: sync_20201005090726.xml.gz
+Results: None
+</pre></div>
+</div>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">results3</span> <span class="o">=</span> <span class="n">job3</span><span class="o">.</span><span class="n">get_results</span><span class="p">()</span>
+<span class="n">results3</span>
+</pre></div>
+</div>
+<p><i>Table length=10</i></p>
+<table id="table140265627700432" class="table-striped table-bordered table-condensed">
+<thead><tr><th>source_id</th><th>ra</th><th>dec</th><th>pmra</th><th>pmdec</th><th>parallax</th><th>parallax_error</th><th>radial_velocity</th></tr></thead>
+<thead><tr><th></th><th>deg</th><th>deg</th><th>mas / yr</th><th>mas / yr</th><th>mas</th><th>mas</th><th>km / s</th></tr></thead>
+<thead><tr><th>int64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th></tr></thead>
+<tr><td>4467710915011802624</td><td>269.9680969307347</td><td>1.1429085038160882</td><td>2.0233280236600626</td><td>-2.5692427875510266</td><td>0.42361471245557913</td><td>0.470352406647465</td><td>--</td></tr>
+<tr><td>4467706551328679552</td><td>270.033164589881</td><td>1.0565747323689927</td><td>-3.414829591355289</td><td>-3.8437215857495737</td><td>0.922888231734588</td><td>0.927008559859825</td><td>--</td></tr>
+<tr><td>4467712255037300096</td><td>270.7724717923047</td><td>0.6581664892880896</td><td>-3.5620173752896025</td><td>-6.595792323153987</td><td>-2.669179465293931</td><td>0.9719742773203504</td><td>--</td></tr>
+<tr><td>4467735001181761792</td><td>270.3628606248308</td><td>0.8947079323599124</td><td>2.1307079926489205</td><td>0.8826727710910712</td><td>0.6117399163086398</td><td>0.509812721702093</td><td>--</td></tr>
+<tr><td>4467737101421916672</td><td>270.5110834661444</td><td>0.9806225910160181</td><td>0.17532366511560785</td><td>-5.113270239706202</td><td>-0.39818224846127004</td><td>0.7549581886719651</td><td>--</td></tr>
+<tr><td>4467707547757327488</td><td>269.88746280594927</td><td>1.0212759940136962</td><td>-2.6382230817672987</td><td>-3.707776532049287</td><td>0.7741412301054209</td><td>0.3022057897812064</td><td>--</td></tr>
+<tr><td>4467732355491087744</td><td>270.6730790702491</td><td>0.9197224705139885</td><td>-2.2735991502653037</td><td>-11.864952855984358</td><td>-0.3464446494840354</td><td>0.4937921513912002</td><td>--</td></tr>
+<tr><td>4467717099766944512</td><td>270.57667173120825</td><td>0.726277659009568</td><td>-3.4598362614808367</td><td>-4.601426893365921</td><td>0.05443955111134051</td><td>0.8867339293525688</td><td>--</td></tr>
+<tr><td>4467719058265781248</td><td>270.7248052971514</td><td>0.8205551921782785</td><td>-3.255079498426542</td><td>-9.249285069111085</td><td>0.3733943917490343</td><td>0.390952370410666</td><td>--</td></tr>
+<tr><td>4467722326741572352</td><td>270.87431291888504</td><td>0.8595565975869158</td><td>0.10696398351859826</td><td>1.2035993780158853</td><td>-0.11850943432864373</td><td>0.1660452431882023</td><td>--</td></tr>
+</table>
+<p>Good so far.</p>
+<p><strong>Exercise:</strong> This query always selects sources with <code class="docutils literal notranslate"><span class="pre">parallax</span></code> less than 1.  But suppose you want to take that upper bound as an input.</p>
+<p>Modify <code class="docutils literal notranslate"><span class="pre">query3_base</span></code> to replace <code class="docutils literal notranslate"><span class="pre">1</span></code> with a format specifier like <code class="docutils literal notranslate"><span class="pre">{max_parallax}</span></code>.  Now, when you call <code class="docutils literal notranslate"><span class="pre">format</span></code>, add a keyword argument that assigns a value to <code class="docutils literal notranslate"><span class="pre">max_parallax</span></code>, and confirm that the format specifier gets replaced with the value you provide.</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Solution</span>
+
+<span class="n">query4_base</span> <span class="o">=</span> <span class="s2">&quot;&quot;&quot;SELECT TOP 10</span>
+<span class="si">{columns}</span><span class="s2"></span>
+<span class="s2">FROM gaiadr2.gaia_source</span>
+<span class="s2">WHERE parallax &lt; </span><span class="si">{max_parallax}</span><span class="s2"> AND </span>
+<span class="s2">bp_rp BETWEEN -0.75 AND 2</span>
+<span class="s2">&quot;&quot;&quot;</span>
+</pre></div>
+</div>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Solution</span>
+
+<span class="n">query4</span> <span class="o">=</span> <span class="n">query4_base</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="n">columns</span><span class="p">,</span>
+                          <span class="n">max_parallax</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">query</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>SELECT TOP 10
+source_id, ra, dec, pmra, pmdec, parallax, parallax_error, radial_velocity
+FROM gaiadr2.gaia_source
+WHERE parallax &lt; 0.5 AND 
+bp_rp BETWEEN -0.75 AND 2
+</pre></div>
+</div>
+<p><strong>Style note:</strong>  You might notice that the variable names in this notebook are numbered, like <code class="docutils literal notranslate"><span class="pre">query1</span></code>, <code class="docutils literal notranslate"><span class="pre">query2</span></code>, etc.</p>
+<p>The advantage of this style is that it isolates each section of the notebook from the others, so if you go back and run the cells out of order, it’s less likely that you will get unexpected interactions.</p>
+<p>A drawback of this style is that it can be a nuisance to update the notebook if you add, remove, or reorder a section.</p>
+<p>What do you think of this choice?  Are there alternatives you prefer?</p>
+</div>
+<div class="section" id="summary">
+<h2>Summary<a class="headerlink" href="#summary" title="Permalink to this headline">¶</a></h2>
+<p>This notebook demonstrates the following steps:</p>
+<ol class="simple">
+<li><p>Making a connection to the Gaia server,</p></li>
+<li><p>Exploring information about the database and the tables it contains,</p></li>
+<li><p>Writing a query and sending it to the server, and finally</p></li>
+<li><p>Downloading the response from the server as an Astropy <code class="docutils literal notranslate"><span class="pre">Table</span></code>.</p></li>
+</ol>
+</div>
+<div class="section" id="best-practices">
+<h2>Best practices<a class="headerlink" href="#best-practices" title="Permalink to this headline">¶</a></h2>
+<ul class="simple">
+<li><p>If you can’t download an entire dataset (or it’s not practical) use queries to select the data you need.</p></li>
+<li><p>Read the metadata and the documentation to make sure you understand the tables, their columns, and what they mean.</p></li>
+<li><p>Develop queries incrementally: start with something simple, test it, and add a little bit at a time.</p></li>
+<li><p>Use ADQL features like <code class="docutils literal notranslate"><span class="pre">TOP</span></code> and <code class="docutils literal notranslate"><span class="pre">COUNT</span></code> to test before you run a query that might return a lot of data.</p></li>
+<li><p>If you know your query will return fewer than 3000 rows, you can run it synchronously, which might complete faster (but it doesn’t seem to make much difference).  If it might return more than 3000 rows, you should run it asynchronously.</p></li>
+<li><p>ADQL and SQL are not case-sensitive, so you don’t have to capitalize the keywords, but you should.</p></li>
+<li><p>ADQL and SQL don’t require you to break a query into multiple lines, but you should.</p></li>
+</ul>
+<p>Jupyter notebooks can be good for developing and testing code, but they have some drawbacks.  In particular, if you run the cells out of order, you might find that variables don’t have the values you expect.</p>
+<p>There are a few things you can do to mitigate these problems:</p>
+<ul class="simple">
+<li><p>Make each section of the notebook self-contained.  Try not to use the same variable name in more than one section.</p></li>
+<li><p>Keep notebooks short.  Look for places where you can break your analysis into phases with one notebook per phase.</p></li>
+</ul>
+</div>
+</div>
+
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+   Chapter 3
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+    <!-- Table of contents -->
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+        
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+            <i class="fas fa-list"></i> Contents
+        </div>
+        <nav id="bd-toc-nav">
+            <ul class="nav section-nav flex-column">
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#outline">
+   Outline
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#installing-libraries">
+   Installing libraries
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#selecting-a-region">
+   Selecting a region
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#getting-gd-1-data">
+   Getting GD-1 Data
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#working-with-coordinates">
+   Working with coordinates
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#selecting-a-rectangle">
+   Selecting a rectangle
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#selecting-a-polygon">
+   Selecting a polygon
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#saving-results">
+   Saving results
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#summary">
+   Summary
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#best-practices">
+   Best practices
+  </a>
+ </li>
+</ul>
+
+        </nav>
+        
+    </div>
+</div>
+    <div id="main-content" class="row">
+        <div class="col-12 col-md-9 pl-md-3 pr-md-0">
+        
+              <div>
+                
+  <div class="section" id="chapter-2">
+<h1>Chapter 2<a class="headerlink" href="#chapter-2" title="Permalink to this headline">¶</a></h1>
+<p>This is the second in a series of notebooks related to astronomy data.</p>
+<p>As a running example, we are replicating parts of the analysis in a recent paper, “<a class="reference external" href="https://arxiv.org/abs/1805.00425">Off the beaten path: Gaia reveals GD-1 stars outside of the main stream</a>� by Adrian M. Price-Whelan and Ana Bonaca.</p>
+<p>In the first notebook, we wrote ADQL queries and used them to select and download data from the Gaia server.</p>
+<p>In this notebook, we’ll pick up where we left off and write a query to select stars from the region of the sky where we expect GD-1 to be.</p>
+<div class="section" id="outline">
+<h2>Outline<a class="headerlink" href="#outline" title="Permalink to this headline">¶</a></h2>
+<p>We’ll start with an example that does a “cone search�; that is, it selects stars that appear in a circular region of the sky.</p>
+<p>Then, to select stars in the vicinity of GD-1, we’ll:</p>
+<ul class="simple">
+<li><p>Use <code class="docutils literal notranslate"><span class="pre">Quantity</span></code> objects to represent measurements with units.</p></li>
+<li><p>Use the <code class="docutils literal notranslate"><span class="pre">Gala</span></code> library to convert coordinates from one frame to another.</p></li>
+<li><p>Use the ADQL keywords <code class="docutils literal notranslate"><span class="pre">POLYGON</span></code>, <code class="docutils literal notranslate"><span class="pre">CONTAINS</span></code>, and <code class="docutils literal notranslate"><span class="pre">POINT</span></code> to select stars that fall within a polygonal region.</p></li>
+<li><p>Submit a query and download the results.</p></li>
+<li><p>Store the results in a FITS file.</p></li>
+</ul>
+<p>After completing this lesson, you should be able to</p>
+<ul class="simple">
+<li><p>Use Python string formatting to compose more complex ADQL queries.</p></li>
+<li><p>Work with coordinates and other quantities that have units.</p></li>
+<li><p>Download the results of a query and store them in a file.</p></li>
+</ul>
+</div>
+<div class="section" id="installing-libraries">
+<h2>Installing libraries<a class="headerlink" href="#installing-libraries" title="Permalink to this headline">¶</a></h2>
+<p>If you are running this notebook on Colab, you can run the following cell to install Astroquery and a the other libraries we’ll use.</p>
+<p>If you are running this notebook on your own computer, you might have to install these libraries yourself.</p>
+<p>If you are using this notebook as part of a Carpentries workshop, you should have received setup instructions.</p>
+<p>TODO: Add a link to the instructions.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># If we&#39;re running on Colab, install libraries</span>
+
+<span class="kn">import</span> <span class="nn">sys</span>
+<span class="n">IN_COLAB</span> <span class="o">=</span> <span class="s1">&#39;google.colab&#39;</span> <span class="ow">in</span> <span class="n">sys</span><span class="o">.</span><span class="n">modules</span>
+
+<span class="k">if</span> <span class="n">IN_COLAB</span><span class="p">:</span>
+    <span class="o">!</span>pip install astroquery astro-gala pyia
+</pre></div>
+</div>
+</div>
+</div>
+</div>
+<div class="section" id="selecting-a-region">
+<h2>Selecting a region<a class="headerlink" href="#selecting-a-region" title="Permalink to this headline">¶</a></h2>
+<p>One of the most common ways to restrict a query is to select stars in a particular region of the sky.</p>
+<p>For example, here’s a query from the <a class="reference external" href="https://gea.esac.esa.int/archive-help/adql/examples/index.html">Gaia archive documentation</a> that selects “all the objects … in a circular region centered at (266.41683, -29.00781) with a search radius of 5 arcmin (0.08333 deg).�</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">query</span> <span class="o">=</span> <span class="s2">&quot;&quot;&quot;</span>
+<span class="s2">SELECT </span>
+<span class="s2">TOP 10 source_id</span>
+<span class="s2">FROM gaiadr2.gaia_source</span>
+<span class="s2">WHERE 1=CONTAINS(</span>
+<span class="s2">  POINT(ra, dec),</span>
+<span class="s2">  CIRCLE(266.41683, -29.00781, 0.08333333))</span>
+<span class="s2">&quot;&quot;&quot;</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>This query uses three keywords that are specific to ADQL (not SQL):</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">POINT</span></code>: a location in <a class="reference external" href="https://en.wikipedia.org/wiki/International_Celestial_Reference_System">ICRS coordinates</a>, specified in degrees of right ascension and declination.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">CIRCLE</span></code>: a circle where the first two values are the coordinates of the center and the third is the radius in degrees.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">CONTAINS</span></code>: a function that returns <code class="docutils literal notranslate"><span class="pre">1</span></code> if a <code class="docutils literal notranslate"><span class="pre">POINT</span></code> is contained in a shape and <code class="docutils literal notranslate"><span class="pre">0</span></code> otherwise.</p></li>
+</ul>
+<p>Here is the <a class="reference external" href="http://www.ivoa.net/documents/ADQL/20180112/PR-ADQL-2.1-20180112.html#tth_sEc4.2.12">documentation of <code class="docutils literal notranslate"><span class="pre">CONTAINS</span></code></a>.</p>
+<p>A query like this is called a cone search because it selects stars in a cone.</p>
+<p>Here’s how we run it.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">astroquery.gaia</span> <span class="kn">import</span> <span class="n">Gaia</span>
+
+<span class="n">job</span> <span class="o">=</span> <span class="n">Gaia</span><span class="o">.</span><span class="n">launch_job</span><span class="p">(</span><span class="n">query</span><span class="p">)</span>
+<span class="n">result</span> <span class="o">=</span> <span class="n">job</span><span class="o">.</span><span class="n">get_results</span><span class="p">()</span>
+<span class="n">result</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Created TAP+ (v1.2.1) - Connection:
+	Host: gea.esac.esa.int
+	Use HTTPS: True
+	Port: 443
+	SSL Port: 443
+Created TAP+ (v1.2.1) - Connection:
+	Host: geadata.esac.esa.int
+	Use HTTPS: True
+	Port: 443
+	SSL Port: 443
+</pre></div>
+</div>
+<div class="output text_html"><i>Table length=10</i>
+<table id="table139949838258672" class="table-striped table-bordered table-condensed">
+<thead><tr><th>source_id</th></tr></thead>
+<thead><tr><th>int64</th></tr></thead>
+<tr><td>4057468321929794432</td></tr>
+<tr><td>4057468287575835392</td></tr>
+<tr><td>4057482027171038976</td></tr>
+<tr><td>4057470349160630656</td></tr>
+<tr><td>4057470039924301696</td></tr>
+<tr><td>4057469868125641984</td></tr>
+<tr><td>4057468351995073024</td></tr>
+<tr><td>4057469661959554560</td></tr>
+<tr><td>4057470520960672640</td></tr>
+<tr><td>4057470555320409600</td></tr>
+</table></div></div>
+</div>
+<p><strong>Exercise:</strong> When you are debugging queries like this, you can use <code class="docutils literal notranslate"><span class="pre">TOP</span></code> to limit the size of the results, but then you still don’t know how big the results will be.</p>
+<p>An alternative is to use <code class="docutils literal notranslate"><span class="pre">COUNT</span></code>, which asks for the number of rows that would be selected, but it does not return them.</p>
+<p>In the previous query, replace <code class="docutils literal notranslate"><span class="pre">TOP</span> <span class="pre">10</span> <span class="pre">source_id</span></code> with <code class="docutils literal notranslate"><span class="pre">COUNT(source_id)</span></code> and run the query again.  How many stars has Gaia identified in the cone we searched?</p>
+</div>
+<div class="section" id="getting-gd-1-data">
+<h2>Getting GD-1 Data<a class="headerlink" href="#getting-gd-1-data" title="Permalink to this headline">¶</a></h2>
+<p>From the Price-Whelan and Bonaca paper, we will try to reproduce Figure 1, which includes this representation of stars likely to belong to GD-1:</p>
+<img src="https://github.com/datacarpentry/astronomy-python/raw/gh-pages/fig/gd1-4.png"><p>Along the axis of right ascension (<span class="math notranslate nohighlight">\(\phi_1\)</span>) the figure extends from -100 to 20 degrees.</p>
+<p>Along the axis of declination (<span class="math notranslate nohighlight">\(\phi_2\)</span>) the figure extends from about -8 to 4 degrees.</p>
+<p>Ideally, we would select all stars from this rectangle, but there are more than 10 million of them, so</p>
+<ul class="simple">
+<li><p>That would be difficult to work with,</p></li>
+<li><p>As anonymous users, we are limited to 3 million rows in a single query, and</p></li>
+<li><p>While we are developing and testing code, it will be faster to work with a smaller dataset.</p></li>
+</ul>
+<p>So we’ll start by selecting stars in a smaller rectangle, from -55 to -45 degrees right ascension and -8 to 4 degrees of declination.</p>
+<p>But first we let’s see how to represent quantities with units like degrees.</p>
+</div>
+<div class="section" id="working-with-coordinates">
+<h2>Working with coordinates<a class="headerlink" href="#working-with-coordinates" title="Permalink to this headline">¶</a></h2>
+<p>Coordinates are physical quantities, which means that they have two parts, a value and a unit.</p>
+<p>For example, the coordinate <span class="math notranslate nohighlight">\(30^{\circ}\)</span> has value 30 and its units are degrees.</p>
+<p>Until recently, most scientific computation was done with values only; units were left out of the program altogether, <a class="reference external" href="https://en.wikipedia.org/wiki/Mars_Climate_Orbiter#Cause_of_failure">often with disastrous results</a>.</p>
+<p>Astropy provides tools for including units explicitly in computations, which makes it possible to detect errors before they cause disasters.</p>
+<p>To use Astropy units, we import them like this:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">astropy.units</span> <span class="k">as</span> <span class="nn">u</span>
+
+<span class="n">u</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;module &#39;astropy.units&#39; from &#39;/home/downey/anaconda3/envs/AstronomicalData/lib/python3.8/site-packages/astropy/units/__init__.py&#39;&gt;
+</pre></div>
+</div>
+</div>
+</div>
+<p><code class="docutils literal notranslate"><span class="pre">u</span></code> is an object that contains most common units and all SI units.</p>
+<p>You can use <code class="docutils literal notranslate"><span class="pre">dir</span></code> to list them, but you should also <a class="reference external" href="https://docs.astropy.org/en/stable/units/">read the documentation</a>.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="nb">dir</span><span class="p">(</span><span class="n">u</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[&#39;A&#39;,
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+ &#39;ABmag&#39;,
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+ &#39;Angstrom&#39;,
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+ &#39;Emin&#39;,
+ &#39;Emol&#39;,
+ &#39;Eohm&#39;,
+ &#39;Epc&#39;,
+ &#39;Eph&#39;,
+ &#39;Ephoton&#39;,
+ &#39;Epix&#39;,
+ &#39;Epixel&#39;,
+ &#39;Erad&#39;,
+ &#39;Es&#39;,
+ &#39;Esr&#39;,
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+ &#39;Mmol&#39;,
+ &#39;Mohm&#39;,
+ &#39;Mpc&#39;,
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+ &#39;__name__&#39;,
+ &#39;__package__&#39;,
+ &#39;__path__&#39;,
+ &#39;__spec__&#39;,
+ &#39;a&#39;,
+ &#39;aA&#39;,
+ &#39;aAU&#39;,
+ &#39;aB&#39;,
+ &#39;aBa&#39;,
+ &#39;aC&#39;,
+ &#39;aD&#39;,
+ &#39;aF&#39;,
+ &#39;aG&#39;,
+ &#39;aGal&#39;,
+ &#39;aH&#39;,
+ &#39;aHz&#39;,
+ &#39;aJ&#39;,
+ &#39;aJy&#39;,
+ &#39;aK&#39;,
+ &#39;aL&#39;,
+ &#39;aN&#39;,
+ &#39;aOhm&#39;,
+ &#39;aP&#39;,
+ &#39;aPa&#39;,
+ &#39;aR&#39;,
+ &#39;aRy&#39;,
+ &#39;aS&#39;,
+ &#39;aSt&#39;,
+ &#39;aT&#39;,
+ &#39;aV&#39;,
+ &#39;aW&#39;,
+ &#39;aWb&#39;,
+ &#39;aa&#39;,
+ &#39;aadu&#39;,
+ &#39;aarcmin&#39;,
+ &#39;aarcsec&#39;,
+ &#39;aau&#39;,
+ &#39;ab&#39;,
+ &#39;abA&#39;,
+ &#39;abC&#39;,
+ &#39;abampere&#39;,
+ &#39;abarn&#39;,
+ &#39;abcoulomb&#39;,
+ &#39;abeam&#39;,
+ &#39;abin&#39;,
+ &#39;abit&#39;,
+ &#39;abyte&#39;,
+ &#39;acd&#39;,
+ &#39;achan&#39;,
+ &#39;acount&#39;,
+ &#39;act&#39;,
+ &#39;ad&#39;,
+ &#39;add_enabled_equivalencies&#39;,
+ &#39;add_enabled_units&#39;,
+ &#39;adeg&#39;,
+ &#39;adu&#39;,
+ &#39;adyn&#39;,
+ &#39;aeV&#39;,
+ &#39;aerg&#39;,
+ &#39;ag&#39;,
+ &#39;ah&#39;,
+ &#39;ak&#39;,
+ &#39;al&#39;,
+ &#39;allclose&#39;,
+ &#39;alm&#39;,
+ &#39;alx&#39;,
+ &#39;alyr&#39;,
+ &#39;am&#39;,
+ &#39;amag&#39;,
+ &#39;amin&#39;,
+ &#39;amol&#39;,
+ &#39;amp&#39;,
+ &#39;ampere&#39;,
+ &#39;angstrom&#39;,
+ &#39;annum&#39;,
+ &#39;aohm&#39;,
+ &#39;apc&#39;,
+ &#39;aph&#39;,
+ &#39;aphoton&#39;,
+ &#39;apix&#39;,
+ &#39;apixel&#39;,
+ &#39;arad&#39;,
+ &#39;arcmin&#39;,
+ &#39;arcminute&#39;,
+ &#39;arcsec&#39;,
+ &#39;arcsecond&#39;,
+ &#39;asr&#39;,
+ &#39;astronomical_unit&#39;,
+ &#39;astrophys&#39;,
+ &#39;attoBarye&#39;,
+ &#39;attoDa&#39;,
+ &#39;attoDalton&#39;,
+ &#39;attoDebye&#39;,
+ &#39;attoFarad&#39;,
+ &#39;attoGauss&#39;,
+ &#39;attoHenry&#39;,
+ &#39;attoHertz&#39;,
+ &#39;attoJansky&#39;,
+ &#39;attoJoule&#39;,
+ &#39;attoKayser&#39;,
+ &#39;attoKelvin&#39;,
+ &#39;attoNewton&#39;,
+ &#39;attoOhm&#39;,
+ &#39;attoPascal&#39;,
+ &#39;attoRayleigh&#39;,
+ &#39;attoSiemens&#39;,
+ &#39;attoTesla&#39;,
+ &#39;attoVolt&#39;,
+ &#39;attoWatt&#39;,
+ &#39;attoWeber&#39;,
+ &#39;attoamp&#39;,
+ &#39;attoampere&#39;,
+ &#39;attoannum&#39;,
+ &#39;attoarcminute&#39;,
+ &#39;attoarcsecond&#39;,
+ &#39;attoastronomical_unit&#39;,
+ &#39;attobarn&#39;,
+ &#39;attobarye&#39;,
+ &#39;attobit&#39;,
+ &#39;attobyte&#39;,
+ &#39;attocandela&#39;,
+ &#39;attocoulomb&#39;,
+ &#39;attocount&#39;,
+ &#39;attoday&#39;,
+ &#39;attodebye&#39;,
+ &#39;attodegree&#39;,
+ &#39;attodyne&#39;,
+ &#39;attoelectronvolt&#39;,
+ &#39;attofarad&#39;,
+ &#39;attogal&#39;,
+ &#39;attogauss&#39;,
+ &#39;attogram&#39;,
+ &#39;attohenry&#39;,
+ &#39;attohertz&#39;,
+ &#39;attohour&#39;,
+ &#39;attohr&#39;,
+ &#39;attojansky&#39;,
+ &#39;attojoule&#39;,
+ &#39;attokayser&#39;,
+ &#39;attolightyear&#39;,
+ &#39;attoliter&#39;,
+ &#39;attolumen&#39;,
+ &#39;attolux&#39;,
+ &#39;attometer&#39;,
+ &#39;attominute&#39;,
+ &#39;attomole&#39;,
+ &#39;attonewton&#39;,
+ &#39;attoparsec&#39;,
+ &#39;attopascal&#39;,
+ &#39;attophoton&#39;,
+ &#39;attopixel&#39;,
+ &#39;attopoise&#39;,
+ &#39;attoradian&#39;,
+ &#39;attorayleigh&#39;,
+ &#39;attorydberg&#39;,
+ &#39;attosecond&#39;,
+ &#39;attosiemens&#39;,
+ &#39;attosteradian&#39;,
+ &#39;attostokes&#39;,
+ &#39;attotesla&#39;,
+ &#39;attovolt&#39;,
+ &#39;attovoxel&#39;,
+ &#39;attowatt&#39;,
+ &#39;attoweber&#39;,
+ &#39;attoyear&#39;,
+ &#39;au&#39;,
+ &#39;avox&#39;,
+ &#39;avoxel&#39;,
+ &#39;ayr&#39;,
+ &#39;b&#39;,
+ &#39;bar&#39;,
+ &#39;barn&#39;,
+ &#39;barye&#39;,
+ &#39;beam&#39;,
+ &#39;beam_angular_area&#39;,
+ &#39;becquerel&#39;,
+ &#39;bin&#39;,
+ &#39;binary_prefixes&#39;,
+ &#39;bit&#39;,
+ &#39;bol&#39;,
+ &#39;brightness_temperature&#39;,
+ &#39;byte&#39;,
+ &#39;cA&#39;,
+ &#39;cAU&#39;,
+ &#39;cB&#39;,
+ &#39;cBa&#39;,
+ &#39;cC&#39;,
+ &#39;cD&#39;,
+ &#39;cF&#39;,
+ &#39;cG&#39;,
+ &#39;cGal&#39;,
+ &#39;cH&#39;,
+ &#39;cHz&#39;,
+ &#39;cJ&#39;,
+ &#39;cJy&#39;,
+ &#39;cK&#39;,
+ &#39;cL&#39;,
+ &#39;cN&#39;,
+ &#39;cOhm&#39;,
+ &#39;cP&#39;,
+ &#39;cPa&#39;,
+ &#39;cR&#39;,
+ &#39;cRy&#39;,
+ &#39;cS&#39;,
+ &#39;cSt&#39;,
+ &#39;cT&#39;,
+ &#39;cV&#39;,
+ &#39;cW&#39;,
+ &#39;cWb&#39;,
+ &#39;ca&#39;,
+ &#39;cadu&#39;,
+ &#39;candela&#39;,
+ &#39;carcmin&#39;,
+ &#39;carcsec&#39;,
+ &#39;cau&#39;,
+ &#39;cb&#39;,
+ &#39;cbarn&#39;,
+ &#39;cbeam&#39;,
+ &#39;cbin&#39;,
+ &#39;cbit&#39;,
+ &#39;cbyte&#39;,
+ &#39;ccd&#39;,
+ &#39;cchan&#39;,
+ &#39;ccount&#39;,
+ &#39;cct&#39;,
+ &#39;cd&#39;,
+ &#39;cdeg&#39;,
+ &#39;cdyn&#39;,
+ &#39;ceV&#39;,
+ &#39;centiBarye&#39;,
+ &#39;centiDa&#39;,
+ &#39;centiDalton&#39;,
+ &#39;centiDebye&#39;,
+ &#39;centiFarad&#39;,
+ &#39;centiGauss&#39;,
+ &#39;centiHenry&#39;,
+ &#39;centiHertz&#39;,
+ &#39;centiJansky&#39;,
+ &#39;centiJoule&#39;,
+ &#39;centiKayser&#39;,
+ &#39;centiKelvin&#39;,
+ &#39;centiNewton&#39;,
+ &#39;centiOhm&#39;,
+ &#39;centiPascal&#39;,
+ &#39;centiRayleigh&#39;,
+ &#39;centiSiemens&#39;,
+ &#39;centiTesla&#39;,
+ &#39;centiVolt&#39;,
+ &#39;centiWatt&#39;,
+ &#39;centiWeber&#39;,
+ &#39;centiamp&#39;,
+ &#39;centiampere&#39;,
+ &#39;centiannum&#39;,
+ &#39;centiarcminute&#39;,
+ &#39;centiarcsecond&#39;,
+ &#39;centiastronomical_unit&#39;,
+ &#39;centibarn&#39;,
+ &#39;centibarye&#39;,
+ &#39;centibit&#39;,
+ &#39;centibyte&#39;,
+ &#39;centicandela&#39;,
+ &#39;centicoulomb&#39;,
+ &#39;centicount&#39;,
+ &#39;centiday&#39;,
+ &#39;centidebye&#39;,
+ &#39;centidegree&#39;,
+ &#39;centidyne&#39;,
+ &#39;centielectronvolt&#39;,
+ &#39;centifarad&#39;,
+ &#39;centigal&#39;,
+ &#39;centigauss&#39;,
+ &#39;centigram&#39;,
+ &#39;centihenry&#39;,
+ &#39;centihertz&#39;,
+ &#39;centihour&#39;,
+ &#39;centihr&#39;,
+ &#39;centijansky&#39;,
+ &#39;centijoule&#39;,
+ &#39;centikayser&#39;,
+ &#39;centilightyear&#39;,
+ &#39;centiliter&#39;,
+ &#39;centilumen&#39;,
+ &#39;centilux&#39;,
+ &#39;centimeter&#39;,
+ &#39;centiminute&#39;,
+ &#39;centimole&#39;,
+ &#39;centinewton&#39;,
+ &#39;centiparsec&#39;,
+ &#39;centipascal&#39;,
+ &#39;centiphoton&#39;,
+ &#39;centipixel&#39;,
+ &#39;centipoise&#39;,
+ &#39;centiradian&#39;,
+ &#39;centirayleigh&#39;,
+ &#39;centirydberg&#39;,
+ &#39;centisecond&#39;,
+ &#39;centisiemens&#39;,
+ &#39;centisteradian&#39;,
+ &#39;centistokes&#39;,
+ &#39;centitesla&#39;,
+ &#39;centivolt&#39;,
+ &#39;centivoxel&#39;,
+ &#39;centiwatt&#39;,
+ &#39;centiweber&#39;,
+ &#39;centiyear&#39;,
+ &#39;cerg&#39;,
+ &#39;cg&#39;,
+ &#39;cgs&#39;,
+ &#39;ch&#39;,
+ &#39;chan&#39;,
+ &#39;ck&#39;,
+ &#39;cl&#39;,
+ &#39;clm&#39;,
+ &#39;clx&#39;,
+ &#39;clyr&#39;,
+ &#39;cm&#39;,
+ &#39;cmag&#39;,
+ &#39;cmin&#39;,
+ &#39;cmol&#39;,
+ &#39;cohm&#39;,
+ &#39;core&#39;,
+ &#39;coulomb&#39;,
+ &#39;count&#39;,
+ &#39;cpc&#39;,
+ &#39;cph&#39;,
+ &#39;cphoton&#39;,
+ &#39;cpix&#39;,
+ &#39;cpixel&#39;,
+ &#39;crad&#39;,
+ &#39;cs&#39;,
+ &#39;csr&#39;,
+ &#39;ct&#39;,
+ &#39;cu&#39;,
+ &#39;curie&#39;,
+ &#39;cvox&#39;,
+ &#39;cvoxel&#39;,
+ &#39;cy&#39;,
+ &#39;cycle&#39;,
+ &#39;cyr&#39;,
+ &#39;d&#39;,
+ &#39;dA&#39;,
+ &#39;dAU&#39;,
+ &#39;dB&#39;,
+ &#39;dBa&#39;,
+ &#39;dC&#39;,
+ &#39;dD&#39;,
+ &#39;dF&#39;,
+ &#39;dG&#39;,
+ &#39;dGal&#39;,
+ &#39;dH&#39;,
+ &#39;dHz&#39;,
+ &#39;dJ&#39;,
+ &#39;dJy&#39;,
+ &#39;dK&#39;,
+ &#39;dL&#39;,
+ &#39;dN&#39;,
+ &#39;dOhm&#39;,
+ &#39;dP&#39;,
+ &#39;dPa&#39;,
+ &#39;dR&#39;,
+ &#39;dRy&#39;,
+ &#39;dS&#39;,
+ &#39;dSt&#39;,
+ &#39;dT&#39;,
+ ...]
+</pre></div>
+</div>
+</div>
+</div>
+<p>To create a quantity, we multiply a value by a unit.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">coord</span> <span class="o">=</span> <span class="mi">30</span> <span class="o">*</span> <span class="n">u</span><span class="o">.</span><span class="n">deg</span>
+<span class="nb">type</span><span class="p">(</span><span class="n">coord</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>astropy.units.quantity.Quantity
+</pre></div>
+</div>
+</div>
+</div>
+<p>The result is a <code class="docutils literal notranslate"><span class="pre">Quantity</span></code> object.</p>
+<p>Jupyter knows how to display <code class="docutils literal notranslate"><span class="pre">Quantities</span></code> like this:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">coord</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_latex math notranslate nohighlight">
+\[30 \; \mathrm{{}^{\circ}}\]</div>
+</div>
+</div>
+</div>
+<div class="section" id="selecting-a-rectangle">
+<h2>Selecting a rectangle<a class="headerlink" href="#selecting-a-rectangle" title="Permalink to this headline">¶</a></h2>
+<p>Now we’ll select a rectangle from -55 to -45 degrees right ascension and -8 to 4 degrees of declination.</p>
+<p>We’ll define variables to contain these limits.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">phi1_min</span> <span class="o">=</span> <span class="o">-</span><span class="mi">55</span>
+<span class="n">phi1_max</span> <span class="o">=</span> <span class="o">-</span><span class="mi">45</span>
+<span class="n">phi2_min</span> <span class="o">=</span> <span class="o">-</span><span class="mi">8</span>
+<span class="n">phi2_max</span> <span class="o">=</span> <span class="mi">4</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>To represent a rectangle, we’ll use two lists of coordinates and multiply by their units.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">phi1_rect</span> <span class="o">=</span> <span class="p">[</span><span class="n">phi1_min</span><span class="p">,</span> <span class="n">phi1_min</span><span class="p">,</span> <span class="n">phi1_max</span><span class="p">,</span> <span class="n">phi1_max</span><span class="p">]</span> <span class="o">*</span> <span class="n">u</span><span class="o">.</span><span class="n">deg</span>
+<span class="n">phi2_rect</span> <span class="o">=</span> <span class="p">[</span><span class="n">phi2_min</span><span class="p">,</span> <span class="n">phi2_max</span><span class="p">,</span> <span class="n">phi2_max</span><span class="p">,</span> <span class="n">phi2_min</span><span class="p">]</span> <span class="o">*</span> <span class="n">u</span><span class="o">.</span><span class="n">deg</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p><code class="docutils literal notranslate"><span class="pre">phi1_rect</span></code> and <code class="docutils literal notranslate"><span class="pre">phi2_rect</span></code> represent the coordinates of the corners of a rectangle.</p>
+<p>But they are in “<a class="reference external" href="https://gala-astro.readthedocs.io/en/latest/_modules/gala/coordinates/gd1.html">a Heliocentric spherical coordinate system defined by the orbit of the GD1 stream</a>�</p>
+<p>In order to use them in a Gaia query, we have to convert them to <a class="reference external" href="https://en.wikipedia.org/wiki/International_Celestial_Reference_System">International Celestial Reference System</a> (ICRS) coordinates.  We can do that by storing the coordinates in a <code class="docutils literal notranslate"><span class="pre">GD1Koposov10</span></code> object provided by <a class="reference external" href="https://gala-astro.readthedocs.io/en/latest/coordinates/">Gala</a>.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">gala.coordinates</span> <span class="k">as</span> <span class="nn">gc</span>
+
+<span class="n">corners</span> <span class="o">=</span> <span class="n">gc</span><span class="o">.</span><span class="n">GD1Koposov10</span><span class="p">(</span><span class="n">phi1</span><span class="o">=</span><span class="n">phi1_rect</span><span class="p">,</span> <span class="n">phi2</span><span class="o">=</span><span class="n">phi2_rect</span><span class="p">)</span>
+<span class="nb">type</span><span class="p">(</span><span class="n">corners</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>gala.coordinates.gd1.GD1Koposov10
+</pre></div>
+</div>
+</div>
+</div>
+<p>We can display the result like this:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">corners</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;GD1Koposov10 Coordinate: (phi1, phi2) in deg
+    [(-55., -8.), (-55.,  4.), (-45.,  4.), (-45., -8.)]&gt;
+</pre></div>
+</div>
+</div>
+</div>
+<p>Now we can use <code class="docutils literal notranslate"><span class="pre">transform_to</span></code> to convert to ICRS coordinates.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">astropy.coordinates</span> <span class="k">as</span> <span class="nn">coord</span>
+
+<span class="n">corners_icrs</span> <span class="o">=</span> <span class="n">corners</span><span class="o">.</span><span class="n">transform_to</span><span class="p">(</span><span class="n">coord</span><span class="o">.</span><span class="n">ICRS</span><span class="p">)</span>
+<span class="nb">type</span><span class="p">(</span><span class="n">corners_icrs</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>astropy.coordinates.builtin_frames.icrs.ICRS
+</pre></div>
+</div>
+</div>
+</div>
+<p>The result is an <code class="docutils literal notranslate"><span class="pre">ICRS</span></code> object.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">corners_icrs</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;ICRS Coordinate: (ra, dec) in deg
+    [(146.27533314, 19.26190982), (135.42163944, 25.87738723),
+     (141.60264825, 34.3048303 ), (152.81671045, 27.13611254)]&gt;
+</pre></div>
+</div>
+</div>
+</div>
+<p>Notice that a rectangle in one coordinate system is not necessarily a rectangle in another.  In this example, the result is a polygon.</p>
+</div>
+<div class="section" id="selecting-a-polygon">
+<h2>Selecting a polygon<a class="headerlink" href="#selecting-a-polygon" title="Permalink to this headline">¶</a></h2>
+<p>In order to use this polygon as part of an ADQL query, we have to convert it to a string with a comma-separated list of coordinates, as in this example:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">POLYGON(143.65, 20.98, </span>
+<span class="sd">        134.46, 26.39, </span>
+<span class="sd">        140.58, 34.85, </span>
+<span class="sd">        150.16, 29.01)</span>
+<span class="sd">&quot;&quot;&quot;</span>
+</pre></div>
+</div>
+<p><code class="docutils literal notranslate"><span class="pre">corners_icrs</span></code> behaves like a list, so we can use a <code class="docutils literal notranslate"><span class="pre">for</span></code> loop to iterate through the points.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">for</span> <span class="n">point</span> <span class="ow">in</span> <span class="n">corners_icrs</span><span class="p">:</span>
+    <span class="nb">print</span><span class="p">(</span><span class="n">point</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;ICRS Coordinate: (ra, dec) in deg
+    (146.27533314, 19.26190982)&gt;
+&lt;ICRS Coordinate: (ra, dec) in deg
+    (135.42163944, 25.87738723)&gt;
+&lt;ICRS Coordinate: (ra, dec) in deg
+    (141.60264825, 34.3048303)&gt;
+&lt;ICRS Coordinate: (ra, dec) in deg
+    (152.81671045, 27.13611254)&gt;
+</pre></div>
+</div>
+</div>
+</div>
+<p>From that, we can select the coordinates <code class="docutils literal notranslate"><span class="pre">ra</span></code> and <code class="docutils literal notranslate"><span class="pre">dec</span></code>:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">for</span> <span class="n">point</span> <span class="ow">in</span> <span class="n">corners_icrs</span><span class="p">:</span>
+    <span class="nb">print</span><span class="p">(</span><span class="n">point</span><span class="o">.</span><span class="n">ra</span><span class="p">,</span> <span class="n">point</span><span class="o">.</span><span class="n">dec</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>146d16m31.1993s 19d15m42.8754s
+135d25m17.902s 25d52m38.594s
+141d36m09.5337s 34d18m17.3891s
+152d49m00.1576s 27d08m10.0051s
+</pre></div>
+</div>
+</div>
+</div>
+<p>The results are quantities with units, but if we select the <code class="docutils literal notranslate"><span class="pre">value</span></code> part, we get a dimensionless floating-point number.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">for</span> <span class="n">point</span> <span class="ow">in</span> <span class="n">corners_icrs</span><span class="p">:</span>
+    <span class="nb">print</span><span class="p">(</span><span class="n">point</span><span class="o">.</span><span class="n">ra</span><span class="o">.</span><span class="n">value</span><span class="p">,</span> <span class="n">point</span><span class="o">.</span><span class="n">dec</span><span class="o">.</span><span class="n">value</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>146.27533313607782 19.261909820533692
+135.42163944306296 25.87738722767213
+141.60264825107333 34.304830296257144
+152.81671044675923 27.136112541397996
+</pre></div>
+</div>
+</div>
+</div>
+<p>We can use string <code class="docutils literal notranslate"><span class="pre">format</span></code> to convert these numbers to strings.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">point_base</span> <span class="o">=</span> <span class="s2">&quot;</span><span class="si">{point.ra.value}</span><span class="s2">, </span><span class="si">{point.dec.value}</span><span class="s2">&quot;</span>
+
+<span class="n">t</span> <span class="o">=</span> <span class="p">[</span><span class="n">point_base</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">point</span><span class="o">=</span><span class="n">point</span><span class="p">)</span>
+     <span class="k">for</span> <span class="n">point</span> <span class="ow">in</span> <span class="n">corners_icrs</span><span class="p">]</span>
+<span class="n">t</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[&#39;146.27533313607782, 19.261909820533692&#39;,
+ &#39;135.42163944306296, 25.87738722767213&#39;,
+ &#39;141.60264825107333, 34.304830296257144&#39;,
+ &#39;152.81671044675923, 27.136112541397996&#39;]
+</pre></div>
+</div>
+</div>
+</div>
+<p>The result is a list of strings, which we can join into a single string using <code class="docutils literal notranslate"><span class="pre">join</span></code>.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">point_list</span> <span class="o">=</span> <span class="s1">&#39;, &#39;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">t</span><span class="p">)</span>
+<span class="n">point_list</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&#39;146.27533313607782, 19.261909820533692, 135.42163944306296, 25.87738722767213, 141.60264825107333, 34.304830296257144, 152.81671044675923, 27.136112541397996&#39;
+</pre></div>
+</div>
+</div>
+</div>
+<p>Notice that we invoke <code class="docutils literal notranslate"><span class="pre">join</span></code> on a string and pass the list as an argument.</p>
+<p>Before we can assemble the query, we need <code class="docutils literal notranslate"><span class="pre">columns</span></code> again (as we saw in the previous notebook).</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">columns</span> <span class="o">=</span> <span class="s1">&#39;source_id, ra, dec, pmra, pmdec, parallax, parallax_error, radial_velocity&#39;</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>Here’s the base for the query, with format specifiers for <code class="docutils literal notranslate"><span class="pre">columns</span></code> and <code class="docutils literal notranslate"><span class="pre">point_list</span></code>.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">query_base</span> <span class="o">=</span> <span class="s2">&quot;&quot;&quot;SELECT </span><span class="si">{columns}</span><span class="s2"></span>
+<span class="s2">FROM gaiadr2.gaia_source</span>
+<span class="s2">WHERE parallax &lt; 1</span>
+<span class="s2">  AND bp_rp BETWEEN -0.75 AND 2 </span>
+<span class="s2">  AND 1 = CONTAINS(POINT(ra, dec), </span>
+<span class="s2">                   POLYGON(</span><span class="si">{point_list}</span><span class="s2">))</span>
+<span class="s2">&quot;&quot;&quot;</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>And here’s the result:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">query</span> <span class="o">=</span> <span class="n">query_base</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="n">columns</span><span class="p">,</span> 
+                          <span class="n">point_list</span><span class="o">=</span><span class="n">point_list</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">query</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>SELECT source_id, ra, dec, pmra, pmdec, parallax, parallax_error, radial_velocity
+FROM gaiadr2.gaia_source
+WHERE parallax &lt; 1
+  AND bp_rp BETWEEN -0.75 AND 2 
+  AND 1 = CONTAINS(POINT(ra, dec), 
+                   POLYGON(146.27533313607782, 19.261909820533692, 135.42163944306296, 25.87738722767213, 141.60264825107333, 34.304830296257144, 152.81671044675923, 27.136112541397996))
+</pre></div>
+</div>
+</div>
+</div>
+<p>As always, we should take a minute to proof-read the query before we launch it.</p>
+<p>The result will be bigger than our previous queries, so it will take a little longer.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">job</span> <span class="o">=</span> <span class="n">Gaia</span><span class="o">.</span><span class="n">launch_job_async</span><span class="p">(</span><span class="n">query</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">job</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>INFO: Query finished. [astroquery.utils.tap.core]
+&lt;Table length=140340&gt;
+      name       dtype    unit                              description                             n_bad 
+--------------- ------- -------- ------------------------------------------------------------------ ------
+      source_id   int64          Unique source identifier (unique within a particular Data Release)      0
+             ra float64      deg                                                    Right ascension      0
+            dec float64      deg                                                        Declination      0
+           pmra float64 mas / yr                         Proper motion in right ascension direction      0
+          pmdec float64 mas / yr                             Proper motion in declination direction      0
+       parallax float64      mas                                                           Parallax      0
+ parallax_error float64      mas                                         Standard error of parallax      0
+radial_velocity float64   km / s                                                    Radial velocity 139374
+Jobid: 1603114980658O
+Phase: COMPLETED
+Owner: None
+Output file: async_20201019094300.vot
+Results: None
+</pre></div>
+</div>
+</div>
+</div>
+<p>Here are the results.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">results</span> <span class="o">=</span> <span class="n">job</span><span class="o">.</span><span class="n">get_results</span><span class="p">()</span>
+<span class="nb">len</span><span class="p">(</span><span class="n">results</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>140340
+</pre></div>
+</div>
+</div>
+</div>
+<p>There are more than 100,000 stars in this polygon, but that’s a manageable size to work with.</p>
+</div>
+<div class="section" id="saving-results">
+<h2>Saving results<a class="headerlink" href="#saving-results" title="Permalink to this headline">¶</a></h2>
+<p>This is the set of stars we’ll work with in the next step.  But since we have a substantial dataset now, this is a good time to save it.</p>
+<p>Storing the data in a file means we can shut down this notebook and pick up where we left off without running the previous query again.</p>
+<p>Astropy <code class="docutils literal notranslate"><span class="pre">Table</span></code> objects provide <code class="docutils literal notranslate"><span class="pre">write</span></code>, which writes the table to disk.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">filename</span> <span class="o">=</span> <span class="s1">&#39;gd1_results.fits&#39;</span>
+<span class="n">results</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="n">filename</span><span class="p">,</span> <span class="n">overwrite</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>Because the filename ends with <code class="docutils literal notranslate"><span class="pre">fits</span></code>, the table is written in the <a class="reference external" href="https://en.wikipedia.org/wiki/FITS">FITS format</a>, which preserves the metadata associated with the table.</p>
+<p>If the file already exists, the <code class="docutils literal notranslate"><span class="pre">overwrite</span></code> argument causes it to be overwritten.</p>
+<p>To see how big the file is, we can use <code class="docutils literal notranslate"><span class="pre">ls</span></code> with the <code class="docutils literal notranslate"><span class="pre">-lh</span></code> option, which prints information about the file including its size in human-readable form.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">!</span>ls -lh gd1_results.fits
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>-rw-rw-r-- 1 downey downey 8.6M Oct 19 09:43 gd1_results.fits
+</pre></div>
+</div>
+</div>
+</div>
+<p>The file is about 8.6 MB.  If you are using Windows, <code class="docutils literal notranslate"><span class="pre">ls</span></code> might not work; in that case, try:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>!dir gd1_results.fits
+</pre></div>
+</div>
+</div>
+<div class="section" id="summary">
+<h2>Summary<a class="headerlink" href="#summary" title="Permalink to this headline">¶</a></h2>
+<p>In this notebook, we composed more complex queries to select stars within a polygonal region of the sky.  Then we downloaded the results and saved them in a FITS file.</p>
+<p>In the next notebook, we’ll reload the data from this file and replicate the next step in the analysis, using proper motion to identify stars likely to be in GD-1.</p>
+</div>
+<div class="section" id="best-practices">
+<h2>Best practices<a class="headerlink" href="#best-practices" title="Permalink to this headline">¶</a></h2>
+<ul class="simple">
+<li><p>For measurements with units, use <code class="docutils literal notranslate"><span class="pre">Quantity</span></code> objects that represent units explicitly and check for errors.</p></li>
+<li><p>Use the <code class="docutils literal notranslate"><span class="pre">format</span></code> function to compose queries; it is often faster and less error-prone.</p></li>
+<li><p>Develop queries incrementally: start with something simple, test it, and add a little bit at a time.</p></li>
+<li><p>Once you have a query working, save the data in a local file.  If you shut down the notebook and come back to it later, you can reload the file; you don’t have to run the query again.</p></li>
+</ul>
+</div>
+</div>
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+        <!-- Download PDF via print -->
+        <button type="button" id="download-print" class="btn btn-secondary topbarbtn" title="Print to PDF"
+            onClick="window.print()" data-toggle="tooltip" data-placement="left">.pdf</button>
+    </div>
+    
+</div>
+        <!-- Source interaction buttons -->
+
+<div class="dropdown-buttons-trigger">
+    <button id="dropdown-buttons-trigger" class="btn btn-secondary topbarbtn"
+        aria-label="Connect with source repository"><i class="fab fa-github"></i></button>
+    <div class="dropdown-buttons sourcebuttons">
+        <a class="repository-button"
+            href="https://github.com/AllenDowney/AstronomyDataPython"><button type="button" class="btn btn-secondary topbarbtn"
+                data-toggle="tooltip" data-placement="left" title="Source repository"><i
+                    class="fab fa-github"></i>repository</button></a>
+        
+        
+    </div>
+</div>
+
+
+        <!-- Full screen (wrap in <a> to have style consistency -->
+        <a class="full-screen-button"><button type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip"
+                data-placement="bottom" onclick="toggleFullScreen()" title="Fullscreen mode"><i
+                    class="fas fa-expand"></i></button></a>
+
+        <!-- Launch buttons -->
+
+<div class="dropdown-buttons-trigger">
+    <button id="dropdown-buttons-trigger" class="btn btn-secondary topbarbtn"
+        aria-label="Launch interactive content"><i class="fas fa-rocket"></i></button>
+    <div class="dropdown-buttons">
+        
+        <a class="binder-button" href="https://mybinder.org/v2/gh/AllenDowney/AstronomyDataPython/master?urlpath=tree/03_motion.ipynb"><button type="button"
+                class="btn btn-secondary topbarbtn" title="Launch Binder" data-toggle="tooltip"
+                data-placement="left"><img class="binder-button-logo"
+                    src="_static/images/logo_binder.svg"
+                    alt="Interact on binder">Binder</button></a>
+        
+        
+        
+        <a class="colab-button" href="https://colab.research.google.com/github/AllenDowney/AstronomyDataPython/blob/master/03_motion.ipynb"><button type="button" class="btn btn-secondary topbarbtn"
+                title="Launch Colab" data-toggle="tooltip" data-placement="left"><img class="colab-button-logo"
+                    src="_static/images/logo_colab.png"
+                    alt="Interact on Colab">Colab</button></a>
+        
+        
+    </div>
+</div>
+
+    </div>
+
+    <!-- Table of contents -->
+    <div class="d-none d-md-block col-md-2 bd-toc show">
+        
+        <div class="tocsection onthispage pt-5 pb-3">
+            <i class="fas fa-list"></i> Contents
+        </div>
+        <nav id="bd-toc-nav">
+            <ul class="nav section-nav flex-column">
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#outline">
+   Outline
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#installing-libraries">
+   Installing libraries
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#reload-the-data">
+   Reload the data
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#selecting-rows-and-columns">
+   Selecting rows and columns
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#scatter-plot">
+   Scatter plot
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#transform-back">
+   Transform back
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#pandas-dataframe">
+   Pandas DataFrame
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#plot-proper-motion">
+   Plot proper motion
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#selecting-the-centerline">
+   Selecting the centerline
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#filtering-based-on-proper-motion">
+   Filtering based on proper motion
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#saving-the-dataframe">
+   Saving the DataFrame
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#summary">
+   Summary
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#best-practices">
+   Best practices
+  </a>
+ </li>
+</ul>
+
+        </nav>
+        
+    </div>
+</div>
+    <div id="main-content" class="row">
+        <div class="col-12 col-md-9 pl-md-3 pr-md-0">
+        
+              <div>
+                
+  <div class="section" id="chapter-3">
+<h1>Chapter 3<a class="headerlink" href="#chapter-3" title="Permalink to this headline">¶</a></h1>
+<p>This is the third in a series of notebooks related to astronomy data.</p>
+<p>As a running example, we are replicating parts of the analysis in a recent paper, “<a class="reference external" href="https://arxiv.org/abs/1805.00425">Off the beaten path: Gaia reveals GD-1 stars outside of the main stream</a>� by Adrian M. Price-Whelan and Ana Bonaca.</p>
+<p>In the first lesson, we wrote ADQL queries and used them to select and download data from the Gaia server.</p>
+<p>In the second lesson, we wrote a query to select stars from the region of the sky where we expect GD-1 to be, and saved the results in a FITS file.</p>
+<p>Now we’ll read that data back and implement the next step in the analysis, identifying stars with the proper motion we expect for GD-1.</p>
+<div class="section" id="outline">
+<h2>Outline<a class="headerlink" href="#outline" title="Permalink to this headline">¶</a></h2>
+<p>Here are the steps in this lesson:</p>
+<ol class="simple">
+<li><p>We’ll read back the results from the previous lesson, which we saved in a FITS file.</p></li>
+<li><p>Then we’ll transform the coordinates and proper motion data from ICRS back to the coordinate frame of GD-1.</p></li>
+<li><p>We’ll put those results into a Pandas <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code>, which we’ll use to select stars near the centerline of GD-1.</p></li>
+<li><p>Plotting the proper motion of those stars, we’ll identify a region of proper motion for stars that are likely to be in GD-1.</p></li>
+<li><p>Finally, we’ll select and plot the stars whose proper motion is in that region.</p></li>
+</ol>
+<p>After completing this lesson, you should be able to</p>
+<ul class="simple">
+<li><p>Select rows and columns from an Astropy <code class="docutils literal notranslate"><span class="pre">Table</span></code>.</p></li>
+<li><p>Use Matplotlib to make a scatter plot.</p></li>
+<li><p>Use Gala to transform coordinates.</p></li>
+<li><p>Make a Pandas <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code> and use a Boolean <code class="docutils literal notranslate"><span class="pre">Series</span></code> to select rows.</p></li>
+<li><p>Save a <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code> in an HDF5 file.</p></li>
+</ul>
+</div>
+<div class="section" id="installing-libraries">
+<h2>Installing libraries<a class="headerlink" href="#installing-libraries" title="Permalink to this headline">¶</a></h2>
+<p>If you are running this notebook on Colab, you can run the following cell to install Astroquery and a the other libraries we’ll use.</p>
+<p>If you are running this notebook on your own computer, you might have to install these libraries yourself.</p>
+<p>If you are using this notebook as part of a Carpentries workshop, you should have received setup instructions.</p>
+<p>TODO: Add a link to the instructions.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># If we&#39;re running on Colab, install libraries</span>
+
+<span class="kn">import</span> <span class="nn">sys</span>
+<span class="n">IN_COLAB</span> <span class="o">=</span> <span class="s1">&#39;google.colab&#39;</span> <span class="ow">in</span> <span class="n">sys</span><span class="o">.</span><span class="n">modules</span>
+
+<span class="k">if</span> <span class="n">IN_COLAB</span><span class="p">:</span>
+    <span class="o">!</span>pip install astroquery astro-gala pyia python-wget
+</pre></div>
+</div>
+</div>
+</div>
+</div>
+<div class="section" id="reload-the-data">
+<h2>Reload the data<a class="headerlink" href="#reload-the-data" title="Permalink to this headline">¶</a></h2>
+<p>In the previous lesson, we ran a query on the Gaia server and downloaded data for roughly 100,000 stars.  We saved the data in a FITS file so that now, picking up where we left off, we can read the data from a local file rather than running the query again.</p>
+<p>If you ran the previous lesson successfully, you should already have a file called <code class="docutils literal notranslate"><span class="pre">gd1_results.fits</span></code> that contains the data we downloaded.</p>
+<p>If not, you can run the following cell, which downloads the data from our repository.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">os</span>
+<span class="kn">from</span> <span class="nn">wget</span> <span class="kn">import</span> <span class="n">download</span>
+
+<span class="n">filename</span> <span class="o">=</span> <span class="s1">&#39;gd1_results.fits&#39;</span>
+<span class="n">path</span> <span class="o">=</span> <span class="s1">&#39;https://github.com/AllenDowney/AstronomicalData/raw/main/data/&#39;</span>
+
+<span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">filename</span><span class="p">):</span>
+    <span class="nb">print</span><span class="p">(</span><span class="n">download</span><span class="p">(</span><span class="n">path</span><span class="o">+</span><span class="n">filename</span><span class="p">))</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>Now here’s how we can read the data from the file back into an Astropy <code class="docutils literal notranslate"><span class="pre">Table</span></code>:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">astropy.table</span> <span class="kn">import</span> <span class="n">Table</span>
+
+<span class="n">results</span> <span class="o">=</span> <span class="n">Table</span><span class="o">.</span><span class="n">read</span><span class="p">(</span><span class="n">filename</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>The result is an Astropy <code class="docutils literal notranslate"><span class="pre">Table</span></code>.</p>
+<p>We can use <code class="docutils literal notranslate"><span class="pre">info</span></code> to refresh our memory of the contents.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">results</span><span class="o">.</span><span class="n">info</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;Table length=140340&gt;
+      name       dtype    unit                              description                            
+--------------- ------- -------- ------------------------------------------------------------------
+      source_id   int64          Unique source identifier (unique within a particular Data Release)
+             ra float64      deg                                                    Right ascension
+            dec float64      deg                                                        Declination
+           pmra float64 mas / yr                         Proper motion in right ascension direction
+          pmdec float64 mas / yr                             Proper motion in declination direction
+       parallax float64      mas                                                           Parallax
+ parallax_error float64      mas                                         Standard error of parallax
+radial_velocity float64   km / s                                                    Radial velocity
+</pre></div>
+</div>
+</div>
+</div>
+</div>
+<div class="section" id="selecting-rows-and-columns">
+<h2>Selecting rows and columns<a class="headerlink" href="#selecting-rows-and-columns" title="Permalink to this headline">¶</a></h2>
+<p>In this section we’ll see operations for selecting columns and rows from an Astropy <code class="docutils literal notranslate"><span class="pre">Table</span></code>.  You can find more information about these operations in the <a class="reference external" href="https://docs.astropy.org/en/stable/table/access_table.html">Astropy documentation</a>.</p>
+<p>We can get the names of the columns like this:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">results</span><span class="o">.</span><span class="n">colnames</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[&#39;source_id&#39;,
+ &#39;ra&#39;,
+ &#39;dec&#39;,
+ &#39;pmra&#39;,
+ &#39;pmdec&#39;,
+ &#39;parallax&#39;,
+ &#39;parallax_error&#39;,
+ &#39;radial_velocity&#39;]
+</pre></div>
+</div>
+</div>
+</div>
+<p>And select an individual column like this:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">results</span><span class="p">[</span><span class="s1">&#39;ra&#39;</span><span class="p">]</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html">&lt;Column name=&apos;ra&apos; dtype=&apos;float64&apos; unit=&apos;deg&apos; description=&apos;Right ascension&apos; length=140340&gt;
+<table>
+<tr><td>142.48301935991023</td></tr>
+<tr><td>142.25452941346344</td></tr>
+<tr><td>142.64528557468074</td></tr>
+<tr><td>142.57739430926034</td></tr>
+<tr><td>142.58913564478618</td></tr>
+<tr><td>141.81762228999614</td></tr>
+<tr><td>143.18339801317677</td></tr>
+<tr><td>142.9347319464589</td></tr>
+<tr><td>142.26769745823267</td></tr>
+<tr><td>142.89551292869012</td></tr>
+<tr><td>142.2780935768316</td></tr>
+<tr><td>142.06138786534987</td></tr>
+<tr><td>...</td></tr>
+<tr><td>143.05456487172972</td></tr>
+<tr><td>144.0436496516182</td></tr>
+<tr><td>144.06566578919313</td></tr>
+<tr><td>144.13177563215973</td></tr>
+<tr><td>143.77696341662764</td></tr>
+<tr><td>142.945956347594</td></tr>
+<tr><td>142.97282480557786</td></tr>
+<tr><td>143.4166017695258</td></tr>
+<tr><td>143.64484588686904</td></tr>
+<tr><td>143.41554585481808</td></tr>
+<tr><td>143.6908739159247</td></tr>
+<tr><td>143.7702681295401</td></tr>
+</table></div></div>
+</div>
+<p>The result is a <code class="docutils literal notranslate"><span class="pre">Column</span></code> object that contains the data, and also the data type, units, and name of the column.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="nb">type</span><span class="p">(</span><span class="n">results</span><span class="p">[</span><span class="s1">&#39;ra&#39;</span><span class="p">])</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>astropy.table.column.Column
+</pre></div>
+</div>
+</div>
+</div>
+<p>The rows in the <code class="docutils literal notranslate"><span class="pre">Table</span></code> are numbered from 0 to <code class="docutils literal notranslate"><span class="pre">n-1</span></code>, where <code class="docutils literal notranslate"><span class="pre">n</span></code> is the number of rows.  We can select the first row like this:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">results</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><i>Row index=0</i>
+<table id="table140510732444000">
+<thead><tr><th>source_id</th><th>ra</th><th>dec</th><th>pmra</th><th>pmdec</th><th>parallax</th><th>parallax_error</th><th>radial_velocity</th></tr></thead>
+<thead><tr><th></th><th>deg</th><th>deg</th><th>mas / yr</th><th>mas / yr</th><th>mas</th><th>mas</th><th>km / s</th></tr></thead>
+<thead><tr><th>int64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th></tr></thead>
+<tr><td>637987125186749568</td><td>142.48301935991023</td><td>21.75771616932985</td><td>-2.5168384683875766</td><td>2.941813096629439</td><td>-0.2573448962333354</td><td>0.823720794509811</td><td>1e+20</td></tr>
+</table></div></div>
+</div>
+<p>As you might have guessed, the result is a <code class="docutils literal notranslate"><span class="pre">Row</span></code> object.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="nb">type</span><span class="p">(</span><span class="n">results</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>astropy.table.row.Row
+</pre></div>
+</div>
+</div>
+</div>
+<p>Notice that the bracket operator selects both columns and rows.  You might wonder how it knows which to select.</p>
+<p>If the expression in brackets is a string, it selects a column; if the expression is an integer, it selects a row.</p>
+<p>If you apply the bracket operator twice, you can select a column and then an element from the column.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">results</span><span class="p">[</span><span class="s1">&#39;ra&#39;</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>142.48301935991023
+</pre></div>
+</div>
+</div>
+</div>
+<p>Or you can select a row and then an element from the row.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">results</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="s1">&#39;ra&#39;</span><span class="p">]</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>142.48301935991023
+</pre></div>
+</div>
+</div>
+</div>
+<p>You get the same result either way.</p>
+</div>
+<div class="section" id="scatter-plot">
+<h2>Scatter plot<a class="headerlink" href="#scatter-plot" title="Permalink to this headline">¶</a></h2>
+<p>To see what the results look like, we’ll use a scatter plot.  The library we’ll use is <a class="reference external" href="https://matplotlib.org/">Matplotlib</a>, which is the most widely-used plotting library for Python.</p>
+<p>The Matplotlib interface is based on MATLAB (hence the name), so if you know MATLAB, some of it will be familiar.</p>
+<p>We’ll import like this.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>Pyplot part of the Matplotlib library.  It is conventional to import it using the shortened name <code class="docutils literal notranslate"><span class="pre">plt</span></code>.</p>
+<p>Pyplot provides two functions that can make scatterplots, <a class="reference external" href="https://matplotlib.org/3.3.0/api/_as_gen/matplotlib.pyplot.scatter.html">plt.scatter</a> and <a class="reference external" href="https://matplotlib.org/api/_as_gen/matplotlib.pyplot.plot.html">plt.plot</a>.</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">scatter</span></code> is more versatile; for example, you can make every point in a scatter plot a different color.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">plot</span></code> is more limited, but for simple cases, it can be substantially faster.</p></li>
+</ul>
+<p>Jake Vanderplas explains these differences in <a class="reference external" href="https://jakevdp.github.io/PythonDataScienceHandbook/04.02-simple-scatter-plots.html">The Python Data Science Handbook</a></p>
+<p>Since we are plotting more than 100,000 points and they are all the same size and color, we’ll use <code class="docutils literal notranslate"><span class="pre">plot</span></code>.</p>
+<p>Here’s a scatter plot with right ascension on the x-axis and declination on the y-axis, both ICRS coordinates in degrees.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="n">results</span><span class="p">[</span><span class="s1">&#39;ra&#39;</span><span class="p">]</span>
+<span class="n">y</span> <span class="o">=</span> <span class="n">results</span><span class="p">[</span><span class="s1">&#39;dec&#39;</span><span class="p">]</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="s1">&#39;ko&#39;</span><span class="p">)</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">&#39;ra (degree ICRS)&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">&#39;dec (degree ICRS)&#39;</span><span class="p">);</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<img alt="_images/03_motion_28_0.png" src="_images/03_motion_28_0.png" />
+</div>
+</div>
+<p>The arguments to <code class="docutils literal notranslate"><span class="pre">plt.plot</span></code> are <code class="docutils literal notranslate"><span class="pre">x</span></code>, <code class="docutils literal notranslate"><span class="pre">y</span></code>, and a string that specifies the style.  In this case, the letters <code class="docutils literal notranslate"><span class="pre">ko</span></code> indicate that we want a black, round marker (<code class="docutils literal notranslate"><span class="pre">k</span></code> is for black because <code class="docutils literal notranslate"><span class="pre">b</span></code> is for blue).</p>
+<p>The functions <code class="docutils literal notranslate"><span class="pre">xlabel</span></code> and <code class="docutils literal notranslate"><span class="pre">ylabel</span></code> put labels on the axes.</p>
+<p>This scatter plot has a problem.  It is “<a class="reference external" href="https://python-graph-gallery.com/134-how-to-avoid-overplotting-with-python/">overplotted</a>�, which means that there are so many overlapping points, we can’t distinguish between high and low density areas.</p>
+<p>To fix this, we can provide optional arguments to control the size and transparency of the points.</p>
+<p><strong>Exercise:</strong> In the call to <code class="docutils literal notranslate"><span class="pre">plt.plot</span></code>, add the keyword argument <code class="docutils literal notranslate"><span class="pre">markersize=0.1</span></code> to make the markers smaller.</p>
+<p>Then add the argument <code class="docutils literal notranslate"><span class="pre">alpha=0.1</span></code> to make the markers nearly transparent.</p>
+<p>Adjust these arguments until you think the figure shows the data most clearly.</p>
+<p>Note: Once you have made these changes, you might notice that the figure shows stripes with lower density of stars.  These stripes are caused by the way Gaia scans the sky, which <a class="reference external" href="https://www.cosmos.esa.int/web/gaia/scanning-law">you can read about here</a>.  The dataset we are using, <a class="reference external" href="https://www.cosmos.esa.int/web/gaia/dr2">Gaia Data Release 2</a>, covers 22 months of observations; during this time, some parts of the sky were scanned more than others.</p>
+</div>
+<div class="section" id="transform-back">
+<h2>Transform back<a class="headerlink" href="#transform-back" title="Permalink to this headline">¶</a></h2>
+<p>Remember that we selected data from a rectangle of coordinates in the <code class="docutils literal notranslate"><span class="pre">GD1Koposov10</span></code> frame, then transformed them to ICRS when we constructed the query.
+The coordinates in <code class="docutils literal notranslate"><span class="pre">results</span></code> are in ICRS.</p>
+<p>To plot them, we will transform them back to the <code class="docutils literal notranslate"><span class="pre">GD1Koposov10</span></code> frame; that way, the axes of the figure are aligned with the GD-1, which will make it easy to select stars near the centerline of the stream.</p>
+<p>To do that, we’ll put the results into a <code class="docutils literal notranslate"><span class="pre">GaiaData</span></code> object, provided by the <a class="reference external" href="https://pyia.readthedocs.io/en/latest/api/pyia.GaiaData.html">pyia library</a>.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">pyia</span> <span class="kn">import</span> <span class="n">GaiaData</span>
+
+<span class="n">gaia_data</span> <span class="o">=</span> <span class="n">GaiaData</span><span class="p">(</span><span class="n">results</span><span class="p">)</span>
+<span class="nb">type</span><span class="p">(</span><span class="n">gaia_data</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>pyia.data.GaiaData
+</pre></div>
+</div>
+</div>
+</div>
+<p>Now we can extract sky coordinates from the <code class="docutils literal notranslate"><span class="pre">GaiaData</span></code> object, like this:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">astropy.units</span> <span class="k">as</span> <span class="nn">u</span>
+
+<span class="n">skycoord</span> <span class="o">=</span> <span class="n">gaia_data</span><span class="o">.</span><span class="n">get_skycoord</span><span class="p">(</span>
+                <span class="n">distance</span><span class="o">=</span><span class="mi">8</span><span class="o">*</span><span class="n">u</span><span class="o">.</span><span class="n">kpc</span><span class="p">,</span> 
+                <span class="n">radial_velocity</span><span class="o">=</span><span class="mi">0</span><span class="o">*</span><span class="n">u</span><span class="o">.</span><span class="n">km</span><span class="o">/</span><span class="n">u</span><span class="o">.</span><span class="n">s</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>We provide <code class="docutils literal notranslate"><span class="pre">distance</span></code> and <code class="docutils literal notranslate"><span class="pre">radial_velocity</span></code> to prepare the data for reflex correction, which we explain below.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="nb">type</span><span class="p">(</span><span class="n">skycoord</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>astropy.coordinates.sky_coordinate.SkyCoord
+</pre></div>
+</div>
+</div>
+</div>
+<p>The result is an Astropy <code class="docutils literal notranslate"><span class="pre">SkyCoord</span></code> object (<a class="reference external" href="https://docs.astropy.org/en/stable/api/astropy.coordinates.SkyCoord.html#astropy.coordinates.SkyCoord">documentation here</a>), which provides <code class="docutils literal notranslate"><span class="pre">transform_to</span></code>, so we can transform the coordinates to other frames.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">gala.coordinates</span> <span class="k">as</span> <span class="nn">gc</span>
+
+<span class="n">transformed</span> <span class="o">=</span> <span class="n">skycoord</span><span class="o">.</span><span class="n">transform_to</span><span class="p">(</span><span class="n">gc</span><span class="o">.</span><span class="n">GD1Koposov10</span><span class="p">)</span>
+<span class="nb">type</span><span class="p">(</span><span class="n">transformed</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>astropy.coordinates.sky_coordinate.SkyCoord
+</pre></div>
+</div>
+</div>
+</div>
+<p>The result is another <code class="docutils literal notranslate"><span class="pre">SkyCoord</span></code> object, now in the <code class="docutils literal notranslate"><span class="pre">GD1Koposov10</span></code> frame.</p>
+<p>The next step is to correct the proper motion measurements from Gaia for reflex due to the motion of our solar system around the Galactic center.</p>
+<p>When we created <code class="docutils literal notranslate"><span class="pre">skycoord</span></code>, we provided <code class="docutils literal notranslate"><span class="pre">distance</span></code> and <code class="docutils literal notranslate"><span class="pre">radial_velocity</span></code> as arguments, which means we ignore the measurements provided by Gaia and replace them with these fixed values.</p>
+<p>That might seem like a strange thing to do, but here’s the motivation:</p>
+<ul class="simple">
+<li><p>Because the stars in GD-1 are so far away, the distance estimates we get from Gaia, which are based on parallax, are not very precise.  So we replace them with our current best estimate of the mean distance to GD-1, about 8 kpc.  See <a class="reference external" href="https://ui.adsabs.harvard.edu/abs/2010ApJ...712..260K/abstract">Koposov, Rix, and Hogg, 2010</a>.</p></li>
+<li><p>For the other stars in the table, this distance estimate will be inaccurate, so reflex correction will not be correct.  But that should have only a small effect on our ability to identify stars with the proper motion we expect for GD-1.</p></li>
+<li><p>The measurement of radial velocity has no effect on the correction for proper motion; the value we provide is arbitrary, but we have to provide a value to avoid errors in the reflex correction calculation.</p></li>
+</ul>
+<p>We are grateful to Adrian Price-Whelen for his help explaining this step in the analysis.</p>
+<p>With this preparation, we can use <code class="docutils literal notranslate"><span class="pre">reflex_correct</span></code> from Gala (<a class="reference external" href="https://gala-astro.readthedocs.io/en/latest/api/gala.coordinates.reflex_correct.html">documentation here</a>) to correct for solar reflex motion.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">gd1_coord</span> <span class="o">=</span> <span class="n">gc</span><span class="o">.</span><span class="n">reflex_correct</span><span class="p">(</span><span class="n">transformed</span><span class="p">)</span>
+
+<span class="nb">type</span><span class="p">(</span><span class="n">gd1_coord</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>astropy.coordinates.sky_coordinate.SkyCoord
+</pre></div>
+</div>
+</div>
+</div>
+<p>The result is a <code class="docutils literal notranslate"><span class="pre">SkyCoord</span></code> object that contains</p>
+<ul class="simple">
+<li><p>The transformed coordinates as attributes named <code class="docutils literal notranslate"><span class="pre">phi1</span></code> and <code class="docutils literal notranslate"><span class="pre">phi2</span></code>, which represent right ascension and declination in the <code class="docutils literal notranslate"><span class="pre">GD1Koposov10</span></code> frame.</p></li>
+<li><p>The transformed and corrected proper motions as <code class="docutils literal notranslate"><span class="pre">pm_phi1_cosphi2</span></code> and <code class="docutils literal notranslate"><span class="pre">pm_phi2</span></code>.</p></li>
+</ul>
+<p>We can select the coordinates like this:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">phi1</span> <span class="o">=</span> <span class="n">gd1_coord</span><span class="o">.</span><span class="n">phi1</span>
+<span class="n">phi2</span> <span class="o">=</span> <span class="n">gd1_coord</span><span class="o">.</span><span class="n">phi2</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>And plot them like this:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">phi1</span><span class="p">,</span> <span class="n">phi2</span><span class="p">,</span> <span class="s1">&#39;ko&#39;</span><span class="p">,</span> <span class="n">markersize</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.2</span><span class="p">)</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">&#39;ra (degree GD1)&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">&#39;dec (degree GD1)&#39;</span><span class="p">);</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<img alt="_images/03_motion_45_0.png" src="_images/03_motion_45_0.png" />
+</div>
+</div>
+<p>Remember that we started with a rectangle in GD-1 coordinates.  When transformed to ICRS, it’s a non-rectangular polygon.  Now that we have transformed back to GD-1 coordinates, it’s a rectangle again.</p>
+</div>
+<div class="section" id="pandas-dataframe">
+<h2>Pandas DataFrame<a class="headerlink" href="#pandas-dataframe" title="Permalink to this headline">¶</a></h2>
+<p>At this point we have three objects containing different subsets of the data.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="nb">type</span><span class="p">(</span><span class="n">results</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>astropy.table.table.Table
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="nb">type</span><span class="p">(</span><span class="n">gaia_data</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>pyia.data.GaiaData
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="nb">type</span><span class="p">(</span><span class="n">gd1_coord</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>astropy.coordinates.sky_coordinate.SkyCoord
+</pre></div>
+</div>
+</div>
+</div>
+<p>On one hand, this makes sense, since each object provides different capabilities.  But working with three different object types can be awkward.</p>
+<p>It will be more convenient to choose one object and get all of the data into it.  We’ll use a Pandas DataFrame, for two reasons:</p>
+<ol class="simple">
+<li><p>It provides capabilities that are pretty much a superset of the other data structures, so it’s the all-in-one solution.</p></li>
+<li><p>Pandas is a general-purpose tool that is useful in many domains, especially data science.  If you are going to develop expertise in one tool, Pandas is a good choice.</p></li>
+</ol>
+<p>However, compared to an Astropy <code class="docutils literal notranslate"><span class="pre">Table</span></code>, Pandas has one big drawback: it does not keep the metadata associated with the table, including the units for the columns.</p>
+<p>It’s easy to convert a <code class="docutils literal notranslate"><span class="pre">Table</span></code> to a Pandas <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code>.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+
+<span class="n">df</span> <span class="o">=</span> <span class="n">results</span><span class="o">.</span><span class="n">to_pandas</span><span class="p">()</span>
+<span class="n">df</span><span class="o">.</span><span class="n">shape</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>(140340, 8)
+</pre></div>
+</div>
+</div>
+</div>
+<p><code class="docutils literal notranslate"><span class="pre">DataFrame</span></code> provides <code class="docutils literal notranslate"><span class="pre">shape</span></code>, which shows the number of rows and columns.</p>
+<p>It also provides <code class="docutils literal notranslate"><span class="pre">head</span></code>, which displays the first few rows.  It is useful for spot-checking large results as you go along.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>source_id</th>
+      <th>ra</th>
+      <th>dec</th>
+      <th>pmra</th>
+      <th>pmdec</th>
+      <th>parallax</th>
+      <th>parallax_error</th>
+      <th>radial_velocity</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>637987125186749568</td>
+      <td>142.483019</td>
+      <td>21.757716</td>
+      <td>-2.516838</td>
+      <td>2.941813</td>
+      <td>-0.257345</td>
+      <td>0.823721</td>
+      <td>1.000000e+20</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>638285195917112960</td>
+      <td>142.254529</td>
+      <td>22.476168</td>
+      <td>2.662702</td>
+      <td>-12.165984</td>
+      <td>0.422728</td>
+      <td>0.297472</td>
+      <td>1.000000e+20</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>638073505568978688</td>
+      <td>142.645286</td>
+      <td>22.166932</td>
+      <td>18.306747</td>
+      <td>-7.950660</td>
+      <td>0.103640</td>
+      <td>0.544584</td>
+      <td>1.000000e+20</td>
+    </tr>
+    <tr>
+      <th>3</th>
+      <td>638086386175786752</td>
+      <td>142.577394</td>
+      <td>22.227920</td>
+      <td>0.987786</td>
+      <td>-2.584105</td>
+      <td>-0.857327</td>
+      <td>1.059607</td>
+      <td>1.000000e+20</td>
+    </tr>
+    <tr>
+      <th>4</th>
+      <td>638049655615392384</td>
+      <td>142.589136</td>
+      <td>22.110783</td>
+      <td>0.244439</td>
+      <td>-4.941079</td>
+      <td>0.099625</td>
+      <td>0.486224</td>
+      <td>1.000000e+20</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+<p>Python detail: <code class="docutils literal notranslate"><span class="pre">shape</span></code> is an attribute, so we can display it’s value without calling it as a function; <code class="docutils literal notranslate"><span class="pre">head</span></code> is a function, so we need the parentheses.</p>
+<p>Now we can extract the columns we want from <code class="docutils literal notranslate"><span class="pre">gd1_coord</span></code> and add them as columns in the <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code>.  <code class="docutils literal notranslate"><span class="pre">phi1</span></code> and <code class="docutils literal notranslate"><span class="pre">phi2</span></code> contain the transformed coordinates.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;phi1&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">gd1_coord</span><span class="o">.</span><span class="n">phi1</span>
+<span class="n">df</span><span class="p">[</span><span class="s1">&#39;phi2&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">gd1_coord</span><span class="o">.</span><span class="n">phi2</span>
+<span class="n">df</span><span class="o">.</span><span class="n">shape</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>(140340, 10)
+</pre></div>
+</div>
+</div>
+</div>
+<p><code class="docutils literal notranslate"><span class="pre">pm_phi1_cosphi2</span></code> and <code class="docutils literal notranslate"><span class="pre">pm_phi2</span></code> contain the components of proper motion in the transformed frame.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;pm_phi1&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">gd1_coord</span><span class="o">.</span><span class="n">pm_phi1_cosphi2</span>
+<span class="n">df</span><span class="p">[</span><span class="s1">&#39;pm_phi2&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">gd1_coord</span><span class="o">.</span><span class="n">pm_phi2</span>
+<span class="n">df</span><span class="o">.</span><span class="n">shape</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>(140340, 12)
+</pre></div>
+</div>
+</div>
+</div>
+<p><strong>Detail:</strong> If you notice that <code class="docutils literal notranslate"><span class="pre">SkyCoord</span></code> has an attribute called <code class="docutils literal notranslate"><span class="pre">proper_motion</span></code>, you might wonder why we are not using it.</p>
+<p>We could have: <code class="docutils literal notranslate"><span class="pre">proper_motion</span></code> contains the same data as <code class="docutils literal notranslate"><span class="pre">pm_phi1_cosphi2</span></code> and <code class="docutils literal notranslate"><span class="pre">pm_phi2</span></code>, but in a different format.</p>
+</div>
+<div class="section" id="plot-proper-motion">
+<h2>Plot proper motion<a class="headerlink" href="#plot-proper-motion" title="Permalink to this headline">¶</a></h2>
+<p>Now we are ready to replicate one of the panels in Figure 1 of the Price-Whelan and Bonaca paper, the one that shows the components of proper motion as a scatter plot:</p>
+<img width="300" src="https://github.com/datacarpentry/astronomy-python/raw/gh-pages/fig/gd1-1.png">
+<p>In this figure, the shaded area is a high-density region of stars with the proper motion we expect for stars in GD-1.</p>
+<ul class="simple">
+<li><p>Due to the nature of tidal streams, we expect the proper motion for most stars to be along the axis of the stream; that is, we expect motion in the direction of <code class="docutils literal notranslate"><span class="pre">phi2</span></code> to be near 0.</p></li>
+<li><p>In the direction of <code class="docutils literal notranslate"><span class="pre">phi1</span></code>, we don’t have a prior expectation for proper motion, except that it should form a cluster at a non-zero value.</p></li>
+</ul>
+<p>To locate this cluster, we’ll select stars near the centerline of GD-1 and plot their proper motion.</p>
+</div>
+<div class="section" id="selecting-the-centerline">
+<h2>Selecting the centerline<a class="headerlink" href="#selecting-the-centerline" title="Permalink to this headline">¶</a></h2>
+<p>As we can see in the following figure, many stars in GD-1 are less than 1 degree of declination from the line <code class="docutils literal notranslate"><span class="pre">phi2=0</span></code>.</p>
+<img src="https://github.com/datacarpentry/astronomy-python/raw/gh-pages/fig/gd1-4.png">
+<p>If we select stars near this line, they are more likely to be in GD-1.</p>
+<p>We’ll start by selecting the <code class="docutils literal notranslate"><span class="pre">phi2</span></code> column from the <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code>:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">phi2</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;phi2&#39;</span><span class="p">]</span>
+<span class="nb">type</span><span class="p">(</span><span class="n">phi2</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>pandas.core.series.Series
+</pre></div>
+</div>
+</div>
+</div>
+<p>The result is a <code class="docutils literal notranslate"><span class="pre">Series</span></code>, which is the structure Pandas uses to represent columns.</p>
+<p>We can use a comparison operator, <code class="docutils literal notranslate"><span class="pre">&gt;</span></code>, to compare the values in a <code class="docutils literal notranslate"><span class="pre">Series</span></code> to a constant.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">phi2_min</span> <span class="o">=</span> <span class="o">-</span><span class="mf">1.0</span> <span class="o">*</span> <span class="n">u</span><span class="o">.</span><span class="n">deg</span>
+<span class="n">phi2_max</span> <span class="o">=</span> <span class="mf">1.0</span> <span class="o">*</span> <span class="n">u</span><span class="o">.</span><span class="n">deg</span>
+
+<span class="n">mask</span> <span class="o">=</span> <span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;phi2&#39;</span><span class="p">]</span> <span class="o">&gt;</span> <span class="n">phi2_min</span><span class="p">)</span>
+<span class="nb">type</span><span class="p">(</span><span class="n">mask</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>pandas.core.series.Series
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">mask</span><span class="o">.</span><span class="n">dtype</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>dtype(&#39;bool&#39;)
+</pre></div>
+</div>
+</div>
+</div>
+<p>The result is a <code class="docutils literal notranslate"><span class="pre">Series</span></code> of Boolean values, that is, <code class="docutils literal notranslate"><span class="pre">True</span></code> and <code class="docutils literal notranslate"><span class="pre">False</span></code>.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">mask</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0    False
+1    False
+2    False
+3    False
+4    False
+Name: phi2, dtype: bool
+</pre></div>
+</div>
+</div>
+</div>
+<p>A Boolean <code class="docutils literal notranslate"><span class="pre">Series</span></code> is sometimes called a “mask� because we can use it to mask out some of the rows in a <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code> and select the rest, like this:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">selected</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">mask</span><span class="p">]</span>
+<span class="nb">type</span><span class="p">(</span><span class="n">selected</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>pandas.core.frame.DataFrame
+</pre></div>
+</div>
+</div>
+</div>
+<p><code class="docutils literal notranslate"><span class="pre">selected</span></code> is a <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code> that contains only the rows from <code class="docutils literal notranslate"><span class="pre">df</span></code> that correspond to <code class="docutils literal notranslate"><span class="pre">True</span></code> values in <code class="docutils literal notranslate"><span class="pre">mask</span></code>.</p>
+<p>The previous mask selects all stars where <code class="docutils literal notranslate"><span class="pre">phi2</span></code> exceeds <code class="docutils literal notranslate"><span class="pre">phi2_min</span></code>; now we’ll select stars where <code class="docutils literal notranslate"><span class="pre">phi2</span></code> falls between <code class="docutils literal notranslate"><span class="pre">phi2_min</span></code> and <code class="docutils literal notranslate"><span class="pre">phi2_max</span></code>.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">phi_mask</span> <span class="o">=</span> <span class="p">((</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;phi2&#39;</span><span class="p">]</span> <span class="o">&gt;</span> <span class="n">phi2_min</span><span class="p">)</span> <span class="o">&amp;</span> 
+            <span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;phi2&#39;</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">phi2_max</span><span class="p">))</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>The <code class="docutils literal notranslate"><span class="pre">&amp;</span></code> operator computes “logical AND�, which means the result is true where elements from both Boolean <code class="docutils literal notranslate"><span class="pre">Series</span></code> are true.</p>
+<p>The sum of a Boolean <code class="docutils literal notranslate"><span class="pre">Series</span></code> is the number of <code class="docutils literal notranslate"><span class="pre">True</span></code> values, so we can use <code class="docutils literal notranslate"><span class="pre">sum</span></code> to see how many stars are in the selected region.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">phi_mask</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>25084
+</pre></div>
+</div>
+</div>
+</div>
+<p>And we can use <code class="docutils literal notranslate"><span class="pre">phi1_mask</span></code> to select stars near the centerline, which are more likely to be in GD-1.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">centerline</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">phi_mask</span><span class="p">]</span>
+<span class="nb">len</span><span class="p">(</span><span class="n">centerline</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>25084
+</pre></div>
+</div>
+</div>
+</div>
+<p>Here’s a scatter plot of proper motion for the selected stars.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pm1</span> <span class="o">=</span> <span class="n">centerline</span><span class="p">[</span><span class="s1">&#39;pm_phi1&#39;</span><span class="p">]</span>
+<span class="n">pm2</span> <span class="o">=</span> <span class="n">centerline</span><span class="p">[</span><span class="s1">&#39;pm_phi2&#39;</span><span class="p">]</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">pm1</span><span class="p">,</span> <span class="n">pm2</span><span class="p">,</span> <span class="s1">&#39;ko&#39;</span><span class="p">,</span> <span class="n">markersize</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.1</span><span class="p">)</span>
+    
+<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">&#39;Proper motion phi1 (GD1 frame)&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">&#39;Proper motion phi2 (GD1 frame)&#39;</span><span class="p">);</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<img alt="_images/03_motion_79_0.png" src="_images/03_motion_79_0.png" />
+</div>
+</div>
+<p>Looking at these results, we see a large cluster around (0, 0), and a smaller cluster near (0, -10).</p>
+<p>We can use <code class="docutils literal notranslate"><span class="pre">xlim</span></code> and <code class="docutils literal notranslate"><span class="pre">ylim</span></code> to set the limits on the axes and zoom in on the region near (0, 0).</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pm1</span> <span class="o">=</span> <span class="n">centerline</span><span class="p">[</span><span class="s1">&#39;pm_phi1&#39;</span><span class="p">]</span>
+<span class="n">pm2</span> <span class="o">=</span> <span class="n">centerline</span><span class="p">[</span><span class="s1">&#39;pm_phi2&#39;</span><span class="p">]</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">pm1</span><span class="p">,</span> <span class="n">pm2</span><span class="p">,</span> <span class="s1">&#39;ko&#39;</span><span class="p">,</span> <span class="n">markersize</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.3</span><span class="p">)</span>
+    
+<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">&#39;Proper motion phi1 (GD1 frame)&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">&#39;Proper motion phi2 (GD1 frame)&#39;</span><span class="p">)</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">xlim</span><span class="p">(</span><span class="o">-</span><span class="mi">12</span><span class="p">,</span> <span class="mi">8</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylim</span><span class="p">(</span><span class="o">-</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">);</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<img alt="_images/03_motion_81_0.png" src="_images/03_motion_81_0.png" />
+</div>
+</div>
+<p>Now we can see the smaller cluster more clearly.</p>
+<p>You might notice that our figure is less dense than the one in the paper.  That’s because we started with a set of stars from a relatively small region.  The figure in the paper is based on a region about 10 times bigger.</p>
+<p>In the next lesson we’ll go back and select stars from a larger region.  But first we’ll use the proper motion data to identify stars likely to be in GD-1.</p>
+</div>
+<div class="section" id="filtering-based-on-proper-motion">
+<h2>Filtering based on proper motion<a class="headerlink" href="#filtering-based-on-proper-motion" title="Permalink to this headline">¶</a></h2>
+<p>The next step is to select stars in the “overdense� region of proper motion, which are candidates to be in GD-1.</p>
+<p>In the original paper, Price-Whelan and Bonaca used a polygon to cover this region, as shown in this figure.</p>
+<img width="300" src="https://github.com/datacarpentry/astronomy-python/raw/gh-pages/fig/gd1-1.png">
+<p>We’ll use a simple rectangle for now, but in a later lesson we’ll see how to select a polygonal region as well.</p>
+<p>Here are bounds on proper motion we chose by eye,</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pm1_min</span> <span class="o">=</span> <span class="o">-</span><span class="mf">8.9</span>
+<span class="n">pm1_max</span> <span class="o">=</span> <span class="o">-</span><span class="mf">6.9</span>
+<span class="n">pm2_min</span> <span class="o">=</span> <span class="o">-</span><span class="mf">2.2</span>
+<span class="n">pm2_max</span> <span class="o">=</span>  <span class="mf">1.0</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>To draw these bounds, we’ll make two lists containing the coordinates of the corners of the rectangle.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pm1_rect</span> <span class="o">=</span> <span class="p">[</span><span class="n">pm1_min</span><span class="p">,</span> <span class="n">pm1_min</span><span class="p">,</span> <span class="n">pm1_max</span><span class="p">,</span> <span class="n">pm1_max</span><span class="p">,</span> <span class="n">pm1_min</span><span class="p">]</span> <span class="o">*</span> <span class="n">u</span><span class="o">.</span><span class="n">mas</span><span class="o">/</span><span class="n">u</span><span class="o">.</span><span class="n">yr</span>
+<span class="n">pm2_rect</span> <span class="o">=</span> <span class="p">[</span><span class="n">pm2_min</span><span class="p">,</span> <span class="n">pm2_max</span><span class="p">,</span> <span class="n">pm2_max</span><span class="p">,</span> <span class="n">pm2_min</span><span class="p">,</span> <span class="n">pm2_min</span><span class="p">]</span> <span class="o">*</span> <span class="n">u</span><span class="o">.</span><span class="n">mas</span><span class="o">/</span><span class="n">u</span><span class="o">.</span><span class="n">yr</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>Here’s what the plot looks like with the bounds we chose.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">pm1</span><span class="p">,</span> <span class="n">pm2</span><span class="p">,</span> <span class="s1">&#39;ko&#39;</span><span class="p">,</span> <span class="n">markersize</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.3</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">pm1_rect</span><span class="p">,</span> <span class="n">pm2_rect</span><span class="p">,</span> <span class="s1">&#39;-&#39;</span><span class="p">)</span>
+    
+<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">&#39;Proper motion phi1 (GD1 frame)&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">&#39;Proper motion phi2 (GD1 frame)&#39;</span><span class="p">)</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">xlim</span><span class="p">(</span><span class="o">-</span><span class="mi">12</span><span class="p">,</span> <span class="mi">8</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylim</span><span class="p">(</span><span class="o">-</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">);</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<img alt="_images/03_motion_88_0.png" src="_images/03_motion_88_0.png" />
+</div>
+</div>
+<p>To select rows that fall within these bounds, we’ll use the following function, which uses Pandas operators to make a mask that selects rows where <code class="docutils literal notranslate"><span class="pre">series</span></code> falls between <code class="docutils literal notranslate"><span class="pre">low</span></code> and <code class="docutils literal notranslate"><span class="pre">high</span></code>.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">between</span><span class="p">(</span><span class="n">series</span><span class="p">,</span> <span class="n">low</span><span class="p">,</span> <span class="n">high</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;Make a Boolean Series.</span>
+<span class="sd">    </span>
+<span class="sd">    series: Pandas Series</span>
+<span class="sd">    low: lower bound</span>
+<span class="sd">    high: upper bound</span>
+<span class="sd">    </span>
+<span class="sd">    returns: Boolean Series</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">return</span> <span class="p">(</span><span class="n">series</span> <span class="o">&gt;</span> <span class="n">low</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">series</span> <span class="o">&lt;</span> <span class="n">high</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>The following mask select stars with proper motion in the region we chose.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pm_mask</span> <span class="o">=</span> <span class="p">(</span><span class="n">between</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;pm_phi1&#39;</span><span class="p">],</span> <span class="n">pm1_min</span><span class="p">,</span> <span class="n">pm1_max</span><span class="p">)</span> <span class="o">&amp;</span> 
+           <span class="n">between</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;pm_phi2&#39;</span><span class="p">],</span> <span class="n">pm2_min</span><span class="p">,</span> <span class="n">pm2_max</span><span class="p">))</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>Again, the sum of a Boolean series is the number of <code class="docutils literal notranslate"><span class="pre">True</span></code> values.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pm_mask</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>1049
+</pre></div>
+</div>
+</div>
+</div>
+<p>Now we can use this mask to select rows from <code class="docutils literal notranslate"><span class="pre">df</span></code>.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">selected</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">pm_mask</span><span class="p">]</span>
+<span class="nb">len</span><span class="p">(</span><span class="n">selected</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>1049
+</pre></div>
+</div>
+</div>
+</div>
+<p>These are the stars we think are likely to be in GD-1.  Let’s see what they look like, plotting their coordinates (not their proper motion).</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">phi1</span> <span class="o">=</span> <span class="n">selected</span><span class="p">[</span><span class="s1">&#39;phi1&#39;</span><span class="p">]</span>
+<span class="n">phi2</span> <span class="o">=</span> <span class="n">selected</span><span class="p">[</span><span class="s1">&#39;phi2&#39;</span><span class="p">]</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">phi1</span><span class="p">,</span> <span class="n">phi2</span><span class="p">,</span> <span class="s1">&#39;ko&#39;</span><span class="p">,</span> <span class="n">markersize</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">&#39;ra (degree GD1)&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">&#39;dec (degree GD1)&#39;</span><span class="p">);</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<img alt="_images/03_motion_98_0.png" src="_images/03_motion_98_0.png" />
+</div>
+</div>
+<p>Now that’s starting to look like a tidal stream!</p>
+</div>
+<div class="section" id="saving-the-dataframe">
+<h2>Saving the DataFrame<a class="headerlink" href="#saving-the-dataframe" title="Permalink to this headline">¶</a></h2>
+<p>At this point we have run a successful query and cleaned up the results; this is a good time to save the data.</p>
+<p>To save a Pandas <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code>, one option is to convert it to an Astropy <code class="docutils literal notranslate"><span class="pre">Table</span></code>, like this:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">selected_table</span> <span class="o">=</span> <span class="n">Table</span><span class="o">.</span><span class="n">from_pandas</span><span class="p">(</span><span class="n">selected</span><span class="p">)</span>
+<span class="nb">type</span><span class="p">(</span><span class="n">selected_table</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>astropy.table.table.Table
+</pre></div>
+</div>
+</div>
+</div>
+<p>Then we could write the <code class="docutils literal notranslate"><span class="pre">Table</span></code> to a FITS file, as we did in the previous lesson.</p>
+<p>But Pandas provides functions to write DataFrames in other formats; to see what they are <a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html">find the functions here that begin with <code class="docutils literal notranslate"><span class="pre">to_</span></code></a>.</p>
+<p>One of the best options is HDF5, which is Version 5 of <a class="reference external" href="https://en.wikipedia.org/wiki/Hierarchical_Data_Format">Hierarchical Data Format</a>.</p>
+<p>HDF5 is a binary format, so files are small and fast to read and write (like FITS, but unlike XML).</p>
+<p>An HDF5 file is similar to an SQL database in the sense that it can contain more than one table, although in HDF5 vocabulary, a table is called a Dataset.  (<a class="reference external" href="https://www.stsci.edu/itt/review/dhb_2011/Intro/intro_ch23.html">Multi-extension FITS files</a> can also contain more than one table.)</p>
+<p>And HDF5 stores the metadata associated with the table, including column names, row labels, and data types (like FITS).</p>
+<p>Finally, HDF5 is a cross-language standard, so if you write an HDF5 file with Pandas, you can read it back with many other software tools (more than FITS).</p>
+<p>Before we write the HDF5, let’s delete the old one, if it exists.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">!</span>rm -f gd1_dataframe.hdf5
+</pre></div>
+</div>
+</div>
+</div>
+<p>We can write a Pandas <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code> to an HDF5 file like this:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">filename</span> <span class="o">=</span> <span class="s1">&#39;gd1_dataframe.hdf5&#39;</span>
+
+<span class="n">df</span><span class="o">.</span><span class="n">to_hdf</span><span class="p">(</span><span class="n">filename</span><span class="p">,</span> <span class="s1">&#39;df&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>Because an HDF5 file can contain more than one Dataset, we have to provide a name, or “key�, that identifies the Dataset in the file.</p>
+<p>We could use any string as the key, but in this example I use the variable name <code class="docutils literal notranslate"><span class="pre">df</span></code>.</p>
+<p><strong>Exercise:</strong> We’re going to need <code class="docutils literal notranslate"><span class="pre">centerline</span></code> and <code class="docutils literal notranslate"><span class="pre">selected</span></code> later as well.  Write a line or two of code to add it as a second Dataset in the HDF5 file.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Solution</span>
+
+<span class="n">centerline</span><span class="o">.</span><span class="n">to_hdf</span><span class="p">(</span><span class="n">filename</span><span class="p">,</span> <span class="s1">&#39;centerline&#39;</span><span class="p">)</span>
+<span class="n">selected</span><span class="o">.</span><span class="n">to_hdf</span><span class="p">(</span><span class="n">filename</span><span class="p">,</span> <span class="s1">&#39;selected&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p><strong>Detail:</strong> Reading and writing HDF5 tables requires a library called <code class="docutils literal notranslate"><span class="pre">PyTables</span></code> that is not always installed with Pandas.  You can install it with pip like this:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">pip</span> <span class="n">install</span> <span class="n">tables</span>
+</pre></div>
+</div>
+<p>If you install it using Conda, the name of the package is <code class="docutils literal notranslate"><span class="pre">pytables</span></code>.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">conda</span> <span class="n">install</span> <span class="n">pytables</span>
+</pre></div>
+</div>
+<p>We can use <code class="docutils literal notranslate"><span class="pre">ls</span></code> to confirm that the file exists and check the size:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">!</span>ls -lh gd1_dataframe.hdf5
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>-rw-rw-r-- 1 downey downey 17M Oct 19 12:05 gd1_dataframe.hdf5
+</pre></div>
+</div>
+</div>
+</div>
+<p>If you are using Windows, <code class="docutils literal notranslate"><span class="pre">ls</span></code> might not work; in that case, try:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>!dir gd1_dataframe.hdf5
+</pre></div>
+</div>
+<p>We can read the file back like this:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">read_back_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_hdf</span><span class="p">(</span><span class="n">filename</span><span class="p">,</span> <span class="s1">&#39;df&#39;</span><span class="p">)</span>
+<span class="n">read_back_df</span><span class="o">.</span><span class="n">shape</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>(140340, 12)
+</pre></div>
+</div>
+</div>
+</div>
+<p>Pandas can write a variety of other formats, <a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html">which you can read about here</a>.</p>
+</div>
+<div class="section" id="summary">
+<h2>Summary<a class="headerlink" href="#summary" title="Permalink to this headline">¶</a></h2>
+<p>In this lesson, we re-loaded the Gaia data we saved from a previous query.</p>
+<p>We transformed the coordinates and proper motion from ICRS to a frame aligned with GD-1, and stored the results in a Pandas <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code>.</p>
+<p>Then we replicated the selection process from the Price-Whelan and Bonaca paper:</p>
+<ul class="simple">
+<li><p>We selected stars near the centerline of GD-1 and made a scatter plot of their proper motion.</p></li>
+<li><p>We identified a region of proper motion that contains stars likely to be in GD-1.</p></li>
+<li><p>We used a Boolean <code class="docutils literal notranslate"><span class="pre">Series</span></code> as a mask to select stars whose proper motion is in that region.</p></li>
+</ul>
+<p>So far, we have used data from a relatively small region of the sky.  In the next lesson, we’ll write a query that selects stars based on proper motion, which will allow us to explore a larger region.</p>
+</div>
+<div class="section" id="best-practices">
+<h2>Best practices<a class="headerlink" href="#best-practices" title="Permalink to this headline">¶</a></h2>
+<ul class="simple">
+<li><p>When you make a scatter plot, adjust the size of the markers and their transparency so the figure is not overplotted; otherwise it can misrepresent the data badly.</p></li>
+<li><p>For simple scatter plots in Matplotlib, <code class="docutils literal notranslate"><span class="pre">plot</span></code> is faster than <code class="docutils literal notranslate"><span class="pre">scatter</span></code>.</p></li>
+<li><p>An Astropy <code class="docutils literal notranslate"><span class="pre">Table</span></code> and a Pandas <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code> are similar in many ways and they provide many of the same functions.  They have pros and cons, but for many projects, either one would be a reasonable choice.</p></li>
+</ul>
+</div>
+</div>
+
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+            onClick="window.print()" data-toggle="tooltip" data-placement="left">.pdf</button>
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+                    class="fab fa-github"></i>repository</button></a>
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+                    class="fas fa-expand"></i></button></a>
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+        aria-label="Launch interactive content"><i class="fas fa-rocket"></i></button>
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+        
+        <a class="binder-button" href="https://mybinder.org/v2/gh/AllenDowney/AstronomyDataPython/master?urlpath=tree/04_select.ipynb"><button type="button"
+                class="btn btn-secondary topbarbtn" title="Launch Binder" data-toggle="tooltip"
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+                    src="_static/images/logo_binder.svg"
+                    alt="Interact on binder">Binder</button></a>
+        
+        
+        
+        <a class="colab-button" href="https://colab.research.google.com/github/AllenDowney/AstronomyDataPython/blob/master/04_select.ipynb"><button type="button" class="btn btn-secondary topbarbtn"
+                title="Launch Colab" data-toggle="tooltip" data-placement="left"><img class="colab-button-logo"
+                    src="_static/images/logo_colab.png"
+                    alt="Interact on Colab">Colab</button></a>
+        
+        
+    </div>
+</div>
+
+    </div>
+
+    <!-- Table of contents -->
+    <div class="d-none d-md-block col-md-2 bd-toc show">
+        
+        <div class="tocsection onthispage pt-5 pb-3">
+            <i class="fas fa-list"></i> Contents
+        </div>
+        <nav id="bd-toc-nav">
+            <ul class="nav section-nav flex-column">
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#outline">
+   Outline
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#installing-libraries">
+   Installing libraries
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#reload-the-data">
+   Reload the data
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#selection-by-proper-motion">
+   Selection by proper motion
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#selecting-the-region">
+   Selecting the region
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#assemble-the-query">
+   Assemble the query
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#plotting-one-more-time">
+   Plotting one more time
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#saving-the-dataframe">
+   Saving the DataFrame
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#csv">
+   CSV
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#summary">
+   Summary
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#best-practices">
+   Best practices
+  </a>
+ </li>
+</ul>
+
+        </nav>
+        
+    </div>
+</div>
+    <div id="main-content" class="row">
+        <div class="col-12 col-md-9 pl-md-3 pr-md-0">
+        
+              <div>
+                
+  <div class="section" id="chapter-4">
+<h1>Chapter 4<a class="headerlink" href="#chapter-4" title="Permalink to this headline">¶</a></h1>
+<p>This is the fourth in a series of notebooks related to astronomy data.</p>
+<p>As a running example, we are replicating parts of the analysis in a recent paper, “<a class="reference external" href="https://arxiv.org/abs/1805.00425">Off the beaten path: Gaia reveals GD-1 stars outside of the main stream</a>� by Adrian M. Price-Whelan and Ana Bonaca.</p>
+<p>In the first lesson, we wrote ADQL queries and used them to select and download data from the Gaia server.</p>
+<p>In the second lesson, we write a query to select stars from the region of the sky where we expect GD-1 to be, and save the results in a FITS file.</p>
+<p>In the third lesson, we read that data back and identified stars with the proper motion we expect for GD-1.</p>
+<div class="section" id="outline">
+<h2>Outline<a class="headerlink" href="#outline" title="Permalink to this headline">¶</a></h2>
+<p>Here are the steps in this lesson:</p>
+<ol class="simple">
+<li><p>Using data from the previous lesson, we’ll identify the values of proper motion for stars likely to be in GD-1.</p></li>
+<li><p>Then we’ll compose an ADQL query that selects stars based on proper motion, so we can download only the data we need.</p></li>
+<li><p>We’ll also see how to write the results to a CSV file.</p></li>
+</ol>
+<p>That will make it possible to search a bigger region of the sky in a single query.</p>
+<p>After completing this lesson, you should be able to</p>
+<ul class="simple">
+<li><p>Convert proper motion between frames.</p></li>
+<li><p>Write an ADQL query that selects based on proper motion.</p></li>
+</ul>
+</div>
+<div class="section" id="installing-libraries">
+<h2>Installing libraries<a class="headerlink" href="#installing-libraries" title="Permalink to this headline">¶</a></h2>
+<p>If you are running this notebook on Colab, you can run the following cell to install Astroquery and a the other libraries we’ll use.</p>
+<p>If you are running this notebook on your own computer, you might have to install these libraries yourself.</p>
+<p>If you are using this notebook as part of a Carpentries workshop, you should have received setup instructions.</p>
+<p>TODO: Add a link to the instructions.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># If we&#39;re running on Colab, install libraries</span>
+
+<span class="kn">import</span> <span class="nn">sys</span>
+<span class="n">IN_COLAB</span> <span class="o">=</span> <span class="s1">&#39;google.colab&#39;</span> <span class="ow">in</span> <span class="n">sys</span><span class="o">.</span><span class="n">modules</span>
+
+<span class="k">if</span> <span class="n">IN_COLAB</span><span class="p">:</span>
+    <span class="o">!</span>pip install astroquery astro-gala pyia python-wget
+</pre></div>
+</div>
+</div>
+</div>
+</div>
+<div class="section" id="reload-the-data">
+<h2>Reload the data<a class="headerlink" href="#reload-the-data" title="Permalink to this headline">¶</a></h2>
+<p>The following cells download the data from the previous lesson, if necessary, and load it into a Pandas <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code>.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">os</span>
+<span class="kn">from</span> <span class="nn">wget</span> <span class="kn">import</span> <span class="n">download</span>
+
+<span class="n">filename</span> <span class="o">=</span> <span class="s1">&#39;gd1_dataframe.hdf5&#39;</span>
+<span class="n">path</span> <span class="o">=</span> <span class="s1">&#39;https://github.com/AllenDowney/AstronomicalData/raw/main/data/&#39;</span>
+
+<span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">filename</span><span class="p">):</span>
+    <span class="nb">print</span><span class="p">(</span><span class="n">download</span><span class="p">(</span><span class="n">path</span><span class="o">+</span><span class="n">filename</span><span class="p">))</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+
+<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_hdf</span><span class="p">(</span><span class="n">filename</span><span class="p">,</span> <span class="s1">&#39;df&#39;</span><span class="p">)</span>
+<span class="n">centerline</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_hdf</span><span class="p">(</span><span class="n">filename</span><span class="p">,</span> <span class="s1">&#39;centerline&#39;</span><span class="p">)</span>
+<span class="n">selected</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_hdf</span><span class="p">(</span><span class="n">filename</span><span class="p">,</span> <span class="s1">&#39;selected&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+</div>
+<div class="section" id="selection-by-proper-motion">
+<h2>Selection by proper motion<a class="headerlink" href="#selection-by-proper-motion" title="Permalink to this headline">¶</a></h2>
+<p>At this point we have downloaded data for a relatively large number of stars (more than 100,000) and selected a relatively small number (around 1000).</p>
+<p>It would be more efficient to use ADQL to select only the stars we need.  That would also make it possible to download data covering a larger region of the sky.</p>
+<p>However, the selection we did was based on proper motion in the <code class="docutils literal notranslate"><span class="pre">GD1Koposov10</span></code> frame.  In order to do the same selection in ADQL, we have to work with proper motions in ICRS.</p>
+<p>As a reminder, here’s the rectangle we selected based on proper motion in the <code class="docutils literal notranslate"><span class="pre">GD1Koposov10</span></code> frame.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pm1_min</span> <span class="o">=</span> <span class="o">-</span><span class="mf">8.9</span>
+<span class="n">pm1_max</span> <span class="o">=</span> <span class="o">-</span><span class="mf">6.9</span>
+<span class="n">pm2_min</span> <span class="o">=</span> <span class="o">-</span><span class="mf">2.2</span>
+<span class="n">pm2_max</span> <span class="o">=</span>  <span class="mf">1.0</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">astropy.units</span> <span class="k">as</span> <span class="nn">u</span>
+
+<span class="n">pm1_rect</span> <span class="o">=</span> <span class="p">[</span><span class="n">pm1_min</span><span class="p">,</span> <span class="n">pm1_min</span><span class="p">,</span> <span class="n">pm1_max</span><span class="p">,</span> <span class="n">pm1_max</span><span class="p">,</span> <span class="n">pm1_min</span><span class="p">]</span> <span class="o">*</span> <span class="n">u</span><span class="o">.</span><span class="n">mas</span><span class="o">/</span><span class="n">u</span><span class="o">.</span><span class="n">yr</span>
+<span class="n">pm2_rect</span> <span class="o">=</span> <span class="p">[</span><span class="n">pm2_min</span><span class="p">,</span> <span class="n">pm2_max</span><span class="p">,</span> <span class="n">pm2_max</span><span class="p">,</span> <span class="n">pm2_min</span><span class="p">,</span> <span class="n">pm2_min</span><span class="p">]</span> <span class="o">*</span> <span class="n">u</span><span class="o">.</span><span class="n">mas</span><span class="o">/</span><span class="n">u</span><span class="o">.</span><span class="n">yr</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>The following figure shows:</p>
+<ul class="simple">
+<li><p>Proper motion for the stars we selected along the center line of GD-1,</p></li>
+<li><p>The rectangle we selected, and</p></li>
+<li><p>The stars inside the rectangle highlighted in green.</p></li>
+</ul>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+
+<span class="n">pm1</span> <span class="o">=</span> <span class="n">centerline</span><span class="p">[</span><span class="s1">&#39;pm_phi1&#39;</span><span class="p">]</span>
+<span class="n">pm2</span> <span class="o">=</span> <span class="n">centerline</span><span class="p">[</span><span class="s1">&#39;pm_phi2&#39;</span><span class="p">]</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">pm1</span><span class="p">,</span> <span class="n">pm2</span><span class="p">,</span> <span class="s1">&#39;ko&#39;</span><span class="p">,</span> <span class="n">markersize</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.3</span><span class="p">)</span>
+
+<span class="n">pm1</span> <span class="o">=</span> <span class="n">selected</span><span class="p">[</span><span class="s1">&#39;pm_phi1&#39;</span><span class="p">]</span>
+<span class="n">pm2</span> <span class="o">=</span> <span class="n">selected</span><span class="p">[</span><span class="s1">&#39;pm_phi2&#39;</span><span class="p">]</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">pm1</span><span class="p">,</span> <span class="n">pm2</span><span class="p">,</span> <span class="s1">&#39;gx&#39;</span><span class="p">,</span> <span class="n">markersize</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.3</span><span class="p">)</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">pm1_rect</span><span class="p">,</span> <span class="n">pm2_rect</span><span class="p">,</span> <span class="s1">&#39;-&#39;</span><span class="p">)</span>
+    
+<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">&#39;Proper motion phi1 (GD1 frame)&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">&#39;Proper motion phi2 (GD1 frame)&#39;</span><span class="p">)</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">xlim</span><span class="p">(</span><span class="o">-</span><span class="mi">12</span><span class="p">,</span> <span class="mi">8</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylim</span><span class="p">(</span><span class="o">-</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">);</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<img alt="_images/04_select_11_0.png" src="_images/04_select_11_0.png" />
+</div>
+</div>
+<p>Now we’ll make the same plot using proper motions in the ICRS frame, which are stored in columns <code class="docutils literal notranslate"><span class="pre">pmra</span></code> and <code class="docutils literal notranslate"><span class="pre">pmdec</span></code>.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pm1</span> <span class="o">=</span> <span class="n">centerline</span><span class="p">[</span><span class="s1">&#39;pmra&#39;</span><span class="p">]</span>
+<span class="n">pm2</span> <span class="o">=</span> <span class="n">centerline</span><span class="p">[</span><span class="s1">&#39;pmdec&#39;</span><span class="p">]</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">pm1</span><span class="p">,</span> <span class="n">pm2</span><span class="p">,</span> <span class="s1">&#39;ko&#39;</span><span class="p">,</span> <span class="n">markersize</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.3</span><span class="p">)</span>
+
+<span class="n">pm1</span> <span class="o">=</span> <span class="n">selected</span><span class="p">[</span><span class="s1">&#39;pmra&#39;</span><span class="p">]</span>
+<span class="n">pm2</span> <span class="o">=</span> <span class="n">selected</span><span class="p">[</span><span class="s1">&#39;pmdec&#39;</span><span class="p">]</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">pm1</span><span class="p">,</span> <span class="n">pm2</span><span class="p">,</span> <span class="s1">&#39;gx&#39;</span><span class="p">,</span> <span class="n">markersize</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.3</span><span class="p">)</span>
+    
+<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">&#39;Proper motion phi1 (ICRS frame)&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">&#39;Proper motion phi2 (ICRS frame)&#39;</span><span class="p">)</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">xlim</span><span class="p">([</span><span class="o">-</span><span class="mi">10</span><span class="p">,</span> <span class="mi">5</span><span class="p">])</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylim</span><span class="p">([</span><span class="o">-</span><span class="mi">20</span><span class="p">,</span> <span class="mi">5</span><span class="p">]);</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<img alt="_images/04_select_13_0.png" src="_images/04_select_13_0.png" />
+</div>
+</div>
+<p>The proper motions of the selected stars are more spread out in this frame, which is why it was preferable to do the selection in the GD-1 frame.</p>
+<p>But now we can define a polygon that encloses the proper motions of these stars in ICRS,
+and use the polygon as a selection criterion in an ADQL query.</p>
+<p>SciPy provides a function that computes the <a class="reference external" href="https://en.wikipedia.org/wiki/Convex_hull">convex hull</a> of a set of points, which is the smallest convex polygon that contains all of the points.</p>
+<p>To use it, I’ll select columns <code class="docutils literal notranslate"><span class="pre">pmra</span></code> and <code class="docutils literal notranslate"><span class="pre">pmdec</span></code> and convert them to a NumPy array.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+
+<span class="n">points</span> <span class="o">=</span> <span class="n">selected</span><span class="p">[[</span><span class="s1">&#39;pmra&#39;</span><span class="p">,</span><span class="s1">&#39;pmdec&#39;</span><span class="p">]]</span><span class="o">.</span><span class="n">to_numpy</span><span class="p">()</span>
+<span class="n">points</span><span class="o">.</span><span class="n">shape</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>(1049, 2)
+</pre></div>
+</div>
+</div>
+</div>
+<p>We’ll pass the points to <code class="docutils literal notranslate"><span class="pre">ConvexHull</span></code>, which returns an object that contains the results.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">scipy.spatial</span> <span class="kn">import</span> <span class="n">ConvexHull</span>
+
+<span class="n">hull</span> <span class="o">=</span> <span class="n">ConvexHull</span><span class="p">(</span><span class="n">points</span><span class="p">)</span>
+<span class="n">hull</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;scipy.spatial.qhull.ConvexHull at 0x7f446b1e8bb0&gt;
+</pre></div>
+</div>
+</div>
+</div>
+<p><code class="docutils literal notranslate"><span class="pre">hull.vertices</span></code> contains the indices of the points that fall on the perimeter of the hull.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">hull</span><span class="o">.</span><span class="n">vertices</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>array([ 692,  873,  141,  303,   42,  622,   45,   83,  127,  182, 1006,
+        971,  967, 1001,  969,  940], dtype=int32)
+</pre></div>
+</div>
+</div>
+</div>
+<p>We can use them as an index into the original array to select the corresponding rows.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pm_vertices</span> <span class="o">=</span> <span class="n">points</span><span class="p">[</span><span class="n">hull</span><span class="o">.</span><span class="n">vertices</span><span class="p">]</span>
+<span class="n">pm_vertices</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>array([[ -4.05037121, -14.75623261],
+       [ -3.41981085, -14.72365546],
+       [ -3.03521988, -14.44357135],
+       [ -2.26847919, -13.7140236 ],
+       [ -2.61172203, -13.24797471],
+       [ -2.73471401, -13.09054471],
+       [ -3.19923146, -12.5942653 ],
+       [ -3.34082546, -12.47611926],
+       [ -5.67489413, -11.16083338],
+       [ -5.95159272, -11.10547884],
+       [ -6.42394023, -11.05981295],
+       [ -7.09631023, -11.95187806],
+       [ -7.30641519, -12.24559977],
+       [ -7.04016696, -12.88580702],
+       [ -6.00347705, -13.75912098],
+       [ -4.42442296, -14.74641176]])
+</pre></div>
+</div>
+</div>
+</div>
+<p>To plot the resulting polygon, we have to pull out the x and y coordinates.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pmra_poly</span><span class="p">,</span> <span class="n">pmdec_poly</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="n">pm_vertices</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>The following figure shows proper motion in ICRS again, along with the convex hull we just computed.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pm1</span> <span class="o">=</span> <span class="n">centerline</span><span class="p">[</span><span class="s1">&#39;pmra&#39;</span><span class="p">]</span>
+<span class="n">pm2</span> <span class="o">=</span> <span class="n">centerline</span><span class="p">[</span><span class="s1">&#39;pmdec&#39;</span><span class="p">]</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">pm1</span><span class="p">,</span> <span class="n">pm2</span><span class="p">,</span> <span class="s1">&#39;ko&#39;</span><span class="p">,</span> <span class="n">markersize</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.3</span><span class="p">)</span>
+
+<span class="n">pm1</span> <span class="o">=</span> <span class="n">selected</span><span class="p">[</span><span class="s1">&#39;pmra&#39;</span><span class="p">]</span>
+<span class="n">pm2</span> <span class="o">=</span> <span class="n">selected</span><span class="p">[</span><span class="s1">&#39;pmdec&#39;</span><span class="p">]</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">pm1</span><span class="p">,</span> <span class="n">pm2</span><span class="p">,</span> <span class="s1">&#39;gx&#39;</span><span class="p">,</span> <span class="n">markersize</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.3</span><span class="p">)</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">pmra_poly</span><span class="p">,</span> <span class="n">pmdec_poly</span><span class="p">)</span>
+    
+<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">&#39;Proper motion phi1 (ICRS frame)&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">&#39;Proper motion phi2 (ICRS frame)&#39;</span><span class="p">)</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">xlim</span><span class="p">([</span><span class="o">-</span><span class="mi">10</span><span class="p">,</span> <span class="mi">5</span><span class="p">])</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylim</span><span class="p">([</span><span class="o">-</span><span class="mi">20</span><span class="p">,</span> <span class="mi">5</span><span class="p">]);</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<img alt="_images/04_select_25_0.png" src="_images/04_select_25_0.png" />
+</div>
+</div>
+<p>To use <code class="docutils literal notranslate"><span class="pre">pm_vertices</span></code> as part of an ADQL query, we have to convert it to a string.</p>
+<p>We’ll use <code class="docutils literal notranslate"><span class="pre">flatten</span></code> to convert from a 2-D array to a 1-D array, and <code class="docutils literal notranslate"><span class="pre">str</span></code> to convert each element to a string.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">t</span> <span class="o">=</span> <span class="p">[</span><span class="nb">str</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">pm_vertices</span><span class="o">.</span><span class="n">flatten</span><span class="p">()]</span>
+<span class="n">t</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[&#39;-4.050371212154984&#39;,
+ &#39;-14.75623260987968&#39;,
+ &#39;-3.4198108491382455&#39;,
+ &#39;-14.723655456335619&#39;,
+ &#39;-3.035219883740934&#39;,
+ &#39;-14.443571352854612&#39;,
+ &#39;-2.268479190206636&#39;,
+ &#39;-13.714023598831554&#39;,
+ &#39;-2.611722027231764&#39;,
+ &#39;-13.247974712069263&#39;,
+ &#39;-2.7347140078529106&#39;,
+ &#39;-13.090544709622938&#39;,
+ &#39;-3.199231461993783&#39;,
+ &#39;-12.594265302440828&#39;,
+ &#39;-3.34082545787549&#39;,
+ &#39;-12.476119260818695&#39;,
+ &#39;-5.674894125178565&#39;,
+ &#39;-11.160833381392624&#39;,
+ &#39;-5.95159272432137&#39;,
+ &#39;-11.105478836426514&#39;,
+ &#39;-6.423940229776128&#39;,
+ &#39;-11.05981294804957&#39;,
+ &#39;-7.096310230579248&#39;,
+ &#39;-11.951878058650085&#39;,
+ &#39;-7.306415190921692&#39;,
+ &#39;-12.245599765990594&#39;,
+ &#39;-7.040166963232815&#39;,
+ &#39;-12.885807024935527&#39;,
+ &#39;-6.0034770546523735&#39;,
+ &#39;-13.759120984106968&#39;,
+ &#39;-4.42442296194263&#39;,
+ &#39;-14.7464117578883&#39;]
+</pre></div>
+</div>
+</div>
+</div>
+<p>Now <code class="docutils literal notranslate"><span class="pre">t</span></code> is a list of strings; we can use <code class="docutils literal notranslate"><span class="pre">join</span></code> to make a single string with commas between the elements.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pm_point_list</span> <span class="o">=</span> <span class="s1">&#39;, &#39;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">t</span><span class="p">)</span>
+<span class="n">pm_point_list</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&#39;-4.050371212154984, -14.75623260987968, -3.4198108491382455, -14.723655456335619, -3.035219883740934, -14.443571352854612, -2.268479190206636, -13.714023598831554, -2.611722027231764, -13.247974712069263, -2.7347140078529106, -13.090544709622938, -3.199231461993783, -12.594265302440828, -3.34082545787549, -12.476119260818695, -5.674894125178565, -11.160833381392624, -5.95159272432137, -11.105478836426514, -6.423940229776128, -11.05981294804957, -7.096310230579248, -11.951878058650085, -7.306415190921692, -12.245599765990594, -7.040166963232815, -12.885807024935527, -6.0034770546523735, -13.759120984106968, -4.42442296194263, -14.7464117578883&#39;
+</pre></div>
+</div>
+</div>
+</div>
+</div>
+<div class="section" id="selecting-the-region">
+<h2>Selecting the region<a class="headerlink" href="#selecting-the-region" title="Permalink to this headline">¶</a></h2>
+<p>Let’s review how we got to this point.</p>
+<ol class="simple">
+<li><p>We made an ADQL query to the Gaia server to get data for stars in the vicinity of GD-1.</p></li>
+<li><p>We transformed to <code class="docutils literal notranslate"><span class="pre">GD1</span></code> coordinates so we could select stars along the centerline of GD-1.</p></li>
+<li><p>We plotted the proper motion of the centerline stars to identify the bounds of the overdense region.</p></li>
+<li><p>We made a mask that selects stars whose proper motion is in the overdense region.</p></li>
+</ol>
+<p>The problem is that we downloaded data for more than 100,000 stars and selected only about 1000 of them.</p>
+<p>It will be more efficient if we select on proper motion as part of the query.  That will allow us to work with a larger region of the sky in a single query, and download less unneeded data.</p>
+<p>This query will select on the following conditions:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">parallax</span> <span class="pre">&lt;</span> <span class="pre">1</span></code></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">bp_rp</span> <span class="pre">BETWEEN</span> <span class="pre">-0.75</span> <span class="pre">AND</span> <span class="pre">2</span></code></p></li>
+<li><p>Coordinates within a rectangle in the GD-1 frame, transformed to ICRS.</p></li>
+<li><p>Proper motion with the polygon we just computed.</p></li>
+</ul>
+<p>The first three conditions are the same as in the previous query.  Only the last one is new.</p>
+<p>Here’s the rectangle in the GD-1 frame we’ll select.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">phi1_min</span> <span class="o">=</span> <span class="o">-</span><span class="mi">70</span>
+<span class="n">phi1_max</span> <span class="o">=</span> <span class="o">-</span><span class="mi">20</span>
+<span class="n">phi2_min</span> <span class="o">=</span> <span class="o">-</span><span class="mi">5</span>
+<span class="n">phi2_max</span> <span class="o">=</span> <span class="mi">5</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">phi1_rect</span> <span class="o">=</span> <span class="p">[</span><span class="n">phi1_min</span><span class="p">,</span> <span class="n">phi1_min</span><span class="p">,</span> <span class="n">phi1_max</span><span class="p">,</span> <span class="n">phi1_max</span><span class="p">]</span> <span class="o">*</span> <span class="n">u</span><span class="o">.</span><span class="n">deg</span>
+<span class="n">phi2_rect</span> <span class="o">=</span> <span class="p">[</span><span class="n">phi2_min</span><span class="p">,</span> <span class="n">phi2_max</span><span class="p">,</span> <span class="n">phi2_max</span><span class="p">,</span> <span class="n">phi2_min</span><span class="p">]</span> <span class="o">*</span> <span class="n">u</span><span class="o">.</span><span class="n">deg</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>Here’s how we transform it to ICRS, as we saw in the previous lesson.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">gala.coordinates</span> <span class="k">as</span> <span class="nn">gc</span>
+<span class="kn">import</span> <span class="nn">astropy.coordinates</span> <span class="k">as</span> <span class="nn">coord</span>
+
+<span class="n">corners</span> <span class="o">=</span> <span class="n">gc</span><span class="o">.</span><span class="n">GD1Koposov10</span><span class="p">(</span><span class="n">phi1</span><span class="o">=</span><span class="n">phi1_rect</span><span class="p">,</span> <span class="n">phi2</span><span class="o">=</span><span class="n">phi2_rect</span><span class="p">)</span>
+<span class="n">corners_icrs</span> <span class="o">=</span> <span class="n">corners</span><span class="o">.</span><span class="n">transform_to</span><span class="p">(</span><span class="n">coord</span><span class="o">.</span><span class="n">ICRS</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>To use <code class="docutils literal notranslate"><span class="pre">corners_icrs</span></code> as part of an ADQL query, we have to convert it to a string.  Here’s how we do that, as we saw in the previous lesson.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">point_base</span> <span class="o">=</span> <span class="s2">&quot;</span><span class="si">{point.ra.value}</span><span class="s2">, </span><span class="si">{point.dec.value}</span><span class="s2">&quot;</span>
+
+<span class="n">t</span> <span class="o">=</span> <span class="p">[</span><span class="n">point_base</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">point</span><span class="o">=</span><span class="n">point</span><span class="p">)</span>
+     <span class="k">for</span> <span class="n">point</span> <span class="ow">in</span> <span class="n">corners_icrs</span><span class="p">]</span>
+
+<span class="n">point_list</span> <span class="o">=</span> <span class="s1">&#39;, &#39;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">t</span><span class="p">)</span>
+<span class="n">point_list</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&#39;135.30559858565638, 8.398623940157561, 126.50951508623503, 13.44494195652069, 163.0173655836748, 54.24242734020255, 172.9328536286811, 46.47260492416258&#39;
+</pre></div>
+</div>
+</div>
+</div>
+<p>Now we have everything we need to assemble the query.</p>
+</div>
+<div class="section" id="assemble-the-query">
+<h2>Assemble the query<a class="headerlink" href="#assemble-the-query" title="Permalink to this headline">¶</a></h2>
+<p>Here’s the base string we used for the query in the previous lesson.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">query_base</span> <span class="o">=</span> <span class="s2">&quot;&quot;&quot;SELECT </span>
+<span class="si">{columns}</span><span class="s2"></span>
+<span class="s2">FROM gaiadr2.gaia_source</span>
+<span class="s2">WHERE parallax &lt; 1</span>
+<span class="s2">  AND bp_rp BETWEEN -0.75 AND 2 </span>
+<span class="s2">  AND 1 = CONTAINS(POINT(ra, dec), </span>
+<span class="s2">                   POLYGON(</span><span class="si">{point_list}</span><span class="s2">))</span>
+<span class="s2">&quot;&quot;&quot;</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p><strong>Exercise:</strong> Modify <code class="docutils literal notranslate"><span class="pre">query_base</span></code> by adding a new clause to select stars whose coordinates of proper motion, <code class="docutils literal notranslate"><span class="pre">pmra</span></code> and <code class="docutils literal notranslate"><span class="pre">pmdec</span></code>, fall within the polygon defined by <code class="docutils literal notranslate"><span class="pre">pm_point_list</span></code>.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Solution</span>
+
+<span class="n">query_base</span> <span class="o">=</span> <span class="s2">&quot;&quot;&quot;SELECT </span>
+<span class="si">{columns}</span><span class="s2"></span>
+<span class="s2">FROM gaiadr2.gaia_source</span>
+<span class="s2">WHERE parallax &lt; 1</span>
+<span class="s2">  AND bp_rp BETWEEN -0.75 AND 2 </span>
+<span class="s2">  AND 1 = CONTAINS(POINT(ra, dec), </span>
+<span class="s2">                   POLYGON(</span><span class="si">{point_list}</span><span class="s2">))</span>
+<span class="s2">  AND 1 = CONTAINS(POINT(pmra, pmdec),</span>
+<span class="s2">                   POLYGON(</span><span class="si">{pm_point_list}</span><span class="s2">))</span>
+<span class="s2">&quot;&quot;&quot;</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>Here again are the columns we want to select.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">columns</span> <span class="o">=</span> <span class="s1">&#39;source_id, ra, dec, pmra, pmdec, parallax, parallax_error, radial_velocity&#39;</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p><strong>Exercise:</strong> Use <code class="docutils literal notranslate"><span class="pre">format</span></code> to format <code class="docutils literal notranslate"><span class="pre">query_base</span></code> and define <code class="docutils literal notranslate"><span class="pre">query</span></code>, filling in the values of <code class="docutils literal notranslate"><span class="pre">columns</span></code>, <code class="docutils literal notranslate"><span class="pre">point_list</span></code>, and <code class="docutils literal notranslate"><span class="pre">pm_point_list</span></code>.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Solution</span>
+
+<span class="n">query</span> <span class="o">=</span> <span class="n">query_base</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="n">columns</span><span class="p">,</span> 
+                            <span class="n">point_list</span><span class="o">=</span><span class="n">point_list</span><span class="p">,</span>
+                            <span class="n">pm_point_list</span><span class="o">=</span><span class="n">pm_point_list</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">query</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>SELECT 
+source_id, ra, dec, pmra, pmdec, parallax, parallax_error, radial_velocity
+FROM gaiadr2.gaia_source
+WHERE parallax &lt; 1
+  AND bp_rp BETWEEN -0.75 AND 2 
+  AND 1 = CONTAINS(POINT(ra, dec), 
+                   POLYGON(135.30559858565638, 8.398623940157561, 126.50951508623503, 13.44494195652069, 163.0173655836748, 54.24242734020255, 172.9328536286811, 46.47260492416258))
+  AND 1 = CONTAINS(POINT(pmra, pmdec),
+                   POLYGON(-4.050371212154984, -14.75623260987968, -3.4198108491382455, -14.723655456335619, -3.035219883740934, -14.443571352854612, -2.268479190206636, -13.714023598831554, -2.611722027231764, -13.247974712069263, -2.7347140078529106, -13.090544709622938, -3.199231461993783, -12.594265302440828, -3.34082545787549, -12.476119260818695, -5.674894125178565, -11.160833381392624, -5.95159272432137, -11.105478836426514, -6.423940229776128, -11.05981294804957, -7.096310230579248, -11.951878058650085, -7.306415190921692, -12.245599765990594, -7.040166963232815, -12.885807024935527, -6.0034770546523735, -13.759120984106968, -4.42442296194263, -14.7464117578883))
+</pre></div>
+</div>
+</div>
+</div>
+<p>Here’s how we run it.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">astroquery.gaia</span> <span class="kn">import</span> <span class="n">Gaia</span>
+
+<span class="n">job</span> <span class="o">=</span> <span class="n">Gaia</span><span class="o">.</span><span class="n">launch_job_async</span><span class="p">(</span><span class="n">query</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">job</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Created TAP+ (v1.2.1) - Connection:
+	Host: gea.esac.esa.int
+	Use HTTPS: True
+	Port: 443
+	SSL Port: 443
+Created TAP+ (v1.2.1) - Connection:
+	Host: geadata.esac.esa.int
+	Use HTTPS: True
+	Port: 443
+	SSL Port: 443
+INFO: Query finished. [astroquery.utils.tap.core]
+&lt;Table length=7346&gt;
+      name       dtype    unit                              description                             n_bad
+--------------- ------- -------- ------------------------------------------------------------------ -----
+      source_id   int64          Unique source identifier (unique within a particular Data Release)     0
+             ra float64      deg                                                    Right ascension     0
+            dec float64      deg                                                        Declination     0
+           pmra float64 mas / yr                         Proper motion in right ascension direction     0
+          pmdec float64 mas / yr                             Proper motion in declination direction     0
+       parallax float64      mas                                                           Parallax     0
+ parallax_error float64      mas                                         Standard error of parallax     0
+radial_velocity float64   km / s                                                    Radial velocity  7295
+Jobid: 1603132746237O
+Phase: COMPLETED
+Owner: None
+Output file: async_20201019143906.vot
+Results: None
+</pre></div>
+</div>
+</div>
+</div>
+<p>And get the results.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">candidate_table</span> <span class="o">=</span> <span class="n">job</span><span class="o">.</span><span class="n">get_results</span><span class="p">()</span>
+<span class="nb">len</span><span class="p">(</span><span class="n">candidate_table</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>7346
+</pre></div>
+</div>
+</div>
+</div>
+</div>
+<div class="section" id="plotting-one-more-time">
+<h2>Plotting one more time<a class="headerlink" href="#plotting-one-more-time" title="Permalink to this headline">¶</a></h2>
+<p>Let’s see what the results look like.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="n">candidate_table</span><span class="p">[</span><span class="s1">&#39;ra&#39;</span><span class="p">]</span>
+<span class="n">y</span> <span class="o">=</span> <span class="n">candidate_table</span><span class="p">[</span><span class="s1">&#39;dec&#39;</span><span class="p">]</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="s1">&#39;ko&#39;</span><span class="p">,</span> <span class="n">markersize</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.3</span><span class="p">)</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">&#39;ra (degree ICRS)&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">&#39;dec (degree ICRS)&#39;</span><span class="p">);</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<img alt="_images/04_select_51_0.png" src="_images/04_select_51_0.png" />
+</div>
+</div>
+<p>Here we can see why it was useful to transform these coordinates.  In ICRS, it is more difficult to identity the stars near the centerline of GD-1.</p>
+<p>So, before we move on to the next step, let’s collect the code we used to transform the coordinates and make a Pandas <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code>:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">pyia</span> <span class="kn">import</span> <span class="n">GaiaData</span>
+
+<span class="k">def</span> <span class="nf">make_dataframe</span><span class="p">(</span><span class="n">table</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;Transform coordinates from ICRS to GD-1 frame.</span>
+<span class="sd">    </span>
+<span class="sd">    table: Astropy Table</span>
+<span class="sd">    </span>
+<span class="sd">    returns: Pandas DataFrame</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">gaia_data</span> <span class="o">=</span> <span class="n">GaiaData</span><span class="p">(</span><span class="n">table</span><span class="p">)</span>
+
+    <span class="n">c_sky</span> <span class="o">=</span> <span class="n">gaia_data</span><span class="o">.</span><span class="n">get_skycoord</span><span class="p">(</span><span class="n">distance</span><span class="o">=</span><span class="mi">8</span><span class="o">*</span><span class="n">u</span><span class="o">.</span><span class="n">kpc</span><span class="p">,</span> 
+                                   <span class="n">radial_velocity</span><span class="o">=</span><span class="mi">0</span><span class="o">*</span><span class="n">u</span><span class="o">.</span><span class="n">km</span><span class="o">/</span><span class="n">u</span><span class="o">.</span><span class="n">s</span><span class="p">)</span>
+    <span class="n">c_gd1</span> <span class="o">=</span> <span class="n">gc</span><span class="o">.</span><span class="n">reflex_correct</span><span class="p">(</span>
+                <span class="n">c_sky</span><span class="o">.</span><span class="n">transform_to</span><span class="p">(</span><span class="n">gc</span><span class="o">.</span><span class="n">GD1Koposov10</span><span class="p">))</span>
+
+    <span class="n">df</span> <span class="o">=</span> <span class="n">table</span><span class="o">.</span><span class="n">to_pandas</span><span class="p">()</span>
+    <span class="n">df</span><span class="p">[</span><span class="s1">&#39;phi1&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">c_gd1</span><span class="o">.</span><span class="n">phi1</span>
+    <span class="n">df</span><span class="p">[</span><span class="s1">&#39;phi2&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">c_gd1</span><span class="o">.</span><span class="n">phi2</span>
+    <span class="n">df</span><span class="p">[</span><span class="s1">&#39;pm_phi1&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">c_gd1</span><span class="o">.</span><span class="n">pm_phi1_cosphi2</span>
+    <span class="n">df</span><span class="p">[</span><span class="s1">&#39;pm_phi2&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">c_gd1</span><span class="o">.</span><span class="n">pm_phi2</span>
+    <span class="k">return</span> <span class="n">df</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>Here’s how we can use this function:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">candidate_df</span> <span class="o">=</span> <span class="n">make_dataframe</span><span class="p">(</span><span class="n">candidate_table</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>And let’s see the results.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="n">candidate_df</span><span class="p">[</span><span class="s1">&#39;phi1&#39;</span><span class="p">]</span>
+<span class="n">y</span> <span class="o">=</span> <span class="n">candidate_df</span><span class="p">[</span><span class="s1">&#39;phi2&#39;</span><span class="p">]</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="s1">&#39;ko&#39;</span><span class="p">,</span> <span class="n">markersize</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">&#39;ra (degree GD1)&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">&#39;dec (degree GD1)&#39;</span><span class="p">);</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<img alt="_images/04_select_57_0.png" src="_images/04_select_57_0.png" />
+</div>
+</div>
+<p>We’re starting to see GD-1 more clearly.</p>
+<p>We can compare this figure with one of these panels in Figure 1 from the original paper:</p>
+<img height="150" src="https://github.com/datacarpentry/astronomy-python/raw/gh-pages/fig/gd1-2.png">
+<img height="150" src="https://github.com/datacarpentry/astronomy-python/raw/gh-pages/fig/gd1-4.png">
+<p>The top panel shows stars selected based on proper motion only, so it is comparable to our figure (although notice that it covers a wider region).</p>
+<p>In the next lesson, we will use photometry data from Pan-STARRS to do a second round of filtering, and see if we can replicate the bottom panel.</p>
+<p>We’ll also learn how to add annotations like the ones in the figure from the paper, and customize the style of the figure to present the results clearly and compellingly.</p>
+</div>
+<div class="section" id="saving-the-dataframe">
+<h2>Saving the DataFrame<a class="headerlink" href="#saving-the-dataframe" title="Permalink to this headline">¶</a></h2>
+<p>Let’s save this <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code> so we can pick up where we left off without running this query again.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">!</span>rm -f gd1_candidates.hdf5
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">filename</span> <span class="o">=</span> <span class="s1">&#39;gd1_candidates.hdf5&#39;</span>
+
+<span class="n">candidate_df</span><span class="o">.</span><span class="n">to_hdf</span><span class="p">(</span><span class="n">filename</span><span class="p">,</span> <span class="s1">&#39;candidate_df&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>We can use <code class="docutils literal notranslate"><span class="pre">ls</span></code> to confirm that the file exists and check the size:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">!</span>ls -lh gd1_candidates.hdf5
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>-rw-rw-r-- 1 downey downey 756K Oct 19 14:39 gd1_candidates.hdf5
+</pre></div>
+</div>
+</div>
+</div>
+<p>If you are using Windows, <code class="docutils literal notranslate"><span class="pre">ls</span></code> might not work; in that case, try:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>!dir gd1_candidates.hdf5
+</pre></div>
+</div>
+</div>
+<div class="section" id="csv">
+<h2>CSV<a class="headerlink" href="#csv" title="Permalink to this headline">¶</a></h2>
+<p>Pandas can write a variety of other formats, <a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html">which you can read about here</a>.</p>
+<p>We won’t cover all of them, but one other important one is <a class="reference external" href="https://en.wikipedia.org/wiki/Comma-separated_values">CSV</a>, which stands for “comma-separated values�.</p>
+<p>CSV is a plain-text format with minimal formatting requirements, so it can be read and written by pretty much any tool that works with data.  In that sense, it is the “least common denominator� of data formats.</p>
+<p>However, it has an important limitation: some information about the data gets lost in translation, notably the data types.  If you read a CSV file from someone else, you might need some additional information to make sure you are getting it right.</p>
+<p>Also, CSV files tend to be big, and slow to read and write.</p>
+<p>With those caveats, here’s how to write one:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">candidate_df</span><span class="o">.</span><span class="n">to_csv</span><span class="p">(</span><span class="s1">&#39;gd1_candidates.csv&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>We can check the file size like this:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">!</span>ls -lh gd1_candidates.csv
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>-rw-rw-r-- 1 downey downey 1.6M Oct 19 14:39 gd1_candidates.csv
+</pre></div>
+</div>
+</div>
+</div>
+<p>The CSV file about 2 times bigger than the HDF5 file (so that’s not that bad, really).</p>
+<p>We can see the first few lines like this:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">!</span>head -3 gd1_candidates.csv
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>,source_id,ra,dec,pmra,pmdec,parallax,parallax_error,radial_velocity,phi1,phi2,pm_phi1,pm_phi2
+0,635559124339440000,137.58671691646745,19.1965441084838,-3.770521900009566,-12.490481778113859,0.7913934419894347,0.2717538145759051,,-59.63048941944396,-1.21648525150429,-7.361362712556612,-0.5926328820420083
+1,635860218726658176,138.5187065217173,19.09233926905897,-5.941679495793577,-11.346409129876392,0.30745551377348623,0.19946557779138105,,-59.247329893833296,-2.0160784008206476,-7.527126084599517,1.7487794924398758
+</pre></div>
+</div>
+</div>
+</div>
+<p>The CSV file contains the names of the columns, but not the data types.</p>
+<p>We can read the CSV file back like this:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">read_back_csv</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s1">&#39;gd1_candidates.csv&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>Let’s compare the first few rows of <code class="docutils literal notranslate"><span class="pre">candidate_df</span></code> and <code class="docutils literal notranslate"><span class="pre">read_back_csv</span></code></p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">candidate_df</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>source_id</th>
+      <th>ra</th>
+      <th>dec</th>
+      <th>pmra</th>
+      <th>pmdec</th>
+      <th>parallax</th>
+      <th>parallax_error</th>
+      <th>radial_velocity</th>
+      <th>phi1</th>
+      <th>phi2</th>
+      <th>pm_phi1</th>
+      <th>pm_phi2</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>635559124339440000</td>
+      <td>137.586717</td>
+      <td>19.196544</td>
+      <td>-3.770522</td>
+      <td>-12.490482</td>
+      <td>0.791393</td>
+      <td>0.271754</td>
+      <td>NaN</td>
+      <td>-59.630489</td>
+      <td>-1.216485</td>
+      <td>-7.361363</td>
+      <td>-0.592633</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>635860218726658176</td>
+      <td>138.518707</td>
+      <td>19.092339</td>
+      <td>-5.941679</td>
+      <td>-11.346409</td>
+      <td>0.307456</td>
+      <td>0.199466</td>
+      <td>NaN</td>
+      <td>-59.247330</td>
+      <td>-2.016078</td>
+      <td>-7.527126</td>
+      <td>1.748779</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>635674126383965568</td>
+      <td>138.842874</td>
+      <td>19.031798</td>
+      <td>-3.897001</td>
+      <td>-12.702780</td>
+      <td>0.779463</td>
+      <td>0.223692</td>
+      <td>NaN</td>
+      <td>-59.133391</td>
+      <td>-2.306901</td>
+      <td>-7.560608</td>
+      <td>-0.741800</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">read_back_csv</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>Unnamed: 0</th>
+      <th>source_id</th>
+      <th>ra</th>
+      <th>dec</th>
+      <th>pmra</th>
+      <th>pmdec</th>
+      <th>parallax</th>
+      <th>parallax_error</th>
+      <th>radial_velocity</th>
+      <th>phi1</th>
+      <th>phi2</th>
+      <th>pm_phi1</th>
+      <th>pm_phi2</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>0</td>
+      <td>635559124339440000</td>
+      <td>137.586717</td>
+      <td>19.196544</td>
+      <td>-3.770522</td>
+      <td>-12.490482</td>
+      <td>0.791393</td>
+      <td>0.271754</td>
+      <td>NaN</td>
+      <td>-59.630489</td>
+      <td>-1.216485</td>
+      <td>-7.361363</td>
+      <td>-0.592633</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>1</td>
+      <td>635860218726658176</td>
+      <td>138.518707</td>
+      <td>19.092339</td>
+      <td>-5.941679</td>
+      <td>-11.346409</td>
+      <td>0.307456</td>
+      <td>0.199466</td>
+      <td>NaN</td>
+      <td>-59.247330</td>
+      <td>-2.016078</td>
+      <td>-7.527126</td>
+      <td>1.748779</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>2</td>
+      <td>635674126383965568</td>
+      <td>138.842874</td>
+      <td>19.031798</td>
+      <td>-3.897001</td>
+      <td>-12.702780</td>
+      <td>0.779463</td>
+      <td>0.223692</td>
+      <td>NaN</td>
+      <td>-59.133391</td>
+      <td>-2.306901</td>
+      <td>-7.560608</td>
+      <td>-0.741800</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+<p>Notice that the index in <code class="docutils literal notranslate"><span class="pre">candidate_df</span></code> has become an unnamed column in <code class="docutils literal notranslate"><span class="pre">read_back_csv</span></code>.  The Pandas functions for writing and reading CSV files provide options to avoid that problem, but this is an example of the kind of thing that can go wrong with CSV files.</p>
+</div>
+<div class="section" id="summary">
+<h2>Summary<a class="headerlink" href="#summary" title="Permalink to this headline">¶</a></h2>
+<p>In the previous lesson we downloaded data for a large number of stars and then selected a small fraction of them based on proper motion.</p>
+<p>In this lesson, we improved this process by writing a more complex query that uses the database to select stars based on proper motion.  This process requires more computation on the Gaia server, but then we’re able to either:</p>
+<ol class="simple">
+<li><p>Search the same region and download less data, or</p></li>
+<li><p>Search a larger region while still downloading a manageable amount of data.</p></li>
+</ol>
+<p>In the next lesson, we’ll learn about the databased <code class="docutils literal notranslate"><span class="pre">JOIN</span></code> operation and use it to download photometry data from Pan-STARRS.</p>
+</div>
+<div class="section" id="best-practices">
+<h2>Best practices<a class="headerlink" href="#best-practices" title="Permalink to this headline">¶</a></h2>
+<ul class="simple">
+<li><p>When possible, “move the computation to the data�; that is, do as much of the work as possible on the database server before downloading the data.</p></li>
+<li><p>For most applications, saving data in FITS or HDF5 is better than CSV.  FITS and HDF5 are binary formats, so the files are usually smaller, and they store metadata, so you don’t lose anything when you read the file back.</p></li>
+<li><p>On the other hand, CSV is a “least common denominator� format; that is, it can be read by practically any application that works with data.</p></li>
+</ul>
+</div>
+</div>
+
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+   Chapter 1
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+   Chapter 2
+  </a>
+ </li>
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+   Chapter 3
+  </a>
+ </li>
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+   Chapter 4
+  </a>
+ </li>
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+   Chapter 5
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+   Chapter 6
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+    <!-- Table of contents -->
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+        
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+            <i class="fas fa-list"></i> Contents
+        </div>
+        <nav id="bd-toc-nav">
+            <ul class="nav section-nav flex-column">
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#outline">
+   Outline
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#installing-libraries">
+   Installing libraries
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#reloading-the-data">
+   Reloading the data
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#getting-photometry-data">
+   Getting photometry data
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#preparing-a-table-for-uploading">
+   Preparing a table for uploading
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#uploading-a-table">
+   Uploading a table
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#joining-with-an-uploaded-table">
+   Joining with an uploaded table
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#getting-the-photometry-data">
+   Getting the photometry data
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#write-the-data">
+   Write the data
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#summary">
+   Summary
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#best-practice">
+   Best practice
+  </a>
+ </li>
+</ul>
+
+        </nav>
+        
+    </div>
+</div>
+    <div id="main-content" class="row">
+        <div class="col-12 col-md-9 pl-md-3 pr-md-0">
+        
+              <div>
+                
+  <div class="section" id="chapter-5">
+<h1>Chapter 5<a class="headerlink" href="#chapter-5" title="Permalink to this headline">¶</a></h1>
+<p>This is the fifth in a series of notebooks related to astronomy data.</p>
+<p>As a continuing example, we will replicate part of the analysis in a recent paper, “<a class="reference external" href="https://arxiv.org/abs/1805.00425">Off the beaten path: Gaia reveals GD-1 stars outside of the main stream</a>� by Adrian M. Price-Whelan and Ana Bonaca.</p>
+<p>Picking up where we left off, the next step in the analysis is to select candidate stars based on photometry.  The following figure from the paper is a color-magnitude diagram for the stars selected based on proper motion:</p>
+<img width="300" src="https://github.com/datacarpentry/astronomy-python/raw/gh-pages/fig/gd1-3.png">
+<p>In red is a theoretical isochrone, showing where we expect the stars in GD-1 to fall based on the metallicity and age of their original globular cluster.</p>
+<p>By selecting stars in the shaded area, we can further distinguish the main sequence of GD-1 from younger background stars.</p>
+<div class="section" id="outline">
+<h2>Outline<a class="headerlink" href="#outline" title="Permalink to this headline">¶</a></h2>
+<p>Here are the steps in this notebook:</p>
+<ol class="simple">
+<li><p>We’ll reload the candidate stars we identified in the previous notebook.</p></li>
+<li><p>Then we’ll run a query on the Gaia server that uploads the table of candidates and uses a <code class="docutils literal notranslate"><span class="pre">JOIN</span></code> operation to select photometry data for the candidate stars.</p></li>
+<li><p>We’ll write the results to a file for use in the next notebook.</p></li>
+</ol>
+<p>After completing this lesson, you should be able to</p>
+<ul class="simple">
+<li><p>Upload a table to the Gaia server.</p></li>
+<li><p>Write ADQL queries involving <code class="docutils literal notranslate"><span class="pre">JOIN</span></code> operations.</p></li>
+</ul>
+</div>
+<div class="section" id="installing-libraries">
+<h2>Installing libraries<a class="headerlink" href="#installing-libraries" title="Permalink to this headline">¶</a></h2>
+<p>If you are running this notebook on Colab, you can run the following cell to install Astroquery and a the other libraries we’ll use.</p>
+<p>If you are running this notebook on your own computer, you might have to install these libraries yourself.</p>
+<p>If you are using this notebook as part of a Carpentries workshop, you should have received setup instructions.</p>
+<p>TODO: Add a link to the instructions.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># If we&#39;re running on Colab, install libraries</span>
+
+<span class="kn">import</span> <span class="nn">sys</span>
+<span class="n">IN_COLAB</span> <span class="o">=</span> <span class="s1">&#39;google.colab&#39;</span> <span class="ow">in</span> <span class="n">sys</span><span class="o">.</span><span class="n">modules</span>
+
+<span class="k">if</span> <span class="n">IN_COLAB</span><span class="p">:</span>
+    <span class="o">!</span>pip install astroquery astro-gala pyia python-wget
+</pre></div>
+</div>
+</div>
+</div>
+</div>
+<div class="section" id="reloading-the-data">
+<h2>Reloading the data<a class="headerlink" href="#reloading-the-data" title="Permalink to this headline">¶</a></h2>
+<p>The following cell downloads the data from the previous notebook.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">os</span>
+<span class="kn">from</span> <span class="nn">wget</span> <span class="kn">import</span> <span class="n">download</span>
+
+<span class="n">filename</span> <span class="o">=</span> <span class="s1">&#39;gd1_candidates.hdf5&#39;</span>
+<span class="n">path</span> <span class="o">=</span> <span class="s1">&#39;https://github.com/AllenDowney/AstronomicalData/raw/main/data/&#39;</span>
+
+<span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">filename</span><span class="p">):</span>
+    <span class="nb">print</span><span class="p">(</span><span class="n">download</span><span class="p">(</span><span class="n">path</span><span class="o">+</span><span class="n">filename</span><span class="p">))</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>And we can read it back.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+
+<span class="n">candidate_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_hdf</span><span class="p">(</span><span class="n">filename</span><span class="p">,</span> <span class="s1">&#39;candidate_df&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p><code class="docutils literal notranslate"><span class="pre">candidate_df</span></code> is the Pandas DataFrame that contains results from the query in the previous notebook, which selects stars likely to be in GD-1 based on proper motion.  It also includes position and proper motion transformed to the ICRS frame.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+
+<span class="n">x</span> <span class="o">=</span> <span class="n">candidate_df</span><span class="p">[</span><span class="s1">&#39;phi1&#39;</span><span class="p">]</span>
+<span class="n">y</span> <span class="o">=</span> <span class="n">candidate_df</span><span class="p">[</span><span class="s1">&#39;phi2&#39;</span><span class="p">]</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="s1">&#39;ko&#39;</span><span class="p">,</span> <span class="n">markersize</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.3</span><span class="p">)</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">&#39;ra (degree GD1)&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">&#39;dec (degree GD1)&#39;</span><span class="p">);</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<img alt="_images/05_join_9_0.png" src="_images/05_join_9_0.png" />
+</div>
+</div>
+<p>This is the same figure we saw at the end of the previous notebook.  GD-1 is visible against the background stars, but we will be able to see it more clearly after selecting based on photometry data.</p>
+</div>
+<div class="section" id="getting-photometry-data">
+<h2>Getting photometry data<a class="headerlink" href="#getting-photometry-data" title="Permalink to this headline">¶</a></h2>
+<p>The Gaia dataset contains some photometry data, including the variable <code class="docutils literal notranslate"><span class="pre">bp_rp</span></code>, which we used in the original query to select stars with BP - RP color between -0.75 and 2.</p>
+<p>Selecting stars with <code class="docutils literal notranslate"><span class="pre">bp-rp</span></code> less than 2 excludes many class M dwarf stars, which are low temperature, low luminosity.  A star like that at GD-1’s distance would be hard to detect, so if it is detected, it it more likely to be in the foreground.</p>
+<p>Now, to select stars with the age and metal richness we expect in GD-1, we will use <code class="docutils literal notranslate"><span class="pre">g</span> <span class="pre">-</span> <span class="pre">i</span></code> color and apparent <code class="docutils literal notranslate"><span class="pre">g</span></code>-band magnitude, which are available from the Pan-STARRS survey.</p>
+<p>Conveniently, the Gaia server provides data from Pan-STARRS as a table in the same database we have been using, so we can access it by making ADQL queries.</p>
+<p>In general, looking up a star from the Gaia catalog and finding the corresponding star in the Pan-STARRS catalog is not easy.  This kind of cross matching is not always possible, because a star might appear in one catalog and not the other.  And even when both stars are present, there might not be a clear one-to-one relationship between stars in the two catalogs.</p>
+<p>Fortunately, smart people have worked on this problem, and the Gaia database includes cross-matching tables that suggest a best neighbor in the Pan-STARRS catalog for many stars in the Gaia catalog.</p>
+<p><a class="reference external" href="https://gea.esac.esa.int/archive/documentation/GDR2/Catalogue_consolidation/chap_cu9val_cu9val/ssec_cu9xma/sssec_cu9xma_extcat.html">This document describes the cross matching process</a>.  Briefly, it uses a cone search to find possible matches in approximately the right position, then uses attributes like color and magnitude to choose pairs of stars most likely to be identical.</p>
+<p>So the hard part of cross-matching has been done for us.  However, using the results is a little tricky.</p>
+<p>But, it is also an opportunity to learn about one of the most important tools for working with databases: “joining� tables.</p>
+<p>In general, a “join� is an operation where you match up records from one table with records from another table using as a “key� a piece of information that is common to both tables, usually some kind of ID code.</p>
+<p>In this example:</p>
+<ul class="simple">
+<li><p>Stars in the Gaia dataset are identified by <code class="docutils literal notranslate"><span class="pre">source_id</span></code>.</p></li>
+<li><p>Stars in the Pan-STARRS dataset are identified by <code class="docutils literal notranslate"><span class="pre">obj_id</span></code>.</p></li>
+</ul>
+<p>For each candidate star we have selected so far, we have the <code class="docutils literal notranslate"><span class="pre">source_id</span></code>; the goal is to find the <code class="docutils literal notranslate"><span class="pre">obj_id</span></code> for the same star (we hope) in the Pan-STARRS catalog.</p>
+<p>To do that we will:</p>
+<ol class="simple">
+<li><p>Make a table that contains the <code class="docutils literal notranslate"><span class="pre">source_id</span></code> for each candidate star and upload the table to the Gaia server;</p></li>
+<li><p>Use the <code class="docutils literal notranslate"><span class="pre">JOIN</span></code> operator to look up each <code class="docutils literal notranslate"><span class="pre">source_id</span></code> in the <code class="docutils literal notranslate"><span class="pre">gaiadr2.panstarrs1_best_neighbour</span></code> table, which contains the <code class="docutils literal notranslate"><span class="pre">obj_id</span></code> of the best match for each star in the Gaia catalog; then</p></li>
+<li><p>Use the <code class="docutils literal notranslate"><span class="pre">JOIN</span></code> operator again to look up each <code class="docutils literal notranslate"><span class="pre">obj_id</span></code> in the <code class="docutils literal notranslate"><span class="pre">panstarrs1_original_valid</span></code> table, which contains the Pan-STARRS photometry data we want.</p></li>
+</ol>
+<p>Let’s start with the first step, uploading a table.</p>
+</div>
+<div class="section" id="preparing-a-table-for-uploading">
+<h2>Preparing a table for uploading<a class="headerlink" href="#preparing-a-table-for-uploading" title="Permalink to this headline">¶</a></h2>
+<p>For each candidate star, we want to find the corresponding row in the <code class="docutils literal notranslate"><span class="pre">gaiadr2.panstarrs1_best_neighbour</span></code> table.</p>
+<p>In order to do that, we have to:</p>
+<ol class="simple">
+<li><p>Write the table in a local file as an XML VOTable, which is a format suitable for transmitting a table over a network.</p></li>
+<li><p>Write an ADQL query that refers to the uploaded table.</p></li>
+<li><p>Change the way we submit the job so it uploads the table before running the query.</p></li>
+</ol>
+<p>The first step is not too difficult because Astropy provides a function called <code class="docutils literal notranslate"><span class="pre">writeto</span></code> that can write a <code class="docutils literal notranslate"><span class="pre">Table</span></code> in <code class="docutils literal notranslate"><span class="pre">XML</span></code>.</p>
+<p><a class="reference external" href="https://docs.astropy.org/en/stable/io/votable/">The documentation of this process is here</a>.</p>
+<p>First we have to convert our Pandas <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code> to an Astropy <code class="docutils literal notranslate"><span class="pre">Table</span></code>.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">astropy.table</span> <span class="kn">import</span> <span class="n">Table</span>
+
+<span class="n">candidate_table</span> <span class="o">=</span> <span class="n">Table</span><span class="o">.</span><span class="n">from_pandas</span><span class="p">(</span><span class="n">candidate_df</span><span class="p">)</span>
+<span class="nb">type</span><span class="p">(</span><span class="n">candidate_table</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>astropy.table.table.Table
+</pre></div>
+</div>
+</div>
+</div>
+<p>To write the file, we can use <code class="docutils literal notranslate"><span class="pre">Table.write</span></code> with <code class="docutils literal notranslate"><span class="pre">format='votable'</span></code>, <a class="reference external" href="https://docs.astropy.org/en/stable/io/unified.html#vo-tables">as described here</a>.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">table</span> <span class="o">=</span> <span class="n">candidate_table</span><span class="p">[[</span><span class="s1">&#39;source_id&#39;</span><span class="p">]]</span>
+<span class="n">table</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="s1">&#39;candidate_df.xml&#39;</span><span class="p">,</span> <span class="nb">format</span><span class="o">=</span><span class="s1">&#39;votable&#39;</span><span class="p">,</span> <span class="n">overwrite</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>Notice that we select a single column from the table, <code class="docutils literal notranslate"><span class="pre">source_id</span></code>.
+We could write the entire table to a file, but that would take longer to transmit over the network, and we really only need one column.</p>
+<p>This process, taking a structure like a <code class="docutils literal notranslate"><span class="pre">Table</span></code> and translating it into a form that can be transmitted over a network, is called <a class="reference external" href="https://en.wikipedia.org/wiki/Serialization">serialization</a>.</p>
+<p>XML is one of the most common serialization formats.  One nice feature is that XML data is plain text, as opposed to binary digits, so you can read the file we just wrote:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">!</span>head candidate_df.xml
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;?xml version=&quot;1.0&quot; encoding=&quot;utf-8&quot;?&gt;
+&lt;!-- Produced with astropy.io.votable version 4.0.1.post1
+     http://www.astropy.org/ --&gt;
+&lt;VOTABLE version=&quot;1.4&quot; xmlns=&quot;http://www.ivoa.net/xml/VOTable/v1.4&quot; xmlns:xsi=&quot;http://www.w3.org/2001/XMLSchema-instance&quot; xsi:noNamespaceSchemaLocation=&quot;http://www.ivoa.net/xml/VOTable/v1.4&quot;&gt;
+ &lt;RESOURCE type=&quot;results&quot;&gt;
+  &lt;TABLE&gt;
+   &lt;FIELD ID=&quot;source_id&quot; datatype=&quot;long&quot; name=&quot;source_id&quot;/&gt;
+   &lt;DATA&gt;
+    &lt;TABLEDATA&gt;
+     &lt;TR&gt;
+</pre></div>
+</div>
+</div>
+</div>
+<p>XML is a general format, so different XML files contain different kinds of data.  In order to read an XML file, it’s not enough to know that it’s XML; you also have to know the data format, which is called a <a class="reference external" href="https://en.wikipedia.org/wiki/XML_schema">schema</a>.</p>
+<p>In this example, the schema is VOTable; notice that one of the first tags in the file specifies the schema, and even includes the URL where you can get its definition.</p>
+<p>So this is an example of a self-documenting format.</p>
+<p>A drawback of XML is that it tends to be big, which is why we wrote just the <code class="docutils literal notranslate"><span class="pre">source_id</span></code> column rather than the whole table.
+The size of the file is about 750 KB, so that’s not too bad.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">!</span>ls -lh candidate_df.xml
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>-rw-rw-r-- 1 downey downey 396K Oct 19 14:48 candidate_df.xml
+</pre></div>
+</div>
+</div>
+</div>
+<p>If you are using Windows, <code class="docutils literal notranslate"><span class="pre">ls</span></code> might not work; in that case, try:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>!dir candidate_df.xml
+</pre></div>
+</div>
+<p><strong>Exercise:</strong> There’s a gotcha here we want to warn you about.  Why do you think we used double brackets to specify the column we wanted?  What happens if you use single brackets?</p>
+<p>Run these cells to find out.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">table</span> <span class="o">=</span> <span class="n">candidate_table</span><span class="p">[[</span><span class="s1">&#39;source_id&#39;</span><span class="p">]]</span>
+<span class="nb">type</span><span class="p">(</span><span class="n">table</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>astropy.table.table.Table
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">column</span> <span class="o">=</span> <span class="n">candidate_table</span><span class="p">[</span><span class="s1">&#39;source_id&#39;</span><span class="p">]</span>
+<span class="nb">type</span><span class="p">(</span><span class="n">column</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>astropy.table.column.Column
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># writeto(column, &#39;candidate_df.xml&#39;)</span>
+</pre></div>
+</div>
+</div>
+</div>
+</div>
+<div class="section" id="uploading-a-table">
+<h2>Uploading a table<a class="headerlink" href="#uploading-a-table" title="Permalink to this headline">¶</a></h2>
+<p>The next step is to upload this table to the Gaia server and use it as part of a query.</p>
+<p><a class="reference external" href="https://astroquery.readthedocs.io/en/latest/gaia/gaia.html#synchronous-query-on-an-on-the-fly-uploaded-table">Here’s the documentation that explains how to run a query with an uploaded table</a>.</p>
+<p>In the spirit of incremental development and testing, let’s start with the simplest possible query.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">query</span> <span class="o">=</span> <span class="s2">&quot;&quot;&quot;SELECT *</span>
+<span class="s2">FROM tap_upload.candidate_df</span>
+<span class="s2">&quot;&quot;&quot;</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>This query downloads all rows and all columns from the uploaded table.  The name of the table has two parts: <code class="docutils literal notranslate"><span class="pre">tap_upload</span></code> specifies a table that was uploaded using TAP+ (remember that’s the name of the protocol we’re using to talk to the Gaia server).</p>
+<p>And <code class="docutils literal notranslate"><span class="pre">candidate_df</span></code> is the name of the table, which we get to choose (unlike <code class="docutils literal notranslate"><span class="pre">tap_upload</span></code>, which we didn’t get to choose).</p>
+<p>Here’s how we run the query:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">astroquery.gaia</span> <span class="kn">import</span> <span class="n">Gaia</span>
+
+<span class="n">job</span> <span class="o">=</span> <span class="n">Gaia</span><span class="o">.</span><span class="n">launch_job_async</span><span class="p">(</span><span class="n">query</span><span class="o">=</span><span class="n">query</span><span class="p">,</span> 
+                            <span class="n">upload_resource</span><span class="o">=</span><span class="s1">&#39;candidate_df.xml&#39;</span><span class="p">,</span> 
+                            <span class="n">upload_table_name</span><span class="o">=</span><span class="s1">&#39;candidate_df&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Created TAP+ (v1.2.1) - Connection:
+	Host: gea.esac.esa.int
+	Use HTTPS: True
+	Port: 443
+	SSL Port: 443
+Created TAP+ (v1.2.1) - Connection:
+	Host: geadata.esac.esa.int
+	Use HTTPS: True
+	Port: 443
+	SSL Port: 443
+INFO: Query finished. [astroquery.utils.tap.core]
+</pre></div>
+</div>
+</div>
+</div>
+<p><code class="docutils literal notranslate"><span class="pre">upload_resource</span></code> specifies the name of the file we want to upload, which is the file we just wrote.</p>
+<p><code class="docutils literal notranslate"><span class="pre">upload_table_name</span></code> is the name we assign to this table, which is the name we used in the query.</p>
+<p>And here are the results:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">results</span> <span class="o">=</span> <span class="n">job</span><span class="o">.</span><span class="n">get_results</span><span class="p">()</span>
+<span class="n">results</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><i>Table length=7346</i>
+<table id="table140665432275552" class="table-striped table-bordered table-condensed">
+<thead><tr><th>source_id</th></tr></thead>
+<thead><tr><th>int64</th></tr></thead>
+<tr><td>635559124339440000</td></tr>
+<tr><td>635860218726658176</td></tr>
+<tr><td>635674126383965568</td></tr>
+<tr><td>635535454774983040</td></tr>
+<tr><td>635497276810313600</td></tr>
+<tr><td>635614168640132864</td></tr>
+<tr><td>635821843194387840</td></tr>
+<tr><td>635551706931167104</td></tr>
+<tr><td>635518889086133376</td></tr>
+<tr><td>635580294233854464</td></tr>
+<tr><td>...</td></tr>
+<tr><td>612282738058264960</td></tr>
+<tr><td>612485911486166656</td></tr>
+<tr><td>612386332668697600</td></tr>
+<tr><td>612296172717818624</td></tr>
+<tr><td>612250375480101760</td></tr>
+<tr><td>612394926899159168</td></tr>
+<tr><td>612288854091187712</td></tr>
+<tr><td>612428870024913152</td></tr>
+<tr><td>612256418500423168</td></tr>
+<tr><td>612429144902815104</td></tr>
+</table></div></div>
+</div>
+<p>If things go according to plan, the result should contain the same rows and columns as the uploaded table.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="nb">len</span><span class="p">(</span><span class="n">candidate_table</span><span class="p">),</span> <span class="nb">len</span><span class="p">(</span><span class="n">results</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>(7346, 7346)
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="nb">set</span><span class="p">(</span><span class="n">candidate_table</span><span class="p">[</span><span class="s1">&#39;source_id&#39;</span><span class="p">])</span> <span class="o">==</span> <span class="nb">set</span><span class="p">(</span><span class="n">results</span><span class="p">[</span><span class="s1">&#39;source_id&#39;</span><span class="p">])</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>True
+</pre></div>
+</div>
+</div>
+</div>
+<p>In this example, we uploaded a table and then downloaded it again, so that’s not too useful.</p>
+<p>But now that we can upload a table, we can join it with other tables on the Gaia server.</p>
+</div>
+<div class="section" id="joining-with-an-uploaded-table">
+<h2>Joining with an uploaded table<a class="headerlink" href="#joining-with-an-uploaded-table" title="Permalink to this headline">¶</a></h2>
+<p>Here’s the first example of a query that contains a <code class="docutils literal notranslate"><span class="pre">JOIN</span></code> clause.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">query1</span> <span class="o">=</span> <span class="s2">&quot;&quot;&quot;SELECT *</span>
+<span class="s2">FROM gaiadr2.panstarrs1_best_neighbour as best</span>
+<span class="s2">JOIN tap_upload.candidate_df as candidate_df</span>
+<span class="s2">ON best.source_id = candidate_df.source_id</span>
+<span class="s2">&quot;&quot;&quot;</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>Let’s break that down one clause at a time:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">SELECT</span> <span class="pre">*</span></code> means we will download all columns from both tables.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">FROM</span> <span class="pre">gaiadr2.panstarrs1_best_neighbour</span> <span class="pre">as</span> <span class="pre">best</span></code> means that we’ll get the columns from the Pan-STARRS best neighbor table, which we’ll refer to using the short name <code class="docutils literal notranslate"><span class="pre">best</span></code>.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">JOIN</span> <span class="pre">tap_upload.candidate_df</span> <span class="pre">as</span> <span class="pre">candidate_df</span></code> means that we’ll also get columns from the uploaded table, which we’ll refer to using the short name <code class="docutils literal notranslate"><span class="pre">candidate_df</span></code>.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">ON</span> <span class="pre">best.source_id</span> <span class="pre">=</span> <span class="pre">candidate_df.source_id</span></code> specifies that we will use <code class="docutils literal notranslate"><span class="pre">source_id</span> </code> to match up the rows from the two tables.</p></li>
+</ul>
+<p>Here’s the <a class="reference external" href="https://gea.esac.esa.int/archive/documentation/GDR2/Gaia_archive/chap_datamodel/sec_dm_crossmatches/ssec_dm_panstarrs1_best_neighbour.html">documentation of the best neighbor table</a>.</p>
+<p>Let’s run the query:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">job1</span> <span class="o">=</span> <span class="n">Gaia</span><span class="o">.</span><span class="n">launch_job_async</span><span class="p">(</span><span class="n">query</span><span class="o">=</span><span class="n">query1</span><span class="p">,</span> 
+                       <span class="n">upload_resource</span><span class="o">=</span><span class="s1">&#39;candidate_df.xml&#39;</span><span class="p">,</span> 
+                       <span class="n">upload_table_name</span><span class="o">=</span><span class="s1">&#39;candidate_df&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>INFO: Query finished. [astroquery.utils.tap.core]
+</pre></div>
+</div>
+</div>
+</div>
+<p>And get the results.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">results1</span> <span class="o">=</span> <span class="n">job1</span><span class="o">.</span><span class="n">get_results</span><span class="p">()</span>
+<span class="n">results1</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><i>Table length=3724</i>
+<table id="table140665411011344" class="table-striped table-bordered table-condensed">
+<thead><tr><th>source_id</th><th>original_ext_source_id</th><th>angular_distance</th><th>number_of_neighbours</th><th>number_of_mates</th><th>best_neighbour_multiplicity</th><th>gaia_astrometric_params</th><th>source_id_2</th></tr></thead>
+<thead><tr><th></th><th></th><th>arcsec</th><th></th><th></th><th></th><th></th><th></th></tr></thead>
+<thead><tr><th>int64</th><th>int64</th><th>float64</th><th>int32</th><th>int16</th><th>int16</th><th>int16</th><th>int64</th></tr></thead>
+<tr><td>635860218726658176</td><td>130911385187671349</td><td>0.053667035895467084</td><td>1</td><td>0</td><td>1</td><td>5</td><td>635860218726658176</td></tr>
+<tr><td>635674126383965568</td><td>130831388428488720</td><td>0.038810268141577516</td><td>1</td><td>0</td><td>1</td><td>5</td><td>635674126383965568</td></tr>
+<tr><td>635535454774983040</td><td>130631378377657369</td><td>0.034323028828991076</td><td>1</td><td>0</td><td>1</td><td>5</td><td>635535454774983040</td></tr>
+<tr><td>635497276810313600</td><td>130811380445631930</td><td>0.04720255413250006</td><td>1</td><td>0</td><td>1</td><td>5</td><td>635497276810313600</td></tr>
+<tr><td>635614168640132864</td><td>130571395922140135</td><td>0.020304189709964143</td><td>1</td><td>0</td><td>1</td><td>5</td><td>635614168640132864</td></tr>
+<tr><td>635598607974369792</td><td>130341392091279513</td><td>0.036524626853403054</td><td>1</td><td>0</td><td>1</td><td>5</td><td>635598607974369792</td></tr>
+<tr><td>635737661835496576</td><td>131001399333502136</td><td>0.036626827820716606</td><td>1</td><td>0</td><td>1</td><td>5</td><td>635737661835496576</td></tr>
+<tr><td>635850945892748672</td><td>132011398654934147</td><td>0.021178742393378396</td><td>1</td><td>0</td><td>1</td><td>5</td><td>635850945892748672</td></tr>
+<tr><td>635600532119713664</td><td>130421392285893623</td><td>0.04518820915043015</td><td>1</td><td>0</td><td>1</td><td>5</td><td>635600532119713664</td></tr>
+<tr><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr>
+<tr><td>612241781249124608</td><td>129751343755995561</td><td>0.04235715830001815</td><td>1</td><td>0</td><td>1</td><td>5</td><td>612241781249124608</td></tr>
+<tr><td>612332147361443072</td><td>130141341458538777</td><td>0.02265249859012977</td><td>1</td><td>0</td><td>1</td><td>5</td><td>612332147361443072</td></tr>
+<tr><td>612426744016802432</td><td>130521346852465656</td><td>0.03247653009961843</td><td>1</td><td>0</td><td>1</td><td>5</td><td>612426744016802432</td></tr>
+<tr><td>612331739340341760</td><td>130111341217793839</td><td>0.036064240818025735</td><td>1</td><td>0</td><td>1</td><td>5</td><td>612331739340341760</td></tr>
+<tr><td>612282738058264960</td><td>129741340445933519</td><td>0.025293237353496898</td><td>1</td><td>0</td><td>1</td><td>5</td><td>612282738058264960</td></tr>
+<tr><td>612386332668697600</td><td>130351354570219774</td><td>0.02010316001403086</td><td>1</td><td>0</td><td>1</td><td>5</td><td>612386332668697600</td></tr>
+<tr><td>612296172717818624</td><td>129691338006168780</td><td>0.051264212025836205</td><td>1</td><td>0</td><td>1</td><td>5</td><td>612296172717818624</td></tr>
+<tr><td>612250375480101760</td><td>129741346475897464</td><td>0.031783740347530905</td><td>1</td><td>0</td><td>1</td><td>5</td><td>612250375480101760</td></tr>
+<tr><td>612394926899159168</td><td>130581355199751795</td><td>0.04019174830546698</td><td>1</td><td>0</td><td>1</td><td>5</td><td>612394926899159168</td></tr>
+<tr><td>612256418500423168</td><td>129931349075297310</td><td>0.009242789669513156</td><td>1</td><td>0</td><td>1</td><td>5</td><td>612256418500423168</td></tr>
+</table></div></div>
+</div>
+<p>This table contains all of the columns from the best neighbor table, plus the single column from the uploaded table.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">results1</span><span class="o">.</span><span class="n">colnames</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[&#39;source_id&#39;,
+ &#39;original_ext_source_id&#39;,
+ &#39;angular_distance&#39;,
+ &#39;number_of_neighbours&#39;,
+ &#39;number_of_mates&#39;,
+ &#39;best_neighbour_multiplicity&#39;,
+ &#39;gaia_astrometric_params&#39;,
+ &#39;source_id_2&#39;]
+</pre></div>
+</div>
+</div>
+</div>
+<p>Because one of the column names appears in both tables, the second instance of <code class="docutils literal notranslate"><span class="pre">source_id</span></code> has been appended with the suffix <code class="docutils literal notranslate"><span class="pre">_2</span></code>.</p>
+<p>The length of the results table is about 2000, which means we were not able to find matches for all stars in the list of candidate_df.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="nb">len</span><span class="p">(</span><span class="n">results1</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>3724
+</pre></div>
+</div>
+</div>
+</div>
+<p>To get more information about the matching process, we can inspect <code class="docutils literal notranslate"><span class="pre">best_neighbour_multiplicity</span></code>, which indicates for each star in Gaia how many stars in Pan-STARRS are equally likely matches.</p>
+<p>For this kind of data exploration, we’ll convert a column from the table to a Pandas <code class="docutils literal notranslate"><span class="pre">Series</span></code> so we can use <code class="docutils literal notranslate"><span class="pre">value_counts</span></code>, which counts the number of times each value appears in a <code class="docutils literal notranslate"><span class="pre">Series</span></code>, like a histogram.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+
+<span class="n">nn</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">results1</span><span class="p">[</span><span class="s1">&#39;best_neighbour_multiplicity&#39;</span><span class="p">])</span>
+<span class="n">nn</span><span class="o">.</span><span class="n">value_counts</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>1    3724
+dtype: int64
+</pre></div>
+</div>
+</div>
+</div>
+<p>The result shows that <code class="docutils literal notranslate"><span class="pre">1</span></code> is the only value in the <code class="docutils literal notranslate"><span class="pre">Series</span></code>, appearing xxx times.</p>
+<p>That means that in every case where a match was found, the matching algorithm identified a single neighbor as the most likely match.</p>
+<p>Similarly, <code class="docutils literal notranslate"><span class="pre">number_of_mates</span></code> indicates the number of other stars in Gaia that match with the same star in Pan-STARRS.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">nm</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">results1</span><span class="p">[</span><span class="s1">&#39;number_of_mates&#39;</span><span class="p">])</span>
+<span class="n">nm</span><span class="o">.</span><span class="n">value_counts</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0    3724
+dtype: int64
+</pre></div>
+</div>
+</div>
+</div>
+<p>For this set of candidate_df, almost all of the stars we’ve selected from Pan-STARRS are only matched with a single star in the Gaia catalog.</p>
+<p><strong>Detail</strong> The table also contains <code class="docutils literal notranslate"><span class="pre">number_of_neighbors</span></code> which is the number of stars in Pan-STARRS that match in terms of position, before using other critieria to choose the most likely match.</p>
+</div>
+<div class="section" id="getting-the-photometry-data">
+<h2>Getting the photometry data<a class="headerlink" href="#getting-the-photometry-data" title="Permalink to this headline">¶</a></h2>
+<p>The most important column in <code class="docutils literal notranslate"><span class="pre">results1</span></code> is <code class="docutils literal notranslate"><span class="pre">original_ext_source_id</span></code> which is the <code class="docutils literal notranslate"><span class="pre">obj_id</span></code> we will use to look up the likely matches in Pan-STARRS to get photometry data.</p>
+<p>The process is similar to what we just did to look up the matches.  We will:</p>
+<ol class="simple">
+<li><p>Make a table that contains <code class="docutils literal notranslate"><span class="pre">source_id</span></code> and <code class="docutils literal notranslate"><span class="pre">original_ext_source_id</span></code>.</p></li>
+<li><p>Write the table to an XML VOTable file.</p></li>
+<li><p>Write a query that joins the uploaded table with <code class="docutils literal notranslate"><span class="pre">gaiadr2.panstarrs1_original_valid</span></code> and selects the photometry data we want.</p></li>
+<li><p>Run the query using the uploaded table.</p></li>
+</ol>
+<p>Since we’ve done everything here before, we’ll do these steps as an exercise.</p>
+<p><strong>Exercise:</strong> Select <code class="docutils literal notranslate"><span class="pre">source_id</span></code> and <code class="docutils literal notranslate"><span class="pre">original_ext_source_id</span></code> from <code class="docutils literal notranslate"><span class="pre">results1</span></code> and write the resulting table as a file named <code class="docutils literal notranslate"><span class="pre">external.xml</span></code>.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Solution</span>
+
+<span class="n">table</span> <span class="o">=</span> <span class="n">results1</span><span class="p">[[</span><span class="s1">&#39;source_id&#39;</span><span class="p">,</span> <span class="s1">&#39;original_ext_source_id&#39;</span><span class="p">]]</span>
+<span class="n">table</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="s1">&#39;external.xml&#39;</span><span class="p">,</span> <span class="nb">format</span><span class="o">=</span><span class="s1">&#39;votable&#39;</span><span class="p">,</span> <span class="n">overwrite</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>Use <code class="docutils literal notranslate"><span class="pre">!head</span></code> to confirm that the file exists and contains an XML VOTable.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">!</span>head external.xml
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;?xml version=&quot;1.0&quot; encoding=&quot;utf-8&quot;?&gt;
+&lt;!-- Produced with astropy.io.votable version 4.0.1.post1
+     http://www.astropy.org/ --&gt;
+&lt;VOTABLE version=&quot;1.4&quot; xmlns=&quot;http://www.ivoa.net/xml/VOTable/v1.4&quot; xmlns:xsi=&quot;http://www.w3.org/2001/XMLSchema-instance&quot; xsi:noNamespaceSchemaLocation=&quot;http://www.ivoa.net/xml/VOTable/v1.4&quot;&gt;
+ &lt;RESOURCE type=&quot;results&quot;&gt;
+  &lt;TABLE&gt;
+   &lt;FIELD ID=&quot;source_id&quot; datatype=&quot;long&quot; name=&quot;source_id&quot; ucd=&quot;meta.id;meta.main&quot;&gt;
+    &lt;DESCRIPTION&gt;
+     Unique Gaia source identifier
+    &lt;/DESCRIPTION&gt;
+</pre></div>
+</div>
+</div>
+</div>
+<p><strong>Exercise:</strong> Read <a class="reference external" href="https://gea.esac.esa.int/archive/documentation/GDR2/Gaia_archive/chap_datamodel/sec_dm_external_catalogues/ssec_dm_panstarrs1_original_valid.html">the documentation of the Pan-STARRS table</a> and make note of <code class="docutils literal notranslate"><span class="pre">obj_id</span></code>, which contains the object IDs we’ll use to find the rows we want.</p>
+<p>Write a query that uses each value of <code class="docutils literal notranslate"><span class="pre">original_ext_source_id</span></code> from the uploaded table to find a row in <code class="docutils literal notranslate"><span class="pre">gaiadr2.panstarrs1_original_valid</span></code> with the same value in <code class="docutils literal notranslate"><span class="pre">obj_id</span></code>, and select all columns from both tables.</p>
+<p>Suggestion: Develop and test your query incrementally.  For example:</p>
+<ol class="simple">
+<li><p>Write a query that downloads all columns from the uploaded table.  Test to make sure we can read the uploaded table.</p></li>
+<li><p>Write a query that downloads the first 10 rows from <code class="docutils literal notranslate"><span class="pre">gaiadr2.panstarrs1_original_valid</span></code>.  Test to make sure we can access Pan-STARRS data.</p></li>
+<li><p>Write a query that joins the two tables and selects all columns.  Test that the join works as expected.</p></li>
+</ol>
+<p>As a bonus exercise, write a query that joins the two tables and selects just the columns we need:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">source_id</span></code> from the uploaded table</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">g_mean_psf_mag</span></code> from <code class="docutils literal notranslate"><span class="pre">gaiadr2.panstarrs1_original_valid</span></code></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">i_mean_psf_mag</span></code> from <code class="docutils literal notranslate"><span class="pre">gaiadr2.panstarrs1_original_valid</span></code></p></li>
+</ul>
+<p>Hint: When you select a column from a join, you have to specify which table the column is in.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Solution</span>
+
+<span class="n">query2</span> <span class="o">=</span> <span class="s2">&quot;&quot;&quot;SELECT *</span>
+<span class="s2">FROM tap_upload.external as external</span>
+<span class="s2">&quot;&quot;&quot;</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Solution</span>
+
+<span class="n">query2</span> <span class="o">=</span> <span class="s2">&quot;&quot;&quot;SELECT TOP 10 *</span>
+<span class="s2">FROM gaiadr2.panstarrs1_original_valid</span>
+<span class="s2">&quot;&quot;&quot;</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Solution</span>
+
+<span class="n">query2</span> <span class="o">=</span> <span class="s2">&quot;&quot;&quot;SELECT *</span>
+<span class="s2">FROM gaiadr2.panstarrs1_original_valid as ps</span>
+<span class="s2">JOIN tap_upload.external as external</span>
+<span class="s2">ON ps.obj_id = external.original_ext_source_id</span>
+<span class="s2">&quot;&quot;&quot;</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Solution</span>
+
+<span class="n">query2</span> <span class="o">=</span> <span class="s2">&quot;&quot;&quot;SELECT</span>
+<span class="s2">external.source_id, ps.g_mean_psf_mag, ps.i_mean_psf_mag</span>
+<span class="s2">FROM gaiadr2.panstarrs1_original_valid as ps</span>
+<span class="s2">JOIN tap_upload.external as external</span>
+<span class="s2">ON ps.obj_id = external.original_ext_source_id</span>
+<span class="s2">&quot;&quot;&quot;</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">query2</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>SELECT
+external.source_id, ps.g_mean_psf_mag, ps.i_mean_psf_mag
+FROM gaiadr2.panstarrs1_original_valid as ps
+JOIN tap_upload.external as external
+ON ps.obj_id = external.original_ext_source_id
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">job2</span> <span class="o">=</span> <span class="n">Gaia</span><span class="o">.</span><span class="n">launch_job_async</span><span class="p">(</span><span class="n">query</span><span class="o">=</span><span class="n">query2</span><span class="p">,</span> 
+                       <span class="n">upload_resource</span><span class="o">=</span><span class="s1">&#39;external.xml&#39;</span><span class="p">,</span> 
+                       <span class="n">upload_table_name</span><span class="o">=</span><span class="s1">&#39;external&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>INFO: Query finished. [astroquery.utils.tap.core]
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">results2</span> <span class="o">=</span> <span class="n">job2</span><span class="o">.</span><span class="n">get_results</span><span class="p">()</span>
+<span class="n">results2</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><i>Table length=3724</i>
+<table id="table140665408248800" class="table-striped table-bordered table-condensed">
+<thead><tr><th>source_id</th><th>g_mean_psf_mag</th><th>i_mean_psf_mag</th></tr></thead>
+<thead><tr><th></th><th></th><th>mag</th></tr></thead>
+<thead><tr><th>int64</th><th>float64</th><th>float64</th></tr></thead>
+<tr><td>635860218726658176</td><td>17.8978004455566</td><td>17.5174007415771</td></tr>
+<tr><td>635674126383965568</td><td>19.2873001098633</td><td>17.6781005859375</td></tr>
+<tr><td>635535454774983040</td><td>16.9237995147705</td><td>16.478099822998</td></tr>
+<tr><td>635497276810313600</td><td>19.9242000579834</td><td>18.3339996337891</td></tr>
+<tr><td>635614168640132864</td><td>16.1515998840332</td><td>14.6662998199463</td></tr>
+<tr><td>635598607974369792</td><td>16.5223999023438</td><td>16.1375007629395</td></tr>
+<tr><td>635737661835496576</td><td>14.5032997131348</td><td>13.9849004745483</td></tr>
+<tr><td>635850945892748672</td><td>16.5174999237061</td><td>16.0450000762939</td></tr>
+<tr><td>635600532119713664</td><td>20.4505996704102</td><td>19.5177001953125</td></tr>
+<tr><td>...</td><td>...</td><td>...</td></tr>
+<tr><td>612241781249124608</td><td>20.2343997955322</td><td>18.6518001556396</td></tr>
+<tr><td>612332147361443072</td><td>21.3848991394043</td><td>20.3076000213623</td></tr>
+<tr><td>612426744016802432</td><td>17.8281002044678</td><td>17.4281005859375</td></tr>
+<tr><td>612331739340341760</td><td>21.8656997680664</td><td>19.5223007202148</td></tr>
+<tr><td>612282738058264960</td><td>22.5151996612549</td><td>19.9743995666504</td></tr>
+<tr><td>612386332668697600</td><td>19.3792991638184</td><td>17.9923000335693</td></tr>
+<tr><td>612296172717818624</td><td>17.4944000244141</td><td>16.926700592041</td></tr>
+<tr><td>612250375480101760</td><td>15.3330001831055</td><td>14.6280002593994</td></tr>
+<tr><td>612394926899159168</td><td>16.4414005279541</td><td>15.8212003707886</td></tr>
+<tr><td>612256418500423168</td><td>20.8715991973877</td><td>19.9612007141113</td></tr>
+</table></div></div>
+</div>
+<p><strong>Challenge exercise</strong></p>
+<p>Do both joins in one query.</p>
+<p>There’s an <a class="reference external" href="https://github.com/smoh/Getting-started-with-Gaia/blob/master/gaia-adql-snippets.md">example here</a> you could start with.</p>
+</div>
+<div class="section" id="write-the-data">
+<h2>Write the data<a class="headerlink" href="#write-the-data" title="Permalink to this headline">¶</a></h2>
+<p>Since we have the data in an Astropy <code class="docutils literal notranslate"><span class="pre">Table</span></code>, let’s store it in a FITS file.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">filename</span> <span class="o">=</span> <span class="s1">&#39;gd1_photo.fits&#39;</span>
+<span class="n">results2</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="n">filename</span><span class="p">,</span> <span class="n">overwrite</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>We can check that the file exists, and see how big it is.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">!</span>ls -lh gd1_photo.fits
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>-rw-rw-r-- 1 downey downey 96K Oct 19 14:49 gd1_photo.fits
+</pre></div>
+</div>
+</div>
+</div>
+<p>At around 175 KB, it is smaller than some of the other files we’ve been working with.</p>
+<p>If you are using Windows, <code class="docutils literal notranslate"><span class="pre">ls</span></code> might not work; in that case, try:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>!dir gd1_photo.fits
+</pre></div>
+</div>
+</div>
+<div class="section" id="summary">
+<h2>Summary<a class="headerlink" href="#summary" title="Permalink to this headline">¶</a></h2>
+<p>In this notebook, we used database <code class="docutils literal notranslate"><span class="pre">JOIN</span></code> operations to select photometry data for the stars we’ve identified as candidates to be in GD-1.</p>
+<p>In the next notebook, we’ll use this data for a second round of selection, identifying stars that have photometry data consistent with GD-1.</p>
+</div>
+<div class="section" id="best-practice">
+<h2>Best practice<a class="headerlink" href="#best-practice" title="Permalink to this headline">¶</a></h2>
+<ul class="simple">
+<li><p>Use <code class="docutils literal notranslate"><span class="pre">JOIN</span></code> operations to combine data from multiple tables in a databased, using some kind of identifier to match up records from one table with records from another.</p></li>
+<li><p>This is another example of a practice we saw in the previous notebook, moving the computation to the data.</p></li>
+</ul>
+</div>
+</div>
+
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+   Chapter 1
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+   Chapter 2
+  </a>
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+   Chapter 3
+  </a>
+ </li>
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+   Chapter 4
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+   Chapter 5
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+        
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+            <i class="fas fa-list"></i> Contents
+        </div>
+        <nav id="bd-toc-nav">
+            <ul class="nav section-nav flex-column">
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#outline">
+   Outline
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#installing-libraries">
+   Installing libraries
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#reload-the-data">
+   Reload the data
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#plotting-photometry-data">
+   Plotting photometry data
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#drawing-a-polygon">
+   Drawing a polygon
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#which-points-are-in-the-polygon">
+   Which points are in the polygon?
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#reloading-the-data">
+   Reloading the data
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#merging-photometry-data">
+   Merging photometry data
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#missing-data">
+   Missing data
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#selecting-based-on-photometry">
+   Selecting based on photometry
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#write-the-data">
+   Write the data
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#save-the-polygon">
+   Save the polygon
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#summary">
+   Summary
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#best-practices">
+   Best practices
+  </a>
+ </li>
+</ul>
+
+        </nav>
+        
+    </div>
+</div>
+    <div id="main-content" class="row">
+        <div class="col-12 col-md-9 pl-md-3 pr-md-0">
+        
+              <div>
+                
+  <div class="section" id="chapter-6">
+<h1>Chapter 6<a class="headerlink" href="#chapter-6" title="Permalink to this headline">¶</a></h1>
+<p>This is the sixth in a series of notebooks related to astronomy data.</p>
+<p>As a continuing example, we will replicate part of the analysis in a recent paper, “<a class="reference external" href="https://arxiv.org/abs/1805.00425">Off the beaten path: Gaia reveals GD-1 stars outside of the main stream</a>� by Adrian M. Price-Whelan and Ana Bonaca.</p>
+<p>In the previous lesson we downloaded photometry data from Pan-STARRS, which is available from the same server we’ve been using to get Gaia data.</p>
+<p>The next step in the analysis is to select candidate stars based on the photometry data.  The following figure from the paper is a color-magnitude diagram for the stars selected based on proper motion:</p>
+<img width="300" src="https://github.com/datacarpentry/astronomy-python/raw/gh-pages/fig/gd1-3.png">
+<p>In red is a theoretical isochrone, showing where we expect the stars in GD-1 to fall based on the metallicity and age of their original globular cluster.</p>
+<p>By selecting stars in the shaded area, we can further distinguish the main sequence of GD-1 from younger background stars.</p>
+<div class="section" id="outline">
+<h2>Outline<a class="headerlink" href="#outline" title="Permalink to this headline">¶</a></h2>
+<p>Here are the steps in this notebook:</p>
+<ol class="simple">
+<li><p>We’ll reload the data from the previous notebook and make a color-magnitude diagram.</p></li>
+<li><p>Then we’ll specify a polygon in the diagram that contains stars with the photometry we expect.</p></li>
+<li><p>Then we’ll merge the photometry data with the list of candidate stars, storing the result in a Pandas <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code>.</p></li>
+</ol>
+<p>After completing this lesson, you should be able to</p>
+<ul class="simple">
+<li><p>Use Matplotlib to specify a <code class="docutils literal notranslate"><span class="pre">Polygon</span></code> and determine which points fall inside it.</p></li>
+<li><p>Use Pandas to merge data from multiple <code class="docutils literal notranslate"><span class="pre">DataFrames</span></code>, much like a database <code class="docutils literal notranslate"><span class="pre">JOIN</span></code> operation.</p></li>
+</ul>
+</div>
+<div class="section" id="installing-libraries">
+<h2>Installing libraries<a class="headerlink" href="#installing-libraries" title="Permalink to this headline">¶</a></h2>
+<p>If you are running this notebook on Colab, you can run the following cell to install Astroquery and a the other libraries we’ll use.</p>
+<p>If you are running this notebook on your own computer, you might have to install these libraries yourself.</p>
+<p>If you are using this notebook as part of a Carpentries workshop, you should have received setup instructions.</p>
+<p>TODO: Add a link to the instructions.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># If we&#39;re running on Colab, install libraries</span>
+
+<span class="kn">import</span> <span class="nn">sys</span>
+<span class="n">IN_COLAB</span> <span class="o">=</span> <span class="s1">&#39;google.colab&#39;</span> <span class="ow">in</span> <span class="n">sys</span><span class="o">.</span><span class="n">modules</span>
+
+<span class="k">if</span> <span class="n">IN_COLAB</span><span class="p">:</span>
+    <span class="o">!</span>pip install astroquery astro-gala pyia python-wget
+</pre></div>
+</div>
+</div>
+</div>
+</div>
+<div class="section" id="reload-the-data">
+<h2>Reload the data<a class="headerlink" href="#reload-the-data" title="Permalink to this headline">¶</a></h2>
+<p>The following cell downloads the photometry data we created in the previous notebook.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">os</span>
+<span class="kn">from</span> <span class="nn">wget</span> <span class="kn">import</span> <span class="n">download</span>
+
+<span class="n">filename</span> <span class="o">=</span> <span class="s1">&#39;gd1_photo.fits&#39;</span>
+<span class="n">filepath</span> <span class="o">=</span> <span class="s1">&#39;https://github.com/AllenDowney/AstronomicalData/raw/main/data/&#39;</span>
+
+<span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">filename</span><span class="p">):</span>
+    <span class="nb">print</span><span class="p">(</span><span class="n">download</span><span class="p">(</span><span class="n">filepath</span><span class="o">+</span><span class="n">filename</span><span class="p">))</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>Now we can read the data back into an Astropy <code class="docutils literal notranslate"><span class="pre">Table</span></code>.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">astropy.table</span> <span class="kn">import</span> <span class="n">Table</span>
+
+<span class="n">photo_table</span> <span class="o">=</span> <span class="n">Table</span><span class="o">.</span><span class="n">read</span><span class="p">(</span><span class="n">filename</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+</div>
+<div class="section" id="plotting-photometry-data">
+<h2>Plotting photometry data<a class="headerlink" href="#plotting-photometry-data" title="Permalink to this headline">¶</a></h2>
+<p>Now that we have photometry data from Pan-STARRS, we can replicate the <a class="reference external" href="https://en.wikipedia.org/wiki/Galaxy_color%E2%80%93magnitude_diagram">color-magnitude diagram</a> from the original paper:</p>
+<img width="300" src="https://github.com/datacarpentry/astronomy-python/raw/gh-pages/fig/gd1-3.png">
+<p>The y-axis shows the apparent magnitude of each source with the <a class="reference external" href="https://en.wikipedia.org/wiki/Photometric_system">g filter</a>.</p>
+<p>The x-axis shows the difference in apparent magnitude between the g and i filters, which indicates color.</p>
+<p>Stars with lower values of (g-i) are brighter in g-band than in i-band, compared to other stars, which means they are bluer.</p>
+<p>Stars in the lower-left quadrant of this diagram are less bright and less metallic than the others, which means they are <a class="reference external" href="http://spiff.rit.edu/classes/ladder/lectures/ordinary_stars/ordinary.html">likely to be older</a>.</p>
+<p>Since we expect the stars in GD-1 to be older than the background stars, the stars in the lower-left are more likely to be in GD-1.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+
+<span class="k">def</span> <span class="nf">plot_cmd</span><span class="p">(</span><span class="n">table</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;Plot a color magnitude diagram.</span>
+<span class="sd">    </span>
+<span class="sd">    table: Table or DataFrame with photometry data</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">y</span> <span class="o">=</span> <span class="n">table</span><span class="p">[</span><span class="s1">&#39;g_mean_psf_mag&#39;</span><span class="p">]</span>
+    <span class="n">x</span> <span class="o">=</span> <span class="n">table</span><span class="p">[</span><span class="s1">&#39;g_mean_psf_mag&#39;</span><span class="p">]</span> <span class="o">-</span> <span class="n">table</span><span class="p">[</span><span class="s1">&#39;i_mean_psf_mag&#39;</span><span class="p">]</span>
+
+    <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="s1">&#39;ko&#39;</span><span class="p">,</span> <span class="n">markersize</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.3</span><span class="p">)</span>
+
+    <span class="n">plt</span><span class="o">.</span><span class="n">xlim</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mf">1.5</span><span class="p">])</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">ylim</span><span class="p">([</span><span class="mi">14</span><span class="p">,</span> <span class="mi">22</span><span class="p">])</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">gca</span><span class="p">()</span><span class="o">.</span><span class="n">invert_yaxis</span><span class="p">()</span>
+
+    <span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">&#39;$g_0$&#39;</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">&#39;$(g-i)_0$&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p><code class="docutils literal notranslate"><span class="pre">plot_cmd</span></code> uses a new function, <code class="docutils literal notranslate"><span class="pre">invert_yaxis</span></code>, to invert the <code class="docutils literal notranslate"><span class="pre">y</span></code> axis, which is conventional when plotting magnitudes, since lower magnitude indicates higher brightness.</p>
+<p><code class="docutils literal notranslate"><span class="pre">invert_yaxis</span></code> is a little different from the other functions we’ve used.  You can’t call it like this:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">invert_yaxis</span><span class="p">()</span>          <span class="c1"># doesn&#39;t work</span>
+</pre></div>
+</div>
+<p>You have to call it like this:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">gca</span><span class="p">()</span><span class="o">.</span><span class="n">invert_yaxis</span><span class="p">()</span>          <span class="c1"># works</span>
+</pre></div>
+</div>
+<p><code class="docutils literal notranslate"><span class="pre">gca</span></code> stands for “get current axis�.  It returns an object that represents the axes of the current figure, and that object provides <code class="docutils literal notranslate"><span class="pre">invert_yaxis</span></code>.</p>
+<p><strong>In case anyone asks:</strong> The most likely reason for this inconsistency in the interface is that <code class="docutils literal notranslate"><span class="pre">invert_yaxis</span></code> is a lesser-used function, so it’s not made available at the top level of the interface.</p>
+<p>Here’s what the results look like.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">plot_cmd</span><span class="p">(</span><span class="n">photo_table</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<img alt="_images/06_photo_12_0.png" src="_images/06_photo_12_0.png" />
+</div>
+</div>
+<p>Our figure does not look exactly like the one in the paper because we are working with a smaller region of the sky, so we don’t have as many stars.  But we can see an overdense region in the lower left that contains stars with the photometry we expect for GD-1.</p>
+<p>The authors of the original paper derive a detailed polygon that defines a boundary between stars that are likely to be in GD-1 or not.</p>
+<p>As a simplification, we’ll choose a boundary by eye that seems to contain the overdense region.</p>
+</div>
+<div class="section" id="drawing-a-polygon">
+<h2>Drawing a polygon<a class="headerlink" href="#drawing-a-polygon" title="Permalink to this headline">¶</a></h2>
+<p>Matplotlib provides a function called <code class="docutils literal notranslate"><span class="pre">ginput</span></code> that lets us click on the figure and make a list of coordinates.</p>
+<p>It’s a little tricky to use <code class="docutils literal notranslate"><span class="pre">ginput</span></code> in a Jupyter notebook.<br />
+Before calling <code class="docutils literal notranslate"><span class="pre">plt.ginput</span></code> we have to tell Matplotlib to use <code class="docutils literal notranslate"><span class="pre">TkAgg</span></code> to draw the figure in a new window.</p>
+<p>When you run the following cell, a figure should appear in a new window.  Click on it 10 times to draw a polygon around the overdense area.  A red cross should appear where you click.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib</span> <span class="k">as</span> <span class="nn">mpl</span>
+
+<span class="k">if</span> <span class="n">IN_COLAB</span><span class="p">:</span>
+    <span class="n">coords</span> <span class="o">=</span> <span class="kc">None</span>
+<span class="k">else</span><span class="p">:</span>
+    <span class="n">mpl</span><span class="o">.</span><span class="n">use</span><span class="p">(</span><span class="s1">&#39;TkAgg&#39;</span><span class="p">)</span>
+    <span class="n">plot_cmd</span><span class="p">(</span><span class="n">photo_table</span><span class="p">)</span>
+    <span class="n">coords</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">ginput</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span>
+    <span class="n">mpl</span><span class="o">.</span><span class="n">use</span><span class="p">(</span><span class="s1">&#39;agg&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>The argument to <code class="docutils literal notranslate"><span class="pre">ginput</span></code> is the number of times the user has to click on the figure.</p>
+<p>The result from <code class="docutils literal notranslate"><span class="pre">ginput</span></code> is a list of coordinate pairs.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">coords</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[(0.2150537634408602, 17.548197203826344),
+ (0.3897849462365591, 18.94628403237675),
+ (0.5376344086021505, 19.902869757174393),
+ (0.7034050179211468, 20.601913171449596),
+ (0.8288530465949819, 21.300956585724798),
+ (0.6630824372759856, 21.52170713760118),
+ (0.4301075268817204, 20.785871964679913),
+ (0.27329749103942647, 19.71891096394408),
+ (0.17473118279569888, 18.688741721854306),
+ (0.17473118279569888, 17.95290654893304)]
+</pre></div>
+</div>
+</div>
+</div>
+<p>If <code class="docutils literal notranslate"><span class="pre">ginput</span></code> doesn’t work for you, you could use the following coordinates.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">if</span> <span class="n">coords</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+    <span class="n">coords</span> <span class="o">=</span> <span class="p">[(</span><span class="mf">0.2</span><span class="p">,</span> <span class="mf">17.5</span><span class="p">),</span> 
+              <span class="p">(</span><span class="mf">0.2</span><span class="p">,</span> <span class="mf">19.5</span><span class="p">),</span> 
+              <span class="p">(</span><span class="mf">0.65</span><span class="p">,</span> <span class="mi">22</span><span class="p">),</span>
+              <span class="p">(</span><span class="mf">0.75</span><span class="p">,</span> <span class="mi">21</span><span class="p">),</span>
+              <span class="p">(</span><span class="mf">0.4</span><span class="p">,</span> <span class="mi">19</span><span class="p">),</span>
+              <span class="p">(</span><span class="mf">0.4</span><span class="p">,</span> <span class="mf">17.5</span><span class="p">)]</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>The next step is to convert the coordinates to a format we can use to plot them, which is a sequence of <code class="docutils literal notranslate"><span class="pre">x</span></code> coordinates and a sequence of <code class="docutils literal notranslate"><span class="pre">y</span></code> coordinates.  The NumPy function <code class="docutils literal notranslate"><span class="pre">transpose</span></code> does what we want.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+
+<span class="n">xs</span><span class="p">,</span> <span class="n">ys</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="n">coords</span><span class="p">)</span>
+<span class="n">xs</span><span class="p">,</span> <span class="n">ys</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>(array([0.21505376, 0.38978495, 0.53763441, 0.70340502, 0.82885305,
+        0.66308244, 0.43010753, 0.27329749, 0.17473118, 0.17473118]),
+ array([17.5481972 , 18.94628403, 19.90286976, 20.60191317, 21.30095659,
+        21.52170714, 20.78587196, 19.71891096, 18.68874172, 17.95290655]))
+</pre></div>
+</div>
+</div>
+</div>
+<p>To display the polygon, we’ll draw the figure again and use <code class="docutils literal notranslate"><span class="pre">plt.plot</span></code> to draw the polygon.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">plot_cmd</span><span class="p">(</span><span class="n">photo_table</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">xs</span><span class="p">,</span> <span class="n">ys</span><span class="p">);</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<img alt="_images/06_photo_23_0.png" src="_images/06_photo_23_0.png" />
+</div>
+</div>
+<p>If it looks like your polygon does a good job surrounding the overdense area, go on to the next section.  Otherwise you can try again.</p>
+<p>If you want a polygon with more points (or fewer), you can change the argument to <code class="docutils literal notranslate"><span class="pre">ginput</span></code>.</p>
+<p>The polygon does not have to be “closed�.  When we use this polygon in the next section, the last and first points will be connected by a straight line.</p>
+</div>
+<div class="section" id="which-points-are-in-the-polygon">
+<h2>Which points are in the polygon?<a class="headerlink" href="#which-points-are-in-the-polygon" title="Permalink to this headline">¶</a></h2>
+<p>Matplotlib provides a <code class="docutils literal notranslate"><span class="pre">Path</span></code> object that we can use to check which points fall in the polygon we selected.</p>
+<p>Here’s how we make a <code class="docutils literal notranslate"><span class="pre">Path</span></code> using a list of coordinates.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">matplotlib.path</span> <span class="kn">import</span> <span class="n">Path</span>
+
+<span class="n">path</span> <span class="o">=</span> <span class="n">Path</span><span class="p">(</span><span class="n">coords</span><span class="p">)</span>
+<span class="n">path</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Path(array([[ 0.21505376, 17.5481972 ],
+       [ 0.38978495, 18.94628403],
+       [ 0.53763441, 19.90286976],
+       [ 0.70340502, 20.60191317],
+       [ 0.82885305, 21.30095659],
+       [ 0.66308244, 21.52170714],
+       [ 0.43010753, 20.78587196],
+       [ 0.27329749, 19.71891096],
+       [ 0.17473118, 18.68874172],
+       [ 0.17473118, 17.95290655]]), None)
+</pre></div>
+</div>
+</div>
+</div>
+<p><code class="docutils literal notranslate"><span class="pre">Path</span></code> provides <code class="docutils literal notranslate"><span class="pre">contains_points</span></code>, which figures out which points are inside the polygon.</p>
+<p>To test it, we’ll create a list with two points, one inside the polygon and one outside.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">points</span> <span class="o">=</span> <span class="p">[(</span><span class="mf">0.4</span><span class="p">,</span> <span class="mi">20</span><span class="p">),</span> 
+          <span class="p">(</span><span class="mf">0.4</span><span class="p">,</span> <span class="mi">30</span><span class="p">)]</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>Now we can make sure <code class="docutils literal notranslate"><span class="pre">contains_points</span></code> does what we expect.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">inside</span> <span class="o">=</span> <span class="n">path</span><span class="o">.</span><span class="n">contains_points</span><span class="p">(</span><span class="n">points</span><span class="p">)</span>
+<span class="n">inside</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>array([ True, False])
+</pre></div>
+</div>
+</div>
+</div>
+<p>The result is an array of Boolean values.</p>
+<p>We are almost ready to select stars whose photometry data falls in this polygon.  But first we need to do some data cleaning.</p>
+</div>
+<div class="section" id="reloading-the-data">
+<h2>Reloading the data<a class="headerlink" href="#reloading-the-data" title="Permalink to this headline">¶</a></h2>
+<p>Now we need to combine the photometry data with the list of candidate stars we identified in a previous notebook.  The following cell downloads it:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">os</span>
+<span class="kn">from</span> <span class="nn">wget</span> <span class="kn">import</span> <span class="n">download</span>
+
+<span class="n">filename</span> <span class="o">=</span> <span class="s1">&#39;gd1_candidates.hdf5&#39;</span>
+<span class="n">filepath</span> <span class="o">=</span> <span class="s1">&#39;https://github.com/AllenDowney/AstronomicalData/raw/main/data/&#39;</span>
+
+<span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">filename</span><span class="p">):</span>
+    <span class="nb">print</span><span class="p">(</span><span class="n">download</span><span class="p">(</span><span class="n">filepath</span><span class="o">+</span><span class="n">filename</span><span class="p">))</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+
+<span class="n">candidate_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_hdf</span><span class="p">(</span><span class="n">filename</span><span class="p">,</span> <span class="s1">&#39;candidate_df&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p><code class="docutils literal notranslate"><span class="pre">candidate_df</span></code> is the Pandas DataFrame that contains the results from Notebook XX, which selects stars likely to be in GD-1 based on proper motion.  It also includes position and proper motion transformed to the ICRS frame.</p>
+</div>
+<div class="section" id="merging-photometry-data">
+<h2>Merging photometry data<a class="headerlink" href="#merging-photometry-data" title="Permalink to this headline">¶</a></h2>
+<p>Before we select stars based on photometry data, we have to solve two problems:</p>
+<ol class="simple">
+<li><p>We only have Pan-STARRS data for some stars in <code class="docutils literal notranslate"><span class="pre">candidate_df</span></code>.</p></li>
+<li><p>Even for the stars where we have Pan-STARRS data in <code class="docutils literal notranslate"><span class="pre">photo_table</span></code>, some photometry data is missing.</p></li>
+</ol>
+<p>We will solve these problems in two step:</p>
+<ol class="simple">
+<li><p>We’ll merge the data from <code class="docutils literal notranslate"><span class="pre">candidate_df</span></code> and <code class="docutils literal notranslate"><span class="pre">photo_table</span></code> into a single Pandas <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code>.</p></li>
+<li><p>We’ll use Pandas functions to deal with missing data.</p></li>
+</ol>
+<p><code class="docutils literal notranslate"><span class="pre">candidate_df</span></code> is already a <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code>, but <code class="docutils literal notranslate"><span class="pre">results</span></code> is an Astropy <code class="docutils literal notranslate"><span class="pre">Table</span></code>.  Let’s convert it to Pandas:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">photo_df</span> <span class="o">=</span> <span class="n">photo_table</span><span class="o">.</span><span class="n">to_pandas</span><span class="p">()</span>
+
+<span class="k">for</span> <span class="n">colname</span> <span class="ow">in</span> <span class="n">photo_df</span><span class="o">.</span><span class="n">columns</span><span class="p">:</span>
+    <span class="nb">print</span><span class="p">(</span><span class="n">colname</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>source_id
+g_mean_psf_mag
+i_mean_psf_mag
+</pre></div>
+</div>
+</div>
+</div>
+<p>Now we want to combine <code class="docutils literal notranslate"><span class="pre">candidate_df</span></code> and <code class="docutils literal notranslate"><span class="pre">photo_df</span></code> into a single table, using <code class="docutils literal notranslate"><span class="pre">source_id</span></code> to match up the rows.</p>
+<p>You might recognize this task; it’s the same as the JOIN operation in ADQL/SQL.</p>
+<p>Pandas provides a function called <code class="docutils literal notranslate"><span class="pre">merge</span></code> that does what we want.  Here’s how we use it.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">merged</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">merge</span><span class="p">(</span><span class="n">candidate_df</span><span class="p">,</span> 
+                  <span class="n">photo_df</span><span class="p">,</span> 
+                  <span class="n">on</span><span class="o">=</span><span class="s1">&#39;source_id&#39;</span><span class="p">,</span> 
+                  <span class="n">how</span><span class="o">=</span><span class="s1">&#39;left&#39;</span><span class="p">)</span>
+<span class="n">merged</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>source_id</th>
+      <th>ra</th>
+      <th>dec</th>
+      <th>pmra</th>
+      <th>pmdec</th>
+      <th>parallax</th>
+      <th>parallax_error</th>
+      <th>radial_velocity</th>
+      <th>phi1</th>
+      <th>phi2</th>
+      <th>pm_phi1</th>
+      <th>pm_phi2</th>
+      <th>g_mean_psf_mag</th>
+      <th>i_mean_psf_mag</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>635559124339440000</td>
+      <td>137.586717</td>
+      <td>19.196544</td>
+      <td>-3.770522</td>
+      <td>-12.490482</td>
+      <td>0.791393</td>
+      <td>0.271754</td>
+      <td>NaN</td>
+      <td>-59.630489</td>
+      <td>-1.216485</td>
+      <td>-7.361363</td>
+      <td>-0.592633</td>
+      <td>NaN</td>
+      <td>NaN</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>635860218726658176</td>
+      <td>138.518707</td>
+      <td>19.092339</td>
+      <td>-5.941679</td>
+      <td>-11.346409</td>
+      <td>0.307456</td>
+      <td>0.199466</td>
+      <td>NaN</td>
+      <td>-59.247330</td>
+      <td>-2.016078</td>
+      <td>-7.527126</td>
+      <td>1.748779</td>
+      <td>17.8978</td>
+      <td>17.517401</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>635674126383965568</td>
+      <td>138.842874</td>
+      <td>19.031798</td>
+      <td>-3.897001</td>
+      <td>-12.702780</td>
+      <td>0.779463</td>
+      <td>0.223692</td>
+      <td>NaN</td>
+      <td>-59.133391</td>
+      <td>-2.306901</td>
+      <td>-7.560608</td>
+      <td>-0.741800</td>
+      <td>19.2873</td>
+      <td>17.678101</td>
+    </tr>
+    <tr>
+      <th>3</th>
+      <td>635535454774983040</td>
+      <td>137.837752</td>
+      <td>18.864007</td>
+      <td>-4.335041</td>
+      <td>-14.492309</td>
+      <td>0.314514</td>
+      <td>0.102775</td>
+      <td>NaN</td>
+      <td>-59.785300</td>
+      <td>-1.594569</td>
+      <td>-9.357536</td>
+      <td>-1.218492</td>
+      <td>16.9238</td>
+      <td>16.478100</td>
+    </tr>
+    <tr>
+      <th>4</th>
+      <td>635497276810313600</td>
+      <td>138.044516</td>
+      <td>19.009471</td>
+      <td>-7.172931</td>
+      <td>-12.291499</td>
+      <td>0.425404</td>
+      <td>0.337689</td>
+      <td>NaN</td>
+      <td>-59.557744</td>
+      <td>-1.682147</td>
+      <td>-9.000831</td>
+      <td>2.334407</td>
+      <td>19.9242</td>
+      <td>18.334000</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+<p>The first argument is the “left� table, the second argument is the “right� table, and the keyword argument <code class="docutils literal notranslate"><span class="pre">on='source_id'</span></code> specifies a column to use to match up the rows.</p>
+<p>The argument <code class="docutils literal notranslate"><span class="pre">how='left'</span></code> means that the result should have all rows from the left table, even if some of them don’t match up with a row in the right table.</p>
+<p>If you are interested in the other options for <code class="docutils literal notranslate"><span class="pre">how</span></code>, you can <a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.merge.html">read the documentation of <code class="docutils literal notranslate"><span class="pre">merge</span></code></a>.</p>
+<p>You can also do different types of join in ADQL/SQL; <a class="reference external" href="https://www.w3schools.com/sql/sql_join.asp">you can read about that here</a>.</p>
+<p>The result is a <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code> that contains the same number of rows as <code class="docutils literal notranslate"><span class="pre">candidate_df</span></code>.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="nb">len</span><span class="p">(</span><span class="n">candidate_df</span><span class="p">),</span> <span class="nb">len</span><span class="p">(</span><span class="n">photo_df</span><span class="p">),</span> <span class="nb">len</span><span class="p">(</span><span class="n">merged</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>(7346, 3724, 7346)
+</pre></div>
+</div>
+</div>
+</div>
+<p>And all columns from both tables.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">for</span> <span class="n">colname</span> <span class="ow">in</span> <span class="n">merged</span><span class="o">.</span><span class="n">columns</span><span class="p">:</span>
+    <span class="nb">print</span><span class="p">(</span><span class="n">colname</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>source_id
+ra
+dec
+pmra
+pmdec
+parallax
+parallax_error
+radial_velocity
+phi1
+phi2
+pm_phi1
+pm_phi2
+g_mean_psf_mag
+i_mean_psf_mag
+</pre></div>
+</div>
+</div>
+</div>
+<p><strong>Detail</strong> You might notice that Pandas also provides a function called <code class="docutils literal notranslate"><span class="pre">join</span></code>; it does almost the same thing, but the interface is slightly different.  We think <code class="docutils literal notranslate"><span class="pre">merge</span></code> is a little easier to use, so that’s what we chose.  It’s also more consistent with JOIN in SQL, so if you learn how to use <code class="docutils literal notranslate"><span class="pre">pd.merge</span></code>, you are also learning how to use SQL JOIN.</p>
+<p>Also, someone might ask why we have to use Pandas to do this join; why didn’t we do it in ADQL.  The answer is that we could have done that, but since we already have the data we need, we should probably do the computation locally rather than make another round trip to the Gaia server.</p>
+</div>
+<div class="section" id="missing-data">
+<h2>Missing data<a class="headerlink" href="#missing-data" title="Permalink to this headline">¶</a></h2>
+<p>Let’s add columns to the merged table for magnitude and color.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">merged</span><span class="p">[</span><span class="s1">&#39;mag&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">merged</span><span class="p">[</span><span class="s1">&#39;g_mean_psf_mag&#39;</span><span class="p">]</span>
+<span class="n">merged</span><span class="p">[</span><span class="s1">&#39;color&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">merged</span><span class="p">[</span><span class="s1">&#39;g_mean_psf_mag&#39;</span><span class="p">]</span> <span class="o">-</span> <span class="n">merged</span><span class="p">[</span><span class="s1">&#39;i_mean_psf_mag&#39;</span><span class="p">]</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>These columns contain the special value <code class="docutils literal notranslate"><span class="pre">NaN</span></code> where we are missing data.</p>
+<p>We can use <code class="docutils literal notranslate"><span class="pre">notnull</span></code> to see which rows contain value data, that is, not null values.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">merged</span><span class="p">[</span><span class="s1">&#39;color&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">notnull</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0       False
+1        True
+2        True
+3        True
+4        True
+        ...  
+7341     True
+7342    False
+7343    False
+7344     True
+7345    False
+Name: color, Length: 7346, dtype: bool
+</pre></div>
+</div>
+</div>
+</div>
+<p>And <code class="docutils literal notranslate"><span class="pre">sum</span></code> to count the number of valid values.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">merged</span><span class="p">[</span><span class="s1">&#39;color&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">notnull</span><span class="p">()</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>3724
+</pre></div>
+</div>
+</div>
+</div>
+<p>For scientific purposes, it’s not obvious what we should do with candidate stars if we don’t have photometry data.  Should we give them the benefit of the doubt or leave them out?</p>
+<p>In part the answer depends on the goal: are we trying to identify more stars that might be in GD-1, or a smaller set of stars that have higher probability?</p>
+<p>In the next section, we’ll leave them out, but you can experiment with the alternative.</p>
+</div>
+<div class="section" id="selecting-based-on-photometry">
+<h2>Selecting based on photometry<a class="headerlink" href="#selecting-based-on-photometry" title="Permalink to this headline">¶</a></h2>
+<p>Now let’s see how many of these points are inside the polygon we chose.</p>
+<p>We can use a list of column names to select <code class="docutils literal notranslate"><span class="pre">color</span></code> and <code class="docutils literal notranslate"><span class="pre">mag</span></code>.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">points</span> <span class="o">=</span> <span class="n">merged</span><span class="p">[[</span><span class="s1">&#39;color&#39;</span><span class="p">,</span> <span class="s1">&#39;mag&#39;</span><span class="p">]]</span>
+<span class="n">points</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>color</th>
+      <th>mag</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>NaN</td>
+      <td>NaN</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>0.3804</td>
+      <td>17.8978</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>1.6092</td>
+      <td>19.2873</td>
+    </tr>
+    <tr>
+      <th>3</th>
+      <td>0.4457</td>
+      <td>16.9238</td>
+    </tr>
+    <tr>
+      <th>4</th>
+      <td>1.5902</td>
+      <td>19.9242</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+<p>The result is a <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code> that can be treated as a sequence of coordinates, so we can pass it to <code class="docutils literal notranslate"><span class="pre">contains_points</span></code>:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">inside</span> <span class="o">=</span> <span class="n">path</span><span class="o">.</span><span class="n">contains_points</span><span class="p">(</span><span class="n">points</span><span class="p">)</span>
+<span class="n">inside</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>array([False, False, False, ..., False, False, False])
+</pre></div>
+</div>
+</div>
+</div>
+<p>The result is a Boolean array.  We can use <code class="docutils literal notranslate"><span class="pre">sum</span></code> to see how many stars fall in the polygon.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">inside</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>496
+</pre></div>
+</div>
+</div>
+</div>
+<p>Now we can use <code class="docutils literal notranslate"><span class="pre">inside</span></code> as a mask to select stars that fall inside the polygon.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">selected</span> <span class="o">=</span> <span class="n">merged</span><span class="p">[</span><span class="n">inside</span><span class="p">]</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>Let’s make a color-magnitude plot one more time, highlighting the selected stars with green <code class="docutils literal notranslate"><span class="pre">x</span></code> marks.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">plot_cmd</span><span class="p">(</span><span class="n">photo_table</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">xs</span><span class="p">,</span> <span class="n">ys</span><span class="p">)</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">selected</span><span class="p">[</span><span class="s1">&#39;color&#39;</span><span class="p">],</span> <span class="n">selected</span><span class="p">[</span><span class="s1">&#39;mag&#39;</span><span class="p">],</span> <span class="s1">&#39;gx&#39;</span><span class="p">);</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<img alt="_images/06_photo_61_0.png" src="_images/06_photo_61_0.png" />
+</div>
+</div>
+<p>It looks like the selected stars are, in fact, inside the polygon, which means they have photometry data consistent with GD-1.</p>
+<p>Finally, we can plot the coordinates of the selected stars:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span><span class="mf">2.5</span><span class="p">))</span>
+
+<span class="n">x</span> <span class="o">=</span> <span class="n">selected</span><span class="p">[</span><span class="s1">&#39;phi1&#39;</span><span class="p">]</span>
+<span class="n">y</span> <span class="o">=</span> <span class="n">selected</span><span class="p">[</span><span class="s1">&#39;phi2&#39;</span><span class="p">]</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="s1">&#39;ko&#39;</span><span class="p">,</span> <span class="n">markersize</span><span class="o">=</span><span class="mf">0.7</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.9</span><span class="p">)</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">&#39;ra (degree GD1)&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">&#39;dec (degree GD1)&#39;</span><span class="p">)</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s1">&#39;equal&#39;</span><span class="p">);</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<img alt="_images/06_photo_63_0.png" src="_images/06_photo_63_0.png" />
+</div>
+</div>
+<p>This example includes two new Matplotlib commands:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">figure</span></code> creates the figure.  In previous examples, we didn’t have to use this function; the figure was created automatically.  But when we call it explicitly, we can provide arguments like <code class="docutils literal notranslate"><span class="pre">figsize</span></code>, which sets the size of the figure.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">axis</span></code> with the parameter <code class="docutils literal notranslate"><span class="pre">equal</span></code> sets up the axes so a unit is the same size along the <code class="docutils literal notranslate"><span class="pre">x</span></code> and <code class="docutils literal notranslate"><span class="pre">y</span></code> axes.</p></li>
+</ul>
+<p>In an example like this, where <code class="docutils literal notranslate"><span class="pre">x</span></code> and <code class="docutils literal notranslate"><span class="pre">y</span></code> represent coordinates in space, equal axes ensures that the distance between points is represented accurately.</p>
+</div>
+<div class="section" id="write-the-data">
+<h2>Write the data<a class="headerlink" href="#write-the-data" title="Permalink to this headline">¶</a></h2>
+<p>Let’s write the merged DataFrame to a file.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">filename</span> <span class="o">=</span> <span class="s1">&#39;gd1_merged.hdf5&#39;</span>
+
+<span class="n">merged</span><span class="o">.</span><span class="n">to_hdf</span><span class="p">(</span><span class="n">filename</span><span class="p">,</span> <span class="s1">&#39;merged&#39;</span><span class="p">)</span>
+<span class="n">selected</span><span class="o">.</span><span class="n">to_hdf</span><span class="p">(</span><span class="n">filename</span><span class="p">,</span> <span class="s1">&#39;selected&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">!</span>ls -lh gd1_merged.hdf5
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>-rw-rw-r-- 1 downey downey 2.0M Oct 19 17:21 gd1_merged.hdf5
+</pre></div>
+</div>
+</div>
+</div>
+<p>If you are using Windows, <code class="docutils literal notranslate"><span class="pre">ls</span></code> might not work; in that case, try:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>!dir gd1_merged.hdf5
+</pre></div>
+</div>
+</div>
+<div class="section" id="save-the-polygon">
+<h2>Save the polygon<a class="headerlink" href="#save-the-polygon" title="Permalink to this headline">¶</a></h2>
+<p><a class="reference external" href="https://en.wikipedia.org/wiki/Reproducibility#Reproducible_research">Reproducibile research</a> is “the idea that … the full computational environment used to produce the results in the paper such as the code, data, etc. can be used to reproduce the results and create new work based on the research.�</p>
+<p>This Jupyter notebook is an example of reproducible research because it contains all of the code needed to reproduce the results, including the database queries that download the data and and analysis.</p>
+<p>However, when we used <code class="docutils literal notranslate"><span class="pre">ginput</span></code> to define a polygon by hand, we introduced a non-reproducible element to the analysis.  If someone running this notebook chooses a different polygon, they will get different results.  So it is important to record the polygon we chose as part of the data analysis pipeline.</p>
+<p>Since <code class="docutils literal notranslate"><span class="pre">coords</span></code> is a NumPy array, we can’t use <code class="docutils literal notranslate"><span class="pre">to_hdf</span></code> to save it in a file.  But we can convert it to a Pandas <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code> and save that.</p>
+<p>As an alternative, we could use <a class="reference external" href="http://www.pytables.org/index.html">PyTables</a>, which is the library Pandas uses to read and write files.  It is a powerful library, but not easy to use directly.  So let’s take advantage of Pandas.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">coords_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">coords</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">filename</span> <span class="o">=</span> <span class="s1">&#39;gd1_polygon.hdf5&#39;</span>
+<span class="n">coords_df</span><span class="o">.</span><span class="n">to_hdf</span><span class="p">(</span><span class="n">filename</span><span class="p">,</span> <span class="s1">&#39;coords_df&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>We can read it back like this.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">coords2_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_hdf</span><span class="p">(</span><span class="n">filename</span><span class="p">,</span> <span class="s1">&#39;coords_df&#39;</span><span class="p">)</span>
+<span class="n">coords2</span> <span class="o">=</span> <span class="n">coords2_df</span><span class="o">.</span><span class="n">to_numpy</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>And verify that the data we read back is the same.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">np</span><span class="o">.</span><span class="n">all</span><span class="p">(</span><span class="n">coords2</span> <span class="o">==</span> <span class="n">coords</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>True
+</pre></div>
+</div>
+</div>
+</div>
+</div>
+<div class="section" id="summary">
+<h2>Summary<a class="headerlink" href="#summary" title="Permalink to this headline">¶</a></h2>
+<p>In this notebook, we worked with two datasets: the list of candidate stars from Gaia and the photometry data from Pan-STARRS.</p>
+<p>We drew a color-magnitude diagram and used it to identify stars we think are likely to be in GD-1.</p>
+<p>Then we used a Pandas <code class="docutils literal notranslate"><span class="pre">merge</span></code> operation to combine the data into a single <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code>.</p>
+</div>
+<div class="section" id="best-practices">
+<h2>Best practices<a class="headerlink" href="#best-practices" title="Permalink to this headline">¶</a></h2>
+<ul class="simple">
+<li><p>If you want to perform something like a database <code class="docutils literal notranslate"><span class="pre">JOIN</span></code> operation with data that is in a Pandas <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code>, you can use the <code class="docutils literal notranslate"><span class="pre">join</span></code> or <code class="docutils literal notranslate"><span class="pre">merge</span></code> function.  In many cases, <code class="docutils literal notranslate"><span class="pre">merge</span></code> is easier to use because the arguments are more like SQL.</p></li>
+<li><p>Use Matplotlib options to control the size and aspect ratio of figures to make them easier to interpret.  In this example, we scaled the axes so the size of a degree is equal along both axes.</p></li>
+<li><p>Matplotlib also provides operations for working with points, polygons, and other geometric entities, so it’s not just for making figures.</p></li>
+<li><p>Be sure to record every element of the data analysis pipeline that would be needed to replicate the results.</p></li>
+</ul>
+</div>
+</div>
+
+    <script type="text/x-thebe-config">
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+   Chapter 2
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+   Chapter 3
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+  <a class="reference internal nav-link" href="#installing-libraries">
+   Installing libraries
+  </a>
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+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#making-figures-that-tell-a-story">
+   Making Figures That Tell a Story
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#plotting-gd-1">
+   Plotting GD-1
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#annotations">
+   Annotations
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#customization">
+   Customization
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#rcparams">
+   rcParams
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#style-sheets">
+   Style sheets
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#latex-fonts">
+   LaTeX fonts
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#multiple-panels">
+   Multiple panels
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#upper-right">
+   Upper right
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#upper-left">
+   Upper left
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#lower-right">
+   Lower right
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#subplots">
+   Subplots
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#adjusting-proportions">
+   Adjusting proportions
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#summary">
+   Summary
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#best-practices">
+   Best practices
+  </a>
+ </li>
+</ul>
+
+        </nav>
+        
+    </div>
+</div>
+    <div id="main-content" class="row">
+        <div class="col-12 col-md-9 pl-md-3 pr-md-0">
+        
+              <div>
+                
+  <div class="section" id="chapter-7">
+<h1>Chapter 7<a class="headerlink" href="#chapter-7" title="Permalink to this headline">¶</a></h1>
+<p>This is the seventh in a series of notebooks related to astronomy data.</p>
+<p>As a continuing example, we will replicate part of the analysis in a recent paper, “<a class="reference external" href="https://arxiv.org/abs/1805.00425">Off the beaten path: Gaia reveals GD-1 stars outside of the main stream</a>� by Adrian M. Price-Whelan and Ana Bonaca.</p>
+<p>In the previous notebook we selected photometry data from Pan-STARRS and used it to identify stars we think are likely to be in GD-1</p>
+<p>In this notebook, we’ll take the results from previous lessons and use them to make a figure that tells a compelling scientific story.</p>
+<div class="section" id="outline">
+<h2>Outline<a class="headerlink" href="#outline" title="Permalink to this headline">¶</a></h2>
+<p>Here are the steps in this notebook:</p>
+<ol class="simple">
+<li><p>Starting with the figure from the previous notebook, we’ll add annotations to present the results more clearly.</p></li>
+<li><p>The we’ll see several ways to customize figures to make them more appealing and effective.</p></li>
+<li><p>Finally, we’ll see how to make a figure with multiple panels or subplots.</p></li>
+</ol>
+<p>After completing this lesson, you should be able to</p>
+<ul class="simple">
+<li><p>Design a figure that tells a compelling story.</p></li>
+<li><p>Use Matplotlib features to customize the appearance of figures.</p></li>
+<li><p>Generate a figure with multiple subplots.</p></li>
+</ul>
+</div>
+<div class="section" id="installing-libraries">
+<h2>Installing libraries<a class="headerlink" href="#installing-libraries" title="Permalink to this headline">¶</a></h2>
+<p>If you are running this notebook on Colab, you can run the following cell to install Astroquery and a the other libraries we’ll use.</p>
+<p>If you are running this notebook on your own computer, you might have to install these libraries yourself.</p>
+<p>If you are using this notebook as part of a Carpentries workshop, you should have received setup instructions.</p>
+<p>TODO: Add a link to the instructions.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># If we&#39;re running on Colab, install libraries</span>
+
+<span class="kn">import</span> <span class="nn">sys</span>
+<span class="n">IN_COLAB</span> <span class="o">=</span> <span class="s1">&#39;google.colab&#39;</span> <span class="ow">in</span> <span class="n">sys</span><span class="o">.</span><span class="n">modules</span>
+
+<span class="k">if</span> <span class="n">IN_COLAB</span><span class="p">:</span>
+    <span class="o">!</span>pip install astroquery astro-gala pyia python-wget
+</pre></div>
+</div>
+</div>
+</div>
+</div>
+<div class="section" id="making-figures-that-tell-a-story">
+<h2>Making Figures That Tell a Story<a class="headerlink" href="#making-figures-that-tell-a-story" title="Permalink to this headline">¶</a></h2>
+<p>So far the figure we’ve made have been “quick and dirty�.  Mostly we have used Matplotlib’s default style, although we have adjusted a few parameters, like <code class="docutils literal notranslate"><span class="pre">markersize</span></code> and <code class="docutils literal notranslate"><span class="pre">alpha</span></code>, to improve legibility.</p>
+<p>Now that the analysis is done, it’s time to think more about:</p>
+<ol class="simple">
+<li><p>Making professional-looking figures that are ready for publication, and</p></li>
+<li><p>Making figures that communicate a scientific result clearly and compellingly.</p></li>
+</ol>
+<p>Not necessarily in that order.</p>
+<p>Let’s start by reviewing Figure 1 from the original paper.  We’ve seen the individual panels, but now let’s look at the whole thing, along with the caption:</p>
+<img width="500" src="https://github.com/datacarpentry/astronomy-python/raw/gh-pages/fig/gd1-5.png"><p><strong>Exercise:</strong> Think about the following questions:</p>
+<ol class="simple">
+<li><p>What is the primary scientific result of this work?</p></li>
+<li><p>What story is this figure telling?</p></li>
+<li><p>In the design of this figure, can you identify 1-2 choices the authors made that you think are effective?  Think about big-picture elements, like the number of panels and how they are arranged, as well as details like the choice of typeface.</p></li>
+<li><p>Can you identify 1-2 elements that could be improved, or that you might have done differently?</p></li>
+</ol>
+<p>Some topics that might come up in this discussion:</p>
+<ol class="simple">
+<li><p>The primary result is that the multiple stages of selection make it possible to separate likely candidates from the background more effectively than in previous work, which makes it possible to see the structure of GD-1 in “unprecedented detail�.</p></li>
+<li><p>The figure documents the selection process as a sequence of steps.  Reading right-to-left, top-to-bottom, we see selection based on proper motion, the results of the first selection, selection based on color and magnitude, and the results of the second selection.  So this figure documents the methodology and presents the primary result.</p></li>
+<li><p>It’s mostly black and white, with minimal use of color, so it will work well in print.  The annotations in the bottom left panel guide the reader to the most important results.  It contains enough technical detail for a professional audience, but most of it is also comprehensible to a more general audience.  The two left panels have the same dimensions and their axes are aligned.</p></li>
+<li><p>Since the panels represent a sequence, it might be better to arrange them left-to-right.  The placement and size of the axis labels could be tweaked.  The entire figure could be a little bigger to match the width and proportion of the caption.  The top left panel has unnused white space (but that leaves space for the annotations in the bottom left).</p></li>
+</ol>
+</div>
+<div class="section" id="plotting-gd-1">
+<h2>Plotting GD-1<a class="headerlink" href="#plotting-gd-1" title="Permalink to this headline">¶</a></h2>
+<p>Let’s start with the panel in the lower left.  The following cell reloads the data.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">os</span>
+<span class="kn">from</span> <span class="nn">wget</span> <span class="kn">import</span> <span class="n">download</span>
+
+<span class="n">filename</span> <span class="o">=</span> <span class="s1">&#39;gd1_merged.hdf5&#39;</span>
+<span class="n">path</span> <span class="o">=</span> <span class="s1">&#39;https://github.com/AllenDowney/AstronomicalData/raw/main/data/&#39;</span>
+
+<span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">filename</span><span class="p">):</span>
+    <span class="nb">print</span><span class="p">(</span><span class="n">download</span><span class="p">(</span><span class="n">path</span><span class="o">+</span><span class="n">filename</span><span class="p">))</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+
+<span class="n">selected</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_hdf</span><span class="p">(</span><span class="n">filename</span><span class="p">,</span> <span class="s1">&#39;selected&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+
+<span class="k">def</span> <span class="nf">plot_second_selection</span><span class="p">(</span><span class="n">df</span><span class="p">):</span>
+    <span class="n">x</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;phi1&#39;</span><span class="p">]</span>
+    <span class="n">y</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;phi2&#39;</span><span class="p">]</span>
+
+    <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="s1">&#39;ko&#39;</span><span class="p">,</span> <span class="n">markersize</span><span class="o">=</span><span class="mf">0.7</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.9</span><span class="p">)</span>
+
+    <span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">&#39;$\phi_1$ [deg]&#39;</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">&#39;$\phi_2$ [deg]&#39;</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;Proper motion + photometry selection&#39;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="s1">&#39;medium&#39;</span><span class="p">)</span>
+
+    <span class="n">plt</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s1">&#39;equal&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>And here’s what it looks like.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span><span class="mf">2.5</span><span class="p">))</span>
+<span class="n">plot_second_selection</span><span class="p">(</span><span class="n">selected</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<img alt="_images/07_plot_13_0.png" src="_images/07_plot_13_0.png" />
+</div>
+</div>
+</div>
+<div class="section" id="annotations">
+<h2>Annotations<a class="headerlink" href="#annotations" title="Permalink to this headline">¶</a></h2>
+<p>The figure in the paper uses three other features to present the results more clearly and compellingly:</p>
+<ul class="simple">
+<li><p>A vertical dashed line to distinguish the previously undetected region of GD-1,</p></li>
+<li><p>A label that identifies the new region, and</p></li>
+<li><p>Several annotations that combine text and arrows to identify features of GD-1.</p></li>
+</ul>
+<p>As an exercise, choose any or all of these features and add them to the figure:</p>
+<ul class="simple">
+<li><p>To draw vertical lines, see <a class="reference external" href="https://matplotlib.org/3.3.1/api/_as_gen/matplotlib.pyplot.vlines.html"><code class="docutils literal notranslate"><span class="pre">plt.vlines</span></code></a> and <a class="reference external" href="https://matplotlib.org/3.3.1/api/_as_gen/matplotlib.pyplot.axvline.html#matplotlib.pyplot.axvline"><code class="docutils literal notranslate"><span class="pre">plt.axvline</span></code></a>.</p></li>
+<li><p>To add text, see <a class="reference external" href="https://matplotlib.org/3.3.1/api/_as_gen/matplotlib.pyplot.text.html"><code class="docutils literal notranslate"><span class="pre">plt.text</span></code></a>.</p></li>
+<li><p>To add an annotation with text and an arrow, see <span class="xref myst">plt.annotate</span>.</p></li>
+</ul>
+<p>And here is some <a class="reference external" href="https://matplotlib.org/3.3.1/tutorials/text/annotations.html#plotting-guide-annotation">additional information about text and arrows</a>.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Solution</span>
+
+<span class="c1"># plt.axvline(-55, ls=&#39;--&#39;, color=&#39;gray&#39;, </span>
+<span class="c1">#             alpha=0.4, dashes=(6,4), lw=2)</span>
+<span class="c1"># plt.text(-60, 5.5, &#39;Previously\nundetected&#39;, </span>
+<span class="c1">#          fontsize=&#39;small&#39;, ha=&#39;right&#39;, va=&#39;top&#39;);</span>
+
+<span class="c1"># arrowprops=dict(color=&#39;gray&#39;, shrink=0.05, width=1.5, </span>
+<span class="c1">#                 headwidth=6, headlength=8, alpha=0.4)</span>
+
+<span class="c1"># plt.annotate(&#39;Spur&#39;, xy=(-33, 2), xytext=(-35, 5.5),</span>
+<span class="c1">#              arrowprops=arrowprops,</span>
+<span class="c1">#              fontsize=&#39;small&#39;)</span>
+
+<span class="c1"># plt.annotate(&#39;Gap&#39;, xy=(-22, -1), xytext=(-25, -5.5),</span>
+<span class="c1">#              arrowprops=arrowprops,</span>
+<span class="c1">#              fontsize=&#39;small&#39;)</span>
+</pre></div>
+</div>
+</div>
+</div>
+</div>
+<div class="section" id="customization">
+<h2>Customization<a class="headerlink" href="#customization" title="Permalink to this headline">¶</a></h2>
+<p>Matplotlib provides a default style that determines things like the colors of lines, the placement of labels and ticks on the axes, and many other properties.</p>
+<p>There are several ways to override these defaults and customize your figures:</p>
+<ul class="simple">
+<li><p>To customize only the current figure, you can call functions like <code class="docutils literal notranslate"><span class="pre">tick_params</span></code>, which we’ll demonstrate below.</p></li>
+<li><p>To customize all figures in a notebook, you use <code class="docutils literal notranslate"><span class="pre">rcParams</span></code>.</p></li>
+<li><p>To override more than a few defaults at the same time, you can use a style sheet.</p></li>
+</ul>
+<p>As a simple example, notice that Matplotlib puts ticks on the outside of the figures by default, and only on the left and bottom sides of the axes.</p>
+<p>To change this behavior, you can use <code class="docutils literal notranslate"><span class="pre">gca()</span></code> to get the current axes and <code class="docutils literal notranslate"><span class="pre">tick_params</span></code> to change the settings.</p>
+<p>Here’s how you can put the ticks on the inside of the figure:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">gca</span><span class="p">()</span><span class="o">.</span><span class="n">tick_params</span><span class="p">(</span><span class="n">direction</span><span class="o">=</span><span class="s1">&#39;in&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+<p><strong>Exercise:</strong> Read the documentation of <a class="reference external" href="https://matplotlib.org/3.1.1/api/_as_gen/matplotlib.axes.Axes.tick_params.html"><code class="docutils literal notranslate"><span class="pre">tick_params</span></code></a> and use it to put ticks on the top and right sides of the axes.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Solution</span>
+
+<span class="c1"># plt.gca().tick_params(top=True, right=True)</span>
+</pre></div>
+</div>
+</div>
+</div>
+</div>
+<div class="section" id="rcparams">
+<h2>rcParams<a class="headerlink" href="#rcparams" title="Permalink to this headline">¶</a></h2>
+<p>If you want to make a customization that applies to all figures in a notebook, you can use <code class="docutils literal notranslate"><span class="pre">rcParams</span></code>.</p>
+<p>Here’s an example that reads the current font size from <code class="docutils literal notranslate"><span class="pre">rcParams</span></code>:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s1">&#39;font.size&#39;</span><span class="p">]</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>10.0
+</pre></div>
+</div>
+</div>
+</div>
+<p>And sets it to a new value:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s1">&#39;font.size&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="mi">14</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p><strong>Exercise:</strong> Plot the previous figure again, and see what font sizes have changed.  Look up any other element of <code class="docutils literal notranslate"><span class="pre">rcParams</span></code>, change its value, and check the effect on the figure.</p>
+<p>If you find yourself making the same customizations in several notebooks, you can put changes to <code class="docutils literal notranslate"><span class="pre">rcParams</span></code> in a <code class="docutils literal notranslate"><span class="pre">matplotlibrc</span></code> file, <a class="reference external" href="https://matplotlib.org/3.3.1/tutorials/introductory/customizing.html#customizing-with-matplotlibrc-files">which you can read about here</a>.</p>
+</div>
+<div class="section" id="style-sheets">
+<h2>Style sheets<a class="headerlink" href="#style-sheets" title="Permalink to this headline">¶</a></h2>
+<p>The <code class="docutils literal notranslate"><span class="pre">matplotlibrc</span></code> file is read when you import Matplotlib, so it is not easy to switch from one set of options to another.</p>
+<p>The solution to this problem is style sheets, <a class="reference external" href="https://matplotlib.org/3.1.1/tutorials/introductory/customizing.html">which you can read about here</a>.</p>
+<p>Matplotlib provides a set of predefined style sheets, or you can make your own.</p>
+<p>The following cell displays a list of style sheets installed on your system.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">style</span><span class="o">.</span><span class="n">available</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[&#39;Solarize_Light2&#39;,
+ &#39;_classic_test_patch&#39;,
+ &#39;bmh&#39;,
+ &#39;classic&#39;,
+ &#39;dark_background&#39;,
+ &#39;fast&#39;,
+ &#39;fivethirtyeight&#39;,
+ &#39;ggplot&#39;,
+ &#39;grayscale&#39;,
+ &#39;seaborn&#39;,
+ &#39;seaborn-bright&#39;,
+ &#39;seaborn-colorblind&#39;,
+ &#39;seaborn-dark&#39;,
+ &#39;seaborn-dark-palette&#39;,
+ &#39;seaborn-darkgrid&#39;,
+ &#39;seaborn-deep&#39;,
+ &#39;seaborn-muted&#39;,
+ &#39;seaborn-notebook&#39;,
+ &#39;seaborn-paper&#39;,
+ &#39;seaborn-pastel&#39;,
+ &#39;seaborn-poster&#39;,
+ &#39;seaborn-talk&#39;,
+ &#39;seaborn-ticks&#39;,
+ &#39;seaborn-white&#39;,
+ &#39;seaborn-whitegrid&#39;,
+ &#39;tableau-colorblind10&#39;]
+</pre></div>
+</div>
+</div>
+</div>
+<p>Note that <code class="docutils literal notranslate"><span class="pre">seaborn-paper</span></code>, <code class="docutils literal notranslate"><span class="pre">seaborn-talk</span></code> and <code class="docutils literal notranslate"><span class="pre">seaborn-poster</span></code> are particularly intended to prepare versions of a figure with text sizes and other features that work well in papers, talks, and posters.</p>
+<p>To use any of these style sheets, run <code class="docutils literal notranslate"><span class="pre">plt.style.use</span></code> like this:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">style</span><span class="o">.</span><span class="n">use</span><span class="p">(</span><span class="s1">&#39;fivethirtyeight&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>The style sheet you choose will affect the appearance of all figures you plot after calling <code class="docutils literal notranslate"><span class="pre">use</span></code>, unless you override any of the options or call <code class="docutils literal notranslate"><span class="pre">use</span></code> again.</p>
+<p><strong>Exercise:</strong> Choose one of the styles on the list and select it by calling <code class="docutils literal notranslate"><span class="pre">use</span></code>.  Then go back and plot one of the figures above and see what effect it has.</p>
+<p>If you can’t find a style sheet that’s exactly what you want, you can make your own.  This repository includes a style sheet called <code class="docutils literal notranslate"><span class="pre">az-paper-twocol.mplstyle</span></code>, with customizations chosen by Azalee Bostroem for publication in astronomy journals.</p>
+<p>The following cell downloads the style sheet.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">os</span>
+
+<span class="n">filename</span> <span class="o">=</span> <span class="s1">&#39;az-paper-twocol.mplstyle&#39;</span>
+<span class="n">path</span> <span class="o">=</span> <span class="s1">&#39;https://github.com/AllenDowney/AstronomicalData/raw/main/data/&#39;</span>
+
+<span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">filename</span><span class="p">):</span>
+    <span class="nb">print</span><span class="p">(</span><span class="n">download</span><span class="p">(</span><span class="n">path</span><span class="o">+</span><span class="n">filename</span><span class="p">))</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>You can use it like this:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">style</span><span class="o">.</span><span class="n">use</span><span class="p">(</span><span class="s1">&#39;./az-paper-twocol.mplstyle&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>The prefix <code class="docutils literal notranslate"><span class="pre">./</span></code> tells Matplotlib to look for the file in the current directory.</p>
+<p>As an alternative, you can install a style sheet for your own use by putting it in your configuration directory.  To find out where that is, you can run the following command:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib</span> <span class="k">as</span> <span class="nn">mpl</span>
+
+<span class="n">mpl</span><span class="o">.</span><span class="n">get_configdir</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="section" id="latex-fonts">
+<h2>LaTeX fonts<a class="headerlink" href="#latex-fonts" title="Permalink to this headline">¶</a></h2>
+<p>When you include mathematical expressions in titles, labels, and annotations, Matplotlib uses <a class="reference external" href="https://matplotlib.org/3.1.0/tutorials/text/mathtext.html"><code class="docutils literal notranslate"><span class="pre">mathtext</span></code></a> to typeset them.  <code class="docutils literal notranslate"><span class="pre">mathtext</span></code> uses the same syntax as LaTeX, but it provides only a subset of its features.</p>
+<p>If you need features that are not provided by <code class="docutils literal notranslate"><span class="pre">mathtext</span></code>, or you prefer the way LaTeX typesets mathematical expressions, you can customize Matplotlib to use LaTeX.</p>
+<p>In <code class="docutils literal notranslate"><span class="pre">matplotlibrc</span></code> or in a style sheet, you can add the following line:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">text</span><span class="o">.</span><span class="n">usetex</span>        <span class="p">:</span> <span class="n">true</span>
+</pre></div>
+</div>
+<p>Or in a notebook you can run the following code.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s1">&#39;text.usetex&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="kc">True</span>
+</pre></div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s1">&#39;text.usetex&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="kc">True</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>If you go back and draw the figure again, you should see the difference.</p>
+<p>If you get an error message like</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>LaTeX Error: File `type1cm.sty&#39; not found.
+</pre></div>
+</div>
+<p>You might have to install a package that contains the fonts LaTeX needs.  On some systems, the packages <code class="docutils literal notranslate"><span class="pre">texlive-latex-extra</span></code> or <code class="docutils literal notranslate"><span class="pre">cm-super</span></code> might be what you need.  <a class="reference external" href="https://stackoverflow.com/questions/11354149/python-unable-to-render-tex-in-matplotlib">See here for more help with this</a>.</p>
+<p>In case you are curious, <code class="docutils literal notranslate"><span class="pre">cm</span></code> stands for <a class="reference external" href="https://en.wikipedia.org/wiki/Computer_Modern">Computer Modern</a>, the font LaTeX uses to typeset math.</p>
+</div>
+<div class="section" id="multiple-panels">
+<h2>Multiple panels<a class="headerlink" href="#multiple-panels" title="Permalink to this headline">¶</a></h2>
+<p>So far we’ve been working with one figure at a time, but the figure we are replicating contains multiple panels, also known as “subplots�.</p>
+<p>Confusingly, Matplotlib provides <em>three</em> functions for making figures like this: <code class="docutils literal notranslate"><span class="pre">subplot</span></code>, <code class="docutils literal notranslate"><span class="pre">subplots</span></code>, and <code class="docutils literal notranslate"><span class="pre">subplot2grid</span></code>.</p>
+<ul class="simple">
+<li><p><a class="reference external" href="https://matplotlib.org/3.3.1/api/_as_gen/matplotlib.pyplot.subplot.html"><code class="docutils literal notranslate"><span class="pre">subplot</span></code></a> is simple and similar to MATLAB, so if you are familiar with that interface, you might like <code class="docutils literal notranslate"><span class="pre">subplot</span></code></p></li>
+<li><p><a class="reference external" href="https://matplotlib.org/3.3.1/api/_as_gen/matplotlib.pyplot.subplots.html"><code class="docutils literal notranslate"><span class="pre">subplots</span></code></a> is more object-oriented, which some people prefer.</p></li>
+<li><p><a class="reference external" href="https://matplotlib.org/3.3.1/api/_as_gen/matplotlib.pyplot.subplot2grid.html"><code class="docutils literal notranslate"><span class="pre">subplot2grid</span></code></a> is most convenient if you want to control the relative sizes of the subplots.</p></li>
+</ul>
+<p>So we’ll use <code class="docutils literal notranslate"><span class="pre">subplot2grid</span></code>.</p>
+<p>All of these functions are easier to use if we put the code that generates each panel in a function.</p>
+</div>
+<div class="section" id="upper-right">
+<h2>Upper right<a class="headerlink" href="#upper-right" title="Permalink to this headline">¶</a></h2>
+<p>To make the panel in the upper right, we have to reload <code class="docutils literal notranslate"><span class="pre">centerline</span></code>.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">os</span>
+
+<span class="n">filename</span> <span class="o">=</span> <span class="s1">&#39;gd1_dataframe.hdf5&#39;</span>
+<span class="n">path</span> <span class="o">=</span> <span class="s1">&#39;https://github.com/AllenDowney/AstronomicalData/raw/main/data/&#39;</span>
+
+<span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">filename</span><span class="p">):</span>
+    <span class="nb">print</span><span class="p">(</span><span class="n">download</span><span class="p">(</span><span class="n">path</span><span class="o">+</span><span class="n">filename</span><span class="p">))</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+
+<span class="n">centerline</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_hdf</span><span class="p">(</span><span class="n">filename</span><span class="p">,</span> <span class="s1">&#39;centerline&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>And define the coordinates of the rectangle we selected.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pm1_min</span> <span class="o">=</span> <span class="o">-</span><span class="mf">8.9</span>
+<span class="n">pm1_max</span> <span class="o">=</span> <span class="o">-</span><span class="mf">6.9</span>
+<span class="n">pm2_min</span> <span class="o">=</span> <span class="o">-</span><span class="mf">2.2</span>
+<span class="n">pm2_max</span> <span class="o">=</span>  <span class="mf">1.0</span>
+
+<span class="n">pm1_rect</span> <span class="o">=</span> <span class="p">[</span><span class="n">pm1_min</span><span class="p">,</span> <span class="n">pm1_min</span><span class="p">,</span> <span class="n">pm1_max</span><span class="p">,</span> <span class="n">pm1_max</span><span class="p">]</span>
+<span class="n">pm2_rect</span> <span class="o">=</span> <span class="p">[</span><span class="n">pm2_min</span><span class="p">,</span> <span class="n">pm2_max</span><span class="p">,</span> <span class="n">pm2_max</span><span class="p">,</span> <span class="n">pm2_min</span><span class="p">]</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>To plot this rectangle, we’ll use a feature we have not seen before: <code class="docutils literal notranslate"><span class="pre">Polygon</span></code>, which is provided by Matplotlib.</p>
+<p>To create a <code class="docutils literal notranslate"><span class="pre">Polygon</span></code>, we have to put the coordinates in an array with <code class="docutils literal notranslate"><span class="pre">x</span></code> values in the first column and <code class="docutils literal notranslate"><span class="pre">y</span></code> values in the second column.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+
+<span class="n">vertices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">transpose</span><span class="p">([</span><span class="n">pm1_rect</span><span class="p">,</span> <span class="n">pm2_rect</span><span class="p">])</span>
+<span class="n">vertices</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>array([[-8.9, -2.2],
+       [-8.9,  1. ],
+       [-6.9,  1. ],
+       [-6.9, -2.2]])
+</pre></div>
+</div>
+</div>
+</div>
+<p>The following function takes a <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code> as a parameter, plots the proper motion for each star, and adds a shaded <code class="docutils literal notranslate"><span class="pre">Polygon</span></code> to show the region we selected.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">matplotlib.patches</span> <span class="kn">import</span> <span class="n">Polygon</span>
+
+<span class="k">def</span> <span class="nf">plot_proper_motion</span><span class="p">(</span><span class="n">df</span><span class="p">):</span>
+    <span class="n">pm1</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;pm_phi1&#39;</span><span class="p">]</span>
+    <span class="n">pm2</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;pm_phi2&#39;</span><span class="p">]</span>
+
+    <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">pm1</span><span class="p">,</span> <span class="n">pm2</span><span class="p">,</span> <span class="s1">&#39;ko&#39;</span><span class="p">,</span> <span class="n">markersize</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.3</span><span class="p">)</span>
+    
+    <span class="n">poly</span> <span class="o">=</span> <span class="n">Polygon</span><span class="p">(</span><span class="n">vertices</span><span class="p">,</span> <span class="n">closed</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> 
+                   <span class="n">facecolor</span><span class="o">=</span><span class="s1">&#39;C1&#39;</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.4</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">gca</span><span class="p">()</span><span class="o">.</span><span class="n">add_patch</span><span class="p">(</span><span class="n">poly</span><span class="p">)</span>
+    
+    <span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">&#39;$\mu_{\phi_1} [\mathrm{mas~yr}^{-1}]$&#39;</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">&#39;$\mu_{\phi_2} [\mathrm{mas~yr}^{-1}]$&#39;</span><span class="p">)</span>
+
+    <span class="n">plt</span><span class="o">.</span><span class="n">xlim</span><span class="p">(</span><span class="o">-</span><span class="mi">12</span><span class="p">,</span> <span class="mi">8</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">ylim</span><span class="p">(</span><span class="o">-</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>Notice that <code class="docutils literal notranslate"><span class="pre">add_patch</span></code> is like <code class="docutils literal notranslate"><span class="pre">invert_yaxis</span></code>; in order to call it, we have to use <code class="docutils literal notranslate"><span class="pre">gca</span></code> to get the current axes.</p>
+<p>Here’s what the new version of the figure looks like.  We’ve changed the labels on the axes to be consistent with the paper.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s1">&#39;text.usetex&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="kc">False</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">style</span><span class="o">.</span><span class="n">use</span><span class="p">(</span><span class="s1">&#39;default&#39;</span><span class="p">)</span>
+
+<span class="n">plot_proper_motion</span><span class="p">(</span><span class="n">centerline</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<img alt="_images/07_plot_50_0.png" src="_images/07_plot_50_0.png" />
+</div>
+</div>
+</div>
+<div class="section" id="upper-left">
+<h2>Upper left<a class="headerlink" href="#upper-left" title="Permalink to this headline">¶</a></h2>
+<p>Now let’s work on the panel in the upper left. We have to reload <code class="docutils literal notranslate"><span class="pre">candidates</span></code>.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">os</span>
+
+<span class="n">filename</span> <span class="o">=</span> <span class="s1">&#39;gd1_candidates.hdf5&#39;</span>
+<span class="n">path</span> <span class="o">=</span> <span class="s1">&#39;https://github.com/AllenDowney/AstronomicalData/raw/main/data/&#39;</span>
+
+<span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">filename</span><span class="p">):</span>
+    <span class="nb">print</span><span class="p">(</span><span class="n">download</span><span class="p">(</span><span class="n">path</span><span class="o">+</span><span class="n">filename</span><span class="p">))</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+
+<span class="n">filename</span> <span class="o">=</span> <span class="s1">&#39;gd1_candidates.hdf5&#39;</span>
+
+<span class="n">candidate_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_hdf</span><span class="p">(</span><span class="n">filename</span><span class="p">,</span> <span class="s1">&#39;candidate_df&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>Here’s a function that takes a <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code> of candidate stars and plots their positions in GD-1 coordindates.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">plot_first_selection</span><span class="p">(</span><span class="n">df</span><span class="p">):</span>
+    <span class="n">x</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;phi1&#39;</span><span class="p">]</span>
+    <span class="n">y</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;phi2&#39;</span><span class="p">]</span>
+
+    <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="s1">&#39;ko&#39;</span><span class="p">,</span> <span class="n">markersize</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.3</span><span class="p">)</span>
+
+    <span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">&#39;$\phi_1$ [deg]&#39;</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">&#39;$\phi_2$ [deg]&#39;</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;Proper motion selection&#39;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="s1">&#39;medium&#39;</span><span class="p">)</span>
+
+    <span class="n">plt</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s1">&#39;equal&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>And here’s what it looks like.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">plot_first_selection</span><span class="p">(</span><span class="n">candidate_df</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<img alt="_images/07_plot_57_0.png" src="_images/07_plot_57_0.png" />
+</div>
+</div>
+</div>
+<div class="section" id="lower-right">
+<h2>Lower right<a class="headerlink" href="#lower-right" title="Permalink to this headline">¶</a></h2>
+<p>For the figure in the lower right, we need to reload the merged <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code>, which contains data from Gaia and photometry data from Pan-STARRS.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+
+<span class="n">filename</span> <span class="o">=</span> <span class="s1">&#39;gd1_merged.hdf5&#39;</span>
+
+<span class="n">merged</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_hdf</span><span class="p">(</span><span class="n">filename</span><span class="p">,</span> <span class="s1">&#39;merged&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>From the previous notebook, here’s the function that plots the color-magnitude diagram.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+
+<span class="k">def</span> <span class="nf">plot_cmd</span><span class="p">(</span><span class="n">table</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;Plot a color magnitude diagram.</span>
+<span class="sd">    </span>
+<span class="sd">    table: Table or DataFrame with photometry data</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">y</span> <span class="o">=</span> <span class="n">table</span><span class="p">[</span><span class="s1">&#39;g_mean_psf_mag&#39;</span><span class="p">]</span>
+    <span class="n">x</span> <span class="o">=</span> <span class="n">table</span><span class="p">[</span><span class="s1">&#39;g_mean_psf_mag&#39;</span><span class="p">]</span> <span class="o">-</span> <span class="n">table</span><span class="p">[</span><span class="s1">&#39;i_mean_psf_mag&#39;</span><span class="p">]</span>
+
+    <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="s1">&#39;ko&#39;</span><span class="p">,</span> <span class="n">markersize</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.3</span><span class="p">)</span>
+
+    <span class="n">plt</span><span class="o">.</span><span class="n">xlim</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mf">1.5</span><span class="p">])</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">ylim</span><span class="p">([</span><span class="mi">14</span><span class="p">,</span> <span class="mi">22</span><span class="p">])</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">gca</span><span class="p">()</span><span class="o">.</span><span class="n">invert_yaxis</span><span class="p">()</span>
+
+    <span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">&#39;$g_0$&#39;</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">&#39;$(g-i)_0$&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>And here’s what it looks like.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">plot_cmd</span><span class="p">(</span><span class="n">merged</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<img alt="_images/07_plot_63_0.png" src="_images/07_plot_63_0.png" />
+</div>
+</div>
+<p><strong>Exercise:</strong> Add a few lines to <code class="docutils literal notranslate"><span class="pre">plot_cmd</span></code> to show the Polygon we selected as a shaded area.</p>
+<p>Run these cells to get the polygon coordinates we saved in the previous notebook.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">os</span>
+
+<span class="n">filename</span> <span class="o">=</span> <span class="s1">&#39;gd1_polygon.hdf5&#39;</span>
+<span class="n">path</span> <span class="o">=</span> <span class="s1">&#39;https://github.com/AllenDowney/AstronomicalData/raw/main/data/&#39;</span>
+
+<span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">filename</span><span class="p">):</span>
+    <span class="nb">print</span><span class="p">(</span><span class="n">download</span><span class="p">(</span><span class="n">path</span><span class="o">+</span><span class="n">filename</span><span class="p">))</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">coords_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_hdf</span><span class="p">(</span><span class="n">filename</span><span class="p">,</span> <span class="s1">&#39;coords_df&#39;</span><span class="p">)</span>
+<span class="n">coords</span> <span class="o">=</span> <span class="n">coords_df</span><span class="o">.</span><span class="n">to_numpy</span><span class="p">()</span>
+<span class="n">coords</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>array([[ 0.21505376, 17.5481972 ],
+       [ 0.38978495, 18.94628403],
+       [ 0.53763441, 19.90286976],
+       [ 0.70340502, 20.60191317],
+       [ 0.82885305, 21.30095659],
+       [ 0.66308244, 21.52170714],
+       [ 0.43010753, 20.78587196],
+       [ 0.27329749, 19.71891096],
+       [ 0.17473118, 18.68874172],
+       [ 0.17473118, 17.95290655]])
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Solution</span>
+
+<span class="c1">#poly = Polygon(coords, closed=True, </span>
+<span class="c1">#               facecolor=&#39;C1&#39;, alpha=0.4)</span>
+<span class="c1">#plt.gca().add_patch(poly)</span>
+</pre></div>
+</div>
+</div>
+</div>
+</div>
+<div class="section" id="subplots">
+<h2>Subplots<a class="headerlink" href="#subplots" title="Permalink to this headline">¶</a></h2>
+<p>Now we’re ready to put it all together.  To make a figure with four subplots, we’ll use <code class="docutils literal notranslate"><span class="pre">subplot2grid</span></code>, <a class="reference external" href="https://matplotlib.org/3.3.1/api/_as_gen/matplotlib.pyplot.subplot2grid.html">which requires two arguments</a>:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">shape</span></code>, which is a tuple with the number of rows and columns in the grid, and</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">loc</span></code>, which is a tuple identifying the location in the grid we’re about to fill.</p></li>
+</ul>
+<p>In this example, <code class="docutils literal notranslate"><span class="pre">shape</span></code> is <code class="docutils literal notranslate"><span class="pre">(2,</span> <span class="pre">2)</span></code> to create two rows and two columns.</p>
+<p>For the first panel, <code class="docutils literal notranslate"><span class="pre">loc</span></code> is <code class="docutils literal notranslate"><span class="pre">(0,</span> <span class="pre">0)</span></code>, which indicates row 0 and column 0, which is the upper-left panel.</p>
+<p>Here’s how we use it to draw the four panels.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot2grid</span><span class="p">(</span><span class="n">shape</span><span class="p">,</span> <span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>
+<span class="n">plot_first_selection</span><span class="p">(</span><span class="n">candidate_df</span><span class="p">)</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot2grid</span><span class="p">(</span><span class="n">shape</span><span class="p">,</span> <span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
+<span class="n">plot_proper_motion</span><span class="p">(</span><span class="n">centerline</span><span class="p">)</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot2grid</span><span class="p">(</span><span class="n">shape</span><span class="p">,</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>
+<span class="n">plot_second_selection</span><span class="p">(</span><span class="n">selected</span><span class="p">)</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot2grid</span><span class="p">(</span><span class="n">shape</span><span class="p">,</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
+<span class="n">plot_cmd</span><span class="p">(</span><span class="n">merged</span><span class="p">)</span>
+<span class="n">poly</span> <span class="o">=</span> <span class="n">Polygon</span><span class="p">(</span><span class="n">coords</span><span class="p">,</span> <span class="n">closed</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> 
+               <span class="n">facecolor</span><span class="o">=</span><span class="s1">&#39;C1&#39;</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.4</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">gca</span><span class="p">()</span><span class="o">.</span><span class="n">add_patch</span><span class="p">(</span><span class="n">poly</span><span class="p">)</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<img alt="_images/07_plot_69_0.png" src="_images/07_plot_69_0.png" />
+</div>
+</div>
+<p>We use <a class="reference external" href="https://matplotlib.org/3.3.1/tutorials/intermediate/tight_layout_guide.html"><code class="docutils literal notranslate"><span class="pre">plt.tight_layout</span></code></a> at the end, which adjusts the sizes of the panels to make sure the titles and axis labels don’t overlap.</p>
+<p><strong>Exercise:</strong> See what happens if you leave out <code class="docutils literal notranslate"><span class="pre">tight_layout</span></code>.</p>
+</div>
+<div class="section" id="adjusting-proportions">
+<h2>Adjusting proportions<a class="headerlink" href="#adjusting-proportions" title="Permalink to this headline">¶</a></h2>
+<p>In the previous figure, the panels are all the same size.  To get a better view of GD-1, we’d like to stretch the panels on the left and compress the ones on the right.</p>
+<p>To do that, we’ll use the <code class="docutils literal notranslate"><span class="pre">colspan</span></code> argument to make a panel that spans multiple columns in the grid.</p>
+<p>In the following example, <code class="docutils literal notranslate"><span class="pre">shape</span></code> is <code class="docutils literal notranslate"><span class="pre">(2,</span> <span class="pre">4)</span></code>, which means 2 rows and 4 columns.</p>
+<p>The panels on the left span three columns, so they are three times wider than the panels on the right.</p>
+<p>At the same time, we use <code class="docutils literal notranslate"><span class="pre">figsize</span></code> to adjust the aspect ratio of the whole figure.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">9</span><span class="p">,</span> <span class="mf">4.5</span><span class="p">))</span>
+
+<span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot2grid</span><span class="p">(</span><span class="n">shape</span><span class="p">,</span> <span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span> <span class="n">colspan</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
+<span class="n">plot_first_selection</span><span class="p">(</span><span class="n">candidate_df</span><span class="p">)</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot2grid</span><span class="p">(</span><span class="n">shape</span><span class="p">,</span> <span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
+<span class="n">plot_proper_motion</span><span class="p">(</span><span class="n">centerline</span><span class="p">)</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot2grid</span><span class="p">(</span><span class="n">shape</span><span class="p">,</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span> <span class="n">colspan</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
+<span class="n">plot_second_selection</span><span class="p">(</span><span class="n">selected</span><span class="p">)</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot2grid</span><span class="p">(</span><span class="n">shape</span><span class="p">,</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
+<span class="n">plot_cmd</span><span class="p">(</span><span class="n">merged</span><span class="p">)</span>
+<span class="n">poly</span> <span class="o">=</span> <span class="n">Polygon</span><span class="p">(</span><span class="n">coords</span><span class="p">,</span> <span class="n">closed</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> 
+               <span class="n">facecolor</span><span class="o">=</span><span class="s1">&#39;C1&#39;</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.4</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">gca</span><span class="p">()</span><span class="o">.</span><span class="n">add_patch</span><span class="p">(</span><span class="n">poly</span><span class="p">)</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<img alt="_images/07_plot_72_0.png" src="_images/07_plot_72_0.png" />
+</div>
+</div>
+<p>This is looking more and more like the figure in the paper.</p>
+<p><strong>Exercise:</strong> In this example, the ratio of the widths of the panels is 3:1.  How would you adjust it if you wanted the ratio to be 3:2?</p>
+</div>
+<div class="section" id="summary">
+<h2>Summary<a class="headerlink" href="#summary" title="Permalink to this headline">¶</a></h2>
+<p>In this notebook, we reverse-engineered the figure we’ve been replicating, identifying elements that seem effective and others that could be improved.</p>
+<p>We explored features Matplotlib provides for adding annotations to figures – including text, lines, arrows, and polygons – and several ways to customize the appearance of figures.  And we learned how to create figures that contain multiple panels.</p>
+</div>
+<div class="section" id="best-practices">
+<h2>Best practices<a class="headerlink" href="#best-practices" title="Permalink to this headline">¶</a></h2>
+<ul class="simple">
+<li><p>The most effective figures focus on telling a single story clearly and compellingly.</p></li>
+<li><p>Consider using annotations to guide the readers attention to the most important elements of a figure.</p></li>
+<li><p>The default Matplotlib style generates good quality figures, but there are several ways you can override the defaults.</p></li>
+<li><p>If you find yourself making the same customizations on several projects, you might want to create your own style sheet.</p></li>
+</ul>
+</div>
+</div>
+
+    <script type="text/x-thebe-config">
+    {
+        requestKernel: true,
+        binderOptions: {
+            repo: "binder-examples/jupyter-stacks-datascience",
+            ref: "master",
+        },
+        codeMirrorConfig: {
+            theme: "abcdef",
+            mode: "python"
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+            kernelName: "python3",
+            path: "./."
+        },
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+    }
+    </script>
+    <script>kernelName = 'python3'</script>
+
+              </div>
+              
+        </div>
+    </div>
+    
+    
+    <div class='prev-next-bottom'>
+        
+    <a class='left-prev' id="prev-link" href="06_photo.html" title="previous page">Chapter 6</a>
+
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+      <p>
+        
+          By Allen B. Downey<br/>
+        
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new file mode 100644
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+
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+
+<html>
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+    <meta charset="utf-8" />
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+   Astronomical Data in Python
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+<ul class="nav sidenav_l1">
+ <li class="toctree-l1">
+  <a class="reference internal" href="../01_query.html">
+   Lesson 1
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../02_coords.html">
+   Lesson 2
+  </a>
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+        
+        <div class="tocsection onthispage pt-5 pb-3">
+            <i class="fas fa-list"></i> Contents
+        </div>
+        <nav id="bd-toc-nav">
+            <ul class="nav section-nav flex-column">
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#introduction">
+   Introduction
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#prerequisites">
+   Prerequisites
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#data">
+   Data
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#id1">
+   Lesson 1
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#query-language">
+   Query Language
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#installing-libraries">
+   Installing libraries
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#connecting-to-gaia">
+   Connecting to Gaia
+  </a>
+  <ul class="nav section-nav flex-column">
+   <li class="toc-h3 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#optional-detail">
+     Optional detail
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#databases-and-tables">
+   Databases and Tables
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#columns">
+   Columns
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#writing-queries">
+   Writing queries
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#asynchronous-queries">
+   Asynchronous queries
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#operators">
+   Operators
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#cleaning-up">
+   Cleaning up
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#formatting-queries">
+   Formatting queries
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#summary">
+   Summary
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#best-practices">
+   Best practices
+  </a>
+ </li>
+</ul>
+
+        </nav>
+        
+    </div>
+</div>
+    <div id="main-content" class="row">
+        <div class="col-12 col-md-9 pl-md-3 pr-md-0">
+        
+              <div>
+                
+  <div class="section" id="lesson-1">
+<h1>Lesson 1<a class="headerlink" href="#lesson-1" title="Permalink to this headline">¶</a></h1>
+<div class="section" id="introduction">
+<h2>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">¶</a></h2>
+<p>This workshop is an introduction to tools and practices for working with astronomical data.  Topics covered include:</p>
+<ul class="simple">
+<li><p>Writing queries that select and download data from a database.</p></li>
+<li><p>Using data stored in an Astropy <code class="docutils literal notranslate"><span class="pre">Table</span></code> or Pandas <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code>.</p></li>
+<li><p>Working with coordinates and other quantities with units.</p></li>
+<li><p>Storing data in various formats.</p></li>
+<li><p>Performing database join operations that combine data from multiple tables.</p></li>
+<li><p>Visualizing data and preparing publication-quality figures.</p></li>
+</ul>
+<p>As a running example, we will replicate part of the analysis in a recent paper, “<a class="reference external" href="https://arxiv.org/abs/1805.00425">Off the beaten path: Gaia reveals GD-1 stars outside of the main stream</a>� by Adrian M. Price-Whelan and Ana Bonaca.</p>
+<p>As the abstract explains, “Using data from the Gaia second data release combined with Pan-STARRS photometry, we present a sample of highly-probable members of the longest cold stream in the Milky Way, GD-1.�</p>
+<p>GD-1 is a <a class="reference external" href="https://en.wikipedia.org/wiki/List_of_stellar_streams">stellar stream</a>, which is “an association of stars orbiting a galaxy that was once a globular cluster or dwarf galaxy that has now been torn apart and stretched out along its orbit by tidal forces.�</p>
+<p><a class="reference external" href="https://www.sciencemag.org/news/2018/10/streams-stars-reveal-galaxy-s-violent-history-and-perhaps-its-unseen-dark-matter">This article in <em>Science</em> magazine</a> explains some of the background, including the process that led to the paper and an discussion of the scientific implications:</p>
+<ul class="simple">
+<li><p>“The streams are particularly useful for … galactic archaeology — rewinding the cosmic clock to reconstruct the assembly of the Milky Way.�</p></li>
+<li><p>“They also are being used as exquisitely sensitive scales to measure the galaxy’s mass.�</p></li>
+<li><p>“… the streams are well-positioned to reveal the presence of dark matter … because the streams are so fragile, theorists say, collisions with marauding clumps of dark matter could leave telltale scars, potential clues to its nature.�</p></li>
+</ul>
+</div>
+<div class="section" id="prerequisites">
+<h2>Prerequisites<a class="headerlink" href="#prerequisites" title="Permalink to this headline">¶</a></h2>
+<p>This workshop is meant for people who are familiar with basic Python, but not necessarily the libraries we will use, like Astropy or Pandas.  If you are familiar with Python lists and dictionaries, and you know how to write a function that takes parameters and returns a value, you know enough Python for this workshop.</p>
+<p>We assume that you have some familiarity with operating systems, like the ability to use a command-line interface.  But we don’t assume you have any prior experience with databases.</p>
+<p>We assume that you are familiar with astronomy at the undergraduate level, but we will not assume specialized knowledge of the datasets or analysis methods we’ll use.</p>
+</div>
+<div class="section" id="data">
+<h2>Data<a class="headerlink" href="#data" title="Permalink to this headline">¶</a></h2>
+<p>The datasets we will work with are:</p>
+<ul class="simple">
+<li><p><a class="reference external" href="https://en.wikipedia.org/wiki/Gaia_(spacecraft)">Gaia</a>, which is “a space observatory of the European Space Agency (ESA), launched in 2013 … designed for astrometry: measuring the positions, distances and motions of stars with unprecedented precision�, and</p></li>
+<li><p><a class="reference external" href="https://en.wikipedia.org/wiki/Pan-STARRS">Pan-STARRS</a>, The Panoramic Survey Telescope and Rapid Response System, which is a survey designed to monitor the sky for transient objects, producing a catalog with accurate astronometry and photometry of detected sources.</p></li>
+</ul>
+<p>Both of these datasets are very large, which can make them challenging to work with.  It might not be possible, or practical, to download the entire dataset.
+One of the goals of this workshop is to provide tools for working with large datasets.</p>
+</div>
+<div class="section" id="id1">
+<h2>Lesson 1<a class="headerlink" href="#id1" title="Permalink to this headline">¶</a></h2>
+<p>The first lesson demonstrates the steps for selecting and downloading data from the Gaia Database:</p>
+<ol class="simple">
+<li><p>First we’ll make a connection to the Gaia server,</p></li>
+<li><p>We will explore information about the database and the tables it contains,</p></li>
+<li><p>We will write a query and send it to the server, and finally</p></li>
+<li><p>We will download the response from the server.</p></li>
+</ol>
+<p>After completing this lesson, you should be able to</p>
+<ul class="simple">
+<li><p>Compose a basic query in ADQL.</p></li>
+<li><p>Use queries to explore a database and its tables.</p></li>
+<li><p>Use queries to download data.</p></li>
+<li><p>Develop, test, and debug a query incrementally.</p></li>
+</ul>
+</div>
+<div class="section" id="query-language">
+<h2>Query Language<a class="headerlink" href="#query-language" title="Permalink to this headline">¶</a></h2>
+<p>In order to select data from a database, you have to compose a query, which is like a program written in a “query language�.
+The query language we’ll use is ADQL, which stands for “Astronomical Data Query Language�.</p>
+<p>ADQL is a dialect of <a class="reference external" href="https://en.wikipedia.org/wiki/SQL">SQL</a> (Structured Query Language), which is by far the most commonly used query language.  Almost everything you will learn about ADQL also works in SQL.</p>
+<p><a class="reference external" href="http://www.ivoa.net/documents/ADQL/20180112/PR-ADQL-2.1-20180112.html">The reference manual for ADQL is here</a>.
+But you might find it easier to learn from <a class="reference external" href="https://www.gaia.ac.uk/data/gaia-data-release-1/adql-cookbook">this ADQL Cookbook</a>.</p>
+</div>
+<div class="section" id="installing-libraries">
+<h2>Installing libraries<a class="headerlink" href="#installing-libraries" title="Permalink to this headline">¶</a></h2>
+<p>The library we’ll use to get Gaia data is <a class="reference external" href="https://astroquery.readthedocs.io/en/latest/">Astroquery</a>.</p>
+<p>If you are running this notebook on Colab, you can run the following cell to install Astroquery and the other libraries we’ll use.</p>
+<p>If you are running this notebook on your own computer, you might have to install these libraries yourself.</p>
+<p>If you are using this notebook as part of a Carpentries workshop, you should have received setup instructions.</p>
+<p>TODO: Add a link to the instructions.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># If we&#39;re running on Colab, install libraries</span>
+
+<span class="kn">import</span> <span class="nn">sys</span>
+<span class="n">IN_COLAB</span> <span class="o">=</span> <span class="s1">&#39;google.colab&#39;</span> <span class="ow">in</span> <span class="n">sys</span><span class="o">.</span><span class="n">modules</span>
+
+<span class="k">if</span> <span class="n">IN_COLAB</span><span class="p">:</span>
+    <span class="o">!</span>pip install astroquery astro-gala pyia
+</pre></div>
+</div>
+</div>
+</div>
+</div>
+<div class="section" id="connecting-to-gaia">
+<h2>Connecting to Gaia<a class="headerlink" href="#connecting-to-gaia" title="Permalink to this headline">¶</a></h2>
+<p>Astroquery provides <code class="docutils literal notranslate"><span class="pre">Gaia</span></code>, which is an <a class="reference external" href="https://astroquery.readthedocs.io/en/latest/gaia/gaia.html">object that represents a connection to the Gaia database</a>.</p>
+<p>We can connect to the Gaia database like this:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">astroquery.gaia</span> <span class="kn">import</span> <span class="n">Gaia</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Created TAP+ (v1.2.1) - Connection:
+	Host: gea.esac.esa.int
+	Use HTTPS: True
+	Port: 443
+	SSL Port: 443
+Created TAP+ (v1.2.1) - Connection:
+	Host: geadata.esac.esa.int
+	Use HTTPS: True
+	Port: 443
+	SSL Port: 443
+</pre></div>
+</div>
+</div>
+</div>
+<div class="section" id="optional-detail">
+<h3>Optional detail<a class="headerlink" href="#optional-detail" title="Permalink to this headline">¶</a></h3>
+<blockquote>
+<div><p>Running this import statement has the effect of creating a <a class="reference external" href="http://www.ivoa.net/documents/TAP/">TAP+</a> connection; TAP stands for “Table Access Protocol�.  It is a network protocol for sending queries to the database and getting back the results.  We’re not sure why it seems to create two connections.</p>
+</div></blockquote>
+</div>
+</div>
+<div class="section" id="databases-and-tables">
+<h2>Databases and Tables<a class="headerlink" href="#databases-and-tables" title="Permalink to this headline">¶</a></h2>
+<p>What is a database, anyway?  Most generally, it can be any collection of data, but when we are talking about ADQL or SQL:</p>
+<ul class="simple">
+<li><p>A database is a collection of one or more named tables.</p></li>
+<li><p>Each table is a 2-D array with one or more named columns of data.</p></li>
+</ul>
+<p>We can use <code class="docutils literal notranslate"><span class="pre">Gaia.load_tables</span></code> to get the names of the tables in the Gaia database.  With the option <code class="docutils literal notranslate"><span class="pre">only_names=True</span></code>, it loads information about the tables, called the “metadata�, not the data itself.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">tables</span> <span class="o">=</span> <span class="n">Gaia</span><span class="o">.</span><span class="n">load_tables</span><span class="p">(</span><span class="n">only_names</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>INFO: Retrieving tables... [astroquery.utils.tap.core]
+INFO: Parsing tables... [astroquery.utils.tap.core]
+INFO: Done. [astroquery.utils.tap.core]
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">for</span> <span class="n">table</span> <span class="ow">in</span> <span class="p">(</span><span class="n">tables</span><span class="p">):</span>
+    <span class="nb">print</span><span class="p">(</span><span class="n">table</span><span class="o">.</span><span class="n">get_qualified_name</span><span class="p">())</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>external.external.apassdr9
+external.external.gaiadr2_geometric_distance
+external.external.galex_ais
+external.external.ravedr5_com
+external.external.ravedr5_dr5
+external.external.ravedr5_gra
+external.external.ravedr5_on
+external.external.sdssdr13_photoprimary
+external.external.skymapperdr1_master
+external.external.tmass_xsc
+public.public.hipparcos
+public.public.hipparcos_newreduction
+public.public.hubble_sc
+public.public.igsl_source
+public.public.igsl_source_catalog_ids
+public.public.tycho2
+public.public.dual
+tap_config.tap_config.coord_sys
+tap_config.tap_config.properties
+tap_schema.tap_schema.columns
+tap_schema.tap_schema.key_columns
+tap_schema.tap_schema.keys
+tap_schema.tap_schema.schemas
+tap_schema.tap_schema.tables
+gaiadr1.gaiadr1.aux_qso_icrf2_match
+gaiadr1.gaiadr1.ext_phot_zero_point
+gaiadr1.gaiadr1.allwise_best_neighbour
+gaiadr1.gaiadr1.allwise_neighbourhood
+gaiadr1.gaiadr1.gsc23_best_neighbour
+gaiadr1.gaiadr1.gsc23_neighbourhood
+gaiadr1.gaiadr1.ppmxl_best_neighbour
+gaiadr1.gaiadr1.ppmxl_neighbourhood
+gaiadr1.gaiadr1.sdss_dr9_best_neighbour
+gaiadr1.gaiadr1.sdss_dr9_neighbourhood
+gaiadr1.gaiadr1.tmass_best_neighbour
+gaiadr1.gaiadr1.tmass_neighbourhood
+gaiadr1.gaiadr1.ucac4_best_neighbour
+gaiadr1.gaiadr1.ucac4_neighbourhood
+gaiadr1.gaiadr1.urat1_best_neighbour
+gaiadr1.gaiadr1.urat1_neighbourhood
+gaiadr1.gaiadr1.cepheid
+gaiadr1.gaiadr1.phot_variable_time_series_gfov
+gaiadr1.gaiadr1.phot_variable_time_series_gfov_statistical_parameters
+gaiadr1.gaiadr1.rrlyrae
+gaiadr1.gaiadr1.variable_summary
+gaiadr1.gaiadr1.allwise_original_valid
+gaiadr1.gaiadr1.gsc23_original_valid
+gaiadr1.gaiadr1.ppmxl_original_valid
+gaiadr1.gaiadr1.sdssdr9_original_valid
+gaiadr1.gaiadr1.tmass_original_valid
+gaiadr1.gaiadr1.ucac4_original_valid
+gaiadr1.gaiadr1.urat1_original_valid
+gaiadr1.gaiadr1.gaia_source
+gaiadr1.gaiadr1.tgas_source
+gaiadr2.gaiadr2.aux_allwise_agn_gdr2_cross_id
+gaiadr2.gaiadr2.aux_iers_gdr2_cross_id
+gaiadr2.gaiadr2.aux_sso_orbit_residuals
+gaiadr2.gaiadr2.aux_sso_orbits
+gaiadr2.gaiadr2.dr1_neighbourhood
+gaiadr2.gaiadr2.allwise_best_neighbour
+gaiadr2.gaiadr2.allwise_neighbourhood
+gaiadr2.gaiadr2.apassdr9_best_neighbour
+gaiadr2.gaiadr2.apassdr9_neighbourhood
+gaiadr2.gaiadr2.gsc23_best_neighbour
+gaiadr2.gaiadr2.gsc23_neighbourhood
+gaiadr2.gaiadr2.hipparcos2_best_neighbour
+gaiadr2.gaiadr2.hipparcos2_neighbourhood
+gaiadr2.gaiadr2.panstarrs1_best_neighbour
+gaiadr2.gaiadr2.panstarrs1_neighbourhood
+gaiadr2.gaiadr2.ppmxl_best_neighbour
+gaiadr2.gaiadr2.ppmxl_neighbourhood
+gaiadr2.gaiadr2.ravedr5_best_neighbour
+gaiadr2.gaiadr2.ravedr5_neighbourhood
+gaiadr2.gaiadr2.sdssdr9_best_neighbour
+gaiadr2.gaiadr2.sdssdr9_neighbourhood
+gaiadr2.gaiadr2.tmass_best_neighbour
+gaiadr2.gaiadr2.tmass_neighbourhood
+gaiadr2.gaiadr2.tycho2_best_neighbour
+gaiadr2.gaiadr2.tycho2_neighbourhood
+gaiadr2.gaiadr2.urat1_best_neighbour
+gaiadr2.gaiadr2.urat1_neighbourhood
+gaiadr2.gaiadr2.sso_observation
+gaiadr2.gaiadr2.sso_source
+gaiadr2.gaiadr2.vari_cepheid
+gaiadr2.gaiadr2.vari_classifier_class_definition
+gaiadr2.gaiadr2.vari_classifier_definition
+gaiadr2.gaiadr2.vari_classifier_result
+gaiadr2.gaiadr2.vari_long_period_variable
+gaiadr2.gaiadr2.vari_rotation_modulation
+gaiadr2.gaiadr2.vari_rrlyrae
+gaiadr2.gaiadr2.vari_short_timescale
+gaiadr2.gaiadr2.vari_time_series_statistics
+gaiadr2.gaiadr2.panstarrs1_original_valid
+gaiadr2.gaiadr2.gaia_source
+gaiadr2.gaiadr2.ruwe
+</pre></div>
+</div>
+</div>
+</div>
+<p>So that’s a lot of tables.  The ones we’ll use are:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">gaiadr2.gaia_source</span></code>, which contains Gaia data from <a class="reference external" href="https://www.cosmos.esa.int/web/gaia/data-release-2">data release 2</a>,</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">gaiadr2.panstarrs1_original_valid</span></code>, which contains the photometry data we’ll use from PanSTARRS, and</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">gaiadr2.panstarrs1_best_neighbour</span></code>, which we’ll use to cross-match each star observed by Gaia with the same star observed by PanSTARRS.</p></li>
+</ul>
+<p>We can use <code class="docutils literal notranslate"><span class="pre">load_table</span></code> (not <code class="docutils literal notranslate"><span class="pre">load_tables</span></code>) to get the metadata for a single table.  The name of this function is misleading, because it only downloads metadata.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">meta</span> <span class="o">=</span> <span class="n">Gaia</span><span class="o">.</span><span class="n">load_table</span><span class="p">(</span><span class="s1">&#39;gaiadr2.gaia_source&#39;</span><span class="p">)</span>
+<span class="n">meta</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Retrieving table &#39;gaiadr2.gaia_source&#39;
+Parsing table &#39;gaiadr2.gaia_source&#39;...
+Done.
+</pre></div>
+</div>
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;astroquery.utils.tap.model.taptable.TapTableMeta at 0x7f922376e0a0&gt;
+</pre></div>
+</div>
+</div>
+</div>
+<p>Jupyter shows that the result is an object of type <code class="docutils literal notranslate"><span class="pre">TapTableMeta</span></code>, but it does not display the contents.</p>
+<p>To see the metadata, we have to print the object.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">meta</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>TAP Table name: gaiadr2.gaiadr2.gaia_source
+Description: This table has an entry for every Gaia observed source as listed in the
+Main Database accumulating catalogue version from which the catalogue
+release has been generated. It contains the basic source parameters,
+that is only final data (no epoch data) and no spectra (neither final
+nor epoch).
+Num. columns: 96
+</pre></div>
+</div>
+</div>
+</div>
+<p>Notice one gotcha: in the list of table names, this table appears as <code class="docutils literal notranslate"><span class="pre">gaiadr2.gaiadr2.gaia_source</span></code>, but when we load the metadata, we refer to it as <code class="docutils literal notranslate"><span class="pre">gaiadr2.gaia_source</span></code>.</p>
+<p><strong>Exercise:</strong> Go back and try</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">meta</span> <span class="o">=</span> <span class="n">Gaia</span><span class="o">.</span><span class="n">load_table</span><span class="p">(</span><span class="s1">&#39;gaiadr2.gaiadr2.gaia_source&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>What happens?  Is the error message helpful?  If you had not made this error deliberately, would you have been able to figure it out?</p>
+</div>
+<div class="section" id="columns">
+<h2>Columns<a class="headerlink" href="#columns" title="Permalink to this headline">¶</a></h2>
+<p>The following loop prints the names of the columns in the table.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">for</span> <span class="n">column</span> <span class="ow">in</span> <span class="n">meta</span><span class="o">.</span><span class="n">columns</span><span class="p">:</span>
+    <span class="nb">print</span><span class="p">(</span><span class="n">column</span><span class="o">.</span><span class="n">name</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>solution_id
+designation
+source_id
+random_index
+ref_epoch
+ra
+ra_error
+dec
+dec_error
+parallax
+parallax_error
+parallax_over_error
+pmra
+pmra_error
+pmdec
+pmdec_error
+ra_dec_corr
+ra_parallax_corr
+ra_pmra_corr
+ra_pmdec_corr
+dec_parallax_corr
+dec_pmra_corr
+dec_pmdec_corr
+parallax_pmra_corr
+parallax_pmdec_corr
+pmra_pmdec_corr
+astrometric_n_obs_al
+astrometric_n_obs_ac
+astrometric_n_good_obs_al
+astrometric_n_bad_obs_al
+astrometric_gof_al
+astrometric_chi2_al
+astrometric_excess_noise
+astrometric_excess_noise_sig
+astrometric_params_solved
+astrometric_primary_flag
+astrometric_weight_al
+astrometric_pseudo_colour
+astrometric_pseudo_colour_error
+mean_varpi_factor_al
+astrometric_matched_observations
+visibility_periods_used
+astrometric_sigma5d_max
+frame_rotator_object_type
+matched_observations
+duplicated_source
+phot_g_n_obs
+phot_g_mean_flux
+phot_g_mean_flux_error
+phot_g_mean_flux_over_error
+phot_g_mean_mag
+phot_bp_n_obs
+phot_bp_mean_flux
+phot_bp_mean_flux_error
+phot_bp_mean_flux_over_error
+phot_bp_mean_mag
+phot_rp_n_obs
+phot_rp_mean_flux
+phot_rp_mean_flux_error
+phot_rp_mean_flux_over_error
+phot_rp_mean_mag
+phot_bp_rp_excess_factor
+phot_proc_mode
+bp_rp
+bp_g
+g_rp
+radial_velocity
+radial_velocity_error
+rv_nb_transits
+rv_template_teff
+rv_template_logg
+rv_template_fe_h
+phot_variable_flag
+l
+b
+ecl_lon
+ecl_lat
+priam_flags
+teff_val
+teff_percentile_lower
+teff_percentile_upper
+a_g_val
+a_g_percentile_lower
+a_g_percentile_upper
+e_bp_min_rp_val
+e_bp_min_rp_percentile_lower
+e_bp_min_rp_percentile_upper
+flame_flags
+radius_val
+radius_percentile_lower
+radius_percentile_upper
+lum_val
+lum_percentile_lower
+lum_percentile_upper
+datalink_url
+epoch_photometry_url
+</pre></div>
+</div>
+</div>
+</div>
+<p>You can probably guess what many of these columns are by looking at the names, but you should resist the temptation to guess.
+To find out what the columns mean, <a class="reference external" href="https://gea.esac.esa.int/archive/documentation/GDR2/Gaia_archive/chap_datamodel/sec_dm_main_tables/ssec_dm_gaia_source.html">read the documentation</a>.</p>
+<p>If you want to know what can go wrong when you don’t read the documentation, <a class="reference external" href="https://www.vox.com/future-perfect/2019/6/4/18650969/married-women-miserable-fake-paul-dolan-happiness">you might like this article</a>.</p>
+<p><strong>Exercise:</strong> One of the other tables we’ll use is <code class="docutils literal notranslate"><span class="pre">gaiadr2.gaiadr2.panstarrs1_original_valid</span></code>.  Use <code class="docutils literal notranslate"><span class="pre">load_table</span></code> to get the metadata for this table.  How many columns are there and what are their names?</p>
+<p>Hint: Remember the gotcha we mentioned earlier.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Solution</span>
+
+<span class="n">meta2</span> <span class="o">=</span> <span class="n">Gaia</span><span class="o">.</span><span class="n">load_table</span><span class="p">(</span><span class="s1">&#39;gaiadr2.panstarrs1_original_valid&#39;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">meta2</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Retrieving table &#39;gaiadr2.panstarrs1_original_valid&#39;
+Parsing table &#39;gaiadr2.panstarrs1_original_valid&#39;...
+Done.
+TAP Table name: gaiadr2.gaiadr2.panstarrs1_original_valid
+Description: The Panoramic Survey Telescope and Rapid Response System (Pan-STARRS) is
+a system for wide-field astronomical imaging developed and operated by
+the Institute for Astronomy at the University of Hawaii. Pan-STARRS1
+(PS1) is the first part of Pan-STARRS to be completed and is the basis
+for Data Release 1 (DR1). The PS1 survey used a 1.8 meter telescope and
+its 1.4 Gigapixel camera to image the sky in five broadband filters (g,
+r, i, z, y).
+
+The current table contains a filtered subsample of the 10 723 304 629
+entries listed in the original ObjectThin table.
+We used only ObjectThin and MeanObject tables to extract
+panstarrs1OriginalValid table, this means that objects detected only in
+stack images are not included here. The main reason for us to avoid the
+use of objects detected in stack images is that their astrometry is not
+as good as the mean objects astrometry: “The stack positions (raStack,
+decStack) have considerably larger systematic astrometric errors than
+the mean epoch positions (raMean, decMean).� The astrometry for the
+MeanObject positions uses Gaia DR1 as a reference catalog, while the
+stack positions use 2MASS as a reference catalog.
+
+In details, we filtered out all objects where:
+
+-   nDetections = 1
+
+-   no good quality data in Pan-STARRS, objInfoFlag 33554432 not set
+
+-   mean astrometry could not be measured, objInfoFlag 524288 set
+
+-   stack position used for mean astrometry, objInfoFlag 1048576 set
+
+-   error on all magnitudes equal to 0 or to -999;
+
+-   all magnitudes set to -999;
+
+-   error on RA or DEC greater than 1 arcsec.
+
+The number of objects in panstarrs1OriginalValid is 2 264 263 282.
+
+The panstarrs1OriginalValid table contains only a subset of the columns
+available in the combined ObjectThin and MeanObject tables. A
+description of the original ObjectThin and MeanObjects tables can be
+found at:
+https://outerspace.stsci.edu/display/PANSTARRS/PS1+Database+object+and+detection+tables
+
+Download:
+http://mastweb.stsci.edu/ps1casjobs/home.aspx
+Documentation:
+https://outerspace.stsci.edu/display/PANSTARRS
+http://pswww.ifa.hawaii.edu/pswww/
+References:
+The Pan-STARRS1 Surveys, Chambers, K.C., et al. 2016, arXiv:1612.05560
+Pan-STARRS Data Processing System, Magnier, E. A., et al. 2016,
+arXiv:1612.05240
+Pan-STARRS Pixel Processing: Detrending, Warping, Stacking, Waters, C.
+Z., et al. 2016, arXiv:1612.05245
+Pan-STARRS Pixel Analysis: Source Detection and Characterization,
+Magnier, E. A., et al. 2016, arXiv:1612.05244
+Pan-STARRS Photometric and Astrometric Calibration, Magnier, E. A., et
+al. 2016, arXiv:1612.05242
+The Pan-STARRS1 Database and Data Products, Flewelling, H. A., et al.
+2016, arXiv:1612.05243
+
+Catalogue curator:
+SSDC - ASI Space Science Data Center
+https://www.ssdc.asi.it/
+Num. columns: 26
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Solution</span>
+
+<span class="k">for</span> <span class="n">column</span> <span class="ow">in</span> <span class="n">meta2</span><span class="o">.</span><span class="n">columns</span><span class="p">:</span>
+    <span class="nb">print</span><span class="p">(</span><span class="n">column</span><span class="o">.</span><span class="n">name</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>obj_name
+obj_id
+ra
+dec
+ra_error
+dec_error
+epoch_mean
+g_mean_psf_mag
+g_mean_psf_mag_error
+g_flags
+r_mean_psf_mag
+r_mean_psf_mag_error
+r_flags
+i_mean_psf_mag
+i_mean_psf_mag_error
+i_flags
+z_mean_psf_mag
+z_mean_psf_mag_error
+z_flags
+y_mean_psf_mag
+y_mean_psf_mag_error
+y_flags
+n_detections
+zone_id
+obj_info_flag
+quality_flag
+</pre></div>
+</div>
+</div>
+</div>
+</div>
+<div class="section" id="writing-queries">
+<h2>Writing queries<a class="headerlink" href="#writing-queries" title="Permalink to this headline">¶</a></h2>
+<p>By now you might be wondering how we actually download the data.  With tables this big, you generally don’t.  Instead, you use queries to select only the data you want.</p>
+<p>A query is a string written in a query language like SQL; for the Gaia database, the query language is a dialect of SQL called ADQL.</p>
+<p>Here’s an example of an ADQL query.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">query1</span> <span class="o">=</span> <span class="s2">&quot;&quot;&quot;SELECT </span>
+<span class="s2">TOP 10</span>
+<span class="s2">source_id, ref_epoch, ra, dec, parallax </span>
+<span class="s2">FROM gaiadr2.gaia_source&quot;&quot;&quot;</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p><strong>Python note:</strong> We use a <a class="reference external" href="https://docs.python.org/3/tutorial/introduction.html#strings">triple-quoted string</a> here so we can include line breaks in the query, which makes it easier to read.</p>
+<p>The words in uppercase are ADQL keywords:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">SELECT</span></code> indicates that we are selecting data (as opposed to adding or modifying data).</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">TOP</span></code> indicates that we only want the first 10 rows of the table, which is useful for testing a query before asking for all of the data.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">FROM</span></code> specifies which table we want data from.</p></li>
+</ul>
+<p>The third line is a list of column names, indicating which columns we want.</p>
+<p>In this example, the keywords are capitalized and the column names are lowercase.  This is a common style, but it is not required.  ADQL and SQL are not case-sensitive.</p>
+<p>To run this query, we use the <code class="docutils literal notranslate"><span class="pre">Gaia</span></code> object, which represents our connection to the Gaia database, and invoke <code class="docutils literal notranslate"><span class="pre">launch_job</span></code>:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">job1</span> <span class="o">=</span> <span class="n">Gaia</span><span class="o">.</span><span class="n">launch_job</span><span class="p">(</span><span class="n">query1</span><span class="p">)</span>
+<span class="n">job1</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;astroquery.utils.tap.model.job.Job at 0x7f9222e9cb20&gt;
+</pre></div>
+</div>
+</div>
+</div>
+<p>The result is an object that represents the job running on a Gaia server.</p>
+<p>If you print it, it displays metadata for the forthcoming table.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">job1</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;Table length=10&gt;
+   name    dtype  unit                            description                            
+--------- ------- ---- ------------------------------------------------------------------
+source_id   int64      Unique source identifier (unique within a particular Data Release)
+ref_epoch float64   yr                                                    Reference epoch
+       ra float64  deg                                                    Right ascension
+      dec float64  deg                                                        Declination
+ parallax float64  mas                                                           Parallax
+Jobid: None
+Phase: COMPLETED
+Owner: None
+Output file: sync_20201005090721.xml.gz
+Results: None
+</pre></div>
+</div>
+</div>
+</div>
+<p>Don’t worry about <code class="docutils literal notranslate"><span class="pre">Results:</span> <span class="pre">None</span></code>.  That does not actually mean there are no results.</p>
+<p>However, <code class="docutils literal notranslate"><span class="pre">Phase:</span> <span class="pre">COMPLETED</span></code> indicates that the job is complete, so we can get the results like this:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">results1</span> <span class="o">=</span> <span class="n">job1</span><span class="o">.</span><span class="n">get_results</span><span class="p">()</span>
+<span class="nb">type</span><span class="p">(</span><span class="n">results1</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>astropy.table.table.Table
+</pre></div>
+</div>
+</div>
+</div>
+<p><strong>Optional detail:</strong>  Why is <code class="docutils literal notranslate"><span class="pre">table</span></code> repeated three times?  The first is the name of the module, the second is the name of the submodule, and the third is the name of the class.  Most of the time we only care about the last one.  It’s like the Linnean name for gorilla, which is <em>Gorilla Gorilla Gorilla</em>.</p>
+<p>The result is an <a class="reference external" href="https://docs.astropy.org/en/stable/table/">Astropy Table</a>, which is similar to a table in an SQL database except:</p>
+<ul class="simple">
+<li><p>SQL databases are stored on disk drives, so they are persistent; that is, they “survive� even if you turn off the computer.  An Astropy <code class="docutils literal notranslate"><span class="pre">Table</span></code> is stored in memory; it disappears when you turn off the computer (or shut down this Jupyter notebook).</p></li>
+<li><p>SQL databases are designed to process queries.  An Astropy <code class="docutils literal notranslate"><span class="pre">Table</span></code> can perform some query-like operations, like selecting columns and rows.  But these operations use Python syntax, not SQL.</p></li>
+</ul>
+<p>Jupyter knows how to display the contents of a <code class="docutils literal notranslate"><span class="pre">Table</span></code>.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">results1</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><i>Table length=10</i>
+<table id="table140265627585264" class="table-striped table-bordered table-condensed">
+<thead><tr><th>source_id</th><th>ref_epoch</th><th>ra</th><th>dec</th><th>parallax</th></tr></thead>
+<thead><tr><th></th><th>yr</th><th>deg</th><th>deg</th><th>mas</th></tr></thead>
+<thead><tr><th>int64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th></tr></thead>
+<tr><td>4530738361793769600</td><td>2015.5</td><td>281.56725362448725</td><td>20.40682117430378</td><td>0.9785380604519425</td></tr>
+<tr><td>4530752651135081216</td><td>2015.5</td><td>281.0861565355257</td><td>20.523350496351846</td><td>0.2674800612552977</td></tr>
+<tr><td>4530743343951405568</td><td>2015.5</td><td>281.37114418299177</td><td>20.474147574053124</td><td>-0.43911323550176806</td></tr>
+<tr><td>4530755060627162368</td><td>2015.5</td><td>281.2676236268299</td><td>20.558523922346158</td><td>1.1422630184554958</td></tr>
+<tr><td>4530746844341315968</td><td>2015.5</td><td>281.1370431749541</td><td>20.377852388898184</td><td>1.0092247424630945</td></tr>
+<tr><td>4530768456615026432</td><td>2015.5</td><td>281.8720921436347</td><td>20.31829694530366</td><td>-0.06900136127674149</td></tr>
+<tr><td>4530763513119137280</td><td>2015.5</td><td>281.9211808864116</td><td>20.20956829578524</td><td>0.1266016679823622</td></tr>
+<tr><td>4530736364618539264</td><td>2015.5</td><td>281.4913475613274</td><td>20.346579041327693</td><td>0.3894019486060072</td></tr>
+<tr><td>4530735952305177728</td><td>2015.5</td><td>281.4085549165704</td><td>20.311030903719928</td><td>0.2041189982608354</td></tr>
+<tr><td>4530751281056022656</td><td>2015.5</td><td>281.0585328377638</td><td>20.460309556214753</td><td>0.10294642821734962</td></tr>
+</table></div></div>
+</div>
+<p>Each column has a name, units, and a data type.</p>
+<p>For example, the units of <code class="docutils literal notranslate"><span class="pre">ra</span></code> and <code class="docutils literal notranslate"><span class="pre">dec</span></code> are degrees, and their data type is <code class="docutils literal notranslate"><span class="pre">float64</span></code>, which is a 64-bit floating-point number, used to store measurements with a fraction part.</p>
+<p>This information comes from the Gaia database, and has been stored in the Astropy <code class="docutils literal notranslate"><span class="pre">Table</span></code> by Astroquery.</p>
+<p><strong>Exercise:</strong> Read <a class="reference external" href="https://gea.esac.esa.int/archive/documentation/GDR2/Gaia_archive/chap_datamodel/sec_dm_main_tables/ssec_dm_gaia_source.html">the documentation of this table</a> and choose a column that looks interesting to you.  Add the column name to the query and run it again.  What are the units of the column you selected?  What is its data type?</p>
+</div>
+<div class="section" id="asynchronous-queries">
+<h2>Asynchronous queries<a class="headerlink" href="#asynchronous-queries" title="Permalink to this headline">¶</a></h2>
+<p><code class="docutils literal notranslate"><span class="pre">launch_job</span></code> asks the server to run the job “synchronously�, which normally means it runs immediately.  But synchronous jobs are limited to 2000 rows.  For queries that return more rows, you should run “asynchronously�, which mean they might take longer to get started.</p>
+<p>If you are not sure how many rows a query will return, you can use the SQL command <code class="docutils literal notranslate"><span class="pre">COUNT</span></code> to find out how many rows are in the result without actually returning them.  We’ll see an example of this later.</p>
+<p>The results of an asynchronous query are stored in a file on the server, so you can start a query and come back later to get the results.</p>
+<p>For anonymous users, files are kept for three days.</p>
+<p>As an example, let’s try a query that’s similar to <code class="docutils literal notranslate"><span class="pre">query1</span></code>, with two changes:</p>
+<ul class="simple">
+<li><p>It selects the first 3000 rows, so it is bigger than we should run synchronously.</p></li>
+<li><p>It uses a new keyword, <code class="docutils literal notranslate"><span class="pre">WHERE</span></code>.</p></li>
+</ul>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">query2</span> <span class="o">=</span> <span class="s2">&quot;&quot;&quot;SELECT TOP 3000</span>
+<span class="s2">source_id, ref_epoch, ra, dec, parallax</span>
+<span class="s2">FROM gaiadr2.gaia_source</span>
+<span class="s2">WHERE parallax &lt; 1</span>
+<span class="s2">&quot;&quot;&quot;</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>A <code class="docutils literal notranslate"><span class="pre">WHERE</span></code> clause indicates which rows we want; in this case, the query selects only rows “where� <code class="docutils literal notranslate"><span class="pre">parallax</span></code> is less than 1.  This has the effect of selecting stars with relatively low parallax, which are farther away.  We’ll use this clause to exclude nearby stars that are unlikely to be part of GD-1.</p>
+<p><code class="docutils literal notranslate"><span class="pre">WHERE</span></code> is one of the most common clauses in ADQL/SQL, and one of the most useful, because it allows us to select only the rows we need from the database.</p>
+<p>We use <code class="docutils literal notranslate"><span class="pre">launch_job_async</span></code> to submit an asynchronous query.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">job2</span> <span class="o">=</span> <span class="n">Gaia</span><span class="o">.</span><span class="n">launch_job_async</span><span class="p">(</span><span class="n">query2</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">job2</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>INFO: Query finished. [astroquery.utils.tap.core]
+&lt;Table length=3000&gt;
+   name    dtype  unit                            description                            
+--------- ------- ---- ------------------------------------------------------------------
+source_id   int64      Unique source identifier (unique within a particular Data Release)
+ref_epoch float64   yr                                                    Reference epoch
+       ra float64  deg                                                    Right ascension
+      dec float64  deg                                                        Declination
+ parallax float64  mas                                                           Parallax
+Jobid: 1601903242219O
+Phase: COMPLETED
+Owner: None
+Output file: async_20201005090722.vot
+Results: None
+</pre></div>
+</div>
+</div>
+</div>
+<p>And here are the results.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">results2</span> <span class="o">=</span> <span class="n">job2</span><span class="o">.</span><span class="n">get_results</span><span class="p">()</span>
+<span class="n">results2</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><i>Table length=3000</i>
+<table id="table140265625141056" class="table-striped table-bordered table-condensed">
+<thead><tr><th>source_id</th><th>ref_epoch</th><th>ra</th><th>dec</th><th>parallax</th></tr></thead>
+<thead><tr><th></th><th>yr</th><th>deg</th><th>deg</th><th>mas</th></tr></thead>
+<thead><tr><th>int64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th></tr></thead>
+<tr><td>4530738361793769600</td><td>2015.5</td><td>281.56725362448725</td><td>20.40682117430378</td><td>0.9785380604519425</td></tr>
+<tr><td>4530752651135081216</td><td>2015.5</td><td>281.0861565355257</td><td>20.523350496351846</td><td>0.2674800612552977</td></tr>
+<tr><td>4530743343951405568</td><td>2015.5</td><td>281.37114418299177</td><td>20.474147574053124</td><td>-0.43911323550176806</td></tr>
+<tr><td>4530768456615026432</td><td>2015.5</td><td>281.8720921436347</td><td>20.31829694530366</td><td>-0.06900136127674149</td></tr>
+<tr><td>4530763513119137280</td><td>2015.5</td><td>281.9211808864116</td><td>20.20956829578524</td><td>0.1266016679823622</td></tr>
+<tr><td>4530736364618539264</td><td>2015.5</td><td>281.4913475613274</td><td>20.346579041327693</td><td>0.3894019486060072</td></tr>
+<tr><td>4530735952305177728</td><td>2015.5</td><td>281.4085549165704</td><td>20.311030903719928</td><td>0.2041189982608354</td></tr>
+<tr><td>4530751281056022656</td><td>2015.5</td><td>281.0585328377638</td><td>20.460309556214753</td><td>0.10294642821734962</td></tr>
+<tr><td>4530740938774409344</td><td>2015.5</td><td>281.3762569536416</td><td>20.436140058941206</td><td>0.9242670062090182</td></tr>
+<tr><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr>
+<tr><td>4467710915011802624</td><td>2015.5</td><td>269.9680969307347</td><td>1.1429085038160882</td><td>0.42361471245557913</td></tr>
+<tr><td>4467706551328679552</td><td>2015.5</td><td>270.033164589881</td><td>1.0565747323689927</td><td>0.922888231734588</td></tr>
+<tr><td>4467712255037300096</td><td>2015.5</td><td>270.7724717923047</td><td>0.6581664892880896</td><td>-2.669179465293931</td></tr>
+<tr><td>4467735001181761792</td><td>2015.5</td><td>270.3628606248308</td><td>0.8947079323599124</td><td>0.6117399163086398</td></tr>
+<tr><td>4467737101421916672</td><td>2015.5</td><td>270.5110834661444</td><td>0.9806225910160181</td><td>-0.39818224846127004</td></tr>
+<tr><td>4467707547757327488</td><td>2015.5</td><td>269.88746280594927</td><td>1.0212759940136962</td><td>0.7741412301054209</td></tr>
+<tr><td>4467732772094573056</td><td>2015.5</td><td>270.55997182760126</td><td>0.9037072088489417</td><td>-1.7920417800164183</td></tr>
+<tr><td>4467732355491087744</td><td>2015.5</td><td>270.6730790702491</td><td>0.9197224705139885</td><td>-0.3464446494840354</td></tr>
+<tr><td>4467717099766944512</td><td>2015.5</td><td>270.57667173120825</td><td>0.726277659009568</td><td>0.05443955111134051</td></tr>
+<tr><td>4467719058265781248</td><td>2015.5</td><td>270.7248052971514</td><td>0.8205551921782785</td><td>0.3733943917490343</td></tr>
+</table></div></div>
+</div>
+<p>You might notice that some values of <code class="docutils literal notranslate"><span class="pre">parallax</span></code> are negative.  As <a class="reference external" href="https://www.cosmos.esa.int/web/gaia/archive-tips#negative%20parallax">this FAQ explains</a>, “Negative parallaxes are caused by errors in the observations.�  Negative parallaxes have “no physical meaning,� but they can be a “useful diagnostic on the quality of the astrometric solution.�</p>
+<p>Later we will see an example where we use <code class="docutils literal notranslate"><span class="pre">parallax</span></code> and <code class="docutils literal notranslate"><span class="pre">parallax_error</span></code> to identify stars where the distance estimate is likely to be inaccurate.</p>
+<p><strong>Exercise:</strong> The clauses in a query have to be in the right order.  Go back and change the order of the clauses in <code class="docutils literal notranslate"><span class="pre">query2</span></code> and run it again.</p>
+<p>The query should fail, but notice that you don’t get much useful debugging information.</p>
+<p>For this reason, developing and debugging ADQL queries can be really hard.  A few suggestions that might help:</p>
+<ul class="simple">
+<li><p>Whenever possible, start with a working query, either an example you find online or a query you have used in the past.</p></li>
+<li><p>Make small changes and test each change before you continue.</p></li>
+<li><p>While you are debugging, use <code class="docutils literal notranslate"><span class="pre">TOP</span></code> to limit the number of rows in the result.  That will make each attempt run faster, which reduces your testing time.</p></li>
+<li><p>Launching test queries synchronously might make them start faster, too.</p></li>
+</ul>
+</div>
+<div class="section" id="operators">
+<h2>Operators<a class="headerlink" href="#operators" title="Permalink to this headline">¶</a></h2>
+<p>In a <code class="docutils literal notranslate"><span class="pre">WHERE</span></code> clause, you can use any of the <a class="reference external" href="https://www.w3schools.com/sql/sql_operators.asp">SQL comparison operators</a>; here are the most common ones:</p>
+<table class="colwidths-auto table">
+<thead>
+<tr class="row-odd"><th class="head"><p>Symbol</p></th>
+<th class="text-align:left head"><p>Operation</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">&gt;</span></code></p></td>
+<td class="text-align:left"><p>greater than</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">&lt;</span></code></p></td>
+<td class="text-align:left"><p>less than</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">&gt;=</span></code></p></td>
+<td class="text-align:left"><p>greater than or equal</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">&lt;=</span></code></p></td>
+<td class="text-align:left"><p>less than or equal</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">=</span></code></p></td>
+<td class="text-align:left"><p>equal</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">!=</span></code> or <code class="docutils literal notranslate"><span class="pre">&lt;&gt;</span></code></p></td>
+<td class="text-align:left"><p>not equal</p></td>
+</tr>
+</tbody>
+</table>
+<p>Most of these are the same as Python, but some are not.  In particular, notice that the equality operator is <code class="docutils literal notranslate"><span class="pre">=</span></code>, not <code class="docutils literal notranslate"><span class="pre">==</span></code>.
+Be careful to keep your Python out of your ADQL!</p>
+<p>You can combine comparisons using the logical operators:</p>
+<ul class="simple">
+<li><p>AND: true if both comparisons are true</p></li>
+<li><p>OR: true if either or both comparisons are true</p></li>
+</ul>
+<p>Finally, you can use <code class="docutils literal notranslate"><span class="pre">NOT</span></code> to invert the result of a comparison.</p>
+<p><strong>Exercise:</strong> <a class="reference external" href="https://www.w3schools.com/sql/sql_operators.asp">Read about SQL operators here</a> and then modify the previous query to select rows where <code class="docutils literal notranslate"><span class="pre">bp_rp</span></code> is between <code class="docutils literal notranslate"><span class="pre">-0.75</span></code> and <code class="docutils literal notranslate"><span class="pre">2</span></code>.</p>
+<p>You can <a class="reference external" href="https://gea.esac.esa.int/archive/documentation/GDR2/Gaia_archive/chap_datamodel/sec_dm_main_tables/ssec_dm_gaia_source.html">read about this variable here</a>.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Solution</span>
+
+<span class="c1"># This is what most people will probably do</span>
+
+<span class="n">query</span> <span class="o">=</span> <span class="s2">&quot;&quot;&quot;SELECT TOP 10</span>
+<span class="s2">source_id, ref_epoch, ra, dec, parallax</span>
+<span class="s2">FROM gaiadr2.gaia_source</span>
+<span class="s2">WHERE parallax &lt; 1 </span>
+<span class="s2">  AND bp_rp &gt; -0.75 AND bp_rp &lt; 2</span>
+<span class="s2">&quot;&quot;&quot;</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Solution</span>
+
+<span class="c1"># But if someone notices the BETWEEN operator, </span>
+<span class="c1"># they might do this</span>
+
+<span class="n">query</span> <span class="o">=</span> <span class="s2">&quot;&quot;&quot;SELECT TOP 10</span>
+<span class="s2">source_id, ref_epoch, ra, dec, parallax</span>
+<span class="s2">FROM gaiadr2.gaia_source</span>
+<span class="s2">WHERE parallax &lt; 1 </span>
+<span class="s2">  AND bp_rp BETWEEN -0.75 AND 2</span>
+<span class="s2">&quot;&quot;&quot;</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>This <a class="reference external" href="https://sci.esa.int/web/gaia/-/60198-gaia-hertzsprung-russell-diagram">Hertzsprung-Russell diagram</a> shows the BP-RP color and luminosity of stars in the Gaia catalog.</p>
+<p>Selecting stars with <code class="docutils literal notranslate"><span class="pre">bp-rp</span></code> less than 2 excludes many <a class="reference external" href="https://xkcd.com/2360/">class M dwarf stars</a>, which are low temperature, low luminosity.  A star like that at GD-1’s distance would be hard to detect, so if it is detected, it it more likely to be in the foreground.</p>
+</div>
+<div class="section" id="cleaning-up">
+<h2>Cleaning up<a class="headerlink" href="#cleaning-up" title="Permalink to this headline">¶</a></h2>
+<p>Asynchronous jobs have a <code class="docutils literal notranslate"><span class="pre">jobid</span></code>.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">job1</span><span class="o">.</span><span class="n">jobid</span><span class="p">,</span> <span class="n">job2</span><span class="o">.</span><span class="n">jobid</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>(None, &#39;1601903242219O&#39;)
+</pre></div>
+</div>
+</div>
+</div>
+<p>Which you can use to remove the job from the server.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">Gaia</span><span class="o">.</span><span class="n">remove_jobs</span><span class="p">([</span><span class="n">job2</span><span class="o">.</span><span class="n">jobid</span><span class="p">])</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Removed jobs: &#39;[&#39;1601903242219O&#39;]&#39;.
+</pre></div>
+</div>
+</div>
+</div>
+<p>If you don’t remove it job from the server, it will be removed eventually, so don’t feel too bad if you don’t clean up after yourself.</p>
+</div>
+<div class="section" id="formatting-queries">
+<h2>Formatting queries<a class="headerlink" href="#formatting-queries" title="Permalink to this headline">¶</a></h2>
+<p>So far the queries have been string “literals�, meaning that the entire string is part of the program.
+But writing queries yourself can be slow, repetitive, and error-prone.</p>
+<p>It is often a good idea to write Python code that assembles a query for you.  One useful tool for that is the <a class="reference external" href="https://www.w3schools.com/python/ref_string_format.asp">string <code class="docutils literal notranslate"><span class="pre">format</span></code> method</a>.</p>
+<p>As an example, we’ll divide the previous query into two parts; a list of column names and a “base� for the query that contains everything except the column names.</p>
+<p>Here’s the list of columns we’ll select.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">columns</span> <span class="o">=</span> <span class="s1">&#39;source_id, ra, dec, pmra, pmdec, parallax, parallax_error, radial_velocity&#39;</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>And here’s the base; it’s a string that contains at least one format specifier in curly brackets (braces).</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">query3_base</span> <span class="o">=</span> <span class="s2">&quot;&quot;&quot;SELECT TOP 10 </span>
+<span class="si">{columns}</span><span class="s2"></span>
+<span class="s2">FROM gaiadr2.gaia_source</span>
+<span class="s2">WHERE parallax &lt; 1</span>
+<span class="s2">  AND bp_rp BETWEEN -0.75 AND 2</span>
+<span class="s2">&quot;&quot;&quot;</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>This base query contains one format specifier, <code class="docutils literal notranslate"><span class="pre">{columns}</span></code>, which is a placeholder for the list of column names we will provide.</p>
+<p>To assemble the query, we invoke <code class="docutils literal notranslate"><span class="pre">format</span></code> on the base string and provide a keyword argument that assigns a value to <code class="docutils literal notranslate"><span class="pre">columns</span></code>.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">query3</span> <span class="o">=</span> <span class="n">query3_base</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="n">columns</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>The result is a string with line breaks.  If you display it, the line breaks appear as <code class="docutils literal notranslate"><span class="pre">\n</span></code>.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">query3</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&#39;SELECT TOP 10 \nsource_id, ra, dec, pmra, pmdec, parallax, parallax_error, radial_velocity\nFROM gaiadr2.gaia_source\nWHERE parallax &lt; 1\n  AND bp_rp BETWEEN -0.75 AND 2\n&#39;
+</pre></div>
+</div>
+</div>
+</div>
+<p>But if you print it, the line breaks appear as… line breaks.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">query3</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>SELECT TOP 10 
+source_id, ra, dec, pmra, pmdec, parallax, parallax_error, radial_velocity
+FROM gaiadr2.gaia_source
+WHERE parallax &lt; 1
+  AND bp_rp BETWEEN -0.75 AND 2
+</pre></div>
+</div>
+</div>
+</div>
+<p>Notice that the format specifier has been replaced with the value of <code class="docutils literal notranslate"><span class="pre">columns</span></code>.</p>
+<p>Let’s run it and see if it works:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">job3</span> <span class="o">=</span> <span class="n">Gaia</span><span class="o">.</span><span class="n">launch_job</span><span class="p">(</span><span class="n">query3</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">job3</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;Table length=10&gt;
+      name       dtype    unit                              description                             n_bad
+--------------- ------- -------- ------------------------------------------------------------------ -----
+      source_id   int64          Unique source identifier (unique within a particular Data Release)     0
+             ra float64      deg                                                    Right ascension     0
+            dec float64      deg                                                        Declination     0
+           pmra float64 mas / yr                         Proper motion in right ascension direction     0
+          pmdec float64 mas / yr                             Proper motion in declination direction     0
+       parallax float64      mas                                                           Parallax     0
+ parallax_error float64      mas                                         Standard error of parallax     0
+radial_velocity float64   km / s                                                    Radial velocity    10
+Jobid: None
+Phase: COMPLETED
+Owner: None
+Output file: sync_20201005090726.xml.gz
+Results: None
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">results3</span> <span class="o">=</span> <span class="n">job3</span><span class="o">.</span><span class="n">get_results</span><span class="p">()</span>
+<span class="n">results3</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><i>Table length=10</i>
+<table id="table140265627700432" class="table-striped table-bordered table-condensed">
+<thead><tr><th>source_id</th><th>ra</th><th>dec</th><th>pmra</th><th>pmdec</th><th>parallax</th><th>parallax_error</th><th>radial_velocity</th></tr></thead>
+<thead><tr><th></th><th>deg</th><th>deg</th><th>mas / yr</th><th>mas / yr</th><th>mas</th><th>mas</th><th>km / s</th></tr></thead>
+<thead><tr><th>int64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th></tr></thead>
+<tr><td>4467710915011802624</td><td>269.9680969307347</td><td>1.1429085038160882</td><td>2.0233280236600626</td><td>-2.5692427875510266</td><td>0.42361471245557913</td><td>0.470352406647465</td><td>--</td></tr>
+<tr><td>4467706551328679552</td><td>270.033164589881</td><td>1.0565747323689927</td><td>-3.414829591355289</td><td>-3.8437215857495737</td><td>0.922888231734588</td><td>0.927008559859825</td><td>--</td></tr>
+<tr><td>4467712255037300096</td><td>270.7724717923047</td><td>0.6581664892880896</td><td>-3.5620173752896025</td><td>-6.595792323153987</td><td>-2.669179465293931</td><td>0.9719742773203504</td><td>--</td></tr>
+<tr><td>4467735001181761792</td><td>270.3628606248308</td><td>0.8947079323599124</td><td>2.1307079926489205</td><td>0.8826727710910712</td><td>0.6117399163086398</td><td>0.509812721702093</td><td>--</td></tr>
+<tr><td>4467737101421916672</td><td>270.5110834661444</td><td>0.9806225910160181</td><td>0.17532366511560785</td><td>-5.113270239706202</td><td>-0.39818224846127004</td><td>0.7549581886719651</td><td>--</td></tr>
+<tr><td>4467707547757327488</td><td>269.88746280594927</td><td>1.0212759940136962</td><td>-2.6382230817672987</td><td>-3.707776532049287</td><td>0.7741412301054209</td><td>0.3022057897812064</td><td>--</td></tr>
+<tr><td>4467732355491087744</td><td>270.6730790702491</td><td>0.9197224705139885</td><td>-2.2735991502653037</td><td>-11.864952855984358</td><td>-0.3464446494840354</td><td>0.4937921513912002</td><td>--</td></tr>
+<tr><td>4467717099766944512</td><td>270.57667173120825</td><td>0.726277659009568</td><td>-3.4598362614808367</td><td>-4.601426893365921</td><td>0.05443955111134051</td><td>0.8867339293525688</td><td>--</td></tr>
+<tr><td>4467719058265781248</td><td>270.7248052971514</td><td>0.8205551921782785</td><td>-3.255079498426542</td><td>-9.249285069111085</td><td>0.3733943917490343</td><td>0.390952370410666</td><td>--</td></tr>
+<tr><td>4467722326741572352</td><td>270.87431291888504</td><td>0.8595565975869158</td><td>0.10696398351859826</td><td>1.2035993780158853</td><td>-0.11850943432864373</td><td>0.1660452431882023</td><td>--</td></tr>
+</table></div></div>
+</div>
+<p>Good so far.</p>
+<p><strong>Exercise:</strong> This query always selects sources with <code class="docutils literal notranslate"><span class="pre">parallax</span></code> less than 1.  But suppose you want to take that upper bound as an input.</p>
+<p>Modify <code class="docutils literal notranslate"><span class="pre">query3_base</span></code> to replace <code class="docutils literal notranslate"><span class="pre">1</span></code> with a format specifier like <code class="docutils literal notranslate"><span class="pre">{max_parallax}</span></code>.  Now, when you call <code class="docutils literal notranslate"><span class="pre">format</span></code>, add a keyword argument that assigns a value to <code class="docutils literal notranslate"><span class="pre">max_parallax</span></code>, and confirm that the format specifier gets replaced with the value you provide.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Solution</span>
+
+<span class="n">query4_base</span> <span class="o">=</span> <span class="s2">&quot;&quot;&quot;SELECT TOP 10</span>
+<span class="si">{columns}</span><span class="s2"></span>
+<span class="s2">FROM gaiadr2.gaia_source</span>
+<span class="s2">WHERE parallax &lt; </span><span class="si">{max_parallax}</span><span class="s2"> AND </span>
+<span class="s2">bp_rp BETWEEN -0.75 AND 2</span>
+<span class="s2">&quot;&quot;&quot;</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Solution</span>
+
+<span class="n">query4</span> <span class="o">=</span> <span class="n">query4_base</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="n">columns</span><span class="p">,</span>
+                          <span class="n">max_parallax</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">query</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>SELECT TOP 10
+source_id, ra, dec, pmra, pmdec, parallax, parallax_error, radial_velocity
+FROM gaiadr2.gaia_source
+WHERE parallax &lt; 0.5 AND 
+bp_rp BETWEEN -0.75 AND 2
+</pre></div>
+</div>
+</div>
+</div>
+<p><strong>Style note:</strong>  You might notice that the variable names in this notebook are numbered, like <code class="docutils literal notranslate"><span class="pre">query1</span></code>, <code class="docutils literal notranslate"><span class="pre">query2</span></code>, etc.</p>
+<p>The advantage of this style is that it isolates each section of the notebook from the others, so if you go back and run the cells out of order, it’s less likely that you will get unexpected interactions.</p>
+<p>A drawback of this style is that it can be a nuisance to update the notebook if you add, remove, or reorder a section.</p>
+<p>What do you think of this choice?  Are there alternatives you prefer?</p>
+</div>
+<div class="section" id="summary">
+<h2>Summary<a class="headerlink" href="#summary" title="Permalink to this headline">¶</a></h2>
+<p>This notebook demonstrates the following steps:</p>
+<ol class="simple">
+<li><p>Making a connection to the Gaia server,</p></li>
+<li><p>Exploring information about the database and the tables it contains,</p></li>
+<li><p>Writing a query and sending it to the server, and finally</p></li>
+<li><p>Downloading the response from the server as an Astropy <code class="docutils literal notranslate"><span class="pre">Table</span></code>.</p></li>
+</ol>
+</div>
+<div class="section" id="best-practices">
+<h2>Best practices<a class="headerlink" href="#best-practices" title="Permalink to this headline">¶</a></h2>
+<ul class="simple">
+<li><p>If you can’t download an entire dataset (or it’s not practical) use queries to select the data you need.</p></li>
+<li><p>Read the metadata and the documentation to make sure you understand the tables, their columns, and what they mean.</p></li>
+<li><p>Develop queries incrementally: start with something simple, test it, and add a little bit at a time.</p></li>
+<li><p>Use ADQL features like <code class="docutils literal notranslate"><span class="pre">TOP</span></code> and <code class="docutils literal notranslate"><span class="pre">COUNT</span></code> to test before you run a query that might return a lot of data.</p></li>
+<li><p>If you know your query will return fewer than 3000 rows, you can run it synchronously, which might complete faster (but it doesn’t seem to make much difference).  If it might return more than 3000 rows, you should run it asynchronously.</p></li>
+<li><p>ADQL and SQL are not case-sensitive, so you don’t have to capitalize the keywords, but you should.</p></li>
+<li><p>ADQL and SQL don’t require you to break a query into multiple lines, but you should.</p></li>
+</ul>
+<p>Jupyter notebooks can be good for developing and testing code, but they have some drawbacks.  In particular, if you run the cells out of order, you might find that variables don’t have the values you expect.</p>
+<p>There are a few things you can do to mitigate these problems:</p>
+<ul class="simple">
+<li><p>Make each section of the notebook self-contained.  Try not to use the same variable name in more than one section.</p></li>
+<li><p>Keep notebooks short.  Look for places where you can break your analysis into phases with one notebook per phase.</p></li>
+</ul>
+</div>
+</div>
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+        
+        
+    </div>
+</div>
+
+    </div>
+
+    <!-- Table of contents -->
+    <div class="d-none d-md-block col-md-2 bd-toc show">
+        
+        <div class="tocsection onthispage pt-5 pb-3">
+            <i class="fas fa-list"></i> Contents
+        </div>
+        <nav id="bd-toc-nav">
+            <ul class="nav section-nav flex-column">
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#installing-libraries">
+   Installing libraries
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#selecting-a-region">
+   Selecting a region
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#getting-gd-1-data">
+   Getting GD-1 Data
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#working-with-coordinates">
+   Working with coordinates
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#selecting-a-rectangle">
+   Selecting a rectangle
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#selecting-a-polygon">
+   Selecting a polygon
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#saving-results">
+   Saving results
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#summary">
+   Summary
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#best-practices">
+   Best practices
+  </a>
+ </li>
+</ul>
+
+        </nav>
+        
+    </div>
+</div>
+    <div id="main-content" class="row">
+        <div class="col-12 col-md-9 pl-md-3 pr-md-0">
+        
+              <div>
+                
+  <div class="section" id="lesson-2">
+<h1>Lesson 2<a class="headerlink" href="#lesson-2" title="Permalink to this headline">¶</a></h1>
+<p>This is the second in a series of lessons related to astronomy data.</p>
+<p>As a running example, we are replicating parts of the analysis in a recent paper, “<a class="reference external" href="https://arxiv.org/abs/1805.00425">Off the beaten path: Gaia reveals GD-1 stars outside of the main stream</a>� by Adrian M. Price-Whelan and Ana Bonaca.</p>
+<p>In the first notebook, we wrote ADQL queries and used them to select and download data from the Gaia server.</p>
+<p>In this notebook, we’ll pick up where we left off and write a query to select stars from the region of the sky where we expect GD-1 to be.</p>
+<p>We’ll start with an example that does a “cone search�; that is, it selects stars that appear in a circular region of the sky.</p>
+<p>Then, to select stars in the vicinity of GD-1, we’ll:</p>
+<ul class="simple">
+<li><p>Use <code class="docutils literal notranslate"><span class="pre">Quantity</span></code> objects to represent measurements with units.</p></li>
+<li><p>Use the <code class="docutils literal notranslate"><span class="pre">Gala</span></code> library to convert coordinates from one frame to another.</p></li>
+<li><p>Use the ADQL keywords <code class="docutils literal notranslate"><span class="pre">POLYGON</span></code>, <code class="docutils literal notranslate"><span class="pre">CONTAINS</span></code>, and <code class="docutils literal notranslate"><span class="pre">POINT</span></code> to select stars that fall within a polygonal region.</p></li>
+<li><p>Submit a query and download the results.</p></li>
+<li><p>Store the results in a FITS file.</p></li>
+</ul>
+<p>After completing this lesson, you should be able to</p>
+<ul class="simple">
+<li><p>Use Python string formatting to compose more complex ADQL queries.</p></li>
+<li><p>Work with coordinates and other quantities that have units.</p></li>
+<li><p>Download the results of a query and store them in a file.</p></li>
+</ul>
+<div class="section" id="installing-libraries">
+<h2>Installing libraries<a class="headerlink" href="#installing-libraries" title="Permalink to this headline">¶</a></h2>
+<p>If you are running this notebook on Colab, you can run the following cell to install Astroquery and a the other libraries we’ll use.</p>
+<p>If you are running this notebook on your own computer, you might have to install these libraries yourself.</p>
+<p>If you are using this notebook as part of a Carpentries workshop, you should have received setup instructions.</p>
+<p>TODO: Add a link to the instructions.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># If we&#39;re running on Colab, install libraries</span>
+
+<span class="kn">import</span> <span class="nn">sys</span>
+<span class="n">IN_COLAB</span> <span class="o">=</span> <span class="s1">&#39;google.colab&#39;</span> <span class="ow">in</span> <span class="n">sys</span><span class="o">.</span><span class="n">modules</span>
+
+<span class="k">if</span> <span class="n">IN_COLAB</span><span class="p">:</span>
+    <span class="o">!</span>pip install astroquery astro-gala pyia
+</pre></div>
+</div>
+</div>
+</div>
+</div>
+<div class="section" id="selecting-a-region">
+<h2>Selecting a region<a class="headerlink" href="#selecting-a-region" title="Permalink to this headline">¶</a></h2>
+<p>One of the most common ways to restrict a query is to select stars in a particular region of the sky.</p>
+<p>For example, here’s a query from the <a class="reference external" href="https://gea.esac.esa.int/archive-help/adql/examples/index.html">Gaia archive documentation</a> that selects “all the objects … in a circular region centered at (266.41683, -29.00781) with a search radius of 5 arcmin (0.08333 deg).�</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">query</span> <span class="o">=</span> <span class="s2">&quot;&quot;&quot;</span>
+<span class="s2">SELECT </span>
+<span class="s2">TOP 10 source_id</span>
+<span class="s2">FROM gaiadr2.gaia_source</span>
+<span class="s2">WHERE 1=CONTAINS(</span>
+<span class="s2">  POINT(ra, dec),</span>
+<span class="s2">  CIRCLE(266.41683, -29.00781, 0.08333333))</span>
+<span class="s2">&quot;&quot;&quot;</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>This query uses three keywords that are specific to ADQL (not SQL):</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">POINT</span></code>: a location in <a class="reference external" href="https://en.wikipedia.org/wiki/International_Celestial_Reference_System">ICRS coordinates</a>, specified in degrees of right ascension and declination.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">CIRCLE</span></code>: a circle where the first two values are the coordinates of the center and the third is the radius in degrees.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">CONTAINS</span></code>: a function that returns <code class="docutils literal notranslate"><span class="pre">1</span></code> if a <code class="docutils literal notranslate"><span class="pre">POINT</span></code> is contained in a shape and <code class="docutils literal notranslate"><span class="pre">0</span></code> otherwise.</p></li>
+</ul>
+<p>Here is the <a class="reference external" href="http://www.ivoa.net/documents/ADQL/20180112/PR-ADQL-2.1-20180112.html#tth_sEc4.2.12">documentation of <code class="docutils literal notranslate"><span class="pre">CONTAINS</span></code></a>.</p>
+<p>A query like this is called a cone search because it selects stars in a cone.</p>
+<p>Here’s how we run it.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">astroquery.gaia</span> <span class="kn">import</span> <span class="n">Gaia</span>
+
+<span class="n">job</span> <span class="o">=</span> <span class="n">Gaia</span><span class="o">.</span><span class="n">launch_job</span><span class="p">(</span><span class="n">query</span><span class="p">)</span>
+<span class="n">result</span> <span class="o">=</span> <span class="n">job</span><span class="o">.</span><span class="n">get_results</span><span class="p">()</span>
+<span class="n">result</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Created TAP+ (v1.2.1) - Connection:
+	Host: gea.esac.esa.int
+	Use HTTPS: True
+	Port: 443
+	SSL Port: 443
+Created TAP+ (v1.2.1) - Connection:
+	Host: geadata.esac.esa.int
+	Use HTTPS: True
+	Port: 443
+	SSL Port: 443
+</pre></div>
+</div>
+<div class="output text_html"><i>Table length=10</i>
+<table id="table139949838258672" class="table-striped table-bordered table-condensed">
+<thead><tr><th>source_id</th></tr></thead>
+<thead><tr><th>int64</th></tr></thead>
+<tr><td>4057468321929794432</td></tr>
+<tr><td>4057468287575835392</td></tr>
+<tr><td>4057482027171038976</td></tr>
+<tr><td>4057470349160630656</td></tr>
+<tr><td>4057470039924301696</td></tr>
+<tr><td>4057469868125641984</td></tr>
+<tr><td>4057468351995073024</td></tr>
+<tr><td>4057469661959554560</td></tr>
+<tr><td>4057470520960672640</td></tr>
+<tr><td>4057470555320409600</td></tr>
+</table></div></div>
+</div>
+<p><strong>Exercise:</strong> When you are debugging queries like this, you can use <code class="docutils literal notranslate"><span class="pre">TOP</span></code> to limit the size of the results, but then you still don’t know how big the results will be.</p>
+<p>An alternative is to use <code class="docutils literal notranslate"><span class="pre">COUNT</span></code>, which asks for the number of rows that would be selected, but it does not return them.</p>
+<p>In the previous query, replace <code class="docutils literal notranslate"><span class="pre">TOP</span> <span class="pre">10</span> <span class="pre">source_id</span></code> with <code class="docutils literal notranslate"><span class="pre">COUNT(source_id)</span></code> and run the query again.  How many stars has Gaia identified in the cone we searched?</p>
+</div>
+<div class="section" id="getting-gd-1-data">
+<h2>Getting GD-1 Data<a class="headerlink" href="#getting-gd-1-data" title="Permalink to this headline">¶</a></h2>
+<p>From the Price-Whelan and Bonaca paper, we will try to reproduce Figure 1, which includes this representation of stars likely to belong to GD-1:</p>
+<img src="https://github.com/datacarpentry/astronomy-python/raw/gh-pages/fig/gd1-4.png"><p>Along the axis of right ascension (<span class="math notranslate nohighlight">\(\phi_1\)</span>) the figure extends from -100 to 20 degrees.</p>
+<p>Along the axis of declination (<span class="math notranslate nohighlight">\(\phi_2\)</span>) the figure extends from about -8 to 4 degrees.</p>
+<p>Ideally, we would select all stars from this rectangle, but there are more than 10 million of them, so</p>
+<ul class="simple">
+<li><p>That would be difficult to work with,</p></li>
+<li><p>As anonymous users, we are limited to 3 million rows in a single query, and</p></li>
+<li><p>While we are developing and testing code, it will be faster to work with a smaller dataset.</p></li>
+</ul>
+<p>So we’ll start by selecting stars in a smaller rectangle, from -55 to -45 degrees right ascension and -8 to 4 degrees of declination.</p>
+<p>But first we let’s see how to represent quantities with units like degrees.</p>
+</div>
+<div class="section" id="working-with-coordinates">
+<h2>Working with coordinates<a class="headerlink" href="#working-with-coordinates" title="Permalink to this headline">¶</a></h2>
+<p>Coordinates are physical quantities, which means that they have two parts, a value and a unit.</p>
+<p>For example, the coordinate <span class="math notranslate nohighlight">\(30^{\circ}\)</span> has value 30 and its units are degrees.</p>
+<p>Until recently, most scientific computation was done with values only; units were left out of the program altogether, <a class="reference external" href="https://en.wikipedia.org/wiki/Mars_Climate_Orbiter#Cause_of_failure">often with disastrous results</a>.</p>
+<p>Astropy provides tools for including units explicitly in computations, which makes it possible to detect errors before they cause disasters.</p>
+<p>To use Astropy units, we import them like this:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">astropy.units</span> <span class="k">as</span> <span class="nn">u</span>
+
+<span class="n">u</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;module &#39;astropy.units&#39; from &#39;/home/downey/anaconda3/envs/AstronomicalData/lib/python3.8/site-packages/astropy/units/__init__.py&#39;&gt;
+</pre></div>
+</div>
+</div>
+</div>
+<p><code class="docutils literal notranslate"><span class="pre">u</span></code> is an object that contains most common units and all SI units.</p>
+<p>You can use <code class="docutils literal notranslate"><span class="pre">dir</span></code> to list them, but you should also <a class="reference external" href="https://docs.astropy.org/en/stable/units/">read the documentation</a>.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="nb">dir</span><span class="p">(</span><span class="n">u</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[&#39;A&#39;,
+ &#39;AA&#39;,
+ &#39;AB&#39;,
+ &#39;ABflux&#39;,
+ &#39;ABmag&#39;,
+ &#39;AU&#39;,
+ &#39;Angstrom&#39;,
+ &#39;B&#39;,
+ &#39;Ba&#39;,
+ &#39;Barye&#39;,
+ &#39;Bi&#39;,
+ &#39;Biot&#39;,
+ &#39;Bol&#39;,
+ &#39;Bq&#39;,
+ &#39;C&#39;,
+ &#39;Celsius&#39;,
+ &#39;Ci&#39;,
+ &#39;CompositeUnit&#39;,
+ &#39;D&#39;,
+ &#39;Da&#39;,
+ &#39;Dalton&#39;,
+ &#39;Debye&#39;,
+ &#39;Decibel&#39;,
+ &#39;DecibelUnit&#39;,
+ &#39;Dex&#39;,
+ &#39;DexUnit&#39;,
+ &#39;EA&#39;,
+ &#39;EAU&#39;,
+ &#39;EB&#39;,
+ &#39;EBa&#39;,
+ &#39;EC&#39;,
+ &#39;ED&#39;,
+ &#39;EF&#39;,
+ &#39;EG&#39;,
+ &#39;EGal&#39;,
+ &#39;EH&#39;,
+ &#39;EHz&#39;,
+ &#39;EJ&#39;,
+ &#39;EJy&#39;,
+ &#39;EK&#39;,
+ &#39;EL&#39;,
+ &#39;EN&#39;,
+ &#39;EOhm&#39;,
+ &#39;EP&#39;,
+ &#39;EPa&#39;,
+ &#39;ER&#39;,
+ &#39;ERy&#39;,
+ &#39;ES&#39;,
+ &#39;ESt&#39;,
+ &#39;ET&#39;,
+ &#39;EV&#39;,
+ &#39;EW&#39;,
+ &#39;EWb&#39;,
+ &#39;Ea&#39;,
+ &#39;Eadu&#39;,
+ &#39;Earcmin&#39;,
+ &#39;Earcsec&#39;,
+ &#39;Eau&#39;,
+ &#39;Eb&#39;,
+ &#39;Ebarn&#39;,
+ &#39;Ebeam&#39;,
+ &#39;Ebin&#39;,
+ &#39;Ebit&#39;,
+ &#39;Ebyte&#39;,
+ &#39;Ecd&#39;,
+ &#39;Echan&#39;,
+ &#39;Ecount&#39;,
+ &#39;Ect&#39;,
+ &#39;Ed&#39;,
+ &#39;Edeg&#39;,
+ &#39;Edyn&#39;,
+ &#39;EeV&#39;,
+ &#39;Eerg&#39;,
+ &#39;Eg&#39;,
+ &#39;Eh&#39;,
+ &#39;EiB&#39;,
+ &#39;Eib&#39;,
+ &#39;Eibit&#39;,
+ &#39;Eibyte&#39;,
+ &#39;Ek&#39;,
+ &#39;El&#39;,
+ &#39;Elm&#39;,
+ &#39;Elx&#39;,
+ &#39;Elyr&#39;,
+ &#39;Em&#39;,
+ &#39;Emag&#39;,
+ &#39;Emin&#39;,
+ &#39;Emol&#39;,
+ &#39;Eohm&#39;,
+ &#39;Epc&#39;,
+ &#39;Eph&#39;,
+ &#39;Ephoton&#39;,
+ &#39;Epix&#39;,
+ &#39;Epixel&#39;,
+ &#39;Erad&#39;,
+ &#39;Es&#39;,
+ &#39;Esr&#39;,
+ &#39;Eu&#39;,
+ &#39;Evox&#39;,
+ &#39;Evoxel&#39;,
+ &#39;Eyr&#39;,
+ &#39;F&#39;,
+ &#39;Farad&#39;,
+ &#39;Fr&#39;,
+ &#39;Franklin&#39;,
+ &#39;FunctionQuantity&#39;,
+ &#39;FunctionUnitBase&#39;,
+ &#39;G&#39;,
+ &#39;GA&#39;,
+ &#39;GAU&#39;,
+ &#39;GB&#39;,
+ &#39;GBa&#39;,
+ &#39;GC&#39;,
+ &#39;GD&#39;,
+ &#39;GF&#39;,
+ &#39;GG&#39;,
+ &#39;GGal&#39;,
+ &#39;GH&#39;,
+ &#39;GHz&#39;,
+ &#39;GJ&#39;,
+ &#39;GJy&#39;,
+ &#39;GK&#39;,
+ &#39;GL&#39;,
+ &#39;GN&#39;,
+ &#39;GOhm&#39;,
+ &#39;GP&#39;,
+ &#39;GPa&#39;,
+ &#39;GR&#39;,
+ &#39;GRy&#39;,
+ &#39;GS&#39;,
+ &#39;GSt&#39;,
+ &#39;GT&#39;,
+ &#39;GV&#39;,
+ &#39;GW&#39;,
+ &#39;GWb&#39;,
+ &#39;Ga&#39;,
+ &#39;Gadu&#39;,
+ &#39;Gal&#39;,
+ &#39;Garcmin&#39;,
+ &#39;Garcsec&#39;,
+ &#39;Gau&#39;,
+ &#39;Gauss&#39;,
+ &#39;Gb&#39;,
+ &#39;Gbarn&#39;,
+ &#39;Gbeam&#39;,
+ &#39;Gbin&#39;,
+ &#39;Gbit&#39;,
+ &#39;Gbyte&#39;,
+ &#39;Gcd&#39;,
+ &#39;Gchan&#39;,
+ &#39;Gcount&#39;,
+ &#39;Gct&#39;,
+ &#39;Gd&#39;,
+ &#39;Gdeg&#39;,
+ &#39;Gdyn&#39;,
+ &#39;GeV&#39;,
+ &#39;Gerg&#39;,
+ &#39;Gg&#39;,
+ &#39;Gh&#39;,
+ &#39;GiB&#39;,
+ &#39;Gib&#39;,
+ &#39;Gibit&#39;,
+ &#39;Gibyte&#39;,
+ &#39;Gk&#39;,
+ &#39;Gl&#39;,
+ &#39;Glm&#39;,
+ &#39;Glx&#39;,
+ &#39;Glyr&#39;,
+ &#39;Gm&#39;,
+ &#39;Gmag&#39;,
+ &#39;Gmin&#39;,
+ &#39;Gmol&#39;,
+ &#39;Gohm&#39;,
+ &#39;Gpc&#39;,
+ &#39;Gph&#39;,
+ &#39;Gphoton&#39;,
+ &#39;Gpix&#39;,
+ &#39;Gpixel&#39;,
+ &#39;Grad&#39;,
+ &#39;Gs&#39;,
+ &#39;Gsr&#39;,
+ &#39;Gu&#39;,
+ &#39;Gvox&#39;,
+ &#39;Gvoxel&#39;,
+ &#39;Gyr&#39;,
+ &#39;H&#39;,
+ &#39;Henry&#39;,
+ &#39;Hertz&#39;,
+ &#39;Hz&#39;,
+ &#39;IrreducibleUnit&#39;,
+ &#39;J&#39;,
+ &#39;Jansky&#39;,
+ &#39;Joule&#39;,
+ &#39;Jy&#39;,
+ &#39;K&#39;,
+ &#39;Kayser&#39;,
+ &#39;Kelvin&#39;,
+ &#39;KiB&#39;,
+ &#39;Kib&#39;,
+ &#39;Kibit&#39;,
+ &#39;Kibyte&#39;,
+ &#39;L&#39;,
+ &#39;L_bol&#39;,
+ &#39;L_sun&#39;,
+ &#39;LogQuantity&#39;,
+ &#39;LogUnit&#39;,
+ &#39;Lsun&#39;,
+ &#39;MA&#39;,
+ &#39;MAU&#39;,
+ &#39;MB&#39;,
+ &#39;MBa&#39;,
+ &#39;MC&#39;,
+ &#39;MD&#39;,
+ &#39;MF&#39;,
+ &#39;MG&#39;,
+ &#39;MGal&#39;,
+ &#39;MH&#39;,
+ &#39;MHz&#39;,
+ &#39;MJ&#39;,
+ &#39;MJy&#39;,
+ &#39;MK&#39;,
+ &#39;ML&#39;,
+ &#39;MN&#39;,
+ &#39;MOhm&#39;,
+ &#39;MP&#39;,
+ &#39;MPa&#39;,
+ &#39;MR&#39;,
+ &#39;MRy&#39;,
+ &#39;MS&#39;,
+ &#39;MSt&#39;,
+ &#39;MT&#39;,
+ &#39;MV&#39;,
+ &#39;MW&#39;,
+ &#39;MWb&#39;,
+ &#39;M_bol&#39;,
+ &#39;M_e&#39;,
+ &#39;M_earth&#39;,
+ &#39;M_jup&#39;,
+ &#39;M_jupiter&#39;,
+ &#39;M_p&#39;,
+ &#39;M_sun&#39;,
+ &#39;Ma&#39;,
+ &#39;Madu&#39;,
+ &#39;MagUnit&#39;,
+ &#39;Magnitude&#39;,
+ &#39;Marcmin&#39;,
+ &#39;Marcsec&#39;,
+ &#39;Mau&#39;,
+ &#39;Mb&#39;,
+ &#39;Mbarn&#39;,
+ &#39;Mbeam&#39;,
+ &#39;Mbin&#39;,
+ &#39;Mbit&#39;,
+ &#39;Mbyte&#39;,
+ &#39;Mcd&#39;,
+ &#39;Mchan&#39;,
+ &#39;Mcount&#39;,
+ &#39;Mct&#39;,
+ &#39;Md&#39;,
+ &#39;Mdeg&#39;,
+ &#39;Mdyn&#39;,
+ &#39;MeV&#39;,
+ &#39;Mearth&#39;,
+ &#39;Merg&#39;,
+ &#39;Mg&#39;,
+ &#39;Mh&#39;,
+ &#39;MiB&#39;,
+ &#39;Mib&#39;,
+ &#39;Mibit&#39;,
+ &#39;Mibyte&#39;,
+ &#39;Mjup&#39;,
+ &#39;Mjupiter&#39;,
+ &#39;Mk&#39;,
+ &#39;Ml&#39;,
+ &#39;Mlm&#39;,
+ &#39;Mlx&#39;,
+ &#39;Mlyr&#39;,
+ &#39;Mm&#39;,
+ &#39;Mmag&#39;,
+ &#39;Mmin&#39;,
+ &#39;Mmol&#39;,
+ &#39;Mohm&#39;,
+ &#39;Mpc&#39;,
+ &#39;Mph&#39;,
+ &#39;Mphoton&#39;,
+ &#39;Mpix&#39;,
+ &#39;Mpixel&#39;,
+ &#39;Mrad&#39;,
+ &#39;Ms&#39;,
+ &#39;Msr&#39;,
+ &#39;Msun&#39;,
+ &#39;Mu&#39;,
+ &#39;Mvox&#39;,
+ &#39;Mvoxel&#39;,
+ &#39;Myr&#39;,
+ &#39;N&#39;,
+ &#39;NamedUnit&#39;,
+ &#39;Newton&#39;,
+ &#39;Ohm&#39;,
+ &#39;P&#39;,
+ &#39;PA&#39;,
+ &#39;PAU&#39;,
+ &#39;PB&#39;,
+ &#39;PBa&#39;,
+ &#39;PC&#39;,
+ &#39;PD&#39;,
+ &#39;PF&#39;,
+ &#39;PG&#39;,
+ &#39;PGal&#39;,
+ &#39;PH&#39;,
+ &#39;PHz&#39;,
+ &#39;PJ&#39;,
+ &#39;PJy&#39;,
+ &#39;PK&#39;,
+ &#39;PL&#39;,
+ &#39;PN&#39;,
+ &#39;POhm&#39;,
+ &#39;PP&#39;,
+ &#39;PPa&#39;,
+ &#39;PR&#39;,
+ &#39;PRy&#39;,
+ &#39;PS&#39;,
+ &#39;PSt&#39;,
+ &#39;PT&#39;,
+ &#39;PV&#39;,
+ &#39;PW&#39;,
+ &#39;PWb&#39;,
+ &#39;Pa&#39;,
+ &#39;Padu&#39;,
+ &#39;Parcmin&#39;,
+ &#39;Parcsec&#39;,
+ &#39;Pascal&#39;,
+ &#39;Pau&#39;,
+ &#39;Pb&#39;,
+ &#39;Pbarn&#39;,
+ &#39;Pbeam&#39;,
+ &#39;Pbin&#39;,
+ &#39;Pbit&#39;,
+ &#39;Pbyte&#39;,
+ &#39;Pcd&#39;,
+ &#39;Pchan&#39;,
+ &#39;Pcount&#39;,
+ &#39;Pct&#39;,
+ &#39;Pd&#39;,
+ &#39;Pdeg&#39;,
+ &#39;Pdyn&#39;,
+ &#39;PeV&#39;,
+ &#39;Perg&#39;,
+ &#39;Pg&#39;,
+ &#39;Ph&#39;,
+ &#39;PiB&#39;,
+ &#39;Pib&#39;,
+ &#39;Pibit&#39;,
+ &#39;Pibyte&#39;,
+ &#39;Pk&#39;,
+ &#39;Pl&#39;,
+ &#39;Plm&#39;,
+ &#39;Plx&#39;,
+ &#39;Plyr&#39;,
+ &#39;Pm&#39;,
+ &#39;Pmag&#39;,
+ &#39;Pmin&#39;,
+ &#39;Pmol&#39;,
+ &#39;Pohm&#39;,
+ &#39;Ppc&#39;,
+ &#39;Pph&#39;,
+ &#39;Pphoton&#39;,
+ &#39;Ppix&#39;,
+ &#39;Ppixel&#39;,
+ &#39;Prad&#39;,
+ &#39;PrefixUnit&#39;,
+ &#39;Ps&#39;,
+ &#39;Psr&#39;,
+ &#39;Pu&#39;,
+ &#39;Pvox&#39;,
+ &#39;Pvoxel&#39;,
+ &#39;Pyr&#39;,
+ &#39;Quantity&#39;,
+ &#39;QuantityInfo&#39;,
+ &#39;QuantityInfoBase&#39;,
+ &#39;R&#39;,
+ &#39;R_earth&#39;,
+ &#39;R_jup&#39;,
+ &#39;R_jupiter&#39;,
+ &#39;R_sun&#39;,
+ &#39;Rayleigh&#39;,
+ &#39;Rearth&#39;,
+ &#39;Rjup&#39;,
+ &#39;Rjupiter&#39;,
+ &#39;Rsun&#39;,
+ &#39;Ry&#39;,
+ &#39;S&#39;,
+ &#39;ST&#39;,
+ &#39;STflux&#39;,
+ &#39;STmag&#39;,
+ &#39;Siemens&#39;,
+ &#39;SpecificTypeQuantity&#39;,
+ &#39;St&#39;,
+ &#39;Sun&#39;,
+ &#39;T&#39;,
+ &#39;TA&#39;,
+ &#39;TAU&#39;,
+ &#39;TB&#39;,
+ &#39;TBa&#39;,
+ &#39;TC&#39;,
+ &#39;TD&#39;,
+ &#39;TF&#39;,
+ &#39;TG&#39;,
+ &#39;TGal&#39;,
+ &#39;TH&#39;,
+ &#39;THz&#39;,
+ &#39;TJ&#39;,
+ &#39;TJy&#39;,
+ &#39;TK&#39;,
+ &#39;TL&#39;,
+ &#39;TN&#39;,
+ &#39;TOhm&#39;,
+ &#39;TP&#39;,
+ &#39;TPa&#39;,
+ &#39;TR&#39;,
+ &#39;TRy&#39;,
+ &#39;TS&#39;,
+ &#39;TSt&#39;,
+ &#39;TT&#39;,
+ &#39;TV&#39;,
+ &#39;TW&#39;,
+ &#39;TWb&#39;,
+ &#39;Ta&#39;,
+ &#39;Tadu&#39;,
+ &#39;Tarcmin&#39;,
+ &#39;Tarcsec&#39;,
+ &#39;Tau&#39;,
+ &#39;Tb&#39;,
+ &#39;Tbarn&#39;,
+ &#39;Tbeam&#39;,
+ &#39;Tbin&#39;,
+ &#39;Tbit&#39;,
+ &#39;Tbyte&#39;,
+ &#39;Tcd&#39;,
+ &#39;Tchan&#39;,
+ &#39;Tcount&#39;,
+ &#39;Tct&#39;,
+ &#39;Td&#39;,
+ &#39;Tdeg&#39;,
+ &#39;Tdyn&#39;,
+ &#39;TeV&#39;,
+ &#39;Terg&#39;,
+ &#39;Tesla&#39;,
+ &#39;Tg&#39;,
+ &#39;Th&#39;,
+ &#39;TiB&#39;,
+ &#39;Tib&#39;,
+ &#39;Tibit&#39;,
+ &#39;Tibyte&#39;,
+ &#39;Tk&#39;,
+ &#39;Tl&#39;,
+ &#39;Tlm&#39;,
+ &#39;Tlx&#39;,
+ &#39;Tlyr&#39;,
+ &#39;Tm&#39;,
+ &#39;Tmag&#39;,
+ &#39;Tmin&#39;,
+ &#39;Tmol&#39;,
+ &#39;Tohm&#39;,
+ &#39;Tpc&#39;,
+ &#39;Tph&#39;,
+ &#39;Tphoton&#39;,
+ &#39;Tpix&#39;,
+ &#39;Tpixel&#39;,
+ &#39;Trad&#39;,
+ &#39;Ts&#39;,
+ &#39;Tsr&#39;,
+ &#39;Tu&#39;,
+ &#39;Tvox&#39;,
+ &#39;Tvoxel&#39;,
+ &#39;Tyr&#39;,
+ &#39;Unit&#39;,
+ &#39;UnitBase&#39;,
+ &#39;UnitConversionError&#39;,
+ &#39;UnitTypeError&#39;,
+ &#39;UnitsError&#39;,
+ &#39;UnitsWarning&#39;,
+ &#39;UnrecognizedUnit&#39;,
+ &#39;V&#39;,
+ &#39;Volt&#39;,
+ &#39;W&#39;,
+ &#39;Watt&#39;,
+ &#39;Wb&#39;,
+ &#39;Weber&#39;,
+ &#39;YA&#39;,
+ &#39;YAU&#39;,
+ &#39;YB&#39;,
+ &#39;YBa&#39;,
+ &#39;YC&#39;,
+ &#39;YD&#39;,
+ &#39;YF&#39;,
+ &#39;YG&#39;,
+ &#39;YGal&#39;,
+ &#39;YH&#39;,
+ &#39;YHz&#39;,
+ &#39;YJ&#39;,
+ &#39;YJy&#39;,
+ &#39;YK&#39;,
+ &#39;YL&#39;,
+ &#39;YN&#39;,
+ &#39;YOhm&#39;,
+ &#39;YP&#39;,
+ &#39;YPa&#39;,
+ &#39;YR&#39;,
+ &#39;YRy&#39;,
+ &#39;YS&#39;,
+ &#39;YSt&#39;,
+ &#39;YT&#39;,
+ &#39;YV&#39;,
+ &#39;YW&#39;,
+ &#39;YWb&#39;,
+ &#39;Ya&#39;,
+ &#39;Yadu&#39;,
+ &#39;Yarcmin&#39;,
+ &#39;Yarcsec&#39;,
+ &#39;Yau&#39;,
+ &#39;Yb&#39;,
+ &#39;Ybarn&#39;,
+ &#39;Ybeam&#39;,
+ &#39;Ybin&#39;,
+ &#39;Ybit&#39;,
+ &#39;Ybyte&#39;,
+ &#39;Ycd&#39;,
+ &#39;Ychan&#39;,
+ &#39;Ycount&#39;,
+ &#39;Yct&#39;,
+ &#39;Yd&#39;,
+ &#39;Ydeg&#39;,
+ &#39;Ydyn&#39;,
+ &#39;YeV&#39;,
+ &#39;Yerg&#39;,
+ &#39;Yg&#39;,
+ &#39;Yh&#39;,
+ &#39;Yk&#39;,
+ &#39;Yl&#39;,
+ &#39;Ylm&#39;,
+ &#39;Ylx&#39;,
+ &#39;Ylyr&#39;,
+ &#39;Ym&#39;,
+ &#39;Ymag&#39;,
+ &#39;Ymin&#39;,
+ &#39;Ymol&#39;,
+ &#39;Yohm&#39;,
+ &#39;Ypc&#39;,
+ &#39;Yph&#39;,
+ &#39;Yphoton&#39;,
+ &#39;Ypix&#39;,
+ &#39;Ypixel&#39;,
+ &#39;Yrad&#39;,
+ &#39;Ys&#39;,
+ &#39;Ysr&#39;,
+ &#39;Yu&#39;,
+ &#39;Yvox&#39;,
+ &#39;Yvoxel&#39;,
+ &#39;Yyr&#39;,
+ &#39;ZA&#39;,
+ &#39;ZAU&#39;,
+ &#39;ZB&#39;,
+ &#39;ZBa&#39;,
+ &#39;ZC&#39;,
+ &#39;ZD&#39;,
+ &#39;ZF&#39;,
+ &#39;ZG&#39;,
+ &#39;ZGal&#39;,
+ &#39;ZH&#39;,
+ &#39;ZHz&#39;,
+ &#39;ZJ&#39;,
+ &#39;ZJy&#39;,
+ &#39;ZK&#39;,
+ &#39;ZL&#39;,
+ &#39;ZN&#39;,
+ &#39;ZOhm&#39;,
+ &#39;ZP&#39;,
+ &#39;ZPa&#39;,
+ &#39;ZR&#39;,
+ &#39;ZRy&#39;,
+ &#39;ZS&#39;,
+ &#39;ZSt&#39;,
+ &#39;ZT&#39;,
+ &#39;ZV&#39;,
+ &#39;ZW&#39;,
+ &#39;ZWb&#39;,
+ &#39;Za&#39;,
+ &#39;Zadu&#39;,
+ &#39;Zarcmin&#39;,
+ &#39;Zarcsec&#39;,
+ &#39;Zau&#39;,
+ &#39;Zb&#39;,
+ &#39;Zbarn&#39;,
+ &#39;Zbeam&#39;,
+ &#39;Zbin&#39;,
+ &#39;Zbit&#39;,
+ &#39;Zbyte&#39;,
+ &#39;Zcd&#39;,
+ &#39;Zchan&#39;,
+ &#39;Zcount&#39;,
+ &#39;Zct&#39;,
+ &#39;Zd&#39;,
+ &#39;Zdeg&#39;,
+ &#39;Zdyn&#39;,
+ &#39;ZeV&#39;,
+ &#39;Zerg&#39;,
+ &#39;Zg&#39;,
+ &#39;Zh&#39;,
+ &#39;Zk&#39;,
+ &#39;Zl&#39;,
+ &#39;Zlm&#39;,
+ &#39;Zlx&#39;,
+ &#39;Zlyr&#39;,
+ &#39;Zm&#39;,
+ &#39;Zmag&#39;,
+ &#39;Zmin&#39;,
+ &#39;Zmol&#39;,
+ &#39;Zohm&#39;,
+ &#39;Zpc&#39;,
+ &#39;Zph&#39;,
+ &#39;Zphoton&#39;,
+ &#39;Zpix&#39;,
+ &#39;Zpixel&#39;,
+ &#39;Zrad&#39;,
+ &#39;Zs&#39;,
+ &#39;Zsr&#39;,
+ &#39;Zu&#39;,
+ &#39;Zvox&#39;,
+ &#39;Zvoxel&#39;,
+ &#39;Zyr&#39;,
+ &#39;__builtins__&#39;,
+ &#39;__cached__&#39;,
+ &#39;__doc__&#39;,
+ &#39;__file__&#39;,
+ &#39;__loader__&#39;,
+ &#39;__name__&#39;,
+ &#39;__package__&#39;,
+ &#39;__path__&#39;,
+ &#39;__spec__&#39;,
+ &#39;a&#39;,
+ &#39;aA&#39;,
+ &#39;aAU&#39;,
+ &#39;aB&#39;,
+ &#39;aBa&#39;,
+ &#39;aC&#39;,
+ &#39;aD&#39;,
+ &#39;aF&#39;,
+ &#39;aG&#39;,
+ &#39;aGal&#39;,
+ &#39;aH&#39;,
+ &#39;aHz&#39;,
+ &#39;aJ&#39;,
+ &#39;aJy&#39;,
+ &#39;aK&#39;,
+ &#39;aL&#39;,
+ &#39;aN&#39;,
+ &#39;aOhm&#39;,
+ &#39;aP&#39;,
+ &#39;aPa&#39;,
+ &#39;aR&#39;,
+ &#39;aRy&#39;,
+ &#39;aS&#39;,
+ &#39;aSt&#39;,
+ &#39;aT&#39;,
+ &#39;aV&#39;,
+ &#39;aW&#39;,
+ &#39;aWb&#39;,
+ &#39;aa&#39;,
+ &#39;aadu&#39;,
+ &#39;aarcmin&#39;,
+ &#39;aarcsec&#39;,
+ &#39;aau&#39;,
+ &#39;ab&#39;,
+ &#39;abA&#39;,
+ &#39;abC&#39;,
+ &#39;abampere&#39;,
+ &#39;abarn&#39;,
+ &#39;abcoulomb&#39;,
+ &#39;abeam&#39;,
+ &#39;abin&#39;,
+ &#39;abit&#39;,
+ &#39;abyte&#39;,
+ &#39;acd&#39;,
+ &#39;achan&#39;,
+ &#39;acount&#39;,
+ &#39;act&#39;,
+ &#39;ad&#39;,
+ &#39;add_enabled_equivalencies&#39;,
+ &#39;add_enabled_units&#39;,
+ &#39;adeg&#39;,
+ &#39;adu&#39;,
+ &#39;adyn&#39;,
+ &#39;aeV&#39;,
+ &#39;aerg&#39;,
+ &#39;ag&#39;,
+ &#39;ah&#39;,
+ &#39;ak&#39;,
+ &#39;al&#39;,
+ &#39;allclose&#39;,
+ &#39;alm&#39;,
+ &#39;alx&#39;,
+ &#39;alyr&#39;,
+ &#39;am&#39;,
+ &#39;amag&#39;,
+ &#39;amin&#39;,
+ &#39;amol&#39;,
+ &#39;amp&#39;,
+ &#39;ampere&#39;,
+ &#39;angstrom&#39;,
+ &#39;annum&#39;,
+ &#39;aohm&#39;,
+ &#39;apc&#39;,
+ &#39;aph&#39;,
+ &#39;aphoton&#39;,
+ &#39;apix&#39;,
+ &#39;apixel&#39;,
+ &#39;arad&#39;,
+ &#39;arcmin&#39;,
+ &#39;arcminute&#39;,
+ &#39;arcsec&#39;,
+ &#39;arcsecond&#39;,
+ &#39;asr&#39;,
+ &#39;astronomical_unit&#39;,
+ &#39;astrophys&#39;,
+ &#39;attoBarye&#39;,
+ &#39;attoDa&#39;,
+ &#39;attoDalton&#39;,
+ &#39;attoDebye&#39;,
+ &#39;attoFarad&#39;,
+ &#39;attoGauss&#39;,
+ &#39;attoHenry&#39;,
+ &#39;attoHertz&#39;,
+ &#39;attoJansky&#39;,
+ &#39;attoJoule&#39;,
+ &#39;attoKayser&#39;,
+ &#39;attoKelvin&#39;,
+ &#39;attoNewton&#39;,
+ &#39;attoOhm&#39;,
+ &#39;attoPascal&#39;,
+ &#39;attoRayleigh&#39;,
+ &#39;attoSiemens&#39;,
+ &#39;attoTesla&#39;,
+ &#39;attoVolt&#39;,
+ &#39;attoWatt&#39;,
+ &#39;attoWeber&#39;,
+ &#39;attoamp&#39;,
+ &#39;attoampere&#39;,
+ &#39;attoannum&#39;,
+ &#39;attoarcminute&#39;,
+ &#39;attoarcsecond&#39;,
+ &#39;attoastronomical_unit&#39;,
+ &#39;attobarn&#39;,
+ &#39;attobarye&#39;,
+ &#39;attobit&#39;,
+ &#39;attobyte&#39;,
+ &#39;attocandela&#39;,
+ &#39;attocoulomb&#39;,
+ &#39;attocount&#39;,
+ &#39;attoday&#39;,
+ &#39;attodebye&#39;,
+ &#39;attodegree&#39;,
+ &#39;attodyne&#39;,
+ &#39;attoelectronvolt&#39;,
+ &#39;attofarad&#39;,
+ &#39;attogal&#39;,
+ &#39;attogauss&#39;,
+ &#39;attogram&#39;,
+ &#39;attohenry&#39;,
+ &#39;attohertz&#39;,
+ &#39;attohour&#39;,
+ &#39;attohr&#39;,
+ &#39;attojansky&#39;,
+ &#39;attojoule&#39;,
+ &#39;attokayser&#39;,
+ &#39;attolightyear&#39;,
+ &#39;attoliter&#39;,
+ &#39;attolumen&#39;,
+ &#39;attolux&#39;,
+ &#39;attometer&#39;,
+ &#39;attominute&#39;,
+ &#39;attomole&#39;,
+ &#39;attonewton&#39;,
+ &#39;attoparsec&#39;,
+ &#39;attopascal&#39;,
+ &#39;attophoton&#39;,
+ &#39;attopixel&#39;,
+ &#39;attopoise&#39;,
+ &#39;attoradian&#39;,
+ &#39;attorayleigh&#39;,
+ &#39;attorydberg&#39;,
+ &#39;attosecond&#39;,
+ &#39;attosiemens&#39;,
+ &#39;attosteradian&#39;,
+ &#39;attostokes&#39;,
+ &#39;attotesla&#39;,
+ &#39;attovolt&#39;,
+ &#39;attovoxel&#39;,
+ &#39;attowatt&#39;,
+ &#39;attoweber&#39;,
+ &#39;attoyear&#39;,
+ &#39;au&#39;,
+ &#39;avox&#39;,
+ &#39;avoxel&#39;,
+ &#39;ayr&#39;,
+ &#39;b&#39;,
+ &#39;bar&#39;,
+ &#39;barn&#39;,
+ &#39;barye&#39;,
+ &#39;beam&#39;,
+ &#39;beam_angular_area&#39;,
+ &#39;becquerel&#39;,
+ &#39;bin&#39;,
+ &#39;binary_prefixes&#39;,
+ &#39;bit&#39;,
+ &#39;bol&#39;,
+ &#39;brightness_temperature&#39;,
+ &#39;byte&#39;,
+ &#39;cA&#39;,
+ &#39;cAU&#39;,
+ &#39;cB&#39;,
+ &#39;cBa&#39;,
+ &#39;cC&#39;,
+ &#39;cD&#39;,
+ &#39;cF&#39;,
+ &#39;cG&#39;,
+ &#39;cGal&#39;,
+ &#39;cH&#39;,
+ &#39;cHz&#39;,
+ &#39;cJ&#39;,
+ &#39;cJy&#39;,
+ &#39;cK&#39;,
+ &#39;cL&#39;,
+ &#39;cN&#39;,
+ &#39;cOhm&#39;,
+ &#39;cP&#39;,
+ &#39;cPa&#39;,
+ &#39;cR&#39;,
+ &#39;cRy&#39;,
+ &#39;cS&#39;,
+ &#39;cSt&#39;,
+ &#39;cT&#39;,
+ &#39;cV&#39;,
+ &#39;cW&#39;,
+ &#39;cWb&#39;,
+ &#39;ca&#39;,
+ &#39;cadu&#39;,
+ &#39;candela&#39;,
+ &#39;carcmin&#39;,
+ &#39;carcsec&#39;,
+ &#39;cau&#39;,
+ &#39;cb&#39;,
+ &#39;cbarn&#39;,
+ &#39;cbeam&#39;,
+ &#39;cbin&#39;,
+ &#39;cbit&#39;,
+ &#39;cbyte&#39;,
+ &#39;ccd&#39;,
+ &#39;cchan&#39;,
+ &#39;ccount&#39;,
+ &#39;cct&#39;,
+ &#39;cd&#39;,
+ &#39;cdeg&#39;,
+ &#39;cdyn&#39;,
+ &#39;ceV&#39;,
+ &#39;centiBarye&#39;,
+ &#39;centiDa&#39;,
+ &#39;centiDalton&#39;,
+ &#39;centiDebye&#39;,
+ &#39;centiFarad&#39;,
+ &#39;centiGauss&#39;,
+ &#39;centiHenry&#39;,
+ &#39;centiHertz&#39;,
+ &#39;centiJansky&#39;,
+ &#39;centiJoule&#39;,
+ &#39;centiKayser&#39;,
+ &#39;centiKelvin&#39;,
+ &#39;centiNewton&#39;,
+ &#39;centiOhm&#39;,
+ &#39;centiPascal&#39;,
+ &#39;centiRayleigh&#39;,
+ &#39;centiSiemens&#39;,
+ &#39;centiTesla&#39;,
+ &#39;centiVolt&#39;,
+ &#39;centiWatt&#39;,
+ &#39;centiWeber&#39;,
+ &#39;centiamp&#39;,
+ &#39;centiampere&#39;,
+ &#39;centiannum&#39;,
+ &#39;centiarcminute&#39;,
+ &#39;centiarcsecond&#39;,
+ &#39;centiastronomical_unit&#39;,
+ &#39;centibarn&#39;,
+ &#39;centibarye&#39;,
+ &#39;centibit&#39;,
+ &#39;centibyte&#39;,
+ &#39;centicandela&#39;,
+ &#39;centicoulomb&#39;,
+ &#39;centicount&#39;,
+ &#39;centiday&#39;,
+ &#39;centidebye&#39;,
+ &#39;centidegree&#39;,
+ &#39;centidyne&#39;,
+ &#39;centielectronvolt&#39;,
+ &#39;centifarad&#39;,
+ &#39;centigal&#39;,
+ &#39;centigauss&#39;,
+ &#39;centigram&#39;,
+ &#39;centihenry&#39;,
+ &#39;centihertz&#39;,
+ &#39;centihour&#39;,
+ &#39;centihr&#39;,
+ &#39;centijansky&#39;,
+ &#39;centijoule&#39;,
+ &#39;centikayser&#39;,
+ &#39;centilightyear&#39;,
+ &#39;centiliter&#39;,
+ &#39;centilumen&#39;,
+ &#39;centilux&#39;,
+ &#39;centimeter&#39;,
+ &#39;centiminute&#39;,
+ &#39;centimole&#39;,
+ &#39;centinewton&#39;,
+ &#39;centiparsec&#39;,
+ &#39;centipascal&#39;,
+ &#39;centiphoton&#39;,
+ &#39;centipixel&#39;,
+ &#39;centipoise&#39;,
+ &#39;centiradian&#39;,
+ &#39;centirayleigh&#39;,
+ &#39;centirydberg&#39;,
+ &#39;centisecond&#39;,
+ &#39;centisiemens&#39;,
+ &#39;centisteradian&#39;,
+ &#39;centistokes&#39;,
+ &#39;centitesla&#39;,
+ &#39;centivolt&#39;,
+ &#39;centivoxel&#39;,
+ &#39;centiwatt&#39;,
+ &#39;centiweber&#39;,
+ &#39;centiyear&#39;,
+ &#39;cerg&#39;,
+ &#39;cg&#39;,
+ &#39;cgs&#39;,
+ &#39;ch&#39;,
+ &#39;chan&#39;,
+ &#39;ck&#39;,
+ &#39;cl&#39;,
+ &#39;clm&#39;,
+ &#39;clx&#39;,
+ &#39;clyr&#39;,
+ &#39;cm&#39;,
+ &#39;cmag&#39;,
+ &#39;cmin&#39;,
+ &#39;cmol&#39;,
+ &#39;cohm&#39;,
+ &#39;core&#39;,
+ &#39;coulomb&#39;,
+ &#39;count&#39;,
+ &#39;cpc&#39;,
+ &#39;cph&#39;,
+ &#39;cphoton&#39;,
+ &#39;cpix&#39;,
+ &#39;cpixel&#39;,
+ &#39;crad&#39;,
+ &#39;cs&#39;,
+ &#39;csr&#39;,
+ &#39;ct&#39;,
+ &#39;cu&#39;,
+ &#39;curie&#39;,
+ &#39;cvox&#39;,
+ &#39;cvoxel&#39;,
+ &#39;cy&#39;,
+ &#39;cycle&#39;,
+ &#39;cyr&#39;,
+ &#39;d&#39;,
+ &#39;dA&#39;,
+ &#39;dAU&#39;,
+ &#39;dB&#39;,
+ &#39;dBa&#39;,
+ &#39;dC&#39;,
+ &#39;dD&#39;,
+ &#39;dF&#39;,
+ &#39;dG&#39;,
+ &#39;dGal&#39;,
+ &#39;dH&#39;,
+ &#39;dHz&#39;,
+ &#39;dJ&#39;,
+ &#39;dJy&#39;,
+ &#39;dK&#39;,
+ &#39;dL&#39;,
+ &#39;dN&#39;,
+ &#39;dOhm&#39;,
+ &#39;dP&#39;,
+ &#39;dPa&#39;,
+ &#39;dR&#39;,
+ &#39;dRy&#39;,
+ &#39;dS&#39;,
+ &#39;dSt&#39;,
+ &#39;dT&#39;,
+ ...]
+</pre></div>
+</div>
+</div>
+</div>
+<p>To create a quantity, we multiply a value by a unit.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">coord</span> <span class="o">=</span> <span class="mi">30</span> <span class="o">*</span> <span class="n">u</span><span class="o">.</span><span class="n">deg</span>
+<span class="nb">type</span><span class="p">(</span><span class="n">coord</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>astropy.units.quantity.Quantity
+</pre></div>
+</div>
+</div>
+</div>
+<p>The result is a <code class="docutils literal notranslate"><span class="pre">Quantity</span></code> object.</p>
+<p>Jupyter knows how to display <code class="docutils literal notranslate"><span class="pre">Quantities</span></code> like this:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">coord</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_latex math notranslate nohighlight">
+\[30 \; \mathrm{{}^{\circ}}\]</div>
+</div>
+</div>
+</div>
+<div class="section" id="selecting-a-rectangle">
+<h2>Selecting a rectangle<a class="headerlink" href="#selecting-a-rectangle" title="Permalink to this headline">¶</a></h2>
+<p>Now we’ll select a rectangle from -55 to -45 degrees right ascension and -8 to 4 degrees of declination.</p>
+<p>We’ll define variables to contain these limits.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">phi1_min</span> <span class="o">=</span> <span class="o">-</span><span class="mi">55</span>
+<span class="n">phi1_max</span> <span class="o">=</span> <span class="o">-</span><span class="mi">45</span>
+<span class="n">phi2_min</span> <span class="o">=</span> <span class="o">-</span><span class="mi">8</span>
+<span class="n">phi2_max</span> <span class="o">=</span> <span class="mi">4</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>To represent a rectangle, we’ll use two lists of coordinates and multiply by their units.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">phi1_rect</span> <span class="o">=</span> <span class="p">[</span><span class="n">phi1_min</span><span class="p">,</span> <span class="n">phi1_min</span><span class="p">,</span> <span class="n">phi1_max</span><span class="p">,</span> <span class="n">phi1_max</span><span class="p">]</span> <span class="o">*</span> <span class="n">u</span><span class="o">.</span><span class="n">deg</span>
+<span class="n">phi2_rect</span> <span class="o">=</span> <span class="p">[</span><span class="n">phi2_min</span><span class="p">,</span> <span class="n">phi2_max</span><span class="p">,</span> <span class="n">phi2_max</span><span class="p">,</span> <span class="n">phi2_min</span><span class="p">]</span> <span class="o">*</span> <span class="n">u</span><span class="o">.</span><span class="n">deg</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p><code class="docutils literal notranslate"><span class="pre">phi1_rect</span></code> and <code class="docutils literal notranslate"><span class="pre">phi2_rect</span></code> represent the coordinates of the corners of a rectangle.</p>
+<p>But they are in “<a class="reference external" href="https://gala-astro.readthedocs.io/en/latest/_modules/gala/coordinates/gd1.html">a Heliocentric spherical coordinate system defined by the orbit of the GD1 stream</a>�</p>
+<p>In order to use them in a Gaia query, we have to convert them to <a class="reference external" href="https://en.wikipedia.org/wiki/International_Celestial_Reference_System">International Celestial Reference System</a> (ICRS) coordinates.  We can do that by storing the coordinates in a <code class="docutils literal notranslate"><span class="pre">GD1Koposov10</span></code> object provided by <a class="reference external" href="https://gala-astro.readthedocs.io/en/latest/coordinates/">Gala</a>.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">gala.coordinates</span> <span class="k">as</span> <span class="nn">gc</span>
+
+<span class="n">corners</span> <span class="o">=</span> <span class="n">gc</span><span class="o">.</span><span class="n">GD1Koposov10</span><span class="p">(</span><span class="n">phi1</span><span class="o">=</span><span class="n">phi1_rect</span><span class="p">,</span> <span class="n">phi2</span><span class="o">=</span><span class="n">phi2_rect</span><span class="p">)</span>
+<span class="nb">type</span><span class="p">(</span><span class="n">corners</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>gala.coordinates.gd1.GD1Koposov10
+</pre></div>
+</div>
+</div>
+</div>
+<p>We can display the result like this:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">corners</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;GD1Koposov10 Coordinate: (phi1, phi2) in deg
+    [(-55., -8.), (-55.,  4.), (-45.,  4.), (-45., -8.)]&gt;
+</pre></div>
+</div>
+</div>
+</div>
+<p>Now we can use <code class="docutils literal notranslate"><span class="pre">transform_to</span></code> to convert to ICRS coordinates.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">astropy.coordinates</span> <span class="k">as</span> <span class="nn">coord</span>
+
+<span class="n">corners_icrs</span> <span class="o">=</span> <span class="n">corners</span><span class="o">.</span><span class="n">transform_to</span><span class="p">(</span><span class="n">coord</span><span class="o">.</span><span class="n">ICRS</span><span class="p">)</span>
+<span class="nb">type</span><span class="p">(</span><span class="n">corners_icrs</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>astropy.coordinates.builtin_frames.icrs.ICRS
+</pre></div>
+</div>
+</div>
+</div>
+<p>The result is an <code class="docutils literal notranslate"><span class="pre">ICRS</span></code> object.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">corners_icrs</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;ICRS Coordinate: (ra, dec) in deg
+    [(146.27533314, 19.26190982), (135.42163944, 25.87738723),
+     (141.60264825, 34.3048303 ), (152.81671045, 27.13611254)]&gt;
+</pre></div>
+</div>
+</div>
+</div>
+<p>Notice that a rectangle in one coordinate system is not necessarily a rectangle in another.  In this example, the result is a polygon.</p>
+</div>
+<div class="section" id="selecting-a-polygon">
+<h2>Selecting a polygon<a class="headerlink" href="#selecting-a-polygon" title="Permalink to this headline">¶</a></h2>
+<p>In order to use this polygon as part of an ADQL query, we have to convert it to a string with a comma-separated list of coordinates, as in this example:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">POLYGON(143.65, 20.98, </span>
+<span class="sd">        134.46, 26.39, </span>
+<span class="sd">        140.58, 34.85, </span>
+<span class="sd">        150.16, 29.01)</span>
+<span class="sd">&quot;&quot;&quot;</span>
+</pre></div>
+</div>
+<p><code class="docutils literal notranslate"><span class="pre">corners_icrs</span></code> behaves like a list, so we can use a <code class="docutils literal notranslate"><span class="pre">for</span></code> loop to iterate through the points.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">for</span> <span class="n">point</span> <span class="ow">in</span> <span class="n">corners_icrs</span><span class="p">:</span>
+    <span class="nb">print</span><span class="p">(</span><span class="n">point</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;ICRS Coordinate: (ra, dec) in deg
+    (146.27533314, 19.26190982)&gt;
+&lt;ICRS Coordinate: (ra, dec) in deg
+    (135.42163944, 25.87738723)&gt;
+&lt;ICRS Coordinate: (ra, dec) in deg
+    (141.60264825, 34.3048303)&gt;
+&lt;ICRS Coordinate: (ra, dec) in deg
+    (152.81671045, 27.13611254)&gt;
+</pre></div>
+</div>
+</div>
+</div>
+<p>From that, we can select the coordinates <code class="docutils literal notranslate"><span class="pre">ra</span></code> and <code class="docutils literal notranslate"><span class="pre">dec</span></code>:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">for</span> <span class="n">point</span> <span class="ow">in</span> <span class="n">corners_icrs</span><span class="p">:</span>
+    <span class="nb">print</span><span class="p">(</span><span class="n">point</span><span class="o">.</span><span class="n">ra</span><span class="p">,</span> <span class="n">point</span><span class="o">.</span><span class="n">dec</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>146d16m31.1993s 19d15m42.8754s
+135d25m17.902s 25d52m38.594s
+141d36m09.5337s 34d18m17.3891s
+152d49m00.1576s 27d08m10.0051s
+</pre></div>
+</div>
+</div>
+</div>
+<p>The results are quantities with units, but if we select the <code class="docutils literal notranslate"><span class="pre">value</span></code> part, we get a dimensionless floating-point number.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">for</span> <span class="n">point</span> <span class="ow">in</span> <span class="n">corners_icrs</span><span class="p">:</span>
+    <span class="nb">print</span><span class="p">(</span><span class="n">point</span><span class="o">.</span><span class="n">ra</span><span class="o">.</span><span class="n">value</span><span class="p">,</span> <span class="n">point</span><span class="o">.</span><span class="n">dec</span><span class="o">.</span><span class="n">value</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>146.27533313607782 19.261909820533692
+135.42163944306296 25.87738722767213
+141.60264825107333 34.304830296257144
+152.81671044675923 27.136112541397996
+</pre></div>
+</div>
+</div>
+</div>
+<p>We can use string <code class="docutils literal notranslate"><span class="pre">format</span></code> to convert these numbers to strings.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">point_base</span> <span class="o">=</span> <span class="s2">&quot;</span><span class="si">{point.ra.value}</span><span class="s2">, </span><span class="si">{point.dec.value}</span><span class="s2">&quot;</span>
+
+<span class="n">t</span> <span class="o">=</span> <span class="p">[</span><span class="n">point_base</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">point</span><span class="o">=</span><span class="n">point</span><span class="p">)</span>
+     <span class="k">for</span> <span class="n">point</span> <span class="ow">in</span> <span class="n">corners_icrs</span><span class="p">]</span>
+<span class="n">t</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[&#39;146.27533313607782, 19.261909820533692&#39;,
+ &#39;135.42163944306296, 25.87738722767213&#39;,
+ &#39;141.60264825107333, 34.304830296257144&#39;,
+ &#39;152.81671044675923, 27.136112541397996&#39;]
+</pre></div>
+</div>
+</div>
+</div>
+<p>The result is a list of strings, which we can join into a single string using <code class="docutils literal notranslate"><span class="pre">join</span></code>.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">point_list</span> <span class="o">=</span> <span class="s1">&#39;, &#39;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">t</span><span class="p">)</span>
+<span class="n">point_list</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&#39;146.27533313607782, 19.261909820533692, 135.42163944306296, 25.87738722767213, 141.60264825107333, 34.304830296257144, 152.81671044675923, 27.136112541397996&#39;
+</pre></div>
+</div>
+</div>
+</div>
+<p>Notice that we invoke <code class="docutils literal notranslate"><span class="pre">join</span></code> on a string and pass the list as an argument.</p>
+<p>Before we can assemble the query, we need <code class="docutils literal notranslate"><span class="pre">columns</span></code> again (as we saw in the previous notebook).</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">columns</span> <span class="o">=</span> <span class="s1">&#39;source_id, ra, dec, pmra, pmdec, parallax, parallax_error, radial_velocity&#39;</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>Here’s the base for the query, with format specifiers for <code class="docutils literal notranslate"><span class="pre">columns</span></code> and <code class="docutils literal notranslate"><span class="pre">point_list</span></code>.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">query_base</span> <span class="o">=</span> <span class="s2">&quot;&quot;&quot;SELECT </span><span class="si">{columns}</span><span class="s2"></span>
+<span class="s2">FROM gaiadr2.gaia_source</span>
+<span class="s2">WHERE parallax &lt; 1</span>
+<span class="s2">  AND bp_rp BETWEEN -0.75 AND 2 </span>
+<span class="s2">  AND 1 = CONTAINS(POINT(ra, dec), </span>
+<span class="s2">                   POLYGON(</span><span class="si">{point_list}</span><span class="s2">))</span>
+<span class="s2">&quot;&quot;&quot;</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>And here’s the result:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">query</span> <span class="o">=</span> <span class="n">query_base</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="n">columns</span><span class="p">,</span> 
+                          <span class="n">point_list</span><span class="o">=</span><span class="n">point_list</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">query</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>SELECT source_id, ra, dec, pmra, pmdec, parallax, parallax_error, radial_velocity
+FROM gaiadr2.gaia_source
+WHERE parallax &lt; 1
+  AND bp_rp BETWEEN -0.75 AND 2 
+  AND 1 = CONTAINS(POINT(ra, dec), 
+                   POLYGON(146.27533313607782, 19.261909820533692, 135.42163944306296, 25.87738722767213, 141.60264825107333, 34.304830296257144, 152.81671044675923, 27.136112541397996))
+</pre></div>
+</div>
+</div>
+</div>
+<p>As always, we should take a minute to proof-read the query before we launch it.</p>
+<p>The result will be bigger than our previous queries, so it will take a little longer.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">job</span> <span class="o">=</span> <span class="n">Gaia</span><span class="o">.</span><span class="n">launch_job_async</span><span class="p">(</span><span class="n">query</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">job</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>INFO: Query finished. [astroquery.utils.tap.core]
+&lt;Table length=140340&gt;
+      name       dtype    unit                              description                             n_bad 
+--------------- ------- -------- ------------------------------------------------------------------ ------
+      source_id   int64          Unique source identifier (unique within a particular Data Release)      0
+             ra float64      deg                                                    Right ascension      0
+            dec float64      deg                                                        Declination      0
+           pmra float64 mas / yr                         Proper motion in right ascension direction      0
+          pmdec float64 mas / yr                             Proper motion in declination direction      0
+       parallax float64      mas                                                           Parallax      0
+ parallax_error float64      mas                                         Standard error of parallax      0
+radial_velocity float64   km / s                                                    Radial velocity 139374
+Jobid: 1603114980658O
+Phase: COMPLETED
+Owner: None
+Output file: async_20201019094300.vot
+Results: None
+</pre></div>
+</div>
+</div>
+</div>
+<p>Here are the results.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">results</span> <span class="o">=</span> <span class="n">job</span><span class="o">.</span><span class="n">get_results</span><span class="p">()</span>
+<span class="nb">len</span><span class="p">(</span><span class="n">results</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>140340
+</pre></div>
+</div>
+</div>
+</div>
+<p>There are more than 100,000 stars in this polygon, but that’s a manageable size to work with.</p>
+</div>
+<div class="section" id="saving-results">
+<h2>Saving results<a class="headerlink" href="#saving-results" title="Permalink to this headline">¶</a></h2>
+<p>This is the set of stars we’ll work with in the next step.  But since we have a substantial dataset now, this is a good time to save it.</p>
+<p>Storing the data in a file means we can shut down this notebook and pick up where we left off without running the previous query again.</p>
+<p>Astropy <code class="docutils literal notranslate"><span class="pre">Table</span></code> objects provide <code class="docutils literal notranslate"><span class="pre">write</span></code>, which writes the table to disk.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">filename</span> <span class="o">=</span> <span class="s1">&#39;gd1_results.fits&#39;</span>
+<span class="n">results</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="n">filename</span><span class="p">,</span> <span class="n">overwrite</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>Because the filename ends with <code class="docutils literal notranslate"><span class="pre">fits</span></code>, the table is written in the <a class="reference external" href="https://en.wikipedia.org/wiki/FITS">FITS format</a>, which preserves the metadata associated with the table.</p>
+<p>If the file already exists, the <code class="docutils literal notranslate"><span class="pre">overwrite</span></code> argument causes it to be overwritten.</p>
+<p>To see how big the file is, we can use <code class="docutils literal notranslate"><span class="pre">ls</span></code> with the <code class="docutils literal notranslate"><span class="pre">-lh</span></code> option, which prints information about the file including its size in human-readable form.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">!</span>ls -lh gd1_results.fits
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>-rw-rw-r-- 1 downey downey 8.6M Oct 19 09:43 gd1_results.fits
+</pre></div>
+</div>
+</div>
+</div>
+<p>The file is about 8.6 MB.  If you are using Windows, <code class="docutils literal notranslate"><span class="pre">ls</span></code> might not work; in that case, try:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>!dir gd1_results.fits
+</pre></div>
+</div>
+</div>
+<div class="section" id="summary">
+<h2>Summary<a class="headerlink" href="#summary" title="Permalink to this headline">¶</a></h2>
+<p>In this notebook, we composed more complex queries to select stars within a polygonal region of the sky.  Then we downloaded the results and saved them in a FITS file.</p>
+<p>In the next notebook, we’ll reload the data from this file and replicate the next step in the analysis, using proper motion to identify stars likely to be in GD-1.</p>
+</div>
+<div class="section" id="best-practices">
+<h2>Best practices<a class="headerlink" href="#best-practices" title="Permalink to this headline">¶</a></h2>
+<ul class="simple">
+<li><p>For measurements with units, use <code class="docutils literal notranslate"><span class="pre">Quantity</span></code> objects that represent units explicitly and check for errors.</p></li>
+<li><p>Use the <code class="docutils literal notranslate"><span class="pre">format</span></code> function to compose queries; it is often faster and less error-prone.</p></li>
+<li><p>Develop queries incrementally: start with something simple, test it, and add a little bit at a time.</p></li>
+<li><p>Once you have a query working, save the data in a local file.  If you shut down the notebook and come back to it later, you can reload the file; you don’t have to run the query again.</p></li>
+</ul>
+</div>
+</div>
+
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+   Astronomical Data in Python
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+   Lesson 1
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+  <a class="reference internal" href="../02_coords.html">
+   Lesson 2
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+                
+  <div class="section" id="astronomical-data-in-python">
+<h1>Astronomical Data in Python<a class="headerlink" href="#astronomical-data-in-python" title="Permalink to this headline">¶</a></h1>
+<p><em>Astronomical Data in Python</em> is an introduction to tools and practices for working with astronomical data.  Topics covered include:</p>
+<ul class="simple">
+<li><p>Writing queries that select and download data from a database.</p></li>
+<li><p>Using data stored in an Astropy <code class="docutils literal notranslate"><span class="pre">Table</span></code> or Pandas <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code>.</p></li>
+<li><p>Working with coordinates and other quantities with units.</p></li>
+<li><p>Storing data in various formats.</p></li>
+<li><p>Performing database join operations that combine data from multiple tables.</p></li>
+<li><p>Visualizing data and preparing publication-quality figures.</p></li>
+</ul>
+<p>As a running example, we will replicate part of the analysis in a recent paper, “<a class="reference external" href="https://arxiv.org/abs/1805.00425">Off the beaten path: Gaia reveals GD-1 stars outside of the main stream</a>� by Adrian M. Price-Whelan and Ana Bonaca.</p>
+<p>This material was developed in collaboration with <a class="reference external" href="https://carpentries.org/">The Carpentries</a> and the Astronomy Curriculum Development Committee, and supported by funding from the American Institute of Physics through the American Astronomical Society.</p>
+<p>I am grateful for contributions from the members of the committee – Azalee Bostroem, Rodolfo Montez, and Phil Rosenfield – and from Erin Becker, Brett Morris and Adrian Price-Whelan.</p>
+<p>The original format of this material is a series of Jupyter notebooks.  Using the
+links below, you can read the notebooks on NBViewer or run them on Colab.  If you
+want to run the notebooks in your own environment, you can download them from
+this repository and follow the instructions below to set up your environment.</p>
+<p>This material is also available in the form of <a class="reference external" href="https://datacarpentry.github.io/astronomy-python">Carpentries lessons</a>, but you should be
+aware that these versions might diverge in the future.</p>
+<p><strong>Prerequisites</strong></p>
+<p>This material should be accessible to people familiar with basic Python, but not necessarily the libraries we will use, like Astropy or Pandas.  If you are familiar with Python lists and dictionaries, and you know how to write a function that takes parameters and returns a value, that should be enough.</p>
+<p>We assume that you are familiar with astronomy at the undergraduate level, but we will not assume specialized knowledge of the datasets or analysis methods we’ll use.</p>
+<p><strong>Notebook 1</strong></p>
+<p>This notebook demonstrates the following steps:</p>
+<ol class="simple">
+<li><p>Making a connection to the Gaia server,</p></li>
+<li><p>Exploring information about the database and the tables it contains,</p></li>
+<li><p>Writing a query and sending it to the server, and finally</p></li>
+<li><p>Downloading the response from the server as an Astropy <code class="docutils literal notranslate"><span class="pre">Table</span></code>.</p></li>
+</ol>
+<p>Press this button to run this notebook on Colab:</p>
+<p><a class="reference external" href="https://colab.research.google.com/github/AllenDowney/AstronomicalData/blob/main/01_query.ipynb"><img src="run_on_colab_small.png"></a></p>
+<p><a class="reference external" href="https://nbviewer.jupyter.org/github/AllenDowney/AstronomicalData/blob/main/01_query.ipynb">or click here to read it on NBViewer</a></p>
+<p><strong>Notebook 2</strong></p>
+<p>This notebook starts with an example that does a “cone search�; that is, it selects stars that appear in a circular region of the sky.</p>
+<p>Then, to select stars in the vicinity of GD-1, we:</p>
+<ul class="simple">
+<li><p>Use <code class="docutils literal notranslate"><span class="pre">Quantity</span></code> objects to represent measurements with units.</p></li>
+<li><p>Use the <code class="docutils literal notranslate"><span class="pre">Gala</span></code> library to convert coordinates from one frame to another.</p></li>
+<li><p>Use the ADQL keywords <code class="docutils literal notranslate"><span class="pre">POLYGON</span></code>, <code class="docutils literal notranslate"><span class="pre">CONTAINS</span></code>, and <code class="docutils literal notranslate"><span class="pre">POINT</span></code> to select stars that fall within a polygonal region.</p></li>
+<li><p>Submit a query and download the results.</p></li>
+<li><p>Store the results in a FITS file.</p></li>
+</ul>
+<p>Press this button to run this notebook on Colab:</p>
+<p><a class="reference external" href="https://colab.research.google.com/github/AllenDowney/AstronomicalData/blob/main/02_coords.ipynb"><img src="run_on_colab_small.png"></a></p>
+<p><a class="reference external" href="https://nbviewer.jupyter.org/github/AllenDowney/AstronomicalData/blob/main/02_coords.ipynb">or click here to read it on NBViewer</a></p>
+<p><strong>Notebook 3</strong></p>
+<p>Here are the steps in this notebook:</p>
+<ol class="simple">
+<li><p>We’ll read back the results from the previous notebook, which we saved in a FITS file.</p></li>
+<li><p>Then we’ll transform the coordinates and proper motion data from ICRS back to the coordinate frame of GD-1.</p></li>
+<li><p>We’ll put those results into a Pandas <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code>, which we’ll use to select stars near the centerline of GD-1.</p></li>
+<li><p>Plotting the proper motion of those stars, we’ll identify a region of proper motion for stars that are likely to be in GD-1.</p></li>
+<li><p>Finally, we’ll select and plot the stars whose proper motion is in that region.</p></li>
+</ol>
+<p>Press this button to run this notebook on Colab:</p>
+<p><a class="reference external" href="https://colab.research.google.com/github/AllenDowney/AstronomicalData/blob/main/03_motion.ipynb"><img src="run_on_colab_small.png"></a></p>
+<p><a class="reference external" href="https://nbviewer.jupyter.org/github/AllenDowney/AstronomicalData/blob/main/03_motion.ipynb">or click here to read it on NBViewer</a></p>
+<p><strong>Notebook 4</strong></p>
+<p>Here are the steps in this notebook:</p>
+<ol class="simple">
+<li><p>Using data from the previous notebook, we’ll identify the values of proper motion for stars likely to be in GD-1.</p></li>
+<li><p>Then we’ll compose an ADQL query that selects stars based on proper motion, so we can download only the data we need.</p></li>
+<li><p>We’ll also see how to write the results to a CSV file.</p></li>
+</ol>
+<p>That will make it possible to search a bigger region of the sky in a single query.</p>
+<p>Press this button to run this notebook on Colab:</p>
+<p><a class="reference external" href="https://colab.research.google.com/github/AllenDowney/AstronomicalData/blob/main/04_select.ipynb"><img src="run_on_colab_small.png"></a></p>
+<p><a class="reference external" href="https://nbviewer.jupyter.org/github/AllenDowney/AstronomicalData/blob/main/04_select.ipynb">or click here to read it on NBViewer</a></p>
+<p><strong>Notebook 5</strong></p>
+<p>Here are the steps in this notebook:</p>
+<ol class="simple">
+<li><p>We’ll reload the candidate stars we identified in the previous notebook.</p></li>
+<li><p>Then we’ll run a query on the Gaia server that uploads the table of candidates and uses a <code class="docutils literal notranslate"><span class="pre">JOIN</span></code> operation to select photometry data for the candidate stars.</p></li>
+<li><p>We’ll write the results to a file for use in the next notebook.</p></li>
+</ol>
+<p>Press this button to run this notebook on Colab:</p>
+<p><a class="reference external" href="https://colab.research.google.com/github/AllenDowney/AstronomicalData/blob/main/05_join.ipynb"><img src="run_on_colab_small.png"></a></p>
+<p><a class="reference external" href="https://nbviewer.jupyter.org/github/AllenDowney/AstronomicalData/blob/main/05_join.ipynb">or click here to read it on NBViewer</a></p>
+<p><strong>Notebook 6</strong></p>
+<p>Here are the steps in this notebook:</p>
+<ol class="simple">
+<li><p>We’ll reload the data from the previous notebook and make a color-magnitude diagram.</p></li>
+<li><p>Then we’ll specify a polygon in the diagram that contains stars with the photometry we expect.</p></li>
+<li><p>Then we’ll merge the photometry data with the list of candidate stars, storing the result in a Pandas <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code>.</p></li>
+</ol>
+<p>Press this button to run this notebook on Colab:</p>
+<p><a class="reference external" href="https://colab.research.google.com/github/AllenDowney/AstronomicalData/blob/main/06_photo.ipynb"><img src="run_on_colab_small.png"></a></p>
+<p><a class="reference external" href="https://nbviewer.jupyter.org/github/AllenDowney/AstronomicalData/blob/main/06_photo.ipynb">or click here to read it on NBViewer</a></p>
+<p><strong>Notebook 7</strong></p>
+<p>Here are the steps in this notebook:</p>
+<ol class="simple">
+<li><p>Starting with the figure from the previous notebook, we’ll add annotations to present the results more clearly.</p></li>
+<li><p>The we’ll see several ways to customize figures to make them more appealing and effective.</p></li>
+<li><p>Finally, we’ll see how to make a figure with multiple panels or subplots.</p></li>
+</ol>
+<p>Press this button to run this notebook on Colab:</p>
+<p><a class="reference external" href="https://colab.research.google.com/github/AllenDowney/AstronomicalData/blob/main/07_plot.ipynb"><img src="run_on_colab_small.png"></a></p>
+<p><a class="reference external" href="https://nbviewer.jupyter.org/github/AllenDowney/AstronomicalData/blob/main/07_plot.ipynb">or click here to read it on NBViewer</a></p>
+<p><strong>Installation instructions</strong></p>
+<p>Coming soon.</p>
+</div>
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+      
+  <link rel="stylesheet"
+    href="../../../../../../../_static/vendor/open-sans_all/1.44.1/index.css">
+  <link rel="stylesheet"
+    href="../../../../../../../_static/vendor/lato_latin-ext/1.44.1/index.css">
+
+    
+    <link rel="stylesheet" href="../../../../../../../_static/sphinx-book-theme.bfb7730f9caf2ec0b46a44615585038c.css" type="text/css" />
+    <link rel="stylesheet" href="../../../../../../../_static/pygments.css" type="text/css" />
+    <link rel="stylesheet" type="text/css" href="../../../../../../../_static/togglebutton.css" />
+    <link rel="stylesheet" type="text/css" href="../../../../../../../_static/copybutton.css" />
+    <link rel="stylesheet" type="text/css" href="../../../../../../../_static/mystnb.css" />
+    <link rel="stylesheet" type="text/css" href="../../../../../../../_static/sphinx-thebe.css" />
+    <link rel="stylesheet" type="text/css" href="../../../../../../../_static/panels-main.c949a650a448cc0ae9fd3441c0e17fb0.css" />
+    <link rel="stylesheet" type="text/css" href="../../../../../../../_static/panels-variables.06eb56fa6e07937060861dad626602ad.css" />
+    
+  <link rel="preload" as="script" href="../../../../../../../_static/js/index.30270b6e4c972e43c488.js">
+
+    <script id="documentation_options" data-url_root="../../../../../../../" src="../../../../../../../_static/documentation_options.js"></script>
+    <script src="../../../../../../../_static/jquery.js"></script>
+    <script src="../../../../../../../_static/underscore.js"></script>
+    <script src="../../../../../../../_static/doctools.js"></script>
+    <script src="../../../../../../../_static/language_data.js"></script>
+    <script src="../../../../../../../_static/togglebutton.js"></script>
+    <script src="../../../../../../../_static/clipboard.min.js"></script>
+    <script src="../../../../../../../_static/copybutton.js"></script>
+    <script >var togglebuttonSelector = '.toggle, .admonition.dropdown, .tag_hide_input div.cell_input, .tag_hide-input div.cell_input, .tag_hide_output div.cell_output, .tag_hide-output div.cell_output, .tag_hide_cell.cell, .tag_hide-cell.cell';</script>
+    <script src="../../../../../../../_static/sphinx-book-theme.be0a4a0c39cd630af62a2fcf693f3f06.js"></script>
+    <script async="async" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.7/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+    <script type="text/x-mathjax-config">MathJax.Hub.Config({"tex2jax": {"inlineMath": [["\\(", "\\)"]], "displayMath": [["\\[", "\\]"]], "processRefs": false, "processEnvironments": false}})</script>
+    <script async="async" src="https://unpkg.com/thebelab@latest/lib/index.js"></script>
+    <script >
+        const thebe_selector = ".thebe"
+        const thebe_selector_input = "pre"
+        const thebe_selector_output = ".output"
+    </script>
+    <script async="async" src="../../../../../../../_static/sphinx-thebe.js"></script>
+    <link rel="index" title="Index" href="../../../../../../../genindex.html" />
+    <link rel="search" title="Search" href="../../../../../../../search.html" />
+
+    <meta name="viewport" content="width=device-width, initial-scale=1" />
+    <meta name="docsearch:language" content="en" />
+
+
+
+  </head>
+  <body data-spy="scroll" data-target="#bd-toc-nav" data-offset="80">
+    
+
+    <div class="container-xl">
+      <div class="row">
+          
+<div class="col-12 col-md-3 bd-sidebar site-navigation show" id="site-navigation">
+    
+        <div class="navbar-brand-box">
+<a class="navbar-brand text-wrap" href="../../../../../../../index.html">
+  
+  
+  <h1 class="site-logo" id="site-title">Astronomical Data in Python</h1>
+  
+</a>
+</div>
+
+<form class="bd-search d-flex align-items-center" action="../../../../../../../search.html" method="get">
+  <i class="icon fas fa-search"></i>
+  <input type="search" class="form-control" name="q" id="search-input" placeholder="Search this book..." aria-label="Search this book..." autocomplete="off" >
+</form>
+
+<nav class="bd-links" id="bd-docs-nav" aria-label="Main navigation">
+  <ul class="nav sidenav_l1">
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../../../../../README.html">
+   Astronomical Data in Python
+  </a>
+ </li>
+</ul>
+<ul class="nav sidenav_l1">
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../../../../../01_query.html">
+   Lesson 1
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../../../../../02_coords.html">
+   Lesson 2
+  </a>
+ </li>
+</ul>
+
+</nav>
+
+ <!-- To handle the deprecated key -->
+
+<div class="navbar_extra_footer">
+  Powered by <a href="https://jupyterbook.org">Jupyter Book</a>
+</div>
+
+</div>
+
+
+          
+
+
+          
+<main class="col py-md-3 pl-md-4 bd-content overflow-auto" role="main">
+    
+    <div class="row topbar fixed-top container-xl">
+    <div class="col-12 col-md-3 bd-topbar-whitespace site-navigation show">
+    </div>
+    <div class="col pl-2 topbar-main">
+        
+        <button id="navbar-toggler" class="navbar-toggler ml-0" type="button" data-toggle="collapse"
+            data-toggle="tooltip" data-placement="bottom" data-target=".site-navigation" aria-controls="navbar-menu"
+            aria-expanded="true" aria-label="Toggle navigation" aria-controls="site-navigation"
+            title="Toggle navigation" data-toggle="tooltip" data-placement="left">
+            <i class="fas fa-bars"></i>
+            <i class="fas fa-arrow-left"></i>
+            <i class="fas fa-arrow-up"></i>
+        </button>
+        
+        <div class="dropdown-buttons-trigger">
+    <button id="dropdown-buttons-trigger" class="btn btn-secondary topbarbtn" aria-label="Download this page"><i
+            class="fas fa-download"></i></button>
+
+    
+    <div class="dropdown-buttons">
+        <!-- ipynb file if we had a myst markdown file -->
+        
+        <!-- Download raw file -->
+        <a class="dropdown-buttons" href="../../../../../../../_sources/AstronomicalData/_build/html/_static/vendor/lato_latin-ext/1.44.1/LICENSE.md"><button type="button"
+                class="btn btn-secondary topbarbtn" title="Download source file" data-toggle="tooltip"
+                data-placement="left">.md</button></a>
+        <!-- Download PDF via print -->
+        <button type="button" id="download-print" class="btn btn-secondary topbarbtn" title="Print to PDF"
+            onClick="window.print()" data-toggle="tooltip" data-placement="left">.pdf</button>
+    </div>
+    
+</div>
+        <!-- Source interaction buttons -->
+
+
+        <!-- Full screen (wrap in <a> to have style consistency -->
+        <a class="full-screen-button"><button type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip"
+                data-placement="bottom" onclick="toggleFullScreen()" title="Fullscreen mode"><i
+                    class="fas fa-expand"></i></button></a>
+
+        <!-- Launch buttons -->
+
+    </div>
+
+    <!-- Table of contents -->
+    <div class="d-none d-md-block col-md-2 bd-toc show">
+        
+        <div class="tocsection onthispage pt-5 pb-3">
+            <i class="fas fa-list"></i> Contents
+        </div>
+        <nav id="bd-toc-nav">
+            <ul class="simple nav section-nav flex-column">
+</ul>
+
+        </nav>
+        
+    </div>
+</div>
+    <div id="main-content" class="row">
+        <div class="col-12 col-md-9 pl-md-3 pr-md-0">
+        
+              <div>
+                
+  <p>Copyright (c) 2019 Jan Bednar</p>
+<p>Permission is hereby granted, free of charge, to any person obtaining
+a copy of this software and associated documentation files (the
+“Software�), to deal in the Software without restriction, including
+without limitation the rights to use, copy, modify, merge, publish,
+distribute, sublicense, and/or sell copies of the Software, and to
+permit persons to whom the Software is furnished to do so, subject to
+the following conditions:</p>
+<p>The above copyright notice and this permission notice shall be
+included in all copies or substantial portions of the Software.</p>
+<p>THE SOFTWARE IS PROVIDED “AS IS�, WITHOUT WARRANTY OF ANY KIND,
+EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE
+LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
+OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION
+WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.</p>
+
+    <script type="text/x-thebe-config">
+    {
+        requestKernel: true,
+        binderOptions: {
+            repo: "binder-examples/jupyter-stacks-datascience",
+            ref: "master",
+        },
+        codeMirrorConfig: {
+            theme: "abcdef",
+            mode: "python"
+        },
+        kernelOptions: {
+            kernelName: "python3",
+            path: "./AstronomicalData/_build/html/_static/vendor/lato_latin-ext/1.44.1"
+        },
+        predefinedOutput: true
+    }
+    </script>
+    <script>kernelName = 'python3'</script>
+
+              </div>
+              
+        </div>
+    </div>
+    
+    
+    <div class='prev-next-bottom'>
+        
+
+    </div>
+    <footer class="footer mt-5 mt-md-0">
+    <div class="container">
+      <p>
+        
+          By Allen B. Downey<br/>
+        
+            &copy; Copyright 2020.<br/>
+      </p>
+    </div>
+  </footer>
+</main>
+
+
+      </div>
+    </div>
+
+    
+  <script src="../../../../../../../_static/js/index.30270b6e4c972e43c488.js"></script>
+
+
+    
+  </body>
+</html>
\ No newline at end of file
diff --git a/_build/html/AstronomicalData/_build/html/_static/vendor/open-sans_all/1.44.1/LICENSE.html b/_build/html/AstronomicalData/_build/html/_static/vendor/open-sans_all/1.44.1/LICENSE.html
new file mode 100644
index 0000000..2b73475
--- /dev/null
+++ b/_build/html/AstronomicalData/_build/html/_static/vendor/open-sans_all/1.44.1/LICENSE.html
@@ -0,0 +1,260 @@
+
+<!DOCTYPE html>
+
+<html>
+  <head>
+    <meta charset="utf-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+    <title>&lt;no title&gt; &#8212; Astronomical Data in Python</title>
+    
+  <link rel="stylesheet" href="../../../../../../../_static/css/index.d431a4ee1c1efae0e38bdfebc22debff.css">
+
+    
+  <link rel="stylesheet"
+    href="../../../../../../../_static/vendor/fontawesome/5.13.0/css/all.min.css">
+  <link rel="preload" as="font" type="font/woff2" crossorigin
+    href="../../../../../../../_static/vendor/fontawesome/5.13.0/webfonts/fa-solid-900.woff2">
+  <link rel="preload" as="font" type="font/woff2" crossorigin
+    href="../../../../../../../_static/vendor/fontawesome/5.13.0/webfonts/fa-brands-400.woff2">
+
+    
+      
+  <link rel="stylesheet"
+    href="../../../../../../../_static/vendor/open-sans_all/1.44.1/index.css">
+  <link rel="stylesheet"
+    href="../../../../../../../_static/vendor/lato_latin-ext/1.44.1/index.css">
+
+    
+    <link rel="stylesheet" href="../../../../../../../_static/sphinx-book-theme.bfb7730f9caf2ec0b46a44615585038c.css" type="text/css" />
+    <link rel="stylesheet" href="../../../../../../../_static/pygments.css" type="text/css" />
+    <link rel="stylesheet" type="text/css" href="../../../../../../../_static/togglebutton.css" />
+    <link rel="stylesheet" type="text/css" href="../../../../../../../_static/copybutton.css" />
+    <link rel="stylesheet" type="text/css" href="../../../../../../../_static/mystnb.css" />
+    <link rel="stylesheet" type="text/css" href="../../../../../../../_static/sphinx-thebe.css" />
+    <link rel="stylesheet" type="text/css" href="../../../../../../../_static/panels-main.c949a650a448cc0ae9fd3441c0e17fb0.css" />
+    <link rel="stylesheet" type="text/css" href="../../../../../../../_static/panels-variables.06eb56fa6e07937060861dad626602ad.css" />
+    
+  <link rel="preload" as="script" href="../../../../../../../_static/js/index.30270b6e4c972e43c488.js">
+
+    <script id="documentation_options" data-url_root="../../../../../../../" src="../../../../../../../_static/documentation_options.js"></script>
+    <script src="../../../../../../../_static/jquery.js"></script>
+    <script src="../../../../../../../_static/underscore.js"></script>
+    <script src="../../../../../../../_static/doctools.js"></script>
+    <script src="../../../../../../../_static/language_data.js"></script>
+    <script src="../../../../../../../_static/togglebutton.js"></script>
+    <script src="../../../../../../../_static/clipboard.min.js"></script>
+    <script src="../../../../../../../_static/copybutton.js"></script>
+    <script >var togglebuttonSelector = '.toggle, .admonition.dropdown, .tag_hide_input div.cell_input, .tag_hide-input div.cell_input, .tag_hide_output div.cell_output, .tag_hide-output div.cell_output, .tag_hide_cell.cell, .tag_hide-cell.cell';</script>
+    <script src="../../../../../../../_static/sphinx-book-theme.be0a4a0c39cd630af62a2fcf693f3f06.js"></script>
+    <script async="async" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.7/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+    <script type="text/x-mathjax-config">MathJax.Hub.Config({"tex2jax": {"inlineMath": [["\\(", "\\)"]], "displayMath": [["\\[", "\\]"]], "processRefs": false, "processEnvironments": false}})</script>
+    <script async="async" src="https://unpkg.com/thebelab@latest/lib/index.js"></script>
+    <script >
+        const thebe_selector = ".thebe"
+        const thebe_selector_input = "pre"
+        const thebe_selector_output = ".output"
+    </script>
+    <script async="async" src="../../../../../../../_static/sphinx-thebe.js"></script>
+    <link rel="index" title="Index" href="../../../../../../../genindex.html" />
+    <link rel="search" title="Search" href="../../../../../../../search.html" />
+
+    <meta name="viewport" content="width=device-width, initial-scale=1" />
+    <meta name="docsearch:language" content="en" />
+
+
+
+  </head>
+  <body data-spy="scroll" data-target="#bd-toc-nav" data-offset="80">
+    
+
+    <div class="container-xl">
+      <div class="row">
+          
+<div class="col-12 col-md-3 bd-sidebar site-navigation show" id="site-navigation">
+    
+        <div class="navbar-brand-box">
+<a class="navbar-brand text-wrap" href="../../../../../../../index.html">
+  
+  
+  <h1 class="site-logo" id="site-title">Astronomical Data in Python</h1>
+  
+</a>
+</div>
+
+<form class="bd-search d-flex align-items-center" action="../../../../../../../search.html" method="get">
+  <i class="icon fas fa-search"></i>
+  <input type="search" class="form-control" name="q" id="search-input" placeholder="Search this book..." aria-label="Search this book..." autocomplete="off" >
+</form>
+
+<nav class="bd-links" id="bd-docs-nav" aria-label="Main navigation">
+  <ul class="nav sidenav_l1">
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../../../../../README.html">
+   Astronomical Data in Python
+  </a>
+ </li>
+</ul>
+<ul class="nav sidenav_l1">
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../../../../../01_query.html">
+   Lesson 1
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../../../../../02_coords.html">
+   Lesson 2
+  </a>
+ </li>
+</ul>
+
+</nav>
+
+ <!-- To handle the deprecated key -->
+
+<div class="navbar_extra_footer">
+  Powered by <a href="https://jupyterbook.org">Jupyter Book</a>
+</div>
+
+</div>
+
+
+          
+
+
+          
+<main class="col py-md-3 pl-md-4 bd-content overflow-auto" role="main">
+    
+    <div class="row topbar fixed-top container-xl">
+    <div class="col-12 col-md-3 bd-topbar-whitespace site-navigation show">
+    </div>
+    <div class="col pl-2 topbar-main">
+        
+        <button id="navbar-toggler" class="navbar-toggler ml-0" type="button" data-toggle="collapse"
+            data-toggle="tooltip" data-placement="bottom" data-target=".site-navigation" aria-controls="navbar-menu"
+            aria-expanded="true" aria-label="Toggle navigation" aria-controls="site-navigation"
+            title="Toggle navigation" data-toggle="tooltip" data-placement="left">
+            <i class="fas fa-bars"></i>
+            <i class="fas fa-arrow-left"></i>
+            <i class="fas fa-arrow-up"></i>
+        </button>
+        
+        <div class="dropdown-buttons-trigger">
+    <button id="dropdown-buttons-trigger" class="btn btn-secondary topbarbtn" aria-label="Download this page"><i
+            class="fas fa-download"></i></button>
+
+    
+    <div class="dropdown-buttons">
+        <!-- ipynb file if we had a myst markdown file -->
+        
+        <!-- Download raw file -->
+        <a class="dropdown-buttons" href="../../../../../../../_sources/AstronomicalData/_build/html/_static/vendor/open-sans_all/1.44.1/LICENSE.md"><button type="button"
+                class="btn btn-secondary topbarbtn" title="Download source file" data-toggle="tooltip"
+                data-placement="left">.md</button></a>
+        <!-- Download PDF via print -->
+        <button type="button" id="download-print" class="btn btn-secondary topbarbtn" title="Print to PDF"
+            onClick="window.print()" data-toggle="tooltip" data-placement="left">.pdf</button>
+    </div>
+    
+</div>
+        <!-- Source interaction buttons -->
+
+
+        <!-- Full screen (wrap in <a> to have style consistency -->
+        <a class="full-screen-button"><button type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip"
+                data-placement="bottom" onclick="toggleFullScreen()" title="Fullscreen mode"><i
+                    class="fas fa-expand"></i></button></a>
+
+        <!-- Launch buttons -->
+
+    </div>
+
+    <!-- Table of contents -->
+    <div class="d-none d-md-block col-md-2 bd-toc show">
+        
+        <div class="tocsection onthispage pt-5 pb-3">
+            <i class="fas fa-list"></i> Contents
+        </div>
+        <nav id="bd-toc-nav">
+            <ul class="simple nav section-nav flex-column">
+</ul>
+
+        </nav>
+        
+    </div>
+</div>
+    <div id="main-content" class="row">
+        <div class="col-12 col-md-9 pl-md-3 pr-md-0">
+        
+              <div>
+                
+  <p>Copyright (c) 2019 Jan Bednar</p>
+<p>Permission is hereby granted, free of charge, to any person obtaining
+a copy of this software and associated documentation files (the
+“Software�), to deal in the Software without restriction, including
+without limitation the rights to use, copy, modify, merge, publish,
+distribute, sublicense, and/or sell copies of the Software, and to
+permit persons to whom the Software is furnished to do so, subject to
+the following conditions:</p>
+<p>The above copyright notice and this permission notice shall be
+included in all copies or substantial portions of the Software.</p>
+<p>THE SOFTWARE IS PROVIDED “AS IS�, WITHOUT WARRANTY OF ANY KIND,
+EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE
+LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
+OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION
+WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.</p>
+
+    <script type="text/x-thebe-config">
+    {
+        requestKernel: true,
+        binderOptions: {
+            repo: "binder-examples/jupyter-stacks-datascience",
+            ref: "master",
+        },
+        codeMirrorConfig: {
+            theme: "abcdef",
+            mode: "python"
+        },
+        kernelOptions: {
+            kernelName: "python3",
+            path: "./AstronomicalData/_build/html/_static/vendor/open-sans_all/1.44.1"
+        },
+        predefinedOutput: true
+    }
+    </script>
+    <script>kernelName = 'python3'</script>
+
+              </div>
+              
+        </div>
+    </div>
+    
+    
+    <div class='prev-next-bottom'>
+        
+
+    </div>
+    <footer class="footer mt-5 mt-md-0">
+    <div class="container">
+      <p>
+        
+          By Allen B. Downey<br/>
+        
+            &copy; Copyright 2020.<br/>
+      </p>
+    </div>
+  </footer>
+</main>
+
+
+      </div>
+    </div>
+
+    
+  <script src="../../../../../../../_static/js/index.30270b6e4c972e43c488.js"></script>
+
+
+    
+  </body>
+</html>
\ No newline at end of file
diff --git a/_build/html/AstronomicalData/_build/jupyter_execute/01_query.html b/_build/html/AstronomicalData/_build/jupyter_execute/01_query.html
new file mode 100644
index 0000000..752cca2
--- /dev/null
+++ b/_build/html/AstronomicalData/_build/jupyter_execute/01_query.html
@@ -0,0 +1,1384 @@
+
+<!DOCTYPE html>
+
+<html>
+  <head>
+    <meta charset="utf-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+    <title>Lesson 1 &#8212; Astronomical Data in Python</title>
+    
+  <link rel="stylesheet" href="../../../_static/css/index.d431a4ee1c1efae0e38bdfebc22debff.css">
+
+    
+  <link rel="stylesheet"
+    href="../../../_static/vendor/fontawesome/5.13.0/css/all.min.css">
+  <link rel="preload" as="font" type="font/woff2" crossorigin
+    href="../../../_static/vendor/fontawesome/5.13.0/webfonts/fa-solid-900.woff2">
+  <link rel="preload" as="font" type="font/woff2" crossorigin
+    href="../../../_static/vendor/fontawesome/5.13.0/webfonts/fa-brands-400.woff2">
+
+    
+      
+  <link rel="stylesheet"
+    href="../../../_static/vendor/open-sans_all/1.44.1/index.css">
+  <link rel="stylesheet"
+    href="../../../_static/vendor/lato_latin-ext/1.44.1/index.css">
+
+    
+    <link rel="stylesheet" href="../../../_static/sphinx-book-theme.bfb7730f9caf2ec0b46a44615585038c.css" type="text/css" />
+    <link rel="stylesheet" href="../../../_static/pygments.css" type="text/css" />
+    <link rel="stylesheet" type="text/css" href="../../../_static/togglebutton.css" />
+    <link rel="stylesheet" type="text/css" href="../../../_static/copybutton.css" />
+    <link rel="stylesheet" type="text/css" href="../../../_static/mystnb.css" />
+    <link rel="stylesheet" type="text/css" href="../../../_static/sphinx-thebe.css" />
+    <link rel="stylesheet" type="text/css" href="../../../_static/panels-main.c949a650a448cc0ae9fd3441c0e17fb0.css" />
+    <link rel="stylesheet" type="text/css" href="../../../_static/panels-variables.06eb56fa6e07937060861dad626602ad.css" />
+    
+  <link rel="preload" as="script" href="../../../_static/js/index.30270b6e4c972e43c488.js">
+
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+   Lesson 1
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+   Lesson 2
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+        
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+            <i class="fas fa-list"></i> Contents
+        </div>
+        <nav id="bd-toc-nav">
+            <ul class="nav section-nav flex-column">
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#introduction">
+   Introduction
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#prerequisites">
+   Prerequisites
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#data">
+   Data
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#id1">
+   Lesson 1
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#query-language">
+   Query Language
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#installing-libraries">
+   Installing libraries
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#connecting-to-gaia">
+   Connecting to Gaia
+  </a>
+  <ul class="nav section-nav flex-column">
+   <li class="toc-h3 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#optional-detail">
+     Optional detail
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#databases-and-tables">
+   Databases and Tables
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#columns">
+   Columns
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#writing-queries">
+   Writing queries
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#asynchronous-queries">
+   Asynchronous queries
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#operators">
+   Operators
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#cleaning-up">
+   Cleaning up
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#formatting-queries">
+   Formatting queries
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#summary">
+   Summary
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#best-practices">
+   Best practices
+  </a>
+ </li>
+</ul>
+
+        </nav>
+        
+    </div>
+</div>
+    <div id="main-content" class="row">
+        <div class="col-12 col-md-9 pl-md-3 pr-md-0">
+        
+              <div>
+                
+  <div class="section" id="lesson-1">
+<h1>Lesson 1<a class="headerlink" href="#lesson-1" title="Permalink to this headline">¶</a></h1>
+<div class="section" id="introduction">
+<h2>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">¶</a></h2>
+<p>This workshop is an introduction to tools and practices for working with astronomical data.  Topics covered include:</p>
+<ul class="simple">
+<li><p>Writing queries that select and download data from a database.</p></li>
+<li><p>Using data stored in an Astropy <code class="docutils literal notranslate"><span class="pre">Table</span></code> or Pandas <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code>.</p></li>
+<li><p>Working with coordinates and other quantities with units.</p></li>
+<li><p>Storing data in various formats.</p></li>
+<li><p>Performing database join operations that combine data from multiple tables.</p></li>
+<li><p>Visualizing data and preparing publication-quality figures.</p></li>
+</ul>
+<p>As a running example, we will replicate part of the analysis in a recent paper, “<a class="reference external" href="https://arxiv.org/abs/1805.00425">Off the beaten path: Gaia reveals GD-1 stars outside of the main stream</a>� by Adrian M. Price-Whelan and Ana Bonaca.</p>
+<p>As the abstract explains, “Using data from the Gaia second data release combined with Pan-STARRS photometry, we present a sample of highly-probable members of the longest cold stream in the Milky Way, GD-1.�</p>
+<p>GD-1 is a <a class="reference external" href="https://en.wikipedia.org/wiki/List_of_stellar_streams">stellar stream</a>, which is “an association of stars orbiting a galaxy that was once a globular cluster or dwarf galaxy that has now been torn apart and stretched out along its orbit by tidal forces.�</p>
+<p><a class="reference external" href="https://www.sciencemag.org/news/2018/10/streams-stars-reveal-galaxy-s-violent-history-and-perhaps-its-unseen-dark-matter">This article in <em>Science</em> magazine</a> explains some of the background, including the process that led to the paper and an discussion of the scientific implications:</p>
+<ul class="simple">
+<li><p>“The streams are particularly useful for … galactic archaeology — rewinding the cosmic clock to reconstruct the assembly of the Milky Way.�</p></li>
+<li><p>“They also are being used as exquisitely sensitive scales to measure the galaxy’s mass.�</p></li>
+<li><p>“… the streams are well-positioned to reveal the presence of dark matter … because the streams are so fragile, theorists say, collisions with marauding clumps of dark matter could leave telltale scars, potential clues to its nature.�</p></li>
+</ul>
+</div>
+<div class="section" id="prerequisites">
+<h2>Prerequisites<a class="headerlink" href="#prerequisites" title="Permalink to this headline">¶</a></h2>
+<p>This workshop is meant for people who are familiar with basic Python, but not necessarily the libraries we will use, like Astropy or Pandas.  If you are familiar with Python lists and dictionaries, and you know how to write a function that takes parameters and returns a value, you know enough Python for this workshop.</p>
+<p>We assume that you have some familiarity with operating systems, like the ability to use a command-line interface.  But we don’t assume you have any prior experience with databases.</p>
+<p>We assume that you are familiar with astronomy at the undergraduate level, but we will not assume specialized knowledge of the datasets or analysis methods we’ll use.</p>
+</div>
+<div class="section" id="data">
+<h2>Data<a class="headerlink" href="#data" title="Permalink to this headline">¶</a></h2>
+<p>The datasets we will work with are:</p>
+<ul class="simple">
+<li><p><a class="reference external" href="https://en.wikipedia.org/wiki/Gaia_(spacecraft)">Gaia</a>, which is “a space observatory of the European Space Agency (ESA), launched in 2013 … designed for astrometry: measuring the positions, distances and motions of stars with unprecedented precision�, and</p></li>
+<li><p><a class="reference external" href="https://en.wikipedia.org/wiki/Pan-STARRS">Pan-STARRS</a>, The Panoramic Survey Telescope and Rapid Response System, which is a survey designed to monitor the sky for transient objects, producing a catalog with accurate astronometry and photometry of detected sources.</p></li>
+</ul>
+<p>Both of these datasets are very large, which can make them challenging to work with.  It might not be possible, or practical, to download the entire dataset.
+One of the goals of this workshop is to provide tools for working with large datasets.</p>
+</div>
+<div class="section" id="id1">
+<h2>Lesson 1<a class="headerlink" href="#id1" title="Permalink to this headline">¶</a></h2>
+<p>The first lesson demonstrates the steps for selecting and downloading data from the Gaia Database:</p>
+<ol class="simple">
+<li><p>First we’ll make a connection to the Gaia server,</p></li>
+<li><p>We will explore information about the database and the tables it contains,</p></li>
+<li><p>We will write a query and send it to the server, and finally</p></li>
+<li><p>We will download the response from the server.</p></li>
+</ol>
+<p>After completing this lesson, you should be able to</p>
+<ul class="simple">
+<li><p>Compose a basic query in ADQL.</p></li>
+<li><p>Use queries to explore a database and its tables.</p></li>
+<li><p>Use queries to download data.</p></li>
+<li><p>Develop, test, and debug a query incrementally.</p></li>
+</ul>
+</div>
+<div class="section" id="query-language">
+<h2>Query Language<a class="headerlink" href="#query-language" title="Permalink to this headline">¶</a></h2>
+<p>In order to select data from a database, you have to compose a query, which is like a program written in a “query language�.
+The query language we’ll use is ADQL, which stands for “Astronomical Data Query Language�.</p>
+<p>ADQL is a dialect of <a class="reference external" href="https://en.wikipedia.org/wiki/SQL">SQL</a> (Structured Query Language), which is by far the most commonly used query language.  Almost everything you will learn about ADQL also works in SQL.</p>
+<p><a class="reference external" href="http://www.ivoa.net/documents/ADQL/20180112/PR-ADQL-2.1-20180112.html">The reference manual for ADQL is here</a>.
+But you might find it easier to learn from <a class="reference external" href="https://www.gaia.ac.uk/data/gaia-data-release-1/adql-cookbook">this ADQL Cookbook</a>.</p>
+</div>
+<div class="section" id="installing-libraries">
+<h2>Installing libraries<a class="headerlink" href="#installing-libraries" title="Permalink to this headline">¶</a></h2>
+<p>The library we’ll use to get Gaia data is <a class="reference external" href="https://astroquery.readthedocs.io/en/latest/">Astroquery</a>.</p>
+<p>If you are running this notebook on Colab, you can run the following cell to install Astroquery and the other libraries we’ll use.</p>
+<p>If you are running this notebook on your own computer, you might have to install these libraries yourself.</p>
+<p>If you are using this notebook as part of a Carpentries workshop, you should have received setup instructions.</p>
+<p>TODO: Add a link to the instructions.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># If we&#39;re running on Colab, install libraries</span>
+
+<span class="kn">import</span> <span class="nn">sys</span>
+<span class="n">IN_COLAB</span> <span class="o">=</span> <span class="s1">&#39;google.colab&#39;</span> <span class="ow">in</span> <span class="n">sys</span><span class="o">.</span><span class="n">modules</span>
+
+<span class="k">if</span> <span class="n">IN_COLAB</span><span class="p">:</span>
+    <span class="o">!</span>pip install astroquery astro-gala pyia
+</pre></div>
+</div>
+</div>
+</div>
+</div>
+<div class="section" id="connecting-to-gaia">
+<h2>Connecting to Gaia<a class="headerlink" href="#connecting-to-gaia" title="Permalink to this headline">¶</a></h2>
+<p>Astroquery provides <code class="docutils literal notranslate"><span class="pre">Gaia</span></code>, which is an <a class="reference external" href="https://astroquery.readthedocs.io/en/latest/gaia/gaia.html">object that represents a connection to the Gaia database</a>.</p>
+<p>We can connect to the Gaia database like this:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">astroquery.gaia</span> <span class="kn">import</span> <span class="n">Gaia</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Created TAP+ (v1.2.1) - Connection:
+	Host: gea.esac.esa.int
+	Use HTTPS: True
+	Port: 443
+	SSL Port: 443
+Created TAP+ (v1.2.1) - Connection:
+	Host: geadata.esac.esa.int
+	Use HTTPS: True
+	Port: 443
+	SSL Port: 443
+</pre></div>
+</div>
+</div>
+</div>
+<div class="section" id="optional-detail">
+<h3>Optional detail<a class="headerlink" href="#optional-detail" title="Permalink to this headline">¶</a></h3>
+<blockquote>
+<div><p>Running this import statement has the effect of creating a <a class="reference external" href="http://www.ivoa.net/documents/TAP/">TAP+</a> connection; TAP stands for “Table Access Protocol�.  It is a network protocol for sending queries to the database and getting back the results.  We’re not sure why it seems to create two connections.</p>
+</div></blockquote>
+</div>
+</div>
+<div class="section" id="databases-and-tables">
+<h2>Databases and Tables<a class="headerlink" href="#databases-and-tables" title="Permalink to this headline">¶</a></h2>
+<p>What is a database, anyway?  Most generally, it can be any collection of data, but when we are talking about ADQL or SQL:</p>
+<ul class="simple">
+<li><p>A database is a collection of one or more named tables.</p></li>
+<li><p>Each table is a 2-D array with one or more named columns of data.</p></li>
+</ul>
+<p>We can use <code class="docutils literal notranslate"><span class="pre">Gaia.load_tables</span></code> to get the names of the tables in the Gaia database.  With the option <code class="docutils literal notranslate"><span class="pre">only_names=True</span></code>, it loads information about the tables, called the “metadata�, not the data itself.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">tables</span> <span class="o">=</span> <span class="n">Gaia</span><span class="o">.</span><span class="n">load_tables</span><span class="p">(</span><span class="n">only_names</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>INFO: Retrieving tables... [astroquery.utils.tap.core]
+INFO: Parsing tables... [astroquery.utils.tap.core]
+INFO: Done. [astroquery.utils.tap.core]
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">for</span> <span class="n">table</span> <span class="ow">in</span> <span class="p">(</span><span class="n">tables</span><span class="p">):</span>
+    <span class="nb">print</span><span class="p">(</span><span class="n">table</span><span class="o">.</span><span class="n">get_qualified_name</span><span class="p">())</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>external.external.apassdr9
+external.external.gaiadr2_geometric_distance
+external.external.galex_ais
+external.external.ravedr5_com
+external.external.ravedr5_dr5
+external.external.ravedr5_gra
+external.external.ravedr5_on
+external.external.sdssdr13_photoprimary
+external.external.skymapperdr1_master
+external.external.tmass_xsc
+public.public.hipparcos
+public.public.hipparcos_newreduction
+public.public.hubble_sc
+public.public.igsl_source
+public.public.igsl_source_catalog_ids
+public.public.tycho2
+public.public.dual
+tap_config.tap_config.coord_sys
+tap_config.tap_config.properties
+tap_schema.tap_schema.columns
+tap_schema.tap_schema.key_columns
+tap_schema.tap_schema.keys
+tap_schema.tap_schema.schemas
+tap_schema.tap_schema.tables
+gaiadr1.gaiadr1.aux_qso_icrf2_match
+gaiadr1.gaiadr1.ext_phot_zero_point
+gaiadr1.gaiadr1.allwise_best_neighbour
+gaiadr1.gaiadr1.allwise_neighbourhood
+gaiadr1.gaiadr1.gsc23_best_neighbour
+gaiadr1.gaiadr1.gsc23_neighbourhood
+gaiadr1.gaiadr1.ppmxl_best_neighbour
+gaiadr1.gaiadr1.ppmxl_neighbourhood
+gaiadr1.gaiadr1.sdss_dr9_best_neighbour
+gaiadr1.gaiadr1.sdss_dr9_neighbourhood
+gaiadr1.gaiadr1.tmass_best_neighbour
+gaiadr1.gaiadr1.tmass_neighbourhood
+gaiadr1.gaiadr1.ucac4_best_neighbour
+gaiadr1.gaiadr1.ucac4_neighbourhood
+gaiadr1.gaiadr1.urat1_best_neighbour
+gaiadr1.gaiadr1.urat1_neighbourhood
+gaiadr1.gaiadr1.cepheid
+gaiadr1.gaiadr1.phot_variable_time_series_gfov
+gaiadr1.gaiadr1.phot_variable_time_series_gfov_statistical_parameters
+gaiadr1.gaiadr1.rrlyrae
+gaiadr1.gaiadr1.variable_summary
+gaiadr1.gaiadr1.allwise_original_valid
+gaiadr1.gaiadr1.gsc23_original_valid
+gaiadr1.gaiadr1.ppmxl_original_valid
+gaiadr1.gaiadr1.sdssdr9_original_valid
+gaiadr1.gaiadr1.tmass_original_valid
+gaiadr1.gaiadr1.ucac4_original_valid
+gaiadr1.gaiadr1.urat1_original_valid
+gaiadr1.gaiadr1.gaia_source
+gaiadr1.gaiadr1.tgas_source
+gaiadr2.gaiadr2.aux_allwise_agn_gdr2_cross_id
+gaiadr2.gaiadr2.aux_iers_gdr2_cross_id
+gaiadr2.gaiadr2.aux_sso_orbit_residuals
+gaiadr2.gaiadr2.aux_sso_orbits
+gaiadr2.gaiadr2.dr1_neighbourhood
+gaiadr2.gaiadr2.allwise_best_neighbour
+gaiadr2.gaiadr2.allwise_neighbourhood
+gaiadr2.gaiadr2.apassdr9_best_neighbour
+gaiadr2.gaiadr2.apassdr9_neighbourhood
+gaiadr2.gaiadr2.gsc23_best_neighbour
+gaiadr2.gaiadr2.gsc23_neighbourhood
+gaiadr2.gaiadr2.hipparcos2_best_neighbour
+gaiadr2.gaiadr2.hipparcos2_neighbourhood
+gaiadr2.gaiadr2.panstarrs1_best_neighbour
+gaiadr2.gaiadr2.panstarrs1_neighbourhood
+gaiadr2.gaiadr2.ppmxl_best_neighbour
+gaiadr2.gaiadr2.ppmxl_neighbourhood
+gaiadr2.gaiadr2.ravedr5_best_neighbour
+gaiadr2.gaiadr2.ravedr5_neighbourhood
+gaiadr2.gaiadr2.sdssdr9_best_neighbour
+gaiadr2.gaiadr2.sdssdr9_neighbourhood
+gaiadr2.gaiadr2.tmass_best_neighbour
+gaiadr2.gaiadr2.tmass_neighbourhood
+gaiadr2.gaiadr2.tycho2_best_neighbour
+gaiadr2.gaiadr2.tycho2_neighbourhood
+gaiadr2.gaiadr2.urat1_best_neighbour
+gaiadr2.gaiadr2.urat1_neighbourhood
+gaiadr2.gaiadr2.sso_observation
+gaiadr2.gaiadr2.sso_source
+gaiadr2.gaiadr2.vari_cepheid
+gaiadr2.gaiadr2.vari_classifier_class_definition
+gaiadr2.gaiadr2.vari_classifier_definition
+gaiadr2.gaiadr2.vari_classifier_result
+gaiadr2.gaiadr2.vari_long_period_variable
+gaiadr2.gaiadr2.vari_rotation_modulation
+gaiadr2.gaiadr2.vari_rrlyrae
+gaiadr2.gaiadr2.vari_short_timescale
+gaiadr2.gaiadr2.vari_time_series_statistics
+gaiadr2.gaiadr2.panstarrs1_original_valid
+gaiadr2.gaiadr2.gaia_source
+gaiadr2.gaiadr2.ruwe
+</pre></div>
+</div>
+</div>
+</div>
+<p>So that’s a lot of tables.  The ones we’ll use are:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">gaiadr2.gaia_source</span></code>, which contains Gaia data from <a class="reference external" href="https://www.cosmos.esa.int/web/gaia/data-release-2">data release 2</a>,</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">gaiadr2.panstarrs1_original_valid</span></code>, which contains the photometry data we’ll use from PanSTARRS, and</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">gaiadr2.panstarrs1_best_neighbour</span></code>, which we’ll use to cross-match each star observed by Gaia with the same star observed by PanSTARRS.</p></li>
+</ul>
+<p>We can use <code class="docutils literal notranslate"><span class="pre">load_table</span></code> (not <code class="docutils literal notranslate"><span class="pre">load_tables</span></code>) to get the metadata for a single table.  The name of this function is misleading, because it only downloads metadata.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">meta</span> <span class="o">=</span> <span class="n">Gaia</span><span class="o">.</span><span class="n">load_table</span><span class="p">(</span><span class="s1">&#39;gaiadr2.gaia_source&#39;</span><span class="p">)</span>
+<span class="n">meta</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Retrieving table &#39;gaiadr2.gaia_source&#39;
+Parsing table &#39;gaiadr2.gaia_source&#39;...
+Done.
+</pre></div>
+</div>
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;astroquery.utils.tap.model.taptable.TapTableMeta at 0x7f922376e0a0&gt;
+</pre></div>
+</div>
+</div>
+</div>
+<p>Jupyter shows that the result is an object of type <code class="docutils literal notranslate"><span class="pre">TapTableMeta</span></code>, but it does not display the contents.</p>
+<p>To see the metadata, we have to print the object.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">meta</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>TAP Table name: gaiadr2.gaiadr2.gaia_source
+Description: This table has an entry for every Gaia observed source as listed in the
+Main Database accumulating catalogue version from which the catalogue
+release has been generated. It contains the basic source parameters,
+that is only final data (no epoch data) and no spectra (neither final
+nor epoch).
+Num. columns: 96
+</pre></div>
+</div>
+</div>
+</div>
+<p>Notice one gotcha: in the list of table names, this table appears as <code class="docutils literal notranslate"><span class="pre">gaiadr2.gaiadr2.gaia_source</span></code>, but when we load the metadata, we refer to it as <code class="docutils literal notranslate"><span class="pre">gaiadr2.gaia_source</span></code>.</p>
+<p><strong>Exercise:</strong> Go back and try</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">meta</span> <span class="o">=</span> <span class="n">Gaia</span><span class="o">.</span><span class="n">load_table</span><span class="p">(</span><span class="s1">&#39;gaiadr2.gaiadr2.gaia_source&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>What happens?  Is the error message helpful?  If you had not made this error deliberately, would you have been able to figure it out?</p>
+</div>
+<div class="section" id="columns">
+<h2>Columns<a class="headerlink" href="#columns" title="Permalink to this headline">¶</a></h2>
+<p>The following loop prints the names of the columns in the table.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">for</span> <span class="n">column</span> <span class="ow">in</span> <span class="n">meta</span><span class="o">.</span><span class="n">columns</span><span class="p">:</span>
+    <span class="nb">print</span><span class="p">(</span><span class="n">column</span><span class="o">.</span><span class="n">name</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>solution_id
+designation
+source_id
+random_index
+ref_epoch
+ra
+ra_error
+dec
+dec_error
+parallax
+parallax_error
+parallax_over_error
+pmra
+pmra_error
+pmdec
+pmdec_error
+ra_dec_corr
+ra_parallax_corr
+ra_pmra_corr
+ra_pmdec_corr
+dec_parallax_corr
+dec_pmra_corr
+dec_pmdec_corr
+parallax_pmra_corr
+parallax_pmdec_corr
+pmra_pmdec_corr
+astrometric_n_obs_al
+astrometric_n_obs_ac
+astrometric_n_good_obs_al
+astrometric_n_bad_obs_al
+astrometric_gof_al
+astrometric_chi2_al
+astrometric_excess_noise
+astrometric_excess_noise_sig
+astrometric_params_solved
+astrometric_primary_flag
+astrometric_weight_al
+astrometric_pseudo_colour
+astrometric_pseudo_colour_error
+mean_varpi_factor_al
+astrometric_matched_observations
+visibility_periods_used
+astrometric_sigma5d_max
+frame_rotator_object_type
+matched_observations
+duplicated_source
+phot_g_n_obs
+phot_g_mean_flux
+phot_g_mean_flux_error
+phot_g_mean_flux_over_error
+phot_g_mean_mag
+phot_bp_n_obs
+phot_bp_mean_flux
+phot_bp_mean_flux_error
+phot_bp_mean_flux_over_error
+phot_bp_mean_mag
+phot_rp_n_obs
+phot_rp_mean_flux
+phot_rp_mean_flux_error
+phot_rp_mean_flux_over_error
+phot_rp_mean_mag
+phot_bp_rp_excess_factor
+phot_proc_mode
+bp_rp
+bp_g
+g_rp
+radial_velocity
+radial_velocity_error
+rv_nb_transits
+rv_template_teff
+rv_template_logg
+rv_template_fe_h
+phot_variable_flag
+l
+b
+ecl_lon
+ecl_lat
+priam_flags
+teff_val
+teff_percentile_lower
+teff_percentile_upper
+a_g_val
+a_g_percentile_lower
+a_g_percentile_upper
+e_bp_min_rp_val
+e_bp_min_rp_percentile_lower
+e_bp_min_rp_percentile_upper
+flame_flags
+radius_val
+radius_percentile_lower
+radius_percentile_upper
+lum_val
+lum_percentile_lower
+lum_percentile_upper
+datalink_url
+epoch_photometry_url
+</pre></div>
+</div>
+</div>
+</div>
+<p>You can probably guess what many of these columns are by looking at the names, but you should resist the temptation to guess.
+To find out what the columns mean, <a class="reference external" href="https://gea.esac.esa.int/archive/documentation/GDR2/Gaia_archive/chap_datamodel/sec_dm_main_tables/ssec_dm_gaia_source.html">read the documentation</a>.</p>
+<p>If you want to know what can go wrong when you don’t read the documentation, <a class="reference external" href="https://www.vox.com/future-perfect/2019/6/4/18650969/married-women-miserable-fake-paul-dolan-happiness">you might like this article</a>.</p>
+<p><strong>Exercise:</strong> One of the other tables we’ll use is <code class="docutils literal notranslate"><span class="pre">gaiadr2.gaiadr2.panstarrs1_original_valid</span></code>.  Use <code class="docutils literal notranslate"><span class="pre">load_table</span></code> to get the metadata for this table.  How many columns are there and what are their names?</p>
+<p>Hint: Remember the gotcha we mentioned earlier.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Solution</span>
+
+<span class="n">meta2</span> <span class="o">=</span> <span class="n">Gaia</span><span class="o">.</span><span class="n">load_table</span><span class="p">(</span><span class="s1">&#39;gaiadr2.panstarrs1_original_valid&#39;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">meta2</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Retrieving table &#39;gaiadr2.panstarrs1_original_valid&#39;
+Parsing table &#39;gaiadr2.panstarrs1_original_valid&#39;...
+Done.
+TAP Table name: gaiadr2.gaiadr2.panstarrs1_original_valid
+Description: The Panoramic Survey Telescope and Rapid Response System (Pan-STARRS) is
+a system for wide-field astronomical imaging developed and operated by
+the Institute for Astronomy at the University of Hawaii. Pan-STARRS1
+(PS1) is the first part of Pan-STARRS to be completed and is the basis
+for Data Release 1 (DR1). The PS1 survey used a 1.8 meter telescope and
+its 1.4 Gigapixel camera to image the sky in five broadband filters (g,
+r, i, z, y).
+
+The current table contains a filtered subsample of the 10 723 304 629
+entries listed in the original ObjectThin table.
+We used only ObjectThin and MeanObject tables to extract
+panstarrs1OriginalValid table, this means that objects detected only in
+stack images are not included here. The main reason for us to avoid the
+use of objects detected in stack images is that their astrometry is not
+as good as the mean objects astrometry: “The stack positions (raStack,
+decStack) have considerably larger systematic astrometric errors than
+the mean epoch positions (raMean, decMean).� The astrometry for the
+MeanObject positions uses Gaia DR1 as a reference catalog, while the
+stack positions use 2MASS as a reference catalog.
+
+In details, we filtered out all objects where:
+
+-   nDetections = 1
+
+-   no good quality data in Pan-STARRS, objInfoFlag 33554432 not set
+
+-   mean astrometry could not be measured, objInfoFlag 524288 set
+
+-   stack position used for mean astrometry, objInfoFlag 1048576 set
+
+-   error on all magnitudes equal to 0 or to -999;
+
+-   all magnitudes set to -999;
+
+-   error on RA or DEC greater than 1 arcsec.
+
+The number of objects in panstarrs1OriginalValid is 2 264 263 282.
+
+The panstarrs1OriginalValid table contains only a subset of the columns
+available in the combined ObjectThin and MeanObject tables. A
+description of the original ObjectThin and MeanObjects tables can be
+found at:
+https://outerspace.stsci.edu/display/PANSTARRS/PS1+Database+object+and+detection+tables
+
+Download:
+http://mastweb.stsci.edu/ps1casjobs/home.aspx
+Documentation:
+https://outerspace.stsci.edu/display/PANSTARRS
+http://pswww.ifa.hawaii.edu/pswww/
+References:
+The Pan-STARRS1 Surveys, Chambers, K.C., et al. 2016, arXiv:1612.05560
+Pan-STARRS Data Processing System, Magnier, E. A., et al. 2016,
+arXiv:1612.05240
+Pan-STARRS Pixel Processing: Detrending, Warping, Stacking, Waters, C.
+Z., et al. 2016, arXiv:1612.05245
+Pan-STARRS Pixel Analysis: Source Detection and Characterization,
+Magnier, E. A., et al. 2016, arXiv:1612.05244
+Pan-STARRS Photometric and Astrometric Calibration, Magnier, E. A., et
+al. 2016, arXiv:1612.05242
+The Pan-STARRS1 Database and Data Products, Flewelling, H. A., et al.
+2016, arXiv:1612.05243
+
+Catalogue curator:
+SSDC - ASI Space Science Data Center
+https://www.ssdc.asi.it/
+Num. columns: 26
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Solution</span>
+
+<span class="k">for</span> <span class="n">column</span> <span class="ow">in</span> <span class="n">meta2</span><span class="o">.</span><span class="n">columns</span><span class="p">:</span>
+    <span class="nb">print</span><span class="p">(</span><span class="n">column</span><span class="o">.</span><span class="n">name</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>obj_name
+obj_id
+ra
+dec
+ra_error
+dec_error
+epoch_mean
+g_mean_psf_mag
+g_mean_psf_mag_error
+g_flags
+r_mean_psf_mag
+r_mean_psf_mag_error
+r_flags
+i_mean_psf_mag
+i_mean_psf_mag_error
+i_flags
+z_mean_psf_mag
+z_mean_psf_mag_error
+z_flags
+y_mean_psf_mag
+y_mean_psf_mag_error
+y_flags
+n_detections
+zone_id
+obj_info_flag
+quality_flag
+</pre></div>
+</div>
+</div>
+</div>
+</div>
+<div class="section" id="writing-queries">
+<h2>Writing queries<a class="headerlink" href="#writing-queries" title="Permalink to this headline">¶</a></h2>
+<p>By now you might be wondering how we actually download the data.  With tables this big, you generally don’t.  Instead, you use queries to select only the data you want.</p>
+<p>A query is a string written in a query language like SQL; for the Gaia database, the query language is a dialect of SQL called ADQL.</p>
+<p>Here’s an example of an ADQL query.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">query1</span> <span class="o">=</span> <span class="s2">&quot;&quot;&quot;SELECT </span>
+<span class="s2">TOP 10</span>
+<span class="s2">source_id, ref_epoch, ra, dec, parallax </span>
+<span class="s2">FROM gaiadr2.gaia_source&quot;&quot;&quot;</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p><strong>Python note:</strong> We use a <a class="reference external" href="https://docs.python.org/3/tutorial/introduction.html#strings">triple-quoted string</a> here so we can include line breaks in the query, which makes it easier to read.</p>
+<p>The words in uppercase are ADQL keywords:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">SELECT</span></code> indicates that we are selecting data (as opposed to adding or modifying data).</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">TOP</span></code> indicates that we only want the first 10 rows of the table, which is useful for testing a query before asking for all of the data.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">FROM</span></code> specifies which table we want data from.</p></li>
+</ul>
+<p>The third line is a list of column names, indicating which columns we want.</p>
+<p>In this example, the keywords are capitalized and the column names are lowercase.  This is a common style, but it is not required.  ADQL and SQL are not case-sensitive.</p>
+<p>To run this query, we use the <code class="docutils literal notranslate"><span class="pre">Gaia</span></code> object, which represents our connection to the Gaia database, and invoke <code class="docutils literal notranslate"><span class="pre">launch_job</span></code>:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">job1</span> <span class="o">=</span> <span class="n">Gaia</span><span class="o">.</span><span class="n">launch_job</span><span class="p">(</span><span class="n">query1</span><span class="p">)</span>
+<span class="n">job1</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;astroquery.utils.tap.model.job.Job at 0x7f9222e9cb20&gt;
+</pre></div>
+</div>
+</div>
+</div>
+<p>The result is an object that represents the job running on a Gaia server.</p>
+<p>If you print it, it displays metadata for the forthcoming table.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">job1</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;Table length=10&gt;
+   name    dtype  unit                            description                            
+--------- ------- ---- ------------------------------------------------------------------
+source_id   int64      Unique source identifier (unique within a particular Data Release)
+ref_epoch float64   yr                                                    Reference epoch
+       ra float64  deg                                                    Right ascension
+      dec float64  deg                                                        Declination
+ parallax float64  mas                                                           Parallax
+Jobid: None
+Phase: COMPLETED
+Owner: None
+Output file: sync_20201005090721.xml.gz
+Results: None
+</pre></div>
+</div>
+</div>
+</div>
+<p>Don’t worry about <code class="docutils literal notranslate"><span class="pre">Results:</span> <span class="pre">None</span></code>.  That does not actually mean there are no results.</p>
+<p>However, <code class="docutils literal notranslate"><span class="pre">Phase:</span> <span class="pre">COMPLETED</span></code> indicates that the job is complete, so we can get the results like this:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">results1</span> <span class="o">=</span> <span class="n">job1</span><span class="o">.</span><span class="n">get_results</span><span class="p">()</span>
+<span class="nb">type</span><span class="p">(</span><span class="n">results1</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>astropy.table.table.Table
+</pre></div>
+</div>
+</div>
+</div>
+<p><strong>Optional detail:</strong>  Why is <code class="docutils literal notranslate"><span class="pre">table</span></code> repeated three times?  The first is the name of the module, the second is the name of the submodule, and the third is the name of the class.  Most of the time we only care about the last one.  It’s like the Linnean name for gorilla, which is <em>Gorilla Gorilla Gorilla</em>.</p>
+<p>The result is an <a class="reference external" href="https://docs.astropy.org/en/stable/table/">Astropy Table</a>, which is similar to a table in an SQL database except:</p>
+<ul class="simple">
+<li><p>SQL databases are stored on disk drives, so they are persistent; that is, they “survive� even if you turn off the computer.  An Astropy <code class="docutils literal notranslate"><span class="pre">Table</span></code> is stored in memory; it disappears when you turn off the computer (or shut down this Jupyter notebook).</p></li>
+<li><p>SQL databases are designed to process queries.  An Astropy <code class="docutils literal notranslate"><span class="pre">Table</span></code> can perform some query-like operations, like selecting columns and rows.  But these operations use Python syntax, not SQL.</p></li>
+</ul>
+<p>Jupyter knows how to display the contents of a <code class="docutils literal notranslate"><span class="pre">Table</span></code>.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">results1</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><i>Table length=10</i>
+<table id="table140265627585264" class="table-striped table-bordered table-condensed">
+<thead><tr><th>source_id</th><th>ref_epoch</th><th>ra</th><th>dec</th><th>parallax</th></tr></thead>
+<thead><tr><th></th><th>yr</th><th>deg</th><th>deg</th><th>mas</th></tr></thead>
+<thead><tr><th>int64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th></tr></thead>
+<tr><td>4530738361793769600</td><td>2015.5</td><td>281.56725362448725</td><td>20.40682117430378</td><td>0.9785380604519425</td></tr>
+<tr><td>4530752651135081216</td><td>2015.5</td><td>281.0861565355257</td><td>20.523350496351846</td><td>0.2674800612552977</td></tr>
+<tr><td>4530743343951405568</td><td>2015.5</td><td>281.37114418299177</td><td>20.474147574053124</td><td>-0.43911323550176806</td></tr>
+<tr><td>4530755060627162368</td><td>2015.5</td><td>281.2676236268299</td><td>20.558523922346158</td><td>1.1422630184554958</td></tr>
+<tr><td>4530746844341315968</td><td>2015.5</td><td>281.1370431749541</td><td>20.377852388898184</td><td>1.0092247424630945</td></tr>
+<tr><td>4530768456615026432</td><td>2015.5</td><td>281.8720921436347</td><td>20.31829694530366</td><td>-0.06900136127674149</td></tr>
+<tr><td>4530763513119137280</td><td>2015.5</td><td>281.9211808864116</td><td>20.20956829578524</td><td>0.1266016679823622</td></tr>
+<tr><td>4530736364618539264</td><td>2015.5</td><td>281.4913475613274</td><td>20.346579041327693</td><td>0.3894019486060072</td></tr>
+<tr><td>4530735952305177728</td><td>2015.5</td><td>281.4085549165704</td><td>20.311030903719928</td><td>0.2041189982608354</td></tr>
+<tr><td>4530751281056022656</td><td>2015.5</td><td>281.0585328377638</td><td>20.460309556214753</td><td>0.10294642821734962</td></tr>
+</table></div></div>
+</div>
+<p>Each column has a name, units, and a data type.</p>
+<p>For example, the units of <code class="docutils literal notranslate"><span class="pre">ra</span></code> and <code class="docutils literal notranslate"><span class="pre">dec</span></code> are degrees, and their data type is <code class="docutils literal notranslate"><span class="pre">float64</span></code>, which is a 64-bit floating-point number, used to store measurements with a fraction part.</p>
+<p>This information comes from the Gaia database, and has been stored in the Astropy <code class="docutils literal notranslate"><span class="pre">Table</span></code> by Astroquery.</p>
+<p><strong>Exercise:</strong> Read <a class="reference external" href="https://gea.esac.esa.int/archive/documentation/GDR2/Gaia_archive/chap_datamodel/sec_dm_main_tables/ssec_dm_gaia_source.html">the documentation of this table</a> and choose a column that looks interesting to you.  Add the column name to the query and run it again.  What are the units of the column you selected?  What is its data type?</p>
+</div>
+<div class="section" id="asynchronous-queries">
+<h2>Asynchronous queries<a class="headerlink" href="#asynchronous-queries" title="Permalink to this headline">¶</a></h2>
+<p><code class="docutils literal notranslate"><span class="pre">launch_job</span></code> asks the server to run the job “synchronously�, which normally means it runs immediately.  But synchronous jobs are limited to 2000 rows.  For queries that return more rows, you should run “asynchronously�, which mean they might take longer to get started.</p>
+<p>If you are not sure how many rows a query will return, you can use the SQL command <code class="docutils literal notranslate"><span class="pre">COUNT</span></code> to find out how many rows are in the result without actually returning them.  We’ll see an example of this later.</p>
+<p>The results of an asynchronous query are stored in a file on the server, so you can start a query and come back later to get the results.</p>
+<p>For anonymous users, files are kept for three days.</p>
+<p>As an example, let’s try a query that’s similar to <code class="docutils literal notranslate"><span class="pre">query1</span></code>, with two changes:</p>
+<ul class="simple">
+<li><p>It selects the first 3000 rows, so it is bigger than we should run synchronously.</p></li>
+<li><p>It uses a new keyword, <code class="docutils literal notranslate"><span class="pre">WHERE</span></code>.</p></li>
+</ul>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">query2</span> <span class="o">=</span> <span class="s2">&quot;&quot;&quot;SELECT TOP 3000</span>
+<span class="s2">source_id, ref_epoch, ra, dec, parallax</span>
+<span class="s2">FROM gaiadr2.gaia_source</span>
+<span class="s2">WHERE parallax &lt; 1</span>
+<span class="s2">&quot;&quot;&quot;</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>A <code class="docutils literal notranslate"><span class="pre">WHERE</span></code> clause indicates which rows we want; in this case, the query selects only rows “where� <code class="docutils literal notranslate"><span class="pre">parallax</span></code> is less than 1.  This has the effect of selecting stars with relatively low parallax, which are farther away.  We’ll use this clause to exclude nearby stars that are unlikely to be part of GD-1.</p>
+<p><code class="docutils literal notranslate"><span class="pre">WHERE</span></code> is one of the most common clauses in ADQL/SQL, and one of the most useful, because it allows us to select only the rows we need from the database.</p>
+<p>We use <code class="docutils literal notranslate"><span class="pre">launch_job_async</span></code> to submit an asynchronous query.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">job2</span> <span class="o">=</span> <span class="n">Gaia</span><span class="o">.</span><span class="n">launch_job_async</span><span class="p">(</span><span class="n">query2</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">job2</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>INFO: Query finished. [astroquery.utils.tap.core]
+&lt;Table length=3000&gt;
+   name    dtype  unit                            description                            
+--------- ------- ---- ------------------------------------------------------------------
+source_id   int64      Unique source identifier (unique within a particular Data Release)
+ref_epoch float64   yr                                                    Reference epoch
+       ra float64  deg                                                    Right ascension
+      dec float64  deg                                                        Declination
+ parallax float64  mas                                                           Parallax
+Jobid: 1601903242219O
+Phase: COMPLETED
+Owner: None
+Output file: async_20201005090722.vot
+Results: None
+</pre></div>
+</div>
+</div>
+</div>
+<p>And here are the results.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">results2</span> <span class="o">=</span> <span class="n">job2</span><span class="o">.</span><span class="n">get_results</span><span class="p">()</span>
+<span class="n">results2</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><i>Table length=3000</i>
+<table id="table140265625141056" class="table-striped table-bordered table-condensed">
+<thead><tr><th>source_id</th><th>ref_epoch</th><th>ra</th><th>dec</th><th>parallax</th></tr></thead>
+<thead><tr><th></th><th>yr</th><th>deg</th><th>deg</th><th>mas</th></tr></thead>
+<thead><tr><th>int64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th></tr></thead>
+<tr><td>4530738361793769600</td><td>2015.5</td><td>281.56725362448725</td><td>20.40682117430378</td><td>0.9785380604519425</td></tr>
+<tr><td>4530752651135081216</td><td>2015.5</td><td>281.0861565355257</td><td>20.523350496351846</td><td>0.2674800612552977</td></tr>
+<tr><td>4530743343951405568</td><td>2015.5</td><td>281.37114418299177</td><td>20.474147574053124</td><td>-0.43911323550176806</td></tr>
+<tr><td>4530768456615026432</td><td>2015.5</td><td>281.8720921436347</td><td>20.31829694530366</td><td>-0.06900136127674149</td></tr>
+<tr><td>4530763513119137280</td><td>2015.5</td><td>281.9211808864116</td><td>20.20956829578524</td><td>0.1266016679823622</td></tr>
+<tr><td>4530736364618539264</td><td>2015.5</td><td>281.4913475613274</td><td>20.346579041327693</td><td>0.3894019486060072</td></tr>
+<tr><td>4530735952305177728</td><td>2015.5</td><td>281.4085549165704</td><td>20.311030903719928</td><td>0.2041189982608354</td></tr>
+<tr><td>4530751281056022656</td><td>2015.5</td><td>281.0585328377638</td><td>20.460309556214753</td><td>0.10294642821734962</td></tr>
+<tr><td>4530740938774409344</td><td>2015.5</td><td>281.3762569536416</td><td>20.436140058941206</td><td>0.9242670062090182</td></tr>
+<tr><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr>
+<tr><td>4467710915011802624</td><td>2015.5</td><td>269.9680969307347</td><td>1.1429085038160882</td><td>0.42361471245557913</td></tr>
+<tr><td>4467706551328679552</td><td>2015.5</td><td>270.033164589881</td><td>1.0565747323689927</td><td>0.922888231734588</td></tr>
+<tr><td>4467712255037300096</td><td>2015.5</td><td>270.7724717923047</td><td>0.6581664892880896</td><td>-2.669179465293931</td></tr>
+<tr><td>4467735001181761792</td><td>2015.5</td><td>270.3628606248308</td><td>0.8947079323599124</td><td>0.6117399163086398</td></tr>
+<tr><td>4467737101421916672</td><td>2015.5</td><td>270.5110834661444</td><td>0.9806225910160181</td><td>-0.39818224846127004</td></tr>
+<tr><td>4467707547757327488</td><td>2015.5</td><td>269.88746280594927</td><td>1.0212759940136962</td><td>0.7741412301054209</td></tr>
+<tr><td>4467732772094573056</td><td>2015.5</td><td>270.55997182760126</td><td>0.9037072088489417</td><td>-1.7920417800164183</td></tr>
+<tr><td>4467732355491087744</td><td>2015.5</td><td>270.6730790702491</td><td>0.9197224705139885</td><td>-0.3464446494840354</td></tr>
+<tr><td>4467717099766944512</td><td>2015.5</td><td>270.57667173120825</td><td>0.726277659009568</td><td>0.05443955111134051</td></tr>
+<tr><td>4467719058265781248</td><td>2015.5</td><td>270.7248052971514</td><td>0.8205551921782785</td><td>0.3733943917490343</td></tr>
+</table></div></div>
+</div>
+<p>You might notice that some values of <code class="docutils literal notranslate"><span class="pre">parallax</span></code> are negative.  As <a class="reference external" href="https://www.cosmos.esa.int/web/gaia/archive-tips#negative%20parallax">this FAQ explains</a>, “Negative parallaxes are caused by errors in the observations.�  Negative parallaxes have “no physical meaning,� but they can be a “useful diagnostic on the quality of the astrometric solution.�</p>
+<p>Later we will see an example where we use <code class="docutils literal notranslate"><span class="pre">parallax</span></code> and <code class="docutils literal notranslate"><span class="pre">parallax_error</span></code> to identify stars where the distance estimate is likely to be inaccurate.</p>
+<p><strong>Exercise:</strong> The clauses in a query have to be in the right order.  Go back and change the order of the clauses in <code class="docutils literal notranslate"><span class="pre">query2</span></code> and run it again.</p>
+<p>The query should fail, but notice that you don’t get much useful debugging information.</p>
+<p>For this reason, developing and debugging ADQL queries can be really hard.  A few suggestions that might help:</p>
+<ul class="simple">
+<li><p>Whenever possible, start with a working query, either an example you find online or a query you have used in the past.</p></li>
+<li><p>Make small changes and test each change before you continue.</p></li>
+<li><p>While you are debugging, use <code class="docutils literal notranslate"><span class="pre">TOP</span></code> to limit the number of rows in the result.  That will make each attempt run faster, which reduces your testing time.</p></li>
+<li><p>Launching test queries synchronously might make them start faster, too.</p></li>
+</ul>
+</div>
+<div class="section" id="operators">
+<h2>Operators<a class="headerlink" href="#operators" title="Permalink to this headline">¶</a></h2>
+<p>In a <code class="docutils literal notranslate"><span class="pre">WHERE</span></code> clause, you can use any of the <a class="reference external" href="https://www.w3schools.com/sql/sql_operators.asp">SQL comparison operators</a>:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">&gt;</span></code>: greater than</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">&lt;</span></code>: less than</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">&gt;=</span></code>: greater than or equal</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">&lt;=</span></code>: less than or equal</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">=</span></code>: equal</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">!=</span></code> or <code class="docutils literal notranslate"><span class="pre">&lt;&gt;</span></code>: not equal</p></li>
+</ul>
+<p>Most of these are the same as Python, but some are not.  In particular, notice that the equality operator is <code class="docutils literal notranslate"><span class="pre">=</span></code>, not <code class="docutils literal notranslate"><span class="pre">==</span></code>.
+Be careful to keep your Python out of your ADQL!</p>
+<p>You can combine comparisons using the logical operators:</p>
+<ul class="simple">
+<li><p>AND: true if both comparisons are true</p></li>
+<li><p>OR: true if either or both comparisons are true</p></li>
+</ul>
+<p>Finally, you can use <code class="docutils literal notranslate"><span class="pre">NOT</span></code> to invert the result of a comparison.</p>
+<p><strong>Exercise:</strong> <a class="reference external" href="https://www.w3schools.com/sql/sql_operators.asp">Read about SQL operators here</a> and then modify the previous query to select rows where <code class="docutils literal notranslate"><span class="pre">bp_rp</span></code> is between <code class="docutils literal notranslate"><span class="pre">-0.75</span></code> and <code class="docutils literal notranslate"><span class="pre">2</span></code>.</p>
+<p>You can <a class="reference external" href="https://gea.esac.esa.int/archive/documentation/GDR2/Gaia_archive/chap_datamodel/sec_dm_main_tables/ssec_dm_gaia_source.html">read about this variable here</a>.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Solution</span>
+
+<span class="c1"># This is what most people will probably do</span>
+
+<span class="n">query</span> <span class="o">=</span> <span class="s2">&quot;&quot;&quot;SELECT TOP 10</span>
+<span class="s2">source_id, ref_epoch, ra, dec, parallax</span>
+<span class="s2">FROM gaiadr2.gaia_source</span>
+<span class="s2">WHERE parallax &lt; 1 </span>
+<span class="s2">  AND bp_rp &gt; -0.75 AND bp_rp &lt; 2</span>
+<span class="s2">&quot;&quot;&quot;</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Solution</span>
+
+<span class="c1"># But if someone notices the BETWEEN operator, </span>
+<span class="c1"># they might do this</span>
+
+<span class="n">query</span> <span class="o">=</span> <span class="s2">&quot;&quot;&quot;SELECT TOP 10</span>
+<span class="s2">source_id, ref_epoch, ra, dec, parallax</span>
+<span class="s2">FROM gaiadr2.gaia_source</span>
+<span class="s2">WHERE parallax &lt; 1 </span>
+<span class="s2">  AND bp_rp BETWEEN -0.75 AND 2</span>
+<span class="s2">&quot;&quot;&quot;</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>This <a class="reference external" href="https://sci.esa.int/web/gaia/-/60198-gaia-hertzsprung-russell-diagram">Hertzsprung-Russell diagram</a> shows the BP-RP color and luminosity of stars in the Gaia catalog.</p>
+<p>Selecting stars with <code class="docutils literal notranslate"><span class="pre">bp-rp</span></code> less than 2 excludes many <a class="reference external" href="https://xkcd.com/2360/">class M dwarf stars</a>, which are low temperature, low luminosity.  A star like that at GD-1’s distance would be hard to detect, so if it is detected, it it more likely to be in the foreground.</p>
+</div>
+<div class="section" id="cleaning-up">
+<h2>Cleaning up<a class="headerlink" href="#cleaning-up" title="Permalink to this headline">¶</a></h2>
+<p>Asynchronous jobs have a <code class="docutils literal notranslate"><span class="pre">jobid</span></code>.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">job1</span><span class="o">.</span><span class="n">jobid</span><span class="p">,</span> <span class="n">job2</span><span class="o">.</span><span class="n">jobid</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>(None, &#39;1601903242219O&#39;)
+</pre></div>
+</div>
+</div>
+</div>
+<p>Which you can use to remove the job from the server.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">Gaia</span><span class="o">.</span><span class="n">remove_jobs</span><span class="p">([</span><span class="n">job2</span><span class="o">.</span><span class="n">jobid</span><span class="p">])</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Removed jobs: &#39;[&#39;1601903242219O&#39;]&#39;.
+</pre></div>
+</div>
+</div>
+</div>
+<p>If you don’t remove it job from the server, it will be removed eventually, so don’t feel too bad if you don’t clean up after yourself.</p>
+</div>
+<div class="section" id="formatting-queries">
+<h2>Formatting queries<a class="headerlink" href="#formatting-queries" title="Permalink to this headline">¶</a></h2>
+<p>So far the queries have been string “literals�, meaning that the entire string is part of the program.
+But writing queries yourself can be slow, repetitive, and error-prone.</p>
+<p>It is often a good idea to write Python code that assembles a query for you.  One useful tool for that is the <a class="reference external" href="https://www.w3schools.com/python/ref_string_format.asp">string <code class="docutils literal notranslate"><span class="pre">format</span></code> method</a>.</p>
+<p>As an example, we’ll divide the previous query into two parts; a list of column names and a “base� for the query that contains everything except the column names.</p>
+<p>Here’s the list of columns we’ll select.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">columns</span> <span class="o">=</span> <span class="s1">&#39;source_id, ra, dec, pmra, pmdec, parallax, parallax_error, radial_velocity&#39;</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>And here’s the base; it’s a string that contains at least one format specifier in curly brackets (braces).</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">query3_base</span> <span class="o">=</span> <span class="s2">&quot;&quot;&quot;SELECT TOP 10 </span>
+<span class="si">{columns}</span><span class="s2"></span>
+<span class="s2">FROM gaiadr2.gaia_source</span>
+<span class="s2">WHERE parallax &lt; 1</span>
+<span class="s2">  AND bp_rp BETWEEN -0.75 AND 2</span>
+<span class="s2">&quot;&quot;&quot;</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>This base query contains one format specifier, <code class="docutils literal notranslate"><span class="pre">{columns}</span></code>, which is a placeholder for the list of column names we will provide.</p>
+<p>To assemble the query, we invoke <code class="docutils literal notranslate"><span class="pre">format</span></code> on the base string and provide a keyword argument that assigns a value to <code class="docutils literal notranslate"><span class="pre">columns</span></code>.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">query3</span> <span class="o">=</span> <span class="n">query3_base</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="n">columns</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>The result is a string with line breaks.  If you display it, the line breaks appear as <code class="docutils literal notranslate"><span class="pre">\n</span></code>.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">query3</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&#39;SELECT TOP 10 \nsource_id, ra, dec, pmra, pmdec, parallax, parallax_error, radial_velocity\nFROM gaiadr2.gaia_source\nWHERE parallax &lt; 1\n  AND bp_rp BETWEEN -0.75 AND 2\n&#39;
+</pre></div>
+</div>
+</div>
+</div>
+<p>But if you print it, the line breaks appear as… line breaks.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">query3</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>SELECT TOP 10 
+source_id, ra, dec, pmra, pmdec, parallax, parallax_error, radial_velocity
+FROM gaiadr2.gaia_source
+WHERE parallax &lt; 1
+  AND bp_rp BETWEEN -0.75 AND 2
+</pre></div>
+</div>
+</div>
+</div>
+<p>Notice that the format specifier has been replaced with the value of <code class="docutils literal notranslate"><span class="pre">columns</span></code>.</p>
+<p>Let’s run it and see if it works:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">job3</span> <span class="o">=</span> <span class="n">Gaia</span><span class="o">.</span><span class="n">launch_job</span><span class="p">(</span><span class="n">query3</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">job3</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;Table length=10&gt;
+      name       dtype    unit                              description                             n_bad
+--------------- ------- -------- ------------------------------------------------------------------ -----
+      source_id   int64          Unique source identifier (unique within a particular Data Release)     0
+             ra float64      deg                                                    Right ascension     0
+            dec float64      deg                                                        Declination     0
+           pmra float64 mas / yr                         Proper motion in right ascension direction     0
+          pmdec float64 mas / yr                             Proper motion in declination direction     0
+       parallax float64      mas                                                           Parallax     0
+ parallax_error float64      mas                                         Standard error of parallax     0
+radial_velocity float64   km / s                                                    Radial velocity    10
+Jobid: None
+Phase: COMPLETED
+Owner: None
+Output file: sync_20201005090726.xml.gz
+Results: None
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">results3</span> <span class="o">=</span> <span class="n">job3</span><span class="o">.</span><span class="n">get_results</span><span class="p">()</span>
+<span class="n">results3</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><i>Table length=10</i>
+<table id="table140265627700432" class="table-striped table-bordered table-condensed">
+<thead><tr><th>source_id</th><th>ra</th><th>dec</th><th>pmra</th><th>pmdec</th><th>parallax</th><th>parallax_error</th><th>radial_velocity</th></tr></thead>
+<thead><tr><th></th><th>deg</th><th>deg</th><th>mas / yr</th><th>mas / yr</th><th>mas</th><th>mas</th><th>km / s</th></tr></thead>
+<thead><tr><th>int64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th></tr></thead>
+<tr><td>4467710915011802624</td><td>269.9680969307347</td><td>1.1429085038160882</td><td>2.0233280236600626</td><td>-2.5692427875510266</td><td>0.42361471245557913</td><td>0.470352406647465</td><td>--</td></tr>
+<tr><td>4467706551328679552</td><td>270.033164589881</td><td>1.0565747323689927</td><td>-3.414829591355289</td><td>-3.8437215857495737</td><td>0.922888231734588</td><td>0.927008559859825</td><td>--</td></tr>
+<tr><td>4467712255037300096</td><td>270.7724717923047</td><td>0.6581664892880896</td><td>-3.5620173752896025</td><td>-6.595792323153987</td><td>-2.669179465293931</td><td>0.9719742773203504</td><td>--</td></tr>
+<tr><td>4467735001181761792</td><td>270.3628606248308</td><td>0.8947079323599124</td><td>2.1307079926489205</td><td>0.8826727710910712</td><td>0.6117399163086398</td><td>0.509812721702093</td><td>--</td></tr>
+<tr><td>4467737101421916672</td><td>270.5110834661444</td><td>0.9806225910160181</td><td>0.17532366511560785</td><td>-5.113270239706202</td><td>-0.39818224846127004</td><td>0.7549581886719651</td><td>--</td></tr>
+<tr><td>4467707547757327488</td><td>269.88746280594927</td><td>1.0212759940136962</td><td>-2.6382230817672987</td><td>-3.707776532049287</td><td>0.7741412301054209</td><td>0.3022057897812064</td><td>--</td></tr>
+<tr><td>4467732355491087744</td><td>270.6730790702491</td><td>0.9197224705139885</td><td>-2.2735991502653037</td><td>-11.864952855984358</td><td>-0.3464446494840354</td><td>0.4937921513912002</td><td>--</td></tr>
+<tr><td>4467717099766944512</td><td>270.57667173120825</td><td>0.726277659009568</td><td>-3.4598362614808367</td><td>-4.601426893365921</td><td>0.05443955111134051</td><td>0.8867339293525688</td><td>--</td></tr>
+<tr><td>4467719058265781248</td><td>270.7248052971514</td><td>0.8205551921782785</td><td>-3.255079498426542</td><td>-9.249285069111085</td><td>0.3733943917490343</td><td>0.390952370410666</td><td>--</td></tr>
+<tr><td>4467722326741572352</td><td>270.87431291888504</td><td>0.8595565975869158</td><td>0.10696398351859826</td><td>1.2035993780158853</td><td>-0.11850943432864373</td><td>0.1660452431882023</td><td>--</td></tr>
+</table></div></div>
+</div>
+<p>Good so far.</p>
+<p><strong>Exercise:</strong> This query always selects sources with <code class="docutils literal notranslate"><span class="pre">parallax</span></code> less than 1.  But suppose you want to take that upper bound as an input.</p>
+<p>Modify <code class="docutils literal notranslate"><span class="pre">query3_base</span></code> to replace <code class="docutils literal notranslate"><span class="pre">1</span></code> with a format specifier like <code class="docutils literal notranslate"><span class="pre">{max_parallax}</span></code>.  Now, when you call <code class="docutils literal notranslate"><span class="pre">format</span></code>, add a keyword argument that assigns a value to <code class="docutils literal notranslate"><span class="pre">max_parallax</span></code>, and confirm that the format specifier gets replaced with the value you provide.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Solution</span>
+
+<span class="n">query4_base</span> <span class="o">=</span> <span class="s2">&quot;&quot;&quot;SELECT TOP 10</span>
+<span class="si">{columns}</span><span class="s2"></span>
+<span class="s2">FROM gaiadr2.gaia_source</span>
+<span class="s2">WHERE parallax &lt; </span><span class="si">{max_parallax}</span><span class="s2"> AND </span>
+<span class="s2">bp_rp BETWEEN -0.75 AND 2</span>
+<span class="s2">&quot;&quot;&quot;</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Solution</span>
+
+<span class="n">query4</span> <span class="o">=</span> <span class="n">query4_base</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="n">columns</span><span class="p">,</span>
+                          <span class="n">max_parallax</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">query</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>SELECT TOP 10
+source_id, ra, dec, pmra, pmdec, parallax, parallax_error, radial_velocity
+FROM gaiadr2.gaia_source
+WHERE parallax &lt; 0.5 AND 
+bp_rp BETWEEN -0.75 AND 2
+</pre></div>
+</div>
+</div>
+</div>
+<p><strong>Style note:</strong>  You might notice that the variable names in this notebook are numbered, like <code class="docutils literal notranslate"><span class="pre">query1</span></code>, <code class="docutils literal notranslate"><span class="pre">query2</span></code>, etc.</p>
+<p>The advantage of this style is that it isolates each section of the notebook from the others, so if you go back and run the cells out of order, it’s less likely that you will get unexpected interactions.</p>
+<p>A drawback of this style is that it can be a nuisance to update the notebook if you add, remove, or reorder a section.</p>
+<p>What do you think of this choice?  Are there alternatives you prefer?</p>
+</div>
+<div class="section" id="summary">
+<h2>Summary<a class="headerlink" href="#summary" title="Permalink to this headline">¶</a></h2>
+<p>This notebook demonstrates the following steps:</p>
+<ol class="simple">
+<li><p>Making a connection to the Gaia server,</p></li>
+<li><p>Exploring information about the database and the tables it contains,</p></li>
+<li><p>Writing a query and sending it to the server, and finally</p></li>
+<li><p>Downloading the response from the server as an Astropy <code class="docutils literal notranslate"><span class="pre">Table</span></code>.</p></li>
+</ol>
+</div>
+<div class="section" id="best-practices">
+<h2>Best practices<a class="headerlink" href="#best-practices" title="Permalink to this headline">¶</a></h2>
+<ul class="simple">
+<li><p>If you can’t download an entire dataset (or it’s not practical) use queries to select the data you need.</p></li>
+<li><p>Read the metadata and the documentation to make sure you understand the tables, their columns, and what they mean.</p></li>
+<li><p>Develop queries incrementally: start with something simple, test it, and add a little bit at a time.</p></li>
+<li><p>Use ADQL features like <code class="docutils literal notranslate"><span class="pre">TOP</span></code> and <code class="docutils literal notranslate"><span class="pre">COUNT</span></code> to test before you run a query that might return a lot of data.</p></li>
+<li><p>If you know your query will return fewer than 3000 rows, you can run it synchronously, which might complete faster (but it doesn’t seem to make much difference).  If it might return more than 3000 rows, you should run it asynchronously.</p></li>
+<li><p>ADQL and SQL are not case-sensitive, so you don’t have to capitalize the keywords, but you should.</p></li>
+<li><p>ADQL and SQL don’t require you to break a query into multiple lines, but you should.</p></li>
+</ul>
+<p>Jupyter notebooks can be good for developing and testing code, but they have some drawbacks.  In particular, if you run the cells out of order, you might find that variables don’t have the values you expect.</p>
+<p>There are a few things you can do to mitigate these problems:</p>
+<ul class="simple">
+<li><p>Make each section of the notebook self-contained.  Try not to use the same variable name in more than one section.</p></li>
+<li><p>Keep notebooks short.  Look for places where you can break your analysis into phases with one notebook per phase.</p></li>
+</ul>
+</div>
+</div>
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+    <div class="d-none d-md-block col-md-2 bd-toc show">
+        
+        <div class="tocsection onthispage pt-5 pb-3">
+            <i class="fas fa-list"></i> Contents
+        </div>
+        <nav id="bd-toc-nav">
+            <ul class="nav section-nav flex-column">
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#installing-libraries">
+   Installing libraries
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#selecting-a-region">
+   Selecting a region
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#getting-gd-1-data">
+   Getting GD-1 Data
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#working-with-coordinates">
+   Working with coordinates
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#selecting-a-rectangle">
+   Selecting a rectangle
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#selecting-a-polygon">
+   Selecting a polygon
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#saving-results">
+   Saving results
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#summary">
+   Summary
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#best-practices">
+   Best practices
+  </a>
+ </li>
+</ul>
+
+        </nav>
+        
+    </div>
+</div>
+    <div id="main-content" class="row">
+        <div class="col-12 col-md-9 pl-md-3 pr-md-0">
+        
+              <div>
+                
+  <div class="section" id="lesson-2">
+<h1>Lesson 2<a class="headerlink" href="#lesson-2" title="Permalink to this headline">¶</a></h1>
+<p>This is the second in a series of lessons related to astronomy data.</p>
+<p>As a running example, we are replicating parts of the analysis in a recent paper, “<a class="reference external" href="https://arxiv.org/abs/1805.00425">Off the beaten path: Gaia reveals GD-1 stars outside of the main stream</a>� by Adrian M. Price-Whelan and Ana Bonaca.</p>
+<p>In the first notebook, we wrote ADQL queries and used them to select and download data from the Gaia server.</p>
+<p>In this notebook, we’ll pick up where we left off and write a query to select stars from the region of the sky where we expect GD-1 to be.</p>
+<p>We’ll start with an example that does a “cone search�; that is, it selects stars that appear in a circular region of the sky.</p>
+<p>Then, to select stars in the vicinity of GD-1, we’ll:</p>
+<ul class="simple">
+<li><p>Use <code class="docutils literal notranslate"><span class="pre">Quantity</span></code> objects to represent measurements with units.</p></li>
+<li><p>Use the <code class="docutils literal notranslate"><span class="pre">Gala</span></code> library to convert coordinates from one frame to another.</p></li>
+<li><p>Use the ADQL keywords <code class="docutils literal notranslate"><span class="pre">POLYGON</span></code>, <code class="docutils literal notranslate"><span class="pre">CONTAINS</span></code>, and <code class="docutils literal notranslate"><span class="pre">POINT</span></code> to select stars that fall within a polygonal region.</p></li>
+<li><p>Submit a query and download the results.</p></li>
+<li><p>Store the results in a FITS file.</p></li>
+</ul>
+<p>After completing this lesson, you should be able to</p>
+<ul class="simple">
+<li><p>Use Python string formatting to compose more complex ADQL queries.</p></li>
+<li><p>Work with coordinates and other quantities that have units.</p></li>
+<li><p>Download the results of a query and store them in a file.</p></li>
+</ul>
+<div class="section" id="installing-libraries">
+<h2>Installing libraries<a class="headerlink" href="#installing-libraries" title="Permalink to this headline">¶</a></h2>
+<p>If you are running this notebook on Colab, you can run the following cell to install Astroquery and a the other libraries we’ll use.</p>
+<p>If you are running this notebook on your own computer, you might have to install these libraries yourself.</p>
+<p>If you are using this notebook as part of a Carpentries workshop, you should have received setup instructions.</p>
+<p>TODO: Add a link to the instructions.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># If we&#39;re running on Colab, install libraries</span>
+
+<span class="kn">import</span> <span class="nn">sys</span>
+<span class="n">IN_COLAB</span> <span class="o">=</span> <span class="s1">&#39;google.colab&#39;</span> <span class="ow">in</span> <span class="n">sys</span><span class="o">.</span><span class="n">modules</span>
+
+<span class="k">if</span> <span class="n">IN_COLAB</span><span class="p">:</span>
+    <span class="o">!</span>pip install astroquery astro-gala pyia
+</pre></div>
+</div>
+</div>
+</div>
+</div>
+<div class="section" id="selecting-a-region">
+<h2>Selecting a region<a class="headerlink" href="#selecting-a-region" title="Permalink to this headline">¶</a></h2>
+<p>One of the most common ways to restrict a query is to select stars in a particular region of the sky.</p>
+<p>For example, here’s a query from the <a class="reference external" href="https://gea.esac.esa.int/archive-help/adql/examples/index.html">Gaia archive documentation</a> that selects “all the objects … in a circular region centered at (266.41683, -29.00781) with a search radius of 5 arcmin (0.08333 deg).�</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">query</span> <span class="o">=</span> <span class="s2">&quot;&quot;&quot;</span>
+<span class="s2">SELECT </span>
+<span class="s2">TOP 10 source_id</span>
+<span class="s2">FROM gaiadr2.gaia_source</span>
+<span class="s2">WHERE 1=CONTAINS(</span>
+<span class="s2">  POINT(ra, dec),</span>
+<span class="s2">  CIRCLE(266.41683, -29.00781, 0.08333333))</span>
+<span class="s2">&quot;&quot;&quot;</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>This query uses three keywords that are specific to ADQL (not SQL):</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">POINT</span></code>: a location in <a class="reference external" href="https://en.wikipedia.org/wiki/International_Celestial_Reference_System">ICRS coordinates</a>, specified in degrees of right ascension and declination.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">CIRCLE</span></code>: a circle where the first two values are the coordinates of the center and the third is the radius in degrees.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">CONTAINS</span></code>: a function that returns <code class="docutils literal notranslate"><span class="pre">1</span></code> if a <code class="docutils literal notranslate"><span class="pre">POINT</span></code> is contained in a shape and <code class="docutils literal notranslate"><span class="pre">0</span></code> otherwise.</p></li>
+</ul>
+<p>Here is the <a class="reference external" href="http://www.ivoa.net/documents/ADQL/20180112/PR-ADQL-2.1-20180112.html#tth_sEc4.2.12">documentation of <code class="docutils literal notranslate"><span class="pre">CONTAINS</span></code></a>.</p>
+<p>A query like this is called a cone search because it selects stars in a cone.</p>
+<p>Here’s how we run it.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">astroquery.gaia</span> <span class="kn">import</span> <span class="n">Gaia</span>
+
+<span class="n">job</span> <span class="o">=</span> <span class="n">Gaia</span><span class="o">.</span><span class="n">launch_job</span><span class="p">(</span><span class="n">query</span><span class="p">)</span>
+<span class="n">result</span> <span class="o">=</span> <span class="n">job</span><span class="o">.</span><span class="n">get_results</span><span class="p">()</span>
+<span class="n">result</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Created TAP+ (v1.2.1) - Connection:
+	Host: gea.esac.esa.int
+	Use HTTPS: True
+	Port: 443
+	SSL Port: 443
+Created TAP+ (v1.2.1) - Connection:
+	Host: geadata.esac.esa.int
+	Use HTTPS: True
+	Port: 443
+	SSL Port: 443
+</pre></div>
+</div>
+<div class="output text_html"><i>Table length=10</i>
+<table id="table140683453947328" class="table-striped table-bordered table-condensed">
+<thead><tr><th>source_id</th></tr></thead>
+<thead><tr><th>int64</th></tr></thead>
+<tr><td>4057468321929794432</td></tr>
+<tr><td>4057468287575835392</td></tr>
+<tr><td>4057482027171038976</td></tr>
+<tr><td>4057470349160630656</td></tr>
+<tr><td>4057470039924301696</td></tr>
+<tr><td>4057469868125641984</td></tr>
+<tr><td>4057468351995073024</td></tr>
+<tr><td>4057469661959554560</td></tr>
+<tr><td>4057470520960672640</td></tr>
+<tr><td>4057470555320409600</td></tr>
+</table></div></div>
+</div>
+<p><strong>Exercise:</strong> When you are debugging queries like this, you can use <code class="docutils literal notranslate"><span class="pre">TOP</span></code> to limit the size of the results, but then you still don’t know how big the results will be.</p>
+<p>An alternative is to use <code class="docutils literal notranslate"><span class="pre">COUNT</span></code>, which asks for the number of rows that would be selected, but it does not return them.</p>
+<p>In the previous query, replace <code class="docutils literal notranslate"><span class="pre">TOP</span> <span class="pre">10</span> <span class="pre">source_id</span></code> with <code class="docutils literal notranslate"><span class="pre">COUNT(source_id)</span></code> and run the query again.  How many stars has Gaia identified in the cone we searched?</p>
+</div>
+<div class="section" id="getting-gd-1-data">
+<h2>Getting GD-1 Data<a class="headerlink" href="#getting-gd-1-data" title="Permalink to this headline">¶</a></h2>
+<p>From the Price-Whelan and Bonaca paper, we will try to reproduce Figure 1, which includes this representation of stars likely to belong to GD-1:</p>
+<img src="https://github.com/datacarpentry/astronomy-python/raw/gh-pages/fig/gd1-4.png"><p>Along the axis of right ascension (<span class="math notranslate nohighlight">\(\phi_1\)</span>) the figure extends from -100 to 20 degrees.</p>
+<p>Along the axis of declination (<span class="math notranslate nohighlight">\(\phi_2\)</span>) the figure extends from about -8 to 4 degrees.</p>
+<p>Ideally, we would select all stars from this rectangle, but there are more than 10 million of them, so</p>
+<ul class="simple">
+<li><p>That would be difficult to work with,</p></li>
+<li><p>As anonymous users, we are limited to 3 million rows in a single query, and</p></li>
+<li><p>While we are developing and testing code, it will be faster to work with a smaller dataset.</p></li>
+</ul>
+<p>So we’ll start by selecting stars in a smaller rectangle, from -55 to -45 degrees right ascension and -8 to 4 degrees of declination.</p>
+<p>But first we let’s see how to represent quantities with units like degrees.</p>
+</div>
+<div class="section" id="working-with-coordinates">
+<h2>Working with coordinates<a class="headerlink" href="#working-with-coordinates" title="Permalink to this headline">¶</a></h2>
+<p>Coordinates are physical quantities, which means that they have two parts, a value and a unit.</p>
+<p>For example, the coordinate <span class="math notranslate nohighlight">\(30^{\circ}\)</span> has value 30 and its units are degrees.</p>
+<p>Until recently, most scientific computation was done with values only; units were left out of the program altogether, <a class="reference external" href="https://en.wikipedia.org/wiki/Mars_Climate_Orbiter#Cause_of_failure">often with disastrous results</a>.</p>
+<p>Astropy provides tools for including units explicitly in computations, which makes it possible to detect errors before they cause disasters.</p>
+<p>To use Astropy units, we import them like this:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">astropy.units</span> <span class="k">as</span> <span class="nn">u</span>
+
+<span class="n">u</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;module &#39;astropy.units&#39; from &#39;/home/downey/anaconda3/envs/AstronomicalData/lib/python3.8/site-packages/astropy/units/__init__.py&#39;&gt;
+</pre></div>
+</div>
+</div>
+</div>
+<p><code class="docutils literal notranslate"><span class="pre">u</span></code> is an object that contains most common units and all SI units.</p>
+<p>You can use <code class="docutils literal notranslate"><span class="pre">dir</span></code> to list them, but you should also <a class="reference external" href="https://docs.astropy.org/en/stable/units/">read the documentation</a>.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="nb">dir</span><span class="p">(</span><span class="n">u</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[&#39;A&#39;,
+ &#39;AA&#39;,
+ &#39;AB&#39;,
+ &#39;ABflux&#39;,
+ &#39;ABmag&#39;,
+ &#39;AU&#39;,
+ &#39;Angstrom&#39;,
+ &#39;B&#39;,
+ &#39;Ba&#39;,
+ &#39;Barye&#39;,
+ &#39;Bi&#39;,
+ &#39;Biot&#39;,
+ &#39;Bol&#39;,
+ &#39;Bq&#39;,
+ &#39;C&#39;,
+ &#39;Celsius&#39;,
+ &#39;Ci&#39;,
+ &#39;CompositeUnit&#39;,
+ &#39;D&#39;,
+ &#39;Da&#39;,
+ &#39;Dalton&#39;,
+ &#39;Debye&#39;,
+ &#39;Decibel&#39;,
+ &#39;DecibelUnit&#39;,
+ &#39;Dex&#39;,
+ &#39;DexUnit&#39;,
+ &#39;EA&#39;,
+ &#39;EAU&#39;,
+ &#39;EB&#39;,
+ &#39;EBa&#39;,
+ &#39;EC&#39;,
+ &#39;ED&#39;,
+ &#39;EF&#39;,
+ &#39;EG&#39;,
+ &#39;EGal&#39;,
+ &#39;EH&#39;,
+ &#39;EHz&#39;,
+ &#39;EJ&#39;,
+ &#39;EJy&#39;,
+ &#39;EK&#39;,
+ &#39;EL&#39;,
+ &#39;EN&#39;,
+ &#39;EOhm&#39;,
+ &#39;EP&#39;,
+ &#39;EPa&#39;,
+ &#39;ER&#39;,
+ &#39;ERy&#39;,
+ &#39;ES&#39;,
+ &#39;ESt&#39;,
+ &#39;ET&#39;,
+ &#39;EV&#39;,
+ &#39;EW&#39;,
+ &#39;EWb&#39;,
+ &#39;Ea&#39;,
+ &#39;Eadu&#39;,
+ &#39;Earcmin&#39;,
+ &#39;Earcsec&#39;,
+ &#39;Eau&#39;,
+ &#39;Eb&#39;,
+ &#39;Ebarn&#39;,
+ &#39;Ebeam&#39;,
+ &#39;Ebin&#39;,
+ &#39;Ebit&#39;,
+ &#39;Ebyte&#39;,
+ &#39;Ecd&#39;,
+ &#39;Echan&#39;,
+ &#39;Ecount&#39;,
+ &#39;Ect&#39;,
+ &#39;Ed&#39;,
+ &#39;Edeg&#39;,
+ &#39;Edyn&#39;,
+ &#39;EeV&#39;,
+ &#39;Eerg&#39;,
+ &#39;Eg&#39;,
+ &#39;Eh&#39;,
+ &#39;EiB&#39;,
+ &#39;Eib&#39;,
+ &#39;Eibit&#39;,
+ &#39;Eibyte&#39;,
+ &#39;Ek&#39;,
+ &#39;El&#39;,
+ &#39;Elm&#39;,
+ &#39;Elx&#39;,
+ &#39;Elyr&#39;,
+ &#39;Em&#39;,
+ &#39;Emag&#39;,
+ &#39;Emin&#39;,
+ &#39;Emol&#39;,
+ &#39;Eohm&#39;,
+ &#39;Epc&#39;,
+ &#39;Eph&#39;,
+ &#39;Ephoton&#39;,
+ &#39;Epix&#39;,
+ &#39;Epixel&#39;,
+ &#39;Erad&#39;,
+ &#39;Es&#39;,
+ &#39;Esr&#39;,
+ &#39;Eu&#39;,
+ &#39;Evox&#39;,
+ &#39;Evoxel&#39;,
+ &#39;Eyr&#39;,
+ &#39;F&#39;,
+ &#39;Farad&#39;,
+ &#39;Fr&#39;,
+ &#39;Franklin&#39;,
+ &#39;FunctionQuantity&#39;,
+ &#39;FunctionUnitBase&#39;,
+ &#39;G&#39;,
+ &#39;GA&#39;,
+ &#39;GAU&#39;,
+ &#39;GB&#39;,
+ &#39;GBa&#39;,
+ &#39;GC&#39;,
+ &#39;GD&#39;,
+ &#39;GF&#39;,
+ &#39;GG&#39;,
+ &#39;GGal&#39;,
+ &#39;GH&#39;,
+ &#39;GHz&#39;,
+ &#39;GJ&#39;,
+ &#39;GJy&#39;,
+ &#39;GK&#39;,
+ &#39;GL&#39;,
+ &#39;GN&#39;,
+ &#39;GOhm&#39;,
+ &#39;GP&#39;,
+ &#39;GPa&#39;,
+ &#39;GR&#39;,
+ &#39;GRy&#39;,
+ &#39;GS&#39;,
+ &#39;GSt&#39;,
+ &#39;GT&#39;,
+ &#39;GV&#39;,
+ &#39;GW&#39;,
+ &#39;GWb&#39;,
+ &#39;Ga&#39;,
+ &#39;Gadu&#39;,
+ &#39;Gal&#39;,
+ &#39;Garcmin&#39;,
+ &#39;Garcsec&#39;,
+ &#39;Gau&#39;,
+ &#39;Gauss&#39;,
+ &#39;Gb&#39;,
+ &#39;Gbarn&#39;,
+ &#39;Gbeam&#39;,
+ &#39;Gbin&#39;,
+ &#39;Gbit&#39;,
+ &#39;Gbyte&#39;,
+ &#39;Gcd&#39;,
+ &#39;Gchan&#39;,
+ &#39;Gcount&#39;,
+ &#39;Gct&#39;,
+ &#39;Gd&#39;,
+ &#39;Gdeg&#39;,
+ &#39;Gdyn&#39;,
+ &#39;GeV&#39;,
+ &#39;Gerg&#39;,
+ &#39;Gg&#39;,
+ &#39;Gh&#39;,
+ &#39;GiB&#39;,
+ &#39;Gib&#39;,
+ &#39;Gibit&#39;,
+ &#39;Gibyte&#39;,
+ &#39;Gk&#39;,
+ &#39;Gl&#39;,
+ &#39;Glm&#39;,
+ &#39;Glx&#39;,
+ &#39;Glyr&#39;,
+ &#39;Gm&#39;,
+ &#39;Gmag&#39;,
+ &#39;Gmin&#39;,
+ &#39;Gmol&#39;,
+ &#39;Gohm&#39;,
+ &#39;Gpc&#39;,
+ &#39;Gph&#39;,
+ &#39;Gphoton&#39;,
+ &#39;Gpix&#39;,
+ &#39;Gpixel&#39;,
+ &#39;Grad&#39;,
+ &#39;Gs&#39;,
+ &#39;Gsr&#39;,
+ &#39;Gu&#39;,
+ &#39;Gvox&#39;,
+ &#39;Gvoxel&#39;,
+ &#39;Gyr&#39;,
+ &#39;H&#39;,
+ &#39;Henry&#39;,
+ &#39;Hertz&#39;,
+ &#39;Hz&#39;,
+ &#39;IrreducibleUnit&#39;,
+ &#39;J&#39;,
+ &#39;Jansky&#39;,
+ &#39;Joule&#39;,
+ &#39;Jy&#39;,
+ &#39;K&#39;,
+ &#39;Kayser&#39;,
+ &#39;Kelvin&#39;,
+ &#39;KiB&#39;,
+ &#39;Kib&#39;,
+ &#39;Kibit&#39;,
+ &#39;Kibyte&#39;,
+ &#39;L&#39;,
+ &#39;L_bol&#39;,
+ &#39;L_sun&#39;,
+ &#39;LogQuantity&#39;,
+ &#39;LogUnit&#39;,
+ &#39;Lsun&#39;,
+ &#39;MA&#39;,
+ &#39;MAU&#39;,
+ &#39;MB&#39;,
+ &#39;MBa&#39;,
+ &#39;MC&#39;,
+ &#39;MD&#39;,
+ &#39;MF&#39;,
+ &#39;MG&#39;,
+ &#39;MGal&#39;,
+ &#39;MH&#39;,
+ &#39;MHz&#39;,
+ &#39;MJ&#39;,
+ &#39;MJy&#39;,
+ &#39;MK&#39;,
+ &#39;ML&#39;,
+ &#39;MN&#39;,
+ &#39;MOhm&#39;,
+ &#39;MP&#39;,
+ &#39;MPa&#39;,
+ &#39;MR&#39;,
+ &#39;MRy&#39;,
+ &#39;MS&#39;,
+ &#39;MSt&#39;,
+ &#39;MT&#39;,
+ &#39;MV&#39;,
+ &#39;MW&#39;,
+ &#39;MWb&#39;,
+ &#39;M_bol&#39;,
+ &#39;M_e&#39;,
+ &#39;M_earth&#39;,
+ &#39;M_jup&#39;,
+ &#39;M_jupiter&#39;,
+ &#39;M_p&#39;,
+ &#39;M_sun&#39;,
+ &#39;Ma&#39;,
+ &#39;Madu&#39;,
+ &#39;MagUnit&#39;,
+ &#39;Magnitude&#39;,
+ &#39;Marcmin&#39;,
+ &#39;Marcsec&#39;,
+ &#39;Mau&#39;,
+ &#39;Mb&#39;,
+ &#39;Mbarn&#39;,
+ &#39;Mbeam&#39;,
+ &#39;Mbin&#39;,
+ &#39;Mbit&#39;,
+ &#39;Mbyte&#39;,
+ &#39;Mcd&#39;,
+ &#39;Mchan&#39;,
+ &#39;Mcount&#39;,
+ &#39;Mct&#39;,
+ &#39;Md&#39;,
+ &#39;Mdeg&#39;,
+ &#39;Mdyn&#39;,
+ &#39;MeV&#39;,
+ &#39;Mearth&#39;,
+ &#39;Merg&#39;,
+ &#39;Mg&#39;,
+ &#39;Mh&#39;,
+ &#39;MiB&#39;,
+ &#39;Mib&#39;,
+ &#39;Mibit&#39;,
+ &#39;Mibyte&#39;,
+ &#39;Mjup&#39;,
+ &#39;Mjupiter&#39;,
+ &#39;Mk&#39;,
+ &#39;Ml&#39;,
+ &#39;Mlm&#39;,
+ &#39;Mlx&#39;,
+ &#39;Mlyr&#39;,
+ &#39;Mm&#39;,
+ &#39;Mmag&#39;,
+ &#39;Mmin&#39;,
+ &#39;Mmol&#39;,
+ &#39;Mohm&#39;,
+ &#39;Mpc&#39;,
+ &#39;Mph&#39;,
+ &#39;Mphoton&#39;,
+ &#39;Mpix&#39;,
+ &#39;Mpixel&#39;,
+ &#39;Mrad&#39;,
+ &#39;Ms&#39;,
+ &#39;Msr&#39;,
+ &#39;Msun&#39;,
+ &#39;Mu&#39;,
+ &#39;Mvox&#39;,
+ &#39;Mvoxel&#39;,
+ &#39;Myr&#39;,
+ &#39;N&#39;,
+ &#39;NamedUnit&#39;,
+ &#39;Newton&#39;,
+ &#39;Ohm&#39;,
+ &#39;P&#39;,
+ &#39;PA&#39;,
+ &#39;PAU&#39;,
+ &#39;PB&#39;,
+ &#39;PBa&#39;,
+ &#39;PC&#39;,
+ &#39;PD&#39;,
+ &#39;PF&#39;,
+ &#39;PG&#39;,
+ &#39;PGal&#39;,
+ &#39;PH&#39;,
+ &#39;PHz&#39;,
+ &#39;PJ&#39;,
+ &#39;PJy&#39;,
+ &#39;PK&#39;,
+ &#39;PL&#39;,
+ &#39;PN&#39;,
+ &#39;POhm&#39;,
+ &#39;PP&#39;,
+ &#39;PPa&#39;,
+ &#39;PR&#39;,
+ &#39;PRy&#39;,
+ &#39;PS&#39;,
+ &#39;PSt&#39;,
+ &#39;PT&#39;,
+ &#39;PV&#39;,
+ &#39;PW&#39;,
+ &#39;PWb&#39;,
+ &#39;Pa&#39;,
+ &#39;Padu&#39;,
+ &#39;Parcmin&#39;,
+ &#39;Parcsec&#39;,
+ &#39;Pascal&#39;,
+ &#39;Pau&#39;,
+ &#39;Pb&#39;,
+ &#39;Pbarn&#39;,
+ &#39;Pbeam&#39;,
+ &#39;Pbin&#39;,
+ &#39;Pbit&#39;,
+ &#39;Pbyte&#39;,
+ &#39;Pcd&#39;,
+ &#39;Pchan&#39;,
+ &#39;Pcount&#39;,
+ &#39;Pct&#39;,
+ &#39;Pd&#39;,
+ &#39;Pdeg&#39;,
+ &#39;Pdyn&#39;,
+ &#39;PeV&#39;,
+ &#39;Perg&#39;,
+ &#39;Pg&#39;,
+ &#39;Ph&#39;,
+ &#39;PiB&#39;,
+ &#39;Pib&#39;,
+ &#39;Pibit&#39;,
+ &#39;Pibyte&#39;,
+ &#39;Pk&#39;,
+ &#39;Pl&#39;,
+ &#39;Plm&#39;,
+ &#39;Plx&#39;,
+ &#39;Plyr&#39;,
+ &#39;Pm&#39;,
+ &#39;Pmag&#39;,
+ &#39;Pmin&#39;,
+ &#39;Pmol&#39;,
+ &#39;Pohm&#39;,
+ &#39;Ppc&#39;,
+ &#39;Pph&#39;,
+ &#39;Pphoton&#39;,
+ &#39;Ppix&#39;,
+ &#39;Ppixel&#39;,
+ &#39;Prad&#39;,
+ &#39;PrefixUnit&#39;,
+ &#39;Ps&#39;,
+ &#39;Psr&#39;,
+ &#39;Pu&#39;,
+ &#39;Pvox&#39;,
+ &#39;Pvoxel&#39;,
+ &#39;Pyr&#39;,
+ &#39;Quantity&#39;,
+ &#39;QuantityInfo&#39;,
+ &#39;QuantityInfoBase&#39;,
+ &#39;R&#39;,
+ &#39;R_earth&#39;,
+ &#39;R_jup&#39;,
+ &#39;R_jupiter&#39;,
+ &#39;R_sun&#39;,
+ &#39;Rayleigh&#39;,
+ &#39;Rearth&#39;,
+ &#39;Rjup&#39;,
+ &#39;Rjupiter&#39;,
+ &#39;Rsun&#39;,
+ &#39;Ry&#39;,
+ &#39;S&#39;,
+ &#39;ST&#39;,
+ &#39;STflux&#39;,
+ &#39;STmag&#39;,
+ &#39;Siemens&#39;,
+ &#39;SpecificTypeQuantity&#39;,
+ &#39;St&#39;,
+ &#39;Sun&#39;,
+ &#39;T&#39;,
+ &#39;TA&#39;,
+ &#39;TAU&#39;,
+ &#39;TB&#39;,
+ &#39;TBa&#39;,
+ &#39;TC&#39;,
+ &#39;TD&#39;,
+ &#39;TF&#39;,
+ &#39;TG&#39;,
+ &#39;TGal&#39;,
+ &#39;TH&#39;,
+ &#39;THz&#39;,
+ &#39;TJ&#39;,
+ &#39;TJy&#39;,
+ &#39;TK&#39;,
+ &#39;TL&#39;,
+ &#39;TN&#39;,
+ &#39;TOhm&#39;,
+ &#39;TP&#39;,
+ &#39;TPa&#39;,
+ &#39;TR&#39;,
+ &#39;TRy&#39;,
+ &#39;TS&#39;,
+ &#39;TSt&#39;,
+ &#39;TT&#39;,
+ &#39;TV&#39;,
+ &#39;TW&#39;,
+ &#39;TWb&#39;,
+ &#39;Ta&#39;,
+ &#39;Tadu&#39;,
+ &#39;Tarcmin&#39;,
+ &#39;Tarcsec&#39;,
+ &#39;Tau&#39;,
+ &#39;Tb&#39;,
+ &#39;Tbarn&#39;,
+ &#39;Tbeam&#39;,
+ &#39;Tbin&#39;,
+ &#39;Tbit&#39;,
+ &#39;Tbyte&#39;,
+ &#39;Tcd&#39;,
+ &#39;Tchan&#39;,
+ &#39;Tcount&#39;,
+ &#39;Tct&#39;,
+ &#39;Td&#39;,
+ &#39;Tdeg&#39;,
+ &#39;Tdyn&#39;,
+ &#39;TeV&#39;,
+ &#39;Terg&#39;,
+ &#39;Tesla&#39;,
+ &#39;Tg&#39;,
+ &#39;Th&#39;,
+ &#39;TiB&#39;,
+ &#39;Tib&#39;,
+ &#39;Tibit&#39;,
+ &#39;Tibyte&#39;,
+ &#39;Tk&#39;,
+ &#39;Tl&#39;,
+ &#39;Tlm&#39;,
+ &#39;Tlx&#39;,
+ &#39;Tlyr&#39;,
+ &#39;Tm&#39;,
+ &#39;Tmag&#39;,
+ &#39;Tmin&#39;,
+ &#39;Tmol&#39;,
+ &#39;Tohm&#39;,
+ &#39;Tpc&#39;,
+ &#39;Tph&#39;,
+ &#39;Tphoton&#39;,
+ &#39;Tpix&#39;,
+ &#39;Tpixel&#39;,
+ &#39;Trad&#39;,
+ &#39;Ts&#39;,
+ &#39;Tsr&#39;,
+ &#39;Tu&#39;,
+ &#39;Tvox&#39;,
+ &#39;Tvoxel&#39;,
+ &#39;Tyr&#39;,
+ &#39;Unit&#39;,
+ &#39;UnitBase&#39;,
+ &#39;UnitConversionError&#39;,
+ &#39;UnitTypeError&#39;,
+ &#39;UnitsError&#39;,
+ &#39;UnitsWarning&#39;,
+ &#39;UnrecognizedUnit&#39;,
+ &#39;V&#39;,
+ &#39;Volt&#39;,
+ &#39;W&#39;,
+ &#39;Watt&#39;,
+ &#39;Wb&#39;,
+ &#39;Weber&#39;,
+ &#39;YA&#39;,
+ &#39;YAU&#39;,
+ &#39;YB&#39;,
+ &#39;YBa&#39;,
+ &#39;YC&#39;,
+ &#39;YD&#39;,
+ &#39;YF&#39;,
+ &#39;YG&#39;,
+ &#39;YGal&#39;,
+ &#39;YH&#39;,
+ &#39;YHz&#39;,
+ &#39;YJ&#39;,
+ &#39;YJy&#39;,
+ &#39;YK&#39;,
+ &#39;YL&#39;,
+ &#39;YN&#39;,
+ &#39;YOhm&#39;,
+ &#39;YP&#39;,
+ &#39;YPa&#39;,
+ &#39;YR&#39;,
+ &#39;YRy&#39;,
+ &#39;YS&#39;,
+ &#39;YSt&#39;,
+ &#39;YT&#39;,
+ &#39;YV&#39;,
+ &#39;YW&#39;,
+ &#39;YWb&#39;,
+ &#39;Ya&#39;,
+ &#39;Yadu&#39;,
+ &#39;Yarcmin&#39;,
+ &#39;Yarcsec&#39;,
+ &#39;Yau&#39;,
+ &#39;Yb&#39;,
+ &#39;Ybarn&#39;,
+ &#39;Ybeam&#39;,
+ &#39;Ybin&#39;,
+ &#39;Ybit&#39;,
+ &#39;Ybyte&#39;,
+ &#39;Ycd&#39;,
+ &#39;Ychan&#39;,
+ &#39;Ycount&#39;,
+ &#39;Yct&#39;,
+ &#39;Yd&#39;,
+ &#39;Ydeg&#39;,
+ &#39;Ydyn&#39;,
+ &#39;YeV&#39;,
+ &#39;Yerg&#39;,
+ &#39;Yg&#39;,
+ &#39;Yh&#39;,
+ &#39;Yk&#39;,
+ &#39;Yl&#39;,
+ &#39;Ylm&#39;,
+ &#39;Ylx&#39;,
+ &#39;Ylyr&#39;,
+ &#39;Ym&#39;,
+ &#39;Ymag&#39;,
+ &#39;Ymin&#39;,
+ &#39;Ymol&#39;,
+ &#39;Yohm&#39;,
+ &#39;Ypc&#39;,
+ &#39;Yph&#39;,
+ &#39;Yphoton&#39;,
+ &#39;Ypix&#39;,
+ &#39;Ypixel&#39;,
+ &#39;Yrad&#39;,
+ &#39;Ys&#39;,
+ &#39;Ysr&#39;,
+ &#39;Yu&#39;,
+ &#39;Yvox&#39;,
+ &#39;Yvoxel&#39;,
+ &#39;Yyr&#39;,
+ &#39;ZA&#39;,
+ &#39;ZAU&#39;,
+ &#39;ZB&#39;,
+ &#39;ZBa&#39;,
+ &#39;ZC&#39;,
+ &#39;ZD&#39;,
+ &#39;ZF&#39;,
+ &#39;ZG&#39;,
+ &#39;ZGal&#39;,
+ &#39;ZH&#39;,
+ &#39;ZHz&#39;,
+ &#39;ZJ&#39;,
+ &#39;ZJy&#39;,
+ &#39;ZK&#39;,
+ &#39;ZL&#39;,
+ &#39;ZN&#39;,
+ &#39;ZOhm&#39;,
+ &#39;ZP&#39;,
+ &#39;ZPa&#39;,
+ &#39;ZR&#39;,
+ &#39;ZRy&#39;,
+ &#39;ZS&#39;,
+ &#39;ZSt&#39;,
+ &#39;ZT&#39;,
+ &#39;ZV&#39;,
+ &#39;ZW&#39;,
+ &#39;ZWb&#39;,
+ &#39;Za&#39;,
+ &#39;Zadu&#39;,
+ &#39;Zarcmin&#39;,
+ &#39;Zarcsec&#39;,
+ &#39;Zau&#39;,
+ &#39;Zb&#39;,
+ &#39;Zbarn&#39;,
+ &#39;Zbeam&#39;,
+ &#39;Zbin&#39;,
+ &#39;Zbit&#39;,
+ &#39;Zbyte&#39;,
+ &#39;Zcd&#39;,
+ &#39;Zchan&#39;,
+ &#39;Zcount&#39;,
+ &#39;Zct&#39;,
+ &#39;Zd&#39;,
+ &#39;Zdeg&#39;,
+ &#39;Zdyn&#39;,
+ &#39;ZeV&#39;,
+ &#39;Zerg&#39;,
+ &#39;Zg&#39;,
+ &#39;Zh&#39;,
+ &#39;Zk&#39;,
+ &#39;Zl&#39;,
+ &#39;Zlm&#39;,
+ &#39;Zlx&#39;,
+ &#39;Zlyr&#39;,
+ &#39;Zm&#39;,
+ &#39;Zmag&#39;,
+ &#39;Zmin&#39;,
+ &#39;Zmol&#39;,
+ &#39;Zohm&#39;,
+ &#39;Zpc&#39;,
+ &#39;Zph&#39;,
+ &#39;Zphoton&#39;,
+ &#39;Zpix&#39;,
+ &#39;Zpixel&#39;,
+ &#39;Zrad&#39;,
+ &#39;Zs&#39;,
+ &#39;Zsr&#39;,
+ &#39;Zu&#39;,
+ &#39;Zvox&#39;,
+ &#39;Zvoxel&#39;,
+ &#39;Zyr&#39;,
+ &#39;__builtins__&#39;,
+ &#39;__cached__&#39;,
+ &#39;__doc__&#39;,
+ &#39;__file__&#39;,
+ &#39;__loader__&#39;,
+ &#39;__name__&#39;,
+ &#39;__package__&#39;,
+ &#39;__path__&#39;,
+ &#39;__spec__&#39;,
+ &#39;a&#39;,
+ &#39;aA&#39;,
+ &#39;aAU&#39;,
+ &#39;aB&#39;,
+ &#39;aBa&#39;,
+ &#39;aC&#39;,
+ &#39;aD&#39;,
+ &#39;aF&#39;,
+ &#39;aG&#39;,
+ &#39;aGal&#39;,
+ &#39;aH&#39;,
+ &#39;aHz&#39;,
+ &#39;aJ&#39;,
+ &#39;aJy&#39;,
+ &#39;aK&#39;,
+ &#39;aL&#39;,
+ &#39;aN&#39;,
+ &#39;aOhm&#39;,
+ &#39;aP&#39;,
+ &#39;aPa&#39;,
+ &#39;aR&#39;,
+ &#39;aRy&#39;,
+ &#39;aS&#39;,
+ &#39;aSt&#39;,
+ &#39;aT&#39;,
+ &#39;aV&#39;,
+ &#39;aW&#39;,
+ &#39;aWb&#39;,
+ &#39;aa&#39;,
+ &#39;aadu&#39;,
+ &#39;aarcmin&#39;,
+ &#39;aarcsec&#39;,
+ &#39;aau&#39;,
+ &#39;ab&#39;,
+ &#39;abA&#39;,
+ &#39;abC&#39;,
+ &#39;abampere&#39;,
+ &#39;abarn&#39;,
+ &#39;abcoulomb&#39;,
+ &#39;abeam&#39;,
+ &#39;abin&#39;,
+ &#39;abit&#39;,
+ &#39;abyte&#39;,
+ &#39;acd&#39;,
+ &#39;achan&#39;,
+ &#39;acount&#39;,
+ &#39;act&#39;,
+ &#39;ad&#39;,
+ &#39;add_enabled_equivalencies&#39;,
+ &#39;add_enabled_units&#39;,
+ &#39;adeg&#39;,
+ &#39;adu&#39;,
+ &#39;adyn&#39;,
+ &#39;aeV&#39;,
+ &#39;aerg&#39;,
+ &#39;ag&#39;,
+ &#39;ah&#39;,
+ &#39;ak&#39;,
+ &#39;al&#39;,
+ &#39;allclose&#39;,
+ &#39;alm&#39;,
+ &#39;alx&#39;,
+ &#39;alyr&#39;,
+ &#39;am&#39;,
+ &#39;amag&#39;,
+ &#39;amin&#39;,
+ &#39;amol&#39;,
+ &#39;amp&#39;,
+ &#39;ampere&#39;,
+ &#39;angstrom&#39;,
+ &#39;annum&#39;,
+ &#39;aohm&#39;,
+ &#39;apc&#39;,
+ &#39;aph&#39;,
+ &#39;aphoton&#39;,
+ &#39;apix&#39;,
+ &#39;apixel&#39;,
+ &#39;arad&#39;,
+ &#39;arcmin&#39;,
+ &#39;arcminute&#39;,
+ &#39;arcsec&#39;,
+ &#39;arcsecond&#39;,
+ &#39;asr&#39;,
+ &#39;astronomical_unit&#39;,
+ &#39;astrophys&#39;,
+ &#39;attoBarye&#39;,
+ &#39;attoDa&#39;,
+ &#39;attoDalton&#39;,
+ &#39;attoDebye&#39;,
+ &#39;attoFarad&#39;,
+ &#39;attoGauss&#39;,
+ &#39;attoHenry&#39;,
+ &#39;attoHertz&#39;,
+ &#39;attoJansky&#39;,
+ &#39;attoJoule&#39;,
+ &#39;attoKayser&#39;,
+ &#39;attoKelvin&#39;,
+ &#39;attoNewton&#39;,
+ &#39;attoOhm&#39;,
+ &#39;attoPascal&#39;,
+ &#39;attoRayleigh&#39;,
+ &#39;attoSiemens&#39;,
+ &#39;attoTesla&#39;,
+ &#39;attoVolt&#39;,
+ &#39;attoWatt&#39;,
+ &#39;attoWeber&#39;,
+ &#39;attoamp&#39;,
+ &#39;attoampere&#39;,
+ &#39;attoannum&#39;,
+ &#39;attoarcminute&#39;,
+ &#39;attoarcsecond&#39;,
+ &#39;attoastronomical_unit&#39;,
+ &#39;attobarn&#39;,
+ &#39;attobarye&#39;,
+ &#39;attobit&#39;,
+ &#39;attobyte&#39;,
+ &#39;attocandela&#39;,
+ &#39;attocoulomb&#39;,
+ &#39;attocount&#39;,
+ &#39;attoday&#39;,
+ &#39;attodebye&#39;,
+ &#39;attodegree&#39;,
+ &#39;attodyne&#39;,
+ &#39;attoelectronvolt&#39;,
+ &#39;attofarad&#39;,
+ &#39;attogal&#39;,
+ &#39;attogauss&#39;,
+ &#39;attogram&#39;,
+ &#39;attohenry&#39;,
+ &#39;attohertz&#39;,
+ &#39;attohour&#39;,
+ &#39;attohr&#39;,
+ &#39;attojansky&#39;,
+ &#39;attojoule&#39;,
+ &#39;attokayser&#39;,
+ &#39;attolightyear&#39;,
+ &#39;attoliter&#39;,
+ &#39;attolumen&#39;,
+ &#39;attolux&#39;,
+ &#39;attometer&#39;,
+ &#39;attominute&#39;,
+ &#39;attomole&#39;,
+ &#39;attonewton&#39;,
+ &#39;attoparsec&#39;,
+ &#39;attopascal&#39;,
+ &#39;attophoton&#39;,
+ &#39;attopixel&#39;,
+ &#39;attopoise&#39;,
+ &#39;attoradian&#39;,
+ &#39;attorayleigh&#39;,
+ &#39;attorydberg&#39;,
+ &#39;attosecond&#39;,
+ &#39;attosiemens&#39;,
+ &#39;attosteradian&#39;,
+ &#39;attostokes&#39;,
+ &#39;attotesla&#39;,
+ &#39;attovolt&#39;,
+ &#39;attovoxel&#39;,
+ &#39;attowatt&#39;,
+ &#39;attoweber&#39;,
+ &#39;attoyear&#39;,
+ &#39;au&#39;,
+ &#39;avox&#39;,
+ &#39;avoxel&#39;,
+ &#39;ayr&#39;,
+ &#39;b&#39;,
+ &#39;bar&#39;,
+ &#39;barn&#39;,
+ &#39;barye&#39;,
+ &#39;beam&#39;,
+ &#39;beam_angular_area&#39;,
+ &#39;becquerel&#39;,
+ &#39;bin&#39;,
+ &#39;binary_prefixes&#39;,
+ &#39;bit&#39;,
+ &#39;bol&#39;,
+ &#39;brightness_temperature&#39;,
+ &#39;byte&#39;,
+ &#39;cA&#39;,
+ &#39;cAU&#39;,
+ &#39;cB&#39;,
+ &#39;cBa&#39;,
+ &#39;cC&#39;,
+ &#39;cD&#39;,
+ &#39;cF&#39;,
+ &#39;cG&#39;,
+ &#39;cGal&#39;,
+ &#39;cH&#39;,
+ &#39;cHz&#39;,
+ &#39;cJ&#39;,
+ &#39;cJy&#39;,
+ &#39;cK&#39;,
+ &#39;cL&#39;,
+ &#39;cN&#39;,
+ &#39;cOhm&#39;,
+ &#39;cP&#39;,
+ &#39;cPa&#39;,
+ &#39;cR&#39;,
+ &#39;cRy&#39;,
+ &#39;cS&#39;,
+ &#39;cSt&#39;,
+ &#39;cT&#39;,
+ &#39;cV&#39;,
+ &#39;cW&#39;,
+ &#39;cWb&#39;,
+ &#39;ca&#39;,
+ &#39;cadu&#39;,
+ &#39;candela&#39;,
+ &#39;carcmin&#39;,
+ &#39;carcsec&#39;,
+ &#39;cau&#39;,
+ &#39;cb&#39;,
+ &#39;cbarn&#39;,
+ &#39;cbeam&#39;,
+ &#39;cbin&#39;,
+ &#39;cbit&#39;,
+ &#39;cbyte&#39;,
+ &#39;ccd&#39;,
+ &#39;cchan&#39;,
+ &#39;ccount&#39;,
+ &#39;cct&#39;,
+ &#39;cd&#39;,
+ &#39;cdeg&#39;,
+ &#39;cdyn&#39;,
+ &#39;ceV&#39;,
+ &#39;centiBarye&#39;,
+ &#39;centiDa&#39;,
+ &#39;centiDalton&#39;,
+ &#39;centiDebye&#39;,
+ &#39;centiFarad&#39;,
+ &#39;centiGauss&#39;,
+ &#39;centiHenry&#39;,
+ &#39;centiHertz&#39;,
+ &#39;centiJansky&#39;,
+ &#39;centiJoule&#39;,
+ &#39;centiKayser&#39;,
+ &#39;centiKelvin&#39;,
+ &#39;centiNewton&#39;,
+ &#39;centiOhm&#39;,
+ &#39;centiPascal&#39;,
+ &#39;centiRayleigh&#39;,
+ &#39;centiSiemens&#39;,
+ &#39;centiTesla&#39;,
+ &#39;centiVolt&#39;,
+ &#39;centiWatt&#39;,
+ &#39;centiWeber&#39;,
+ &#39;centiamp&#39;,
+ &#39;centiampere&#39;,
+ &#39;centiannum&#39;,
+ &#39;centiarcminute&#39;,
+ &#39;centiarcsecond&#39;,
+ &#39;centiastronomical_unit&#39;,
+ &#39;centibarn&#39;,
+ &#39;centibarye&#39;,
+ &#39;centibit&#39;,
+ &#39;centibyte&#39;,
+ &#39;centicandela&#39;,
+ &#39;centicoulomb&#39;,
+ &#39;centicount&#39;,
+ &#39;centiday&#39;,
+ &#39;centidebye&#39;,
+ &#39;centidegree&#39;,
+ &#39;centidyne&#39;,
+ &#39;centielectronvolt&#39;,
+ &#39;centifarad&#39;,
+ &#39;centigal&#39;,
+ &#39;centigauss&#39;,
+ &#39;centigram&#39;,
+ &#39;centihenry&#39;,
+ &#39;centihertz&#39;,
+ &#39;centihour&#39;,
+ &#39;centihr&#39;,
+ &#39;centijansky&#39;,
+ &#39;centijoule&#39;,
+ &#39;centikayser&#39;,
+ &#39;centilightyear&#39;,
+ &#39;centiliter&#39;,
+ &#39;centilumen&#39;,
+ &#39;centilux&#39;,
+ &#39;centimeter&#39;,
+ &#39;centiminute&#39;,
+ &#39;centimole&#39;,
+ &#39;centinewton&#39;,
+ &#39;centiparsec&#39;,
+ &#39;centipascal&#39;,
+ &#39;centiphoton&#39;,
+ &#39;centipixel&#39;,
+ &#39;centipoise&#39;,
+ &#39;centiradian&#39;,
+ &#39;centirayleigh&#39;,
+ &#39;centirydberg&#39;,
+ &#39;centisecond&#39;,
+ &#39;centisiemens&#39;,
+ &#39;centisteradian&#39;,
+ &#39;centistokes&#39;,
+ &#39;centitesla&#39;,
+ &#39;centivolt&#39;,
+ &#39;centivoxel&#39;,
+ &#39;centiwatt&#39;,
+ &#39;centiweber&#39;,
+ &#39;centiyear&#39;,
+ &#39;cerg&#39;,
+ &#39;cg&#39;,
+ &#39;cgs&#39;,
+ &#39;ch&#39;,
+ &#39;chan&#39;,
+ &#39;ck&#39;,
+ &#39;cl&#39;,
+ &#39;clm&#39;,
+ &#39;clx&#39;,
+ &#39;clyr&#39;,
+ &#39;cm&#39;,
+ &#39;cmag&#39;,
+ &#39;cmin&#39;,
+ &#39;cmol&#39;,
+ &#39;cohm&#39;,
+ &#39;core&#39;,
+ &#39;coulomb&#39;,
+ &#39;count&#39;,
+ &#39;cpc&#39;,
+ &#39;cph&#39;,
+ &#39;cphoton&#39;,
+ &#39;cpix&#39;,
+ &#39;cpixel&#39;,
+ &#39;crad&#39;,
+ &#39;cs&#39;,
+ &#39;csr&#39;,
+ &#39;ct&#39;,
+ &#39;cu&#39;,
+ &#39;curie&#39;,
+ &#39;cvox&#39;,
+ &#39;cvoxel&#39;,
+ &#39;cy&#39;,
+ &#39;cycle&#39;,
+ &#39;cyr&#39;,
+ &#39;d&#39;,
+ &#39;dA&#39;,
+ &#39;dAU&#39;,
+ &#39;dB&#39;,
+ &#39;dBa&#39;,
+ &#39;dC&#39;,
+ &#39;dD&#39;,
+ &#39;dF&#39;,
+ &#39;dG&#39;,
+ &#39;dGal&#39;,
+ &#39;dH&#39;,
+ &#39;dHz&#39;,
+ &#39;dJ&#39;,
+ &#39;dJy&#39;,
+ &#39;dK&#39;,
+ &#39;dL&#39;,
+ &#39;dN&#39;,
+ &#39;dOhm&#39;,
+ &#39;dP&#39;,
+ &#39;dPa&#39;,
+ &#39;dR&#39;,
+ &#39;dRy&#39;,
+ &#39;dS&#39;,
+ &#39;dSt&#39;,
+ &#39;dT&#39;,
+ ...]
+</pre></div>
+</div>
+</div>
+</div>
+<p>To create a quantity, we multiply a value by a unit.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">coord</span> <span class="o">=</span> <span class="mi">30</span> <span class="o">*</span> <span class="n">u</span><span class="o">.</span><span class="n">deg</span>
+<span class="nb">type</span><span class="p">(</span><span class="n">coord</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>astropy.units.quantity.Quantity
+</pre></div>
+</div>
+</div>
+</div>
+<p>The result is a <code class="docutils literal notranslate"><span class="pre">Quantity</span></code> object.</p>
+<p>Jupyter knows how to display <code class="docutils literal notranslate"><span class="pre">Quantities</span></code> like this:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">coord</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_latex math notranslate nohighlight">
+\[30 \; \mathrm{{}^{\circ}}\]</div>
+</div>
+</div>
+</div>
+<div class="section" id="selecting-a-rectangle">
+<h2>Selecting a rectangle<a class="headerlink" href="#selecting-a-rectangle" title="Permalink to this headline">¶</a></h2>
+<p>Now we’ll select a rectangle from -55 to -45 degrees right ascension and -8 to 4 degrees of declination.</p>
+<p>We’ll define variables to contain these limits.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">phi1_min</span> <span class="o">=</span> <span class="o">-</span><span class="mi">55</span>
+<span class="n">phi1_max</span> <span class="o">=</span> <span class="o">-</span><span class="mi">45</span>
+<span class="n">phi2_min</span> <span class="o">=</span> <span class="o">-</span><span class="mi">8</span>
+<span class="n">phi2_max</span> <span class="o">=</span> <span class="mi">4</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>To represent a rectangle, we’ll use two lists of coordinates and multiply by their units.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">phi1_rect</span> <span class="o">=</span> <span class="p">[</span><span class="n">phi1_min</span><span class="p">,</span> <span class="n">phi1_min</span><span class="p">,</span> <span class="n">phi1_max</span><span class="p">,</span> <span class="n">phi1_max</span><span class="p">]</span> <span class="o">*</span> <span class="n">u</span><span class="o">.</span><span class="n">deg</span>
+<span class="n">phi2_rect</span> <span class="o">=</span> <span class="p">[</span><span class="n">phi2_min</span><span class="p">,</span> <span class="n">phi2_max</span><span class="p">,</span> <span class="n">phi2_max</span><span class="p">,</span> <span class="n">phi2_min</span><span class="p">]</span> <span class="o">*</span> <span class="n">u</span><span class="o">.</span><span class="n">deg</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p><code class="docutils literal notranslate"><span class="pre">phi1_rect</span></code> and <code class="docutils literal notranslate"><span class="pre">phi2_rect</span></code> represent the coordinates of the corners of a rectangle.</p>
+<p>But they are in “<a class="reference external" href="https://gala-astro.readthedocs.io/en/latest/_modules/gala/coordinates/gd1.html">a Heliocentric spherical coordinate system defined by the orbit of the GD1 stream</a>�</p>
+<p>In order to use them in a Gaia query, we have to convert them to <a class="reference external" href="https://en.wikipedia.org/wiki/International_Celestial_Reference_System">International Celestial Reference System</a> (ICRS) coordinates.  We can do that by storing the coordinates in a <code class="docutils literal notranslate"><span class="pre">GD1Koposov10</span></code> object provided by <a class="reference external" href="https://gala-astro.readthedocs.io/en/latest/coordinates/">Gala</a>.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">gala.coordinates</span> <span class="k">as</span> <span class="nn">gc</span>
+
+<span class="n">corners</span> <span class="o">=</span> <span class="n">gc</span><span class="o">.</span><span class="n">GD1Koposov10</span><span class="p">(</span><span class="n">phi1</span><span class="o">=</span><span class="n">phi1_rect</span><span class="p">,</span> <span class="n">phi2</span><span class="o">=</span><span class="n">phi2_rect</span><span class="p">)</span>
+<span class="nb">type</span><span class="p">(</span><span class="n">corners</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>gala.coordinates.gd1.GD1Koposov10
+</pre></div>
+</div>
+</div>
+</div>
+<p>We can display the result like this:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">corners</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;GD1Koposov10 Coordinate: (phi1, phi2) in deg
+    [(-55., -8.), (-55.,  4.), (-45.,  4.), (-45., -8.)]&gt;
+</pre></div>
+</div>
+</div>
+</div>
+<p>Now we can use <code class="docutils literal notranslate"><span class="pre">transform_to</span></code> to convert to ICRS coordinates.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">astropy.coordinates</span> <span class="k">as</span> <span class="nn">coord</span>
+
+<span class="n">corners_icrs</span> <span class="o">=</span> <span class="n">corners</span><span class="o">.</span><span class="n">transform_to</span><span class="p">(</span><span class="n">coord</span><span class="o">.</span><span class="n">ICRS</span><span class="p">)</span>
+<span class="nb">type</span><span class="p">(</span><span class="n">corners_icrs</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>astropy.coordinates.builtin_frames.icrs.ICRS
+</pre></div>
+</div>
+</div>
+</div>
+<p>The result is an <code class="docutils literal notranslate"><span class="pre">ICRS</span></code> object.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">corners_icrs</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;ICRS Coordinate: (ra, dec) in deg
+    [(146.27533314, 19.26190982), (135.42163944, 25.87738723),
+     (141.60264825, 34.3048303 ), (152.81671045, 27.13611254)]&gt;
+</pre></div>
+</div>
+</div>
+</div>
+<p>Notice that a rectangle in one coordinate system is not necessarily a rectangle in another.  In this example, the result is a polygon.</p>
+</div>
+<div class="section" id="selecting-a-polygon">
+<h2>Selecting a polygon<a class="headerlink" href="#selecting-a-polygon" title="Permalink to this headline">¶</a></h2>
+<p>In order to use this polygon as part of an ADQL query, we have to convert it to a string with a comma-separated list of coordinates, as in this example:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">POLYGON(143.65, 20.98, </span>
+<span class="sd">        134.46, 26.39, </span>
+<span class="sd">        140.58, 34.85, </span>
+<span class="sd">        150.16, 29.01)</span>
+<span class="sd">&quot;&quot;&quot;</span>
+</pre></div>
+</div>
+<p><code class="docutils literal notranslate"><span class="pre">corners_icrs</span></code> behaves like a list, so we can use a <code class="docutils literal notranslate"><span class="pre">for</span></code> loop to iterate through the points.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">for</span> <span class="n">point</span> <span class="ow">in</span> <span class="n">corners_icrs</span><span class="p">:</span>
+    <span class="nb">print</span><span class="p">(</span><span class="n">point</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;ICRS Coordinate: (ra, dec) in deg
+    (146.27533314, 19.26190982)&gt;
+&lt;ICRS Coordinate: (ra, dec) in deg
+    (135.42163944, 25.87738723)&gt;
+&lt;ICRS Coordinate: (ra, dec) in deg
+    (141.60264825, 34.3048303)&gt;
+&lt;ICRS Coordinate: (ra, dec) in deg
+    (152.81671045, 27.13611254)&gt;
+</pre></div>
+</div>
+</div>
+</div>
+<p>From that, we can select the coordinates <code class="docutils literal notranslate"><span class="pre">ra</span></code> and <code class="docutils literal notranslate"><span class="pre">dec</span></code>:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">for</span> <span class="n">point</span> <span class="ow">in</span> <span class="n">corners_icrs</span><span class="p">:</span>
+    <span class="nb">print</span><span class="p">(</span><span class="n">point</span><span class="o">.</span><span class="n">ra</span><span class="p">,</span> <span class="n">point</span><span class="o">.</span><span class="n">dec</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>146d16m31.1993s 19d15m42.8754s
+135d25m17.902s 25d52m38.594s
+141d36m09.5337s 34d18m17.3891s
+152d49m00.1576s 27d08m10.0051s
+</pre></div>
+</div>
+</div>
+</div>
+<p>The results are quantities with units, but if we select the <code class="docutils literal notranslate"><span class="pre">value</span></code> part, we get a dimensionless floating-point number.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">for</span> <span class="n">point</span> <span class="ow">in</span> <span class="n">corners_icrs</span><span class="p">:</span>
+    <span class="nb">print</span><span class="p">(</span><span class="n">point</span><span class="o">.</span><span class="n">ra</span><span class="o">.</span><span class="n">value</span><span class="p">,</span> <span class="n">point</span><span class="o">.</span><span class="n">dec</span><span class="o">.</span><span class="n">value</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>146.27533313607782 19.261909820533692
+135.42163944306296 25.87738722767213
+141.60264825107333 34.304830296257144
+152.81671044675923 27.136112541397996
+</pre></div>
+</div>
+</div>
+</div>
+<p>We can use string <code class="docutils literal notranslate"><span class="pre">format</span></code> to convert these numbers to strings.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">point_base</span> <span class="o">=</span> <span class="s2">&quot;</span><span class="si">{point.ra.value}</span><span class="s2">, </span><span class="si">{point.dec.value}</span><span class="s2">&quot;</span>
+
+<span class="n">t</span> <span class="o">=</span> <span class="p">[</span><span class="n">point_base</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">point</span><span class="o">=</span><span class="n">point</span><span class="p">)</span>
+     <span class="k">for</span> <span class="n">point</span> <span class="ow">in</span> <span class="n">corners_icrs</span><span class="p">]</span>
+<span class="n">t</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[&#39;146.27533313607782, 19.261909820533692&#39;,
+ &#39;135.42163944306296, 25.87738722767213&#39;,
+ &#39;141.60264825107333, 34.304830296257144&#39;,
+ &#39;152.81671044675923, 27.136112541397996&#39;]
+</pre></div>
+</div>
+</div>
+</div>
+<p>The result is a list of strings, which we can join into a single string using <code class="docutils literal notranslate"><span class="pre">join</span></code>.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">point_list</span> <span class="o">=</span> <span class="s1">&#39;, &#39;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">t</span><span class="p">)</span>
+<span class="n">point_list</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&#39;146.27533313607782, 19.261909820533692, 135.42163944306296, 25.87738722767213, 141.60264825107333, 34.304830296257144, 152.81671044675923, 27.136112541397996&#39;
+</pre></div>
+</div>
+</div>
+</div>
+<p>Notice that we invoke <code class="docutils literal notranslate"><span class="pre">join</span></code> on a string and pass the list as an argument.</p>
+<p>Before we can assemble the query, we need <code class="docutils literal notranslate"><span class="pre">columns</span></code> again (as we saw in the previous notebook).</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">columns</span> <span class="o">=</span> <span class="s1">&#39;source_id, ra, dec, pmra, pmdec, parallax, parallax_error, radial_velocity&#39;</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>Here’s the base for the query, with format specifiers for <code class="docutils literal notranslate"><span class="pre">columns</span></code> and <code class="docutils literal notranslate"><span class="pre">point_list</span></code>.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">query_base</span> <span class="o">=</span> <span class="s2">&quot;&quot;&quot;SELECT </span><span class="si">{columns}</span><span class="s2"></span>
+<span class="s2">FROM gaiadr2.gaia_source</span>
+<span class="s2">WHERE parallax &lt; 1</span>
+<span class="s2">  AND bp_rp BETWEEN -0.75 AND 2 </span>
+<span class="s2">  AND 1 = CONTAINS(POINT(ra, dec), </span>
+<span class="s2">                   POLYGON(</span><span class="si">{point_list}</span><span class="s2">))</span>
+<span class="s2">&quot;&quot;&quot;</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>And here’s the result:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">query</span> <span class="o">=</span> <span class="n">query_base</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="n">columns</span><span class="p">,</span> 
+                          <span class="n">point_list</span><span class="o">=</span><span class="n">point_list</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">query</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>SELECT source_id, ra, dec, pmra, pmdec, parallax, parallax_error, radial_velocity
+FROM gaiadr2.gaia_source
+WHERE parallax &lt; 1
+  AND bp_rp BETWEEN -0.75 AND 2 
+  AND 1 = CONTAINS(POINT(ra, dec), 
+                   POLYGON(146.27533313607782, 19.261909820533692, 135.42163944306296, 25.87738722767213, 141.60264825107333, 34.304830296257144, 152.81671044675923, 27.136112541397996))
+</pre></div>
+</div>
+</div>
+</div>
+<p>As always, we should take a minute to proof-read the query before we launch it.</p>
+<p>The result will be bigger than our previous queries, so it will take a little longer.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">job</span> <span class="o">=</span> <span class="n">Gaia</span><span class="o">.</span><span class="n">launch_job_async</span><span class="p">(</span><span class="n">query</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">job</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>INFO: Query finished. [astroquery.utils.tap.core]
+&lt;Table length=140340&gt;
+      name       dtype    unit                              description                             n_bad 
+--------------- ------- -------- ------------------------------------------------------------------ ------
+      source_id   int64          Unique source identifier (unique within a particular Data Release)      0
+             ra float64      deg                                                    Right ascension      0
+            dec float64      deg                                                        Declination      0
+           pmra float64 mas / yr                         Proper motion in right ascension direction      0
+          pmdec float64 mas / yr                             Proper motion in declination direction      0
+       parallax float64      mas                                                           Parallax      0
+ parallax_error float64      mas                                         Standard error of parallax      0
+radial_velocity float64   km / s                                                    Radial velocity 139374
+Jobid: 1601903357321O
+Phase: COMPLETED
+Owner: None
+Output file: async_20201005090917.vot
+Results: None
+</pre></div>
+</div>
+</div>
+</div>
+<p>Here are the results.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">results</span> <span class="o">=</span> <span class="n">job</span><span class="o">.</span><span class="n">get_results</span><span class="p">()</span>
+<span class="nb">len</span><span class="p">(</span><span class="n">results</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>140340
+</pre></div>
+</div>
+</div>
+</div>
+<p>There are more than 100,000 stars in this polygon, but that’s a manageable size to work with.</p>
+</div>
+<div class="section" id="saving-results">
+<h2>Saving results<a class="headerlink" href="#saving-results" title="Permalink to this headline">¶</a></h2>
+<p>This is the set of stars we’ll work with in the next step.  But since we have a substantial dataset now, this is a good time to save it.</p>
+<p>Storing the data in a file means we can shut down this notebook and pick up where we left off without running the previous query again.</p>
+<p>Astropy <code class="docutils literal notranslate"><span class="pre">Table</span></code> objects provide <code class="docutils literal notranslate"><span class="pre">write</span></code>, which writes the table to disk.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">filename</span> <span class="o">=</span> <span class="s1">&#39;gd1_results.fits&#39;</span>
+<span class="n">results</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="n">filename</span><span class="p">,</span> <span class="n">overwrite</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>Because the filename ends with <code class="docutils literal notranslate"><span class="pre">fits</span></code>, the table is written in the <a class="reference external" href="https://en.wikipedia.org/wiki/FITS">FITS format</a>, which preserves the metadata associated with the table.</p>
+<p>If the file already exists, the <code class="docutils literal notranslate"><span class="pre">overwrite</span></code> argument causes it to be overwritten.</p>
+<p>To see how big the file is, we can use <code class="docutils literal notranslate"><span class="pre">ls</span></code> with the <code class="docutils literal notranslate"><span class="pre">-lh</span></code> option, which prints information about the file including its size in human-readable form.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">!</span>ls -lh gd1_results.fits
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>-rw-rw-r-- 1 downey downey 8.6M Oct  5 09:09 gd1_results.fits
+</pre></div>
+</div>
+</div>
+</div>
+<p>The file is about 8.6 MB.</p>
+</div>
+<div class="section" id="summary">
+<h2>Summary<a class="headerlink" href="#summary" title="Permalink to this headline">¶</a></h2>
+<p>In this notebook, we composed more complex queries to select stars within a polygonal region of the sky.  Then we downloaded the results and saved them in a FITS file.</p>
+<p>In the next notebook, we’ll reload the data from this file and replicate the next step in the analysis, using proper motion to identify stars likely to be in GD-1.</p>
+</div>
+<div class="section" id="best-practices">
+<h2>Best practices<a class="headerlink" href="#best-practices" title="Permalink to this headline">¶</a></h2>
+<ul class="simple">
+<li><p>For measurements with units, use <code class="docutils literal notranslate"><span class="pre">Quantity</span></code> objects that represent units explicitly and check for errors.</p></li>
+<li><p>Use the <code class="docutils literal notranslate"><span class="pre">format</span></code> function to compose queries; it is often faster and less error-prone.</p></li>
+<li><p>Develop queries incrementally: start with something simple, test it, and add a little bit at a time.</p></li>
+<li><p>Once you have a query working, save the data in a local file.  If you shut down the notebook and come back to it later, you can reload the file; you don’t have to run the query again.</p></li>
+</ul>
+</div>
+</div>
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+   Astronomical Data in Python
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+  <a class="reference internal" href="01_query.html">
+   Chapter 1
+  </a>
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+ <li class="toctree-l1">
+  <a class="reference internal" href="02_coords.html">
+   Chapter 2
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="03_motion.html">
+   Chapter 3
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="04_select.html">
+   Chapter 4
+  </a>
+ </li>
+ <li class="toctree-l1">
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+   Chapter 5
+  </a>
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+   Chapter 6
+  </a>
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+                
+  <div class="section" id="astronomical-data-in-python">
+<h1>Astronomical Data in Python<a class="headerlink" href="#astronomical-data-in-python" title="Permalink to this headline">¶</a></h1>
+<p><em>Astronomical Data in Python</em> is an introduction to tools and practices for working with astronomical data.  Topics covered include:</p>
+<ul class="simple">
+<li><p>Writing queries that select and download data from a database.</p></li>
+<li><p>Using data stored in an Astropy <code class="docutils literal notranslate"><span class="pre">Table</span></code> or Pandas <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code>.</p></li>
+<li><p>Working with coordinates and other quantities with units.</p></li>
+<li><p>Storing data in various formats.</p></li>
+<li><p>Performing database join operations that combine data from multiple tables.</p></li>
+<li><p>Visualizing data and preparing publication-quality figures.</p></li>
+</ul>
+<p>As a running example, we will replicate part of the analysis in a recent paper, “<a class="reference external" href="https://arxiv.org/abs/1805.00425">Off the beaten path: Gaia reveals GD-1 stars outside of the main stream</a>� by Adrian M. Price-Whelan and Ana Bonaca.</p>
+<p>This material was developed in collaboration with <a class="reference external" href="https://carpentries.org/">The Carpentries</a> and the Astronomy Curriculum Development Committee, and supported by funding from the American Institute of Physics through the American Astronomical Society.</p>
+<p>I am grateful for contributions from the members of the committee – Azalee Bostroem, Rodolfo Montez, and Phil Rosenfield – and from Erin Becker, Brett Morris and Adrian Price-Whelan.</p>
+<p>The original format of this material is a series of Jupyter notebooks.  Using the
+links below, you can read the notebooks on NBViewer or run them on Colab.  If you
+want to run the notebooks in your own environment, you can download them from
+this repository and follow the instructions below to set up your environment.</p>
+<p>This material is also available in the form of <a class="reference external" href="https://datacarpentry.github.io/astronomy-python">Carpentries lessons</a>, but you should be
+aware that these versions might diverge in the future.</p>
+<p><strong>Prerequisites</strong></p>
+<p>This material should be accessible to people familiar with basic Python, but not necessarily the libraries we will use, like Astropy or Pandas.  If you are familiar with Python lists and dictionaries, and you know how to write a function that takes parameters and returns a value, that should be enough.</p>
+<p>We assume that you are familiar with astronomy at the undergraduate level, but we will not assume specialized knowledge of the datasets or analysis methods we’ll use.</p>
+<p><strong>Notebook 1</strong></p>
+<p>This notebook demonstrates the following steps:</p>
+<ol class="simple">
+<li><p>Making a connection to the Gaia server,</p></li>
+<li><p>Exploring information about the database and the tables it contains,</p></li>
+<li><p>Writing a query and sending it to the server, and finally</p></li>
+<li><p>Downloading the response from the server as an Astropy <code class="docutils literal notranslate"><span class="pre">Table</span></code>.</p></li>
+</ol>
+<p>Press this button to run this notebook on Colab:</p>
+<p><a class="reference external" href="https://colab.research.google.com/github/AllenDowney/AstronomicalData/blob/main/01_query.ipynb"><img src="run_on_colab_small.png"></a></p>
+<p><a class="reference external" href="https://nbviewer.jupyter.org/github/AllenDowney/AstronomicalData/blob/main/01_query.ipynb">or click here to read it on NBViewer</a></p>
+<p><strong>Notebook 2</strong></p>
+<p>This notebook starts with an example that does a “cone search�; that is, it selects stars that appear in a circular region of the sky.</p>
+<p>Then, to select stars in the vicinity of GD-1, we:</p>
+<ul class="simple">
+<li><p>Use <code class="docutils literal notranslate"><span class="pre">Quantity</span></code> objects to represent measurements with units.</p></li>
+<li><p>Use the <code class="docutils literal notranslate"><span class="pre">Gala</span></code> library to convert coordinates from one frame to another.</p></li>
+<li><p>Use the ADQL keywords <code class="docutils literal notranslate"><span class="pre">POLYGON</span></code>, <code class="docutils literal notranslate"><span class="pre">CONTAINS</span></code>, and <code class="docutils literal notranslate"><span class="pre">POINT</span></code> to select stars that fall within a polygonal region.</p></li>
+<li><p>Submit a query and download the results.</p></li>
+<li><p>Store the results in a FITS file.</p></li>
+</ul>
+<p>Press this button to run this notebook on Colab:</p>
+<p><a class="reference external" href="https://colab.research.google.com/github/AllenDowney/AstronomicalData/blob/main/02_coords.ipynb"><img src="run_on_colab_small.png"></a></p>
+<p><a class="reference external" href="https://nbviewer.jupyter.org/github/AllenDowney/AstronomicalData/blob/main/02_coords.ipynb">or click here to read it on NBViewer</a></p>
+<p><strong>Notebook 3</strong></p>
+<p>Here are the steps in this notebook:</p>
+<ol class="simple">
+<li><p>We’ll read back the results from the previous notebook, which we saved in a FITS file.</p></li>
+<li><p>Then we’ll transform the coordinates and proper motion data from ICRS back to the coordinate frame of GD-1.</p></li>
+<li><p>We’ll put those results into a Pandas <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code>, which we’ll use to select stars near the centerline of GD-1.</p></li>
+<li><p>Plotting the proper motion of those stars, we’ll identify a region of proper motion for stars that are likely to be in GD-1.</p></li>
+<li><p>Finally, we’ll select and plot the stars whose proper motion is in that region.</p></li>
+</ol>
+<p>Press this button to run this notebook on Colab:</p>
+<p><a class="reference external" href="https://colab.research.google.com/github/AllenDowney/AstronomicalData/blob/main/03_motion.ipynb"><img src="run_on_colab_small.png"></a></p>
+<p><a class="reference external" href="https://nbviewer.jupyter.org/github/AllenDowney/AstronomicalData/blob/main/03_motion.ipynb">or click here to read it on NBViewer</a></p>
+<p><strong>Notebook 4</strong></p>
+<p>Here are the steps in this notebook:</p>
+<ol class="simple">
+<li><p>Using data from the previous notebook, we’ll identify the values of proper motion for stars likely to be in GD-1.</p></li>
+<li><p>Then we’ll compose an ADQL query that selects stars based on proper motion, so we can download only the data we need.</p></li>
+<li><p>We’ll also see how to write the results to a CSV file.</p></li>
+</ol>
+<p>That will make it possible to search a bigger region of the sky in a single query.</p>
+<p>Press this button to run this notebook on Colab:</p>
+<p><a class="reference external" href="https://colab.research.google.com/github/AllenDowney/AstronomicalData/blob/main/04_select.ipynb"><img src="run_on_colab_small.png"></a></p>
+<p><a class="reference external" href="https://nbviewer.jupyter.org/github/AllenDowney/AstronomicalData/blob/main/04_select.ipynb">or click here to read it on NBViewer</a></p>
+<p><strong>Notebook 5</strong></p>
+<p>Here are the steps in this notebook:</p>
+<ol class="simple">
+<li><p>We’ll reload the candidate stars we identified in the previous notebook.</p></li>
+<li><p>Then we’ll run a query on the Gaia server that uploads the table of candidates and uses a <code class="docutils literal notranslate"><span class="pre">JOIN</span></code> operation to select photometry data for the candidate stars.</p></li>
+<li><p>We’ll write the results to a file for use in the next notebook.</p></li>
+</ol>
+<p>Press this button to run this notebook on Colab:</p>
+<p><a class="reference external" href="https://colab.research.google.com/github/AllenDowney/AstronomicalData/blob/main/05_join.ipynb"><img src="run_on_colab_small.png"></a></p>
+<p><a class="reference external" href="https://nbviewer.jupyter.org/github/AllenDowney/AstronomicalData/blob/main/05_join.ipynb">or click here to read it on NBViewer</a></p>
+<p><strong>Notebook 6</strong></p>
+<p>Here are the steps in this notebook:</p>
+<ol class="simple">
+<li><p>We’ll reload the data from the previous notebook and make a color-magnitude diagram.</p></li>
+<li><p>Then we’ll specify a polygon in the diagram that contains stars with the photometry we expect.</p></li>
+<li><p>Then we’ll merge the photometry data with the list of candidate stars, storing the result in a Pandas <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code>.</p></li>
+</ol>
+<p>Press this button to run this notebook on Colab:</p>
+<p><a class="reference external" href="https://colab.research.google.com/github/AllenDowney/AstronomicalData/blob/main/06_photo.ipynb"><img src="run_on_colab_small.png"></a></p>
+<p><a class="reference external" href="https://nbviewer.jupyter.org/github/AllenDowney/AstronomicalData/blob/main/06_photo.ipynb">or click here to read it on NBViewer</a></p>
+<p><strong>Notebook 7</strong></p>
+<p>Here are the steps in this notebook:</p>
+<ol class="simple">
+<li><p>Starting with the figure from the previous notebook, we’ll add annotations to present the results more clearly.</p></li>
+<li><p>The we’ll see several ways to customize figures to make them more appealing and effective.</p></li>
+<li><p>Finally, we’ll see how to make a figure with multiple panels or subplots.</p></li>
+</ol>
+<p>Press this button to run this notebook on Colab:</p>
+<p><a class="reference external" href="https://colab.research.google.com/github/AllenDowney/AstronomicalData/blob/main/07_plot.ipynb"><img src="run_on_colab_small.png"></a></p>
+<p><a class="reference external" href="https://nbviewer.jupyter.org/github/AllenDowney/AstronomicalData/blob/main/07_plot.ipynb">or click here to read it on NBViewer</a></p>
+<p><strong>Installation instructions</strong></p>
+<p>Coming soon.</p>
+<div class="toctree-wrapper compound">
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diff --git a/_build/html/_sources/01_query.ipynb b/_build/html/_sources/01_query.ipynb
new file mode 100644
index 0000000..cccc8e4
--- /dev/null
+++ b/_build/html/_sources/01_query.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Chapter 1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*Astronomical Data in Python* is an introduction to tools and practices for working with astronomical data.  Topics covered include:\n",
+    "\n",
+    "* Writing queries that select and download data from a database.\n",
+    "\n",
+    "* Using data stored in an Astropy `Table` or Pandas `DataFrame`.\n",
+    "\n",
+    "* Working with coordinates and other quantities with units.\n",
+    "\n",
+    "* Storing data in various formats.\n",
+    "\n",
+    "* Performing database join operations that combine data from multiple tables.\n",
+    "\n",
+    "* Visualizing data and preparing publication-quality figures."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As a running example, we will replicate part of the analysis in a recent paper, \"[Off the beaten path: Gaia reveals GD-1 stars outside of the main stream](https://arxiv.org/abs/1805.00425)\" by Adrian M. Price-Whelan and Ana Bonaca.\n",
+    "\n",
+    "As the abstract explains, \"Using data from the Gaia second data release combined with Pan-STARRS photometry, we present a sample of highly-probable members of the longest cold stream in the Milky Way, GD-1.\"\n",
+    "\n",
+    "GD-1 is a [stellar stream](https://en.wikipedia.org/wiki/List_of_stellar_streams), which is \"an association of stars orbiting a galaxy that was once a globular cluster or dwarf galaxy that has now been torn apart and stretched out along its orbit by tidal forces.\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[This article in *Science* magazine](https://www.sciencemag.org/news/2018/10/streams-stars-reveal-galaxy-s-violent-history-and-perhaps-its-unseen-dark-matter) explains some of the background, including the process that led to the paper and an discussion of the scientific implications:\n",
+    "\n",
+    "* \"The streams are particularly useful for ... galactic archaeology --- rewinding the cosmic clock to reconstruct the assembly of the Milky Way.\"\n",
+    "\n",
+    "* \"They also are being used as exquisitely sensitive scales to measure the galaxy's mass.\"\n",
+    "\n",
+    "* \"... the streams are well-positioned to reveal the presence of dark matter ... because the streams are so fragile, theorists say, collisions with marauding clumps of dark matter could leave telltale scars, potential clues to its nature.\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Data\n",
+    "\n",
+    "The datasets we will work with are:\n",
+    " \n",
+    "* [Gaia](https://en.wikipedia.org/wiki/Gaia_(spacecraft)), which is \"a space observatory of the European Space Agency (ESA), launched in 2013 ... designed for astrometry: measuring the positions, distances and motions of stars with unprecedented precision\", and\n",
+    "\n",
+    "* [Pan-STARRS](https://en.wikipedia.org/wiki/Pan-STARRS), The Panoramic Survey Telescope and Rapid Response System, which is a survey designed to monitor the sky for transient objects, producing a catalog with accurate astronometry and photometry of detected sources.\n",
+    "\n",
+    "Both of these datasets are very large, which can make them challenging to work with.  It might not be possible, or practical, to download the entire dataset.\n",
+    "One of the goals of this workshop is to provide tools for working with large datasets."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Prerequisites\n",
+    "\n",
+    "These notebooks are meant for people who are familiar with basic Python, but not necessarily the libraries we will use, like Astropy or Pandas.  If you are familiar with Python lists and dictionaries, and you know how to write a function that takes parameters and returns a value, you know enough Python to get started.\n",
+    "\n",
+    "We assume that you have some familiarity with operating systems, like the ability to use a command-line interface.  But we don't assume you have any prior experience with databases.\n",
+    "\n",
+    "We assume that you are familiar with astronomy at the undergraduate level, but we will not assume specialized knowledge of the datasets or analysis methods we'll use. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Outline\n",
+    "\n",
+    "The first lesson demonstrates the steps for selecting and downloading data from the Gaia Database:\n",
+    "\n",
+    "1. First we'll make a connection to the Gaia server,\n",
+    "\n",
+    "2. We will explore information about the database and the tables it contains,\n",
+    "\n",
+    "3. We will write a query and send it to the server, and finally\n",
+    "\n",
+    "4. We will download the response from the server.\n",
+    "\n",
+    "After completing this lesson, you should be able to\n",
+    "\n",
+    "* Compose a basic query in ADQL.\n",
+    "\n",
+    "* Use queries to explore a database and its tables.\n",
+    "\n",
+    "* Use queries to download data.\n",
+    "\n",
+    "* Develop, test, and debug a query incrementally."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Query Language\n",
+    "\n",
+    "In order to select data from a database, you have to compose a query, which is like a program written in a \"query language\".\n",
+    "The query language we'll use is ADQL, which stands for \"Astronomical Data Query Language\".\n",
+    "\n",
+    "ADQL is a dialect of [SQL](https://en.wikipedia.org/wiki/SQL) (Structured Query Language), which is by far the most commonly used query language.  Almost everything you will learn about ADQL also works in SQL.\n",
+    "\n",
+    "[The reference manual for ADQL is here](http://www.ivoa.net/documents/ADQL/20180112/PR-ADQL-2.1-20180112.html).\n",
+    "But you might find it easier to learn from [this ADQL Cookbook](https://www.gaia.ac.uk/data/gaia-data-release-1/adql-cookbook)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Installing libraries\n",
+    "\n",
+    "The library we'll use to get Gaia data is [Astroquery](https://astroquery.readthedocs.io/en/latest/).\n",
+    "\n",
+    "If you are running this notebook on Colab, you can run the following cell to install Astroquery and the other libraries we'll use.\n",
+    "\n",
+    "If you are running this notebook on your own computer, you might have to install these libraries yourself.  \n",
+    "\n",
+    "If you are using this notebook as part of a Carpentries workshop, you should have received setup instructions.\n",
+    "\n",
+    "TODO: Add a link to the instructions.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "tags": [
+     "hide-cell"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# If we're running on Colab, install libraries\n",
+    "\n",
+    "import sys\n",
+    "IN_COLAB = 'google.colab' in sys.modules\n",
+    "\n",
+    "if IN_COLAB:\n",
+    "    !pip install astroquery astro-gala pyia"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Connecting to Gaia\n",
+    "\n",
+    "Astroquery provides `Gaia`, which is an [object that represents a connection to the Gaia database](https://astroquery.readthedocs.io/en/latest/gaia/gaia.html).\n",
+    "\n",
+    "We can connect to the Gaia database like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Created TAP+ (v1.2.1) - Connection:\n",
+      "\tHost: gea.esac.esa.int\n",
+      "\tUse HTTPS: True\n",
+      "\tPort: 443\n",
+      "\tSSL Port: 443\n",
+      "Created TAP+ (v1.2.1) - Connection:\n",
+      "\tHost: geadata.esac.esa.int\n",
+      "\tUse HTTPS: True\n",
+      "\tPort: 443\n",
+      "\tSSL Port: 443\n"
+     ]
+    }
+   ],
+   "source": [
+    "from astroquery.gaia import Gaia"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Running this import statement has the effect of creating a [TAP+](http://www.ivoa.net/documents/TAP/) connection; TAP stands for \"Table Access Protocol\".  It is a network protocol for sending queries to the database and getting back the results.  We're not sure why it seems to create two connections."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Databases and Tables\n",
+    "\n",
+    "What is a database, anyway?  Most generally, it can be any collection of data, but when we are talking about ADQL or SQL:\n",
+    "\n",
+    "* A database is a collection of one or more named tables.\n",
+    "\n",
+    "* Each table is a 2-D array with one or more named columns of data.\n",
+    "\n",
+    "We can use `Gaia.load_tables` to get the names of the tables in the Gaia database.  With the option `only_names=True`, it loads information about the tables, called the \"metadata\", not the data itself."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO: Retrieving tables... [astroquery.utils.tap.core]\n",
+      "INFO: Parsing tables... [astroquery.utils.tap.core]\n",
+      "INFO: Done. [astroquery.utils.tap.core]\n"
+     ]
+    }
+   ],
+   "source": [
+    "tables = Gaia.load_tables(only_names=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "tags": [
+     "hide-output"
+    ]
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "external.external.apassdr9\n",
+      "external.external.gaiadr2_geometric_distance\n",
+      "external.external.galex_ais\n",
+      "external.external.ravedr5_com\n",
+      "external.external.ravedr5_dr5\n",
+      "external.external.ravedr5_gra\n",
+      "external.external.ravedr5_on\n",
+      "external.external.sdssdr13_photoprimary\n",
+      "external.external.skymapperdr1_master\n",
+      "external.external.tmass_xsc\n",
+      "public.public.hipparcos\n",
+      "public.public.hipparcos_newreduction\n",
+      "public.public.hubble_sc\n",
+      "public.public.igsl_source\n",
+      "public.public.igsl_source_catalog_ids\n",
+      "public.public.tycho2\n",
+      "public.public.dual\n",
+      "tap_config.tap_config.coord_sys\n",
+      "tap_config.tap_config.properties\n",
+      "tap_schema.tap_schema.columns\n",
+      "tap_schema.tap_schema.key_columns\n",
+      "tap_schema.tap_schema.keys\n",
+      "tap_schema.tap_schema.schemas\n",
+      "tap_schema.tap_schema.tables\n",
+      "gaiadr1.gaiadr1.aux_qso_icrf2_match\n",
+      "gaiadr1.gaiadr1.ext_phot_zero_point\n",
+      "gaiadr1.gaiadr1.allwise_best_neighbour\n",
+      "gaiadr1.gaiadr1.allwise_neighbourhood\n",
+      "gaiadr1.gaiadr1.gsc23_best_neighbour\n",
+      "gaiadr1.gaiadr1.gsc23_neighbourhood\n",
+      "gaiadr1.gaiadr1.ppmxl_best_neighbour\n",
+      "gaiadr1.gaiadr1.ppmxl_neighbourhood\n",
+      "gaiadr1.gaiadr1.sdss_dr9_best_neighbour\n",
+      "gaiadr1.gaiadr1.sdss_dr9_neighbourhood\n",
+      "gaiadr1.gaiadr1.tmass_best_neighbour\n",
+      "gaiadr1.gaiadr1.tmass_neighbourhood\n",
+      "gaiadr1.gaiadr1.ucac4_best_neighbour\n",
+      "gaiadr1.gaiadr1.ucac4_neighbourhood\n",
+      "gaiadr1.gaiadr1.urat1_best_neighbour\n",
+      "gaiadr1.gaiadr1.urat1_neighbourhood\n",
+      "gaiadr1.gaiadr1.cepheid\n",
+      "gaiadr1.gaiadr1.phot_variable_time_series_gfov\n",
+      "gaiadr1.gaiadr1.phot_variable_time_series_gfov_statistical_parameters\n",
+      "gaiadr1.gaiadr1.rrlyrae\n",
+      "gaiadr1.gaiadr1.variable_summary\n",
+      "gaiadr1.gaiadr1.allwise_original_valid\n",
+      "gaiadr1.gaiadr1.gsc23_original_valid\n",
+      "gaiadr1.gaiadr1.ppmxl_original_valid\n",
+      "gaiadr1.gaiadr1.sdssdr9_original_valid\n",
+      "gaiadr1.gaiadr1.tmass_original_valid\n",
+      "gaiadr1.gaiadr1.ucac4_original_valid\n",
+      "gaiadr1.gaiadr1.urat1_original_valid\n",
+      "gaiadr1.gaiadr1.gaia_source\n",
+      "gaiadr1.gaiadr1.tgas_source\n",
+      "gaiadr2.gaiadr2.aux_allwise_agn_gdr2_cross_id\n",
+      "gaiadr2.gaiadr2.aux_iers_gdr2_cross_id\n",
+      "gaiadr2.gaiadr2.aux_sso_orbit_residuals\n",
+      "gaiadr2.gaiadr2.aux_sso_orbits\n",
+      "gaiadr2.gaiadr2.dr1_neighbourhood\n",
+      "gaiadr2.gaiadr2.allwise_best_neighbour\n",
+      "gaiadr2.gaiadr2.allwise_neighbourhood\n",
+      "gaiadr2.gaiadr2.apassdr9_best_neighbour\n",
+      "gaiadr2.gaiadr2.apassdr9_neighbourhood\n",
+      "gaiadr2.gaiadr2.gsc23_best_neighbour\n",
+      "gaiadr2.gaiadr2.gsc23_neighbourhood\n",
+      "gaiadr2.gaiadr2.hipparcos2_best_neighbour\n",
+      "gaiadr2.gaiadr2.hipparcos2_neighbourhood\n",
+      "gaiadr2.gaiadr2.panstarrs1_best_neighbour\n",
+      "gaiadr2.gaiadr2.panstarrs1_neighbourhood\n",
+      "gaiadr2.gaiadr2.ppmxl_best_neighbour\n",
+      "gaiadr2.gaiadr2.ppmxl_neighbourhood\n",
+      "gaiadr2.gaiadr2.ravedr5_best_neighbour\n",
+      "gaiadr2.gaiadr2.ravedr5_neighbourhood\n",
+      "gaiadr2.gaiadr2.sdssdr9_best_neighbour\n",
+      "gaiadr2.gaiadr2.sdssdr9_neighbourhood\n",
+      "gaiadr2.gaiadr2.tmass_best_neighbour\n",
+      "gaiadr2.gaiadr2.tmass_neighbourhood\n",
+      "gaiadr2.gaiadr2.tycho2_best_neighbour\n",
+      "gaiadr2.gaiadr2.tycho2_neighbourhood\n",
+      "gaiadr2.gaiadr2.urat1_best_neighbour\n",
+      "gaiadr2.gaiadr2.urat1_neighbourhood\n",
+      "gaiadr2.gaiadr2.sso_observation\n",
+      "gaiadr2.gaiadr2.sso_source\n",
+      "gaiadr2.gaiadr2.vari_cepheid\n",
+      "gaiadr2.gaiadr2.vari_classifier_class_definition\n",
+      "gaiadr2.gaiadr2.vari_classifier_definition\n",
+      "gaiadr2.gaiadr2.vari_classifier_result\n",
+      "gaiadr2.gaiadr2.vari_long_period_variable\n",
+      "gaiadr2.gaiadr2.vari_rotation_modulation\n",
+      "gaiadr2.gaiadr2.vari_rrlyrae\n",
+      "gaiadr2.gaiadr2.vari_short_timescale\n",
+      "gaiadr2.gaiadr2.vari_time_series_statistics\n",
+      "gaiadr2.gaiadr2.panstarrs1_original_valid\n",
+      "gaiadr2.gaiadr2.gaia_source\n",
+      "gaiadr2.gaiadr2.ruwe\n"
+     ]
+    }
+   ],
+   "source": [
+    "for table in (tables):\n",
+    "    print(table.get_qualified_name())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "So that's a lot of tables.  The ones we'll use are:\n",
+    "\n",
+    "* `gaiadr2.gaia_source`, which contains Gaia data from [data release 2](https://www.cosmos.esa.int/web/gaia/data-release-2),\n",
+    "\n",
+    "* `gaiadr2.panstarrs1_original_valid`, which contains the photometry data we'll use from PanSTARRS, and\n",
+    "\n",
+    "* `gaiadr2.panstarrs1_best_neighbour`, which we'll use to cross-match each star observed by Gaia with the same star observed by PanSTARRS.\n",
+    "\n",
+    "We can use `load_table` (not `load_tables`) to get the metadata for a single table.  The name of this function is misleading, because it only downloads metadata. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Retrieving table 'gaiadr2.gaia_source'\n",
+      "Parsing table 'gaiadr2.gaia_source'...\n",
+      "Done.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<astroquery.utils.tap.model.taptable.TapTableMeta at 0x7f922376e0a0>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "meta = Gaia.load_table('gaiadr2.gaia_source')\n",
+    "meta"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Jupyter shows that the result is an object of type `TapTableMeta`, but it does not display the contents.\n",
+    "\n",
+    "To see the metadata, we have to print the object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "TAP Table name: gaiadr2.gaiadr2.gaia_source\n",
+      "Description: This table has an entry for every Gaia observed source as listed in the\n",
+      "Main Database accumulating catalogue version from which the catalogue\n",
+      "release has been generated. It contains the basic source parameters,\n",
+      "that is only final data (no epoch data) and no spectra (neither final\n",
+      "nor epoch).\n",
+      "Num. columns: 96\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(meta)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notice one gotcha: in the list of table names, this table appears as `gaiadr2.gaiadr2.gaia_source`, but when we load the metadata, we refer to it as `gaiadr2.gaia_source`.\n",
+    "\n",
+    "**Exercise:** Go back and try\n",
+    "\n",
+    "```\n",
+    "meta = Gaia.load_table('gaiadr2.gaiadr2.gaia_source')\n",
+    "```\n",
+    "\n",
+    "What happens?  Is the error message helpful?  If you had not made this error deliberately, would you have been able to figure it out?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Columns\n",
+    "\n",
+    "The following loop prints the names of the columns in the table."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "tags": [
+     "hide-output"
+    ]
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "solution_id\n",
+      "designation\n",
+      "source_id\n",
+      "random_index\n",
+      "ref_epoch\n",
+      "ra\n",
+      "ra_error\n",
+      "dec\n",
+      "dec_error\n",
+      "parallax\n",
+      "parallax_error\n",
+      "parallax_over_error\n",
+      "pmra\n",
+      "pmra_error\n",
+      "pmdec\n",
+      "pmdec_error\n",
+      "ra_dec_corr\n",
+      "ra_parallax_corr\n",
+      "ra_pmra_corr\n",
+      "ra_pmdec_corr\n",
+      "dec_parallax_corr\n",
+      "dec_pmra_corr\n",
+      "dec_pmdec_corr\n",
+      "parallax_pmra_corr\n",
+      "parallax_pmdec_corr\n",
+      "pmra_pmdec_corr\n",
+      "astrometric_n_obs_al\n",
+      "astrometric_n_obs_ac\n",
+      "astrometric_n_good_obs_al\n",
+      "astrometric_n_bad_obs_al\n",
+      "astrometric_gof_al\n",
+      "astrometric_chi2_al\n",
+      "astrometric_excess_noise\n",
+      "astrometric_excess_noise_sig\n",
+      "astrometric_params_solved\n",
+      "astrometric_primary_flag\n",
+      "astrometric_weight_al\n",
+      "astrometric_pseudo_colour\n",
+      "astrometric_pseudo_colour_error\n",
+      "mean_varpi_factor_al\n",
+      "astrometric_matched_observations\n",
+      "visibility_periods_used\n",
+      "astrometric_sigma5d_max\n",
+      "frame_rotator_object_type\n",
+      "matched_observations\n",
+      "duplicated_source\n",
+      "phot_g_n_obs\n",
+      "phot_g_mean_flux\n",
+      "phot_g_mean_flux_error\n",
+      "phot_g_mean_flux_over_error\n",
+      "phot_g_mean_mag\n",
+      "phot_bp_n_obs\n",
+      "phot_bp_mean_flux\n",
+      "phot_bp_mean_flux_error\n",
+      "phot_bp_mean_flux_over_error\n",
+      "phot_bp_mean_mag\n",
+      "phot_rp_n_obs\n",
+      "phot_rp_mean_flux\n",
+      "phot_rp_mean_flux_error\n",
+      "phot_rp_mean_flux_over_error\n",
+      "phot_rp_mean_mag\n",
+      "phot_bp_rp_excess_factor\n",
+      "phot_proc_mode\n",
+      "bp_rp\n",
+      "bp_g\n",
+      "g_rp\n",
+      "radial_velocity\n",
+      "radial_velocity_error\n",
+      "rv_nb_transits\n",
+      "rv_template_teff\n",
+      "rv_template_logg\n",
+      "rv_template_fe_h\n",
+      "phot_variable_flag\n",
+      "l\n",
+      "b\n",
+      "ecl_lon\n",
+      "ecl_lat\n",
+      "priam_flags\n",
+      "teff_val\n",
+      "teff_percentile_lower\n",
+      "teff_percentile_upper\n",
+      "a_g_val\n",
+      "a_g_percentile_lower\n",
+      "a_g_percentile_upper\n",
+      "e_bp_min_rp_val\n",
+      "e_bp_min_rp_percentile_lower\n",
+      "e_bp_min_rp_percentile_upper\n",
+      "flame_flags\n",
+      "radius_val\n",
+      "radius_percentile_lower\n",
+      "radius_percentile_upper\n",
+      "lum_val\n",
+      "lum_percentile_lower\n",
+      "lum_percentile_upper\n",
+      "datalink_url\n",
+      "epoch_photometry_url\n"
+     ]
+    }
+   ],
+   "source": [
+    "for column in meta.columns:\n",
+    "    print(column.name)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can probably guess what many of these columns are by looking at the names, but you should resist the temptation to guess.\n",
+    "To find out what the columns mean, [read the documentation](https://gea.esac.esa.int/archive/documentation/GDR2/Gaia_archive/chap_datamodel/sec_dm_main_tables/ssec_dm_gaia_source.html).\n",
+    "\n",
+    "If you want to know what can go wrong when you don't read the documentation, [you might like this article](https://www.vox.com/future-perfect/2019/6/4/18650969/married-women-miserable-fake-paul-dolan-happiness)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** One of the other tables we'll use is `gaiadr2.gaiadr2.panstarrs1_original_valid`.  Use `load_table` to get the metadata for this table.  How many columns are there and what are their names?\n",
+    "\n",
+    "Hint: Remember the gotcha we mentioned earlier."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "tags": [
+     "hide-cell"
+    ]
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Retrieving table 'gaiadr2.panstarrs1_original_valid'\n",
+      "Parsing table 'gaiadr2.panstarrs1_original_valid'...\n",
+      "Done.\n",
+      "TAP Table name: gaiadr2.gaiadr2.panstarrs1_original_valid\n",
+      "Description: The Panoramic Survey Telescope and Rapid Response System (Pan-STARRS) is\n",
+      "a system for wide-field astronomical imaging developed and operated by\n",
+      "the Institute for Astronomy at the University of Hawaii. Pan-STARRS1\n",
+      "(PS1) is the first part of Pan-STARRS to be completed and is the basis\n",
+      "for Data Release 1 (DR1). The PS1 survey used a 1.8 meter telescope and\n",
+      "its 1.4 Gigapixel camera to image the sky in five broadband filters (g,\n",
+      "r, i, z, y).\n",
+      "\n",
+      "The current table contains a filtered subsample of the 10 723 304 629\n",
+      "entries listed in the original ObjectThin table.\n",
+      "We used only ObjectThin and MeanObject tables to extract\n",
+      "panstarrs1OriginalValid table, this means that objects detected only in\n",
+      "stack images are not included here. The main reason for us to avoid the\n",
+      "use of objects detected in stack images is that their astrometry is not\n",
+      "as good as the mean objects astrometry: “The stack positions (raStack,\n",
+      "decStack) have considerably larger systematic astrometric errors than\n",
+      "the mean epoch positions (raMean, decMean).� The astrometry for the\n",
+      "MeanObject positions uses Gaia DR1 as a reference catalog, while the\n",
+      "stack positions use 2MASS as a reference catalog.\n",
+      "\n",
+      "In details, we filtered out all objects where:\n",
+      "\n",
+      "-   nDetections = 1\n",
+      "\n",
+      "-   no good quality data in Pan-STARRS, objInfoFlag 33554432 not set\n",
+      "\n",
+      "-   mean astrometry could not be measured, objInfoFlag 524288 set\n",
+      "\n",
+      "-   stack position used for mean astrometry, objInfoFlag 1048576 set\n",
+      "\n",
+      "-   error on all magnitudes equal to 0 or to -999;\n",
+      "\n",
+      "-   all magnitudes set to -999;\n",
+      "\n",
+      "-   error on RA or DEC greater than 1 arcsec.\n",
+      "\n",
+      "The number of objects in panstarrs1OriginalValid is 2 264 263 282.\n",
+      "\n",
+      "The panstarrs1OriginalValid table contains only a subset of the columns\n",
+      "available in the combined ObjectThin and MeanObject tables. A\n",
+      "description of the original ObjectThin and MeanObjects tables can be\n",
+      "found at:\n",
+      "https://outerspace.stsci.edu/display/PANSTARRS/PS1+Database+object+and+detection+tables\n",
+      "\n",
+      "Download:\n",
+      "http://mastweb.stsci.edu/ps1casjobs/home.aspx\n",
+      "Documentation:\n",
+      "https://outerspace.stsci.edu/display/PANSTARRS\n",
+      "http://pswww.ifa.hawaii.edu/pswww/\n",
+      "References:\n",
+      "The Pan-STARRS1 Surveys, Chambers, K.C., et al. 2016, arXiv:1612.05560\n",
+      "Pan-STARRS Data Processing System, Magnier, E. A., et al. 2016,\n",
+      "arXiv:1612.05240\n",
+      "Pan-STARRS Pixel Processing: Detrending, Warping, Stacking, Waters, C.\n",
+      "Z., et al. 2016, arXiv:1612.05245\n",
+      "Pan-STARRS Pixel Analysis: Source Detection and Characterization,\n",
+      "Magnier, E. A., et al. 2016, arXiv:1612.05244\n",
+      "Pan-STARRS Photometric and Astrometric Calibration, Magnier, E. A., et\n",
+      "al. 2016, arXiv:1612.05242\n",
+      "The Pan-STARRS1 Database and Data Products, Flewelling, H. A., et al.\n",
+      "2016, arXiv:1612.05243\n",
+      "\n",
+      "Catalogue curator:\n",
+      "SSDC - ASI Space Science Data Center\n",
+      "https://www.ssdc.asi.it/\n",
+      "Num. columns: 26\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "meta2 = Gaia.load_table('gaiadr2.panstarrs1_original_valid')\n",
+    "print(meta2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "tags": [
+     "hide-cell"
+    ]
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "obj_name\n",
+      "obj_id\n",
+      "ra\n",
+      "dec\n",
+      "ra_error\n",
+      "dec_error\n",
+      "epoch_mean\n",
+      "g_mean_psf_mag\n",
+      "g_mean_psf_mag_error\n",
+      "g_flags\n",
+      "r_mean_psf_mag\n",
+      "r_mean_psf_mag_error\n",
+      "r_flags\n",
+      "i_mean_psf_mag\n",
+      "i_mean_psf_mag_error\n",
+      "i_flags\n",
+      "z_mean_psf_mag\n",
+      "z_mean_psf_mag_error\n",
+      "z_flags\n",
+      "y_mean_psf_mag\n",
+      "y_mean_psf_mag_error\n",
+      "y_flags\n",
+      "n_detections\n",
+      "zone_id\n",
+      "obj_info_flag\n",
+      "quality_flag\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "for column in meta2.columns:\n",
+    "    print(column.name)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Writing queries\n",
+    "\n",
+    "By now you might be wondering how we actually download the data.  With tables this big, you generally don't.  Instead, you use queries to select only the data you want.\n",
+    "\n",
+    "A query is a string written in a query language like SQL; for the Gaia database, the query language is a dialect of SQL called ADQL.\n",
+    "\n",
+    "Here's an example of an ADQL query."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query1 = \"\"\"SELECT \n",
+    "TOP 10\n",
+    "source_id, ref_epoch, ra, dec, parallax \n",
+    "FROM gaiadr2.gaia_source\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Python note:** We use a [triple-quoted string](https://docs.python.org/3/tutorial/introduction.html#strings) here so we can include line breaks in the query, which makes it easier to read.\n",
+    "\n",
+    "The words in uppercase are ADQL keywords:\n",
+    "\n",
+    "* `SELECT` indicates that we are selecting data (as opposed to adding or modifying data).\n",
+    "\n",
+    "* `TOP` indicates that we only want the first 10 rows of the table, which is useful for testing a query before asking for all of the data.\n",
+    "\n",
+    "* `FROM` specifies which table we want data from.\n",
+    "\n",
+    "The third line is a list of column names, indicating which columns we want.  \n",
+    "\n",
+    "In this example, the keywords are capitalized and the column names are lowercase.  This is a common style, but it is not required.  ADQL and SQL are not case-sensitive."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To run this query, we use the `Gaia` object, which represents our connection to the Gaia database, and invoke `launch_job`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<astroquery.utils.tap.model.job.Job at 0x7f9222e9cb20>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "job1 = Gaia.launch_job(query1)\n",
+    "job1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The result is an object that represents the job running on a Gaia server.\n",
+    "\n",
+    "If you print it, it displays metadata for the forthcoming table."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<Table length=10>\n",
+      "   name    dtype  unit                            description                            \n",
+      "--------- ------- ---- ------------------------------------------------------------------\n",
+      "source_id   int64      Unique source identifier (unique within a particular Data Release)\n",
+      "ref_epoch float64   yr                                                    Reference epoch\n",
+      "       ra float64  deg                                                    Right ascension\n",
+      "      dec float64  deg                                                        Declination\n",
+      " parallax float64  mas                                                           Parallax\n",
+      "Jobid: None\n",
+      "Phase: COMPLETED\n",
+      "Owner: None\n",
+      "Output file: sync_20201005090721.xml.gz\n",
+      "Results: None\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(job1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Don't worry about `Results: None`.  That does not actually mean there are no results.\n",
+    "\n",
+    "However, `Phase: COMPLETED` indicates that the job is complete, so we can get the results like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "astropy.table.table.Table"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results1 = job1.get_results()\n",
+    "type(results1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Optional detail:**  Why is `table` repeated three times?  The first is the name of the module, the second is the name of the submodule, and the third is the name of the class.  Most of the time we only care about the last one.  It's like the Linnean name for gorilla, which is *Gorilla Gorilla Gorilla*."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The result is an [Astropy Table](https://docs.astropy.org/en/stable/table/), which is similar to a table in an SQL database except:\n",
+    "\n",
+    "* SQL databases are stored on disk drives, so they are persistent; that is, they \"survive\" even if you turn off the computer.  An Astropy `Table` is stored in memory; it disappears when you turn off the computer (or shut down this Jupyter notebook).\n",
+    "\n",
+    "* SQL databases are designed to process queries.  An Astropy `Table` can perform some query-like operations, like selecting columns and rows.  But these operations use Python syntax, not SQL.\n",
+    "\n",
+    "Jupyter knows how to display the contents of a `Table`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<i>Table length=10</i>\n",
+       "<table id=\"table140265627585264\" class=\"table-striped table-bordered table-condensed\">\n",
+       "<thead><tr><th>source_id</th><th>ref_epoch</th><th>ra</th><th>dec</th><th>parallax</th></tr></thead>\n",
+       "<thead><tr><th></th><th>yr</th><th>deg</th><th>deg</th><th>mas</th></tr></thead>\n",
+       "<thead><tr><th>int64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th></tr></thead>\n",
+       "<tr><td>4530738361793769600</td><td>2015.5</td><td>281.56725362448725</td><td>20.40682117430378</td><td>0.9785380604519425</td></tr>\n",
+       "<tr><td>4530752651135081216</td><td>2015.5</td><td>281.0861565355257</td><td>20.523350496351846</td><td>0.2674800612552977</td></tr>\n",
+       "<tr><td>4530743343951405568</td><td>2015.5</td><td>281.37114418299177</td><td>20.474147574053124</td><td>-0.43911323550176806</td></tr>\n",
+       "<tr><td>4530755060627162368</td><td>2015.5</td><td>281.2676236268299</td><td>20.558523922346158</td><td>1.1422630184554958</td></tr>\n",
+       "<tr><td>4530746844341315968</td><td>2015.5</td><td>281.1370431749541</td><td>20.377852388898184</td><td>1.0092247424630945</td></tr>\n",
+       "<tr><td>4530768456615026432</td><td>2015.5</td><td>281.8720921436347</td><td>20.31829694530366</td><td>-0.06900136127674149</td></tr>\n",
+       "<tr><td>4530763513119137280</td><td>2015.5</td><td>281.9211808864116</td><td>20.20956829578524</td><td>0.1266016679823622</td></tr>\n",
+       "<tr><td>4530736364618539264</td><td>2015.5</td><td>281.4913475613274</td><td>20.346579041327693</td><td>0.3894019486060072</td></tr>\n",
+       "<tr><td>4530735952305177728</td><td>2015.5</td><td>281.4085549165704</td><td>20.311030903719928</td><td>0.2041189982608354</td></tr>\n",
+       "<tr><td>4530751281056022656</td><td>2015.5</td><td>281.0585328377638</td><td>20.460309556214753</td><td>0.10294642821734962</td></tr>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<Table length=10>\n",
+       "     source_id      ref_epoch ...        dec               parallax      \n",
+       "                        yr    ...        deg                 mas         \n",
+       "       int64         float64  ...      float64             float64       \n",
+       "------------------- --------- ... ------------------ --------------------\n",
+       "4530738361793769600    2015.5 ...  20.40682117430378   0.9785380604519425\n",
+       "4530752651135081216    2015.5 ... 20.523350496351846   0.2674800612552977\n",
+       "4530743343951405568    2015.5 ... 20.474147574053124 -0.43911323550176806\n",
+       "4530755060627162368    2015.5 ... 20.558523922346158   1.1422630184554958\n",
+       "4530746844341315968    2015.5 ... 20.377852388898184   1.0092247424630945\n",
+       "4530768456615026432    2015.5 ...  20.31829694530366 -0.06900136127674149\n",
+       "4530763513119137280    2015.5 ...  20.20956829578524   0.1266016679823622\n",
+       "4530736364618539264    2015.5 ... 20.346579041327693   0.3894019486060072\n",
+       "4530735952305177728    2015.5 ... 20.311030903719928   0.2041189982608354\n",
+       "4530751281056022656    2015.5 ... 20.460309556214753  0.10294642821734962"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Each column has a name, units, and a data type.\n",
+    "\n",
+    "For example, the units of `ra` and `dec` are degrees, and their data type is `float64`, which is a 64-bit floating-point number, used to store measurements with a fraction part.\n",
+    "\n",
+    "This information comes from the Gaia database, and has been stored in the Astropy `Table` by Astroquery."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Read [the documentation of this table](https://gea.esac.esa.int/archive/documentation/GDR2/Gaia_archive/chap_datamodel/sec_dm_main_tables/ssec_dm_gaia_source.html) and choose a column that looks interesting to you.  Add the column name to the query and run it again.  What are the units of the column you selected?  What is its data type?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Asynchronous queries\n",
+    "\n",
+    "`launch_job` asks the server to run the job \"synchronously\", which normally means it runs immediately.  But synchronous jobs are limited to 2000 rows.  For queries that return more rows, you should run \"asynchronously\", which mean they might take longer to get started.\n",
+    "\n",
+    "If you are not sure how many rows a query will return, you can use the SQL command `COUNT` to find out how many rows are in the result without actually returning them.  We'll see an example of this later.\n",
+    "\n",
+    "The results of an asynchronous query are stored in a file on the server, so you can start a query and come back later to get the results.\n",
+    "\n",
+    "For anonymous users, files are kept for three days.\n",
+    "\n",
+    "As an example, let's try a query that's similar to `query1`, with two changes:\n",
+    "\n",
+    "* It selects the first 3000 rows, so it is bigger than we should run synchronously.\n",
+    "\n",
+    "* It uses a new keyword, `WHERE`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query2 = \"\"\"SELECT TOP 3000\n",
+    "source_id, ref_epoch, ra, dec, parallax\n",
+    "FROM gaiadr2.gaia_source\n",
+    "WHERE parallax < 1\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A `WHERE` clause indicates which rows we want; in this case, the query selects only rows \"where\" `parallax` is less than 1.  This has the effect of selecting stars with relatively low parallax, which are farther away.  We'll use this clause to exclude nearby stars that are unlikely to be part of GD-1.\n",
+    "\n",
+    "`WHERE` is one of the most common clauses in ADQL/SQL, and one of the most useful, because it allows us to select only the rows we need from the database.\n",
+    "\n",
+    "We use `launch_job_async` to submit an asynchronous query."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO: Query finished. [astroquery.utils.tap.core]\n",
+      "<Table length=3000>\n",
+      "   name    dtype  unit                            description                            \n",
+      "--------- ------- ---- ------------------------------------------------------------------\n",
+      "source_id   int64      Unique source identifier (unique within a particular Data Release)\n",
+      "ref_epoch float64   yr                                                    Reference epoch\n",
+      "       ra float64  deg                                                    Right ascension\n",
+      "      dec float64  deg                                                        Declination\n",
+      " parallax float64  mas                                                           Parallax\n",
+      "Jobid: 1601903242219O\n",
+      "Phase: COMPLETED\n",
+      "Owner: None\n",
+      "Output file: async_20201005090722.vot\n",
+      "Results: None\n"
+     ]
+    }
+   ],
+   "source": [
+    "job2 = Gaia.launch_job_async(query2)\n",
+    "print(job2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And here are the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<i>Table length=3000</i>\n",
+       "<table id=\"table140265625141056\" class=\"table-striped table-bordered table-condensed\">\n",
+       "<thead><tr><th>source_id</th><th>ref_epoch</th><th>ra</th><th>dec</th><th>parallax</th></tr></thead>\n",
+       "<thead><tr><th></th><th>yr</th><th>deg</th><th>deg</th><th>mas</th></tr></thead>\n",
+       "<thead><tr><th>int64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th></tr></thead>\n",
+       "<tr><td>4530738361793769600</td><td>2015.5</td><td>281.56725362448725</td><td>20.40682117430378</td><td>0.9785380604519425</td></tr>\n",
+       "<tr><td>4530752651135081216</td><td>2015.5</td><td>281.0861565355257</td><td>20.523350496351846</td><td>0.2674800612552977</td></tr>\n",
+       "<tr><td>4530743343951405568</td><td>2015.5</td><td>281.37114418299177</td><td>20.474147574053124</td><td>-0.43911323550176806</td></tr>\n",
+       "<tr><td>4530768456615026432</td><td>2015.5</td><td>281.8720921436347</td><td>20.31829694530366</td><td>-0.06900136127674149</td></tr>\n",
+       "<tr><td>4530763513119137280</td><td>2015.5</td><td>281.9211808864116</td><td>20.20956829578524</td><td>0.1266016679823622</td></tr>\n",
+       "<tr><td>4530736364618539264</td><td>2015.5</td><td>281.4913475613274</td><td>20.346579041327693</td><td>0.3894019486060072</td></tr>\n",
+       "<tr><td>4530735952305177728</td><td>2015.5</td><td>281.4085549165704</td><td>20.311030903719928</td><td>0.2041189982608354</td></tr>\n",
+       "<tr><td>4530751281056022656</td><td>2015.5</td><td>281.0585328377638</td><td>20.460309556214753</td><td>0.10294642821734962</td></tr>\n",
+       "<tr><td>4530740938774409344</td><td>2015.5</td><td>281.3762569536416</td><td>20.436140058941206</td><td>0.9242670062090182</td></tr>\n",
+       "<tr><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr>\n",
+       "<tr><td>4467710915011802624</td><td>2015.5</td><td>269.9680969307347</td><td>1.1429085038160882</td><td>0.42361471245557913</td></tr>\n",
+       "<tr><td>4467706551328679552</td><td>2015.5</td><td>270.033164589881</td><td>1.0565747323689927</td><td>0.922888231734588</td></tr>\n",
+       "<tr><td>4467712255037300096</td><td>2015.5</td><td>270.7724717923047</td><td>0.6581664892880896</td><td>-2.669179465293931</td></tr>\n",
+       "<tr><td>4467735001181761792</td><td>2015.5</td><td>270.3628606248308</td><td>0.8947079323599124</td><td>0.6117399163086398</td></tr>\n",
+       "<tr><td>4467737101421916672</td><td>2015.5</td><td>270.5110834661444</td><td>0.9806225910160181</td><td>-0.39818224846127004</td></tr>\n",
+       "<tr><td>4467707547757327488</td><td>2015.5</td><td>269.88746280594927</td><td>1.0212759940136962</td><td>0.7741412301054209</td></tr>\n",
+       "<tr><td>4467732772094573056</td><td>2015.5</td><td>270.55997182760126</td><td>0.9037072088489417</td><td>-1.7920417800164183</td></tr>\n",
+       "<tr><td>4467732355491087744</td><td>2015.5</td><td>270.6730790702491</td><td>0.9197224705139885</td><td>-0.3464446494840354</td></tr>\n",
+       "<tr><td>4467717099766944512</td><td>2015.5</td><td>270.57667173120825</td><td>0.726277659009568</td><td>0.05443955111134051</td></tr>\n",
+       "<tr><td>4467719058265781248</td><td>2015.5</td><td>270.7248052971514</td><td>0.8205551921782785</td><td>0.3733943917490343</td></tr>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<Table length=3000>\n",
+       "     source_id      ref_epoch ...        dec               parallax      \n",
+       "                        yr    ...        deg                 mas         \n",
+       "       int64         float64  ...      float64             float64       \n",
+       "------------------- --------- ... ------------------ --------------------\n",
+       "4530738361793769600    2015.5 ...  20.40682117430378   0.9785380604519425\n",
+       "4530752651135081216    2015.5 ... 20.523350496351846   0.2674800612552977\n",
+       "4530743343951405568    2015.5 ... 20.474147574053124 -0.43911323550176806\n",
+       "4530768456615026432    2015.5 ...  20.31829694530366 -0.06900136127674149\n",
+       "4530763513119137280    2015.5 ...  20.20956829578524   0.1266016679823622\n",
+       "4530736364618539264    2015.5 ... 20.346579041327693   0.3894019486060072\n",
+       "4530735952305177728    2015.5 ... 20.311030903719928   0.2041189982608354\n",
+       "4530751281056022656    2015.5 ... 20.460309556214753  0.10294642821734962\n",
+       "4530740938774409344    2015.5 ... 20.436140058941206   0.9242670062090182\n",
+       "                ...       ... ...                ...                  ...\n",
+       "4467710915011802624    2015.5 ... 1.1429085038160882  0.42361471245557913\n",
+       "4467706551328679552    2015.5 ... 1.0565747323689927    0.922888231734588\n",
+       "4467712255037300096    2015.5 ... 0.6581664892880896   -2.669179465293931\n",
+       "4467735001181761792    2015.5 ... 0.8947079323599124   0.6117399163086398\n",
+       "4467737101421916672    2015.5 ... 0.9806225910160181 -0.39818224846127004\n",
+       "4467707547757327488    2015.5 ... 1.0212759940136962   0.7741412301054209\n",
+       "4467732772094573056    2015.5 ... 0.9037072088489417  -1.7920417800164183\n",
+       "4467732355491087744    2015.5 ... 0.9197224705139885  -0.3464446494840354\n",
+       "4467717099766944512    2015.5 ...  0.726277659009568  0.05443955111134051\n",
+       "4467719058265781248    2015.5 ... 0.8205551921782785   0.3733943917490343"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results2 = job2.get_results()\n",
+    "results2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You might notice that some values of `parallax` are negative.  As [this FAQ explains](https://www.cosmos.esa.int/web/gaia/archive-tips#negative%20parallax), \"Negative parallaxes are caused by errors in the observations.\"  Negative parallaxes have \"no physical meaning,\" but they can be a \"useful diagnostic on the quality of the astrometric solution.\"\n",
+    "\n",
+    "Later we will see an example where we use `parallax` and `parallax_error` to identify stars where the distance estimate is likely to be inaccurate."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** The clauses in a query have to be in the right order.  Go back and change the order of the clauses in `query2` and run it again.  \n",
+    "\n",
+    "The query should fail, but notice that you don't get much useful debugging information.  \n",
+    "\n",
+    "For this reason, developing and debugging ADQL queries can be really hard.  A few suggestions that might help:\n",
+    "\n",
+    "* Whenever possible, start with a working query, either an example you find online or a query you have used in the past.\n",
+    "\n",
+    "* Make small changes and test each change before you continue.\n",
+    "\n",
+    "* While you are debugging, use `TOP` to limit the number of rows in the result.  That will make each attempt run faster, which reduces your testing time.  \n",
+    "\n",
+    "* Launching test queries synchronously might make them start faster, too."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Operators\n",
+    "\n",
+    "In a `WHERE` clause, you can use any of the [SQL comparison operators](https://www.w3schools.com/sql/sql_operators.asp); here are the most common ones:\n",
+    "\n",
+    "| Symbol | Operation\n",
+    "|--------| :---\n",
+    "| `>` | greater than\n",
+    "| `<` | less than\n",
+    "| `>=` | greater than or equal\n",
+    "| `<=` | less than or equal\n",
+    "| `=` | equal\n",
+    "| `!=` or `<>` | not equal\n",
+    "\n",
+    "Most of these are the same as Python, but some are not.  In particular, notice that the equality operator is `=`, not `==`.\n",
+    "Be careful to keep your Python out of your ADQL!\n",
+    "\n",
+    "You can combine comparisons using the logical operators:\n",
+    "\n",
+    "* AND: true if both comparisons are true\n",
+    "* OR: true if either or both comparisons are true\n",
+    "\n",
+    "Finally, you can use `NOT` to invert the result of a comparison. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** [Read about SQL operators here](https://www.w3schools.com/sql/sql_operators.asp) and then modify the previous query to select rows where `bp_rp` is between `-0.75` and `2`.\n",
+    "\n",
+    "You can [read about this variable here](https://gea.esac.esa.int/archive/documentation/GDR2/Gaia_archive/chap_datamodel/sec_dm_main_tables/ssec_dm_gaia_source.html)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "tags": [
+     "hide-cell"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "# This is what most people will probably do\n",
+    "\n",
+    "query = \"\"\"SELECT TOP 10\n",
+    "source_id, ref_epoch, ra, dec, parallax\n",
+    "FROM gaiadr2.gaia_source\n",
+    "WHERE parallax < 1 \n",
+    "  AND bp_rp > -0.75 AND bp_rp < 2\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "tags": [
+     "hide-cell"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "# But if someone notices the BETWEEN operator, \n",
+    "# they might do this\n",
+    "\n",
+    "query = \"\"\"SELECT TOP 10\n",
+    "source_id, ref_epoch, ra, dec, parallax\n",
+    "FROM gaiadr2.gaia_source\n",
+    "WHERE parallax < 1 \n",
+    "  AND bp_rp BETWEEN -0.75 AND 2\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This [Hertzsprung-Russell diagram](https://sci.esa.int/web/gaia/-/60198-gaia-hertzsprung-russell-diagram) shows the BP-RP color and luminosity of stars in the Gaia catalog.\n",
+    "\n",
+    "Selecting stars with `bp-rp` less than 2 excludes many [class M dwarf stars](https://xkcd.com/2360/), which are low temperature, low luminosity.  A star like that at GD-1's distance would be hard to detect, so if it is detected, it it more likely to be in the foreground."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Cleaning up\n",
+    "\n",
+    "Asynchronous jobs have a `jobid`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(None, '1601903242219O')"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "job1.jobid, job2.jobid"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Which you can use to remove the job from the server."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Removed jobs: '['1601903242219O']'.\n"
+     ]
+    }
+   ],
+   "source": [
+    "Gaia.remove_jobs([job2.jobid])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you don't remove it job from the server, it will be removed eventually, so don't feel too bad if you don't clean up after yourself."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Formatting queries\n",
+    "\n",
+    "So far the queries have been string \"literals\", meaning that the entire string is part of the program.\n",
+    "But writing queries yourself can be slow, repetitive, and error-prone.\n",
+    "\n",
+    "It is often a good idea to write Python code that assembles a query for you.  One useful tool for that is the [string `format` method](https://www.w3schools.com/python/ref_string_format.asp).\n",
+    "\n",
+    "As an example, we'll divide the previous query into two parts; a list of column names and a \"base\" for the query that contains everything except the column names.\n",
+    "\n",
+    "Here's the list of columns we'll select.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "columns = 'source_id, ra, dec, pmra, pmdec, parallax, parallax_error, radial_velocity'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And here's the base; it's a string that contains at least one format specifier in curly brackets (braces)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query3_base = \"\"\"SELECT TOP 10 \n",
+    "{columns}\n",
+    "FROM gaiadr2.gaia_source\n",
+    "WHERE parallax < 1\n",
+    "  AND bp_rp BETWEEN -0.75 AND 2\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This base query contains one format specifier, `{columns}`, which is a placeholder for the list of column names we will provide.\n",
+    "\n",
+    "To assemble the query, we invoke `format` on the base string and provide a keyword argument that assigns a value to `columns`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query3 = query3_base.format(columns=columns)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The result is a string with line breaks.  If you display it, the line breaks appear as `\\n`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'SELECT TOP 10 \\nsource_id, ra, dec, pmra, pmdec, parallax, parallax_error, radial_velocity\\nFROM gaiadr2.gaia_source\\nWHERE parallax < 1\\n  AND bp_rp BETWEEN -0.75 AND 2\\n'"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "query3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "But if you print it, the line breaks appear as... line breaks."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "SELECT TOP 10 \n",
+      "source_id, ra, dec, pmra, pmdec, parallax, parallax_error, radial_velocity\n",
+      "FROM gaiadr2.gaia_source\n",
+      "WHERE parallax < 1\n",
+      "  AND bp_rp BETWEEN -0.75 AND 2\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(query3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notice that the format specifier has been replaced with the value of `columns`.\n",
+    "\n",
+    "Let's run it and see if it works:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<Table length=10>\n",
+      "      name       dtype    unit                              description                             n_bad\n",
+      "--------------- ------- -------- ------------------------------------------------------------------ -----\n",
+      "      source_id   int64          Unique source identifier (unique within a particular Data Release)     0\n",
+      "             ra float64      deg                                                    Right ascension     0\n",
+      "            dec float64      deg                                                        Declination     0\n",
+      "           pmra float64 mas / yr                         Proper motion in right ascension direction     0\n",
+      "          pmdec float64 mas / yr                             Proper motion in declination direction     0\n",
+      "       parallax float64      mas                                                           Parallax     0\n",
+      " parallax_error float64      mas                                         Standard error of parallax     0\n",
+      "radial_velocity float64   km / s                                                    Radial velocity    10\n",
+      "Jobid: None\n",
+      "Phase: COMPLETED\n",
+      "Owner: None\n",
+      "Output file: sync_20201005090726.xml.gz\n",
+      "Results: None\n"
+     ]
+    }
+   ],
+   "source": [
+    "job3 = Gaia.launch_job(query3)\n",
+    "print(job3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<i>Table length=10</i>\n",
+       "<table id=\"table140265627700432\" class=\"table-striped table-bordered table-condensed\">\n",
+       "<thead><tr><th>source_id</th><th>ra</th><th>dec</th><th>pmra</th><th>pmdec</th><th>parallax</th><th>parallax_error</th><th>radial_velocity</th></tr></thead>\n",
+       "<thead><tr><th></th><th>deg</th><th>deg</th><th>mas / yr</th><th>mas / yr</th><th>mas</th><th>mas</th><th>km / s</th></tr></thead>\n",
+       "<thead><tr><th>int64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th></tr></thead>\n",
+       "<tr><td>4467710915011802624</td><td>269.9680969307347</td><td>1.1429085038160882</td><td>2.0233280236600626</td><td>-2.5692427875510266</td><td>0.42361471245557913</td><td>0.470352406647465</td><td>--</td></tr>\n",
+       "<tr><td>4467706551328679552</td><td>270.033164589881</td><td>1.0565747323689927</td><td>-3.414829591355289</td><td>-3.8437215857495737</td><td>0.922888231734588</td><td>0.927008559859825</td><td>--</td></tr>\n",
+       "<tr><td>4467712255037300096</td><td>270.7724717923047</td><td>0.6581664892880896</td><td>-3.5620173752896025</td><td>-6.595792323153987</td><td>-2.669179465293931</td><td>0.9719742773203504</td><td>--</td></tr>\n",
+       "<tr><td>4467735001181761792</td><td>270.3628606248308</td><td>0.8947079323599124</td><td>2.1307079926489205</td><td>0.8826727710910712</td><td>0.6117399163086398</td><td>0.509812721702093</td><td>--</td></tr>\n",
+       "<tr><td>4467737101421916672</td><td>270.5110834661444</td><td>0.9806225910160181</td><td>0.17532366511560785</td><td>-5.113270239706202</td><td>-0.39818224846127004</td><td>0.7549581886719651</td><td>--</td></tr>\n",
+       "<tr><td>4467707547757327488</td><td>269.88746280594927</td><td>1.0212759940136962</td><td>-2.6382230817672987</td><td>-3.707776532049287</td><td>0.7741412301054209</td><td>0.3022057897812064</td><td>--</td></tr>\n",
+       "<tr><td>4467732355491087744</td><td>270.6730790702491</td><td>0.9197224705139885</td><td>-2.2735991502653037</td><td>-11.864952855984358</td><td>-0.3464446494840354</td><td>0.4937921513912002</td><td>--</td></tr>\n",
+       "<tr><td>4467717099766944512</td><td>270.57667173120825</td><td>0.726277659009568</td><td>-3.4598362614808367</td><td>-4.601426893365921</td><td>0.05443955111134051</td><td>0.8867339293525688</td><td>--</td></tr>\n",
+       "<tr><td>4467719058265781248</td><td>270.7248052971514</td><td>0.8205551921782785</td><td>-3.255079498426542</td><td>-9.249285069111085</td><td>0.3733943917490343</td><td>0.390952370410666</td><td>--</td></tr>\n",
+       "<tr><td>4467722326741572352</td><td>270.87431291888504</td><td>0.8595565975869158</td><td>0.10696398351859826</td><td>1.2035993780158853</td><td>-0.11850943432864373</td><td>0.1660452431882023</td><td>--</td></tr>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<Table length=10>\n",
+       "     source_id              ra         ...   parallax_error   radial_velocity\n",
+       "                           deg         ...        mas              km / s    \n",
+       "       int64             float64       ...      float64           float64    \n",
+       "------------------- ------------------ ... ------------------ ---------------\n",
+       "4467710915011802624  269.9680969307347 ...  0.470352406647465              --\n",
+       "4467706551328679552   270.033164589881 ...  0.927008559859825              --\n",
+       "4467712255037300096  270.7724717923047 ... 0.9719742773203504              --\n",
+       "4467735001181761792  270.3628606248308 ...  0.509812721702093              --\n",
+       "4467737101421916672  270.5110834661444 ... 0.7549581886719651              --\n",
+       "4467707547757327488 269.88746280594927 ... 0.3022057897812064              --\n",
+       "4467732355491087744  270.6730790702491 ... 0.4937921513912002              --\n",
+       "4467717099766944512 270.57667173120825 ... 0.8867339293525688              --\n",
+       "4467719058265781248  270.7248052971514 ...  0.390952370410666              --\n",
+       "4467722326741572352 270.87431291888504 ... 0.1660452431882023              --"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results3 = job3.get_results()\n",
+    "results3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Good so far."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** This query always selects sources with `parallax` less than 1.  But suppose you want to take that upper bound as an input.\n",
+    "\n",
+    "Modify `query3_base` to replace `1` with a format specifier like `{max_parallax}`.  Now, when you call `format`, add a keyword argument that assigns a value to `max_parallax`, and confirm that the format specifier gets replaced with the value you provide."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "tags": [
+     "hide-cell"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "query4_base = \"\"\"SELECT TOP 10\n",
+    "{columns}\n",
+    "FROM gaiadr2.gaia_source\n",
+    "WHERE parallax < {max_parallax} AND \n",
+    "bp_rp BETWEEN -0.75 AND 2\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "tags": [
+     "hide-cell"
+    ]
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "SELECT TOP 10\n",
+      "source_id, ra, dec, pmra, pmdec, parallax, parallax_error, radial_velocity\n",
+      "FROM gaiadr2.gaia_source\n",
+      "WHERE parallax < 0.5 AND \n",
+      "bp_rp BETWEEN -0.75 AND 2\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "query4 = query4_base.format(columns=columns,\n",
+    "                          max_parallax=0.5)\n",
+    "print(query)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Style note:**  You might notice that the variable names in this notebook are numbered, like `query1`, `query2`, etc.  \n",
+    "\n",
+    "The advantage of this style is that it isolates each section of the notebook from the others, so if you go back and run the cells out of order, it's less likely that you will get unexpected interactions.\n",
+    "\n",
+    "A drawback of this style is that it can be a nuisance to update the notebook if you add, remove, or reorder a section.\n",
+    "\n",
+    "What do you think of this choice?  Are there alternatives you prefer?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "This notebook demonstrates the following steps:\n",
+    "\n",
+    "1. Making a connection to the Gaia server,\n",
+    "\n",
+    "2. Exploring information about the database and the tables it contains,\n",
+    "\n",
+    "3. Writing a query and sending it to the server, and finally\n",
+    "\n",
+    "4. Downloading the response from the server as an Astropy `Table`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Best practices\n",
+    "\n",
+    "* If you can't download an entire dataset (or it's not practical) use queries to select the data you need.\n",
+    "\n",
+    "* Read the metadata and the documentation to make sure you understand the tables, their columns, and what they mean.\n",
+    "\n",
+    "* Develop queries incrementally: start with something simple, test it, and add a little bit at a time.\n",
+    "\n",
+    "* Use ADQL features like `TOP` and `COUNT` to test before you run a query that might return a lot of data.\n",
+    "\n",
+    "* If you know your query will return fewer than 3000 rows, you can run it synchronously, which might complete faster (but it doesn't seem to make much difference).  If it might return more than 3000 rows, you should run it asynchronously.\n",
+    "\n",
+    "* ADQL and SQL are not case-sensitive, so you don't have to capitalize the keywords, but you should.\n",
+    "\n",
+    "* ADQL and SQL don't require you to break a query into multiple lines, but you should.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Jupyter notebooks can be good for developing and testing code, but they have some drawbacks.  In particular, if you run the cells out of order, you might find that variables don't have the values you expect.\n",
+    "\n",
+    "There are a few things you can do to mitigate these problems:\n",
+    "\n",
+    "* Make each section of the notebook self-contained.  Try not to use the same variable name in more than one section.\n",
+    "\n",
+    "* Keep notebooks short.  Look for places where you can break your analysis into phases with one notebook per phase."
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "celltoolbar": "Tags",
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/_build/html/_sources/01_query.md b/_build/html/_sources/01_query.md
new file mode 100644
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--- /dev/null
+++ b/_build/html/_sources/01_query.md
@@ -0,0 +1,1073 @@
+# Chapter 1
+
+*Astronomical Data in Python* is an introduction to tools and practices for working with astronomical data.  Topics covered include:
+
+* Writing queries that select and download data from a database.
+
+* Using data stored in an Astropy `Table` or Pandas `DataFrame`.
+
+* Working with coordinates and other quantities with units.
+
+* Storing data in various formats.
+
+* Performing database join operations that combine data from multiple tables.
+
+* Visualizing data and preparing publication-quality figures.
+
+As a running example, we will replicate part of the analysis in a recent paper, "[Off the beaten path: Gaia reveals GD-1 stars outside of the main stream](https://arxiv.org/abs/1805.00425)" by Adrian M. Price-Whelan and Ana Bonaca.
+
+As the abstract explains, "Using data from the Gaia second data release combined with Pan-STARRS photometry, we present a sample of highly-probable members of the longest cold stream in the Milky Way, GD-1."
+
+GD-1 is a [stellar stream](https://en.wikipedia.org/wiki/List_of_stellar_streams), which is "an association of stars orbiting a galaxy that was once a globular cluster or dwarf galaxy that has now been torn apart and stretched out along its orbit by tidal forces."
+
+[This article in *Science* magazine](https://www.sciencemag.org/news/2018/10/streams-stars-reveal-galaxy-s-violent-history-and-perhaps-its-unseen-dark-matter) explains some of the background, including the process that led to the paper and an discussion of the scientific implications:
+
+* "The streams are particularly useful for ... galactic archaeology --- rewinding the cosmic clock to reconstruct the assembly of the Milky Way."
+
+* "They also are being used as exquisitely sensitive scales to measure the galaxy's mass."
+
+* "... the streams are well-positioned to reveal the presence of dark matter ... because the streams are so fragile, theorists say, collisions with marauding clumps of dark matter could leave telltale scars, potential clues to its nature."
+
+## Data
+
+The datasets we will work with are:
+ 
+* [Gaia](https://en.wikipedia.org/wiki/Gaia_(spacecraft)), which is "a space observatory of the European Space Agency (ESA), launched in 2013 ... designed for astrometry: measuring the positions, distances and motions of stars with unprecedented precision", and
+
+* [Pan-STARRS](https://en.wikipedia.org/wiki/Pan-STARRS), The Panoramic Survey Telescope and Rapid Response System, which is a survey designed to monitor the sky for transient objects, producing a catalog with accurate astronometry and photometry of detected sources.
+
+Both of these datasets are very large, which can make them challenging to work with.  It might not be possible, or practical, to download the entire dataset.
+One of the goals of this workshop is to provide tools for working with large datasets.
+
+## Prerequisites
+
+These notebooks are meant for people who are familiar with basic Python, but not necessarily the libraries we will use, like Astropy or Pandas.  If you are familiar with Python lists and dictionaries, and you know how to write a function that takes parameters and returns a value, you know enough Python to get started.
+
+We assume that you have some familiarity with operating systems, like the ability to use a command-line interface.  But we don't assume you have any prior experience with databases.
+
+We assume that you are familiar with astronomy at the undergraduate level, but we will not assume specialized knowledge of the datasets or analysis methods we'll use. 
+
+## Outline
+
+The first lesson demonstrates the steps for selecting and downloading data from the Gaia Database:
+
+1. First we'll make a connection to the Gaia server,
+
+2. We will explore information about the database and the tables it contains,
+
+3. We will write a query and send it to the server, and finally
+
+4. We will download the response from the server.
+
+After completing this lesson, you should be able to
+
+* Compose a basic query in ADQL.
+
+* Use queries to explore a database and its tables.
+
+* Use queries to download data.
+
+* Develop, test, and debug a query incrementally.
+
+## Query Language
+
+In order to select data from a database, you have to compose a query, which is like a program written in a "query language".
+The query language we'll use is ADQL, which stands for "Astronomical Data Query Language".
+
+ADQL is a dialect of [SQL](https://en.wikipedia.org/wiki/SQL) (Structured Query Language), which is by far the most commonly used query language.  Almost everything you will learn about ADQL also works in SQL.
+
+[The reference manual for ADQL is here](http://www.ivoa.net/documents/ADQL/20180112/PR-ADQL-2.1-20180112.html).
+But you might find it easier to learn from [this ADQL Cookbook](https://www.gaia.ac.uk/data/gaia-data-release-1/adql-cookbook).
+
+## Installing libraries
+
+The library we'll use to get Gaia data is [Astroquery](https://astroquery.readthedocs.io/en/latest/).
+
+If you are running this notebook on Colab, you can run the following cell to install Astroquery and the other libraries we'll use.
+
+If you are running this notebook on your own computer, you might have to install these libraries yourself.  
+
+If you are using this notebook as part of a Carpentries workshop, you should have received setup instructions.
+
+TODO: Add a link to the instructions.
+
+
+
+```python
+# If we're running on Colab, install libraries
+
+import sys
+IN_COLAB = 'google.colab' in sys.modules
+
+if IN_COLAB:
+    !pip install astroquery astro-gala pyia
+```
+
+## Connecting to Gaia
+
+Astroquery provides `Gaia`, which is an [object that represents a connection to the Gaia database](https://astroquery.readthedocs.io/en/latest/gaia/gaia.html).
+
+We can connect to the Gaia database like this:
+
+
+```python
+from astroquery.gaia import Gaia
+```
+
+    Created TAP+ (v1.2.1) - Connection:
+    	Host: gea.esac.esa.int
+    	Use HTTPS: True
+    	Port: 443
+    	SSL Port: 443
+    Created TAP+ (v1.2.1) - Connection:
+    	Host: geadata.esac.esa.int
+    	Use HTTPS: True
+    	Port: 443
+    	SSL Port: 443
+
+
+Running this import statement has the effect of creating a [TAP+](http://www.ivoa.net/documents/TAP/) connection; TAP stands for "Table Access Protocol".  It is a network protocol for sending queries to the database and getting back the results.  We're not sure why it seems to create two connections.
+
+## Databases and Tables
+
+What is a database, anyway?  Most generally, it can be any collection of data, but when we are talking about ADQL or SQL:
+
+* A database is a collection of one or more named tables.
+
+* Each table is a 2-D array with one or more named columns of data.
+
+We can use `Gaia.load_tables` to get the names of the tables in the Gaia database.  With the option `only_names=True`, it loads information about the tables, called the "metadata", not the data itself.
+
+
+```python
+tables = Gaia.load_tables(only_names=True)
+```
+
+    INFO: Retrieving tables... [astroquery.utils.tap.core]
+    INFO: Parsing tables... [astroquery.utils.tap.core]
+    INFO: Done. [astroquery.utils.tap.core]
+
+
+
+```python
+for table in (tables):
+    print(table.get_qualified_name())
+```
+
+    external.external.apassdr9
+    external.external.gaiadr2_geometric_distance
+    external.external.galex_ais
+    external.external.ravedr5_com
+    external.external.ravedr5_dr5
+    external.external.ravedr5_gra
+    external.external.ravedr5_on
+    external.external.sdssdr13_photoprimary
+    external.external.skymapperdr1_master
+    external.external.tmass_xsc
+    public.public.hipparcos
+    public.public.hipparcos_newreduction
+    public.public.hubble_sc
+    public.public.igsl_source
+    public.public.igsl_source_catalog_ids
+    public.public.tycho2
+    public.public.dual
+    tap_config.tap_config.coord_sys
+    tap_config.tap_config.properties
+    tap_schema.tap_schema.columns
+    tap_schema.tap_schema.key_columns
+    tap_schema.tap_schema.keys
+    tap_schema.tap_schema.schemas
+    tap_schema.tap_schema.tables
+    gaiadr1.gaiadr1.aux_qso_icrf2_match
+    gaiadr1.gaiadr1.ext_phot_zero_point
+    gaiadr1.gaiadr1.allwise_best_neighbour
+    gaiadr1.gaiadr1.allwise_neighbourhood
+    gaiadr1.gaiadr1.gsc23_best_neighbour
+    gaiadr1.gaiadr1.gsc23_neighbourhood
+    gaiadr1.gaiadr1.ppmxl_best_neighbour
+    gaiadr1.gaiadr1.ppmxl_neighbourhood
+    gaiadr1.gaiadr1.sdss_dr9_best_neighbour
+    gaiadr1.gaiadr1.sdss_dr9_neighbourhood
+    gaiadr1.gaiadr1.tmass_best_neighbour
+    gaiadr1.gaiadr1.tmass_neighbourhood
+    gaiadr1.gaiadr1.ucac4_best_neighbour
+    gaiadr1.gaiadr1.ucac4_neighbourhood
+    gaiadr1.gaiadr1.urat1_best_neighbour
+    gaiadr1.gaiadr1.urat1_neighbourhood
+    gaiadr1.gaiadr1.cepheid
+    gaiadr1.gaiadr1.phot_variable_time_series_gfov
+    gaiadr1.gaiadr1.phot_variable_time_series_gfov_statistical_parameters
+    gaiadr1.gaiadr1.rrlyrae
+    gaiadr1.gaiadr1.variable_summary
+    gaiadr1.gaiadr1.allwise_original_valid
+    gaiadr1.gaiadr1.gsc23_original_valid
+    gaiadr1.gaiadr1.ppmxl_original_valid
+    gaiadr1.gaiadr1.sdssdr9_original_valid
+    gaiadr1.gaiadr1.tmass_original_valid
+    gaiadr1.gaiadr1.ucac4_original_valid
+    gaiadr1.gaiadr1.urat1_original_valid
+    gaiadr1.gaiadr1.gaia_source
+    gaiadr1.gaiadr1.tgas_source
+    gaiadr2.gaiadr2.aux_allwise_agn_gdr2_cross_id
+    gaiadr2.gaiadr2.aux_iers_gdr2_cross_id
+    gaiadr2.gaiadr2.aux_sso_orbit_residuals
+    gaiadr2.gaiadr2.aux_sso_orbits
+    gaiadr2.gaiadr2.dr1_neighbourhood
+    gaiadr2.gaiadr2.allwise_best_neighbour
+    gaiadr2.gaiadr2.allwise_neighbourhood
+    gaiadr2.gaiadr2.apassdr9_best_neighbour
+    gaiadr2.gaiadr2.apassdr9_neighbourhood
+    gaiadr2.gaiadr2.gsc23_best_neighbour
+    gaiadr2.gaiadr2.gsc23_neighbourhood
+    gaiadr2.gaiadr2.hipparcos2_best_neighbour
+    gaiadr2.gaiadr2.hipparcos2_neighbourhood
+    gaiadr2.gaiadr2.panstarrs1_best_neighbour
+    gaiadr2.gaiadr2.panstarrs1_neighbourhood
+    gaiadr2.gaiadr2.ppmxl_best_neighbour
+    gaiadr2.gaiadr2.ppmxl_neighbourhood
+    gaiadr2.gaiadr2.ravedr5_best_neighbour
+    gaiadr2.gaiadr2.ravedr5_neighbourhood
+    gaiadr2.gaiadr2.sdssdr9_best_neighbour
+    gaiadr2.gaiadr2.sdssdr9_neighbourhood
+    gaiadr2.gaiadr2.tmass_best_neighbour
+    gaiadr2.gaiadr2.tmass_neighbourhood
+    gaiadr2.gaiadr2.tycho2_best_neighbour
+    gaiadr2.gaiadr2.tycho2_neighbourhood
+    gaiadr2.gaiadr2.urat1_best_neighbour
+    gaiadr2.gaiadr2.urat1_neighbourhood
+    gaiadr2.gaiadr2.sso_observation
+    gaiadr2.gaiadr2.sso_source
+    gaiadr2.gaiadr2.vari_cepheid
+    gaiadr2.gaiadr2.vari_classifier_class_definition
+    gaiadr2.gaiadr2.vari_classifier_definition
+    gaiadr2.gaiadr2.vari_classifier_result
+    gaiadr2.gaiadr2.vari_long_period_variable
+    gaiadr2.gaiadr2.vari_rotation_modulation
+    gaiadr2.gaiadr2.vari_rrlyrae
+    gaiadr2.gaiadr2.vari_short_timescale
+    gaiadr2.gaiadr2.vari_time_series_statistics
+    gaiadr2.gaiadr2.panstarrs1_original_valid
+    gaiadr2.gaiadr2.gaia_source
+    gaiadr2.gaiadr2.ruwe
+
+
+So that's a lot of tables.  The ones we'll use are:
+
+* `gaiadr2.gaia_source`, which contains Gaia data from [data release 2](https://www.cosmos.esa.int/web/gaia/data-release-2),
+
+* `gaiadr2.panstarrs1_original_valid`, which contains the photometry data we'll use from PanSTARRS, and
+
+* `gaiadr2.panstarrs1_best_neighbour`, which we'll use to cross-match each star observed by Gaia with the same star observed by PanSTARRS.
+
+We can use `load_table` (not `load_tables`) to get the metadata for a single table.  The name of this function is misleading, because it only downloads metadata. 
+
+
+```python
+meta = Gaia.load_table('gaiadr2.gaia_source')
+meta
+```
+
+    Retrieving table 'gaiadr2.gaia_source'
+    Parsing table 'gaiadr2.gaia_source'...
+    Done.
+
+
+
+
+
+    <astroquery.utils.tap.model.taptable.TapTableMeta at 0x7f922376e0a0>
+
+
+
+Jupyter shows that the result is an object of type `TapTableMeta`, but it does not display the contents.
+
+To see the metadata, we have to print the object.
+
+
+```python
+print(meta)
+```
+
+    TAP Table name: gaiadr2.gaiadr2.gaia_source
+    Description: This table has an entry for every Gaia observed source as listed in the
+    Main Database accumulating catalogue version from which the catalogue
+    release has been generated. It contains the basic source parameters,
+    that is only final data (no epoch data) and no spectra (neither final
+    nor epoch).
+    Num. columns: 96
+
+
+Notice one gotcha: in the list of table names, this table appears as `gaiadr2.gaiadr2.gaia_source`, but when we load the metadata, we refer to it as `gaiadr2.gaia_source`.
+
+**Exercise:** Go back and try
+
+```
+meta = Gaia.load_table('gaiadr2.gaiadr2.gaia_source')
+```
+
+What happens?  Is the error message helpful?  If you had not made this error deliberately, would you have been able to figure it out?
+
+## Columns
+
+The following loop prints the names of the columns in the table.
+
+
+```python
+for column in meta.columns:
+    print(column.name)
+```
+
+    solution_id
+    designation
+    source_id
+    random_index
+    ref_epoch
+    ra
+    ra_error
+    dec
+    dec_error
+    parallax
+    parallax_error
+    parallax_over_error
+    pmra
+    pmra_error
+    pmdec
+    pmdec_error
+    ra_dec_corr
+    ra_parallax_corr
+    ra_pmra_corr
+    ra_pmdec_corr
+    dec_parallax_corr
+    dec_pmra_corr
+    dec_pmdec_corr
+    parallax_pmra_corr
+    parallax_pmdec_corr
+    pmra_pmdec_corr
+    astrometric_n_obs_al
+    astrometric_n_obs_ac
+    astrometric_n_good_obs_al
+    astrometric_n_bad_obs_al
+    astrometric_gof_al
+    astrometric_chi2_al
+    astrometric_excess_noise
+    astrometric_excess_noise_sig
+    astrometric_params_solved
+    astrometric_primary_flag
+    astrometric_weight_al
+    astrometric_pseudo_colour
+    astrometric_pseudo_colour_error
+    mean_varpi_factor_al
+    astrometric_matched_observations
+    visibility_periods_used
+    astrometric_sigma5d_max
+    frame_rotator_object_type
+    matched_observations
+    duplicated_source
+    phot_g_n_obs
+    phot_g_mean_flux
+    phot_g_mean_flux_error
+    phot_g_mean_flux_over_error
+    phot_g_mean_mag
+    phot_bp_n_obs
+    phot_bp_mean_flux
+    phot_bp_mean_flux_error
+    phot_bp_mean_flux_over_error
+    phot_bp_mean_mag
+    phot_rp_n_obs
+    phot_rp_mean_flux
+    phot_rp_mean_flux_error
+    phot_rp_mean_flux_over_error
+    phot_rp_mean_mag
+    phot_bp_rp_excess_factor
+    phot_proc_mode
+    bp_rp
+    bp_g
+    g_rp
+    radial_velocity
+    radial_velocity_error
+    rv_nb_transits
+    rv_template_teff
+    rv_template_logg
+    rv_template_fe_h
+    phot_variable_flag
+    l
+    b
+    ecl_lon
+    ecl_lat
+    priam_flags
+    teff_val
+    teff_percentile_lower
+    teff_percentile_upper
+    a_g_val
+    a_g_percentile_lower
+    a_g_percentile_upper
+    e_bp_min_rp_val
+    e_bp_min_rp_percentile_lower
+    e_bp_min_rp_percentile_upper
+    flame_flags
+    radius_val
+    radius_percentile_lower
+    radius_percentile_upper
+    lum_val
+    lum_percentile_lower
+    lum_percentile_upper
+    datalink_url
+    epoch_photometry_url
+
+
+You can probably guess what many of these columns are by looking at the names, but you should resist the temptation to guess.
+To find out what the columns mean, [read the documentation](https://gea.esac.esa.int/archive/documentation/GDR2/Gaia_archive/chap_datamodel/sec_dm_main_tables/ssec_dm_gaia_source.html).
+
+If you want to know what can go wrong when you don't read the documentation, [you might like this article](https://www.vox.com/future-perfect/2019/6/4/18650969/married-women-miserable-fake-paul-dolan-happiness).
+
+**Exercise:** One of the other tables we'll use is `gaiadr2.gaiadr2.panstarrs1_original_valid`.  Use `load_table` to get the metadata for this table.  How many columns are there and what are their names?
+
+Hint: Remember the gotcha we mentioned earlier.
+
+
+```python
+# Solution
+
+meta2 = Gaia.load_table('gaiadr2.panstarrs1_original_valid')
+print(meta2)
+```
+
+    Retrieving table 'gaiadr2.panstarrs1_original_valid'
+    Parsing table 'gaiadr2.panstarrs1_original_valid'...
+    Done.
+    TAP Table name: gaiadr2.gaiadr2.panstarrs1_original_valid
+    Description: The Panoramic Survey Telescope and Rapid Response System (Pan-STARRS) is
+    a system for wide-field astronomical imaging developed and operated by
+    the Institute for Astronomy at the University of Hawaii. Pan-STARRS1
+    (PS1) is the first part of Pan-STARRS to be completed and is the basis
+    for Data Release 1 (DR1). The PS1 survey used a 1.8 meter telescope and
+    its 1.4 Gigapixel camera to image the sky in five broadband filters (g,
+    r, i, z, y).
+    
+    The current table contains a filtered subsample of the 10 723 304 629
+    entries listed in the original ObjectThin table.
+    We used only ObjectThin and MeanObject tables to extract
+    panstarrs1OriginalValid table, this means that objects detected only in
+    stack images are not included here. The main reason for us to avoid the
+    use of objects detected in stack images is that their astrometry is not
+    as good as the mean objects astrometry: “The stack positions (raStack,
+    decStack) have considerably larger systematic astrometric errors than
+    the mean epoch positions (raMean, decMean).� The astrometry for the
+    MeanObject positions uses Gaia DR1 as a reference catalog, while the
+    stack positions use 2MASS as a reference catalog.
+    
+    In details, we filtered out all objects where:
+    
+    -   nDetections = 1
+    
+    -   no good quality data in Pan-STARRS, objInfoFlag 33554432 not set
+    
+    -   mean astrometry could not be measured, objInfoFlag 524288 set
+    
+    -   stack position used for mean astrometry, objInfoFlag 1048576 set
+    
+    -   error on all magnitudes equal to 0 or to -999;
+    
+    -   all magnitudes set to -999;
+    
+    -   error on RA or DEC greater than 1 arcsec.
+    
+    The number of objects in panstarrs1OriginalValid is 2 264 263 282.
+    
+    The panstarrs1OriginalValid table contains only a subset of the columns
+    available in the combined ObjectThin and MeanObject tables. A
+    description of the original ObjectThin and MeanObjects tables can be
+    found at:
+    https://outerspace.stsci.edu/display/PANSTARRS/PS1+Database+object+and+detection+tables
+    
+    Download:
+    http://mastweb.stsci.edu/ps1casjobs/home.aspx
+    Documentation:
+    https://outerspace.stsci.edu/display/PANSTARRS
+    http://pswww.ifa.hawaii.edu/pswww/
+    References:
+    The Pan-STARRS1 Surveys, Chambers, K.C., et al. 2016, arXiv:1612.05560
+    Pan-STARRS Data Processing System, Magnier, E. A., et al. 2016,
+    arXiv:1612.05240
+    Pan-STARRS Pixel Processing: Detrending, Warping, Stacking, Waters, C.
+    Z., et al. 2016, arXiv:1612.05245
+    Pan-STARRS Pixel Analysis: Source Detection and Characterization,
+    Magnier, E. A., et al. 2016, arXiv:1612.05244
+    Pan-STARRS Photometric and Astrometric Calibration, Magnier, E. A., et
+    al. 2016, arXiv:1612.05242
+    The Pan-STARRS1 Database and Data Products, Flewelling, H. A., et al.
+    2016, arXiv:1612.05243
+    
+    Catalogue curator:
+    SSDC - ASI Space Science Data Center
+    https://www.ssdc.asi.it/
+    Num. columns: 26
+
+
+
+```python
+# Solution
+
+for column in meta2.columns:
+    print(column.name)
+```
+
+    obj_name
+    obj_id
+    ra
+    dec
+    ra_error
+    dec_error
+    epoch_mean
+    g_mean_psf_mag
+    g_mean_psf_mag_error
+    g_flags
+    r_mean_psf_mag
+    r_mean_psf_mag_error
+    r_flags
+    i_mean_psf_mag
+    i_mean_psf_mag_error
+    i_flags
+    z_mean_psf_mag
+    z_mean_psf_mag_error
+    z_flags
+    y_mean_psf_mag
+    y_mean_psf_mag_error
+    y_flags
+    n_detections
+    zone_id
+    obj_info_flag
+    quality_flag
+
+
+## Writing queries
+
+By now you might be wondering how we actually download the data.  With tables this big, you generally don't.  Instead, you use queries to select only the data you want.
+
+A query is a string written in a query language like SQL; for the Gaia database, the query language is a dialect of SQL called ADQL.
+
+Here's an example of an ADQL query.
+
+
+```python
+query1 = """SELECT 
+TOP 10
+source_id, ref_epoch, ra, dec, parallax 
+FROM gaiadr2.gaia_source"""
+```
+
+**Python note:** We use a [triple-quoted string](https://docs.python.org/3/tutorial/introduction.html#strings) here so we can include line breaks in the query, which makes it easier to read.
+
+The words in uppercase are ADQL keywords:
+
+* `SELECT` indicates that we are selecting data (as opposed to adding or modifying data).
+
+* `TOP` indicates that we only want the first 10 rows of the table, which is useful for testing a query before asking for all of the data.
+
+* `FROM` specifies which table we want data from.
+
+The third line is a list of column names, indicating which columns we want.  
+
+In this example, the keywords are capitalized and the column names are lowercase.  This is a common style, but it is not required.  ADQL and SQL are not case-sensitive.
+
+To run this query, we use the `Gaia` object, which represents our connection to the Gaia database, and invoke `launch_job`:
+
+
+```python
+job1 = Gaia.launch_job(query1)
+job1
+```
+
+
+
+
+    <astroquery.utils.tap.model.job.Job at 0x7f9222e9cb20>
+
+
+
+The result is an object that represents the job running on a Gaia server.
+
+If you print it, it displays metadata for the forthcoming table.
+
+
+```python
+print(job1)
+```
+
+    <Table length=10>
+       name    dtype  unit                            description                            
+    --------- ------- ---- ------------------------------------------------------------------
+    source_id   int64      Unique source identifier (unique within a particular Data Release)
+    ref_epoch float64   yr                                                    Reference epoch
+           ra float64  deg                                                    Right ascension
+          dec float64  deg                                                        Declination
+     parallax float64  mas                                                           Parallax
+    Jobid: None
+    Phase: COMPLETED
+    Owner: None
+    Output file: sync_20201005090721.xml.gz
+    Results: None
+
+
+Don't worry about `Results: None`.  That does not actually mean there are no results.
+
+However, `Phase: COMPLETED` indicates that the job is complete, so we can get the results like this:
+
+
+```python
+results1 = job1.get_results()
+type(results1)
+```
+
+
+
+
+    astropy.table.table.Table
+
+
+
+**Optional detail:**  Why is `table` repeated three times?  The first is the name of the module, the second is the name of the submodule, and the third is the name of the class.  Most of the time we only care about the last one.  It's like the Linnean name for gorilla, which is *Gorilla Gorilla Gorilla*.
+
+The result is an [Astropy Table](https://docs.astropy.org/en/stable/table/), which is similar to a table in an SQL database except:
+
+* SQL databases are stored on disk drives, so they are persistent; that is, they "survive" even if you turn off the computer.  An Astropy `Table` is stored in memory; it disappears when you turn off the computer (or shut down this Jupyter notebook).
+
+* SQL databases are designed to process queries.  An Astropy `Table` can perform some query-like operations, like selecting columns and rows.  But these operations use Python syntax, not SQL.
+
+Jupyter knows how to display the contents of a `Table`.
+
+
+```python
+results1
+```
+
+
+
+
+<i>Table length=10</i>
+<table id="table140265627585264" class="table-striped table-bordered table-condensed">
+<thead><tr><th>source_id</th><th>ref_epoch</th><th>ra</th><th>dec</th><th>parallax</th></tr></thead>
+<thead><tr><th></th><th>yr</th><th>deg</th><th>deg</th><th>mas</th></tr></thead>
+<thead><tr><th>int64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th></tr></thead>
+<tr><td>4530738361793769600</td><td>2015.5</td><td>281.56725362448725</td><td>20.40682117430378</td><td>0.9785380604519425</td></tr>
+<tr><td>4530752651135081216</td><td>2015.5</td><td>281.0861565355257</td><td>20.523350496351846</td><td>0.2674800612552977</td></tr>
+<tr><td>4530743343951405568</td><td>2015.5</td><td>281.37114418299177</td><td>20.474147574053124</td><td>-0.43911323550176806</td></tr>
+<tr><td>4530755060627162368</td><td>2015.5</td><td>281.2676236268299</td><td>20.558523922346158</td><td>1.1422630184554958</td></tr>
+<tr><td>4530746844341315968</td><td>2015.5</td><td>281.1370431749541</td><td>20.377852388898184</td><td>1.0092247424630945</td></tr>
+<tr><td>4530768456615026432</td><td>2015.5</td><td>281.8720921436347</td><td>20.31829694530366</td><td>-0.06900136127674149</td></tr>
+<tr><td>4530763513119137280</td><td>2015.5</td><td>281.9211808864116</td><td>20.20956829578524</td><td>0.1266016679823622</td></tr>
+<tr><td>4530736364618539264</td><td>2015.5</td><td>281.4913475613274</td><td>20.346579041327693</td><td>0.3894019486060072</td></tr>
+<tr><td>4530735952305177728</td><td>2015.5</td><td>281.4085549165704</td><td>20.311030903719928</td><td>0.2041189982608354</td></tr>
+<tr><td>4530751281056022656</td><td>2015.5</td><td>281.0585328377638</td><td>20.460309556214753</td><td>0.10294642821734962</td></tr>
+</table>
+
+
+
+Each column has a name, units, and a data type.
+
+For example, the units of `ra` and `dec` are degrees, and their data type is `float64`, which is a 64-bit floating-point number, used to store measurements with a fraction part.
+
+This information comes from the Gaia database, and has been stored in the Astropy `Table` by Astroquery.
+
+**Exercise:** Read [the documentation of this table](https://gea.esac.esa.int/archive/documentation/GDR2/Gaia_archive/chap_datamodel/sec_dm_main_tables/ssec_dm_gaia_source.html) and choose a column that looks interesting to you.  Add the column name to the query and run it again.  What are the units of the column you selected?  What is its data type?
+
+## Asynchronous queries
+
+`launch_job` asks the server to run the job "synchronously", which normally means it runs immediately.  But synchronous jobs are limited to 2000 rows.  For queries that return more rows, you should run "asynchronously", which mean they might take longer to get started.
+
+If you are not sure how many rows a query will return, you can use the SQL command `COUNT` to find out how many rows are in the result without actually returning them.  We'll see an example of this later.
+
+The results of an asynchronous query are stored in a file on the server, so you can start a query and come back later to get the results.
+
+For anonymous users, files are kept for three days.
+
+As an example, let's try a query that's similar to `query1`, with two changes:
+
+* It selects the first 3000 rows, so it is bigger than we should run synchronously.
+
+* It uses a new keyword, `WHERE`.
+
+
+```python
+query2 = """SELECT TOP 3000
+source_id, ref_epoch, ra, dec, parallax
+FROM gaiadr2.gaia_source
+WHERE parallax < 1
+"""
+```
+
+A `WHERE` clause indicates which rows we want; in this case, the query selects only rows "where" `parallax` is less than 1.  This has the effect of selecting stars with relatively low parallax, which are farther away.  We'll use this clause to exclude nearby stars that are unlikely to be part of GD-1.
+
+`WHERE` is one of the most common clauses in ADQL/SQL, and one of the most useful, because it allows us to select only the rows we need from the database.
+
+We use `launch_job_async` to submit an asynchronous query.
+
+
+```python
+job2 = Gaia.launch_job_async(query2)
+print(job2)
+```
+
+    INFO: Query finished. [astroquery.utils.tap.core]
+    <Table length=3000>
+       name    dtype  unit                            description                            
+    --------- ------- ---- ------------------------------------------------------------------
+    source_id   int64      Unique source identifier (unique within a particular Data Release)
+    ref_epoch float64   yr                                                    Reference epoch
+           ra float64  deg                                                    Right ascension
+          dec float64  deg                                                        Declination
+     parallax float64  mas                                                           Parallax
+    Jobid: 1601903242219O
+    Phase: COMPLETED
+    Owner: None
+    Output file: async_20201005090722.vot
+    Results: None
+
+
+And here are the results.
+
+
+```python
+results2 = job2.get_results()
+results2
+```
+
+
+
+
+<i>Table length=3000</i>
+<table id="table140265625141056" class="table-striped table-bordered table-condensed">
+<thead><tr><th>source_id</th><th>ref_epoch</th><th>ra</th><th>dec</th><th>parallax</th></tr></thead>
+<thead><tr><th></th><th>yr</th><th>deg</th><th>deg</th><th>mas</th></tr></thead>
+<thead><tr><th>int64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th></tr></thead>
+<tr><td>4530738361793769600</td><td>2015.5</td><td>281.56725362448725</td><td>20.40682117430378</td><td>0.9785380604519425</td></tr>
+<tr><td>4530752651135081216</td><td>2015.5</td><td>281.0861565355257</td><td>20.523350496351846</td><td>0.2674800612552977</td></tr>
+<tr><td>4530743343951405568</td><td>2015.5</td><td>281.37114418299177</td><td>20.474147574053124</td><td>-0.43911323550176806</td></tr>
+<tr><td>4530768456615026432</td><td>2015.5</td><td>281.8720921436347</td><td>20.31829694530366</td><td>-0.06900136127674149</td></tr>
+<tr><td>4530763513119137280</td><td>2015.5</td><td>281.9211808864116</td><td>20.20956829578524</td><td>0.1266016679823622</td></tr>
+<tr><td>4530736364618539264</td><td>2015.5</td><td>281.4913475613274</td><td>20.346579041327693</td><td>0.3894019486060072</td></tr>
+<tr><td>4530735952305177728</td><td>2015.5</td><td>281.4085549165704</td><td>20.311030903719928</td><td>0.2041189982608354</td></tr>
+<tr><td>4530751281056022656</td><td>2015.5</td><td>281.0585328377638</td><td>20.460309556214753</td><td>0.10294642821734962</td></tr>
+<tr><td>4530740938774409344</td><td>2015.5</td><td>281.3762569536416</td><td>20.436140058941206</td><td>0.9242670062090182</td></tr>
+<tr><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr>
+<tr><td>4467710915011802624</td><td>2015.5</td><td>269.9680969307347</td><td>1.1429085038160882</td><td>0.42361471245557913</td></tr>
+<tr><td>4467706551328679552</td><td>2015.5</td><td>270.033164589881</td><td>1.0565747323689927</td><td>0.922888231734588</td></tr>
+<tr><td>4467712255037300096</td><td>2015.5</td><td>270.7724717923047</td><td>0.6581664892880896</td><td>-2.669179465293931</td></tr>
+<tr><td>4467735001181761792</td><td>2015.5</td><td>270.3628606248308</td><td>0.8947079323599124</td><td>0.6117399163086398</td></tr>
+<tr><td>4467737101421916672</td><td>2015.5</td><td>270.5110834661444</td><td>0.9806225910160181</td><td>-0.39818224846127004</td></tr>
+<tr><td>4467707547757327488</td><td>2015.5</td><td>269.88746280594927</td><td>1.0212759940136962</td><td>0.7741412301054209</td></tr>
+<tr><td>4467732772094573056</td><td>2015.5</td><td>270.55997182760126</td><td>0.9037072088489417</td><td>-1.7920417800164183</td></tr>
+<tr><td>4467732355491087744</td><td>2015.5</td><td>270.6730790702491</td><td>0.9197224705139885</td><td>-0.3464446494840354</td></tr>
+<tr><td>4467717099766944512</td><td>2015.5</td><td>270.57667173120825</td><td>0.726277659009568</td><td>0.05443955111134051</td></tr>
+<tr><td>4467719058265781248</td><td>2015.5</td><td>270.7248052971514</td><td>0.8205551921782785</td><td>0.3733943917490343</td></tr>
+</table>
+
+
+
+You might notice that some values of `parallax` are negative.  As [this FAQ explains](https://www.cosmos.esa.int/web/gaia/archive-tips#negative%20parallax), "Negative parallaxes are caused by errors in the observations."  Negative parallaxes have "no physical meaning," but they can be a "useful diagnostic on the quality of the astrometric solution."
+
+Later we will see an example where we use `parallax` and `parallax_error` to identify stars where the distance estimate is likely to be inaccurate.
+
+**Exercise:** The clauses in a query have to be in the right order.  Go back and change the order of the clauses in `query2` and run it again.  
+
+The query should fail, but notice that you don't get much useful debugging information.  
+
+For this reason, developing and debugging ADQL queries can be really hard.  A few suggestions that might help:
+
+* Whenever possible, start with a working query, either an example you find online or a query you have used in the past.
+
+* Make small changes and test each change before you continue.
+
+* While you are debugging, use `TOP` to limit the number of rows in the result.  That will make each attempt run faster, which reduces your testing time.  
+
+* Launching test queries synchronously might make them start faster, too.
+
+## Operators
+
+In a `WHERE` clause, you can use any of the [SQL comparison operators](https://www.w3schools.com/sql/sql_operators.asp); here are the most common ones:
+
+| Symbol | Operation
+|--------| :---
+| `>` | greater than
+| `<` | less than
+| `>=` | greater than or equal
+| `<=` | less than or equal
+| `=` | equal
+| `!=` or `<>` | not equal
+
+Most of these are the same as Python, but some are not.  In particular, notice that the equality operator is `=`, not `==`.
+Be careful to keep your Python out of your ADQL!
+
+You can combine comparisons using the logical operators:
+
+* AND: true if both comparisons are true
+* OR: true if either or both comparisons are true
+
+Finally, you can use `NOT` to invert the result of a comparison. 
+
+**Exercise:** [Read about SQL operators here](https://www.w3schools.com/sql/sql_operators.asp) and then modify the previous query to select rows where `bp_rp` is between `-0.75` and `2`.
+
+You can [read about this variable here](https://gea.esac.esa.int/archive/documentation/GDR2/Gaia_archive/chap_datamodel/sec_dm_main_tables/ssec_dm_gaia_source.html).
+
+
+```python
+# Solution
+
+# This is what most people will probably do
+
+query = """SELECT TOP 10
+source_id, ref_epoch, ra, dec, parallax
+FROM gaiadr2.gaia_source
+WHERE parallax < 1 
+  AND bp_rp > -0.75 AND bp_rp < 2
+"""
+```
+
+
+```python
+# Solution
+
+# But if someone notices the BETWEEN operator, 
+# they might do this
+
+query = """SELECT TOP 10
+source_id, ref_epoch, ra, dec, parallax
+FROM gaiadr2.gaia_source
+WHERE parallax < 1 
+  AND bp_rp BETWEEN -0.75 AND 2
+"""
+```
+
+This [Hertzsprung-Russell diagram](https://sci.esa.int/web/gaia/-/60198-gaia-hertzsprung-russell-diagram) shows the BP-RP color and luminosity of stars in the Gaia catalog.
+
+Selecting stars with `bp-rp` less than 2 excludes many [class M dwarf stars](https://xkcd.com/2360/), which are low temperature, low luminosity.  A star like that at GD-1's distance would be hard to detect, so if it is detected, it it more likely to be in the foreground.
+
+## Cleaning up
+
+Asynchronous jobs have a `jobid`.
+
+
+```python
+job1.jobid, job2.jobid
+```
+
+
+
+
+    (None, '1601903242219O')
+
+
+
+Which you can use to remove the job from the server.
+
+
+```python
+Gaia.remove_jobs([job2.jobid])
+```
+
+    Removed jobs: '['1601903242219O']'.
+
+
+If you don't remove it job from the server, it will be removed eventually, so don't feel too bad if you don't clean up after yourself.
+
+## Formatting queries
+
+So far the queries have been string "literals", meaning that the entire string is part of the program.
+But writing queries yourself can be slow, repetitive, and error-prone.
+
+It is often a good idea to write Python code that assembles a query for you.  One useful tool for that is the [string `format` method](https://www.w3schools.com/python/ref_string_format.asp).
+
+As an example, we'll divide the previous query into two parts; a list of column names and a "base" for the query that contains everything except the column names.
+
+Here's the list of columns we'll select.  
+
+
+```python
+columns = 'source_id, ra, dec, pmra, pmdec, parallax, parallax_error, radial_velocity'
+```
+
+And here's the base; it's a string that contains at least one format specifier in curly brackets (braces).
+
+
+```python
+query3_base = """SELECT TOP 10 
+{columns}
+FROM gaiadr2.gaia_source
+WHERE parallax < 1
+  AND bp_rp BETWEEN -0.75 AND 2
+"""
+```
+
+This base query contains one format specifier, `{columns}`, which is a placeholder for the list of column names we will provide.
+
+To assemble the query, we invoke `format` on the base string and provide a keyword argument that assigns a value to `columns`.
+
+
+```python
+query3 = query3_base.format(columns=columns)
+```
+
+The result is a string with line breaks.  If you display it, the line breaks appear as `\n`.
+
+
+```python
+query3
+```
+
+
+
+
+    'SELECT TOP 10 \nsource_id, ra, dec, pmra, pmdec, parallax, parallax_error, radial_velocity\nFROM gaiadr2.gaia_source\nWHERE parallax < 1\n  AND bp_rp BETWEEN -0.75 AND 2\n'
+
+
+
+But if you print it, the line breaks appear as... line breaks.
+
+
+```python
+print(query3)
+```
+
+    SELECT TOP 10 
+    source_id, ra, dec, pmra, pmdec, parallax, parallax_error, radial_velocity
+    FROM gaiadr2.gaia_source
+    WHERE parallax < 1
+      AND bp_rp BETWEEN -0.75 AND 2
+    
+
+
+Notice that the format specifier has been replaced with the value of `columns`.
+
+Let's run it and see if it works:
+
+
+```python
+job3 = Gaia.launch_job(query3)
+print(job3)
+```
+
+    <Table length=10>
+          name       dtype    unit                              description                             n_bad
+    --------------- ------- -------- ------------------------------------------------------------------ -----
+          source_id   int64          Unique source identifier (unique within a particular Data Release)     0
+                 ra float64      deg                                                    Right ascension     0
+                dec float64      deg                                                        Declination     0
+               pmra float64 mas / yr                         Proper motion in right ascension direction     0
+              pmdec float64 mas / yr                             Proper motion in declination direction     0
+           parallax float64      mas                                                           Parallax     0
+     parallax_error float64      mas                                         Standard error of parallax     0
+    radial_velocity float64   km / s                                                    Radial velocity    10
+    Jobid: None
+    Phase: COMPLETED
+    Owner: None
+    Output file: sync_20201005090726.xml.gz
+    Results: None
+
+
+
+```python
+results3 = job3.get_results()
+results3
+```
+
+
+
+
+<i>Table length=10</i>
+<table id="table140265627700432" class="table-striped table-bordered table-condensed">
+<thead><tr><th>source_id</th><th>ra</th><th>dec</th><th>pmra</th><th>pmdec</th><th>parallax</th><th>parallax_error</th><th>radial_velocity</th></tr></thead>
+<thead><tr><th></th><th>deg</th><th>deg</th><th>mas / yr</th><th>mas / yr</th><th>mas</th><th>mas</th><th>km / s</th></tr></thead>
+<thead><tr><th>int64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th></tr></thead>
+<tr><td>4467710915011802624</td><td>269.9680969307347</td><td>1.1429085038160882</td><td>2.0233280236600626</td><td>-2.5692427875510266</td><td>0.42361471245557913</td><td>0.470352406647465</td><td>--</td></tr>
+<tr><td>4467706551328679552</td><td>270.033164589881</td><td>1.0565747323689927</td><td>-3.414829591355289</td><td>-3.8437215857495737</td><td>0.922888231734588</td><td>0.927008559859825</td><td>--</td></tr>
+<tr><td>4467712255037300096</td><td>270.7724717923047</td><td>0.6581664892880896</td><td>-3.5620173752896025</td><td>-6.595792323153987</td><td>-2.669179465293931</td><td>0.9719742773203504</td><td>--</td></tr>
+<tr><td>4467735001181761792</td><td>270.3628606248308</td><td>0.8947079323599124</td><td>2.1307079926489205</td><td>0.8826727710910712</td><td>0.6117399163086398</td><td>0.509812721702093</td><td>--</td></tr>
+<tr><td>4467737101421916672</td><td>270.5110834661444</td><td>0.9806225910160181</td><td>0.17532366511560785</td><td>-5.113270239706202</td><td>-0.39818224846127004</td><td>0.7549581886719651</td><td>--</td></tr>
+<tr><td>4467707547757327488</td><td>269.88746280594927</td><td>1.0212759940136962</td><td>-2.6382230817672987</td><td>-3.707776532049287</td><td>0.7741412301054209</td><td>0.3022057897812064</td><td>--</td></tr>
+<tr><td>4467732355491087744</td><td>270.6730790702491</td><td>0.9197224705139885</td><td>-2.2735991502653037</td><td>-11.864952855984358</td><td>-0.3464446494840354</td><td>0.4937921513912002</td><td>--</td></tr>
+<tr><td>4467717099766944512</td><td>270.57667173120825</td><td>0.726277659009568</td><td>-3.4598362614808367</td><td>-4.601426893365921</td><td>0.05443955111134051</td><td>0.8867339293525688</td><td>--</td></tr>
+<tr><td>4467719058265781248</td><td>270.7248052971514</td><td>0.8205551921782785</td><td>-3.255079498426542</td><td>-9.249285069111085</td><td>0.3733943917490343</td><td>0.390952370410666</td><td>--</td></tr>
+<tr><td>4467722326741572352</td><td>270.87431291888504</td><td>0.8595565975869158</td><td>0.10696398351859826</td><td>1.2035993780158853</td><td>-0.11850943432864373</td><td>0.1660452431882023</td><td>--</td></tr>
+</table>
+
+
+
+Good so far.
+
+**Exercise:** This query always selects sources with `parallax` less than 1.  But suppose you want to take that upper bound as an input.
+
+Modify `query3_base` to replace `1` with a format specifier like `{max_parallax}`.  Now, when you call `format`, add a keyword argument that assigns a value to `max_parallax`, and confirm that the format specifier gets replaced with the value you provide.
+
+
+```python
+# Solution
+
+query4_base = """SELECT TOP 10
+{columns}
+FROM gaiadr2.gaia_source
+WHERE parallax < {max_parallax} AND 
+bp_rp BETWEEN -0.75 AND 2
+"""
+```
+
+
+```python
+# Solution
+
+query4 = query4_base.format(columns=columns,
+                          max_parallax=0.5)
+print(query)
+```
+
+    SELECT TOP 10
+    source_id, ra, dec, pmra, pmdec, parallax, parallax_error, radial_velocity
+    FROM gaiadr2.gaia_source
+    WHERE parallax < 0.5 AND 
+    bp_rp BETWEEN -0.75 AND 2
+    
+
+
+**Style note:**  You might notice that the variable names in this notebook are numbered, like `query1`, `query2`, etc.  
+
+The advantage of this style is that it isolates each section of the notebook from the others, so if you go back and run the cells out of order, it's less likely that you will get unexpected interactions.
+
+A drawback of this style is that it can be a nuisance to update the notebook if you add, remove, or reorder a section.
+
+What do you think of this choice?  Are there alternatives you prefer?
+
+## Summary
+
+This notebook demonstrates the following steps:
+
+1. Making a connection to the Gaia server,
+
+2. Exploring information about the database and the tables it contains,
+
+3. Writing a query and sending it to the server, and finally
+
+4. Downloading the response from the server as an Astropy `Table`.
+
+## Best practices
+
+* If you can't download an entire dataset (or it's not practical) use queries to select the data you need.
+
+* Read the metadata and the documentation to make sure you understand the tables, their columns, and what they mean.
+
+* Develop queries incrementally: start with something simple, test it, and add a little bit at a time.
+
+* Use ADQL features like `TOP` and `COUNT` to test before you run a query that might return a lot of data.
+
+* If you know your query will return fewer than 3000 rows, you can run it synchronously, which might complete faster (but it doesn't seem to make much difference).  If it might return more than 3000 rows, you should run it asynchronously.
+
+* ADQL and SQL are not case-sensitive, so you don't have to capitalize the keywords, but you should.
+
+* ADQL and SQL don't require you to break a query into multiple lines, but you should.
+
+
+Jupyter notebooks can be good for developing and testing code, but they have some drawbacks.  In particular, if you run the cells out of order, you might find that variables don't have the values you expect.
+
+There are a few things you can do to mitigate these problems:
+
+* Make each section of the notebook self-contained.  Try not to use the same variable name in more than one section.
+
+* Keep notebooks short.  Look for places where you can break your analysis into phases with one notebook per phase.
diff --git a/_build/html/_sources/02_coords.ipynb b/_build/html/_sources/02_coords.ipynb
new file mode 100644
index 0000000..4e4966e
--- /dev/null
+++ b/_build/html/_sources/02_coords.ipynb
@@ -0,0 +1,1972 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Chapter 2\n",
+    "\n",
+    "This is the second in a series of notebooks related to astronomy data.\n",
+    "\n",
+    "As a running example, we are replicating parts of the analysis in a recent paper, \"[Off the beaten path: Gaia reveals GD-1 stars outside of the main stream](https://arxiv.org/abs/1805.00425)\" by Adrian M. Price-Whelan and Ana Bonaca.\n",
+    "\n",
+    "In the first notebook, we wrote ADQL queries and used them to select and download data from the Gaia server.\n",
+    "\n",
+    "In this notebook, we'll pick up where we left off and write a query to select stars from the region of the sky where we expect GD-1 to be."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Outline\n",
+    "\n",
+    "We'll start with an example that does a \"cone search\"; that is, it selects stars that appear in a circular region of the sky.\n",
+    "\n",
+    "Then, to select stars in the vicinity of GD-1, we'll:\n",
+    "\n",
+    "* Use `Quantity` objects to represent measurements with units.\n",
+    "\n",
+    "* Use the `Gala` library to convert coordinates from one frame to another.\n",
+    "\n",
+    "* Use the ADQL keywords `POLYGON`, `CONTAINS`, and `POINT` to select stars that fall within a polygonal region.\n",
+    "\n",
+    "* Submit a query and download the results.\n",
+    "\n",
+    "* Store the results in a FITS file.\n",
+    "\n",
+    "After completing this lesson, you should be able to\n",
+    "\n",
+    "* Use Python string formatting to compose more complex ADQL queries.\n",
+    "\n",
+    "* Work with coordinates and other quantities that have units.\n",
+    "\n",
+    "* Download the results of a query and store them in a file."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Installing libraries\n",
+    "\n",
+    "If you are running this notebook on Colab, you can run the following cell to install Astroquery and a the other libraries we'll use.\n",
+    "\n",
+    "If you are running this notebook on your own computer, you might have to install these libraries yourself.  \n",
+    "\n",
+    "If you are using this notebook as part of a Carpentries workshop, you should have received setup instructions.\n",
+    "\n",
+    "TODO: Add a link to the instructions.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# If we're running on Colab, install libraries\n",
+    "\n",
+    "import sys\n",
+    "IN_COLAB = 'google.colab' in sys.modules\n",
+    "\n",
+    "if IN_COLAB:\n",
+    "    !pip install astroquery astro-gala pyia"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Selecting a region"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "One of the most common ways to restrict a query is to select stars in a particular region of the sky.\n",
+    "\n",
+    "For example, here's a query from the [Gaia archive documentation](https://gea.esac.esa.int/archive-help/adql/examples/index.html) that selects \"all the objects ... in a circular region centered at (266.41683, -29.00781) with a search radius of 5 arcmin (0.08333 deg).\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query = \"\"\"\n",
+    "SELECT \n",
+    "TOP 10 source_id\n",
+    "FROM gaiadr2.gaia_source\n",
+    "WHERE 1=CONTAINS(\n",
+    "  POINT(ra, dec),\n",
+    "  CIRCLE(266.41683, -29.00781, 0.08333333))\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This query uses three keywords that are specific to ADQL (not SQL):\n",
+    "\n",
+    "* `POINT`: a location in [ICRS coordinates](https://en.wikipedia.org/wiki/International_Celestial_Reference_System), specified in degrees of right ascension and declination.\n",
+    "\n",
+    "* `CIRCLE`: a circle where the first two values are the coordinates of the center and the third is the radius in degrees.\n",
+    "\n",
+    "* `CONTAINS`: a function that returns `1` if a `POINT` is contained in a shape and `0` otherwise.\n",
+    "\n",
+    "Here is the [documentation of `CONTAINS`](http://www.ivoa.net/documents/ADQL/20180112/PR-ADQL-2.1-20180112.html#tth_sEc4.2.12).\n",
+    "\n",
+    "A query like this is called a cone search because it selects stars in a cone.\n",
+    "\n",
+    "Here's how we run it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Created TAP+ (v1.2.1) - Connection:\n",
+      "\tHost: gea.esac.esa.int\n",
+      "\tUse HTTPS: True\n",
+      "\tPort: 443\n",
+      "\tSSL Port: 443\n",
+      "Created TAP+ (v1.2.1) - Connection:\n",
+      "\tHost: geadata.esac.esa.int\n",
+      "\tUse HTTPS: True\n",
+      "\tPort: 443\n",
+      "\tSSL Port: 443\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<i>Table length=10</i>\n",
+       "<table id=\"table139949838258672\" class=\"table-striped table-bordered table-condensed\">\n",
+       "<thead><tr><th>source_id</th></tr></thead>\n",
+       "<thead><tr><th>int64</th></tr></thead>\n",
+       "<tr><td>4057468321929794432</td></tr>\n",
+       "<tr><td>4057468287575835392</td></tr>\n",
+       "<tr><td>4057482027171038976</td></tr>\n",
+       "<tr><td>4057470349160630656</td></tr>\n",
+       "<tr><td>4057470039924301696</td></tr>\n",
+       "<tr><td>4057469868125641984</td></tr>\n",
+       "<tr><td>4057468351995073024</td></tr>\n",
+       "<tr><td>4057469661959554560</td></tr>\n",
+       "<tr><td>4057470520960672640</td></tr>\n",
+       "<tr><td>4057470555320409600</td></tr>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<Table length=10>\n",
+       "     source_id     \n",
+       "       int64       \n",
+       "-------------------\n",
+       "4057468321929794432\n",
+       "4057468287575835392\n",
+       "4057482027171038976\n",
+       "4057470349160630656\n",
+       "4057470039924301696\n",
+       "4057469868125641984\n",
+       "4057468351995073024\n",
+       "4057469661959554560\n",
+       "4057470520960672640\n",
+       "4057470555320409600"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from astroquery.gaia import Gaia\n",
+    "\n",
+    "job = Gaia.launch_job(query)\n",
+    "result = job.get_results()\n",
+    "result"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** When you are debugging queries like this, you can use `TOP` to limit the size of the results, but then you still don't know how big the results will be.\n",
+    "\n",
+    "An alternative is to use `COUNT`, which asks for the number of rows that would be selected, but it does not return them.\n",
+    "\n",
+    "In the previous query, replace `TOP 10 source_id` with `COUNT(source_id)` and run the query again.  How many stars has Gaia identified in the cone we searched?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Getting GD-1 Data\n",
+    "\n",
+    "From the Price-Whelan and Bonaca paper, we will try to reproduce Figure 1, which includes this representation of stars likely to belong to GD-1:\n",
+    "\n",
+    "<img src=\"https://github.com/datacarpentry/astronomy-python/raw/gh-pages/fig/gd1-4.png\">"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Along the axis of right ascension ($\\phi_1$) the figure extends from -100 to 20 degrees.\n",
+    "\n",
+    "Along the axis of declination ($\\phi_2$) the figure extends from about -8 to 4 degrees.\n",
+    "\n",
+    "Ideally, we would select all stars from this rectangle, but there are more than 10 million of them, so\n",
+    "\n",
+    "* That would be difficult to work with,\n",
+    "\n",
+    "* As anonymous users, we are limited to 3 million rows in a single query, and\n",
+    "\n",
+    "* While we are developing and testing code, it will be faster to work with a smaller dataset.\n",
+    "\n",
+    "So we'll start by selecting stars in a smaller rectangle, from -55 to -45 degrees right ascension and -8 to 4 degrees of declination.\n",
+    "\n",
+    "But first we let's see how to represent quantities with units like degrees."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Working with coordinates\n",
+    "\n",
+    "Coordinates are physical quantities, which means that they have two parts, a value and a unit.\n",
+    "\n",
+    "For example, the coordinate $30^{\\circ}$ has value 30 and its units are degrees.\n",
+    "\n",
+    "Until recently, most scientific computation was done with values only; units were left out of the program altogether, [often with disastrous results](https://en.wikipedia.org/wiki/Mars_Climate_Orbiter#Cause_of_failure).\n",
+    "\n",
+    "Astropy provides tools for including units explicitly in computations, which makes it possible to detect errors before they cause disasters.\n",
+    "\n",
+    "To use Astropy units, we import them like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<module 'astropy.units' from '/home/downey/anaconda3/envs/AstronomicalData/lib/python3.8/site-packages/astropy/units/__init__.py'>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import astropy.units as u\n",
+    "\n",
+    "u"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`u` is an object that contains most common units and all SI units.\n",
+    "\n",
+    "You can use `dir` to list them, but you should also [read the documentation](https://docs.astropy.org/en/stable/units/)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['A',\n",
+       " 'AA',\n",
+       " 'AB',\n",
+       " 'ABflux',\n",
+       " 'ABmag',\n",
+       " 'AU',\n",
+       " 'Angstrom',\n",
+       " 'B',\n",
+       " 'Ba',\n",
+       " 'Barye',\n",
+       " 'Bi',\n",
+       " 'Biot',\n",
+       " 'Bol',\n",
+       " 'Bq',\n",
+       " 'C',\n",
+       " 'Celsius',\n",
+       " 'Ci',\n",
+       " 'CompositeUnit',\n",
+       " 'D',\n",
+       " 'Da',\n",
+       " 'Dalton',\n",
+       " 'Debye',\n",
+       " 'Decibel',\n",
+       " 'DecibelUnit',\n",
+       " 'Dex',\n",
+       " 'DexUnit',\n",
+       " 'EA',\n",
+       " 'EAU',\n",
+       " 'EB',\n",
+       " 'EBa',\n",
+       " 'EC',\n",
+       " 'ED',\n",
+       " 'EF',\n",
+       " 'EG',\n",
+       " 'EGal',\n",
+       " 'EH',\n",
+       " 'EHz',\n",
+       " 'EJ',\n",
+       " 'EJy',\n",
+       " 'EK',\n",
+       " 'EL',\n",
+       " 'EN',\n",
+       " 'EOhm',\n",
+       " 'EP',\n",
+       " 'EPa',\n",
+       " 'ER',\n",
+       " 'ERy',\n",
+       " 'ES',\n",
+       " 'ESt',\n",
+       " 'ET',\n",
+       " 'EV',\n",
+       " 'EW',\n",
+       " 'EWb',\n",
+       " 'Ea',\n",
+       " 'Eadu',\n",
+       " 'Earcmin',\n",
+       " 'Earcsec',\n",
+       " 'Eau',\n",
+       " 'Eb',\n",
+       " 'Ebarn',\n",
+       " 'Ebeam',\n",
+       " 'Ebin',\n",
+       " 'Ebit',\n",
+       " 'Ebyte',\n",
+       " 'Ecd',\n",
+       " 'Echan',\n",
+       " 'Ecount',\n",
+       " 'Ect',\n",
+       " 'Ed',\n",
+       " 'Edeg',\n",
+       " 'Edyn',\n",
+       " 'EeV',\n",
+       " 'Eerg',\n",
+       " 'Eg',\n",
+       " 'Eh',\n",
+       " 'EiB',\n",
+       " 'Eib',\n",
+       " 'Eibit',\n",
+       " 'Eibyte',\n",
+       " 'Ek',\n",
+       " 'El',\n",
+       " 'Elm',\n",
+       " 'Elx',\n",
+       " 'Elyr',\n",
+       " 'Em',\n",
+       " 'Emag',\n",
+       " 'Emin',\n",
+       " 'Emol',\n",
+       " 'Eohm',\n",
+       " 'Epc',\n",
+       " 'Eph',\n",
+       " 'Ephoton',\n",
+       " 'Epix',\n",
+       " 'Epixel',\n",
+       " 'Erad',\n",
+       " 'Es',\n",
+       " 'Esr',\n",
+       " 'Eu',\n",
+       " 'Evox',\n",
+       " 'Evoxel',\n",
+       " 'Eyr',\n",
+       " 'F',\n",
+       " 'Farad',\n",
+       " 'Fr',\n",
+       " 'Franklin',\n",
+       " 'FunctionQuantity',\n",
+       " 'FunctionUnitBase',\n",
+       " 'G',\n",
+       " 'GA',\n",
+       " 'GAU',\n",
+       " 'GB',\n",
+       " 'GBa',\n",
+       " 'GC',\n",
+       " 'GD',\n",
+       " 'GF',\n",
+       " 'GG',\n",
+       " 'GGal',\n",
+       " 'GH',\n",
+       " 'GHz',\n",
+       " 'GJ',\n",
+       " 'GJy',\n",
+       " 'GK',\n",
+       " 'GL',\n",
+       " 'GN',\n",
+       " 'GOhm',\n",
+       " 'GP',\n",
+       " 'GPa',\n",
+       " 'GR',\n",
+       " 'GRy',\n",
+       " 'GS',\n",
+       " 'GSt',\n",
+       " 'GT',\n",
+       " 'GV',\n",
+       " 'GW',\n",
+       " 'GWb',\n",
+       " 'Ga',\n",
+       " 'Gadu',\n",
+       " 'Gal',\n",
+       " 'Garcmin',\n",
+       " 'Garcsec',\n",
+       " 'Gau',\n",
+       " 'Gauss',\n",
+       " 'Gb',\n",
+       " 'Gbarn',\n",
+       " 'Gbeam',\n",
+       " 'Gbin',\n",
+       " 'Gbit',\n",
+       " 'Gbyte',\n",
+       " 'Gcd',\n",
+       " 'Gchan',\n",
+       " 'Gcount',\n",
+       " 'Gct',\n",
+       " 'Gd',\n",
+       " 'Gdeg',\n",
+       " 'Gdyn',\n",
+       " 'GeV',\n",
+       " 'Gerg',\n",
+       " 'Gg',\n",
+       " 'Gh',\n",
+       " 'GiB',\n",
+       " 'Gib',\n",
+       " 'Gibit',\n",
+       " 'Gibyte',\n",
+       " 'Gk',\n",
+       " 'Gl',\n",
+       " 'Glm',\n",
+       " 'Glx',\n",
+       " 'Glyr',\n",
+       " 'Gm',\n",
+       " 'Gmag',\n",
+       " 'Gmin',\n",
+       " 'Gmol',\n",
+       " 'Gohm',\n",
+       " 'Gpc',\n",
+       " 'Gph',\n",
+       " 'Gphoton',\n",
+       " 'Gpix',\n",
+       " 'Gpixel',\n",
+       " 'Grad',\n",
+       " 'Gs',\n",
+       " 'Gsr',\n",
+       " 'Gu',\n",
+       " 'Gvox',\n",
+       " 'Gvoxel',\n",
+       " 'Gyr',\n",
+       " 'H',\n",
+       " 'Henry',\n",
+       " 'Hertz',\n",
+       " 'Hz',\n",
+       " 'IrreducibleUnit',\n",
+       " 'J',\n",
+       " 'Jansky',\n",
+       " 'Joule',\n",
+       " 'Jy',\n",
+       " 'K',\n",
+       " 'Kayser',\n",
+       " 'Kelvin',\n",
+       " 'KiB',\n",
+       " 'Kib',\n",
+       " 'Kibit',\n",
+       " 'Kibyte',\n",
+       " 'L',\n",
+       " 'L_bol',\n",
+       " 'L_sun',\n",
+       " 'LogQuantity',\n",
+       " 'LogUnit',\n",
+       " 'Lsun',\n",
+       " 'MA',\n",
+       " 'MAU',\n",
+       " 'MB',\n",
+       " 'MBa',\n",
+       " 'MC',\n",
+       " 'MD',\n",
+       " 'MF',\n",
+       " 'MG',\n",
+       " 'MGal',\n",
+       " 'MH',\n",
+       " 'MHz',\n",
+       " 'MJ',\n",
+       " 'MJy',\n",
+       " 'MK',\n",
+       " 'ML',\n",
+       " 'MN',\n",
+       " 'MOhm',\n",
+       " 'MP',\n",
+       " 'MPa',\n",
+       " 'MR',\n",
+       " 'MRy',\n",
+       " 'MS',\n",
+       " 'MSt',\n",
+       " 'MT',\n",
+       " 'MV',\n",
+       " 'MW',\n",
+       " 'MWb',\n",
+       " 'M_bol',\n",
+       " 'M_e',\n",
+       " 'M_earth',\n",
+       " 'M_jup',\n",
+       " 'M_jupiter',\n",
+       " 'M_p',\n",
+       " 'M_sun',\n",
+       " 'Ma',\n",
+       " 'Madu',\n",
+       " 'MagUnit',\n",
+       " 'Magnitude',\n",
+       " 'Marcmin',\n",
+       " 'Marcsec',\n",
+       " 'Mau',\n",
+       " 'Mb',\n",
+       " 'Mbarn',\n",
+       " 'Mbeam',\n",
+       " 'Mbin',\n",
+       " 'Mbit',\n",
+       " 'Mbyte',\n",
+       " 'Mcd',\n",
+       " 'Mchan',\n",
+       " 'Mcount',\n",
+       " 'Mct',\n",
+       " 'Md',\n",
+       " 'Mdeg',\n",
+       " 'Mdyn',\n",
+       " 'MeV',\n",
+       " 'Mearth',\n",
+       " 'Merg',\n",
+       " 'Mg',\n",
+       " 'Mh',\n",
+       " 'MiB',\n",
+       " 'Mib',\n",
+       " 'Mibit',\n",
+       " 'Mibyte',\n",
+       " 'Mjup',\n",
+       " 'Mjupiter',\n",
+       " 'Mk',\n",
+       " 'Ml',\n",
+       " 'Mlm',\n",
+       " 'Mlx',\n",
+       " 'Mlyr',\n",
+       " 'Mm',\n",
+       " 'Mmag',\n",
+       " 'Mmin',\n",
+       " 'Mmol',\n",
+       " 'Mohm',\n",
+       " 'Mpc',\n",
+       " 'Mph',\n",
+       " 'Mphoton',\n",
+       " 'Mpix',\n",
+       " 'Mpixel',\n",
+       " 'Mrad',\n",
+       " 'Ms',\n",
+       " 'Msr',\n",
+       " 'Msun',\n",
+       " 'Mu',\n",
+       " 'Mvox',\n",
+       " 'Mvoxel',\n",
+       " 'Myr',\n",
+       " 'N',\n",
+       " 'NamedUnit',\n",
+       " 'Newton',\n",
+       " 'Ohm',\n",
+       " 'P',\n",
+       " 'PA',\n",
+       " 'PAU',\n",
+       " 'PB',\n",
+       " 'PBa',\n",
+       " 'PC',\n",
+       " 'PD',\n",
+       " 'PF',\n",
+       " 'PG',\n",
+       " 'PGal',\n",
+       " 'PH',\n",
+       " 'PHz',\n",
+       " 'PJ',\n",
+       " 'PJy',\n",
+       " 'PK',\n",
+       " 'PL',\n",
+       " 'PN',\n",
+       " 'POhm',\n",
+       " 'PP',\n",
+       " 'PPa',\n",
+       " 'PR',\n",
+       " 'PRy',\n",
+       " 'PS',\n",
+       " 'PSt',\n",
+       " 'PT',\n",
+       " 'PV',\n",
+       " 'PW',\n",
+       " 'PWb',\n",
+       " 'Pa',\n",
+       " 'Padu',\n",
+       " 'Parcmin',\n",
+       " 'Parcsec',\n",
+       " 'Pascal',\n",
+       " 'Pau',\n",
+       " 'Pb',\n",
+       " 'Pbarn',\n",
+       " 'Pbeam',\n",
+       " 'Pbin',\n",
+       " 'Pbit',\n",
+       " 'Pbyte',\n",
+       " 'Pcd',\n",
+       " 'Pchan',\n",
+       " 'Pcount',\n",
+       " 'Pct',\n",
+       " 'Pd',\n",
+       " 'Pdeg',\n",
+       " 'Pdyn',\n",
+       " 'PeV',\n",
+       " 'Perg',\n",
+       " 'Pg',\n",
+       " 'Ph',\n",
+       " 'PiB',\n",
+       " 'Pib',\n",
+       " 'Pibit',\n",
+       " 'Pibyte',\n",
+       " 'Pk',\n",
+       " 'Pl',\n",
+       " 'Plm',\n",
+       " 'Plx',\n",
+       " 'Plyr',\n",
+       " 'Pm',\n",
+       " 'Pmag',\n",
+       " 'Pmin',\n",
+       " 'Pmol',\n",
+       " 'Pohm',\n",
+       " 'Ppc',\n",
+       " 'Pph',\n",
+       " 'Pphoton',\n",
+       " 'Ppix',\n",
+       " 'Ppixel',\n",
+       " 'Prad',\n",
+       " 'PrefixUnit',\n",
+       " 'Ps',\n",
+       " 'Psr',\n",
+       " 'Pu',\n",
+       " 'Pvox',\n",
+       " 'Pvoxel',\n",
+       " 'Pyr',\n",
+       " 'Quantity',\n",
+       " 'QuantityInfo',\n",
+       " 'QuantityInfoBase',\n",
+       " 'R',\n",
+       " 'R_earth',\n",
+       " 'R_jup',\n",
+       " 'R_jupiter',\n",
+       " 'R_sun',\n",
+       " 'Rayleigh',\n",
+       " 'Rearth',\n",
+       " 'Rjup',\n",
+       " 'Rjupiter',\n",
+       " 'Rsun',\n",
+       " 'Ry',\n",
+       " 'S',\n",
+       " 'ST',\n",
+       " 'STflux',\n",
+       " 'STmag',\n",
+       " 'Siemens',\n",
+       " 'SpecificTypeQuantity',\n",
+       " 'St',\n",
+       " 'Sun',\n",
+       " 'T',\n",
+       " 'TA',\n",
+       " 'TAU',\n",
+       " 'TB',\n",
+       " 'TBa',\n",
+       " 'TC',\n",
+       " 'TD',\n",
+       " 'TF',\n",
+       " 'TG',\n",
+       " 'TGal',\n",
+       " 'TH',\n",
+       " 'THz',\n",
+       " 'TJ',\n",
+       " 'TJy',\n",
+       " 'TK',\n",
+       " 'TL',\n",
+       " 'TN',\n",
+       " 'TOhm',\n",
+       " 'TP',\n",
+       " 'TPa',\n",
+       " 'TR',\n",
+       " 'TRy',\n",
+       " 'TS',\n",
+       " 'TSt',\n",
+       " 'TT',\n",
+       " 'TV',\n",
+       " 'TW',\n",
+       " 'TWb',\n",
+       " 'Ta',\n",
+       " 'Tadu',\n",
+       " 'Tarcmin',\n",
+       " 'Tarcsec',\n",
+       " 'Tau',\n",
+       " 'Tb',\n",
+       " 'Tbarn',\n",
+       " 'Tbeam',\n",
+       " 'Tbin',\n",
+       " 'Tbit',\n",
+       " 'Tbyte',\n",
+       " 'Tcd',\n",
+       " 'Tchan',\n",
+       " 'Tcount',\n",
+       " 'Tct',\n",
+       " 'Td',\n",
+       " 'Tdeg',\n",
+       " 'Tdyn',\n",
+       " 'TeV',\n",
+       " 'Terg',\n",
+       " 'Tesla',\n",
+       " 'Tg',\n",
+       " 'Th',\n",
+       " 'TiB',\n",
+       " 'Tib',\n",
+       " 'Tibit',\n",
+       " 'Tibyte',\n",
+       " 'Tk',\n",
+       " 'Tl',\n",
+       " 'Tlm',\n",
+       " 'Tlx',\n",
+       " 'Tlyr',\n",
+       " 'Tm',\n",
+       " 'Tmag',\n",
+       " 'Tmin',\n",
+       " 'Tmol',\n",
+       " 'Tohm',\n",
+       " 'Tpc',\n",
+       " 'Tph',\n",
+       " 'Tphoton',\n",
+       " 'Tpix',\n",
+       " 'Tpixel',\n",
+       " 'Trad',\n",
+       " 'Ts',\n",
+       " 'Tsr',\n",
+       " 'Tu',\n",
+       " 'Tvox',\n",
+       " 'Tvoxel',\n",
+       " 'Tyr',\n",
+       " 'Unit',\n",
+       " 'UnitBase',\n",
+       " 'UnitConversionError',\n",
+       " 'UnitTypeError',\n",
+       " 'UnitsError',\n",
+       " 'UnitsWarning',\n",
+       " 'UnrecognizedUnit',\n",
+       " 'V',\n",
+       " 'Volt',\n",
+       " 'W',\n",
+       " 'Watt',\n",
+       " 'Wb',\n",
+       " 'Weber',\n",
+       " 'YA',\n",
+       " 'YAU',\n",
+       " 'YB',\n",
+       " 'YBa',\n",
+       " 'YC',\n",
+       " 'YD',\n",
+       " 'YF',\n",
+       " 'YG',\n",
+       " 'YGal',\n",
+       " 'YH',\n",
+       " 'YHz',\n",
+       " 'YJ',\n",
+       " 'YJy',\n",
+       " 'YK',\n",
+       " 'YL',\n",
+       " 'YN',\n",
+       " 'YOhm',\n",
+       " 'YP',\n",
+       " 'YPa',\n",
+       " 'YR',\n",
+       " 'YRy',\n",
+       " 'YS',\n",
+       " 'YSt',\n",
+       " 'YT',\n",
+       " 'YV',\n",
+       " 'YW',\n",
+       " 'YWb',\n",
+       " 'Ya',\n",
+       " 'Yadu',\n",
+       " 'Yarcmin',\n",
+       " 'Yarcsec',\n",
+       " 'Yau',\n",
+       " 'Yb',\n",
+       " 'Ybarn',\n",
+       " 'Ybeam',\n",
+       " 'Ybin',\n",
+       " 'Ybit',\n",
+       " 'Ybyte',\n",
+       " 'Ycd',\n",
+       " 'Ychan',\n",
+       " 'Ycount',\n",
+       " 'Yct',\n",
+       " 'Yd',\n",
+       " 'Ydeg',\n",
+       " 'Ydyn',\n",
+       " 'YeV',\n",
+       " 'Yerg',\n",
+       " 'Yg',\n",
+       " 'Yh',\n",
+       " 'Yk',\n",
+       " 'Yl',\n",
+       " 'Ylm',\n",
+       " 'Ylx',\n",
+       " 'Ylyr',\n",
+       " 'Ym',\n",
+       " 'Ymag',\n",
+       " 'Ymin',\n",
+       " 'Ymol',\n",
+       " 'Yohm',\n",
+       " 'Ypc',\n",
+       " 'Yph',\n",
+       " 'Yphoton',\n",
+       " 'Ypix',\n",
+       " 'Ypixel',\n",
+       " 'Yrad',\n",
+       " 'Ys',\n",
+       " 'Ysr',\n",
+       " 'Yu',\n",
+       " 'Yvox',\n",
+       " 'Yvoxel',\n",
+       " 'Yyr',\n",
+       " 'ZA',\n",
+       " 'ZAU',\n",
+       " 'ZB',\n",
+       " 'ZBa',\n",
+       " 'ZC',\n",
+       " 'ZD',\n",
+       " 'ZF',\n",
+       " 'ZG',\n",
+       " 'ZGal',\n",
+       " 'ZH',\n",
+       " 'ZHz',\n",
+       " 'ZJ',\n",
+       " 'ZJy',\n",
+       " 'ZK',\n",
+       " 'ZL',\n",
+       " 'ZN',\n",
+       " 'ZOhm',\n",
+       " 'ZP',\n",
+       " 'ZPa',\n",
+       " 'ZR',\n",
+       " 'ZRy',\n",
+       " 'ZS',\n",
+       " 'ZSt',\n",
+       " 'ZT',\n",
+       " 'ZV',\n",
+       " 'ZW',\n",
+       " 'ZWb',\n",
+       " 'Za',\n",
+       " 'Zadu',\n",
+       " 'Zarcmin',\n",
+       " 'Zarcsec',\n",
+       " 'Zau',\n",
+       " 'Zb',\n",
+       " 'Zbarn',\n",
+       " 'Zbeam',\n",
+       " 'Zbin',\n",
+       " 'Zbit',\n",
+       " 'Zbyte',\n",
+       " 'Zcd',\n",
+       " 'Zchan',\n",
+       " 'Zcount',\n",
+       " 'Zct',\n",
+       " 'Zd',\n",
+       " 'Zdeg',\n",
+       " 'Zdyn',\n",
+       " 'ZeV',\n",
+       " 'Zerg',\n",
+       " 'Zg',\n",
+       " 'Zh',\n",
+       " 'Zk',\n",
+       " 'Zl',\n",
+       " 'Zlm',\n",
+       " 'Zlx',\n",
+       " 'Zlyr',\n",
+       " 'Zm',\n",
+       " 'Zmag',\n",
+       " 'Zmin',\n",
+       " 'Zmol',\n",
+       " 'Zohm',\n",
+       " 'Zpc',\n",
+       " 'Zph',\n",
+       " 'Zphoton',\n",
+       " 'Zpix',\n",
+       " 'Zpixel',\n",
+       " 'Zrad',\n",
+       " 'Zs',\n",
+       " 'Zsr',\n",
+       " 'Zu',\n",
+       " 'Zvox',\n",
+       " 'Zvoxel',\n",
+       " 'Zyr',\n",
+       " '__builtins__',\n",
+       " '__cached__',\n",
+       " '__doc__',\n",
+       " '__file__',\n",
+       " '__loader__',\n",
+       " '__name__',\n",
+       " '__package__',\n",
+       " '__path__',\n",
+       " '__spec__',\n",
+       " 'a',\n",
+       " 'aA',\n",
+       " 'aAU',\n",
+       " 'aB',\n",
+       " 'aBa',\n",
+       " 'aC',\n",
+       " 'aD',\n",
+       " 'aF',\n",
+       " 'aG',\n",
+       " 'aGal',\n",
+       " 'aH',\n",
+       " 'aHz',\n",
+       " 'aJ',\n",
+       " 'aJy',\n",
+       " 'aK',\n",
+       " 'aL',\n",
+       " 'aN',\n",
+       " 'aOhm',\n",
+       " 'aP',\n",
+       " 'aPa',\n",
+       " 'aR',\n",
+       " 'aRy',\n",
+       " 'aS',\n",
+       " 'aSt',\n",
+       " 'aT',\n",
+       " 'aV',\n",
+       " 'aW',\n",
+       " 'aWb',\n",
+       " 'aa',\n",
+       " 'aadu',\n",
+       " 'aarcmin',\n",
+       " 'aarcsec',\n",
+       " 'aau',\n",
+       " 'ab',\n",
+       " 'abA',\n",
+       " 'abC',\n",
+       " 'abampere',\n",
+       " 'abarn',\n",
+       " 'abcoulomb',\n",
+       " 'abeam',\n",
+       " 'abin',\n",
+       " 'abit',\n",
+       " 'abyte',\n",
+       " 'acd',\n",
+       " 'achan',\n",
+       " 'acount',\n",
+       " 'act',\n",
+       " 'ad',\n",
+       " 'add_enabled_equivalencies',\n",
+       " 'add_enabled_units',\n",
+       " 'adeg',\n",
+       " 'adu',\n",
+       " 'adyn',\n",
+       " 'aeV',\n",
+       " 'aerg',\n",
+       " 'ag',\n",
+       " 'ah',\n",
+       " 'ak',\n",
+       " 'al',\n",
+       " 'allclose',\n",
+       " 'alm',\n",
+       " 'alx',\n",
+       " 'alyr',\n",
+       " 'am',\n",
+       " 'amag',\n",
+       " 'amin',\n",
+       " 'amol',\n",
+       " 'amp',\n",
+       " 'ampere',\n",
+       " 'angstrom',\n",
+       " 'annum',\n",
+       " 'aohm',\n",
+       " 'apc',\n",
+       " 'aph',\n",
+       " 'aphoton',\n",
+       " 'apix',\n",
+       " 'apixel',\n",
+       " 'arad',\n",
+       " 'arcmin',\n",
+       " 'arcminute',\n",
+       " 'arcsec',\n",
+       " 'arcsecond',\n",
+       " 'asr',\n",
+       " 'astronomical_unit',\n",
+       " 'astrophys',\n",
+       " 'attoBarye',\n",
+       " 'attoDa',\n",
+       " 'attoDalton',\n",
+       " 'attoDebye',\n",
+       " 'attoFarad',\n",
+       " 'attoGauss',\n",
+       " 'attoHenry',\n",
+       " 'attoHertz',\n",
+       " 'attoJansky',\n",
+       " 'attoJoule',\n",
+       " 'attoKayser',\n",
+       " 'attoKelvin',\n",
+       " 'attoNewton',\n",
+       " 'attoOhm',\n",
+       " 'attoPascal',\n",
+       " 'attoRayleigh',\n",
+       " 'attoSiemens',\n",
+       " 'attoTesla',\n",
+       " 'attoVolt',\n",
+       " 'attoWatt',\n",
+       " 'attoWeber',\n",
+       " 'attoamp',\n",
+       " 'attoampere',\n",
+       " 'attoannum',\n",
+       " 'attoarcminute',\n",
+       " 'attoarcsecond',\n",
+       " 'attoastronomical_unit',\n",
+       " 'attobarn',\n",
+       " 'attobarye',\n",
+       " 'attobit',\n",
+       " 'attobyte',\n",
+       " 'attocandela',\n",
+       " 'attocoulomb',\n",
+       " 'attocount',\n",
+       " 'attoday',\n",
+       " 'attodebye',\n",
+       " 'attodegree',\n",
+       " 'attodyne',\n",
+       " 'attoelectronvolt',\n",
+       " 'attofarad',\n",
+       " 'attogal',\n",
+       " 'attogauss',\n",
+       " 'attogram',\n",
+       " 'attohenry',\n",
+       " 'attohertz',\n",
+       " 'attohour',\n",
+       " 'attohr',\n",
+       " 'attojansky',\n",
+       " 'attojoule',\n",
+       " 'attokayser',\n",
+       " 'attolightyear',\n",
+       " 'attoliter',\n",
+       " 'attolumen',\n",
+       " 'attolux',\n",
+       " 'attometer',\n",
+       " 'attominute',\n",
+       " 'attomole',\n",
+       " 'attonewton',\n",
+       " 'attoparsec',\n",
+       " 'attopascal',\n",
+       " 'attophoton',\n",
+       " 'attopixel',\n",
+       " 'attopoise',\n",
+       " 'attoradian',\n",
+       " 'attorayleigh',\n",
+       " 'attorydberg',\n",
+       " 'attosecond',\n",
+       " 'attosiemens',\n",
+       " 'attosteradian',\n",
+       " 'attostokes',\n",
+       " 'attotesla',\n",
+       " 'attovolt',\n",
+       " 'attovoxel',\n",
+       " 'attowatt',\n",
+       " 'attoweber',\n",
+       " 'attoyear',\n",
+       " 'au',\n",
+       " 'avox',\n",
+       " 'avoxel',\n",
+       " 'ayr',\n",
+       " 'b',\n",
+       " 'bar',\n",
+       " 'barn',\n",
+       " 'barye',\n",
+       " 'beam',\n",
+       " 'beam_angular_area',\n",
+       " 'becquerel',\n",
+       " 'bin',\n",
+       " 'binary_prefixes',\n",
+       " 'bit',\n",
+       " 'bol',\n",
+       " 'brightness_temperature',\n",
+       " 'byte',\n",
+       " 'cA',\n",
+       " 'cAU',\n",
+       " 'cB',\n",
+       " 'cBa',\n",
+       " 'cC',\n",
+       " 'cD',\n",
+       " 'cF',\n",
+       " 'cG',\n",
+       " 'cGal',\n",
+       " 'cH',\n",
+       " 'cHz',\n",
+       " 'cJ',\n",
+       " 'cJy',\n",
+       " 'cK',\n",
+       " 'cL',\n",
+       " 'cN',\n",
+       " 'cOhm',\n",
+       " 'cP',\n",
+       " 'cPa',\n",
+       " 'cR',\n",
+       " 'cRy',\n",
+       " 'cS',\n",
+       " 'cSt',\n",
+       " 'cT',\n",
+       " 'cV',\n",
+       " 'cW',\n",
+       " 'cWb',\n",
+       " 'ca',\n",
+       " 'cadu',\n",
+       " 'candela',\n",
+       " 'carcmin',\n",
+       " 'carcsec',\n",
+       " 'cau',\n",
+       " 'cb',\n",
+       " 'cbarn',\n",
+       " 'cbeam',\n",
+       " 'cbin',\n",
+       " 'cbit',\n",
+       " 'cbyte',\n",
+       " 'ccd',\n",
+       " 'cchan',\n",
+       " 'ccount',\n",
+       " 'cct',\n",
+       " 'cd',\n",
+       " 'cdeg',\n",
+       " 'cdyn',\n",
+       " 'ceV',\n",
+       " 'centiBarye',\n",
+       " 'centiDa',\n",
+       " 'centiDalton',\n",
+       " 'centiDebye',\n",
+       " 'centiFarad',\n",
+       " 'centiGauss',\n",
+       " 'centiHenry',\n",
+       " 'centiHertz',\n",
+       " 'centiJansky',\n",
+       " 'centiJoule',\n",
+       " 'centiKayser',\n",
+       " 'centiKelvin',\n",
+       " 'centiNewton',\n",
+       " 'centiOhm',\n",
+       " 'centiPascal',\n",
+       " 'centiRayleigh',\n",
+       " 'centiSiemens',\n",
+       " 'centiTesla',\n",
+       " 'centiVolt',\n",
+       " 'centiWatt',\n",
+       " 'centiWeber',\n",
+       " 'centiamp',\n",
+       " 'centiampere',\n",
+       " 'centiannum',\n",
+       " 'centiarcminute',\n",
+       " 'centiarcsecond',\n",
+       " 'centiastronomical_unit',\n",
+       " 'centibarn',\n",
+       " 'centibarye',\n",
+       " 'centibit',\n",
+       " 'centibyte',\n",
+       " 'centicandela',\n",
+       " 'centicoulomb',\n",
+       " 'centicount',\n",
+       " 'centiday',\n",
+       " 'centidebye',\n",
+       " 'centidegree',\n",
+       " 'centidyne',\n",
+       " 'centielectronvolt',\n",
+       " 'centifarad',\n",
+       " 'centigal',\n",
+       " 'centigauss',\n",
+       " 'centigram',\n",
+       " 'centihenry',\n",
+       " 'centihertz',\n",
+       " 'centihour',\n",
+       " 'centihr',\n",
+       " 'centijansky',\n",
+       " 'centijoule',\n",
+       " 'centikayser',\n",
+       " 'centilightyear',\n",
+       " 'centiliter',\n",
+       " 'centilumen',\n",
+       " 'centilux',\n",
+       " 'centimeter',\n",
+       " 'centiminute',\n",
+       " 'centimole',\n",
+       " 'centinewton',\n",
+       " 'centiparsec',\n",
+       " 'centipascal',\n",
+       " 'centiphoton',\n",
+       " 'centipixel',\n",
+       " 'centipoise',\n",
+       " 'centiradian',\n",
+       " 'centirayleigh',\n",
+       " 'centirydberg',\n",
+       " 'centisecond',\n",
+       " 'centisiemens',\n",
+       " 'centisteradian',\n",
+       " 'centistokes',\n",
+       " 'centitesla',\n",
+       " 'centivolt',\n",
+       " 'centivoxel',\n",
+       " 'centiwatt',\n",
+       " 'centiweber',\n",
+       " 'centiyear',\n",
+       " 'cerg',\n",
+       " 'cg',\n",
+       " 'cgs',\n",
+       " 'ch',\n",
+       " 'chan',\n",
+       " 'ck',\n",
+       " 'cl',\n",
+       " 'clm',\n",
+       " 'clx',\n",
+       " 'clyr',\n",
+       " 'cm',\n",
+       " 'cmag',\n",
+       " 'cmin',\n",
+       " 'cmol',\n",
+       " 'cohm',\n",
+       " 'core',\n",
+       " 'coulomb',\n",
+       " 'count',\n",
+       " 'cpc',\n",
+       " 'cph',\n",
+       " 'cphoton',\n",
+       " 'cpix',\n",
+       " 'cpixel',\n",
+       " 'crad',\n",
+       " 'cs',\n",
+       " 'csr',\n",
+       " 'ct',\n",
+       " 'cu',\n",
+       " 'curie',\n",
+       " 'cvox',\n",
+       " 'cvoxel',\n",
+       " 'cy',\n",
+       " 'cycle',\n",
+       " 'cyr',\n",
+       " 'd',\n",
+       " 'dA',\n",
+       " 'dAU',\n",
+       " 'dB',\n",
+       " 'dBa',\n",
+       " 'dC',\n",
+       " 'dD',\n",
+       " 'dF',\n",
+       " 'dG',\n",
+       " 'dGal',\n",
+       " 'dH',\n",
+       " 'dHz',\n",
+       " 'dJ',\n",
+       " 'dJy',\n",
+       " 'dK',\n",
+       " 'dL',\n",
+       " 'dN',\n",
+       " 'dOhm',\n",
+       " 'dP',\n",
+       " 'dPa',\n",
+       " 'dR',\n",
+       " 'dRy',\n",
+       " 'dS',\n",
+       " 'dSt',\n",
+       " 'dT',\n",
+       " ...]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dir(u)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To create a quantity, we multiply a value by a unit."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "astropy.units.quantity.Quantity"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "coord = 30 * u.deg\n",
+    "type(coord)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The result is a `Quantity` object.\n",
+    "\n",
+    "Jupyter knows how to display `Quantities` like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$30 \\; \\mathrm{{}^{\\circ}}$"
+      ],
+      "text/plain": [
+       "<Quantity 30. deg>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "coord"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Selecting a rectangle\n",
+    "\n",
+    "Now we'll select a rectangle from -55 to -45 degrees right ascension and -8 to 4 degrees of declination.\n",
+    "\n",
+    "We'll define variables to contain these limits."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "phi1_min = -55\n",
+    "phi1_max = -45\n",
+    "phi2_min = -8\n",
+    "phi2_max = 4"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To represent a rectangle, we'll use two lists of coordinates and multiply by their units."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "phi1_rect = [phi1_min, phi1_min, phi1_max, phi1_max] * u.deg\n",
+    "phi2_rect = [phi2_min, phi2_max, phi2_max, phi2_min] * u.deg"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`phi1_rect` and `phi2_rect` represent the coordinates of the corners of a rectangle.  \n",
+    "\n",
+    "But they are in \"[a Heliocentric spherical coordinate system defined by the orbit of the GD1 stream](https://gala-astro.readthedocs.io/en/latest/_modules/gala/coordinates/gd1.html)\"\n",
+    "\n",
+    "In order to use them in a Gaia query, we have to convert them to [International Celestial Reference System](https://en.wikipedia.org/wiki/International_Celestial_Reference_System) (ICRS) coordinates.  We can do that by storing the coordinates in a `GD1Koposov10` object provided by [Gala](https://gala-astro.readthedocs.io/en/latest/coordinates/)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "gala.coordinates.gd1.GD1Koposov10"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import gala.coordinates as gc\n",
+    "\n",
+    "corners = gc.GD1Koposov10(phi1=phi1_rect, phi2=phi2_rect)\n",
+    "type(corners)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can display the result like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<GD1Koposov10 Coordinate: (phi1, phi2) in deg\n",
+       "    [(-55., -8.), (-55.,  4.), (-45.,  4.), (-45., -8.)]>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "corners"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can use `transform_to` to convert to ICRS coordinates."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "astropy.coordinates.builtin_frames.icrs.ICRS"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import astropy.coordinates as coord\n",
+    "\n",
+    "corners_icrs = corners.transform_to(coord.ICRS)\n",
+    "type(corners_icrs)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The result is an `ICRS` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<ICRS Coordinate: (ra, dec) in deg\n",
+       "    [(146.27533314, 19.26190982), (135.42163944, 25.87738723),\n",
+       "     (141.60264825, 34.3048303 ), (152.81671045, 27.13611254)]>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "corners_icrs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notice that a rectangle in one coordinate system is not necessarily a rectangle in another.  In this example, the result is a polygon."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Selecting a polygon\n",
+    "\n",
+    "In order to use this polygon as part of an ADQL query, we have to convert it to a string with a comma-separated list of coordinates, as in this example:\n",
+    "\n",
+    "```\n",
+    "\"\"\"\n",
+    "POLYGON(143.65, 20.98, \n",
+    "        134.46, 26.39, \n",
+    "        140.58, 34.85, \n",
+    "        150.16, 29.01)\n",
+    "\"\"\"\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`corners_icrs` behaves like a list, so we can use a `for` loop to iterate through the points."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<ICRS Coordinate: (ra, dec) in deg\n",
+      "    (146.27533314, 19.26190982)>\n",
+      "<ICRS Coordinate: (ra, dec) in deg\n",
+      "    (135.42163944, 25.87738723)>\n",
+      "<ICRS Coordinate: (ra, dec) in deg\n",
+      "    (141.60264825, 34.3048303)>\n",
+      "<ICRS Coordinate: (ra, dec) in deg\n",
+      "    (152.81671045, 27.13611254)>\n"
+     ]
+    }
+   ],
+   "source": [
+    "for point in corners_icrs:\n",
+    "    print(point)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "From that, we can select the coordinates `ra` and `dec`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "146d16m31.1993s 19d15m42.8754s\n",
+      "135d25m17.902s 25d52m38.594s\n",
+      "141d36m09.5337s 34d18m17.3891s\n",
+      "152d49m00.1576s 27d08m10.0051s\n"
+     ]
+    }
+   ],
+   "source": [
+    "for point in corners_icrs:\n",
+    "    print(point.ra, point.dec)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The results are quantities with units, but if we select the `value` part, we get a dimensionless floating-point number."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "146.27533313607782 19.261909820533692\n",
+      "135.42163944306296 25.87738722767213\n",
+      "141.60264825107333 34.304830296257144\n",
+      "152.81671044675923 27.136112541397996\n"
+     ]
+    }
+   ],
+   "source": [
+    "for point in corners_icrs:\n",
+    "    print(point.ra.value, point.dec.value)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can use string `format` to convert these numbers to strings."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['146.27533313607782, 19.261909820533692',\n",
+       " '135.42163944306296, 25.87738722767213',\n",
+       " '141.60264825107333, 34.304830296257144',\n",
+       " '152.81671044675923, 27.136112541397996']"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "point_base = \"{point.ra.value}, {point.dec.value}\"\n",
+    "\n",
+    "t = [point_base.format(point=point)\n",
+    "     for point in corners_icrs]\n",
+    "t"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The result is a list of strings, which we can join into a single string using `join`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'146.27533313607782, 19.261909820533692, 135.42163944306296, 25.87738722767213, 141.60264825107333, 34.304830296257144, 152.81671044675923, 27.136112541397996'"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "point_list = ', '.join(t)\n",
+    "point_list"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notice that we invoke `join` on a string and pass the list as an argument.\n",
+    "\n",
+    "Before we can assemble the query, we need `columns` again (as we saw in the previous notebook)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "columns = 'source_id, ra, dec, pmra, pmdec, parallax, parallax_error, radial_velocity'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's the base for the query, with format specifiers for `columns` and `point_list`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query_base = \"\"\"SELECT {columns}\n",
+    "FROM gaiadr2.gaia_source\n",
+    "WHERE parallax < 1\n",
+    "  AND bp_rp BETWEEN -0.75 AND 2 \n",
+    "  AND 1 = CONTAINS(POINT(ra, dec), \n",
+    "                   POLYGON({point_list}))\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And here's the result:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "SELECT source_id, ra, dec, pmra, pmdec, parallax, parallax_error, radial_velocity\n",
+      "FROM gaiadr2.gaia_source\n",
+      "WHERE parallax < 1\n",
+      "  AND bp_rp BETWEEN -0.75 AND 2 \n",
+      "  AND 1 = CONTAINS(POINT(ra, dec), \n",
+      "                   POLYGON(146.27533313607782, 19.261909820533692, 135.42163944306296, 25.87738722767213, 141.60264825107333, 34.304830296257144, 152.81671044675923, 27.136112541397996))\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "query = query_base.format(columns=columns, \n",
+    "                          point_list=point_list)\n",
+    "print(query)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As always, we should take a minute to proof-read the query before we launch it.\n",
+    "\n",
+    "The result will be bigger than our previous queries, so it will take a little longer."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO: Query finished. [astroquery.utils.tap.core]\n",
+      "<Table length=140340>\n",
+      "      name       dtype    unit                              description                             n_bad \n",
+      "--------------- ------- -------- ------------------------------------------------------------------ ------\n",
+      "      source_id   int64          Unique source identifier (unique within a particular Data Release)      0\n",
+      "             ra float64      deg                                                    Right ascension      0\n",
+      "            dec float64      deg                                                        Declination      0\n",
+      "           pmra float64 mas / yr                         Proper motion in right ascension direction      0\n",
+      "          pmdec float64 mas / yr                             Proper motion in declination direction      0\n",
+      "       parallax float64      mas                                                           Parallax      0\n",
+      " parallax_error float64      mas                                         Standard error of parallax      0\n",
+      "radial_velocity float64   km / s                                                    Radial velocity 139374\n",
+      "Jobid: 1603114980658O\n",
+      "Phase: COMPLETED\n",
+      "Owner: None\n",
+      "Output file: async_20201019094300.vot\n",
+      "Results: None\n"
+     ]
+    }
+   ],
+   "source": [
+    "job = Gaia.launch_job_async(query)\n",
+    "print(job)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here are the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "140340"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results = job.get_results()\n",
+    "len(results)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There are more than 100,000 stars in this polygon, but that's a manageable size to work with."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Saving results\n",
+    "\n",
+    "This is the set of stars we'll work with in the next step.  But since we have a substantial dataset now, this is a good time to save it.\n",
+    "\n",
+    "Storing the data in a file means we can shut down this notebook and pick up where we left off without running the previous query again.\n",
+    "\n",
+    "Astropy `Table` objects provide `write`, which writes the table to disk."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "filename = 'gd1_results.fits'\n",
+    "results.write(filename, overwrite=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Because the filename ends with `fits`, the table is written in the [FITS format](https://en.wikipedia.org/wiki/FITS), which preserves the metadata associated with the table.\n",
+    "\n",
+    "If the file already exists, the `overwrite` argument causes it to be overwritten.\n",
+    "\n",
+    "To see how big the file is, we can use `ls` with the `-lh` option, which prints information about the file including its size in human-readable form."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-rw-rw-r-- 1 downey downey 8.6M Oct 19 09:43 gd1_results.fits\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!ls -lh gd1_results.fits"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The file is about 8.6 MB.  If you are using Windows, `ls` might not work; in that case, try:\n",
+    "\n",
+    "```\n",
+    "!dir gd1_results.fits\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "In this notebook, we composed more complex queries to select stars within a polygonal region of the sky.  Then we downloaded the results and saved them in a FITS file.\n",
+    "\n",
+    "In the next notebook, we'll reload the data from this file and replicate the next step in the analysis, using proper motion to identify stars likely to be in GD-1."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Best practices\n",
+    "\n",
+    "* For measurements with units, use `Quantity` objects that represent units explicitly and check for errors.\n",
+    "\n",
+    "* Use the `format` function to compose queries; it is often faster and less error-prone.\n",
+    "\n",
+    "* Develop queries incrementally: start with something simple, test it, and add a little bit at a time.\n",
+    "\n",
+    "* Once you have a query working, save the data in a local file.  If you shut down the notebook and come back to it later, you can reload the file; you don't have to run the query again."
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/_build/html/_sources/03_motion.ipynb b/_build/html/_sources/03_motion.ipynb
new file mode 100644
index 0000000..baf52c9
--- /dev/null
+++ b/_build/html/_sources/03_motion.ipynb
@@ -0,0 +1,1896 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Chapter 3\n",
+    "\n",
+    "This is the third in a series of notebooks related to astronomy data.\n",
+    "\n",
+    "As a running example, we are replicating parts of the analysis in a recent paper, \"[Off the beaten path: Gaia reveals GD-1 stars outside of the main stream](https://arxiv.org/abs/1805.00425)\" by Adrian M. Price-Whelan and Ana Bonaca.\n",
+    "\n",
+    "In the first lesson, we wrote ADQL queries and used them to select and download data from the Gaia server.\n",
+    "\n",
+    "In the second lesson, we wrote a query to select stars from the region of the sky where we expect GD-1 to be, and saved the results in a FITS file.\n",
+    "\n",
+    "Now we'll read that data back and implement the next step in the analysis, identifying stars with the proper motion we expect for GD-1."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Outline\n",
+    "\n",
+    "Here are the steps in this lesson:\n",
+    "\n",
+    "1. We'll read back the results from the previous lesson, which we saved in a FITS file.\n",
+    "\n",
+    "2. Then we'll transform the coordinates and proper motion data from ICRS back to the coordinate frame of GD-1.\n",
+    "\n",
+    "3. We'll put those results into a Pandas `DataFrame`, which we'll use to select stars near the centerline of GD-1.\n",
+    "\n",
+    "4. Plotting the proper motion of those stars, we'll identify a region of proper motion for stars that are likely to be in GD-1.\n",
+    "\n",
+    "5. Finally, we'll select and plot the stars whose proper motion is in that region.\n",
+    "\n",
+    "After completing this lesson, you should be able to\n",
+    "\n",
+    "* Select rows and columns from an Astropy `Table`.\n",
+    "\n",
+    "* Use Matplotlib to make a scatter plot.\n",
+    "\n",
+    "* Use Gala to transform coordinates.\n",
+    "\n",
+    "* Make a Pandas `DataFrame` and use a Boolean `Series` to select rows.\n",
+    "\n",
+    "* Save a `DataFrame` in an HDF5 file.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Installing libraries\n",
+    "\n",
+    "If you are running this notebook on Colab, you can run the following cell to install Astroquery and a the other libraries we'll use.\n",
+    "\n",
+    "If you are running this notebook on your own computer, you might have to install these libraries yourself.  \n",
+    "\n",
+    "If you are using this notebook as part of a Carpentries workshop, you should have received setup instructions.\n",
+    "\n",
+    "TODO: Add a link to the instructions.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# If we're running on Colab, install libraries\n",
+    "\n",
+    "import sys\n",
+    "IN_COLAB = 'google.colab' in sys.modules\n",
+    "\n",
+    "if IN_COLAB:\n",
+    "    !pip install astroquery astro-gala pyia python-wget"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Reload the data\n",
+    "\n",
+    "In the previous lesson, we ran a query on the Gaia server and downloaded data for roughly 100,000 stars.  We saved the data in a FITS file so that now, picking up where we left off, we can read the data from a local file rather than running the query again.\n",
+    "\n",
+    "If you ran the previous lesson successfully, you should already have a file called `gd1_results.fits` that contains the data we downloaded.\n",
+    "\n",
+    "If not, you can run the following cell, which downloads the data from our repository."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from wget import download\n",
+    "\n",
+    "filename = 'gd1_results.fits'\n",
+    "path = 'https://github.com/AllenDowney/AstronomicalData/raw/main/data/'\n",
+    "\n",
+    "if not os.path.exists(filename):\n",
+    "    print(download(path+filename))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now here's how we can read the data from the file back into an Astropy `Table`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from astropy.table import Table\n",
+    "\n",
+    "results = Table.read(filename)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The result is an Astropy `Table`.\n",
+    "\n",
+    "We can use `info` to refresh our memory of the contents."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Table length=140340>\n",
+       "      name       dtype    unit                              description                            \n",
+       "--------------- ------- -------- ------------------------------------------------------------------\n",
+       "      source_id   int64          Unique source identifier (unique within a particular Data Release)\n",
+       "             ra float64      deg                                                    Right ascension\n",
+       "            dec float64      deg                                                        Declination\n",
+       "           pmra float64 mas / yr                         Proper motion in right ascension direction\n",
+       "          pmdec float64 mas / yr                             Proper motion in declination direction\n",
+       "       parallax float64      mas                                                           Parallax\n",
+       " parallax_error float64      mas                                         Standard error of parallax\n",
+       "radial_velocity float64   km / s                                                    Radial velocity"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results.info"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Selecting rows and columns\n",
+    "\n",
+    "In this section we'll see operations for selecting columns and rows from an Astropy `Table`.  You can find more information about these operations in the [Astropy documentation](https://docs.astropy.org/en/stable/table/access_table.html).\n",
+    "\n",
+    "We can get the names of the columns like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['source_id',\n",
+       " 'ra',\n",
+       " 'dec',\n",
+       " 'pmra',\n",
+       " 'pmdec',\n",
+       " 'parallax',\n",
+       " 'parallax_error',\n",
+       " 'radial_velocity']"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results.colnames"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And select an individual column like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "&lt;Column name=&apos;ra&apos; dtype=&apos;float64&apos; unit=&apos;deg&apos; description=&apos;Right ascension&apos; length=140340&gt;\n",
+       "<table>\n",
+       "<tr><td>142.48301935991023</td></tr>\n",
+       "<tr><td>142.25452941346344</td></tr>\n",
+       "<tr><td>142.64528557468074</td></tr>\n",
+       "<tr><td>142.57739430926034</td></tr>\n",
+       "<tr><td>142.58913564478618</td></tr>\n",
+       "<tr><td>141.81762228999614</td></tr>\n",
+       "<tr><td>143.18339801317677</td></tr>\n",
+       "<tr><td>142.9347319464589</td></tr>\n",
+       "<tr><td>142.26769745823267</td></tr>\n",
+       "<tr><td>142.89551292869012</td></tr>\n",
+       "<tr><td>142.2780935768316</td></tr>\n",
+       "<tr><td>142.06138786534987</td></tr>\n",
+       "<tr><td>...</td></tr>\n",
+       "<tr><td>143.05456487172972</td></tr>\n",
+       "<tr><td>144.0436496516182</td></tr>\n",
+       "<tr><td>144.06566578919313</td></tr>\n",
+       "<tr><td>144.13177563215973</td></tr>\n",
+       "<tr><td>143.77696341662764</td></tr>\n",
+       "<tr><td>142.945956347594</td></tr>\n",
+       "<tr><td>142.97282480557786</td></tr>\n",
+       "<tr><td>143.4166017695258</td></tr>\n",
+       "<tr><td>143.64484588686904</td></tr>\n",
+       "<tr><td>143.41554585481808</td></tr>\n",
+       "<tr><td>143.6908739159247</td></tr>\n",
+       "<tr><td>143.7702681295401</td></tr>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<Column name='ra' dtype='float64' unit='deg' description='Right ascension' length=140340>\n",
+       "142.48301935991023\n",
+       "142.25452941346344\n",
+       "142.64528557468074\n",
+       "142.57739430926034\n",
+       "142.58913564478618\n",
+       "141.81762228999614\n",
+       "143.18339801317677\n",
+       " 142.9347319464589\n",
+       "142.26769745823267\n",
+       "142.89551292869012\n",
+       " 142.2780935768316\n",
+       "142.06138786534987\n",
+       "               ...\n",
+       "143.05456487172972\n",
+       " 144.0436496516182\n",
+       "144.06566578919313\n",
+       "144.13177563215973\n",
+       "143.77696341662764\n",
+       "  142.945956347594\n",
+       "142.97282480557786\n",
+       " 143.4166017695258\n",
+       "143.64484588686904\n",
+       "143.41554585481808\n",
+       " 143.6908739159247\n",
+       " 143.7702681295401"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results['ra']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The result is a `Column` object that contains the data, and also the data type, units, and name of the column."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "astropy.table.column.Column"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(results['ra'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The rows in the `Table` are numbered from 0 to `n-1`, where `n` is the number of rows.  We can select the first row like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<i>Row index=0</i>\n",
+       "<table id=\"table140510732444000\">\n",
+       "<thead><tr><th>source_id</th><th>ra</th><th>dec</th><th>pmra</th><th>pmdec</th><th>parallax</th><th>parallax_error</th><th>radial_velocity</th></tr></thead>\n",
+       "<thead><tr><th></th><th>deg</th><th>deg</th><th>mas / yr</th><th>mas / yr</th><th>mas</th><th>mas</th><th>km / s</th></tr></thead>\n",
+       "<thead><tr><th>int64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th></tr></thead>\n",
+       "<tr><td>637987125186749568</td><td>142.48301935991023</td><td>21.75771616932985</td><td>-2.5168384683875766</td><td>2.941813096629439</td><td>-0.2573448962333354</td><td>0.823720794509811</td><td>1e+20</td></tr>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<Row index=0>\n",
+       "    source_id              ra                dec                pmra              pmdec             parallax        parallax_error  radial_velocity\n",
+       "                          deg                deg              mas / yr           mas / yr             mas                mas             km / s    \n",
+       "      int64             float64            float64            float64            float64            float64            float64          float64    \n",
+       "------------------ ------------------ ----------------- ------------------- ----------------- ------------------- ----------------- ---------------\n",
+       "637987125186749568 142.48301935991023 21.75771616932985 -2.5168384683875766 2.941813096629439 -0.2573448962333354 0.823720794509811           1e+20"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As you might have guessed, the result is a `Row` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "astropy.table.row.Row"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(results[0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notice that the bracket operator selects both columns and rows.  You might wonder how it knows which to select.\n",
+    "\n",
+    "If the expression in brackets is a string, it selects a column; if the expression is an integer, it selects a row.\n",
+    "\n",
+    "If you apply the bracket operator twice, you can select a column and then an element from the column."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "142.48301935991023"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results['ra'][0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Or you can select a row and then an element from the row."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "142.48301935991023"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results[0]['ra']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You get the same result either way."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Scatter plot\n",
+    "\n",
+    "To see what the results look like, we'll use a scatter plot.  The library we'll use is [Matplotlib](https://matplotlib.org/), which is the most widely-used plotting library for Python.\n",
+    "\n",
+    "The Matplotlib interface is based on MATLAB (hence the name), so if you know MATLAB, some of it will be familiar.\n",
+    "\n",
+    "We'll import like this."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Pyplot part of the Matplotlib library.  It is conventional to import it using the shortened name `plt`.\n",
+    "\n",
+    "Pyplot provides two functions that can make scatterplots, [plt.scatter](https://matplotlib.org/3.3.0/api/_as_gen/matplotlib.pyplot.scatter.html) and [plt.plot](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.plot.html).\n",
+    "\n",
+    "* `scatter` is more versatile; for example, you can make every point in a scatter plot a different color.\n",
+    "\n",
+    "* `plot` is more limited, but for simple cases, it can be substantially faster.  \n",
+    "\n",
+    "Jake Vanderplas explains these differences in [The Python Data Science Handbook](https://jakevdp.github.io/PythonDataScienceHandbook/04.02-simple-scatter-plots.html)\n",
+    "\n",
+    "Since we are plotting more than 100,000 points and they are all the same size and color, we'll use `plot`.\n",
+    "\n",
+    "Here's a scatter plot with right ascension on the x-axis and declination on the y-axis, both ICRS coordinates in degrees."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x = results['ra']\n",
+    "y = results['dec']\n",
+    "plt.plot(x, y, 'ko')\n",
+    "\n",
+    "plt.xlabel('ra (degree ICRS)')\n",
+    "plt.ylabel('dec (degree ICRS)');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The arguments to `plt.plot` are `x`, `y`, and a string that specifies the style.  In this case, the letters `ko` indicate that we want a black, round marker (`k` is for black because `b` is for blue).\n",
+    "\n",
+    "The functions `xlabel` and `ylabel` put labels on the axes.\n",
+    "\n",
+    "This scatter plot has a problem.  It is \"[overplotted](https://python-graph-gallery.com/134-how-to-avoid-overplotting-with-python/)\", which means that there are so many overlapping points, we can't distinguish between high and low density areas.\n",
+    "\n",
+    "To fix this, we can provide optional arguments to control the size and transparency of the points.\n",
+    "\n",
+    "**Exercise:** In the call to `plt.plot`, add the keyword argument `markersize=0.1` to make the markers smaller.\n",
+    "\n",
+    "Then add the argument `alpha=0.1` to make the markers nearly transparent.\n",
+    "\n",
+    "Adjust these arguments until you think the figure shows the data most clearly.\n",
+    "\n",
+    "Note: Once you have made these changes, you might notice that the figure shows stripes with lower density of stars.  These stripes are caused by the way Gaia scans the sky, which [you can read about here](https://www.cosmos.esa.int/web/gaia/scanning-law).  The dataset we are using, [Gaia Data Release 2](https://www.cosmos.esa.int/web/gaia/dr2), covers 22 months of observations; during this time, some parts of the sky were scanned more than others."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Transform back\n",
+    "\n",
+    "Remember that we selected data from a rectangle of coordinates in the `GD1Koposov10` frame, then transformed them to ICRS when we constructed the query.\n",
+    "The coordinates in `results` are in ICRS.\n",
+    "\n",
+    "To plot them, we will transform them back to the `GD1Koposov10` frame; that way, the axes of the figure are aligned with the GD-1, which will make it easy to select stars near the centerline of the stream.\n",
+    "\n",
+    "To do that, we'll put the results into a `GaiaData` object, provided by the [pyia library](https://pyia.readthedocs.io/en/latest/api/pyia.GaiaData.html)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "pyia.data.GaiaData"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from pyia import GaiaData\n",
+    "\n",
+    "gaia_data = GaiaData(results)\n",
+    "type(gaia_data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can extract sky coordinates from the `GaiaData` object, like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import astropy.units as u\n",
+    "\n",
+    "skycoord = gaia_data.get_skycoord(\n",
+    "                distance=8*u.kpc, \n",
+    "                radial_velocity=0*u.km/u.s)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We provide `distance` and `radial_velocity` to prepare the data for reflex correction, which we explain below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "astropy.coordinates.sky_coordinate.SkyCoord"
+      ]
+     },
+     "execution_count": 75,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(skycoord)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The result is an Astropy `SkyCoord` object ([documentation here](https://docs.astropy.org/en/stable/api/astropy.coordinates.SkyCoord.html#astropy.coordinates.SkyCoord)), which provides `transform_to`, so we can transform the coordinates to other frames."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "astropy.coordinates.sky_coordinate.SkyCoord"
+      ]
+     },
+     "execution_count": 76,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import gala.coordinates as gc\n",
+    "\n",
+    "transformed = skycoord.transform_to(gc.GD1Koposov10)\n",
+    "type(transformed)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The result is another `SkyCoord` object, now in the `GD1Koposov10` frame."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The next step is to correct the proper motion measurements from Gaia for reflex due to the motion of our solar system around the Galactic center.\n",
+    "\n",
+    "When we created `skycoord`, we provided `distance` and `radial_velocity` as arguments, which means we ignore the measurements provided by Gaia and replace them with these fixed values.\n",
+    "\n",
+    "That might seem like a strange thing to do, but here's the motivation:\n",
+    "\n",
+    "* Because the stars in GD-1 are so far away, the distance estimates we get from Gaia, which are based on parallax, are not very precise.  So we replace them with our current best estimate of the mean distance to GD-1, about 8 kpc.  See [Koposov, Rix, and Hogg, 2010](https://ui.adsabs.harvard.edu/abs/2010ApJ...712..260K/abstract).\n",
+    "\n",
+    "* For the other stars in the table, this distance estimate will be inaccurate, so reflex correction will not be correct.  But that should have only a small effect on our ability to identify stars with the proper motion we expect for GD-1.\n",
+    "\n",
+    "* The measurement of radial velocity has no effect on the correction for proper motion; the value we provide is arbitrary, but we have to provide a value to avoid errors in the reflex correction calculation.\n",
+    "\n",
+    "We are grateful to Adrian Price-Whelen for his help explaining this step in the analysis."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "With this preparation, we can use `reflex_correct` from Gala ([documentation here](https://gala-astro.readthedocs.io/en/latest/api/gala.coordinates.reflex_correct.html)) to correct for solar reflex motion."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "astropy.coordinates.sky_coordinate.SkyCoord"
+      ]
+     },
+     "execution_count": 77,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gd1_coord = gc.reflex_correct(transformed)\n",
+    "\n",
+    "type(gd1_coord)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The result is a `SkyCoord` object that contains \n",
+    "\n",
+    "* The transformed coordinates as attributes named `phi1` and `phi2`, which represent right ascension and declination in the `GD1Koposov10` frame.\n",
+    "\n",
+    "* The transformed and corrected proper motions as `pm_phi1_cosphi2` and `pm_phi2`.\n",
+    "\n",
+    "We can select the coordinates like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "phi1 = gd1_coord.phi1\n",
+    "phi2 = gd1_coord.phi2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And plot them like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(phi1, phi2, 'ko', markersize=0.1, alpha=0.2)\n",
+    "\n",
+    "plt.xlabel('ra (degree GD1)')\n",
+    "plt.ylabel('dec (degree GD1)');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Remember that we started with a rectangle in GD-1 coordinates.  When transformed to ICRS, it's a non-rectangular polygon.  Now that we have transformed back to GD-1 coordinates, it's a rectangle again."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Pandas DataFrame\n",
+    "\n",
+    "At this point we have three objects containing different subsets of the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "astropy.table.table.Table"
+      ]
+     },
+     "execution_count": 80,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(results)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "pyia.data.GaiaData"
+      ]
+     },
+     "execution_count": 81,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(gaia_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "astropy.coordinates.sky_coordinate.SkyCoord"
+      ]
+     },
+     "execution_count": 82,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(gd1_coord)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "On one hand, this makes sense, since each object provides different capabilities.  But working with three different object types can be awkward.\n",
+    "\n",
+    "It will be more convenient to choose one object and get all of the data into it.  We'll use a Pandas DataFrame, for two reasons:\n",
+    "\n",
+    "1. It provides capabilities that are pretty much a superset of the other data structures, so it's the all-in-one solution.\n",
+    "\n",
+    "2. Pandas is a general-purpose tool that is useful in many domains, especially data science.  If you are going to develop expertise in one tool, Pandas is a good choice.\n",
+    "\n",
+    "However, compared to an Astropy `Table`, Pandas has one big drawback: it does not keep the metadata associated with the table, including the units for the columns."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It's easy to convert a `Table` to a Pandas `DataFrame`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(140340, 8)"
+      ]
+     },
+     "execution_count": 83,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "df = results.to_pandas()\n",
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`DataFrame` provides `shape`, which shows the number of rows and columns.\n",
+    "\n",
+    "It also provides `head`, which displays the first few rows.  It is useful for spot-checking large results as you go along."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>source_id</th>\n",
+       "      <th>ra</th>\n",
+       "      <th>dec</th>\n",
+       "      <th>pmra</th>\n",
+       "      <th>pmdec</th>\n",
+       "      <th>parallax</th>\n",
+       "      <th>parallax_error</th>\n",
+       "      <th>radial_velocity</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>637987125186749568</td>\n",
+       "      <td>142.483019</td>\n",
+       "      <td>21.757716</td>\n",
+       "      <td>-2.516838</td>\n",
+       "      <td>2.941813</td>\n",
+       "      <td>-0.257345</td>\n",
+       "      <td>0.823721</td>\n",
+       "      <td>1.000000e+20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>638285195917112960</td>\n",
+       "      <td>142.254529</td>\n",
+       "      <td>22.476168</td>\n",
+       "      <td>2.662702</td>\n",
+       "      <td>-12.165984</td>\n",
+       "      <td>0.422728</td>\n",
+       "      <td>0.297472</td>\n",
+       "      <td>1.000000e+20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>638073505568978688</td>\n",
+       "      <td>142.645286</td>\n",
+       "      <td>22.166932</td>\n",
+       "      <td>18.306747</td>\n",
+       "      <td>-7.950660</td>\n",
+       "      <td>0.103640</td>\n",
+       "      <td>0.544584</td>\n",
+       "      <td>1.000000e+20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>638086386175786752</td>\n",
+       "      <td>142.577394</td>\n",
+       "      <td>22.227920</td>\n",
+       "      <td>0.987786</td>\n",
+       "      <td>-2.584105</td>\n",
+       "      <td>-0.857327</td>\n",
+       "      <td>1.059607</td>\n",
+       "      <td>1.000000e+20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>638049655615392384</td>\n",
+       "      <td>142.589136</td>\n",
+       "      <td>22.110783</td>\n",
+       "      <td>0.244439</td>\n",
+       "      <td>-4.941079</td>\n",
+       "      <td>0.099625</td>\n",
+       "      <td>0.486224</td>\n",
+       "      <td>1.000000e+20</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            source_id          ra        dec       pmra      pmdec  parallax  \\\n",
+       "0  637987125186749568  142.483019  21.757716  -2.516838   2.941813 -0.257345   \n",
+       "1  638285195917112960  142.254529  22.476168   2.662702 -12.165984  0.422728   \n",
+       "2  638073505568978688  142.645286  22.166932  18.306747  -7.950660  0.103640   \n",
+       "3  638086386175786752  142.577394  22.227920   0.987786  -2.584105 -0.857327   \n",
+       "4  638049655615392384  142.589136  22.110783   0.244439  -4.941079  0.099625   \n",
+       "\n",
+       "   parallax_error  radial_velocity  \n",
+       "0        0.823721     1.000000e+20  \n",
+       "1        0.297472     1.000000e+20  \n",
+       "2        0.544584     1.000000e+20  \n",
+       "3        1.059607     1.000000e+20  \n",
+       "4        0.486224     1.000000e+20  "
+      ]
+     },
+     "execution_count": 84,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Python detail: `shape` is an attribute, so we can display it's value without calling it as a function; `head` is a function, so we need the parentheses."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can extract the columns we want from `gd1_coord` and add them as columns in the `DataFrame`.  `phi1` and `phi2` contain the transformed coordinates."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(140340, 10)"
+      ]
+     },
+     "execution_count": 85,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['phi1'] = gd1_coord.phi1\n",
+    "df['phi2'] = gd1_coord.phi2\n",
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`pm_phi1_cosphi2` and `pm_phi2` contain the components of proper motion in the transformed frame."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(140340, 12)"
+      ]
+     },
+     "execution_count": 86,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['pm_phi1'] = gd1_coord.pm_phi1_cosphi2\n",
+    "df['pm_phi2'] = gd1_coord.pm_phi2\n",
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Detail:** If you notice that `SkyCoord` has an attribute called `proper_motion`, you might wonder why we are not using it.\n",
+    "\n",
+    "We could have: `proper_motion` contains the same data as `pm_phi1_cosphi2` and `pm_phi2`, but in a different format."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plot proper motion\n",
+    "\n",
+    "Now we are ready to replicate one of the panels in Figure 1 of the Price-Whelan and Bonaca paper, the one that shows the components of proper motion as a scatter plot:\n",
+    "\n",
+    "<img width=\"300\" src=\"https://github.com/datacarpentry/astronomy-python/raw/gh-pages/fig/gd1-1.png\">\n",
+    "\n",
+    "In this figure, the shaded area is a high-density region of stars with the proper motion we expect for stars in GD-1.  \n",
+    "\n",
+    "* Due to the nature of tidal streams, we expect the proper motion for most stars to be along the axis of the stream; that is, we expect motion in the direction of `phi2` to be near 0.\n",
+    "\n",
+    "* In the direction of `phi1`, we don't have a prior expectation for proper motion, except that it should form a cluster at a non-zero value. \n",
+    "\n",
+    "To locate this cluster, we'll select stars near the centerline of GD-1 and plot their proper motion."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Selecting the centerline\n",
+    "\n",
+    "As we can see in the following figure, many stars in GD-1 are less than 1 degree of declination from the line `phi2=0`.\n",
+    "\n",
+    "<img src=\"https://github.com/datacarpentry/astronomy-python/raw/gh-pages/fig/gd1-4.png\">\n",
+    "\n",
+    "If we select stars near this line, they are more likely to be in GD-1.\n",
+    "\n",
+    "We'll start by selecting the `phi2` column from the `DataFrame`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "pandas.core.series.Series"
+      ]
+     },
+     "execution_count": 99,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "phi2 = df['phi2']\n",
+    "type(phi2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The result is a `Series`, which is the structure Pandas uses to represent columns.\n",
+    "\n",
+    "We can use a comparison operator, `>`, to compare the values in a `Series` to a constant."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "pandas.core.series.Series"
+      ]
+     },
+     "execution_count": 100,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "phi2_min = -1.0 * u.deg\n",
+    "phi2_max = 1.0 * u.deg\n",
+    "\n",
+    "mask = (df['phi2'] > phi2_min)\n",
+    "type(mask)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dtype('bool')"
+      ]
+     },
+     "execution_count": 101,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mask.dtype"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The result is a `Series` of Boolean values, that is, `True` and `False`. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    False\n",
+       "1    False\n",
+       "2    False\n",
+       "3    False\n",
+       "4    False\n",
+       "Name: phi2, dtype: bool"
+      ]
+     },
+     "execution_count": 102,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mask.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A Boolean `Series` is sometimes called a \"mask\" because we can use it to mask out some of the rows in a `DataFrame` and select the rest, like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "pandas.core.frame.DataFrame"
+      ]
+     },
+     "execution_count": 103,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "selected = df[mask]\n",
+    "type(selected)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`selected` is a `DataFrame` that contains only the rows from `df` that correspond to `True` values in `mask`.\n",
+    "\n",
+    "The previous mask selects all stars where `phi2` exceeds `phi2_min`; now we'll select stars where `phi2` falls between `phi2_min` and `phi2_max`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "phi_mask = ((df['phi2'] > phi2_min) & \n",
+    "            (df['phi2'] < phi2_max))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `&` operator computes \"logical AND\", which means the result is true where elements from both Boolean `Series` are true.\n",
+    "\n",
+    "The sum of a Boolean `Series` is the number of `True` values, so we can use `sum` to see how many stars are in the selected region."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "25084"
+      ]
+     },
+     "execution_count": 105,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "phi_mask.sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And we can use `phi1_mask` to select stars near the centerline, which are more likely to be in GD-1."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "25084"
+      ]
+     },
+     "execution_count": 106,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "centerline = df[phi_mask]\n",
+    "len(centerline)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's a scatter plot of proper motion for the selected stars."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pm1 = centerline['pm_phi1']\n",
+    "pm2 = centerline['pm_phi2']\n",
+    "\n",
+    "plt.plot(pm1, pm2, 'ko', markersize=0.1, alpha=0.1)\n",
+    "    \n",
+    "plt.xlabel('Proper motion phi1 (GD1 frame)')\n",
+    "plt.ylabel('Proper motion phi2 (GD1 frame)');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Looking at these results, we see a large cluster around (0, 0), and a smaller cluster near (0, -10).\n",
+    "\n",
+    "We can use `xlim` and `ylim` to set the limits on the axes and zoom in on the region near (0, 0)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pm1 = centerline['pm_phi1']\n",
+    "pm2 = centerline['pm_phi2']\n",
+    "\n",
+    "plt.plot(pm1, pm2, 'ko', markersize=0.3, alpha=0.3)\n",
+    "    \n",
+    "plt.xlabel('Proper motion phi1 (GD1 frame)')\n",
+    "plt.ylabel('Proper motion phi2 (GD1 frame)')\n",
+    "\n",
+    "plt.xlim(-12, 8)\n",
+    "plt.ylim(-10, 10);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can see the smaller cluster more clearly.\n",
+    "\n",
+    "You might notice that our figure is less dense than the one in the paper.  That's because we started with a set of stars from a relatively small region.  The figure in the paper is based on a region about 10 times bigger.\n",
+    "\n",
+    "In the next lesson we'll go back and select stars from a larger region.  But first we'll use the proper motion data to identify stars likely to be in GD-1."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Filtering based on proper motion\n",
+    "\n",
+    "The next step is to select stars in the \"overdense\" region of proper motion, which are candidates to be in GD-1.\n",
+    "\n",
+    "In the original paper, Price-Whelan and Bonaca used a polygon to cover this region, as shown in this figure.\n",
+    "\n",
+    "<img width=\"300\" src=\"https://github.com/datacarpentry/astronomy-python/raw/gh-pages/fig/gd1-1.png\">\n",
+    "\n",
+    "We'll use a simple rectangle for now, but in a later lesson we'll see how to select a polygonal region as well.\n",
+    "\n",
+    "Here are bounds on proper motion we chose by eye,"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pm1_min = -8.9\n",
+    "pm1_max = -6.9\n",
+    "pm2_min = -2.2\n",
+    "pm2_max =  1.0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To draw these bounds, we'll make two lists containing the coordinates of the corners of the rectangle."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pm1_rect = [pm1_min, pm1_min, pm1_max, pm1_max, pm1_min] * u.mas/u.yr\n",
+    "pm2_rect = [pm2_min, pm2_max, pm2_max, pm2_min, pm2_min] * u.mas/u.yr"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's what the plot looks like with the bounds we chose."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 118,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(pm1, pm2, 'ko', markersize=0.3, alpha=0.3)\n",
+    "plt.plot(pm1_rect, pm2_rect, '-')\n",
+    "    \n",
+    "plt.xlabel('Proper motion phi1 (GD1 frame)')\n",
+    "plt.ylabel('Proper motion phi2 (GD1 frame)')\n",
+    "\n",
+    "plt.xlim(-12, 8)\n",
+    "plt.ylim(-10, 10);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To select rows that fall within these bounds, we'll use the following function, which uses Pandas operators to make a mask that selects rows where `series` falls between `low` and `high`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 119,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def between(series, low, high):\n",
+    "    \"\"\"Make a Boolean Series.\n",
+    "    \n",
+    "    series: Pandas Series\n",
+    "    low: lower bound\n",
+    "    high: upper bound\n",
+    "    \n",
+    "    returns: Boolean Series\n",
+    "    \"\"\"\n",
+    "    return (series > low) & (series < high)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The following mask select stars with proper motion in the region we chose."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 120,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pm_mask = (between(df['pm_phi1'], pm1_min, pm1_max) & \n",
+    "           between(df['pm_phi2'], pm2_min, pm2_max))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Again, the sum of a Boolean series is the number of `True` values."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 121,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1049"
+      ]
+     },
+     "execution_count": 121,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pm_mask.sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can use this mask to select rows from `df`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 122,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1049"
+      ]
+     },
+     "execution_count": 122,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "selected = df[pm_mask]\n",
+    "len(selected)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "These are the stars we think are likely to be in GD-1.  Let's see what they look like, plotting their coordinates (not their proper motion)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 123,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "phi1 = selected['phi1']\n",
+    "phi2 = selected['phi2']\n",
+    "\n",
+    "plt.plot(phi1, phi2, 'ko', markersize=0.5, alpha=0.5)\n",
+    "\n",
+    "plt.xlabel('ra (degree GD1)')\n",
+    "plt.ylabel('dec (degree GD1)');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now that's starting to look like a tidal stream!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Saving the DataFrame\n",
+    "\n",
+    "At this point we have run a successful query and cleaned up the results; this is a good time to save the data.\n",
+    "\n",
+    "To save a Pandas `DataFrame`, one option is to convert it to an Astropy `Table`, like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 124,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "astropy.table.table.Table"
+      ]
+     },
+     "execution_count": 124,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "selected_table = Table.from_pandas(selected)\n",
+    "type(selected_table)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Then we could write the `Table` to a FITS file, as we did in the previous lesson.  \n",
+    "\n",
+    "But Pandas provides functions to write DataFrames in other formats; to see what they are [find the functions here that begin with `to_`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html).\n",
+    "\n",
+    "One of the best options is HDF5, which is Version 5 of [Hierarchical Data Format](https://en.wikipedia.org/wiki/Hierarchical_Data_Format).\n",
+    "\n",
+    "HDF5 is a binary format, so files are small and fast to read and write (like FITS, but unlike XML).\n",
+    "\n",
+    "An HDF5 file is similar to an SQL database in the sense that it can contain more than one table, although in HDF5 vocabulary, a table is called a Dataset.  ([Multi-extension FITS files](https://www.stsci.edu/itt/review/dhb_2011/Intro/intro_ch23.html) can also contain more than one table.)\n",
+    "\n",
+    "And HDF5 stores the metadata associated with the table, including column names, row labels, and data types (like FITS).\n",
+    "\n",
+    "Finally, HDF5 is a cross-language standard, so if you write an HDF5 file with Pandas, you can read it back with many other software tools (more than FITS)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Before we write the HDF5, let's delete the old one, if it exists."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!rm -f gd1_dataframe.hdf5"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can write a Pandas `DataFrame` to an HDF5 file like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "filename = 'gd1_dataframe.hdf5'\n",
+    "\n",
+    "df.to_hdf(filename, 'df')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Because an HDF5 file can contain more than one Dataset, we have to provide a name, or \"key\", that identifies the Dataset in the file.\n",
+    "\n",
+    "We could use any string as the key, but in this example I use the variable name `df`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** We're going to need `centerline` and `selected` later as well.  Write a line or two of code to add it as a second Dataset in the HDF5 file."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 127,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "centerline.to_hdf(filename, 'centerline')\n",
+    "selected.to_hdf(filename, 'selected')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Detail:** Reading and writing HDF5 tables requires a library called `PyTables` that is not always installed with Pandas.  You can install it with pip like this:\n",
+    "\n",
+    "```\n",
+    "pip install tables\n",
+    "```\n",
+    "\n",
+    "If you install it using Conda, the name of the package is `pytables`.\n",
+    "\n",
+    "```\n",
+    "conda install pytables\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can use `ls` to confirm that the file exists and check the size:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 128,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-rw-rw-r-- 1 downey downey 17M Oct 19 12:05 gd1_dataframe.hdf5\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!ls -lh gd1_dataframe.hdf5"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you are using Windows, `ls` might not work; in that case, try:\n",
+    "\n",
+    "```\n",
+    "!dir gd1_dataframe.hdf5\n",
+    "```\n",
+    "\n",
+    "We can read the file back like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 129,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(140340, 12)"
+      ]
+     },
+     "execution_count": 129,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "read_back_df = pd.read_hdf(filename, 'df')\n",
+    "read_back_df.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Pandas can write a variety of other formats, [which you can read about here](https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "In this lesson, we re-loaded the Gaia data we saved from a previous query.\n",
+    "\n",
+    "We transformed the coordinates and proper motion from ICRS to a frame aligned with GD-1, and stored the results in a Pandas `DataFrame`.\n",
+    "\n",
+    "Then we replicated the selection process from the Price-Whelan and Bonaca paper:\n",
+    "\n",
+    "* We selected stars near the centerline of GD-1 and made a scatter plot of their proper motion.\n",
+    "\n",
+    "* We identified a region of proper motion that contains stars likely to be in GD-1.\n",
+    "\n",
+    "* We used a Boolean `Series` as a mask to select stars whose proper motion is in that region.\n",
+    "\n",
+    "So far, we have used data from a relatively small region of the sky.  In the next lesson, we'll write a query that selects stars based on proper motion, which will allow us to explore a larger region."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Best practices\n",
+    "\n",
+    "* When you make a scatter plot, adjust the size of the markers and their transparency so the figure is not overplotted; otherwise it can misrepresent the data badly.\n",
+    "\n",
+    "* For simple scatter plots in Matplotlib, `plot` is faster than `scatter`.\n",
+    "\n",
+    "* An Astropy `Table` and a Pandas `DataFrame` are similar in many ways and they provide many of the same functions.  They have pros and cons, but for many projects, either one would be a reasonable choice."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/_build/html/_sources/04_select.ipynb b/_build/html/_sources/04_select.ipynb
new file mode 100644
index 0000000..56cda75
--- /dev/null
+++ b/_build/html/_sources/04_select.ipynb
@@ -0,0 +1,1445 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Chapter 4\n",
+    "\n",
+    "This is the fourth in a series of notebooks related to astronomy data.\n",
+    "\n",
+    "As a running example, we are replicating parts of the analysis in a recent paper, \"[Off the beaten path: Gaia reveals GD-1 stars outside of the main stream](https://arxiv.org/abs/1805.00425)\" by Adrian M. Price-Whelan and Ana Bonaca.\n",
+    "\n",
+    "In the first lesson, we wrote ADQL queries and used them to select and download data from the Gaia server.\n",
+    "\n",
+    "In the second lesson, we write a query to select stars from the region of the sky where we expect GD-1 to be, and save the results in a FITS file.\n",
+    "\n",
+    "In the third lesson, we read that data back and identified stars with the proper motion we expect for GD-1."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Outline\n",
+    "\n",
+    "Here are the steps in this lesson:\n",
+    "\n",
+    "1. Using data from the previous lesson, we'll identify the values of proper motion for stars likely to be in GD-1.\n",
+    "\n",
+    "2. Then we'll compose an ADQL query that selects stars based on proper motion, so we can download only the data we need.\n",
+    "\n",
+    "3. We'll also see how to write the results to a CSV file.\n",
+    "\n",
+    "That will make it possible to search a bigger region of the sky in a single query.\n",
+    "\n",
+    "After completing this lesson, you should be able to\n",
+    "\n",
+    "* Convert proper motion between frames.\n",
+    "\n",
+    "* Write an ADQL query that selects based on proper motion."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Installing libraries\n",
+    "\n",
+    "If you are running this notebook on Colab, you can run the following cell to install Astroquery and a the other libraries we'll use.\n",
+    "\n",
+    "If you are running this notebook on your own computer, you might have to install these libraries yourself.  \n",
+    "\n",
+    "If you are using this notebook as part of a Carpentries workshop, you should have received setup instructions.\n",
+    "\n",
+    "TODO: Add a link to the instructions.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# If we're running on Colab, install libraries\n",
+    "\n",
+    "import sys\n",
+    "IN_COLAB = 'google.colab' in sys.modules\n",
+    "\n",
+    "if IN_COLAB:\n",
+    "    !pip install astroquery astro-gala pyia python-wget"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Reload the data\n",
+    "\n",
+    "The following cells download the data from the previous lesson, if necessary, and load it into a Pandas `DataFrame`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from wget import download\n",
+    "\n",
+    "filename = 'gd1_dataframe.hdf5'\n",
+    "path = 'https://github.com/AllenDowney/AstronomicalData/raw/main/data/'\n",
+    "\n",
+    "if not os.path.exists(filename):\n",
+    "    print(download(path+filename))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "df = pd.read_hdf(filename, 'df')\n",
+    "centerline = pd.read_hdf(filename, 'centerline')\n",
+    "selected = pd.read_hdf(filename, 'selected')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Selection by proper motion\n",
+    "\n",
+    "At this point we have downloaded data for a relatively large number of stars (more than 100,000) and selected a relatively small number (around 1000).\n",
+    "\n",
+    "It would be more efficient to use ADQL to select only the stars we need.  That would also make it possible to download data covering a larger region of the sky.\n",
+    "\n",
+    "However, the selection we did was based on proper motion in the `GD1Koposov10` frame.  In order to do the same selection in ADQL, we have to work with proper motions in ICRS.\n",
+    "\n",
+    "As a reminder, here's the rectangle we selected based on proper motion in the `GD1Koposov10` frame."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pm1_min = -8.9\n",
+    "pm1_max = -6.9\n",
+    "pm2_min = -2.2\n",
+    "pm2_max =  1.0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import astropy.units as u\n",
+    "\n",
+    "pm1_rect = [pm1_min, pm1_min, pm1_max, pm1_max, pm1_min] * u.mas/u.yr\n",
+    "pm2_rect = [pm2_min, pm2_max, pm2_max, pm2_min, pm2_min] * u.mas/u.yr"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The following figure shows:\n",
+    "\n",
+    "* Proper motion for the stars we selected along the center line of GD-1,\n",
+    "\n",
+    "* The rectangle we selected, and\n",
+    "\n",
+    "* The stars inside the rectangle highlighted in green."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "pm1 = centerline['pm_phi1']\n",
+    "pm2 = centerline['pm_phi2']\n",
+    "plt.plot(pm1, pm2, 'ko', markersize=0.3, alpha=0.3)\n",
+    "\n",
+    "pm1 = selected['pm_phi1']\n",
+    "pm2 = selected['pm_phi2']\n",
+    "plt.plot(pm1, pm2, 'gx', markersize=0.3, alpha=0.3)\n",
+    "\n",
+    "plt.plot(pm1_rect, pm2_rect, '-')\n",
+    "    \n",
+    "plt.xlabel('Proper motion phi1 (GD1 frame)')\n",
+    "plt.ylabel('Proper motion phi2 (GD1 frame)')\n",
+    "\n",
+    "plt.xlim(-12, 8)\n",
+    "plt.ylim(-10, 10);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we'll make the same plot using proper motions in the ICRS frame, which are stored in columns `pmra` and `pmdec`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pm1 = centerline['pmra']\n",
+    "pm2 = centerline['pmdec']\n",
+    "plt.plot(pm1, pm2, 'ko', markersize=0.3, alpha=0.3)\n",
+    "\n",
+    "pm1 = selected['pmra']\n",
+    "pm2 = selected['pmdec']\n",
+    "plt.plot(pm1, pm2, 'gx', markersize=1, alpha=0.3)\n",
+    "    \n",
+    "plt.xlabel('Proper motion phi1 (ICRS frame)')\n",
+    "plt.ylabel('Proper motion phi2 (ICRS frame)')\n",
+    "\n",
+    "plt.xlim([-10, 5])\n",
+    "plt.ylim([-20, 5]);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The proper motions of the selected stars are more spread out in this frame, which is why it was preferable to do the selection in the GD-1 frame.\n",
+    "\n",
+    "But now we can define a polygon that encloses the proper motions of these stars in ICRS, \n",
+    "and use the polygon as a selection criterion in an ADQL query.\n",
+    "\n",
+    "SciPy provides a function that computes the [convex hull](https://en.wikipedia.org/wiki/Convex_hull) of a set of points, which is the smallest convex polygon that contains all of the points.\n",
+    "\n",
+    "To use it, I'll select columns `pmra` and `pmdec` and convert them to a NumPy array."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1049, 2)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "points = selected[['pmra','pmdec']].to_numpy()\n",
+    "points.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We'll pass the points to `ConvexHull`, which returns an object that contains the results. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<scipy.spatial.qhull.ConvexHull at 0x7f446b1e8bb0>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from scipy.spatial import ConvexHull\n",
+    "\n",
+    "hull = ConvexHull(points)\n",
+    "hull"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`hull.vertices` contains the indices of the points that fall on the perimeter of the hull."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 692,  873,  141,  303,   42,  622,   45,   83,  127,  182, 1006,\n",
+       "        971,  967, 1001,  969,  940], dtype=int32)"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hull.vertices"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can use them as an index into the original array to select the corresponding rows."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ -4.05037121, -14.75623261],\n",
+       "       [ -3.41981085, -14.72365546],\n",
+       "       [ -3.03521988, -14.44357135],\n",
+       "       [ -2.26847919, -13.7140236 ],\n",
+       "       [ -2.61172203, -13.24797471],\n",
+       "       [ -2.73471401, -13.09054471],\n",
+       "       [ -3.19923146, -12.5942653 ],\n",
+       "       [ -3.34082546, -12.47611926],\n",
+       "       [ -5.67489413, -11.16083338],\n",
+       "       [ -5.95159272, -11.10547884],\n",
+       "       [ -6.42394023, -11.05981295],\n",
+       "       [ -7.09631023, -11.95187806],\n",
+       "       [ -7.30641519, -12.24559977],\n",
+       "       [ -7.04016696, -12.88580702],\n",
+       "       [ -6.00347705, -13.75912098],\n",
+       "       [ -4.42442296, -14.74641176]])"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pm_vertices = points[hull.vertices]\n",
+    "pm_vertices"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To plot the resulting polygon, we have to pull out the x and y coordinates."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pmra_poly, pmdec_poly = np.transpose(pm_vertices)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The following figure shows proper motion in ICRS again, along with the convex hull we just computed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pm1 = centerline['pmra']\n",
+    "pm2 = centerline['pmdec']\n",
+    "plt.plot(pm1, pm2, 'ko', markersize=0.3, alpha=0.3)\n",
+    "\n",
+    "pm1 = selected['pmra']\n",
+    "pm2 = selected['pmdec']\n",
+    "plt.plot(pm1, pm2, 'gx', markersize=0.3, alpha=0.3)\n",
+    "\n",
+    "plt.plot(pmra_poly, pmdec_poly)\n",
+    "    \n",
+    "plt.xlabel('Proper motion phi1 (ICRS frame)')\n",
+    "plt.ylabel('Proper motion phi2 (ICRS frame)')\n",
+    "\n",
+    "plt.xlim([-10, 5])\n",
+    "plt.ylim([-20, 5]);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To use `pm_vertices` as part of an ADQL query, we have to convert it to a string.\n",
+    "\n",
+    "We'll use `flatten` to convert from a 2-D array to a 1-D array, and `str` to convert each element to a string."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['-4.050371212154984',\n",
+       " '-14.75623260987968',\n",
+       " '-3.4198108491382455',\n",
+       " '-14.723655456335619',\n",
+       " '-3.035219883740934',\n",
+       " '-14.443571352854612',\n",
+       " '-2.268479190206636',\n",
+       " '-13.714023598831554',\n",
+       " '-2.611722027231764',\n",
+       " '-13.247974712069263',\n",
+       " '-2.7347140078529106',\n",
+       " '-13.090544709622938',\n",
+       " '-3.199231461993783',\n",
+       " '-12.594265302440828',\n",
+       " '-3.34082545787549',\n",
+       " '-12.476119260818695',\n",
+       " '-5.674894125178565',\n",
+       " '-11.160833381392624',\n",
+       " '-5.95159272432137',\n",
+       " '-11.105478836426514',\n",
+       " '-6.423940229776128',\n",
+       " '-11.05981294804957',\n",
+       " '-7.096310230579248',\n",
+       " '-11.951878058650085',\n",
+       " '-7.306415190921692',\n",
+       " '-12.245599765990594',\n",
+       " '-7.040166963232815',\n",
+       " '-12.885807024935527',\n",
+       " '-6.0034770546523735',\n",
+       " '-13.759120984106968',\n",
+       " '-4.42442296194263',\n",
+       " '-14.7464117578883']"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "t = [str(x) for x in pm_vertices.flatten()]\n",
+    "t"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now `t` is a list of strings; we can use `join` to make a single string with commas between the elements."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'-4.050371212154984, -14.75623260987968, -3.4198108491382455, -14.723655456335619, -3.035219883740934, -14.443571352854612, -2.268479190206636, -13.714023598831554, -2.611722027231764, -13.247974712069263, -2.7347140078529106, -13.090544709622938, -3.199231461993783, -12.594265302440828, -3.34082545787549, -12.476119260818695, -5.674894125178565, -11.160833381392624, -5.95159272432137, -11.105478836426514, -6.423940229776128, -11.05981294804957, -7.096310230579248, -11.951878058650085, -7.306415190921692, -12.245599765990594, -7.040166963232815, -12.885807024935527, -6.0034770546523735, -13.759120984106968, -4.42442296194263, -14.7464117578883'"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pm_point_list = ', '.join(t)\n",
+    "pm_point_list"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Selecting the region\n",
+    "\n",
+    "Let's review how we got to this point.\n",
+    "\n",
+    "1. We made an ADQL query to the Gaia server to get data for stars in the vicinity of GD-1.\n",
+    "\n",
+    "2. We transformed to `GD1` coordinates so we could select stars along the centerline of GD-1.\n",
+    "\n",
+    "3. We plotted the proper motion of the centerline stars to identify the bounds of the overdense region.\n",
+    "\n",
+    "4. We made a mask that selects stars whose proper motion is in the overdense region.\n",
+    "\n",
+    "The problem is that we downloaded data for more than 100,000 stars and selected only about 1000 of them.\n",
+    "\n",
+    "It will be more efficient if we select on proper motion as part of the query.  That will allow us to work with a larger region of the sky in a single query, and download less unneeded data.\n",
+    "\n",
+    "This query will select on the following conditions:\n",
+    "\n",
+    "* `parallax < 1`\n",
+    "\n",
+    "* `bp_rp BETWEEN -0.75 AND 2`\n",
+    "\n",
+    "* Coordinates within a rectangle in the GD-1 frame, transformed to ICRS.\n",
+    "\n",
+    "* Proper motion with the polygon we just computed.\n",
+    "\n",
+    "The first three conditions are the same as in the previous query.  Only the last one is new.\n",
+    "\n",
+    "Here's the rectangle in the GD-1 frame we'll select."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "phi1_min = -70\n",
+    "phi1_max = -20\n",
+    "phi2_min = -5\n",
+    "phi2_max = 5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "phi1_rect = [phi1_min, phi1_min, phi1_max, phi1_max] * u.deg\n",
+    "phi2_rect = [phi2_min, phi2_max, phi2_max, phi2_min] * u.deg"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's how we transform it to ICRS, as we saw in the previous lesson."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import gala.coordinates as gc\n",
+    "import astropy.coordinates as coord\n",
+    "\n",
+    "corners = gc.GD1Koposov10(phi1=phi1_rect, phi2=phi2_rect)\n",
+    "corners_icrs = corners.transform_to(coord.ICRS)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To use `corners_icrs` as part of an ADQL query, we have to convert it to a string.  Here's how we do that, as we saw in the previous lesson."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'135.30559858565638, 8.398623940157561, 126.50951508623503, 13.44494195652069, 163.0173655836748, 54.24242734020255, 172.9328536286811, 46.47260492416258'"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "point_base = \"{point.ra.value}, {point.dec.value}\"\n",
+    "\n",
+    "t = [point_base.format(point=point)\n",
+    "     for point in corners_icrs]\n",
+    "\n",
+    "point_list = ', '.join(t)\n",
+    "point_list"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we have everything we need to assemble the query."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Assemble the query\n",
+    "\n",
+    "Here's the base string we used for the query in the previous lesson."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query_base = \"\"\"SELECT \n",
+    "{columns}\n",
+    "FROM gaiadr2.gaia_source\n",
+    "WHERE parallax < 1\n",
+    "  AND bp_rp BETWEEN -0.75 AND 2 \n",
+    "  AND 1 = CONTAINS(POINT(ra, dec), \n",
+    "                   POLYGON({point_list}))\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Modify `query_base` by adding a new clause to select stars whose coordinates of proper motion, `pmra` and `pmdec`, fall within the polygon defined by `pm_point_list`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "query_base = \"\"\"SELECT \n",
+    "{columns}\n",
+    "FROM gaiadr2.gaia_source\n",
+    "WHERE parallax < 1\n",
+    "  AND bp_rp BETWEEN -0.75 AND 2 \n",
+    "  AND 1 = CONTAINS(POINT(ra, dec), \n",
+    "                   POLYGON({point_list}))\n",
+    "  AND 1 = CONTAINS(POINT(pmra, pmdec),\n",
+    "                   POLYGON({pm_point_list}))\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here again are the columns we want to select."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "columns = 'source_id, ra, dec, pmra, pmdec, parallax, parallax_error, radial_velocity'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Use `format` to format `query_base` and define `query`, filling in the values of `columns`, `point_list`, and `pm_point_list`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "SELECT \n",
+      "source_id, ra, dec, pmra, pmdec, parallax, parallax_error, radial_velocity\n",
+      "FROM gaiadr2.gaia_source\n",
+      "WHERE parallax < 1\n",
+      "  AND bp_rp BETWEEN -0.75 AND 2 \n",
+      "  AND 1 = CONTAINS(POINT(ra, dec), \n",
+      "                   POLYGON(135.30559858565638, 8.398623940157561, 126.50951508623503, 13.44494195652069, 163.0173655836748, 54.24242734020255, 172.9328536286811, 46.47260492416258))\n",
+      "  AND 1 = CONTAINS(POINT(pmra, pmdec),\n",
+      "                   POLYGON(-4.050371212154984, -14.75623260987968, -3.4198108491382455, -14.723655456335619, -3.035219883740934, -14.443571352854612, -2.268479190206636, -13.714023598831554, -2.611722027231764, -13.247974712069263, -2.7347140078529106, -13.090544709622938, -3.199231461993783, -12.594265302440828, -3.34082545787549, -12.476119260818695, -5.674894125178565, -11.160833381392624, -5.95159272432137, -11.105478836426514, -6.423940229776128, -11.05981294804957, -7.096310230579248, -11.951878058650085, -7.306415190921692, -12.245599765990594, -7.040166963232815, -12.885807024935527, -6.0034770546523735, -13.759120984106968, -4.42442296194263, -14.7464117578883))\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "query = query_base.format(columns=columns, \n",
+    "                            point_list=point_list,\n",
+    "                            pm_point_list=pm_point_list)\n",
+    "print(query)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's how we run it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Created TAP+ (v1.2.1) - Connection:\n",
+      "\tHost: gea.esac.esa.int\n",
+      "\tUse HTTPS: True\n",
+      "\tPort: 443\n",
+      "\tSSL Port: 443\n",
+      "Created TAP+ (v1.2.1) - Connection:\n",
+      "\tHost: geadata.esac.esa.int\n",
+      "\tUse HTTPS: True\n",
+      "\tPort: 443\n",
+      "\tSSL Port: 443\n",
+      "INFO: Query finished. [astroquery.utils.tap.core]\n",
+      "<Table length=7346>\n",
+      "      name       dtype    unit                              description                             n_bad\n",
+      "--------------- ------- -------- ------------------------------------------------------------------ -----\n",
+      "      source_id   int64          Unique source identifier (unique within a particular Data Release)     0\n",
+      "             ra float64      deg                                                    Right ascension     0\n",
+      "            dec float64      deg                                                        Declination     0\n",
+      "           pmra float64 mas / yr                         Proper motion in right ascension direction     0\n",
+      "          pmdec float64 mas / yr                             Proper motion in declination direction     0\n",
+      "       parallax float64      mas                                                           Parallax     0\n",
+      " parallax_error float64      mas                                         Standard error of parallax     0\n",
+      "radial_velocity float64   km / s                                                    Radial velocity  7295\n",
+      "Jobid: 1603132746237O\n",
+      "Phase: COMPLETED\n",
+      "Owner: None\n",
+      "Output file: async_20201019143906.vot\n",
+      "Results: None\n"
+     ]
+    }
+   ],
+   "source": [
+    "from astroquery.gaia import Gaia\n",
+    "\n",
+    "job = Gaia.launch_job_async(query)\n",
+    "print(job)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And get the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "7346"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "candidate_table = job.get_results()\n",
+    "len(candidate_table)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plotting one more time\n",
+    "\n",
+    "Let's see what the results look like."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x = candidate_table['ra']\n",
+    "y = candidate_table['dec']\n",
+    "plt.plot(x, y, 'ko', markersize=0.3, alpha=0.3)\n",
+    "\n",
+    "plt.xlabel('ra (degree ICRS)')\n",
+    "plt.ylabel('dec (degree ICRS)');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here we can see why it was useful to transform these coordinates.  In ICRS, it is more difficult to identity the stars near the centerline of GD-1.\n",
+    "\n",
+    "So, before we move on to the next step, let's collect the code we used to transform the coordinates and make a Pandas `DataFrame`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pyia import GaiaData\n",
+    "\n",
+    "def make_dataframe(table):\n",
+    "    \"\"\"Transform coordinates from ICRS to GD-1 frame.\n",
+    "    \n",
+    "    table: Astropy Table\n",
+    "    \n",
+    "    returns: Pandas DataFrame\n",
+    "    \"\"\"\n",
+    "    gaia_data = GaiaData(table)\n",
+    "\n",
+    "    c_sky = gaia_data.get_skycoord(distance=8*u.kpc, \n",
+    "                                   radial_velocity=0*u.km/u.s)\n",
+    "    c_gd1 = gc.reflex_correct(\n",
+    "                c_sky.transform_to(gc.GD1Koposov10))\n",
+    "\n",
+    "    df = table.to_pandas()\n",
+    "    df['phi1'] = c_gd1.phi1\n",
+    "    df['phi2'] = c_gd1.phi2\n",
+    "    df['pm_phi1'] = c_gd1.pm_phi1_cosphi2\n",
+    "    df['pm_phi2'] = c_gd1.pm_phi2\n",
+    "    return df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's how we can use this function:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "candidate_df = make_dataframe(candidate_table)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And let's see the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 648x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x = candidate_df['phi1']\n",
+    "y = candidate_df['phi2']\n",
+    "\n",
+    "plt.plot(x, y, 'ko', markersize=0.5, alpha=0.5)\n",
+    "\n",
+    "plt.xlabel('ra (degree GD1)')\n",
+    "plt.ylabel('dec (degree GD1)');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We're starting to see GD-1 more clearly.\n",
+    "\n",
+    "We can compare this figure with one of these panels in Figure 1 from the original paper:\n",
+    "\n",
+    "<img height=\"150\" src=\"https://github.com/datacarpentry/astronomy-python/raw/gh-pages/fig/gd1-2.png\">\n",
+    "\n",
+    "<img height=\"150\" src=\"https://github.com/datacarpentry/astronomy-python/raw/gh-pages/fig/gd1-4.png\">\n",
+    "\n",
+    "The top panel shows stars selected based on proper motion only, so it is comparable to our figure (although notice that it covers a wider region).\n",
+    "\n",
+    "In the next lesson, we will use photometry data from Pan-STARRS to do a second round of filtering, and see if we can replicate the bottom panel.\n",
+    "\n",
+    "We'll also learn how to add annotations like the ones in the figure from the paper, and customize the style of the figure to present the results clearly and compellingly."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Saving the DataFrame\n",
+    "\n",
+    "Let's save this `DataFrame` so we can pick up where we left off without running this query again."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!rm -f gd1_candidates.hdf5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "filename = 'gd1_candidates.hdf5'\n",
+    "\n",
+    "candidate_df.to_hdf(filename, 'candidate_df')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can use `ls` to confirm that the file exists and check the size:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-rw-rw-r-- 1 downey downey 756K Oct 19 14:39 gd1_candidates.hdf5\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!ls -lh gd1_candidates.hdf5"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you are using Windows, `ls` might not work; in that case, try:\n",
+    "\n",
+    "```\n",
+    "!dir gd1_candidates.hdf5\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## CSV\n",
+    "\n",
+    "Pandas can write a variety of other formats, [which you can read about here](https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html).\n",
+    "\n",
+    "We won't cover all of them, but one other important one is [CSV](https://en.wikipedia.org/wiki/Comma-separated_values), which stands for \"comma-separated values\".\n",
+    "\n",
+    "CSV is a plain-text format with minimal formatting requirements, so it can be read and written by pretty much any tool that works with data.  In that sense, it is the \"least common denominator\" of data formats.\n",
+    "\n",
+    "However, it has an important limitation: some information about the data gets lost in translation, notably the data types.  If you read a CSV file from someone else, you might need some additional information to make sure you are getting it right.\n",
+    "\n",
+    "Also, CSV files tend to be big, and slow to read and write.\n",
+    "\n",
+    "With those caveats, here's how to write one:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "candidate_df.to_csv('gd1_candidates.csv')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can check the file size like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-rw-rw-r-- 1 downey downey 1.6M Oct 19 14:39 gd1_candidates.csv\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!ls -lh gd1_candidates.csv"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The CSV file about 2 times bigger than the HDF5 file (so that's not that bad, really).\n",
+    "\n",
+    "We can see the first few lines like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      ",source_id,ra,dec,pmra,pmdec,parallax,parallax_error,radial_velocity,phi1,phi2,pm_phi1,pm_phi2\r\n",
+      "0,635559124339440000,137.58671691646745,19.1965441084838,-3.770521900009566,-12.490481778113859,0.7913934419894347,0.2717538145759051,,-59.63048941944396,-1.21648525150429,-7.361362712556612,-0.5926328820420083\r\n",
+      "1,635860218726658176,138.5187065217173,19.09233926905897,-5.941679495793577,-11.346409129876392,0.30745551377348623,0.19946557779138105,,-59.247329893833296,-2.0160784008206476,-7.527126084599517,1.7487794924398758\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!head -3 gd1_candidates.csv"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The CSV file contains the names of the columns, but not the data types.\n",
+    "\n",
+    "We can read the CSV file back like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "read_back_csv = pd.read_csv('gd1_candidates.csv')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's compare the first few rows of `candidate_df` and `read_back_csv`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>source_id</th>\n",
+       "      <th>ra</th>\n",
+       "      <th>dec</th>\n",
+       "      <th>pmra</th>\n",
+       "      <th>pmdec</th>\n",
+       "      <th>parallax</th>\n",
+       "      <th>parallax_error</th>\n",
+       "      <th>radial_velocity</th>\n",
+       "      <th>phi1</th>\n",
+       "      <th>phi2</th>\n",
+       "      <th>pm_phi1</th>\n",
+       "      <th>pm_phi2</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>635559124339440000</td>\n",
+       "      <td>137.586717</td>\n",
+       "      <td>19.196544</td>\n",
+       "      <td>-3.770522</td>\n",
+       "      <td>-12.490482</td>\n",
+       "      <td>0.791393</td>\n",
+       "      <td>0.271754</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-59.630489</td>\n",
+       "      <td>-1.216485</td>\n",
+       "      <td>-7.361363</td>\n",
+       "      <td>-0.592633</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>635860218726658176</td>\n",
+       "      <td>138.518707</td>\n",
+       "      <td>19.092339</td>\n",
+       "      <td>-5.941679</td>\n",
+       "      <td>-11.346409</td>\n",
+       "      <td>0.307456</td>\n",
+       "      <td>0.199466</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-59.247330</td>\n",
+       "      <td>-2.016078</td>\n",
+       "      <td>-7.527126</td>\n",
+       "      <td>1.748779</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>635674126383965568</td>\n",
+       "      <td>138.842874</td>\n",
+       "      <td>19.031798</td>\n",
+       "      <td>-3.897001</td>\n",
+       "      <td>-12.702780</td>\n",
+       "      <td>0.779463</td>\n",
+       "      <td>0.223692</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-59.133391</td>\n",
+       "      <td>-2.306901</td>\n",
+       "      <td>-7.560608</td>\n",
+       "      <td>-0.741800</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            source_id          ra        dec      pmra      pmdec  parallax  \\\n",
+       "0  635559124339440000  137.586717  19.196544 -3.770522 -12.490482  0.791393   \n",
+       "1  635860218726658176  138.518707  19.092339 -5.941679 -11.346409  0.307456   \n",
+       "2  635674126383965568  138.842874  19.031798 -3.897001 -12.702780  0.779463   \n",
+       "\n",
+       "   parallax_error  radial_velocity       phi1      phi2   pm_phi1   pm_phi2  \n",
+       "0        0.271754              NaN -59.630489 -1.216485 -7.361363 -0.592633  \n",
+       "1        0.199466              NaN -59.247330 -2.016078 -7.527126  1.748779  \n",
+       "2        0.223692              NaN -59.133391 -2.306901 -7.560608 -0.741800  "
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "candidate_df.head(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Unnamed: 0</th>\n",
+       "      <th>source_id</th>\n",
+       "      <th>ra</th>\n",
+       "      <th>dec</th>\n",
+       "      <th>pmra</th>\n",
+       "      <th>pmdec</th>\n",
+       "      <th>parallax</th>\n",
+       "      <th>parallax_error</th>\n",
+       "      <th>radial_velocity</th>\n",
+       "      <th>phi1</th>\n",
+       "      <th>phi2</th>\n",
+       "      <th>pm_phi1</th>\n",
+       "      <th>pm_phi2</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>635559124339440000</td>\n",
+       "      <td>137.586717</td>\n",
+       "      <td>19.196544</td>\n",
+       "      <td>-3.770522</td>\n",
+       "      <td>-12.490482</td>\n",
+       "      <td>0.791393</td>\n",
+       "      <td>0.271754</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-59.630489</td>\n",
+       "      <td>-1.216485</td>\n",
+       "      <td>-7.361363</td>\n",
+       "      <td>-0.592633</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>635860218726658176</td>\n",
+       "      <td>138.518707</td>\n",
+       "      <td>19.092339</td>\n",
+       "      <td>-5.941679</td>\n",
+       "      <td>-11.346409</td>\n",
+       "      <td>0.307456</td>\n",
+       "      <td>0.199466</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-59.247330</td>\n",
+       "      <td>-2.016078</td>\n",
+       "      <td>-7.527126</td>\n",
+       "      <td>1.748779</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>635674126383965568</td>\n",
+       "      <td>138.842874</td>\n",
+       "      <td>19.031798</td>\n",
+       "      <td>-3.897001</td>\n",
+       "      <td>-12.702780</td>\n",
+       "      <td>0.779463</td>\n",
+       "      <td>0.223692</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-59.133391</td>\n",
+       "      <td>-2.306901</td>\n",
+       "      <td>-7.560608</td>\n",
+       "      <td>-0.741800</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Unnamed: 0           source_id          ra        dec      pmra      pmdec  \\\n",
+       "0           0  635559124339440000  137.586717  19.196544 -3.770522 -12.490482   \n",
+       "1           1  635860218726658176  138.518707  19.092339 -5.941679 -11.346409   \n",
+       "2           2  635674126383965568  138.842874  19.031798 -3.897001 -12.702780   \n",
+       "\n",
+       "   parallax  parallax_error  radial_velocity       phi1      phi2   pm_phi1  \\\n",
+       "0  0.791393        0.271754              NaN -59.630489 -1.216485 -7.361363   \n",
+       "1  0.307456        0.199466              NaN -59.247330 -2.016078 -7.527126   \n",
+       "2  0.779463        0.223692              NaN -59.133391 -2.306901 -7.560608   \n",
+       "\n",
+       "    pm_phi2  \n",
+       "0 -0.592633  \n",
+       "1  1.748779  \n",
+       "2 -0.741800  "
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "read_back_csv.head(3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notice that the index in `candidate_df` has become an unnamed column in `read_back_csv`.  The Pandas functions for writing and reading CSV files provide options to avoid that problem, but this is an example of the kind of thing that can go wrong with CSV files."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "In the previous lesson we downloaded data for a large number of stars and then selected a small fraction of them based on proper motion.\n",
+    "\n",
+    "In this lesson, we improved this process by writing a more complex query that uses the database to select stars based on proper motion.  This process requires more computation on the Gaia server, but then we're able to either:\n",
+    "\n",
+    "1. Search the same region and download less data, or\n",
+    "\n",
+    "2. Search a larger region while still downloading a manageable amount of data.\n",
+    "\n",
+    "In the next lesson, we'll learn about the databased `JOIN` operation and use it to download photometry data from Pan-STARRS."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Best practices\n",
+    "\n",
+    "* When possible, \"move the computation to the data\"; that is, do as much of the work as possible on the database server before downloading the data.\n",
+    "\n",
+    "* For most applications, saving data in FITS or HDF5 is better than CSV.  FITS and HDF5 are binary formats, so the files are usually smaller, and they store metadata, so you don't lose anything when you read the file back.\n",
+    "\n",
+    "* On the other hand, CSV is a \"least common denominator\" format; that is, it can be read by practically any application that works with data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/_build/html/_sources/05_join.ipynb b/_build/html/_sources/05_join.ipynb
new file mode 100644
index 0000000..e4d80bf
--- /dev/null
+++ b/_build/html/_sources/05_join.ipynb
@@ -0,0 +1,1301 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Chapter 5\n",
+    "\n",
+    "This is the fifth in a series of notebooks related to astronomy data.\n",
+    "\n",
+    "As a continuing example, we will replicate part of the analysis in a recent paper, \"[Off the beaten path: Gaia reveals GD-1 stars outside of the main stream](https://arxiv.org/abs/1805.00425)\" by Adrian M. Price-Whelan and Ana Bonaca.\n",
+    "\n",
+    "Picking up where we left off, the next step in the analysis is to select candidate stars based on photometry.  The following figure from the paper is a color-magnitude diagram for the stars selected based on proper motion:\n",
+    "\n",
+    "<img width=\"300\" src=\"https://github.com/datacarpentry/astronomy-python/raw/gh-pages/fig/gd1-3.png\">\n",
+    "\n",
+    "In red is a theoretical isochrone, showing where we expect the stars in GD-1 to fall based on the metallicity and age of their original globular cluster. \n",
+    "\n",
+    "By selecting stars in the shaded area, we can further distinguish the main sequence of GD-1 from younger background stars."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Outline\n",
+    "\n",
+    "Here are the steps in this notebook:\n",
+    "\n",
+    "1. We'll reload the candidate stars we identified in the previous notebook.\n",
+    "\n",
+    "2. Then we'll run a query on the Gaia server that uploads the table of candidates and uses a `JOIN` operation to select photometry data for the candidate stars.\n",
+    "\n",
+    "3. We'll write the results to a file for use in the next notebook.\n",
+    "\n",
+    "After completing this lesson, you should be able to\n",
+    "\n",
+    "* Upload a table to the Gaia server.\n",
+    "\n",
+    "* Write ADQL queries involving `JOIN` operations."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Installing libraries\n",
+    "\n",
+    "If you are running this notebook on Colab, you can run the following cell to install Astroquery and a the other libraries we'll use.\n",
+    "\n",
+    "If you are running this notebook on your own computer, you might have to install these libraries yourself.  \n",
+    "\n",
+    "If you are using this notebook as part of a Carpentries workshop, you should have received setup instructions.\n",
+    "\n",
+    "TODO: Add a link to the instructions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# If we're running on Colab, install libraries\n",
+    "\n",
+    "import sys\n",
+    "IN_COLAB = 'google.colab' in sys.modules\n",
+    "\n",
+    "if IN_COLAB:\n",
+    "    !pip install astroquery astro-gala pyia python-wget"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Reloading the data\n",
+    "\n",
+    "The following cell downloads the data from the previous notebook."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from wget import download\n",
+    "\n",
+    "filename = 'gd1_candidates.hdf5'\n",
+    "path = 'https://github.com/AllenDowney/AstronomicalData/raw/main/data/'\n",
+    "\n",
+    "if not os.path.exists(filename):\n",
+    "    print(download(path+filename))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And we can read it back."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "candidate_df = pd.read_hdf(filename, 'candidate_df')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`candidate_df` is the Pandas DataFrame that contains results from the query in the previous notebook, which selects stars likely to be in GD-1 based on proper motion.  It also includes position and proper motion transformed to the ICRS frame."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "x = candidate_df['phi1']\n",
+    "y = candidate_df['phi2']\n",
+    "\n",
+    "plt.plot(x, y, 'ko', markersize=0.3, alpha=0.3)\n",
+    "\n",
+    "plt.xlabel('ra (degree GD1)')\n",
+    "plt.ylabel('dec (degree GD1)');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is the same figure we saw at the end of the previous notebook.  GD-1 is visible against the background stars, but we will be able to see it more clearly after selecting based on photometry data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Getting photometry data\n",
+    "\n",
+    "The Gaia dataset contains some photometry data, including the variable `bp_rp`, which we used in the original query to select stars with BP - RP color between -0.75 and 2.\n",
+    "\n",
+    "Selecting stars with `bp-rp` less than 2 excludes many class M dwarf stars, which are low temperature, low luminosity.  A star like that at GD-1's distance would be hard to detect, so if it is detected, it it more likely to be in the foreground.\n",
+    "\n",
+    "Now, to select stars with the age and metal richness we expect in GD-1, we will use `g - i` color and apparent `g`-band magnitude, which are available from the Pan-STARRS survey."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Conveniently, the Gaia server provides data from Pan-STARRS as a table in the same database we have been using, so we can access it by making ADQL queries.\n",
+    "\n",
+    "In general, looking up a star from the Gaia catalog and finding the corresponding star in the Pan-STARRS catalog is not easy.  This kind of cross matching is not always possible, because a star might appear in one catalog and not the other.  And even when both stars are present, there might not be a clear one-to-one relationship between stars in the two catalogs.\n",
+    "\n",
+    "Fortunately, smart people have worked on this problem, and the Gaia database includes cross-matching tables that suggest a best neighbor in the Pan-STARRS catalog for many stars in the Gaia catalog.\n",
+    "\n",
+    "[This document describes the cross matching process](https://gea.esac.esa.int/archive/documentation/GDR2/Catalogue_consolidation/chap_cu9val_cu9val/ssec_cu9xma/sssec_cu9xma_extcat.html).  Briefly, it uses a cone search to find possible matches in approximately the right position, then uses attributes like color and magnitude to choose pairs of stars most likely to be identical."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "So the hard part of cross-matching has been done for us.  However, using the results is a little tricky.\n",
+    "\n",
+    "But, it is also an opportunity to learn about one of the most important tools for working with databases: \"joining\" tables.\n",
+    "\n",
+    "In general, a \"join\" is an operation where you match up records from one table with records from another table using as a \"key\" a piece of information that is common to both tables, usually some kind of ID code.\n",
+    "\n",
+    "In this example:\n",
+    "\n",
+    "* Stars in the Gaia dataset are identified by `source_id`.\n",
+    "\n",
+    "* Stars in the Pan-STARRS dataset are identified by `obj_id`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For each candidate star we have selected so far, we have the `source_id`; the goal is to find the `obj_id` for the same star (we hope) in the Pan-STARRS catalog.\n",
+    "\n",
+    "To do that we will:\n",
+    "\n",
+    "1. Make a table that contains the `source_id` for each candidate star and upload the table to the Gaia server;\n",
+    "\n",
+    "2. Use the `JOIN` operator to look up each `source_id` in the `gaiadr2.panstarrs1_best_neighbour` table, which contains the `obj_id` of the best match for each star in the Gaia catalog; then\n",
+    "\n",
+    "3. Use the `JOIN` operator again to look up each `obj_id` in the `panstarrs1_original_valid` table, which contains the Pan-STARRS photometry data we want.\n",
+    "\n",
+    "Let's start with the first step, uploading a table."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Preparing a table for uploading\n",
+    "\n",
+    "For each candidate star, we want to find the corresponding row in the `gaiadr2.panstarrs1_best_neighbour` table.\n",
+    "\n",
+    "In order to do that, we have to:\n",
+    "\n",
+    "1. Write the table in a local file as an XML VOTable, which is a format suitable for transmitting a table over a network.\n",
+    "\n",
+    "2. Write an ADQL query that refers to the uploaded table.\n",
+    "\n",
+    "3. Change the way we submit the job so it uploads the table before running the query."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The first step is not too difficult because Astropy provides a function called `writeto` that can write a `Table` in `XML`.\n",
+    "\n",
+    "[The documentation of this process is here](https://docs.astropy.org/en/stable/io/votable/).\n",
+    "\n",
+    "First we have to convert our Pandas `DataFrame` to an Astropy `Table`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "astropy.table.table.Table"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from astropy.table import Table\n",
+    "\n",
+    "candidate_table = Table.from_pandas(candidate_df)\n",
+    "type(candidate_table)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To write the file, we can use `Table.write` with `format='votable'`, [as described here](https://docs.astropy.org/en/stable/io/unified.html#vo-tables)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "table = candidate_table[['source_id']]\n",
+    "table.write('candidate_df.xml', format='votable', overwrite=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notice that we select a single column from the table, `source_id`.\n",
+    "We could write the entire table to a file, but that would take longer to transmit over the network, and we really only need one column.\n",
+    "\n",
+    "This process, taking a structure like a `Table` and translating it into a form that can be transmitted over a network, is called [serialization](https://en.wikipedia.org/wiki/Serialization).\n",
+    "\n",
+    "XML is one of the most common serialization formats.  One nice feature is that XML data is plain text, as opposed to binary digits, so you can read the file we just wrote:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<?xml version=\"1.0\" encoding=\"utf-8\"?>\r\n",
+      "<!-- Produced with astropy.io.votable version 4.0.1.post1\r\n",
+      "     http://www.astropy.org/ -->\r\n",
+      "<VOTABLE version=\"1.4\" xmlns=\"http://www.ivoa.net/xml/VOTable/v1.4\" xmlns:xsi=\"http://www.w3.org/2001/XMLSchema-instance\" xsi:noNamespaceSchemaLocation=\"http://www.ivoa.net/xml/VOTable/v1.4\">\r\n",
+      " <RESOURCE type=\"results\">\r\n",
+      "  <TABLE>\r\n",
+      "   <FIELD ID=\"source_id\" datatype=\"long\" name=\"source_id\"/>\r\n",
+      "   <DATA>\r\n",
+      "    <TABLEDATA>\r\n",
+      "     <TR>\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!head candidate_df.xml"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "XML is a general format, so different XML files contain different kinds of data.  In order to read an XML file, it's not enough to know that it's XML; you also have to know the data format, which is called a [schema](https://en.wikipedia.org/wiki/XML_schema).\n",
+    "\n",
+    "In this example, the schema is VOTable; notice that one of the first tags in the file specifies the schema, and even includes the URL where you can get its definition.\n",
+    "\n",
+    "So this is an example of a self-documenting format.\n",
+    "\n",
+    "A drawback of XML is that it tends to be big, which is why we wrote just the `source_id` column rather than the whole table.\n",
+    "The size of the file is about 750 KB, so that's not too bad."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-rw-rw-r-- 1 downey downey 396K Oct 19 14:48 candidate_df.xml\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!ls -lh candidate_df.xml"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you are using Windows, `ls` might not work; in that case, try:\n",
+    "\n",
+    "```\n",
+    "!dir candidate_df.xml\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** There's a gotcha here we want to warn you about.  Why do you think we used double brackets to specify the column we wanted?  What happens if you use single brackets?\n",
+    "\n",
+    "Run these cells to find out."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "astropy.table.table.Table"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "table = candidate_table[['source_id']]\n",
+    "type(table)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "astropy.table.column.Column"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "column = candidate_table['source_id']\n",
+    "type(column)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# writeto(column, 'candidate_df.xml')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Uploading a table\n",
+    "\n",
+    "The next step is to upload this table to the Gaia server and use it as part of a query.\n",
+    "\n",
+    "[Here's the documentation that explains how to run a query with an uploaded table](https://astroquery.readthedocs.io/en/latest/gaia/gaia.html#synchronous-query-on-an-on-the-fly-uploaded-table).\n",
+    "\n",
+    "In the spirit of incremental development and testing, let's start with the simplest possible query."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query = \"\"\"SELECT *\n",
+    "FROM tap_upload.candidate_df\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This query downloads all rows and all columns from the uploaded table.  The name of the table has two parts: `tap_upload` specifies a table that was uploaded using TAP+ (remember that's the name of the protocol we're using to talk to the Gaia server).\n",
+    "\n",
+    "And `candidate_df` is the name of the table, which we get to choose (unlike `tap_upload`, which we didn't get to choose).\n",
+    "\n",
+    "Here's how we run the query:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Created TAP+ (v1.2.1) - Connection:\n",
+      "\tHost: gea.esac.esa.int\n",
+      "\tUse HTTPS: True\n",
+      "\tPort: 443\n",
+      "\tSSL Port: 443\n",
+      "Created TAP+ (v1.2.1) - Connection:\n",
+      "\tHost: geadata.esac.esa.int\n",
+      "\tUse HTTPS: True\n",
+      "\tPort: 443\n",
+      "\tSSL Port: 443\n",
+      "INFO: Query finished. [astroquery.utils.tap.core]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from astroquery.gaia import Gaia\n",
+    "\n",
+    "job = Gaia.launch_job_async(query=query, \n",
+    "                            upload_resource='candidate_df.xml', \n",
+    "                            upload_table_name='candidate_df')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`upload_resource` specifies the name of the file we want to upload, which is the file we just wrote.\n",
+    "\n",
+    "`upload_table_name` is the name we assign to this table, which is the name we used in the query.\n",
+    "\n",
+    "And here are the results:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<i>Table length=7346</i>\n",
+       "<table id=\"table140665432275552\" class=\"table-striped table-bordered table-condensed\">\n",
+       "<thead><tr><th>source_id</th></tr></thead>\n",
+       "<thead><tr><th>int64</th></tr></thead>\n",
+       "<tr><td>635559124339440000</td></tr>\n",
+       "<tr><td>635860218726658176</td></tr>\n",
+       "<tr><td>635674126383965568</td></tr>\n",
+       "<tr><td>635535454774983040</td></tr>\n",
+       "<tr><td>635497276810313600</td></tr>\n",
+       "<tr><td>635614168640132864</td></tr>\n",
+       "<tr><td>635821843194387840</td></tr>\n",
+       "<tr><td>635551706931167104</td></tr>\n",
+       "<tr><td>635518889086133376</td></tr>\n",
+       "<tr><td>635580294233854464</td></tr>\n",
+       "<tr><td>...</td></tr>\n",
+       "<tr><td>612282738058264960</td></tr>\n",
+       "<tr><td>612485911486166656</td></tr>\n",
+       "<tr><td>612386332668697600</td></tr>\n",
+       "<tr><td>612296172717818624</td></tr>\n",
+       "<tr><td>612250375480101760</td></tr>\n",
+       "<tr><td>612394926899159168</td></tr>\n",
+       "<tr><td>612288854091187712</td></tr>\n",
+       "<tr><td>612428870024913152</td></tr>\n",
+       "<tr><td>612256418500423168</td></tr>\n",
+       "<tr><td>612429144902815104</td></tr>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<Table length=7346>\n",
+       "    source_id     \n",
+       "      int64       \n",
+       "------------------\n",
+       "635559124339440000\n",
+       "635860218726658176\n",
+       "635674126383965568\n",
+       "635535454774983040\n",
+       "635497276810313600\n",
+       "635614168640132864\n",
+       "635821843194387840\n",
+       "635551706931167104\n",
+       "635518889086133376\n",
+       "635580294233854464\n",
+       "               ...\n",
+       "612282738058264960\n",
+       "612485911486166656\n",
+       "612386332668697600\n",
+       "612296172717818624\n",
+       "612250375480101760\n",
+       "612394926899159168\n",
+       "612288854091187712\n",
+       "612428870024913152\n",
+       "612256418500423168\n",
+       "612429144902815104"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results = job.get_results()\n",
+    "results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If things go according to plan, the result should contain the same rows and columns as the uploaded table."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(7346, 7346)"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(candidate_table), len(results)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "set(candidate_table['source_id']) == set(results['source_id'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this example, we uploaded a table and then downloaded it again, so that's not too useful.\n",
+    "\n",
+    "But now that we can upload a table, we can join it with other tables on the Gaia server."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Joining with an uploaded table\n",
+    "\n",
+    "Here's the first example of a query that contains a `JOIN` clause."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query1 = \"\"\"SELECT *\n",
+    "FROM gaiadr2.panstarrs1_best_neighbour as best\n",
+    "JOIN tap_upload.candidate_df as candidate_df\n",
+    "ON best.source_id = candidate_df.source_id\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's break that down one clause at a time:\n",
+    "\n",
+    "* `SELECT *` means we will download all columns from both tables.\n",
+    "\n",
+    "* `FROM gaiadr2.panstarrs1_best_neighbour as best` means that we'll get the columns from the Pan-STARRS best neighbor table, which we'll refer to using the short name `best`.\n",
+    "\n",
+    "* `JOIN tap_upload.candidate_df as candidate_df` means that we'll also get columns from the uploaded table, which we'll refer to using the short name `candidate_df`.\n",
+    "\n",
+    "* `ON best.source_id = candidate_df.source_id` specifies that we will use `source_id ` to match up the rows from the two tables.\n",
+    "\n",
+    "Here's the [documentation of the best neighbor table](https://gea.esac.esa.int/archive/documentation/GDR2/Gaia_archive/chap_datamodel/sec_dm_crossmatches/ssec_dm_panstarrs1_best_neighbour.html).\n",
+    "\n",
+    "Let's run the query:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO: Query finished. [astroquery.utils.tap.core]\n"
+     ]
+    }
+   ],
+   "source": [
+    "job1 = Gaia.launch_job_async(query=query1, \n",
+    "                       upload_resource='candidate_df.xml', \n",
+    "                       upload_table_name='candidate_df')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And get the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<i>Table length=3724</i>\n",
+       "<table id=\"table140665411011344\" class=\"table-striped table-bordered table-condensed\">\n",
+       "<thead><tr><th>source_id</th><th>original_ext_source_id</th><th>angular_distance</th><th>number_of_neighbours</th><th>number_of_mates</th><th>best_neighbour_multiplicity</th><th>gaia_astrometric_params</th><th>source_id_2</th></tr></thead>\n",
+       "<thead><tr><th></th><th></th><th>arcsec</th><th></th><th></th><th></th><th></th><th></th></tr></thead>\n",
+       "<thead><tr><th>int64</th><th>int64</th><th>float64</th><th>int32</th><th>int16</th><th>int16</th><th>int16</th><th>int64</th></tr></thead>\n",
+       "<tr><td>635860218726658176</td><td>130911385187671349</td><td>0.053667035895467084</td><td>1</td><td>0</td><td>1</td><td>5</td><td>635860218726658176</td></tr>\n",
+       "<tr><td>635674126383965568</td><td>130831388428488720</td><td>0.038810268141577516</td><td>1</td><td>0</td><td>1</td><td>5</td><td>635674126383965568</td></tr>\n",
+       "<tr><td>635535454774983040</td><td>130631378377657369</td><td>0.034323028828991076</td><td>1</td><td>0</td><td>1</td><td>5</td><td>635535454774983040</td></tr>\n",
+       "<tr><td>635497276810313600</td><td>130811380445631930</td><td>0.04720255413250006</td><td>1</td><td>0</td><td>1</td><td>5</td><td>635497276810313600</td></tr>\n",
+       "<tr><td>635614168640132864</td><td>130571395922140135</td><td>0.020304189709964143</td><td>1</td><td>0</td><td>1</td><td>5</td><td>635614168640132864</td></tr>\n",
+       "<tr><td>635598607974369792</td><td>130341392091279513</td><td>0.036524626853403054</td><td>1</td><td>0</td><td>1</td><td>5</td><td>635598607974369792</td></tr>\n",
+       "<tr><td>635737661835496576</td><td>131001399333502136</td><td>0.036626827820716606</td><td>1</td><td>0</td><td>1</td><td>5</td><td>635737661835496576</td></tr>\n",
+       "<tr><td>635850945892748672</td><td>132011398654934147</td><td>0.021178742393378396</td><td>1</td><td>0</td><td>1</td><td>5</td><td>635850945892748672</td></tr>\n",
+       "<tr><td>635600532119713664</td><td>130421392285893623</td><td>0.04518820915043015</td><td>1</td><td>0</td><td>1</td><td>5</td><td>635600532119713664</td></tr>\n",
+       "<tr><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr>\n",
+       "<tr><td>612241781249124608</td><td>129751343755995561</td><td>0.04235715830001815</td><td>1</td><td>0</td><td>1</td><td>5</td><td>612241781249124608</td></tr>\n",
+       "<tr><td>612332147361443072</td><td>130141341458538777</td><td>0.02265249859012977</td><td>1</td><td>0</td><td>1</td><td>5</td><td>612332147361443072</td></tr>\n",
+       "<tr><td>612426744016802432</td><td>130521346852465656</td><td>0.03247653009961843</td><td>1</td><td>0</td><td>1</td><td>5</td><td>612426744016802432</td></tr>\n",
+       "<tr><td>612331739340341760</td><td>130111341217793839</td><td>0.036064240818025735</td><td>1</td><td>0</td><td>1</td><td>5</td><td>612331739340341760</td></tr>\n",
+       "<tr><td>612282738058264960</td><td>129741340445933519</td><td>0.025293237353496898</td><td>1</td><td>0</td><td>1</td><td>5</td><td>612282738058264960</td></tr>\n",
+       "<tr><td>612386332668697600</td><td>130351354570219774</td><td>0.02010316001403086</td><td>1</td><td>0</td><td>1</td><td>5</td><td>612386332668697600</td></tr>\n",
+       "<tr><td>612296172717818624</td><td>129691338006168780</td><td>0.051264212025836205</td><td>1</td><td>0</td><td>1</td><td>5</td><td>612296172717818624</td></tr>\n",
+       "<tr><td>612250375480101760</td><td>129741346475897464</td><td>0.031783740347530905</td><td>1</td><td>0</td><td>1</td><td>5</td><td>612250375480101760</td></tr>\n",
+       "<tr><td>612394926899159168</td><td>130581355199751795</td><td>0.04019174830546698</td><td>1</td><td>0</td><td>1</td><td>5</td><td>612394926899159168</td></tr>\n",
+       "<tr><td>612256418500423168</td><td>129931349075297310</td><td>0.009242789669513156</td><td>1</td><td>0</td><td>1</td><td>5</td><td>612256418500423168</td></tr>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<Table length=3724>\n",
+       "    source_id      original_ext_source_id ...    source_id_2    \n",
+       "                                          ...                   \n",
+       "      int64                int64          ...       int64       \n",
+       "------------------ ---------------------- ... ------------------\n",
+       "635860218726658176     130911385187671349 ... 635860218726658176\n",
+       "635674126383965568     130831388428488720 ... 635674126383965568\n",
+       "635535454774983040     130631378377657369 ... 635535454774983040\n",
+       "635497276810313600     130811380445631930 ... 635497276810313600\n",
+       "635614168640132864     130571395922140135 ... 635614168640132864\n",
+       "635598607974369792     130341392091279513 ... 635598607974369792\n",
+       "635737661835496576     131001399333502136 ... 635737661835496576\n",
+       "635850945892748672     132011398654934147 ... 635850945892748672\n",
+       "635600532119713664     130421392285893623 ... 635600532119713664\n",
+       "               ...                    ... ...                ...\n",
+       "612241781249124608     129751343755995561 ... 612241781249124608\n",
+       "612332147361443072     130141341458538777 ... 612332147361443072\n",
+       "612426744016802432     130521346852465656 ... 612426744016802432\n",
+       "612331739340341760     130111341217793839 ... 612331739340341760\n",
+       "612282738058264960     129741340445933519 ... 612282738058264960\n",
+       "612386332668697600     130351354570219774 ... 612386332668697600\n",
+       "612296172717818624     129691338006168780 ... 612296172717818624\n",
+       "612250375480101760     129741346475897464 ... 612250375480101760\n",
+       "612394926899159168     130581355199751795 ... 612394926899159168\n",
+       "612256418500423168     129931349075297310 ... 612256418500423168"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results1 = job1.get_results()\n",
+    "results1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This table contains all of the columns from the best neighbor table, plus the single column from the uploaded table."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['source_id',\n",
+       " 'original_ext_source_id',\n",
+       " 'angular_distance',\n",
+       " 'number_of_neighbours',\n",
+       " 'number_of_mates',\n",
+       " 'best_neighbour_multiplicity',\n",
+       " 'gaia_astrometric_params',\n",
+       " 'source_id_2']"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results1.colnames"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Because one of the column names appears in both tables, the second instance of `source_id` has been appended with the suffix `_2`.\n",
+    "\n",
+    "The length of the results table is about 2000, which means we were not able to find matches for all stars in the list of candidate_df."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3724"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(results1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To get more information about the matching process, we can inspect `best_neighbour_multiplicity`, which indicates for each star in Gaia how many stars in Pan-STARRS are equally likely matches.\n",
+    "\n",
+    "For this kind of data exploration, we'll convert a column from the table to a Pandas `Series` so we can use `value_counts`, which counts the number of times each value appears in a `Series`, like a histogram."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1    3724\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "nn = pd.Series(results1['best_neighbour_multiplicity'])\n",
+    "nn.value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The result shows that `1` is the only value in the `Series`, appearing xxx times.\n",
+    "\n",
+    "That means that in every case where a match was found, the matching algorithm identified a single neighbor as the most likely match.\n",
+    "\n",
+    "Similarly, `number_of_mates` indicates the number of other stars in Gaia that match with the same star in Pan-STARRS."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    3724\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nm = pd.Series(results1['number_of_mates'])\n",
+    "nm.value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For this set of candidate_df, almost all of the stars we've selected from Pan-STARRS are only matched with a single star in the Gaia catalog.\n",
+    "\n",
+    "**Detail** The table also contains `number_of_neighbors` which is the number of stars in Pan-STARRS that match in terms of position, before using other critieria to choose the most likely match."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Getting the photometry data\n",
+    "\n",
+    "The most important column in `results1` is `original_ext_source_id` which is the `obj_id` we will use to look up the likely matches in Pan-STARRS to get photometry data.\n",
+    "\n",
+    "The process is similar to what we just did to look up the matches.  We will:\n",
+    "\n",
+    "1. Make a table that contains `source_id` and `original_ext_source_id`.\n",
+    "\n",
+    "2. Write the table to an XML VOTable file.\n",
+    "\n",
+    "3. Write a query that joins the uploaded table with `gaiadr2.panstarrs1_original_valid` and selects the photometry data we want.\n",
+    "\n",
+    "4. Run the query using the uploaded table.\n",
+    "\n",
+    "Since we've done everything here before, we'll do these steps as an exercise.\n",
+    "\n",
+    "**Exercise:** Select `source_id` and `original_ext_source_id` from `results1` and write the resulting table as a file named `external.xml`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "table = results1[['source_id', 'original_ext_source_id']]\n",
+    "table.write('external.xml', format='votable', overwrite=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Use `!head` to confirm that the file exists and contains an XML VOTable."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<?xml version=\"1.0\" encoding=\"utf-8\"?>\r\n",
+      "<!-- Produced with astropy.io.votable version 4.0.1.post1\r\n",
+      "     http://www.astropy.org/ -->\r\n",
+      "<VOTABLE version=\"1.4\" xmlns=\"http://www.ivoa.net/xml/VOTable/v1.4\" xmlns:xsi=\"http://www.w3.org/2001/XMLSchema-instance\" xsi:noNamespaceSchemaLocation=\"http://www.ivoa.net/xml/VOTable/v1.4\">\r\n",
+      " <RESOURCE type=\"results\">\r\n",
+      "  <TABLE>\r\n",
+      "   <FIELD ID=\"source_id\" datatype=\"long\" name=\"source_id\" ucd=\"meta.id;meta.main\">\r\n",
+      "    <DESCRIPTION>\r\n",
+      "     Unique Gaia source identifier\r\n",
+      "    </DESCRIPTION>\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!head external.xml"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Read [the documentation of the Pan-STARRS table](https://gea.esac.esa.int/archive/documentation/GDR2/Gaia_archive/chap_datamodel/sec_dm_external_catalogues/ssec_dm_panstarrs1_original_valid.html) and make note of `obj_id`, which contains the object IDs we'll use to find the rows we want.\n",
+    "\n",
+    "Write a query that uses each value of `original_ext_source_id` from the uploaded table to find a row in `gaiadr2.panstarrs1_original_valid` with the same value in `obj_id`, and select all columns from both tables.\n",
+    "\n",
+    "Suggestion: Develop and test your query incrementally.  For example:\n",
+    "\n",
+    "1. Write a query that downloads all columns from the uploaded table.  Test to make sure we can read the uploaded table.\n",
+    "\n",
+    "2. Write a query that downloads the first 10 rows from `gaiadr2.panstarrs1_original_valid`.  Test to make sure we can access Pan-STARRS data.\n",
+    "\n",
+    "3. Write a query that joins the two tables and selects all columns.  Test that the join works as expected.\n",
+    "\n",
+    "\n",
+    "As a bonus exercise, write a query that joins the two tables and selects just the columns we need:\n",
+    "\n",
+    "* `source_id` from the uploaded table\n",
+    "\n",
+    "* `g_mean_psf_mag` from `gaiadr2.panstarrs1_original_valid`\n",
+    "\n",
+    "* `i_mean_psf_mag` from `gaiadr2.panstarrs1_original_valid`\n",
+    "\n",
+    "Hint: When you select a column from a join, you have to specify which table the column is in."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "query2 = \"\"\"SELECT *\n",
+    "FROM tap_upload.external as external\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "query2 = \"\"\"SELECT TOP 10 *\n",
+    "FROM gaiadr2.panstarrs1_original_valid\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "query2 = \"\"\"SELECT *\n",
+    "FROM gaiadr2.panstarrs1_original_valid as ps\n",
+    "JOIN tap_upload.external as external\n",
+    "ON ps.obj_id = external.original_ext_source_id\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "query2 = \"\"\"SELECT\n",
+    "external.source_id, ps.g_mean_psf_mag, ps.i_mean_psf_mag\n",
+    "FROM gaiadr2.panstarrs1_original_valid as ps\n",
+    "JOIN tap_upload.external as external\n",
+    "ON ps.obj_id = external.original_ext_source_id\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "SELECT\n",
+      "external.source_id, ps.g_mean_psf_mag, ps.i_mean_psf_mag\n",
+      "FROM gaiadr2.panstarrs1_original_valid as ps\n",
+      "JOIN tap_upload.external as external\n",
+      "ON ps.obj_id = external.original_ext_source_id\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(query2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO: Query finished. [astroquery.utils.tap.core]\n"
+     ]
+    }
+   ],
+   "source": [
+    "job2 = Gaia.launch_job_async(query=query2, \n",
+    "                       upload_resource='external.xml', \n",
+    "                       upload_table_name='external')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<i>Table length=3724</i>\n",
+       "<table id=\"table140665408248800\" class=\"table-striped table-bordered table-condensed\">\n",
+       "<thead><tr><th>source_id</th><th>g_mean_psf_mag</th><th>i_mean_psf_mag</th></tr></thead>\n",
+       "<thead><tr><th></th><th></th><th>mag</th></tr></thead>\n",
+       "<thead><tr><th>int64</th><th>float64</th><th>float64</th></tr></thead>\n",
+       "<tr><td>635860218726658176</td><td>17.8978004455566</td><td>17.5174007415771</td></tr>\n",
+       "<tr><td>635674126383965568</td><td>19.2873001098633</td><td>17.6781005859375</td></tr>\n",
+       "<tr><td>635535454774983040</td><td>16.9237995147705</td><td>16.478099822998</td></tr>\n",
+       "<tr><td>635497276810313600</td><td>19.9242000579834</td><td>18.3339996337891</td></tr>\n",
+       "<tr><td>635614168640132864</td><td>16.1515998840332</td><td>14.6662998199463</td></tr>\n",
+       "<tr><td>635598607974369792</td><td>16.5223999023438</td><td>16.1375007629395</td></tr>\n",
+       "<tr><td>635737661835496576</td><td>14.5032997131348</td><td>13.9849004745483</td></tr>\n",
+       "<tr><td>635850945892748672</td><td>16.5174999237061</td><td>16.0450000762939</td></tr>\n",
+       "<tr><td>635600532119713664</td><td>20.4505996704102</td><td>19.5177001953125</td></tr>\n",
+       "<tr><td>...</td><td>...</td><td>...</td></tr>\n",
+       "<tr><td>612241781249124608</td><td>20.2343997955322</td><td>18.6518001556396</td></tr>\n",
+       "<tr><td>612332147361443072</td><td>21.3848991394043</td><td>20.3076000213623</td></tr>\n",
+       "<tr><td>612426744016802432</td><td>17.8281002044678</td><td>17.4281005859375</td></tr>\n",
+       "<tr><td>612331739340341760</td><td>21.8656997680664</td><td>19.5223007202148</td></tr>\n",
+       "<tr><td>612282738058264960</td><td>22.5151996612549</td><td>19.9743995666504</td></tr>\n",
+       "<tr><td>612386332668697600</td><td>19.3792991638184</td><td>17.9923000335693</td></tr>\n",
+       "<tr><td>612296172717818624</td><td>17.4944000244141</td><td>16.926700592041</td></tr>\n",
+       "<tr><td>612250375480101760</td><td>15.3330001831055</td><td>14.6280002593994</td></tr>\n",
+       "<tr><td>612394926899159168</td><td>16.4414005279541</td><td>15.8212003707886</td></tr>\n",
+       "<tr><td>612256418500423168</td><td>20.8715991973877</td><td>19.9612007141113</td></tr>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<Table length=3724>\n",
+       "    source_id       g_mean_psf_mag   i_mean_psf_mag \n",
+       "                                          mag       \n",
+       "      int64            float64          float64     \n",
+       "------------------ ---------------- ----------------\n",
+       "635860218726658176 17.8978004455566 17.5174007415771\n",
+       "635674126383965568 19.2873001098633 17.6781005859375\n",
+       "635535454774983040 16.9237995147705  16.478099822998\n",
+       "635497276810313600 19.9242000579834 18.3339996337891\n",
+       "635614168640132864 16.1515998840332 14.6662998199463\n",
+       "635598607974369792 16.5223999023438 16.1375007629395\n",
+       "635737661835496576 14.5032997131348 13.9849004745483\n",
+       "635850945892748672 16.5174999237061 16.0450000762939\n",
+       "635600532119713664 20.4505996704102 19.5177001953125\n",
+       "               ...              ...              ...\n",
+       "612241781249124608 20.2343997955322 18.6518001556396\n",
+       "612332147361443072 21.3848991394043 20.3076000213623\n",
+       "612426744016802432 17.8281002044678 17.4281005859375\n",
+       "612331739340341760 21.8656997680664 19.5223007202148\n",
+       "612282738058264960 22.5151996612549 19.9743995666504\n",
+       "612386332668697600 19.3792991638184 17.9923000335693\n",
+       "612296172717818624 17.4944000244141  16.926700592041\n",
+       "612250375480101760 15.3330001831055 14.6280002593994\n",
+       "612394926899159168 16.4414005279541 15.8212003707886\n",
+       "612256418500423168 20.8715991973877 19.9612007141113"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results2 = job2.get_results()\n",
+    "results2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Challenge exercise**\n",
+    "\n",
+    "Do both joins in one query.\n",
+    "\n",
+    "There's an [example here](https://github.com/smoh/Getting-started-with-Gaia/blob/master/gaia-adql-snippets.md) you could start with."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Write the data\n",
+    "\n",
+    "Since we have the data in an Astropy `Table`, let's store it in a FITS file."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "filename = 'gd1_photo.fits'\n",
+    "results2.write(filename, overwrite=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can check that the file exists, and see how big it is."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-rw-rw-r-- 1 downey downey 96K Oct 19 14:49 gd1_photo.fits\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!ls -lh gd1_photo.fits"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "At around 175 KB, it is smaller than some of the other files we've been working with.\n",
+    "\n",
+    "If you are using Windows, `ls` might not work; in that case, try:\n",
+    "\n",
+    "```\n",
+    "!dir gd1_photo.fits\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "In this notebook, we used database `JOIN` operations to select photometry data for the stars we've identified as candidates to be in GD-1.\n",
+    "\n",
+    "In the next notebook, we'll use this data for a second round of selection, identifying stars that have photometry data consistent with GD-1."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Best practice\n",
+    "\n",
+    "* Use `JOIN` operations to combine data from multiple tables in a databased, using some kind of identifier to match up records from one table with records from another.\n",
+    "\n",
+    "* This is another example of a practice we saw in the previous notebook, moving the computation to the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/_build/html/_sources/06_photo.ipynb b/_build/html/_sources/06_photo.ipynb
new file mode 100644
index 0000000..1375ac2
--- /dev/null
+++ b/_build/html/_sources/06_photo.ipynb
@@ -0,0 +1,1372 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Chapter 6\n",
+    "\n",
+    "This is the sixth in a series of notebooks related to astronomy data.\n",
+    "\n",
+    "As a continuing example, we will replicate part of the analysis in a recent paper, \"[Off the beaten path: Gaia reveals GD-1 stars outside of the main stream](https://arxiv.org/abs/1805.00425)\" by Adrian M. Price-Whelan and Ana Bonaca.\n",
+    "\n",
+    "In the previous lesson we downloaded photometry data from Pan-STARRS, which is available from the same server we've been using to get Gaia data. \n",
+    "\n",
+    "The next step in the analysis is to select candidate stars based on the photometry data.  The following figure from the paper is a color-magnitude diagram for the stars selected based on proper motion:\n",
+    "\n",
+    "<img width=\"300\" src=\"https://github.com/datacarpentry/astronomy-python/raw/gh-pages/fig/gd1-3.png\">\n",
+    "\n",
+    "In red is a theoretical isochrone, showing where we expect the stars in GD-1 to fall based on the metallicity and age of their original globular cluster. \n",
+    "\n",
+    "By selecting stars in the shaded area, we can further distinguish the main sequence of GD-1 from younger background stars."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Outline\n",
+    "\n",
+    "Here are the steps in this notebook:\n",
+    "\n",
+    "1. We'll reload the data from the previous notebook and make a color-magnitude diagram.\n",
+    "\n",
+    "2. Then we'll specify a polygon in the diagram that contains stars with the photometry we expect.\n",
+    "\n",
+    "3. Then we'll merge the photometry data with the list of candidate stars, storing the result in a Pandas `DataFrame`.\n",
+    "\n",
+    "After completing this lesson, you should be able to\n",
+    "\n",
+    "* Use Matplotlib to specify a `Polygon` and determine which points fall inside it.\n",
+    "\n",
+    "* Use Pandas to merge data from multiple `DataFrames`, much like a database `JOIN` operation."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Installing libraries\n",
+    "\n",
+    "If you are running this notebook on Colab, you can run the following cell to install Astroquery and a the other libraries we'll use.\n",
+    "\n",
+    "If you are running this notebook on your own computer, you might have to install these libraries yourself.  \n",
+    "\n",
+    "If you are using this notebook as part of a Carpentries workshop, you should have received setup instructions.\n",
+    "\n",
+    "TODO: Add a link to the instructions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# If we're running on Colab, install libraries\n",
+    "\n",
+    "import sys\n",
+    "IN_COLAB = 'google.colab' in sys.modules\n",
+    "\n",
+    "if IN_COLAB:\n",
+    "    !pip install astroquery astro-gala pyia python-wget"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Reload the data\n",
+    "\n",
+    "The following cell downloads the photometry data we created in the previous notebook."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from wget import download\n",
+    "\n",
+    "filename = 'gd1_photo.fits'\n",
+    "filepath = 'https://github.com/AllenDowney/AstronomicalData/raw/main/data/'\n",
+    "\n",
+    "if not os.path.exists(filename):\n",
+    "    print(download(filepath+filename))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can read the data back into an Astropy `Table`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from astropy.table import Table\n",
+    "\n",
+    "photo_table = Table.read(filename)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plotting photometry data\n",
+    "\n",
+    "Now that we have photometry data from Pan-STARRS, we can replicate the [color-magnitude diagram](https://en.wikipedia.org/wiki/Galaxy_color%E2%80%93magnitude_diagram) from the original paper:\n",
+    "\n",
+    "<img width=\"300\" src=\"https://github.com/datacarpentry/astronomy-python/raw/gh-pages/fig/gd1-3.png\">\n",
+    "\n",
+    "The y-axis shows the apparent magnitude of each source with the [g filter](https://en.wikipedia.org/wiki/Photometric_system).\n",
+    "\n",
+    "The x-axis shows the difference in apparent magnitude between the g and i filters, which indicates color.\n",
+    "\n",
+    "Stars with lower values of (g-i) are brighter in g-band than in i-band, compared to other stars, which means they are bluer.\n",
+    "\n",
+    "Stars in the lower-left quadrant of this diagram are less bright and less metallic than the others, which means they are [likely to be older](http://spiff.rit.edu/classes/ladder/lectures/ordinary_stars/ordinary.html).\n",
+    "\n",
+    "Since we expect the stars in GD-1 to be older than the background stars, the stars in the lower-left are more likely to be in GD-1."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "def plot_cmd(table):\n",
+    "    \"\"\"Plot a color magnitude diagram.\n",
+    "    \n",
+    "    table: Table or DataFrame with photometry data\n",
+    "    \"\"\"\n",
+    "    y = table['g_mean_psf_mag']\n",
+    "    x = table['g_mean_psf_mag'] - table['i_mean_psf_mag']\n",
+    "\n",
+    "    plt.plot(x, y, 'ko', markersize=0.3, alpha=0.3)\n",
+    "\n",
+    "    plt.xlim([0, 1.5])\n",
+    "    plt.ylim([14, 22])\n",
+    "    plt.gca().invert_yaxis()\n",
+    "\n",
+    "    plt.ylabel('$g_0$')\n",
+    "    plt.xlabel('$(g-i)_0$')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`plot_cmd` uses a new function, `invert_yaxis`, to invert the `y` axis, which is conventional when plotting magnitudes, since lower magnitude indicates higher brightness.\n",
+    "\n",
+    "`invert_yaxis` is a little different from the other functions we've used.  You can't call it like this:\n",
+    "\n",
+    "```\n",
+    "plt.invert_yaxis()          # doesn't work\n",
+    "```\n",
+    "\n",
+    "You have to call it like this:\n",
+    "\n",
+    "```\n",
+    "plt.gca().invert_yaxis()          # works\n",
+    "```\n",
+    "\n",
+    "`gca` stands for \"get current axis\".  It returns an object that represents the axes of the current figure, and that object provides `invert_yaxis`.\n",
+    "\n",
+    "**In case anyone asks:** The most likely reason for this inconsistency in the interface is that `invert_yaxis` is a lesser-used function, so it's not made available at the top level of the interface."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's what the results look like."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_cmd(photo_table)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Our figure does not look exactly like the one in the paper because we are working with a smaller region of the sky, so we don't have as many stars.  But we can see an overdense region in the lower left that contains stars with the photometry we expect for GD-1.\n",
+    "\n",
+    "The authors of the original paper derive a detailed polygon that defines a boundary between stars that are likely to be in GD-1 or not.\n",
+    "\n",
+    "As a simplification, we'll choose a boundary by eye that seems to contain the overdense region."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Drawing a polygon\n",
+    "\n",
+    "Matplotlib provides a function called `ginput` that lets us click on the figure and make a list of coordinates.\n",
+    "\n",
+    "It's a little tricky to use `ginput` in a Jupyter notebook.  \n",
+    "Before calling `plt.ginput` we have to tell Matplotlib to use `TkAgg` to draw the figure in a new window.\n",
+    "\n",
+    "When you run the following cell, a figure should appear in a new window.  Click on it 10 times to draw a polygon around the overdense area.  A red cross should appear where you click."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib as mpl\n",
+    "\n",
+    "if IN_COLAB:\n",
+    "    coords = None\n",
+    "else:\n",
+    "    mpl.use('TkAgg')\n",
+    "    plot_cmd(photo_table)\n",
+    "    coords = plt.ginput(10)\n",
+    "    mpl.use('agg')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The argument to `ginput` is the number of times the user has to click on the figure.\n",
+    "\n",
+    "The result from `ginput` is a list of coordinate pairs."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[(0.2150537634408602, 17.548197203826344),\n",
+       " (0.3897849462365591, 18.94628403237675),\n",
+       " (0.5376344086021505, 19.902869757174393),\n",
+       " (0.7034050179211468, 20.601913171449596),\n",
+       " (0.8288530465949819, 21.300956585724798),\n",
+       " (0.6630824372759856, 21.52170713760118),\n",
+       " (0.4301075268817204, 20.785871964679913),\n",
+       " (0.27329749103942647, 19.71891096394408),\n",
+       " (0.17473118279569888, 18.688741721854306),\n",
+       " (0.17473118279569888, 17.95290654893304)]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "coords"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If `ginput` doesn't work for you, you could use the following coordinates."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if coords is None:\n",
+    "    coords = [(0.2, 17.5), \n",
+    "              (0.2, 19.5), \n",
+    "              (0.65, 22),\n",
+    "              (0.75, 21),\n",
+    "              (0.4, 19),\n",
+    "              (0.4, 17.5)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The next step is to convert the coordinates to a format we can use to plot them, which is a sequence of `x` coordinates and a sequence of `y` coordinates.  The NumPy function `transpose` does what we want. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([0.21505376, 0.38978495, 0.53763441, 0.70340502, 0.82885305,\n",
+       "        0.66308244, 0.43010753, 0.27329749, 0.17473118, 0.17473118]),\n",
+       " array([17.5481972 , 18.94628403, 19.90286976, 20.60191317, 21.30095659,\n",
+       "        21.52170714, 20.78587196, 19.71891096, 18.68874172, 17.95290655]))"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "xs, ys = np.transpose(coords)\n",
+    "xs, ys"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To display the polygon, we'll draw the figure again and use `plt.plot` to draw the polygon."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_cmd(photo_table)\n",
+    "plt.plot(xs, ys);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If it looks like your polygon does a good job surrounding the overdense area, go on to the next section.  Otherwise you can try again.\n",
+    "\n",
+    "If you want a polygon with more points (or fewer), you can change the argument to `ginput`.\n",
+    "\n",
+    "The polygon does not have to be \"closed\".  When we use this polygon in the next section, the last and first points will be connected by a straight line.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Which points are in the polygon?\n",
+    "\n",
+    "Matplotlib provides a `Path` object that we can use to check which points fall in the polygon we selected.\n",
+    "\n",
+    "Here's how we make a `Path` using a list of coordinates."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Path(array([[ 0.21505376, 17.5481972 ],\n",
+       "       [ 0.38978495, 18.94628403],\n",
+       "       [ 0.53763441, 19.90286976],\n",
+       "       [ 0.70340502, 20.60191317],\n",
+       "       [ 0.82885305, 21.30095659],\n",
+       "       [ 0.66308244, 21.52170714],\n",
+       "       [ 0.43010753, 20.78587196],\n",
+       "       [ 0.27329749, 19.71891096],\n",
+       "       [ 0.17473118, 18.68874172],\n",
+       "       [ 0.17473118, 17.95290655]]), None)"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from matplotlib.path import Path\n",
+    "\n",
+    "path = Path(coords)\n",
+    "path"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`Path` provides `contains_points`, which figures out which points are inside the polygon.\n",
+    "\n",
+    "To test it, we'll create a list with two points, one inside the polygon and one outside."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "points = [(0.4, 20), \n",
+    "          (0.4, 30)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can make sure `contains_points` does what we expect."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ True, False])"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "inside = path.contains_points(points)\n",
+    "inside"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The result is an array of Boolean values.\n",
+    "\n",
+    "We are almost ready to select stars whose photometry data falls in this polygon.  But first we need to do some data cleaning."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Reloading the data\n",
+    "\n",
+    "Now we need to combine the photometry data with the list of candidate stars we identified in a previous notebook.  The following cell downloads it:\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from wget import download\n",
+    "\n",
+    "filename = 'gd1_candidates.hdf5'\n",
+    "filepath = 'https://github.com/AllenDowney/AstronomicalData/raw/main/data/'\n",
+    "\n",
+    "if not os.path.exists(filename):\n",
+    "    print(download(filepath+filename))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "candidate_df = pd.read_hdf(filename, 'candidate_df')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`candidate_df` is the Pandas DataFrame that contains the results from Notebook XX, which selects stars likely to be in GD-1 based on proper motion.  It also includes position and proper motion transformed to the ICRS frame."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Merging photometry data\n",
+    "\n",
+    "Before we select stars based on photometry data, we have to solve two problems:\n",
+    "\n",
+    "1. We only have Pan-STARRS data for some stars in `candidate_df`.\n",
+    "\n",
+    "2. Even for the stars where we have Pan-STARRS data in `photo_table`, some photometry data is missing.\n",
+    "\n",
+    "We will solve these problems in two step:\n",
+    "\n",
+    "1. We'll merge the data from `candidate_df` and `photo_table` into a single Pandas `DataFrame`.\n",
+    "\n",
+    "2. We'll use Pandas functions to deal with missing data.\n",
+    "\n",
+    "`candidate_df` is already a `DataFrame`, but `results` is an Astropy `Table`.  Let's convert it to Pandas:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "source_id\n",
+      "g_mean_psf_mag\n",
+      "i_mean_psf_mag\n"
+     ]
+    }
+   ],
+   "source": [
+    "photo_df = photo_table.to_pandas()\n",
+    "\n",
+    "for colname in photo_df.columns:\n",
+    "    print(colname)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we want to combine `candidate_df` and `photo_df` into a single table, using `source_id` to match up the rows.\n",
+    "\n",
+    "You might recognize this task; it's the same as the JOIN operation in ADQL/SQL.\n",
+    "\n",
+    "Pandas provides a function called `merge` that does what we want.  Here's how we use it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>source_id</th>\n",
+       "      <th>ra</th>\n",
+       "      <th>dec</th>\n",
+       "      <th>pmra</th>\n",
+       "      <th>pmdec</th>\n",
+       "      <th>parallax</th>\n",
+       "      <th>parallax_error</th>\n",
+       "      <th>radial_velocity</th>\n",
+       "      <th>phi1</th>\n",
+       "      <th>phi2</th>\n",
+       "      <th>pm_phi1</th>\n",
+       "      <th>pm_phi2</th>\n",
+       "      <th>g_mean_psf_mag</th>\n",
+       "      <th>i_mean_psf_mag</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>635559124339440000</td>\n",
+       "      <td>137.586717</td>\n",
+       "      <td>19.196544</td>\n",
+       "      <td>-3.770522</td>\n",
+       "      <td>-12.490482</td>\n",
+       "      <td>0.791393</td>\n",
+       "      <td>0.271754</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-59.630489</td>\n",
+       "      <td>-1.216485</td>\n",
+       "      <td>-7.361363</td>\n",
+       "      <td>-0.592633</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>635860218726658176</td>\n",
+       "      <td>138.518707</td>\n",
+       "      <td>19.092339</td>\n",
+       "      <td>-5.941679</td>\n",
+       "      <td>-11.346409</td>\n",
+       "      <td>0.307456</td>\n",
+       "      <td>0.199466</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-59.247330</td>\n",
+       "      <td>-2.016078</td>\n",
+       "      <td>-7.527126</td>\n",
+       "      <td>1.748779</td>\n",
+       "      <td>17.8978</td>\n",
+       "      <td>17.517401</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>635674126383965568</td>\n",
+       "      <td>138.842874</td>\n",
+       "      <td>19.031798</td>\n",
+       "      <td>-3.897001</td>\n",
+       "      <td>-12.702780</td>\n",
+       "      <td>0.779463</td>\n",
+       "      <td>0.223692</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-59.133391</td>\n",
+       "      <td>-2.306901</td>\n",
+       "      <td>-7.560608</td>\n",
+       "      <td>-0.741800</td>\n",
+       "      <td>19.2873</td>\n",
+       "      <td>17.678101</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>635535454774983040</td>\n",
+       "      <td>137.837752</td>\n",
+       "      <td>18.864007</td>\n",
+       "      <td>-4.335041</td>\n",
+       "      <td>-14.492309</td>\n",
+       "      <td>0.314514</td>\n",
+       "      <td>0.102775</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-59.785300</td>\n",
+       "      <td>-1.594569</td>\n",
+       "      <td>-9.357536</td>\n",
+       "      <td>-1.218492</td>\n",
+       "      <td>16.9238</td>\n",
+       "      <td>16.478100</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>635497276810313600</td>\n",
+       "      <td>138.044516</td>\n",
+       "      <td>19.009471</td>\n",
+       "      <td>-7.172931</td>\n",
+       "      <td>-12.291499</td>\n",
+       "      <td>0.425404</td>\n",
+       "      <td>0.337689</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-59.557744</td>\n",
+       "      <td>-1.682147</td>\n",
+       "      <td>-9.000831</td>\n",
+       "      <td>2.334407</td>\n",
+       "      <td>19.9242</td>\n",
+       "      <td>18.334000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            source_id          ra        dec      pmra      pmdec  parallax  \\\n",
+       "0  635559124339440000  137.586717  19.196544 -3.770522 -12.490482  0.791393   \n",
+       "1  635860218726658176  138.518707  19.092339 -5.941679 -11.346409  0.307456   \n",
+       "2  635674126383965568  138.842874  19.031798 -3.897001 -12.702780  0.779463   \n",
+       "3  635535454774983040  137.837752  18.864007 -4.335041 -14.492309  0.314514   \n",
+       "4  635497276810313600  138.044516  19.009471 -7.172931 -12.291499  0.425404   \n",
+       "\n",
+       "   parallax_error  radial_velocity       phi1      phi2   pm_phi1   pm_phi2  \\\n",
+       "0        0.271754              NaN -59.630489 -1.216485 -7.361363 -0.592633   \n",
+       "1        0.199466              NaN -59.247330 -2.016078 -7.527126  1.748779   \n",
+       "2        0.223692              NaN -59.133391 -2.306901 -7.560608 -0.741800   \n",
+       "3        0.102775              NaN -59.785300 -1.594569 -9.357536 -1.218492   \n",
+       "4        0.337689              NaN -59.557744 -1.682147 -9.000831  2.334407   \n",
+       "\n",
+       "   g_mean_psf_mag  i_mean_psf_mag  \n",
+       "0             NaN             NaN  \n",
+       "1         17.8978       17.517401  \n",
+       "2         19.2873       17.678101  \n",
+       "3         16.9238       16.478100  \n",
+       "4         19.9242       18.334000  "
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "merged = pd.merge(candidate_df, \n",
+    "                  photo_df, \n",
+    "                  on='source_id', \n",
+    "                  how='left')\n",
+    "merged.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The first argument is the \"left\" table, the second argument is the \"right\" table, and the keyword argument `on='source_id'` specifies a column to use to match up the rows.\n",
+    "\n",
+    "The argument `how='left'` means that the result should have all rows from the left table, even if some of them don't match up with a row in the right table.\n",
+    "\n",
+    "If you are interested in the other options for `how`, you can [read the documentation of `merge`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.merge.html).\n",
+    "\n",
+    "You can also do different types of join in ADQL/SQL; [you can read about that here](https://www.w3schools.com/sql/sql_join.asp).\n",
+    "\n",
+    "The result is a `DataFrame` that contains the same number of rows as `candidate_df`. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(7346, 3724, 7346)"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(candidate_df), len(photo_df), len(merged)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And all columns from both tables."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "source_id\n",
+      "ra\n",
+      "dec\n",
+      "pmra\n",
+      "pmdec\n",
+      "parallax\n",
+      "parallax_error\n",
+      "radial_velocity\n",
+      "phi1\n",
+      "phi2\n",
+      "pm_phi1\n",
+      "pm_phi2\n",
+      "g_mean_psf_mag\n",
+      "i_mean_psf_mag\n"
+     ]
+    }
+   ],
+   "source": [
+    "for colname in merged.columns:\n",
+    "    print(colname)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Detail** You might notice that Pandas also provides a function called `join`; it does almost the same thing, but the interface is slightly different.  We think `merge` is a little easier to use, so that's what we chose.  It's also more consistent with JOIN in SQL, so if you learn how to use `pd.merge`, you are also learning how to use SQL JOIN.\n",
+    "\n",
+    "Also, someone might ask why we have to use Pandas to do this join; why didn't we do it in ADQL.  The answer is that we could have done that, but since we already have the data we need, we should probably do the computation locally rather than make another round trip to the Gaia server."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Missing data\n",
+    "\n",
+    "Let's add columns to the merged table for magnitude and color."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "merged['mag'] = merged['g_mean_psf_mag']\n",
+    "merged['color'] = merged['g_mean_psf_mag'] - merged['i_mean_psf_mag']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "These columns contain the special value `NaN` where we are missing data.\n",
+    "\n",
+    "We can use `notnull` to see which rows contain value data, that is, not null values."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0       False\n",
+       "1        True\n",
+       "2        True\n",
+       "3        True\n",
+       "4        True\n",
+       "        ...  \n",
+       "7341     True\n",
+       "7342    False\n",
+       "7343    False\n",
+       "7344     True\n",
+       "7345    False\n",
+       "Name: color, Length: 7346, dtype: bool"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "merged['color'].notnull()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And `sum` to count the number of valid values."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3724"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "merged['color'].notnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For scientific purposes, it's not obvious what we should do with candidate stars if we don't have photometry data.  Should we give them the benefit of the doubt or leave them out?\n",
+    "\n",
+    "In part the answer depends on the goal: are we trying to identify more stars that might be in GD-1, or a smaller set of stars that have higher probability?\n",
+    "\n",
+    "In the next section, we'll leave them out, but you can experiment with the alternative."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Selecting based on photometry\n",
+    "\n",
+    "Now let's see how many of these points are inside the polygon we chose.\n",
+    "\n",
+    "We can use a list of column names to select `color` and `mag`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>color</th>\n",
+       "      <th>mag</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.3804</td>\n",
+       "      <td>17.8978</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.6092</td>\n",
+       "      <td>19.2873</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.4457</td>\n",
+       "      <td>16.9238</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1.5902</td>\n",
+       "      <td>19.9242</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    color      mag\n",
+       "0     NaN      NaN\n",
+       "1  0.3804  17.8978\n",
+       "2  1.6092  19.2873\n",
+       "3  0.4457  16.9238\n",
+       "4  1.5902  19.9242"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "points = merged[['color', 'mag']]\n",
+    "points.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The result is a `DataFrame` that can be treated as a sequence of coordinates, so we can pass it to `contains_points`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([False, False, False, ..., False, False, False])"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "inside = path.contains_points(points)\n",
+    "inside"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The result is a Boolean array.  We can use `sum` to see how many stars fall in the polygon."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "496"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "inside.sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can use `inside` as a mask to select stars that fall inside the polygon."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "selected = merged[inside]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's make a color-magnitude plot one more time, highlighting the selected stars with green `x` marks."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_cmd(photo_table)\n",
+    "plt.plot(xs, ys)\n",
+    "\n",
+    "plt.plot(selected['color'], selected['mag'], 'gx');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It looks like the selected stars are, in fact, inside the polygon, which means they have photometry data consistent with GD-1.\n",
+    "\n",
+    "Finally, we can plot the coordinates of the selected stars:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x180 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10,2.5))\n",
+    "\n",
+    "x = selected['phi1']\n",
+    "y = selected['phi2']\n",
+    "\n",
+    "plt.plot(x, y, 'ko', markersize=0.7, alpha=0.9)\n",
+    "\n",
+    "plt.xlabel('ra (degree GD1)')\n",
+    "plt.ylabel('dec (degree GD1)')\n",
+    "\n",
+    "plt.axis('equal');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This example includes two new Matplotlib commands:\n",
+    "\n",
+    "* `figure` creates the figure.  In previous examples, we didn't have to use this function; the figure was created automatically.  But when we call it explicitly, we can provide arguments like `figsize`, which sets the size of the figure.\n",
+    "\n",
+    "* `axis` with the parameter `equal` sets up the axes so a unit is the same size along the `x` and `y` axes.\n",
+    "\n",
+    "In an example like this, where `x` and `y` represent coordinates in space, equal axes ensures that the distance between points is represented accurately.   "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Write the data\n",
+    "\n",
+    "Let's write the merged DataFrame to a file."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "filename = 'gd1_merged.hdf5'\n",
+    "\n",
+    "merged.to_hdf(filename, 'merged')\n",
+    "selected.to_hdf(filename, 'selected')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-rw-rw-r-- 1 downey downey 2.0M Oct 19 17:21 gd1_merged.hdf5\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!ls -lh gd1_merged.hdf5"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you are using Windows, `ls` might not work; in that case, try:\n",
+    "\n",
+    "```\n",
+    "!dir gd1_merged.hdf5\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Save the polygon\n",
+    "\n",
+    "[Reproducibile research](https://en.wikipedia.org/wiki/Reproducibility#Reproducible_research) is \"the idea that ... the full computational environment used to produce the results in the paper such as the code, data, etc. can be used to reproduce the results and create new work based on the research.\"\n",
+    "\n",
+    "This Jupyter notebook is an example of reproducible research because it contains all of the code needed to reproduce the results, including the database queries that download the data and and analysis.\n",
+    "\n",
+    "However, when we used `ginput` to define a polygon by hand, we introduced a non-reproducible element to the analysis.  If someone running this notebook chooses a different polygon, they will get different results.  So it is important to record the polygon we chose as part of the data analysis pipeline.\n",
+    "\n",
+    "Since `coords` is a NumPy array, we can't use `to_hdf` to save it in a file.  But we can convert it to a Pandas `DataFrame` and save that.\n",
+    "\n",
+    "As an alternative, we could use [PyTables](http://www.pytables.org/index.html), which is the library Pandas uses to read and write files.  It is a powerful library, but not easy to use directly.  So let's take advantage of Pandas."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "coords_df = pd.DataFrame(coords)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "filename = 'gd1_polygon.hdf5'\n",
+    "coords_df.to_hdf(filename, 'coords_df')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can read it back like this."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "coords2_df = pd.read_hdf(filename, 'coords_df')\n",
+    "coords2 = coords2_df.to_numpy()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And verify that the data we read back is the same."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.all(coords2 == coords)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "In this notebook, we worked with two datasets: the list of candidate stars from Gaia and the photometry data from Pan-STARRS.\n",
+    "\n",
+    "We drew a color-magnitude diagram and used it to identify stars we think are likely to be in GD-1.\n",
+    "\n",
+    "Then we used a Pandas `merge` operation to combine the data into a single `DataFrame`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Best practices\n",
+    "\n",
+    "* If you want to perform something like a database `JOIN` operation with data that is in a Pandas `DataFrame`, you can use the `join` or `merge` function.  In many cases, `merge` is easier to use because the arguments are more like SQL.\n",
+    "\n",
+    "* Use Matplotlib options to control the size and aspect ratio of figures to make them easier to interpret.  In this example, we scaled the axes so the size of a degree is equal along both axes.\n",
+    "\n",
+    "* Matplotlib also provides operations for working with points, polygons, and other geometric entities, so it's not just for making figures.\n",
+    "\n",
+    "* Be sure to record every element of the data analysis pipeline that would be needed to replicate the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/_build/html/_sources/07_plot.ipynb b/_build/html/_sources/07_plot.ipynb
new file mode 100644
index 0000000..77778eb
--- /dev/null
+++ b/_build/html/_sources/07_plot.ipynb
@@ -0,0 +1,1174 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Chapter 7\n",
+    "\n",
+    "This is the seventh in a series of notebooks related to astronomy data.\n",
+    "\n",
+    "As a continuing example, we will replicate part of the analysis in a recent paper, \"[Off the beaten path: Gaia reveals GD-1 stars outside of the main stream](https://arxiv.org/abs/1805.00425)\" by Adrian M. Price-Whelan and Ana Bonaca.\n",
+    "\n",
+    "In the previous notebook we selected photometry data from Pan-STARRS and used it to identify stars we think are likely to be in GD-1\n",
+    "\n",
+    "In this notebook, we'll take the results from previous lessons and use them to make a figure that tells a compelling scientific story."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Outline\n",
+    "\n",
+    "Here are the steps in this notebook:\n",
+    "\n",
+    "1. Starting with the figure from the previous notebook, we'll add annotations to present the results more clearly.\n",
+    "\n",
+    "2. The we'll see several ways to customize figures to make them more appealing and effective.\n",
+    "\n",
+    "3. Finally, we'll see how to make a figure with multiple panels or subplots.\n",
+    "\n",
+    "After completing this lesson, you should be able to\n",
+    "\n",
+    "* Design a figure that tells a compelling story.\n",
+    "\n",
+    "* Use Matplotlib features to customize the appearance of figures.\n",
+    "\n",
+    "* Generate a figure with multiple subplots."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Installing libraries\n",
+    "\n",
+    "If you are running this notebook on Colab, you can run the following cell to install Astroquery and a the other libraries we'll use.\n",
+    "\n",
+    "If you are running this notebook on your own computer, you might have to install these libraries yourself.  \n",
+    "\n",
+    "If you are using this notebook as part of a Carpentries workshop, you should have received setup instructions.\n",
+    "\n",
+    "TODO: Add a link to the instructions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# If we're running on Colab, install libraries\n",
+    "\n",
+    "import sys\n",
+    "IN_COLAB = 'google.colab' in sys.modules\n",
+    "\n",
+    "if IN_COLAB:\n",
+    "    !pip install astroquery astro-gala pyia python-wget"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Making Figures That Tell a Story\n",
+    "\n",
+    "So far the figure we've made have been \"quick and dirty\".  Mostly we have used Matplotlib's default style, although we have adjusted a few parameters, like `markersize` and `alpha`, to improve legibility.\n",
+    "\n",
+    "Now that the analysis is done, it's time to think more about:\n",
+    "\n",
+    "1. Making professional-looking figures that are ready for publication, and\n",
+    "\n",
+    "2. Making figures that communicate a scientific result clearly and compellingly.\n",
+    "\n",
+    "Not necessarily in that order."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's start by reviewing Figure 1 from the original paper.  We've seen the individual panels, but now let's look at the whole thing, along with the caption:\n",
+    "\n",
+    "<img width=\"500\" src=\"https://github.com/datacarpentry/astronomy-python/raw/gh-pages/fig/gd1-5.png\">"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Think about the following questions:\n",
+    "\n",
+    "1. What is the primary scientific result of this work?\n",
+    "\n",
+    "2. What story is this figure telling?\n",
+    "\n",
+    "3. In the design of this figure, can you identify 1-2 choices the authors made that you think are effective?  Think about big-picture elements, like the number of panels and how they are arranged, as well as details like the choice of typeface.\n",
+    "\n",
+    "4. Can you identify 1-2 elements that could be improved, or that you might have done differently?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Some topics that might come up in this discussion:\n",
+    "\n",
+    "1. The primary result is that the multiple stages of selection make it possible to separate likely candidates from the background more effectively than in previous work, which makes it possible to see the structure of GD-1 in \"unprecedented detail\".\n",
+    "\n",
+    "2. The figure documents the selection process as a sequence of steps.  Reading right-to-left, top-to-bottom, we see selection based on proper motion, the results of the first selection, selection based on color and magnitude, and the results of the second selection.  So this figure documents the methodology and presents the primary result.\n",
+    "\n",
+    "3. It's mostly black and white, with minimal use of color, so it will work well in print.  The annotations in the bottom left panel guide the reader to the most important results.  It contains enough technical detail for a professional audience, but most of it is also comprehensible to a more general audience.  The two left panels have the same dimensions and their axes are aligned.\n",
+    "\n",
+    "4. Since the panels represent a sequence, it might be better to arrange them left-to-right.  The placement and size of the axis labels could be tweaked.  The entire figure could be a little bigger to match the width and proportion of the caption.  The top left panel has unnused white space (but that leaves space for the annotations in the bottom left)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plotting GD-1\n",
+    "\n",
+    "Let's start with the panel in the lower left.  The following cell reloads the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from wget import download\n",
+    "\n",
+    "filename = 'gd1_merged.hdf5'\n",
+    "path = 'https://github.com/AllenDowney/AstronomicalData/raw/main/data/'\n",
+    "\n",
+    "if not os.path.exists(filename):\n",
+    "    print(download(path+filename))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "selected = pd.read_hdf(filename, 'selected')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "def plot_second_selection(df):\n",
+    "    x = df['phi1']\n",
+    "    y = df['phi2']\n",
+    "\n",
+    "    plt.plot(x, y, 'ko', markersize=0.7, alpha=0.9)\n",
+    "\n",
+    "    plt.xlabel('$\\phi_1$ [deg]')\n",
+    "    plt.ylabel('$\\phi_2$ [deg]')\n",
+    "    plt.title('Proper motion + photometry selection', fontsize='medium')\n",
+    "\n",
+    "    plt.axis('equal')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And here's what it looks like."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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kofLmqvK1EWvWrOGqq65izZo1xVesIgYdEHXTX1XY1toGbRHxoyKWMTOzzg1yqLwKmYa6+nDjFVdc0XXb1Nf3kUceabvevWSInDkffEDUTZs329bKtF3kvntU0jsypt+RtLKP9TMzK6VB7cgHecAv6sCap216bb/Z2VnWr18/P/TYTdn19b300ksHNpRWpgBgkNqdfJShXZr9PmypMnARkWsCrgS+Abw/me4C/ha4CfjDvOUMYzrllFPCzKxIMzMzcdRRR8XMzEzf32vfvn0xMzMT+/btG8r7pOfnqUuetsnbfnnq1GvZe/fu7bh9u+3/QX5uqqSodul1W8mqx6C2vzRgR2TFYlkzMxeEzwGHpx4fDnwWOAz4Wt5yhjE5aDPr3TB2XP3U6/oMsj2KPNB3U+/0++epS573yFuPbtY9q+yi+6vb8kZtOypKUe3S7bbSSwDfD0UEbXcCT0k9PoTanaUAX81bzjAmB21m2TrZUY5ahqBK61Pkgb7XIKixLp3UrZv1KGrdp6enY+nSpTE9Pd1TOb1y0NZf3bZv2fYHRQRtfwrsBN6VTDuAC4DFwCfzljOMyUGbWbZOdlSjdrAZZgAxSL0EWXl08hka5oExHbQNsx/LFhwUoYrbRaOyrUPPQVutDE4BzgXeDqzq5LXDnBy0mWUr244qopx1qivbAbeo68v6UYdOhigH0efp9xhmP5b5892tsm0Xo6CITJuAdcAFyePjgRfnff0wJwdtZuXQa5Ax7ANekddqFWFQ15f1ejNAu/Lry05PTw/shovp6en5rJv1Ztjb5SgqImj7MPCh1HVsTwVuyvv6YU4O2sy6V+QOOc91RUXcFdipVu+Z9dzevXtj06ZNsXfv3qZ1HEQAUsTNFFNTU7F+/fpYtmzZQe1aL396ejrzjrpNmzbF8uXLMwOzTjJbrd6naIN8L7NuFRG07Uz+fjU179a8rx/m5KCt2nwWN1ydZHPa3XnVLGjLmw3qNDtSxB2KWc9t2rQpJMWmTZuavueggoJeto+ZmZlYvHhxSIoNGzYcVEarAHRmZiaWL18emzZtahuY5c3UtQqGW+nmhppBZfUaeX9meRQRtN1A7Yfh68Hb8rLfNVqfHLRVm6+XGLx0EJYnUKr30aZNm3JlVJoFB636OG9QkP4/7x2Dnd4duWfPntiwYUPs3r27owxdP8zMzMSyZctiw4YNuYOddP9OTU3F5ORk7Nmzp+k1aFnBeBE3ODT2aWMg2G05rda71ed5EH3WSV27qcswgsJxCUQHuZ5FBG1vBKaA7wAXAV8HXp/39Tnf41VJuXcDf5zxvIBLk+dvA07OU66Dtt4Me4Mc9vsPwiDXsZPrytJBWJ7Aplmmrd0BPk/GJe+wafrAPzU11XFmL0+g1659st6rk+9/6uTzsG/fvtiwYUPTzF9avV2npqYOqnerzGBjoFFUQJH1OD3kmre8dm2bN/s5iBPErLbLmpeuS972TrdfOvDt9/6lm3ar4n59kAmEou4efT5wTjK9oJPX5ij7ycA9wE8BTwFuBU5oWObVwLVJ8PYS4IY8ZTto640zXf3V6YGq8bW9Zjcay0pn19IHw8bAqD7E1EkmLp1RSQ9R1d9zamoqpqenY3Jycj4IabeO+/btm88W7d2794D2TJefLqPVAbGT6+6y2ifdJvUgqZ4Jy9vHnW5zeYPc+rDu5ORkZr3bZdrqf7sd+s1q98a+aTw5aPx8dZNJbbWO6XWdnp6OrVu3zn+WmrVjVhvnDcby1i/92ryfh/oJS+NnrdsMZifBYlH7oTKrRKYNeEerqd3r807AS4HPpR6/E3hnwzIfBX4r9fjrwDPale2grTfDPiMqYhim33XqRS871G6CvWZ1n5qaisWLF8fGjRtbBnXpA0r9oLl06dKWdWh2IJ6enp4/yCxZsiQWL14cS5cujampqfkgpLF9Gus/MzMz/9r6wbsxAN24cWMsWbIkpqamYmZmJvbs2ROTk5MxNTV1UDau3YG43Tqm26TePxs2bDhoCLNV2d1e25WWdVDMKrc+L2t4tF2w3S6j2qyN0oFIq2zlzMxMLF26NJYsWRKTk5Pzn5n68s2+ey0rkG/Xj/X6NGYts9oxK3OX1TbtPruN5U1NTcWSJUti48aNB5w05c0kNlu+cV/RaTCZtW33ug8sw368zHoJ2upfpnsl8E2e+O3RbwAfb/f6vBNwWro84E3AXzQsczXwC6nHn2/2fXHAWckXAO84/vjj+9Ss5TcKG0azA0eeQKUf699LZqxZed3UcWam82uZWtm4cWMAcf755+cemqwHYPUMWZ4sQ2O2pZ6JqgdRWeWkM2f15dMHwsnJyTjyyCNj/fr1BwQf9YP+okWL4tBDD50PSDdt2jQfWE1OTh6Urck6KOeVDkrTGcTJycmDsh95goF275EnQGolK3hqVka7gLaxbxsztum/7bK56fWs9+/ixYvnA+/G7GxjANXqBotWWbE9e/Yc8PlvtZ71ft2yZUtmPRr3Fe32XdPT07F48eJYtGjRfNCfN8OWd5i+k8xdq89Qq89vEfvbspY1KEVc0zYDHJF6fATw2byvz1H+6zOCtg82LHNNRtB2SruyxznTVtYUdDcZjG4ybf1Y//rZc/3MfxA7gqxMVVYgkGeH3Ti/fqDZsmVLLF68OC644IKDgpjGYajG8rKGqZplZRoDjjyBX3pYLmv4Z9++J67r2rBhw0GBQz3TtmXLlvmsUlbQ0K7uefqnUfoAnzcYaPe+6QxUpxnaZnXv9DcXs+raGBikM45HHXXUfIA8OTnZNpvbOH9ycjKWLFky/xmrf+bqZTUOtW/ZsiXOPPPMOPLIIw8aPu014EgH10uXLp3PEDcGTXv37p0/GckKdLMep7eFvH3SKiBrFaB287lp9Xkpcn9b1rIGpYig7S7gkNTjQ4C78r4+R/keHu1R0RtnP/VrI8ob4BWx06oHEIMI3tIHyE2bNsWSJUvmg6T0erTKVDQOjdSHxepl1YOKq666aj6jUTc9PR1LliyJCy64YP4glH6frKCtVdaosZ7pA3y6jvXXNl6blnXAqB8gt2zZkhn8NbZDM+kDZ1Yg2ap/ssptXJfG4C1r2TwH6bzrk0c320O7rGBWpq0+FF3PlnXzfo0nCo1lpfui2TVv7bKEed4/vY7NPidZXw2TN+PV7T4qzwlVL/pR5yxlLWtQigjazk9uDrgwGS69pTGo6mUCFgD3As9K3YjwwoZlfqXhRoQb85Q9LkFbVc4m2u3o2r22XQai0+GEbu3bt28+6Okl45EnsGwMFDdu3BgbN26cP4NvVlZWRqa+7vVhm/S1QsuXL4/169cfkNFIr+uiRYsCiI0bNx6U+WvWl3lOJhrLSAepnQRbjdmYtLwZ0nT7NLter9nJQavMSH2Z9E0Wrdooj/rrGof0Oi23l+2hmwxVPw/EWdtNEXWrf3462c6zriFMt3Wr9yxqf1500FbFAKiKeg7aamVwMrXfHj0XOKmT1+Ys/9XJtXL3AOcn884Gzk7+F7VfZbgH2NXserbGaVyCtkFtTL2+T/qg2OlOqZuz4V6Wa6eIjEfWOu3du3f+4vX0/HTwVg8omt0hma5f1t13jcun3zMrsNm3b19s3LgxFi1adEAWrh8nC43ZiDwHy5mZg29IyCqzHpw2Ky/dPs2C0WbrnKct2h3Iu9Hsy37zljvqB+J265dn/esnLr1mNoexj2p30mT5DLLdCgnaqjqNS9DWSpE7il4PMK3uVmunX2fq3ZTb6+uzyqmrH4SzLqKuvyZ9B2b6eqE8Q0FZ0sFRqyGvQR8AOvnstrs4v75c3oNvnmxOq/l5g4E89W6l2d2mo3hw7mad2u2zxi24rcqoTNkMst16uXt0ZxHLDHPqd9DW64Y8iB1BkcOGvX4dQacf/H61T7oe6eG4vO/V6wbcar3q37i/Z8+e3OU0+yLZvMMxRQShnQY5wzKIYcO8r/cBtDPdtFcRmbZBybuN9us9rLlKZNqAR6n9+kCzaRfw7+3KGebU76BtEDv2XnV7xp/1uiKClU4yC43v148dV3roLO+69TN4bRdEdpLx6maosVu9DBuW0SBOyHwA7cyot1ee/ULVtqMqGvbnrJeg7Zk5pmPblTPMyZm2fLJ2CFnzihgKzPM9VM2yPnkzR93UrZdgst16dPNcs3bqdufdydBg3rrmWa4sn3Gzsutkn+Htqn+GHSD7mrYKGvQG2c31Sp3WMe8wZKsNpjFz1O/v8uklEOnXEGoRgXOR1wSZ2eB5u+yfYQfEDtoqqAobZL+uTyt6uV7q1ks/DHvDL0o36zEq625WVqOwjY3COvRDs6BNtedG26pVq2LHjh3DrkbH5ubmmJ2dZWJigoULFw67OpmKqmOZ17XMdSuzbdu2sW7dOjZv3szatWuHXR2zShmX/Y73E9kk3RwRqxrnP2kYlbF8Fi5cyNq1a0u9wRZVx9nZWdatW8fs7GwxFStQFfqhjCYmJti8eTMTExPDrsrYmJubY9u2bczNzQ27KtajMu8Ti+T9RGc6DtokrZX0MUkrk8dnFV4rGzurV6/mvPPOY/Xq1cOuihXEwe7gVf1A76DzCeMSzHg/0ZluMm1vBf4AWCfpFcDKQmtkY+m6667jkksu4brrrht2VawHPugOV9UP9FUPOovkYMaydBO07Y6IPRHx+8CpwIsKrpONoaofbKzGB93hqvqB3vsBs9Y6CtokPQX4Qv1xRPwx8DdFV8rGT9UPNlbjg671wvuBGmesizGK7Zg7aJN0LvBd4GJJd0p6G0BEfLBflTOzavFB16x3zlgXYxTbsW3QJukDks4EzgVeEBHHAL8InCDpPf2uoJmZDccoZiqqwBnrYoxiO+bJtH0ReA6wDPgXSTuB9wH3AG+QtLR/1bMieQdsZp0YxUxFFThjXYxRbMe2QVtEbI2IC4DrgdcCa4BPAPuBpwGzku7uay2tEN4Bm1knRjFTYeNl1JIVndyIcA6wGXg/cBJwIrArIlYCJxRfNSuad8Bm1olRzFTY6MoK0EYtWZE7aIuIbwI/D/wDcBhwG/C65Ll9famdFco7YDOD0cs+mEF2gDZqyYqOvvIjIvZFxDURcVFEfDAi/quISkh6n6S7JN0maWuz6+Qk3Sdpl6RbJFXvx0TNzEqgqtkHB5vWSlaAlidZUaXPVVl+e3QbcGJE/CzwDeCdLZZ9eUSszPohVTMza6+q2YeqBps2GN2OJlXpc1WKoC0iZiJif/LweuDYYdbHzGyUVfVSiaoGm9aZQWe+qvS5KkXQ1mADcG2T5wKYkXRzux+ql3SWpB2SduzevbvwSpqZ2WBVNdjsVpWG7YrUa+ar03ar0udqYEGbpO2Sbs+YXpta5nxqXyXyySbFrI6Ik4FfBs6R9IvN3i8iLouIVRGxavny5YWui5mZWb9VadiuSL1mvka53RQRw64DAJLeDJwNvDIiHsmx/IXAwxFxcbtlV61aFTt2+L6FXs3NzTE7O8vExEQlzkjMbLSN+j5p1NZvUOszCu0m6easa/dLMTwq6VXAHwGvaRawSVos6Yj6/8CpwO2Dq6WN8tnLoI3rsIdZkUZ9n5Q1bFflfceg+qtKw52dKkXQBvwFcASwLfk6j48ASDpa0nSyzE8AX5F0K3AjcE1EfHY41R1PVbpYs+xG/WBjNgjjuE+q8r5jHPuraKUZHu0nD49a2YxC+t7Mn+PBc5uPh1IPj5qNm1FO39v4qHLWp6q87xhvDtrMzKwrHu4q7hqzKl+rNmjj3FYO2szMrCujmvXpJCgoKtvorGV+49xWDtrMzMxSOgkKiso2OmuZX9FtVaXMnW9EMDMzS/HF/uNl27ZtrFu3js2bN7N27dphVwdofiOCgzYzMzMbW2UM0n33qJmZjbQqDXNZeVTp2kwHbWZmbTgYKI9WfTHOF6jbeHDQZmbWhoOB8mjVF1W7mN8nA9YpB21mZm1ULRgYZa36okrDXDDYkwEHiKPBQZuZWRtVCwZGWad9UeZgZZAnA70GiGVux3HioM3MbMCKOAD6IJpPmYe2B3ky0GuAWOZ2HCcO2szMBqyIA6APovl4aLum1wDR7VgO/p42M7MBK+J7ocr43VJmVVambcrf02ZmgIfVyqCIYTFfZze6+rmNevtvrgrZawdtZmOmCjsmGy++xu9A/dxGvf03V4UhYAdtNlJGacfdL1XYMeXl/h6eItve1/gdqJ/b6Cht/0WrQva6FEGbpAslfUfSLcn06ibLvUrS1yXdLemPB11PK79R2nH3SxV2THm5v4enyLYvIpAYpWCkn9voKG3/46gUNyJIuhB4OCIubrHMk4FvAGuB+4GbgN+KiK+1K983IoyPMl1Iav3n/h6eTtre/WTWmVG4EeHFwN0RcW9E7AP+DnjtkOtkJeOzyPHi/h6eTtreGVGrojJeflGmoO1tkm6TdLmkp2Y8fwzw7dTj+5N5ZmZWYqM0dDkqyhiQlE0ZTzYGFrRJ2i7p9ozptcCHgWcDK4HvAu/PKiJjXtOxXUlnSdohacfu3buLWAWzg3jHZ9aeM6LlU8aApGzKeLIxsKAtItZExIkZ02ci4j8j4vGI+DHwMWpDoY3uB45LPT4WeKDF+10WEasiYtXy5cuLXRmzhHd8VmU+6RhfZQxIyqaMJxulGB6V9IzUw9cBt2csdhPwXEnPkvQU4A3A1CDqZ9aMd3y9c+AwPD7pGF9lDEisvVIEbcB7Je2SdBvwcuA8AElHS5oGiIj9wNuAzwF3AldFxB3DqrAZeMdXBAcOwzOMkw4H6WbdK8VXfvSbv/LDrLz8dRDjZdu2baxbt47Nmzezdu3aYVfHrJSafeWHgzYzMxsYB+lm7TUL2hYMozJmZjae6pcUmFnnynJNm5mZmZm14KDNzMzMrAIctJmZmZlVgIM2MzMzswpw0GZmZmZWAQ7azMzMzCrAQZuZmZlZBThoMzMzM6sAB21mZmZmFeCgzczMzKwCxuK3RyXtBr6Vc/FlwEN9rI71zn1Ubu6f8nMflZv7p9wG0T/PjIjljTPHImjrhKQdWT/SauXhPio390/5uY/Kzf1TbsPsHw+PmpmZmVWAgzYzMzOzCnDQdrDLhl0Ba8t9VG7un/JzH5Wb+6fchtY/vqbNzMzMrAKcaTMzMzOrAAdtCUl/L+mWZLpP0i2p594p6W5JX5f034dYzbEm6X8lfXCHpPem5rt/SkDShZK+k9qOXp16zn1UEpJ+X1JIWpaa5/4pAUnvkXRbsv3MSDo69Zz7aMgkvU/SXUkfbZW0NPXcQPrHw6MZJL0f+H5EvFvSCcD/A14MHA1sB54XEY8Ps47jRtLLgfOBX4mIxyQdFREPun/KQ9KFwMMRcXHDfPdRSUg6Dvg48HzglIh4yP1THpKOjIgfJP//LnBCRJztPioHSacC/xwR+yX9OUBE/NEg+8eZtgaSBJxOrQMAXgv8XUQ8FhH/BtxNrWNssN4C/FlEPAYQEQ8m890/5ec+Ko9LgD8E0mfr7p+SqAdsicU80U/uoxKIiJmI2J88vB44Nvl/YP3joO1gLwP+MyK+mTw+Bvh26vn7k3k2WM8DXibpBklflPSiZL77p1zelgwdXC7pqck891EJSHoN8J2IuLXhKfdPiUi6SNK3gTcCFySz3UflswG4Nvl/YP2zoB+FlpWk7cBPZjx1fkR8Jvn/t3giywagjOU9ptwHrfqH2mf1qcBLgBcBV0n6Kdw/A9Wmjz4MvIda+78HeD+1HZv7aEDa9M//Bk7NelnGPPdPn7Q7DkXE+cD5kt4JvA14F+6jgckTJ0g6H9gPfLL+sozl+9I/YxW0RcSaVs9LWgD8OnBKavb9wHGpx8cCDxRfO2vVP5LeAmyJ2kWYN0r6MbXff3P/DFC7bahO0seAq5OH7qMBadY/kn4GeBZwa+0KEI4Fdkp6Me6fgcq7DQFXAtdQC9rcRwOSI054M/A/gFfGEzcFDKx/PDx6oDXAXRFxf2reFPAGSYdIehbwXODGodRuvH0aeAWApOcBT6H2g73un5KQ9IzUw9cBtyf/u4+GLCJ2RcRREbEiIlZQO8icHBH/gfunNCQ9N/XwNcBdyf/uoxKQ9Crgj4DXRMQjqacG1j9jlWnL4Q0cODRKRNwh6Srga9TSoef4jp2huBy4XNLtwD7gzclZjvunPN4raSW1YYH7gP8J3obKzv1TKn8m6aeBHwPfAs4G91GJ/AVwCLAtyVhfHxFnD7J//JUfZmZmZhXg4VEzMzOzCnDQZmZmZlYBDtrMzMzMKsBBm5mZmVkFOGgzMzMzqwAHbWZmZmYV4KDNzMzMrAIctJnZ2JH065IuzbnsCkmPSrqlyfMXSvr9LutxmKRbJO2TtKybMsxsfDhoM7NxdBKws4Pl74mIlUVXIiIeTcr170iaWVv+GSszGxvJ79Z+CHgJ8D1JSyPiA12Ucz5wJvBtYDdwczJ/HfC71H4b9wbgrRHxuKQ/Bd6YLP8QcHNEXNz7GpnZOHGmzczGgqRDgKuAd1ALtF4CXCDp0A7LOYXa7xSfBPw68KJk/guA3wRWJ9mzx4E3SloF/EZq+VVFrI+ZjR9n2sxsXKwFbqU2FPmDiPgPST8Cni3pHcCSiDgtRzkvA7ZGxCMAkqaS+a8ETgFuSn5M+jDgQeBpwGci4tFk+X8qcJ3MbIw402Zm42IlsAv4OeA2SUcBP4yIOyLidzosKzLmCfhERKxMpp+OiAuT+WZmPXPQZmbj4gfA84GfBW4DNlK7vq1TXwJel9z5eQTwq8n8zwOnJcEgkp4m6ZnAV4BflXSopMOBX+lxPcxsTHl41MzGxWZgK7Xryv4L+Dvgg50WEhE7Jf09cAvwLeDLyfyvSfoTYEbSk4A54JyIuD4ZQr01WX4H8P3eV8fMxo0isrL8ZmajSdKtwCsi4nvJ46cDF1G75u3jEbGpYfkVwNURcWIP73l4RDwsaRG1TN1ZEbEz9fx9wKqIeKjb9zCz0edMm5mNjeQO0iPqARtA8v/ZLV72OLBE0i09fFfbZZJOAA6ldt3bzqQ+hwH/CiwEftxl2WY2JpxpMzMzM6sA34hgZmZmVgEO2szMzMwqwEGbmZmZWQU4aDMzMzOrAAdtZmZmZhXgoM3MzMysAhy0mZmZmVWAgzYzMzOzCvj/b1DvmsUPsooAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 720x180 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10,2.5))\n",
+    "plot_second_selection(selected)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Annotations\n",
+    "\n",
+    "The figure in the paper uses three other features to present the results more clearly and compellingly:\n",
+    "\n",
+    "* A vertical dashed line to distinguish the previously undetected region of GD-1,\n",
+    "\n",
+    "* A label that identifies the new region, and\n",
+    "\n",
+    "* Several annotations that combine text and arrows to identify features of GD-1.\n",
+    "\n",
+    "As an exercise, choose any or all of these features and add them to the figure:\n",
+    "\n",
+    "* To draw vertical lines, see [`plt.vlines`](https://matplotlib.org/3.3.1/api/_as_gen/matplotlib.pyplot.vlines.html) and [`plt.axvline`](https://matplotlib.org/3.3.1/api/_as_gen/matplotlib.pyplot.axvline.html#matplotlib.pyplot.axvline).\n",
+    "\n",
+    "* To add text, see [`plt.text`](https://matplotlib.org/3.3.1/api/_as_gen/matplotlib.pyplot.text.html).\n",
+    "\n",
+    "* To add an annotation with text and an arrow, see [plt.annotate]().\n",
+    "\n",
+    "And here is some [additional information about text and arrows](https://matplotlib.org/3.3.1/tutorials/text/annotations.html#plotting-guide-annotation)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "# plt.axvline(-55, ls='--', color='gray', \n",
+    "#             alpha=0.4, dashes=(6,4), lw=2)\n",
+    "# plt.text(-60, 5.5, 'Previously\\nundetected', \n",
+    "#          fontsize='small', ha='right', va='top');\n",
+    "\n",
+    "# arrowprops=dict(color='gray', shrink=0.05, width=1.5, \n",
+    "#                 headwidth=6, headlength=8, alpha=0.4)\n",
+    "\n",
+    "# plt.annotate('Spur', xy=(-33, 2), xytext=(-35, 5.5),\n",
+    "#              arrowprops=arrowprops,\n",
+    "#              fontsize='small')\n",
+    "\n",
+    "# plt.annotate('Gap', xy=(-22, -1), xytext=(-25, -5.5),\n",
+    "#              arrowprops=arrowprops,\n",
+    "#              fontsize='small')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Customization\n",
+    "\n",
+    "Matplotlib provides a default style that determines things like the colors of lines, the placement of labels and ticks on the axes, and many other properties.\n",
+    "\n",
+    "There are several ways to override these defaults and customize your figures:\n",
+    "\n",
+    "* To customize only the current figure, you can call functions like `tick_params`, which we'll demonstrate below.\n",
+    "\n",
+    "* To customize all figures in a notebook, you use `rcParams`.\n",
+    "\n",
+    "* To override more than a few defaults at the same time, you can use a style sheet."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As a simple example, notice that Matplotlib puts ticks on the outside of the figures by default, and only on the left and bottom sides of the axes.\n",
+    "\n",
+    "To change this behavior, you can use `gca()` to get the current axes and `tick_params` to change the settings.\n",
+    "\n",
+    "Here's how you can put the ticks on the inside of the figure:\n",
+    "\n",
+    "```\n",
+    "plt.gca().tick_params(direction='in')\n",
+    "```\n",
+    "\n",
+    "**Exercise:** Read the documentation of [`tick_params`](https://matplotlib.org/3.1.1/api/_as_gen/matplotlib.axes.Axes.tick_params.html) and use it to put ticks on the top and right sides of the axes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "# plt.gca().tick_params(top=True, right=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## rcParams\n",
+    "\n",
+    "If you want to make a customization that applies to all figures in a notebook, you can use `rcParams`.\n",
+    "\n",
+    "Here's an example that reads the current font size from `rcParams`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "10.0"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "plt.rcParams['font.size']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And sets it to a new value:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.rcParams['font.size'] = 14"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Plot the previous figure again, and see what font sizes have changed.  Look up any other element of `rcParams`, change its value, and check the effect on the figure."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you find yourself making the same customizations in several notebooks, you can put changes to `rcParams` in a `matplotlibrc` file, [which you can read about here](https://matplotlib.org/3.3.1/tutorials/introductory/customizing.html#customizing-with-matplotlibrc-files)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Style sheets\n",
+    "\n",
+    "The `matplotlibrc` file is read when you import Matplotlib, so it is not easy to switch from one set of options to another.\n",
+    "\n",
+    "The solution to this problem is style sheets, [which you can read about here](https://matplotlib.org/3.1.1/tutorials/introductory/customizing.html).\n",
+    "\n",
+    "Matplotlib provides a set of predefined style sheets, or you can make your own.\n",
+    "\n",
+    "The following cell displays a list of style sheets installed on your system."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Solarize_Light2',\n",
+       " '_classic_test_patch',\n",
+       " 'bmh',\n",
+       " 'classic',\n",
+       " 'dark_background',\n",
+       " 'fast',\n",
+       " 'fivethirtyeight',\n",
+       " 'ggplot',\n",
+       " 'grayscale',\n",
+       " 'seaborn',\n",
+       " 'seaborn-bright',\n",
+       " 'seaborn-colorblind',\n",
+       " 'seaborn-dark',\n",
+       " 'seaborn-dark-palette',\n",
+       " 'seaborn-darkgrid',\n",
+       " 'seaborn-deep',\n",
+       " 'seaborn-muted',\n",
+       " 'seaborn-notebook',\n",
+       " 'seaborn-paper',\n",
+       " 'seaborn-pastel',\n",
+       " 'seaborn-poster',\n",
+       " 'seaborn-talk',\n",
+       " 'seaborn-ticks',\n",
+       " 'seaborn-white',\n",
+       " 'seaborn-whitegrid',\n",
+       " 'tableau-colorblind10']"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "plt.style.available"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that `seaborn-paper`, `seaborn-talk` and `seaborn-poster` are particularly intended to prepare versions of a figure with text sizes and other features that work well in papers, talks, and posters.\n",
+    "\n",
+    "To use any of these style sheets, run `plt.style.use` like this:\n",
+    "\n",
+    "```\n",
+    "plt.style.use('fivethirtyeight')\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The style sheet you choose will affect the appearance of all figures you plot after calling `use`, unless you override any of the options or call `use` again.\n",
+    "\n",
+    "**Exercise:** Choose one of the styles on the list and select it by calling `use`.  Then go back and plot one of the figures above and see what effect it has."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you can't find a style sheet that's exactly what you want, you can make your own.  This repository includes a style sheet called `az-paper-twocol.mplstyle`, with customizations chosen by Azalee Bostroem for publication in astronomy journals.\n",
+    "\n",
+    "The following cell downloads the style sheet."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "filename = 'az-paper-twocol.mplstyle'\n",
+    "path = 'https://github.com/AllenDowney/AstronomicalData/raw/main/data/'\n",
+    "\n",
+    "if not os.path.exists(filename):\n",
+    "    print(download(path+filename))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can use it like this:\n",
+    "\n",
+    "```\n",
+    "plt.style.use('./az-paper-twocol.mplstyle')\n",
+    "```\n",
+    "\n",
+    "The prefix `./` tells Matplotlib to look for the file in the current directory."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As an alternative, you can install a style sheet for your own use by putting it in your configuration directory.  To find out where that is, you can run the following command:\n",
+    "\n",
+    "```\n",
+    "import matplotlib as mpl\n",
+    "\n",
+    "mpl.get_configdir()\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## LaTeX fonts\n",
+    "\n",
+    "When you include mathematical expressions in titles, labels, and annotations, Matplotlib uses [`mathtext`](https://matplotlib.org/3.1.0/tutorials/text/mathtext.html) to typeset them.  `mathtext` uses the same syntax as LaTeX, but it provides only a subset of its features.\n",
+    "\n",
+    "If you need features that are not provided by `mathtext`, or you prefer the way LaTeX typesets mathematical expressions, you can customize Matplotlib to use LaTeX.\n",
+    "\n",
+    "In `matplotlibrc` or in a style sheet, you can add the following line:\n",
+    "\n",
+    "```\n",
+    "text.usetex        : true\n",
+    "```\n",
+    "\n",
+    "Or in a notebook you can run the following code.\n",
+    "\n",
+    "```\n",
+    "plt.rcParams['text.usetex'] = True\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.rcParams['text.usetex'] = True"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you go back and draw the figure again, you should see the difference.\n",
+    "\n",
+    "If you get an error message like\n",
+    "\n",
+    "```\n",
+    "LaTeX Error: File `type1cm.sty' not found.\n",
+    "```\n",
+    "\n",
+    "You might have to install a package that contains the fonts LaTeX needs.  On some systems, the packages `texlive-latex-extra` or `cm-super` might be what you need.  [See here for more help with this](https://stackoverflow.com/questions/11354149/python-unable-to-render-tex-in-matplotlib).\n",
+    "\n",
+    "In case you are curious, `cm` stands for [Computer Modern](https://en.wikipedia.org/wiki/Computer_Modern), the font LaTeX uses to typeset math."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Multiple panels\n",
+    "\n",
+    "So far we've been working with one figure at a time, but the figure we are replicating contains multiple panels, also known as \"subplots\".\n",
+    "\n",
+    "Confusingly, Matplotlib provides *three* functions for making figures like this: `subplot`, `subplots`, and `subplot2grid`.\n",
+    "\n",
+    "* [`subplot`](https://matplotlib.org/3.3.1/api/_as_gen/matplotlib.pyplot.subplot.html) is simple and similar to MATLAB, so if you are familiar with that interface, you might like `subplot`\n",
+    "\n",
+    "* [`subplots`](https://matplotlib.org/3.3.1/api/_as_gen/matplotlib.pyplot.subplots.html) is more object-oriented, which some people prefer.\n",
+    "\n",
+    "* [`subplot2grid`](https://matplotlib.org/3.3.1/api/_as_gen/matplotlib.pyplot.subplot2grid.html) is most convenient if you want to control the relative sizes of the subplots. \n",
+    "\n",
+    "So we'll use `subplot2grid`.\n",
+    "\n",
+    "All of these functions are easier to use if we put the code that generates each panel in a function."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Upper right\n",
+    "\n",
+    "To make the panel in the upper right, we have to reload `centerline`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "filename = 'gd1_dataframe.hdf5'\n",
+    "path = 'https://github.com/AllenDowney/AstronomicalData/raw/main/data/'\n",
+    "\n",
+    "if not os.path.exists(filename):\n",
+    "    print(download(path+filename))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "centerline = pd.read_hdf(filename, 'centerline')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And define the coordinates of the rectangle we selected."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pm1_min = -8.9\n",
+    "pm1_max = -6.9\n",
+    "pm2_min = -2.2\n",
+    "pm2_max =  1.0\n",
+    "\n",
+    "pm1_rect = [pm1_min, pm1_min, pm1_max, pm1_max]\n",
+    "pm2_rect = [pm2_min, pm2_max, pm2_max, pm2_min]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To plot this rectangle, we'll use a feature we have not seen before: `Polygon`, which is provided by Matplotlib.\n",
+    "\n",
+    "To create a `Polygon`, we have to put the coordinates in an array with `x` values in the first column and `y` values in the second column. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[-8.9, -2.2],\n",
+       "       [-8.9,  1. ],\n",
+       "       [-6.9,  1. ],\n",
+       "       [-6.9, -2.2]])"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "vertices = np.transpose([pm1_rect, pm2_rect])\n",
+    "vertices"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The following function takes a `DataFrame` as a parameter, plots the proper motion for each star, and adds a shaded `Polygon` to show the region we selected."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from matplotlib.patches import Polygon\n",
+    "\n",
+    "def plot_proper_motion(df):\n",
+    "    pm1 = df['pm_phi1']\n",
+    "    pm2 = df['pm_phi2']\n",
+    "\n",
+    "    plt.plot(pm1, pm2, 'ko', markersize=0.3, alpha=0.3)\n",
+    "    \n",
+    "    poly = Polygon(vertices, closed=True, \n",
+    "                   facecolor='C1', alpha=0.4)\n",
+    "    plt.gca().add_patch(poly)\n",
+    "    \n",
+    "    plt.xlabel('$\\mu_{\\phi_1} [\\mathrm{mas~yr}^{-1}]$')\n",
+    "    plt.ylabel('$\\mu_{\\phi_2} [\\mathrm{mas~yr}^{-1}]$')\n",
+    "\n",
+    "    plt.xlim(-12, 8)\n",
+    "    plt.ylim(-10, 10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notice that `add_patch` is like `invert_yaxis`; in order to call it, we have to use `gca` to get the current axes.\n",
+    "\n",
+    "Here's what the new version of the figure looks like.  We've changed the labels on the axes to be consistent with the paper."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.rcParams['text.usetex'] = False\n",
+    "plt.style.use('default')\n",
+    "\n",
+    "plot_proper_motion(centerline)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Upper left\n",
+    "\n",
+    "Now let's work on the panel in the upper left. We have to reload `candidates`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "filename = 'gd1_candidates.hdf5'\n",
+    "path = 'https://github.com/AllenDowney/AstronomicalData/raw/main/data/'\n",
+    "\n",
+    "if not os.path.exists(filename):\n",
+    "    print(download(path+filename))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "filename = 'gd1_candidates.hdf5'\n",
+    "\n",
+    "candidate_df = pd.read_hdf(filename, 'candidate_df')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's a function that takes a `DataFrame` of candidate stars and plots their positions in GD-1 coordindates. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_first_selection(df):\n",
+    "    x = df['phi1']\n",
+    "    y = df['phi2']\n",
+    "\n",
+    "    plt.plot(x, y, 'ko', markersize=0.3, alpha=0.3)\n",
+    "\n",
+    "    plt.xlabel('$\\phi_1$ [deg]')\n",
+    "    plt.ylabel('$\\phi_2$ [deg]')\n",
+    "    plt.title('Proper motion selection', fontsize='medium')\n",
+    "\n",
+    "    plt.axis('equal')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And here's what it looks like."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_first_selection(candidate_df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Lower right\n",
+    "\n",
+    "For the figure in the lower right, we need to reload the merged `DataFrame`, which contains data from Gaia and photometry data from Pan-STARRS."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "filename = 'gd1_merged.hdf5'\n",
+    "\n",
+    "merged = pd.read_hdf(filename, 'merged')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "From the previous notebook, here's the function that plots the color-magnitude diagram."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "def plot_cmd(table):\n",
+    "    \"\"\"Plot a color magnitude diagram.\n",
+    "    \n",
+    "    table: Table or DataFrame with photometry data\n",
+    "    \"\"\"\n",
+    "    y = table['g_mean_psf_mag']\n",
+    "    x = table['g_mean_psf_mag'] - table['i_mean_psf_mag']\n",
+    "\n",
+    "    plt.plot(x, y, 'ko', markersize=0.3, alpha=0.3)\n",
+    "\n",
+    "    plt.xlim([0, 1.5])\n",
+    "    plt.ylim([14, 22])\n",
+    "    plt.gca().invert_yaxis()\n",
+    "\n",
+    "    plt.ylabel('$g_0$')\n",
+    "    plt.xlabel('$(g-i)_0$')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And here's what it looks like."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_cmd(merged)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Add a few lines to `plot_cmd` to show the Polygon we selected as a shaded area.  \n",
+    "\n",
+    "Run these cells to get the polygon coordinates we saved in the previous notebook."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "filename = 'gd1_polygon.hdf5'\n",
+    "path = 'https://github.com/AllenDowney/AstronomicalData/raw/main/data/'\n",
+    "\n",
+    "if not os.path.exists(filename):\n",
+    "    print(download(path+filename))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 0.21505376, 17.5481972 ],\n",
+       "       [ 0.38978495, 18.94628403],\n",
+       "       [ 0.53763441, 19.90286976],\n",
+       "       [ 0.70340502, 20.60191317],\n",
+       "       [ 0.82885305, 21.30095659],\n",
+       "       [ 0.66308244, 21.52170714],\n",
+       "       [ 0.43010753, 20.78587196],\n",
+       "       [ 0.27329749, 19.71891096],\n",
+       "       [ 0.17473118, 18.68874172],\n",
+       "       [ 0.17473118, 17.95290655]])"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "coords_df = pd.read_hdf(filename, 'coords_df')\n",
+    "coords = coords_df.to_numpy()\n",
+    "coords"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "#poly = Polygon(coords, closed=True, \n",
+    "#               facecolor='C1', alpha=0.4)\n",
+    "#plt.gca().add_patch(poly)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Subplots\n",
+    "\n",
+    "Now we're ready to put it all together.  To make a figure with four subplots, we'll use `subplot2grid`, [which requires two arguments](https://matplotlib.org/3.3.1/api/_as_gen/matplotlib.pyplot.subplot2grid.html):\n",
+    "\n",
+    "* `shape`, which is a tuple with the number of rows and columns in the grid, and\n",
+    "\n",
+    "* `loc`, which is a tuple identifying the location in the grid we're about to fill.\n",
+    "\n",
+    "In this example, `shape` is `(2, 2)` to create two rows and two columns.\n",
+    "\n",
+    "For the first panel, `loc` is `(0, 0)`, which indicates row 0 and column 0, which is the upper-left panel.\n",
+    "\n",
+    "Here's how we use it to draw the four panels."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "shape = (2, 2)\n",
+    "plt.subplot2grid(shape, (0, 0))\n",
+    "plot_first_selection(candidate_df)\n",
+    "\n",
+    "plt.subplot2grid(shape, (0, 1))\n",
+    "plot_proper_motion(centerline)\n",
+    "\n",
+    "plt.subplot2grid(shape, (1, 0))\n",
+    "plot_second_selection(selected)\n",
+    "\n",
+    "plt.subplot2grid(shape, (1, 1))\n",
+    "plot_cmd(merged)\n",
+    "poly = Polygon(coords, closed=True, \n",
+    "               facecolor='C1', alpha=0.4)\n",
+    "plt.gca().add_patch(poly)\n",
+    "\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We use [`plt.tight_layout`](https://matplotlib.org/3.3.1/tutorials/intermediate/tight_layout_guide.html) at the end, which adjusts the sizes of the panels to make sure the titles and axis labels don't overlap.\n",
+    "\n",
+    "**Exercise:** See what happens if you leave out `tight_layout`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Adjusting proportions\n",
+    "\n",
+    "In the previous figure, the panels are all the same size.  To get a better view of GD-1, we'd like to stretch the panels on the left and compress the ones on the right.\n",
+    "\n",
+    "To do that, we'll use the `colspan` argument to make a panel that spans multiple columns in the grid.\n",
+    "\n",
+    "In the following example, `shape` is `(2, 4)`, which means 2 rows and 4 columns.\n",
+    "\n",
+    "The panels on the left span three columns, so they are three times wider than the panels on the right.\n",
+    "\n",
+    "At the same time, we use `figsize` to adjust the aspect ratio of the whole figure."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 900x450 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(9, 4.5))\n",
+    "\n",
+    "shape = (2, 4)\n",
+    "plt.subplot2grid(shape, (0, 0), colspan=3)\n",
+    "plot_first_selection(candidate_df)\n",
+    "\n",
+    "plt.subplot2grid(shape, (0, 3))\n",
+    "plot_proper_motion(centerline)\n",
+    "\n",
+    "plt.subplot2grid(shape, (1, 0), colspan=3)\n",
+    "plot_second_selection(selected)\n",
+    "\n",
+    "plt.subplot2grid(shape, (1, 3))\n",
+    "plot_cmd(merged)\n",
+    "poly = Polygon(coords, closed=True, \n",
+    "               facecolor='C1', alpha=0.4)\n",
+    "plt.gca().add_patch(poly)\n",
+    "\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is looking more and more like the figure in the paper.\n",
+    "\n",
+    "**Exercise:** In this example, the ratio of the widths of the panels is 3:1.  How would you adjust it if you wanted the ratio to be 3:2?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "In this notebook, we reverse-engineered the figure we've been replicating, identifying elements that seem effective and others that could be improved.\n",
+    "\n",
+    "We explored features Matplotlib provides for adding annotations to figures -- including text, lines, arrows, and polygons -- and several ways to customize the appearance of figures.  And we learned how to create figures that contain multiple panels."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Best practices\n",
+    "\n",
+    "* The most effective figures focus on telling a single story clearly and compellingly.\n",
+    "\n",
+    "* Consider using annotations to guide the readers attention to the most important elements of a figure.\n",
+    "\n",
+    "* The default Matplotlib style generates good quality figures, but there are several ways you can override the defaults.\n",
+    "\n",
+    "* If you find yourself making the same customizations on several projects, you might want to create your own style sheet."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/_build/html/_sources/AstronomicalData/01_query.ipynb b/_build/html/_sources/AstronomicalData/01_query.ipynb
new file mode 100644
index 0000000..9a7f17d
--- /dev/null
+++ b/_build/html/_sources/AstronomicalData/01_query.ipynb
@@ -0,0 +1,1642 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Lesson 1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Introduction\n",
+    "\n",
+    "This workshop is an introduction to tools and practices for working with astronomical data.  Topics covered include:\n",
+    "\n",
+    "* Writing queries that select and download data from a database.\n",
+    "\n",
+    "* Using data stored in an Astropy `Table` or Pandas `DataFrame`.\n",
+    "\n",
+    "* Working with coordinates and other quantities with units.\n",
+    "\n",
+    "* Storing data in various formats.\n",
+    "\n",
+    "* Performing database join operations that combine data from multiple tables.\n",
+    "\n",
+    "* Visualizing data and preparing publication-quality figures."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As a running example, we will replicate part of the analysis in a recent paper, \"[Off the beaten path: Gaia reveals GD-1 stars outside of the main stream](https://arxiv.org/abs/1805.00425)\" by Adrian M. Price-Whelan and Ana Bonaca.\n",
+    "\n",
+    "As the abstract explains, \"Using data from the Gaia second data release combined with Pan-STARRS photometry, we present a sample of highly-probable members of the longest cold stream in the Milky Way, GD-1.\"\n",
+    "\n",
+    "GD-1 is a [stellar stream](https://en.wikipedia.org/wiki/List_of_stellar_streams), which is \"an association of stars orbiting a galaxy that was once a globular cluster or dwarf galaxy that has now been torn apart and stretched out along its orbit by tidal forces.\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[This article in *Science* magazine](https://www.sciencemag.org/news/2018/10/streams-stars-reveal-galaxy-s-violent-history-and-perhaps-its-unseen-dark-matter) explains some of the background, including the process that led to the paper and an discussion of the scientific implications:\n",
+    "\n",
+    "* \"The streams are particularly useful for ... galactic archaeology --- rewinding the cosmic clock to reconstruct the assembly of the Milky Way.\"\n",
+    "\n",
+    "* \"They also are being used as exquisitely sensitive scales to measure the galaxy's mass.\"\n",
+    "\n",
+    "* \"... the streams are well-positioned to reveal the presence of dark matter ... because the streams are so fragile, theorists say, collisions with marauding clumps of dark matter could leave telltale scars, potential clues to its nature.\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Prerequisites\n",
+    "\n",
+    "This workshop is meant for people who are familiar with basic Python, but not necessarily the libraries we will use, like Astropy or Pandas.  If you are familiar with Python lists and dictionaries, and you know how to write a function that takes parameters and returns a value, you know enough Python for this workshop.\n",
+    "\n",
+    "We assume that you have some familiarity with operating systems, like the ability to use a command-line interface.  But we don't assume you have any prior experience with databases.\n",
+    "\n",
+    "We assume that you are familiar with astronomy at the undergraduate level, but we will not assume specialized knowledge of the datasets or analysis methods we'll use. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Data\n",
+    "\n",
+    "The datasets we will work with are:\n",
+    " \n",
+    "* [Gaia](https://en.wikipedia.org/wiki/Gaia_(spacecraft)), which is \"a space observatory of the European Space Agency (ESA), launched in 2013 ... designed for astrometry: measuring the positions, distances and motions of stars with unprecedented precision\", and\n",
+    "\n",
+    "* [Pan-STARRS](https://en.wikipedia.org/wiki/Pan-STARRS), The Panoramic Survey Telescope and Rapid Response System, which is a survey designed to monitor the sky for transient objects, producing a catalog with accurate astronometry and photometry of detected sources.\n",
+    "\n",
+    "Both of these datasets are very large, which can make them challenging to work with.  It might not be possible, or practical, to download the entire dataset.\n",
+    "One of the goals of this workshop is to provide tools for working with large datasets."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Lesson 1\n",
+    "\n",
+    "The first lesson demonstrates the steps for selecting and downloading data from the Gaia Database:\n",
+    "\n",
+    "1. First we'll make a connection to the Gaia server,\n",
+    "\n",
+    "2. We will explore information about the database and the tables it contains,\n",
+    "\n",
+    "3. We will write a query and send it to the server, and finally\n",
+    "\n",
+    "4. We will download the response from the server.\n",
+    "\n",
+    "After completing this lesson, you should be able to\n",
+    "\n",
+    "* Compose a basic query in ADQL.\n",
+    "\n",
+    "* Use queries to explore a database and its tables.\n",
+    "\n",
+    "* Use queries to download data.\n",
+    "\n",
+    "* Develop, test, and debug a query incrementally."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Query Language\n",
+    "\n",
+    "In order to select data from a database, you have to compose a query, which is like a program written in a \"query language\".\n",
+    "The query language we'll use is ADQL, which stands for \"Astronomical Data Query Language\".\n",
+    "\n",
+    "ADQL is a dialect of [SQL](https://en.wikipedia.org/wiki/SQL) (Structured Query Language), which is by far the most commonly used query language.  Almost everything you will learn about ADQL also works in SQL.\n",
+    "\n",
+    "[The reference manual for ADQL is here](http://www.ivoa.net/documents/ADQL/20180112/PR-ADQL-2.1-20180112.html).\n",
+    "But you might find it easier to learn from [this ADQL Cookbook](https://www.gaia.ac.uk/data/gaia-data-release-1/adql-cookbook)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Installing libraries\n",
+    "\n",
+    "The library we'll use to get Gaia data is [Astroquery](https://astroquery.readthedocs.io/en/latest/).\n",
+    "\n",
+    "If you are running this notebook on Colab, you can run the following cell to install Astroquery and the other libraries we'll use.\n",
+    "\n",
+    "If you are running this notebook on your own computer, you might have to install these libraries yourself.  \n",
+    "\n",
+    "If you are using this notebook as part of a Carpentries workshop, you should have received setup instructions.\n",
+    "\n",
+    "TODO: Add a link to the instructions.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# If we're running on Colab, install libraries\n",
+    "\n",
+    "import sys\n",
+    "IN_COLAB = 'google.colab' in sys.modules\n",
+    "\n",
+    "if IN_COLAB:\n",
+    "    !pip install astroquery astro-gala pyia"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Connecting to Gaia\n",
+    "\n",
+    "Astroquery provides `Gaia`, which is an [object that represents a connection to the Gaia database](https://astroquery.readthedocs.io/en/latest/gaia/gaia.html).\n",
+    "\n",
+    "We can connect to the Gaia database like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Created TAP+ (v1.2.1) - Connection:\n",
+      "\tHost: gea.esac.esa.int\n",
+      "\tUse HTTPS: True\n",
+      "\tPort: 443\n",
+      "\tSSL Port: 443\n",
+      "Created TAP+ (v1.2.1) - Connection:\n",
+      "\tHost: geadata.esac.esa.int\n",
+      "\tUse HTTPS: True\n",
+      "\tPort: 443\n",
+      "\tSSL Port: 443\n"
+     ]
+    }
+   ],
+   "source": [
+    "from astroquery.gaia import Gaia"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Optional detail \n",
+    "\n",
+    "> Running this import statement has the effect of creating a [TAP+](http://www.ivoa.net/documents/TAP/) connection; TAP stands for \"Table Access Protocol\".  It is a network protocol for sending queries to the database and getting back the results.  We're not sure why it seems to create two connections."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Databases and Tables\n",
+    "\n",
+    "What is a database, anyway?  Most generally, it can be any collection of data, but when we are talking about ADQL or SQL:\n",
+    "\n",
+    "* A database is a collection of one or more named tables.\n",
+    "\n",
+    "* Each table is a 2-D array with one or more named columns of data.\n",
+    "\n",
+    "We can use `Gaia.load_tables` to get the names of the tables in the Gaia database.  With the option `only_names=True`, it loads information about the tables, called the \"metadata\", not the data itself."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO: Retrieving tables... [astroquery.utils.tap.core]\n",
+      "INFO: Parsing tables... [astroquery.utils.tap.core]\n",
+      "INFO: Done. [astroquery.utils.tap.core]\n"
+     ]
+    }
+   ],
+   "source": [
+    "tables = Gaia.load_tables(only_names=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "external.external.apassdr9\n",
+      "external.external.gaiadr2_geometric_distance\n",
+      "external.external.galex_ais\n",
+      "external.external.ravedr5_com\n",
+      "external.external.ravedr5_dr5\n",
+      "external.external.ravedr5_gra\n",
+      "external.external.ravedr5_on\n",
+      "external.external.sdssdr13_photoprimary\n",
+      "external.external.skymapperdr1_master\n",
+      "external.external.tmass_xsc\n",
+      "public.public.hipparcos\n",
+      "public.public.hipparcos_newreduction\n",
+      "public.public.hubble_sc\n",
+      "public.public.igsl_source\n",
+      "public.public.igsl_source_catalog_ids\n",
+      "public.public.tycho2\n",
+      "public.public.dual\n",
+      "tap_config.tap_config.coord_sys\n",
+      "tap_config.tap_config.properties\n",
+      "tap_schema.tap_schema.columns\n",
+      "tap_schema.tap_schema.key_columns\n",
+      "tap_schema.tap_schema.keys\n",
+      "tap_schema.tap_schema.schemas\n",
+      "tap_schema.tap_schema.tables\n",
+      "gaiadr1.gaiadr1.aux_qso_icrf2_match\n",
+      "gaiadr1.gaiadr1.ext_phot_zero_point\n",
+      "gaiadr1.gaiadr1.allwise_best_neighbour\n",
+      "gaiadr1.gaiadr1.allwise_neighbourhood\n",
+      "gaiadr1.gaiadr1.gsc23_best_neighbour\n",
+      "gaiadr1.gaiadr1.gsc23_neighbourhood\n",
+      "gaiadr1.gaiadr1.ppmxl_best_neighbour\n",
+      "gaiadr1.gaiadr1.ppmxl_neighbourhood\n",
+      "gaiadr1.gaiadr1.sdss_dr9_best_neighbour\n",
+      "gaiadr1.gaiadr1.sdss_dr9_neighbourhood\n",
+      "gaiadr1.gaiadr1.tmass_best_neighbour\n",
+      "gaiadr1.gaiadr1.tmass_neighbourhood\n",
+      "gaiadr1.gaiadr1.ucac4_best_neighbour\n",
+      "gaiadr1.gaiadr1.ucac4_neighbourhood\n",
+      "gaiadr1.gaiadr1.urat1_best_neighbour\n",
+      "gaiadr1.gaiadr1.urat1_neighbourhood\n",
+      "gaiadr1.gaiadr1.cepheid\n",
+      "gaiadr1.gaiadr1.phot_variable_time_series_gfov\n",
+      "gaiadr1.gaiadr1.phot_variable_time_series_gfov_statistical_parameters\n",
+      "gaiadr1.gaiadr1.rrlyrae\n",
+      "gaiadr1.gaiadr1.variable_summary\n",
+      "gaiadr1.gaiadr1.allwise_original_valid\n",
+      "gaiadr1.gaiadr1.gsc23_original_valid\n",
+      "gaiadr1.gaiadr1.ppmxl_original_valid\n",
+      "gaiadr1.gaiadr1.sdssdr9_original_valid\n",
+      "gaiadr1.gaiadr1.tmass_original_valid\n",
+      "gaiadr1.gaiadr1.ucac4_original_valid\n",
+      "gaiadr1.gaiadr1.urat1_original_valid\n",
+      "gaiadr1.gaiadr1.gaia_source\n",
+      "gaiadr1.gaiadr1.tgas_source\n",
+      "gaiadr2.gaiadr2.aux_allwise_agn_gdr2_cross_id\n",
+      "gaiadr2.gaiadr2.aux_iers_gdr2_cross_id\n",
+      "gaiadr2.gaiadr2.aux_sso_orbit_residuals\n",
+      "gaiadr2.gaiadr2.aux_sso_orbits\n",
+      "gaiadr2.gaiadr2.dr1_neighbourhood\n",
+      "gaiadr2.gaiadr2.allwise_best_neighbour\n",
+      "gaiadr2.gaiadr2.allwise_neighbourhood\n",
+      "gaiadr2.gaiadr2.apassdr9_best_neighbour\n",
+      "gaiadr2.gaiadr2.apassdr9_neighbourhood\n",
+      "gaiadr2.gaiadr2.gsc23_best_neighbour\n",
+      "gaiadr2.gaiadr2.gsc23_neighbourhood\n",
+      "gaiadr2.gaiadr2.hipparcos2_best_neighbour\n",
+      "gaiadr2.gaiadr2.hipparcos2_neighbourhood\n",
+      "gaiadr2.gaiadr2.panstarrs1_best_neighbour\n",
+      "gaiadr2.gaiadr2.panstarrs1_neighbourhood\n",
+      "gaiadr2.gaiadr2.ppmxl_best_neighbour\n",
+      "gaiadr2.gaiadr2.ppmxl_neighbourhood\n",
+      "gaiadr2.gaiadr2.ravedr5_best_neighbour\n",
+      "gaiadr2.gaiadr2.ravedr5_neighbourhood\n",
+      "gaiadr2.gaiadr2.sdssdr9_best_neighbour\n",
+      "gaiadr2.gaiadr2.sdssdr9_neighbourhood\n",
+      "gaiadr2.gaiadr2.tmass_best_neighbour\n",
+      "gaiadr2.gaiadr2.tmass_neighbourhood\n",
+      "gaiadr2.gaiadr2.tycho2_best_neighbour\n",
+      "gaiadr2.gaiadr2.tycho2_neighbourhood\n",
+      "gaiadr2.gaiadr2.urat1_best_neighbour\n",
+      "gaiadr2.gaiadr2.urat1_neighbourhood\n",
+      "gaiadr2.gaiadr2.sso_observation\n",
+      "gaiadr2.gaiadr2.sso_source\n",
+      "gaiadr2.gaiadr2.vari_cepheid\n",
+      "gaiadr2.gaiadr2.vari_classifier_class_definition\n",
+      "gaiadr2.gaiadr2.vari_classifier_definition\n",
+      "gaiadr2.gaiadr2.vari_classifier_result\n",
+      "gaiadr2.gaiadr2.vari_long_period_variable\n",
+      "gaiadr2.gaiadr2.vari_rotation_modulation\n",
+      "gaiadr2.gaiadr2.vari_rrlyrae\n",
+      "gaiadr2.gaiadr2.vari_short_timescale\n",
+      "gaiadr2.gaiadr2.vari_time_series_statistics\n",
+      "gaiadr2.gaiadr2.panstarrs1_original_valid\n",
+      "gaiadr2.gaiadr2.gaia_source\n",
+      "gaiadr2.gaiadr2.ruwe\n"
+     ]
+    }
+   ],
+   "source": [
+    "for table in (tables):\n",
+    "    print(table.get_qualified_name())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "So that's a lot of tables.  The ones we'll use are:\n",
+    "\n",
+    "* `gaiadr2.gaia_source`, which contains Gaia data from [data release 2](https://www.cosmos.esa.int/web/gaia/data-release-2),\n",
+    "\n",
+    "* `gaiadr2.panstarrs1_original_valid`, which contains the photometry data we'll use from PanSTARRS, and\n",
+    "\n",
+    "* `gaiadr2.panstarrs1_best_neighbour`, which we'll use to cross-match each star observed by Gaia with the same star observed by PanSTARRS.\n",
+    "\n",
+    "We can use `load_table` (not `load_tables`) to get the metadata for a single table.  The name of this function is misleading, because it only downloads metadata. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Retrieving table 'gaiadr2.gaia_source'\n",
+      "Parsing table 'gaiadr2.gaia_source'...\n",
+      "Done.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<astroquery.utils.tap.model.taptable.TapTableMeta at 0x7f922376e0a0>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "meta = Gaia.load_table('gaiadr2.gaia_source')\n",
+    "meta"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Jupyter shows that the result is an object of type `TapTableMeta`, but it does not display the contents.\n",
+    "\n",
+    "To see the metadata, we have to print the object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "TAP Table name: gaiadr2.gaiadr2.gaia_source\n",
+      "Description: This table has an entry for every Gaia observed source as listed in the\n",
+      "Main Database accumulating catalogue version from which the catalogue\n",
+      "release has been generated. It contains the basic source parameters,\n",
+      "that is only final data (no epoch data) and no spectra (neither final\n",
+      "nor epoch).\n",
+      "Num. columns: 96\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(meta)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notice one gotcha: in the list of table names, this table appears as `gaiadr2.gaiadr2.gaia_source`, but when we load the metadata, we refer to it as `gaiadr2.gaia_source`.\n",
+    "\n",
+    "**Exercise:** Go back and try\n",
+    "\n",
+    "```\n",
+    "meta = Gaia.load_table('gaiadr2.gaiadr2.gaia_source')\n",
+    "```\n",
+    "\n",
+    "What happens?  Is the error message helpful?  If you had not made this error deliberately, would you have been able to figure it out?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Columns\n",
+    "\n",
+    "The following loop prints the names of the columns in the table."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "solution_id\n",
+      "designation\n",
+      "source_id\n",
+      "random_index\n",
+      "ref_epoch\n",
+      "ra\n",
+      "ra_error\n",
+      "dec\n",
+      "dec_error\n",
+      "parallax\n",
+      "parallax_error\n",
+      "parallax_over_error\n",
+      "pmra\n",
+      "pmra_error\n",
+      "pmdec\n",
+      "pmdec_error\n",
+      "ra_dec_corr\n",
+      "ra_parallax_corr\n",
+      "ra_pmra_corr\n",
+      "ra_pmdec_corr\n",
+      "dec_parallax_corr\n",
+      "dec_pmra_corr\n",
+      "dec_pmdec_corr\n",
+      "parallax_pmra_corr\n",
+      "parallax_pmdec_corr\n",
+      "pmra_pmdec_corr\n",
+      "astrometric_n_obs_al\n",
+      "astrometric_n_obs_ac\n",
+      "astrometric_n_good_obs_al\n",
+      "astrometric_n_bad_obs_al\n",
+      "astrometric_gof_al\n",
+      "astrometric_chi2_al\n",
+      "astrometric_excess_noise\n",
+      "astrometric_excess_noise_sig\n",
+      "astrometric_params_solved\n",
+      "astrometric_primary_flag\n",
+      "astrometric_weight_al\n",
+      "astrometric_pseudo_colour\n",
+      "astrometric_pseudo_colour_error\n",
+      "mean_varpi_factor_al\n",
+      "astrometric_matched_observations\n",
+      "visibility_periods_used\n",
+      "astrometric_sigma5d_max\n",
+      "frame_rotator_object_type\n",
+      "matched_observations\n",
+      "duplicated_source\n",
+      "phot_g_n_obs\n",
+      "phot_g_mean_flux\n",
+      "phot_g_mean_flux_error\n",
+      "phot_g_mean_flux_over_error\n",
+      "phot_g_mean_mag\n",
+      "phot_bp_n_obs\n",
+      "phot_bp_mean_flux\n",
+      "phot_bp_mean_flux_error\n",
+      "phot_bp_mean_flux_over_error\n",
+      "phot_bp_mean_mag\n",
+      "phot_rp_n_obs\n",
+      "phot_rp_mean_flux\n",
+      "phot_rp_mean_flux_error\n",
+      "phot_rp_mean_flux_over_error\n",
+      "phot_rp_mean_mag\n",
+      "phot_bp_rp_excess_factor\n",
+      "phot_proc_mode\n",
+      "bp_rp\n",
+      "bp_g\n",
+      "g_rp\n",
+      "radial_velocity\n",
+      "radial_velocity_error\n",
+      "rv_nb_transits\n",
+      "rv_template_teff\n",
+      "rv_template_logg\n",
+      "rv_template_fe_h\n",
+      "phot_variable_flag\n",
+      "l\n",
+      "b\n",
+      "ecl_lon\n",
+      "ecl_lat\n",
+      "priam_flags\n",
+      "teff_val\n",
+      "teff_percentile_lower\n",
+      "teff_percentile_upper\n",
+      "a_g_val\n",
+      "a_g_percentile_lower\n",
+      "a_g_percentile_upper\n",
+      "e_bp_min_rp_val\n",
+      "e_bp_min_rp_percentile_lower\n",
+      "e_bp_min_rp_percentile_upper\n",
+      "flame_flags\n",
+      "radius_val\n",
+      "radius_percentile_lower\n",
+      "radius_percentile_upper\n",
+      "lum_val\n",
+      "lum_percentile_lower\n",
+      "lum_percentile_upper\n",
+      "datalink_url\n",
+      "epoch_photometry_url\n"
+     ]
+    }
+   ],
+   "source": [
+    "for column in meta.columns:\n",
+    "    print(column.name)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can probably guess what many of these columns are by looking at the names, but you should resist the temptation to guess.\n",
+    "To find out what the columns mean, [read the documentation](https://gea.esac.esa.int/archive/documentation/GDR2/Gaia_archive/chap_datamodel/sec_dm_main_tables/ssec_dm_gaia_source.html).\n",
+    "\n",
+    "If you want to know what can go wrong when you don't read the documentation, [you might like this article](https://www.vox.com/future-perfect/2019/6/4/18650969/married-women-miserable-fake-paul-dolan-happiness)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** One of the other tables we'll use is `gaiadr2.gaiadr2.panstarrs1_original_valid`.  Use `load_table` to get the metadata for this table.  How many columns are there and what are their names?\n",
+    "\n",
+    "Hint: Remember the gotcha we mentioned earlier."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Retrieving table 'gaiadr2.panstarrs1_original_valid'\n",
+      "Parsing table 'gaiadr2.panstarrs1_original_valid'...\n",
+      "Done.\n",
+      "TAP Table name: gaiadr2.gaiadr2.panstarrs1_original_valid\n",
+      "Description: The Panoramic Survey Telescope and Rapid Response System (Pan-STARRS) is\n",
+      "a system for wide-field astronomical imaging developed and operated by\n",
+      "the Institute for Astronomy at the University of Hawaii. Pan-STARRS1\n",
+      "(PS1) is the first part of Pan-STARRS to be completed and is the basis\n",
+      "for Data Release 1 (DR1). The PS1 survey used a 1.8 meter telescope and\n",
+      "its 1.4 Gigapixel camera to image the sky in five broadband filters (g,\n",
+      "r, i, z, y).\n",
+      "\n",
+      "The current table contains a filtered subsample of the 10 723 304 629\n",
+      "entries listed in the original ObjectThin table.\n",
+      "We used only ObjectThin and MeanObject tables to extract\n",
+      "panstarrs1OriginalValid table, this means that objects detected only in\n",
+      "stack images are not included here. The main reason for us to avoid the\n",
+      "use of objects detected in stack images is that their astrometry is not\n",
+      "as good as the mean objects astrometry: “The stack positions (raStack,\n",
+      "decStack) have considerably larger systematic astrometric errors than\n",
+      "the mean epoch positions (raMean, decMean).� The astrometry for the\n",
+      "MeanObject positions uses Gaia DR1 as a reference catalog, while the\n",
+      "stack positions use 2MASS as a reference catalog.\n",
+      "\n",
+      "In details, we filtered out all objects where:\n",
+      "\n",
+      "-   nDetections = 1\n",
+      "\n",
+      "-   no good quality data in Pan-STARRS, objInfoFlag 33554432 not set\n",
+      "\n",
+      "-   mean astrometry could not be measured, objInfoFlag 524288 set\n",
+      "\n",
+      "-   stack position used for mean astrometry, objInfoFlag 1048576 set\n",
+      "\n",
+      "-   error on all magnitudes equal to 0 or to -999;\n",
+      "\n",
+      "-   all magnitudes set to -999;\n",
+      "\n",
+      "-   error on RA or DEC greater than 1 arcsec.\n",
+      "\n",
+      "The number of objects in panstarrs1OriginalValid is 2 264 263 282.\n",
+      "\n",
+      "The panstarrs1OriginalValid table contains only a subset of the columns\n",
+      "available in the combined ObjectThin and MeanObject tables. A\n",
+      "description of the original ObjectThin and MeanObjects tables can be\n",
+      "found at:\n",
+      "https://outerspace.stsci.edu/display/PANSTARRS/PS1+Database+object+and+detection+tables\n",
+      "\n",
+      "Download:\n",
+      "http://mastweb.stsci.edu/ps1casjobs/home.aspx\n",
+      "Documentation:\n",
+      "https://outerspace.stsci.edu/display/PANSTARRS\n",
+      "http://pswww.ifa.hawaii.edu/pswww/\n",
+      "References:\n",
+      "The Pan-STARRS1 Surveys, Chambers, K.C., et al. 2016, arXiv:1612.05560\n",
+      "Pan-STARRS Data Processing System, Magnier, E. A., et al. 2016,\n",
+      "arXiv:1612.05240\n",
+      "Pan-STARRS Pixel Processing: Detrending, Warping, Stacking, Waters, C.\n",
+      "Z., et al. 2016, arXiv:1612.05245\n",
+      "Pan-STARRS Pixel Analysis: Source Detection and Characterization,\n",
+      "Magnier, E. A., et al. 2016, arXiv:1612.05244\n",
+      "Pan-STARRS Photometric and Astrometric Calibration, Magnier, E. A., et\n",
+      "al. 2016, arXiv:1612.05242\n",
+      "The Pan-STARRS1 Database and Data Products, Flewelling, H. A., et al.\n",
+      "2016, arXiv:1612.05243\n",
+      "\n",
+      "Catalogue curator:\n",
+      "SSDC - ASI Space Science Data Center\n",
+      "https://www.ssdc.asi.it/\n",
+      "Num. columns: 26\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "meta2 = Gaia.load_table('gaiadr2.panstarrs1_original_valid')\n",
+    "print(meta2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "obj_name\n",
+      "obj_id\n",
+      "ra\n",
+      "dec\n",
+      "ra_error\n",
+      "dec_error\n",
+      "epoch_mean\n",
+      "g_mean_psf_mag\n",
+      "g_mean_psf_mag_error\n",
+      "g_flags\n",
+      "r_mean_psf_mag\n",
+      "r_mean_psf_mag_error\n",
+      "r_flags\n",
+      "i_mean_psf_mag\n",
+      "i_mean_psf_mag_error\n",
+      "i_flags\n",
+      "z_mean_psf_mag\n",
+      "z_mean_psf_mag_error\n",
+      "z_flags\n",
+      "y_mean_psf_mag\n",
+      "y_mean_psf_mag_error\n",
+      "y_flags\n",
+      "n_detections\n",
+      "zone_id\n",
+      "obj_info_flag\n",
+      "quality_flag\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "for column in meta2.columns:\n",
+    "    print(column.name)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Writing queries\n",
+    "\n",
+    "By now you might be wondering how we actually download the data.  With tables this big, you generally don't.  Instead, you use queries to select only the data you want.\n",
+    "\n",
+    "A query is a string written in a query language like SQL; for the Gaia database, the query language is a dialect of SQL called ADQL.\n",
+    "\n",
+    "Here's an example of an ADQL query."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query1 = \"\"\"SELECT \n",
+    "TOP 10\n",
+    "source_id, ref_epoch, ra, dec, parallax \n",
+    "FROM gaiadr2.gaia_source\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Python note:** We use a [triple-quoted string](https://docs.python.org/3/tutorial/introduction.html#strings) here so we can include line breaks in the query, which makes it easier to read.\n",
+    "\n",
+    "The words in uppercase are ADQL keywords:\n",
+    "\n",
+    "* `SELECT` indicates that we are selecting data (as opposed to adding or modifying data).\n",
+    "\n",
+    "* `TOP` indicates that we only want the first 10 rows of the table, which is useful for testing a query before asking for all of the data.\n",
+    "\n",
+    "* `FROM` specifies which table we want data from.\n",
+    "\n",
+    "The third line is a list of column names, indicating which columns we want.  \n",
+    "\n",
+    "In this example, the keywords are capitalized and the column names are lowercase.  This is a common style, but it is not required.  ADQL and SQL are not case-sensitive."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To run this query, we use the `Gaia` object, which represents our connection to the Gaia database, and invoke `launch_job`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<astroquery.utils.tap.model.job.Job at 0x7f9222e9cb20>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "job1 = Gaia.launch_job(query1)\n",
+    "job1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The result is an object that represents the job running on a Gaia server.\n",
+    "\n",
+    "If you print it, it displays metadata for the forthcoming table."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<Table length=10>\n",
+      "   name    dtype  unit                            description                            \n",
+      "--------- ------- ---- ------------------------------------------------------------------\n",
+      "source_id   int64      Unique source identifier (unique within a particular Data Release)\n",
+      "ref_epoch float64   yr                                                    Reference epoch\n",
+      "       ra float64  deg                                                    Right ascension\n",
+      "      dec float64  deg                                                        Declination\n",
+      " parallax float64  mas                                                           Parallax\n",
+      "Jobid: None\n",
+      "Phase: COMPLETED\n",
+      "Owner: None\n",
+      "Output file: sync_20201005090721.xml.gz\n",
+      "Results: None\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(job1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Don't worry about `Results: None`.  That does not actually mean there are no results.\n",
+    "\n",
+    "However, `Phase: COMPLETED` indicates that the job is complete, so we can get the results like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "astropy.table.table.Table"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results1 = job1.get_results()\n",
+    "type(results1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Optional detail:**  Why is `table` repeated three times?  The first is the name of the module, the second is the name of the submodule, and the third is the name of the class.  Most of the time we only care about the last one.  It's like the Linnean name for gorilla, which is *Gorilla Gorilla Gorilla*."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The result is an [Astropy Table](https://docs.astropy.org/en/stable/table/), which is similar to a table in an SQL database except:\n",
+    "\n",
+    "* SQL databases are stored on disk drives, so they are persistent; that is, they \"survive\" even if you turn off the computer.  An Astropy `Table` is stored in memory; it disappears when you turn off the computer (or shut down this Jupyter notebook).\n",
+    "\n",
+    "* SQL databases are designed to process queries.  An Astropy `Table` can perform some query-like operations, like selecting columns and rows.  But these operations use Python syntax, not SQL.\n",
+    "\n",
+    "Jupyter knows how to display the contents of a `Table`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<i>Table length=10</i>\n",
+       "<table id=\"table140265627585264\" class=\"table-striped table-bordered table-condensed\">\n",
+       "<thead><tr><th>source_id</th><th>ref_epoch</th><th>ra</th><th>dec</th><th>parallax</th></tr></thead>\n",
+       "<thead><tr><th></th><th>yr</th><th>deg</th><th>deg</th><th>mas</th></tr></thead>\n",
+       "<thead><tr><th>int64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th></tr></thead>\n",
+       "<tr><td>4530738361793769600</td><td>2015.5</td><td>281.56725362448725</td><td>20.40682117430378</td><td>0.9785380604519425</td></tr>\n",
+       "<tr><td>4530752651135081216</td><td>2015.5</td><td>281.0861565355257</td><td>20.523350496351846</td><td>0.2674800612552977</td></tr>\n",
+       "<tr><td>4530743343951405568</td><td>2015.5</td><td>281.37114418299177</td><td>20.474147574053124</td><td>-0.43911323550176806</td></tr>\n",
+       "<tr><td>4530755060627162368</td><td>2015.5</td><td>281.2676236268299</td><td>20.558523922346158</td><td>1.1422630184554958</td></tr>\n",
+       "<tr><td>4530746844341315968</td><td>2015.5</td><td>281.1370431749541</td><td>20.377852388898184</td><td>1.0092247424630945</td></tr>\n",
+       "<tr><td>4530768456615026432</td><td>2015.5</td><td>281.8720921436347</td><td>20.31829694530366</td><td>-0.06900136127674149</td></tr>\n",
+       "<tr><td>4530763513119137280</td><td>2015.5</td><td>281.9211808864116</td><td>20.20956829578524</td><td>0.1266016679823622</td></tr>\n",
+       "<tr><td>4530736364618539264</td><td>2015.5</td><td>281.4913475613274</td><td>20.346579041327693</td><td>0.3894019486060072</td></tr>\n",
+       "<tr><td>4530735952305177728</td><td>2015.5</td><td>281.4085549165704</td><td>20.311030903719928</td><td>0.2041189982608354</td></tr>\n",
+       "<tr><td>4530751281056022656</td><td>2015.5</td><td>281.0585328377638</td><td>20.460309556214753</td><td>0.10294642821734962</td></tr>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<Table length=10>\n",
+       "     source_id      ref_epoch ...        dec               parallax      \n",
+       "                        yr    ...        deg                 mas         \n",
+       "       int64         float64  ...      float64             float64       \n",
+       "------------------- --------- ... ------------------ --------------------\n",
+       "4530738361793769600    2015.5 ...  20.40682117430378   0.9785380604519425\n",
+       "4530752651135081216    2015.5 ... 20.523350496351846   0.2674800612552977\n",
+       "4530743343951405568    2015.5 ... 20.474147574053124 -0.43911323550176806\n",
+       "4530755060627162368    2015.5 ... 20.558523922346158   1.1422630184554958\n",
+       "4530746844341315968    2015.5 ... 20.377852388898184   1.0092247424630945\n",
+       "4530768456615026432    2015.5 ...  20.31829694530366 -0.06900136127674149\n",
+       "4530763513119137280    2015.5 ...  20.20956829578524   0.1266016679823622\n",
+       "4530736364618539264    2015.5 ... 20.346579041327693   0.3894019486060072\n",
+       "4530735952305177728    2015.5 ... 20.311030903719928   0.2041189982608354\n",
+       "4530751281056022656    2015.5 ... 20.460309556214753  0.10294642821734962"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Each column has a name, units, and a data type.\n",
+    "\n",
+    "For example, the units of `ra` and `dec` are degrees, and their data type is `float64`, which is a 64-bit floating-point number, used to store measurements with a fraction part.\n",
+    "\n",
+    "This information comes from the Gaia database, and has been stored in the Astropy `Table` by Astroquery."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Read [the documentation of this table](https://gea.esac.esa.int/archive/documentation/GDR2/Gaia_archive/chap_datamodel/sec_dm_main_tables/ssec_dm_gaia_source.html) and choose a column that looks interesting to you.  Add the column name to the query and run it again.  What are the units of the column you selected?  What is its data type?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Asynchronous queries\n",
+    "\n",
+    "`launch_job` asks the server to run the job \"synchronously\", which normally means it runs immediately.  But synchronous jobs are limited to 2000 rows.  For queries that return more rows, you should run \"asynchronously\", which mean they might take longer to get started.\n",
+    "\n",
+    "If you are not sure how many rows a query will return, you can use the SQL command `COUNT` to find out how many rows are in the result without actually returning them.  We'll see an example of this later.\n",
+    "\n",
+    "The results of an asynchronous query are stored in a file on the server, so you can start a query and come back later to get the results.\n",
+    "\n",
+    "For anonymous users, files are kept for three days.\n",
+    "\n",
+    "As an example, let's try a query that's similar to `query1`, with two changes:\n",
+    "\n",
+    "* It selects the first 3000 rows, so it is bigger than we should run synchronously.\n",
+    "\n",
+    "* It uses a new keyword, `WHERE`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query2 = \"\"\"SELECT TOP 3000\n",
+    "source_id, ref_epoch, ra, dec, parallax\n",
+    "FROM gaiadr2.gaia_source\n",
+    "WHERE parallax < 1\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A `WHERE` clause indicates which rows we want; in this case, the query selects only rows \"where\" `parallax` is less than 1.  This has the effect of selecting stars with relatively low parallax, which are farther away.  We'll use this clause to exclude nearby stars that are unlikely to be part of GD-1.\n",
+    "\n",
+    "`WHERE` is one of the most common clauses in ADQL/SQL, and one of the most useful, because it allows us to select only the rows we need from the database.\n",
+    "\n",
+    "We use `launch_job_async` to submit an asynchronous query."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO: Query finished. [astroquery.utils.tap.core]\n",
+      "<Table length=3000>\n",
+      "   name    dtype  unit                            description                            \n",
+      "--------- ------- ---- ------------------------------------------------------------------\n",
+      "source_id   int64      Unique source identifier (unique within a particular Data Release)\n",
+      "ref_epoch float64   yr                                                    Reference epoch\n",
+      "       ra float64  deg                                                    Right ascension\n",
+      "      dec float64  deg                                                        Declination\n",
+      " parallax float64  mas                                                           Parallax\n",
+      "Jobid: 1601903242219O\n",
+      "Phase: COMPLETED\n",
+      "Owner: None\n",
+      "Output file: async_20201005090722.vot\n",
+      "Results: None\n"
+     ]
+    }
+   ],
+   "source": [
+    "job2 = Gaia.launch_job_async(query2)\n",
+    "print(job2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And here are the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<i>Table length=3000</i>\n",
+       "<table id=\"table140265625141056\" class=\"table-striped table-bordered table-condensed\">\n",
+       "<thead><tr><th>source_id</th><th>ref_epoch</th><th>ra</th><th>dec</th><th>parallax</th></tr></thead>\n",
+       "<thead><tr><th></th><th>yr</th><th>deg</th><th>deg</th><th>mas</th></tr></thead>\n",
+       "<thead><tr><th>int64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th></tr></thead>\n",
+       "<tr><td>4530738361793769600</td><td>2015.5</td><td>281.56725362448725</td><td>20.40682117430378</td><td>0.9785380604519425</td></tr>\n",
+       "<tr><td>4530752651135081216</td><td>2015.5</td><td>281.0861565355257</td><td>20.523350496351846</td><td>0.2674800612552977</td></tr>\n",
+       "<tr><td>4530743343951405568</td><td>2015.5</td><td>281.37114418299177</td><td>20.474147574053124</td><td>-0.43911323550176806</td></tr>\n",
+       "<tr><td>4530768456615026432</td><td>2015.5</td><td>281.8720921436347</td><td>20.31829694530366</td><td>-0.06900136127674149</td></tr>\n",
+       "<tr><td>4530763513119137280</td><td>2015.5</td><td>281.9211808864116</td><td>20.20956829578524</td><td>0.1266016679823622</td></tr>\n",
+       "<tr><td>4530736364618539264</td><td>2015.5</td><td>281.4913475613274</td><td>20.346579041327693</td><td>0.3894019486060072</td></tr>\n",
+       "<tr><td>4530735952305177728</td><td>2015.5</td><td>281.4085549165704</td><td>20.311030903719928</td><td>0.2041189982608354</td></tr>\n",
+       "<tr><td>4530751281056022656</td><td>2015.5</td><td>281.0585328377638</td><td>20.460309556214753</td><td>0.10294642821734962</td></tr>\n",
+       "<tr><td>4530740938774409344</td><td>2015.5</td><td>281.3762569536416</td><td>20.436140058941206</td><td>0.9242670062090182</td></tr>\n",
+       "<tr><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr>\n",
+       "<tr><td>4467710915011802624</td><td>2015.5</td><td>269.9680969307347</td><td>1.1429085038160882</td><td>0.42361471245557913</td></tr>\n",
+       "<tr><td>4467706551328679552</td><td>2015.5</td><td>270.033164589881</td><td>1.0565747323689927</td><td>0.922888231734588</td></tr>\n",
+       "<tr><td>4467712255037300096</td><td>2015.5</td><td>270.7724717923047</td><td>0.6581664892880896</td><td>-2.669179465293931</td></tr>\n",
+       "<tr><td>4467735001181761792</td><td>2015.5</td><td>270.3628606248308</td><td>0.8947079323599124</td><td>0.6117399163086398</td></tr>\n",
+       "<tr><td>4467737101421916672</td><td>2015.5</td><td>270.5110834661444</td><td>0.9806225910160181</td><td>-0.39818224846127004</td></tr>\n",
+       "<tr><td>4467707547757327488</td><td>2015.5</td><td>269.88746280594927</td><td>1.0212759940136962</td><td>0.7741412301054209</td></tr>\n",
+       "<tr><td>4467732772094573056</td><td>2015.5</td><td>270.55997182760126</td><td>0.9037072088489417</td><td>-1.7920417800164183</td></tr>\n",
+       "<tr><td>4467732355491087744</td><td>2015.5</td><td>270.6730790702491</td><td>0.9197224705139885</td><td>-0.3464446494840354</td></tr>\n",
+       "<tr><td>4467717099766944512</td><td>2015.5</td><td>270.57667173120825</td><td>0.726277659009568</td><td>0.05443955111134051</td></tr>\n",
+       "<tr><td>4467719058265781248</td><td>2015.5</td><td>270.7248052971514</td><td>0.8205551921782785</td><td>0.3733943917490343</td></tr>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<Table length=3000>\n",
+       "     source_id      ref_epoch ...        dec               parallax      \n",
+       "                        yr    ...        deg                 mas         \n",
+       "       int64         float64  ...      float64             float64       \n",
+       "------------------- --------- ... ------------------ --------------------\n",
+       "4530738361793769600    2015.5 ...  20.40682117430378   0.9785380604519425\n",
+       "4530752651135081216    2015.5 ... 20.523350496351846   0.2674800612552977\n",
+       "4530743343951405568    2015.5 ... 20.474147574053124 -0.43911323550176806\n",
+       "4530768456615026432    2015.5 ...  20.31829694530366 -0.06900136127674149\n",
+       "4530763513119137280    2015.5 ...  20.20956829578524   0.1266016679823622\n",
+       "4530736364618539264    2015.5 ... 20.346579041327693   0.3894019486060072\n",
+       "4530735952305177728    2015.5 ... 20.311030903719928   0.2041189982608354\n",
+       "4530751281056022656    2015.5 ... 20.460309556214753  0.10294642821734962\n",
+       "4530740938774409344    2015.5 ... 20.436140058941206   0.9242670062090182\n",
+       "                ...       ... ...                ...                  ...\n",
+       "4467710915011802624    2015.5 ... 1.1429085038160882  0.42361471245557913\n",
+       "4467706551328679552    2015.5 ... 1.0565747323689927    0.922888231734588\n",
+       "4467712255037300096    2015.5 ... 0.6581664892880896   -2.669179465293931\n",
+       "4467735001181761792    2015.5 ... 0.8947079323599124   0.6117399163086398\n",
+       "4467737101421916672    2015.5 ... 0.9806225910160181 -0.39818224846127004\n",
+       "4467707547757327488    2015.5 ... 1.0212759940136962   0.7741412301054209\n",
+       "4467732772094573056    2015.5 ... 0.9037072088489417  -1.7920417800164183\n",
+       "4467732355491087744    2015.5 ... 0.9197224705139885  -0.3464446494840354\n",
+       "4467717099766944512    2015.5 ...  0.726277659009568  0.05443955111134051\n",
+       "4467719058265781248    2015.5 ... 0.8205551921782785   0.3733943917490343"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results2 = job2.get_results()\n",
+    "results2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You might notice that some values of `parallax` are negative.  As [this FAQ explains](https://www.cosmos.esa.int/web/gaia/archive-tips#negative%20parallax), \"Negative parallaxes are caused by errors in the observations.\"  Negative parallaxes have \"no physical meaning,\" but they can be a \"useful diagnostic on the quality of the astrometric solution.\"\n",
+    "\n",
+    "Later we will see an example where we use `parallax` and `parallax_error` to identify stars where the distance estimate is likely to be inaccurate."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** The clauses in a query have to be in the right order.  Go back and change the order of the clauses in `query2` and run it again.  \n",
+    "\n",
+    "The query should fail, but notice that you don't get much useful debugging information.  \n",
+    "\n",
+    "For this reason, developing and debugging ADQL queries can be really hard.  A few suggestions that might help:\n",
+    "\n",
+    "* Whenever possible, start with a working query, either an example you find online or a query you have used in the past.\n",
+    "\n",
+    "* Make small changes and test each change before you continue.\n",
+    "\n",
+    "* While you are debugging, use `TOP` to limit the number of rows in the result.  That will make each attempt run faster, which reduces your testing time.  \n",
+    "\n",
+    "* Launching test queries synchronously might make them start faster, too."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Operators\n",
+    "\n",
+    "In a `WHERE` clause, you can use any of the [SQL comparison operators](https://www.w3schools.com/sql/sql_operators.asp); here are the most common ones:\n",
+    "\n",
+    "| Symbol | Operation\n",
+    "|--------| :---\n",
+    "| `>` | greater than\n",
+    "| `<` | less than\n",
+    "| `>=` | greater than or equal\n",
+    "| `<=` | less than or equal\n",
+    "| `=` | equal\n",
+    "| `!=` or `<>` | not equal\n",
+    "\n",
+    "Most of these are the same as Python, but some are not.  In particular, notice that the equality operator is `=`, not `==`.\n",
+    "Be careful to keep your Python out of your ADQL!\n",
+    "\n",
+    "You can combine comparisons using the logical operators:\n",
+    "\n",
+    "* AND: true if both comparisons are true\n",
+    "* OR: true if either or both comparisons are true\n",
+    "\n",
+    "Finally, you can use `NOT` to invert the result of a comparison. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** [Read about SQL operators here](https://www.w3schools.com/sql/sql_operators.asp) and then modify the previous query to select rows where `bp_rp` is between `-0.75` and `2`.\n",
+    "\n",
+    "You can [read about this variable here](https://gea.esac.esa.int/archive/documentation/GDR2/Gaia_archive/chap_datamodel/sec_dm_main_tables/ssec_dm_gaia_source.html)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "# This is what most people will probably do\n",
+    "\n",
+    "query = \"\"\"SELECT TOP 10\n",
+    "source_id, ref_epoch, ra, dec, parallax\n",
+    "FROM gaiadr2.gaia_source\n",
+    "WHERE parallax < 1 \n",
+    "  AND bp_rp > -0.75 AND bp_rp < 2\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "# But if someone notices the BETWEEN operator, \n",
+    "# they might do this\n",
+    "\n",
+    "query = \"\"\"SELECT TOP 10\n",
+    "source_id, ref_epoch, ra, dec, parallax\n",
+    "FROM gaiadr2.gaia_source\n",
+    "WHERE parallax < 1 \n",
+    "  AND bp_rp BETWEEN -0.75 AND 2\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This [Hertzsprung-Russell diagram](https://sci.esa.int/web/gaia/-/60198-gaia-hertzsprung-russell-diagram) shows the BP-RP color and luminosity of stars in the Gaia catalog.\n",
+    "\n",
+    "Selecting stars with `bp-rp` less than 2 excludes many [class M dwarf stars](https://xkcd.com/2360/), which are low temperature, low luminosity.  A star like that at GD-1's distance would be hard to detect, so if it is detected, it it more likely to be in the foreground."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Cleaning up\n",
+    "\n",
+    "Asynchronous jobs have a `jobid`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(None, '1601903242219O')"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "job1.jobid, job2.jobid"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Which you can use to remove the job from the server."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Removed jobs: '['1601903242219O']'.\n"
+     ]
+    }
+   ],
+   "source": [
+    "Gaia.remove_jobs([job2.jobid])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you don't remove it job from the server, it will be removed eventually, so don't feel too bad if you don't clean up after yourself."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Formatting queries\n",
+    "\n",
+    "So far the queries have been string \"literals\", meaning that the entire string is part of the program.\n",
+    "But writing queries yourself can be slow, repetitive, and error-prone.\n",
+    "\n",
+    "It is often a good idea to write Python code that assembles a query for you.  One useful tool for that is the [string `format` method](https://www.w3schools.com/python/ref_string_format.asp).\n",
+    "\n",
+    "As an example, we'll divide the previous query into two parts; a list of column names and a \"base\" for the query that contains everything except the column names.\n",
+    "\n",
+    "Here's the list of columns we'll select.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "columns = 'source_id, ra, dec, pmra, pmdec, parallax, parallax_error, radial_velocity'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And here's the base; it's a string that contains at least one format specifier in curly brackets (braces)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query3_base = \"\"\"SELECT TOP 10 \n",
+    "{columns}\n",
+    "FROM gaiadr2.gaia_source\n",
+    "WHERE parallax < 1\n",
+    "  AND bp_rp BETWEEN -0.75 AND 2\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This base query contains one format specifier, `{columns}`, which is a placeholder for the list of column names we will provide.\n",
+    "\n",
+    "To assemble the query, we invoke `format` on the base string and provide a keyword argument that assigns a value to `columns`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query3 = query3_base.format(columns=columns)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The result is a string with line breaks.  If you display it, the line breaks appear as `\\n`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'SELECT TOP 10 \\nsource_id, ra, dec, pmra, pmdec, parallax, parallax_error, radial_velocity\\nFROM gaiadr2.gaia_source\\nWHERE parallax < 1\\n  AND bp_rp BETWEEN -0.75 AND 2\\n'"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "query3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "But if you print it, the line breaks appear as... line breaks."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "SELECT TOP 10 \n",
+      "source_id, ra, dec, pmra, pmdec, parallax, parallax_error, radial_velocity\n",
+      "FROM gaiadr2.gaia_source\n",
+      "WHERE parallax < 1\n",
+      "  AND bp_rp BETWEEN -0.75 AND 2\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(query3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notice that the format specifier has been replaced with the value of `columns`.\n",
+    "\n",
+    "Let's run it and see if it works:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<Table length=10>\n",
+      "      name       dtype    unit                              description                             n_bad\n",
+      "--------------- ------- -------- ------------------------------------------------------------------ -----\n",
+      "      source_id   int64          Unique source identifier (unique within a particular Data Release)     0\n",
+      "             ra float64      deg                                                    Right ascension     0\n",
+      "            dec float64      deg                                                        Declination     0\n",
+      "           pmra float64 mas / yr                         Proper motion in right ascension direction     0\n",
+      "          pmdec float64 mas / yr                             Proper motion in declination direction     0\n",
+      "       parallax float64      mas                                                           Parallax     0\n",
+      " parallax_error float64      mas                                         Standard error of parallax     0\n",
+      "radial_velocity float64   km / s                                                    Radial velocity    10\n",
+      "Jobid: None\n",
+      "Phase: COMPLETED\n",
+      "Owner: None\n",
+      "Output file: sync_20201005090726.xml.gz\n",
+      "Results: None\n"
+     ]
+    }
+   ],
+   "source": [
+    "job3 = Gaia.launch_job(query3)\n",
+    "print(job3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<i>Table length=10</i>\n",
+       "<table id=\"table140265627700432\" class=\"table-striped table-bordered table-condensed\">\n",
+       "<thead><tr><th>source_id</th><th>ra</th><th>dec</th><th>pmra</th><th>pmdec</th><th>parallax</th><th>parallax_error</th><th>radial_velocity</th></tr></thead>\n",
+       "<thead><tr><th></th><th>deg</th><th>deg</th><th>mas / yr</th><th>mas / yr</th><th>mas</th><th>mas</th><th>km / s</th></tr></thead>\n",
+       "<thead><tr><th>int64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th></tr></thead>\n",
+       "<tr><td>4467710915011802624</td><td>269.9680969307347</td><td>1.1429085038160882</td><td>2.0233280236600626</td><td>-2.5692427875510266</td><td>0.42361471245557913</td><td>0.470352406647465</td><td>--</td></tr>\n",
+       "<tr><td>4467706551328679552</td><td>270.033164589881</td><td>1.0565747323689927</td><td>-3.414829591355289</td><td>-3.8437215857495737</td><td>0.922888231734588</td><td>0.927008559859825</td><td>--</td></tr>\n",
+       "<tr><td>4467712255037300096</td><td>270.7724717923047</td><td>0.6581664892880896</td><td>-3.5620173752896025</td><td>-6.595792323153987</td><td>-2.669179465293931</td><td>0.9719742773203504</td><td>--</td></tr>\n",
+       "<tr><td>4467735001181761792</td><td>270.3628606248308</td><td>0.8947079323599124</td><td>2.1307079926489205</td><td>0.8826727710910712</td><td>0.6117399163086398</td><td>0.509812721702093</td><td>--</td></tr>\n",
+       "<tr><td>4467737101421916672</td><td>270.5110834661444</td><td>0.9806225910160181</td><td>0.17532366511560785</td><td>-5.113270239706202</td><td>-0.39818224846127004</td><td>0.7549581886719651</td><td>--</td></tr>\n",
+       "<tr><td>4467707547757327488</td><td>269.88746280594927</td><td>1.0212759940136962</td><td>-2.6382230817672987</td><td>-3.707776532049287</td><td>0.7741412301054209</td><td>0.3022057897812064</td><td>--</td></tr>\n",
+       "<tr><td>4467732355491087744</td><td>270.6730790702491</td><td>0.9197224705139885</td><td>-2.2735991502653037</td><td>-11.864952855984358</td><td>-0.3464446494840354</td><td>0.4937921513912002</td><td>--</td></tr>\n",
+       "<tr><td>4467717099766944512</td><td>270.57667173120825</td><td>0.726277659009568</td><td>-3.4598362614808367</td><td>-4.601426893365921</td><td>0.05443955111134051</td><td>0.8867339293525688</td><td>--</td></tr>\n",
+       "<tr><td>4467719058265781248</td><td>270.7248052971514</td><td>0.8205551921782785</td><td>-3.255079498426542</td><td>-9.249285069111085</td><td>0.3733943917490343</td><td>0.390952370410666</td><td>--</td></tr>\n",
+       "<tr><td>4467722326741572352</td><td>270.87431291888504</td><td>0.8595565975869158</td><td>0.10696398351859826</td><td>1.2035993780158853</td><td>-0.11850943432864373</td><td>0.1660452431882023</td><td>--</td></tr>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<Table length=10>\n",
+       "     source_id              ra         ...   parallax_error   radial_velocity\n",
+       "                           deg         ...        mas              km / s    \n",
+       "       int64             float64       ...      float64           float64    \n",
+       "------------------- ------------------ ... ------------------ ---------------\n",
+       "4467710915011802624  269.9680969307347 ...  0.470352406647465              --\n",
+       "4467706551328679552   270.033164589881 ...  0.927008559859825              --\n",
+       "4467712255037300096  270.7724717923047 ... 0.9719742773203504              --\n",
+       "4467735001181761792  270.3628606248308 ...  0.509812721702093              --\n",
+       "4467737101421916672  270.5110834661444 ... 0.7549581886719651              --\n",
+       "4467707547757327488 269.88746280594927 ... 0.3022057897812064              --\n",
+       "4467732355491087744  270.6730790702491 ... 0.4937921513912002              --\n",
+       "4467717099766944512 270.57667173120825 ... 0.8867339293525688              --\n",
+       "4467719058265781248  270.7248052971514 ...  0.390952370410666              --\n",
+       "4467722326741572352 270.87431291888504 ... 0.1660452431882023              --"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results3 = job3.get_results()\n",
+    "results3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Good so far."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** This query always selects sources with `parallax` less than 1.  But suppose you want to take that upper bound as an input.\n",
+    "\n",
+    "Modify `query3_base` to replace `1` with a format specifier like `{max_parallax}`.  Now, when you call `format`, add a keyword argument that assigns a value to `max_parallax`, and confirm that the format specifier gets replaced with the value you provide."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "query4_base = \"\"\"SELECT TOP 10\n",
+    "{columns}\n",
+    "FROM gaiadr2.gaia_source\n",
+    "WHERE parallax < {max_parallax} AND \n",
+    "bp_rp BETWEEN -0.75 AND 2\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "SELECT TOP 10\n",
+      "source_id, ra, dec, pmra, pmdec, parallax, parallax_error, radial_velocity\n",
+      "FROM gaiadr2.gaia_source\n",
+      "WHERE parallax < 0.5 AND \n",
+      "bp_rp BETWEEN -0.75 AND 2\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "query4 = query4_base.format(columns=columns,\n",
+    "                          max_parallax=0.5)\n",
+    "print(query)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Style note:**  You might notice that the variable names in this notebook are numbered, like `query1`, `query2`, etc.  \n",
+    "\n",
+    "The advantage of this style is that it isolates each section of the notebook from the others, so if you go back and run the cells out of order, it's less likely that you will get unexpected interactions.\n",
+    "\n",
+    "A drawback of this style is that it can be a nuisance to update the notebook if you add, remove, or reorder a section.\n",
+    "\n",
+    "What do you think of this choice?  Are there alternatives you prefer?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "This notebook demonstrates the following steps:\n",
+    "\n",
+    "1. Making a connection to the Gaia server,\n",
+    "\n",
+    "2. Exploring information about the database and the tables it contains,\n",
+    "\n",
+    "3. Writing a query and sending it to the server, and finally\n",
+    "\n",
+    "4. Downloading the response from the server as an Astropy `Table`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Best practices\n",
+    "\n",
+    "* If you can't download an entire dataset (or it's not practical) use queries to select the data you need.\n",
+    "\n",
+    "* Read the metadata and the documentation to make sure you understand the tables, their columns, and what they mean.\n",
+    "\n",
+    "* Develop queries incrementally: start with something simple, test it, and add a little bit at a time.\n",
+    "\n",
+    "* Use ADQL features like `TOP` and `COUNT` to test before you run a query that might return a lot of data.\n",
+    "\n",
+    "* If you know your query will return fewer than 3000 rows, you can run it synchronously, which might complete faster (but it doesn't seem to make much difference).  If it might return more than 3000 rows, you should run it asynchronously.\n",
+    "\n",
+    "* ADQL and SQL are not case-sensitive, so you don't have to capitalize the keywords, but you should.\n",
+    "\n",
+    "* ADQL and SQL don't require you to break a query into multiple lines, but you should.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Jupyter notebooks can be good for developing and testing code, but they have some drawbacks.  In particular, if you run the cells out of order, you might find that variables don't have the values you expect.\n",
+    "\n",
+    "There are a few things you can do to mitigate these problems:\n",
+    "\n",
+    "* Make each section of the notebook self-contained.  Try not to use the same variable name in more than one section.\n",
+    "\n",
+    "* Keep notebooks short.  Look for places where you can break your analysis into phases with one notebook per phase."
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/_build/html/_sources/AstronomicalData/02_coords.ipynb b/_build/html/_sources/AstronomicalData/02_coords.ipynb
new file mode 100644
index 0000000..54821b3
--- /dev/null
+++ b/_build/html/_sources/AstronomicalData/02_coords.ipynb
@@ -0,0 +1,1970 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Lesson 2\n",
+    "\n",
+    "This is the second in a series of lessons related to astronomy data.\n",
+    "\n",
+    "As a running example, we are replicating parts of the analysis in a recent paper, \"[Off the beaten path: Gaia reveals GD-1 stars outside of the main stream](https://arxiv.org/abs/1805.00425)\" by Adrian M. Price-Whelan and Ana Bonaca.\n",
+    "\n",
+    "In the first notebook, we wrote ADQL queries and used them to select and download data from the Gaia server.\n",
+    "\n",
+    "In this notebook, we'll pick up where we left off and write a query to select stars from the region of the sky where we expect GD-1 to be."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We'll start with an example that does a \"cone search\"; that is, it selects stars that appear in a circular region of the sky.\n",
+    "\n",
+    "Then, to select stars in the vicinity of GD-1, we'll:\n",
+    "\n",
+    "* Use `Quantity` objects to represent measurements with units.\n",
+    "\n",
+    "* Use the `Gala` library to convert coordinates from one frame to another.\n",
+    "\n",
+    "* Use the ADQL keywords `POLYGON`, `CONTAINS`, and `POINT` to select stars that fall within a polygonal region.\n",
+    "\n",
+    "* Submit a query and download the results.\n",
+    "\n",
+    "* Store the results in a FITS file.\n",
+    "\n",
+    "After completing this lesson, you should be able to\n",
+    "\n",
+    "* Use Python string formatting to compose more complex ADQL queries.\n",
+    "\n",
+    "* Work with coordinates and other quantities that have units.\n",
+    "\n",
+    "* Download the results of a query and store them in a file."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Installing libraries\n",
+    "\n",
+    "If you are running this notebook on Colab, you can run the following cell to install Astroquery and a the other libraries we'll use.\n",
+    "\n",
+    "If you are running this notebook on your own computer, you might have to install these libraries yourself.  \n",
+    "\n",
+    "If you are using this notebook as part of a Carpentries workshop, you should have received setup instructions.\n",
+    "\n",
+    "TODO: Add a link to the instructions.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# If we're running on Colab, install libraries\n",
+    "\n",
+    "import sys\n",
+    "IN_COLAB = 'google.colab' in sys.modules\n",
+    "\n",
+    "if IN_COLAB:\n",
+    "    !pip install astroquery astro-gala pyia"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Selecting a region"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "One of the most common ways to restrict a query is to select stars in a particular region of the sky.\n",
+    "\n",
+    "For example, here's a query from the [Gaia archive documentation](https://gea.esac.esa.int/archive-help/adql/examples/index.html) that selects \"all the objects ... in a circular region centered at (266.41683, -29.00781) with a search radius of 5 arcmin (0.08333 deg).\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query = \"\"\"\n",
+    "SELECT \n",
+    "TOP 10 source_id\n",
+    "FROM gaiadr2.gaia_source\n",
+    "WHERE 1=CONTAINS(\n",
+    "  POINT(ra, dec),\n",
+    "  CIRCLE(266.41683, -29.00781, 0.08333333))\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This query uses three keywords that are specific to ADQL (not SQL):\n",
+    "\n",
+    "* `POINT`: a location in [ICRS coordinates](https://en.wikipedia.org/wiki/International_Celestial_Reference_System), specified in degrees of right ascension and declination.\n",
+    "\n",
+    "* `CIRCLE`: a circle where the first two values are the coordinates of the center and the third is the radius in degrees.\n",
+    "\n",
+    "* `CONTAINS`: a function that returns `1` if a `POINT` is contained in a shape and `0` otherwise.\n",
+    "\n",
+    "Here is the [documentation of `CONTAINS`](http://www.ivoa.net/documents/ADQL/20180112/PR-ADQL-2.1-20180112.html#tth_sEc4.2.12).\n",
+    "\n",
+    "A query like this is called a cone search because it selects stars in a cone.\n",
+    "\n",
+    "Here's how we run it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Created TAP+ (v1.2.1) - Connection:\n",
+      "\tHost: gea.esac.esa.int\n",
+      "\tUse HTTPS: True\n",
+      "\tPort: 443\n",
+      "\tSSL Port: 443\n",
+      "Created TAP+ (v1.2.1) - Connection:\n",
+      "\tHost: geadata.esac.esa.int\n",
+      "\tUse HTTPS: True\n",
+      "\tPort: 443\n",
+      "\tSSL Port: 443\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<i>Table length=10</i>\n",
+       "<table id=\"table139949838258672\" class=\"table-striped table-bordered table-condensed\">\n",
+       "<thead><tr><th>source_id</th></tr></thead>\n",
+       "<thead><tr><th>int64</th></tr></thead>\n",
+       "<tr><td>4057468321929794432</td></tr>\n",
+       "<tr><td>4057468287575835392</td></tr>\n",
+       "<tr><td>4057482027171038976</td></tr>\n",
+       "<tr><td>4057470349160630656</td></tr>\n",
+       "<tr><td>4057470039924301696</td></tr>\n",
+       "<tr><td>4057469868125641984</td></tr>\n",
+       "<tr><td>4057468351995073024</td></tr>\n",
+       "<tr><td>4057469661959554560</td></tr>\n",
+       "<tr><td>4057470520960672640</td></tr>\n",
+       "<tr><td>4057470555320409600</td></tr>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<Table length=10>\n",
+       "     source_id     \n",
+       "       int64       \n",
+       "-------------------\n",
+       "4057468321929794432\n",
+       "4057468287575835392\n",
+       "4057482027171038976\n",
+       "4057470349160630656\n",
+       "4057470039924301696\n",
+       "4057469868125641984\n",
+       "4057468351995073024\n",
+       "4057469661959554560\n",
+       "4057470520960672640\n",
+       "4057470555320409600"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from astroquery.gaia import Gaia\n",
+    "\n",
+    "job = Gaia.launch_job(query)\n",
+    "result = job.get_results()\n",
+    "result"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** When you are debugging queries like this, you can use `TOP` to limit the size of the results, but then you still don't know how big the results will be.\n",
+    "\n",
+    "An alternative is to use `COUNT`, which asks for the number of rows that would be selected, but it does not return them.\n",
+    "\n",
+    "In the previous query, replace `TOP 10 source_id` with `COUNT(source_id)` and run the query again.  How many stars has Gaia identified in the cone we searched?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Getting GD-1 Data\n",
+    "\n",
+    "From the Price-Whelan and Bonaca paper, we will try to reproduce Figure 1, which includes this representation of stars likely to belong to GD-1:\n",
+    "\n",
+    "<img src=\"https://github.com/datacarpentry/astronomy-python/raw/gh-pages/fig/gd1-4.png\">"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Along the axis of right ascension ($\\phi_1$) the figure extends from -100 to 20 degrees.\n",
+    "\n",
+    "Along the axis of declination ($\\phi_2$) the figure extends from about -8 to 4 degrees.\n",
+    "\n",
+    "Ideally, we would select all stars from this rectangle, but there are more than 10 million of them, so\n",
+    "\n",
+    "* That would be difficult to work with,\n",
+    "\n",
+    "* As anonymous users, we are limited to 3 million rows in a single query, and\n",
+    "\n",
+    "* While we are developing and testing code, it will be faster to work with a smaller dataset.\n",
+    "\n",
+    "So we'll start by selecting stars in a smaller rectangle, from -55 to -45 degrees right ascension and -8 to 4 degrees of declination.\n",
+    "\n",
+    "But first we let's see how to represent quantities with units like degrees."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Working with coordinates\n",
+    "\n",
+    "Coordinates are physical quantities, which means that they have two parts, a value and a unit.\n",
+    "\n",
+    "For example, the coordinate $30^{\\circ}$ has value 30 and its units are degrees.\n",
+    "\n",
+    "Until recently, most scientific computation was done with values only; units were left out of the program altogether, [often with disastrous results](https://en.wikipedia.org/wiki/Mars_Climate_Orbiter#Cause_of_failure).\n",
+    "\n",
+    "Astropy provides tools for including units explicitly in computations, which makes it possible to detect errors before they cause disasters.\n",
+    "\n",
+    "To use Astropy units, we import them like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<module 'astropy.units' from '/home/downey/anaconda3/envs/AstronomicalData/lib/python3.8/site-packages/astropy/units/__init__.py'>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import astropy.units as u\n",
+    "\n",
+    "u"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`u` is an object that contains most common units and all SI units.\n",
+    "\n",
+    "You can use `dir` to list them, but you should also [read the documentation](https://docs.astropy.org/en/stable/units/)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['A',\n",
+       " 'AA',\n",
+       " 'AB',\n",
+       " 'ABflux',\n",
+       " 'ABmag',\n",
+       " 'AU',\n",
+       " 'Angstrom',\n",
+       " 'B',\n",
+       " 'Ba',\n",
+       " 'Barye',\n",
+       " 'Bi',\n",
+       " 'Biot',\n",
+       " 'Bol',\n",
+       " 'Bq',\n",
+       " 'C',\n",
+       " 'Celsius',\n",
+       " 'Ci',\n",
+       " 'CompositeUnit',\n",
+       " 'D',\n",
+       " 'Da',\n",
+       " 'Dalton',\n",
+       " 'Debye',\n",
+       " 'Decibel',\n",
+       " 'DecibelUnit',\n",
+       " 'Dex',\n",
+       " 'DexUnit',\n",
+       " 'EA',\n",
+       " 'EAU',\n",
+       " 'EB',\n",
+       " 'EBa',\n",
+       " 'EC',\n",
+       " 'ED',\n",
+       " 'EF',\n",
+       " 'EG',\n",
+       " 'EGal',\n",
+       " 'EH',\n",
+       " 'EHz',\n",
+       " 'EJ',\n",
+       " 'EJy',\n",
+       " 'EK',\n",
+       " 'EL',\n",
+       " 'EN',\n",
+       " 'EOhm',\n",
+       " 'EP',\n",
+       " 'EPa',\n",
+       " 'ER',\n",
+       " 'ERy',\n",
+       " 'ES',\n",
+       " 'ESt',\n",
+       " 'ET',\n",
+       " 'EV',\n",
+       " 'EW',\n",
+       " 'EWb',\n",
+       " 'Ea',\n",
+       " 'Eadu',\n",
+       " 'Earcmin',\n",
+       " 'Earcsec',\n",
+       " 'Eau',\n",
+       " 'Eb',\n",
+       " 'Ebarn',\n",
+       " 'Ebeam',\n",
+       " 'Ebin',\n",
+       " 'Ebit',\n",
+       " 'Ebyte',\n",
+       " 'Ecd',\n",
+       " 'Echan',\n",
+       " 'Ecount',\n",
+       " 'Ect',\n",
+       " 'Ed',\n",
+       " 'Edeg',\n",
+       " 'Edyn',\n",
+       " 'EeV',\n",
+       " 'Eerg',\n",
+       " 'Eg',\n",
+       " 'Eh',\n",
+       " 'EiB',\n",
+       " 'Eib',\n",
+       " 'Eibit',\n",
+       " 'Eibyte',\n",
+       " 'Ek',\n",
+       " 'El',\n",
+       " 'Elm',\n",
+       " 'Elx',\n",
+       " 'Elyr',\n",
+       " 'Em',\n",
+       " 'Emag',\n",
+       " 'Emin',\n",
+       " 'Emol',\n",
+       " 'Eohm',\n",
+       " 'Epc',\n",
+       " 'Eph',\n",
+       " 'Ephoton',\n",
+       " 'Epix',\n",
+       " 'Epixel',\n",
+       " 'Erad',\n",
+       " 'Es',\n",
+       " 'Esr',\n",
+       " 'Eu',\n",
+       " 'Evox',\n",
+       " 'Evoxel',\n",
+       " 'Eyr',\n",
+       " 'F',\n",
+       " 'Farad',\n",
+       " 'Fr',\n",
+       " 'Franklin',\n",
+       " 'FunctionQuantity',\n",
+       " 'FunctionUnitBase',\n",
+       " 'G',\n",
+       " 'GA',\n",
+       " 'GAU',\n",
+       " 'GB',\n",
+       " 'GBa',\n",
+       " 'GC',\n",
+       " 'GD',\n",
+       " 'GF',\n",
+       " 'GG',\n",
+       " 'GGal',\n",
+       " 'GH',\n",
+       " 'GHz',\n",
+       " 'GJ',\n",
+       " 'GJy',\n",
+       " 'GK',\n",
+       " 'GL',\n",
+       " 'GN',\n",
+       " 'GOhm',\n",
+       " 'GP',\n",
+       " 'GPa',\n",
+       " 'GR',\n",
+       " 'GRy',\n",
+       " 'GS',\n",
+       " 'GSt',\n",
+       " 'GT',\n",
+       " 'GV',\n",
+       " 'GW',\n",
+       " 'GWb',\n",
+       " 'Ga',\n",
+       " 'Gadu',\n",
+       " 'Gal',\n",
+       " 'Garcmin',\n",
+       " 'Garcsec',\n",
+       " 'Gau',\n",
+       " 'Gauss',\n",
+       " 'Gb',\n",
+       " 'Gbarn',\n",
+       " 'Gbeam',\n",
+       " 'Gbin',\n",
+       " 'Gbit',\n",
+       " 'Gbyte',\n",
+       " 'Gcd',\n",
+       " 'Gchan',\n",
+       " 'Gcount',\n",
+       " 'Gct',\n",
+       " 'Gd',\n",
+       " 'Gdeg',\n",
+       " 'Gdyn',\n",
+       " 'GeV',\n",
+       " 'Gerg',\n",
+       " 'Gg',\n",
+       " 'Gh',\n",
+       " 'GiB',\n",
+       " 'Gib',\n",
+       " 'Gibit',\n",
+       " 'Gibyte',\n",
+       " 'Gk',\n",
+       " 'Gl',\n",
+       " 'Glm',\n",
+       " 'Glx',\n",
+       " 'Glyr',\n",
+       " 'Gm',\n",
+       " 'Gmag',\n",
+       " 'Gmin',\n",
+       " 'Gmol',\n",
+       " 'Gohm',\n",
+       " 'Gpc',\n",
+       " 'Gph',\n",
+       " 'Gphoton',\n",
+       " 'Gpix',\n",
+       " 'Gpixel',\n",
+       " 'Grad',\n",
+       " 'Gs',\n",
+       " 'Gsr',\n",
+       " 'Gu',\n",
+       " 'Gvox',\n",
+       " 'Gvoxel',\n",
+       " 'Gyr',\n",
+       " 'H',\n",
+       " 'Henry',\n",
+       " 'Hertz',\n",
+       " 'Hz',\n",
+       " 'IrreducibleUnit',\n",
+       " 'J',\n",
+       " 'Jansky',\n",
+       " 'Joule',\n",
+       " 'Jy',\n",
+       " 'K',\n",
+       " 'Kayser',\n",
+       " 'Kelvin',\n",
+       " 'KiB',\n",
+       " 'Kib',\n",
+       " 'Kibit',\n",
+       " 'Kibyte',\n",
+       " 'L',\n",
+       " 'L_bol',\n",
+       " 'L_sun',\n",
+       " 'LogQuantity',\n",
+       " 'LogUnit',\n",
+       " 'Lsun',\n",
+       " 'MA',\n",
+       " 'MAU',\n",
+       " 'MB',\n",
+       " 'MBa',\n",
+       " 'MC',\n",
+       " 'MD',\n",
+       " 'MF',\n",
+       " 'MG',\n",
+       " 'MGal',\n",
+       " 'MH',\n",
+       " 'MHz',\n",
+       " 'MJ',\n",
+       " 'MJy',\n",
+       " 'MK',\n",
+       " 'ML',\n",
+       " 'MN',\n",
+       " 'MOhm',\n",
+       " 'MP',\n",
+       " 'MPa',\n",
+       " 'MR',\n",
+       " 'MRy',\n",
+       " 'MS',\n",
+       " 'MSt',\n",
+       " 'MT',\n",
+       " 'MV',\n",
+       " 'MW',\n",
+       " 'MWb',\n",
+       " 'M_bol',\n",
+       " 'M_e',\n",
+       " 'M_earth',\n",
+       " 'M_jup',\n",
+       " 'M_jupiter',\n",
+       " 'M_p',\n",
+       " 'M_sun',\n",
+       " 'Ma',\n",
+       " 'Madu',\n",
+       " 'MagUnit',\n",
+       " 'Magnitude',\n",
+       " 'Marcmin',\n",
+       " 'Marcsec',\n",
+       " 'Mau',\n",
+       " 'Mb',\n",
+       " 'Mbarn',\n",
+       " 'Mbeam',\n",
+       " 'Mbin',\n",
+       " 'Mbit',\n",
+       " 'Mbyte',\n",
+       " 'Mcd',\n",
+       " 'Mchan',\n",
+       " 'Mcount',\n",
+       " 'Mct',\n",
+       " 'Md',\n",
+       " 'Mdeg',\n",
+       " 'Mdyn',\n",
+       " 'MeV',\n",
+       " 'Mearth',\n",
+       " 'Merg',\n",
+       " 'Mg',\n",
+       " 'Mh',\n",
+       " 'MiB',\n",
+       " 'Mib',\n",
+       " 'Mibit',\n",
+       " 'Mibyte',\n",
+       " 'Mjup',\n",
+       " 'Mjupiter',\n",
+       " 'Mk',\n",
+       " 'Ml',\n",
+       " 'Mlm',\n",
+       " 'Mlx',\n",
+       " 'Mlyr',\n",
+       " 'Mm',\n",
+       " 'Mmag',\n",
+       " 'Mmin',\n",
+       " 'Mmol',\n",
+       " 'Mohm',\n",
+       " 'Mpc',\n",
+       " 'Mph',\n",
+       " 'Mphoton',\n",
+       " 'Mpix',\n",
+       " 'Mpixel',\n",
+       " 'Mrad',\n",
+       " 'Ms',\n",
+       " 'Msr',\n",
+       " 'Msun',\n",
+       " 'Mu',\n",
+       " 'Mvox',\n",
+       " 'Mvoxel',\n",
+       " 'Myr',\n",
+       " 'N',\n",
+       " 'NamedUnit',\n",
+       " 'Newton',\n",
+       " 'Ohm',\n",
+       " 'P',\n",
+       " 'PA',\n",
+       " 'PAU',\n",
+       " 'PB',\n",
+       " 'PBa',\n",
+       " 'PC',\n",
+       " 'PD',\n",
+       " 'PF',\n",
+       " 'PG',\n",
+       " 'PGal',\n",
+       " 'PH',\n",
+       " 'PHz',\n",
+       " 'PJ',\n",
+       " 'PJy',\n",
+       " 'PK',\n",
+       " 'PL',\n",
+       " 'PN',\n",
+       " 'POhm',\n",
+       " 'PP',\n",
+       " 'PPa',\n",
+       " 'PR',\n",
+       " 'PRy',\n",
+       " 'PS',\n",
+       " 'PSt',\n",
+       " 'PT',\n",
+       " 'PV',\n",
+       " 'PW',\n",
+       " 'PWb',\n",
+       " 'Pa',\n",
+       " 'Padu',\n",
+       " 'Parcmin',\n",
+       " 'Parcsec',\n",
+       " 'Pascal',\n",
+       " 'Pau',\n",
+       " 'Pb',\n",
+       " 'Pbarn',\n",
+       " 'Pbeam',\n",
+       " 'Pbin',\n",
+       " 'Pbit',\n",
+       " 'Pbyte',\n",
+       " 'Pcd',\n",
+       " 'Pchan',\n",
+       " 'Pcount',\n",
+       " 'Pct',\n",
+       " 'Pd',\n",
+       " 'Pdeg',\n",
+       " 'Pdyn',\n",
+       " 'PeV',\n",
+       " 'Perg',\n",
+       " 'Pg',\n",
+       " 'Ph',\n",
+       " 'PiB',\n",
+       " 'Pib',\n",
+       " 'Pibit',\n",
+       " 'Pibyte',\n",
+       " 'Pk',\n",
+       " 'Pl',\n",
+       " 'Plm',\n",
+       " 'Plx',\n",
+       " 'Plyr',\n",
+       " 'Pm',\n",
+       " 'Pmag',\n",
+       " 'Pmin',\n",
+       " 'Pmol',\n",
+       " 'Pohm',\n",
+       " 'Ppc',\n",
+       " 'Pph',\n",
+       " 'Pphoton',\n",
+       " 'Ppix',\n",
+       " 'Ppixel',\n",
+       " 'Prad',\n",
+       " 'PrefixUnit',\n",
+       " 'Ps',\n",
+       " 'Psr',\n",
+       " 'Pu',\n",
+       " 'Pvox',\n",
+       " 'Pvoxel',\n",
+       " 'Pyr',\n",
+       " 'Quantity',\n",
+       " 'QuantityInfo',\n",
+       " 'QuantityInfoBase',\n",
+       " 'R',\n",
+       " 'R_earth',\n",
+       " 'R_jup',\n",
+       " 'R_jupiter',\n",
+       " 'R_sun',\n",
+       " 'Rayleigh',\n",
+       " 'Rearth',\n",
+       " 'Rjup',\n",
+       " 'Rjupiter',\n",
+       " 'Rsun',\n",
+       " 'Ry',\n",
+       " 'S',\n",
+       " 'ST',\n",
+       " 'STflux',\n",
+       " 'STmag',\n",
+       " 'Siemens',\n",
+       " 'SpecificTypeQuantity',\n",
+       " 'St',\n",
+       " 'Sun',\n",
+       " 'T',\n",
+       " 'TA',\n",
+       " 'TAU',\n",
+       " 'TB',\n",
+       " 'TBa',\n",
+       " 'TC',\n",
+       " 'TD',\n",
+       " 'TF',\n",
+       " 'TG',\n",
+       " 'TGal',\n",
+       " 'TH',\n",
+       " 'THz',\n",
+       " 'TJ',\n",
+       " 'TJy',\n",
+       " 'TK',\n",
+       " 'TL',\n",
+       " 'TN',\n",
+       " 'TOhm',\n",
+       " 'TP',\n",
+       " 'TPa',\n",
+       " 'TR',\n",
+       " 'TRy',\n",
+       " 'TS',\n",
+       " 'TSt',\n",
+       " 'TT',\n",
+       " 'TV',\n",
+       " 'TW',\n",
+       " 'TWb',\n",
+       " 'Ta',\n",
+       " 'Tadu',\n",
+       " 'Tarcmin',\n",
+       " 'Tarcsec',\n",
+       " 'Tau',\n",
+       " 'Tb',\n",
+       " 'Tbarn',\n",
+       " 'Tbeam',\n",
+       " 'Tbin',\n",
+       " 'Tbit',\n",
+       " 'Tbyte',\n",
+       " 'Tcd',\n",
+       " 'Tchan',\n",
+       " 'Tcount',\n",
+       " 'Tct',\n",
+       " 'Td',\n",
+       " 'Tdeg',\n",
+       " 'Tdyn',\n",
+       " 'TeV',\n",
+       " 'Terg',\n",
+       " 'Tesla',\n",
+       " 'Tg',\n",
+       " 'Th',\n",
+       " 'TiB',\n",
+       " 'Tib',\n",
+       " 'Tibit',\n",
+       " 'Tibyte',\n",
+       " 'Tk',\n",
+       " 'Tl',\n",
+       " 'Tlm',\n",
+       " 'Tlx',\n",
+       " 'Tlyr',\n",
+       " 'Tm',\n",
+       " 'Tmag',\n",
+       " 'Tmin',\n",
+       " 'Tmol',\n",
+       " 'Tohm',\n",
+       " 'Tpc',\n",
+       " 'Tph',\n",
+       " 'Tphoton',\n",
+       " 'Tpix',\n",
+       " 'Tpixel',\n",
+       " 'Trad',\n",
+       " 'Ts',\n",
+       " 'Tsr',\n",
+       " 'Tu',\n",
+       " 'Tvox',\n",
+       " 'Tvoxel',\n",
+       " 'Tyr',\n",
+       " 'Unit',\n",
+       " 'UnitBase',\n",
+       " 'UnitConversionError',\n",
+       " 'UnitTypeError',\n",
+       " 'UnitsError',\n",
+       " 'UnitsWarning',\n",
+       " 'UnrecognizedUnit',\n",
+       " 'V',\n",
+       " 'Volt',\n",
+       " 'W',\n",
+       " 'Watt',\n",
+       " 'Wb',\n",
+       " 'Weber',\n",
+       " 'YA',\n",
+       " 'YAU',\n",
+       " 'YB',\n",
+       " 'YBa',\n",
+       " 'YC',\n",
+       " 'YD',\n",
+       " 'YF',\n",
+       " 'YG',\n",
+       " 'YGal',\n",
+       " 'YH',\n",
+       " 'YHz',\n",
+       " 'YJ',\n",
+       " 'YJy',\n",
+       " 'YK',\n",
+       " 'YL',\n",
+       " 'YN',\n",
+       " 'YOhm',\n",
+       " 'YP',\n",
+       " 'YPa',\n",
+       " 'YR',\n",
+       " 'YRy',\n",
+       " 'YS',\n",
+       " 'YSt',\n",
+       " 'YT',\n",
+       " 'YV',\n",
+       " 'YW',\n",
+       " 'YWb',\n",
+       " 'Ya',\n",
+       " 'Yadu',\n",
+       " 'Yarcmin',\n",
+       " 'Yarcsec',\n",
+       " 'Yau',\n",
+       " 'Yb',\n",
+       " 'Ybarn',\n",
+       " 'Ybeam',\n",
+       " 'Ybin',\n",
+       " 'Ybit',\n",
+       " 'Ybyte',\n",
+       " 'Ycd',\n",
+       " 'Ychan',\n",
+       " 'Ycount',\n",
+       " 'Yct',\n",
+       " 'Yd',\n",
+       " 'Ydeg',\n",
+       " 'Ydyn',\n",
+       " 'YeV',\n",
+       " 'Yerg',\n",
+       " 'Yg',\n",
+       " 'Yh',\n",
+       " 'Yk',\n",
+       " 'Yl',\n",
+       " 'Ylm',\n",
+       " 'Ylx',\n",
+       " 'Ylyr',\n",
+       " 'Ym',\n",
+       " 'Ymag',\n",
+       " 'Ymin',\n",
+       " 'Ymol',\n",
+       " 'Yohm',\n",
+       " 'Ypc',\n",
+       " 'Yph',\n",
+       " 'Yphoton',\n",
+       " 'Ypix',\n",
+       " 'Ypixel',\n",
+       " 'Yrad',\n",
+       " 'Ys',\n",
+       " 'Ysr',\n",
+       " 'Yu',\n",
+       " 'Yvox',\n",
+       " 'Yvoxel',\n",
+       " 'Yyr',\n",
+       " 'ZA',\n",
+       " 'ZAU',\n",
+       " 'ZB',\n",
+       " 'ZBa',\n",
+       " 'ZC',\n",
+       " 'ZD',\n",
+       " 'ZF',\n",
+       " 'ZG',\n",
+       " 'ZGal',\n",
+       " 'ZH',\n",
+       " 'ZHz',\n",
+       " 'ZJ',\n",
+       " 'ZJy',\n",
+       " 'ZK',\n",
+       " 'ZL',\n",
+       " 'ZN',\n",
+       " 'ZOhm',\n",
+       " 'ZP',\n",
+       " 'ZPa',\n",
+       " 'ZR',\n",
+       " 'ZRy',\n",
+       " 'ZS',\n",
+       " 'ZSt',\n",
+       " 'ZT',\n",
+       " 'ZV',\n",
+       " 'ZW',\n",
+       " 'ZWb',\n",
+       " 'Za',\n",
+       " 'Zadu',\n",
+       " 'Zarcmin',\n",
+       " 'Zarcsec',\n",
+       " 'Zau',\n",
+       " 'Zb',\n",
+       " 'Zbarn',\n",
+       " 'Zbeam',\n",
+       " 'Zbin',\n",
+       " 'Zbit',\n",
+       " 'Zbyte',\n",
+       " 'Zcd',\n",
+       " 'Zchan',\n",
+       " 'Zcount',\n",
+       " 'Zct',\n",
+       " 'Zd',\n",
+       " 'Zdeg',\n",
+       " 'Zdyn',\n",
+       " 'ZeV',\n",
+       " 'Zerg',\n",
+       " 'Zg',\n",
+       " 'Zh',\n",
+       " 'Zk',\n",
+       " 'Zl',\n",
+       " 'Zlm',\n",
+       " 'Zlx',\n",
+       " 'Zlyr',\n",
+       " 'Zm',\n",
+       " 'Zmag',\n",
+       " 'Zmin',\n",
+       " 'Zmol',\n",
+       " 'Zohm',\n",
+       " 'Zpc',\n",
+       " 'Zph',\n",
+       " 'Zphoton',\n",
+       " 'Zpix',\n",
+       " 'Zpixel',\n",
+       " 'Zrad',\n",
+       " 'Zs',\n",
+       " 'Zsr',\n",
+       " 'Zu',\n",
+       " 'Zvox',\n",
+       " 'Zvoxel',\n",
+       " 'Zyr',\n",
+       " '__builtins__',\n",
+       " '__cached__',\n",
+       " '__doc__',\n",
+       " '__file__',\n",
+       " '__loader__',\n",
+       " '__name__',\n",
+       " '__package__',\n",
+       " '__path__',\n",
+       " '__spec__',\n",
+       " 'a',\n",
+       " 'aA',\n",
+       " 'aAU',\n",
+       " 'aB',\n",
+       " 'aBa',\n",
+       " 'aC',\n",
+       " 'aD',\n",
+       " 'aF',\n",
+       " 'aG',\n",
+       " 'aGal',\n",
+       " 'aH',\n",
+       " 'aHz',\n",
+       " 'aJ',\n",
+       " 'aJy',\n",
+       " 'aK',\n",
+       " 'aL',\n",
+       " 'aN',\n",
+       " 'aOhm',\n",
+       " 'aP',\n",
+       " 'aPa',\n",
+       " 'aR',\n",
+       " 'aRy',\n",
+       " 'aS',\n",
+       " 'aSt',\n",
+       " 'aT',\n",
+       " 'aV',\n",
+       " 'aW',\n",
+       " 'aWb',\n",
+       " 'aa',\n",
+       " 'aadu',\n",
+       " 'aarcmin',\n",
+       " 'aarcsec',\n",
+       " 'aau',\n",
+       " 'ab',\n",
+       " 'abA',\n",
+       " 'abC',\n",
+       " 'abampere',\n",
+       " 'abarn',\n",
+       " 'abcoulomb',\n",
+       " 'abeam',\n",
+       " 'abin',\n",
+       " 'abit',\n",
+       " 'abyte',\n",
+       " 'acd',\n",
+       " 'achan',\n",
+       " 'acount',\n",
+       " 'act',\n",
+       " 'ad',\n",
+       " 'add_enabled_equivalencies',\n",
+       " 'add_enabled_units',\n",
+       " 'adeg',\n",
+       " 'adu',\n",
+       " 'adyn',\n",
+       " 'aeV',\n",
+       " 'aerg',\n",
+       " 'ag',\n",
+       " 'ah',\n",
+       " 'ak',\n",
+       " 'al',\n",
+       " 'allclose',\n",
+       " 'alm',\n",
+       " 'alx',\n",
+       " 'alyr',\n",
+       " 'am',\n",
+       " 'amag',\n",
+       " 'amin',\n",
+       " 'amol',\n",
+       " 'amp',\n",
+       " 'ampere',\n",
+       " 'angstrom',\n",
+       " 'annum',\n",
+       " 'aohm',\n",
+       " 'apc',\n",
+       " 'aph',\n",
+       " 'aphoton',\n",
+       " 'apix',\n",
+       " 'apixel',\n",
+       " 'arad',\n",
+       " 'arcmin',\n",
+       " 'arcminute',\n",
+       " 'arcsec',\n",
+       " 'arcsecond',\n",
+       " 'asr',\n",
+       " 'astronomical_unit',\n",
+       " 'astrophys',\n",
+       " 'attoBarye',\n",
+       " 'attoDa',\n",
+       " 'attoDalton',\n",
+       " 'attoDebye',\n",
+       " 'attoFarad',\n",
+       " 'attoGauss',\n",
+       " 'attoHenry',\n",
+       " 'attoHertz',\n",
+       " 'attoJansky',\n",
+       " 'attoJoule',\n",
+       " 'attoKayser',\n",
+       " 'attoKelvin',\n",
+       " 'attoNewton',\n",
+       " 'attoOhm',\n",
+       " 'attoPascal',\n",
+       " 'attoRayleigh',\n",
+       " 'attoSiemens',\n",
+       " 'attoTesla',\n",
+       " 'attoVolt',\n",
+       " 'attoWatt',\n",
+       " 'attoWeber',\n",
+       " 'attoamp',\n",
+       " 'attoampere',\n",
+       " 'attoannum',\n",
+       " 'attoarcminute',\n",
+       " 'attoarcsecond',\n",
+       " 'attoastronomical_unit',\n",
+       " 'attobarn',\n",
+       " 'attobarye',\n",
+       " 'attobit',\n",
+       " 'attobyte',\n",
+       " 'attocandela',\n",
+       " 'attocoulomb',\n",
+       " 'attocount',\n",
+       " 'attoday',\n",
+       " 'attodebye',\n",
+       " 'attodegree',\n",
+       " 'attodyne',\n",
+       " 'attoelectronvolt',\n",
+       " 'attofarad',\n",
+       " 'attogal',\n",
+       " 'attogauss',\n",
+       " 'attogram',\n",
+       " 'attohenry',\n",
+       " 'attohertz',\n",
+       " 'attohour',\n",
+       " 'attohr',\n",
+       " 'attojansky',\n",
+       " 'attojoule',\n",
+       " 'attokayser',\n",
+       " 'attolightyear',\n",
+       " 'attoliter',\n",
+       " 'attolumen',\n",
+       " 'attolux',\n",
+       " 'attometer',\n",
+       " 'attominute',\n",
+       " 'attomole',\n",
+       " 'attonewton',\n",
+       " 'attoparsec',\n",
+       " 'attopascal',\n",
+       " 'attophoton',\n",
+       " 'attopixel',\n",
+       " 'attopoise',\n",
+       " 'attoradian',\n",
+       " 'attorayleigh',\n",
+       " 'attorydberg',\n",
+       " 'attosecond',\n",
+       " 'attosiemens',\n",
+       " 'attosteradian',\n",
+       " 'attostokes',\n",
+       " 'attotesla',\n",
+       " 'attovolt',\n",
+       " 'attovoxel',\n",
+       " 'attowatt',\n",
+       " 'attoweber',\n",
+       " 'attoyear',\n",
+       " 'au',\n",
+       " 'avox',\n",
+       " 'avoxel',\n",
+       " 'ayr',\n",
+       " 'b',\n",
+       " 'bar',\n",
+       " 'barn',\n",
+       " 'barye',\n",
+       " 'beam',\n",
+       " 'beam_angular_area',\n",
+       " 'becquerel',\n",
+       " 'bin',\n",
+       " 'binary_prefixes',\n",
+       " 'bit',\n",
+       " 'bol',\n",
+       " 'brightness_temperature',\n",
+       " 'byte',\n",
+       " 'cA',\n",
+       " 'cAU',\n",
+       " 'cB',\n",
+       " 'cBa',\n",
+       " 'cC',\n",
+       " 'cD',\n",
+       " 'cF',\n",
+       " 'cG',\n",
+       " 'cGal',\n",
+       " 'cH',\n",
+       " 'cHz',\n",
+       " 'cJ',\n",
+       " 'cJy',\n",
+       " 'cK',\n",
+       " 'cL',\n",
+       " 'cN',\n",
+       " 'cOhm',\n",
+       " 'cP',\n",
+       " 'cPa',\n",
+       " 'cR',\n",
+       " 'cRy',\n",
+       " 'cS',\n",
+       " 'cSt',\n",
+       " 'cT',\n",
+       " 'cV',\n",
+       " 'cW',\n",
+       " 'cWb',\n",
+       " 'ca',\n",
+       " 'cadu',\n",
+       " 'candela',\n",
+       " 'carcmin',\n",
+       " 'carcsec',\n",
+       " 'cau',\n",
+       " 'cb',\n",
+       " 'cbarn',\n",
+       " 'cbeam',\n",
+       " 'cbin',\n",
+       " 'cbit',\n",
+       " 'cbyte',\n",
+       " 'ccd',\n",
+       " 'cchan',\n",
+       " 'ccount',\n",
+       " 'cct',\n",
+       " 'cd',\n",
+       " 'cdeg',\n",
+       " 'cdyn',\n",
+       " 'ceV',\n",
+       " 'centiBarye',\n",
+       " 'centiDa',\n",
+       " 'centiDalton',\n",
+       " 'centiDebye',\n",
+       " 'centiFarad',\n",
+       " 'centiGauss',\n",
+       " 'centiHenry',\n",
+       " 'centiHertz',\n",
+       " 'centiJansky',\n",
+       " 'centiJoule',\n",
+       " 'centiKayser',\n",
+       " 'centiKelvin',\n",
+       " 'centiNewton',\n",
+       " 'centiOhm',\n",
+       " 'centiPascal',\n",
+       " 'centiRayleigh',\n",
+       " 'centiSiemens',\n",
+       " 'centiTesla',\n",
+       " 'centiVolt',\n",
+       " 'centiWatt',\n",
+       " 'centiWeber',\n",
+       " 'centiamp',\n",
+       " 'centiampere',\n",
+       " 'centiannum',\n",
+       " 'centiarcminute',\n",
+       " 'centiarcsecond',\n",
+       " 'centiastronomical_unit',\n",
+       " 'centibarn',\n",
+       " 'centibarye',\n",
+       " 'centibit',\n",
+       " 'centibyte',\n",
+       " 'centicandela',\n",
+       " 'centicoulomb',\n",
+       " 'centicount',\n",
+       " 'centiday',\n",
+       " 'centidebye',\n",
+       " 'centidegree',\n",
+       " 'centidyne',\n",
+       " 'centielectronvolt',\n",
+       " 'centifarad',\n",
+       " 'centigal',\n",
+       " 'centigauss',\n",
+       " 'centigram',\n",
+       " 'centihenry',\n",
+       " 'centihertz',\n",
+       " 'centihour',\n",
+       " 'centihr',\n",
+       " 'centijansky',\n",
+       " 'centijoule',\n",
+       " 'centikayser',\n",
+       " 'centilightyear',\n",
+       " 'centiliter',\n",
+       " 'centilumen',\n",
+       " 'centilux',\n",
+       " 'centimeter',\n",
+       " 'centiminute',\n",
+       " 'centimole',\n",
+       " 'centinewton',\n",
+       " 'centiparsec',\n",
+       " 'centipascal',\n",
+       " 'centiphoton',\n",
+       " 'centipixel',\n",
+       " 'centipoise',\n",
+       " 'centiradian',\n",
+       " 'centirayleigh',\n",
+       " 'centirydberg',\n",
+       " 'centisecond',\n",
+       " 'centisiemens',\n",
+       " 'centisteradian',\n",
+       " 'centistokes',\n",
+       " 'centitesla',\n",
+       " 'centivolt',\n",
+       " 'centivoxel',\n",
+       " 'centiwatt',\n",
+       " 'centiweber',\n",
+       " 'centiyear',\n",
+       " 'cerg',\n",
+       " 'cg',\n",
+       " 'cgs',\n",
+       " 'ch',\n",
+       " 'chan',\n",
+       " 'ck',\n",
+       " 'cl',\n",
+       " 'clm',\n",
+       " 'clx',\n",
+       " 'clyr',\n",
+       " 'cm',\n",
+       " 'cmag',\n",
+       " 'cmin',\n",
+       " 'cmol',\n",
+       " 'cohm',\n",
+       " 'core',\n",
+       " 'coulomb',\n",
+       " 'count',\n",
+       " 'cpc',\n",
+       " 'cph',\n",
+       " 'cphoton',\n",
+       " 'cpix',\n",
+       " 'cpixel',\n",
+       " 'crad',\n",
+       " 'cs',\n",
+       " 'csr',\n",
+       " 'ct',\n",
+       " 'cu',\n",
+       " 'curie',\n",
+       " 'cvox',\n",
+       " 'cvoxel',\n",
+       " 'cy',\n",
+       " 'cycle',\n",
+       " 'cyr',\n",
+       " 'd',\n",
+       " 'dA',\n",
+       " 'dAU',\n",
+       " 'dB',\n",
+       " 'dBa',\n",
+       " 'dC',\n",
+       " 'dD',\n",
+       " 'dF',\n",
+       " 'dG',\n",
+       " 'dGal',\n",
+       " 'dH',\n",
+       " 'dHz',\n",
+       " 'dJ',\n",
+       " 'dJy',\n",
+       " 'dK',\n",
+       " 'dL',\n",
+       " 'dN',\n",
+       " 'dOhm',\n",
+       " 'dP',\n",
+       " 'dPa',\n",
+       " 'dR',\n",
+       " 'dRy',\n",
+       " 'dS',\n",
+       " 'dSt',\n",
+       " 'dT',\n",
+       " ...]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dir(u)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To create a quantity, we multiply a value by a unit."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "astropy.units.quantity.Quantity"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "coord = 30 * u.deg\n",
+    "type(coord)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The result is a `Quantity` object.\n",
+    "\n",
+    "Jupyter knows how to display `Quantities` like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$30 \\; \\mathrm{{}^{\\circ}}$"
+      ],
+      "text/plain": [
+       "<Quantity 30. deg>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "coord"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Selecting a rectangle\n",
+    "\n",
+    "Now we'll select a rectangle from -55 to -45 degrees right ascension and -8 to 4 degrees of declination.\n",
+    "\n",
+    "We'll define variables to contain these limits."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "phi1_min = -55\n",
+    "phi1_max = -45\n",
+    "phi2_min = -8\n",
+    "phi2_max = 4"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To represent a rectangle, we'll use two lists of coordinates and multiply by their units."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "phi1_rect = [phi1_min, phi1_min, phi1_max, phi1_max] * u.deg\n",
+    "phi2_rect = [phi2_min, phi2_max, phi2_max, phi2_min] * u.deg"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`phi1_rect` and `phi2_rect` represent the coordinates of the corners of a rectangle.  \n",
+    "\n",
+    "But they are in \"[a Heliocentric spherical coordinate system defined by the orbit of the GD1 stream](https://gala-astro.readthedocs.io/en/latest/_modules/gala/coordinates/gd1.html)\"\n",
+    "\n",
+    "In order to use them in a Gaia query, we have to convert them to [International Celestial Reference System](https://en.wikipedia.org/wiki/International_Celestial_Reference_System) (ICRS) coordinates.  We can do that by storing the coordinates in a `GD1Koposov10` object provided by [Gala](https://gala-astro.readthedocs.io/en/latest/coordinates/)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "gala.coordinates.gd1.GD1Koposov10"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import gala.coordinates as gc\n",
+    "\n",
+    "corners = gc.GD1Koposov10(phi1=phi1_rect, phi2=phi2_rect)\n",
+    "type(corners)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can display the result like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<GD1Koposov10 Coordinate: (phi1, phi2) in deg\n",
+       "    [(-55., -8.), (-55.,  4.), (-45.,  4.), (-45., -8.)]>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "corners"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can use `transform_to` to convert to ICRS coordinates."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "astropy.coordinates.builtin_frames.icrs.ICRS"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import astropy.coordinates as coord\n",
+    "\n",
+    "corners_icrs = corners.transform_to(coord.ICRS)\n",
+    "type(corners_icrs)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The result is an `ICRS` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<ICRS Coordinate: (ra, dec) in deg\n",
+       "    [(146.27533314, 19.26190982), (135.42163944, 25.87738723),\n",
+       "     (141.60264825, 34.3048303 ), (152.81671045, 27.13611254)]>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "corners_icrs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notice that a rectangle in one coordinate system is not necessarily a rectangle in another.  In this example, the result is a polygon."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Selecting a polygon\n",
+    "\n",
+    "In order to use this polygon as part of an ADQL query, we have to convert it to a string with a comma-separated list of coordinates, as in this example:\n",
+    "\n",
+    "```\n",
+    "\"\"\"\n",
+    "POLYGON(143.65, 20.98, \n",
+    "        134.46, 26.39, \n",
+    "        140.58, 34.85, \n",
+    "        150.16, 29.01)\n",
+    "\"\"\"\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`corners_icrs` behaves like a list, so we can use a `for` loop to iterate through the points."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<ICRS Coordinate: (ra, dec) in deg\n",
+      "    (146.27533314, 19.26190982)>\n",
+      "<ICRS Coordinate: (ra, dec) in deg\n",
+      "    (135.42163944, 25.87738723)>\n",
+      "<ICRS Coordinate: (ra, dec) in deg\n",
+      "    (141.60264825, 34.3048303)>\n",
+      "<ICRS Coordinate: (ra, dec) in deg\n",
+      "    (152.81671045, 27.13611254)>\n"
+     ]
+    }
+   ],
+   "source": [
+    "for point in corners_icrs:\n",
+    "    print(point)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "From that, we can select the coordinates `ra` and `dec`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "146d16m31.1993s 19d15m42.8754s\n",
+      "135d25m17.902s 25d52m38.594s\n",
+      "141d36m09.5337s 34d18m17.3891s\n",
+      "152d49m00.1576s 27d08m10.0051s\n"
+     ]
+    }
+   ],
+   "source": [
+    "for point in corners_icrs:\n",
+    "    print(point.ra, point.dec)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The results are quantities with units, but if we select the `value` part, we get a dimensionless floating-point number."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "146.27533313607782 19.261909820533692\n",
+      "135.42163944306296 25.87738722767213\n",
+      "141.60264825107333 34.304830296257144\n",
+      "152.81671044675923 27.136112541397996\n"
+     ]
+    }
+   ],
+   "source": [
+    "for point in corners_icrs:\n",
+    "    print(point.ra.value, point.dec.value)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can use string `format` to convert these numbers to strings."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['146.27533313607782, 19.261909820533692',\n",
+       " '135.42163944306296, 25.87738722767213',\n",
+       " '141.60264825107333, 34.304830296257144',\n",
+       " '152.81671044675923, 27.136112541397996']"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "point_base = \"{point.ra.value}, {point.dec.value}\"\n",
+    "\n",
+    "t = [point_base.format(point=point)\n",
+    "     for point in corners_icrs]\n",
+    "t"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The result is a list of strings, which we can join into a single string using `join`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'146.27533313607782, 19.261909820533692, 135.42163944306296, 25.87738722767213, 141.60264825107333, 34.304830296257144, 152.81671044675923, 27.136112541397996'"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "point_list = ', '.join(t)\n",
+    "point_list"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notice that we invoke `join` on a string and pass the list as an argument.\n",
+    "\n",
+    "Before we can assemble the query, we need `columns` again (as we saw in the previous notebook)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "columns = 'source_id, ra, dec, pmra, pmdec, parallax, parallax_error, radial_velocity'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's the base for the query, with format specifiers for `columns` and `point_list`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query_base = \"\"\"SELECT {columns}\n",
+    "FROM gaiadr2.gaia_source\n",
+    "WHERE parallax < 1\n",
+    "  AND bp_rp BETWEEN -0.75 AND 2 \n",
+    "  AND 1 = CONTAINS(POINT(ra, dec), \n",
+    "                   POLYGON({point_list}))\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And here's the result:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "SELECT source_id, ra, dec, pmra, pmdec, parallax, parallax_error, radial_velocity\n",
+      "FROM gaiadr2.gaia_source\n",
+      "WHERE parallax < 1\n",
+      "  AND bp_rp BETWEEN -0.75 AND 2 \n",
+      "  AND 1 = CONTAINS(POINT(ra, dec), \n",
+      "                   POLYGON(146.27533313607782, 19.261909820533692, 135.42163944306296, 25.87738722767213, 141.60264825107333, 34.304830296257144, 152.81671044675923, 27.136112541397996))\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "query = query_base.format(columns=columns, \n",
+    "                          point_list=point_list)\n",
+    "print(query)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As always, we should take a minute to proof-read the query before we launch it.\n",
+    "\n",
+    "The result will be bigger than our previous queries, so it will take a little longer."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO: Query finished. [astroquery.utils.tap.core]\n",
+      "<Table length=140340>\n",
+      "      name       dtype    unit                              description                             n_bad \n",
+      "--------------- ------- -------- ------------------------------------------------------------------ ------\n",
+      "      source_id   int64          Unique source identifier (unique within a particular Data Release)      0\n",
+      "             ra float64      deg                                                    Right ascension      0\n",
+      "            dec float64      deg                                                        Declination      0\n",
+      "           pmra float64 mas / yr                         Proper motion in right ascension direction      0\n",
+      "          pmdec float64 mas / yr                             Proper motion in declination direction      0\n",
+      "       parallax float64      mas                                                           Parallax      0\n",
+      " parallax_error float64      mas                                         Standard error of parallax      0\n",
+      "radial_velocity float64   km / s                                                    Radial velocity 139374\n",
+      "Jobid: 1603114980658O\n",
+      "Phase: COMPLETED\n",
+      "Owner: None\n",
+      "Output file: async_20201019094300.vot\n",
+      "Results: None\n"
+     ]
+    }
+   ],
+   "source": [
+    "job = Gaia.launch_job_async(query)\n",
+    "print(job)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here are the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "140340"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results = job.get_results()\n",
+    "len(results)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There are more than 100,000 stars in this polygon, but that's a manageable size to work with."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Saving results\n",
+    "\n",
+    "This is the set of stars we'll work with in the next step.  But since we have a substantial dataset now, this is a good time to save it.\n",
+    "\n",
+    "Storing the data in a file means we can shut down this notebook and pick up where we left off without running the previous query again.\n",
+    "\n",
+    "Astropy `Table` objects provide `write`, which writes the table to disk."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "filename = 'gd1_results.fits'\n",
+    "results.write(filename, overwrite=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Because the filename ends with `fits`, the table is written in the [FITS format](https://en.wikipedia.org/wiki/FITS), which preserves the metadata associated with the table.\n",
+    "\n",
+    "If the file already exists, the `overwrite` argument causes it to be overwritten.\n",
+    "\n",
+    "To see how big the file is, we can use `ls` with the `-lh` option, which prints information about the file including its size in human-readable form."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-rw-rw-r-- 1 downey downey 8.6M Oct 19 09:43 gd1_results.fits\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!ls -lh gd1_results.fits"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The file is about 8.6 MB.  If you are using Windows, `ls` might not work; in that case, try:\n",
+    "\n",
+    "```\n",
+    "!dir gd1_results.fits\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "In this notebook, we composed more complex queries to select stars within a polygonal region of the sky.  Then we downloaded the results and saved them in a FITS file.\n",
+    "\n",
+    "In the next notebook, we'll reload the data from this file and replicate the next step in the analysis, using proper motion to identify stars likely to be in GD-1."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Best practices\n",
+    "\n",
+    "* For measurements with units, use `Quantity` objects that represent units explicitly and check for errors.\n",
+    "\n",
+    "* Use the `format` function to compose queries; it is often faster and less error-prone.\n",
+    "\n",
+    "* Develop queries incrementally: start with something simple, test it, and add a little bit at a time.\n",
+    "\n",
+    "* Once you have a query working, save the data in a local file.  If you shut down the notebook and come back to it later, you can reload the file; you don't have to run the query again."
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/_build/html/_sources/AstronomicalData/README.md b/_build/html/_sources/AstronomicalData/README.md
new file mode 100644
index 0000000..190277b
--- /dev/null
+++ b/_build/html/_sources/AstronomicalData/README.md
@@ -0,0 +1,172 @@
+# Astronomical Data in Python
+
+*Astronomical Data in Python* is an introduction to tools and practices for working with astronomical data.  Topics covered include:
+
+* Writing queries that select and download data from a database.
+
+* Using data stored in an Astropy `Table` or Pandas `DataFrame`.
+
+* Working with coordinates and other quantities with units.
+
+* Storing data in various formats.
+
+* Performing database join operations that combine data from multiple tables.
+
+* Visualizing data and preparing publication-quality figures.
+
+As a running example, we will replicate part of the analysis in a recent paper, "[Off the beaten path: Gaia reveals GD-1 stars outside of the main stream](https://arxiv.org/abs/1805.00425)" by Adrian M. Price-Whelan and Ana Bonaca.
+
+This material was developed in collaboration with [The Carpentries](https://carpentries.org/) and the Astronomy Curriculum Development Committee, and supported by funding from the American Institute of Physics through the American Astronomical Society.
+
+I am grateful for contributions from the members of the committee -- Azalee Bostroem, Rodolfo Montez, and Phil Rosenfield -- and from Erin Becker, Brett Morris and Adrian Price-Whelan.
+
+The original format of this material is a series of Jupyter notebooks.  Using the
+links below, you can read the notebooks on NBViewer or run them on Colab.  If you
+want to run the notebooks in your own environment, you can download them from
+this repository and follow the instructions below to set up your environment.
+
+This material is also available in the form of [Carpentries lessons](https://datacarpentry.github.io/astronomy-python), but you should be
+aware that these versions might diverge in the future.
+
+**Prerequisites**
+
+This material should be accessible to people familiar with basic Python, but not necessarily the libraries we will use, like Astropy or Pandas.  If you are familiar with Python lists and dictionaries, and you know how to write a function that takes parameters and returns a value, that should be enough.
+
+We assume that you are familiar with astronomy at the undergraduate level, but we will not assume specialized knowledge of the datasets or analysis methods we'll use.
+
+**Notebook 1**
+
+This notebook demonstrates the following steps:
+
+1. Making a connection to the Gaia server,
+
+2. Exploring information about the database and the tables it contains,
+
+3. Writing a query and sending it to the server, and finally
+
+4. Downloading the response from the server as an Astropy `Table`.
+
+Press this button to run this notebook on Colab:
+
+[<img src="run_on_colab_small.png">](https://colab.research.google.com/github/AllenDowney/AstronomicalData/blob/main/01_query.ipynb)
+
+[or click here to read it on NBViewer](https://nbviewer.jupyter.org/github/AllenDowney/AstronomicalData/blob/main/01_query.ipynb)
+
+
+**Notebook 2**
+
+This notebook starts with an example that does a "cone search"; that is, it selects stars that appear in a circular region of the sky.
+
+Then, to select stars in the vicinity of GD-1, we:
+
+* Use `Quantity` objects to represent measurements with units.
+
+* Use the `Gala` library to convert coordinates from one frame to another.
+
+* Use the ADQL keywords `POLYGON`, `CONTAINS`, and `POINT` to select stars that fall within a polygonal region.
+
+* Submit a query and download the results.
+
+* Store the results in a FITS file.
+
+Press this button to run this notebook on Colab:
+
+[<img src="run_on_colab_small.png">](https://colab.research.google.com/github/AllenDowney/AstronomicalData/blob/main/02_coords.ipynb)
+
+[or click here to read it on NBViewer](https://nbviewer.jupyter.org/github/AllenDowney/AstronomicalData/blob/main/02_coords.ipynb)
+
+
+**Notebook 3**
+
+Here are the steps in this notebook:
+
+1. We'll read back the results from the previous notebook, which we saved in a FITS file.
+
+2. Then we'll transform the coordinates and proper motion data from ICRS back to the coordinate frame of GD-1.
+
+3. We'll put those results into a Pandas `DataFrame`, which we'll use to select stars near the centerline of GD-1.
+
+4. Plotting the proper motion of those stars, we'll identify a region of proper motion for stars that are likely to be in GD-1.
+
+5. Finally, we'll select and plot the stars whose proper motion is in that region.
+
+Press this button to run this notebook on Colab:
+
+[<img src="run_on_colab_small.png">](https://colab.research.google.com/github/AllenDowney/AstronomicalData/blob/main/03_motion.ipynb)
+
+[or click here to read it on NBViewer](https://nbviewer.jupyter.org/github/AllenDowney/AstronomicalData/blob/main/03_motion.ipynb)
+
+
+**Notebook 4**
+
+Here are the steps in this notebook:
+
+1. Using data from the previous notebook, we'll identify the values of proper motion for stars likely to be in GD-1.
+
+2. Then we'll compose an ADQL query that selects stars based on proper motion, so we can download only the data we need.
+
+3. We'll also see how to write the results to a CSV file.
+
+That will make it possible to search a bigger region of the sky in a single query.
+
+Press this button to run this notebook on Colab:
+
+[<img src="run_on_colab_small.png">](https://colab.research.google.com/github/AllenDowney/AstronomicalData/blob/main/04_select.ipynb)
+
+[or click here to read it on NBViewer](https://nbviewer.jupyter.org/github/AllenDowney/AstronomicalData/blob/main/04_select.ipynb)
+
+
+**Notebook 5**
+
+Here are the steps in this notebook:
+
+1. We'll reload the candidate stars we identified in the previous notebook.
+
+2. Then we'll run a query on the Gaia server that uploads the table of candidates and uses a `JOIN` operation to select photometry data for the candidate stars.
+
+3. We'll write the results to a file for use in the next notebook.
+
+Press this button to run this notebook on Colab:
+
+[<img src="run_on_colab_small.png">](https://colab.research.google.com/github/AllenDowney/AstronomicalData/blob/main/05_join.ipynb)
+
+[or click here to read it on NBViewer](https://nbviewer.jupyter.org/github/AllenDowney/AstronomicalData/blob/main/05_join.ipynb)
+
+
+**Notebook 6**
+
+Here are the steps in this notebook:
+
+1. We'll reload the data from the previous notebook and make a color-magnitude diagram.
+
+2. Then we'll specify a polygon in the diagram that contains stars with the photometry we expect.
+
+3. Then we'll merge the photometry data with the list of candidate stars, storing the result in a Pandas `DataFrame`.
+
+Press this button to run this notebook on Colab:
+
+[<img src="run_on_colab_small.png">](https://colab.research.google.com/github/AllenDowney/AstronomicalData/blob/main/06_photo.ipynb)
+
+[or click here to read it on NBViewer](https://nbviewer.jupyter.org/github/AllenDowney/AstronomicalData/blob/main/06_photo.ipynb)
+
+
+**Notebook 7**
+
+Here are the steps in this notebook:
+
+1. Starting with the figure from the previous notebook, we'll add annotations to present the results more clearly.
+
+2. The we'll see several ways to customize figures to make them more appealing and effective.
+
+3. Finally, we'll see how to make a figure with multiple panels or subplots.
+
+Press this button to run this notebook on Colab:
+
+[<img src="run_on_colab_small.png">](https://colab.research.google.com/github/AllenDowney/AstronomicalData/blob/main/07_plot.ipynb)
+
+[or click here to read it on NBViewer](https://nbviewer.jupyter.org/github/AllenDowney/AstronomicalData/blob/main/07_plot.ipynb)
+
+
+**Installation instructions**
+
+Coming soon.
diff --git a/_build/html/_sources/AstronomicalData/_build/html/_static/vendor/lato_latin-ext/1.44.1/LICENSE.md b/_build/html/_sources/AstronomicalData/_build/html/_static/vendor/lato_latin-ext/1.44.1/LICENSE.md
new file mode 100644
index 0000000..89bc0f2
--- /dev/null
+++ b/_build/html/_sources/AstronomicalData/_build/html/_static/vendor/lato_latin-ext/1.44.1/LICENSE.md
@@ -0,0 +1,20 @@
+Copyright (c) 2019 Jan Bednar
+
+Permission is hereby granted, free of charge, to any person obtaining
+a copy of this software and associated documentation files (the
+"Software"), to deal in the Software without restriction, including
+without limitation the rights to use, copy, modify, merge, publish,
+distribute, sublicense, and/or sell copies of the Software, and to
+permit persons to whom the Software is furnished to do so, subject to
+the following conditions:
+
+The above copyright notice and this permission notice shall be
+included in all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE
+LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
+OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION
+WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
diff --git a/_build/html/_sources/AstronomicalData/_build/html/_static/vendor/open-sans_all/1.44.1/LICENSE.md b/_build/html/_sources/AstronomicalData/_build/html/_static/vendor/open-sans_all/1.44.1/LICENSE.md
new file mode 100644
index 0000000..89bc0f2
--- /dev/null
+++ b/_build/html/_sources/AstronomicalData/_build/html/_static/vendor/open-sans_all/1.44.1/LICENSE.md
@@ -0,0 +1,20 @@
+Copyright (c) 2019 Jan Bednar
+
+Permission is hereby granted, free of charge, to any person obtaining
+a copy of this software and associated documentation files (the
+"Software"), to deal in the Software without restriction, including
+without limitation the rights to use, copy, modify, merge, publish,
+distribute, sublicense, and/or sell copies of the Software, and to
+permit persons to whom the Software is furnished to do so, subject to
+the following conditions:
+
+The above copyright notice and this permission notice shall be
+included in all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE
+LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
+OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION
+WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
diff --git a/_build/html/_sources/AstronomicalData/_build/jupyter_execute/01_query.ipynb b/_build/html/_sources/AstronomicalData/_build/jupyter_execute/01_query.ipynb
new file mode 100644
index 0000000..94813e9
--- /dev/null
+++ b/_build/html/_sources/AstronomicalData/_build/jupyter_execute/01_query.ipynb
@@ -0,0 +1,1640 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Lesson 1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Introduction\n",
+    "\n",
+    "This workshop is an introduction to tools and practices for working with astronomical data.  Topics covered include:\n",
+    "\n",
+    "* Writing queries that select and download data from a database.\n",
+    "\n",
+    "* Using data stored in an Astropy `Table` or Pandas `DataFrame`.\n",
+    "\n",
+    "* Working with coordinates and other quantities with units.\n",
+    "\n",
+    "* Storing data in various formats.\n",
+    "\n",
+    "* Performing database join operations that combine data from multiple tables.\n",
+    "\n",
+    "* Visualizing data and preparing publication-quality figures."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As a running example, we will replicate part of the analysis in a recent paper, \"[Off the beaten path: Gaia reveals GD-1 stars outside of the main stream](https://arxiv.org/abs/1805.00425)\" by Adrian M. Price-Whelan and Ana Bonaca.\n",
+    "\n",
+    "As the abstract explains, \"Using data from the Gaia second data release combined with Pan-STARRS photometry, we present a sample of highly-probable members of the longest cold stream in the Milky Way, GD-1.\"\n",
+    "\n",
+    "GD-1 is a [stellar stream](https://en.wikipedia.org/wiki/List_of_stellar_streams), which is \"an association of stars orbiting a galaxy that was once a globular cluster or dwarf galaxy that has now been torn apart and stretched out along its orbit by tidal forces.\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[This article in *Science* magazine](https://www.sciencemag.org/news/2018/10/streams-stars-reveal-galaxy-s-violent-history-and-perhaps-its-unseen-dark-matter) explains some of the background, including the process that led to the paper and an discussion of the scientific implications:\n",
+    "\n",
+    "* \"The streams are particularly useful for ... galactic archaeology --- rewinding the cosmic clock to reconstruct the assembly of the Milky Way.\"\n",
+    "\n",
+    "* \"They also are being used as exquisitely sensitive scales to measure the galaxy's mass.\"\n",
+    "\n",
+    "* \"... the streams are well-positioned to reveal the presence of dark matter ... because the streams are so fragile, theorists say, collisions with marauding clumps of dark matter could leave telltale scars, potential clues to its nature.\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Prerequisites\n",
+    "\n",
+    "This workshop is meant for people who are familiar with basic Python, but not necessarily the libraries we will use, like Astropy or Pandas.  If you are familiar with Python lists and dictionaries, and you know how to write a function that takes parameters and returns a value, you know enough Python for this workshop.\n",
+    "\n",
+    "We assume that you have some familiarity with operating systems, like the ability to use a command-line interface.  But we don't assume you have any prior experience with databases.\n",
+    "\n",
+    "We assume that you are familiar with astronomy at the undergraduate level, but we will not assume specialized knowledge of the datasets or analysis methods we'll use. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Data\n",
+    "\n",
+    "The datasets we will work with are:\n",
+    " \n",
+    "* [Gaia](https://en.wikipedia.org/wiki/Gaia_(spacecraft)), which is \"a space observatory of the European Space Agency (ESA), launched in 2013 ... designed for astrometry: measuring the positions, distances and motions of stars with unprecedented precision\", and\n",
+    "\n",
+    "* [Pan-STARRS](https://en.wikipedia.org/wiki/Pan-STARRS), The Panoramic Survey Telescope and Rapid Response System, which is a survey designed to monitor the sky for transient objects, producing a catalog with accurate astronometry and photometry of detected sources.\n",
+    "\n",
+    "Both of these datasets are very large, which can make them challenging to work with.  It might not be possible, or practical, to download the entire dataset.\n",
+    "One of the goals of this workshop is to provide tools for working with large datasets."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Lesson 1\n",
+    "\n",
+    "The first lesson demonstrates the steps for selecting and downloading data from the Gaia Database:\n",
+    "\n",
+    "1. First we'll make a connection to the Gaia server,\n",
+    "\n",
+    "2. We will explore information about the database and the tables it contains,\n",
+    "\n",
+    "3. We will write a query and send it to the server, and finally\n",
+    "\n",
+    "4. We will download the response from the server.\n",
+    "\n",
+    "After completing this lesson, you should be able to\n",
+    "\n",
+    "* Compose a basic query in ADQL.\n",
+    "\n",
+    "* Use queries to explore a database and its tables.\n",
+    "\n",
+    "* Use queries to download data.\n",
+    "\n",
+    "* Develop, test, and debug a query incrementally."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Query Language\n",
+    "\n",
+    "In order to select data from a database, you have to compose a query, which is like a program written in a \"query language\".\n",
+    "The query language we'll use is ADQL, which stands for \"Astronomical Data Query Language\".\n",
+    "\n",
+    "ADQL is a dialect of [SQL](https://en.wikipedia.org/wiki/SQL) (Structured Query Language), which is by far the most commonly used query language.  Almost everything you will learn about ADQL also works in SQL.\n",
+    "\n",
+    "[The reference manual for ADQL is here](http://www.ivoa.net/documents/ADQL/20180112/PR-ADQL-2.1-20180112.html).\n",
+    "But you might find it easier to learn from [this ADQL Cookbook](https://www.gaia.ac.uk/data/gaia-data-release-1/adql-cookbook)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Installing libraries\n",
+    "\n",
+    "The library we'll use to get Gaia data is [Astroquery](https://astroquery.readthedocs.io/en/latest/).\n",
+    "\n",
+    "If you are running this notebook on Colab, you can run the following cell to install Astroquery and the other libraries we'll use.\n",
+    "\n",
+    "If you are running this notebook on your own computer, you might have to install these libraries yourself.  \n",
+    "\n",
+    "If you are using this notebook as part of a Carpentries workshop, you should have received setup instructions.\n",
+    "\n",
+    "TODO: Add a link to the instructions.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# If we're running on Colab, install libraries\n",
+    "\n",
+    "import sys\n",
+    "IN_COLAB = 'google.colab' in sys.modules\n",
+    "\n",
+    "if IN_COLAB:\n",
+    "    !pip install astroquery astro-gala pyia"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Connecting to Gaia\n",
+    "\n",
+    "Astroquery provides `Gaia`, which is an [object that represents a connection to the Gaia database](https://astroquery.readthedocs.io/en/latest/gaia/gaia.html).\n",
+    "\n",
+    "We can connect to the Gaia database like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Created TAP+ (v1.2.1) - Connection:\n",
+      "\tHost: gea.esac.esa.int\n",
+      "\tUse HTTPS: True\n",
+      "\tPort: 443\n",
+      "\tSSL Port: 443\n",
+      "Created TAP+ (v1.2.1) - Connection:\n",
+      "\tHost: geadata.esac.esa.int\n",
+      "\tUse HTTPS: True\n",
+      "\tPort: 443\n",
+      "\tSSL Port: 443\n"
+     ]
+    }
+   ],
+   "source": [
+    "from astroquery.gaia import Gaia"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Optional detail \n",
+    "\n",
+    "> Running this import statement has the effect of creating a [TAP+](http://www.ivoa.net/documents/TAP/) connection; TAP stands for \"Table Access Protocol\".  It is a network protocol for sending queries to the database and getting back the results.  We're not sure why it seems to create two connections."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Databases and Tables\n",
+    "\n",
+    "What is a database, anyway?  Most generally, it can be any collection of data, but when we are talking about ADQL or SQL:\n",
+    "\n",
+    "* A database is a collection of one or more named tables.\n",
+    "\n",
+    "* Each table is a 2-D array with one or more named columns of data.\n",
+    "\n",
+    "We can use `Gaia.load_tables` to get the names of the tables in the Gaia database.  With the option `only_names=True`, it loads information about the tables, called the \"metadata\", not the data itself."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO: Retrieving tables... [astroquery.utils.tap.core]\n",
+      "INFO: Parsing tables... [astroquery.utils.tap.core]\n",
+      "INFO: Done. [astroquery.utils.tap.core]\n"
+     ]
+    }
+   ],
+   "source": [
+    "tables = Gaia.load_tables(only_names=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "external.external.apassdr9\n",
+      "external.external.gaiadr2_geometric_distance\n",
+      "external.external.galex_ais\n",
+      "external.external.ravedr5_com\n",
+      "external.external.ravedr5_dr5\n",
+      "external.external.ravedr5_gra\n",
+      "external.external.ravedr5_on\n",
+      "external.external.sdssdr13_photoprimary\n",
+      "external.external.skymapperdr1_master\n",
+      "external.external.tmass_xsc\n",
+      "public.public.hipparcos\n",
+      "public.public.hipparcos_newreduction\n",
+      "public.public.hubble_sc\n",
+      "public.public.igsl_source\n",
+      "public.public.igsl_source_catalog_ids\n",
+      "public.public.tycho2\n",
+      "public.public.dual\n",
+      "tap_config.tap_config.coord_sys\n",
+      "tap_config.tap_config.properties\n",
+      "tap_schema.tap_schema.columns\n",
+      "tap_schema.tap_schema.key_columns\n",
+      "tap_schema.tap_schema.keys\n",
+      "tap_schema.tap_schema.schemas\n",
+      "tap_schema.tap_schema.tables\n",
+      "gaiadr1.gaiadr1.aux_qso_icrf2_match\n",
+      "gaiadr1.gaiadr1.ext_phot_zero_point\n",
+      "gaiadr1.gaiadr1.allwise_best_neighbour\n",
+      "gaiadr1.gaiadr1.allwise_neighbourhood\n",
+      "gaiadr1.gaiadr1.gsc23_best_neighbour\n",
+      "gaiadr1.gaiadr1.gsc23_neighbourhood\n",
+      "gaiadr1.gaiadr1.ppmxl_best_neighbour\n",
+      "gaiadr1.gaiadr1.ppmxl_neighbourhood\n",
+      "gaiadr1.gaiadr1.sdss_dr9_best_neighbour\n",
+      "gaiadr1.gaiadr1.sdss_dr9_neighbourhood\n",
+      "gaiadr1.gaiadr1.tmass_best_neighbour\n",
+      "gaiadr1.gaiadr1.tmass_neighbourhood\n",
+      "gaiadr1.gaiadr1.ucac4_best_neighbour\n",
+      "gaiadr1.gaiadr1.ucac4_neighbourhood\n",
+      "gaiadr1.gaiadr1.urat1_best_neighbour\n",
+      "gaiadr1.gaiadr1.urat1_neighbourhood\n",
+      "gaiadr1.gaiadr1.cepheid\n",
+      "gaiadr1.gaiadr1.phot_variable_time_series_gfov\n",
+      "gaiadr1.gaiadr1.phot_variable_time_series_gfov_statistical_parameters\n",
+      "gaiadr1.gaiadr1.rrlyrae\n",
+      "gaiadr1.gaiadr1.variable_summary\n",
+      "gaiadr1.gaiadr1.allwise_original_valid\n",
+      "gaiadr1.gaiadr1.gsc23_original_valid\n",
+      "gaiadr1.gaiadr1.ppmxl_original_valid\n",
+      "gaiadr1.gaiadr1.sdssdr9_original_valid\n",
+      "gaiadr1.gaiadr1.tmass_original_valid\n",
+      "gaiadr1.gaiadr1.ucac4_original_valid\n",
+      "gaiadr1.gaiadr1.urat1_original_valid\n",
+      "gaiadr1.gaiadr1.gaia_source\n",
+      "gaiadr1.gaiadr1.tgas_source\n",
+      "gaiadr2.gaiadr2.aux_allwise_agn_gdr2_cross_id\n",
+      "gaiadr2.gaiadr2.aux_iers_gdr2_cross_id\n",
+      "gaiadr2.gaiadr2.aux_sso_orbit_residuals\n",
+      "gaiadr2.gaiadr2.aux_sso_orbits\n",
+      "gaiadr2.gaiadr2.dr1_neighbourhood\n",
+      "gaiadr2.gaiadr2.allwise_best_neighbour\n",
+      "gaiadr2.gaiadr2.allwise_neighbourhood\n",
+      "gaiadr2.gaiadr2.apassdr9_best_neighbour\n",
+      "gaiadr2.gaiadr2.apassdr9_neighbourhood\n",
+      "gaiadr2.gaiadr2.gsc23_best_neighbour\n",
+      "gaiadr2.gaiadr2.gsc23_neighbourhood\n",
+      "gaiadr2.gaiadr2.hipparcos2_best_neighbour\n",
+      "gaiadr2.gaiadr2.hipparcos2_neighbourhood\n",
+      "gaiadr2.gaiadr2.panstarrs1_best_neighbour\n",
+      "gaiadr2.gaiadr2.panstarrs1_neighbourhood\n",
+      "gaiadr2.gaiadr2.ppmxl_best_neighbour\n",
+      "gaiadr2.gaiadr2.ppmxl_neighbourhood\n",
+      "gaiadr2.gaiadr2.ravedr5_best_neighbour\n",
+      "gaiadr2.gaiadr2.ravedr5_neighbourhood\n",
+      "gaiadr2.gaiadr2.sdssdr9_best_neighbour\n",
+      "gaiadr2.gaiadr2.sdssdr9_neighbourhood\n",
+      "gaiadr2.gaiadr2.tmass_best_neighbour\n",
+      "gaiadr2.gaiadr2.tmass_neighbourhood\n",
+      "gaiadr2.gaiadr2.tycho2_best_neighbour\n",
+      "gaiadr2.gaiadr2.tycho2_neighbourhood\n",
+      "gaiadr2.gaiadr2.urat1_best_neighbour\n",
+      "gaiadr2.gaiadr2.urat1_neighbourhood\n",
+      "gaiadr2.gaiadr2.sso_observation\n",
+      "gaiadr2.gaiadr2.sso_source\n",
+      "gaiadr2.gaiadr2.vari_cepheid\n",
+      "gaiadr2.gaiadr2.vari_classifier_class_definition\n",
+      "gaiadr2.gaiadr2.vari_classifier_definition\n",
+      "gaiadr2.gaiadr2.vari_classifier_result\n",
+      "gaiadr2.gaiadr2.vari_long_period_variable\n",
+      "gaiadr2.gaiadr2.vari_rotation_modulation\n",
+      "gaiadr2.gaiadr2.vari_rrlyrae\n",
+      "gaiadr2.gaiadr2.vari_short_timescale\n",
+      "gaiadr2.gaiadr2.vari_time_series_statistics\n",
+      "gaiadr2.gaiadr2.panstarrs1_original_valid\n",
+      "gaiadr2.gaiadr2.gaia_source\n",
+      "gaiadr2.gaiadr2.ruwe\n"
+     ]
+    }
+   ],
+   "source": [
+    "for table in (tables):\n",
+    "    print(table.get_qualified_name())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "So that's a lot of tables.  The ones we'll use are:\n",
+    "\n",
+    "* `gaiadr2.gaia_source`, which contains Gaia data from [data release 2](https://www.cosmos.esa.int/web/gaia/data-release-2),\n",
+    "\n",
+    "* `gaiadr2.panstarrs1_original_valid`, which contains the photometry data we'll use from PanSTARRS, and\n",
+    "\n",
+    "* `gaiadr2.panstarrs1_best_neighbour`, which we'll use to cross-match each star observed by Gaia with the same star observed by PanSTARRS.\n",
+    "\n",
+    "We can use `load_table` (not `load_tables`) to get the metadata for a single table.  The name of this function is misleading, because it only downloads metadata. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Retrieving table 'gaiadr2.gaia_source'\n",
+      "Parsing table 'gaiadr2.gaia_source'...\n",
+      "Done.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<astroquery.utils.tap.model.taptable.TapTableMeta at 0x7f922376e0a0>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "meta = Gaia.load_table('gaiadr2.gaia_source')\n",
+    "meta"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Jupyter shows that the result is an object of type `TapTableMeta`, but it does not display the contents.\n",
+    "\n",
+    "To see the metadata, we have to print the object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "TAP Table name: gaiadr2.gaiadr2.gaia_source\n",
+      "Description: This table has an entry for every Gaia observed source as listed in the\n",
+      "Main Database accumulating catalogue version from which the catalogue\n",
+      "release has been generated. It contains the basic source parameters,\n",
+      "that is only final data (no epoch data) and no spectra (neither final\n",
+      "nor epoch).\n",
+      "Num. columns: 96\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(meta)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notice one gotcha: in the list of table names, this table appears as `gaiadr2.gaiadr2.gaia_source`, but when we load the metadata, we refer to it as `gaiadr2.gaia_source`.\n",
+    "\n",
+    "**Exercise:** Go back and try\n",
+    "\n",
+    "```\n",
+    "meta = Gaia.load_table('gaiadr2.gaiadr2.gaia_source')\n",
+    "```\n",
+    "\n",
+    "What happens?  Is the error message helpful?  If you had not made this error deliberately, would you have been able to figure it out?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Columns\n",
+    "\n",
+    "The following loop prints the names of the columns in the table."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "solution_id\n",
+      "designation\n",
+      "source_id\n",
+      "random_index\n",
+      "ref_epoch\n",
+      "ra\n",
+      "ra_error\n",
+      "dec\n",
+      "dec_error\n",
+      "parallax\n",
+      "parallax_error\n",
+      "parallax_over_error\n",
+      "pmra\n",
+      "pmra_error\n",
+      "pmdec\n",
+      "pmdec_error\n",
+      "ra_dec_corr\n",
+      "ra_parallax_corr\n",
+      "ra_pmra_corr\n",
+      "ra_pmdec_corr\n",
+      "dec_parallax_corr\n",
+      "dec_pmra_corr\n",
+      "dec_pmdec_corr\n",
+      "parallax_pmra_corr\n",
+      "parallax_pmdec_corr\n",
+      "pmra_pmdec_corr\n",
+      "astrometric_n_obs_al\n",
+      "astrometric_n_obs_ac\n",
+      "astrometric_n_good_obs_al\n",
+      "astrometric_n_bad_obs_al\n",
+      "astrometric_gof_al\n",
+      "astrometric_chi2_al\n",
+      "astrometric_excess_noise\n",
+      "astrometric_excess_noise_sig\n",
+      "astrometric_params_solved\n",
+      "astrometric_primary_flag\n",
+      "astrometric_weight_al\n",
+      "astrometric_pseudo_colour\n",
+      "astrometric_pseudo_colour_error\n",
+      "mean_varpi_factor_al\n",
+      "astrometric_matched_observations\n",
+      "visibility_periods_used\n",
+      "astrometric_sigma5d_max\n",
+      "frame_rotator_object_type\n",
+      "matched_observations\n",
+      "duplicated_source\n",
+      "phot_g_n_obs\n",
+      "phot_g_mean_flux\n",
+      "phot_g_mean_flux_error\n",
+      "phot_g_mean_flux_over_error\n",
+      "phot_g_mean_mag\n",
+      "phot_bp_n_obs\n",
+      "phot_bp_mean_flux\n",
+      "phot_bp_mean_flux_error\n",
+      "phot_bp_mean_flux_over_error\n",
+      "phot_bp_mean_mag\n",
+      "phot_rp_n_obs\n",
+      "phot_rp_mean_flux\n",
+      "phot_rp_mean_flux_error\n",
+      "phot_rp_mean_flux_over_error\n",
+      "phot_rp_mean_mag\n",
+      "phot_bp_rp_excess_factor\n",
+      "phot_proc_mode\n",
+      "bp_rp\n",
+      "bp_g\n",
+      "g_rp\n",
+      "radial_velocity\n",
+      "radial_velocity_error\n",
+      "rv_nb_transits\n",
+      "rv_template_teff\n",
+      "rv_template_logg\n",
+      "rv_template_fe_h\n",
+      "phot_variable_flag\n",
+      "l\n",
+      "b\n",
+      "ecl_lon\n",
+      "ecl_lat\n",
+      "priam_flags\n",
+      "teff_val\n",
+      "teff_percentile_lower\n",
+      "teff_percentile_upper\n",
+      "a_g_val\n",
+      "a_g_percentile_lower\n",
+      "a_g_percentile_upper\n",
+      "e_bp_min_rp_val\n",
+      "e_bp_min_rp_percentile_lower\n",
+      "e_bp_min_rp_percentile_upper\n",
+      "flame_flags\n",
+      "radius_val\n",
+      "radius_percentile_lower\n",
+      "radius_percentile_upper\n",
+      "lum_val\n",
+      "lum_percentile_lower\n",
+      "lum_percentile_upper\n",
+      "datalink_url\n",
+      "epoch_photometry_url\n"
+     ]
+    }
+   ],
+   "source": [
+    "for column in meta.columns:\n",
+    "    print(column.name)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can probably guess what many of these columns are by looking at the names, but you should resist the temptation to guess.\n",
+    "To find out what the columns mean, [read the documentation](https://gea.esac.esa.int/archive/documentation/GDR2/Gaia_archive/chap_datamodel/sec_dm_main_tables/ssec_dm_gaia_source.html).\n",
+    "\n",
+    "If you want to know what can go wrong when you don't read the documentation, [you might like this article](https://www.vox.com/future-perfect/2019/6/4/18650969/married-women-miserable-fake-paul-dolan-happiness)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** One of the other tables we'll use is `gaiadr2.gaiadr2.panstarrs1_original_valid`.  Use `load_table` to get the metadata for this table.  How many columns are there and what are their names?\n",
+    "\n",
+    "Hint: Remember the gotcha we mentioned earlier."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Retrieving table 'gaiadr2.panstarrs1_original_valid'\n",
+      "Parsing table 'gaiadr2.panstarrs1_original_valid'...\n",
+      "Done.\n",
+      "TAP Table name: gaiadr2.gaiadr2.panstarrs1_original_valid\n",
+      "Description: The Panoramic Survey Telescope and Rapid Response System (Pan-STARRS) is\n",
+      "a system for wide-field astronomical imaging developed and operated by\n",
+      "the Institute for Astronomy at the University of Hawaii. Pan-STARRS1\n",
+      "(PS1) is the first part of Pan-STARRS to be completed and is the basis\n",
+      "for Data Release 1 (DR1). The PS1 survey used a 1.8 meter telescope and\n",
+      "its 1.4 Gigapixel camera to image the sky in five broadband filters (g,\n",
+      "r, i, z, y).\n",
+      "\n",
+      "The current table contains a filtered subsample of the 10 723 304 629\n",
+      "entries listed in the original ObjectThin table.\n",
+      "We used only ObjectThin and MeanObject tables to extract\n",
+      "panstarrs1OriginalValid table, this means that objects detected only in\n",
+      "stack images are not included here. The main reason for us to avoid the\n",
+      "use of objects detected in stack images is that their astrometry is not\n",
+      "as good as the mean objects astrometry: “The stack positions (raStack,\n",
+      "decStack) have considerably larger systematic astrometric errors than\n",
+      "the mean epoch positions (raMean, decMean).� The astrometry for the\n",
+      "MeanObject positions uses Gaia DR1 as a reference catalog, while the\n",
+      "stack positions use 2MASS as a reference catalog.\n",
+      "\n",
+      "In details, we filtered out all objects where:\n",
+      "\n",
+      "-   nDetections = 1\n",
+      "\n",
+      "-   no good quality data in Pan-STARRS, objInfoFlag 33554432 not set\n",
+      "\n",
+      "-   mean astrometry could not be measured, objInfoFlag 524288 set\n",
+      "\n",
+      "-   stack position used for mean astrometry, objInfoFlag 1048576 set\n",
+      "\n",
+      "-   error on all magnitudes equal to 0 or to -999;\n",
+      "\n",
+      "-   all magnitudes set to -999;\n",
+      "\n",
+      "-   error on RA or DEC greater than 1 arcsec.\n",
+      "\n",
+      "The number of objects in panstarrs1OriginalValid is 2 264 263 282.\n",
+      "\n",
+      "The panstarrs1OriginalValid table contains only a subset of the columns\n",
+      "available in the combined ObjectThin and MeanObject tables. A\n",
+      "description of the original ObjectThin and MeanObjects tables can be\n",
+      "found at:\n",
+      "https://outerspace.stsci.edu/display/PANSTARRS/PS1+Database+object+and+detection+tables\n",
+      "\n",
+      "Download:\n",
+      "http://mastweb.stsci.edu/ps1casjobs/home.aspx\n",
+      "Documentation:\n",
+      "https://outerspace.stsci.edu/display/PANSTARRS\n",
+      "http://pswww.ifa.hawaii.edu/pswww/\n",
+      "References:\n",
+      "The Pan-STARRS1 Surveys, Chambers, K.C., et al. 2016, arXiv:1612.05560\n",
+      "Pan-STARRS Data Processing System, Magnier, E. A., et al. 2016,\n",
+      "arXiv:1612.05240\n",
+      "Pan-STARRS Pixel Processing: Detrending, Warping, Stacking, Waters, C.\n",
+      "Z., et al. 2016, arXiv:1612.05245\n",
+      "Pan-STARRS Pixel Analysis: Source Detection and Characterization,\n",
+      "Magnier, E. A., et al. 2016, arXiv:1612.05244\n",
+      "Pan-STARRS Photometric and Astrometric Calibration, Magnier, E. A., et\n",
+      "al. 2016, arXiv:1612.05242\n",
+      "The Pan-STARRS1 Database and Data Products, Flewelling, H. A., et al.\n",
+      "2016, arXiv:1612.05243\n",
+      "\n",
+      "Catalogue curator:\n",
+      "SSDC - ASI Space Science Data Center\n",
+      "https://www.ssdc.asi.it/\n",
+      "Num. columns: 26\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "meta2 = Gaia.load_table('gaiadr2.panstarrs1_original_valid')\n",
+    "print(meta2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "obj_name\n",
+      "obj_id\n",
+      "ra\n",
+      "dec\n",
+      "ra_error\n",
+      "dec_error\n",
+      "epoch_mean\n",
+      "g_mean_psf_mag\n",
+      "g_mean_psf_mag_error\n",
+      "g_flags\n",
+      "r_mean_psf_mag\n",
+      "r_mean_psf_mag_error\n",
+      "r_flags\n",
+      "i_mean_psf_mag\n",
+      "i_mean_psf_mag_error\n",
+      "i_flags\n",
+      "z_mean_psf_mag\n",
+      "z_mean_psf_mag_error\n",
+      "z_flags\n",
+      "y_mean_psf_mag\n",
+      "y_mean_psf_mag_error\n",
+      "y_flags\n",
+      "n_detections\n",
+      "zone_id\n",
+      "obj_info_flag\n",
+      "quality_flag\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "for column in meta2.columns:\n",
+    "    print(column.name)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Writing queries\n",
+    "\n",
+    "By now you might be wondering how we actually download the data.  With tables this big, you generally don't.  Instead, you use queries to select only the data you want.\n",
+    "\n",
+    "A query is a string written in a query language like SQL; for the Gaia database, the query language is a dialect of SQL called ADQL.\n",
+    "\n",
+    "Here's an example of an ADQL query."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query1 = \"\"\"SELECT \n",
+    "TOP 10\n",
+    "source_id, ref_epoch, ra, dec, parallax \n",
+    "FROM gaiadr2.gaia_source\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Python note:** We use a [triple-quoted string](https://docs.python.org/3/tutorial/introduction.html#strings) here so we can include line breaks in the query, which makes it easier to read.\n",
+    "\n",
+    "The words in uppercase are ADQL keywords:\n",
+    "\n",
+    "* `SELECT` indicates that we are selecting data (as opposed to adding or modifying data).\n",
+    "\n",
+    "* `TOP` indicates that we only want the first 10 rows of the table, which is useful for testing a query before asking for all of the data.\n",
+    "\n",
+    "* `FROM` specifies which table we want data from.\n",
+    "\n",
+    "The third line is a list of column names, indicating which columns we want.  \n",
+    "\n",
+    "In this example, the keywords are capitalized and the column names are lowercase.  This is a common style, but it is not required.  ADQL and SQL are not case-sensitive."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To run this query, we use the `Gaia` object, which represents our connection to the Gaia database, and invoke `launch_job`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<astroquery.utils.tap.model.job.Job at 0x7f9222e9cb20>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "job1 = Gaia.launch_job(query1)\n",
+    "job1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The result is an object that represents the job running on a Gaia server.\n",
+    "\n",
+    "If you print it, it displays metadata for the forthcoming table."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<Table length=10>\n",
+      "   name    dtype  unit                            description                            \n",
+      "--------- ------- ---- ------------------------------------------------------------------\n",
+      "source_id   int64      Unique source identifier (unique within a particular Data Release)\n",
+      "ref_epoch float64   yr                                                    Reference epoch\n",
+      "       ra float64  deg                                                    Right ascension\n",
+      "      dec float64  deg                                                        Declination\n",
+      " parallax float64  mas                                                           Parallax\n",
+      "Jobid: None\n",
+      "Phase: COMPLETED\n",
+      "Owner: None\n",
+      "Output file: sync_20201005090721.xml.gz\n",
+      "Results: None\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(job1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Don't worry about `Results: None`.  That does not actually mean there are no results.\n",
+    "\n",
+    "However, `Phase: COMPLETED` indicates that the job is complete, so we can get the results like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "astropy.table.table.Table"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results1 = job1.get_results()\n",
+    "type(results1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Optional detail:**  Why is `table` repeated three times?  The first is the name of the module, the second is the name of the submodule, and the third is the name of the class.  Most of the time we only care about the last one.  It's like the Linnean name for gorilla, which is *Gorilla Gorilla Gorilla*."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The result is an [Astropy Table](https://docs.astropy.org/en/stable/table/), which is similar to a table in an SQL database except:\n",
+    "\n",
+    "* SQL databases are stored on disk drives, so they are persistent; that is, they \"survive\" even if you turn off the computer.  An Astropy `Table` is stored in memory; it disappears when you turn off the computer (or shut down this Jupyter notebook).\n",
+    "\n",
+    "* SQL databases are designed to process queries.  An Astropy `Table` can perform some query-like operations, like selecting columns and rows.  But these operations use Python syntax, not SQL.\n",
+    "\n",
+    "Jupyter knows how to display the contents of a `Table`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<i>Table length=10</i>\n",
+       "<table id=\"table140265627585264\" class=\"table-striped table-bordered table-condensed\">\n",
+       "<thead><tr><th>source_id</th><th>ref_epoch</th><th>ra</th><th>dec</th><th>parallax</th></tr></thead>\n",
+       "<thead><tr><th></th><th>yr</th><th>deg</th><th>deg</th><th>mas</th></tr></thead>\n",
+       "<thead><tr><th>int64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th></tr></thead>\n",
+       "<tr><td>4530738361793769600</td><td>2015.5</td><td>281.56725362448725</td><td>20.40682117430378</td><td>0.9785380604519425</td></tr>\n",
+       "<tr><td>4530752651135081216</td><td>2015.5</td><td>281.0861565355257</td><td>20.523350496351846</td><td>0.2674800612552977</td></tr>\n",
+       "<tr><td>4530743343951405568</td><td>2015.5</td><td>281.37114418299177</td><td>20.474147574053124</td><td>-0.43911323550176806</td></tr>\n",
+       "<tr><td>4530755060627162368</td><td>2015.5</td><td>281.2676236268299</td><td>20.558523922346158</td><td>1.1422630184554958</td></tr>\n",
+       "<tr><td>4530746844341315968</td><td>2015.5</td><td>281.1370431749541</td><td>20.377852388898184</td><td>1.0092247424630945</td></tr>\n",
+       "<tr><td>4530768456615026432</td><td>2015.5</td><td>281.8720921436347</td><td>20.31829694530366</td><td>-0.06900136127674149</td></tr>\n",
+       "<tr><td>4530763513119137280</td><td>2015.5</td><td>281.9211808864116</td><td>20.20956829578524</td><td>0.1266016679823622</td></tr>\n",
+       "<tr><td>4530736364618539264</td><td>2015.5</td><td>281.4913475613274</td><td>20.346579041327693</td><td>0.3894019486060072</td></tr>\n",
+       "<tr><td>4530735952305177728</td><td>2015.5</td><td>281.4085549165704</td><td>20.311030903719928</td><td>0.2041189982608354</td></tr>\n",
+       "<tr><td>4530751281056022656</td><td>2015.5</td><td>281.0585328377638</td><td>20.460309556214753</td><td>0.10294642821734962</td></tr>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<Table length=10>\n",
+       "     source_id      ref_epoch ...        dec               parallax      \n",
+       "                        yr    ...        deg                 mas         \n",
+       "       int64         float64  ...      float64             float64       \n",
+       "------------------- --------- ... ------------------ --------------------\n",
+       "4530738361793769600    2015.5 ...  20.40682117430378   0.9785380604519425\n",
+       "4530752651135081216    2015.5 ... 20.523350496351846   0.2674800612552977\n",
+       "4530743343951405568    2015.5 ... 20.474147574053124 -0.43911323550176806\n",
+       "4530755060627162368    2015.5 ... 20.558523922346158   1.1422630184554958\n",
+       "4530746844341315968    2015.5 ... 20.377852388898184   1.0092247424630945\n",
+       "4530768456615026432    2015.5 ...  20.31829694530366 -0.06900136127674149\n",
+       "4530763513119137280    2015.5 ...  20.20956829578524   0.1266016679823622\n",
+       "4530736364618539264    2015.5 ... 20.346579041327693   0.3894019486060072\n",
+       "4530735952305177728    2015.5 ... 20.311030903719928   0.2041189982608354\n",
+       "4530751281056022656    2015.5 ... 20.460309556214753  0.10294642821734962"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Each column has a name, units, and a data type.\n",
+    "\n",
+    "For example, the units of `ra` and `dec` are degrees, and their data type is `float64`, which is a 64-bit floating-point number, used to store measurements with a fraction part.\n",
+    "\n",
+    "This information comes from the Gaia database, and has been stored in the Astropy `Table` by Astroquery."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Read [the documentation of this table](https://gea.esac.esa.int/archive/documentation/GDR2/Gaia_archive/chap_datamodel/sec_dm_main_tables/ssec_dm_gaia_source.html) and choose a column that looks interesting to you.  Add the column name to the query and run it again.  What are the units of the column you selected?  What is its data type?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Asynchronous queries\n",
+    "\n",
+    "`launch_job` asks the server to run the job \"synchronously\", which normally means it runs immediately.  But synchronous jobs are limited to 2000 rows.  For queries that return more rows, you should run \"asynchronously\", which mean they might take longer to get started.\n",
+    "\n",
+    "If you are not sure how many rows a query will return, you can use the SQL command `COUNT` to find out how many rows are in the result without actually returning them.  We'll see an example of this later.\n",
+    "\n",
+    "The results of an asynchronous query are stored in a file on the server, so you can start a query and come back later to get the results.\n",
+    "\n",
+    "For anonymous users, files are kept for three days.\n",
+    "\n",
+    "As an example, let's try a query that's similar to `query1`, with two changes:\n",
+    "\n",
+    "* It selects the first 3000 rows, so it is bigger than we should run synchronously.\n",
+    "\n",
+    "* It uses a new keyword, `WHERE`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query2 = \"\"\"SELECT TOP 3000\n",
+    "source_id, ref_epoch, ra, dec, parallax\n",
+    "FROM gaiadr2.gaia_source\n",
+    "WHERE parallax < 1\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A `WHERE` clause indicates which rows we want; in this case, the query selects only rows \"where\" `parallax` is less than 1.  This has the effect of selecting stars with relatively low parallax, which are farther away.  We'll use this clause to exclude nearby stars that are unlikely to be part of GD-1.\n",
+    "\n",
+    "`WHERE` is one of the most common clauses in ADQL/SQL, and one of the most useful, because it allows us to select only the rows we need from the database.\n",
+    "\n",
+    "We use `launch_job_async` to submit an asynchronous query."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO: Query finished. [astroquery.utils.tap.core]\n",
+      "<Table length=3000>\n",
+      "   name    dtype  unit                            description                            \n",
+      "--------- ------- ---- ------------------------------------------------------------------\n",
+      "source_id   int64      Unique source identifier (unique within a particular Data Release)\n",
+      "ref_epoch float64   yr                                                    Reference epoch\n",
+      "       ra float64  deg                                                    Right ascension\n",
+      "      dec float64  deg                                                        Declination\n",
+      " parallax float64  mas                                                           Parallax\n",
+      "Jobid: 1601903242219O\n",
+      "Phase: COMPLETED\n",
+      "Owner: None\n",
+      "Output file: async_20201005090722.vot\n",
+      "Results: None\n"
+     ]
+    }
+   ],
+   "source": [
+    "job2 = Gaia.launch_job_async(query2)\n",
+    "print(job2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And here are the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<i>Table length=3000</i>\n",
+       "<table id=\"table140265625141056\" class=\"table-striped table-bordered table-condensed\">\n",
+       "<thead><tr><th>source_id</th><th>ref_epoch</th><th>ra</th><th>dec</th><th>parallax</th></tr></thead>\n",
+       "<thead><tr><th></th><th>yr</th><th>deg</th><th>deg</th><th>mas</th></tr></thead>\n",
+       "<thead><tr><th>int64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th></tr></thead>\n",
+       "<tr><td>4530738361793769600</td><td>2015.5</td><td>281.56725362448725</td><td>20.40682117430378</td><td>0.9785380604519425</td></tr>\n",
+       "<tr><td>4530752651135081216</td><td>2015.5</td><td>281.0861565355257</td><td>20.523350496351846</td><td>0.2674800612552977</td></tr>\n",
+       "<tr><td>4530743343951405568</td><td>2015.5</td><td>281.37114418299177</td><td>20.474147574053124</td><td>-0.43911323550176806</td></tr>\n",
+       "<tr><td>4530768456615026432</td><td>2015.5</td><td>281.8720921436347</td><td>20.31829694530366</td><td>-0.06900136127674149</td></tr>\n",
+       "<tr><td>4530763513119137280</td><td>2015.5</td><td>281.9211808864116</td><td>20.20956829578524</td><td>0.1266016679823622</td></tr>\n",
+       "<tr><td>4530736364618539264</td><td>2015.5</td><td>281.4913475613274</td><td>20.346579041327693</td><td>0.3894019486060072</td></tr>\n",
+       "<tr><td>4530735952305177728</td><td>2015.5</td><td>281.4085549165704</td><td>20.311030903719928</td><td>0.2041189982608354</td></tr>\n",
+       "<tr><td>4530751281056022656</td><td>2015.5</td><td>281.0585328377638</td><td>20.460309556214753</td><td>0.10294642821734962</td></tr>\n",
+       "<tr><td>4530740938774409344</td><td>2015.5</td><td>281.3762569536416</td><td>20.436140058941206</td><td>0.9242670062090182</td></tr>\n",
+       "<tr><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr>\n",
+       "<tr><td>4467710915011802624</td><td>2015.5</td><td>269.9680969307347</td><td>1.1429085038160882</td><td>0.42361471245557913</td></tr>\n",
+       "<tr><td>4467706551328679552</td><td>2015.5</td><td>270.033164589881</td><td>1.0565747323689927</td><td>0.922888231734588</td></tr>\n",
+       "<tr><td>4467712255037300096</td><td>2015.5</td><td>270.7724717923047</td><td>0.6581664892880896</td><td>-2.669179465293931</td></tr>\n",
+       "<tr><td>4467735001181761792</td><td>2015.5</td><td>270.3628606248308</td><td>0.8947079323599124</td><td>0.6117399163086398</td></tr>\n",
+       "<tr><td>4467737101421916672</td><td>2015.5</td><td>270.5110834661444</td><td>0.9806225910160181</td><td>-0.39818224846127004</td></tr>\n",
+       "<tr><td>4467707547757327488</td><td>2015.5</td><td>269.88746280594927</td><td>1.0212759940136962</td><td>0.7741412301054209</td></tr>\n",
+       "<tr><td>4467732772094573056</td><td>2015.5</td><td>270.55997182760126</td><td>0.9037072088489417</td><td>-1.7920417800164183</td></tr>\n",
+       "<tr><td>4467732355491087744</td><td>2015.5</td><td>270.6730790702491</td><td>0.9197224705139885</td><td>-0.3464446494840354</td></tr>\n",
+       "<tr><td>4467717099766944512</td><td>2015.5</td><td>270.57667173120825</td><td>0.726277659009568</td><td>0.05443955111134051</td></tr>\n",
+       "<tr><td>4467719058265781248</td><td>2015.5</td><td>270.7248052971514</td><td>0.8205551921782785</td><td>0.3733943917490343</td></tr>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<Table length=3000>\n",
+       "     source_id      ref_epoch ...        dec               parallax      \n",
+       "                        yr    ...        deg                 mas         \n",
+       "       int64         float64  ...      float64             float64       \n",
+       "------------------- --------- ... ------------------ --------------------\n",
+       "4530738361793769600    2015.5 ...  20.40682117430378   0.9785380604519425\n",
+       "4530752651135081216    2015.5 ... 20.523350496351846   0.2674800612552977\n",
+       "4530743343951405568    2015.5 ... 20.474147574053124 -0.43911323550176806\n",
+       "4530768456615026432    2015.5 ...  20.31829694530366 -0.06900136127674149\n",
+       "4530763513119137280    2015.5 ...  20.20956829578524   0.1266016679823622\n",
+       "4530736364618539264    2015.5 ... 20.346579041327693   0.3894019486060072\n",
+       "4530735952305177728    2015.5 ... 20.311030903719928   0.2041189982608354\n",
+       "4530751281056022656    2015.5 ... 20.460309556214753  0.10294642821734962\n",
+       "4530740938774409344    2015.5 ... 20.436140058941206   0.9242670062090182\n",
+       "                ...       ... ...                ...                  ...\n",
+       "4467710915011802624    2015.5 ... 1.1429085038160882  0.42361471245557913\n",
+       "4467706551328679552    2015.5 ... 1.0565747323689927    0.922888231734588\n",
+       "4467712255037300096    2015.5 ... 0.6581664892880896   -2.669179465293931\n",
+       "4467735001181761792    2015.5 ... 0.8947079323599124   0.6117399163086398\n",
+       "4467737101421916672    2015.5 ... 0.9806225910160181 -0.39818224846127004\n",
+       "4467707547757327488    2015.5 ... 1.0212759940136962   0.7741412301054209\n",
+       "4467732772094573056    2015.5 ... 0.9037072088489417  -1.7920417800164183\n",
+       "4467732355491087744    2015.5 ... 0.9197224705139885  -0.3464446494840354\n",
+       "4467717099766944512    2015.5 ...  0.726277659009568  0.05443955111134051\n",
+       "4467719058265781248    2015.5 ... 0.8205551921782785   0.3733943917490343"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results2 = job2.get_results()\n",
+    "results2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You might notice that some values of `parallax` are negative.  As [this FAQ explains](https://www.cosmos.esa.int/web/gaia/archive-tips#negative%20parallax), \"Negative parallaxes are caused by errors in the observations.\"  Negative parallaxes have \"no physical meaning,\" but they can be a \"useful diagnostic on the quality of the astrometric solution.\"\n",
+    "\n",
+    "Later we will see an example where we use `parallax` and `parallax_error` to identify stars where the distance estimate is likely to be inaccurate."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** The clauses in a query have to be in the right order.  Go back and change the order of the clauses in `query2` and run it again.  \n",
+    "\n",
+    "The query should fail, but notice that you don't get much useful debugging information.  \n",
+    "\n",
+    "For this reason, developing and debugging ADQL queries can be really hard.  A few suggestions that might help:\n",
+    "\n",
+    "* Whenever possible, start with a working query, either an example you find online or a query you have used in the past.\n",
+    "\n",
+    "* Make small changes and test each change before you continue.\n",
+    "\n",
+    "* While you are debugging, use `TOP` to limit the number of rows in the result.  That will make each attempt run faster, which reduces your testing time.  \n",
+    "\n",
+    "* Launching test queries synchronously might make them start faster, too."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Operators\n",
+    "\n",
+    "In a `WHERE` clause, you can use any of the [SQL comparison operators](https://www.w3schools.com/sql/sql_operators.asp):\n",
+    "\n",
+    "* `>`: greater than\n",
+    "* `<`: less than\n",
+    "* `>=`: greater than or equal\n",
+    "* `<=`: less than or equal\n",
+    "* `=`: equal\n",
+    "* `!=` or `<>`: not equal\n",
+    "\n",
+    "Most of these are the same as Python, but some are not.  In particular, notice that the equality operator is `=`, not `==`.\n",
+    "Be careful to keep your Python out of your ADQL!\n",
+    "\n",
+    "You can combine comparisons using the logical operators:\n",
+    "\n",
+    "* AND: true if both comparisons are true\n",
+    "* OR: true if either or both comparisons are true\n",
+    "\n",
+    "Finally, you can use `NOT` to invert the result of a comparison. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** [Read about SQL operators here](https://www.w3schools.com/sql/sql_operators.asp) and then modify the previous query to select rows where `bp_rp` is between `-0.75` and `2`.\n",
+    "\n",
+    "You can [read about this variable here](https://gea.esac.esa.int/archive/documentation/GDR2/Gaia_archive/chap_datamodel/sec_dm_main_tables/ssec_dm_gaia_source.html)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "# This is what most people will probably do\n",
+    "\n",
+    "query = \"\"\"SELECT TOP 10\n",
+    "source_id, ref_epoch, ra, dec, parallax\n",
+    "FROM gaiadr2.gaia_source\n",
+    "WHERE parallax < 1 \n",
+    "  AND bp_rp > -0.75 AND bp_rp < 2\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "# But if someone notices the BETWEEN operator, \n",
+    "# they might do this\n",
+    "\n",
+    "query = \"\"\"SELECT TOP 10\n",
+    "source_id, ref_epoch, ra, dec, parallax\n",
+    "FROM gaiadr2.gaia_source\n",
+    "WHERE parallax < 1 \n",
+    "  AND bp_rp BETWEEN -0.75 AND 2\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This [Hertzsprung-Russell diagram](https://sci.esa.int/web/gaia/-/60198-gaia-hertzsprung-russell-diagram) shows the BP-RP color and luminosity of stars in the Gaia catalog.\n",
+    "\n",
+    "Selecting stars with `bp-rp` less than 2 excludes many [class M dwarf stars](https://xkcd.com/2360/), which are low temperature, low luminosity.  A star like that at GD-1's distance would be hard to detect, so if it is detected, it it more likely to be in the foreground."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Cleaning up\n",
+    "\n",
+    "Asynchronous jobs have a `jobid`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(None, '1601903242219O')"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "job1.jobid, job2.jobid"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Which you can use to remove the job from the server."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Removed jobs: '['1601903242219O']'.\n"
+     ]
+    }
+   ],
+   "source": [
+    "Gaia.remove_jobs([job2.jobid])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you don't remove it job from the server, it will be removed eventually, so don't feel too bad if you don't clean up after yourself."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Formatting queries\n",
+    "\n",
+    "So far the queries have been string \"literals\", meaning that the entire string is part of the program.\n",
+    "But writing queries yourself can be slow, repetitive, and error-prone.\n",
+    "\n",
+    "It is often a good idea to write Python code that assembles a query for you.  One useful tool for that is the [string `format` method](https://www.w3schools.com/python/ref_string_format.asp).\n",
+    "\n",
+    "As an example, we'll divide the previous query into two parts; a list of column names and a \"base\" for the query that contains everything except the column names.\n",
+    "\n",
+    "Here's the list of columns we'll select.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "columns = 'source_id, ra, dec, pmra, pmdec, parallax, parallax_error, radial_velocity'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And here's the base; it's a string that contains at least one format specifier in curly brackets (braces)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query3_base = \"\"\"SELECT TOP 10 \n",
+    "{columns}\n",
+    "FROM gaiadr2.gaia_source\n",
+    "WHERE parallax < 1\n",
+    "  AND bp_rp BETWEEN -0.75 AND 2\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This base query contains one format specifier, `{columns}`, which is a placeholder for the list of column names we will provide.\n",
+    "\n",
+    "To assemble the query, we invoke `format` on the base string and provide a keyword argument that assigns a value to `columns`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query3 = query3_base.format(columns=columns)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The result is a string with line breaks.  If you display it, the line breaks appear as `\\n`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'SELECT TOP 10 \\nsource_id, ra, dec, pmra, pmdec, parallax, parallax_error, radial_velocity\\nFROM gaiadr2.gaia_source\\nWHERE parallax < 1\\n  AND bp_rp BETWEEN -0.75 AND 2\\n'"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "query3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "But if you print it, the line breaks appear as... line breaks."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "SELECT TOP 10 \n",
+      "source_id, ra, dec, pmra, pmdec, parallax, parallax_error, radial_velocity\n",
+      "FROM gaiadr2.gaia_source\n",
+      "WHERE parallax < 1\n",
+      "  AND bp_rp BETWEEN -0.75 AND 2\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(query3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notice that the format specifier has been replaced with the value of `columns`.\n",
+    "\n",
+    "Let's run it and see if it works:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<Table length=10>\n",
+      "      name       dtype    unit                              description                             n_bad\n",
+      "--------------- ------- -------- ------------------------------------------------------------------ -----\n",
+      "      source_id   int64          Unique source identifier (unique within a particular Data Release)     0\n",
+      "             ra float64      deg                                                    Right ascension     0\n",
+      "            dec float64      deg                                                        Declination     0\n",
+      "           pmra float64 mas / yr                         Proper motion in right ascension direction     0\n",
+      "          pmdec float64 mas / yr                             Proper motion in declination direction     0\n",
+      "       parallax float64      mas                                                           Parallax     0\n",
+      " parallax_error float64      mas                                         Standard error of parallax     0\n",
+      "radial_velocity float64   km / s                                                    Radial velocity    10\n",
+      "Jobid: None\n",
+      "Phase: COMPLETED\n",
+      "Owner: None\n",
+      "Output file: sync_20201005090726.xml.gz\n",
+      "Results: None\n"
+     ]
+    }
+   ],
+   "source": [
+    "job3 = Gaia.launch_job(query3)\n",
+    "print(job3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<i>Table length=10</i>\n",
+       "<table id=\"table140265627700432\" class=\"table-striped table-bordered table-condensed\">\n",
+       "<thead><tr><th>source_id</th><th>ra</th><th>dec</th><th>pmra</th><th>pmdec</th><th>parallax</th><th>parallax_error</th><th>radial_velocity</th></tr></thead>\n",
+       "<thead><tr><th></th><th>deg</th><th>deg</th><th>mas / yr</th><th>mas / yr</th><th>mas</th><th>mas</th><th>km / s</th></tr></thead>\n",
+       "<thead><tr><th>int64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th></tr></thead>\n",
+       "<tr><td>4467710915011802624</td><td>269.9680969307347</td><td>1.1429085038160882</td><td>2.0233280236600626</td><td>-2.5692427875510266</td><td>0.42361471245557913</td><td>0.470352406647465</td><td>--</td></tr>\n",
+       "<tr><td>4467706551328679552</td><td>270.033164589881</td><td>1.0565747323689927</td><td>-3.414829591355289</td><td>-3.8437215857495737</td><td>0.922888231734588</td><td>0.927008559859825</td><td>--</td></tr>\n",
+       "<tr><td>4467712255037300096</td><td>270.7724717923047</td><td>0.6581664892880896</td><td>-3.5620173752896025</td><td>-6.595792323153987</td><td>-2.669179465293931</td><td>0.9719742773203504</td><td>--</td></tr>\n",
+       "<tr><td>4467735001181761792</td><td>270.3628606248308</td><td>0.8947079323599124</td><td>2.1307079926489205</td><td>0.8826727710910712</td><td>0.6117399163086398</td><td>0.509812721702093</td><td>--</td></tr>\n",
+       "<tr><td>4467737101421916672</td><td>270.5110834661444</td><td>0.9806225910160181</td><td>0.17532366511560785</td><td>-5.113270239706202</td><td>-0.39818224846127004</td><td>0.7549581886719651</td><td>--</td></tr>\n",
+       "<tr><td>4467707547757327488</td><td>269.88746280594927</td><td>1.0212759940136962</td><td>-2.6382230817672987</td><td>-3.707776532049287</td><td>0.7741412301054209</td><td>0.3022057897812064</td><td>--</td></tr>\n",
+       "<tr><td>4467732355491087744</td><td>270.6730790702491</td><td>0.9197224705139885</td><td>-2.2735991502653037</td><td>-11.864952855984358</td><td>-0.3464446494840354</td><td>0.4937921513912002</td><td>--</td></tr>\n",
+       "<tr><td>4467717099766944512</td><td>270.57667173120825</td><td>0.726277659009568</td><td>-3.4598362614808367</td><td>-4.601426893365921</td><td>0.05443955111134051</td><td>0.8867339293525688</td><td>--</td></tr>\n",
+       "<tr><td>4467719058265781248</td><td>270.7248052971514</td><td>0.8205551921782785</td><td>-3.255079498426542</td><td>-9.249285069111085</td><td>0.3733943917490343</td><td>0.390952370410666</td><td>--</td></tr>\n",
+       "<tr><td>4467722326741572352</td><td>270.87431291888504</td><td>0.8595565975869158</td><td>0.10696398351859826</td><td>1.2035993780158853</td><td>-0.11850943432864373</td><td>0.1660452431882023</td><td>--</td></tr>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<Table length=10>\n",
+       "     source_id              ra         ...   parallax_error   radial_velocity\n",
+       "                           deg         ...        mas              km / s    \n",
+       "       int64             float64       ...      float64           float64    \n",
+       "------------------- ------------------ ... ------------------ ---------------\n",
+       "4467710915011802624  269.9680969307347 ...  0.470352406647465              --\n",
+       "4467706551328679552   270.033164589881 ...  0.927008559859825              --\n",
+       "4467712255037300096  270.7724717923047 ... 0.9719742773203504              --\n",
+       "4467735001181761792  270.3628606248308 ...  0.509812721702093              --\n",
+       "4467737101421916672  270.5110834661444 ... 0.7549581886719651              --\n",
+       "4467707547757327488 269.88746280594927 ... 0.3022057897812064              --\n",
+       "4467732355491087744  270.6730790702491 ... 0.4937921513912002              --\n",
+       "4467717099766944512 270.57667173120825 ... 0.8867339293525688              --\n",
+       "4467719058265781248  270.7248052971514 ...  0.390952370410666              --\n",
+       "4467722326741572352 270.87431291888504 ... 0.1660452431882023              --"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results3 = job3.get_results()\n",
+    "results3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Good so far."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** This query always selects sources with `parallax` less than 1.  But suppose you want to take that upper bound as an input.\n",
+    "\n",
+    "Modify `query3_base` to replace `1` with a format specifier like `{max_parallax}`.  Now, when you call `format`, add a keyword argument that assigns a value to `max_parallax`, and confirm that the format specifier gets replaced with the value you provide."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "query4_base = \"\"\"SELECT TOP 10\n",
+    "{columns}\n",
+    "FROM gaiadr2.gaia_source\n",
+    "WHERE parallax < {max_parallax} AND \n",
+    "bp_rp BETWEEN -0.75 AND 2\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "SELECT TOP 10\n",
+      "source_id, ra, dec, pmra, pmdec, parallax, parallax_error, radial_velocity\n",
+      "FROM gaiadr2.gaia_source\n",
+      "WHERE parallax < 0.5 AND \n",
+      "bp_rp BETWEEN -0.75 AND 2\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "query4 = query4_base.format(columns=columns,\n",
+    "                          max_parallax=0.5)\n",
+    "print(query)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Style note:**  You might notice that the variable names in this notebook are numbered, like `query1`, `query2`, etc.  \n",
+    "\n",
+    "The advantage of this style is that it isolates each section of the notebook from the others, so if you go back and run the cells out of order, it's less likely that you will get unexpected interactions.\n",
+    "\n",
+    "A drawback of this style is that it can be a nuisance to update the notebook if you add, remove, or reorder a section.\n",
+    "\n",
+    "What do you think of this choice?  Are there alternatives you prefer?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "This notebook demonstrates the following steps:\n",
+    "\n",
+    "1. Making a connection to the Gaia server,\n",
+    "\n",
+    "2. Exploring information about the database and the tables it contains,\n",
+    "\n",
+    "3. Writing a query and sending it to the server, and finally\n",
+    "\n",
+    "4. Downloading the response from the server as an Astropy `Table`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Best practices\n",
+    "\n",
+    "* If you can't download an entire dataset (or it's not practical) use queries to select the data you need.\n",
+    "\n",
+    "* Read the metadata and the documentation to make sure you understand the tables, their columns, and what they mean.\n",
+    "\n",
+    "* Develop queries incrementally: start with something simple, test it, and add a little bit at a time.\n",
+    "\n",
+    "* Use ADQL features like `TOP` and `COUNT` to test before you run a query that might return a lot of data.\n",
+    "\n",
+    "* If you know your query will return fewer than 3000 rows, you can run it synchronously, which might complete faster (but it doesn't seem to make much difference).  If it might return more than 3000 rows, you should run it asynchronously.\n",
+    "\n",
+    "* ADQL and SQL are not case-sensitive, so you don't have to capitalize the keywords, but you should.\n",
+    "\n",
+    "* ADQL and SQL don't require you to break a query into multiple lines, but you should.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Jupyter notebooks can be good for developing and testing code, but they have some drawbacks.  In particular, if you run the cells out of order, you might find that variables don't have the values you expect.\n",
+    "\n",
+    "There are a few things you can do to mitigate these problems:\n",
+    "\n",
+    "* Make each section of the notebook self-contained.  Try not to use the same variable name in more than one section.\n",
+    "\n",
+    "* Keep notebooks short.  Look for places where you can break your analysis into phases with one notebook per phase."
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/_build/html/_sources/AstronomicalData/_build/jupyter_execute/02_coords.ipynb b/_build/html/_sources/AstronomicalData/_build/jupyter_execute/02_coords.ipynb
new file mode 100644
index 0000000..7707176
--- /dev/null
+++ b/_build/html/_sources/AstronomicalData/_build/jupyter_execute/02_coords.ipynb
@@ -0,0 +1,1966 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Lesson 2\n",
+    "\n",
+    "This is the second in a series of lessons related to astronomy data.\n",
+    "\n",
+    "As a running example, we are replicating parts of the analysis in a recent paper, \"[Off the beaten path: Gaia reveals GD-1 stars outside of the main stream](https://arxiv.org/abs/1805.00425)\" by Adrian M. Price-Whelan and Ana Bonaca.\n",
+    "\n",
+    "In the first notebook, we wrote ADQL queries and used them to select and download data from the Gaia server.\n",
+    "\n",
+    "In this notebook, we'll pick up where we left off and write a query to select stars from the region of the sky where we expect GD-1 to be."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We'll start with an example that does a \"cone search\"; that is, it selects stars that appear in a circular region of the sky.\n",
+    "\n",
+    "Then, to select stars in the vicinity of GD-1, we'll:\n",
+    "\n",
+    "* Use `Quantity` objects to represent measurements with units.\n",
+    "\n",
+    "* Use the `Gala` library to convert coordinates from one frame to another.\n",
+    "\n",
+    "* Use the ADQL keywords `POLYGON`, `CONTAINS`, and `POINT` to select stars that fall within a polygonal region.\n",
+    "\n",
+    "* Submit a query and download the results.\n",
+    "\n",
+    "* Store the results in a FITS file.\n",
+    "\n",
+    "After completing this lesson, you should be able to\n",
+    "\n",
+    "* Use Python string formatting to compose more complex ADQL queries.\n",
+    "\n",
+    "* Work with coordinates and other quantities that have units.\n",
+    "\n",
+    "* Download the results of a query and store them in a file."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Installing libraries\n",
+    "\n",
+    "If you are running this notebook on Colab, you can run the following cell to install Astroquery and a the other libraries we'll use.\n",
+    "\n",
+    "If you are running this notebook on your own computer, you might have to install these libraries yourself.  \n",
+    "\n",
+    "If you are using this notebook as part of a Carpentries workshop, you should have received setup instructions.\n",
+    "\n",
+    "TODO: Add a link to the instructions.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# If we're running on Colab, install libraries\n",
+    "\n",
+    "import sys\n",
+    "IN_COLAB = 'google.colab' in sys.modules\n",
+    "\n",
+    "if IN_COLAB:\n",
+    "    !pip install astroquery astro-gala pyia"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Selecting a region"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "One of the most common ways to restrict a query is to select stars in a particular region of the sky.\n",
+    "\n",
+    "For example, here's a query from the [Gaia archive documentation](https://gea.esac.esa.int/archive-help/adql/examples/index.html) that selects \"all the objects ... in a circular region centered at (266.41683, -29.00781) with a search radius of 5 arcmin (0.08333 deg).\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query = \"\"\"\n",
+    "SELECT \n",
+    "TOP 10 source_id\n",
+    "FROM gaiadr2.gaia_source\n",
+    "WHERE 1=CONTAINS(\n",
+    "  POINT(ra, dec),\n",
+    "  CIRCLE(266.41683, -29.00781, 0.08333333))\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This query uses three keywords that are specific to ADQL (not SQL):\n",
+    "\n",
+    "* `POINT`: a location in [ICRS coordinates](https://en.wikipedia.org/wiki/International_Celestial_Reference_System), specified in degrees of right ascension and declination.\n",
+    "\n",
+    "* `CIRCLE`: a circle where the first two values are the coordinates of the center and the third is the radius in degrees.\n",
+    "\n",
+    "* `CONTAINS`: a function that returns `1` if a `POINT` is contained in a shape and `0` otherwise.\n",
+    "\n",
+    "Here is the [documentation of `CONTAINS`](http://www.ivoa.net/documents/ADQL/20180112/PR-ADQL-2.1-20180112.html#tth_sEc4.2.12).\n",
+    "\n",
+    "A query like this is called a cone search because it selects stars in a cone.\n",
+    "\n",
+    "Here's how we run it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Created TAP+ (v1.2.1) - Connection:\n",
+      "\tHost: gea.esac.esa.int\n",
+      "\tUse HTTPS: True\n",
+      "\tPort: 443\n",
+      "\tSSL Port: 443\n",
+      "Created TAP+ (v1.2.1) - Connection:\n",
+      "\tHost: geadata.esac.esa.int\n",
+      "\tUse HTTPS: True\n",
+      "\tPort: 443\n",
+      "\tSSL Port: 443\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<i>Table length=10</i>\n",
+       "<table id=\"table140683453947328\" class=\"table-striped table-bordered table-condensed\">\n",
+       "<thead><tr><th>source_id</th></tr></thead>\n",
+       "<thead><tr><th>int64</th></tr></thead>\n",
+       "<tr><td>4057468321929794432</td></tr>\n",
+       "<tr><td>4057468287575835392</td></tr>\n",
+       "<tr><td>4057482027171038976</td></tr>\n",
+       "<tr><td>4057470349160630656</td></tr>\n",
+       "<tr><td>4057470039924301696</td></tr>\n",
+       "<tr><td>4057469868125641984</td></tr>\n",
+       "<tr><td>4057468351995073024</td></tr>\n",
+       "<tr><td>4057469661959554560</td></tr>\n",
+       "<tr><td>4057470520960672640</td></tr>\n",
+       "<tr><td>4057470555320409600</td></tr>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<Table length=10>\n",
+       "     source_id     \n",
+       "       int64       \n",
+       "-------------------\n",
+       "4057468321929794432\n",
+       "4057468287575835392\n",
+       "4057482027171038976\n",
+       "4057470349160630656\n",
+       "4057470039924301696\n",
+       "4057469868125641984\n",
+       "4057468351995073024\n",
+       "4057469661959554560\n",
+       "4057470520960672640\n",
+       "4057470555320409600"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from astroquery.gaia import Gaia\n",
+    "\n",
+    "job = Gaia.launch_job(query)\n",
+    "result = job.get_results()\n",
+    "result"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** When you are debugging queries like this, you can use `TOP` to limit the size of the results, but then you still don't know how big the results will be.\n",
+    "\n",
+    "An alternative is to use `COUNT`, which asks for the number of rows that would be selected, but it does not return them.\n",
+    "\n",
+    "In the previous query, replace `TOP 10 source_id` with `COUNT(source_id)` and run the query again.  How many stars has Gaia identified in the cone we searched?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Getting GD-1 Data\n",
+    "\n",
+    "From the Price-Whelan and Bonaca paper, we will try to reproduce Figure 1, which includes this representation of stars likely to belong to GD-1:\n",
+    "\n",
+    "<img src=\"https://github.com/datacarpentry/astronomy-python/raw/gh-pages/fig/gd1-4.png\">"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Along the axis of right ascension ($\\phi_1$) the figure extends from -100 to 20 degrees.\n",
+    "\n",
+    "Along the axis of declination ($\\phi_2$) the figure extends from about -8 to 4 degrees.\n",
+    "\n",
+    "Ideally, we would select all stars from this rectangle, but there are more than 10 million of them, so\n",
+    "\n",
+    "* That would be difficult to work with,\n",
+    "\n",
+    "* As anonymous users, we are limited to 3 million rows in a single query, and\n",
+    "\n",
+    "* While we are developing and testing code, it will be faster to work with a smaller dataset.\n",
+    "\n",
+    "So we'll start by selecting stars in a smaller rectangle, from -55 to -45 degrees right ascension and -8 to 4 degrees of declination.\n",
+    "\n",
+    "But first we let's see how to represent quantities with units like degrees."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Working with coordinates\n",
+    "\n",
+    "Coordinates are physical quantities, which means that they have two parts, a value and a unit.\n",
+    "\n",
+    "For example, the coordinate $30^{\\circ}$ has value 30 and its units are degrees.\n",
+    "\n",
+    "Until recently, most scientific computation was done with values only; units were left out of the program altogether, [often with disastrous results](https://en.wikipedia.org/wiki/Mars_Climate_Orbiter#Cause_of_failure).\n",
+    "\n",
+    "Astropy provides tools for including units explicitly in computations, which makes it possible to detect errors before they cause disasters.\n",
+    "\n",
+    "To use Astropy units, we import them like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<module 'astropy.units' from '/home/downey/anaconda3/envs/AstronomicalData/lib/python3.8/site-packages/astropy/units/__init__.py'>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import astropy.units as u\n",
+    "\n",
+    "u"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`u` is an object that contains most common units and all SI units.\n",
+    "\n",
+    "You can use `dir` to list them, but you should also [read the documentation](https://docs.astropy.org/en/stable/units/)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['A',\n",
+       " 'AA',\n",
+       " 'AB',\n",
+       " 'ABflux',\n",
+       " 'ABmag',\n",
+       " 'AU',\n",
+       " 'Angstrom',\n",
+       " 'B',\n",
+       " 'Ba',\n",
+       " 'Barye',\n",
+       " 'Bi',\n",
+       " 'Biot',\n",
+       " 'Bol',\n",
+       " 'Bq',\n",
+       " 'C',\n",
+       " 'Celsius',\n",
+       " 'Ci',\n",
+       " 'CompositeUnit',\n",
+       " 'D',\n",
+       " 'Da',\n",
+       " 'Dalton',\n",
+       " 'Debye',\n",
+       " 'Decibel',\n",
+       " 'DecibelUnit',\n",
+       " 'Dex',\n",
+       " 'DexUnit',\n",
+       " 'EA',\n",
+       " 'EAU',\n",
+       " 'EB',\n",
+       " 'EBa',\n",
+       " 'EC',\n",
+       " 'ED',\n",
+       " 'EF',\n",
+       " 'EG',\n",
+       " 'EGal',\n",
+       " 'EH',\n",
+       " 'EHz',\n",
+       " 'EJ',\n",
+       " 'EJy',\n",
+       " 'EK',\n",
+       " 'EL',\n",
+       " 'EN',\n",
+       " 'EOhm',\n",
+       " 'EP',\n",
+       " 'EPa',\n",
+       " 'ER',\n",
+       " 'ERy',\n",
+       " 'ES',\n",
+       " 'ESt',\n",
+       " 'ET',\n",
+       " 'EV',\n",
+       " 'EW',\n",
+       " 'EWb',\n",
+       " 'Ea',\n",
+       " 'Eadu',\n",
+       " 'Earcmin',\n",
+       " 'Earcsec',\n",
+       " 'Eau',\n",
+       " 'Eb',\n",
+       " 'Ebarn',\n",
+       " 'Ebeam',\n",
+       " 'Ebin',\n",
+       " 'Ebit',\n",
+       " 'Ebyte',\n",
+       " 'Ecd',\n",
+       " 'Echan',\n",
+       " 'Ecount',\n",
+       " 'Ect',\n",
+       " 'Ed',\n",
+       " 'Edeg',\n",
+       " 'Edyn',\n",
+       " 'EeV',\n",
+       " 'Eerg',\n",
+       " 'Eg',\n",
+       " 'Eh',\n",
+       " 'EiB',\n",
+       " 'Eib',\n",
+       " 'Eibit',\n",
+       " 'Eibyte',\n",
+       " 'Ek',\n",
+       " 'El',\n",
+       " 'Elm',\n",
+       " 'Elx',\n",
+       " 'Elyr',\n",
+       " 'Em',\n",
+       " 'Emag',\n",
+       " 'Emin',\n",
+       " 'Emol',\n",
+       " 'Eohm',\n",
+       " 'Epc',\n",
+       " 'Eph',\n",
+       " 'Ephoton',\n",
+       " 'Epix',\n",
+       " 'Epixel',\n",
+       " 'Erad',\n",
+       " 'Es',\n",
+       " 'Esr',\n",
+       " 'Eu',\n",
+       " 'Evox',\n",
+       " 'Evoxel',\n",
+       " 'Eyr',\n",
+       " 'F',\n",
+       " 'Farad',\n",
+       " 'Fr',\n",
+       " 'Franklin',\n",
+       " 'FunctionQuantity',\n",
+       " 'FunctionUnitBase',\n",
+       " 'G',\n",
+       " 'GA',\n",
+       " 'GAU',\n",
+       " 'GB',\n",
+       " 'GBa',\n",
+       " 'GC',\n",
+       " 'GD',\n",
+       " 'GF',\n",
+       " 'GG',\n",
+       " 'GGal',\n",
+       " 'GH',\n",
+       " 'GHz',\n",
+       " 'GJ',\n",
+       " 'GJy',\n",
+       " 'GK',\n",
+       " 'GL',\n",
+       " 'GN',\n",
+       " 'GOhm',\n",
+       " 'GP',\n",
+       " 'GPa',\n",
+       " 'GR',\n",
+       " 'GRy',\n",
+       " 'GS',\n",
+       " 'GSt',\n",
+       " 'GT',\n",
+       " 'GV',\n",
+       " 'GW',\n",
+       " 'GWb',\n",
+       " 'Ga',\n",
+       " 'Gadu',\n",
+       " 'Gal',\n",
+       " 'Garcmin',\n",
+       " 'Garcsec',\n",
+       " 'Gau',\n",
+       " 'Gauss',\n",
+       " 'Gb',\n",
+       " 'Gbarn',\n",
+       " 'Gbeam',\n",
+       " 'Gbin',\n",
+       " 'Gbit',\n",
+       " 'Gbyte',\n",
+       " 'Gcd',\n",
+       " 'Gchan',\n",
+       " 'Gcount',\n",
+       " 'Gct',\n",
+       " 'Gd',\n",
+       " 'Gdeg',\n",
+       " 'Gdyn',\n",
+       " 'GeV',\n",
+       " 'Gerg',\n",
+       " 'Gg',\n",
+       " 'Gh',\n",
+       " 'GiB',\n",
+       " 'Gib',\n",
+       " 'Gibit',\n",
+       " 'Gibyte',\n",
+       " 'Gk',\n",
+       " 'Gl',\n",
+       " 'Glm',\n",
+       " 'Glx',\n",
+       " 'Glyr',\n",
+       " 'Gm',\n",
+       " 'Gmag',\n",
+       " 'Gmin',\n",
+       " 'Gmol',\n",
+       " 'Gohm',\n",
+       " 'Gpc',\n",
+       " 'Gph',\n",
+       " 'Gphoton',\n",
+       " 'Gpix',\n",
+       " 'Gpixel',\n",
+       " 'Grad',\n",
+       " 'Gs',\n",
+       " 'Gsr',\n",
+       " 'Gu',\n",
+       " 'Gvox',\n",
+       " 'Gvoxel',\n",
+       " 'Gyr',\n",
+       " 'H',\n",
+       " 'Henry',\n",
+       " 'Hertz',\n",
+       " 'Hz',\n",
+       " 'IrreducibleUnit',\n",
+       " 'J',\n",
+       " 'Jansky',\n",
+       " 'Joule',\n",
+       " 'Jy',\n",
+       " 'K',\n",
+       " 'Kayser',\n",
+       " 'Kelvin',\n",
+       " 'KiB',\n",
+       " 'Kib',\n",
+       " 'Kibit',\n",
+       " 'Kibyte',\n",
+       " 'L',\n",
+       " 'L_bol',\n",
+       " 'L_sun',\n",
+       " 'LogQuantity',\n",
+       " 'LogUnit',\n",
+       " 'Lsun',\n",
+       " 'MA',\n",
+       " 'MAU',\n",
+       " 'MB',\n",
+       " 'MBa',\n",
+       " 'MC',\n",
+       " 'MD',\n",
+       " 'MF',\n",
+       " 'MG',\n",
+       " 'MGal',\n",
+       " 'MH',\n",
+       " 'MHz',\n",
+       " 'MJ',\n",
+       " 'MJy',\n",
+       " 'MK',\n",
+       " 'ML',\n",
+       " 'MN',\n",
+       " 'MOhm',\n",
+       " 'MP',\n",
+       " 'MPa',\n",
+       " 'MR',\n",
+       " 'MRy',\n",
+       " 'MS',\n",
+       " 'MSt',\n",
+       " 'MT',\n",
+       " 'MV',\n",
+       " 'MW',\n",
+       " 'MWb',\n",
+       " 'M_bol',\n",
+       " 'M_e',\n",
+       " 'M_earth',\n",
+       " 'M_jup',\n",
+       " 'M_jupiter',\n",
+       " 'M_p',\n",
+       " 'M_sun',\n",
+       " 'Ma',\n",
+       " 'Madu',\n",
+       " 'MagUnit',\n",
+       " 'Magnitude',\n",
+       " 'Marcmin',\n",
+       " 'Marcsec',\n",
+       " 'Mau',\n",
+       " 'Mb',\n",
+       " 'Mbarn',\n",
+       " 'Mbeam',\n",
+       " 'Mbin',\n",
+       " 'Mbit',\n",
+       " 'Mbyte',\n",
+       " 'Mcd',\n",
+       " 'Mchan',\n",
+       " 'Mcount',\n",
+       " 'Mct',\n",
+       " 'Md',\n",
+       " 'Mdeg',\n",
+       " 'Mdyn',\n",
+       " 'MeV',\n",
+       " 'Mearth',\n",
+       " 'Merg',\n",
+       " 'Mg',\n",
+       " 'Mh',\n",
+       " 'MiB',\n",
+       " 'Mib',\n",
+       " 'Mibit',\n",
+       " 'Mibyte',\n",
+       " 'Mjup',\n",
+       " 'Mjupiter',\n",
+       " 'Mk',\n",
+       " 'Ml',\n",
+       " 'Mlm',\n",
+       " 'Mlx',\n",
+       " 'Mlyr',\n",
+       " 'Mm',\n",
+       " 'Mmag',\n",
+       " 'Mmin',\n",
+       " 'Mmol',\n",
+       " 'Mohm',\n",
+       " 'Mpc',\n",
+       " 'Mph',\n",
+       " 'Mphoton',\n",
+       " 'Mpix',\n",
+       " 'Mpixel',\n",
+       " 'Mrad',\n",
+       " 'Ms',\n",
+       " 'Msr',\n",
+       " 'Msun',\n",
+       " 'Mu',\n",
+       " 'Mvox',\n",
+       " 'Mvoxel',\n",
+       " 'Myr',\n",
+       " 'N',\n",
+       " 'NamedUnit',\n",
+       " 'Newton',\n",
+       " 'Ohm',\n",
+       " 'P',\n",
+       " 'PA',\n",
+       " 'PAU',\n",
+       " 'PB',\n",
+       " 'PBa',\n",
+       " 'PC',\n",
+       " 'PD',\n",
+       " 'PF',\n",
+       " 'PG',\n",
+       " 'PGal',\n",
+       " 'PH',\n",
+       " 'PHz',\n",
+       " 'PJ',\n",
+       " 'PJy',\n",
+       " 'PK',\n",
+       " 'PL',\n",
+       " 'PN',\n",
+       " 'POhm',\n",
+       " 'PP',\n",
+       " 'PPa',\n",
+       " 'PR',\n",
+       " 'PRy',\n",
+       " 'PS',\n",
+       " 'PSt',\n",
+       " 'PT',\n",
+       " 'PV',\n",
+       " 'PW',\n",
+       " 'PWb',\n",
+       " 'Pa',\n",
+       " 'Padu',\n",
+       " 'Parcmin',\n",
+       " 'Parcsec',\n",
+       " 'Pascal',\n",
+       " 'Pau',\n",
+       " 'Pb',\n",
+       " 'Pbarn',\n",
+       " 'Pbeam',\n",
+       " 'Pbin',\n",
+       " 'Pbit',\n",
+       " 'Pbyte',\n",
+       " 'Pcd',\n",
+       " 'Pchan',\n",
+       " 'Pcount',\n",
+       " 'Pct',\n",
+       " 'Pd',\n",
+       " 'Pdeg',\n",
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+       " 'PeV',\n",
+       " 'Perg',\n",
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+       " 'Ph',\n",
+       " 'PiB',\n",
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+       " 'Pmol',\n",
+       " 'Pohm',\n",
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+       " 'Ppixel',\n",
+       " 'Prad',\n",
+       " 'PrefixUnit',\n",
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+       " 'Psr',\n",
+       " 'Pu',\n",
+       " 'Pvox',\n",
+       " 'Pvoxel',\n",
+       " 'Pyr',\n",
+       " 'Quantity',\n",
+       " 'QuantityInfo',\n",
+       " 'QuantityInfoBase',\n",
+       " 'R',\n",
+       " 'R_earth',\n",
+       " 'R_jup',\n",
+       " 'R_jupiter',\n",
+       " 'R_sun',\n",
+       " 'Rayleigh',\n",
+       " 'Rearth',\n",
+       " 'Rjup',\n",
+       " 'Rjupiter',\n",
+       " 'Rsun',\n",
+       " 'Ry',\n",
+       " 'S',\n",
+       " 'ST',\n",
+       " 'STflux',\n",
+       " 'STmag',\n",
+       " 'Siemens',\n",
+       " 'SpecificTypeQuantity',\n",
+       " 'St',\n",
+       " 'Sun',\n",
+       " 'T',\n",
+       " 'TA',\n",
+       " 'TAU',\n",
+       " 'TB',\n",
+       " 'TBa',\n",
+       " 'TC',\n",
+       " 'TD',\n",
+       " 'TF',\n",
+       " 'TG',\n",
+       " 'TGal',\n",
+       " 'TH',\n",
+       " 'THz',\n",
+       " 'TJ',\n",
+       " 'TJy',\n",
+       " 'TK',\n",
+       " 'TL',\n",
+       " 'TN',\n",
+       " 'TOhm',\n",
+       " 'TP',\n",
+       " 'TPa',\n",
+       " 'TR',\n",
+       " 'TRy',\n",
+       " 'TS',\n",
+       " 'TSt',\n",
+       " 'TT',\n",
+       " 'TV',\n",
+       " 'TW',\n",
+       " 'TWb',\n",
+       " 'Ta',\n",
+       " 'Tadu',\n",
+       " 'Tarcmin',\n",
+       " 'Tarcsec',\n",
+       " 'Tau',\n",
+       " 'Tb',\n",
+       " 'Tbarn',\n",
+       " 'Tbeam',\n",
+       " 'Tbin',\n",
+       " 'Tbit',\n",
+       " 'Tbyte',\n",
+       " 'Tcd',\n",
+       " 'Tchan',\n",
+       " 'Tcount',\n",
+       " 'Tct',\n",
+       " 'Td',\n",
+       " 'Tdeg',\n",
+       " 'Tdyn',\n",
+       " 'TeV',\n",
+       " 'Terg',\n",
+       " 'Tesla',\n",
+       " 'Tg',\n",
+       " 'Th',\n",
+       " 'TiB',\n",
+       " 'Tib',\n",
+       " 'Tibit',\n",
+       " 'Tibyte',\n",
+       " 'Tk',\n",
+       " 'Tl',\n",
+       " 'Tlm',\n",
+       " 'Tlx',\n",
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+       " 'Tm',\n",
+       " 'Tmag',\n",
+       " 'Tmin',\n",
+       " 'Tmol',\n",
+       " 'Tohm',\n",
+       " 'Tpc',\n",
+       " 'Tph',\n",
+       " 'Tphoton',\n",
+       " 'Tpix',\n",
+       " 'Tpixel',\n",
+       " 'Trad',\n",
+       " 'Ts',\n",
+       " 'Tsr',\n",
+       " 'Tu',\n",
+       " 'Tvox',\n",
+       " 'Tvoxel',\n",
+       " 'Tyr',\n",
+       " 'Unit',\n",
+       " 'UnitBase',\n",
+       " 'UnitConversionError',\n",
+       " 'UnitTypeError',\n",
+       " 'UnitsError',\n",
+       " 'UnitsWarning',\n",
+       " 'UnrecognizedUnit',\n",
+       " 'V',\n",
+       " 'Volt',\n",
+       " 'W',\n",
+       " 'Watt',\n",
+       " 'Wb',\n",
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+       " 'YA',\n",
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+       " 'YS',\n",
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+       " 'YT',\n",
+       " 'YV',\n",
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+       " 'ag',\n",
+       " 'ah',\n",
+       " 'ak',\n",
+       " 'al',\n",
+       " 'allclose',\n",
+       " 'alm',\n",
+       " 'alx',\n",
+       " 'alyr',\n",
+       " 'am',\n",
+       " 'amag',\n",
+       " 'amin',\n",
+       " 'amol',\n",
+       " 'amp',\n",
+       " 'ampere',\n",
+       " 'angstrom',\n",
+       " 'annum',\n",
+       " 'aohm',\n",
+       " 'apc',\n",
+       " 'aph',\n",
+       " 'aphoton',\n",
+       " 'apix',\n",
+       " 'apixel',\n",
+       " 'arad',\n",
+       " 'arcmin',\n",
+       " 'arcminute',\n",
+       " 'arcsec',\n",
+       " 'arcsecond',\n",
+       " 'asr',\n",
+       " 'astronomical_unit',\n",
+       " 'astrophys',\n",
+       " 'attoBarye',\n",
+       " 'attoDa',\n",
+       " 'attoDalton',\n",
+       " 'attoDebye',\n",
+       " 'attoFarad',\n",
+       " 'attoGauss',\n",
+       " 'attoHenry',\n",
+       " 'attoHertz',\n",
+       " 'attoJansky',\n",
+       " 'attoJoule',\n",
+       " 'attoKayser',\n",
+       " 'attoKelvin',\n",
+       " 'attoNewton',\n",
+       " 'attoOhm',\n",
+       " 'attoPascal',\n",
+       " 'attoRayleigh',\n",
+       " 'attoSiemens',\n",
+       " 'attoTesla',\n",
+       " 'attoVolt',\n",
+       " 'attoWatt',\n",
+       " 'attoWeber',\n",
+       " 'attoamp',\n",
+       " 'attoampere',\n",
+       " 'attoannum',\n",
+       " 'attoarcminute',\n",
+       " 'attoarcsecond',\n",
+       " 'attoastronomical_unit',\n",
+       " 'attobarn',\n",
+       " 'attobarye',\n",
+       " 'attobit',\n",
+       " 'attobyte',\n",
+       " 'attocandela',\n",
+       " 'attocoulomb',\n",
+       " 'attocount',\n",
+       " 'attoday',\n",
+       " 'attodebye',\n",
+       " 'attodegree',\n",
+       " 'attodyne',\n",
+       " 'attoelectronvolt',\n",
+       " 'attofarad',\n",
+       " 'attogal',\n",
+       " 'attogauss',\n",
+       " 'attogram',\n",
+       " 'attohenry',\n",
+       " 'attohertz',\n",
+       " 'attohour',\n",
+       " 'attohr',\n",
+       " 'attojansky',\n",
+       " 'attojoule',\n",
+       " 'attokayser',\n",
+       " 'attolightyear',\n",
+       " 'attoliter',\n",
+       " 'attolumen',\n",
+       " 'attolux',\n",
+       " 'attometer',\n",
+       " 'attominute',\n",
+       " 'attomole',\n",
+       " 'attonewton',\n",
+       " 'attoparsec',\n",
+       " 'attopascal',\n",
+       " 'attophoton',\n",
+       " 'attopixel',\n",
+       " 'attopoise',\n",
+       " 'attoradian',\n",
+       " 'attorayleigh',\n",
+       " 'attorydberg',\n",
+       " 'attosecond',\n",
+       " 'attosiemens',\n",
+       " 'attosteradian',\n",
+       " 'attostokes',\n",
+       " 'attotesla',\n",
+       " 'attovolt',\n",
+       " 'attovoxel',\n",
+       " 'attowatt',\n",
+       " 'attoweber',\n",
+       " 'attoyear',\n",
+       " 'au',\n",
+       " 'avox',\n",
+       " 'avoxel',\n",
+       " 'ayr',\n",
+       " 'b',\n",
+       " 'bar',\n",
+       " 'barn',\n",
+       " 'barye',\n",
+       " 'beam',\n",
+       " 'beam_angular_area',\n",
+       " 'becquerel',\n",
+       " 'bin',\n",
+       " 'binary_prefixes',\n",
+       " 'bit',\n",
+       " 'bol',\n",
+       " 'brightness_temperature',\n",
+       " 'byte',\n",
+       " 'cA',\n",
+       " 'cAU',\n",
+       " 'cB',\n",
+       " 'cBa',\n",
+       " 'cC',\n",
+       " 'cD',\n",
+       " 'cF',\n",
+       " 'cG',\n",
+       " 'cGal',\n",
+       " 'cH',\n",
+       " 'cHz',\n",
+       " 'cJ',\n",
+       " 'cJy',\n",
+       " 'cK',\n",
+       " 'cL',\n",
+       " 'cN',\n",
+       " 'cOhm',\n",
+       " 'cP',\n",
+       " 'cPa',\n",
+       " 'cR',\n",
+       " 'cRy',\n",
+       " 'cS',\n",
+       " 'cSt',\n",
+       " 'cT',\n",
+       " 'cV',\n",
+       " 'cW',\n",
+       " 'cWb',\n",
+       " 'ca',\n",
+       " 'cadu',\n",
+       " 'candela',\n",
+       " 'carcmin',\n",
+       " 'carcsec',\n",
+       " 'cau',\n",
+       " 'cb',\n",
+       " 'cbarn',\n",
+       " 'cbeam',\n",
+       " 'cbin',\n",
+       " 'cbit',\n",
+       " 'cbyte',\n",
+       " 'ccd',\n",
+       " 'cchan',\n",
+       " 'ccount',\n",
+       " 'cct',\n",
+       " 'cd',\n",
+       " 'cdeg',\n",
+       " 'cdyn',\n",
+       " 'ceV',\n",
+       " 'centiBarye',\n",
+       " 'centiDa',\n",
+       " 'centiDalton',\n",
+       " 'centiDebye',\n",
+       " 'centiFarad',\n",
+       " 'centiGauss',\n",
+       " 'centiHenry',\n",
+       " 'centiHertz',\n",
+       " 'centiJansky',\n",
+       " 'centiJoule',\n",
+       " 'centiKayser',\n",
+       " 'centiKelvin',\n",
+       " 'centiNewton',\n",
+       " 'centiOhm',\n",
+       " 'centiPascal',\n",
+       " 'centiRayleigh',\n",
+       " 'centiSiemens',\n",
+       " 'centiTesla',\n",
+       " 'centiVolt',\n",
+       " 'centiWatt',\n",
+       " 'centiWeber',\n",
+       " 'centiamp',\n",
+       " 'centiampere',\n",
+       " 'centiannum',\n",
+       " 'centiarcminute',\n",
+       " 'centiarcsecond',\n",
+       " 'centiastronomical_unit',\n",
+       " 'centibarn',\n",
+       " 'centibarye',\n",
+       " 'centibit',\n",
+       " 'centibyte',\n",
+       " 'centicandela',\n",
+       " 'centicoulomb',\n",
+       " 'centicount',\n",
+       " 'centiday',\n",
+       " 'centidebye',\n",
+       " 'centidegree',\n",
+       " 'centidyne',\n",
+       " 'centielectronvolt',\n",
+       " 'centifarad',\n",
+       " 'centigal',\n",
+       " 'centigauss',\n",
+       " 'centigram',\n",
+       " 'centihenry',\n",
+       " 'centihertz',\n",
+       " 'centihour',\n",
+       " 'centihr',\n",
+       " 'centijansky',\n",
+       " 'centijoule',\n",
+       " 'centikayser',\n",
+       " 'centilightyear',\n",
+       " 'centiliter',\n",
+       " 'centilumen',\n",
+       " 'centilux',\n",
+       " 'centimeter',\n",
+       " 'centiminute',\n",
+       " 'centimole',\n",
+       " 'centinewton',\n",
+       " 'centiparsec',\n",
+       " 'centipascal',\n",
+       " 'centiphoton',\n",
+       " 'centipixel',\n",
+       " 'centipoise',\n",
+       " 'centiradian',\n",
+       " 'centirayleigh',\n",
+       " 'centirydberg',\n",
+       " 'centisecond',\n",
+       " 'centisiemens',\n",
+       " 'centisteradian',\n",
+       " 'centistokes',\n",
+       " 'centitesla',\n",
+       " 'centivolt',\n",
+       " 'centivoxel',\n",
+       " 'centiwatt',\n",
+       " 'centiweber',\n",
+       " 'centiyear',\n",
+       " 'cerg',\n",
+       " 'cg',\n",
+       " 'cgs',\n",
+       " 'ch',\n",
+       " 'chan',\n",
+       " 'ck',\n",
+       " 'cl',\n",
+       " 'clm',\n",
+       " 'clx',\n",
+       " 'clyr',\n",
+       " 'cm',\n",
+       " 'cmag',\n",
+       " 'cmin',\n",
+       " 'cmol',\n",
+       " 'cohm',\n",
+       " 'core',\n",
+       " 'coulomb',\n",
+       " 'count',\n",
+       " 'cpc',\n",
+       " 'cph',\n",
+       " 'cphoton',\n",
+       " 'cpix',\n",
+       " 'cpixel',\n",
+       " 'crad',\n",
+       " 'cs',\n",
+       " 'csr',\n",
+       " 'ct',\n",
+       " 'cu',\n",
+       " 'curie',\n",
+       " 'cvox',\n",
+       " 'cvoxel',\n",
+       " 'cy',\n",
+       " 'cycle',\n",
+       " 'cyr',\n",
+       " 'd',\n",
+       " 'dA',\n",
+       " 'dAU',\n",
+       " 'dB',\n",
+       " 'dBa',\n",
+       " 'dC',\n",
+       " 'dD',\n",
+       " 'dF',\n",
+       " 'dG',\n",
+       " 'dGal',\n",
+       " 'dH',\n",
+       " 'dHz',\n",
+       " 'dJ',\n",
+       " 'dJy',\n",
+       " 'dK',\n",
+       " 'dL',\n",
+       " 'dN',\n",
+       " 'dOhm',\n",
+       " 'dP',\n",
+       " 'dPa',\n",
+       " 'dR',\n",
+       " 'dRy',\n",
+       " 'dS',\n",
+       " 'dSt',\n",
+       " 'dT',\n",
+       " ...]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dir(u)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To create a quantity, we multiply a value by a unit."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "astropy.units.quantity.Quantity"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "coord = 30 * u.deg\n",
+    "type(coord)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The result is a `Quantity` object.\n",
+    "\n",
+    "Jupyter knows how to display `Quantities` like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$30 \\; \\mathrm{{}^{\\circ}}$"
+      ],
+      "text/plain": [
+       "<Quantity 30. deg>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "coord"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Selecting a rectangle\n",
+    "\n",
+    "Now we'll select a rectangle from -55 to -45 degrees right ascension and -8 to 4 degrees of declination.\n",
+    "\n",
+    "We'll define variables to contain these limits."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "phi1_min = -55\n",
+    "phi1_max = -45\n",
+    "phi2_min = -8\n",
+    "phi2_max = 4"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To represent a rectangle, we'll use two lists of coordinates and multiply by their units."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "phi1_rect = [phi1_min, phi1_min, phi1_max, phi1_max] * u.deg\n",
+    "phi2_rect = [phi2_min, phi2_max, phi2_max, phi2_min] * u.deg"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`phi1_rect` and `phi2_rect` represent the coordinates of the corners of a rectangle.  \n",
+    "\n",
+    "But they are in \"[a Heliocentric spherical coordinate system defined by the orbit of the GD1 stream](https://gala-astro.readthedocs.io/en/latest/_modules/gala/coordinates/gd1.html)\"\n",
+    "\n",
+    "In order to use them in a Gaia query, we have to convert them to [International Celestial Reference System](https://en.wikipedia.org/wiki/International_Celestial_Reference_System) (ICRS) coordinates.  We can do that by storing the coordinates in a `GD1Koposov10` object provided by [Gala](https://gala-astro.readthedocs.io/en/latest/coordinates/)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "gala.coordinates.gd1.GD1Koposov10"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import gala.coordinates as gc\n",
+    "\n",
+    "corners = gc.GD1Koposov10(phi1=phi1_rect, phi2=phi2_rect)\n",
+    "type(corners)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can display the result like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<GD1Koposov10 Coordinate: (phi1, phi2) in deg\n",
+       "    [(-55., -8.), (-55.,  4.), (-45.,  4.), (-45., -8.)]>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "corners"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can use `transform_to` to convert to ICRS coordinates."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "astropy.coordinates.builtin_frames.icrs.ICRS"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import astropy.coordinates as coord\n",
+    "\n",
+    "corners_icrs = corners.transform_to(coord.ICRS)\n",
+    "type(corners_icrs)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The result is an `ICRS` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<ICRS Coordinate: (ra, dec) in deg\n",
+       "    [(146.27533314, 19.26190982), (135.42163944, 25.87738723),\n",
+       "     (141.60264825, 34.3048303 ), (152.81671045, 27.13611254)]>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "corners_icrs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notice that a rectangle in one coordinate system is not necessarily a rectangle in another.  In this example, the result is a polygon."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Selecting a polygon\n",
+    "\n",
+    "In order to use this polygon as part of an ADQL query, we have to convert it to a string with a comma-separated list of coordinates, as in this example:\n",
+    "\n",
+    "```\n",
+    "\"\"\"\n",
+    "POLYGON(143.65, 20.98, \n",
+    "        134.46, 26.39, \n",
+    "        140.58, 34.85, \n",
+    "        150.16, 29.01)\n",
+    "\"\"\"\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`corners_icrs` behaves like a list, so we can use a `for` loop to iterate through the points."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<ICRS Coordinate: (ra, dec) in deg\n",
+      "    (146.27533314, 19.26190982)>\n",
+      "<ICRS Coordinate: (ra, dec) in deg\n",
+      "    (135.42163944, 25.87738723)>\n",
+      "<ICRS Coordinate: (ra, dec) in deg\n",
+      "    (141.60264825, 34.3048303)>\n",
+      "<ICRS Coordinate: (ra, dec) in deg\n",
+      "    (152.81671045, 27.13611254)>\n"
+     ]
+    }
+   ],
+   "source": [
+    "for point in corners_icrs:\n",
+    "    print(point)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "From that, we can select the coordinates `ra` and `dec`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "146d16m31.1993s 19d15m42.8754s\n",
+      "135d25m17.902s 25d52m38.594s\n",
+      "141d36m09.5337s 34d18m17.3891s\n",
+      "152d49m00.1576s 27d08m10.0051s\n"
+     ]
+    }
+   ],
+   "source": [
+    "for point in corners_icrs:\n",
+    "    print(point.ra, point.dec)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The results are quantities with units, but if we select the `value` part, we get a dimensionless floating-point number."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "146.27533313607782 19.261909820533692\n",
+      "135.42163944306296 25.87738722767213\n",
+      "141.60264825107333 34.304830296257144\n",
+      "152.81671044675923 27.136112541397996\n"
+     ]
+    }
+   ],
+   "source": [
+    "for point in corners_icrs:\n",
+    "    print(point.ra.value, point.dec.value)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can use string `format` to convert these numbers to strings."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['146.27533313607782, 19.261909820533692',\n",
+       " '135.42163944306296, 25.87738722767213',\n",
+       " '141.60264825107333, 34.304830296257144',\n",
+       " '152.81671044675923, 27.136112541397996']"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "point_base = \"{point.ra.value}, {point.dec.value}\"\n",
+    "\n",
+    "t = [point_base.format(point=point)\n",
+    "     for point in corners_icrs]\n",
+    "t"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The result is a list of strings, which we can join into a single string using `join`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'146.27533313607782, 19.261909820533692, 135.42163944306296, 25.87738722767213, 141.60264825107333, 34.304830296257144, 152.81671044675923, 27.136112541397996'"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "point_list = ', '.join(t)\n",
+    "point_list"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notice that we invoke `join` on a string and pass the list as an argument.\n",
+    "\n",
+    "Before we can assemble the query, we need `columns` again (as we saw in the previous notebook)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "columns = 'source_id, ra, dec, pmra, pmdec, parallax, parallax_error, radial_velocity'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's the base for the query, with format specifiers for `columns` and `point_list`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query_base = \"\"\"SELECT {columns}\n",
+    "FROM gaiadr2.gaia_source\n",
+    "WHERE parallax < 1\n",
+    "  AND bp_rp BETWEEN -0.75 AND 2 \n",
+    "  AND 1 = CONTAINS(POINT(ra, dec), \n",
+    "                   POLYGON({point_list}))\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And here's the result:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "SELECT source_id, ra, dec, pmra, pmdec, parallax, parallax_error, radial_velocity\n",
+      "FROM gaiadr2.gaia_source\n",
+      "WHERE parallax < 1\n",
+      "  AND bp_rp BETWEEN -0.75 AND 2 \n",
+      "  AND 1 = CONTAINS(POINT(ra, dec), \n",
+      "                   POLYGON(146.27533313607782, 19.261909820533692, 135.42163944306296, 25.87738722767213, 141.60264825107333, 34.304830296257144, 152.81671044675923, 27.136112541397996))\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "query = query_base.format(columns=columns, \n",
+    "                          point_list=point_list)\n",
+    "print(query)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As always, we should take a minute to proof-read the query before we launch it.\n",
+    "\n",
+    "The result will be bigger than our previous queries, so it will take a little longer."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO: Query finished. [astroquery.utils.tap.core]\n",
+      "<Table length=140340>\n",
+      "      name       dtype    unit                              description                             n_bad \n",
+      "--------------- ------- -------- ------------------------------------------------------------------ ------\n",
+      "      source_id   int64          Unique source identifier (unique within a particular Data Release)      0\n",
+      "             ra float64      deg                                                    Right ascension      0\n",
+      "            dec float64      deg                                                        Declination      0\n",
+      "           pmra float64 mas / yr                         Proper motion in right ascension direction      0\n",
+      "          pmdec float64 mas / yr                             Proper motion in declination direction      0\n",
+      "       parallax float64      mas                                                           Parallax      0\n",
+      " parallax_error float64      mas                                         Standard error of parallax      0\n",
+      "radial_velocity float64   km / s                                                    Radial velocity 139374\n",
+      "Jobid: 1601903357321O\n",
+      "Phase: COMPLETED\n",
+      "Owner: None\n",
+      "Output file: async_20201005090917.vot\n",
+      "Results: None\n"
+     ]
+    }
+   ],
+   "source": [
+    "job = Gaia.launch_job_async(query)\n",
+    "print(job)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here are the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "140340"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results = job.get_results()\n",
+    "len(results)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There are more than 100,000 stars in this polygon, but that's a manageable size to work with."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Saving results\n",
+    "\n",
+    "This is the set of stars we'll work with in the next step.  But since we have a substantial dataset now, this is a good time to save it.\n",
+    "\n",
+    "Storing the data in a file means we can shut down this notebook and pick up where we left off without running the previous query again.\n",
+    "\n",
+    "Astropy `Table` objects provide `write`, which writes the table to disk."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "filename = 'gd1_results.fits'\n",
+    "results.write(filename, overwrite=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Because the filename ends with `fits`, the table is written in the [FITS format](https://en.wikipedia.org/wiki/FITS), which preserves the metadata associated with the table.\n",
+    "\n",
+    "If the file already exists, the `overwrite` argument causes it to be overwritten.\n",
+    "\n",
+    "To see how big the file is, we can use `ls` with the `-lh` option, which prints information about the file including its size in human-readable form."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-rw-rw-r-- 1 downey downey 8.6M Oct  5 09:09 gd1_results.fits\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!ls -lh gd1_results.fits"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The file is about 8.6 MB."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "In this notebook, we composed more complex queries to select stars within a polygonal region of the sky.  Then we downloaded the results and saved them in a FITS file.\n",
+    "\n",
+    "In the next notebook, we'll reload the data from this file and replicate the next step in the analysis, using proper motion to identify stars likely to be in GD-1."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Best practices\n",
+    "\n",
+    "* For measurements with units, use `Quantity` objects that represent units explicitly and check for errors.\n",
+    "\n",
+    "* Use the `format` function to compose queries; it is often faster and less error-prone.\n",
+    "\n",
+    "* Develop queries incrementally: start with something simple, test it, and add a little bit at a time.\n",
+    "\n",
+    "* Once you have a query working, save the data in a local file.  If you shut down the notebook and come back to it later, you can reload the file; you don't have to run the query again."
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/_build/html/_sources/README.md b/_build/html/_sources/README.md
new file mode 100644
index 0000000..190277b
--- /dev/null
+++ b/_build/html/_sources/README.md
@@ -0,0 +1,172 @@
+# Astronomical Data in Python
+
+*Astronomical Data in Python* is an introduction to tools and practices for working with astronomical data.  Topics covered include:
+
+* Writing queries that select and download data from a database.
+
+* Using data stored in an Astropy `Table` or Pandas `DataFrame`.
+
+* Working with coordinates and other quantities with units.
+
+* Storing data in various formats.
+
+* Performing database join operations that combine data from multiple tables.
+
+* Visualizing data and preparing publication-quality figures.
+
+As a running example, we will replicate part of the analysis in a recent paper, "[Off the beaten path: Gaia reveals GD-1 stars outside of the main stream](https://arxiv.org/abs/1805.00425)" by Adrian M. Price-Whelan and Ana Bonaca.
+
+This material was developed in collaboration with [The Carpentries](https://carpentries.org/) and the Astronomy Curriculum Development Committee, and supported by funding from the American Institute of Physics through the American Astronomical Society.
+
+I am grateful for contributions from the members of the committee -- Azalee Bostroem, Rodolfo Montez, and Phil Rosenfield -- and from Erin Becker, Brett Morris and Adrian Price-Whelan.
+
+The original format of this material is a series of Jupyter notebooks.  Using the
+links below, you can read the notebooks on NBViewer or run them on Colab.  If you
+want to run the notebooks in your own environment, you can download them from
+this repository and follow the instructions below to set up your environment.
+
+This material is also available in the form of [Carpentries lessons](https://datacarpentry.github.io/astronomy-python), but you should be
+aware that these versions might diverge in the future.
+
+**Prerequisites**
+
+This material should be accessible to people familiar with basic Python, but not necessarily the libraries we will use, like Astropy or Pandas.  If you are familiar with Python lists and dictionaries, and you know how to write a function that takes parameters and returns a value, that should be enough.
+
+We assume that you are familiar with astronomy at the undergraduate level, but we will not assume specialized knowledge of the datasets or analysis methods we'll use.
+
+**Notebook 1**
+
+This notebook demonstrates the following steps:
+
+1. Making a connection to the Gaia server,
+
+2. Exploring information about the database and the tables it contains,
+
+3. Writing a query and sending it to the server, and finally
+
+4. Downloading the response from the server as an Astropy `Table`.
+
+Press this button to run this notebook on Colab:
+
+[<img src="run_on_colab_small.png">](https://colab.research.google.com/github/AllenDowney/AstronomicalData/blob/main/01_query.ipynb)
+
+[or click here to read it on NBViewer](https://nbviewer.jupyter.org/github/AllenDowney/AstronomicalData/blob/main/01_query.ipynb)
+
+
+**Notebook 2**
+
+This notebook starts with an example that does a "cone search"; that is, it selects stars that appear in a circular region of the sky.
+
+Then, to select stars in the vicinity of GD-1, we:
+
+* Use `Quantity` objects to represent measurements with units.
+
+* Use the `Gala` library to convert coordinates from one frame to another.
+
+* Use the ADQL keywords `POLYGON`, `CONTAINS`, and `POINT` to select stars that fall within a polygonal region.
+
+* Submit a query and download the results.
+
+* Store the results in a FITS file.
+
+Press this button to run this notebook on Colab:
+
+[<img src="run_on_colab_small.png">](https://colab.research.google.com/github/AllenDowney/AstronomicalData/blob/main/02_coords.ipynb)
+
+[or click here to read it on NBViewer](https://nbviewer.jupyter.org/github/AllenDowney/AstronomicalData/blob/main/02_coords.ipynb)
+
+
+**Notebook 3**
+
+Here are the steps in this notebook:
+
+1. We'll read back the results from the previous notebook, which we saved in a FITS file.
+
+2. Then we'll transform the coordinates and proper motion data from ICRS back to the coordinate frame of GD-1.
+
+3. We'll put those results into a Pandas `DataFrame`, which we'll use to select stars near the centerline of GD-1.
+
+4. Plotting the proper motion of those stars, we'll identify a region of proper motion for stars that are likely to be in GD-1.
+
+5. Finally, we'll select and plot the stars whose proper motion is in that region.
+
+Press this button to run this notebook on Colab:
+
+[<img src="run_on_colab_small.png">](https://colab.research.google.com/github/AllenDowney/AstronomicalData/blob/main/03_motion.ipynb)
+
+[or click here to read it on NBViewer](https://nbviewer.jupyter.org/github/AllenDowney/AstronomicalData/blob/main/03_motion.ipynb)
+
+
+**Notebook 4**
+
+Here are the steps in this notebook:
+
+1. Using data from the previous notebook, we'll identify the values of proper motion for stars likely to be in GD-1.
+
+2. Then we'll compose an ADQL query that selects stars based on proper motion, so we can download only the data we need.
+
+3. We'll also see how to write the results to a CSV file.
+
+That will make it possible to search a bigger region of the sky in a single query.
+
+Press this button to run this notebook on Colab:
+
+[<img src="run_on_colab_small.png">](https://colab.research.google.com/github/AllenDowney/AstronomicalData/blob/main/04_select.ipynb)
+
+[or click here to read it on NBViewer](https://nbviewer.jupyter.org/github/AllenDowney/AstronomicalData/blob/main/04_select.ipynb)
+
+
+**Notebook 5**
+
+Here are the steps in this notebook:
+
+1. We'll reload the candidate stars we identified in the previous notebook.
+
+2. Then we'll run a query on the Gaia server that uploads the table of candidates and uses a `JOIN` operation to select photometry data for the candidate stars.
+
+3. We'll write the results to a file for use in the next notebook.
+
+Press this button to run this notebook on Colab:
+
+[<img src="run_on_colab_small.png">](https://colab.research.google.com/github/AllenDowney/AstronomicalData/blob/main/05_join.ipynb)
+
+[or click here to read it on NBViewer](https://nbviewer.jupyter.org/github/AllenDowney/AstronomicalData/blob/main/05_join.ipynb)
+
+
+**Notebook 6**
+
+Here are the steps in this notebook:
+
+1. We'll reload the data from the previous notebook and make a color-magnitude diagram.
+
+2. Then we'll specify a polygon in the diagram that contains stars with the photometry we expect.
+
+3. Then we'll merge the photometry data with the list of candidate stars, storing the result in a Pandas `DataFrame`.
+
+Press this button to run this notebook on Colab:
+
+[<img src="run_on_colab_small.png">](https://colab.research.google.com/github/AllenDowney/AstronomicalData/blob/main/06_photo.ipynb)
+
+[or click here to read it on NBViewer](https://nbviewer.jupyter.org/github/AllenDowney/AstronomicalData/blob/main/06_photo.ipynb)
+
+
+**Notebook 7**
+
+Here are the steps in this notebook:
+
+1. Starting with the figure from the previous notebook, we'll add annotations to present the results more clearly.
+
+2. The we'll see several ways to customize figures to make them more appealing and effective.
+
+3. Finally, we'll see how to make a figure with multiple panels or subplots.
+
+Press this button to run this notebook on Colab:
+
+[<img src="run_on_colab_small.png">](https://colab.research.google.com/github/AllenDowney/AstronomicalData/blob/main/07_plot.ipynb)
+
+[or click here to read it on NBViewer](https://nbviewer.jupyter.org/github/AllenDowney/AstronomicalData/blob/main/07_plot.ipynb)
+
+
+**Installation instructions**
+
+Coming soon.
diff --git a/_build/html/_sources/index.md b/_build/html/_sources/index.md
new file mode 100644
index 0000000..a6a7b98
--- /dev/null
+++ b/_build/html/_sources/index.md
@@ -0,0 +1,169 @@
+# Astronomical Data in Python
+
+*Astronomical Data in Python* is an introduction to tools and practices for working with astronomical data.  Topics covered include:
+
+* Writing queries that select and download data from a database.
+
+* Using data stored in an Astropy `Table` or Pandas `DataFrame`.
+
+* Working with coordinates and other quantities with units.
+
+* Storing data in various formats.
+
+* Performing database join operations that combine data from multiple tables.
+
+* Visualizing data and preparing publication-quality figures.
+
+As a running example, we will replicate part of the analysis in a recent paper, "[Off the beaten path: Gaia reveals GD-1 stars outside of the main stream](https://arxiv.org/abs/1805.00425)" by Adrian M. Price-Whelan and Ana Bonaca.
+
+This material was developed in collaboration with [The Carpentries](https://carpentries.org/) and the Astronomy Curriculum Development Committee, and supported by funding from the American Institute of Physics through the American Astronomical Society.
+
+I am grateful for contributions from the members of the committee -- Azalee Bostroem, Rodolfo Montez, and Phil Rosenfield -- and from Erin Becker, Brett Morris and Adrian Price-Whelan.
+
+The original format of this material is a series of Jupyter notebooks.  Using the
+links below, you can read the notebooks on NBViewer or run them on Colab.  If you
+want to run the notebooks in your own environment, you can download them from
+this repository and follow the instructions below to set up your environment.
+
+### Prerequisites
+
+This material should be accessible to people familiar with basic Python, but not necessarily the libraries we will use, like Astropy or Pandas.  If you are familiar with Python lists and dictionaries, and you know how to write a function that takes parameters and returns a value, that should be enough.
+
+We assume that you are familiar with astronomy at the undergraduate level, but we will not assume specialized knowledge of the datasets or analysis methods we'll use.
+
+### Notebook 1
+
+This notebook demonstrates the following steps:
+
+1. Making a connection to the Gaia server,
+
+2. Exploring information about the database and the tables it contains,
+
+3. Writing a query and sending it to the server, and finally
+
+4. Downloading the response from the server as an Astropy `Table`.
+
+Press this button to run this notebook on Colab:
+
+[<img src="run_on_colab_small.png">](https://colab.research.google.com/github/AllenDowney/AstronomicalData/blob/main/01_query.ipynb)
+
+[or click here to read it on NBViewer](https://nbviewer.jupyter.org/github/AllenDowney/AstronomicalData/blob/main/01_query.ipynb)
+
+
+### Notebook 2
+
+This notebook starts with an example that does a "cone search"; that is, it selects stars that appear in a circular region of the sky.
+
+Then, to select stars in the vicinity of GD-1, we:
+
+* Use `Quantity` objects to represent measurements with units.
+
+* Use the `Gala` library to convert coordinates from one frame to another.
+
+* Use the ADQL keywords `POLYGON`, `CONTAINS`, and `POINT` to select stars that fall within a polygonal region.
+
+* Submit a query and download the results.
+
+* Store the results in a FITS file.
+
+Press this button to run this notebook on Colab:
+
+[<img src="run_on_colab_small.png">](https://colab.research.google.com/github/AllenDowney/AstronomicalData/blob/main/02_coords.ipynb)
+
+[or click here to read it on NBViewer](https://nbviewer.jupyter.org/github/AllenDowney/AstronomicalData/blob/main/02_coords.ipynb)
+
+
+### Notebook 3
+
+Here are the steps in this notebook:
+
+1. We'll read back the results from the previous notebook, which we saved in a FITS file.
+
+2. Then we'll transform the coordinates and proper motion data from ICRS back to the coordinate frame of GD-1.
+
+3. We'll put those results into a Pandas `DataFrame`, which we'll use to select stars near the centerline of GD-1.
+
+4. Plotting the proper motion of those stars, we'll identify a region of proper motion for stars that are likely to be in GD-1.
+
+5. Finally, we'll select and plot the stars whose proper motion is in that region.
+
+Press this button to run this notebook on Colab:
+
+[<img src="run_on_colab_small.png">](https://colab.research.google.com/github/AllenDowney/AstronomicalData/blob/main/03_motion.ipynb)
+
+[or click here to read it on NBViewer](https://nbviewer.jupyter.org/github/AllenDowney/AstronomicalData/blob/main/03_motion.ipynb)
+
+
+### Notebook 4
+
+Here are the steps in this notebook:
+
+1. Using data from the previous notebook, we'll identify the values of proper motion for stars likely to be in GD-1.
+
+2. Then we'll compose an ADQL query that selects stars based on proper motion, so we can download only the data we need.
+
+3. We'll also see how to write the results to a CSV file.
+
+That will make it possible to search a bigger region of the sky in a single query.
+
+Press this button to run this notebook on Colab:
+
+[<img src="run_on_colab_small.png">](https://colab.research.google.com/github/AllenDowney/AstronomicalData/blob/main/04_select.ipynb)
+
+[or click here to read it on NBViewer](https://nbviewer.jupyter.org/github/AllenDowney/AstronomicalData/blob/main/04_select.ipynb)
+
+
+### Notebook 5
+
+Here are the steps in this notebook:
+
+1. We'll reload the candidate stars we identified in the previous notebook.
+
+2. Then we'll run a query on the Gaia server that uploads the table of candidates and uses a `JOIN` operation to select photometry data for the candidate stars.
+
+3. We'll write the results to a file for use in the next notebook.
+
+Press this button to run this notebook on Colab:
+
+[<img src="run_on_colab_small.png">](https://colab.research.google.com/github/AllenDowney/AstronomicalData/blob/main/05_join.ipynb)
+
+[or click here to read it on NBViewer](https://nbviewer.jupyter.org/github/AllenDowney/AstronomicalData/blob/main/05_join.ipynb)
+
+
+### Notebook 6
+
+Here are the steps in this notebook:
+
+1. We'll reload the data from the previous notebook and make a color-magnitude diagram.
+
+2. Then we'll specify a polygon in the diagram that contains stars with the photometry we expect.
+
+3. Then we'll merge the photometry data with the list of candidate stars, storing the result in a Pandas `DataFrame`.
+
+Press this button to run this notebook on Colab:
+
+[<img src="run_on_colab_small.png">](https://colab.research.google.com/github/AllenDowney/AstronomicalData/blob/main/06_photo.ipynb)
+
+[or click here to read it on NBViewer](https://nbviewer.jupyter.org/github/AllenDowney/AstronomicalData/blob/main/06_photo.ipynb)
+
+
+### Notebook 7
+
+Here are the steps in this notebook:
+
+1. Starting with the figure from the previous notebook, we'll add annotations to present the results more clearly.
+
+2. The we'll see several ways to customize figures to make them more appealing and effective.
+
+3. Finally, we'll see how to make a figure with multiple panels or subplots.
+
+Press this button to run this notebook on Colab:
+
+[<img src="run_on_colab_small.png">](https://colab.research.google.com/github/AllenDowney/AstronomicalData/blob/main/07_plot.ipynb)
+
+[or click here to read it on NBViewer](https://nbviewer.jupyter.org/github/AllenDowney/AstronomicalData/blob/main/07_plot.ipynb)
+
+
+## Installation instructions
+
+Coming soon.
diff --git a/_build/html/_sources/last_resort.ipynb b/_build/html/_sources/last_resort.ipynb
new file mode 100644
index 0000000..7982d3c
--- /dev/null
+++ b/_build/html/_sources/last_resort.ipynb
@@ -0,0 +1,72 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# The Notebook of Last Resort"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you are not able to get everything installed that we need for the workshop, you have the option of running this notebook on Colab.\n",
+    "\n",
+    "Before you get started, you probably want to press the Save button!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "tags": [
+     "hide-cell"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# If we're running on Colab, install libraries\n",
+    "\n",
+    "import sys\n",
+    "IN_COLAB = 'google.colab' in sys.modules\n",
+    "\n",
+    "if IN_COLAB:\n",
+    "    !pip install astroquery astro-gala pyia"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "That should be everything you need.  Now you can type code and run it in the following cells."
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/_build/html/_sources/test_setup.ipynb b/_build/html/_sources/test_setup.ipynb
new file mode 100644
index 0000000..10b3888
--- /dev/null
+++ b/_build/html/_sources/test_setup.ipynb
@@ -0,0 +1,136 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Astronomical Data in Python\n",
+    "\n",
+    "This notebook imports the libraries we need for the workshop.\n",
+    "\n",
+    "If any of them are missing, you'll get an error message.\n",
+    "\n",
+    "If you don't get any error messages, you are all set."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from wget import download"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib as mpl\n",
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib.path import Path\n",
+    "from matplotlib.patches import Polygon"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import astropy.coordinates as coord\n",
+    "import astropy.units as u\n",
+    "from astropy.table import Table"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import gala.coordinates as gc\n",
+    "from pyia import GaiaData"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Created TAP+ (v1.2.1) - Connection:\n",
+      "\tHost: gea.esac.esa.int\n",
+      "\tUse HTTPS: True\n",
+      "\tPort: 443\n",
+      "\tSSL Port: 443\n",
+      "Created TAP+ (v1.2.1) - Connection:\n",
+      "\tHost: geadata.esac.esa.int\n",
+      "\tUse HTTPS: True\n",
+      "\tPort: 443\n",
+      "\tSSL Port: 443\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Note: running this import statement opens a connection\n",
+    "# to a Gaia server, so it will fail if you are not connected\n",
+    "# to the internet.\n",
+    "\n",
+    "from astroquery.gaia import Gaia"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "During the workshop, we might put some code on Slack and ask you to cut and paste it into the notebook.\n",
+    "\n",
+    "If you are on a Mac, you might encounter a problem: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/_build/html/_static/__init__.py b/_build/html/_static/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/_build/html/_static/basic.css b/_build/html/_static/basic.css
new file mode 100644
index 0000000..616111c
--- /dev/null
+++ b/_build/html/_static/basic.css
@@ -0,0 +1,855 @@
+/*
+ * basic.css
+ * ~~~~~~~~~
+ *
+ * Sphinx stylesheet -- basic theme.
+ *
+ * :copyright: Copyright 2007-2020 by the Sphinx team, see AUTHORS.
+ * :license: BSD, see LICENSE for details.
+ *
+ */
+
+/* -- main layout ----------------------------------------------------------- */
+
+div.clearer {
+    clear: both;
+}
+
+div.section::after {
+    display: block;
+    content: '';
+    clear: left;
+}
+
+/* -- relbar ---------------------------------------------------------------- */
+
+div.related {
+    width: 100%;
+    font-size: 90%;
+}
+
+div.related h3 {
+    display: none;
+}
+
+div.related ul {
+    margin: 0;
+    padding: 0 0 0 10px;
+    list-style: none;
+}
+
+div.related li {
+    display: inline;
+}
+
+div.related li.right {
+    float: right;
+    margin-right: 5px;
+}
+
+/* -- sidebar --------------------------------------------------------------- */
+
+div.sphinxsidebarwrapper {
+    padding: 10px 5px 0 10px;
+}
+
+div.sphinxsidebar {
+    float: left;
+    width: 270px;
+    margin-left: -100%;
+    font-size: 90%;
+    word-wrap: break-word;
+    overflow-wrap : break-word;
+}
+
+div.sphinxsidebar ul {
+    list-style: none;
+}
+
+div.sphinxsidebar ul ul,
+div.sphinxsidebar ul.want-points {
+    margin-left: 20px;
+    list-style: square;
+}
+
+div.sphinxsidebar ul ul {
+    margin-top: 0;
+    margin-bottom: 0;
+}
+
+div.sphinxsidebar form {
+    margin-top: 10px;
+}
+
+div.sphinxsidebar input {
+    border: 1px solid #98dbcc;
+    font-family: sans-serif;
+    font-size: 1em;
+}
+
+div.sphinxsidebar #searchbox form.search {
+    overflow: hidden;
+}
+
+div.sphinxsidebar #searchbox input[type="text"] {
+    float: left;
+    width: 80%;
+    padding: 0.25em;
+    box-sizing: border-box;
+}
+
+div.sphinxsidebar #searchbox input[type="submit"] {
+    float: left;
+    width: 20%;
+    border-left: none;
+    padding: 0.25em;
+    box-sizing: border-box;
+}
+
+
+img {
+    border: 0;
+    max-width: 100%;
+}
+
+/* -- search page ----------------------------------------------------------- */
+
+ul.search {
+    margin: 10px 0 0 20px;
+    padding: 0;
+}
+
+ul.search li {
+    padding: 5px 0 5px 20px;
+    background-image: url(file.png);
+    background-repeat: no-repeat;
+    background-position: 0 7px;
+}
+
+ul.search li a {
+    font-weight: bold;
+}
+
+ul.search li div.context {
+    color: #888;
+    margin: 2px 0 0 30px;
+    text-align: left;
+}
+
+ul.keywordmatches li.goodmatch a {
+    font-weight: bold;
+}
+
+/* -- index page ------------------------------------------------------------ */
+
+table.contentstable {
+    width: 90%;
+    margin-left: auto;
+    margin-right: auto;
+}
+
+table.contentstable p.biglink {
+    line-height: 150%;
+}
+
+a.biglink {
+    font-size: 1.3em;
+}
+
+span.linkdescr {
+    font-style: italic;
+    padding-top: 5px;
+    font-size: 90%;
+}
+
+/* -- general index --------------------------------------------------------- */
+
+table.indextable {
+    width: 100%;
+}
+
+table.indextable td {
+    text-align: left;
+    vertical-align: top;
+}
+
+table.indextable ul {
+    margin-top: 0;
+    margin-bottom: 0;
+    list-style-type: none;
+}
+
+table.indextable > tbody > tr > td > ul {
+    padding-left: 0em;
+}
+
+table.indextable tr.pcap {
+    height: 10px;
+}
+
+table.indextable tr.cap {
+    margin-top: 10px;
+    background-color: #f2f2f2;
+}
+
+img.toggler {
+    margin-right: 3px;
+    margin-top: 3px;
+    cursor: pointer;
+}
+
+div.modindex-jumpbox {
+    border-top: 1px solid #ddd;
+    border-bottom: 1px solid #ddd;
+    margin: 1em 0 1em 0;
+    padding: 0.4em;
+}
+
+div.genindex-jumpbox {
+    border-top: 1px solid #ddd;
+    border-bottom: 1px solid #ddd;
+    margin: 1em 0 1em 0;
+    padding: 0.4em;
+}
+
+/* -- domain module index --------------------------------------------------- */
+
+table.modindextable td {
+    padding: 2px;
+    border-collapse: collapse;
+}
+
+/* -- general body styles --------------------------------------------------- */
+
+div.body {
+    min-width: 450px;
+    max-width: 800px;
+}
+
+div.body p, div.body dd, div.body li, div.body blockquote {
+    -moz-hyphens: auto;
+    -ms-hyphens: auto;
+    -webkit-hyphens: auto;
+    hyphens: auto;
+}
+
+a.headerlink {
+    visibility: hidden;
+}
+
+a.brackets:before,
+span.brackets > a:before{
+    content: "[";
+}
+
+a.brackets:after,
+span.brackets > a:after {
+    content: "]";
+}
+
+h1:hover > a.headerlink,
+h2:hover > a.headerlink,
+h3:hover > a.headerlink,
+h4:hover > a.headerlink,
+h5:hover > a.headerlink,
+h6:hover > a.headerlink,
+dt:hover > a.headerlink,
+caption:hover > a.headerlink,
+p.caption:hover > a.headerlink,
+div.code-block-caption:hover > a.headerlink {
+    visibility: visible;
+}
+
+div.body p.caption {
+    text-align: inherit;
+}
+
+div.body td {
+    text-align: left;
+}
+
+.first {
+    margin-top: 0 !important;
+}
+
+p.rubric {
+    margin-top: 30px;
+    font-weight: bold;
+}
+
+img.align-left, .figure.align-left, object.align-left {
+    clear: left;
+    float: left;
+    margin-right: 1em;
+}
+
+img.align-right, .figure.align-right, object.align-right {
+    clear: right;
+    float: right;
+    margin-left: 1em;
+}
+
+img.align-center, .figure.align-center, object.align-center {
+  display: block;
+  margin-left: auto;
+  margin-right: auto;
+}
+
+img.align-default, .figure.align-default {
+  display: block;
+  margin-left: auto;
+  margin-right: auto;
+}
+
+.align-left {
+    text-align: left;
+}
+
+.align-center {
+    text-align: center;
+}
+
+.align-default {
+    text-align: center;
+}
+
+.align-right {
+    text-align: right;
+}
+
+/* -- sidebars -------------------------------------------------------------- */
+
+div.sidebar {
+    margin: 0 0 0.5em 1em;
+    border: 1px solid #ddb;
+    padding: 7px;
+    background-color: #ffe;
+    width: 40%;
+    float: right;
+    clear: right;
+    overflow-x: auto;
+}
+
+p.sidebar-title {
+    font-weight: bold;
+}
+
+div.admonition, div.topic, blockquote {
+    clear: left;
+}
+
+/* -- topics ---------------------------------------------------------------- */
+
+div.topic {
+    border: 1px solid #ccc;
+    padding: 7px;
+    margin: 10px 0 10px 0;
+}
+
+p.topic-title {
+    font-size: 1.1em;
+    font-weight: bold;
+    margin-top: 10px;
+}
+
+/* -- admonitions ----------------------------------------------------------- */
+
+div.admonition {
+    margin-top: 10px;
+    margin-bottom: 10px;
+    padding: 7px;
+}
+
+div.admonition dt {
+    font-weight: bold;
+}
+
+p.admonition-title {
+    margin: 0px 10px 5px 0px;
+    font-weight: bold;
+}
+
+div.body p.centered {
+    text-align: center;
+    margin-top: 25px;
+}
+
+/* -- content of sidebars/topics/admonitions -------------------------------- */
+
+div.sidebar > :last-child,
+div.topic > :last-child,
+div.admonition > :last-child {
+    margin-bottom: 0;
+}
+
+div.sidebar::after,
+div.topic::after,
+div.admonition::after,
+blockquote::after {
+    display: block;
+    content: '';
+    clear: both;
+}
+
+/* -- tables ---------------------------------------------------------------- */
+
+table.docutils {
+    margin-top: 10px;
+    margin-bottom: 10px;
+    border: 0;
+    border-collapse: collapse;
+}
+
+table.align-center {
+    margin-left: auto;
+    margin-right: auto;
+}
+
+table.align-default {
+    margin-left: auto;
+    margin-right: auto;
+}
+
+table caption span.caption-number {
+    font-style: italic;
+}
+
+table caption span.caption-text {
+}
+
+table.docutils td, table.docutils th {
+    padding: 1px 8px 1px 5px;
+    border-top: 0;
+    border-left: 0;
+    border-right: 0;
+    border-bottom: 1px solid #aaa;
+}
+
+table.footnote td, table.footnote th {
+    border: 0 !important;
+}
+
+th {
+    text-align: left;
+    padding-right: 5px;
+}
+
+table.citation {
+    border-left: solid 1px gray;
+    margin-left: 1px;
+}
+
+table.citation td {
+    border-bottom: none;
+}
+
+th > :first-child,
+td > :first-child {
+    margin-top: 0px;
+}
+
+th > :last-child,
+td > :last-child {
+    margin-bottom: 0px;
+}
+
+/* -- figures --------------------------------------------------------------- */
+
+div.figure {
+    margin: 0.5em;
+    padding: 0.5em;
+}
+
+div.figure p.caption {
+    padding: 0.3em;
+}
+
+div.figure p.caption span.caption-number {
+    font-style: italic;
+}
+
+div.figure p.caption span.caption-text {
+}
+
+/* -- field list styles ----------------------------------------------------- */
+
+table.field-list td, table.field-list th {
+    border: 0 !important;
+}
+
+.field-list ul {
+    margin: 0;
+    padding-left: 1em;
+}
+
+.field-list p {
+    margin: 0;
+}
+
+.field-name {
+    -moz-hyphens: manual;
+    -ms-hyphens: manual;
+    -webkit-hyphens: manual;
+    hyphens: manual;
+}
+
+/* -- hlist styles ---------------------------------------------------------- */
+
+table.hlist {
+    margin: 1em 0;
+}
+
+table.hlist td {
+    vertical-align: top;
+}
+
+
+/* -- other body styles ----------------------------------------------------- */
+
+ol.arabic {
+    list-style: decimal;
+}
+
+ol.loweralpha {
+    list-style: lower-alpha;
+}
+
+ol.upperalpha {
+    list-style: upper-alpha;
+}
+
+ol.lowerroman {
+    list-style: lower-roman;
+}
+
+ol.upperroman {
+    list-style: upper-roman;
+}
+
+:not(li) > ol > li:first-child > :first-child,
+:not(li) > ul > li:first-child > :first-child {
+    margin-top: 0px;
+}
+
+:not(li) > ol > li:last-child > :last-child,
+:not(li) > ul > li:last-child > :last-child {
+    margin-bottom: 0px;
+}
+
+ol.simple ol p,
+ol.simple ul p,
+ul.simple ol p,
+ul.simple ul p {
+    margin-top: 0;
+}
+
+ol.simple > li:not(:first-child) > p,
+ul.simple > li:not(:first-child) > p {
+    margin-top: 0;
+}
+
+ol.simple p,
+ul.simple p {
+    margin-bottom: 0;
+}
+
+dl.footnote > dt,
+dl.citation > dt {
+    float: left;
+    margin-right: 0.5em;
+}
+
+dl.footnote > dd,
+dl.citation > dd {
+    margin-bottom: 0em;
+}
+
+dl.footnote > dd:after,
+dl.citation > dd:after {
+    content: "";
+    clear: both;
+}
+
+dl.field-list {
+    display: grid;
+    grid-template-columns: fit-content(30%) auto;
+}
+
+dl.field-list > dt {
+    font-weight: bold;
+    word-break: break-word;
+    padding-left: 0.5em;
+    padding-right: 5px;
+}
+
+dl.field-list > dt:after {
+    content: ":";
+}
+
+dl.field-list > dd {
+    padding-left: 0.5em;
+    margin-top: 0em;
+    margin-left: 0em;
+    margin-bottom: 0em;
+}
+
+dl {
+    margin-bottom: 15px;
+}
+
+dd > :first-child {
+    margin-top: 0px;
+}
+
+dd ul, dd table {
+    margin-bottom: 10px;
+}
+
+dd {
+    margin-top: 3px;
+    margin-bottom: 10px;
+    margin-left: 30px;
+}
+
+dl > dd:last-child,
+dl > dd:last-child > :last-child {
+    margin-bottom: 0;
+}
+
+dt:target, span.highlighted {
+    background-color: #fbe54e;
+}
+
+rect.highlighted {
+    fill: #fbe54e;
+}
+
+dl.glossary dt {
+    font-weight: bold;
+    font-size: 1.1em;
+}
+
+.optional {
+    font-size: 1.3em;
+}
+
+.sig-paren {
+    font-size: larger;
+}
+
+.versionmodified {
+    font-style: italic;
+}
+
+.system-message {
+    background-color: #fda;
+    padding: 5px;
+    border: 3px solid red;
+}
+
+.footnote:target  {
+    background-color: #ffa;
+}
+
+.line-block {
+    display: block;
+    margin-top: 1em;
+    margin-bottom: 1em;
+}
+
+.line-block .line-block {
+    margin-top: 0;
+    margin-bottom: 0;
+    margin-left: 1.5em;
+}
+
+.guilabel, .menuselection {
+    font-family: sans-serif;
+}
+
+.accelerator {
+    text-decoration: underline;
+}
+
+.classifier {
+    font-style: oblique;
+}
+
+.classifier:before {
+    font-style: normal;
+    margin: 0.5em;
+    content: ":";
+}
+
+abbr, acronym {
+    border-bottom: dotted 1px;
+    cursor: help;
+}
+
+/* -- code displays --------------------------------------------------------- */
+
+pre {
+    overflow: auto;
+    overflow-y: hidden;  /* fixes display issues on Chrome browsers */
+}
+
+pre, div[class*="highlight-"] {
+    clear: both;
+}
+
+span.pre {
+    -moz-hyphens: none;
+    -ms-hyphens: none;
+    -webkit-hyphens: none;
+    hyphens: none;
+}
+
+div[class*="highlight-"] {
+    margin: 1em 0;
+}
+
+td.linenos pre {
+    border: 0;
+    background-color: transparent;
+    color: #aaa;
+}
+
+table.highlighttable {
+    display: block;
+}
+
+table.highlighttable tbody {
+    display: block;
+}
+
+table.highlighttable tr {
+    display: flex;
+}
+
+table.highlighttable td {
+    margin: 0;
+    padding: 0;
+}
+
+table.highlighttable td.linenos {
+    padding-right: 0.5em;
+}
+
+table.highlighttable td.code {
+    flex: 1;
+    overflow: hidden;
+}
+
+.highlight .hll {
+    display: block;
+}
+
+div.highlight pre,
+table.highlighttable pre {
+    margin: 0;
+}
+
+div.code-block-caption + div {
+    margin-top: 0;
+}
+
+div.code-block-caption {
+    margin-top: 1em;
+    padding: 2px 5px;
+    font-size: small;
+}
+
+div.code-block-caption code {
+    background-color: transparent;
+}
+
+table.highlighttable td.linenos,
+div.doctest > div.highlight span.gp {  /* gp: Generic.Prompt */
+    user-select: none;
+}
+
+div.code-block-caption span.caption-number {
+    padding: 0.1em 0.3em;
+    font-style: italic;
+}
+
+div.code-block-caption span.caption-text {
+}
+
+div.literal-block-wrapper {
+    margin: 1em 0;
+}
+
+code.descname {
+    background-color: transparent;
+    font-weight: bold;
+    font-size: 1.2em;
+}
+
+code.descclassname {
+    background-color: transparent;
+}
+
+code.xref, a code {
+    background-color: transparent;
+    font-weight: bold;
+}
+
+h1 code, h2 code, h3 code, h4 code, h5 code, h6 code {
+    background-color: transparent;
+}
+
+.viewcode-link {
+    float: right;
+}
+
+.viewcode-back {
+    float: right;
+    font-family: sans-serif;
+}
+
+div.viewcode-block:target {
+    margin: -1px -10px;
+    padding: 0 10px;
+}
+
+/* -- math display ---------------------------------------------------------- */
+
+img.math {
+    vertical-align: middle;
+}
+
+div.body div.math p {
+    text-align: center;
+}
+
+span.eqno {
+    float: right;
+}
+
+span.eqno a.headerlink {
+    position: absolute;
+    z-index: 1;
+}
+
+div.math:hover a.headerlink {
+    visibility: visible;
+}
+
+/* -- printout stylesheet --------------------------------------------------- */
+
+@media print {
+    div.document,
+    div.documentwrapper,
+    div.bodywrapper {
+        margin: 0 !important;
+        width: 100%;
+    }
+
+    div.sphinxsidebar,
+    div.related,
+    div.footer,
+    #top-link {
+        display: none;
+    }
+}
\ No newline at end of file
diff --git a/_build/html/_static/clipboard.min.js b/_build/html/_static/clipboard.min.js
new file mode 100644
index 0000000..02c549e
--- /dev/null
+++ b/_build/html/_static/clipboard.min.js
@@ -0,0 +1,7 @@
+/*!
+ * clipboard.js v2.0.4
+ * https://zenorocha.github.io/clipboard.js
+ * 
+ * Licensed MIT © Zeno Rocha
+ */
+!function(t,e){"object"==typeof exports&&"object"==typeof module?module.exports=e():"function"==typeof define&&define.amd?define([],e):"object"==typeof exports?exports.ClipboardJS=e():t.ClipboardJS=e()}(this,function(){return function(n){var o={};function r(t){if(o[t])return o[t].exports;var e=o[t]={i:t,l:!1,exports:{}};return n[t].call(e.exports,e,e.exports,r),e.l=!0,e.exports}return r.m=n,r.c=o,r.d=function(t,e,n){r.o(t,e)||Object.defineProperty(t,e,{enumerable:!0,get:n})},r.r=function(t){"undefined"!=typeof Symbol&&Symbol.toStringTag&&Object.defineProperty(t,Symbol.toStringTag,{value:"Module"}),Object.defineProperty(t,"__esModule",{value:!0})},r.t=function(e,t){if(1&t&&(e=r(e)),8&t)return e;if(4&t&&"object"==typeof e&&e&&e.__esModule)return e;var n=Object.create(null);if(r.r(n),Object.defineProperty(n,"default",{enumerable:!0,value:e}),2&t&&"string"!=typeof e)for(var o in e)r.d(n,o,function(t){return e[t]}.bind(null,o));return n},r.n=function(t){var e=t&&t.__esModule?function(){return t.default}:function(){return t};return r.d(e,"a",e),e},r.o=function(t,e){return Object.prototype.hasOwnProperty.call(t,e)},r.p="",r(r.s=0)}([function(t,e,n){"use strict";var r="function"==typeof Symbol&&"symbol"==typeof Symbol.iterator?function(t){return typeof t}:function(t){return t&&"function"==typeof Symbol&&t.constructor===Symbol&&t!==Symbol.prototype?"symbol":typeof t},i=function(){function o(t,e){for(var n=0;n<e.length;n++){var o=e[n];o.enumerable=o.enumerable||!1,o.configurable=!0,"value"in o&&(o.writable=!0),Object.defineProperty(t,o.key,o)}}return function(t,e,n){return e&&o(t.prototype,e),n&&o(t,n),t}}(),a=o(n(1)),c=o(n(3)),u=o(n(4));function o(t){return t&&t.__esModule?t:{default:t}}var l=function(t){function o(t,e){!function(t,e){if(!(t instanceof e))throw new TypeError("Cannot call a class as a function")}(this,o);var n=function(t,e){if(!t)throw new ReferenceError("this hasn't been initialised - super() hasn't been called");return!e||"object"!=typeof e&&"function"!=typeof e?t:e}(this,(o.__proto__||Object.getPrototypeOf(o)).call(this));return n.resolveOptions(e),n.listenClick(t),n}return function(t,e){if("function"!=typeof e&&null!==e)throw new TypeError("Super expression must either be null or a function, not "+typeof e);t.prototype=Object.create(e&&e.prototype,{constructor:{value:t,enumerable:!1,writable:!0,configurable:!0}}),e&&(Object.setPrototypeOf?Object.setPrototypeOf(t,e):t.__proto__=e)}(o,c.default),i(o,[{key:"resolveOptions",value:function(){var t=0<arguments.length&&void 0!==arguments[0]?arguments[0]:{};this.action="function"==typeof t.action?t.action:this.defaultAction,this.target="function"==typeof t.target?t.target:this.defaultTarget,this.text="function"==typeof t.text?t.text:this.defaultText,this.container="object"===r(t.container)?t.container:document.body}},{key:"listenClick",value:function(t){var e=this;this.listener=(0,u.default)(t,"click",function(t){return e.onClick(t)})}},{key:"onClick",value:function(t){var e=t.delegateTarget||t.currentTarget;this.clipboardAction&&(this.clipboardAction=null),this.clipboardAction=new a.default({action:this.action(e),target:this.target(e),text:this.text(e),container:this.container,trigger:e,emitter:this})}},{key:"defaultAction",value:function(t){return s("action",t)}},{key:"defaultTarget",value:function(t){var e=s("target",t);if(e)return document.querySelector(e)}},{key:"defaultText",value:function(t){return s("text",t)}},{key:"destroy",value:function(){this.listener.destroy(),this.clipboardAction&&(this.clipboardAction.destroy(),this.clipboardAction=null)}}],[{key:"isSupported",value:function(){var t=0<arguments.length&&void 0!==arguments[0]?arguments[0]:["copy","cut"],e="string"==typeof t?[t]:t,n=!!document.queryCommandSupported;return e.forEach(function(t){n=n&&!!document.queryCommandSupported(t)}),n}}]),o}();function s(t,e){var n="data-clipboard-"+t;if(e.hasAttribute(n))return e.getAttribute(n)}t.exports=l},function(t,e,n){"use strict";var o,r="function"==typeof Symbol&&"symbol"==typeof Symbol.iterator?function(t){return typeof t}:function(t){return t&&"function"==typeof Symbol&&t.constructor===Symbol&&t!==Symbol.prototype?"symbol":typeof t},i=function(){function o(t,e){for(var n=0;n<e.length;n++){var o=e[n];o.enumerable=o.enumerable||!1,o.configurable=!0,"value"in o&&(o.writable=!0),Object.defineProperty(t,o.key,o)}}return function(t,e,n){return e&&o(t.prototype,e),n&&o(t,n),t}}(),a=n(2),c=(o=a)&&o.__esModule?o:{default:o};var u=function(){function e(t){!function(t,e){if(!(t instanceof e))throw new TypeError("Cannot call a class as a function")}(this,e),this.resolveOptions(t),this.initSelection()}return i(e,[{key:"resolveOptions",value:function(){var t=0<arguments.length&&void 0!==arguments[0]?arguments[0]:{};this.action=t.action,this.container=t.container,this.emitter=t.emitter,this.target=t.target,this.text=t.text,this.trigger=t.trigger,this.selectedText=""}},{key:"initSelection",value:function(){this.text?this.selectFake():this.target&&this.selectTarget()}},{key:"selectFake",value:function(){var t=this,e="rtl"==document.documentElement.getAttribute("dir");this.removeFake(),this.fakeHandlerCallback=function(){return t.removeFake()},this.fakeHandler=this.container.addEventListener("click",this.fakeHandlerCallback)||!0,this.fakeElem=document.createElement("textarea"),this.fakeElem.style.fontSize="12pt",this.fakeElem.style.border="0",this.fakeElem.style.padding="0",this.fakeElem.style.margin="0",this.fakeElem.style.position="absolute",this.fakeElem.style[e?"right":"left"]="-9999px";var n=window.pageYOffset||document.documentElement.scrollTop;this.fakeElem.style.top=n+"px",this.fakeElem.setAttribute("readonly",""),this.fakeElem.value=this.text,this.container.appendChild(this.fakeElem),this.selectedText=(0,c.default)(this.fakeElem),this.copyText()}},{key:"removeFake",value:function(){this.fakeHandler&&(this.container.removeEventListener("click",this.fakeHandlerCallback),this.fakeHandler=null,this.fakeHandlerCallback=null),this.fakeElem&&(this.container.removeChild(this.fakeElem),this.fakeElem=null)}},{key:"selectTarget",value:function(){this.selectedText=(0,c.default)(this.target),this.copyText()}},{key:"copyText",value:function(){var e=void 0;try{e=document.execCommand(this.action)}catch(t){e=!1}this.handleResult(e)}},{key:"handleResult",value:function(t){this.emitter.emit(t?"success":"error",{action:this.action,text:this.selectedText,trigger:this.trigger,clearSelection:this.clearSelection.bind(this)})}},{key:"clearSelection",value:function(){this.trigger&&this.trigger.focus(),window.getSelection().removeAllRanges()}},{key:"destroy",value:function(){this.removeFake()}},{key:"action",set:function(){var t=0<arguments.length&&void 0!==arguments[0]?arguments[0]:"copy";if(this._action=t,"copy"!==this._action&&"cut"!==this._action)throw new Error('Invalid "action" value, use either "copy" or "cut"')},get:function(){return this._action}},{key:"target",set:function(t){if(void 0!==t){if(!t||"object"!==(void 0===t?"undefined":r(t))||1!==t.nodeType)throw new Error('Invalid "target" value, use a valid Element');if("copy"===this.action&&t.hasAttribute("disabled"))throw new Error('Invalid "target" attribute. Please use "readonly" instead of "disabled" attribute');if("cut"===this.action&&(t.hasAttribute("readonly")||t.hasAttribute("disabled")))throw new Error('Invalid "target" attribute. You can\'t cut text from elements with "readonly" or "disabled" attributes');this._target=t}},get:function(){return this._target}}]),e}();t.exports=u},function(t,e){t.exports=function(t){var e;if("SELECT"===t.nodeName)t.focus(),e=t.value;else if("INPUT"===t.nodeName||"TEXTAREA"===t.nodeName){var n=t.hasAttribute("readonly");n||t.setAttribute("readonly",""),t.select(),t.setSelectionRange(0,t.value.length),n||t.removeAttribute("readonly"),e=t.value}else{t.hasAttribute("contenteditable")&&t.focus();var o=window.getSelection(),r=document.createRange();r.selectNodeContents(t),o.removeAllRanges(),o.addRange(r),e=o.toString()}return e}},function(t,e){function n(){}n.prototype={on:function(t,e,n){var o=this.e||(this.e={});return(o[t]||(o[t]=[])).push({fn:e,ctx:n}),this},once:function(t,e,n){var o=this;function r(){o.off(t,r),e.apply(n,arguments)}return r._=e,this.on(t,r,n)},emit:function(t){for(var e=[].slice.call(arguments,1),n=((this.e||(this.e={}))[t]||[]).slice(),o=0,r=n.length;o<r;o++)n[o].fn.apply(n[o].ctx,e);return this},off:function(t,e){var n=this.e||(this.e={}),o=n[t],r=[];if(o&&e)for(var i=0,a=o.length;i<a;i++)o[i].fn!==e&&o[i].fn._!==e&&r.push(o[i]);return r.length?n[t]=r:delete n[t],this}},t.exports=n},function(t,e,n){var d=n(5),h=n(6);t.exports=function(t,e,n){if(!t&&!e&&!n)throw new Error("Missing required arguments");if(!d.string(e))throw new TypeError("Second argument must be a String");if(!d.fn(n))throw new TypeError("Third argument must be a Function");if(d.node(t))return s=e,f=n,(l=t).addEventListener(s,f),{destroy:function(){l.removeEventListener(s,f)}};if(d.nodeList(t))return a=t,c=e,u=n,Array.prototype.forEach.call(a,function(t){t.addEventListener(c,u)}),{destroy:function(){Array.prototype.forEach.call(a,function(t){t.removeEventListener(c,u)})}};if(d.string(t))return o=t,r=e,i=n,h(document.body,o,r,i);throw new TypeError("First argument must be a String, HTMLElement, HTMLCollection, or NodeList");var o,r,i,a,c,u,l,s,f}},function(t,n){n.node=function(t){return void 0!==t&&t instanceof HTMLElement&&1===t.nodeType},n.nodeList=function(t){var e=Object.prototype.toString.call(t);return void 0!==t&&("[object NodeList]"===e||"[object HTMLCollection]"===e)&&"length"in t&&(0===t.length||n.node(t[0]))},n.string=function(t){return"string"==typeof t||t instanceof String},n.fn=function(t){return"[object Function]"===Object.prototype.toString.call(t)}},function(t,e,n){var a=n(7);function i(t,e,n,o,r){var i=function(e,n,t,o){return function(t){t.delegateTarget=a(t.target,n),t.delegateTarget&&o.call(e,t)}}.apply(this,arguments);return t.addEventListener(n,i,r),{destroy:function(){t.removeEventListener(n,i,r)}}}t.exports=function(t,e,n,o,r){return"function"==typeof t.addEventListener?i.apply(null,arguments):"function"==typeof n?i.bind(null,document).apply(null,arguments):("string"==typeof t&&(t=document.querySelectorAll(t)),Array.prototype.map.call(t,function(t){return i(t,e,n,o,r)}))}},function(t,e){if("undefined"!=typeof Element&&!Element.prototype.matches){var n=Element.prototype;n.matches=n.matchesSelector||n.mozMatchesSelector||n.msMatchesSelector||n.oMatchesSelector||n.webkitMatchesSelector}t.exports=function(t,e){for(;t&&9!==t.nodeType;){if("function"==typeof t.matches&&t.matches(e))return t;t=t.parentNode}}}])});
\ No newline at end of file
diff --git a/_build/html/_static/copy-button.svg b/_build/html/_static/copy-button.svg
new file mode 100644
index 0000000..7cefcff
--- /dev/null
+++ b/_build/html/_static/copy-button.svg
@@ -0,0 +1 @@
+<svg aria-hidden="true" data-prefix="far" data-icon="copy" class="svg-inline--fa fa-copy fa-w-14" role="img" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><path fill="#777" d="M433.941 65.941l-51.882-51.882A48 48 0 0 0 348.118 0H176c-26.51 0-48 21.49-48 48v48H48c-26.51 0-48 21.49-48 48v320c0 26.51 21.49 48 48 48h224c26.51 0 48-21.49 48-48v-48h80c26.51 0 48-21.49 48-48V99.882a48 48 0 0 0-14.059-33.941zM266 464H54a6 6 0 0 1-6-6V150a6 6 0 0 1 6-6h74v224c0 26.51 21.49 48 48 48h96v42a6 6 0 0 1-6 6zm128-96H182a6 6 0 0 1-6-6V54a6 6 0 0 1 6-6h106v88c0 13.255 10.745 24 24 24h88v202a6 6 0 0 1-6 6zm6-256h-64V48h9.632c1.591 0 3.117.632 4.243 1.757l48.368 48.368a6 6 0 0 1 1.757 4.243V112z"></path></svg>
diff --git a/_build/html/_static/copybutton.css b/_build/html/_static/copybutton.css
new file mode 100644
index 0000000..75b17a8
--- /dev/null
+++ b/_build/html/_static/copybutton.css
@@ -0,0 +1,67 @@
+/* Copy buttons */
+a.copybtn {
+    position: absolute;
+    top: .2em;
+    right: .2em;
+    width: 1em;
+    height: 1em;
+	opacity: .3;
+    transition: opacity 0.5s;
+    border: none;
+    user-select: none;
+}
+
+div.highlight  {
+    position: relative;
+}
+
+a.copybtn > img {
+    vertical-align: top;
+    margin: 0;
+    top: 0;
+    left: 0;
+    position: absolute;
+}
+
+.highlight:hover .copybtn {
+	opacity: 1;
+}
+
+/**
+ * A minimal CSS-only tooltip copied from:
+ *   https://codepen.io/mildrenben/pen/rVBrpK
+ *
+ * To use, write HTML like the following:
+ *
+ * <p class="o-tooltip--left" data-tooltip="Hey">Short</p>
+ */
+ .o-tooltip--left {
+  position: relative;
+ }
+
+ .o-tooltip--left:after {
+    opacity: 0;
+    visibility: hidden;
+    position: absolute;
+    content: attr(data-tooltip);
+    padding: 2px;
+    top: 0;
+    left: -.2em;
+    background: grey;
+    font-size: 1rem;
+    color: white;
+    white-space: nowrap;
+    z-index: 2;
+    border-radius: 2px;
+    transform: translateX(-102%) translateY(0);
+    transition: opacity 0.2s cubic-bezier(0.64, 0.09, 0.08, 1), transform 0.2s cubic-bezier(0.64, 0.09, 0.08, 1);
+}
+
+.o-tooltip--left:hover:after {
+    display: block;
+    opacity: 1;
+    visibility: visible;
+    transform: translateX(-100%) translateY(0);
+    transition: opacity 0.2s cubic-bezier(0.64, 0.09, 0.08, 1), transform 0.2s cubic-bezier(0.64, 0.09, 0.08, 1);
+    transition-delay: .5s;
+}
diff --git a/_build/html/_static/copybutton.js b/_build/html/_static/copybutton.js
new file mode 100644
index 0000000..65a5916
--- /dev/null
+++ b/_build/html/_static/copybutton.js
@@ -0,0 +1,153 @@
+// Localization support
+const messages = {
+  'en': {
+    'copy': 'Copy',
+    'copy_to_clipboard': 'Copy to clipboard',
+    'copy_success': 'Copied!',
+    'copy_failure': 'Failed to copy',
+  },
+  'es' : {
+    'copy': 'Copiar',
+    'copy_to_clipboard': 'Copiar al portapapeles',
+    'copy_success': '¡Copiado!',
+    'copy_failure': 'Error al copiar',
+  },
+  'de' : {
+    'copy': 'Kopieren',
+    'copy_to_clipboard': 'In die Zwischenablage kopieren',
+    'copy_success': 'Kopiert!',
+    'copy_failure': 'Fehler beim Kopieren',
+  }
+}
+
+let locale = 'en'
+if( document.documentElement.lang !== undefined
+    && messages[document.documentElement.lang] !== undefined ) {
+  locale = document.documentElement.lang
+}
+
+/**
+ * Set up copy/paste for code blocks
+ */
+
+const runWhenDOMLoaded = cb => {
+  if (document.readyState != 'loading') {
+    cb()
+  } else if (document.addEventListener) {
+    document.addEventListener('DOMContentLoaded', cb)
+  } else {
+    document.attachEvent('onreadystatechange', function() {
+      if (document.readyState == 'complete') cb()
+    })
+  }
+}
+
+const codeCellId = index => `codecell${index}`
+
+// Clears selected text since ClipboardJS will select the text when copying
+const clearSelection = () => {
+  if (window.getSelection) {
+    window.getSelection().removeAllRanges()
+  } else if (document.selection) {
+    document.selection.empty()
+  }
+}
+
+// Changes tooltip text for two seconds, then changes it back
+const temporarilyChangeTooltip = (el, newText) => {
+  const oldText = el.getAttribute('data-tooltip')
+  el.setAttribute('data-tooltip', newText)
+  setTimeout(() => el.setAttribute('data-tooltip', oldText), 2000)
+}
+
+const addCopyButtonToCodeCells = () => {
+  // If ClipboardJS hasn't loaded, wait a bit and try again. This
+  // happens because we load ClipboardJS asynchronously.
+  if (window.ClipboardJS === undefined) {
+    setTimeout(addCopyButtonToCodeCells, 250)
+    return
+  }
+
+  // Add copybuttons to all of our code cells
+  const codeCells = document.querySelectorAll('div.highlight pre')
+  codeCells.forEach((codeCell, index) => {
+    const id = codeCellId(index)
+    codeCell.setAttribute('id', id)
+    const pre_bg = getComputedStyle(codeCell).backgroundColor;
+
+    const clipboardButton = id =>
+    `<a class="copybtn o-tooltip--left" style="background-color: ${pre_bg}" data-tooltip="${messages[locale]['copy']}" data-clipboard-target="#${id}">
+      <img src="${DOCUMENTATION_OPTIONS.URL_ROOT}_static/copy-button.svg" alt="${messages[locale]['copy_to_clipboard']}">
+    </a>`
+    codeCell.insertAdjacentHTML('afterend', clipboardButton(id))
+  })
+
+function escapeRegExp(string) {
+    return string.replace(/[.*+?^${}()|[\]\\]/g, '\\$&'); // $& means the whole matched string
+}
+
+// Callback when a copy button is clicked. Will be passed the node that was clicked
+// should then grab the text and replace pieces of text that shouldn't be used in output
+function formatCopyText(textContent, copybuttonPromptText, isRegexp = false, onlyCopyPromptLines = true, removePrompts = true) {
+
+    var regexp;
+    var match;
+
+    // create regexp to capture prompt and remaining line
+    if (isRegexp) {
+        regexp = new RegExp('^(' + copybuttonPromptText + ')(.*)')
+    } else {
+        regexp = new RegExp('^(' + escapeRegExp(copybuttonPromptText) + ')(.*)')
+    }
+
+    const outputLines = [];
+    var promptFound = false;
+    for (const line of textContent.split('\n')) {
+        match = line.match(regexp)
+        if (match) {
+            promptFound = true
+            if (removePrompts) {
+                outputLines.push(match[2])
+            } else {
+                outputLines.push(line)
+            }
+        } else {
+            if (!onlyCopyPromptLines) {
+                outputLines.push(line)
+            }
+        }
+    }
+
+    // If no lines with the prompt were found then just use original lines
+    if (promptFound) {
+        textContent = outputLines.join('\n');
+    }
+
+    // Remove a trailing newline to avoid auto-running when pasting
+    if (textContent.endsWith("\n")) {
+        textContent = textContent.slice(0, -1)
+    }
+    return textContent
+}
+
+
+var copyTargetText = (trigger) => {
+  var target = document.querySelector(trigger.attributes['data-clipboard-target'].value);
+  return formatCopyText(target.innerText, '', false, true,  true)
+}
+
+  // Initialize with a callback so we can modify the text before copy
+  const clipboard = new ClipboardJS('.copybtn', {text: copyTargetText})
+
+  // Update UI with error/success messages
+  clipboard.on('success', event => {
+    clearSelection()
+    temporarilyChangeTooltip(event.trigger, messages[locale]['copy_success'])
+  })
+
+  clipboard.on('error', event => {
+    temporarilyChangeTooltip(event.trigger, messages[locale]['copy_failure'])
+  })
+}
+
+runWhenDOMLoaded(addCopyButtonToCodeCells)
\ No newline at end of file
diff --git a/_build/html/_static/copybutton_funcs.js b/_build/html/_static/copybutton_funcs.js
new file mode 100644
index 0000000..57caa55
--- /dev/null
+++ b/_build/html/_static/copybutton_funcs.js
@@ -0,0 +1,47 @@
+function escapeRegExp(string) {
+    return string.replace(/[.*+?^${}()|[\]\\]/g, '\\$&'); // $& means the whole matched string
+}
+
+// Callback when a copy button is clicked. Will be passed the node that was clicked
+// should then grab the text and replace pieces of text that shouldn't be used in output
+export function formatCopyText(textContent, copybuttonPromptText, isRegexp = false, onlyCopyPromptLines = true, removePrompts = true) {
+
+    var regexp;
+    var match;
+
+    // create regexp to capture prompt and remaining line
+    if (isRegexp) {
+        regexp = new RegExp('^(' + copybuttonPromptText + ')(.*)')
+    } else {
+        regexp = new RegExp('^(' + escapeRegExp(copybuttonPromptText) + ')(.*)')
+    }
+
+    const outputLines = [];
+    var promptFound = false;
+    for (const line of textContent.split('\n')) {
+        match = line.match(regexp)
+        if (match) {
+            promptFound = true
+            if (removePrompts) {
+                outputLines.push(match[2])
+            } else {
+                outputLines.push(line)
+            }
+        } else {
+            if (!onlyCopyPromptLines) {
+                outputLines.push(line)
+            }
+        }
+    }
+
+    // If no lines with the prompt were found then just use original lines
+    if (promptFound) {
+        textContent = outputLines.join('\n');
+    }
+
+    // Remove a trailing newline to avoid auto-running when pasting
+    if (textContent.endsWith("\n")) {
+        textContent = textContent.slice(0, -1)
+    }
+    return textContent
+}
diff --git a/_build/html/_static/css/index.d431a4ee1c1efae0e38bdfebc22debff.css b/_build/html/_static/css/index.d431a4ee1c1efae0e38bdfebc22debff.css
new file mode 100644
index 0000000..9025998
--- /dev/null
+++ b/_build/html/_static/css/index.d431a4ee1c1efae0e38bdfebc22debff.css
@@ -0,0 +1,6 @@
+/*!
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diff --git a/_build/html/_static/doctools.js b/_build/html/_static/doctools.js
new file mode 100644
index 0000000..daccd20
--- /dev/null
+++ b/_build/html/_static/doctools.js
@@ -0,0 +1,315 @@
+/*
+ * doctools.js
+ * ~~~~~~~~~~~
+ *
+ * Sphinx JavaScript utilities for all documentation.
+ *
+ * :copyright: Copyright 2007-2020 by the Sphinx team, see AUTHORS.
+ * :license: BSD, see LICENSE for details.
+ *
+ */
+
+/**
+ * select a different prefix for underscore
+ */
+$u = _.noConflict();
+
+/**
+ * make the code below compatible with browsers without
+ * an installed firebug like debugger
+if (!window.console || !console.firebug) {
+  var names = ["log", "debug", "info", "warn", "error", "assert", "dir",
+    "dirxml", "group", "groupEnd", "time", "timeEnd", "count", "trace",
+    "profile", "profileEnd"];
+  window.console = {};
+  for (var i = 0; i < names.length; ++i)
+    window.console[names[i]] = function() {};
+}
+ */
+
+/**
+ * small helper function to urldecode strings
+ */
+jQuery.urldecode = function(x) {
+  return decodeURIComponent(x).replace(/\+/g, ' ');
+};
+
+/**
+ * small helper function to urlencode strings
+ */
+jQuery.urlencode = encodeURIComponent;
+
+/**
+ * This function returns the parsed url parameters of the
+ * current request. Multiple values per key are supported,
+ * it will always return arrays of strings for the value parts.
+ */
+jQuery.getQueryParameters = function(s) {
+  if (typeof s === 'undefined')
+    s = document.location.search;
+  var parts = s.substr(s.indexOf('?') + 1).split('&');
+  var result = {};
+  for (var i = 0; i < parts.length; i++) {
+    var tmp = parts[i].split('=', 2);
+    var key = jQuery.urldecode(tmp[0]);
+    var value = jQuery.urldecode(tmp[1]);
+    if (key in result)
+      result[key].push(value);
+    else
+      result[key] = [value];
+  }
+  return result;
+};
+
+/**
+ * highlight a given string on a jquery object by wrapping it in
+ * span elements with the given class name.
+ */
+jQuery.fn.highlightText = function(text, className) {
+  function highlight(node, addItems) {
+    if (node.nodeType === 3) {
+      var val = node.nodeValue;
+      var pos = val.toLowerCase().indexOf(text);
+      if (pos >= 0 &&
+          !jQuery(node.parentNode).hasClass(className) &&
+          !jQuery(node.parentNode).hasClass("nohighlight")) {
+        var span;
+        var isInSVG = jQuery(node).closest("body, svg, foreignObject").is("svg");
+        if (isInSVG) {
+          span = document.createElementNS("http://www.w3.org/2000/svg", "tspan");
+        } else {
+          span = document.createElement("span");
+          span.className = className;
+        }
+        span.appendChild(document.createTextNode(val.substr(pos, text.length)));
+        node.parentNode.insertBefore(span, node.parentNode.insertBefore(
+          document.createTextNode(val.substr(pos + text.length)),
+          node.nextSibling));
+        node.nodeValue = val.substr(0, pos);
+        if (isInSVG) {
+          var rect = document.createElementNS("http://www.w3.org/2000/svg", "rect");
+          var bbox = node.parentElement.getBBox();
+          rect.x.baseVal.value = bbox.x;
+          rect.y.baseVal.value = bbox.y;
+          rect.width.baseVal.value = bbox.width;
+          rect.height.baseVal.value = bbox.height;
+          rect.setAttribute('class', className);
+          addItems.push({
+              "parent": node.parentNode,
+              "target": rect});
+        }
+      }
+    }
+    else if (!jQuery(node).is("button, select, textarea")) {
+      jQuery.each(node.childNodes, function() {
+        highlight(this, addItems);
+      });
+    }
+  }
+  var addItems = [];
+  var result = this.each(function() {
+    highlight(this, addItems);
+  });
+  for (var i = 0; i < addItems.length; ++i) {
+    jQuery(addItems[i].parent).before(addItems[i].target);
+  }
+  return result;
+};
+
+/*
+ * backward compatibility for jQuery.browser
+ * This will be supported until firefox bug is fixed.
+ */
+if (!jQuery.browser) {
+  jQuery.uaMatch = function(ua) {
+    ua = ua.toLowerCase();
+
+    var match = /(chrome)[ \/]([\w.]+)/.exec(ua) ||
+      /(webkit)[ \/]([\w.]+)/.exec(ua) ||
+      /(opera)(?:.*version|)[ \/]([\w.]+)/.exec(ua) ||
+      /(msie) ([\w.]+)/.exec(ua) ||
+      ua.indexOf("compatible") < 0 && /(mozilla)(?:.*? rv:([\w.]+)|)/.exec(ua) ||
+      [];
+
+    return {
+      browser: match[ 1 ] || "",
+      version: match[ 2 ] || "0"
+    };
+  };
+  jQuery.browser = {};
+  jQuery.browser[jQuery.uaMatch(navigator.userAgent).browser] = true;
+}
+
+/**
+ * Small JavaScript module for the documentation.
+ */
+var Documentation = {
+
+  init : function() {
+    this.fixFirefoxAnchorBug();
+    this.highlightSearchWords();
+    this.initIndexTable();
+    if (DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) {
+      this.initOnKeyListeners();
+    }
+  },
+
+  /**
+   * i18n support
+   */
+  TRANSLATIONS : {},
+  PLURAL_EXPR : function(n) { return n === 1 ? 0 : 1; },
+  LOCALE : 'unknown',
+
+  // gettext and ngettext don't access this so that the functions
+  // can safely bound to a different name (_ = Documentation.gettext)
+  gettext : function(string) {
+    var translated = Documentation.TRANSLATIONS[string];
+    if (typeof translated === 'undefined')
+      return string;
+    return (typeof translated === 'string') ? translated : translated[0];
+  },
+
+  ngettext : function(singular, plural, n) {
+    var translated = Documentation.TRANSLATIONS[singular];
+    if (typeof translated === 'undefined')
+      return (n == 1) ? singular : plural;
+    return translated[Documentation.PLURALEXPR(n)];
+  },
+
+  addTranslations : function(catalog) {
+    for (var key in catalog.messages)
+      this.TRANSLATIONS[key] = catalog.messages[key];
+    this.PLURAL_EXPR = new Function('n', 'return +(' + catalog.plural_expr + ')');
+    this.LOCALE = catalog.locale;
+  },
+
+  /**
+   * add context elements like header anchor links
+   */
+  addContextElements : function() {
+    $('div[id] > :header:first').each(function() {
+      $('<a class="headerlink">\u00B6</a>').
+      attr('href', '#' + this.id).
+      attr('title', _('Permalink to this headline')).
+      appendTo(this);
+    });
+    $('dt[id]').each(function() {
+      $('<a class="headerlink">\u00B6</a>').
+      attr('href', '#' + this.id).
+      attr('title', _('Permalink to this definition')).
+      appendTo(this);
+    });
+  },
+
+  /**
+   * workaround a firefox stupidity
+   * see: https://bugzilla.mozilla.org/show_bug.cgi?id=645075
+   */
+  fixFirefoxAnchorBug : function() {
+    if (document.location.hash && $.browser.mozilla)
+      window.setTimeout(function() {
+        document.location.href += '';
+      }, 10);
+  },
+
+  /**
+   * highlight the search words provided in the url in the text
+   */
+  highlightSearchWords : function() {
+    var params = $.getQueryParameters();
+    var terms = (params.highlight) ? params.highlight[0].split(/\s+/) : [];
+    if (terms.length) {
+      var body = $('div.body');
+      if (!body.length) {
+        body = $('body');
+      }
+      window.setTimeout(function() {
+        $.each(terms, function() {
+          body.highlightText(this.toLowerCase(), 'highlighted');
+        });
+      }, 10);
+      $('<p class="highlight-link"><a href="javascript:Documentation.' +
+        'hideSearchWords()">' + _('Hide Search Matches') + '</a></p>')
+          .appendTo($('#searchbox'));
+    }
+  },
+
+  /**
+   * init the domain index toggle buttons
+   */
+  initIndexTable : function() {
+    var togglers = $('img.toggler').click(function() {
+      var src = $(this).attr('src');
+      var idnum = $(this).attr('id').substr(7);
+      $('tr.cg-' + idnum).toggle();
+      if (src.substr(-9) === 'minus.png')
+        $(this).attr('src', src.substr(0, src.length-9) + 'plus.png');
+      else
+        $(this).attr('src', src.substr(0, src.length-8) + 'minus.png');
+    }).css('display', '');
+    if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) {
+        togglers.click();
+    }
+  },
+
+  /**
+   * helper function to hide the search marks again
+   */
+  hideSearchWords : function() {
+    $('#searchbox .highlight-link').fadeOut(300);
+    $('span.highlighted').removeClass('highlighted');
+  },
+
+  /**
+   * make the url absolute
+   */
+  makeURL : function(relativeURL) {
+    return DOCUMENTATION_OPTIONS.URL_ROOT + '/' + relativeURL;
+  },
+
+  /**
+   * get the current relative url
+   */
+  getCurrentURL : function() {
+    var path = document.location.pathname;
+    var parts = path.split(/\//);
+    $.each(DOCUMENTATION_OPTIONS.URL_ROOT.split(/\//), function() {
+      if (this === '..')
+        parts.pop();
+    });
+    var url = parts.join('/');
+    return path.substring(url.lastIndexOf('/') + 1, path.length - 1);
+  },
+
+  initOnKeyListeners: function() {
+    $(document).keydown(function(event) {
+      var activeElementType = document.activeElement.tagName;
+      // don't navigate when in search box or textarea
+      if (activeElementType !== 'TEXTAREA' && activeElementType !== 'INPUT' && activeElementType !== 'SELECT'
+          && !event.altKey && !event.ctrlKey && !event.metaKey && !event.shiftKey) {
+        switch (event.keyCode) {
+          case 37: // left
+            var prevHref = $('link[rel="prev"]').prop('href');
+            if (prevHref) {
+              window.location.href = prevHref;
+              return false;
+            }
+          case 39: // right
+            var nextHref = $('link[rel="next"]').prop('href');
+            if (nextHref) {
+              window.location.href = nextHref;
+              return false;
+            }
+        }
+      }
+    });
+  }
+};
+
+// quick alias for translations
+_ = Documentation.gettext;
+
+$(document).ready(function() {
+  Documentation.init();
+});
diff --git a/_build/html/_static/documentation_options.js b/_build/html/_static/documentation_options.js
new file mode 100644
index 0000000..93b7c24
--- /dev/null
+++ b/_build/html/_static/documentation_options.js
@@ -0,0 +1,12 @@
+var DOCUMENTATION_OPTIONS = {
+    URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'),
+    VERSION: '',
+    LANGUAGE: 'None',
+    COLLAPSE_INDEX: false,
+    BUILDER: 'html',
+    FILE_SUFFIX: '.html',
+    LINK_SUFFIX: '.html',
+    HAS_SOURCE: true,
+    SOURCELINK_SUFFIX: '',
+    NAVIGATION_WITH_KEYS: true
+};
\ No newline at end of file
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diff --git a/_build/html/_static/images/logo_binder.svg b/_build/html/_static/images/logo_binder.svg
new file mode 100644
index 0000000..16390ee
--- /dev/null
+++ b/_build/html/_static/images/logo_binder.svg
@@ -0,0 +1,19 @@
+<?xml version="1.0" encoding="utf-8"?>
+<!-- Generator: Adobe Illustrator 23.0.1, SVG Export Plug-In . SVG Version: 6.00 Build 0)  -->
+<svg version="1.1" id="Layer_1" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" x="0px" y="0px"
+	 viewBox="0 0 44.4 44.4" style="enable-background:new 0 0 44.4 44.4;" xml:space="preserve">
+<style type="text/css">
+	.st0{fill:none;stroke:#F5A252;stroke-width:5;stroke-miterlimit:10;}
+	.st1{fill:none;stroke:#579ACA;stroke-width:5;stroke-miterlimit:10;}
+	.st2{fill:none;stroke:#E66581;stroke-width:5;stroke-miterlimit:10;}
+</style>
+<title>logo</title>
+<g>
+	<path class="st0" d="M33.9,6.4c3.6,3.9,3.4,9.9-0.5,13.5s-9.9,3.4-13.5-0.5s-3.4-9.9,0.5-13.5l0,0C24.2,2.4,30.2,2.6,33.9,6.4z"/>
+	<path class="st1" d="M35.1,27.3c2.6,4.6,1.1,10.4-3.5,13c-4.6,2.6-10.4,1.1-13-3.5s-1.1-10.4,3.5-13l0,0
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+	<path class="st2" d="M25.9,17.8c2.6,4.6,1.1,10.4-3.5,13s-10.4,1.1-13-3.5s-1.1-10.4,3.5-13l0,0C17.5,11.7,23.3,13.2,25.9,17.8z"/>
+	<path class="st1" d="M19.2,26.4c3.1-4.3,9.1-5.2,13.3-2.1c1.1,0.8,2,1.8,2.7,3"/>
+	<path class="st0" d="M19.9,19.4c-3.6-3.9-3.4-9.9,0.5-13.5s9.9-3.4,13.5,0.5"/>
+</g>
+</svg>
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+/*!
+ * jQuery JavaScript Library v3.5.1
+ * https://jquery.com/
+ *
+ * Includes Sizzle.js
+ * https://sizzlejs.com/
+ *
+ * Copyright JS Foundation and other contributors
+ * Released under the MIT license
+ * https://jquery.org/license
+ *
+ * Date: 2020-05-04T22:49Z
+ */
+( function( global, factory ) {
+
+	"use strict";
+
+	if ( typeof module === "object" && typeof module.exports === "object" ) {
+
+		// For CommonJS and CommonJS-like environments where a proper `window`
+		// is present, execute the factory and get jQuery.
+		// For environments that do not have a `window` with a `document`
+		// (such as Node.js), expose a factory as module.exports.
+		// This accentuates the need for the creation of a real `window`.
+		// e.g. var jQuery = require("jquery")(window);
+		// See ticket #14549 for more info.
+		module.exports = global.document ?
+			factory( global, true ) :
+			function( w ) {
+				if ( !w.document ) {
+					throw new Error( "jQuery requires a window with a document" );
+				}
+				return factory( w );
+			};
+	} else {
+		factory( global );
+	}
+
+// Pass this if window is not defined yet
+} )( typeof window !== "undefined" ? window : this, function( window, noGlobal ) {
+
+// Edge <= 12 - 13+, Firefox <=18 - 45+, IE 10 - 11, Safari 5.1 - 9+, iOS 6 - 9.1
+// throw exceptions when non-strict code (e.g., ASP.NET 4.5) accesses strict mode
+// arguments.callee.caller (trac-13335). But as of jQuery 3.0 (2016), strict mode should be common
+// enough that all such attempts are guarded in a try block.
+"use strict";
+
+var arr = [];
+
+var getProto = Object.getPrototypeOf;
+
+var slice = arr.slice;
+
+var flat = arr.flat ? function( array ) {
+	return arr.flat.call( array );
+} : function( array ) {
+	return arr.concat.apply( [], array );
+};
+
+
+var push = arr.push;
+
+var indexOf = arr.indexOf;
+
+var class2type = {};
+
+var toString = class2type.toString;
+
+var hasOwn = class2type.hasOwnProperty;
+
+var fnToString = hasOwn.toString;
+
+var ObjectFunctionString = fnToString.call( Object );
+
+var support = {};
+
+var isFunction = function isFunction( obj ) {
+
+      // Support: Chrome <=57, Firefox <=52
+      // In some browsers, typeof returns "function" for HTML <object> elements
+      // (i.e., `typeof document.createElement( "object" ) === "function"`).
+      // We don't want to classify *any* DOM node as a function.
+      return typeof obj === "function" && typeof obj.nodeType !== "number";
+  };
+
+
+var isWindow = function isWindow( obj ) {
+		return obj != null && obj === obj.window;
+	};
+
+
+var document = window.document;
+
+
+
+	var preservedScriptAttributes = {
+		type: true,
+		src: true,
+		nonce: true,
+		noModule: true
+	};
+
+	function DOMEval( code, node, doc ) {
+		doc = doc || document;
+
+		var i, val,
+			script = doc.createElement( "script" );
+
+		script.text = code;
+		if ( node ) {
+			for ( i in preservedScriptAttributes ) {
+
+				// Support: Firefox 64+, Edge 18+
+				// Some browsers don't support the "nonce" property on scripts.
+				// On the other hand, just using `getAttribute` is not enough as
+				// the `nonce` attribute is reset to an empty string whenever it
+				// becomes browsing-context connected.
+				// See https://github.com/whatwg/html/issues/2369
+				// See https://html.spec.whatwg.org/#nonce-attributes
+				// The `node.getAttribute` check was added for the sake of
+				// `jQuery.globalEval` so that it can fake a nonce-containing node
+				// via an object.
+				val = node[ i ] || node.getAttribute && node.getAttribute( i );
+				if ( val ) {
+					script.setAttribute( i, val );
+				}
+			}
+		}
+		doc.head.appendChild( script ).parentNode.removeChild( script );
+	}
+
+
+function toType( obj ) {
+	if ( obj == null ) {
+		return obj + "";
+	}
+
+	// Support: Android <=2.3 only (functionish RegExp)
+	return typeof obj === "object" || typeof obj === "function" ?
+		class2type[ toString.call( obj ) ] || "object" :
+		typeof obj;
+}
+/* global Symbol */
+// Defining this global in .eslintrc.json would create a danger of using the global
+// unguarded in another place, it seems safer to define global only for this module
+
+
+
+var
+	version = "3.5.1",
+
+	// Define a local copy of jQuery
+	jQuery = function( selector, context ) {
+
+		// The jQuery object is actually just the init constructor 'enhanced'
+		// Need init if jQuery is called (just allow error to be thrown if not included)
+		return new jQuery.fn.init( selector, context );
+	};
+
+jQuery.fn = jQuery.prototype = {
+
+	// The current version of jQuery being used
+	jquery: version,
+
+	constructor: jQuery,
+
+	// The default length of a jQuery object is 0
+	length: 0,
+
+	toArray: function() {
+		return slice.call( this );
+	},
+
+	// Get the Nth element in the matched element set OR
+	// Get the whole matched element set as a clean array
+	get: function( num ) {
+
+		// Return all the elements in a clean array
+		if ( num == null ) {
+			return slice.call( this );
+		}
+
+		// Return just the one element from the set
+		return num < 0 ? this[ num + this.length ] : this[ num ];
+	},
+
+	// Take an array of elements and push it onto the stack
+	// (returning the new matched element set)
+	pushStack: function( elems ) {
+
+		// Build a new jQuery matched element set
+		var ret = jQuery.merge( this.constructor(), elems );
+
+		// Add the old object onto the stack (as a reference)
+		ret.prevObject = this;
+
+		// Return the newly-formed element set
+		return ret;
+	},
+
+	// Execute a callback for every element in the matched set.
+	each: function( callback ) {
+		return jQuery.each( this, callback );
+	},
+
+	map: function( callback ) {
+		return this.pushStack( jQuery.map( this, function( elem, i ) {
+			return callback.call( elem, i, elem );
+		} ) );
+	},
+
+	slice: function() {
+		return this.pushStack( slice.apply( this, arguments ) );
+	},
+
+	first: function() {
+		return this.eq( 0 );
+	},
+
+	last: function() {
+		return this.eq( -1 );
+	},
+
+	even: function() {
+		return this.pushStack( jQuery.grep( this, function( _elem, i ) {
+			return ( i + 1 ) % 2;
+		} ) );
+	},
+
+	odd: function() {
+		return this.pushStack( jQuery.grep( this, function( _elem, i ) {
+			return i % 2;
+		} ) );
+	},
+
+	eq: function( i ) {
+		var len = this.length,
+			j = +i + ( i < 0 ? len : 0 );
+		return this.pushStack( j >= 0 && j < len ? [ this[ j ] ] : [] );
+	},
+
+	end: function() {
+		return this.prevObject || this.constructor();
+	},
+
+	// For internal use only.
+	// Behaves like an Array's method, not like a jQuery method.
+	push: push,
+	sort: arr.sort,
+	splice: arr.splice
+};
+
+jQuery.extend = jQuery.fn.extend = function() {
+	var options, name, src, copy, copyIsArray, clone,
+		target = arguments[ 0 ] || {},
+		i = 1,
+		length = arguments.length,
+		deep = false;
+
+	// Handle a deep copy situation
+	if ( typeof target === "boolean" ) {
+		deep = target;
+
+		// Skip the boolean and the target
+		target = arguments[ i ] || {};
+		i++;
+	}
+
+	// Handle case when target is a string or something (possible in deep copy)
+	if ( typeof target !== "object" && !isFunction( target ) ) {
+		target = {};
+	}
+
+	// Extend jQuery itself if only one argument is passed
+	if ( i === length ) {
+		target = this;
+		i--;
+	}
+
+	for ( ; i < length; i++ ) {
+
+		// Only deal with non-null/undefined values
+		if ( ( options = arguments[ i ] ) != null ) {
+
+			// Extend the base object
+			for ( name in options ) {
+				copy = options[ name ];
+
+				// Prevent Object.prototype pollution
+				// Prevent never-ending loop
+				if ( name === "__proto__" || target === copy ) {
+					continue;
+				}
+
+				// Recurse if we're merging plain objects or arrays
+				if ( deep && copy && ( jQuery.isPlainObject( copy ) ||
+					( copyIsArray = Array.isArray( copy ) ) ) ) {
+					src = target[ name ];
+
+					// Ensure proper type for the source value
+					if ( copyIsArray && !Array.isArray( src ) ) {
+						clone = [];
+					} else if ( !copyIsArray && !jQuery.isPlainObject( src ) ) {
+						clone = {};
+					} else {
+						clone = src;
+					}
+					copyIsArray = false;
+
+					// Never move original objects, clone them
+					target[ name ] = jQuery.extend( deep, clone, copy );
+
+				// Don't bring in undefined values
+				} else if ( copy !== undefined ) {
+					target[ name ] = copy;
+				}
+			}
+		}
+	}
+
+	// Return the modified object
+	return target;
+};
+
+jQuery.extend( {
+
+	// Unique for each copy of jQuery on the page
+	expando: "jQuery" + ( version + Math.random() ).replace( /\D/g, "" ),
+
+	// Assume jQuery is ready without the ready module
+	isReady: true,
+
+	error: function( msg ) {
+		throw new Error( msg );
+	},
+
+	noop: function() {},
+
+	isPlainObject: function( obj ) {
+		var proto, Ctor;
+
+		// Detect obvious negatives
+		// Use toString instead of jQuery.type to catch host objects
+		if ( !obj || toString.call( obj ) !== "[object Object]" ) {
+			return false;
+		}
+
+		proto = getProto( obj );
+
+		// Objects with no prototype (e.g., `Object.create( null )`) are plain
+		if ( !proto ) {
+			return true;
+		}
+
+		// Objects with prototype are plain iff they were constructed by a global Object function
+		Ctor = hasOwn.call( proto, "constructor" ) && proto.constructor;
+		return typeof Ctor === "function" && fnToString.call( Ctor ) === ObjectFunctionString;
+	},
+
+	isEmptyObject: function( obj ) {
+		var name;
+
+		for ( name in obj ) {
+			return false;
+		}
+		return true;
+	},
+
+	// Evaluates a script in a provided context; falls back to the global one
+	// if not specified.
+	globalEval: function( code, options, doc ) {
+		DOMEval( code, { nonce: options && options.nonce }, doc );
+	},
+
+	each: function( obj, callback ) {
+		var length, i = 0;
+
+		if ( isArrayLike( obj ) ) {
+			length = obj.length;
+			for ( ; i < length; i++ ) {
+				if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) {
+					break;
+				}
+			}
+		} else {
+			for ( i in obj ) {
+				if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) {
+					break;
+				}
+			}
+		}
+
+		return obj;
+	},
+
+	// results is for internal usage only
+	makeArray: function( arr, results ) {
+		var ret = results || [];
+
+		if ( arr != null ) {
+			if ( isArrayLike( Object( arr ) ) ) {
+				jQuery.merge( ret,
+					typeof arr === "string" ?
+					[ arr ] : arr
+				);
+			} else {
+				push.call( ret, arr );
+			}
+		}
+
+		return ret;
+	},
+
+	inArray: function( elem, arr, i ) {
+		return arr == null ? -1 : indexOf.call( arr, elem, i );
+	},
+
+	// Support: Android <=4.0 only, PhantomJS 1 only
+	// push.apply(_, arraylike) throws on ancient WebKit
+	merge: function( first, second ) {
+		var len = +second.length,
+			j = 0,
+			i = first.length;
+
+		for ( ; j < len; j++ ) {
+			first[ i++ ] = second[ j ];
+		}
+
+		first.length = i;
+
+		return first;
+	},
+
+	grep: function( elems, callback, invert ) {
+		var callbackInverse,
+			matches = [],
+			i = 0,
+			length = elems.length,
+			callbackExpect = !invert;
+
+		// Go through the array, only saving the items
+		// that pass the validator function
+		for ( ; i < length; i++ ) {
+			callbackInverse = !callback( elems[ i ], i );
+			if ( callbackInverse !== callbackExpect ) {
+				matches.push( elems[ i ] );
+			}
+		}
+
+		return matches;
+	},
+
+	// arg is for internal usage only
+	map: function( elems, callback, arg ) {
+		var length, value,
+			i = 0,
+			ret = [];
+
+		// Go through the array, translating each of the items to their new values
+		if ( isArrayLike( elems ) ) {
+			length = elems.length;
+			for ( ; i < length; i++ ) {
+				value = callback( elems[ i ], i, arg );
+
+				if ( value != null ) {
+					ret.push( value );
+				}
+			}
+
+		// Go through every key on the object,
+		} else {
+			for ( i in elems ) {
+				value = callback( elems[ i ], i, arg );
+
+				if ( value != null ) {
+					ret.push( value );
+				}
+			}
+		}
+
+		// Flatten any nested arrays
+		return flat( ret );
+	},
+
+	// A global GUID counter for objects
+	guid: 1,
+
+	// jQuery.support is not used in Core but other projects attach their
+	// properties to it so it needs to exist.
+	support: support
+} );
+
+if ( typeof Symbol === "function" ) {
+	jQuery.fn[ Symbol.iterator ] = arr[ Symbol.iterator ];
+}
+
+// Populate the class2type map
+jQuery.each( "Boolean Number String Function Array Date RegExp Object Error Symbol".split( " " ),
+function( _i, name ) {
+	class2type[ "[object " + name + "]" ] = name.toLowerCase();
+} );
+
+function isArrayLike( obj ) {
+
+	// Support: real iOS 8.2 only (not reproducible in simulator)
+	// `in` check used to prevent JIT error (gh-2145)
+	// hasOwn isn't used here due to false negatives
+	// regarding Nodelist length in IE
+	var length = !!obj && "length" in obj && obj.length,
+		type = toType( obj );
+
+	if ( isFunction( obj ) || isWindow( obj ) ) {
+		return false;
+	}
+
+	return type === "array" || length === 0 ||
+		typeof length === "number" && length > 0 && ( length - 1 ) in obj;
+}
+var Sizzle =
+/*!
+ * Sizzle CSS Selector Engine v2.3.5
+ * https://sizzlejs.com/
+ *
+ * Copyright JS Foundation and other contributors
+ * Released under the MIT license
+ * https://js.foundation/
+ *
+ * Date: 2020-03-14
+ */
+( function( window ) {
+var i,
+	support,
+	Expr,
+	getText,
+	isXML,
+	tokenize,
+	compile,
+	select,
+	outermostContext,
+	sortInput,
+	hasDuplicate,
+
+	// Local document vars
+	setDocument,
+	document,
+	docElem,
+	documentIsHTML,
+	rbuggyQSA,
+	rbuggyMatches,
+	matches,
+	contains,
+
+	// Instance-specific data
+	expando = "sizzle" + 1 * new Date(),
+	preferredDoc = window.document,
+	dirruns = 0,
+	done = 0,
+	classCache = createCache(),
+	tokenCache = createCache(),
+	compilerCache = createCache(),
+	nonnativeSelectorCache = createCache(),
+	sortOrder = function( a, b ) {
+		if ( a === b ) {
+			hasDuplicate = true;
+		}
+		return 0;
+	},
+
+	// Instance methods
+	hasOwn = ( {} ).hasOwnProperty,
+	arr = [],
+	pop = arr.pop,
+	pushNative = arr.push,
+	push = arr.push,
+	slice = arr.slice,
+
+	// Use a stripped-down indexOf as it's faster than native
+	// https://jsperf.com/thor-indexof-vs-for/5
+	indexOf = function( list, elem ) {
+		var i = 0,
+			len = list.length;
+		for ( ; i < len; i++ ) {
+			if ( list[ i ] === elem ) {
+				return i;
+			}
+		}
+		return -1;
+	},
+
+	booleans = "checked|selected|async|autofocus|autoplay|controls|defer|disabled|hidden|" +
+		"ismap|loop|multiple|open|readonly|required|scoped",
+
+	// Regular expressions
+
+	// http://www.w3.org/TR/css3-selectors/#whitespace
+	whitespace = "[\\x20\\t\\r\\n\\f]",
+
+	// https://www.w3.org/TR/css-syntax-3/#ident-token-diagram
+	identifier = "(?:\\\\[\\da-fA-F]{1,6}" + whitespace +
+		"?|\\\\[^\\r\\n\\f]|[\\w-]|[^\0-\\x7f])+",
+
+	// Attribute selectors: http://www.w3.org/TR/selectors/#attribute-selectors
+	attributes = "\\[" + whitespace + "*(" + identifier + ")(?:" + whitespace +
+
+		// Operator (capture 2)
+		"*([*^$|!~]?=)" + whitespace +
+
+		// "Attribute values must be CSS identifiers [capture 5]
+		// or strings [capture 3 or capture 4]"
+		"*(?:'((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\"|(" + identifier + "))|)" +
+		whitespace + "*\\]",
+
+	pseudos = ":(" + identifier + ")(?:\\((" +
+
+		// To reduce the number of selectors needing tokenize in the preFilter, prefer arguments:
+		// 1. quoted (capture 3; capture 4 or capture 5)
+		"('((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\")|" +
+
+		// 2. simple (capture 6)
+		"((?:\\\\.|[^\\\\()[\\]]|" + attributes + ")*)|" +
+
+		// 3. anything else (capture 2)
+		".*" +
+		")\\)|)",
+
+	// Leading and non-escaped trailing whitespace, capturing some non-whitespace characters preceding the latter
+	rwhitespace = new RegExp( whitespace + "+", "g" ),
+	rtrim = new RegExp( "^" + whitespace + "+|((?:^|[^\\\\])(?:\\\\.)*)" +
+		whitespace + "+$", "g" ),
+
+	rcomma = new RegExp( "^" + whitespace + "*," + whitespace + "*" ),
+	rcombinators = new RegExp( "^" + whitespace + "*([>+~]|" + whitespace + ")" + whitespace +
+		"*" ),
+	rdescend = new RegExp( whitespace + "|>" ),
+
+	rpseudo = new RegExp( pseudos ),
+	ridentifier = new RegExp( "^" + identifier + "$" ),
+
+	matchExpr = {
+		"ID": new RegExp( "^#(" + identifier + ")" ),
+		"CLASS": new RegExp( "^\\.(" + identifier + ")" ),
+		"TAG": new RegExp( "^(" + identifier + "|[*])" ),
+		"ATTR": new RegExp( "^" + attributes ),
+		"PSEUDO": new RegExp( "^" + pseudos ),
+		"CHILD": new RegExp( "^:(only|first|last|nth|nth-last)-(child|of-type)(?:\\(" +
+			whitespace + "*(even|odd|(([+-]|)(\\d*)n|)" + whitespace + "*(?:([+-]|)" +
+			whitespace + "*(\\d+)|))" + whitespace + "*\\)|)", "i" ),
+		"bool": new RegExp( "^(?:" + booleans + ")$", "i" ),
+
+		// For use in libraries implementing .is()
+		// We use this for POS matching in `select`
+		"needsContext": new RegExp( "^" + whitespace +
+			"*[>+~]|:(even|odd|eq|gt|lt|nth|first|last)(?:\\(" + whitespace +
+			"*((?:-\\d)?\\d*)" + whitespace + "*\\)|)(?=[^-]|$)", "i" )
+	},
+
+	rhtml = /HTML$/i,
+	rinputs = /^(?:input|select|textarea|button)$/i,
+	rheader = /^h\d$/i,
+
+	rnative = /^[^{]+\{\s*\[native \w/,
+
+	// Easily-parseable/retrievable ID or TAG or CLASS selectors
+	rquickExpr = /^(?:#([\w-]+)|(\w+)|\.([\w-]+))$/,
+
+	rsibling = /[+~]/,
+
+	// CSS escapes
+	// http://www.w3.org/TR/CSS21/syndata.html#escaped-characters
+	runescape = new RegExp( "\\\\[\\da-fA-F]{1,6}" + whitespace + "?|\\\\([^\\r\\n\\f])", "g" ),
+	funescape = function( escape, nonHex ) {
+		var high = "0x" + escape.slice( 1 ) - 0x10000;
+
+		return nonHex ?
+
+			// Strip the backslash prefix from a non-hex escape sequence
+			nonHex :
+
+			// Replace a hexadecimal escape sequence with the encoded Unicode code point
+			// Support: IE <=11+
+			// For values outside the Basic Multilingual Plane (BMP), manually construct a
+			// surrogate pair
+			high < 0 ?
+				String.fromCharCode( high + 0x10000 ) :
+				String.fromCharCode( high >> 10 | 0xD800, high & 0x3FF | 0xDC00 );
+	},
+
+	// CSS string/identifier serialization
+	// https://drafts.csswg.org/cssom/#common-serializing-idioms
+	rcssescape = /([\0-\x1f\x7f]|^-?\d)|^-$|[^\0-\x1f\x7f-\uFFFF\w-]/g,
+	fcssescape = function( ch, asCodePoint ) {
+		if ( asCodePoint ) {
+
+			// U+0000 NULL becomes U+FFFD REPLACEMENT CHARACTER
+			if ( ch === "\0" ) {
+				return "\uFFFD";
+			}
+
+			// Control characters and (dependent upon position) numbers get escaped as code points
+			return ch.slice( 0, -1 ) + "\\" +
+				ch.charCodeAt( ch.length - 1 ).toString( 16 ) + " ";
+		}
+
+		// Other potentially-special ASCII characters get backslash-escaped
+		return "\\" + ch;
+	},
+
+	// Used for iframes
+	// See setDocument()
+	// Removing the function wrapper causes a "Permission Denied"
+	// error in IE
+	unloadHandler = function() {
+		setDocument();
+	},
+
+	inDisabledFieldset = addCombinator(
+		function( elem ) {
+			return elem.disabled === true && elem.nodeName.toLowerCase() === "fieldset";
+		},
+		{ dir: "parentNode", next: "legend" }
+	);
+
+// Optimize for push.apply( _, NodeList )
+try {
+	push.apply(
+		( arr = slice.call( preferredDoc.childNodes ) ),
+		preferredDoc.childNodes
+	);
+
+	// Support: Android<4.0
+	// Detect silently failing push.apply
+	// eslint-disable-next-line no-unused-expressions
+	arr[ preferredDoc.childNodes.length ].nodeType;
+} catch ( e ) {
+	push = { apply: arr.length ?
+
+		// Leverage slice if possible
+		function( target, els ) {
+			pushNative.apply( target, slice.call( els ) );
+		} :
+
+		// Support: IE<9
+		// Otherwise append directly
+		function( target, els ) {
+			var j = target.length,
+				i = 0;
+
+			// Can't trust NodeList.length
+			while ( ( target[ j++ ] = els[ i++ ] ) ) {}
+			target.length = j - 1;
+		}
+	};
+}
+
+function Sizzle( selector, context, results, seed ) {
+	var m, i, elem, nid, match, groups, newSelector,
+		newContext = context && context.ownerDocument,
+
+		// nodeType defaults to 9, since context defaults to document
+		nodeType = context ? context.nodeType : 9;
+
+	results = results || [];
+
+	// Return early from calls with invalid selector or context
+	if ( typeof selector !== "string" || !selector ||
+		nodeType !== 1 && nodeType !== 9 && nodeType !== 11 ) {
+
+		return results;
+	}
+
+	// Try to shortcut find operations (as opposed to filters) in HTML documents
+	if ( !seed ) {
+		setDocument( context );
+		context = context || document;
+
+		if ( documentIsHTML ) {
+
+			// If the selector is sufficiently simple, try using a "get*By*" DOM method
+			// (excepting DocumentFragment context, where the methods don't exist)
+			if ( nodeType !== 11 && ( match = rquickExpr.exec( selector ) ) ) {
+
+				// ID selector
+				if ( ( m = match[ 1 ] ) ) {
+
+					// Document context
+					if ( nodeType === 9 ) {
+						if ( ( elem = context.getElementById( m ) ) ) {
+
+							// Support: IE, Opera, Webkit
+							// TODO: identify versions
+							// getElementById can match elements by name instead of ID
+							if ( elem.id === m ) {
+								results.push( elem );
+								return results;
+							}
+						} else {
+							return results;
+						}
+
+					// Element context
+					} else {
+
+						// Support: IE, Opera, Webkit
+						// TODO: identify versions
+						// getElementById can match elements by name instead of ID
+						if ( newContext && ( elem = newContext.getElementById( m ) ) &&
+							contains( context, elem ) &&
+							elem.id === m ) {
+
+							results.push( elem );
+							return results;
+						}
+					}
+
+				// Type selector
+				} else if ( match[ 2 ] ) {
+					push.apply( results, context.getElementsByTagName( selector ) );
+					return results;
+
+				// Class selector
+				} else if ( ( m = match[ 3 ] ) && support.getElementsByClassName &&
+					context.getElementsByClassName ) {
+
+					push.apply( results, context.getElementsByClassName( m ) );
+					return results;
+				}
+			}
+
+			// Take advantage of querySelectorAll
+			if ( support.qsa &&
+				!nonnativeSelectorCache[ selector + " " ] &&
+				( !rbuggyQSA || !rbuggyQSA.test( selector ) ) &&
+
+				// Support: IE 8 only
+				// Exclude object elements
+				( nodeType !== 1 || context.nodeName.toLowerCase() !== "object" ) ) {
+
+				newSelector = selector;
+				newContext = context;
+
+				// qSA considers elements outside a scoping root when evaluating child or
+				// descendant combinators, which is not what we want.
+				// In such cases, we work around the behavior by prefixing every selector in the
+				// list with an ID selector referencing the scope context.
+				// The technique has to be used as well when a leading combinator is used
+				// as such selectors are not recognized by querySelectorAll.
+				// Thanks to Andrew Dupont for this technique.
+				if ( nodeType === 1 &&
+					( rdescend.test( selector ) || rcombinators.test( selector ) ) ) {
+
+					// Expand context for sibling selectors
+					newContext = rsibling.test( selector ) && testContext( context.parentNode ) ||
+						context;
+
+					// We can use :scope instead of the ID hack if the browser
+					// supports it & if we're not changing the context.
+					if ( newContext !== context || !support.scope ) {
+
+						// Capture the context ID, setting it first if necessary
+						if ( ( nid = context.getAttribute( "id" ) ) ) {
+							nid = nid.replace( rcssescape, fcssescape );
+						} else {
+							context.setAttribute( "id", ( nid = expando ) );
+						}
+					}
+
+					// Prefix every selector in the list
+					groups = tokenize( selector );
+					i = groups.length;
+					while ( i-- ) {
+						groups[ i ] = ( nid ? "#" + nid : ":scope" ) + " " +
+							toSelector( groups[ i ] );
+					}
+					newSelector = groups.join( "," );
+				}
+
+				try {
+					push.apply( results,
+						newContext.querySelectorAll( newSelector )
+					);
+					return results;
+				} catch ( qsaError ) {
+					nonnativeSelectorCache( selector, true );
+				} finally {
+					if ( nid === expando ) {
+						context.removeAttribute( "id" );
+					}
+				}
+			}
+		}
+	}
+
+	// All others
+	return select( selector.replace( rtrim, "$1" ), context, results, seed );
+}
+
+/**
+ * Create key-value caches of limited size
+ * @returns {function(string, object)} Returns the Object data after storing it on itself with
+ *	property name the (space-suffixed) string and (if the cache is larger than Expr.cacheLength)
+ *	deleting the oldest entry
+ */
+function createCache() {
+	var keys = [];
+
+	function cache( key, value ) {
+
+		// Use (key + " ") to avoid collision with native prototype properties (see Issue #157)
+		if ( keys.push( key + " " ) > Expr.cacheLength ) {
+
+			// Only keep the most recent entries
+			delete cache[ keys.shift() ];
+		}
+		return ( cache[ key + " " ] = value );
+	}
+	return cache;
+}
+
+/**
+ * Mark a function for special use by Sizzle
+ * @param {Function} fn The function to mark
+ */
+function markFunction( fn ) {
+	fn[ expando ] = true;
+	return fn;
+}
+
+/**
+ * Support testing using an element
+ * @param {Function} fn Passed the created element and returns a boolean result
+ */
+function assert( fn ) {
+	var el = document.createElement( "fieldset" );
+
+	try {
+		return !!fn( el );
+	} catch ( e ) {
+		return false;
+	} finally {
+
+		// Remove from its parent by default
+		if ( el.parentNode ) {
+			el.parentNode.removeChild( el );
+		}
+
+		// release memory in IE
+		el = null;
+	}
+}
+
+/**
+ * Adds the same handler for all of the specified attrs
+ * @param {String} attrs Pipe-separated list of attributes
+ * @param {Function} handler The method that will be applied
+ */
+function addHandle( attrs, handler ) {
+	var arr = attrs.split( "|" ),
+		i = arr.length;
+
+	while ( i-- ) {
+		Expr.attrHandle[ arr[ i ] ] = handler;
+	}
+}
+
+/**
+ * Checks document order of two siblings
+ * @param {Element} a
+ * @param {Element} b
+ * @returns {Number} Returns less than 0 if a precedes b, greater than 0 if a follows b
+ */
+function siblingCheck( a, b ) {
+	var cur = b && a,
+		diff = cur && a.nodeType === 1 && b.nodeType === 1 &&
+			a.sourceIndex - b.sourceIndex;
+
+	// Use IE sourceIndex if available on both nodes
+	if ( diff ) {
+		return diff;
+	}
+
+	// Check if b follows a
+	if ( cur ) {
+		while ( ( cur = cur.nextSibling ) ) {
+			if ( cur === b ) {
+				return -1;
+			}
+		}
+	}
+
+	return a ? 1 : -1;
+}
+
+/**
+ * Returns a function to use in pseudos for input types
+ * @param {String} type
+ */
+function createInputPseudo( type ) {
+	return function( elem ) {
+		var name = elem.nodeName.toLowerCase();
+		return name === "input" && elem.type === type;
+	};
+}
+
+/**
+ * Returns a function to use in pseudos for buttons
+ * @param {String} type
+ */
+function createButtonPseudo( type ) {
+	return function( elem ) {
+		var name = elem.nodeName.toLowerCase();
+		return ( name === "input" || name === "button" ) && elem.type === type;
+	};
+}
+
+/**
+ * Returns a function to use in pseudos for :enabled/:disabled
+ * @param {Boolean} disabled true for :disabled; false for :enabled
+ */
+function createDisabledPseudo( disabled ) {
+
+	// Known :disabled false positives: fieldset[disabled] > legend:nth-of-type(n+2) :can-disable
+	return function( elem ) {
+
+		// Only certain elements can match :enabled or :disabled
+		// https://html.spec.whatwg.org/multipage/scripting.html#selector-enabled
+		// https://html.spec.whatwg.org/multipage/scripting.html#selector-disabled
+		if ( "form" in elem ) {
+
+			// Check for inherited disabledness on relevant non-disabled elements:
+			// * listed form-associated elements in a disabled fieldset
+			//   https://html.spec.whatwg.org/multipage/forms.html#category-listed
+			//   https://html.spec.whatwg.org/multipage/forms.html#concept-fe-disabled
+			// * option elements in a disabled optgroup
+			//   https://html.spec.whatwg.org/multipage/forms.html#concept-option-disabled
+			// All such elements have a "form" property.
+			if ( elem.parentNode && elem.disabled === false ) {
+
+				// Option elements defer to a parent optgroup if present
+				if ( "label" in elem ) {
+					if ( "label" in elem.parentNode ) {
+						return elem.parentNode.disabled === disabled;
+					} else {
+						return elem.disabled === disabled;
+					}
+				}
+
+				// Support: IE 6 - 11
+				// Use the isDisabled shortcut property to check for disabled fieldset ancestors
+				return elem.isDisabled === disabled ||
+
+					// Where there is no isDisabled, check manually
+					/* jshint -W018 */
+					elem.isDisabled !== !disabled &&
+					inDisabledFieldset( elem ) === disabled;
+			}
+
+			return elem.disabled === disabled;
+
+		// Try to winnow out elements that can't be disabled before trusting the disabled property.
+		// Some victims get caught in our net (label, legend, menu, track), but it shouldn't
+		// even exist on them, let alone have a boolean value.
+		} else if ( "label" in elem ) {
+			return elem.disabled === disabled;
+		}
+
+		// Remaining elements are neither :enabled nor :disabled
+		return false;
+	};
+}
+
+/**
+ * Returns a function to use in pseudos for positionals
+ * @param {Function} fn
+ */
+function createPositionalPseudo( fn ) {
+	return markFunction( function( argument ) {
+		argument = +argument;
+		return markFunction( function( seed, matches ) {
+			var j,
+				matchIndexes = fn( [], seed.length, argument ),
+				i = matchIndexes.length;
+
+			// Match elements found at the specified indexes
+			while ( i-- ) {
+				if ( seed[ ( j = matchIndexes[ i ] ) ] ) {
+					seed[ j ] = !( matches[ j ] = seed[ j ] );
+				}
+			}
+		} );
+	} );
+}
+
+/**
+ * Checks a node for validity as a Sizzle context
+ * @param {Element|Object=} context
+ * @returns {Element|Object|Boolean} The input node if acceptable, otherwise a falsy value
+ */
+function testContext( context ) {
+	return context && typeof context.getElementsByTagName !== "undefined" && context;
+}
+
+// Expose support vars for convenience
+support = Sizzle.support = {};
+
+/**
+ * Detects XML nodes
+ * @param {Element|Object} elem An element or a document
+ * @returns {Boolean} True iff elem is a non-HTML XML node
+ */
+isXML = Sizzle.isXML = function( elem ) {
+	var namespace = elem.namespaceURI,
+		docElem = ( elem.ownerDocument || elem ).documentElement;
+
+	// Support: IE <=8
+	// Assume HTML when documentElement doesn't yet exist, such as inside loading iframes
+	// https://bugs.jquery.com/ticket/4833
+	return !rhtml.test( namespace || docElem && docElem.nodeName || "HTML" );
+};
+
+/**
+ * Sets document-related variables once based on the current document
+ * @param {Element|Object} [doc] An element or document object to use to set the document
+ * @returns {Object} Returns the current document
+ */
+setDocument = Sizzle.setDocument = function( node ) {
+	var hasCompare, subWindow,
+		doc = node ? node.ownerDocument || node : preferredDoc;
+
+	// Return early if doc is invalid or already selected
+	// Support: IE 11+, Edge 17 - 18+
+	// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+	// two documents; shallow comparisons work.
+	// eslint-disable-next-line eqeqeq
+	if ( doc == document || doc.nodeType !== 9 || !doc.documentElement ) {
+		return document;
+	}
+
+	// Update global variables
+	document = doc;
+	docElem = document.documentElement;
+	documentIsHTML = !isXML( document );
+
+	// Support: IE 9 - 11+, Edge 12 - 18+
+	// Accessing iframe documents after unload throws "permission denied" errors (jQuery #13936)
+	// Support: IE 11+, Edge 17 - 18+
+	// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+	// two documents; shallow comparisons work.
+	// eslint-disable-next-line eqeqeq
+	if ( preferredDoc != document &&
+		( subWindow = document.defaultView ) && subWindow.top !== subWindow ) {
+
+		// Support: IE 11, Edge
+		if ( subWindow.addEventListener ) {
+			subWindow.addEventListener( "unload", unloadHandler, false );
+
+		// Support: IE 9 - 10 only
+		} else if ( subWindow.attachEvent ) {
+			subWindow.attachEvent( "onunload", unloadHandler );
+		}
+	}
+
+	// Support: IE 8 - 11+, Edge 12 - 18+, Chrome <=16 - 25 only, Firefox <=3.6 - 31 only,
+	// Safari 4 - 5 only, Opera <=11.6 - 12.x only
+	// IE/Edge & older browsers don't support the :scope pseudo-class.
+	// Support: Safari 6.0 only
+	// Safari 6.0 supports :scope but it's an alias of :root there.
+	support.scope = assert( function( el ) {
+		docElem.appendChild( el ).appendChild( document.createElement( "div" ) );
+		return typeof el.querySelectorAll !== "undefined" &&
+			!el.querySelectorAll( ":scope fieldset div" ).length;
+	} );
+
+	/* Attributes
+	---------------------------------------------------------------------- */
+
+	// Support: IE<8
+	// Verify that getAttribute really returns attributes and not properties
+	// (excepting IE8 booleans)
+	support.attributes = assert( function( el ) {
+		el.className = "i";
+		return !el.getAttribute( "className" );
+	} );
+
+	/* getElement(s)By*
+	---------------------------------------------------------------------- */
+
+	// Check if getElementsByTagName("*") returns only elements
+	support.getElementsByTagName = assert( function( el ) {
+		el.appendChild( document.createComment( "" ) );
+		return !el.getElementsByTagName( "*" ).length;
+	} );
+
+	// Support: IE<9
+	support.getElementsByClassName = rnative.test( document.getElementsByClassName );
+
+	// Support: IE<10
+	// Check if getElementById returns elements by name
+	// The broken getElementById methods don't pick up programmatically-set names,
+	// so use a roundabout getElementsByName test
+	support.getById = assert( function( el ) {
+		docElem.appendChild( el ).id = expando;
+		return !document.getElementsByName || !document.getElementsByName( expando ).length;
+	} );
+
+	// ID filter and find
+	if ( support.getById ) {
+		Expr.filter[ "ID" ] = function( id ) {
+			var attrId = id.replace( runescape, funescape );
+			return function( elem ) {
+				return elem.getAttribute( "id" ) === attrId;
+			};
+		};
+		Expr.find[ "ID" ] = function( id, context ) {
+			if ( typeof context.getElementById !== "undefined" && documentIsHTML ) {
+				var elem = context.getElementById( id );
+				return elem ? [ elem ] : [];
+			}
+		};
+	} else {
+		Expr.filter[ "ID" ] =  function( id ) {
+			var attrId = id.replace( runescape, funescape );
+			return function( elem ) {
+				var node = typeof elem.getAttributeNode !== "undefined" &&
+					elem.getAttributeNode( "id" );
+				return node && node.value === attrId;
+			};
+		};
+
+		// Support: IE 6 - 7 only
+		// getElementById is not reliable as a find shortcut
+		Expr.find[ "ID" ] = function( id, context ) {
+			if ( typeof context.getElementById !== "undefined" && documentIsHTML ) {
+				var node, i, elems,
+					elem = context.getElementById( id );
+
+				if ( elem ) {
+
+					// Verify the id attribute
+					node = elem.getAttributeNode( "id" );
+					if ( node && node.value === id ) {
+						return [ elem ];
+					}
+
+					// Fall back on getElementsByName
+					elems = context.getElementsByName( id );
+					i = 0;
+					while ( ( elem = elems[ i++ ] ) ) {
+						node = elem.getAttributeNode( "id" );
+						if ( node && node.value === id ) {
+							return [ elem ];
+						}
+					}
+				}
+
+				return [];
+			}
+		};
+	}
+
+	// Tag
+	Expr.find[ "TAG" ] = support.getElementsByTagName ?
+		function( tag, context ) {
+			if ( typeof context.getElementsByTagName !== "undefined" ) {
+				return context.getElementsByTagName( tag );
+
+			// DocumentFragment nodes don't have gEBTN
+			} else if ( support.qsa ) {
+				return context.querySelectorAll( tag );
+			}
+		} :
+
+		function( tag, context ) {
+			var elem,
+				tmp = [],
+				i = 0,
+
+				// By happy coincidence, a (broken) gEBTN appears on DocumentFragment nodes too
+				results = context.getElementsByTagName( tag );
+
+			// Filter out possible comments
+			if ( tag === "*" ) {
+				while ( ( elem = results[ i++ ] ) ) {
+					if ( elem.nodeType === 1 ) {
+						tmp.push( elem );
+					}
+				}
+
+				return tmp;
+			}
+			return results;
+		};
+
+	// Class
+	Expr.find[ "CLASS" ] = support.getElementsByClassName && function( className, context ) {
+		if ( typeof context.getElementsByClassName !== "undefined" && documentIsHTML ) {
+			return context.getElementsByClassName( className );
+		}
+	};
+
+	/* QSA/matchesSelector
+	---------------------------------------------------------------------- */
+
+	// QSA and matchesSelector support
+
+	// matchesSelector(:active) reports false when true (IE9/Opera 11.5)
+	rbuggyMatches = [];
+
+	// qSa(:focus) reports false when true (Chrome 21)
+	// We allow this because of a bug in IE8/9 that throws an error
+	// whenever `document.activeElement` is accessed on an iframe
+	// So, we allow :focus to pass through QSA all the time to avoid the IE error
+	// See https://bugs.jquery.com/ticket/13378
+	rbuggyQSA = [];
+
+	if ( ( support.qsa = rnative.test( document.querySelectorAll ) ) ) {
+
+		// Build QSA regex
+		// Regex strategy adopted from Diego Perini
+		assert( function( el ) {
+
+			var input;
+
+			// Select is set to empty string on purpose
+			// This is to test IE's treatment of not explicitly
+			// setting a boolean content attribute,
+			// since its presence should be enough
+			// https://bugs.jquery.com/ticket/12359
+			docElem.appendChild( el ).innerHTML = "<a id='" + expando + "'></a>" +
+				"<select id='" + expando + "-\r\\' msallowcapture=''>" +
+				"<option selected=''></option></select>";
+
+			// Support: IE8, Opera 11-12.16
+			// Nothing should be selected when empty strings follow ^= or $= or *=
+			// The test attribute must be unknown in Opera but "safe" for WinRT
+			// https://msdn.microsoft.com/en-us/library/ie/hh465388.aspx#attribute_section
+			if ( el.querySelectorAll( "[msallowcapture^='']" ).length ) {
+				rbuggyQSA.push( "[*^$]=" + whitespace + "*(?:''|\"\")" );
+			}
+
+			// Support: IE8
+			// Boolean attributes and "value" are not treated correctly
+			if ( !el.querySelectorAll( "[selected]" ).length ) {
+				rbuggyQSA.push( "\\[" + whitespace + "*(?:value|" + booleans + ")" );
+			}
+
+			// Support: Chrome<29, Android<4.4, Safari<7.0+, iOS<7.0+, PhantomJS<1.9.8+
+			if ( !el.querySelectorAll( "[id~=" + expando + "-]" ).length ) {
+				rbuggyQSA.push( "~=" );
+			}
+
+			// Support: IE 11+, Edge 15 - 18+
+			// IE 11/Edge don't find elements on a `[name='']` query in some cases.
+			// Adding a temporary attribute to the document before the selection works
+			// around the issue.
+			// Interestingly, IE 10 & older don't seem to have the issue.
+			input = document.createElement( "input" );
+			input.setAttribute( "name", "" );
+			el.appendChild( input );
+			if ( !el.querySelectorAll( "[name='']" ).length ) {
+				rbuggyQSA.push( "\\[" + whitespace + "*name" + whitespace + "*=" +
+					whitespace + "*(?:''|\"\")" );
+			}
+
+			// Webkit/Opera - :checked should return selected option elements
+			// http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked
+			// IE8 throws error here and will not see later tests
+			if ( !el.querySelectorAll( ":checked" ).length ) {
+				rbuggyQSA.push( ":checked" );
+			}
+
+			// Support: Safari 8+, iOS 8+
+			// https://bugs.webkit.org/show_bug.cgi?id=136851
+			// In-page `selector#id sibling-combinator selector` fails
+			if ( !el.querySelectorAll( "a#" + expando + "+*" ).length ) {
+				rbuggyQSA.push( ".#.+[+~]" );
+			}
+
+			// Support: Firefox <=3.6 - 5 only
+			// Old Firefox doesn't throw on a badly-escaped identifier.
+			el.querySelectorAll( "\\\f" );
+			rbuggyQSA.push( "[\\r\\n\\f]" );
+		} );
+
+		assert( function( el ) {
+			el.innerHTML = "<a href='' disabled='disabled'></a>" +
+				"<select disabled='disabled'><option/></select>";
+
+			// Support: Windows 8 Native Apps
+			// The type and name attributes are restricted during .innerHTML assignment
+			var input = document.createElement( "input" );
+			input.setAttribute( "type", "hidden" );
+			el.appendChild( input ).setAttribute( "name", "D" );
+
+			// Support: IE8
+			// Enforce case-sensitivity of name attribute
+			if ( el.querySelectorAll( "[name=d]" ).length ) {
+				rbuggyQSA.push( "name" + whitespace + "*[*^$|!~]?=" );
+			}
+
+			// FF 3.5 - :enabled/:disabled and hidden elements (hidden elements are still enabled)
+			// IE8 throws error here and will not see later tests
+			if ( el.querySelectorAll( ":enabled" ).length !== 2 ) {
+				rbuggyQSA.push( ":enabled", ":disabled" );
+			}
+
+			// Support: IE9-11+
+			// IE's :disabled selector does not pick up the children of disabled fieldsets
+			docElem.appendChild( el ).disabled = true;
+			if ( el.querySelectorAll( ":disabled" ).length !== 2 ) {
+				rbuggyQSA.push( ":enabled", ":disabled" );
+			}
+
+			// Support: Opera 10 - 11 only
+			// Opera 10-11 does not throw on post-comma invalid pseudos
+			el.querySelectorAll( "*,:x" );
+			rbuggyQSA.push( ",.*:" );
+		} );
+	}
+
+	if ( ( support.matchesSelector = rnative.test( ( matches = docElem.matches ||
+		docElem.webkitMatchesSelector ||
+		docElem.mozMatchesSelector ||
+		docElem.oMatchesSelector ||
+		docElem.msMatchesSelector ) ) ) ) {
+
+		assert( function( el ) {
+
+			// Check to see if it's possible to do matchesSelector
+			// on a disconnected node (IE 9)
+			support.disconnectedMatch = matches.call( el, "*" );
+
+			// This should fail with an exception
+			// Gecko does not error, returns false instead
+			matches.call( el, "[s!='']:x" );
+			rbuggyMatches.push( "!=", pseudos );
+		} );
+	}
+
+	rbuggyQSA = rbuggyQSA.length && new RegExp( rbuggyQSA.join( "|" ) );
+	rbuggyMatches = rbuggyMatches.length && new RegExp( rbuggyMatches.join( "|" ) );
+
+	/* Contains
+	---------------------------------------------------------------------- */
+	hasCompare = rnative.test( docElem.compareDocumentPosition );
+
+	// Element contains another
+	// Purposefully self-exclusive
+	// As in, an element does not contain itself
+	contains = hasCompare || rnative.test( docElem.contains ) ?
+		function( a, b ) {
+			var adown = a.nodeType === 9 ? a.documentElement : a,
+				bup = b && b.parentNode;
+			return a === bup || !!( bup && bup.nodeType === 1 && (
+				adown.contains ?
+					adown.contains( bup ) :
+					a.compareDocumentPosition && a.compareDocumentPosition( bup ) & 16
+			) );
+		} :
+		function( a, b ) {
+			if ( b ) {
+				while ( ( b = b.parentNode ) ) {
+					if ( b === a ) {
+						return true;
+					}
+				}
+			}
+			return false;
+		};
+
+	/* Sorting
+	---------------------------------------------------------------------- */
+
+	// Document order sorting
+	sortOrder = hasCompare ?
+	function( a, b ) {
+
+		// Flag for duplicate removal
+		if ( a === b ) {
+			hasDuplicate = true;
+			return 0;
+		}
+
+		// Sort on method existence if only one input has compareDocumentPosition
+		var compare = !a.compareDocumentPosition - !b.compareDocumentPosition;
+		if ( compare ) {
+			return compare;
+		}
+
+		// Calculate position if both inputs belong to the same document
+		// Support: IE 11+, Edge 17 - 18+
+		// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+		// two documents; shallow comparisons work.
+		// eslint-disable-next-line eqeqeq
+		compare = ( a.ownerDocument || a ) == ( b.ownerDocument || b ) ?
+			a.compareDocumentPosition( b ) :
+
+			// Otherwise we know they are disconnected
+			1;
+
+		// Disconnected nodes
+		if ( compare & 1 ||
+			( !support.sortDetached && b.compareDocumentPosition( a ) === compare ) ) {
+
+			// Choose the first element that is related to our preferred document
+			// Support: IE 11+, Edge 17 - 18+
+			// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+			// two documents; shallow comparisons work.
+			// eslint-disable-next-line eqeqeq
+			if ( a == document || a.ownerDocument == preferredDoc &&
+				contains( preferredDoc, a ) ) {
+				return -1;
+			}
+
+			// Support: IE 11+, Edge 17 - 18+
+			// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+			// two documents; shallow comparisons work.
+			// eslint-disable-next-line eqeqeq
+			if ( b == document || b.ownerDocument == preferredDoc &&
+				contains( preferredDoc, b ) ) {
+				return 1;
+			}
+
+			// Maintain original order
+			return sortInput ?
+				( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) :
+				0;
+		}
+
+		return compare & 4 ? -1 : 1;
+	} :
+	function( a, b ) {
+
+		// Exit early if the nodes are identical
+		if ( a === b ) {
+			hasDuplicate = true;
+			return 0;
+		}
+
+		var cur,
+			i = 0,
+			aup = a.parentNode,
+			bup = b.parentNode,
+			ap = [ a ],
+			bp = [ b ];
+
+		// Parentless nodes are either documents or disconnected
+		if ( !aup || !bup ) {
+
+			// Support: IE 11+, Edge 17 - 18+
+			// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+			// two documents; shallow comparisons work.
+			/* eslint-disable eqeqeq */
+			return a == document ? -1 :
+				b == document ? 1 :
+				/* eslint-enable eqeqeq */
+				aup ? -1 :
+				bup ? 1 :
+				sortInput ?
+				( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) :
+				0;
+
+		// If the nodes are siblings, we can do a quick check
+		} else if ( aup === bup ) {
+			return siblingCheck( a, b );
+		}
+
+		// Otherwise we need full lists of their ancestors for comparison
+		cur = a;
+		while ( ( cur = cur.parentNode ) ) {
+			ap.unshift( cur );
+		}
+		cur = b;
+		while ( ( cur = cur.parentNode ) ) {
+			bp.unshift( cur );
+		}
+
+		// Walk down the tree looking for a discrepancy
+		while ( ap[ i ] === bp[ i ] ) {
+			i++;
+		}
+
+		return i ?
+
+			// Do a sibling check if the nodes have a common ancestor
+			siblingCheck( ap[ i ], bp[ i ] ) :
+
+			// Otherwise nodes in our document sort first
+			// Support: IE 11+, Edge 17 - 18+
+			// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+			// two documents; shallow comparisons work.
+			/* eslint-disable eqeqeq */
+			ap[ i ] == preferredDoc ? -1 :
+			bp[ i ] == preferredDoc ? 1 :
+			/* eslint-enable eqeqeq */
+			0;
+	};
+
+	return document;
+};
+
+Sizzle.matches = function( expr, elements ) {
+	return Sizzle( expr, null, null, elements );
+};
+
+Sizzle.matchesSelector = function( elem, expr ) {
+	setDocument( elem );
+
+	if ( support.matchesSelector && documentIsHTML &&
+		!nonnativeSelectorCache[ expr + " " ] &&
+		( !rbuggyMatches || !rbuggyMatches.test( expr ) ) &&
+		( !rbuggyQSA     || !rbuggyQSA.test( expr ) ) ) {
+
+		try {
+			var ret = matches.call( elem, expr );
+
+			// IE 9's matchesSelector returns false on disconnected nodes
+			if ( ret || support.disconnectedMatch ||
+
+				// As well, disconnected nodes are said to be in a document
+				// fragment in IE 9
+				elem.document && elem.document.nodeType !== 11 ) {
+				return ret;
+			}
+		} catch ( e ) {
+			nonnativeSelectorCache( expr, true );
+		}
+	}
+
+	return Sizzle( expr, document, null, [ elem ] ).length > 0;
+};
+
+Sizzle.contains = function( context, elem ) {
+
+	// Set document vars if needed
+	// Support: IE 11+, Edge 17 - 18+
+	// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+	// two documents; shallow comparisons work.
+	// eslint-disable-next-line eqeqeq
+	if ( ( context.ownerDocument || context ) != document ) {
+		setDocument( context );
+	}
+	return contains( context, elem );
+};
+
+Sizzle.attr = function( elem, name ) {
+
+	// Set document vars if needed
+	// Support: IE 11+, Edge 17 - 18+
+	// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+	// two documents; shallow comparisons work.
+	// eslint-disable-next-line eqeqeq
+	if ( ( elem.ownerDocument || elem ) != document ) {
+		setDocument( elem );
+	}
+
+	var fn = Expr.attrHandle[ name.toLowerCase() ],
+
+		// Don't get fooled by Object.prototype properties (jQuery #13807)
+		val = fn && hasOwn.call( Expr.attrHandle, name.toLowerCase() ) ?
+			fn( elem, name, !documentIsHTML ) :
+			undefined;
+
+	return val !== undefined ?
+		val :
+		support.attributes || !documentIsHTML ?
+			elem.getAttribute( name ) :
+			( val = elem.getAttributeNode( name ) ) && val.specified ?
+				val.value :
+				null;
+};
+
+Sizzle.escape = function( sel ) {
+	return ( sel + "" ).replace( rcssescape, fcssescape );
+};
+
+Sizzle.error = function( msg ) {
+	throw new Error( "Syntax error, unrecognized expression: " + msg );
+};
+
+/**
+ * Document sorting and removing duplicates
+ * @param {ArrayLike} results
+ */
+Sizzle.uniqueSort = function( results ) {
+	var elem,
+		duplicates = [],
+		j = 0,
+		i = 0;
+
+	// Unless we *know* we can detect duplicates, assume their presence
+	hasDuplicate = !support.detectDuplicates;
+	sortInput = !support.sortStable && results.slice( 0 );
+	results.sort( sortOrder );
+
+	if ( hasDuplicate ) {
+		while ( ( elem = results[ i++ ] ) ) {
+			if ( elem === results[ i ] ) {
+				j = duplicates.push( i );
+			}
+		}
+		while ( j-- ) {
+			results.splice( duplicates[ j ], 1 );
+		}
+	}
+
+	// Clear input after sorting to release objects
+	// See https://github.com/jquery/sizzle/pull/225
+	sortInput = null;
+
+	return results;
+};
+
+/**
+ * Utility function for retrieving the text value of an array of DOM nodes
+ * @param {Array|Element} elem
+ */
+getText = Sizzle.getText = function( elem ) {
+	var node,
+		ret = "",
+		i = 0,
+		nodeType = elem.nodeType;
+
+	if ( !nodeType ) {
+
+		// If no nodeType, this is expected to be an array
+		while ( ( node = elem[ i++ ] ) ) {
+
+			// Do not traverse comment nodes
+			ret += getText( node );
+		}
+	} else if ( nodeType === 1 || nodeType === 9 || nodeType === 11 ) {
+
+		// Use textContent for elements
+		// innerText usage removed for consistency of new lines (jQuery #11153)
+		if ( typeof elem.textContent === "string" ) {
+			return elem.textContent;
+		} else {
+
+			// Traverse its children
+			for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) {
+				ret += getText( elem );
+			}
+		}
+	} else if ( nodeType === 3 || nodeType === 4 ) {
+		return elem.nodeValue;
+	}
+
+	// Do not include comment or processing instruction nodes
+
+	return ret;
+};
+
+Expr = Sizzle.selectors = {
+
+	// Can be adjusted by the user
+	cacheLength: 50,
+
+	createPseudo: markFunction,
+
+	match: matchExpr,
+
+	attrHandle: {},
+
+	find: {},
+
+	relative: {
+		">": { dir: "parentNode", first: true },
+		" ": { dir: "parentNode" },
+		"+": { dir: "previousSibling", first: true },
+		"~": { dir: "previousSibling" }
+	},
+
+	preFilter: {
+		"ATTR": function( match ) {
+			match[ 1 ] = match[ 1 ].replace( runescape, funescape );
+
+			// Move the given value to match[3] whether quoted or unquoted
+			match[ 3 ] = ( match[ 3 ] || match[ 4 ] ||
+				match[ 5 ] || "" ).replace( runescape, funescape );
+
+			if ( match[ 2 ] === "~=" ) {
+				match[ 3 ] = " " + match[ 3 ] + " ";
+			}
+
+			return match.slice( 0, 4 );
+		},
+
+		"CHILD": function( match ) {
+
+			/* matches from matchExpr["CHILD"]
+				1 type (only|nth|...)
+				2 what (child|of-type)
+				3 argument (even|odd|\d*|\d*n([+-]\d+)?|...)
+				4 xn-component of xn+y argument ([+-]?\d*n|)
+				5 sign of xn-component
+				6 x of xn-component
+				7 sign of y-component
+				8 y of y-component
+			*/
+			match[ 1 ] = match[ 1 ].toLowerCase();
+
+			if ( match[ 1 ].slice( 0, 3 ) === "nth" ) {
+
+				// nth-* requires argument
+				if ( !match[ 3 ] ) {
+					Sizzle.error( match[ 0 ] );
+				}
+
+				// numeric x and y parameters for Expr.filter.CHILD
+				// remember that false/true cast respectively to 0/1
+				match[ 4 ] = +( match[ 4 ] ?
+					match[ 5 ] + ( match[ 6 ] || 1 ) :
+					2 * ( match[ 3 ] === "even" || match[ 3 ] === "odd" ) );
+				match[ 5 ] = +( ( match[ 7 ] + match[ 8 ] ) || match[ 3 ] === "odd" );
+
+				// other types prohibit arguments
+			} else if ( match[ 3 ] ) {
+				Sizzle.error( match[ 0 ] );
+			}
+
+			return match;
+		},
+
+		"PSEUDO": function( match ) {
+			var excess,
+				unquoted = !match[ 6 ] && match[ 2 ];
+
+			if ( matchExpr[ "CHILD" ].test( match[ 0 ] ) ) {
+				return null;
+			}
+
+			// Accept quoted arguments as-is
+			if ( match[ 3 ] ) {
+				match[ 2 ] = match[ 4 ] || match[ 5 ] || "";
+
+			// Strip excess characters from unquoted arguments
+			} else if ( unquoted && rpseudo.test( unquoted ) &&
+
+				// Get excess from tokenize (recursively)
+				( excess = tokenize( unquoted, true ) ) &&
+
+				// advance to the next closing parenthesis
+				( excess = unquoted.indexOf( ")", unquoted.length - excess ) - unquoted.length ) ) {
+
+				// excess is a negative index
+				match[ 0 ] = match[ 0 ].slice( 0, excess );
+				match[ 2 ] = unquoted.slice( 0, excess );
+			}
+
+			// Return only captures needed by the pseudo filter method (type and argument)
+			return match.slice( 0, 3 );
+		}
+	},
+
+	filter: {
+
+		"TAG": function( nodeNameSelector ) {
+			var nodeName = nodeNameSelector.replace( runescape, funescape ).toLowerCase();
+			return nodeNameSelector === "*" ?
+				function() {
+					return true;
+				} :
+				function( elem ) {
+					return elem.nodeName && elem.nodeName.toLowerCase() === nodeName;
+				};
+		},
+
+		"CLASS": function( className ) {
+			var pattern = classCache[ className + " " ];
+
+			return pattern ||
+				( pattern = new RegExp( "(^|" + whitespace +
+					")" + className + "(" + whitespace + "|$)" ) ) && classCache(
+						className, function( elem ) {
+							return pattern.test(
+								typeof elem.className === "string" && elem.className ||
+								typeof elem.getAttribute !== "undefined" &&
+									elem.getAttribute( "class" ) ||
+								""
+							);
+				} );
+		},
+
+		"ATTR": function( name, operator, check ) {
+			return function( elem ) {
+				var result = Sizzle.attr( elem, name );
+
+				if ( result == null ) {
+					return operator === "!=";
+				}
+				if ( !operator ) {
+					return true;
+				}
+
+				result += "";
+
+				/* eslint-disable max-len */
+
+				return operator === "=" ? result === check :
+					operator === "!=" ? result !== check :
+					operator === "^=" ? check && result.indexOf( check ) === 0 :
+					operator === "*=" ? check && result.indexOf( check ) > -1 :
+					operator === "$=" ? check && result.slice( -check.length ) === check :
+					operator === "~=" ? ( " " + result.replace( rwhitespace, " " ) + " " ).indexOf( check ) > -1 :
+					operator === "|=" ? result === check || result.slice( 0, check.length + 1 ) === check + "-" :
+					false;
+				/* eslint-enable max-len */
+
+			};
+		},
+
+		"CHILD": function( type, what, _argument, first, last ) {
+			var simple = type.slice( 0, 3 ) !== "nth",
+				forward = type.slice( -4 ) !== "last",
+				ofType = what === "of-type";
+
+			return first === 1 && last === 0 ?
+
+				// Shortcut for :nth-*(n)
+				function( elem ) {
+					return !!elem.parentNode;
+				} :
+
+				function( elem, _context, xml ) {
+					var cache, uniqueCache, outerCache, node, nodeIndex, start,
+						dir = simple !== forward ? "nextSibling" : "previousSibling",
+						parent = elem.parentNode,
+						name = ofType && elem.nodeName.toLowerCase(),
+						useCache = !xml && !ofType,
+						diff = false;
+
+					if ( parent ) {
+
+						// :(first|last|only)-(child|of-type)
+						if ( simple ) {
+							while ( dir ) {
+								node = elem;
+								while ( ( node = node[ dir ] ) ) {
+									if ( ofType ?
+										node.nodeName.toLowerCase() === name :
+										node.nodeType === 1 ) {
+
+										return false;
+									}
+								}
+
+								// Reverse direction for :only-* (if we haven't yet done so)
+								start = dir = type === "only" && !start && "nextSibling";
+							}
+							return true;
+						}
+
+						start = [ forward ? parent.firstChild : parent.lastChild ];
+
+						// non-xml :nth-child(...) stores cache data on `parent`
+						if ( forward && useCache ) {
+
+							// Seek `elem` from a previously-cached index
+
+							// ...in a gzip-friendly way
+							node = parent;
+							outerCache = node[ expando ] || ( node[ expando ] = {} );
+
+							// Support: IE <9 only
+							// Defend against cloned attroperties (jQuery gh-1709)
+							uniqueCache = outerCache[ node.uniqueID ] ||
+								( outerCache[ node.uniqueID ] = {} );
+
+							cache = uniqueCache[ type ] || [];
+							nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ];
+							diff = nodeIndex && cache[ 2 ];
+							node = nodeIndex && parent.childNodes[ nodeIndex ];
+
+							while ( ( node = ++nodeIndex && node && node[ dir ] ||
+
+								// Fallback to seeking `elem` from the start
+								( diff = nodeIndex = 0 ) || start.pop() ) ) {
+
+								// When found, cache indexes on `parent` and break
+								if ( node.nodeType === 1 && ++diff && node === elem ) {
+									uniqueCache[ type ] = [ dirruns, nodeIndex, diff ];
+									break;
+								}
+							}
+
+						} else {
+
+							// Use previously-cached element index if available
+							if ( useCache ) {
+
+								// ...in a gzip-friendly way
+								node = elem;
+								outerCache = node[ expando ] || ( node[ expando ] = {} );
+
+								// Support: IE <9 only
+								// Defend against cloned attroperties (jQuery gh-1709)
+								uniqueCache = outerCache[ node.uniqueID ] ||
+									( outerCache[ node.uniqueID ] = {} );
+
+								cache = uniqueCache[ type ] || [];
+								nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ];
+								diff = nodeIndex;
+							}
+
+							// xml :nth-child(...)
+							// or :nth-last-child(...) or :nth(-last)?-of-type(...)
+							if ( diff === false ) {
+
+								// Use the same loop as above to seek `elem` from the start
+								while ( ( node = ++nodeIndex && node && node[ dir ] ||
+									( diff = nodeIndex = 0 ) || start.pop() ) ) {
+
+									if ( ( ofType ?
+										node.nodeName.toLowerCase() === name :
+										node.nodeType === 1 ) &&
+										++diff ) {
+
+										// Cache the index of each encountered element
+										if ( useCache ) {
+											outerCache = node[ expando ] ||
+												( node[ expando ] = {} );
+
+											// Support: IE <9 only
+											// Defend against cloned attroperties (jQuery gh-1709)
+											uniqueCache = outerCache[ node.uniqueID ] ||
+												( outerCache[ node.uniqueID ] = {} );
+
+											uniqueCache[ type ] = [ dirruns, diff ];
+										}
+
+										if ( node === elem ) {
+											break;
+										}
+									}
+								}
+							}
+						}
+
+						// Incorporate the offset, then check against cycle size
+						diff -= last;
+						return diff === first || ( diff % first === 0 && diff / first >= 0 );
+					}
+				};
+		},
+
+		"PSEUDO": function( pseudo, argument ) {
+
+			// pseudo-class names are case-insensitive
+			// http://www.w3.org/TR/selectors/#pseudo-classes
+			// Prioritize by case sensitivity in case custom pseudos are added with uppercase letters
+			// Remember that setFilters inherits from pseudos
+			var args,
+				fn = Expr.pseudos[ pseudo ] || Expr.setFilters[ pseudo.toLowerCase() ] ||
+					Sizzle.error( "unsupported pseudo: " + pseudo );
+
+			// The user may use createPseudo to indicate that
+			// arguments are needed to create the filter function
+			// just as Sizzle does
+			if ( fn[ expando ] ) {
+				return fn( argument );
+			}
+
+			// But maintain support for old signatures
+			if ( fn.length > 1 ) {
+				args = [ pseudo, pseudo, "", argument ];
+				return Expr.setFilters.hasOwnProperty( pseudo.toLowerCase() ) ?
+					markFunction( function( seed, matches ) {
+						var idx,
+							matched = fn( seed, argument ),
+							i = matched.length;
+						while ( i-- ) {
+							idx = indexOf( seed, matched[ i ] );
+							seed[ idx ] = !( matches[ idx ] = matched[ i ] );
+						}
+					} ) :
+					function( elem ) {
+						return fn( elem, 0, args );
+					};
+			}
+
+			return fn;
+		}
+	},
+
+	pseudos: {
+
+		// Potentially complex pseudos
+		"not": markFunction( function( selector ) {
+
+			// Trim the selector passed to compile
+			// to avoid treating leading and trailing
+			// spaces as combinators
+			var input = [],
+				results = [],
+				matcher = compile( selector.replace( rtrim, "$1" ) );
+
+			return matcher[ expando ] ?
+				markFunction( function( seed, matches, _context, xml ) {
+					var elem,
+						unmatched = matcher( seed, null, xml, [] ),
+						i = seed.length;
+
+					// Match elements unmatched by `matcher`
+					while ( i-- ) {
+						if ( ( elem = unmatched[ i ] ) ) {
+							seed[ i ] = !( matches[ i ] = elem );
+						}
+					}
+				} ) :
+				function( elem, _context, xml ) {
+					input[ 0 ] = elem;
+					matcher( input, null, xml, results );
+
+					// Don't keep the element (issue #299)
+					input[ 0 ] = null;
+					return !results.pop();
+				};
+		} ),
+
+		"has": markFunction( function( selector ) {
+			return function( elem ) {
+				return Sizzle( selector, elem ).length > 0;
+			};
+		} ),
+
+		"contains": markFunction( function( text ) {
+			text = text.replace( runescape, funescape );
+			return function( elem ) {
+				return ( elem.textContent || getText( elem ) ).indexOf( text ) > -1;
+			};
+		} ),
+
+		// "Whether an element is represented by a :lang() selector
+		// is based solely on the element's language value
+		// being equal to the identifier C,
+		// or beginning with the identifier C immediately followed by "-".
+		// The matching of C against the element's language value is performed case-insensitively.
+		// The identifier C does not have to be a valid language name."
+		// http://www.w3.org/TR/selectors/#lang-pseudo
+		"lang": markFunction( function( lang ) {
+
+			// lang value must be a valid identifier
+			if ( !ridentifier.test( lang || "" ) ) {
+				Sizzle.error( "unsupported lang: " + lang );
+			}
+			lang = lang.replace( runescape, funescape ).toLowerCase();
+			return function( elem ) {
+				var elemLang;
+				do {
+					if ( ( elemLang = documentIsHTML ?
+						elem.lang :
+						elem.getAttribute( "xml:lang" ) || elem.getAttribute( "lang" ) ) ) {
+
+						elemLang = elemLang.toLowerCase();
+						return elemLang === lang || elemLang.indexOf( lang + "-" ) === 0;
+					}
+				} while ( ( elem = elem.parentNode ) && elem.nodeType === 1 );
+				return false;
+			};
+		} ),
+
+		// Miscellaneous
+		"target": function( elem ) {
+			var hash = window.location && window.location.hash;
+			return hash && hash.slice( 1 ) === elem.id;
+		},
+
+		"root": function( elem ) {
+			return elem === docElem;
+		},
+
+		"focus": function( elem ) {
+			return elem === document.activeElement &&
+				( !document.hasFocus || document.hasFocus() ) &&
+				!!( elem.type || elem.href || ~elem.tabIndex );
+		},
+
+		// Boolean properties
+		"enabled": createDisabledPseudo( false ),
+		"disabled": createDisabledPseudo( true ),
+
+		"checked": function( elem ) {
+
+			// In CSS3, :checked should return both checked and selected elements
+			// http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked
+			var nodeName = elem.nodeName.toLowerCase();
+			return ( nodeName === "input" && !!elem.checked ) ||
+				( nodeName === "option" && !!elem.selected );
+		},
+
+		"selected": function( elem ) {
+
+			// Accessing this property makes selected-by-default
+			// options in Safari work properly
+			if ( elem.parentNode ) {
+				// eslint-disable-next-line no-unused-expressions
+				elem.parentNode.selectedIndex;
+			}
+
+			return elem.selected === true;
+		},
+
+		// Contents
+		"empty": function( elem ) {
+
+			// http://www.w3.org/TR/selectors/#empty-pseudo
+			// :empty is negated by element (1) or content nodes (text: 3; cdata: 4; entity ref: 5),
+			//   but not by others (comment: 8; processing instruction: 7; etc.)
+			// nodeType < 6 works because attributes (2) do not appear as children
+			for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) {
+				if ( elem.nodeType < 6 ) {
+					return false;
+				}
+			}
+			return true;
+		},
+
+		"parent": function( elem ) {
+			return !Expr.pseudos[ "empty" ]( elem );
+		},
+
+		// Element/input types
+		"header": function( elem ) {
+			return rheader.test( elem.nodeName );
+		},
+
+		"input": function( elem ) {
+			return rinputs.test( elem.nodeName );
+		},
+
+		"button": function( elem ) {
+			var name = elem.nodeName.toLowerCase();
+			return name === "input" && elem.type === "button" || name === "button";
+		},
+
+		"text": function( elem ) {
+			var attr;
+			return elem.nodeName.toLowerCase() === "input" &&
+				elem.type === "text" &&
+
+				// Support: IE<8
+				// New HTML5 attribute values (e.g., "search") appear with elem.type === "text"
+				( ( attr = elem.getAttribute( "type" ) ) == null ||
+					attr.toLowerCase() === "text" );
+		},
+
+		// Position-in-collection
+		"first": createPositionalPseudo( function() {
+			return [ 0 ];
+		} ),
+
+		"last": createPositionalPseudo( function( _matchIndexes, length ) {
+			return [ length - 1 ];
+		} ),
+
+		"eq": createPositionalPseudo( function( _matchIndexes, length, argument ) {
+			return [ argument < 0 ? argument + length : argument ];
+		} ),
+
+		"even": createPositionalPseudo( function( matchIndexes, length ) {
+			var i = 0;
+			for ( ; i < length; i += 2 ) {
+				matchIndexes.push( i );
+			}
+			return matchIndexes;
+		} ),
+
+		"odd": createPositionalPseudo( function( matchIndexes, length ) {
+			var i = 1;
+			for ( ; i < length; i += 2 ) {
+				matchIndexes.push( i );
+			}
+			return matchIndexes;
+		} ),
+
+		"lt": createPositionalPseudo( function( matchIndexes, length, argument ) {
+			var i = argument < 0 ?
+				argument + length :
+				argument > length ?
+					length :
+					argument;
+			for ( ; --i >= 0; ) {
+				matchIndexes.push( i );
+			}
+			return matchIndexes;
+		} ),
+
+		"gt": createPositionalPseudo( function( matchIndexes, length, argument ) {
+			var i = argument < 0 ? argument + length : argument;
+			for ( ; ++i < length; ) {
+				matchIndexes.push( i );
+			}
+			return matchIndexes;
+		} )
+	}
+};
+
+Expr.pseudos[ "nth" ] = Expr.pseudos[ "eq" ];
+
+// Add button/input type pseudos
+for ( i in { radio: true, checkbox: true, file: true, password: true, image: true } ) {
+	Expr.pseudos[ i ] = createInputPseudo( i );
+}
+for ( i in { submit: true, reset: true } ) {
+	Expr.pseudos[ i ] = createButtonPseudo( i );
+}
+
+// Easy API for creating new setFilters
+function setFilters() {}
+setFilters.prototype = Expr.filters = Expr.pseudos;
+Expr.setFilters = new setFilters();
+
+tokenize = Sizzle.tokenize = function( selector, parseOnly ) {
+	var matched, match, tokens, type,
+		soFar, groups, preFilters,
+		cached = tokenCache[ selector + " " ];
+
+	if ( cached ) {
+		return parseOnly ? 0 : cached.slice( 0 );
+	}
+
+	soFar = selector;
+	groups = [];
+	preFilters = Expr.preFilter;
+
+	while ( soFar ) {
+
+		// Comma and first run
+		if ( !matched || ( match = rcomma.exec( soFar ) ) ) {
+			if ( match ) {
+
+				// Don't consume trailing commas as valid
+				soFar = soFar.slice( match[ 0 ].length ) || soFar;
+			}
+			groups.push( ( tokens = [] ) );
+		}
+
+		matched = false;
+
+		// Combinators
+		if ( ( match = rcombinators.exec( soFar ) ) ) {
+			matched = match.shift();
+			tokens.push( {
+				value: matched,
+
+				// Cast descendant combinators to space
+				type: match[ 0 ].replace( rtrim, " " )
+			} );
+			soFar = soFar.slice( matched.length );
+		}
+
+		// Filters
+		for ( type in Expr.filter ) {
+			if ( ( match = matchExpr[ type ].exec( soFar ) ) && ( !preFilters[ type ] ||
+				( match = preFilters[ type ]( match ) ) ) ) {
+				matched = match.shift();
+				tokens.push( {
+					value: matched,
+					type: type,
+					matches: match
+				} );
+				soFar = soFar.slice( matched.length );
+			}
+		}
+
+		if ( !matched ) {
+			break;
+		}
+	}
+
+	// Return the length of the invalid excess
+	// if we're just parsing
+	// Otherwise, throw an error or return tokens
+	return parseOnly ?
+		soFar.length :
+		soFar ?
+			Sizzle.error( selector ) :
+
+			// Cache the tokens
+			tokenCache( selector, groups ).slice( 0 );
+};
+
+function toSelector( tokens ) {
+	var i = 0,
+		len = tokens.length,
+		selector = "";
+	for ( ; i < len; i++ ) {
+		selector += tokens[ i ].value;
+	}
+	return selector;
+}
+
+function addCombinator( matcher, combinator, base ) {
+	var dir = combinator.dir,
+		skip = combinator.next,
+		key = skip || dir,
+		checkNonElements = base && key === "parentNode",
+		doneName = done++;
+
+	return combinator.first ?
+
+		// Check against closest ancestor/preceding element
+		function( elem, context, xml ) {
+			while ( ( elem = elem[ dir ] ) ) {
+				if ( elem.nodeType === 1 || checkNonElements ) {
+					return matcher( elem, context, xml );
+				}
+			}
+			return false;
+		} :
+
+		// Check against all ancestor/preceding elements
+		function( elem, context, xml ) {
+			var oldCache, uniqueCache, outerCache,
+				newCache = [ dirruns, doneName ];
+
+			// We can't set arbitrary data on XML nodes, so they don't benefit from combinator caching
+			if ( xml ) {
+				while ( ( elem = elem[ dir ] ) ) {
+					if ( elem.nodeType === 1 || checkNonElements ) {
+						if ( matcher( elem, context, xml ) ) {
+							return true;
+						}
+					}
+				}
+			} else {
+				while ( ( elem = elem[ dir ] ) ) {
+					if ( elem.nodeType === 1 || checkNonElements ) {
+						outerCache = elem[ expando ] || ( elem[ expando ] = {} );
+
+						// Support: IE <9 only
+						// Defend against cloned attroperties (jQuery gh-1709)
+						uniqueCache = outerCache[ elem.uniqueID ] ||
+							( outerCache[ elem.uniqueID ] = {} );
+
+						if ( skip && skip === elem.nodeName.toLowerCase() ) {
+							elem = elem[ dir ] || elem;
+						} else if ( ( oldCache = uniqueCache[ key ] ) &&
+							oldCache[ 0 ] === dirruns && oldCache[ 1 ] === doneName ) {
+
+							// Assign to newCache so results back-propagate to previous elements
+							return ( newCache[ 2 ] = oldCache[ 2 ] );
+						} else {
+
+							// Reuse newcache so results back-propagate to previous elements
+							uniqueCache[ key ] = newCache;
+
+							// A match means we're done; a fail means we have to keep checking
+							if ( ( newCache[ 2 ] = matcher( elem, context, xml ) ) ) {
+								return true;
+							}
+						}
+					}
+				}
+			}
+			return false;
+		};
+}
+
+function elementMatcher( matchers ) {
+	return matchers.length > 1 ?
+		function( elem, context, xml ) {
+			var i = matchers.length;
+			while ( i-- ) {
+				if ( !matchers[ i ]( elem, context, xml ) ) {
+					return false;
+				}
+			}
+			return true;
+		} :
+		matchers[ 0 ];
+}
+
+function multipleContexts( selector, contexts, results ) {
+	var i = 0,
+		len = contexts.length;
+	for ( ; i < len; i++ ) {
+		Sizzle( selector, contexts[ i ], results );
+	}
+	return results;
+}
+
+function condense( unmatched, map, filter, context, xml ) {
+	var elem,
+		newUnmatched = [],
+		i = 0,
+		len = unmatched.length,
+		mapped = map != null;
+
+	for ( ; i < len; i++ ) {
+		if ( ( elem = unmatched[ i ] ) ) {
+			if ( !filter || filter( elem, context, xml ) ) {
+				newUnmatched.push( elem );
+				if ( mapped ) {
+					map.push( i );
+				}
+			}
+		}
+	}
+
+	return newUnmatched;
+}
+
+function setMatcher( preFilter, selector, matcher, postFilter, postFinder, postSelector ) {
+	if ( postFilter && !postFilter[ expando ] ) {
+		postFilter = setMatcher( postFilter );
+	}
+	if ( postFinder && !postFinder[ expando ] ) {
+		postFinder = setMatcher( postFinder, postSelector );
+	}
+	return markFunction( function( seed, results, context, xml ) {
+		var temp, i, elem,
+			preMap = [],
+			postMap = [],
+			preexisting = results.length,
+
+			// Get initial elements from seed or context
+			elems = seed || multipleContexts(
+				selector || "*",
+				context.nodeType ? [ context ] : context,
+				[]
+			),
+
+			// Prefilter to get matcher input, preserving a map for seed-results synchronization
+			matcherIn = preFilter && ( seed || !selector ) ?
+				condense( elems, preMap, preFilter, context, xml ) :
+				elems,
+
+			matcherOut = matcher ?
+
+				// If we have a postFinder, or filtered seed, or non-seed postFilter or preexisting results,
+				postFinder || ( seed ? preFilter : preexisting || postFilter ) ?
+
+					// ...intermediate processing is necessary
+					[] :
+
+					// ...otherwise use results directly
+					results :
+				matcherIn;
+
+		// Find primary matches
+		if ( matcher ) {
+			matcher( matcherIn, matcherOut, context, xml );
+		}
+
+		// Apply postFilter
+		if ( postFilter ) {
+			temp = condense( matcherOut, postMap );
+			postFilter( temp, [], context, xml );
+
+			// Un-match failing elements by moving them back to matcherIn
+			i = temp.length;
+			while ( i-- ) {
+				if ( ( elem = temp[ i ] ) ) {
+					matcherOut[ postMap[ i ] ] = !( matcherIn[ postMap[ i ] ] = elem );
+				}
+			}
+		}
+
+		if ( seed ) {
+			if ( postFinder || preFilter ) {
+				if ( postFinder ) {
+
+					// Get the final matcherOut by condensing this intermediate into postFinder contexts
+					temp = [];
+					i = matcherOut.length;
+					while ( i-- ) {
+						if ( ( elem = matcherOut[ i ] ) ) {
+
+							// Restore matcherIn since elem is not yet a final match
+							temp.push( ( matcherIn[ i ] = elem ) );
+						}
+					}
+					postFinder( null, ( matcherOut = [] ), temp, xml );
+				}
+
+				// Move matched elements from seed to results to keep them synchronized
+				i = matcherOut.length;
+				while ( i-- ) {
+					if ( ( elem = matcherOut[ i ] ) &&
+						( temp = postFinder ? indexOf( seed, elem ) : preMap[ i ] ) > -1 ) {
+
+						seed[ temp ] = !( results[ temp ] = elem );
+					}
+				}
+			}
+
+		// Add elements to results, through postFinder if defined
+		} else {
+			matcherOut = condense(
+				matcherOut === results ?
+					matcherOut.splice( preexisting, matcherOut.length ) :
+					matcherOut
+			);
+			if ( postFinder ) {
+				postFinder( null, results, matcherOut, xml );
+			} else {
+				push.apply( results, matcherOut );
+			}
+		}
+	} );
+}
+
+function matcherFromTokens( tokens ) {
+	var checkContext, matcher, j,
+		len = tokens.length,
+		leadingRelative = Expr.relative[ tokens[ 0 ].type ],
+		implicitRelative = leadingRelative || Expr.relative[ " " ],
+		i = leadingRelative ? 1 : 0,
+
+		// The foundational matcher ensures that elements are reachable from top-level context(s)
+		matchContext = addCombinator( function( elem ) {
+			return elem === checkContext;
+		}, implicitRelative, true ),
+		matchAnyContext = addCombinator( function( elem ) {
+			return indexOf( checkContext, elem ) > -1;
+		}, implicitRelative, true ),
+		matchers = [ function( elem, context, xml ) {
+			var ret = ( !leadingRelative && ( xml || context !== outermostContext ) ) || (
+				( checkContext = context ).nodeType ?
+					matchContext( elem, context, xml ) :
+					matchAnyContext( elem, context, xml ) );
+
+			// Avoid hanging onto element (issue #299)
+			checkContext = null;
+			return ret;
+		} ];
+
+	for ( ; i < len; i++ ) {
+		if ( ( matcher = Expr.relative[ tokens[ i ].type ] ) ) {
+			matchers = [ addCombinator( elementMatcher( matchers ), matcher ) ];
+		} else {
+			matcher = Expr.filter[ tokens[ i ].type ].apply( null, tokens[ i ].matches );
+
+			// Return special upon seeing a positional matcher
+			if ( matcher[ expando ] ) {
+
+				// Find the next relative operator (if any) for proper handling
+				j = ++i;
+				for ( ; j < len; j++ ) {
+					if ( Expr.relative[ tokens[ j ].type ] ) {
+						break;
+					}
+				}
+				return setMatcher(
+					i > 1 && elementMatcher( matchers ),
+					i > 1 && toSelector(
+
+					// If the preceding token was a descendant combinator, insert an implicit any-element `*`
+					tokens
+						.slice( 0, i - 1 )
+						.concat( { value: tokens[ i - 2 ].type === " " ? "*" : "" } )
+					).replace( rtrim, "$1" ),
+					matcher,
+					i < j && matcherFromTokens( tokens.slice( i, j ) ),
+					j < len && matcherFromTokens( ( tokens = tokens.slice( j ) ) ),
+					j < len && toSelector( tokens )
+				);
+			}
+			matchers.push( matcher );
+		}
+	}
+
+	return elementMatcher( matchers );
+}
+
+function matcherFromGroupMatchers( elementMatchers, setMatchers ) {
+	var bySet = setMatchers.length > 0,
+		byElement = elementMatchers.length > 0,
+		superMatcher = function( seed, context, xml, results, outermost ) {
+			var elem, j, matcher,
+				matchedCount = 0,
+				i = "0",
+				unmatched = seed && [],
+				setMatched = [],
+				contextBackup = outermostContext,
+
+				// We must always have either seed elements or outermost context
+				elems = seed || byElement && Expr.find[ "TAG" ]( "*", outermost ),
+
+				// Use integer dirruns iff this is the outermost matcher
+				dirrunsUnique = ( dirruns += contextBackup == null ? 1 : Math.random() || 0.1 ),
+				len = elems.length;
+
+			if ( outermost ) {
+
+				// Support: IE 11+, Edge 17 - 18+
+				// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+				// two documents; shallow comparisons work.
+				// eslint-disable-next-line eqeqeq
+				outermostContext = context == document || context || outermost;
+			}
+
+			// Add elements passing elementMatchers directly to results
+			// Support: IE<9, Safari
+			// Tolerate NodeList properties (IE: "length"; Safari: <number>) matching elements by id
+			for ( ; i !== len && ( elem = elems[ i ] ) != null; i++ ) {
+				if ( byElement && elem ) {
+					j = 0;
+
+					// Support: IE 11+, Edge 17 - 18+
+					// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+					// two documents; shallow comparisons work.
+					// eslint-disable-next-line eqeqeq
+					if ( !context && elem.ownerDocument != document ) {
+						setDocument( elem );
+						xml = !documentIsHTML;
+					}
+					while ( ( matcher = elementMatchers[ j++ ] ) ) {
+						if ( matcher( elem, context || document, xml ) ) {
+							results.push( elem );
+							break;
+						}
+					}
+					if ( outermost ) {
+						dirruns = dirrunsUnique;
+					}
+				}
+
+				// Track unmatched elements for set filters
+				if ( bySet ) {
+
+					// They will have gone through all possible matchers
+					if ( ( elem = !matcher && elem ) ) {
+						matchedCount--;
+					}
+
+					// Lengthen the array for every element, matched or not
+					if ( seed ) {
+						unmatched.push( elem );
+					}
+				}
+			}
+
+			// `i` is now the count of elements visited above, and adding it to `matchedCount`
+			// makes the latter nonnegative.
+			matchedCount += i;
+
+			// Apply set filters to unmatched elements
+			// NOTE: This can be skipped if there are no unmatched elements (i.e., `matchedCount`
+			// equals `i`), unless we didn't visit _any_ elements in the above loop because we have
+			// no element matchers and no seed.
+			// Incrementing an initially-string "0" `i` allows `i` to remain a string only in that
+			// case, which will result in a "00" `matchedCount` that differs from `i` but is also
+			// numerically zero.
+			if ( bySet && i !== matchedCount ) {
+				j = 0;
+				while ( ( matcher = setMatchers[ j++ ] ) ) {
+					matcher( unmatched, setMatched, context, xml );
+				}
+
+				if ( seed ) {
+
+					// Reintegrate element matches to eliminate the need for sorting
+					if ( matchedCount > 0 ) {
+						while ( i-- ) {
+							if ( !( unmatched[ i ] || setMatched[ i ] ) ) {
+								setMatched[ i ] = pop.call( results );
+							}
+						}
+					}
+
+					// Discard index placeholder values to get only actual matches
+					setMatched = condense( setMatched );
+				}
+
+				// Add matches to results
+				push.apply( results, setMatched );
+
+				// Seedless set matches succeeding multiple successful matchers stipulate sorting
+				if ( outermost && !seed && setMatched.length > 0 &&
+					( matchedCount + setMatchers.length ) > 1 ) {
+
+					Sizzle.uniqueSort( results );
+				}
+			}
+
+			// Override manipulation of globals by nested matchers
+			if ( outermost ) {
+				dirruns = dirrunsUnique;
+				outermostContext = contextBackup;
+			}
+
+			return unmatched;
+		};
+
+	return bySet ?
+		markFunction( superMatcher ) :
+		superMatcher;
+}
+
+compile = Sizzle.compile = function( selector, match /* Internal Use Only */ ) {
+	var i,
+		setMatchers = [],
+		elementMatchers = [],
+		cached = compilerCache[ selector + " " ];
+
+	if ( !cached ) {
+
+		// Generate a function of recursive functions that can be used to check each element
+		if ( !match ) {
+			match = tokenize( selector );
+		}
+		i = match.length;
+		while ( i-- ) {
+			cached = matcherFromTokens( match[ i ] );
+			if ( cached[ expando ] ) {
+				setMatchers.push( cached );
+			} else {
+				elementMatchers.push( cached );
+			}
+		}
+
+		// Cache the compiled function
+		cached = compilerCache(
+			selector,
+			matcherFromGroupMatchers( elementMatchers, setMatchers )
+		);
+
+		// Save selector and tokenization
+		cached.selector = selector;
+	}
+	return cached;
+};
+
+/**
+ * A low-level selection function that works with Sizzle's compiled
+ *  selector functions
+ * @param {String|Function} selector A selector or a pre-compiled
+ *  selector function built with Sizzle.compile
+ * @param {Element} context
+ * @param {Array} [results]
+ * @param {Array} [seed] A set of elements to match against
+ */
+select = Sizzle.select = function( selector, context, results, seed ) {
+	var i, tokens, token, type, find,
+		compiled = typeof selector === "function" && selector,
+		match = !seed && tokenize( ( selector = compiled.selector || selector ) );
+
+	results = results || [];
+
+	// Try to minimize operations if there is only one selector in the list and no seed
+	// (the latter of which guarantees us context)
+	if ( match.length === 1 ) {
+
+		// Reduce context if the leading compound selector is an ID
+		tokens = match[ 0 ] = match[ 0 ].slice( 0 );
+		if ( tokens.length > 2 && ( token = tokens[ 0 ] ).type === "ID" &&
+			context.nodeType === 9 && documentIsHTML && Expr.relative[ tokens[ 1 ].type ] ) {
+
+			context = ( Expr.find[ "ID" ]( token.matches[ 0 ]
+				.replace( runescape, funescape ), context ) || [] )[ 0 ];
+			if ( !context ) {
+				return results;
+
+			// Precompiled matchers will still verify ancestry, so step up a level
+			} else if ( compiled ) {
+				context = context.parentNode;
+			}
+
+			selector = selector.slice( tokens.shift().value.length );
+		}
+
+		// Fetch a seed set for right-to-left matching
+		i = matchExpr[ "needsContext" ].test( selector ) ? 0 : tokens.length;
+		while ( i-- ) {
+			token = tokens[ i ];
+
+			// Abort if we hit a combinator
+			if ( Expr.relative[ ( type = token.type ) ] ) {
+				break;
+			}
+			if ( ( find = Expr.find[ type ] ) ) {
+
+				// Search, expanding context for leading sibling combinators
+				if ( ( seed = find(
+					token.matches[ 0 ].replace( runescape, funescape ),
+					rsibling.test( tokens[ 0 ].type ) && testContext( context.parentNode ) ||
+						context
+				) ) ) {
+
+					// If seed is empty or no tokens remain, we can return early
+					tokens.splice( i, 1 );
+					selector = seed.length && toSelector( tokens );
+					if ( !selector ) {
+						push.apply( results, seed );
+						return results;
+					}
+
+					break;
+				}
+			}
+		}
+	}
+
+	// Compile and execute a filtering function if one is not provided
+	// Provide `match` to avoid retokenization if we modified the selector above
+	( compiled || compile( selector, match ) )(
+		seed,
+		context,
+		!documentIsHTML,
+		results,
+		!context || rsibling.test( selector ) && testContext( context.parentNode ) || context
+	);
+	return results;
+};
+
+// One-time assignments
+
+// Sort stability
+support.sortStable = expando.split( "" ).sort( sortOrder ).join( "" ) === expando;
+
+// Support: Chrome 14-35+
+// Always assume duplicates if they aren't passed to the comparison function
+support.detectDuplicates = !!hasDuplicate;
+
+// Initialize against the default document
+setDocument();
+
+// Support: Webkit<537.32 - Safari 6.0.3/Chrome 25 (fixed in Chrome 27)
+// Detached nodes confoundingly follow *each other*
+support.sortDetached = assert( function( el ) {
+
+	// Should return 1, but returns 4 (following)
+	return el.compareDocumentPosition( document.createElement( "fieldset" ) ) & 1;
+} );
+
+// Support: IE<8
+// Prevent attribute/property "interpolation"
+// https://msdn.microsoft.com/en-us/library/ms536429%28VS.85%29.aspx
+if ( !assert( function( el ) {
+	el.innerHTML = "<a href='#'></a>";
+	return el.firstChild.getAttribute( "href" ) === "#";
+} ) ) {
+	addHandle( "type|href|height|width", function( elem, name, isXML ) {
+		if ( !isXML ) {
+			return elem.getAttribute( name, name.toLowerCase() === "type" ? 1 : 2 );
+		}
+	} );
+}
+
+// Support: IE<9
+// Use defaultValue in place of getAttribute("value")
+if ( !support.attributes || !assert( function( el ) {
+	el.innerHTML = "<input/>";
+	el.firstChild.setAttribute( "value", "" );
+	return el.firstChild.getAttribute( "value" ) === "";
+} ) ) {
+	addHandle( "value", function( elem, _name, isXML ) {
+		if ( !isXML && elem.nodeName.toLowerCase() === "input" ) {
+			return elem.defaultValue;
+		}
+	} );
+}
+
+// Support: IE<9
+// Use getAttributeNode to fetch booleans when getAttribute lies
+if ( !assert( function( el ) {
+	return el.getAttribute( "disabled" ) == null;
+} ) ) {
+	addHandle( booleans, function( elem, name, isXML ) {
+		var val;
+		if ( !isXML ) {
+			return elem[ name ] === true ? name.toLowerCase() :
+				( val = elem.getAttributeNode( name ) ) && val.specified ?
+					val.value :
+					null;
+		}
+	} );
+}
+
+return Sizzle;
+
+} )( window );
+
+
+
+jQuery.find = Sizzle;
+jQuery.expr = Sizzle.selectors;
+
+// Deprecated
+jQuery.expr[ ":" ] = jQuery.expr.pseudos;
+jQuery.uniqueSort = jQuery.unique = Sizzle.uniqueSort;
+jQuery.text = Sizzle.getText;
+jQuery.isXMLDoc = Sizzle.isXML;
+jQuery.contains = Sizzle.contains;
+jQuery.escapeSelector = Sizzle.escape;
+
+
+
+
+var dir = function( elem, dir, until ) {
+	var matched = [],
+		truncate = until !== undefined;
+
+	while ( ( elem = elem[ dir ] ) && elem.nodeType !== 9 ) {
+		if ( elem.nodeType === 1 ) {
+			if ( truncate && jQuery( elem ).is( until ) ) {
+				break;
+			}
+			matched.push( elem );
+		}
+	}
+	return matched;
+};
+
+
+var siblings = function( n, elem ) {
+	var matched = [];
+
+	for ( ; n; n = n.nextSibling ) {
+		if ( n.nodeType === 1 && n !== elem ) {
+			matched.push( n );
+		}
+	}
+
+	return matched;
+};
+
+
+var rneedsContext = jQuery.expr.match.needsContext;
+
+
+
+function nodeName( elem, name ) {
+
+  return elem.nodeName && elem.nodeName.toLowerCase() === name.toLowerCase();
+
+};
+var rsingleTag = ( /^<([a-z][^\/\0>:\x20\t\r\n\f]*)[\x20\t\r\n\f]*\/?>(?:<\/\1>|)$/i );
+
+
+
+// Implement the identical functionality for filter and not
+function winnow( elements, qualifier, not ) {
+	if ( isFunction( qualifier ) ) {
+		return jQuery.grep( elements, function( elem, i ) {
+			return !!qualifier.call( elem, i, elem ) !== not;
+		} );
+	}
+
+	// Single element
+	if ( qualifier.nodeType ) {
+		return jQuery.grep( elements, function( elem ) {
+			return ( elem === qualifier ) !== not;
+		} );
+	}
+
+	// Arraylike of elements (jQuery, arguments, Array)
+	if ( typeof qualifier !== "string" ) {
+		return jQuery.grep( elements, function( elem ) {
+			return ( indexOf.call( qualifier, elem ) > -1 ) !== not;
+		} );
+	}
+
+	// Filtered directly for both simple and complex selectors
+	return jQuery.filter( qualifier, elements, not );
+}
+
+jQuery.filter = function( expr, elems, not ) {
+	var elem = elems[ 0 ];
+
+	if ( not ) {
+		expr = ":not(" + expr + ")";
+	}
+
+	if ( elems.length === 1 && elem.nodeType === 1 ) {
+		return jQuery.find.matchesSelector( elem, expr ) ? [ elem ] : [];
+	}
+
+	return jQuery.find.matches( expr, jQuery.grep( elems, function( elem ) {
+		return elem.nodeType === 1;
+	} ) );
+};
+
+jQuery.fn.extend( {
+	find: function( selector ) {
+		var i, ret,
+			len = this.length,
+			self = this;
+
+		if ( typeof selector !== "string" ) {
+			return this.pushStack( jQuery( selector ).filter( function() {
+				for ( i = 0; i < len; i++ ) {
+					if ( jQuery.contains( self[ i ], this ) ) {
+						return true;
+					}
+				}
+			} ) );
+		}
+
+		ret = this.pushStack( [] );
+
+		for ( i = 0; i < len; i++ ) {
+			jQuery.find( selector, self[ i ], ret );
+		}
+
+		return len > 1 ? jQuery.uniqueSort( ret ) : ret;
+	},
+	filter: function( selector ) {
+		return this.pushStack( winnow( this, selector || [], false ) );
+	},
+	not: function( selector ) {
+		return this.pushStack( winnow( this, selector || [], true ) );
+	},
+	is: function( selector ) {
+		return !!winnow(
+			this,
+
+			// If this is a positional/relative selector, check membership in the returned set
+			// so $("p:first").is("p:last") won't return true for a doc with two "p".
+			typeof selector === "string" && rneedsContext.test( selector ) ?
+				jQuery( selector ) :
+				selector || [],
+			false
+		).length;
+	}
+} );
+
+
+// Initialize a jQuery object
+
+
+// A central reference to the root jQuery(document)
+var rootjQuery,
+
+	// A simple way to check for HTML strings
+	// Prioritize #id over <tag> to avoid XSS via location.hash (#9521)
+	// Strict HTML recognition (#11290: must start with <)
+	// Shortcut simple #id case for speed
+	rquickExpr = /^(?:\s*(<[\w\W]+>)[^>]*|#([\w-]+))$/,
+
+	init = jQuery.fn.init = function( selector, context, root ) {
+		var match, elem;
+
+		// HANDLE: $(""), $(null), $(undefined), $(false)
+		if ( !selector ) {
+			return this;
+		}
+
+		// Method init() accepts an alternate rootjQuery
+		// so migrate can support jQuery.sub (gh-2101)
+		root = root || rootjQuery;
+
+		// Handle HTML strings
+		if ( typeof selector === "string" ) {
+			if ( selector[ 0 ] === "<" &&
+				selector[ selector.length - 1 ] === ">" &&
+				selector.length >= 3 ) {
+
+				// Assume that strings that start and end with <> are HTML and skip the regex check
+				match = [ null, selector, null ];
+
+			} else {
+				match = rquickExpr.exec( selector );
+			}
+
+			// Match html or make sure no context is specified for #id
+			if ( match && ( match[ 1 ] || !context ) ) {
+
+				// HANDLE: $(html) -> $(array)
+				if ( match[ 1 ] ) {
+					context = context instanceof jQuery ? context[ 0 ] : context;
+
+					// Option to run scripts is true for back-compat
+					// Intentionally let the error be thrown if parseHTML is not present
+					jQuery.merge( this, jQuery.parseHTML(
+						match[ 1 ],
+						context && context.nodeType ? context.ownerDocument || context : document,
+						true
+					) );
+
+					// HANDLE: $(html, props)
+					if ( rsingleTag.test( match[ 1 ] ) && jQuery.isPlainObject( context ) ) {
+						for ( match in context ) {
+
+							// Properties of context are called as methods if possible
+							if ( isFunction( this[ match ] ) ) {
+								this[ match ]( context[ match ] );
+
+							// ...and otherwise set as attributes
+							} else {
+								this.attr( match, context[ match ] );
+							}
+						}
+					}
+
+					return this;
+
+				// HANDLE: $(#id)
+				} else {
+					elem = document.getElementById( match[ 2 ] );
+
+					if ( elem ) {
+
+						// Inject the element directly into the jQuery object
+						this[ 0 ] = elem;
+						this.length = 1;
+					}
+					return this;
+				}
+
+			// HANDLE: $(expr, $(...))
+			} else if ( !context || context.jquery ) {
+				return ( context || root ).find( selector );
+
+			// HANDLE: $(expr, context)
+			// (which is just equivalent to: $(context).find(expr)
+			} else {
+				return this.constructor( context ).find( selector );
+			}
+
+		// HANDLE: $(DOMElement)
+		} else if ( selector.nodeType ) {
+			this[ 0 ] = selector;
+			this.length = 1;
+			return this;
+
+		// HANDLE: $(function)
+		// Shortcut for document ready
+		} else if ( isFunction( selector ) ) {
+			return root.ready !== undefined ?
+				root.ready( selector ) :
+
+				// Execute immediately if ready is not present
+				selector( jQuery );
+		}
+
+		return jQuery.makeArray( selector, this );
+	};
+
+// Give the init function the jQuery prototype for later instantiation
+init.prototype = jQuery.fn;
+
+// Initialize central reference
+rootjQuery = jQuery( document );
+
+
+var rparentsprev = /^(?:parents|prev(?:Until|All))/,
+
+	// Methods guaranteed to produce a unique set when starting from a unique set
+	guaranteedUnique = {
+		children: true,
+		contents: true,
+		next: true,
+		prev: true
+	};
+
+jQuery.fn.extend( {
+	has: function( target ) {
+		var targets = jQuery( target, this ),
+			l = targets.length;
+
+		return this.filter( function() {
+			var i = 0;
+			for ( ; i < l; i++ ) {
+				if ( jQuery.contains( this, targets[ i ] ) ) {
+					return true;
+				}
+			}
+		} );
+	},
+
+	closest: function( selectors, context ) {
+		var cur,
+			i = 0,
+			l = this.length,
+			matched = [],
+			targets = typeof selectors !== "string" && jQuery( selectors );
+
+		// Positional selectors never match, since there's no _selection_ context
+		if ( !rneedsContext.test( selectors ) ) {
+			for ( ; i < l; i++ ) {
+				for ( cur = this[ i ]; cur && cur !== context; cur = cur.parentNode ) {
+
+					// Always skip document fragments
+					if ( cur.nodeType < 11 && ( targets ?
+						targets.index( cur ) > -1 :
+
+						// Don't pass non-elements to Sizzle
+						cur.nodeType === 1 &&
+							jQuery.find.matchesSelector( cur, selectors ) ) ) {
+
+						matched.push( cur );
+						break;
+					}
+				}
+			}
+		}
+
+		return this.pushStack( matched.length > 1 ? jQuery.uniqueSort( matched ) : matched );
+	},
+
+	// Determine the position of an element within the set
+	index: function( elem ) {
+
+		// No argument, return index in parent
+		if ( !elem ) {
+			return ( this[ 0 ] && this[ 0 ].parentNode ) ? this.first().prevAll().length : -1;
+		}
+
+		// Index in selector
+		if ( typeof elem === "string" ) {
+			return indexOf.call( jQuery( elem ), this[ 0 ] );
+		}
+
+		// Locate the position of the desired element
+		return indexOf.call( this,
+
+			// If it receives a jQuery object, the first element is used
+			elem.jquery ? elem[ 0 ] : elem
+		);
+	},
+
+	add: function( selector, context ) {
+		return this.pushStack(
+			jQuery.uniqueSort(
+				jQuery.merge( this.get(), jQuery( selector, context ) )
+			)
+		);
+	},
+
+	addBack: function( selector ) {
+		return this.add( selector == null ?
+			this.prevObject : this.prevObject.filter( selector )
+		);
+	}
+} );
+
+function sibling( cur, dir ) {
+	while ( ( cur = cur[ dir ] ) && cur.nodeType !== 1 ) {}
+	return cur;
+}
+
+jQuery.each( {
+	parent: function( elem ) {
+		var parent = elem.parentNode;
+		return parent && parent.nodeType !== 11 ? parent : null;
+	},
+	parents: function( elem ) {
+		return dir( elem, "parentNode" );
+	},
+	parentsUntil: function( elem, _i, until ) {
+		return dir( elem, "parentNode", until );
+	},
+	next: function( elem ) {
+		return sibling( elem, "nextSibling" );
+	},
+	prev: function( elem ) {
+		return sibling( elem, "previousSibling" );
+	},
+	nextAll: function( elem ) {
+		return dir( elem, "nextSibling" );
+	},
+	prevAll: function( elem ) {
+		return dir( elem, "previousSibling" );
+	},
+	nextUntil: function( elem, _i, until ) {
+		return dir( elem, "nextSibling", until );
+	},
+	prevUntil: function( elem, _i, until ) {
+		return dir( elem, "previousSibling", until );
+	},
+	siblings: function( elem ) {
+		return siblings( ( elem.parentNode || {} ).firstChild, elem );
+	},
+	children: function( elem ) {
+		return siblings( elem.firstChild );
+	},
+	contents: function( elem ) {
+		if ( elem.contentDocument != null &&
+
+			// Support: IE 11+
+			// <object> elements with no `data` attribute has an object
+			// `contentDocument` with a `null` prototype.
+			getProto( elem.contentDocument ) ) {
+
+			return elem.contentDocument;
+		}
+
+		// Support: IE 9 - 11 only, iOS 7 only, Android Browser <=4.3 only
+		// Treat the template element as a regular one in browsers that
+		// don't support it.
+		if ( nodeName( elem, "template" ) ) {
+			elem = elem.content || elem;
+		}
+
+		return jQuery.merge( [], elem.childNodes );
+	}
+}, function( name, fn ) {
+	jQuery.fn[ name ] = function( until, selector ) {
+		var matched = jQuery.map( this, fn, until );
+
+		if ( name.slice( -5 ) !== "Until" ) {
+			selector = until;
+		}
+
+		if ( selector && typeof selector === "string" ) {
+			matched = jQuery.filter( selector, matched );
+		}
+
+		if ( this.length > 1 ) {
+
+			// Remove duplicates
+			if ( !guaranteedUnique[ name ] ) {
+				jQuery.uniqueSort( matched );
+			}
+
+			// Reverse order for parents* and prev-derivatives
+			if ( rparentsprev.test( name ) ) {
+				matched.reverse();
+			}
+		}
+
+		return this.pushStack( matched );
+	};
+} );
+var rnothtmlwhite = ( /[^\x20\t\r\n\f]+/g );
+
+
+
+// Convert String-formatted options into Object-formatted ones
+function createOptions( options ) {
+	var object = {};
+	jQuery.each( options.match( rnothtmlwhite ) || [], function( _, flag ) {
+		object[ flag ] = true;
+	} );
+	return object;
+}
+
+/*
+ * Create a callback list using the following parameters:
+ *
+ *	options: an optional list of space-separated options that will change how
+ *			the callback list behaves or a more traditional option object
+ *
+ * By default a callback list will act like an event callback list and can be
+ * "fired" multiple times.
+ *
+ * Possible options:
+ *
+ *	once:			will ensure the callback list can only be fired once (like a Deferred)
+ *
+ *	memory:			will keep track of previous values and will call any callback added
+ *					after the list has been fired right away with the latest "memorized"
+ *					values (like a Deferred)
+ *
+ *	unique:			will ensure a callback can only be added once (no duplicate in the list)
+ *
+ *	stopOnFalse:	interrupt callings when a callback returns false
+ *
+ */
+jQuery.Callbacks = function( options ) {
+
+	// Convert options from String-formatted to Object-formatted if needed
+	// (we check in cache first)
+	options = typeof options === "string" ?
+		createOptions( options ) :
+		jQuery.extend( {}, options );
+
+	var // Flag to know if list is currently firing
+		firing,
+
+		// Last fire value for non-forgettable lists
+		memory,
+
+		// Flag to know if list was already fired
+		fired,
+
+		// Flag to prevent firing
+		locked,
+
+		// Actual callback list
+		list = [],
+
+		// Queue of execution data for repeatable lists
+		queue = [],
+
+		// Index of currently firing callback (modified by add/remove as needed)
+		firingIndex = -1,
+
+		// Fire callbacks
+		fire = function() {
+
+			// Enforce single-firing
+			locked = locked || options.once;
+
+			// Execute callbacks for all pending executions,
+			// respecting firingIndex overrides and runtime changes
+			fired = firing = true;
+			for ( ; queue.length; firingIndex = -1 ) {
+				memory = queue.shift();
+				while ( ++firingIndex < list.length ) {
+
+					// Run callback and check for early termination
+					if ( list[ firingIndex ].apply( memory[ 0 ], memory[ 1 ] ) === false &&
+						options.stopOnFalse ) {
+
+						// Jump to end and forget the data so .add doesn't re-fire
+						firingIndex = list.length;
+						memory = false;
+					}
+				}
+			}
+
+			// Forget the data if we're done with it
+			if ( !options.memory ) {
+				memory = false;
+			}
+
+			firing = false;
+
+			// Clean up if we're done firing for good
+			if ( locked ) {
+
+				// Keep an empty list if we have data for future add calls
+				if ( memory ) {
+					list = [];
+
+				// Otherwise, this object is spent
+				} else {
+					list = "";
+				}
+			}
+		},
+
+		// Actual Callbacks object
+		self = {
+
+			// Add a callback or a collection of callbacks to the list
+			add: function() {
+				if ( list ) {
+
+					// If we have memory from a past run, we should fire after adding
+					if ( memory && !firing ) {
+						firingIndex = list.length - 1;
+						queue.push( memory );
+					}
+
+					( function add( args ) {
+						jQuery.each( args, function( _, arg ) {
+							if ( isFunction( arg ) ) {
+								if ( !options.unique || !self.has( arg ) ) {
+									list.push( arg );
+								}
+							} else if ( arg && arg.length && toType( arg ) !== "string" ) {
+
+								// Inspect recursively
+								add( arg );
+							}
+						} );
+					} )( arguments );
+
+					if ( memory && !firing ) {
+						fire();
+					}
+				}
+				return this;
+			},
+
+			// Remove a callback from the list
+			remove: function() {
+				jQuery.each( arguments, function( _, arg ) {
+					var index;
+					while ( ( index = jQuery.inArray( arg, list, index ) ) > -1 ) {
+						list.splice( index, 1 );
+
+						// Handle firing indexes
+						if ( index <= firingIndex ) {
+							firingIndex--;
+						}
+					}
+				} );
+				return this;
+			},
+
+			// Check if a given callback is in the list.
+			// If no argument is given, return whether or not list has callbacks attached.
+			has: function( fn ) {
+				return fn ?
+					jQuery.inArray( fn, list ) > -1 :
+					list.length > 0;
+			},
+
+			// Remove all callbacks from the list
+			empty: function() {
+				if ( list ) {
+					list = [];
+				}
+				return this;
+			},
+
+			// Disable .fire and .add
+			// Abort any current/pending executions
+			// Clear all callbacks and values
+			disable: function() {
+				locked = queue = [];
+				list = memory = "";
+				return this;
+			},
+			disabled: function() {
+				return !list;
+			},
+
+			// Disable .fire
+			// Also disable .add unless we have memory (since it would have no effect)
+			// Abort any pending executions
+			lock: function() {
+				locked = queue = [];
+				if ( !memory && !firing ) {
+					list = memory = "";
+				}
+				return this;
+			},
+			locked: function() {
+				return !!locked;
+			},
+
+			// Call all callbacks with the given context and arguments
+			fireWith: function( context, args ) {
+				if ( !locked ) {
+					args = args || [];
+					args = [ context, args.slice ? args.slice() : args ];
+					queue.push( args );
+					if ( !firing ) {
+						fire();
+					}
+				}
+				return this;
+			},
+
+			// Call all the callbacks with the given arguments
+			fire: function() {
+				self.fireWith( this, arguments );
+				return this;
+			},
+
+			// To know if the callbacks have already been called at least once
+			fired: function() {
+				return !!fired;
+			}
+		};
+
+	return self;
+};
+
+
+function Identity( v ) {
+	return v;
+}
+function Thrower( ex ) {
+	throw ex;
+}
+
+function adoptValue( value, resolve, reject, noValue ) {
+	var method;
+
+	try {
+
+		// Check for promise aspect first to privilege synchronous behavior
+		if ( value && isFunction( ( method = value.promise ) ) ) {
+			method.call( value ).done( resolve ).fail( reject );
+
+		// Other thenables
+		} else if ( value && isFunction( ( method = value.then ) ) ) {
+			method.call( value, resolve, reject );
+
+		// Other non-thenables
+		} else {
+
+			// Control `resolve` arguments by letting Array#slice cast boolean `noValue` to integer:
+			// * false: [ value ].slice( 0 ) => resolve( value )
+			// * true: [ value ].slice( 1 ) => resolve()
+			resolve.apply( undefined, [ value ].slice( noValue ) );
+		}
+
+	// For Promises/A+, convert exceptions into rejections
+	// Since jQuery.when doesn't unwrap thenables, we can skip the extra checks appearing in
+	// Deferred#then to conditionally suppress rejection.
+	} catch ( value ) {
+
+		// Support: Android 4.0 only
+		// Strict mode functions invoked without .call/.apply get global-object context
+		reject.apply( undefined, [ value ] );
+	}
+}
+
+jQuery.extend( {
+
+	Deferred: function( func ) {
+		var tuples = [
+
+				// action, add listener, callbacks,
+				// ... .then handlers, argument index, [final state]
+				[ "notify", "progress", jQuery.Callbacks( "memory" ),
+					jQuery.Callbacks( "memory" ), 2 ],
+				[ "resolve", "done", jQuery.Callbacks( "once memory" ),
+					jQuery.Callbacks( "once memory" ), 0, "resolved" ],
+				[ "reject", "fail", jQuery.Callbacks( "once memory" ),
+					jQuery.Callbacks( "once memory" ), 1, "rejected" ]
+			],
+			state = "pending",
+			promise = {
+				state: function() {
+					return state;
+				},
+				always: function() {
+					deferred.done( arguments ).fail( arguments );
+					return this;
+				},
+				"catch": function( fn ) {
+					return promise.then( null, fn );
+				},
+
+				// Keep pipe for back-compat
+				pipe: function( /* fnDone, fnFail, fnProgress */ ) {
+					var fns = arguments;
+
+					return jQuery.Deferred( function( newDefer ) {
+						jQuery.each( tuples, function( _i, tuple ) {
+
+							// Map tuples (progress, done, fail) to arguments (done, fail, progress)
+							var fn = isFunction( fns[ tuple[ 4 ] ] ) && fns[ tuple[ 4 ] ];
+
+							// deferred.progress(function() { bind to newDefer or newDefer.notify })
+							// deferred.done(function() { bind to newDefer or newDefer.resolve })
+							// deferred.fail(function() { bind to newDefer or newDefer.reject })
+							deferred[ tuple[ 1 ] ]( function() {
+								var returned = fn && fn.apply( this, arguments );
+								if ( returned && isFunction( returned.promise ) ) {
+									returned.promise()
+										.progress( newDefer.notify )
+										.done( newDefer.resolve )
+										.fail( newDefer.reject );
+								} else {
+									newDefer[ tuple[ 0 ] + "With" ](
+										this,
+										fn ? [ returned ] : arguments
+									);
+								}
+							} );
+						} );
+						fns = null;
+					} ).promise();
+				},
+				then: function( onFulfilled, onRejected, onProgress ) {
+					var maxDepth = 0;
+					function resolve( depth, deferred, handler, special ) {
+						return function() {
+							var that = this,
+								args = arguments,
+								mightThrow = function() {
+									var returned, then;
+
+									// Support: Promises/A+ section 2.3.3.3.3
+									// https://promisesaplus.com/#point-59
+									// Ignore double-resolution attempts
+									if ( depth < maxDepth ) {
+										return;
+									}
+
+									returned = handler.apply( that, args );
+
+									// Support: Promises/A+ section 2.3.1
+									// https://promisesaplus.com/#point-48
+									if ( returned === deferred.promise() ) {
+										throw new TypeError( "Thenable self-resolution" );
+									}
+
+									// Support: Promises/A+ sections 2.3.3.1, 3.5
+									// https://promisesaplus.com/#point-54
+									// https://promisesaplus.com/#point-75
+									// Retrieve `then` only once
+									then = returned &&
+
+										// Support: Promises/A+ section 2.3.4
+										// https://promisesaplus.com/#point-64
+										// Only check objects and functions for thenability
+										( typeof returned === "object" ||
+											typeof returned === "function" ) &&
+										returned.then;
+
+									// Handle a returned thenable
+									if ( isFunction( then ) ) {
+
+										// Special processors (notify) just wait for resolution
+										if ( special ) {
+											then.call(
+												returned,
+												resolve( maxDepth, deferred, Identity, special ),
+												resolve( maxDepth, deferred, Thrower, special )
+											);
+
+										// Normal processors (resolve) also hook into progress
+										} else {
+
+											// ...and disregard older resolution values
+											maxDepth++;
+
+											then.call(
+												returned,
+												resolve( maxDepth, deferred, Identity, special ),
+												resolve( maxDepth, deferred, Thrower, special ),
+												resolve( maxDepth, deferred, Identity,
+													deferred.notifyWith )
+											);
+										}
+
+									// Handle all other returned values
+									} else {
+
+										// Only substitute handlers pass on context
+										// and multiple values (non-spec behavior)
+										if ( handler !== Identity ) {
+											that = undefined;
+											args = [ returned ];
+										}
+
+										// Process the value(s)
+										// Default process is resolve
+										( special || deferred.resolveWith )( that, args );
+									}
+								},
+
+								// Only normal processors (resolve) catch and reject exceptions
+								process = special ?
+									mightThrow :
+									function() {
+										try {
+											mightThrow();
+										} catch ( e ) {
+
+											if ( jQuery.Deferred.exceptionHook ) {
+												jQuery.Deferred.exceptionHook( e,
+													process.stackTrace );
+											}
+
+											// Support: Promises/A+ section 2.3.3.3.4.1
+											// https://promisesaplus.com/#point-61
+											// Ignore post-resolution exceptions
+											if ( depth + 1 >= maxDepth ) {
+
+												// Only substitute handlers pass on context
+												// and multiple values (non-spec behavior)
+												if ( handler !== Thrower ) {
+													that = undefined;
+													args = [ e ];
+												}
+
+												deferred.rejectWith( that, args );
+											}
+										}
+									};
+
+							// Support: Promises/A+ section 2.3.3.3.1
+							// https://promisesaplus.com/#point-57
+							// Re-resolve promises immediately to dodge false rejection from
+							// subsequent errors
+							if ( depth ) {
+								process();
+							} else {
+
+								// Call an optional hook to record the stack, in case of exception
+								// since it's otherwise lost when execution goes async
+								if ( jQuery.Deferred.getStackHook ) {
+									process.stackTrace = jQuery.Deferred.getStackHook();
+								}
+								window.setTimeout( process );
+							}
+						};
+					}
+
+					return jQuery.Deferred( function( newDefer ) {
+
+						// progress_handlers.add( ... )
+						tuples[ 0 ][ 3 ].add(
+							resolve(
+								0,
+								newDefer,
+								isFunction( onProgress ) ?
+									onProgress :
+									Identity,
+								newDefer.notifyWith
+							)
+						);
+
+						// fulfilled_handlers.add( ... )
+						tuples[ 1 ][ 3 ].add(
+							resolve(
+								0,
+								newDefer,
+								isFunction( onFulfilled ) ?
+									onFulfilled :
+									Identity
+							)
+						);
+
+						// rejected_handlers.add( ... )
+						tuples[ 2 ][ 3 ].add(
+							resolve(
+								0,
+								newDefer,
+								isFunction( onRejected ) ?
+									onRejected :
+									Thrower
+							)
+						);
+					} ).promise();
+				},
+
+				// Get a promise for this deferred
+				// If obj is provided, the promise aspect is added to the object
+				promise: function( obj ) {
+					return obj != null ? jQuery.extend( obj, promise ) : promise;
+				}
+			},
+			deferred = {};
+
+		// Add list-specific methods
+		jQuery.each( tuples, function( i, tuple ) {
+			var list = tuple[ 2 ],
+				stateString = tuple[ 5 ];
+
+			// promise.progress = list.add
+			// promise.done = list.add
+			// promise.fail = list.add
+			promise[ tuple[ 1 ] ] = list.add;
+
+			// Handle state
+			if ( stateString ) {
+				list.add(
+					function() {
+
+						// state = "resolved" (i.e., fulfilled)
+						// state = "rejected"
+						state = stateString;
+					},
+
+					// rejected_callbacks.disable
+					// fulfilled_callbacks.disable
+					tuples[ 3 - i ][ 2 ].disable,
+
+					// rejected_handlers.disable
+					// fulfilled_handlers.disable
+					tuples[ 3 - i ][ 3 ].disable,
+
+					// progress_callbacks.lock
+					tuples[ 0 ][ 2 ].lock,
+
+					// progress_handlers.lock
+					tuples[ 0 ][ 3 ].lock
+				);
+			}
+
+			// progress_handlers.fire
+			// fulfilled_handlers.fire
+			// rejected_handlers.fire
+			list.add( tuple[ 3 ].fire );
+
+			// deferred.notify = function() { deferred.notifyWith(...) }
+			// deferred.resolve = function() { deferred.resolveWith(...) }
+			// deferred.reject = function() { deferred.rejectWith(...) }
+			deferred[ tuple[ 0 ] ] = function() {
+				deferred[ tuple[ 0 ] + "With" ]( this === deferred ? undefined : this, arguments );
+				return this;
+			};
+
+			// deferred.notifyWith = list.fireWith
+			// deferred.resolveWith = list.fireWith
+			// deferred.rejectWith = list.fireWith
+			deferred[ tuple[ 0 ] + "With" ] = list.fireWith;
+		} );
+
+		// Make the deferred a promise
+		promise.promise( deferred );
+
+		// Call given func if any
+		if ( func ) {
+			func.call( deferred, deferred );
+		}
+
+		// All done!
+		return deferred;
+	},
+
+	// Deferred helper
+	when: function( singleValue ) {
+		var
+
+			// count of uncompleted subordinates
+			remaining = arguments.length,
+
+			// count of unprocessed arguments
+			i = remaining,
+
+			// subordinate fulfillment data
+			resolveContexts = Array( i ),
+			resolveValues = slice.call( arguments ),
+
+			// the master Deferred
+			master = jQuery.Deferred(),
+
+			// subordinate callback factory
+			updateFunc = function( i ) {
+				return function( value ) {
+					resolveContexts[ i ] = this;
+					resolveValues[ i ] = arguments.length > 1 ? slice.call( arguments ) : value;
+					if ( !( --remaining ) ) {
+						master.resolveWith( resolveContexts, resolveValues );
+					}
+				};
+			};
+
+		// Single- and empty arguments are adopted like Promise.resolve
+		if ( remaining <= 1 ) {
+			adoptValue( singleValue, master.done( updateFunc( i ) ).resolve, master.reject,
+				!remaining );
+
+			// Use .then() to unwrap secondary thenables (cf. gh-3000)
+			if ( master.state() === "pending" ||
+				isFunction( resolveValues[ i ] && resolveValues[ i ].then ) ) {
+
+				return master.then();
+			}
+		}
+
+		// Multiple arguments are aggregated like Promise.all array elements
+		while ( i-- ) {
+			adoptValue( resolveValues[ i ], updateFunc( i ), master.reject );
+		}
+
+		return master.promise();
+	}
+} );
+
+
+// These usually indicate a programmer mistake during development,
+// warn about them ASAP rather than swallowing them by default.
+var rerrorNames = /^(Eval|Internal|Range|Reference|Syntax|Type|URI)Error$/;
+
+jQuery.Deferred.exceptionHook = function( error, stack ) {
+
+	// Support: IE 8 - 9 only
+	// Console exists when dev tools are open, which can happen at any time
+	if ( window.console && window.console.warn && error && rerrorNames.test( error.name ) ) {
+		window.console.warn( "jQuery.Deferred exception: " + error.message, error.stack, stack );
+	}
+};
+
+
+
+
+jQuery.readyException = function( error ) {
+	window.setTimeout( function() {
+		throw error;
+	} );
+};
+
+
+
+
+// The deferred used on DOM ready
+var readyList = jQuery.Deferred();
+
+jQuery.fn.ready = function( fn ) {
+
+	readyList
+		.then( fn )
+
+		// Wrap jQuery.readyException in a function so that the lookup
+		// happens at the time of error handling instead of callback
+		// registration.
+		.catch( function( error ) {
+			jQuery.readyException( error );
+		} );
+
+	return this;
+};
+
+jQuery.extend( {
+
+	// Is the DOM ready to be used? Set to true once it occurs.
+	isReady: false,
+
+	// A counter to track how many items to wait for before
+	// the ready event fires. See #6781
+	readyWait: 1,
+
+	// Handle when the DOM is ready
+	ready: function( wait ) {
+
+		// Abort if there are pending holds or we're already ready
+		if ( wait === true ? --jQuery.readyWait : jQuery.isReady ) {
+			return;
+		}
+
+		// Remember that the DOM is ready
+		jQuery.isReady = true;
+
+		// If a normal DOM Ready event fired, decrement, and wait if need be
+		if ( wait !== true && --jQuery.readyWait > 0 ) {
+			return;
+		}
+
+		// If there are functions bound, to execute
+		readyList.resolveWith( document, [ jQuery ] );
+	}
+} );
+
+jQuery.ready.then = readyList.then;
+
+// The ready event handler and self cleanup method
+function completed() {
+	document.removeEventListener( "DOMContentLoaded", completed );
+	window.removeEventListener( "load", completed );
+	jQuery.ready();
+}
+
+// Catch cases where $(document).ready() is called
+// after the browser event has already occurred.
+// Support: IE <=9 - 10 only
+// Older IE sometimes signals "interactive" too soon
+if ( document.readyState === "complete" ||
+	( document.readyState !== "loading" && !document.documentElement.doScroll ) ) {
+
+	// Handle it asynchronously to allow scripts the opportunity to delay ready
+	window.setTimeout( jQuery.ready );
+
+} else {
+
+	// Use the handy event callback
+	document.addEventListener( "DOMContentLoaded", completed );
+
+	// A fallback to window.onload, that will always work
+	window.addEventListener( "load", completed );
+}
+
+
+
+
+// Multifunctional method to get and set values of a collection
+// The value/s can optionally be executed if it's a function
+var access = function( elems, fn, key, value, chainable, emptyGet, raw ) {
+	var i = 0,
+		len = elems.length,
+		bulk = key == null;
+
+	// Sets many values
+	if ( toType( key ) === "object" ) {
+		chainable = true;
+		for ( i in key ) {
+			access( elems, fn, i, key[ i ], true, emptyGet, raw );
+		}
+
+	// Sets one value
+	} else if ( value !== undefined ) {
+		chainable = true;
+
+		if ( !isFunction( value ) ) {
+			raw = true;
+		}
+
+		if ( bulk ) {
+
+			// Bulk operations run against the entire set
+			if ( raw ) {
+				fn.call( elems, value );
+				fn = null;
+
+			// ...except when executing function values
+			} else {
+				bulk = fn;
+				fn = function( elem, _key, value ) {
+					return bulk.call( jQuery( elem ), value );
+				};
+			}
+		}
+
+		if ( fn ) {
+			for ( ; i < len; i++ ) {
+				fn(
+					elems[ i ], key, raw ?
+					value :
+					value.call( elems[ i ], i, fn( elems[ i ], key ) )
+				);
+			}
+		}
+	}
+
+	if ( chainable ) {
+		return elems;
+	}
+
+	// Gets
+	if ( bulk ) {
+		return fn.call( elems );
+	}
+
+	return len ? fn( elems[ 0 ], key ) : emptyGet;
+};
+
+
+// Matches dashed string for camelizing
+var rmsPrefix = /^-ms-/,
+	rdashAlpha = /-([a-z])/g;
+
+// Used by camelCase as callback to replace()
+function fcamelCase( _all, letter ) {
+	return letter.toUpperCase();
+}
+
+// Convert dashed to camelCase; used by the css and data modules
+// Support: IE <=9 - 11, Edge 12 - 15
+// Microsoft forgot to hump their vendor prefix (#9572)
+function camelCase( string ) {
+	return string.replace( rmsPrefix, "ms-" ).replace( rdashAlpha, fcamelCase );
+}
+var acceptData = function( owner ) {
+
+	// Accepts only:
+	//  - Node
+	//    - Node.ELEMENT_NODE
+	//    - Node.DOCUMENT_NODE
+	//  - Object
+	//    - Any
+	return owner.nodeType === 1 || owner.nodeType === 9 || !( +owner.nodeType );
+};
+
+
+
+
+function Data() {
+	this.expando = jQuery.expando + Data.uid++;
+}
+
+Data.uid = 1;
+
+Data.prototype = {
+
+	cache: function( owner ) {
+
+		// Check if the owner object already has a cache
+		var value = owner[ this.expando ];
+
+		// If not, create one
+		if ( !value ) {
+			value = {};
+
+			// We can accept data for non-element nodes in modern browsers,
+			// but we should not, see #8335.
+			// Always return an empty object.
+			if ( acceptData( owner ) ) {
+
+				// If it is a node unlikely to be stringify-ed or looped over
+				// use plain assignment
+				if ( owner.nodeType ) {
+					owner[ this.expando ] = value;
+
+				// Otherwise secure it in a non-enumerable property
+				// configurable must be true to allow the property to be
+				// deleted when data is removed
+				} else {
+					Object.defineProperty( owner, this.expando, {
+						value: value,
+						configurable: true
+					} );
+				}
+			}
+		}
+
+		return value;
+	},
+	set: function( owner, data, value ) {
+		var prop,
+			cache = this.cache( owner );
+
+		// Handle: [ owner, key, value ] args
+		// Always use camelCase key (gh-2257)
+		if ( typeof data === "string" ) {
+			cache[ camelCase( data ) ] = value;
+
+		// Handle: [ owner, { properties } ] args
+		} else {
+
+			// Copy the properties one-by-one to the cache object
+			for ( prop in data ) {
+				cache[ camelCase( prop ) ] = data[ prop ];
+			}
+		}
+		return cache;
+	},
+	get: function( owner, key ) {
+		return key === undefined ?
+			this.cache( owner ) :
+
+			// Always use camelCase key (gh-2257)
+			owner[ this.expando ] && owner[ this.expando ][ camelCase( key ) ];
+	},
+	access: function( owner, key, value ) {
+
+		// In cases where either:
+		//
+		//   1. No key was specified
+		//   2. A string key was specified, but no value provided
+		//
+		// Take the "read" path and allow the get method to determine
+		// which value to return, respectively either:
+		//
+		//   1. The entire cache object
+		//   2. The data stored at the key
+		//
+		if ( key === undefined ||
+				( ( key && typeof key === "string" ) && value === undefined ) ) {
+
+			return this.get( owner, key );
+		}
+
+		// When the key is not a string, or both a key and value
+		// are specified, set or extend (existing objects) with either:
+		//
+		//   1. An object of properties
+		//   2. A key and value
+		//
+		this.set( owner, key, value );
+
+		// Since the "set" path can have two possible entry points
+		// return the expected data based on which path was taken[*]
+		return value !== undefined ? value : key;
+	},
+	remove: function( owner, key ) {
+		var i,
+			cache = owner[ this.expando ];
+
+		if ( cache === undefined ) {
+			return;
+		}
+
+		if ( key !== undefined ) {
+
+			// Support array or space separated string of keys
+			if ( Array.isArray( key ) ) {
+
+				// If key is an array of keys...
+				// We always set camelCase keys, so remove that.
+				key = key.map( camelCase );
+			} else {
+				key = camelCase( key );
+
+				// If a key with the spaces exists, use it.
+				// Otherwise, create an array by matching non-whitespace
+				key = key in cache ?
+					[ key ] :
+					( key.match( rnothtmlwhite ) || [] );
+			}
+
+			i = key.length;
+
+			while ( i-- ) {
+				delete cache[ key[ i ] ];
+			}
+		}
+
+		// Remove the expando if there's no more data
+		if ( key === undefined || jQuery.isEmptyObject( cache ) ) {
+
+			// Support: Chrome <=35 - 45
+			// Webkit & Blink performance suffers when deleting properties
+			// from DOM nodes, so set to undefined instead
+			// https://bugs.chromium.org/p/chromium/issues/detail?id=378607 (bug restricted)
+			if ( owner.nodeType ) {
+				owner[ this.expando ] = undefined;
+			} else {
+				delete owner[ this.expando ];
+			}
+		}
+	},
+	hasData: function( owner ) {
+		var cache = owner[ this.expando ];
+		return cache !== undefined && !jQuery.isEmptyObject( cache );
+	}
+};
+var dataPriv = new Data();
+
+var dataUser = new Data();
+
+
+
+//	Implementation Summary
+//
+//	1. Enforce API surface and semantic compatibility with 1.9.x branch
+//	2. Improve the module's maintainability by reducing the storage
+//		paths to a single mechanism.
+//	3. Use the same single mechanism to support "private" and "user" data.
+//	4. _Never_ expose "private" data to user code (TODO: Drop _data, _removeData)
+//	5. Avoid exposing implementation details on user objects (eg. expando properties)
+//	6. Provide a clear path for implementation upgrade to WeakMap in 2014
+
+var rbrace = /^(?:\{[\w\W]*\}|\[[\w\W]*\])$/,
+	rmultiDash = /[A-Z]/g;
+
+function getData( data ) {
+	if ( data === "true" ) {
+		return true;
+	}
+
+	if ( data === "false" ) {
+		return false;
+	}
+
+	if ( data === "null" ) {
+		return null;
+	}
+
+	// Only convert to a number if it doesn't change the string
+	if ( data === +data + "" ) {
+		return +data;
+	}
+
+	if ( rbrace.test( data ) ) {
+		return JSON.parse( data );
+	}
+
+	return data;
+}
+
+function dataAttr( elem, key, data ) {
+	var name;
+
+	// If nothing was found internally, try to fetch any
+	// data from the HTML5 data-* attribute
+	if ( data === undefined && elem.nodeType === 1 ) {
+		name = "data-" + key.replace( rmultiDash, "-$&" ).toLowerCase();
+		data = elem.getAttribute( name );
+
+		if ( typeof data === "string" ) {
+			try {
+				data = getData( data );
+			} catch ( e ) {}
+
+			// Make sure we set the data so it isn't changed later
+			dataUser.set( elem, key, data );
+		} else {
+			data = undefined;
+		}
+	}
+	return data;
+}
+
+jQuery.extend( {
+	hasData: function( elem ) {
+		return dataUser.hasData( elem ) || dataPriv.hasData( elem );
+	},
+
+	data: function( elem, name, data ) {
+		return dataUser.access( elem, name, data );
+	},
+
+	removeData: function( elem, name ) {
+		dataUser.remove( elem, name );
+	},
+
+	// TODO: Now that all calls to _data and _removeData have been replaced
+	// with direct calls to dataPriv methods, these can be deprecated.
+	_data: function( elem, name, data ) {
+		return dataPriv.access( elem, name, data );
+	},
+
+	_removeData: function( elem, name ) {
+		dataPriv.remove( elem, name );
+	}
+} );
+
+jQuery.fn.extend( {
+	data: function( key, value ) {
+		var i, name, data,
+			elem = this[ 0 ],
+			attrs = elem && elem.attributes;
+
+		// Gets all values
+		if ( key === undefined ) {
+			if ( this.length ) {
+				data = dataUser.get( elem );
+
+				if ( elem.nodeType === 1 && !dataPriv.get( elem, "hasDataAttrs" ) ) {
+					i = attrs.length;
+					while ( i-- ) {
+
+						// Support: IE 11 only
+						// The attrs elements can be null (#14894)
+						if ( attrs[ i ] ) {
+							name = attrs[ i ].name;
+							if ( name.indexOf( "data-" ) === 0 ) {
+								name = camelCase( name.slice( 5 ) );
+								dataAttr( elem, name, data[ name ] );
+							}
+						}
+					}
+					dataPriv.set( elem, "hasDataAttrs", true );
+				}
+			}
+
+			return data;
+		}
+
+		// Sets multiple values
+		if ( typeof key === "object" ) {
+			return this.each( function() {
+				dataUser.set( this, key );
+			} );
+		}
+
+		return access( this, function( value ) {
+			var data;
+
+			// The calling jQuery object (element matches) is not empty
+			// (and therefore has an element appears at this[ 0 ]) and the
+			// `value` parameter was not undefined. An empty jQuery object
+			// will result in `undefined` for elem = this[ 0 ] which will
+			// throw an exception if an attempt to read a data cache is made.
+			if ( elem && value === undefined ) {
+
+				// Attempt to get data from the cache
+				// The key will always be camelCased in Data
+				data = dataUser.get( elem, key );
+				if ( data !== undefined ) {
+					return data;
+				}
+
+				// Attempt to "discover" the data in
+				// HTML5 custom data-* attrs
+				data = dataAttr( elem, key );
+				if ( data !== undefined ) {
+					return data;
+				}
+
+				// We tried really hard, but the data doesn't exist.
+				return;
+			}
+
+			// Set the data...
+			this.each( function() {
+
+				// We always store the camelCased key
+				dataUser.set( this, key, value );
+			} );
+		}, null, value, arguments.length > 1, null, true );
+	},
+
+	removeData: function( key ) {
+		return this.each( function() {
+			dataUser.remove( this, key );
+		} );
+	}
+} );
+
+
+jQuery.extend( {
+	queue: function( elem, type, data ) {
+		var queue;
+
+		if ( elem ) {
+			type = ( type || "fx" ) + "queue";
+			queue = dataPriv.get( elem, type );
+
+			// Speed up dequeue by getting out quickly if this is just a lookup
+			if ( data ) {
+				if ( !queue || Array.isArray( data ) ) {
+					queue = dataPriv.access( elem, type, jQuery.makeArray( data ) );
+				} else {
+					queue.push( data );
+				}
+			}
+			return queue || [];
+		}
+	},
+
+	dequeue: function( elem, type ) {
+		type = type || "fx";
+
+		var queue = jQuery.queue( elem, type ),
+			startLength = queue.length,
+			fn = queue.shift(),
+			hooks = jQuery._queueHooks( elem, type ),
+			next = function() {
+				jQuery.dequeue( elem, type );
+			};
+
+		// If the fx queue is dequeued, always remove the progress sentinel
+		if ( fn === "inprogress" ) {
+			fn = queue.shift();
+			startLength--;
+		}
+
+		if ( fn ) {
+
+			// Add a progress sentinel to prevent the fx queue from being
+			// automatically dequeued
+			if ( type === "fx" ) {
+				queue.unshift( "inprogress" );
+			}
+
+			// Clear up the last queue stop function
+			delete hooks.stop;
+			fn.call( elem, next, hooks );
+		}
+
+		if ( !startLength && hooks ) {
+			hooks.empty.fire();
+		}
+	},
+
+	// Not public - generate a queueHooks object, or return the current one
+	_queueHooks: function( elem, type ) {
+		var key = type + "queueHooks";
+		return dataPriv.get( elem, key ) || dataPriv.access( elem, key, {
+			empty: jQuery.Callbacks( "once memory" ).add( function() {
+				dataPriv.remove( elem, [ type + "queue", key ] );
+			} )
+		} );
+	}
+} );
+
+jQuery.fn.extend( {
+	queue: function( type, data ) {
+		var setter = 2;
+
+		if ( typeof type !== "string" ) {
+			data = type;
+			type = "fx";
+			setter--;
+		}
+
+		if ( arguments.length < setter ) {
+			return jQuery.queue( this[ 0 ], type );
+		}
+
+		return data === undefined ?
+			this :
+			this.each( function() {
+				var queue = jQuery.queue( this, type, data );
+
+				// Ensure a hooks for this queue
+				jQuery._queueHooks( this, type );
+
+				if ( type === "fx" && queue[ 0 ] !== "inprogress" ) {
+					jQuery.dequeue( this, type );
+				}
+			} );
+	},
+	dequeue: function( type ) {
+		return this.each( function() {
+			jQuery.dequeue( this, type );
+		} );
+	},
+	clearQueue: function( type ) {
+		return this.queue( type || "fx", [] );
+	},
+
+	// Get a promise resolved when queues of a certain type
+	// are emptied (fx is the type by default)
+	promise: function( type, obj ) {
+		var tmp,
+			count = 1,
+			defer = jQuery.Deferred(),
+			elements = this,
+			i = this.length,
+			resolve = function() {
+				if ( !( --count ) ) {
+					defer.resolveWith( elements, [ elements ] );
+				}
+			};
+
+		if ( typeof type !== "string" ) {
+			obj = type;
+			type = undefined;
+		}
+		type = type || "fx";
+
+		while ( i-- ) {
+			tmp = dataPriv.get( elements[ i ], type + "queueHooks" );
+			if ( tmp && tmp.empty ) {
+				count++;
+				tmp.empty.add( resolve );
+			}
+		}
+		resolve();
+		return defer.promise( obj );
+	}
+} );
+var pnum = ( /[+-]?(?:\d*\.|)\d+(?:[eE][+-]?\d+|)/ ).source;
+
+var rcssNum = new RegExp( "^(?:([+-])=|)(" + pnum + ")([a-z%]*)$", "i" );
+
+
+var cssExpand = [ "Top", "Right", "Bottom", "Left" ];
+
+var documentElement = document.documentElement;
+
+
+
+	var isAttached = function( elem ) {
+			return jQuery.contains( elem.ownerDocument, elem );
+		},
+		composed = { composed: true };
+
+	// Support: IE 9 - 11+, Edge 12 - 18+, iOS 10.0 - 10.2 only
+	// Check attachment across shadow DOM boundaries when possible (gh-3504)
+	// Support: iOS 10.0-10.2 only
+	// Early iOS 10 versions support `attachShadow` but not `getRootNode`,
+	// leading to errors. We need to check for `getRootNode`.
+	if ( documentElement.getRootNode ) {
+		isAttached = function( elem ) {
+			return jQuery.contains( elem.ownerDocument, elem ) ||
+				elem.getRootNode( composed ) === elem.ownerDocument;
+		};
+	}
+var isHiddenWithinTree = function( elem, el ) {
+
+		// isHiddenWithinTree might be called from jQuery#filter function;
+		// in that case, element will be second argument
+		elem = el || elem;
+
+		// Inline style trumps all
+		return elem.style.display === "none" ||
+			elem.style.display === "" &&
+
+			// Otherwise, check computed style
+			// Support: Firefox <=43 - 45
+			// Disconnected elements can have computed display: none, so first confirm that elem is
+			// in the document.
+			isAttached( elem ) &&
+
+			jQuery.css( elem, "display" ) === "none";
+	};
+
+
+
+function adjustCSS( elem, prop, valueParts, tween ) {
+	var adjusted, scale,
+		maxIterations = 20,
+		currentValue = tween ?
+			function() {
+				return tween.cur();
+			} :
+			function() {
+				return jQuery.css( elem, prop, "" );
+			},
+		initial = currentValue(),
+		unit = valueParts && valueParts[ 3 ] || ( jQuery.cssNumber[ prop ] ? "" : "px" ),
+
+		// Starting value computation is required for potential unit mismatches
+		initialInUnit = elem.nodeType &&
+			( jQuery.cssNumber[ prop ] || unit !== "px" && +initial ) &&
+			rcssNum.exec( jQuery.css( elem, prop ) );
+
+	if ( initialInUnit && initialInUnit[ 3 ] !== unit ) {
+
+		// Support: Firefox <=54
+		// Halve the iteration target value to prevent interference from CSS upper bounds (gh-2144)
+		initial = initial / 2;
+
+		// Trust units reported by jQuery.css
+		unit = unit || initialInUnit[ 3 ];
+
+		// Iteratively approximate from a nonzero starting point
+		initialInUnit = +initial || 1;
+
+		while ( maxIterations-- ) {
+
+			// Evaluate and update our best guess (doubling guesses that zero out).
+			// Finish if the scale equals or crosses 1 (making the old*new product non-positive).
+			jQuery.style( elem, prop, initialInUnit + unit );
+			if ( ( 1 - scale ) * ( 1 - ( scale = currentValue() / initial || 0.5 ) ) <= 0 ) {
+				maxIterations = 0;
+			}
+			initialInUnit = initialInUnit / scale;
+
+		}
+
+		initialInUnit = initialInUnit * 2;
+		jQuery.style( elem, prop, initialInUnit + unit );
+
+		// Make sure we update the tween properties later on
+		valueParts = valueParts || [];
+	}
+
+	if ( valueParts ) {
+		initialInUnit = +initialInUnit || +initial || 0;
+
+		// Apply relative offset (+=/-=) if specified
+		adjusted = valueParts[ 1 ] ?
+			initialInUnit + ( valueParts[ 1 ] + 1 ) * valueParts[ 2 ] :
+			+valueParts[ 2 ];
+		if ( tween ) {
+			tween.unit = unit;
+			tween.start = initialInUnit;
+			tween.end = adjusted;
+		}
+	}
+	return adjusted;
+}
+
+
+var defaultDisplayMap = {};
+
+function getDefaultDisplay( elem ) {
+	var temp,
+		doc = elem.ownerDocument,
+		nodeName = elem.nodeName,
+		display = defaultDisplayMap[ nodeName ];
+
+	if ( display ) {
+		return display;
+	}
+
+	temp = doc.body.appendChild( doc.createElement( nodeName ) );
+	display = jQuery.css( temp, "display" );
+
+	temp.parentNode.removeChild( temp );
+
+	if ( display === "none" ) {
+		display = "block";
+	}
+	defaultDisplayMap[ nodeName ] = display;
+
+	return display;
+}
+
+function showHide( elements, show ) {
+	var display, elem,
+		values = [],
+		index = 0,
+		length = elements.length;
+
+	// Determine new display value for elements that need to change
+	for ( ; index < length; index++ ) {
+		elem = elements[ index ];
+		if ( !elem.style ) {
+			continue;
+		}
+
+		display = elem.style.display;
+		if ( show ) {
+
+			// Since we force visibility upon cascade-hidden elements, an immediate (and slow)
+			// check is required in this first loop unless we have a nonempty display value (either
+			// inline or about-to-be-restored)
+			if ( display === "none" ) {
+				values[ index ] = dataPriv.get( elem, "display" ) || null;
+				if ( !values[ index ] ) {
+					elem.style.display = "";
+				}
+			}
+			if ( elem.style.display === "" && isHiddenWithinTree( elem ) ) {
+				values[ index ] = getDefaultDisplay( elem );
+			}
+		} else {
+			if ( display !== "none" ) {
+				values[ index ] = "none";
+
+				// Remember what we're overwriting
+				dataPriv.set( elem, "display", display );
+			}
+		}
+	}
+
+	// Set the display of the elements in a second loop to avoid constant reflow
+	for ( index = 0; index < length; index++ ) {
+		if ( values[ index ] != null ) {
+			elements[ index ].style.display = values[ index ];
+		}
+	}
+
+	return elements;
+}
+
+jQuery.fn.extend( {
+	show: function() {
+		return showHide( this, true );
+	},
+	hide: function() {
+		return showHide( this );
+	},
+	toggle: function( state ) {
+		if ( typeof state === "boolean" ) {
+			return state ? this.show() : this.hide();
+		}
+
+		return this.each( function() {
+			if ( isHiddenWithinTree( this ) ) {
+				jQuery( this ).show();
+			} else {
+				jQuery( this ).hide();
+			}
+		} );
+	}
+} );
+var rcheckableType = ( /^(?:checkbox|radio)$/i );
+
+var rtagName = ( /<([a-z][^\/\0>\x20\t\r\n\f]*)/i );
+
+var rscriptType = ( /^$|^module$|\/(?:java|ecma)script/i );
+
+
+
+( function() {
+	var fragment = document.createDocumentFragment(),
+		div = fragment.appendChild( document.createElement( "div" ) ),
+		input = document.createElement( "input" );
+
+	// Support: Android 4.0 - 4.3 only
+	// Check state lost if the name is set (#11217)
+	// Support: Windows Web Apps (WWA)
+	// `name` and `type` must use .setAttribute for WWA (#14901)
+	input.setAttribute( "type", "radio" );
+	input.setAttribute( "checked", "checked" );
+	input.setAttribute( "name", "t" );
+
+	div.appendChild( input );
+
+	// Support: Android <=4.1 only
+	// Older WebKit doesn't clone checked state correctly in fragments
+	support.checkClone = div.cloneNode( true ).cloneNode( true ).lastChild.checked;
+
+	// Support: IE <=11 only
+	// Make sure textarea (and checkbox) defaultValue is properly cloned
+	div.innerHTML = "<textarea>x</textarea>";
+	support.noCloneChecked = !!div.cloneNode( true ).lastChild.defaultValue;
+
+	// Support: IE <=9 only
+	// IE <=9 replaces <option> tags with their contents when inserted outside of
+	// the select element.
+	div.innerHTML = "<option></option>";
+	support.option = !!div.lastChild;
+} )();
+
+
+// We have to close these tags to support XHTML (#13200)
+var wrapMap = {
+
+	// XHTML parsers do not magically insert elements in the
+	// same way that tag soup parsers do. So we cannot shorten
+	// this by omitting <tbody> or other required elements.
+	thead: [ 1, "<table>", "</table>" ],
+	col: [ 2, "<table><colgroup>", "</colgroup></table>" ],
+	tr: [ 2, "<table><tbody>", "</tbody></table>" ],
+	td: [ 3, "<table><tbody><tr>", "</tr></tbody></table>" ],
+
+	_default: [ 0, "", "" ]
+};
+
+wrapMap.tbody = wrapMap.tfoot = wrapMap.colgroup = wrapMap.caption = wrapMap.thead;
+wrapMap.th = wrapMap.td;
+
+// Support: IE <=9 only
+if ( !support.option ) {
+	wrapMap.optgroup = wrapMap.option = [ 1, "<select multiple='multiple'>", "</select>" ];
+}
+
+
+function getAll( context, tag ) {
+
+	// Support: IE <=9 - 11 only
+	// Use typeof to avoid zero-argument method invocation on host objects (#15151)
+	var ret;
+
+	if ( typeof context.getElementsByTagName !== "undefined" ) {
+		ret = context.getElementsByTagName( tag || "*" );
+
+	} else if ( typeof context.querySelectorAll !== "undefined" ) {
+		ret = context.querySelectorAll( tag || "*" );
+
+	} else {
+		ret = [];
+	}
+
+	if ( tag === undefined || tag && nodeName( context, tag ) ) {
+		return jQuery.merge( [ context ], ret );
+	}
+
+	return ret;
+}
+
+
+// Mark scripts as having already been evaluated
+function setGlobalEval( elems, refElements ) {
+	var i = 0,
+		l = elems.length;
+
+	for ( ; i < l; i++ ) {
+		dataPriv.set(
+			elems[ i ],
+			"globalEval",
+			!refElements || dataPriv.get( refElements[ i ], "globalEval" )
+		);
+	}
+}
+
+
+var rhtml = /<|&#?\w+;/;
+
+function buildFragment( elems, context, scripts, selection, ignored ) {
+	var elem, tmp, tag, wrap, attached, j,
+		fragment = context.createDocumentFragment(),
+		nodes = [],
+		i = 0,
+		l = elems.length;
+
+	for ( ; i < l; i++ ) {
+		elem = elems[ i ];
+
+		if ( elem || elem === 0 ) {
+
+			// Add nodes directly
+			if ( toType( elem ) === "object" ) {
+
+				// Support: Android <=4.0 only, PhantomJS 1 only
+				// push.apply(_, arraylike) throws on ancient WebKit
+				jQuery.merge( nodes, elem.nodeType ? [ elem ] : elem );
+
+			// Convert non-html into a text node
+			} else if ( !rhtml.test( elem ) ) {
+				nodes.push( context.createTextNode( elem ) );
+
+			// Convert html into DOM nodes
+			} else {
+				tmp = tmp || fragment.appendChild( context.createElement( "div" ) );
+
+				// Deserialize a standard representation
+				tag = ( rtagName.exec( elem ) || [ "", "" ] )[ 1 ].toLowerCase();
+				wrap = wrapMap[ tag ] || wrapMap._default;
+				tmp.innerHTML = wrap[ 1 ] + jQuery.htmlPrefilter( elem ) + wrap[ 2 ];
+
+				// Descend through wrappers to the right content
+				j = wrap[ 0 ];
+				while ( j-- ) {
+					tmp = tmp.lastChild;
+				}
+
+				// Support: Android <=4.0 only, PhantomJS 1 only
+				// push.apply(_, arraylike) throws on ancient WebKit
+				jQuery.merge( nodes, tmp.childNodes );
+
+				// Remember the top-level container
+				tmp = fragment.firstChild;
+
+				// Ensure the created nodes are orphaned (#12392)
+				tmp.textContent = "";
+			}
+		}
+	}
+
+	// Remove wrapper from fragment
+	fragment.textContent = "";
+
+	i = 0;
+	while ( ( elem = nodes[ i++ ] ) ) {
+
+		// Skip elements already in the context collection (trac-4087)
+		if ( selection && jQuery.inArray( elem, selection ) > -1 ) {
+			if ( ignored ) {
+				ignored.push( elem );
+			}
+			continue;
+		}
+
+		attached = isAttached( elem );
+
+		// Append to fragment
+		tmp = getAll( fragment.appendChild( elem ), "script" );
+
+		// Preserve script evaluation history
+		if ( attached ) {
+			setGlobalEval( tmp );
+		}
+
+		// Capture executables
+		if ( scripts ) {
+			j = 0;
+			while ( ( elem = tmp[ j++ ] ) ) {
+				if ( rscriptType.test( elem.type || "" ) ) {
+					scripts.push( elem );
+				}
+			}
+		}
+	}
+
+	return fragment;
+}
+
+
+var
+	rkeyEvent = /^key/,
+	rmouseEvent = /^(?:mouse|pointer|contextmenu|drag|drop)|click/,
+	rtypenamespace = /^([^.]*)(?:\.(.+)|)/;
+
+function returnTrue() {
+	return true;
+}
+
+function returnFalse() {
+	return false;
+}
+
+// Support: IE <=9 - 11+
+// focus() and blur() are asynchronous, except when they are no-op.
+// So expect focus to be synchronous when the element is already active,
+// and blur to be synchronous when the element is not already active.
+// (focus and blur are always synchronous in other supported browsers,
+// this just defines when we can count on it).
+function expectSync( elem, type ) {
+	return ( elem === safeActiveElement() ) === ( type === "focus" );
+}
+
+// Support: IE <=9 only
+// Accessing document.activeElement can throw unexpectedly
+// https://bugs.jquery.com/ticket/13393
+function safeActiveElement() {
+	try {
+		return document.activeElement;
+	} catch ( err ) { }
+}
+
+function on( elem, types, selector, data, fn, one ) {
+	var origFn, type;
+
+	// Types can be a map of types/handlers
+	if ( typeof types === "object" ) {
+
+		// ( types-Object, selector, data )
+		if ( typeof selector !== "string" ) {
+
+			// ( types-Object, data )
+			data = data || selector;
+			selector = undefined;
+		}
+		for ( type in types ) {
+			on( elem, type, selector, data, types[ type ], one );
+		}
+		return elem;
+	}
+
+	if ( data == null && fn == null ) {
+
+		// ( types, fn )
+		fn = selector;
+		data = selector = undefined;
+	} else if ( fn == null ) {
+		if ( typeof selector === "string" ) {
+
+			// ( types, selector, fn )
+			fn = data;
+			data = undefined;
+		} else {
+
+			// ( types, data, fn )
+			fn = data;
+			data = selector;
+			selector = undefined;
+		}
+	}
+	if ( fn === false ) {
+		fn = returnFalse;
+	} else if ( !fn ) {
+		return elem;
+	}
+
+	if ( one === 1 ) {
+		origFn = fn;
+		fn = function( event ) {
+
+			// Can use an empty set, since event contains the info
+			jQuery().off( event );
+			return origFn.apply( this, arguments );
+		};
+
+		// Use same guid so caller can remove using origFn
+		fn.guid = origFn.guid || ( origFn.guid = jQuery.guid++ );
+	}
+	return elem.each( function() {
+		jQuery.event.add( this, types, fn, data, selector );
+	} );
+}
+
+/*
+ * Helper functions for managing events -- not part of the public interface.
+ * Props to Dean Edwards' addEvent library for many of the ideas.
+ */
+jQuery.event = {
+
+	global: {},
+
+	add: function( elem, types, handler, data, selector ) {
+
+		var handleObjIn, eventHandle, tmp,
+			events, t, handleObj,
+			special, handlers, type, namespaces, origType,
+			elemData = dataPriv.get( elem );
+
+		// Only attach events to objects that accept data
+		if ( !acceptData( elem ) ) {
+			return;
+		}
+
+		// Caller can pass in an object of custom data in lieu of the handler
+		if ( handler.handler ) {
+			handleObjIn = handler;
+			handler = handleObjIn.handler;
+			selector = handleObjIn.selector;
+		}
+
+		// Ensure that invalid selectors throw exceptions at attach time
+		// Evaluate against documentElement in case elem is a non-element node (e.g., document)
+		if ( selector ) {
+			jQuery.find.matchesSelector( documentElement, selector );
+		}
+
+		// Make sure that the handler has a unique ID, used to find/remove it later
+		if ( !handler.guid ) {
+			handler.guid = jQuery.guid++;
+		}
+
+		// Init the element's event structure and main handler, if this is the first
+		if ( !( events = elemData.events ) ) {
+			events = elemData.events = Object.create( null );
+		}
+		if ( !( eventHandle = elemData.handle ) ) {
+			eventHandle = elemData.handle = function( e ) {
+
+				// Discard the second event of a jQuery.event.trigger() and
+				// when an event is called after a page has unloaded
+				return typeof jQuery !== "undefined" && jQuery.event.triggered !== e.type ?
+					jQuery.event.dispatch.apply( elem, arguments ) : undefined;
+			};
+		}
+
+		// Handle multiple events separated by a space
+		types = ( types || "" ).match( rnothtmlwhite ) || [ "" ];
+		t = types.length;
+		while ( t-- ) {
+			tmp = rtypenamespace.exec( types[ t ] ) || [];
+			type = origType = tmp[ 1 ];
+			namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort();
+
+			// There *must* be a type, no attaching namespace-only handlers
+			if ( !type ) {
+				continue;
+			}
+
+			// If event changes its type, use the special event handlers for the changed type
+			special = jQuery.event.special[ type ] || {};
+
+			// If selector defined, determine special event api type, otherwise given type
+			type = ( selector ? special.delegateType : special.bindType ) || type;
+
+			// Update special based on newly reset type
+			special = jQuery.event.special[ type ] || {};
+
+			// handleObj is passed to all event handlers
+			handleObj = jQuery.extend( {
+				type: type,
+				origType: origType,
+				data: data,
+				handler: handler,
+				guid: handler.guid,
+				selector: selector,
+				needsContext: selector && jQuery.expr.match.needsContext.test( selector ),
+				namespace: namespaces.join( "." )
+			}, handleObjIn );
+
+			// Init the event handler queue if we're the first
+			if ( !( handlers = events[ type ] ) ) {
+				handlers = events[ type ] = [];
+				handlers.delegateCount = 0;
+
+				// Only use addEventListener if the special events handler returns false
+				if ( !special.setup ||
+					special.setup.call( elem, data, namespaces, eventHandle ) === false ) {
+
+					if ( elem.addEventListener ) {
+						elem.addEventListener( type, eventHandle );
+					}
+				}
+			}
+
+			if ( special.add ) {
+				special.add.call( elem, handleObj );
+
+				if ( !handleObj.handler.guid ) {
+					handleObj.handler.guid = handler.guid;
+				}
+			}
+
+			// Add to the element's handler list, delegates in front
+			if ( selector ) {
+				handlers.splice( handlers.delegateCount++, 0, handleObj );
+			} else {
+				handlers.push( handleObj );
+			}
+
+			// Keep track of which events have ever been used, for event optimization
+			jQuery.event.global[ type ] = true;
+		}
+
+	},
+
+	// Detach an event or set of events from an element
+	remove: function( elem, types, handler, selector, mappedTypes ) {
+
+		var j, origCount, tmp,
+			events, t, handleObj,
+			special, handlers, type, namespaces, origType,
+			elemData = dataPriv.hasData( elem ) && dataPriv.get( elem );
+
+		if ( !elemData || !( events = elemData.events ) ) {
+			return;
+		}
+
+		// Once for each type.namespace in types; type may be omitted
+		types = ( types || "" ).match( rnothtmlwhite ) || [ "" ];
+		t = types.length;
+		while ( t-- ) {
+			tmp = rtypenamespace.exec( types[ t ] ) || [];
+			type = origType = tmp[ 1 ];
+			namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort();
+
+			// Unbind all events (on this namespace, if provided) for the element
+			if ( !type ) {
+				for ( type in events ) {
+					jQuery.event.remove( elem, type + types[ t ], handler, selector, true );
+				}
+				continue;
+			}
+
+			special = jQuery.event.special[ type ] || {};
+			type = ( selector ? special.delegateType : special.bindType ) || type;
+			handlers = events[ type ] || [];
+			tmp = tmp[ 2 ] &&
+				new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" );
+
+			// Remove matching events
+			origCount = j = handlers.length;
+			while ( j-- ) {
+				handleObj = handlers[ j ];
+
+				if ( ( mappedTypes || origType === handleObj.origType ) &&
+					( !handler || handler.guid === handleObj.guid ) &&
+					( !tmp || tmp.test( handleObj.namespace ) ) &&
+					( !selector || selector === handleObj.selector ||
+						selector === "**" && handleObj.selector ) ) {
+					handlers.splice( j, 1 );
+
+					if ( handleObj.selector ) {
+						handlers.delegateCount--;
+					}
+					if ( special.remove ) {
+						special.remove.call( elem, handleObj );
+					}
+				}
+			}
+
+			// Remove generic event handler if we removed something and no more handlers exist
+			// (avoids potential for endless recursion during removal of special event handlers)
+			if ( origCount && !handlers.length ) {
+				if ( !special.teardown ||
+					special.teardown.call( elem, namespaces, elemData.handle ) === false ) {
+
+					jQuery.removeEvent( elem, type, elemData.handle );
+				}
+
+				delete events[ type ];
+			}
+		}
+
+		// Remove data and the expando if it's no longer used
+		if ( jQuery.isEmptyObject( events ) ) {
+			dataPriv.remove( elem, "handle events" );
+		}
+	},
+
+	dispatch: function( nativeEvent ) {
+
+		var i, j, ret, matched, handleObj, handlerQueue,
+			args = new Array( arguments.length ),
+
+			// Make a writable jQuery.Event from the native event object
+			event = jQuery.event.fix( nativeEvent ),
+
+			handlers = (
+					dataPriv.get( this, "events" ) || Object.create( null )
+				)[ event.type ] || [],
+			special = jQuery.event.special[ event.type ] || {};
+
+		// Use the fix-ed jQuery.Event rather than the (read-only) native event
+		args[ 0 ] = event;
+
+		for ( i = 1; i < arguments.length; i++ ) {
+			args[ i ] = arguments[ i ];
+		}
+
+		event.delegateTarget = this;
+
+		// Call the preDispatch hook for the mapped type, and let it bail if desired
+		if ( special.preDispatch && special.preDispatch.call( this, event ) === false ) {
+			return;
+		}
+
+		// Determine handlers
+		handlerQueue = jQuery.event.handlers.call( this, event, handlers );
+
+		// Run delegates first; they may want to stop propagation beneath us
+		i = 0;
+		while ( ( matched = handlerQueue[ i++ ] ) && !event.isPropagationStopped() ) {
+			event.currentTarget = matched.elem;
+
+			j = 0;
+			while ( ( handleObj = matched.handlers[ j++ ] ) &&
+				!event.isImmediatePropagationStopped() ) {
+
+				// If the event is namespaced, then each handler is only invoked if it is
+				// specially universal or its namespaces are a superset of the event's.
+				if ( !event.rnamespace || handleObj.namespace === false ||
+					event.rnamespace.test( handleObj.namespace ) ) {
+
+					event.handleObj = handleObj;
+					event.data = handleObj.data;
+
+					ret = ( ( jQuery.event.special[ handleObj.origType ] || {} ).handle ||
+						handleObj.handler ).apply( matched.elem, args );
+
+					if ( ret !== undefined ) {
+						if ( ( event.result = ret ) === false ) {
+							event.preventDefault();
+							event.stopPropagation();
+						}
+					}
+				}
+			}
+		}
+
+		// Call the postDispatch hook for the mapped type
+		if ( special.postDispatch ) {
+			special.postDispatch.call( this, event );
+		}
+
+		return event.result;
+	},
+
+	handlers: function( event, handlers ) {
+		var i, handleObj, sel, matchedHandlers, matchedSelectors,
+			handlerQueue = [],
+			delegateCount = handlers.delegateCount,
+			cur = event.target;
+
+		// Find delegate handlers
+		if ( delegateCount &&
+
+			// Support: IE <=9
+			// Black-hole SVG <use> instance trees (trac-13180)
+			cur.nodeType &&
+
+			// Support: Firefox <=42
+			// Suppress spec-violating clicks indicating a non-primary pointer button (trac-3861)
+			// https://www.w3.org/TR/DOM-Level-3-Events/#event-type-click
+			// Support: IE 11 only
+			// ...but not arrow key "clicks" of radio inputs, which can have `button` -1 (gh-2343)
+			!( event.type === "click" && event.button >= 1 ) ) {
+
+			for ( ; cur !== this; cur = cur.parentNode || this ) {
+
+				// Don't check non-elements (#13208)
+				// Don't process clicks on disabled elements (#6911, #8165, #11382, #11764)
+				if ( cur.nodeType === 1 && !( event.type === "click" && cur.disabled === true ) ) {
+					matchedHandlers = [];
+					matchedSelectors = {};
+					for ( i = 0; i < delegateCount; i++ ) {
+						handleObj = handlers[ i ];
+
+						// Don't conflict with Object.prototype properties (#13203)
+						sel = handleObj.selector + " ";
+
+						if ( matchedSelectors[ sel ] === undefined ) {
+							matchedSelectors[ sel ] = handleObj.needsContext ?
+								jQuery( sel, this ).index( cur ) > -1 :
+								jQuery.find( sel, this, null, [ cur ] ).length;
+						}
+						if ( matchedSelectors[ sel ] ) {
+							matchedHandlers.push( handleObj );
+						}
+					}
+					if ( matchedHandlers.length ) {
+						handlerQueue.push( { elem: cur, handlers: matchedHandlers } );
+					}
+				}
+			}
+		}
+
+		// Add the remaining (directly-bound) handlers
+		cur = this;
+		if ( delegateCount < handlers.length ) {
+			handlerQueue.push( { elem: cur, handlers: handlers.slice( delegateCount ) } );
+		}
+
+		return handlerQueue;
+	},
+
+	addProp: function( name, hook ) {
+		Object.defineProperty( jQuery.Event.prototype, name, {
+			enumerable: true,
+			configurable: true,
+
+			get: isFunction( hook ) ?
+				function() {
+					if ( this.originalEvent ) {
+							return hook( this.originalEvent );
+					}
+				} :
+				function() {
+					if ( this.originalEvent ) {
+							return this.originalEvent[ name ];
+					}
+				},
+
+			set: function( value ) {
+				Object.defineProperty( this, name, {
+					enumerable: true,
+					configurable: true,
+					writable: true,
+					value: value
+				} );
+			}
+		} );
+	},
+
+	fix: function( originalEvent ) {
+		return originalEvent[ jQuery.expando ] ?
+			originalEvent :
+			new jQuery.Event( originalEvent );
+	},
+
+	special: {
+		load: {
+
+			// Prevent triggered image.load events from bubbling to window.load
+			noBubble: true
+		},
+		click: {
+
+			// Utilize native event to ensure correct state for checkable inputs
+			setup: function( data ) {
+
+				// For mutual compressibility with _default, replace `this` access with a local var.
+				// `|| data` is dead code meant only to preserve the variable through minification.
+				var el = this || data;
+
+				// Claim the first handler
+				if ( rcheckableType.test( el.type ) &&
+					el.click && nodeName( el, "input" ) ) {
+
+					// dataPriv.set( el, "click", ... )
+					leverageNative( el, "click", returnTrue );
+				}
+
+				// Return false to allow normal processing in the caller
+				return false;
+			},
+			trigger: function( data ) {
+
+				// For mutual compressibility with _default, replace `this` access with a local var.
+				// `|| data` is dead code meant only to preserve the variable through minification.
+				var el = this || data;
+
+				// Force setup before triggering a click
+				if ( rcheckableType.test( el.type ) &&
+					el.click && nodeName( el, "input" ) ) {
+
+					leverageNative( el, "click" );
+				}
+
+				// Return non-false to allow normal event-path propagation
+				return true;
+			},
+
+			// For cross-browser consistency, suppress native .click() on links
+			// Also prevent it if we're currently inside a leveraged native-event stack
+			_default: function( event ) {
+				var target = event.target;
+				return rcheckableType.test( target.type ) &&
+					target.click && nodeName( target, "input" ) &&
+					dataPriv.get( target, "click" ) ||
+					nodeName( target, "a" );
+			}
+		},
+
+		beforeunload: {
+			postDispatch: function( event ) {
+
+				// Support: Firefox 20+
+				// Firefox doesn't alert if the returnValue field is not set.
+				if ( event.result !== undefined && event.originalEvent ) {
+					event.originalEvent.returnValue = event.result;
+				}
+			}
+		}
+	}
+};
+
+// Ensure the presence of an event listener that handles manually-triggered
+// synthetic events by interrupting progress until reinvoked in response to
+// *native* events that it fires directly, ensuring that state changes have
+// already occurred before other listeners are invoked.
+function leverageNative( el, type, expectSync ) {
+
+	// Missing expectSync indicates a trigger call, which must force setup through jQuery.event.add
+	if ( !expectSync ) {
+		if ( dataPriv.get( el, type ) === undefined ) {
+			jQuery.event.add( el, type, returnTrue );
+		}
+		return;
+	}
+
+	// Register the controller as a special universal handler for all event namespaces
+	dataPriv.set( el, type, false );
+	jQuery.event.add( el, type, {
+		namespace: false,
+		handler: function( event ) {
+			var notAsync, result,
+				saved = dataPriv.get( this, type );
+
+			if ( ( event.isTrigger & 1 ) && this[ type ] ) {
+
+				// Interrupt processing of the outer synthetic .trigger()ed event
+				// Saved data should be false in such cases, but might be a leftover capture object
+				// from an async native handler (gh-4350)
+				if ( !saved.length ) {
+
+					// Store arguments for use when handling the inner native event
+					// There will always be at least one argument (an event object), so this array
+					// will not be confused with a leftover capture object.
+					saved = slice.call( arguments );
+					dataPriv.set( this, type, saved );
+
+					// Trigger the native event and capture its result
+					// Support: IE <=9 - 11+
+					// focus() and blur() are asynchronous
+					notAsync = expectSync( this, type );
+					this[ type ]();
+					result = dataPriv.get( this, type );
+					if ( saved !== result || notAsync ) {
+						dataPriv.set( this, type, false );
+					} else {
+						result = {};
+					}
+					if ( saved !== result ) {
+
+						// Cancel the outer synthetic event
+						event.stopImmediatePropagation();
+						event.preventDefault();
+						return result.value;
+					}
+
+				// If this is an inner synthetic event for an event with a bubbling surrogate
+				// (focus or blur), assume that the surrogate already propagated from triggering the
+				// native event and prevent that from happening again here.
+				// This technically gets the ordering wrong w.r.t. to `.trigger()` (in which the
+				// bubbling surrogate propagates *after* the non-bubbling base), but that seems
+				// less bad than duplication.
+				} else if ( ( jQuery.event.special[ type ] || {} ).delegateType ) {
+					event.stopPropagation();
+				}
+
+			// If this is a native event triggered above, everything is now in order
+			// Fire an inner synthetic event with the original arguments
+			} else if ( saved.length ) {
+
+				// ...and capture the result
+				dataPriv.set( this, type, {
+					value: jQuery.event.trigger(
+
+						// Support: IE <=9 - 11+
+						// Extend with the prototype to reset the above stopImmediatePropagation()
+						jQuery.extend( saved[ 0 ], jQuery.Event.prototype ),
+						saved.slice( 1 ),
+						this
+					)
+				} );
+
+				// Abort handling of the native event
+				event.stopImmediatePropagation();
+			}
+		}
+	} );
+}
+
+jQuery.removeEvent = function( elem, type, handle ) {
+
+	// This "if" is needed for plain objects
+	if ( elem.removeEventListener ) {
+		elem.removeEventListener( type, handle );
+	}
+};
+
+jQuery.Event = function( src, props ) {
+
+	// Allow instantiation without the 'new' keyword
+	if ( !( this instanceof jQuery.Event ) ) {
+		return new jQuery.Event( src, props );
+	}
+
+	// Event object
+	if ( src && src.type ) {
+		this.originalEvent = src;
+		this.type = src.type;
+
+		// Events bubbling up the document may have been marked as prevented
+		// by a handler lower down the tree; reflect the correct value.
+		this.isDefaultPrevented = src.defaultPrevented ||
+				src.defaultPrevented === undefined &&
+
+				// Support: Android <=2.3 only
+				src.returnValue === false ?
+			returnTrue :
+			returnFalse;
+
+		// Create target properties
+		// Support: Safari <=6 - 7 only
+		// Target should not be a text node (#504, #13143)
+		this.target = ( src.target && src.target.nodeType === 3 ) ?
+			src.target.parentNode :
+			src.target;
+
+		this.currentTarget = src.currentTarget;
+		this.relatedTarget = src.relatedTarget;
+
+	// Event type
+	} else {
+		this.type = src;
+	}
+
+	// Put explicitly provided properties onto the event object
+	if ( props ) {
+		jQuery.extend( this, props );
+	}
+
+	// Create a timestamp if incoming event doesn't have one
+	this.timeStamp = src && src.timeStamp || Date.now();
+
+	// Mark it as fixed
+	this[ jQuery.expando ] = true;
+};
+
+// jQuery.Event is based on DOM3 Events as specified by the ECMAScript Language Binding
+// https://www.w3.org/TR/2003/WD-DOM-Level-3-Events-20030331/ecma-script-binding.html
+jQuery.Event.prototype = {
+	constructor: jQuery.Event,
+	isDefaultPrevented: returnFalse,
+	isPropagationStopped: returnFalse,
+	isImmediatePropagationStopped: returnFalse,
+	isSimulated: false,
+
+	preventDefault: function() {
+		var e = this.originalEvent;
+
+		this.isDefaultPrevented = returnTrue;
+
+		if ( e && !this.isSimulated ) {
+			e.preventDefault();
+		}
+	},
+	stopPropagation: function() {
+		var e = this.originalEvent;
+
+		this.isPropagationStopped = returnTrue;
+
+		if ( e && !this.isSimulated ) {
+			e.stopPropagation();
+		}
+	},
+	stopImmediatePropagation: function() {
+		var e = this.originalEvent;
+
+		this.isImmediatePropagationStopped = returnTrue;
+
+		if ( e && !this.isSimulated ) {
+			e.stopImmediatePropagation();
+		}
+
+		this.stopPropagation();
+	}
+};
+
+// Includes all common event props including KeyEvent and MouseEvent specific props
+jQuery.each( {
+	altKey: true,
+	bubbles: true,
+	cancelable: true,
+	changedTouches: true,
+	ctrlKey: true,
+	detail: true,
+	eventPhase: true,
+	metaKey: true,
+	pageX: true,
+	pageY: true,
+	shiftKey: true,
+	view: true,
+	"char": true,
+	code: true,
+	charCode: true,
+	key: true,
+	keyCode: true,
+	button: true,
+	buttons: true,
+	clientX: true,
+	clientY: true,
+	offsetX: true,
+	offsetY: true,
+	pointerId: true,
+	pointerType: true,
+	screenX: true,
+	screenY: true,
+	targetTouches: true,
+	toElement: true,
+	touches: true,
+
+	which: function( event ) {
+		var button = event.button;
+
+		// Add which for key events
+		if ( event.which == null && rkeyEvent.test( event.type ) ) {
+			return event.charCode != null ? event.charCode : event.keyCode;
+		}
+
+		// Add which for click: 1 === left; 2 === middle; 3 === right
+		if ( !event.which && button !== undefined && rmouseEvent.test( event.type ) ) {
+			if ( button & 1 ) {
+				return 1;
+			}
+
+			if ( button & 2 ) {
+				return 3;
+			}
+
+			if ( button & 4 ) {
+				return 2;
+			}
+
+			return 0;
+		}
+
+		return event.which;
+	}
+}, jQuery.event.addProp );
+
+jQuery.each( { focus: "focusin", blur: "focusout" }, function( type, delegateType ) {
+	jQuery.event.special[ type ] = {
+
+		// Utilize native event if possible so blur/focus sequence is correct
+		setup: function() {
+
+			// Claim the first handler
+			// dataPriv.set( this, "focus", ... )
+			// dataPriv.set( this, "blur", ... )
+			leverageNative( this, type, expectSync );
+
+			// Return false to allow normal processing in the caller
+			return false;
+		},
+		trigger: function() {
+
+			// Force setup before trigger
+			leverageNative( this, type );
+
+			// Return non-false to allow normal event-path propagation
+			return true;
+		},
+
+		delegateType: delegateType
+	};
+} );
+
+// Create mouseenter/leave events using mouseover/out and event-time checks
+// so that event delegation works in jQuery.
+// Do the same for pointerenter/pointerleave and pointerover/pointerout
+//
+// Support: Safari 7 only
+// Safari sends mouseenter too often; see:
+// https://bugs.chromium.org/p/chromium/issues/detail?id=470258
+// for the description of the bug (it existed in older Chrome versions as well).
+jQuery.each( {
+	mouseenter: "mouseover",
+	mouseleave: "mouseout",
+	pointerenter: "pointerover",
+	pointerleave: "pointerout"
+}, function( orig, fix ) {
+	jQuery.event.special[ orig ] = {
+		delegateType: fix,
+		bindType: fix,
+
+		handle: function( event ) {
+			var ret,
+				target = this,
+				related = event.relatedTarget,
+				handleObj = event.handleObj;
+
+			// For mouseenter/leave call the handler if related is outside the target.
+			// NB: No relatedTarget if the mouse left/entered the browser window
+			if ( !related || ( related !== target && !jQuery.contains( target, related ) ) ) {
+				event.type = handleObj.origType;
+				ret = handleObj.handler.apply( this, arguments );
+				event.type = fix;
+			}
+			return ret;
+		}
+	};
+} );
+
+jQuery.fn.extend( {
+
+	on: function( types, selector, data, fn ) {
+		return on( this, types, selector, data, fn );
+	},
+	one: function( types, selector, data, fn ) {
+		return on( this, types, selector, data, fn, 1 );
+	},
+	off: function( types, selector, fn ) {
+		var handleObj, type;
+		if ( types && types.preventDefault && types.handleObj ) {
+
+			// ( event )  dispatched jQuery.Event
+			handleObj = types.handleObj;
+			jQuery( types.delegateTarget ).off(
+				handleObj.namespace ?
+					handleObj.origType + "." + handleObj.namespace :
+					handleObj.origType,
+				handleObj.selector,
+				handleObj.handler
+			);
+			return this;
+		}
+		if ( typeof types === "object" ) {
+
+			// ( types-object [, selector] )
+			for ( type in types ) {
+				this.off( type, selector, types[ type ] );
+			}
+			return this;
+		}
+		if ( selector === false || typeof selector === "function" ) {
+
+			// ( types [, fn] )
+			fn = selector;
+			selector = undefined;
+		}
+		if ( fn === false ) {
+			fn = returnFalse;
+		}
+		return this.each( function() {
+			jQuery.event.remove( this, types, fn, selector );
+		} );
+	}
+} );
+
+
+var
+
+	// Support: IE <=10 - 11, Edge 12 - 13 only
+	// In IE/Edge using regex groups here causes severe slowdowns.
+	// See https://connect.microsoft.com/IE/feedback/details/1736512/
+	rnoInnerhtml = /<script|<style|<link/i,
+
+	// checked="checked" or checked
+	rchecked = /checked\s*(?:[^=]|=\s*.checked.)/i,
+	rcleanScript = /^\s*<!(?:\[CDATA\[|--)|(?:\]\]|--)>\s*$/g;
+
+// Prefer a tbody over its parent table for containing new rows
+function manipulationTarget( elem, content ) {
+	if ( nodeName( elem, "table" ) &&
+		nodeName( content.nodeType !== 11 ? content : content.firstChild, "tr" ) ) {
+
+		return jQuery( elem ).children( "tbody" )[ 0 ] || elem;
+	}
+
+	return elem;
+}
+
+// Replace/restore the type attribute of script elements for safe DOM manipulation
+function disableScript( elem ) {
+	elem.type = ( elem.getAttribute( "type" ) !== null ) + "/" + elem.type;
+	return elem;
+}
+function restoreScript( elem ) {
+	if ( ( elem.type || "" ).slice( 0, 5 ) === "true/" ) {
+		elem.type = elem.type.slice( 5 );
+	} else {
+		elem.removeAttribute( "type" );
+	}
+
+	return elem;
+}
+
+function cloneCopyEvent( src, dest ) {
+	var i, l, type, pdataOld, udataOld, udataCur, events;
+
+	if ( dest.nodeType !== 1 ) {
+		return;
+	}
+
+	// 1. Copy private data: events, handlers, etc.
+	if ( dataPriv.hasData( src ) ) {
+		pdataOld = dataPriv.get( src );
+		events = pdataOld.events;
+
+		if ( events ) {
+			dataPriv.remove( dest, "handle events" );
+
+			for ( type in events ) {
+				for ( i = 0, l = events[ type ].length; i < l; i++ ) {
+					jQuery.event.add( dest, type, events[ type ][ i ] );
+				}
+			}
+		}
+	}
+
+	// 2. Copy user data
+	if ( dataUser.hasData( src ) ) {
+		udataOld = dataUser.access( src );
+		udataCur = jQuery.extend( {}, udataOld );
+
+		dataUser.set( dest, udataCur );
+	}
+}
+
+// Fix IE bugs, see support tests
+function fixInput( src, dest ) {
+	var nodeName = dest.nodeName.toLowerCase();
+
+	// Fails to persist the checked state of a cloned checkbox or radio button.
+	if ( nodeName === "input" && rcheckableType.test( src.type ) ) {
+		dest.checked = src.checked;
+
+	// Fails to return the selected option to the default selected state when cloning options
+	} else if ( nodeName === "input" || nodeName === "textarea" ) {
+		dest.defaultValue = src.defaultValue;
+	}
+}
+
+function domManip( collection, args, callback, ignored ) {
+
+	// Flatten any nested arrays
+	args = flat( args );
+
+	var fragment, first, scripts, hasScripts, node, doc,
+		i = 0,
+		l = collection.length,
+		iNoClone = l - 1,
+		value = args[ 0 ],
+		valueIsFunction = isFunction( value );
+
+	// We can't cloneNode fragments that contain checked, in WebKit
+	if ( valueIsFunction ||
+			( l > 1 && typeof value === "string" &&
+				!support.checkClone && rchecked.test( value ) ) ) {
+		return collection.each( function( index ) {
+			var self = collection.eq( index );
+			if ( valueIsFunction ) {
+				args[ 0 ] = value.call( this, index, self.html() );
+			}
+			domManip( self, args, callback, ignored );
+		} );
+	}
+
+	if ( l ) {
+		fragment = buildFragment( args, collection[ 0 ].ownerDocument, false, collection, ignored );
+		first = fragment.firstChild;
+
+		if ( fragment.childNodes.length === 1 ) {
+			fragment = first;
+		}
+
+		// Require either new content or an interest in ignored elements to invoke the callback
+		if ( first || ignored ) {
+			scripts = jQuery.map( getAll( fragment, "script" ), disableScript );
+			hasScripts = scripts.length;
+
+			// Use the original fragment for the last item
+			// instead of the first because it can end up
+			// being emptied incorrectly in certain situations (#8070).
+			for ( ; i < l; i++ ) {
+				node = fragment;
+
+				if ( i !== iNoClone ) {
+					node = jQuery.clone( node, true, true );
+
+					// Keep references to cloned scripts for later restoration
+					if ( hasScripts ) {
+
+						// Support: Android <=4.0 only, PhantomJS 1 only
+						// push.apply(_, arraylike) throws on ancient WebKit
+						jQuery.merge( scripts, getAll( node, "script" ) );
+					}
+				}
+
+				callback.call( collection[ i ], node, i );
+			}
+
+			if ( hasScripts ) {
+				doc = scripts[ scripts.length - 1 ].ownerDocument;
+
+				// Reenable scripts
+				jQuery.map( scripts, restoreScript );
+
+				// Evaluate executable scripts on first document insertion
+				for ( i = 0; i < hasScripts; i++ ) {
+					node = scripts[ i ];
+					if ( rscriptType.test( node.type || "" ) &&
+						!dataPriv.access( node, "globalEval" ) &&
+						jQuery.contains( doc, node ) ) {
+
+						if ( node.src && ( node.type || "" ).toLowerCase()  !== "module" ) {
+
+							// Optional AJAX dependency, but won't run scripts if not present
+							if ( jQuery._evalUrl && !node.noModule ) {
+								jQuery._evalUrl( node.src, {
+									nonce: node.nonce || node.getAttribute( "nonce" )
+								}, doc );
+							}
+						} else {
+							DOMEval( node.textContent.replace( rcleanScript, "" ), node, doc );
+						}
+					}
+				}
+			}
+		}
+	}
+
+	return collection;
+}
+
+function remove( elem, selector, keepData ) {
+	var node,
+		nodes = selector ? jQuery.filter( selector, elem ) : elem,
+		i = 0;
+
+	for ( ; ( node = nodes[ i ] ) != null; i++ ) {
+		if ( !keepData && node.nodeType === 1 ) {
+			jQuery.cleanData( getAll( node ) );
+		}
+
+		if ( node.parentNode ) {
+			if ( keepData && isAttached( node ) ) {
+				setGlobalEval( getAll( node, "script" ) );
+			}
+			node.parentNode.removeChild( node );
+		}
+	}
+
+	return elem;
+}
+
+jQuery.extend( {
+	htmlPrefilter: function( html ) {
+		return html;
+	},
+
+	clone: function( elem, dataAndEvents, deepDataAndEvents ) {
+		var i, l, srcElements, destElements,
+			clone = elem.cloneNode( true ),
+			inPage = isAttached( elem );
+
+		// Fix IE cloning issues
+		if ( !support.noCloneChecked && ( elem.nodeType === 1 || elem.nodeType === 11 ) &&
+				!jQuery.isXMLDoc( elem ) ) {
+
+			// We eschew Sizzle here for performance reasons: https://jsperf.com/getall-vs-sizzle/2
+			destElements = getAll( clone );
+			srcElements = getAll( elem );
+
+			for ( i = 0, l = srcElements.length; i < l; i++ ) {
+				fixInput( srcElements[ i ], destElements[ i ] );
+			}
+		}
+
+		// Copy the events from the original to the clone
+		if ( dataAndEvents ) {
+			if ( deepDataAndEvents ) {
+				srcElements = srcElements || getAll( elem );
+				destElements = destElements || getAll( clone );
+
+				for ( i = 0, l = srcElements.length; i < l; i++ ) {
+					cloneCopyEvent( srcElements[ i ], destElements[ i ] );
+				}
+			} else {
+				cloneCopyEvent( elem, clone );
+			}
+		}
+
+		// Preserve script evaluation history
+		destElements = getAll( clone, "script" );
+		if ( destElements.length > 0 ) {
+			setGlobalEval( destElements, !inPage && getAll( elem, "script" ) );
+		}
+
+		// Return the cloned set
+		return clone;
+	},
+
+	cleanData: function( elems ) {
+		var data, elem, type,
+			special = jQuery.event.special,
+			i = 0;
+
+		for ( ; ( elem = elems[ i ] ) !== undefined; i++ ) {
+			if ( acceptData( elem ) ) {
+				if ( ( data = elem[ dataPriv.expando ] ) ) {
+					if ( data.events ) {
+						for ( type in data.events ) {
+							if ( special[ type ] ) {
+								jQuery.event.remove( elem, type );
+
+							// This is a shortcut to avoid jQuery.event.remove's overhead
+							} else {
+								jQuery.removeEvent( elem, type, data.handle );
+							}
+						}
+					}
+
+					// Support: Chrome <=35 - 45+
+					// Assign undefined instead of using delete, see Data#remove
+					elem[ dataPriv.expando ] = undefined;
+				}
+				if ( elem[ dataUser.expando ] ) {
+
+					// Support: Chrome <=35 - 45+
+					// Assign undefined instead of using delete, see Data#remove
+					elem[ dataUser.expando ] = undefined;
+				}
+			}
+		}
+	}
+} );
+
+jQuery.fn.extend( {
+	detach: function( selector ) {
+		return remove( this, selector, true );
+	},
+
+	remove: function( selector ) {
+		return remove( this, selector );
+	},
+
+	text: function( value ) {
+		return access( this, function( value ) {
+			return value === undefined ?
+				jQuery.text( this ) :
+				this.empty().each( function() {
+					if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {
+						this.textContent = value;
+					}
+				} );
+		}, null, value, arguments.length );
+	},
+
+	append: function() {
+		return domManip( this, arguments, function( elem ) {
+			if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {
+				var target = manipulationTarget( this, elem );
+				target.appendChild( elem );
+			}
+		} );
+	},
+
+	prepend: function() {
+		return domManip( this, arguments, function( elem ) {
+			if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {
+				var target = manipulationTarget( this, elem );
+				target.insertBefore( elem, target.firstChild );
+			}
+		} );
+	},
+
+	before: function() {
+		return domManip( this, arguments, function( elem ) {
+			if ( this.parentNode ) {
+				this.parentNode.insertBefore( elem, this );
+			}
+		} );
+	},
+
+	after: function() {
+		return domManip( this, arguments, function( elem ) {
+			if ( this.parentNode ) {
+				this.parentNode.insertBefore( elem, this.nextSibling );
+			}
+		} );
+	},
+
+	empty: function() {
+		var elem,
+			i = 0;
+
+		for ( ; ( elem = this[ i ] ) != null; i++ ) {
+			if ( elem.nodeType === 1 ) {
+
+				// Prevent memory leaks
+				jQuery.cleanData( getAll( elem, false ) );
+
+				// Remove any remaining nodes
+				elem.textContent = "";
+			}
+		}
+
+		return this;
+	},
+
+	clone: function( dataAndEvents, deepDataAndEvents ) {
+		dataAndEvents = dataAndEvents == null ? false : dataAndEvents;
+		deepDataAndEvents = deepDataAndEvents == null ? dataAndEvents : deepDataAndEvents;
+
+		return this.map( function() {
+			return jQuery.clone( this, dataAndEvents, deepDataAndEvents );
+		} );
+	},
+
+	html: function( value ) {
+		return access( this, function( value ) {
+			var elem = this[ 0 ] || {},
+				i = 0,
+				l = this.length;
+
+			if ( value === undefined && elem.nodeType === 1 ) {
+				return elem.innerHTML;
+			}
+
+			// See if we can take a shortcut and just use innerHTML
+			if ( typeof value === "string" && !rnoInnerhtml.test( value ) &&
+				!wrapMap[ ( rtagName.exec( value ) || [ "", "" ] )[ 1 ].toLowerCase() ] ) {
+
+				value = jQuery.htmlPrefilter( value );
+
+				try {
+					for ( ; i < l; i++ ) {
+						elem = this[ i ] || {};
+
+						// Remove element nodes and prevent memory leaks
+						if ( elem.nodeType === 1 ) {
+							jQuery.cleanData( getAll( elem, false ) );
+							elem.innerHTML = value;
+						}
+					}
+
+					elem = 0;
+
+				// If using innerHTML throws an exception, use the fallback method
+				} catch ( e ) {}
+			}
+
+			if ( elem ) {
+				this.empty().append( value );
+			}
+		}, null, value, arguments.length );
+	},
+
+	replaceWith: function() {
+		var ignored = [];
+
+		// Make the changes, replacing each non-ignored context element with the new content
+		return domManip( this, arguments, function( elem ) {
+			var parent = this.parentNode;
+
+			if ( jQuery.inArray( this, ignored ) < 0 ) {
+				jQuery.cleanData( getAll( this ) );
+				if ( parent ) {
+					parent.replaceChild( elem, this );
+				}
+			}
+
+		// Force callback invocation
+		}, ignored );
+	}
+} );
+
+jQuery.each( {
+	appendTo: "append",
+	prependTo: "prepend",
+	insertBefore: "before",
+	insertAfter: "after",
+	replaceAll: "replaceWith"
+}, function( name, original ) {
+	jQuery.fn[ name ] = function( selector ) {
+		var elems,
+			ret = [],
+			insert = jQuery( selector ),
+			last = insert.length - 1,
+			i = 0;
+
+		for ( ; i <= last; i++ ) {
+			elems = i === last ? this : this.clone( true );
+			jQuery( insert[ i ] )[ original ]( elems );
+
+			// Support: Android <=4.0 only, PhantomJS 1 only
+			// .get() because push.apply(_, arraylike) throws on ancient WebKit
+			push.apply( ret, elems.get() );
+		}
+
+		return this.pushStack( ret );
+	};
+} );
+var rnumnonpx = new RegExp( "^(" + pnum + ")(?!px)[a-z%]+$", "i" );
+
+var getStyles = function( elem ) {
+
+		// Support: IE <=11 only, Firefox <=30 (#15098, #14150)
+		// IE throws on elements created in popups
+		// FF meanwhile throws on frame elements through "defaultView.getComputedStyle"
+		var view = elem.ownerDocument.defaultView;
+
+		if ( !view || !view.opener ) {
+			view = window;
+		}
+
+		return view.getComputedStyle( elem );
+	};
+
+var swap = function( elem, options, callback ) {
+	var ret, name,
+		old = {};
+
+	// Remember the old values, and insert the new ones
+	for ( name in options ) {
+		old[ name ] = elem.style[ name ];
+		elem.style[ name ] = options[ name ];
+	}
+
+	ret = callback.call( elem );
+
+	// Revert the old values
+	for ( name in options ) {
+		elem.style[ name ] = old[ name ];
+	}
+
+	return ret;
+};
+
+
+var rboxStyle = new RegExp( cssExpand.join( "|" ), "i" );
+
+
+
+( function() {
+
+	// Executing both pixelPosition & boxSizingReliable tests require only one layout
+	// so they're executed at the same time to save the second computation.
+	function computeStyleTests() {
+
+		// This is a singleton, we need to execute it only once
+		if ( !div ) {
+			return;
+		}
+
+		container.style.cssText = "position:absolute;left:-11111px;width:60px;" +
+			"margin-top:1px;padding:0;border:0";
+		div.style.cssText =
+			"position:relative;display:block;box-sizing:border-box;overflow:scroll;" +
+			"margin:auto;border:1px;padding:1px;" +
+			"width:60%;top:1%";
+		documentElement.appendChild( container ).appendChild( div );
+
+		var divStyle = window.getComputedStyle( div );
+		pixelPositionVal = divStyle.top !== "1%";
+
+		// Support: Android 4.0 - 4.3 only, Firefox <=3 - 44
+		reliableMarginLeftVal = roundPixelMeasures( divStyle.marginLeft ) === 12;
+
+		// Support: Android 4.0 - 4.3 only, Safari <=9.1 - 10.1, iOS <=7.0 - 9.3
+		// Some styles come back with percentage values, even though they shouldn't
+		div.style.right = "60%";
+		pixelBoxStylesVal = roundPixelMeasures( divStyle.right ) === 36;
+
+		// Support: IE 9 - 11 only
+		// Detect misreporting of content dimensions for box-sizing:border-box elements
+		boxSizingReliableVal = roundPixelMeasures( divStyle.width ) === 36;
+
+		// Support: IE 9 only
+		// Detect overflow:scroll screwiness (gh-3699)
+		// Support: Chrome <=64
+		// Don't get tricked when zoom affects offsetWidth (gh-4029)
+		div.style.position = "absolute";
+		scrollboxSizeVal = roundPixelMeasures( div.offsetWidth / 3 ) === 12;
+
+		documentElement.removeChild( container );
+
+		// Nullify the div so it wouldn't be stored in the memory and
+		// it will also be a sign that checks already performed
+		div = null;
+	}
+
+	function roundPixelMeasures( measure ) {
+		return Math.round( parseFloat( measure ) );
+	}
+
+	var pixelPositionVal, boxSizingReliableVal, scrollboxSizeVal, pixelBoxStylesVal,
+		reliableTrDimensionsVal, reliableMarginLeftVal,
+		container = document.createElement( "div" ),
+		div = document.createElement( "div" );
+
+	// Finish early in limited (non-browser) environments
+	if ( !div.style ) {
+		return;
+	}
+
+	// Support: IE <=9 - 11 only
+	// Style of cloned element affects source element cloned (#8908)
+	div.style.backgroundClip = "content-box";
+	div.cloneNode( true ).style.backgroundClip = "";
+	support.clearCloneStyle = div.style.backgroundClip === "content-box";
+
+	jQuery.extend( support, {
+		boxSizingReliable: function() {
+			computeStyleTests();
+			return boxSizingReliableVal;
+		},
+		pixelBoxStyles: function() {
+			computeStyleTests();
+			return pixelBoxStylesVal;
+		},
+		pixelPosition: function() {
+			computeStyleTests();
+			return pixelPositionVal;
+		},
+		reliableMarginLeft: function() {
+			computeStyleTests();
+			return reliableMarginLeftVal;
+		},
+		scrollboxSize: function() {
+			computeStyleTests();
+			return scrollboxSizeVal;
+		},
+
+		// Support: IE 9 - 11+, Edge 15 - 18+
+		// IE/Edge misreport `getComputedStyle` of table rows with width/height
+		// set in CSS while `offset*` properties report correct values.
+		// Behavior in IE 9 is more subtle than in newer versions & it passes
+		// some versions of this test; make sure not to make it pass there!
+		reliableTrDimensions: function() {
+			var table, tr, trChild, trStyle;
+			if ( reliableTrDimensionsVal == null ) {
+				table = document.createElement( "table" );
+				tr = document.createElement( "tr" );
+				trChild = document.createElement( "div" );
+
+				table.style.cssText = "position:absolute;left:-11111px";
+				tr.style.height = "1px";
+				trChild.style.height = "9px";
+
+				documentElement
+					.appendChild( table )
+					.appendChild( tr )
+					.appendChild( trChild );
+
+				trStyle = window.getComputedStyle( tr );
+				reliableTrDimensionsVal = parseInt( trStyle.height ) > 3;
+
+				documentElement.removeChild( table );
+			}
+			return reliableTrDimensionsVal;
+		}
+	} );
+} )();
+
+
+function curCSS( elem, name, computed ) {
+	var width, minWidth, maxWidth, ret,
+
+		// Support: Firefox 51+
+		// Retrieving style before computed somehow
+		// fixes an issue with getting wrong values
+		// on detached elements
+		style = elem.style;
+
+	computed = computed || getStyles( elem );
+
+	// getPropertyValue is needed for:
+	//   .css('filter') (IE 9 only, #12537)
+	//   .css('--customProperty) (#3144)
+	if ( computed ) {
+		ret = computed.getPropertyValue( name ) || computed[ name ];
+
+		if ( ret === "" && !isAttached( elem ) ) {
+			ret = jQuery.style( elem, name );
+		}
+
+		// A tribute to the "awesome hack by Dean Edwards"
+		// Android Browser returns percentage for some values,
+		// but width seems to be reliably pixels.
+		// This is against the CSSOM draft spec:
+		// https://drafts.csswg.org/cssom/#resolved-values
+		if ( !support.pixelBoxStyles() && rnumnonpx.test( ret ) && rboxStyle.test( name ) ) {
+
+			// Remember the original values
+			width = style.width;
+			minWidth = style.minWidth;
+			maxWidth = style.maxWidth;
+
+			// Put in the new values to get a computed value out
+			style.minWidth = style.maxWidth = style.width = ret;
+			ret = computed.width;
+
+			// Revert the changed values
+			style.width = width;
+			style.minWidth = minWidth;
+			style.maxWidth = maxWidth;
+		}
+	}
+
+	return ret !== undefined ?
+
+		// Support: IE <=9 - 11 only
+		// IE returns zIndex value as an integer.
+		ret + "" :
+		ret;
+}
+
+
+function addGetHookIf( conditionFn, hookFn ) {
+
+	// Define the hook, we'll check on the first run if it's really needed.
+	return {
+		get: function() {
+			if ( conditionFn() ) {
+
+				// Hook not needed (or it's not possible to use it due
+				// to missing dependency), remove it.
+				delete this.get;
+				return;
+			}
+
+			// Hook needed; redefine it so that the support test is not executed again.
+			return ( this.get = hookFn ).apply( this, arguments );
+		}
+	};
+}
+
+
+var cssPrefixes = [ "Webkit", "Moz", "ms" ],
+	emptyStyle = document.createElement( "div" ).style,
+	vendorProps = {};
+
+// Return a vendor-prefixed property or undefined
+function vendorPropName( name ) {
+
+	// Check for vendor prefixed names
+	var capName = name[ 0 ].toUpperCase() + name.slice( 1 ),
+		i = cssPrefixes.length;
+
+	while ( i-- ) {
+		name = cssPrefixes[ i ] + capName;
+		if ( name in emptyStyle ) {
+			return name;
+		}
+	}
+}
+
+// Return a potentially-mapped jQuery.cssProps or vendor prefixed property
+function finalPropName( name ) {
+	var final = jQuery.cssProps[ name ] || vendorProps[ name ];
+
+	if ( final ) {
+		return final;
+	}
+	if ( name in emptyStyle ) {
+		return name;
+	}
+	return vendorProps[ name ] = vendorPropName( name ) || name;
+}
+
+
+var
+
+	// Swappable if display is none or starts with table
+	// except "table", "table-cell", or "table-caption"
+	// See here for display values: https://developer.mozilla.org/en-US/docs/CSS/display
+	rdisplayswap = /^(none|table(?!-c[ea]).+)/,
+	rcustomProp = /^--/,
+	cssShow = { position: "absolute", visibility: "hidden", display: "block" },
+	cssNormalTransform = {
+		letterSpacing: "0",
+		fontWeight: "400"
+	};
+
+function setPositiveNumber( _elem, value, subtract ) {
+
+	// Any relative (+/-) values have already been
+	// normalized at this point
+	var matches = rcssNum.exec( value );
+	return matches ?
+
+		// Guard against undefined "subtract", e.g., when used as in cssHooks
+		Math.max( 0, matches[ 2 ] - ( subtract || 0 ) ) + ( matches[ 3 ] || "px" ) :
+		value;
+}
+
+function boxModelAdjustment( elem, dimension, box, isBorderBox, styles, computedVal ) {
+	var i = dimension === "width" ? 1 : 0,
+		extra = 0,
+		delta = 0;
+
+	// Adjustment may not be necessary
+	if ( box === ( isBorderBox ? "border" : "content" ) ) {
+		return 0;
+	}
+
+	for ( ; i < 4; i += 2 ) {
+
+		// Both box models exclude margin
+		if ( box === "margin" ) {
+			delta += jQuery.css( elem, box + cssExpand[ i ], true, styles );
+		}
+
+		// If we get here with a content-box, we're seeking "padding" or "border" or "margin"
+		if ( !isBorderBox ) {
+
+			// Add padding
+			delta += jQuery.css( elem, "padding" + cssExpand[ i ], true, styles );
+
+			// For "border" or "margin", add border
+			if ( box !== "padding" ) {
+				delta += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles );
+
+			// But still keep track of it otherwise
+			} else {
+				extra += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles );
+			}
+
+		// If we get here with a border-box (content + padding + border), we're seeking "content" or
+		// "padding" or "margin"
+		} else {
+
+			// For "content", subtract padding
+			if ( box === "content" ) {
+				delta -= jQuery.css( elem, "padding" + cssExpand[ i ], true, styles );
+			}
+
+			// For "content" or "padding", subtract border
+			if ( box !== "margin" ) {
+				delta -= jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles );
+			}
+		}
+	}
+
+	// Account for positive content-box scroll gutter when requested by providing computedVal
+	if ( !isBorderBox && computedVal >= 0 ) {
+
+		// offsetWidth/offsetHeight is a rounded sum of content, padding, scroll gutter, and border
+		// Assuming integer scroll gutter, subtract the rest and round down
+		delta += Math.max( 0, Math.ceil(
+			elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] -
+			computedVal -
+			delta -
+			extra -
+			0.5
+
+		// If offsetWidth/offsetHeight is unknown, then we can't determine content-box scroll gutter
+		// Use an explicit zero to avoid NaN (gh-3964)
+		) ) || 0;
+	}
+
+	return delta;
+}
+
+function getWidthOrHeight( elem, dimension, extra ) {
+
+	// Start with computed style
+	var styles = getStyles( elem ),
+
+		// To avoid forcing a reflow, only fetch boxSizing if we need it (gh-4322).
+		// Fake content-box until we know it's needed to know the true value.
+		boxSizingNeeded = !support.boxSizingReliable() || extra,
+		isBorderBox = boxSizingNeeded &&
+			jQuery.css( elem, "boxSizing", false, styles ) === "border-box",
+		valueIsBorderBox = isBorderBox,
+
+		val = curCSS( elem, dimension, styles ),
+		offsetProp = "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 );
+
+	// Support: Firefox <=54
+	// Return a confounding non-pixel value or feign ignorance, as appropriate.
+	if ( rnumnonpx.test( val ) ) {
+		if ( !extra ) {
+			return val;
+		}
+		val = "auto";
+	}
+
+
+	// Support: IE 9 - 11 only
+	// Use offsetWidth/offsetHeight for when box sizing is unreliable.
+	// In those cases, the computed value can be trusted to be border-box.
+	if ( ( !support.boxSizingReliable() && isBorderBox ||
+
+		// Support: IE 10 - 11+, Edge 15 - 18+
+		// IE/Edge misreport `getComputedStyle` of table rows with width/height
+		// set in CSS while `offset*` properties report correct values.
+		// Interestingly, in some cases IE 9 doesn't suffer from this issue.
+		!support.reliableTrDimensions() && nodeName( elem, "tr" ) ||
+
+		// Fall back to offsetWidth/offsetHeight when value is "auto"
+		// This happens for inline elements with no explicit setting (gh-3571)
+		val === "auto" ||
+
+		// Support: Android <=4.1 - 4.3 only
+		// Also use offsetWidth/offsetHeight for misreported inline dimensions (gh-3602)
+		!parseFloat( val ) && jQuery.css( elem, "display", false, styles ) === "inline" ) &&
+
+		// Make sure the element is visible & connected
+		elem.getClientRects().length ) {
+
+		isBorderBox = jQuery.css( elem, "boxSizing", false, styles ) === "border-box";
+
+		// Where available, offsetWidth/offsetHeight approximate border box dimensions.
+		// Where not available (e.g., SVG), assume unreliable box-sizing and interpret the
+		// retrieved value as a content box dimension.
+		valueIsBorderBox = offsetProp in elem;
+		if ( valueIsBorderBox ) {
+			val = elem[ offsetProp ];
+		}
+	}
+
+	// Normalize "" and auto
+	val = parseFloat( val ) || 0;
+
+	// Adjust for the element's box model
+	return ( val +
+		boxModelAdjustment(
+			elem,
+			dimension,
+			extra || ( isBorderBox ? "border" : "content" ),
+			valueIsBorderBox,
+			styles,
+
+			// Provide the current computed size to request scroll gutter calculation (gh-3589)
+			val
+		)
+	) + "px";
+}
+
+jQuery.extend( {
+
+	// Add in style property hooks for overriding the default
+	// behavior of getting and setting a style property
+	cssHooks: {
+		opacity: {
+			get: function( elem, computed ) {
+				if ( computed ) {
+
+					// We should always get a number back from opacity
+					var ret = curCSS( elem, "opacity" );
+					return ret === "" ? "1" : ret;
+				}
+			}
+		}
+	},
+
+	// Don't automatically add "px" to these possibly-unitless properties
+	cssNumber: {
+		"animationIterationCount": true,
+		"columnCount": true,
+		"fillOpacity": true,
+		"flexGrow": true,
+		"flexShrink": true,
+		"fontWeight": true,
+		"gridArea": true,
+		"gridColumn": true,
+		"gridColumnEnd": true,
+		"gridColumnStart": true,
+		"gridRow": true,
+		"gridRowEnd": true,
+		"gridRowStart": true,
+		"lineHeight": true,
+		"opacity": true,
+		"order": true,
+		"orphans": true,
+		"widows": true,
+		"zIndex": true,
+		"zoom": true
+	},
+
+	// Add in properties whose names you wish to fix before
+	// setting or getting the value
+	cssProps: {},
+
+	// Get and set the style property on a DOM Node
+	style: function( elem, name, value, extra ) {
+
+		// Don't set styles on text and comment nodes
+		if ( !elem || elem.nodeType === 3 || elem.nodeType === 8 || !elem.style ) {
+			return;
+		}
+
+		// Make sure that we're working with the right name
+		var ret, type, hooks,
+			origName = camelCase( name ),
+			isCustomProp = rcustomProp.test( name ),
+			style = elem.style;
+
+		// Make sure that we're working with the right name. We don't
+		// want to query the value if it is a CSS custom property
+		// since they are user-defined.
+		if ( !isCustomProp ) {
+			name = finalPropName( origName );
+		}
+
+		// Gets hook for the prefixed version, then unprefixed version
+		hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ];
+
+		// Check if we're setting a value
+		if ( value !== undefined ) {
+			type = typeof value;
+
+			// Convert "+=" or "-=" to relative numbers (#7345)
+			if ( type === "string" && ( ret = rcssNum.exec( value ) ) && ret[ 1 ] ) {
+				value = adjustCSS( elem, name, ret );
+
+				// Fixes bug #9237
+				type = "number";
+			}
+
+			// Make sure that null and NaN values aren't set (#7116)
+			if ( value == null || value !== value ) {
+				return;
+			}
+
+			// If a number was passed in, add the unit (except for certain CSS properties)
+			// The isCustomProp check can be removed in jQuery 4.0 when we only auto-append
+			// "px" to a few hardcoded values.
+			if ( type === "number" && !isCustomProp ) {
+				value += ret && ret[ 3 ] || ( jQuery.cssNumber[ origName ] ? "" : "px" );
+			}
+
+			// background-* props affect original clone's values
+			if ( !support.clearCloneStyle && value === "" && name.indexOf( "background" ) === 0 ) {
+				style[ name ] = "inherit";
+			}
+
+			// If a hook was provided, use that value, otherwise just set the specified value
+			if ( !hooks || !( "set" in hooks ) ||
+				( value = hooks.set( elem, value, extra ) ) !== undefined ) {
+
+				if ( isCustomProp ) {
+					style.setProperty( name, value );
+				} else {
+					style[ name ] = value;
+				}
+			}
+
+		} else {
+
+			// If a hook was provided get the non-computed value from there
+			if ( hooks && "get" in hooks &&
+				( ret = hooks.get( elem, false, extra ) ) !== undefined ) {
+
+				return ret;
+			}
+
+			// Otherwise just get the value from the style object
+			return style[ name ];
+		}
+	},
+
+	css: function( elem, name, extra, styles ) {
+		var val, num, hooks,
+			origName = camelCase( name ),
+			isCustomProp = rcustomProp.test( name );
+
+		// Make sure that we're working with the right name. We don't
+		// want to modify the value if it is a CSS custom property
+		// since they are user-defined.
+		if ( !isCustomProp ) {
+			name = finalPropName( origName );
+		}
+
+		// Try prefixed name followed by the unprefixed name
+		hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ];
+
+		// If a hook was provided get the computed value from there
+		if ( hooks && "get" in hooks ) {
+			val = hooks.get( elem, true, extra );
+		}
+
+		// Otherwise, if a way to get the computed value exists, use that
+		if ( val === undefined ) {
+			val = curCSS( elem, name, styles );
+		}
+
+		// Convert "normal" to computed value
+		if ( val === "normal" && name in cssNormalTransform ) {
+			val = cssNormalTransform[ name ];
+		}
+
+		// Make numeric if forced or a qualifier was provided and val looks numeric
+		if ( extra === "" || extra ) {
+			num = parseFloat( val );
+			return extra === true || isFinite( num ) ? num || 0 : val;
+		}
+
+		return val;
+	}
+} );
+
+jQuery.each( [ "height", "width" ], function( _i, dimension ) {
+	jQuery.cssHooks[ dimension ] = {
+		get: function( elem, computed, extra ) {
+			if ( computed ) {
+
+				// Certain elements can have dimension info if we invisibly show them
+				// but it must have a current display style that would benefit
+				return rdisplayswap.test( jQuery.css( elem, "display" ) ) &&
+
+					// Support: Safari 8+
+					// Table columns in Safari have non-zero offsetWidth & zero
+					// getBoundingClientRect().width unless display is changed.
+					// Support: IE <=11 only
+					// Running getBoundingClientRect on a disconnected node
+					// in IE throws an error.
+					( !elem.getClientRects().length || !elem.getBoundingClientRect().width ) ?
+						swap( elem, cssShow, function() {
+							return getWidthOrHeight( elem, dimension, extra );
+						} ) :
+						getWidthOrHeight( elem, dimension, extra );
+			}
+		},
+
+		set: function( elem, value, extra ) {
+			var matches,
+				styles = getStyles( elem ),
+
+				// Only read styles.position if the test has a chance to fail
+				// to avoid forcing a reflow.
+				scrollboxSizeBuggy = !support.scrollboxSize() &&
+					styles.position === "absolute",
+
+				// To avoid forcing a reflow, only fetch boxSizing if we need it (gh-3991)
+				boxSizingNeeded = scrollboxSizeBuggy || extra,
+				isBorderBox = boxSizingNeeded &&
+					jQuery.css( elem, "boxSizing", false, styles ) === "border-box",
+				subtract = extra ?
+					boxModelAdjustment(
+						elem,
+						dimension,
+						extra,
+						isBorderBox,
+						styles
+					) :
+					0;
+
+			// Account for unreliable border-box dimensions by comparing offset* to computed and
+			// faking a content-box to get border and padding (gh-3699)
+			if ( isBorderBox && scrollboxSizeBuggy ) {
+				subtract -= Math.ceil(
+					elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] -
+					parseFloat( styles[ dimension ] ) -
+					boxModelAdjustment( elem, dimension, "border", false, styles ) -
+					0.5
+				);
+			}
+
+			// Convert to pixels if value adjustment is needed
+			if ( subtract && ( matches = rcssNum.exec( value ) ) &&
+				( matches[ 3 ] || "px" ) !== "px" ) {
+
+				elem.style[ dimension ] = value;
+				value = jQuery.css( elem, dimension );
+			}
+
+			return setPositiveNumber( elem, value, subtract );
+		}
+	};
+} );
+
+jQuery.cssHooks.marginLeft = addGetHookIf( support.reliableMarginLeft,
+	function( elem, computed ) {
+		if ( computed ) {
+			return ( parseFloat( curCSS( elem, "marginLeft" ) ) ||
+				elem.getBoundingClientRect().left -
+					swap( elem, { marginLeft: 0 }, function() {
+						return elem.getBoundingClientRect().left;
+					} )
+				) + "px";
+		}
+	}
+);
+
+// These hooks are used by animate to expand properties
+jQuery.each( {
+	margin: "",
+	padding: "",
+	border: "Width"
+}, function( prefix, suffix ) {
+	jQuery.cssHooks[ prefix + suffix ] = {
+		expand: function( value ) {
+			var i = 0,
+				expanded = {},
+
+				// Assumes a single number if not a string
+				parts = typeof value === "string" ? value.split( " " ) : [ value ];
+
+			for ( ; i < 4; i++ ) {
+				expanded[ prefix + cssExpand[ i ] + suffix ] =
+					parts[ i ] || parts[ i - 2 ] || parts[ 0 ];
+			}
+
+			return expanded;
+		}
+	};
+
+	if ( prefix !== "margin" ) {
+		jQuery.cssHooks[ prefix + suffix ].set = setPositiveNumber;
+	}
+} );
+
+jQuery.fn.extend( {
+	css: function( name, value ) {
+		return access( this, function( elem, name, value ) {
+			var styles, len,
+				map = {},
+				i = 0;
+
+			if ( Array.isArray( name ) ) {
+				styles = getStyles( elem );
+				len = name.length;
+
+				for ( ; i < len; i++ ) {
+					map[ name[ i ] ] = jQuery.css( elem, name[ i ], false, styles );
+				}
+
+				return map;
+			}
+
+			return value !== undefined ?
+				jQuery.style( elem, name, value ) :
+				jQuery.css( elem, name );
+		}, name, value, arguments.length > 1 );
+	}
+} );
+
+
+function Tween( elem, options, prop, end, easing ) {
+	return new Tween.prototype.init( elem, options, prop, end, easing );
+}
+jQuery.Tween = Tween;
+
+Tween.prototype = {
+	constructor: Tween,
+	init: function( elem, options, prop, end, easing, unit ) {
+		this.elem = elem;
+		this.prop = prop;
+		this.easing = easing || jQuery.easing._default;
+		this.options = options;
+		this.start = this.now = this.cur();
+		this.end = end;
+		this.unit = unit || ( jQuery.cssNumber[ prop ] ? "" : "px" );
+	},
+	cur: function() {
+		var hooks = Tween.propHooks[ this.prop ];
+
+		return hooks && hooks.get ?
+			hooks.get( this ) :
+			Tween.propHooks._default.get( this );
+	},
+	run: function( percent ) {
+		var eased,
+			hooks = Tween.propHooks[ this.prop ];
+
+		if ( this.options.duration ) {
+			this.pos = eased = jQuery.easing[ this.easing ](
+				percent, this.options.duration * percent, 0, 1, this.options.duration
+			);
+		} else {
+			this.pos = eased = percent;
+		}
+		this.now = ( this.end - this.start ) * eased + this.start;
+
+		if ( this.options.step ) {
+			this.options.step.call( this.elem, this.now, this );
+		}
+
+		if ( hooks && hooks.set ) {
+			hooks.set( this );
+		} else {
+			Tween.propHooks._default.set( this );
+		}
+		return this;
+	}
+};
+
+Tween.prototype.init.prototype = Tween.prototype;
+
+Tween.propHooks = {
+	_default: {
+		get: function( tween ) {
+			var result;
+
+			// Use a property on the element directly when it is not a DOM element,
+			// or when there is no matching style property that exists.
+			if ( tween.elem.nodeType !== 1 ||
+				tween.elem[ tween.prop ] != null && tween.elem.style[ tween.prop ] == null ) {
+				return tween.elem[ tween.prop ];
+			}
+
+			// Passing an empty string as a 3rd parameter to .css will automatically
+			// attempt a parseFloat and fallback to a string if the parse fails.
+			// Simple values such as "10px" are parsed to Float;
+			// complex values such as "rotate(1rad)" are returned as-is.
+			result = jQuery.css( tween.elem, tween.prop, "" );
+
+			// Empty strings, null, undefined and "auto" are converted to 0.
+			return !result || result === "auto" ? 0 : result;
+		},
+		set: function( tween ) {
+
+			// Use step hook for back compat.
+			// Use cssHook if its there.
+			// Use .style if available and use plain properties where available.
+			if ( jQuery.fx.step[ tween.prop ] ) {
+				jQuery.fx.step[ tween.prop ]( tween );
+			} else if ( tween.elem.nodeType === 1 && (
+					jQuery.cssHooks[ tween.prop ] ||
+					tween.elem.style[ finalPropName( tween.prop ) ] != null ) ) {
+				jQuery.style( tween.elem, tween.prop, tween.now + tween.unit );
+			} else {
+				tween.elem[ tween.prop ] = tween.now;
+			}
+		}
+	}
+};
+
+// Support: IE <=9 only
+// Panic based approach to setting things on disconnected nodes
+Tween.propHooks.scrollTop = Tween.propHooks.scrollLeft = {
+	set: function( tween ) {
+		if ( tween.elem.nodeType && tween.elem.parentNode ) {
+			tween.elem[ tween.prop ] = tween.now;
+		}
+	}
+};
+
+jQuery.easing = {
+	linear: function( p ) {
+		return p;
+	},
+	swing: function( p ) {
+		return 0.5 - Math.cos( p * Math.PI ) / 2;
+	},
+	_default: "swing"
+};
+
+jQuery.fx = Tween.prototype.init;
+
+// Back compat <1.8 extension point
+jQuery.fx.step = {};
+
+
+
+
+var
+	fxNow, inProgress,
+	rfxtypes = /^(?:toggle|show|hide)$/,
+	rrun = /queueHooks$/;
+
+function schedule() {
+	if ( inProgress ) {
+		if ( document.hidden === false && window.requestAnimationFrame ) {
+			window.requestAnimationFrame( schedule );
+		} else {
+			window.setTimeout( schedule, jQuery.fx.interval );
+		}
+
+		jQuery.fx.tick();
+	}
+}
+
+// Animations created synchronously will run synchronously
+function createFxNow() {
+	window.setTimeout( function() {
+		fxNow = undefined;
+	} );
+	return ( fxNow = Date.now() );
+}
+
+// Generate parameters to create a standard animation
+function genFx( type, includeWidth ) {
+	var which,
+		i = 0,
+		attrs = { height: type };
+
+	// If we include width, step value is 1 to do all cssExpand values,
+	// otherwise step value is 2 to skip over Left and Right
+	includeWidth = includeWidth ? 1 : 0;
+	for ( ; i < 4; i += 2 - includeWidth ) {
+		which = cssExpand[ i ];
+		attrs[ "margin" + which ] = attrs[ "padding" + which ] = type;
+	}
+
+	if ( includeWidth ) {
+		attrs.opacity = attrs.width = type;
+	}
+
+	return attrs;
+}
+
+function createTween( value, prop, animation ) {
+	var tween,
+		collection = ( Animation.tweeners[ prop ] || [] ).concat( Animation.tweeners[ "*" ] ),
+		index = 0,
+		length = collection.length;
+	for ( ; index < length; index++ ) {
+		if ( ( tween = collection[ index ].call( animation, prop, value ) ) ) {
+
+			// We're done with this property
+			return tween;
+		}
+	}
+}
+
+function defaultPrefilter( elem, props, opts ) {
+	var prop, value, toggle, hooks, oldfire, propTween, restoreDisplay, display,
+		isBox = "width" in props || "height" in props,
+		anim = this,
+		orig = {},
+		style = elem.style,
+		hidden = elem.nodeType && isHiddenWithinTree( elem ),
+		dataShow = dataPriv.get( elem, "fxshow" );
+
+	// Queue-skipping animations hijack the fx hooks
+	if ( !opts.queue ) {
+		hooks = jQuery._queueHooks( elem, "fx" );
+		if ( hooks.unqueued == null ) {
+			hooks.unqueued = 0;
+			oldfire = hooks.empty.fire;
+			hooks.empty.fire = function() {
+				if ( !hooks.unqueued ) {
+					oldfire();
+				}
+			};
+		}
+		hooks.unqueued++;
+
+		anim.always( function() {
+
+			// Ensure the complete handler is called before this completes
+			anim.always( function() {
+				hooks.unqueued--;
+				if ( !jQuery.queue( elem, "fx" ).length ) {
+					hooks.empty.fire();
+				}
+			} );
+		} );
+	}
+
+	// Detect show/hide animations
+	for ( prop in props ) {
+		value = props[ prop ];
+		if ( rfxtypes.test( value ) ) {
+			delete props[ prop ];
+			toggle = toggle || value === "toggle";
+			if ( value === ( hidden ? "hide" : "show" ) ) {
+
+				// Pretend to be hidden if this is a "show" and
+				// there is still data from a stopped show/hide
+				if ( value === "show" && dataShow && dataShow[ prop ] !== undefined ) {
+					hidden = true;
+
+				// Ignore all other no-op show/hide data
+				} else {
+					continue;
+				}
+			}
+			orig[ prop ] = dataShow && dataShow[ prop ] || jQuery.style( elem, prop );
+		}
+	}
+
+	// Bail out if this is a no-op like .hide().hide()
+	propTween = !jQuery.isEmptyObject( props );
+	if ( !propTween && jQuery.isEmptyObject( orig ) ) {
+		return;
+	}
+
+	// Restrict "overflow" and "display" styles during box animations
+	if ( isBox && elem.nodeType === 1 ) {
+
+		// Support: IE <=9 - 11, Edge 12 - 15
+		// Record all 3 overflow attributes because IE does not infer the shorthand
+		// from identically-valued overflowX and overflowY and Edge just mirrors
+		// the overflowX value there.
+		opts.overflow = [ style.overflow, style.overflowX, style.overflowY ];
+
+		// Identify a display type, preferring old show/hide data over the CSS cascade
+		restoreDisplay = dataShow && dataShow.display;
+		if ( restoreDisplay == null ) {
+			restoreDisplay = dataPriv.get( elem, "display" );
+		}
+		display = jQuery.css( elem, "display" );
+		if ( display === "none" ) {
+			if ( restoreDisplay ) {
+				display = restoreDisplay;
+			} else {
+
+				// Get nonempty value(s) by temporarily forcing visibility
+				showHide( [ elem ], true );
+				restoreDisplay = elem.style.display || restoreDisplay;
+				display = jQuery.css( elem, "display" );
+				showHide( [ elem ] );
+			}
+		}
+
+		// Animate inline elements as inline-block
+		if ( display === "inline" || display === "inline-block" && restoreDisplay != null ) {
+			if ( jQuery.css( elem, "float" ) === "none" ) {
+
+				// Restore the original display value at the end of pure show/hide animations
+				if ( !propTween ) {
+					anim.done( function() {
+						style.display = restoreDisplay;
+					} );
+					if ( restoreDisplay == null ) {
+						display = style.display;
+						restoreDisplay = display === "none" ? "" : display;
+					}
+				}
+				style.display = "inline-block";
+			}
+		}
+	}
+
+	if ( opts.overflow ) {
+		style.overflow = "hidden";
+		anim.always( function() {
+			style.overflow = opts.overflow[ 0 ];
+			style.overflowX = opts.overflow[ 1 ];
+			style.overflowY = opts.overflow[ 2 ];
+		} );
+	}
+
+	// Implement show/hide animations
+	propTween = false;
+	for ( prop in orig ) {
+
+		// General show/hide setup for this element animation
+		if ( !propTween ) {
+			if ( dataShow ) {
+				if ( "hidden" in dataShow ) {
+					hidden = dataShow.hidden;
+				}
+			} else {
+				dataShow = dataPriv.access( elem, "fxshow", { display: restoreDisplay } );
+			}
+
+			// Store hidden/visible for toggle so `.stop().toggle()` "reverses"
+			if ( toggle ) {
+				dataShow.hidden = !hidden;
+			}
+
+			// Show elements before animating them
+			if ( hidden ) {
+				showHide( [ elem ], true );
+			}
+
+			/* eslint-disable no-loop-func */
+
+			anim.done( function() {
+
+			/* eslint-enable no-loop-func */
+
+				// The final step of a "hide" animation is actually hiding the element
+				if ( !hidden ) {
+					showHide( [ elem ] );
+				}
+				dataPriv.remove( elem, "fxshow" );
+				for ( prop in orig ) {
+					jQuery.style( elem, prop, orig[ prop ] );
+				}
+			} );
+		}
+
+		// Per-property setup
+		propTween = createTween( hidden ? dataShow[ prop ] : 0, prop, anim );
+		if ( !( prop in dataShow ) ) {
+			dataShow[ prop ] = propTween.start;
+			if ( hidden ) {
+				propTween.end = propTween.start;
+				propTween.start = 0;
+			}
+		}
+	}
+}
+
+function propFilter( props, specialEasing ) {
+	var index, name, easing, value, hooks;
+
+	// camelCase, specialEasing and expand cssHook pass
+	for ( index in props ) {
+		name = camelCase( index );
+		easing = specialEasing[ name ];
+		value = props[ index ];
+		if ( Array.isArray( value ) ) {
+			easing = value[ 1 ];
+			value = props[ index ] = value[ 0 ];
+		}
+
+		if ( index !== name ) {
+			props[ name ] = value;
+			delete props[ index ];
+		}
+
+		hooks = jQuery.cssHooks[ name ];
+		if ( hooks && "expand" in hooks ) {
+			value = hooks.expand( value );
+			delete props[ name ];
+
+			// Not quite $.extend, this won't overwrite existing keys.
+			// Reusing 'index' because we have the correct "name"
+			for ( index in value ) {
+				if ( !( index in props ) ) {
+					props[ index ] = value[ index ];
+					specialEasing[ index ] = easing;
+				}
+			}
+		} else {
+			specialEasing[ name ] = easing;
+		}
+	}
+}
+
+function Animation( elem, properties, options ) {
+	var result,
+		stopped,
+		index = 0,
+		length = Animation.prefilters.length,
+		deferred = jQuery.Deferred().always( function() {
+
+			// Don't match elem in the :animated selector
+			delete tick.elem;
+		} ),
+		tick = function() {
+			if ( stopped ) {
+				return false;
+			}
+			var currentTime = fxNow || createFxNow(),
+				remaining = Math.max( 0, animation.startTime + animation.duration - currentTime ),
+
+				// Support: Android 2.3 only
+				// Archaic crash bug won't allow us to use `1 - ( 0.5 || 0 )` (#12497)
+				temp = remaining / animation.duration || 0,
+				percent = 1 - temp,
+				index = 0,
+				length = animation.tweens.length;
+
+			for ( ; index < length; index++ ) {
+				animation.tweens[ index ].run( percent );
+			}
+
+			deferred.notifyWith( elem, [ animation, percent, remaining ] );
+
+			// If there's more to do, yield
+			if ( percent < 1 && length ) {
+				return remaining;
+			}
+
+			// If this was an empty animation, synthesize a final progress notification
+			if ( !length ) {
+				deferred.notifyWith( elem, [ animation, 1, 0 ] );
+			}
+
+			// Resolve the animation and report its conclusion
+			deferred.resolveWith( elem, [ animation ] );
+			return false;
+		},
+		animation = deferred.promise( {
+			elem: elem,
+			props: jQuery.extend( {}, properties ),
+			opts: jQuery.extend( true, {
+				specialEasing: {},
+				easing: jQuery.easing._default
+			}, options ),
+			originalProperties: properties,
+			originalOptions: options,
+			startTime: fxNow || createFxNow(),
+			duration: options.duration,
+			tweens: [],
+			createTween: function( prop, end ) {
+				var tween = jQuery.Tween( elem, animation.opts, prop, end,
+						animation.opts.specialEasing[ prop ] || animation.opts.easing );
+				animation.tweens.push( tween );
+				return tween;
+			},
+			stop: function( gotoEnd ) {
+				var index = 0,
+
+					// If we are going to the end, we want to run all the tweens
+					// otherwise we skip this part
+					length = gotoEnd ? animation.tweens.length : 0;
+				if ( stopped ) {
+					return this;
+				}
+				stopped = true;
+				for ( ; index < length; index++ ) {
+					animation.tweens[ index ].run( 1 );
+				}
+
+				// Resolve when we played the last frame; otherwise, reject
+				if ( gotoEnd ) {
+					deferred.notifyWith( elem, [ animation, 1, 0 ] );
+					deferred.resolveWith( elem, [ animation, gotoEnd ] );
+				} else {
+					deferred.rejectWith( elem, [ animation, gotoEnd ] );
+				}
+				return this;
+			}
+		} ),
+		props = animation.props;
+
+	propFilter( props, animation.opts.specialEasing );
+
+	for ( ; index < length; index++ ) {
+		result = Animation.prefilters[ index ].call( animation, elem, props, animation.opts );
+		if ( result ) {
+			if ( isFunction( result.stop ) ) {
+				jQuery._queueHooks( animation.elem, animation.opts.queue ).stop =
+					result.stop.bind( result );
+			}
+			return result;
+		}
+	}
+
+	jQuery.map( props, createTween, animation );
+
+	if ( isFunction( animation.opts.start ) ) {
+		animation.opts.start.call( elem, animation );
+	}
+
+	// Attach callbacks from options
+	animation
+		.progress( animation.opts.progress )
+		.done( animation.opts.done, animation.opts.complete )
+		.fail( animation.opts.fail )
+		.always( animation.opts.always );
+
+	jQuery.fx.timer(
+		jQuery.extend( tick, {
+			elem: elem,
+			anim: animation,
+			queue: animation.opts.queue
+		} )
+	);
+
+	return animation;
+}
+
+jQuery.Animation = jQuery.extend( Animation, {
+
+	tweeners: {
+		"*": [ function( prop, value ) {
+			var tween = this.createTween( prop, value );
+			adjustCSS( tween.elem, prop, rcssNum.exec( value ), tween );
+			return tween;
+		} ]
+	},
+
+	tweener: function( props, callback ) {
+		if ( isFunction( props ) ) {
+			callback = props;
+			props = [ "*" ];
+		} else {
+			props = props.match( rnothtmlwhite );
+		}
+
+		var prop,
+			index = 0,
+			length = props.length;
+
+		for ( ; index < length; index++ ) {
+			prop = props[ index ];
+			Animation.tweeners[ prop ] = Animation.tweeners[ prop ] || [];
+			Animation.tweeners[ prop ].unshift( callback );
+		}
+	},
+
+	prefilters: [ defaultPrefilter ],
+
+	prefilter: function( callback, prepend ) {
+		if ( prepend ) {
+			Animation.prefilters.unshift( callback );
+		} else {
+			Animation.prefilters.push( callback );
+		}
+	}
+} );
+
+jQuery.speed = function( speed, easing, fn ) {
+	var opt = speed && typeof speed === "object" ? jQuery.extend( {}, speed ) : {
+		complete: fn || !fn && easing ||
+			isFunction( speed ) && speed,
+		duration: speed,
+		easing: fn && easing || easing && !isFunction( easing ) && easing
+	};
+
+	// Go to the end state if fx are off
+	if ( jQuery.fx.off ) {
+		opt.duration = 0;
+
+	} else {
+		if ( typeof opt.duration !== "number" ) {
+			if ( opt.duration in jQuery.fx.speeds ) {
+				opt.duration = jQuery.fx.speeds[ opt.duration ];
+
+			} else {
+				opt.duration = jQuery.fx.speeds._default;
+			}
+		}
+	}
+
+	// Normalize opt.queue - true/undefined/null -> "fx"
+	if ( opt.queue == null || opt.queue === true ) {
+		opt.queue = "fx";
+	}
+
+	// Queueing
+	opt.old = opt.complete;
+
+	opt.complete = function() {
+		if ( isFunction( opt.old ) ) {
+			opt.old.call( this );
+		}
+
+		if ( opt.queue ) {
+			jQuery.dequeue( this, opt.queue );
+		}
+	};
+
+	return opt;
+};
+
+jQuery.fn.extend( {
+	fadeTo: function( speed, to, easing, callback ) {
+
+		// Show any hidden elements after setting opacity to 0
+		return this.filter( isHiddenWithinTree ).css( "opacity", 0 ).show()
+
+			// Animate to the value specified
+			.end().animate( { opacity: to }, speed, easing, callback );
+	},
+	animate: function( prop, speed, easing, callback ) {
+		var empty = jQuery.isEmptyObject( prop ),
+			optall = jQuery.speed( speed, easing, callback ),
+			doAnimation = function() {
+
+				// Operate on a copy of prop so per-property easing won't be lost
+				var anim = Animation( this, jQuery.extend( {}, prop ), optall );
+
+				// Empty animations, or finishing resolves immediately
+				if ( empty || dataPriv.get( this, "finish" ) ) {
+					anim.stop( true );
+				}
+			};
+			doAnimation.finish = doAnimation;
+
+		return empty || optall.queue === false ?
+			this.each( doAnimation ) :
+			this.queue( optall.queue, doAnimation );
+	},
+	stop: function( type, clearQueue, gotoEnd ) {
+		var stopQueue = function( hooks ) {
+			var stop = hooks.stop;
+			delete hooks.stop;
+			stop( gotoEnd );
+		};
+
+		if ( typeof type !== "string" ) {
+			gotoEnd = clearQueue;
+			clearQueue = type;
+			type = undefined;
+		}
+		if ( clearQueue ) {
+			this.queue( type || "fx", [] );
+		}
+
+		return this.each( function() {
+			var dequeue = true,
+				index = type != null && type + "queueHooks",
+				timers = jQuery.timers,
+				data = dataPriv.get( this );
+
+			if ( index ) {
+				if ( data[ index ] && data[ index ].stop ) {
+					stopQueue( data[ index ] );
+				}
+			} else {
+				for ( index in data ) {
+					if ( data[ index ] && data[ index ].stop && rrun.test( index ) ) {
+						stopQueue( data[ index ] );
+					}
+				}
+			}
+
+			for ( index = timers.length; index--; ) {
+				if ( timers[ index ].elem === this &&
+					( type == null || timers[ index ].queue === type ) ) {
+
+					timers[ index ].anim.stop( gotoEnd );
+					dequeue = false;
+					timers.splice( index, 1 );
+				}
+			}
+
+			// Start the next in the queue if the last step wasn't forced.
+			// Timers currently will call their complete callbacks, which
+			// will dequeue but only if they were gotoEnd.
+			if ( dequeue || !gotoEnd ) {
+				jQuery.dequeue( this, type );
+			}
+		} );
+	},
+	finish: function( type ) {
+		if ( type !== false ) {
+			type = type || "fx";
+		}
+		return this.each( function() {
+			var index,
+				data = dataPriv.get( this ),
+				queue = data[ type + "queue" ],
+				hooks = data[ type + "queueHooks" ],
+				timers = jQuery.timers,
+				length = queue ? queue.length : 0;
+
+			// Enable finishing flag on private data
+			data.finish = true;
+
+			// Empty the queue first
+			jQuery.queue( this, type, [] );
+
+			if ( hooks && hooks.stop ) {
+				hooks.stop.call( this, true );
+			}
+
+			// Look for any active animations, and finish them
+			for ( index = timers.length; index--; ) {
+				if ( timers[ index ].elem === this && timers[ index ].queue === type ) {
+					timers[ index ].anim.stop( true );
+					timers.splice( index, 1 );
+				}
+			}
+
+			// Look for any animations in the old queue and finish them
+			for ( index = 0; index < length; index++ ) {
+				if ( queue[ index ] && queue[ index ].finish ) {
+					queue[ index ].finish.call( this );
+				}
+			}
+
+			// Turn off finishing flag
+			delete data.finish;
+		} );
+	}
+} );
+
+jQuery.each( [ "toggle", "show", "hide" ], function( _i, name ) {
+	var cssFn = jQuery.fn[ name ];
+	jQuery.fn[ name ] = function( speed, easing, callback ) {
+		return speed == null || typeof speed === "boolean" ?
+			cssFn.apply( this, arguments ) :
+			this.animate( genFx( name, true ), speed, easing, callback );
+	};
+} );
+
+// Generate shortcuts for custom animations
+jQuery.each( {
+	slideDown: genFx( "show" ),
+	slideUp: genFx( "hide" ),
+	slideToggle: genFx( "toggle" ),
+	fadeIn: { opacity: "show" },
+	fadeOut: { opacity: "hide" },
+	fadeToggle: { opacity: "toggle" }
+}, function( name, props ) {
+	jQuery.fn[ name ] = function( speed, easing, callback ) {
+		return this.animate( props, speed, easing, callback );
+	};
+} );
+
+jQuery.timers = [];
+jQuery.fx.tick = function() {
+	var timer,
+		i = 0,
+		timers = jQuery.timers;
+
+	fxNow = Date.now();
+
+	for ( ; i < timers.length; i++ ) {
+		timer = timers[ i ];
+
+		// Run the timer and safely remove it when done (allowing for external removal)
+		if ( !timer() && timers[ i ] === timer ) {
+			timers.splice( i--, 1 );
+		}
+	}
+
+	if ( !timers.length ) {
+		jQuery.fx.stop();
+	}
+	fxNow = undefined;
+};
+
+jQuery.fx.timer = function( timer ) {
+	jQuery.timers.push( timer );
+	jQuery.fx.start();
+};
+
+jQuery.fx.interval = 13;
+jQuery.fx.start = function() {
+	if ( inProgress ) {
+		return;
+	}
+
+	inProgress = true;
+	schedule();
+};
+
+jQuery.fx.stop = function() {
+	inProgress = null;
+};
+
+jQuery.fx.speeds = {
+	slow: 600,
+	fast: 200,
+
+	// Default speed
+	_default: 400
+};
+
+
+// Based off of the plugin by Clint Helfers, with permission.
+// https://web.archive.org/web/20100324014747/http://blindsignals.com/index.php/2009/07/jquery-delay/
+jQuery.fn.delay = function( time, type ) {
+	time = jQuery.fx ? jQuery.fx.speeds[ time ] || time : time;
+	type = type || "fx";
+
+	return this.queue( type, function( next, hooks ) {
+		var timeout = window.setTimeout( next, time );
+		hooks.stop = function() {
+			window.clearTimeout( timeout );
+		};
+	} );
+};
+
+
+( function() {
+	var input = document.createElement( "input" ),
+		select = document.createElement( "select" ),
+		opt = select.appendChild( document.createElement( "option" ) );
+
+	input.type = "checkbox";
+
+	// Support: Android <=4.3 only
+	// Default value for a checkbox should be "on"
+	support.checkOn = input.value !== "";
+
+	// Support: IE <=11 only
+	// Must access selectedIndex to make default options select
+	support.optSelected = opt.selected;
+
+	// Support: IE <=11 only
+	// An input loses its value after becoming a radio
+	input = document.createElement( "input" );
+	input.value = "t";
+	input.type = "radio";
+	support.radioValue = input.value === "t";
+} )();
+
+
+var boolHook,
+	attrHandle = jQuery.expr.attrHandle;
+
+jQuery.fn.extend( {
+	attr: function( name, value ) {
+		return access( this, jQuery.attr, name, value, arguments.length > 1 );
+	},
+
+	removeAttr: function( name ) {
+		return this.each( function() {
+			jQuery.removeAttr( this, name );
+		} );
+	}
+} );
+
+jQuery.extend( {
+	attr: function( elem, name, value ) {
+		var ret, hooks,
+			nType = elem.nodeType;
+
+		// Don't get/set attributes on text, comment and attribute nodes
+		if ( nType === 3 || nType === 8 || nType === 2 ) {
+			return;
+		}
+
+		// Fallback to prop when attributes are not supported
+		if ( typeof elem.getAttribute === "undefined" ) {
+			return jQuery.prop( elem, name, value );
+		}
+
+		// Attribute hooks are determined by the lowercase version
+		// Grab necessary hook if one is defined
+		if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) {
+			hooks = jQuery.attrHooks[ name.toLowerCase() ] ||
+				( jQuery.expr.match.bool.test( name ) ? boolHook : undefined );
+		}
+
+		if ( value !== undefined ) {
+			if ( value === null ) {
+				jQuery.removeAttr( elem, name );
+				return;
+			}
+
+			if ( hooks && "set" in hooks &&
+				( ret = hooks.set( elem, value, name ) ) !== undefined ) {
+				return ret;
+			}
+
+			elem.setAttribute( name, value + "" );
+			return value;
+		}
+
+		if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) {
+			return ret;
+		}
+
+		ret = jQuery.find.attr( elem, name );
+
+		// Non-existent attributes return null, we normalize to undefined
+		return ret == null ? undefined : ret;
+	},
+
+	attrHooks: {
+		type: {
+			set: function( elem, value ) {
+				if ( !support.radioValue && value === "radio" &&
+					nodeName( elem, "input" ) ) {
+					var val = elem.value;
+					elem.setAttribute( "type", value );
+					if ( val ) {
+						elem.value = val;
+					}
+					return value;
+				}
+			}
+		}
+	},
+
+	removeAttr: function( elem, value ) {
+		var name,
+			i = 0,
+
+			// Attribute names can contain non-HTML whitespace characters
+			// https://html.spec.whatwg.org/multipage/syntax.html#attributes-2
+			attrNames = value && value.match( rnothtmlwhite );
+
+		if ( attrNames && elem.nodeType === 1 ) {
+			while ( ( name = attrNames[ i++ ] ) ) {
+				elem.removeAttribute( name );
+			}
+		}
+	}
+} );
+
+// Hooks for boolean attributes
+boolHook = {
+	set: function( elem, value, name ) {
+		if ( value === false ) {
+
+			// Remove boolean attributes when set to false
+			jQuery.removeAttr( elem, name );
+		} else {
+			elem.setAttribute( name, name );
+		}
+		return name;
+	}
+};
+
+jQuery.each( jQuery.expr.match.bool.source.match( /\w+/g ), function( _i, name ) {
+	var getter = attrHandle[ name ] || jQuery.find.attr;
+
+	attrHandle[ name ] = function( elem, name, isXML ) {
+		var ret, handle,
+			lowercaseName = name.toLowerCase();
+
+		if ( !isXML ) {
+
+			// Avoid an infinite loop by temporarily removing this function from the getter
+			handle = attrHandle[ lowercaseName ];
+			attrHandle[ lowercaseName ] = ret;
+			ret = getter( elem, name, isXML ) != null ?
+				lowercaseName :
+				null;
+			attrHandle[ lowercaseName ] = handle;
+		}
+		return ret;
+	};
+} );
+
+
+
+
+var rfocusable = /^(?:input|select|textarea|button)$/i,
+	rclickable = /^(?:a|area)$/i;
+
+jQuery.fn.extend( {
+	prop: function( name, value ) {
+		return access( this, jQuery.prop, name, value, arguments.length > 1 );
+	},
+
+	removeProp: function( name ) {
+		return this.each( function() {
+			delete this[ jQuery.propFix[ name ] || name ];
+		} );
+	}
+} );
+
+jQuery.extend( {
+	prop: function( elem, name, value ) {
+		var ret, hooks,
+			nType = elem.nodeType;
+
+		// Don't get/set properties on text, comment and attribute nodes
+		if ( nType === 3 || nType === 8 || nType === 2 ) {
+			return;
+		}
+
+		if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) {
+
+			// Fix name and attach hooks
+			name = jQuery.propFix[ name ] || name;
+			hooks = jQuery.propHooks[ name ];
+		}
+
+		if ( value !== undefined ) {
+			if ( hooks && "set" in hooks &&
+				( ret = hooks.set( elem, value, name ) ) !== undefined ) {
+				return ret;
+			}
+
+			return ( elem[ name ] = value );
+		}
+
+		if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) {
+			return ret;
+		}
+
+		return elem[ name ];
+	},
+
+	propHooks: {
+		tabIndex: {
+			get: function( elem ) {
+
+				// Support: IE <=9 - 11 only
+				// elem.tabIndex doesn't always return the
+				// correct value when it hasn't been explicitly set
+				// https://web.archive.org/web/20141116233347/http://fluidproject.org/blog/2008/01/09/getting-setting-and-removing-tabindex-values-with-javascript/
+				// Use proper attribute retrieval(#12072)
+				var tabindex = jQuery.find.attr( elem, "tabindex" );
+
+				if ( tabindex ) {
+					return parseInt( tabindex, 10 );
+				}
+
+				if (
+					rfocusable.test( elem.nodeName ) ||
+					rclickable.test( elem.nodeName ) &&
+					elem.href
+				) {
+					return 0;
+				}
+
+				return -1;
+			}
+		}
+	},
+
+	propFix: {
+		"for": "htmlFor",
+		"class": "className"
+	}
+} );
+
+// Support: IE <=11 only
+// Accessing the selectedIndex property
+// forces the browser to respect setting selected
+// on the option
+// The getter ensures a default option is selected
+// when in an optgroup
+// eslint rule "no-unused-expressions" is disabled for this code
+// since it considers such accessions noop
+if ( !support.optSelected ) {
+	jQuery.propHooks.selected = {
+		get: function( elem ) {
+
+			/* eslint no-unused-expressions: "off" */
+
+			var parent = elem.parentNode;
+			if ( parent && parent.parentNode ) {
+				parent.parentNode.selectedIndex;
+			}
+			return null;
+		},
+		set: function( elem ) {
+
+			/* eslint no-unused-expressions: "off" */
+
+			var parent = elem.parentNode;
+			if ( parent ) {
+				parent.selectedIndex;
+
+				if ( parent.parentNode ) {
+					parent.parentNode.selectedIndex;
+				}
+			}
+		}
+	};
+}
+
+jQuery.each( [
+	"tabIndex",
+	"readOnly",
+	"maxLength",
+	"cellSpacing",
+	"cellPadding",
+	"rowSpan",
+	"colSpan",
+	"useMap",
+	"frameBorder",
+	"contentEditable"
+], function() {
+	jQuery.propFix[ this.toLowerCase() ] = this;
+} );
+
+
+
+
+	// Strip and collapse whitespace according to HTML spec
+	// https://infra.spec.whatwg.org/#strip-and-collapse-ascii-whitespace
+	function stripAndCollapse( value ) {
+		var tokens = value.match( rnothtmlwhite ) || [];
+		return tokens.join( " " );
+	}
+
+
+function getClass( elem ) {
+	return elem.getAttribute && elem.getAttribute( "class" ) || "";
+}
+
+function classesToArray( value ) {
+	if ( Array.isArray( value ) ) {
+		return value;
+	}
+	if ( typeof value === "string" ) {
+		return value.match( rnothtmlwhite ) || [];
+	}
+	return [];
+}
+
+jQuery.fn.extend( {
+	addClass: function( value ) {
+		var classes, elem, cur, curValue, clazz, j, finalValue,
+			i = 0;
+
+		if ( isFunction( value ) ) {
+			return this.each( function( j ) {
+				jQuery( this ).addClass( value.call( this, j, getClass( this ) ) );
+			} );
+		}
+
+		classes = classesToArray( value );
+
+		if ( classes.length ) {
+			while ( ( elem = this[ i++ ] ) ) {
+				curValue = getClass( elem );
+				cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " );
+
+				if ( cur ) {
+					j = 0;
+					while ( ( clazz = classes[ j++ ] ) ) {
+						if ( cur.indexOf( " " + clazz + " " ) < 0 ) {
+							cur += clazz + " ";
+						}
+					}
+
+					// Only assign if different to avoid unneeded rendering.
+					finalValue = stripAndCollapse( cur );
+					if ( curValue !== finalValue ) {
+						elem.setAttribute( "class", finalValue );
+					}
+				}
+			}
+		}
+
+		return this;
+	},
+
+	removeClass: function( value ) {
+		var classes, elem, cur, curValue, clazz, j, finalValue,
+			i = 0;
+
+		if ( isFunction( value ) ) {
+			return this.each( function( j ) {
+				jQuery( this ).removeClass( value.call( this, j, getClass( this ) ) );
+			} );
+		}
+
+		if ( !arguments.length ) {
+			return this.attr( "class", "" );
+		}
+
+		classes = classesToArray( value );
+
+		if ( classes.length ) {
+			while ( ( elem = this[ i++ ] ) ) {
+				curValue = getClass( elem );
+
+				// This expression is here for better compressibility (see addClass)
+				cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " );
+
+				if ( cur ) {
+					j = 0;
+					while ( ( clazz = classes[ j++ ] ) ) {
+
+						// Remove *all* instances
+						while ( cur.indexOf( " " + clazz + " " ) > -1 ) {
+							cur = cur.replace( " " + clazz + " ", " " );
+						}
+					}
+
+					// Only assign if different to avoid unneeded rendering.
+					finalValue = stripAndCollapse( cur );
+					if ( curValue !== finalValue ) {
+						elem.setAttribute( "class", finalValue );
+					}
+				}
+			}
+		}
+
+		return this;
+	},
+
+	toggleClass: function( value, stateVal ) {
+		var type = typeof value,
+			isValidValue = type === "string" || Array.isArray( value );
+
+		if ( typeof stateVal === "boolean" && isValidValue ) {
+			return stateVal ? this.addClass( value ) : this.removeClass( value );
+		}
+
+		if ( isFunction( value ) ) {
+			return this.each( function( i ) {
+				jQuery( this ).toggleClass(
+					value.call( this, i, getClass( this ), stateVal ),
+					stateVal
+				);
+			} );
+		}
+
+		return this.each( function() {
+			var className, i, self, classNames;
+
+			if ( isValidValue ) {
+
+				// Toggle individual class names
+				i = 0;
+				self = jQuery( this );
+				classNames = classesToArray( value );
+
+				while ( ( className = classNames[ i++ ] ) ) {
+
+					// Check each className given, space separated list
+					if ( self.hasClass( className ) ) {
+						self.removeClass( className );
+					} else {
+						self.addClass( className );
+					}
+				}
+
+			// Toggle whole class name
+			} else if ( value === undefined || type === "boolean" ) {
+				className = getClass( this );
+				if ( className ) {
+
+					// Store className if set
+					dataPriv.set( this, "__className__", className );
+				}
+
+				// If the element has a class name or if we're passed `false`,
+				// then remove the whole classname (if there was one, the above saved it).
+				// Otherwise bring back whatever was previously saved (if anything),
+				// falling back to the empty string if nothing was stored.
+				if ( this.setAttribute ) {
+					this.setAttribute( "class",
+						className || value === false ?
+						"" :
+						dataPriv.get( this, "__className__" ) || ""
+					);
+				}
+			}
+		} );
+	},
+
+	hasClass: function( selector ) {
+		var className, elem,
+			i = 0;
+
+		className = " " + selector + " ";
+		while ( ( elem = this[ i++ ] ) ) {
+			if ( elem.nodeType === 1 &&
+				( " " + stripAndCollapse( getClass( elem ) ) + " " ).indexOf( className ) > -1 ) {
+					return true;
+			}
+		}
+
+		return false;
+	}
+} );
+
+
+
+
+var rreturn = /\r/g;
+
+jQuery.fn.extend( {
+	val: function( value ) {
+		var hooks, ret, valueIsFunction,
+			elem = this[ 0 ];
+
+		if ( !arguments.length ) {
+			if ( elem ) {
+				hooks = jQuery.valHooks[ elem.type ] ||
+					jQuery.valHooks[ elem.nodeName.toLowerCase() ];
+
+				if ( hooks &&
+					"get" in hooks &&
+					( ret = hooks.get( elem, "value" ) ) !== undefined
+				) {
+					return ret;
+				}
+
+				ret = elem.value;
+
+				// Handle most common string cases
+				if ( typeof ret === "string" ) {
+					return ret.replace( rreturn, "" );
+				}
+
+				// Handle cases where value is null/undef or number
+				return ret == null ? "" : ret;
+			}
+
+			return;
+		}
+
+		valueIsFunction = isFunction( value );
+
+		return this.each( function( i ) {
+			var val;
+
+			if ( this.nodeType !== 1 ) {
+				return;
+			}
+
+			if ( valueIsFunction ) {
+				val = value.call( this, i, jQuery( this ).val() );
+			} else {
+				val = value;
+			}
+
+			// Treat null/undefined as ""; convert numbers to string
+			if ( val == null ) {
+				val = "";
+
+			} else if ( typeof val === "number" ) {
+				val += "";
+
+			} else if ( Array.isArray( val ) ) {
+				val = jQuery.map( val, function( value ) {
+					return value == null ? "" : value + "";
+				} );
+			}
+
+			hooks = jQuery.valHooks[ this.type ] || jQuery.valHooks[ this.nodeName.toLowerCase() ];
+
+			// If set returns undefined, fall back to normal setting
+			if ( !hooks || !( "set" in hooks ) || hooks.set( this, val, "value" ) === undefined ) {
+				this.value = val;
+			}
+		} );
+	}
+} );
+
+jQuery.extend( {
+	valHooks: {
+		option: {
+			get: function( elem ) {
+
+				var val = jQuery.find.attr( elem, "value" );
+				return val != null ?
+					val :
+
+					// Support: IE <=10 - 11 only
+					// option.text throws exceptions (#14686, #14858)
+					// Strip and collapse whitespace
+					// https://html.spec.whatwg.org/#strip-and-collapse-whitespace
+					stripAndCollapse( jQuery.text( elem ) );
+			}
+		},
+		select: {
+			get: function( elem ) {
+				var value, option, i,
+					options = elem.options,
+					index = elem.selectedIndex,
+					one = elem.type === "select-one",
+					values = one ? null : [],
+					max = one ? index + 1 : options.length;
+
+				if ( index < 0 ) {
+					i = max;
+
+				} else {
+					i = one ? index : 0;
+				}
+
+				// Loop through all the selected options
+				for ( ; i < max; i++ ) {
+					option = options[ i ];
+
+					// Support: IE <=9 only
+					// IE8-9 doesn't update selected after form reset (#2551)
+					if ( ( option.selected || i === index ) &&
+
+							// Don't return options that are disabled or in a disabled optgroup
+							!option.disabled &&
+							( !option.parentNode.disabled ||
+								!nodeName( option.parentNode, "optgroup" ) ) ) {
+
+						// Get the specific value for the option
+						value = jQuery( option ).val();
+
+						// We don't need an array for one selects
+						if ( one ) {
+							return value;
+						}
+
+						// Multi-Selects return an array
+						values.push( value );
+					}
+				}
+
+				return values;
+			},
+
+			set: function( elem, value ) {
+				var optionSet, option,
+					options = elem.options,
+					values = jQuery.makeArray( value ),
+					i = options.length;
+
+				while ( i-- ) {
+					option = options[ i ];
+
+					/* eslint-disable no-cond-assign */
+
+					if ( option.selected =
+						jQuery.inArray( jQuery.valHooks.option.get( option ), values ) > -1
+					) {
+						optionSet = true;
+					}
+
+					/* eslint-enable no-cond-assign */
+				}
+
+				// Force browsers to behave consistently when non-matching value is set
+				if ( !optionSet ) {
+					elem.selectedIndex = -1;
+				}
+				return values;
+			}
+		}
+	}
+} );
+
+// Radios and checkboxes getter/setter
+jQuery.each( [ "radio", "checkbox" ], function() {
+	jQuery.valHooks[ this ] = {
+		set: function( elem, value ) {
+			if ( Array.isArray( value ) ) {
+				return ( elem.checked = jQuery.inArray( jQuery( elem ).val(), value ) > -1 );
+			}
+		}
+	};
+	if ( !support.checkOn ) {
+		jQuery.valHooks[ this ].get = function( elem ) {
+			return elem.getAttribute( "value" ) === null ? "on" : elem.value;
+		};
+	}
+} );
+
+
+
+
+// Return jQuery for attributes-only inclusion
+
+
+support.focusin = "onfocusin" in window;
+
+
+var rfocusMorph = /^(?:focusinfocus|focusoutblur)$/,
+	stopPropagationCallback = function( e ) {
+		e.stopPropagation();
+	};
+
+jQuery.extend( jQuery.event, {
+
+	trigger: function( event, data, elem, onlyHandlers ) {
+
+		var i, cur, tmp, bubbleType, ontype, handle, special, lastElement,
+			eventPath = [ elem || document ],
+			type = hasOwn.call( event, "type" ) ? event.type : event,
+			namespaces = hasOwn.call( event, "namespace" ) ? event.namespace.split( "." ) : [];
+
+		cur = lastElement = tmp = elem = elem || document;
+
+		// Don't do events on text and comment nodes
+		if ( elem.nodeType === 3 || elem.nodeType === 8 ) {
+			return;
+		}
+
+		// focus/blur morphs to focusin/out; ensure we're not firing them right now
+		if ( rfocusMorph.test( type + jQuery.event.triggered ) ) {
+			return;
+		}
+
+		if ( type.indexOf( "." ) > -1 ) {
+
+			// Namespaced trigger; create a regexp to match event type in handle()
+			namespaces = type.split( "." );
+			type = namespaces.shift();
+			namespaces.sort();
+		}
+		ontype = type.indexOf( ":" ) < 0 && "on" + type;
+
+		// Caller can pass in a jQuery.Event object, Object, or just an event type string
+		event = event[ jQuery.expando ] ?
+			event :
+			new jQuery.Event( type, typeof event === "object" && event );
+
+		// Trigger bitmask: & 1 for native handlers; & 2 for jQuery (always true)
+		event.isTrigger = onlyHandlers ? 2 : 3;
+		event.namespace = namespaces.join( "." );
+		event.rnamespace = event.namespace ?
+			new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" ) :
+			null;
+
+		// Clean up the event in case it is being reused
+		event.result = undefined;
+		if ( !event.target ) {
+			event.target = elem;
+		}
+
+		// Clone any incoming data and prepend the event, creating the handler arg list
+		data = data == null ?
+			[ event ] :
+			jQuery.makeArray( data, [ event ] );
+
+		// Allow special events to draw outside the lines
+		special = jQuery.event.special[ type ] || {};
+		if ( !onlyHandlers && special.trigger && special.trigger.apply( elem, data ) === false ) {
+			return;
+		}
+
+		// Determine event propagation path in advance, per W3C events spec (#9951)
+		// Bubble up to document, then to window; watch for a global ownerDocument var (#9724)
+		if ( !onlyHandlers && !special.noBubble && !isWindow( elem ) ) {
+
+			bubbleType = special.delegateType || type;
+			if ( !rfocusMorph.test( bubbleType + type ) ) {
+				cur = cur.parentNode;
+			}
+			for ( ; cur; cur = cur.parentNode ) {
+				eventPath.push( cur );
+				tmp = cur;
+			}
+
+			// Only add window if we got to document (e.g., not plain obj or detached DOM)
+			if ( tmp === ( elem.ownerDocument || document ) ) {
+				eventPath.push( tmp.defaultView || tmp.parentWindow || window );
+			}
+		}
+
+		// Fire handlers on the event path
+		i = 0;
+		while ( ( cur = eventPath[ i++ ] ) && !event.isPropagationStopped() ) {
+			lastElement = cur;
+			event.type = i > 1 ?
+				bubbleType :
+				special.bindType || type;
+
+			// jQuery handler
+			handle = (
+					dataPriv.get( cur, "events" ) || Object.create( null )
+				)[ event.type ] &&
+				dataPriv.get( cur, "handle" );
+			if ( handle ) {
+				handle.apply( cur, data );
+			}
+
+			// Native handler
+			handle = ontype && cur[ ontype ];
+			if ( handle && handle.apply && acceptData( cur ) ) {
+				event.result = handle.apply( cur, data );
+				if ( event.result === false ) {
+					event.preventDefault();
+				}
+			}
+		}
+		event.type = type;
+
+		// If nobody prevented the default action, do it now
+		if ( !onlyHandlers && !event.isDefaultPrevented() ) {
+
+			if ( ( !special._default ||
+				special._default.apply( eventPath.pop(), data ) === false ) &&
+				acceptData( elem ) ) {
+
+				// Call a native DOM method on the target with the same name as the event.
+				// Don't do default actions on window, that's where global variables be (#6170)
+				if ( ontype && isFunction( elem[ type ] ) && !isWindow( elem ) ) {
+
+					// Don't re-trigger an onFOO event when we call its FOO() method
+					tmp = elem[ ontype ];
+
+					if ( tmp ) {
+						elem[ ontype ] = null;
+					}
+
+					// Prevent re-triggering of the same event, since we already bubbled it above
+					jQuery.event.triggered = type;
+
+					if ( event.isPropagationStopped() ) {
+						lastElement.addEventListener( type, stopPropagationCallback );
+					}
+
+					elem[ type ]();
+
+					if ( event.isPropagationStopped() ) {
+						lastElement.removeEventListener( type, stopPropagationCallback );
+					}
+
+					jQuery.event.triggered = undefined;
+
+					if ( tmp ) {
+						elem[ ontype ] = tmp;
+					}
+				}
+			}
+		}
+
+		return event.result;
+	},
+
+	// Piggyback on a donor event to simulate a different one
+	// Used only for `focus(in | out)` events
+	simulate: function( type, elem, event ) {
+		var e = jQuery.extend(
+			new jQuery.Event(),
+			event,
+			{
+				type: type,
+				isSimulated: true
+			}
+		);
+
+		jQuery.event.trigger( e, null, elem );
+	}
+
+} );
+
+jQuery.fn.extend( {
+
+	trigger: function( type, data ) {
+		return this.each( function() {
+			jQuery.event.trigger( type, data, this );
+		} );
+	},
+	triggerHandler: function( type, data ) {
+		var elem = this[ 0 ];
+		if ( elem ) {
+			return jQuery.event.trigger( type, data, elem, true );
+		}
+	}
+} );
+
+
+// Support: Firefox <=44
+// Firefox doesn't have focus(in | out) events
+// Related ticket - https://bugzilla.mozilla.org/show_bug.cgi?id=687787
+//
+// Support: Chrome <=48 - 49, Safari <=9.0 - 9.1
+// focus(in | out) events fire after focus & blur events,
+// which is spec violation - http://www.w3.org/TR/DOM-Level-3-Events/#events-focusevent-event-order
+// Related ticket - https://bugs.chromium.org/p/chromium/issues/detail?id=449857
+if ( !support.focusin ) {
+	jQuery.each( { focus: "focusin", blur: "focusout" }, function( orig, fix ) {
+
+		// Attach a single capturing handler on the document while someone wants focusin/focusout
+		var handler = function( event ) {
+			jQuery.event.simulate( fix, event.target, jQuery.event.fix( event ) );
+		};
+
+		jQuery.event.special[ fix ] = {
+			setup: function() {
+
+				// Handle: regular nodes (via `this.ownerDocument`), window
+				// (via `this.document`) & document (via `this`).
+				var doc = this.ownerDocument || this.document || this,
+					attaches = dataPriv.access( doc, fix );
+
+				if ( !attaches ) {
+					doc.addEventListener( orig, handler, true );
+				}
+				dataPriv.access( doc, fix, ( attaches || 0 ) + 1 );
+			},
+			teardown: function() {
+				var doc = this.ownerDocument || this.document || this,
+					attaches = dataPriv.access( doc, fix ) - 1;
+
+				if ( !attaches ) {
+					doc.removeEventListener( orig, handler, true );
+					dataPriv.remove( doc, fix );
+
+				} else {
+					dataPriv.access( doc, fix, attaches );
+				}
+			}
+		};
+	} );
+}
+var location = window.location;
+
+var nonce = { guid: Date.now() };
+
+var rquery = ( /\?/ );
+
+
+
+// Cross-browser xml parsing
+jQuery.parseXML = function( data ) {
+	var xml;
+	if ( !data || typeof data !== "string" ) {
+		return null;
+	}
+
+	// Support: IE 9 - 11 only
+	// IE throws on parseFromString with invalid input.
+	try {
+		xml = ( new window.DOMParser() ).parseFromString( data, "text/xml" );
+	} catch ( e ) {
+		xml = undefined;
+	}
+
+	if ( !xml || xml.getElementsByTagName( "parsererror" ).length ) {
+		jQuery.error( "Invalid XML: " + data );
+	}
+	return xml;
+};
+
+
+var
+	rbracket = /\[\]$/,
+	rCRLF = /\r?\n/g,
+	rsubmitterTypes = /^(?:submit|button|image|reset|file)$/i,
+	rsubmittable = /^(?:input|select|textarea|keygen)/i;
+
+function buildParams( prefix, obj, traditional, add ) {
+	var name;
+
+	if ( Array.isArray( obj ) ) {
+
+		// Serialize array item.
+		jQuery.each( obj, function( i, v ) {
+			if ( traditional || rbracket.test( prefix ) ) {
+
+				// Treat each array item as a scalar.
+				add( prefix, v );
+
+			} else {
+
+				// Item is non-scalar (array or object), encode its numeric index.
+				buildParams(
+					prefix + "[" + ( typeof v === "object" && v != null ? i : "" ) + "]",
+					v,
+					traditional,
+					add
+				);
+			}
+		} );
+
+	} else if ( !traditional && toType( obj ) === "object" ) {
+
+		// Serialize object item.
+		for ( name in obj ) {
+			buildParams( prefix + "[" + name + "]", obj[ name ], traditional, add );
+		}
+
+	} else {
+
+		// Serialize scalar item.
+		add( prefix, obj );
+	}
+}
+
+// Serialize an array of form elements or a set of
+// key/values into a query string
+jQuery.param = function( a, traditional ) {
+	var prefix,
+		s = [],
+		add = function( key, valueOrFunction ) {
+
+			// If value is a function, invoke it and use its return value
+			var value = isFunction( valueOrFunction ) ?
+				valueOrFunction() :
+				valueOrFunction;
+
+			s[ s.length ] = encodeURIComponent( key ) + "=" +
+				encodeURIComponent( value == null ? "" : value );
+		};
+
+	if ( a == null ) {
+		return "";
+	}
+
+	// If an array was passed in, assume that it is an array of form elements.
+	if ( Array.isArray( a ) || ( a.jquery && !jQuery.isPlainObject( a ) ) ) {
+
+		// Serialize the form elements
+		jQuery.each( a, function() {
+			add( this.name, this.value );
+		} );
+
+	} else {
+
+		// If traditional, encode the "old" way (the way 1.3.2 or older
+		// did it), otherwise encode params recursively.
+		for ( prefix in a ) {
+			buildParams( prefix, a[ prefix ], traditional, add );
+		}
+	}
+
+	// Return the resulting serialization
+	return s.join( "&" );
+};
+
+jQuery.fn.extend( {
+	serialize: function() {
+		return jQuery.param( this.serializeArray() );
+	},
+	serializeArray: function() {
+		return this.map( function() {
+
+			// Can add propHook for "elements" to filter or add form elements
+			var elements = jQuery.prop( this, "elements" );
+			return elements ? jQuery.makeArray( elements ) : this;
+		} )
+		.filter( function() {
+			var type = this.type;
+
+			// Use .is( ":disabled" ) so that fieldset[disabled] works
+			return this.name && !jQuery( this ).is( ":disabled" ) &&
+				rsubmittable.test( this.nodeName ) && !rsubmitterTypes.test( type ) &&
+				( this.checked || !rcheckableType.test( type ) );
+		} )
+		.map( function( _i, elem ) {
+			var val = jQuery( this ).val();
+
+			if ( val == null ) {
+				return null;
+			}
+
+			if ( Array.isArray( val ) ) {
+				return jQuery.map( val, function( val ) {
+					return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) };
+				} );
+			}
+
+			return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) };
+		} ).get();
+	}
+} );
+
+
+var
+	r20 = /%20/g,
+	rhash = /#.*$/,
+	rantiCache = /([?&])_=[^&]*/,
+	rheaders = /^(.*?):[ \t]*([^\r\n]*)$/mg,
+
+	// #7653, #8125, #8152: local protocol detection
+	rlocalProtocol = /^(?:about|app|app-storage|.+-extension|file|res|widget):$/,
+	rnoContent = /^(?:GET|HEAD)$/,
+	rprotocol = /^\/\//,
+
+	/* Prefilters
+	 * 1) They are useful to introduce custom dataTypes (see ajax/jsonp.js for an example)
+	 * 2) These are called:
+	 *    - BEFORE asking for a transport
+	 *    - AFTER param serialization (s.data is a string if s.processData is true)
+	 * 3) key is the dataType
+	 * 4) the catchall symbol "*" can be used
+	 * 5) execution will start with transport dataType and THEN continue down to "*" if needed
+	 */
+	prefilters = {},
+
+	/* Transports bindings
+	 * 1) key is the dataType
+	 * 2) the catchall symbol "*" can be used
+	 * 3) selection will start with transport dataType and THEN go to "*" if needed
+	 */
+	transports = {},
+
+	// Avoid comment-prolog char sequence (#10098); must appease lint and evade compression
+	allTypes = "*/".concat( "*" ),
+
+	// Anchor tag for parsing the document origin
+	originAnchor = document.createElement( "a" );
+	originAnchor.href = location.href;
+
+// Base "constructor" for jQuery.ajaxPrefilter and jQuery.ajaxTransport
+function addToPrefiltersOrTransports( structure ) {
+
+	// dataTypeExpression is optional and defaults to "*"
+	return function( dataTypeExpression, func ) {
+
+		if ( typeof dataTypeExpression !== "string" ) {
+			func = dataTypeExpression;
+			dataTypeExpression = "*";
+		}
+
+		var dataType,
+			i = 0,
+			dataTypes = dataTypeExpression.toLowerCase().match( rnothtmlwhite ) || [];
+
+		if ( isFunction( func ) ) {
+
+			// For each dataType in the dataTypeExpression
+			while ( ( dataType = dataTypes[ i++ ] ) ) {
+
+				// Prepend if requested
+				if ( dataType[ 0 ] === "+" ) {
+					dataType = dataType.slice( 1 ) || "*";
+					( structure[ dataType ] = structure[ dataType ] || [] ).unshift( func );
+
+				// Otherwise append
+				} else {
+					( structure[ dataType ] = structure[ dataType ] || [] ).push( func );
+				}
+			}
+		}
+	};
+}
+
+// Base inspection function for prefilters and transports
+function inspectPrefiltersOrTransports( structure, options, originalOptions, jqXHR ) {
+
+	var inspected = {},
+		seekingTransport = ( structure === transports );
+
+	function inspect( dataType ) {
+		var selected;
+		inspected[ dataType ] = true;
+		jQuery.each( structure[ dataType ] || [], function( _, prefilterOrFactory ) {
+			var dataTypeOrTransport = prefilterOrFactory( options, originalOptions, jqXHR );
+			if ( typeof dataTypeOrTransport === "string" &&
+				!seekingTransport && !inspected[ dataTypeOrTransport ] ) {
+
+				options.dataTypes.unshift( dataTypeOrTransport );
+				inspect( dataTypeOrTransport );
+				return false;
+			} else if ( seekingTransport ) {
+				return !( selected = dataTypeOrTransport );
+			}
+		} );
+		return selected;
+	}
+
+	return inspect( options.dataTypes[ 0 ] ) || !inspected[ "*" ] && inspect( "*" );
+}
+
+// A special extend for ajax options
+// that takes "flat" options (not to be deep extended)
+// Fixes #9887
+function ajaxExtend( target, src ) {
+	var key, deep,
+		flatOptions = jQuery.ajaxSettings.flatOptions || {};
+
+	for ( key in src ) {
+		if ( src[ key ] !== undefined ) {
+			( flatOptions[ key ] ? target : ( deep || ( deep = {} ) ) )[ key ] = src[ key ];
+		}
+	}
+	if ( deep ) {
+		jQuery.extend( true, target, deep );
+	}
+
+	return target;
+}
+
+/* Handles responses to an ajax request:
+ * - finds the right dataType (mediates between content-type and expected dataType)
+ * - returns the corresponding response
+ */
+function ajaxHandleResponses( s, jqXHR, responses ) {
+
+	var ct, type, finalDataType, firstDataType,
+		contents = s.contents,
+		dataTypes = s.dataTypes;
+
+	// Remove auto dataType and get content-type in the process
+	while ( dataTypes[ 0 ] === "*" ) {
+		dataTypes.shift();
+		if ( ct === undefined ) {
+			ct = s.mimeType || jqXHR.getResponseHeader( "Content-Type" );
+		}
+	}
+
+	// Check if we're dealing with a known content-type
+	if ( ct ) {
+		for ( type in contents ) {
+			if ( contents[ type ] && contents[ type ].test( ct ) ) {
+				dataTypes.unshift( type );
+				break;
+			}
+		}
+	}
+
+	// Check to see if we have a response for the expected dataType
+	if ( dataTypes[ 0 ] in responses ) {
+		finalDataType = dataTypes[ 0 ];
+	} else {
+
+		// Try convertible dataTypes
+		for ( type in responses ) {
+			if ( !dataTypes[ 0 ] || s.converters[ type + " " + dataTypes[ 0 ] ] ) {
+				finalDataType = type;
+				break;
+			}
+			if ( !firstDataType ) {
+				firstDataType = type;
+			}
+		}
+
+		// Or just use first one
+		finalDataType = finalDataType || firstDataType;
+	}
+
+	// If we found a dataType
+	// We add the dataType to the list if needed
+	// and return the corresponding response
+	if ( finalDataType ) {
+		if ( finalDataType !== dataTypes[ 0 ] ) {
+			dataTypes.unshift( finalDataType );
+		}
+		return responses[ finalDataType ];
+	}
+}
+
+/* Chain conversions given the request and the original response
+ * Also sets the responseXXX fields on the jqXHR instance
+ */
+function ajaxConvert( s, response, jqXHR, isSuccess ) {
+	var conv2, current, conv, tmp, prev,
+		converters = {},
+
+		// Work with a copy of dataTypes in case we need to modify it for conversion
+		dataTypes = s.dataTypes.slice();
+
+	// Create converters map with lowercased keys
+	if ( dataTypes[ 1 ] ) {
+		for ( conv in s.converters ) {
+			converters[ conv.toLowerCase() ] = s.converters[ conv ];
+		}
+	}
+
+	current = dataTypes.shift();
+
+	// Convert to each sequential dataType
+	while ( current ) {
+
+		if ( s.responseFields[ current ] ) {
+			jqXHR[ s.responseFields[ current ] ] = response;
+		}
+
+		// Apply the dataFilter if provided
+		if ( !prev && isSuccess && s.dataFilter ) {
+			response = s.dataFilter( response, s.dataType );
+		}
+
+		prev = current;
+		current = dataTypes.shift();
+
+		if ( current ) {
+
+			// There's only work to do if current dataType is non-auto
+			if ( current === "*" ) {
+
+				current = prev;
+
+			// Convert response if prev dataType is non-auto and differs from current
+			} else if ( prev !== "*" && prev !== current ) {
+
+				// Seek a direct converter
+				conv = converters[ prev + " " + current ] || converters[ "* " + current ];
+
+				// If none found, seek a pair
+				if ( !conv ) {
+					for ( conv2 in converters ) {
+
+						// If conv2 outputs current
+						tmp = conv2.split( " " );
+						if ( tmp[ 1 ] === current ) {
+
+							// If prev can be converted to accepted input
+							conv = converters[ prev + " " + tmp[ 0 ] ] ||
+								converters[ "* " + tmp[ 0 ] ];
+							if ( conv ) {
+
+								// Condense equivalence converters
+								if ( conv === true ) {
+									conv = converters[ conv2 ];
+
+								// Otherwise, insert the intermediate dataType
+								} else if ( converters[ conv2 ] !== true ) {
+									current = tmp[ 0 ];
+									dataTypes.unshift( tmp[ 1 ] );
+								}
+								break;
+							}
+						}
+					}
+				}
+
+				// Apply converter (if not an equivalence)
+				if ( conv !== true ) {
+
+					// Unless errors are allowed to bubble, catch and return them
+					if ( conv && s.throws ) {
+						response = conv( response );
+					} else {
+						try {
+							response = conv( response );
+						} catch ( e ) {
+							return {
+								state: "parsererror",
+								error: conv ? e : "No conversion from " + prev + " to " + current
+							};
+						}
+					}
+				}
+			}
+		}
+	}
+
+	return { state: "success", data: response };
+}
+
+jQuery.extend( {
+
+	// Counter for holding the number of active queries
+	active: 0,
+
+	// Last-Modified header cache for next request
+	lastModified: {},
+	etag: {},
+
+	ajaxSettings: {
+		url: location.href,
+		type: "GET",
+		isLocal: rlocalProtocol.test( location.protocol ),
+		global: true,
+		processData: true,
+		async: true,
+		contentType: "application/x-www-form-urlencoded; charset=UTF-8",
+
+		/*
+		timeout: 0,
+		data: null,
+		dataType: null,
+		username: null,
+		password: null,
+		cache: null,
+		throws: false,
+		traditional: false,
+		headers: {},
+		*/
+
+		accepts: {
+			"*": allTypes,
+			text: "text/plain",
+			html: "text/html",
+			xml: "application/xml, text/xml",
+			json: "application/json, text/javascript"
+		},
+
+		contents: {
+			xml: /\bxml\b/,
+			html: /\bhtml/,
+			json: /\bjson\b/
+		},
+
+		responseFields: {
+			xml: "responseXML",
+			text: "responseText",
+			json: "responseJSON"
+		},
+
+		// Data converters
+		// Keys separate source (or catchall "*") and destination types with a single space
+		converters: {
+
+			// Convert anything to text
+			"* text": String,
+
+			// Text to html (true = no transformation)
+			"text html": true,
+
+			// Evaluate text as a json expression
+			"text json": JSON.parse,
+
+			// Parse text as xml
+			"text xml": jQuery.parseXML
+		},
+
+		// For options that shouldn't be deep extended:
+		// you can add your own custom options here if
+		// and when you create one that shouldn't be
+		// deep extended (see ajaxExtend)
+		flatOptions: {
+			url: true,
+			context: true
+		}
+	},
+
+	// Creates a full fledged settings object into target
+	// with both ajaxSettings and settings fields.
+	// If target is omitted, writes into ajaxSettings.
+	ajaxSetup: function( target, settings ) {
+		return settings ?
+
+			// Building a settings object
+			ajaxExtend( ajaxExtend( target, jQuery.ajaxSettings ), settings ) :
+
+			// Extending ajaxSettings
+			ajaxExtend( jQuery.ajaxSettings, target );
+	},
+
+	ajaxPrefilter: addToPrefiltersOrTransports( prefilters ),
+	ajaxTransport: addToPrefiltersOrTransports( transports ),
+
+	// Main method
+	ajax: function( url, options ) {
+
+		// If url is an object, simulate pre-1.5 signature
+		if ( typeof url === "object" ) {
+			options = url;
+			url = undefined;
+		}
+
+		// Force options to be an object
+		options = options || {};
+
+		var transport,
+
+			// URL without anti-cache param
+			cacheURL,
+
+			// Response headers
+			responseHeadersString,
+			responseHeaders,
+
+			// timeout handle
+			timeoutTimer,
+
+			// Url cleanup var
+			urlAnchor,
+
+			// Request state (becomes false upon send and true upon completion)
+			completed,
+
+			// To know if global events are to be dispatched
+			fireGlobals,
+
+			// Loop variable
+			i,
+
+			// uncached part of the url
+			uncached,
+
+			// Create the final options object
+			s = jQuery.ajaxSetup( {}, options ),
+
+			// Callbacks context
+			callbackContext = s.context || s,
+
+			// Context for global events is callbackContext if it is a DOM node or jQuery collection
+			globalEventContext = s.context &&
+				( callbackContext.nodeType || callbackContext.jquery ) ?
+					jQuery( callbackContext ) :
+					jQuery.event,
+
+			// Deferreds
+			deferred = jQuery.Deferred(),
+			completeDeferred = jQuery.Callbacks( "once memory" ),
+
+			// Status-dependent callbacks
+			statusCode = s.statusCode || {},
+
+			// Headers (they are sent all at once)
+			requestHeaders = {},
+			requestHeadersNames = {},
+
+			// Default abort message
+			strAbort = "canceled",
+
+			// Fake xhr
+			jqXHR = {
+				readyState: 0,
+
+				// Builds headers hashtable if needed
+				getResponseHeader: function( key ) {
+					var match;
+					if ( completed ) {
+						if ( !responseHeaders ) {
+							responseHeaders = {};
+							while ( ( match = rheaders.exec( responseHeadersString ) ) ) {
+								responseHeaders[ match[ 1 ].toLowerCase() + " " ] =
+									( responseHeaders[ match[ 1 ].toLowerCase() + " " ] || [] )
+										.concat( match[ 2 ] );
+							}
+						}
+						match = responseHeaders[ key.toLowerCase() + " " ];
+					}
+					return match == null ? null : match.join( ", " );
+				},
+
+				// Raw string
+				getAllResponseHeaders: function() {
+					return completed ? responseHeadersString : null;
+				},
+
+				// Caches the header
+				setRequestHeader: function( name, value ) {
+					if ( completed == null ) {
+						name = requestHeadersNames[ name.toLowerCase() ] =
+							requestHeadersNames[ name.toLowerCase() ] || name;
+						requestHeaders[ name ] = value;
+					}
+					return this;
+				},
+
+				// Overrides response content-type header
+				overrideMimeType: function( type ) {
+					if ( completed == null ) {
+						s.mimeType = type;
+					}
+					return this;
+				},
+
+				// Status-dependent callbacks
+				statusCode: function( map ) {
+					var code;
+					if ( map ) {
+						if ( completed ) {
+
+							// Execute the appropriate callbacks
+							jqXHR.always( map[ jqXHR.status ] );
+						} else {
+
+							// Lazy-add the new callbacks in a way that preserves old ones
+							for ( code in map ) {
+								statusCode[ code ] = [ statusCode[ code ], map[ code ] ];
+							}
+						}
+					}
+					return this;
+				},
+
+				// Cancel the request
+				abort: function( statusText ) {
+					var finalText = statusText || strAbort;
+					if ( transport ) {
+						transport.abort( finalText );
+					}
+					done( 0, finalText );
+					return this;
+				}
+			};
+
+		// Attach deferreds
+		deferred.promise( jqXHR );
+
+		// Add protocol if not provided (prefilters might expect it)
+		// Handle falsy url in the settings object (#10093: consistency with old signature)
+		// We also use the url parameter if available
+		s.url = ( ( url || s.url || location.href ) + "" )
+			.replace( rprotocol, location.protocol + "//" );
+
+		// Alias method option to type as per ticket #12004
+		s.type = options.method || options.type || s.method || s.type;
+
+		// Extract dataTypes list
+		s.dataTypes = ( s.dataType || "*" ).toLowerCase().match( rnothtmlwhite ) || [ "" ];
+
+		// A cross-domain request is in order when the origin doesn't match the current origin.
+		if ( s.crossDomain == null ) {
+			urlAnchor = document.createElement( "a" );
+
+			// Support: IE <=8 - 11, Edge 12 - 15
+			// IE throws exception on accessing the href property if url is malformed,
+			// e.g. http://example.com:80x/
+			try {
+				urlAnchor.href = s.url;
+
+				// Support: IE <=8 - 11 only
+				// Anchor's host property isn't correctly set when s.url is relative
+				urlAnchor.href = urlAnchor.href;
+				s.crossDomain = originAnchor.protocol + "//" + originAnchor.host !==
+					urlAnchor.protocol + "//" + urlAnchor.host;
+			} catch ( e ) {
+
+				// If there is an error parsing the URL, assume it is crossDomain,
+				// it can be rejected by the transport if it is invalid
+				s.crossDomain = true;
+			}
+		}
+
+		// Convert data if not already a string
+		if ( s.data && s.processData && typeof s.data !== "string" ) {
+			s.data = jQuery.param( s.data, s.traditional );
+		}
+
+		// Apply prefilters
+		inspectPrefiltersOrTransports( prefilters, s, options, jqXHR );
+
+		// If request was aborted inside a prefilter, stop there
+		if ( completed ) {
+			return jqXHR;
+		}
+
+		// We can fire global events as of now if asked to
+		// Don't fire events if jQuery.event is undefined in an AMD-usage scenario (#15118)
+		fireGlobals = jQuery.event && s.global;
+
+		// Watch for a new set of requests
+		if ( fireGlobals && jQuery.active++ === 0 ) {
+			jQuery.event.trigger( "ajaxStart" );
+		}
+
+		// Uppercase the type
+		s.type = s.type.toUpperCase();
+
+		// Determine if request has content
+		s.hasContent = !rnoContent.test( s.type );
+
+		// Save the URL in case we're toying with the If-Modified-Since
+		// and/or If-None-Match header later on
+		// Remove hash to simplify url manipulation
+		cacheURL = s.url.replace( rhash, "" );
+
+		// More options handling for requests with no content
+		if ( !s.hasContent ) {
+
+			// Remember the hash so we can put it back
+			uncached = s.url.slice( cacheURL.length );
+
+			// If data is available and should be processed, append data to url
+			if ( s.data && ( s.processData || typeof s.data === "string" ) ) {
+				cacheURL += ( rquery.test( cacheURL ) ? "&" : "?" ) + s.data;
+
+				// #9682: remove data so that it's not used in an eventual retry
+				delete s.data;
+			}
+
+			// Add or update anti-cache param if needed
+			if ( s.cache === false ) {
+				cacheURL = cacheURL.replace( rantiCache, "$1" );
+				uncached = ( rquery.test( cacheURL ) ? "&" : "?" ) + "_=" + ( nonce.guid++ ) +
+					uncached;
+			}
+
+			// Put hash and anti-cache on the URL that will be requested (gh-1732)
+			s.url = cacheURL + uncached;
+
+		// Change '%20' to '+' if this is encoded form body content (gh-2658)
+		} else if ( s.data && s.processData &&
+			( s.contentType || "" ).indexOf( "application/x-www-form-urlencoded" ) === 0 ) {
+			s.data = s.data.replace( r20, "+" );
+		}
+
+		// Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode.
+		if ( s.ifModified ) {
+			if ( jQuery.lastModified[ cacheURL ] ) {
+				jqXHR.setRequestHeader( "If-Modified-Since", jQuery.lastModified[ cacheURL ] );
+			}
+			if ( jQuery.etag[ cacheURL ] ) {
+				jqXHR.setRequestHeader( "If-None-Match", jQuery.etag[ cacheURL ] );
+			}
+		}
+
+		// Set the correct header, if data is being sent
+		if ( s.data && s.hasContent && s.contentType !== false || options.contentType ) {
+			jqXHR.setRequestHeader( "Content-Type", s.contentType );
+		}
+
+		// Set the Accepts header for the server, depending on the dataType
+		jqXHR.setRequestHeader(
+			"Accept",
+			s.dataTypes[ 0 ] && s.accepts[ s.dataTypes[ 0 ] ] ?
+				s.accepts[ s.dataTypes[ 0 ] ] +
+					( s.dataTypes[ 0 ] !== "*" ? ", " + allTypes + "; q=0.01" : "" ) :
+				s.accepts[ "*" ]
+		);
+
+		// Check for headers option
+		for ( i in s.headers ) {
+			jqXHR.setRequestHeader( i, s.headers[ i ] );
+		}
+
+		// Allow custom headers/mimetypes and early abort
+		if ( s.beforeSend &&
+			( s.beforeSend.call( callbackContext, jqXHR, s ) === false || completed ) ) {
+
+			// Abort if not done already and return
+			return jqXHR.abort();
+		}
+
+		// Aborting is no longer a cancellation
+		strAbort = "abort";
+
+		// Install callbacks on deferreds
+		completeDeferred.add( s.complete );
+		jqXHR.done( s.success );
+		jqXHR.fail( s.error );
+
+		// Get transport
+		transport = inspectPrefiltersOrTransports( transports, s, options, jqXHR );
+
+		// If no transport, we auto-abort
+		if ( !transport ) {
+			done( -1, "No Transport" );
+		} else {
+			jqXHR.readyState = 1;
+
+			// Send global event
+			if ( fireGlobals ) {
+				globalEventContext.trigger( "ajaxSend", [ jqXHR, s ] );
+			}
+
+			// If request was aborted inside ajaxSend, stop there
+			if ( completed ) {
+				return jqXHR;
+			}
+
+			// Timeout
+			if ( s.async && s.timeout > 0 ) {
+				timeoutTimer = window.setTimeout( function() {
+					jqXHR.abort( "timeout" );
+				}, s.timeout );
+			}
+
+			try {
+				completed = false;
+				transport.send( requestHeaders, done );
+			} catch ( e ) {
+
+				// Rethrow post-completion exceptions
+				if ( completed ) {
+					throw e;
+				}
+
+				// Propagate others as results
+				done( -1, e );
+			}
+		}
+
+		// Callback for when everything is done
+		function done( status, nativeStatusText, responses, headers ) {
+			var isSuccess, success, error, response, modified,
+				statusText = nativeStatusText;
+
+			// Ignore repeat invocations
+			if ( completed ) {
+				return;
+			}
+
+			completed = true;
+
+			// Clear timeout if it exists
+			if ( timeoutTimer ) {
+				window.clearTimeout( timeoutTimer );
+			}
+
+			// Dereference transport for early garbage collection
+			// (no matter how long the jqXHR object will be used)
+			transport = undefined;
+
+			// Cache response headers
+			responseHeadersString = headers || "";
+
+			// Set readyState
+			jqXHR.readyState = status > 0 ? 4 : 0;
+
+			// Determine if successful
+			isSuccess = status >= 200 && status < 300 || status === 304;
+
+			// Get response data
+			if ( responses ) {
+				response = ajaxHandleResponses( s, jqXHR, responses );
+			}
+
+			// Use a noop converter for missing script
+			if ( !isSuccess && jQuery.inArray( "script", s.dataTypes ) > -1 ) {
+				s.converters[ "text script" ] = function() {};
+			}
+
+			// Convert no matter what (that way responseXXX fields are always set)
+			response = ajaxConvert( s, response, jqXHR, isSuccess );
+
+			// If successful, handle type chaining
+			if ( isSuccess ) {
+
+				// Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode.
+				if ( s.ifModified ) {
+					modified = jqXHR.getResponseHeader( "Last-Modified" );
+					if ( modified ) {
+						jQuery.lastModified[ cacheURL ] = modified;
+					}
+					modified = jqXHR.getResponseHeader( "etag" );
+					if ( modified ) {
+						jQuery.etag[ cacheURL ] = modified;
+					}
+				}
+
+				// if no content
+				if ( status === 204 || s.type === "HEAD" ) {
+					statusText = "nocontent";
+
+				// if not modified
+				} else if ( status === 304 ) {
+					statusText = "notmodified";
+
+				// If we have data, let's convert it
+				} else {
+					statusText = response.state;
+					success = response.data;
+					error = response.error;
+					isSuccess = !error;
+				}
+			} else {
+
+				// Extract error from statusText and normalize for non-aborts
+				error = statusText;
+				if ( status || !statusText ) {
+					statusText = "error";
+					if ( status < 0 ) {
+						status = 0;
+					}
+				}
+			}
+
+			// Set data for the fake xhr object
+			jqXHR.status = status;
+			jqXHR.statusText = ( nativeStatusText || statusText ) + "";
+
+			// Success/Error
+			if ( isSuccess ) {
+				deferred.resolveWith( callbackContext, [ success, statusText, jqXHR ] );
+			} else {
+				deferred.rejectWith( callbackContext, [ jqXHR, statusText, error ] );
+			}
+
+			// Status-dependent callbacks
+			jqXHR.statusCode( statusCode );
+			statusCode = undefined;
+
+			if ( fireGlobals ) {
+				globalEventContext.trigger( isSuccess ? "ajaxSuccess" : "ajaxError",
+					[ jqXHR, s, isSuccess ? success : error ] );
+			}
+
+			// Complete
+			completeDeferred.fireWith( callbackContext, [ jqXHR, statusText ] );
+
+			if ( fireGlobals ) {
+				globalEventContext.trigger( "ajaxComplete", [ jqXHR, s ] );
+
+				// Handle the global AJAX counter
+				if ( !( --jQuery.active ) ) {
+					jQuery.event.trigger( "ajaxStop" );
+				}
+			}
+		}
+
+		return jqXHR;
+	},
+
+	getJSON: function( url, data, callback ) {
+		return jQuery.get( url, data, callback, "json" );
+	},
+
+	getScript: function( url, callback ) {
+		return jQuery.get( url, undefined, callback, "script" );
+	}
+} );
+
+jQuery.each( [ "get", "post" ], function( _i, method ) {
+	jQuery[ method ] = function( url, data, callback, type ) {
+
+		// Shift arguments if data argument was omitted
+		if ( isFunction( data ) ) {
+			type = type || callback;
+			callback = data;
+			data = undefined;
+		}
+
+		// The url can be an options object (which then must have .url)
+		return jQuery.ajax( jQuery.extend( {
+			url: url,
+			type: method,
+			dataType: type,
+			data: data,
+			success: callback
+		}, jQuery.isPlainObject( url ) && url ) );
+	};
+} );
+
+jQuery.ajaxPrefilter( function( s ) {
+	var i;
+	for ( i in s.headers ) {
+		if ( i.toLowerCase() === "content-type" ) {
+			s.contentType = s.headers[ i ] || "";
+		}
+	}
+} );
+
+
+jQuery._evalUrl = function( url, options, doc ) {
+	return jQuery.ajax( {
+		url: url,
+
+		// Make this explicit, since user can override this through ajaxSetup (#11264)
+		type: "GET",
+		dataType: "script",
+		cache: true,
+		async: false,
+		global: false,
+
+		// Only evaluate the response if it is successful (gh-4126)
+		// dataFilter is not invoked for failure responses, so using it instead
+		// of the default converter is kludgy but it works.
+		converters: {
+			"text script": function() {}
+		},
+		dataFilter: function( response ) {
+			jQuery.globalEval( response, options, doc );
+		}
+	} );
+};
+
+
+jQuery.fn.extend( {
+	wrapAll: function( html ) {
+		var wrap;
+
+		if ( this[ 0 ] ) {
+			if ( isFunction( html ) ) {
+				html = html.call( this[ 0 ] );
+			}
+
+			// The elements to wrap the target around
+			wrap = jQuery( html, this[ 0 ].ownerDocument ).eq( 0 ).clone( true );
+
+			if ( this[ 0 ].parentNode ) {
+				wrap.insertBefore( this[ 0 ] );
+			}
+
+			wrap.map( function() {
+				var elem = this;
+
+				while ( elem.firstElementChild ) {
+					elem = elem.firstElementChild;
+				}
+
+				return elem;
+			} ).append( this );
+		}
+
+		return this;
+	},
+
+	wrapInner: function( html ) {
+		if ( isFunction( html ) ) {
+			return this.each( function( i ) {
+				jQuery( this ).wrapInner( html.call( this, i ) );
+			} );
+		}
+
+		return this.each( function() {
+			var self = jQuery( this ),
+				contents = self.contents();
+
+			if ( contents.length ) {
+				contents.wrapAll( html );
+
+			} else {
+				self.append( html );
+			}
+		} );
+	},
+
+	wrap: function( html ) {
+		var htmlIsFunction = isFunction( html );
+
+		return this.each( function( i ) {
+			jQuery( this ).wrapAll( htmlIsFunction ? html.call( this, i ) : html );
+		} );
+	},
+
+	unwrap: function( selector ) {
+		this.parent( selector ).not( "body" ).each( function() {
+			jQuery( this ).replaceWith( this.childNodes );
+		} );
+		return this;
+	}
+} );
+
+
+jQuery.expr.pseudos.hidden = function( elem ) {
+	return !jQuery.expr.pseudos.visible( elem );
+};
+jQuery.expr.pseudos.visible = function( elem ) {
+	return !!( elem.offsetWidth || elem.offsetHeight || elem.getClientRects().length );
+};
+
+
+
+
+jQuery.ajaxSettings.xhr = function() {
+	try {
+		return new window.XMLHttpRequest();
+	} catch ( e ) {}
+};
+
+var xhrSuccessStatus = {
+
+		// File protocol always yields status code 0, assume 200
+		0: 200,
+
+		// Support: IE <=9 only
+		// #1450: sometimes IE returns 1223 when it should be 204
+		1223: 204
+	},
+	xhrSupported = jQuery.ajaxSettings.xhr();
+
+support.cors = !!xhrSupported && ( "withCredentials" in xhrSupported );
+support.ajax = xhrSupported = !!xhrSupported;
+
+jQuery.ajaxTransport( function( options ) {
+	var callback, errorCallback;
+
+	// Cross domain only allowed if supported through XMLHttpRequest
+	if ( support.cors || xhrSupported && !options.crossDomain ) {
+		return {
+			send: function( headers, complete ) {
+				var i,
+					xhr = options.xhr();
+
+				xhr.open(
+					options.type,
+					options.url,
+					options.async,
+					options.username,
+					options.password
+				);
+
+				// Apply custom fields if provided
+				if ( options.xhrFields ) {
+					for ( i in options.xhrFields ) {
+						xhr[ i ] = options.xhrFields[ i ];
+					}
+				}
+
+				// Override mime type if needed
+				if ( options.mimeType && xhr.overrideMimeType ) {
+					xhr.overrideMimeType( options.mimeType );
+				}
+
+				// X-Requested-With header
+				// For cross-domain requests, seeing as conditions for a preflight are
+				// akin to a jigsaw puzzle, we simply never set it to be sure.
+				// (it can always be set on a per-request basis or even using ajaxSetup)
+				// For same-domain requests, won't change header if already provided.
+				if ( !options.crossDomain && !headers[ "X-Requested-With" ] ) {
+					headers[ "X-Requested-With" ] = "XMLHttpRequest";
+				}
+
+				// Set headers
+				for ( i in headers ) {
+					xhr.setRequestHeader( i, headers[ i ] );
+				}
+
+				// Callback
+				callback = function( type ) {
+					return function() {
+						if ( callback ) {
+							callback = errorCallback = xhr.onload =
+								xhr.onerror = xhr.onabort = xhr.ontimeout =
+									xhr.onreadystatechange = null;
+
+							if ( type === "abort" ) {
+								xhr.abort();
+							} else if ( type === "error" ) {
+
+								// Support: IE <=9 only
+								// On a manual native abort, IE9 throws
+								// errors on any property access that is not readyState
+								if ( typeof xhr.status !== "number" ) {
+									complete( 0, "error" );
+								} else {
+									complete(
+
+										// File: protocol always yields status 0; see #8605, #14207
+										xhr.status,
+										xhr.statusText
+									);
+								}
+							} else {
+								complete(
+									xhrSuccessStatus[ xhr.status ] || xhr.status,
+									xhr.statusText,
+
+									// Support: IE <=9 only
+									// IE9 has no XHR2 but throws on binary (trac-11426)
+									// For XHR2 non-text, let the caller handle it (gh-2498)
+									( xhr.responseType || "text" ) !== "text"  ||
+									typeof xhr.responseText !== "string" ?
+										{ binary: xhr.response } :
+										{ text: xhr.responseText },
+									xhr.getAllResponseHeaders()
+								);
+							}
+						}
+					};
+				};
+
+				// Listen to events
+				xhr.onload = callback();
+				errorCallback = xhr.onerror = xhr.ontimeout = callback( "error" );
+
+				// Support: IE 9 only
+				// Use onreadystatechange to replace onabort
+				// to handle uncaught aborts
+				if ( xhr.onabort !== undefined ) {
+					xhr.onabort = errorCallback;
+				} else {
+					xhr.onreadystatechange = function() {
+
+						// Check readyState before timeout as it changes
+						if ( xhr.readyState === 4 ) {
+
+							// Allow onerror to be called first,
+							// but that will not handle a native abort
+							// Also, save errorCallback to a variable
+							// as xhr.onerror cannot be accessed
+							window.setTimeout( function() {
+								if ( callback ) {
+									errorCallback();
+								}
+							} );
+						}
+					};
+				}
+
+				// Create the abort callback
+				callback = callback( "abort" );
+
+				try {
+
+					// Do send the request (this may raise an exception)
+					xhr.send( options.hasContent && options.data || null );
+				} catch ( e ) {
+
+					// #14683: Only rethrow if this hasn't been notified as an error yet
+					if ( callback ) {
+						throw e;
+					}
+				}
+			},
+
+			abort: function() {
+				if ( callback ) {
+					callback();
+				}
+			}
+		};
+	}
+} );
+
+
+
+
+// Prevent auto-execution of scripts when no explicit dataType was provided (See gh-2432)
+jQuery.ajaxPrefilter( function( s ) {
+	if ( s.crossDomain ) {
+		s.contents.script = false;
+	}
+} );
+
+// Install script dataType
+jQuery.ajaxSetup( {
+	accepts: {
+		script: "text/javascript, application/javascript, " +
+			"application/ecmascript, application/x-ecmascript"
+	},
+	contents: {
+		script: /\b(?:java|ecma)script\b/
+	},
+	converters: {
+		"text script": function( text ) {
+			jQuery.globalEval( text );
+			return text;
+		}
+	}
+} );
+
+// Handle cache's special case and crossDomain
+jQuery.ajaxPrefilter( "script", function( s ) {
+	if ( s.cache === undefined ) {
+		s.cache = false;
+	}
+	if ( s.crossDomain ) {
+		s.type = "GET";
+	}
+} );
+
+// Bind script tag hack transport
+jQuery.ajaxTransport( "script", function( s ) {
+
+	// This transport only deals with cross domain or forced-by-attrs requests
+	if ( s.crossDomain || s.scriptAttrs ) {
+		var script, callback;
+		return {
+			send: function( _, complete ) {
+				script = jQuery( "<script>" )
+					.attr( s.scriptAttrs || {} )
+					.prop( { charset: s.scriptCharset, src: s.url } )
+					.on( "load error", callback = function( evt ) {
+						script.remove();
+						callback = null;
+						if ( evt ) {
+							complete( evt.type === "error" ? 404 : 200, evt.type );
+						}
+					} );
+
+				// Use native DOM manipulation to avoid our domManip AJAX trickery
+				document.head.appendChild( script[ 0 ] );
+			},
+			abort: function() {
+				if ( callback ) {
+					callback();
+				}
+			}
+		};
+	}
+} );
+
+
+
+
+var oldCallbacks = [],
+	rjsonp = /(=)\?(?=&|$)|\?\?/;
+
+// Default jsonp settings
+jQuery.ajaxSetup( {
+	jsonp: "callback",
+	jsonpCallback: function() {
+		var callback = oldCallbacks.pop() || ( jQuery.expando + "_" + ( nonce.guid++ ) );
+		this[ callback ] = true;
+		return callback;
+	}
+} );
+
+// Detect, normalize options and install callbacks for jsonp requests
+jQuery.ajaxPrefilter( "json jsonp", function( s, originalSettings, jqXHR ) {
+
+	var callbackName, overwritten, responseContainer,
+		jsonProp = s.jsonp !== false && ( rjsonp.test( s.url ) ?
+			"url" :
+			typeof s.data === "string" &&
+				( s.contentType || "" )
+					.indexOf( "application/x-www-form-urlencoded" ) === 0 &&
+				rjsonp.test( s.data ) && "data"
+		);
+
+	// Handle iff the expected data type is "jsonp" or we have a parameter to set
+	if ( jsonProp || s.dataTypes[ 0 ] === "jsonp" ) {
+
+		// Get callback name, remembering preexisting value associated with it
+		callbackName = s.jsonpCallback = isFunction( s.jsonpCallback ) ?
+			s.jsonpCallback() :
+			s.jsonpCallback;
+
+		// Insert callback into url or form data
+		if ( jsonProp ) {
+			s[ jsonProp ] = s[ jsonProp ].replace( rjsonp, "$1" + callbackName );
+		} else if ( s.jsonp !== false ) {
+			s.url += ( rquery.test( s.url ) ? "&" : "?" ) + s.jsonp + "=" + callbackName;
+		}
+
+		// Use data converter to retrieve json after script execution
+		s.converters[ "script json" ] = function() {
+			if ( !responseContainer ) {
+				jQuery.error( callbackName + " was not called" );
+			}
+			return responseContainer[ 0 ];
+		};
+
+		// Force json dataType
+		s.dataTypes[ 0 ] = "json";
+
+		// Install callback
+		overwritten = window[ callbackName ];
+		window[ callbackName ] = function() {
+			responseContainer = arguments;
+		};
+
+		// Clean-up function (fires after converters)
+		jqXHR.always( function() {
+
+			// If previous value didn't exist - remove it
+			if ( overwritten === undefined ) {
+				jQuery( window ).removeProp( callbackName );
+
+			// Otherwise restore preexisting value
+			} else {
+				window[ callbackName ] = overwritten;
+			}
+
+			// Save back as free
+			if ( s[ callbackName ] ) {
+
+				// Make sure that re-using the options doesn't screw things around
+				s.jsonpCallback = originalSettings.jsonpCallback;
+
+				// Save the callback name for future use
+				oldCallbacks.push( callbackName );
+			}
+
+			// Call if it was a function and we have a response
+			if ( responseContainer && isFunction( overwritten ) ) {
+				overwritten( responseContainer[ 0 ] );
+			}
+
+			responseContainer = overwritten = undefined;
+		} );
+
+		// Delegate to script
+		return "script";
+	}
+} );
+
+
+
+
+// Support: Safari 8 only
+// In Safari 8 documents created via document.implementation.createHTMLDocument
+// collapse sibling forms: the second one becomes a child of the first one.
+// Because of that, this security measure has to be disabled in Safari 8.
+// https://bugs.webkit.org/show_bug.cgi?id=137337
+support.createHTMLDocument = ( function() {
+	var body = document.implementation.createHTMLDocument( "" ).body;
+	body.innerHTML = "<form></form><form></form>";
+	return body.childNodes.length === 2;
+} )();
+
+
+// Argument "data" should be string of html
+// context (optional): If specified, the fragment will be created in this context,
+// defaults to document
+// keepScripts (optional): If true, will include scripts passed in the html string
+jQuery.parseHTML = function( data, context, keepScripts ) {
+	if ( typeof data !== "string" ) {
+		return [];
+	}
+	if ( typeof context === "boolean" ) {
+		keepScripts = context;
+		context = false;
+	}
+
+	var base, parsed, scripts;
+
+	if ( !context ) {
+
+		// Stop scripts or inline event handlers from being executed immediately
+		// by using document.implementation
+		if ( support.createHTMLDocument ) {
+			context = document.implementation.createHTMLDocument( "" );
+
+			// Set the base href for the created document
+			// so any parsed elements with URLs
+			// are based on the document's URL (gh-2965)
+			base = context.createElement( "base" );
+			base.href = document.location.href;
+			context.head.appendChild( base );
+		} else {
+			context = document;
+		}
+	}
+
+	parsed = rsingleTag.exec( data );
+	scripts = !keepScripts && [];
+
+	// Single tag
+	if ( parsed ) {
+		return [ context.createElement( parsed[ 1 ] ) ];
+	}
+
+	parsed = buildFragment( [ data ], context, scripts );
+
+	if ( scripts && scripts.length ) {
+		jQuery( scripts ).remove();
+	}
+
+	return jQuery.merge( [], parsed.childNodes );
+};
+
+
+/**
+ * Load a url into a page
+ */
+jQuery.fn.load = function( url, params, callback ) {
+	var selector, type, response,
+		self = this,
+		off = url.indexOf( " " );
+
+	if ( off > -1 ) {
+		selector = stripAndCollapse( url.slice( off ) );
+		url = url.slice( 0, off );
+	}
+
+	// If it's a function
+	if ( isFunction( params ) ) {
+
+		// We assume that it's the callback
+		callback = params;
+		params = undefined;
+
+	// Otherwise, build a param string
+	} else if ( params && typeof params === "object" ) {
+		type = "POST";
+	}
+
+	// If we have elements to modify, make the request
+	if ( self.length > 0 ) {
+		jQuery.ajax( {
+			url: url,
+
+			// If "type" variable is undefined, then "GET" method will be used.
+			// Make value of this field explicit since
+			// user can override it through ajaxSetup method
+			type: type || "GET",
+			dataType: "html",
+			data: params
+		} ).done( function( responseText ) {
+
+			// Save response for use in complete callback
+			response = arguments;
+
+			self.html( selector ?
+
+				// If a selector was specified, locate the right elements in a dummy div
+				// Exclude scripts to avoid IE 'Permission Denied' errors
+				jQuery( "<div>" ).append( jQuery.parseHTML( responseText ) ).find( selector ) :
+
+				// Otherwise use the full result
+				responseText );
+
+		// If the request succeeds, this function gets "data", "status", "jqXHR"
+		// but they are ignored because response was set above.
+		// If it fails, this function gets "jqXHR", "status", "error"
+		} ).always( callback && function( jqXHR, status ) {
+			self.each( function() {
+				callback.apply( this, response || [ jqXHR.responseText, status, jqXHR ] );
+			} );
+		} );
+	}
+
+	return this;
+};
+
+
+
+
+jQuery.expr.pseudos.animated = function( elem ) {
+	return jQuery.grep( jQuery.timers, function( fn ) {
+		return elem === fn.elem;
+	} ).length;
+};
+
+
+
+
+jQuery.offset = {
+	setOffset: function( elem, options, i ) {
+		var curPosition, curLeft, curCSSTop, curTop, curOffset, curCSSLeft, calculatePosition,
+			position = jQuery.css( elem, "position" ),
+			curElem = jQuery( elem ),
+			props = {};
+
+		// Set position first, in-case top/left are set even on static elem
+		if ( position === "static" ) {
+			elem.style.position = "relative";
+		}
+
+		curOffset = curElem.offset();
+		curCSSTop = jQuery.css( elem, "top" );
+		curCSSLeft = jQuery.css( elem, "left" );
+		calculatePosition = ( position === "absolute" || position === "fixed" ) &&
+			( curCSSTop + curCSSLeft ).indexOf( "auto" ) > -1;
+
+		// Need to be able to calculate position if either
+		// top or left is auto and position is either absolute or fixed
+		if ( calculatePosition ) {
+			curPosition = curElem.position();
+			curTop = curPosition.top;
+			curLeft = curPosition.left;
+
+		} else {
+			curTop = parseFloat( curCSSTop ) || 0;
+			curLeft = parseFloat( curCSSLeft ) || 0;
+		}
+
+		if ( isFunction( options ) ) {
+
+			// Use jQuery.extend here to allow modification of coordinates argument (gh-1848)
+			options = options.call( elem, i, jQuery.extend( {}, curOffset ) );
+		}
+
+		if ( options.top != null ) {
+			props.top = ( options.top - curOffset.top ) + curTop;
+		}
+		if ( options.left != null ) {
+			props.left = ( options.left - curOffset.left ) + curLeft;
+		}
+
+		if ( "using" in options ) {
+			options.using.call( elem, props );
+
+		} else {
+			if ( typeof props.top === "number" ) {
+				props.top += "px";
+			}
+			if ( typeof props.left === "number" ) {
+				props.left += "px";
+			}
+			curElem.css( props );
+		}
+	}
+};
+
+jQuery.fn.extend( {
+
+	// offset() relates an element's border box to the document origin
+	offset: function( options ) {
+
+		// Preserve chaining for setter
+		if ( arguments.length ) {
+			return options === undefined ?
+				this :
+				this.each( function( i ) {
+					jQuery.offset.setOffset( this, options, i );
+				} );
+		}
+
+		var rect, win,
+			elem = this[ 0 ];
+
+		if ( !elem ) {
+			return;
+		}
+
+		// Return zeros for disconnected and hidden (display: none) elements (gh-2310)
+		// Support: IE <=11 only
+		// Running getBoundingClientRect on a
+		// disconnected node in IE throws an error
+		if ( !elem.getClientRects().length ) {
+			return { top: 0, left: 0 };
+		}
+
+		// Get document-relative position by adding viewport scroll to viewport-relative gBCR
+		rect = elem.getBoundingClientRect();
+		win = elem.ownerDocument.defaultView;
+		return {
+			top: rect.top + win.pageYOffset,
+			left: rect.left + win.pageXOffset
+		};
+	},
+
+	// position() relates an element's margin box to its offset parent's padding box
+	// This corresponds to the behavior of CSS absolute positioning
+	position: function() {
+		if ( !this[ 0 ] ) {
+			return;
+		}
+
+		var offsetParent, offset, doc,
+			elem = this[ 0 ],
+			parentOffset = { top: 0, left: 0 };
+
+		// position:fixed elements are offset from the viewport, which itself always has zero offset
+		if ( jQuery.css( elem, "position" ) === "fixed" ) {
+
+			// Assume position:fixed implies availability of getBoundingClientRect
+			offset = elem.getBoundingClientRect();
+
+		} else {
+			offset = this.offset();
+
+			// Account for the *real* offset parent, which can be the document or its root element
+			// when a statically positioned element is identified
+			doc = elem.ownerDocument;
+			offsetParent = elem.offsetParent || doc.documentElement;
+			while ( offsetParent &&
+				( offsetParent === doc.body || offsetParent === doc.documentElement ) &&
+				jQuery.css( offsetParent, "position" ) === "static" ) {
+
+				offsetParent = offsetParent.parentNode;
+			}
+			if ( offsetParent && offsetParent !== elem && offsetParent.nodeType === 1 ) {
+
+				// Incorporate borders into its offset, since they are outside its content origin
+				parentOffset = jQuery( offsetParent ).offset();
+				parentOffset.top += jQuery.css( offsetParent, "borderTopWidth", true );
+				parentOffset.left += jQuery.css( offsetParent, "borderLeftWidth", true );
+			}
+		}
+
+		// Subtract parent offsets and element margins
+		return {
+			top: offset.top - parentOffset.top - jQuery.css( elem, "marginTop", true ),
+			left: offset.left - parentOffset.left - jQuery.css( elem, "marginLeft", true )
+		};
+	},
+
+	// This method will return documentElement in the following cases:
+	// 1) For the element inside the iframe without offsetParent, this method will return
+	//    documentElement of the parent window
+	// 2) For the hidden or detached element
+	// 3) For body or html element, i.e. in case of the html node - it will return itself
+	//
+	// but those exceptions were never presented as a real life use-cases
+	// and might be considered as more preferable results.
+	//
+	// This logic, however, is not guaranteed and can change at any point in the future
+	offsetParent: function() {
+		return this.map( function() {
+			var offsetParent = this.offsetParent;
+
+			while ( offsetParent && jQuery.css( offsetParent, "position" ) === "static" ) {
+				offsetParent = offsetParent.offsetParent;
+			}
+
+			return offsetParent || documentElement;
+		} );
+	}
+} );
+
+// Create scrollLeft and scrollTop methods
+jQuery.each( { scrollLeft: "pageXOffset", scrollTop: "pageYOffset" }, function( method, prop ) {
+	var top = "pageYOffset" === prop;
+
+	jQuery.fn[ method ] = function( val ) {
+		return access( this, function( elem, method, val ) {
+
+			// Coalesce documents and windows
+			var win;
+			if ( isWindow( elem ) ) {
+				win = elem;
+			} else if ( elem.nodeType === 9 ) {
+				win = elem.defaultView;
+			}
+
+			if ( val === undefined ) {
+				return win ? win[ prop ] : elem[ method ];
+			}
+
+			if ( win ) {
+				win.scrollTo(
+					!top ? val : win.pageXOffset,
+					top ? val : win.pageYOffset
+				);
+
+			} else {
+				elem[ method ] = val;
+			}
+		}, method, val, arguments.length );
+	};
+} );
+
+// Support: Safari <=7 - 9.1, Chrome <=37 - 49
+// Add the top/left cssHooks using jQuery.fn.position
+// Webkit bug: https://bugs.webkit.org/show_bug.cgi?id=29084
+// Blink bug: https://bugs.chromium.org/p/chromium/issues/detail?id=589347
+// getComputedStyle returns percent when specified for top/left/bottom/right;
+// rather than make the css module depend on the offset module, just check for it here
+jQuery.each( [ "top", "left" ], function( _i, prop ) {
+	jQuery.cssHooks[ prop ] = addGetHookIf( support.pixelPosition,
+		function( elem, computed ) {
+			if ( computed ) {
+				computed = curCSS( elem, prop );
+
+				// If curCSS returns percentage, fallback to offset
+				return rnumnonpx.test( computed ) ?
+					jQuery( elem ).position()[ prop ] + "px" :
+					computed;
+			}
+		}
+	);
+} );
+
+
+// Create innerHeight, innerWidth, height, width, outerHeight and outerWidth methods
+jQuery.each( { Height: "height", Width: "width" }, function( name, type ) {
+	jQuery.each( { padding: "inner" + name, content: type, "": "outer" + name },
+		function( defaultExtra, funcName ) {
+
+		// Margin is only for outerHeight, outerWidth
+		jQuery.fn[ funcName ] = function( margin, value ) {
+			var chainable = arguments.length && ( defaultExtra || typeof margin !== "boolean" ),
+				extra = defaultExtra || ( margin === true || value === true ? "margin" : "border" );
+
+			return access( this, function( elem, type, value ) {
+				var doc;
+
+				if ( isWindow( elem ) ) {
+
+					// $( window ).outerWidth/Height return w/h including scrollbars (gh-1729)
+					return funcName.indexOf( "outer" ) === 0 ?
+						elem[ "inner" + name ] :
+						elem.document.documentElement[ "client" + name ];
+				}
+
+				// Get document width or height
+				if ( elem.nodeType === 9 ) {
+					doc = elem.documentElement;
+
+					// Either scroll[Width/Height] or offset[Width/Height] or client[Width/Height],
+					// whichever is greatest
+					return Math.max(
+						elem.body[ "scroll" + name ], doc[ "scroll" + name ],
+						elem.body[ "offset" + name ], doc[ "offset" + name ],
+						doc[ "client" + name ]
+					);
+				}
+
+				return value === undefined ?
+
+					// Get width or height on the element, requesting but not forcing parseFloat
+					jQuery.css( elem, type, extra ) :
+
+					// Set width or height on the element
+					jQuery.style( elem, type, value, extra );
+			}, type, chainable ? margin : undefined, chainable );
+		};
+	} );
+} );
+
+
+jQuery.each( [
+	"ajaxStart",
+	"ajaxStop",
+	"ajaxComplete",
+	"ajaxError",
+	"ajaxSuccess",
+	"ajaxSend"
+], function( _i, type ) {
+	jQuery.fn[ type ] = function( fn ) {
+		return this.on( type, fn );
+	};
+} );
+
+
+
+
+jQuery.fn.extend( {
+
+	bind: function( types, data, fn ) {
+		return this.on( types, null, data, fn );
+	},
+	unbind: function( types, fn ) {
+		return this.off( types, null, fn );
+	},
+
+	delegate: function( selector, types, data, fn ) {
+		return this.on( types, selector, data, fn );
+	},
+	undelegate: function( selector, types, fn ) {
+
+		// ( namespace ) or ( selector, types [, fn] )
+		return arguments.length === 1 ?
+			this.off( selector, "**" ) :
+			this.off( types, selector || "**", fn );
+	},
+
+	hover: function( fnOver, fnOut ) {
+		return this.mouseenter( fnOver ).mouseleave( fnOut || fnOver );
+	}
+} );
+
+jQuery.each( ( "blur focus focusin focusout resize scroll click dblclick " +
+	"mousedown mouseup mousemove mouseover mouseout mouseenter mouseleave " +
+	"change select submit keydown keypress keyup contextmenu" ).split( " " ),
+	function( _i, name ) {
+
+		// Handle event binding
+		jQuery.fn[ name ] = function( data, fn ) {
+			return arguments.length > 0 ?
+				this.on( name, null, data, fn ) :
+				this.trigger( name );
+		};
+	} );
+
+
+
+
+// Support: Android <=4.0 only
+// Make sure we trim BOM and NBSP
+var rtrim = /^[\s\uFEFF\xA0]+|[\s\uFEFF\xA0]+$/g;
+
+// Bind a function to a context, optionally partially applying any
+// arguments.
+// jQuery.proxy is deprecated to promote standards (specifically Function#bind)
+// However, it is not slated for removal any time soon
+jQuery.proxy = function( fn, context ) {
+	var tmp, args, proxy;
+
+	if ( typeof context === "string" ) {
+		tmp = fn[ context ];
+		context = fn;
+		fn = tmp;
+	}
+
+	// Quick check to determine if target is callable, in the spec
+	// this throws a TypeError, but we will just return undefined.
+	if ( !isFunction( fn ) ) {
+		return undefined;
+	}
+
+	// Simulated bind
+	args = slice.call( arguments, 2 );
+	proxy = function() {
+		return fn.apply( context || this, args.concat( slice.call( arguments ) ) );
+	};
+
+	// Set the guid of unique handler to the same of original handler, so it can be removed
+	proxy.guid = fn.guid = fn.guid || jQuery.guid++;
+
+	return proxy;
+};
+
+jQuery.holdReady = function( hold ) {
+	if ( hold ) {
+		jQuery.readyWait++;
+	} else {
+		jQuery.ready( true );
+	}
+};
+jQuery.isArray = Array.isArray;
+jQuery.parseJSON = JSON.parse;
+jQuery.nodeName = nodeName;
+jQuery.isFunction = isFunction;
+jQuery.isWindow = isWindow;
+jQuery.camelCase = camelCase;
+jQuery.type = toType;
+
+jQuery.now = Date.now;
+
+jQuery.isNumeric = function( obj ) {
+
+	// As of jQuery 3.0, isNumeric is limited to
+	// strings and numbers (primitives or objects)
+	// that can be coerced to finite numbers (gh-2662)
+	var type = jQuery.type( obj );
+	return ( type === "number" || type === "string" ) &&
+
+		// parseFloat NaNs numeric-cast false positives ("")
+		// ...but misinterprets leading-number strings, particularly hex literals ("0x...")
+		// subtraction forces infinities to NaN
+		!isNaN( obj - parseFloat( obj ) );
+};
+
+jQuery.trim = function( text ) {
+	return text == null ?
+		"" :
+		( text + "" ).replace( rtrim, "" );
+};
+
+
+
+// Register as a named AMD module, since jQuery can be concatenated with other
+// files that may use define, but not via a proper concatenation script that
+// understands anonymous AMD modules. A named AMD is safest and most robust
+// way to register. Lowercase jquery is used because AMD module names are
+// derived from file names, and jQuery is normally delivered in a lowercase
+// file name. Do this after creating the global so that if an AMD module wants
+// to call noConflict to hide this version of jQuery, it will work.
+
+// Note that for maximum portability, libraries that are not jQuery should
+// declare themselves as anonymous modules, and avoid setting a global if an
+// AMD loader is present. jQuery is a special case. For more information, see
+// https://github.com/jrburke/requirejs/wiki/Updating-existing-libraries#wiki-anon
+
+if ( typeof define === "function" && define.amd ) {
+	define( "jquery", [], function() {
+		return jQuery;
+	} );
+}
+
+
+
+
+var
+
+	// Map over jQuery in case of overwrite
+	_jQuery = window.jQuery,
+
+	// Map over the $ in case of overwrite
+	_$ = window.$;
+
+jQuery.noConflict = function( deep ) {
+	if ( window.$ === jQuery ) {
+		window.$ = _$;
+	}
+
+	if ( deep && window.jQuery === jQuery ) {
+		window.jQuery = _jQuery;
+	}
+
+	return jQuery;
+};
+
+// Expose jQuery and $ identifiers, even in AMD
+// (#7102#comment:10, https://github.com/jquery/jquery/pull/557)
+// and CommonJS for browser emulators (#13566)
+if ( typeof noGlobal === "undefined" ) {
+	window.jQuery = window.$ = jQuery;
+}
+
+
+
+
+return jQuery;
+} );
diff --git a/_build/html/_static/jquery.js b/_build/html/_static/jquery.js
new file mode 100644
index 0000000..b061403
--- /dev/null
+++ b/_build/html/_static/jquery.js
@@ -0,0 +1,2 @@
+/*! jQuery v3.5.1 | (c) JS Foundation and other contributors | jquery.org/license */
+!function(e,t){"use strict";"object"==typeof module&&"object"==typeof module.exports?module.exports=e.document?t(e,!0):function(e){if(!e.document)throw new Error("jQuery requires a window with a document");return t(e)}:t(e)}("undefined"!=typeof window?window:this,function(C,e){"use strict";var t=[],r=Object.getPrototypeOf,s=t.slice,g=t.flat?function(e){return t.flat.call(e)}:function(e){return t.concat.apply([],e)},u=t.push,i=t.indexOf,n={},o=n.toString,v=n.hasOwnProperty,a=v.toString,l=a.call(Object),y={},m=function(e){return"function"==typeof e&&"number"!=typeof e.nodeType},x=function(e){return null!=e&&e===e.window},E=C.document,c={type:!0,src:!0,nonce:!0,noModule:!0};function b(e,t,n){var r,i,o=(n=n||E).createElement("script");if(o.text=e,t)for(r in c)(i=t[r]||t.getAttribute&&t.getAttribute(r))&&o.setAttribute(r,i);n.head.appendChild(o).parentNode.removeChild(o)}function w(e){return null==e?e+"":"object"==typeof e||"function"==typeof e?n[o.call(e)]||"object":typeof e}var 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+ * @license
+ * Copyright (c) 2016 Federico Zivolo and contributors
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to deal
+ * in the Software without restriction, including without limitation the rights
+ * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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+/*!
+  * Bootstrap v4.5.0 (https://getbootstrap.com/)
+  * Copyright 2011-2020 The Bootstrap Authors (https://github.com/twbs/bootstrap/graphs/contributors)
+  * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)
+  */
+!function(t,e,n){"use strict";function i(t,e){for(var n=0;n<e.length;n++){var i=e[n];i.enumerable=i.enumerable||!1,i.configurable=!0,"value"in i&&(i.writable=!0),Object.defineProperty(t,i.key,i)}}function o(t,e,n){return e&&i(t.prototype,e),n&&i(t,n),t}function r(t,e,n){return e in t?Object.defineProperty(t,e,{value:n,enumerable:!0,configurable:!0,writable:!0}):t[e]=n,t}function s(t,e){var n=Object.keys(t);if(Object.getOwnPropertySymbols){var i=Object.getOwnPropertySymbols(t);e&&(i=i.filter((function(e){return Object.getOwnPropertyDescriptor(t,e).enumerable}))),n.push.apply(n,i)}return n}function a(t){for(var e=1;e<arguments.length;e++){var n=null!=arguments[e]?arguments[e]:{};e%2?s(Object(n),!0).forEach((function(e){r(t,e,n[e])})):Object.getOwnPropertyDescriptors?Object.defineProperties(t,Object.getOwnPropertyDescriptors(n)):s(Object(n)).forEach((function(e){Object.defineProperty(t,e,Object.getOwnPropertyDescriptor(n,e))}))}return t}function l(t){var n=this,i=!1;return 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t=this;e(document).off("focusin.bs.modal").on("focusin.bs.modal",(function(n){document!==n.target&&t._element!==n.target&&0===e(t._element).has(n.target).length&&t._element.focus()}))},n._setEscapeEvent=function(){var t=this;this._isShown?e(this._element).on("keydown.dismiss.bs.modal",(function(e){t._config.keyboard&&27===e.which?(e.preventDefault(),t.hide()):t._config.keyboard||27!==e.which||t._triggerBackdropTransition()})):this._isShown||e(this._element).off("keydown.dismiss.bs.modal")},n._setResizeEvent=function(){var t=this;this._isShown?e(window).on("resize.bs.modal",(function(e){return t.handleUpdate(e)})):e(window).off("resize.bs.modal")},n._hideModal=function(){var t=this;this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._isTransitioning=!1,this._showBackdrop((function(){e(document.body).removeClass("modal-open"),t._resetAdjustments(),t._resetScrollbar(),e(t._element).trigger("hidden.bs.modal")}))},n._removeBackdrop=function(){this._backdrop&&(e(this._backdrop).remove(),this._backdrop=null)},n._showBackdrop=function(t){var n=this,i=e(this._element).hasClass("fade")?"fade":"";if(this._isShown&&this._config.backdrop){if(this._backdrop=document.createElement("div"),this._backdrop.className="modal-backdrop",i&&this._backdrop.classList.add(i),e(this._backdrop).appendTo(document.body),e(this._element).on("click.dismiss.bs.modal",(function(t){n._ignoreBackdropClick?n._ignoreBackdropClick=!1:t.target===t.currentTarget&&n._triggerBackdropTransition()})),i&&c.reflow(this._backdrop),e(this._backdrop).addClass("show"),!t)return;if(!i)return void t();var 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diff --git a/_build/html/_static/language_data.js b/_build/html/_static/language_data.js
new file mode 100644
index 0000000..d2b4ee9
--- /dev/null
+++ b/_build/html/_static/language_data.js
@@ -0,0 +1,297 @@
+/*
+ * language_data.js
+ * ~~~~~~~~~~~~~~~~
+ *
+ * This script contains the language-specific data used by searchtools.js,
+ * namely the list of stopwords, stemmer, scorer and splitter.
+ *
+ * :copyright: Copyright 2007-2020 by the Sphinx team, see AUTHORS.
+ * :license: BSD, see LICENSE for details.
+ *
+ */
+
+var stopwords = ["a","and","are","as","at","be","but","by","for","if","in","into","is","it","near","no","not","of","on","or","such","that","the","their","then","there","these","they","this","to","was","will","with"];
+
+
+/* Non-minified version JS is _stemmer.js if file is provided */ 
+/**
+ * Porter Stemmer
+ */
+var Stemmer = function() {
+
+  var step2list = {
+    ational: 'ate',
+    tional: 'tion',
+    enci: 'ence',
+    anci: 'ance',
+    izer: 'ize',
+    bli: 'ble',
+    alli: 'al',
+    entli: 'ent',
+    eli: 'e',
+    ousli: 'ous',
+    ization: 'ize',
+    ation: 'ate',
+    ator: 'ate',
+    alism: 'al',
+    iveness: 'ive',
+    fulness: 'ful',
+    ousness: 'ous',
+    aliti: 'al',
+    iviti: 'ive',
+    biliti: 'ble',
+    logi: 'log'
+  };
+
+  var step3list = {
+    icate: 'ic',
+    ative: '',
+    alize: 'al',
+    iciti: 'ic',
+    ical: 'ic',
+    ful: '',
+    ness: ''
+  };
+
+  var c = "[^aeiou]";          // consonant
+  var v = "[aeiouy]";          // vowel
+  var C = c + "[^aeiouy]*";    // consonant sequence
+  var V = v + "[aeiou]*";      // vowel sequence
+
+  var mgr0 = "^(" + C + ")?" + V + C;                      // [C]VC... is m>0
+  var meq1 = "^(" + C + ")?" + V + C + "(" + V + ")?$";    // [C]VC[V] is m=1
+  var mgr1 = "^(" + C + ")?" + V + C + V + C;              // [C]VCVC... is m>1
+  var s_v   = "^(" + C + ")?" + v;                         // vowel in stem
+
+  this.stemWord = function (w) {
+    var stem;
+    var suffix;
+    var firstch;
+    var origword = w;
+
+    if (w.length < 3)
+      return w;
+
+    var re;
+    var re2;
+    var re3;
+    var re4;
+
+    firstch = w.substr(0,1);
+    if (firstch == "y")
+      w = firstch.toUpperCase() + w.substr(1);
+
+    // Step 1a
+    re = /^(.+?)(ss|i)es$/;
+    re2 = /^(.+?)([^s])s$/;
+
+    if (re.test(w))
+      w = w.replace(re,"$1$2");
+    else if (re2.test(w))
+      w = w.replace(re2,"$1$2");
+
+    // Step 1b
+    re = /^(.+?)eed$/;
+    re2 = /^(.+?)(ed|ing)$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      re = new RegExp(mgr0);
+      if (re.test(fp[1])) {
+        re = /.$/;
+        w = w.replace(re,"");
+      }
+    }
+    else if (re2.test(w)) {
+      var fp = re2.exec(w);
+      stem = fp[1];
+      re2 = new RegExp(s_v);
+      if (re2.test(stem)) {
+        w = stem;
+        re2 = /(at|bl|iz)$/;
+        re3 = new RegExp("([^aeiouylsz])\\1$");
+        re4 = new RegExp("^" + C + v + "[^aeiouwxy]$");
+        if (re2.test(w))
+          w = w + "e";
+        else if (re3.test(w)) {
+          re = /.$/;
+          w = w.replace(re,"");
+        }
+        else if (re4.test(w))
+          w = w + "e";
+      }
+    }
+
+    // Step 1c
+    re = /^(.+?)y$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      re = new RegExp(s_v);
+      if (re.test(stem))
+        w = stem + "i";
+    }
+
+    // Step 2
+    re = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      suffix = fp[2];
+      re = new RegExp(mgr0);
+      if (re.test(stem))
+        w = stem + step2list[suffix];
+    }
+
+    // Step 3
+    re = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      suffix = fp[2];
+      re = new RegExp(mgr0);
+      if (re.test(stem))
+        w = stem + step3list[suffix];
+    }
+
+    // Step 4
+    re = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/;
+    re2 = /^(.+?)(s|t)(ion)$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      re = new RegExp(mgr1);
+      if (re.test(stem))
+        w = stem;
+    }
+    else if (re2.test(w)) {
+      var fp = re2.exec(w);
+      stem = fp[1] + fp[2];
+      re2 = new RegExp(mgr1);
+      if (re2.test(stem))
+        w = stem;
+    }
+
+    // Step 5
+    re = /^(.+?)e$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      re = new RegExp(mgr1);
+      re2 = new RegExp(meq1);
+      re3 = new RegExp("^" + C + v + "[^aeiouwxy]$");
+      if (re.test(stem) || (re2.test(stem) && !(re3.test(stem))))
+        w = stem;
+    }
+    re = /ll$/;
+    re2 = new RegExp(mgr1);
+    if (re.test(w) && re2.test(w)) {
+      re = /.$/;
+      w = w.replace(re,"");
+    }
+
+    // and turn initial Y back to y
+    if (firstch == "y")
+      w = firstch.toLowerCase() + w.substr(1);
+    return w;
+  }
+}
+
+
+
+
+
+var splitChars = (function() {
+    var result = {};
+    var singles = [96, 180, 187, 191, 215, 247, 749, 885, 903, 907, 909, 930, 1014, 1648,
+         1748, 1809, 2416, 2473, 2481, 2526, 2601, 2609, 2612, 2615, 2653, 2702,
+         2706, 2729, 2737, 2740, 2857, 2865, 2868, 2910, 2928, 2948, 2961, 2971,
+         2973, 3085, 3089, 3113, 3124, 3213, 3217, 3241, 3252, 3295, 3341, 3345,
+         3369, 3506, 3516, 3633, 3715, 3721, 3736, 3744, 3748, 3750, 3756, 3761,
+         3781, 3912, 4239, 4347, 4681, 4695, 4697, 4745, 4785, 4799, 4801, 4823,
+         4881, 5760, 5901, 5997, 6313, 7405, 8024, 8026, 8028, 8030, 8117, 8125,
+         8133, 8181, 8468, 8485, 8487, 8489, 8494, 8527, 11311, 11359, 11687, 11695,
+         11703, 11711, 11719, 11727, 11735, 12448, 12539, 43010, 43014, 43019, 43587,
+         43696, 43713, 64286, 64297, 64311, 64317, 64319, 64322, 64325, 65141];
+    var i, j, start, end;
+    for (i = 0; i < singles.length; i++) {
+        result[singles[i]] = true;
+    }
+    var ranges = [[0, 47], [58, 64], [91, 94], [123, 169], [171, 177], [182, 184], [706, 709],
+         [722, 735], [741, 747], [751, 879], [888, 889], [894, 901], [1154, 1161],
+         [1318, 1328], [1367, 1368], [1370, 1376], [1416, 1487], [1515, 1519], [1523, 1568],
+         [1611, 1631], [1642, 1645], [1750, 1764], [1767, 1773], [1789, 1790], [1792, 1807],
+         [1840, 1868], [1958, 1968], [1970, 1983], [2027, 2035], [2038, 2041], [2043, 2047],
+         [2070, 2073], [2075, 2083], [2085, 2087], [2089, 2307], [2362, 2364], [2366, 2383],
+         [2385, 2391], [2402, 2405], [2419, 2424], [2432, 2436], [2445, 2446], [2449, 2450],
+         [2483, 2485], [2490, 2492], [2494, 2509], [2511, 2523], [2530, 2533], [2546, 2547],
+         [2554, 2564], [2571, 2574], [2577, 2578], [2618, 2648], [2655, 2661], [2672, 2673],
+         [2677, 2692], [2746, 2748], [2750, 2767], [2769, 2783], [2786, 2789], [2800, 2820],
+         [2829, 2830], [2833, 2834], [2874, 2876], [2878, 2907], [2914, 2917], [2930, 2946],
+         [2955, 2957], [2966, 2968], [2976, 2978], [2981, 2983], [2987, 2989], [3002, 3023],
+         [3025, 3045], [3059, 3076], [3130, 3132], [3134, 3159], [3162, 3167], [3170, 3173],
+         [3184, 3191], [3199, 3204], [3258, 3260], [3262, 3293], [3298, 3301], [3312, 3332],
+         [3386, 3388], [3390, 3423], [3426, 3429], [3446, 3449], [3456, 3460], [3479, 3481],
+         [3518, 3519], [3527, 3584], [3636, 3647], [3655, 3663], [3674, 3712], [3717, 3718],
+         [3723, 3724], [3726, 3731], [3752, 3753], [3764, 3772], [3774, 3775], [3783, 3791],
+         [3802, 3803], [3806, 3839], [3841, 3871], [3892, 3903], [3949, 3975], [3980, 4095],
+         [4139, 4158], [4170, 4175], [4182, 4185], [4190, 4192], [4194, 4196], [4199, 4205],
+         [4209, 4212], [4226, 4237], [4250, 4255], [4294, 4303], [4349, 4351], [4686, 4687],
+         [4702, 4703], [4750, 4751], [4790, 4791], [4806, 4807], [4886, 4887], [4955, 4968],
+         [4989, 4991], [5008, 5023], [5109, 5120], [5741, 5742], [5787, 5791], [5867, 5869],
+         [5873, 5887], [5906, 5919], [5938, 5951], [5970, 5983], [6001, 6015], [6068, 6102],
+         [6104, 6107], [6109, 6111], [6122, 6127], [6138, 6159], [6170, 6175], [6264, 6271],
+         [6315, 6319], [6390, 6399], [6429, 6469], [6510, 6511], [6517, 6527], [6572, 6592],
+         [6600, 6607], [6619, 6655], [6679, 6687], [6741, 6783], [6794, 6799], [6810, 6822],
+         [6824, 6916], [6964, 6980], [6988, 6991], [7002, 7042], [7073, 7085], [7098, 7167],
+         [7204, 7231], [7242, 7244], [7294, 7400], [7410, 7423], [7616, 7679], [7958, 7959],
+         [7966, 7967], [8006, 8007], [8014, 8015], [8062, 8063], [8127, 8129], [8141, 8143],
+         [8148, 8149], [8156, 8159], [8173, 8177], [8189, 8303], [8306, 8307], [8314, 8318],
+         [8330, 8335], [8341, 8449], [8451, 8454], [8456, 8457], [8470, 8472], [8478, 8483],
+         [8506, 8507], [8512, 8516], [8522, 8525], [8586, 9311], [9372, 9449], [9472, 10101],
+         [10132, 11263], [11493, 11498], [11503, 11516], [11518, 11519], [11558, 11567],
+         [11622, 11630], [11632, 11647], [11671, 11679], [11743, 11822], [11824, 12292],
+         [12296, 12320], [12330, 12336], [12342, 12343], [12349, 12352], [12439, 12444],
+         [12544, 12548], [12590, 12592], [12687, 12689], [12694, 12703], [12728, 12783],
+         [12800, 12831], [12842, 12880], [12896, 12927], [12938, 12976], [12992, 13311],
+         [19894, 19967], [40908, 40959], [42125, 42191], [42238, 42239], [42509, 42511],
+         [42540, 42559], [42592, 42593], [42607, 42622], [42648, 42655], [42736, 42774],
+         [42784, 42785], [42889, 42890], [42893, 43002], [43043, 43055], [43062, 43071],
+         [43124, 43137], [43188, 43215], [43226, 43249], [43256, 43258], [43260, 43263],
+         [43302, 43311], [43335, 43359], [43389, 43395], [43443, 43470], [43482, 43519],
+         [43561, 43583], [43596, 43599], [43610, 43615], [43639, 43641], [43643, 43647],
+         [43698, 43700], [43703, 43704], [43710, 43711], [43715, 43738], [43742, 43967],
+         [44003, 44015], [44026, 44031], [55204, 55215], [55239, 55242], [55292, 55295],
+         [57344, 63743], [64046, 64047], [64110, 64111], [64218, 64255], [64263, 64274],
+         [64280, 64284], [64434, 64466], [64830, 64847], [64912, 64913], [64968, 65007],
+         [65020, 65135], [65277, 65295], [65306, 65312], [65339, 65344], [65371, 65381],
+         [65471, 65473], [65480, 65481], [65488, 65489], [65496, 65497]];
+    for (i = 0; i < ranges.length; i++) {
+        start = ranges[i][0];
+        end = ranges[i][1];
+        for (j = start; j <= end; j++) {
+            result[j] = true;
+        }
+    }
+    return result;
+})();
+
+function splitQuery(query) {
+    var result = [];
+    var start = -1;
+    for (var i = 0; i < query.length; i++) {
+        if (splitChars[query.charCodeAt(i)]) {
+            if (start !== -1) {
+                result.push(query.slice(start, i));
+                start = -1;
+            }
+        } else if (start === -1) {
+            start = i;
+        }
+    }
+    if (start !== -1) {
+        result.push(query.slice(start));
+    }
+    return result;
+}
+
+
diff --git a/_build/html/_static/minus.png b/_build/html/_static/minus.png
new file mode 100644
index 0000000000000000000000000000000000000000..d96755fdaf8bb2214971e0db9c1fd3077d7c419d
GIT binary patch
literal 90
zcmeAS@N?(olHy`uVBq!ia0vp^+#t*WBp7;*Yy1LIik>cxAr*|t7R?Mi>2?kWtu=nj
kDsEF_5m^0CR;1wuP-*O&G^0G}KYk!hp00i_>zopr08q^qX#fBK

literal 0
HcmV?d00001

diff --git a/_build/html/_static/mystnb.css b/_build/html/_static/mystnb.css
new file mode 100644
index 0000000..37134ee
--- /dev/null
+++ b/_build/html/_static/mystnb.css
@@ -0,0 +1,184 @@
+/* Whole cell */
+div.container.cell {
+  padding-left: 0;
+  margin-bottom: 1em;
+}
+
+/* Removing all background formatting so we can control at the div level */
+.cell_input div.highlight, .cell_input pre, .cell_output .output * {
+  border: none;
+  background-color: transparent;
+  box-shadow: none;
+}
+
+.cell_output .output pre, .cell_input pre {
+  margin: 0px;
+}
+
+/* Input cells */
+div.cell div.cell_input {
+  padding-left: 0em;
+  padding-right: 0em;
+  border: 1px #ccc solid;
+  background-color: #f7f7f7;
+  border-left-color: green;
+  border-left-width: medium;
+}
+
+div.cell_input > div, div.cell_output div.output > div.highlight {
+  margin: 0em !important;
+  border: none !important;
+}
+
+/* All cell outputs */
+.cell_output {
+  padding-left: 1em;
+  padding-right: 0em;
+  margin-top: 1em;
+}
+
+/* Outputs from jupyter_sphinx overrides to remove extra CSS */
+div.section div.jupyter_container {
+  padding: .4em;
+  margin: 0 0 .4em 0;
+  background-color: none;
+  border: none;
+  -moz-box-shadow: none;
+  -webkit-box-shadow: none;
+  box-shadow: none;
+}
+
+/* Text outputs from cells */
+.cell_output .output.text_plain,
+.cell_output .output.traceback,
+.cell_output .output.stream,
+.cell_output .output.stderr
+  {
+  background: #fcfcfc;
+  margin-top: 1em;
+  margin-bottom: 0em;
+  box-shadow: none;
+}
+
+.cell_output .output.text_plain,
+.cell_output .output.stream,
+.cell_output .output.stderr {
+  border: 1px solid #f7f7f7;
+}
+
+.cell_output .output.stderr {
+  background: #fdd;
+}
+
+.cell_output .output.traceback {
+  border: 1px solid #ffd6d6;
+}
+
+/* Math align to the left */
+.cell_output .MathJax_Display {
+  text-align: left !important;
+}
+
+/* Pandas tables. Pulled from the Jupyter / nbsphinx CSS */
+div.cell_output table {
+    border: none;
+    border-collapse: collapse;
+    border-spacing: 0;
+    color: black;
+    font-size: 1em;
+    table-layout: fixed;
+  }
+  div.cell_output thead {
+    border-bottom: 1px solid black;
+    vertical-align: bottom;
+  }
+  div.cell_output tr,
+  div.cell_output th,
+  div.cell_output td {
+    text-align: right;
+    vertical-align: middle;
+    padding: 0.5em 0.5em;
+    line-height: normal;
+    white-space: normal;
+    max-width: none;
+    border: none;
+  }
+  div.cell_output th {
+    font-weight: bold;
+  }
+  div.cell_output tbody tr:nth-child(odd) {
+    background: #f5f5f5;
+  }
+  div.cell_output tbody tr:hover {
+    background: rgba(66, 165, 245, 0.2);
+  }
+
+
+/* Inline text from `paste` operation */
+
+span.pasted-text {
+  font-weight: bold;
+}
+
+span.pasted-inline img {
+  max-height: 2em;
+}
+
+tbody span.pasted-inline img {
+  max-height: none;
+}
+
+/* Font colors for translated ANSI escape sequences
+Color values are adapted from share/jupyter/nbconvert/templates/classic/static/style.css
+*/
+div.highlight .-Color-Bold {
+  font-weight: bold;
+}
+div.highlight .-Color[class*=-Black] {
+  color :#3E424D
+}
+div.highlight .-Color[class*=-Red] {
+  color: #E75C58
+}
+div.highlight .-Color[class*=-Green] {
+  color: #00A250
+}
+div.highlight .-Color[class*=-Yellow] {
+  color: yellow
+}
+div.highlight .-Color[class*=-Blue] {
+  color: #208FFB
+}
+div.highlight .-Color[class*=-Magenta] {
+  color: #D160C4
+}
+div.highlight .-Color[class*=-Cyan] {
+  color: #60C6C8
+}
+div.highlight .-Color[class*=-White] {
+  color: #C5C1B4
+}
+div.highlight .-Color[class*=-BGBlack] {
+  background-color: #3E424D
+}
+div.highlight .-Color[class*=-BGRed] {
+  background-color: #E75C58
+}
+div.highlight .-Color[class*=-BGGreen] {
+  background-color: #00A250
+}
+div.highlight .-Color[class*=-BGYellow] {
+  background-color: yellow
+}
+div.highlight .-Color[class*=-BGBlue] {
+  background-color: #208FFB
+}
+div.highlight .-Color[class*=-BGMagenta] {
+  background-color: #D160C4
+}
+div.highlight .-Color[class*=-BGCyan] {
+  background-color: #60C6C8
+}
+div.highlight .-Color[class*=-BGWhite] {
+  background-color: #C5C1B4
+}
diff --git a/_build/html/_static/panels-main.c949a650a448cc0ae9fd3441c0e17fb0.css b/_build/html/_static/panels-main.c949a650a448cc0ae9fd3441c0e17fb0.css
new file mode 100644
index 0000000..fc14abc
--- /dev/null
+++ b/_build/html/_static/panels-main.c949a650a448cc0ae9fd3441c0e17fb0.css
@@ -0,0 +1 @@
+details.dropdown .summary-title{padding-right:3em !important;-moz-user-select:none;-ms-user-select:none;-webkit-user-select:none;user-select:none}details.dropdown:hover{cursor:pointer}details.dropdown .summary-content{cursor:default}details.dropdown summary{list-style:none;padding:1em}details.dropdown summary .octicon.no-title{vertical-align:middle}details.dropdown[open] summary .octicon.no-title{visibility:hidden}details.dropdown summary::-webkit-details-marker{display:none}details.dropdown summary:focus{outline:none}details.dropdown summary:hover .summary-up svg,details.dropdown summary:hover .summary-down svg{opacity:1}details.dropdown .summary-up svg,details.dropdown .summary-down svg{display:block;opacity:.6}details.dropdown .summary-up,details.dropdown .summary-down{pointer-events:none;position:absolute;right:1em;top:.75em}details.dropdown[open] .summary-down{visibility:hidden}details.dropdown:not([open]) .summary-up{visibility:hidden}details.dropdown.fade-in[open] summary~*{-moz-animation:panels-fade-in .5s ease-in-out;-webkit-animation:panels-fade-in .5s ease-in-out;animation:panels-fade-in .5s ease-in-out}details.dropdown.fade-in-slide-down[open] summary~*{-moz-animation:panels-fade-in .5s ease-in-out, panels-slide-down .5s ease-in-out;-webkit-animation:panels-fade-in .5s ease-in-out, panels-slide-down .5s ease-in-out;animation:panels-fade-in .5s ease-in-out, panels-slide-down .5s ease-in-out}@keyframes panels-fade-in{0%{opacity:0}100%{opacity:1}}@keyframes panels-slide-down{0%{transform:translate(0, -10px)}100%{transform:translate(0, 0)}}.octicon{display:inline-block;fill:currentColor;vertical-align:text-top}.tabbed-content{box-shadow:0 -.0625rem var(--tabs-color-overline),0 .0625rem var(--tabs-color-underline);display:none;order:99;padding-bottom:.75rem;padding-top:.75rem;width:100%}.tabbed-content>:first-child{margin-top:0 !important}.tabbed-content>:last-child{margin-bottom:0 !important}.tabbed-content>.tabbed-set{margin:0}.tabbed-set{border-radius:.125rem;display:flex;flex-wrap:wrap;margin:1em 0;position:relative}.tabbed-set>input{opacity:0;position:absolute}.tabbed-set>input:checked+label{border-color:var(--tabs-color-label-active);color:var(--tabs-color-label-active)}.tabbed-set>input:checked+label+.tabbed-content{display:block}.tabbed-set>input:focus+label{outline-style:auto}.tabbed-set>input:not(.focus-visible)+label{outline:none;-webkit-tap-highlight-color:transparent}.tabbed-set>label{border-bottom:.125rem solid transparent;color:var(--tabs-color-label-inactive);cursor:pointer;font-size:var(--tabs-size-label);font-weight:700;padding:1em 1.25em .5em;transition:color 250ms;width:auto;z-index:1}html .tabbed-set>label:hover{color:var(--tabs-color-label-active)}
diff --git a/_build/html/_static/panels-variables.06eb56fa6e07937060861dad626602ad.css b/_build/html/_static/panels-variables.06eb56fa6e07937060861dad626602ad.css
new file mode 100644
index 0000000..adc6166
--- /dev/null
+++ b/_build/html/_static/panels-variables.06eb56fa6e07937060861dad626602ad.css
@@ -0,0 +1,7 @@
+:root {
+--tabs-color-label-active: hsla(231, 99%, 66%, 1);
+--tabs-color-label-inactive: rgba(178, 206, 245, 0.62);
+--tabs-color-overline: rgb(207, 236, 238);
+--tabs-color-underline: rgb(207, 236, 238);
+--tabs-size-label: 1rem;
+}
\ No newline at end of file
diff --git a/_build/html/_static/plus.png b/_build/html/_static/plus.png
new file mode 100644
index 0000000000000000000000000000000000000000..7107cec93a979b9a5f64843235a16651d563ce2d
GIT binary patch
literal 90
zcmeAS@N?(olHy`uVBq!ia0vp^+#t*WBp7;*Yy1LIik>cxAr*|t7R?Mi>2?kWtu>-2
m3q%Vub%g%s<8sJhVPMczOq}xhg9DJoz~JfX=d#Wzp$Pyb1r*Kz

literal 0
HcmV?d00001

diff --git a/_build/html/_static/pygments.css b/_build/html/_static/pygments.css
new file mode 100644
index 0000000..d14395e
--- /dev/null
+++ b/_build/html/_static/pygments.css
@@ -0,0 +1,74 @@
+pre { line-height: 125%; margin: 0; }
+td.linenos pre { color: #000000; background-color: #f0f0f0; padding: 0 5px 0 5px; }
+span.linenos { color: #000000; background-color: #f0f0f0; padding: 0 5px 0 5px; }
+td.linenos pre.special { color: #000000; background-color: #ffffc0; padding: 0 5px 0 5px; }
+span.linenos.special { color: #000000; background-color: #ffffc0; padding: 0 5px 0 5px; }
+.highlight .hll { background-color: #ffffcc }
+.highlight { background: #eeffcc; }
+.highlight .c { color: #408090; font-style: italic } /* Comment */
+.highlight .err { border: 1px solid #FF0000 } /* Error */
+.highlight .k { color: #007020; font-weight: bold } /* Keyword */
+.highlight .o { color: #666666 } /* Operator */
+.highlight .ch { color: #408090; font-style: italic } /* Comment.Hashbang */
+.highlight .cm { color: #408090; font-style: italic } /* Comment.Multiline */
+.highlight .cp { color: #007020 } /* Comment.Preproc */
+.highlight .cpf { color: #408090; font-style: italic } /* Comment.PreprocFile */
+.highlight .c1 { color: #408090; font-style: italic } /* Comment.Single */
+.highlight .cs { color: #408090; background-color: #fff0f0 } /* Comment.Special */
+.highlight .gd { color: #A00000 } /* Generic.Deleted */
+.highlight .ge { font-style: italic } /* Generic.Emph */
+.highlight .gr { color: #FF0000 } /* Generic.Error */
+.highlight .gh { color: #000080; font-weight: bold } /* Generic.Heading */
+.highlight .gi { color: #00A000 } /* Generic.Inserted */
+.highlight .go { color: #333333 } /* Generic.Output */
+.highlight .gp { color: #c65d09; font-weight: bold } /* Generic.Prompt */
+.highlight .gs { font-weight: bold } /* Generic.Strong */
+.highlight .gu { color: #800080; font-weight: bold } /* Generic.Subheading */
+.highlight .gt { color: #0044DD } /* Generic.Traceback */
+.highlight .kc { color: #007020; font-weight: bold } /* Keyword.Constant */
+.highlight .kd { color: #007020; font-weight: bold } /* Keyword.Declaration */
+.highlight .kn { color: #007020; font-weight: bold } /* Keyword.Namespace */
+.highlight .kp { color: #007020 } /* Keyword.Pseudo */
+.highlight .kr { color: #007020; font-weight: bold } /* Keyword.Reserved */
+.highlight .kt { color: #902000 } /* Keyword.Type */
+.highlight .m { color: #208050 } /* Literal.Number */
+.highlight .s { color: #4070a0 } /* Literal.String */
+.highlight .na { color: #4070a0 } /* Name.Attribute */
+.highlight .nb { color: #007020 } /* Name.Builtin */
+.highlight .nc { color: #0e84b5; font-weight: bold } /* Name.Class */
+.highlight .no { color: #60add5 } /* Name.Constant */
+.highlight .nd { color: #555555; font-weight: bold } /* Name.Decorator */
+.highlight .ni { color: #d55537; font-weight: bold } /* Name.Entity */
+.highlight .ne { color: #007020 } /* Name.Exception */
+.highlight .nf { color: #06287e } /* Name.Function */
+.highlight .nl { color: #002070; font-weight: bold } /* Name.Label */
+.highlight .nn { color: #0e84b5; font-weight: bold } /* Name.Namespace */
+.highlight .nt { color: #062873; font-weight: bold } /* Name.Tag */
+.highlight .nv { color: #bb60d5 } /* Name.Variable */
+.highlight .ow { color: #007020; font-weight: bold } /* Operator.Word */
+.highlight .w { color: #bbbbbb } /* Text.Whitespace */
+.highlight .mb { color: #208050 } /* Literal.Number.Bin */
+.highlight .mf { color: #208050 } /* Literal.Number.Float */
+.highlight .mh { color: #208050 } /* Literal.Number.Hex */
+.highlight .mi { color: #208050 } /* Literal.Number.Integer */
+.highlight .mo { color: #208050 } /* Literal.Number.Oct */
+.highlight .sa { color: #4070a0 } /* Literal.String.Affix */
+.highlight .sb { color: #4070a0 } /* Literal.String.Backtick */
+.highlight .sc { color: #4070a0 } /* Literal.String.Char */
+.highlight .dl { color: #4070a0 } /* Literal.String.Delimiter */
+.highlight .sd { color: #4070a0; font-style: italic } /* Literal.String.Doc */
+.highlight .s2 { color: #4070a0 } /* Literal.String.Double */
+.highlight .se { color: #4070a0; font-weight: bold } /* Literal.String.Escape */
+.highlight .sh { color: #4070a0 } /* Literal.String.Heredoc */
+.highlight .si { color: #70a0d0; font-style: italic } /* Literal.String.Interpol */
+.highlight .sx { color: #c65d09 } /* Literal.String.Other */
+.highlight .sr { color: #235388 } /* Literal.String.Regex */
+.highlight .s1 { color: #4070a0 } /* Literal.String.Single */
+.highlight .ss { color: #517918 } /* Literal.String.Symbol */
+.highlight .bp { color: #007020 } /* Name.Builtin.Pseudo */
+.highlight .fm { color: #06287e } /* Name.Function.Magic */
+.highlight .vc { color: #bb60d5 } /* Name.Variable.Class */
+.highlight .vg { color: #bb60d5 } /* Name.Variable.Global */
+.highlight .vi { color: #bb60d5 } /* Name.Variable.Instance */
+.highlight .vm { color: #bb60d5 } /* Name.Variable.Magic */
+.highlight .il { color: #208050 } /* Literal.Number.Integer.Long */
\ No newline at end of file
diff --git a/_build/html/_static/searchtools.js b/_build/html/_static/searchtools.js
new file mode 100644
index 0000000..970d0d9
--- /dev/null
+++ b/_build/html/_static/searchtools.js
@@ -0,0 +1,514 @@
+/*
+ * searchtools.js
+ * ~~~~~~~~~~~~~~~~
+ *
+ * Sphinx JavaScript utilities for the full-text search.
+ *
+ * :copyright: Copyright 2007-2020 by the Sphinx team, see AUTHORS.
+ * :license: BSD, see LICENSE for details.
+ *
+ */
+
+if (!Scorer) {
+  /**
+   * Simple result scoring code.
+   */
+  var Scorer = {
+    // Implement the following function to further tweak the score for each result
+    // The function takes a result array [filename, title, anchor, descr, score]
+    // and returns the new score.
+    /*
+    score: function(result) {
+      return result[4];
+    },
+    */
+
+    // query matches the full name of an object
+    objNameMatch: 11,
+    // or matches in the last dotted part of the object name
+    objPartialMatch: 6,
+    // Additive scores depending on the priority of the object
+    objPrio: {0:  15,   // used to be importantResults
+              1:  5,   // used to be objectResults
+              2: -5},  // used to be unimportantResults
+    //  Used when the priority is not in the mapping.
+    objPrioDefault: 0,
+
+    // query found in title
+    title: 15,
+    partialTitle: 7,
+    // query found in terms
+    term: 5,
+    partialTerm: 2
+  };
+}
+
+if (!splitQuery) {
+  function splitQuery(query) {
+    return query.split(/\s+/);
+  }
+}
+
+/**
+ * Search Module
+ */
+var Search = {
+
+  _index : null,
+  _queued_query : null,
+  _pulse_status : -1,
+
+  htmlToText : function(htmlString) {
+      var htmlElement = document.createElement('span');
+      htmlElement.innerHTML = htmlString;
+      $(htmlElement).find('.headerlink').remove();
+      docContent = $(htmlElement).find('[role=main]')[0];
+      if(docContent === undefined) {
+          console.warn("Content block not found. Sphinx search tries to obtain it " +
+                       "via '[role=main]'. Could you check your theme or template.");
+          return "";
+      }
+      return docContent.textContent || docContent.innerText;
+  },
+
+  init : function() {
+      var params = $.getQueryParameters();
+      if (params.q) {
+          var query = params.q[0];
+          $('input[name="q"]')[0].value = query;
+          this.performSearch(query);
+      }
+  },
+
+  loadIndex : function(url) {
+    $.ajax({type: "GET", url: url, data: null,
+            dataType: "script", cache: true,
+            complete: function(jqxhr, textstatus) {
+              if (textstatus != "success") {
+                document.getElementById("searchindexloader").src = url;
+              }
+            }});
+  },
+
+  setIndex : function(index) {
+    var q;
+    this._index = index;
+    if ((q = this._queued_query) !== null) {
+      this._queued_query = null;
+      Search.query(q);
+    }
+  },
+
+  hasIndex : function() {
+      return this._index !== null;
+  },
+
+  deferQuery : function(query) {
+      this._queued_query = query;
+  },
+
+  stopPulse : function() {
+      this._pulse_status = 0;
+  },
+
+  startPulse : function() {
+    if (this._pulse_status >= 0)
+        return;
+    function pulse() {
+      var i;
+      Search._pulse_status = (Search._pulse_status + 1) % 4;
+      var dotString = '';
+      for (i = 0; i < Search._pulse_status; i++)
+        dotString += '.';
+      Search.dots.text(dotString);
+      if (Search._pulse_status > -1)
+        window.setTimeout(pulse, 500);
+    }
+    pulse();
+  },
+
+  /**
+   * perform a search for something (or wait until index is loaded)
+   */
+  performSearch : function(query) {
+    // create the required interface elements
+    this.out = $('#search-results');
+    this.title = $('<h2>' + _('Searching') + '</h2>').appendTo(this.out);
+    this.dots = $('<span></span>').appendTo(this.title);
+    this.status = $('<p class="search-summary">&nbsp;</p>').appendTo(this.out);
+    this.output = $('<ul class="search"/>').appendTo(this.out);
+
+    $('#search-progress').text(_('Preparing search...'));
+    this.startPulse();
+
+    // index already loaded, the browser was quick!
+    if (this.hasIndex())
+      this.query(query);
+    else
+      this.deferQuery(query);
+  },
+
+  /**
+   * execute search (requires search index to be loaded)
+   */
+  query : function(query) {
+    var i;
+
+    // stem the searchterms and add them to the correct list
+    var stemmer = new Stemmer();
+    var searchterms = [];
+    var excluded = [];
+    var hlterms = [];
+    var tmp = splitQuery(query);
+    var objectterms = [];
+    for (i = 0; i < tmp.length; i++) {
+      if (tmp[i] !== "") {
+          objectterms.push(tmp[i].toLowerCase());
+      }
+
+      if ($u.indexOf(stopwords, tmp[i].toLowerCase()) != -1 || tmp[i] === "") {
+        // skip this "word"
+        continue;
+      }
+      // stem the word
+      var word = stemmer.stemWord(tmp[i].toLowerCase());
+      // prevent stemmer from cutting word smaller than two chars
+      if(word.length < 3 && tmp[i].length >= 3) {
+        word = tmp[i];
+      }
+      var toAppend;
+      // select the correct list
+      if (word[0] == '-') {
+        toAppend = excluded;
+        word = word.substr(1);
+      }
+      else {
+        toAppend = searchterms;
+        hlterms.push(tmp[i].toLowerCase());
+      }
+      // only add if not already in the list
+      if (!$u.contains(toAppend, word))
+        toAppend.push(word);
+    }
+    var highlightstring = '?highlight=' + $.urlencode(hlterms.join(" "));
+
+    // console.debug('SEARCH: searching for:');
+    // console.info('required: ', searchterms);
+    // console.info('excluded: ', excluded);
+
+    // prepare search
+    var terms = this._index.terms;
+    var titleterms = this._index.titleterms;
+
+    // array of [filename, title, anchor, descr, score]
+    var results = [];
+    $('#search-progress').empty();
+
+    // lookup as object
+    for (i = 0; i < objectterms.length; i++) {
+      var others = [].concat(objectterms.slice(0, i),
+                             objectterms.slice(i+1, objectterms.length));
+      results = results.concat(this.performObjectSearch(objectterms[i], others));
+    }
+
+    // lookup as search terms in fulltext
+    results = results.concat(this.performTermsSearch(searchterms, excluded, terms, titleterms));
+
+    // let the scorer override scores with a custom scoring function
+    if (Scorer.score) {
+      for (i = 0; i < results.length; i++)
+        results[i][4] = Scorer.score(results[i]);
+    }
+
+    // now sort the results by score (in opposite order of appearance, since the
+    // display function below uses pop() to retrieve items) and then
+    // alphabetically
+    results.sort(function(a, b) {
+      var left = a[4];
+      var right = b[4];
+      if (left > right) {
+        return 1;
+      } else if (left < right) {
+        return -1;
+      } else {
+        // same score: sort alphabetically
+        left = a[1].toLowerCase();
+        right = b[1].toLowerCase();
+        return (left > right) ? -1 : ((left < right) ? 1 : 0);
+      }
+    });
+
+    // for debugging
+    //Search.lastresults = results.slice();  // a copy
+    //console.info('search results:', Search.lastresults);
+
+    // print the results
+    var resultCount = results.length;
+    function displayNextItem() {
+      // results left, load the summary and display it
+      if (results.length) {
+        var item = results.pop();
+        var listItem = $('<li style="display:none"></li>');
+        var requestUrl = "";
+        var linkUrl = "";
+        if (DOCUMENTATION_OPTIONS.BUILDER === 'dirhtml') {
+          // dirhtml builder
+          var dirname = item[0] + '/';
+          if (dirname.match(/\/index\/$/)) {
+            dirname = dirname.substring(0, dirname.length-6);
+          } else if (dirname == 'index/') {
+            dirname = '';
+          }
+          requestUrl = DOCUMENTATION_OPTIONS.URL_ROOT + dirname;
+          linkUrl = requestUrl;
+
+        } else {
+          // normal html builders
+          requestUrl = DOCUMENTATION_OPTIONS.URL_ROOT + item[0] + DOCUMENTATION_OPTIONS.FILE_SUFFIX;
+          linkUrl = item[0] + DOCUMENTATION_OPTIONS.LINK_SUFFIX;
+        }
+        listItem.append($('<a/>').attr('href',
+            linkUrl +
+            highlightstring + item[2]).html(item[1]));
+        if (item[3]) {
+          listItem.append($('<span> (' + item[3] + ')</span>'));
+          Search.output.append(listItem);
+          listItem.slideDown(5, function() {
+            displayNextItem();
+          });
+        } else if (DOCUMENTATION_OPTIONS.HAS_SOURCE) {
+          $.ajax({url: requestUrl,
+                  dataType: "text",
+                  complete: function(jqxhr, textstatus) {
+                    var data = jqxhr.responseText;
+                    if (data !== '' && data !== undefined) {
+                      listItem.append(Search.makeSearchSummary(data, searchterms, hlterms));
+                    }
+                    Search.output.append(listItem);
+                    listItem.slideDown(5, function() {
+                      displayNextItem();
+                    });
+                  }});
+        } else {
+          // no source available, just display title
+          Search.output.append(listItem);
+          listItem.slideDown(5, function() {
+            displayNextItem();
+          });
+        }
+      }
+      // search finished, update title and status message
+      else {
+        Search.stopPulse();
+        Search.title.text(_('Search Results'));
+        if (!resultCount)
+          Search.status.text(_('Your search did not match any documents. Please make sure that all words are spelled correctly and that you\'ve selected enough categories.'));
+        else
+            Search.status.text(_('Search finished, found %s page(s) matching the search query.').replace('%s', resultCount));
+        Search.status.fadeIn(500);
+      }
+    }
+    displayNextItem();
+  },
+
+  /**
+   * search for object names
+   */
+  performObjectSearch : function(object, otherterms) {
+    var filenames = this._index.filenames;
+    var docnames = this._index.docnames;
+    var objects = this._index.objects;
+    var objnames = this._index.objnames;
+    var titles = this._index.titles;
+
+    var i;
+    var results = [];
+
+    for (var prefix in objects) {
+      for (var name in objects[prefix]) {
+        var fullname = (prefix ? prefix + '.' : '') + name;
+        var fullnameLower = fullname.toLowerCase()
+        if (fullnameLower.indexOf(object) > -1) {
+          var score = 0;
+          var parts = fullnameLower.split('.');
+          // check for different match types: exact matches of full name or
+          // "last name" (i.e. last dotted part)
+          if (fullnameLower == object || parts[parts.length - 1] == object) {
+            score += Scorer.objNameMatch;
+          // matches in last name
+          } else if (parts[parts.length - 1].indexOf(object) > -1) {
+            score += Scorer.objPartialMatch;
+          }
+          var match = objects[prefix][name];
+          var objname = objnames[match[1]][2];
+          var title = titles[match[0]];
+          // If more than one term searched for, we require other words to be
+          // found in the name/title/description
+          if (otherterms.length > 0) {
+            var haystack = (prefix + ' ' + name + ' ' +
+                            objname + ' ' + title).toLowerCase();
+            var allfound = true;
+            for (i = 0; i < otherterms.length; i++) {
+              if (haystack.indexOf(otherterms[i]) == -1) {
+                allfound = false;
+                break;
+              }
+            }
+            if (!allfound) {
+              continue;
+            }
+          }
+          var descr = objname + _(', in ') + title;
+
+          var anchor = match[3];
+          if (anchor === '')
+            anchor = fullname;
+          else if (anchor == '-')
+            anchor = objnames[match[1]][1] + '-' + fullname;
+          // add custom score for some objects according to scorer
+          if (Scorer.objPrio.hasOwnProperty(match[2])) {
+            score += Scorer.objPrio[match[2]];
+          } else {
+            score += Scorer.objPrioDefault;
+          }
+          results.push([docnames[match[0]], fullname, '#'+anchor, descr, score, filenames[match[0]]]);
+        }
+      }
+    }
+
+    return results;
+  },
+
+  /**
+   * search for full-text terms in the index
+   */
+  performTermsSearch : function(searchterms, excluded, terms, titleterms) {
+    var docnames = this._index.docnames;
+    var filenames = this._index.filenames;
+    var titles = this._index.titles;
+
+    var i, j, file;
+    var fileMap = {};
+    var scoreMap = {};
+    var results = [];
+
+    // perform the search on the required terms
+    for (i = 0; i < searchterms.length; i++) {
+      var word = searchterms[i];
+      var files = [];
+      var _o = [
+        {files: terms[word], score: Scorer.term},
+        {files: titleterms[word], score: Scorer.title}
+      ];
+      // add support for partial matches
+      if (word.length > 2) {
+        for (var w in terms) {
+          if (w.match(word) && !terms[word]) {
+            _o.push({files: terms[w], score: Scorer.partialTerm})
+          }
+        }
+        for (var w in titleterms) {
+          if (w.match(word) && !titleterms[word]) {
+              _o.push({files: titleterms[w], score: Scorer.partialTitle})
+          }
+        }
+      }
+
+      // no match but word was a required one
+      if ($u.every(_o, function(o){return o.files === undefined;})) {
+        break;
+      }
+      // found search word in contents
+      $u.each(_o, function(o) {
+        var _files = o.files;
+        if (_files === undefined)
+          return
+
+        if (_files.length === undefined)
+          _files = [_files];
+        files = files.concat(_files);
+
+        // set score for the word in each file to Scorer.term
+        for (j = 0; j < _files.length; j++) {
+          file = _files[j];
+          if (!(file in scoreMap))
+            scoreMap[file] = {};
+          scoreMap[file][word] = o.score;
+        }
+      });
+
+      // create the mapping
+      for (j = 0; j < files.length; j++) {
+        file = files[j];
+        if (file in fileMap && fileMap[file].indexOf(word) === -1)
+          fileMap[file].push(word);
+        else
+          fileMap[file] = [word];
+      }
+    }
+
+    // now check if the files don't contain excluded terms
+    for (file in fileMap) {
+      var valid = true;
+
+      // check if all requirements are matched
+      var filteredTermCount = // as search terms with length < 3 are discarded: ignore
+        searchterms.filter(function(term){return term.length > 2}).length
+      if (
+        fileMap[file].length != searchterms.length &&
+        fileMap[file].length != filteredTermCount
+      ) continue;
+
+      // ensure that none of the excluded terms is in the search result
+      for (i = 0; i < excluded.length; i++) {
+        if (terms[excluded[i]] == file ||
+            titleterms[excluded[i]] == file ||
+            $u.contains(terms[excluded[i]] || [], file) ||
+            $u.contains(titleterms[excluded[i]] || [], file)) {
+          valid = false;
+          break;
+        }
+      }
+
+      // if we have still a valid result we can add it to the result list
+      if (valid) {
+        // select one (max) score for the file.
+        // for better ranking, we should calculate ranking by using words statistics like basic tf-idf...
+        var score = $u.max($u.map(fileMap[file], function(w){return scoreMap[file][w]}));
+        results.push([docnames[file], titles[file], '', null, score, filenames[file]]);
+      }
+    }
+    return results;
+  },
+
+  /**
+   * helper function to return a node containing the
+   * search summary for a given text. keywords is a list
+   * of stemmed words, hlwords is the list of normal, unstemmed
+   * words. the first one is used to find the occurrence, the
+   * latter for highlighting it.
+   */
+  makeSearchSummary : function(htmlText, keywords, hlwords) {
+    var text = Search.htmlToText(htmlText);
+    var textLower = text.toLowerCase();
+    var start = 0;
+    $.each(keywords, function() {
+      var i = textLower.indexOf(this.toLowerCase());
+      if (i > -1)
+        start = i;
+    });
+    start = Math.max(start - 120, 0);
+    var excerpt = ((start > 0) ? '...' : '') +
+      $.trim(text.substr(start, 240)) +
+      ((start + 240 - text.length) ? '...' : '');
+    var rv = $('<div class="context"></div>').text(excerpt);
+    $.each(hlwords, function() {
+      rv = rv.highlightText(this, 'highlighted');
+    });
+    return rv;
+  }
+};
+
+$(document).ready(function() {
+  Search.init();
+});
diff --git a/_build/html/_static/sphinx-book-theme.be0a4a0c39cd630af62a2fcf693f3f06.js b/_build/html/_static/sphinx-book-theme.be0a4a0c39cd630af62a2fcf693f3f06.js
new file mode 100644
index 0000000..f876f77
--- /dev/null
+++ b/_build/html/_static/sphinx-book-theme.be0a4a0c39cd630af62a2fcf693f3f06.js
@@ -0,0 +1,17 @@
+var initTriggerNavBar=()=>{if($(window).width()<768){$("#navbar-toggler").trigger("click")}}
+var scrollToActive=()=>{var navbar=document.getElementById('site-navigation')
+var active_pages=navbar.querySelectorAll(".active")
+var active_page=active_pages[active_pages.length-1]
+if(active_page!==undefined&&active_page.offsetTop>($(window).height()*.5)){navbar.scrollTop=active_page.offsetTop-($(window).height()*.2)}}
+var sbRunWhenDOMLoaded=cb=>{if(document.readyState!='loading'){cb()}else if(document.addEventListener){document.addEventListener('DOMContentLoaded',cb)}else{document.attachEvent('onreadystatechange',function(){if(document.readyState=='complete')cb()})}}
+function toggleFullScreen(){var navToggler=$("#navbar-toggler");if(!document.fullscreenElement){document.documentElement.requestFullscreen();if(!navToggler.hasClass("collapsed")){navToggler.click();}}else{if(document.exitFullscreen){document.exitFullscreen();if(navToggler.hasClass("collapsed")){navToggler.click();}}}}
+var initTooltips=()=>{$(document).ready(function(){$('[data-toggle="tooltip"]').tooltip();});}
+var initTocHide=()=>{var scrollTimeout;var throttle=200;var tocHeight=$("#bd-toc-nav").outerHeight(true)+$(".bd-toc").outerHeight(true);var hideTocAfter=tocHeight+200;var checkTocScroll=function(){var margin_content=$(".margin, .tag_margin, .full-width, .full_width, .tag_full-width, .tag_full_width, .sidebar, .tag_sidebar, .popout, .tag_popout");margin_content.each((index,item)=>{var topOffset=$(item).offset().top-$(window).scrollTop();var bottomOffset=topOffset+$(item).outerHeight(true);var topOverlaps=((topOffset>=0)&&(topOffset<hideTocAfter));var bottomOverlaps=((bottomOffset>=0)&&(bottomOffset<hideTocAfter));var removeToc=(topOverlaps||bottomOverlaps);if(removeToc&&window.pageYOffset>20){$("div.bd-toc").removeClass("show")
+return false}else{$("div.bd-toc").addClass("show")};})};$(window).on('scroll',function(){if(!scrollTimeout){scrollTimeout=setTimeout(function(){checkTocScroll();scrollTimeout=null;},throttle);}});}
+var initThebeSBT=()=>{var title=$("div.section h1")[0]
+if(!$(title).next().hasClass("thebe-launch-button")){$("<button class='thebe-launch-button'></button>").insertAfter($(title))}
+initThebe();}
+sbRunWhenDOMLoaded(initTooltips)
+sbRunWhenDOMLoaded(initTriggerNavBar)
+sbRunWhenDOMLoaded(scrollToActive)
+sbRunWhenDOMLoaded(initTocHide)
diff --git a/_build/html/_static/sphinx-book-theme.bfb7730f9caf2ec0b46a44615585038c.css b/_build/html/_static/sphinx-book-theme.bfb7730f9caf2ec0b46a44615585038c.css
new file mode 100644
index 0000000..b6616f8
--- /dev/null
+++ b/_build/html/_static/sphinx-book-theme.bfb7730f9caf2ec0b46a44615585038c.css
@@ -0,0 +1,5 @@
+/*! sphinx-book-theme CSS
+ * BSD 3-Clause License
+ * Copyright (c) 2020, EBP
+ * All rights reserved.
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img{height:1.15em;padding-right:6px;margin-left:-5px}#navbar-toggler{position:relative;margin-right:1em;margin-left:.5em;color:#5a5a5a}#navbar-toggler i{transition:opacity .3s, transform .3s;position:absolute;top:16%;left:0;display:block;font-size:1.2em}#navbar-toggler i.fa-bars{opacity:0;transform:rotate(180deg) scale(0.5)}#navbar-toggler i.fa-arrow-left,#navbar-toggler i.fa-arrow-up{opacity:1}#navbar-toggler.collapsed i.fa-bars{opacity:1;transform:rotate(0) scale(1)}#navbar-toggler.collapsed i.fa-arrow-left,#navbar-toggler.collapsed i.fa-arrow-up{opacity:0;transform:rotate(-180deg) scale(0.5)}@media (max-width: 768px){#navbar-toggler i.fa-arrow-up{display:inherit}#navbar-toggler i.fa-arrow-left{display:none}}@media (min-width: 768px){#navbar-toggler i.fa-arrow-up{display:none}#navbar-toggler i.fa-arrow-left{display:inherit}}.bd-toc{padding:0px !important;overflow-y:visible;background:white;right:0;z-index:999;transition:height .35s ease}.bd-toc div.onthispage,.bd-toc .toc-entry a{color:#5a5a5a}.bd-toc nav{opacity:0;max-height:0;transition:opacity 0.2s ease, max-height .7s ease;overflow-y:hidden;background:white}.bd-toc nav a:hover,.bd-toc nav li.active>a.active{color:#0071bc}.bd-toc nav li.active>a.active{border-left:2px solid #0071bc}.bd-toc:hover nav,.bd-toc.show nav{max-height:100vh;opacity:1}.bd-toc:hover .tocsection:after,.bd-toc.show .tocsection:after{opacity:0}.bd-toc .tocsection{padding:.5rem 0 .5rem 1rem !important}.bd-toc .tocsection:after{content:"\f107";font-family:"Font Awesome 5 Free";font-weight:900;padding-left:.5em;transition:opacity .3s ease}.bd-toc .toc-entry a{padding:.125rem 1rem !important}.bd-toc div.editthispage{display:none}.bd-sidebar{top:0px !important;overflow-y:auto;height:100vh !important;scrollbar-width:thin}.bd-sidebar nav ul.nav li a,.bd-sidebar nav ul.nav ul li a{color:#5a5a5a}.bd-sidebar nav ul.nav a:hover,.bd-sidebar nav ul.nav li.active>a,.bd-sidebar nav ul.nav li.active>a:hover{color:#0071bc}.bd-sidebar::-webkit-scrollbar{width:5px}.bd-sidebar::-webkit-scrollbar{background:#f1f1f1}.bd-sidebar::-webkit-scrollbar-thumb{background:#c1c1c1}@media (min-width: 992px){.bd-sidebar:not(:hover){-ms-overflow-style:none}.bd-sidebar:not(:hover)::-webkit-scrollbar{background:#FFFFFF}.bd-sidebar:not(:hover)::-webkit-scrollbar-thumb{background:#FFFFFF}}.bd-sidebar h1.site-logo{margin:.5em 0 0 0;font-size:1.1em;color:black;text-align:center}.bd-sidebar div.navbar_extra_footer{text-align:center;font-size:.9em;color:#5a5a5a;margin-bottom:3em}.bd-sidebar.collapsing{border:none;overflow:hidden;position:relative;padding-top:0}.bd-sidebar p.caption{margin-top:1.25em;margin-bottom:0;font-size:1.2em}.bd-sidebar li>a>i{font-size:.8em;margin-left:0.3em}.toc-h1,.toc-h2,.toc-h3,.toc-h4{font-size:1em}.toc-h1>a,.toc-h2>a,.toc-h3>a,.toc-h4>a{font-size:0.9em}.site-navigation,.site-navigation.collapsing{transition:flex .2s ease 0s, height .35s ease, opacity 0.2s ease}@media (max-width: 768px){#site-navigation{position:fixed;margin-top:3em;z-index:2000;background:white}}@media (max-width: 768px){.bd-sidebar{height:60vh !important;border-bottom:3px solid #c3c3c3}.bd-sidebar.collapsing{height:0px !important}.bd-sidebar.single-page{display:none}}@media (min-width: 768px){.bd-sidebar{z-index:2000 !important}.site-navigation.collapsing{flex:0 0 0px;padding:0}}@media (min-width: 768px){div.navbar-brand-box{padding-top:2em}}div.navbar-brand-box a.navbar-brand{width:100%;height:auto}div.navbar-brand-box a.navbar-brand img{display:block;height:auto;width:auto;max-height:10vh;max-width:100%;margin:0 auto}@media (min-width: 768px){div.navbar-brand-box a.navbar-brand img{max-height:15vh !important}}nav.bd-links{margin-left:0px;max-height:none !important}nav.bd-links p.caption{font-size:0.9em;text-transform:uppercase;font-weight:bold}nav.bd-links p.caption:first-child{margin-top:0}nav.bd-links ul{list-style:none}nav.bd-links li{width:100%}nav.bd-links li.toctree-l1,nav.bd-links li.toctree-l2,nav.bd-links li.toctree-l3,nav.bd-links li.toctree-l4,nav.bd-links li.toctree-l5{font-size:1em}nav.bd-links li.toctree-l1>a,nav.bd-links li.toctree-l2>a,nav.bd-links li.toctree-l3>a,nav.bd-links li.toctree-l4>a,nav.bd-links li.toctree-l5>a{font-size:0.9em}nav.bd-links>ul.nav{padding-left:0}nav.bd-links>ul.nav ul{padding:0 0 0 1rem}nav.bd-links>ul.nav a{padding:.25rem 0 !important}@media (min-width: 768px){.bd-sidebar,.bd-topbar-whitespace{max-width:275px}}.prev-next-bottom{height:3em}@media print{.tag_popout,div.margin{float:right;clear:right;width:50%;margin-right:-56%;margin-top:0;margin-bottom:0;padding-right:1em;font-size:0.9rem;line-height:1.3;vertical-align:baseline;position:relative;border-left:none;padding-left:0}.bd-content div#main-content>div{flex:0 0 75%;max-width:75%}h1,h2,h3,h4{break-after:avoid}table{break-inside:avoid}pre{word-wrap:break-word}a.copybtn,a.headerlink{display:none}.tag-fullwidth{width:145%;clear:both}div.toggle-hidden{visibility:inherit;opacity:1;height:auto}button.toggle-button{display:none}blockquote.epigraph{border:none}div.container{min-width:50% !important}div.bd-sidebar,div.prev-next-bottom{display:none}div.topbar{height:0;padding:0;position:inherit}div.topbar div.topbar-main{opacity:0}div.topbar div.bd-toc{flex:0 0 25%;max-width:25%;height:auto !important}div.topbar div.bd-toc nav,div.topbar div.bd-toc nav>ul.nav,div.topbar div.bd-toc nav>ul.nav>li>ul.nav{opacity:1;display:block}div.topbar div.bd-toc .nav-link.active{font-weight:inherit;color:inherit;background-color:inherit;border-left:inherit}}
diff --git a/_build/html/_static/sphinx-thebe.css b/_build/html/_static/sphinx-thebe.css
new file mode 100644
index 0000000..b19a7b0
--- /dev/null
+++ b/_build/html/_static/sphinx-thebe.css
@@ -0,0 +1,120 @@
+/* Thebelab Buttons */
+.thebelab-button {
+    z-index: 999;
+    display: inline-block;
+    padding: 0.35em 1.2em;
+    margin: 0px 1px;
+    border-radius: 0.12em;
+    box-sizing:  border-box;
+    text-decoration: none;
+    font-family: 'Roboto', sans-serif;
+    font-weight: 300;
+    text-align: center;
+    transition: all 0.2s;
+    background-color: #dddddd;
+    border: 0.05em solid white;
+    color: #000000;
+}
+
+.thebelab-button:hover{
+    border: 0.05em solid black;
+    background-color: #fcfcfc;
+}
+
+.thebe-launch-button {
+    height: 2.2em;
+    font-size: .8em;
+    border: 1px black solid;
+}
+
+/* Thebelab Cell */
+.thebelab-cell pre {
+  background: none;
+}
+
+.thebelab-cell .thebelab-input {
+    padding-left: 1em;
+    margin-bottom: .5em;
+    margin-top: .5em;
+}
+
+.thebelab-cell .jp-OutputArea {
+    margin-top: .5em;
+    margin-left: 1em;
+}
+
+button.thebelab-button.thebelab-run-button {
+    margin-left: 1.5em;
+    margin-bottom: .5em;
+}
+
+/* Loading button */
+button.thebe-launch-button div.spinner {
+    float: left;
+    margin-right: 1em;
+}
+
+/* Remove the spinner when thebelab is ready */
+.thebe-launch-button.thebe-status-ready .spinner {
+    display: none;
+}
+
+.thebe-launch-button span.status {
+    font-family: monospace;
+    font-weight: bold;
+}
+
+.thebe-launch-button.thebe-status-ready span.status {
+    color: green;
+}
+
+.spinner {
+    height: 2em;
+    text-align: center;
+    font-size: 0.7em;
+  }
+
+  .spinner > div {
+    background-color: #F37726;
+    height: 100%;
+    width: 6px;
+    display: inline-block;
+
+    -webkit-animation: sk-stretchdelay 1.2s infinite ease-in-out;
+    animation: sk-stretchdelay 1.2s infinite ease-in-out;
+  }
+
+  .spinner .rect2 {
+    -webkit-animation-delay: -1.1s;
+    animation-delay: -1.1s;
+  }
+
+  .spinner .rect3 {
+    -webkit-animation-delay: -1.0s;
+    animation-delay: -1.0s;
+  }
+
+  .spinner .rect4 {
+    -webkit-animation-delay: -0.9s;
+    animation-delay: -0.9s;
+  }
+
+  .spinner .rect5 {
+    -webkit-animation-delay: -0.8s;
+    animation-delay: -0.8s;
+  }
+
+  @-webkit-keyframes sk-stretchdelay {
+    0%, 40%, 100% { -webkit-transform: scaleY(0.4) }
+    20% { -webkit-transform: scaleY(1.0) }
+  }
+
+  @keyframes sk-stretchdelay {
+    0%, 40%, 100% {
+      transform: scaleY(0.4);
+      -webkit-transform: scaleY(0.4);
+    }  20% {
+      transform: scaleY(1.0);
+      -webkit-transform: scaleY(1.0);
+    }
+  }
diff --git a/_build/html/_static/sphinx-thebe.js b/_build/html/_static/sphinx-thebe.js
new file mode 100644
index 0000000..4842c44
--- /dev/null
+++ b/_build/html/_static/sphinx-thebe.js
@@ -0,0 +1,96 @@
+/**
+ * Add attributes to Thebe blocks to initialize thebe properly
+ */
+
+var initThebe = () => {
+    // If Thebelab hasn't loaded, wait a bit and try again. This
+    // happens because we load ClipboardJS asynchronously.
+    if (window.thebelab === undefined) {
+        console.log("thebe not loaded, retrying...");
+        setTimeout(initThebe, 500)
+        return
+    }
+
+    console.log("Adding thebe to code cells...");
+
+    // Load thebe config in case we want to update it as some point
+    thebe_config = $('script[type="text/x-thebe-config"]')[0]
+
+
+    // If we already detect a Thebe cell, don't re-run
+    if (document.querySelectorAll('div.thebe-cell').length > 0) {
+        return;
+    }
+
+    // Update thebe buttons with loading message
+    $(".thebe-launch-button").each((ii, button) => {
+        button.innerHTML = `
+        <div class="spinner">
+            <div class="rect1"></div>
+            <div class="rect2"></div>
+            <div class="rect3"></div>
+            <div class="rect4"></div>
+        </div>
+        <span class="loading-text"></span>`;
+    })
+
+    // Set thebe event hooks
+    var thebeStatus;
+    thebelab.on("status", function (evt, data) {
+        console.log("Status changed:", data.status, data.message);
+
+        $(".thebe-launch-button ")
+        .removeClass("thebe-status-" + thebeStatus)
+        .addClass("thebe-status-" + data.status)
+        .find(".loading-text").html("<span class='launch_msg'>Launching from mybinder.org: </span><span class='status'>" + data.status + "</span>");
+
+        // Now update our thebe status
+        thebeStatus = data.status;
+
+        // Find any cells with an initialization tag and ask thebe to run them when ready
+        if (data.status === "ready") {
+            var thebeInitCells = document.querySelectorAll('.thebe-init, .tag_thebe-init');
+            thebeInitCells.forEach((cell) => {
+                console.log("Initializing Thebe with cell: " + cell.id);
+                cell.querySelector('.thebelab-run-button').click();
+            });
+        }
+    });
+
+
+    // Find all code cells, replace with Thebe interactive code cells
+    const codeCells = document.querySelectorAll(thebe_selector)
+    codeCells.forEach((codeCell, index) => {
+        const codeCellId = index => `codecell${index}`;
+        codeCell.id = codeCellId(index);
+        codeCellText = codeCell.querySelector(thebe_selector_input);
+        codeCellOutput = codeCell.querySelector(thebe_selector_output);
+
+        // Clean up the language to make it work w/ CodeMirror and add it to the cell
+        dataLanguage = detectLanguage(kernelName);
+
+        if (codeCellText) {
+            codeCellText.setAttribute('data-language', dataLanguage);
+            codeCellText.setAttribute('data-executable', 'true');
+
+            // If we had an output, insert it just after the `pre` cell
+            if (codeCellOutput) {
+                $(codeCellOutput).attr("data-output", "");
+                $(codeCellOutput).insertAfter(codeCellText);
+            }
+        }
+    });
+
+    // Init thebe
+    thebelab.bootstrap();
+}
+
+// Helper function to munge the language name
+var detectLanguage = (language) => {
+    if (language.indexOf('python') > -1) {
+        language = "python";
+    } else if (language === 'ir') {
+        language = "r"
+    }
+    return language;
+}
diff --git a/_build/html/_static/togglebutton.css b/_build/html/_static/togglebutton.css
new file mode 100644
index 0000000..d5b5f6f
--- /dev/null
+++ b/_build/html/_static/togglebutton.css
@@ -0,0 +1,90 @@
+/* Visibility of the target */
+.toggle, div.admonition.toggle .admonition-title ~ * {
+    transition: opacity .5s, height .5s;
+}
+
+.toggle-hidden:not(.admonition) {
+    visibility: hidden;
+    opacity: 0;
+    height: 1.5em;
+    margin: 0px;
+    padding: 0px;
+}
+
+/* Overrides for admonition toggles */
+
+/* Titles should cut off earlier to avoid overlapping w/ button */
+div.admonition.toggle p.admonition-title {
+    padding-right: 25%;
+}
+
+/* hides all the content of a page until de-toggled */
+div.admonition.toggle-hidden .admonition-title ~ * {
+    height: 0;
+    margin: 0;
+    float: left; /* so they overlap when hidden */
+    opacity: 0;
+    visibility: hidden;
+}
+
+/* Toggle buttons inside admonitions so we see the title */
+.toggle.admonition {
+    position: relative;
+}
+
+.toggle.admonition.admonition-title:after {
+    content: "" !important;
+}
+
+/* Note, we'll over-ride this in sphinx-book-theme */
+.toggle.admonition button.toggle-button {
+    margin-right: 0.5em;
+    right: 0em;
+    position: absolute;
+    top: .2em;
+}
+
+/* General button style */
+button.toggle-button {
+    background: #999;
+    border: none;
+    z-index: 999;
+    right: -2.5em;
+    margin-left: -2.5em; /* A hack to keep code blocks from being pushed left */
+    position: relative;
+    float: right;
+    border-radius: 100%;
+    width: 1.5em;
+    height: 1.5em;
+    padding: 0px;
+}
+
+@media (min-width: 768px) {
+    button.toggle-button.toggle-button-hidden:before {
+        content: "Click to show";
+        position: absolute;
+        font-size: .8em;
+        left: -6.5em;
+        bottom: .4em;
+    }
+}
+
+
+/* Plus / minus toggles */
+.toggle-button .bar {
+    background-color: white;
+    position: absolute;
+    left: 15%;
+    top: 43%;
+    width: 16px;
+    height: 3px;
+}
+
+.toggle-button .vertical {
+    transition: all 0.25s ease-in-out;
+    transform-origin: center;
+}
+
+.toggle-button-hidden .vertical {
+    transform: rotate(-90deg);
+}
\ No newline at end of file
diff --git a/_build/html/_static/togglebutton.js b/_build/html/_static/togglebutton.js
new file mode 100644
index 0000000..19d38c1
--- /dev/null
+++ b/_build/html/_static/togglebutton.js
@@ -0,0 +1,76 @@
+var initToggleItems = () => {
+  var itemsToToggle = document.querySelectorAll(togglebuttonSelector);
+  console.log(itemsToToggle, togglebuttonSelector)
+  // Add the button to each admonition and hook up a callback to toggle visibility
+  itemsToToggle.forEach((item, index) => {
+    var toggleID = `toggle-${index}`;
+    var buttonID = `button-${toggleID}`;
+    var collapseButton = `
+      <button id="${buttonID}" class="toggle-button" data-target="${toggleID}" data-button="${buttonID}">
+          <div class="bar horizontal" data-button="${buttonID}"></div>
+          <div class="bar vertical" data-button="${buttonID}"></div>
+      </button>`;
+
+    item.setAttribute('id', toggleID);
+
+    if (!item.classList.contains("toggle")){
+      item.classList.add("toggle");
+    }
+
+    // If it's an admonition block, then we'll add the button inside
+    if (item.classList.contains("admonition")) {
+      item.insertAdjacentHTML("afterbegin", collapseButton);
+    } else {
+      item.insertAdjacentHTML('beforebegin', collapseButton);
+    }
+
+    thisButton = $(`#${buttonID}`);
+    thisButton.on('click', toggleClickHandler);
+    if (!item.classList.contains("toggle-shown")) {
+      toggleHidden(thisButton[0]);
+    }
+  })
+};
+
+// This should simply add / remove the collapsed class and change the button text
+var toggleHidden = (button) => {
+  target = button.dataset['target']
+  var itemToToggle = document.getElementById(target);
+  if (itemToToggle.classList.contains("toggle-hidden")) {
+    itemToToggle.classList.remove("toggle-hidden");
+    button.classList.remove("toggle-button-hidden");
+  } else {
+    itemToToggle.classList.add("toggle-hidden");
+    button.classList.add("toggle-button-hidden");
+  }
+}
+
+var toggleClickHandler = (click) => {
+  button = document.getElementById(click.target.dataset['button']);
+  toggleHidden(button);
+}
+
+// If we want to blanket-add toggle classes to certain cells
+var addToggleToSelector = () => {
+  const selector = "";
+  if (selector.length > 0) {
+    document.querySelectorAll(selector).forEach((item) => {
+      item.classList.add("toggle");
+    })
+  }
+}
+
+// Helper function to run when the DOM is finished
+const sphinxToggleRunWhenDOMLoaded = cb => {
+  if (document.readyState != 'loading') {
+    cb()
+  } else if (document.addEventListener) {
+    document.addEventListener('DOMContentLoaded', cb)
+  } else {
+    document.attachEvent('onreadystatechange', function() {
+      if (document.readyState == 'complete') cb()
+    })
+  }
+}
+sphinxToggleRunWhenDOMLoaded(addToggleToSelector)
+sphinxToggleRunWhenDOMLoaded(initToggleItems)
diff --git a/_build/html/_static/underscore-1.3.1.js b/_build/html/_static/underscore-1.3.1.js
new file mode 100644
index 0000000..208d4cd
--- /dev/null
+++ b/_build/html/_static/underscore-1.3.1.js
@@ -0,0 +1,999 @@
+//     Underscore.js 1.3.1
+//     (c) 2009-2012 Jeremy Ashkenas, DocumentCloud Inc.
+//     Underscore is freely distributable under the MIT license.
+//     Portions of Underscore are inspired or borrowed from Prototype,
+//     Oliver Steele's Functional, and John Resig's Micro-Templating.
+//     For all details and documentation:
+//     http://documentcloud.github.com/underscore
+
+(function() {
+
+  // Baseline setup
+  // --------------
+
+  // Establish the root object, `window` in the browser, or `global` on the server.
+  var root = this;
+
+  // Save the previous value of the `_` variable.
+  var previousUnderscore = root._;
+
+  // Establish the object that gets returned to break out of a loop iteration.
+  var breaker = {};
+
+  // Save bytes in the minified (but not gzipped) version:
+  var ArrayProto = Array.prototype, ObjProto = Object.prototype, FuncProto = Function.prototype;
+
+  // Create quick reference variables for speed access to core prototypes.
+  var slice            = ArrayProto.slice,
+      unshift          = ArrayProto.unshift,
+      toString         = ObjProto.toString,
+      hasOwnProperty   = ObjProto.hasOwnProperty;
+
+  // All **ECMAScript 5** native function implementations that we hope to use
+  // are declared here.
+  var
+    nativeForEach      = ArrayProto.forEach,
+    nativeMap          = ArrayProto.map,
+    nativeReduce       = ArrayProto.reduce,
+    nativeReduceRight  = ArrayProto.reduceRight,
+    nativeFilter       = ArrayProto.filter,
+    nativeEvery        = ArrayProto.every,
+    nativeSome         = ArrayProto.some,
+    nativeIndexOf      = ArrayProto.indexOf,
+    nativeLastIndexOf  = ArrayProto.lastIndexOf,
+    nativeIsArray      = Array.isArray,
+    nativeKeys         = Object.keys,
+    nativeBind         = FuncProto.bind;
+
+  // Create a safe reference to the Underscore object for use below.
+  var _ = function(obj) { return new wrapper(obj); };
+
+  // Export the Underscore object for **Node.js**, with
+  // backwards-compatibility for the old `require()` API. If we're in
+  // the browser, add `_` as a global object via a string identifier,
+  // for Closure Compiler "advanced" mode.
+  if (typeof exports !== 'undefined') {
+    if (typeof module !== 'undefined' && module.exports) {
+      exports = module.exports = _;
+    }
+    exports._ = _;
+  } else {
+    root['_'] = _;
+  }
+
+  // Current version.
+  _.VERSION = '1.3.1';
+
+  // Collection Functions
+  // --------------------
+
+  // The cornerstone, an `each` implementation, aka `forEach`.
+  // Handles objects with the built-in `forEach`, arrays, and raw objects.
+  // Delegates to **ECMAScript 5**'s native `forEach` if available.
+  var each = _.each = _.forEach = function(obj, iterator, context) {
+    if (obj == null) return;
+    if (nativeForEach && obj.forEach === nativeForEach) {
+      obj.forEach(iterator, context);
+    } else if (obj.length === +obj.length) {
+      for (var i = 0, l = obj.length; i < l; i++) {
+        if (i in obj && iterator.call(context, obj[i], i, obj) === breaker) return;
+      }
+    } else {
+      for (var key in obj) {
+        if (_.has(obj, key)) {
+          if (iterator.call(context, obj[key], key, obj) === breaker) return;
+        }
+      }
+    }
+  };
+
+  // Return the results of applying the iterator to each element.
+  // Delegates to **ECMAScript 5**'s native `map` if available.
+  _.map = _.collect = function(obj, iterator, context) {
+    var results = [];
+    if (obj == null) return results;
+    if (nativeMap && obj.map === nativeMap) return obj.map(iterator, context);
+    each(obj, function(value, index, list) {
+      results[results.length] = iterator.call(context, value, index, list);
+    });
+    if (obj.length === +obj.length) results.length = obj.length;
+    return results;
+  };
+
+  // **Reduce** builds up a single result from a list of values, aka `inject`,
+  // or `foldl`. Delegates to **ECMAScript 5**'s native `reduce` if available.
+  _.reduce = _.foldl = _.inject = function(obj, iterator, memo, context) {
+    var initial = arguments.length > 2;
+    if (obj == null) obj = [];
+    if (nativeReduce && obj.reduce === nativeReduce) {
+      if (context) iterator = _.bind(iterator, context);
+      return initial ? obj.reduce(iterator, memo) : obj.reduce(iterator);
+    }
+    each(obj, function(value, index, list) {
+      if (!initial) {
+        memo = value;
+        initial = true;
+      } else {
+        memo = iterator.call(context, memo, value, index, list);
+      }
+    });
+    if (!initial) throw new TypeError('Reduce of empty array with no initial value');
+    return memo;
+  };
+
+  // The right-associative version of reduce, also known as `foldr`.
+  // Delegates to **ECMAScript 5**'s native `reduceRight` if available.
+  _.reduceRight = _.foldr = function(obj, iterator, memo, context) {
+    var initial = arguments.length > 2;
+    if (obj == null) obj = [];
+    if (nativeReduceRight && obj.reduceRight === nativeReduceRight) {
+      if (context) iterator = _.bind(iterator, context);
+      return initial ? obj.reduceRight(iterator, memo) : obj.reduceRight(iterator);
+    }
+    var reversed = _.toArray(obj).reverse();
+    if (context && !initial) iterator = _.bind(iterator, context);
+    return initial ? _.reduce(reversed, iterator, memo, context) : _.reduce(reversed, iterator);
+  };
+
+  // Return the first value which passes a truth test. Aliased as `detect`.
+  _.find = _.detect = function(obj, iterator, context) {
+    var result;
+    any(obj, function(value, index, list) {
+      if (iterator.call(context, value, index, list)) {
+        result = value;
+        return true;
+      }
+    });
+    return result;
+  };
+
+  // Return all the elements that pass a truth test.
+  // Delegates to **ECMAScript 5**'s native `filter` if available.
+  // Aliased as `select`.
+  _.filter = _.select = function(obj, iterator, context) {
+    var results = [];
+    if (obj == null) return results;
+    if (nativeFilter && obj.filter === nativeFilter) return obj.filter(iterator, context);
+    each(obj, function(value, index, list) {
+      if (iterator.call(context, value, index, list)) results[results.length] = value;
+    });
+    return results;
+  };
+
+  // Return all the elements for which a truth test fails.
+  _.reject = function(obj, iterator, context) {
+    var results = [];
+    if (obj == null) return results;
+    each(obj, function(value, index, list) {
+      if (!iterator.call(context, value, index, list)) results[results.length] = value;
+    });
+    return results;
+  };
+
+  // Determine whether all of the elements match a truth test.
+  // Delegates to **ECMAScript 5**'s native `every` if available.
+  // Aliased as `all`.
+  _.every = _.all = function(obj, iterator, context) {
+    var result = true;
+    if (obj == null) return result;
+    if (nativeEvery && obj.every === nativeEvery) return obj.every(iterator, context);
+    each(obj, function(value, index, list) {
+      if (!(result = result && iterator.call(context, value, index, list))) return breaker;
+    });
+    return result;
+  };
+
+  // Determine if at least one element in the object matches a truth test.
+  // Delegates to **ECMAScript 5**'s native `some` if available.
+  // Aliased as `any`.
+  var any = _.some = _.any = function(obj, iterator, context) {
+    iterator || (iterator = _.identity);
+    var result = false;
+    if (obj == null) return result;
+    if (nativeSome && obj.some === nativeSome) return obj.some(iterator, context);
+    each(obj, function(value, index, list) {
+      if (result || (result = iterator.call(context, value, index, list))) return breaker;
+    });
+    return !!result;
+  };
+
+  // Determine if a given value is included in the array or object using `===`.
+  // Aliased as `contains`.
+  _.include = _.contains = function(obj, target) {
+    var found = false;
+    if (obj == null) return found;
+    if (nativeIndexOf && obj.indexOf === nativeIndexOf) return obj.indexOf(target) != -1;
+    found = any(obj, function(value) {
+      return value === target;
+    });
+    return found;
+  };
+
+  // Invoke a method (with arguments) on every item in a collection.
+  _.invoke = function(obj, method) {
+    var args = slice.call(arguments, 2);
+    return _.map(obj, function(value) {
+      return (_.isFunction(method) ? method || value : value[method]).apply(value, args);
+    });
+  };
+
+  // Convenience version of a common use case of `map`: fetching a property.
+  _.pluck = function(obj, key) {
+    return _.map(obj, function(value){ return value[key]; });
+  };
+
+  // Return the maximum element or (element-based computation).
+  _.max = function(obj, iterator, context) {
+    if (!iterator && _.isArray(obj)) return Math.max.apply(Math, obj);
+    if (!iterator && _.isEmpty(obj)) return -Infinity;
+    var result = {computed : -Infinity};
+    each(obj, function(value, index, list) {
+      var computed = iterator ? iterator.call(context, value, index, list) : value;
+      computed >= result.computed && (result = {value : value, computed : computed});
+    });
+    return result.value;
+  };
+
+  // Return the minimum element (or element-based computation).
+  _.min = function(obj, iterator, context) {
+    if (!iterator && _.isArray(obj)) return Math.min.apply(Math, obj);
+    if (!iterator && _.isEmpty(obj)) return Infinity;
+    var result = {computed : Infinity};
+    each(obj, function(value, index, list) {
+      var computed = iterator ? iterator.call(context, value, index, list) : value;
+      computed < result.computed && (result = {value : value, computed : computed});
+    });
+    return result.value;
+  };
+
+  // Shuffle an array.
+  _.shuffle = function(obj) {
+    var shuffled = [], rand;
+    each(obj, function(value, index, list) {
+      if (index == 0) {
+        shuffled[0] = value;
+      } else {
+        rand = Math.floor(Math.random() * (index + 1));
+        shuffled[index] = shuffled[rand];
+        shuffled[rand] = value;
+      }
+    });
+    return shuffled;
+  };
+
+  // Sort the object's values by a criterion produced by an iterator.
+  _.sortBy = function(obj, iterator, context) {
+    return _.pluck(_.map(obj, function(value, index, list) {
+      return {
+        value : value,
+        criteria : iterator.call(context, value, index, list)
+      };
+    }).sort(function(left, right) {
+      var a = left.criteria, b = right.criteria;
+      return a < b ? -1 : a > b ? 1 : 0;
+    }), 'value');
+  };
+
+  // Groups the object's values by a criterion. Pass either a string attribute
+  // to group by, or a function that returns the criterion.
+  _.groupBy = function(obj, val) {
+    var result = {};
+    var iterator = _.isFunction(val) ? val : function(obj) { return obj[val]; };
+    each(obj, function(value, index) {
+      var key = iterator(value, index);
+      (result[key] || (result[key] = [])).push(value);
+    });
+    return result;
+  };
+
+  // Use a comparator function to figure out at what index an object should
+  // be inserted so as to maintain order. Uses binary search.
+  _.sortedIndex = function(array, obj, iterator) {
+    iterator || (iterator = _.identity);
+    var low = 0, high = array.length;
+    while (low < high) {
+      var mid = (low + high) >> 1;
+      iterator(array[mid]) < iterator(obj) ? low = mid + 1 : high = mid;
+    }
+    return low;
+  };
+
+  // Safely convert anything iterable into a real, live array.
+  _.toArray = function(iterable) {
+    if (!iterable)                return [];
+    if (iterable.toArray)         return iterable.toArray();
+    if (_.isArray(iterable))      return slice.call(iterable);
+    if (_.isArguments(iterable))  return slice.call(iterable);
+    return _.values(iterable);
+  };
+
+  // Return the number of elements in an object.
+  _.size = function(obj) {
+    return _.toArray(obj).length;
+  };
+
+  // Array Functions
+  // ---------------
+
+  // Get the first element of an array. Passing **n** will return the first N
+  // values in the array. Aliased as `head`. The **guard** check allows it to work
+  // with `_.map`.
+  _.first = _.head = function(array, n, guard) {
+    return (n != null) && !guard ? slice.call(array, 0, n) : array[0];
+  };
+
+  // Returns everything but the last entry of the array. Especcialy useful on
+  // the arguments object. Passing **n** will return all the values in
+  // the array, excluding the last N. The **guard** check allows it to work with
+  // `_.map`.
+  _.initial = function(array, n, guard) {
+    return slice.call(array, 0, array.length - ((n == null) || guard ? 1 : n));
+  };
+
+  // Get the last element of an array. Passing **n** will return the last N
+  // values in the array. The **guard** check allows it to work with `_.map`.
+  _.last = function(array, n, guard) {
+    if ((n != null) && !guard) {
+      return slice.call(array, Math.max(array.length - n, 0));
+    } else {
+      return array[array.length - 1];
+    }
+  };
+
+  // Returns everything but the first entry of the array. Aliased as `tail`.
+  // Especially useful on the arguments object. Passing an **index** will return
+  // the rest of the values in the array from that index onward. The **guard**
+  // check allows it to work with `_.map`.
+  _.rest = _.tail = function(array, index, guard) {
+    return slice.call(array, (index == null) || guard ? 1 : index);
+  };
+
+  // Trim out all falsy values from an array.
+  _.compact = function(array) {
+    return _.filter(array, function(value){ return !!value; });
+  };
+
+  // Return a completely flattened version of an array.
+  _.flatten = function(array, shallow) {
+    return _.reduce(array, function(memo, value) {
+      if (_.isArray(value)) return memo.concat(shallow ? value : _.flatten(value));
+      memo[memo.length] = value;
+      return memo;
+    }, []);
+  };
+
+  // Return a version of the array that does not contain the specified value(s).
+  _.without = function(array) {
+    return _.difference(array, slice.call(arguments, 1));
+  };
+
+  // Produce a duplicate-free version of the array. If the array has already
+  // been sorted, you have the option of using a faster algorithm.
+  // Aliased as `unique`.
+  _.uniq = _.unique = function(array, isSorted, iterator) {
+    var initial = iterator ? _.map(array, iterator) : array;
+    var result = [];
+    _.reduce(initial, function(memo, el, i) {
+      if (0 == i || (isSorted === true ? _.last(memo) != el : !_.include(memo, el))) {
+        memo[memo.length] = el;
+        result[result.length] = array[i];
+      }
+      return memo;
+    }, []);
+    return result;
+  };
+
+  // Produce an array that contains the union: each distinct element from all of
+  // the passed-in arrays.
+  _.union = function() {
+    return _.uniq(_.flatten(arguments, true));
+  };
+
+  // Produce an array that contains every item shared between all the
+  // passed-in arrays. (Aliased as "intersect" for back-compat.)
+  _.intersection = _.intersect = function(array) {
+    var rest = slice.call(arguments, 1);
+    return _.filter(_.uniq(array), function(item) {
+      return _.every(rest, function(other) {
+        return _.indexOf(other, item) >= 0;
+      });
+    });
+  };
+
+  // Take the difference between one array and a number of other arrays.
+  // Only the elements present in just the first array will remain.
+  _.difference = function(array) {
+    var rest = _.flatten(slice.call(arguments, 1));
+    return _.filter(array, function(value){ return !_.include(rest, value); });
+  };
+
+  // Zip together multiple lists into a single array -- elements that share
+  // an index go together.
+  _.zip = function() {
+    var args = slice.call(arguments);
+    var length = _.max(_.pluck(args, 'length'));
+    var results = new Array(length);
+    for (var i = 0; i < length; i++) results[i] = _.pluck(args, "" + i);
+    return results;
+  };
+
+  // If the browser doesn't supply us with indexOf (I'm looking at you, **MSIE**),
+  // we need this function. Return the position of the first occurrence of an
+  // item in an array, or -1 if the item is not included in the array.
+  // Delegates to **ECMAScript 5**'s native `indexOf` if available.
+  // If the array is large and already in sort order, pass `true`
+  // for **isSorted** to use binary search.
+  _.indexOf = function(array, item, isSorted) {
+    if (array == null) return -1;
+    var i, l;
+    if (isSorted) {
+      i = _.sortedIndex(array, item);
+      return array[i] === item ? i : -1;
+    }
+    if (nativeIndexOf && array.indexOf === nativeIndexOf) return array.indexOf(item);
+    for (i = 0, l = array.length; i < l; i++) if (i in array && array[i] === item) return i;
+    return -1;
+  };
+
+  // Delegates to **ECMAScript 5**'s native `lastIndexOf` if available.
+  _.lastIndexOf = function(array, item) {
+    if (array == null) return -1;
+    if (nativeLastIndexOf && array.lastIndexOf === nativeLastIndexOf) return array.lastIndexOf(item);
+    var i = array.length;
+    while (i--) if (i in array && array[i] === item) return i;
+    return -1;
+  };
+
+  // Generate an integer Array containing an arithmetic progression. A port of
+  // the native Python `range()` function. See
+  // [the Python documentation](http://docs.python.org/library/functions.html#range).
+  _.range = function(start, stop, step) {
+    if (arguments.length <= 1) {
+      stop = start || 0;
+      start = 0;
+    }
+    step = arguments[2] || 1;
+
+    var len = Math.max(Math.ceil((stop - start) / step), 0);
+    var idx = 0;
+    var range = new Array(len);
+
+    while(idx < len) {
+      range[idx++] = start;
+      start += step;
+    }
+
+    return range;
+  };
+
+  // Function (ahem) Functions
+  // ------------------
+
+  // Reusable constructor function for prototype setting.
+  var ctor = function(){};
+
+  // Create a function bound to a given object (assigning `this`, and arguments,
+  // optionally). Binding with arguments is also known as `curry`.
+  // Delegates to **ECMAScript 5**'s native `Function.bind` if available.
+  // We check for `func.bind` first, to fail fast when `func` is undefined.
+  _.bind = function bind(func, context) {
+    var bound, args;
+    if (func.bind === nativeBind && nativeBind) return nativeBind.apply(func, slice.call(arguments, 1));
+    if (!_.isFunction(func)) throw new TypeError;
+    args = slice.call(arguments, 2);
+    return bound = function() {
+      if (!(this instanceof bound)) return func.apply(context, args.concat(slice.call(arguments)));
+      ctor.prototype = func.prototype;
+      var self = new ctor;
+      var result = func.apply(self, args.concat(slice.call(arguments)));
+      if (Object(result) === result) return result;
+      return self;
+    };
+  };
+
+  // Bind all of an object's methods to that object. Useful for ensuring that
+  // all callbacks defined on an object belong to it.
+  _.bindAll = function(obj) {
+    var funcs = slice.call(arguments, 1);
+    if (funcs.length == 0) funcs = _.functions(obj);
+    each(funcs, function(f) { obj[f] = _.bind(obj[f], obj); });
+    return obj;
+  };
+
+  // Memoize an expensive function by storing its results.
+  _.memoize = function(func, hasher) {
+    var memo = {};
+    hasher || (hasher = _.identity);
+    return function() {
+      var key = hasher.apply(this, arguments);
+      return _.has(memo, key) ? memo[key] : (memo[key] = func.apply(this, arguments));
+    };
+  };
+
+  // Delays a function for the given number of milliseconds, and then calls
+  // it with the arguments supplied.
+  _.delay = function(func, wait) {
+    var args = slice.call(arguments, 2);
+    return setTimeout(function(){ return func.apply(func, args); }, wait);
+  };
+
+  // Defers a function, scheduling it to run after the current call stack has
+  // cleared.
+  _.defer = function(func) {
+    return _.delay.apply(_, [func, 1].concat(slice.call(arguments, 1)));
+  };
+
+  // Returns a function, that, when invoked, will only be triggered at most once
+  // during a given window of time.
+  _.throttle = function(func, wait) {
+    var context, args, timeout, throttling, more;
+    var whenDone = _.debounce(function(){ more = throttling = false; }, wait);
+    return function() {
+      context = this; args = arguments;
+      var later = function() {
+        timeout = null;
+        if (more) func.apply(context, args);
+        whenDone();
+      };
+      if (!timeout) timeout = setTimeout(later, wait);
+      if (throttling) {
+        more = true;
+      } else {
+        func.apply(context, args);
+      }
+      whenDone();
+      throttling = true;
+    };
+  };
+
+  // Returns a function, that, as long as it continues to be invoked, will not
+  // be triggered. The function will be called after it stops being called for
+  // N milliseconds.
+  _.debounce = function(func, wait) {
+    var timeout;
+    return function() {
+      var context = this, args = arguments;
+      var later = function() {
+        timeout = null;
+        func.apply(context, args);
+      };
+      clearTimeout(timeout);
+      timeout = setTimeout(later, wait);
+    };
+  };
+
+  // Returns a function that will be executed at most one time, no matter how
+  // often you call it. Useful for lazy initialization.
+  _.once = function(func) {
+    var ran = false, memo;
+    return function() {
+      if (ran) return memo;
+      ran = true;
+      return memo = func.apply(this, arguments);
+    };
+  };
+
+  // Returns the first function passed as an argument to the second,
+  // allowing you to adjust arguments, run code before and after, and
+  // conditionally execute the original function.
+  _.wrap = function(func, wrapper) {
+    return function() {
+      var args = [func].concat(slice.call(arguments, 0));
+      return wrapper.apply(this, args);
+    };
+  };
+
+  // Returns a function that is the composition of a list of functions, each
+  // consuming the return value of the function that follows.
+  _.compose = function() {
+    var funcs = arguments;
+    return function() {
+      var args = arguments;
+      for (var i = funcs.length - 1; i >= 0; i--) {
+        args = [funcs[i].apply(this, args)];
+      }
+      return args[0];
+    };
+  };
+
+  // Returns a function that will only be executed after being called N times.
+  _.after = function(times, func) {
+    if (times <= 0) return func();
+    return function() {
+      if (--times < 1) { return func.apply(this, arguments); }
+    };
+  };
+
+  // Object Functions
+  // ----------------
+
+  // Retrieve the names of an object's properties.
+  // Delegates to **ECMAScript 5**'s native `Object.keys`
+  _.keys = nativeKeys || function(obj) {
+    if (obj !== Object(obj)) throw new TypeError('Invalid object');
+    var keys = [];
+    for (var key in obj) if (_.has(obj, key)) keys[keys.length] = key;
+    return keys;
+  };
+
+  // Retrieve the values of an object's properties.
+  _.values = function(obj) {
+    return _.map(obj, _.identity);
+  };
+
+  // Return a sorted list of the function names available on the object.
+  // Aliased as `methods`
+  _.functions = _.methods = function(obj) {
+    var names = [];
+    for (var key in obj) {
+      if (_.isFunction(obj[key])) names.push(key);
+    }
+    return names.sort();
+  };
+
+  // Extend a given object with all the properties in passed-in object(s).
+  _.extend = function(obj) {
+    each(slice.call(arguments, 1), function(source) {
+      for (var prop in source) {
+        obj[prop] = source[prop];
+      }
+    });
+    return obj;
+  };
+
+  // Fill in a given object with default properties.
+  _.defaults = function(obj) {
+    each(slice.call(arguments, 1), function(source) {
+      for (var prop in source) {
+        if (obj[prop] == null) obj[prop] = source[prop];
+      }
+    });
+    return obj;
+  };
+
+  // Create a (shallow-cloned) duplicate of an object.
+  _.clone = function(obj) {
+    if (!_.isObject(obj)) return obj;
+    return _.isArray(obj) ? obj.slice() : _.extend({}, obj);
+  };
+
+  // Invokes interceptor with the obj, and then returns obj.
+  // The primary purpose of this method is to "tap into" a method chain, in
+  // order to perform operations on intermediate results within the chain.
+  _.tap = function(obj, interceptor) {
+    interceptor(obj);
+    return obj;
+  };
+
+  // Internal recursive comparison function.
+  function eq(a, b, stack) {
+    // Identical objects are equal. `0 === -0`, but they aren't identical.
+    // See the Harmony `egal` proposal: http://wiki.ecmascript.org/doku.php?id=harmony:egal.
+    if (a === b) return a !== 0 || 1 / a == 1 / b;
+    // A strict comparison is necessary because `null == undefined`.
+    if (a == null || b == null) return a === b;
+    // Unwrap any wrapped objects.
+    if (a._chain) a = a._wrapped;
+    if (b._chain) b = b._wrapped;
+    // Invoke a custom `isEqual` method if one is provided.
+    if (a.isEqual && _.isFunction(a.isEqual)) return a.isEqual(b);
+    if (b.isEqual && _.isFunction(b.isEqual)) return b.isEqual(a);
+    // Compare `[[Class]]` names.
+    var className = toString.call(a);
+    if (className != toString.call(b)) return false;
+    switch (className) {
+      // Strings, numbers, dates, and booleans are compared by value.
+      case '[object String]':
+        // Primitives and their corresponding object wrappers are equivalent; thus, `"5"` is
+        // equivalent to `new String("5")`.
+        return a == String(b);
+      case '[object Number]':
+        // `NaN`s are equivalent, but non-reflexive. An `egal` comparison is performed for
+        // other numeric values.
+        return a != +a ? b != +b : (a == 0 ? 1 / a == 1 / b : a == +b);
+      case '[object Date]':
+      case '[object Boolean]':
+        // Coerce dates and booleans to numeric primitive values. Dates are compared by their
+        // millisecond representations. Note that invalid dates with millisecond representations
+        // of `NaN` are not equivalent.
+        return +a == +b;
+      // RegExps are compared by their source patterns and flags.
+      case '[object RegExp]':
+        return a.source == b.source &&
+               a.global == b.global &&
+               a.multiline == b.multiline &&
+               a.ignoreCase == b.ignoreCase;
+    }
+    if (typeof a != 'object' || typeof b != 'object') return false;
+    // Assume equality for cyclic structures. The algorithm for detecting cyclic
+    // structures is adapted from ES 5.1 section 15.12.3, abstract operation `JO`.
+    var length = stack.length;
+    while (length--) {
+      // Linear search. Performance is inversely proportional to the number of
+      // unique nested structures.
+      if (stack[length] == a) return true;
+    }
+    // Add the first object to the stack of traversed objects.
+    stack.push(a);
+    var size = 0, result = true;
+    // Recursively compare objects and arrays.
+    if (className == '[object Array]') {
+      // Compare array lengths to determine if a deep comparison is necessary.
+      size = a.length;
+      result = size == b.length;
+      if (result) {
+        // Deep compare the contents, ignoring non-numeric properties.
+        while (size--) {
+          // Ensure commutative equality for sparse arrays.
+          if (!(result = size in a == size in b && eq(a[size], b[size], stack))) break;
+        }
+      }
+    } else {
+      // Objects with different constructors are not equivalent.
+      if ('constructor' in a != 'constructor' in b || a.constructor != b.constructor) return false;
+      // Deep compare objects.
+      for (var key in a) {
+        if (_.has(a, key)) {
+          // Count the expected number of properties.
+          size++;
+          // Deep compare each member.
+          if (!(result = _.has(b, key) && eq(a[key], b[key], stack))) break;
+        }
+      }
+      // Ensure that both objects contain the same number of properties.
+      if (result) {
+        for (key in b) {
+          if (_.has(b, key) && !(size--)) break;
+        }
+        result = !size;
+      }
+    }
+    // Remove the first object from the stack of traversed objects.
+    stack.pop();
+    return result;
+  }
+
+  // Perform a deep comparison to check if two objects are equal.
+  _.isEqual = function(a, b) {
+    return eq(a, b, []);
+  };
+
+  // Is a given array, string, or object empty?
+  // An "empty" object has no enumerable own-properties.
+  _.isEmpty = function(obj) {
+    if (_.isArray(obj) || _.isString(obj)) return obj.length === 0;
+    for (var key in obj) if (_.has(obj, key)) return false;
+    return true;
+  };
+
+  // Is a given value a DOM element?
+  _.isElement = function(obj) {
+    return !!(obj && obj.nodeType == 1);
+  };
+
+  // Is a given value an array?
+  // Delegates to ECMA5's native Array.isArray
+  _.isArray = nativeIsArray || function(obj) {
+    return toString.call(obj) == '[object Array]';
+  };
+
+  // Is a given variable an object?
+  _.isObject = function(obj) {
+    return obj === Object(obj);
+  };
+
+  // Is a given variable an arguments object?
+  _.isArguments = function(obj) {
+    return toString.call(obj) == '[object Arguments]';
+  };
+  if (!_.isArguments(arguments)) {
+    _.isArguments = function(obj) {
+      return !!(obj && _.has(obj, 'callee'));
+    };
+  }
+
+  // Is a given value a function?
+  _.isFunction = function(obj) {
+    return toString.call(obj) == '[object Function]';
+  };
+
+  // Is a given value a string?
+  _.isString = function(obj) {
+    return toString.call(obj) == '[object String]';
+  };
+
+  // Is a given value a number?
+  _.isNumber = function(obj) {
+    return toString.call(obj) == '[object Number]';
+  };
+
+  // Is the given value `NaN`?
+  _.isNaN = function(obj) {
+    // `NaN` is the only value for which `===` is not reflexive.
+    return obj !== obj;
+  };
+
+  // Is a given value a boolean?
+  _.isBoolean = function(obj) {
+    return obj === true || obj === false || toString.call(obj) == '[object Boolean]';
+  };
+
+  // Is a given value a date?
+  _.isDate = function(obj) {
+    return toString.call(obj) == '[object Date]';
+  };
+
+  // Is the given value a regular expression?
+  _.isRegExp = function(obj) {
+    return toString.call(obj) == '[object RegExp]';
+  };
+
+  // Is a given value equal to null?
+  _.isNull = function(obj) {
+    return obj === null;
+  };
+
+  // Is a given variable undefined?
+  _.isUndefined = function(obj) {
+    return obj === void 0;
+  };
+
+  // Has own property?
+  _.has = function(obj, key) {
+    return hasOwnProperty.call(obj, key);
+  };
+
+  // Utility Functions
+  // -----------------
+
+  // Run Underscore.js in *noConflict* mode, returning the `_` variable to its
+  // previous owner. Returns a reference to the Underscore object.
+  _.noConflict = function() {
+    root._ = previousUnderscore;
+    return this;
+  };
+
+  // Keep the identity function around for default iterators.
+  _.identity = function(value) {
+    return value;
+  };
+
+  // Run a function **n** times.
+  _.times = function (n, iterator, context) {
+    for (var i = 0; i < n; i++) iterator.call(context, i);
+  };
+
+  // Escape a string for HTML interpolation.
+  _.escape = function(string) {
+    return (''+string).replace(/&/g, '&amp;').replace(/</g, '&lt;').replace(/>/g, '&gt;').replace(/"/g, '&quot;').replace(/'/g, '&#x27;').replace(/\//g,'&#x2F;');
+  };
+
+  // Add your own custom functions to the Underscore object, ensuring that
+  // they're correctly added to the OOP wrapper as well.
+  _.mixin = function(obj) {
+    each(_.functions(obj), function(name){
+      addToWrapper(name, _[name] = obj[name]);
+    });
+  };
+
+  // Generate a unique integer id (unique within the entire client session).
+  // Useful for temporary DOM ids.
+  var idCounter = 0;
+  _.uniqueId = function(prefix) {
+    var id = idCounter++;
+    return prefix ? prefix + id : id;
+  };
+
+  // By default, Underscore uses ERB-style template delimiters, change the
+  // following template settings to use alternative delimiters.
+  _.templateSettings = {
+    evaluate    : /<%([\s\S]+?)%>/g,
+    interpolate : /<%=([\s\S]+?)%>/g,
+    escape      : /<%-([\s\S]+?)%>/g
+  };
+
+  // When customizing `templateSettings`, if you don't want to define an
+  // interpolation, evaluation or escaping regex, we need one that is
+  // guaranteed not to match.
+  var noMatch = /.^/;
+
+  // Within an interpolation, evaluation, or escaping, remove HTML escaping
+  // that had been previously added.
+  var unescape = function(code) {
+    return code.replace(/\\\\/g, '\\').replace(/\\'/g, "'");
+  };
+
+  // JavaScript micro-templating, similar to John Resig's implementation.
+  // Underscore templating handles arbitrary delimiters, preserves whitespace,
+  // and correctly escapes quotes within interpolated code.
+  _.template = function(str, data) {
+    var c  = _.templateSettings;
+    var tmpl = 'var __p=[],print=function(){__p.push.apply(__p,arguments);};' +
+      'with(obj||{}){__p.push(\'' +
+      str.replace(/\\/g, '\\\\')
+         .replace(/'/g, "\\'")
+         .replace(c.escape || noMatch, function(match, code) {
+           return "',_.escape(" + unescape(code) + "),'";
+         })
+         .replace(c.interpolate || noMatch, function(match, code) {
+           return "'," + unescape(code) + ",'";
+         })
+         .replace(c.evaluate || noMatch, function(match, code) {
+           return "');" + unescape(code).replace(/[\r\n\t]/g, ' ') + ";__p.push('";
+         })
+         .replace(/\r/g, '\\r')
+         .replace(/\n/g, '\\n')
+         .replace(/\t/g, '\\t')
+         + "');}return __p.join('');";
+    var func = new Function('obj', '_', tmpl);
+    if (data) return func(data, _);
+    return function(data) {
+      return func.call(this, data, _);
+    };
+  };
+
+  // Add a "chain" function, which will delegate to the wrapper.
+  _.chain = function(obj) {
+    return _(obj).chain();
+  };
+
+  // The OOP Wrapper
+  // ---------------
+
+  // If Underscore is called as a function, it returns a wrapped object that
+  // can be used OO-style. This wrapper holds altered versions of all the
+  // underscore functions. Wrapped objects may be chained.
+  var wrapper = function(obj) { this._wrapped = obj; };
+
+  // Expose `wrapper.prototype` as `_.prototype`
+  _.prototype = wrapper.prototype;
+
+  // Helper function to continue chaining intermediate results.
+  var result = function(obj, chain) {
+    return chain ? _(obj).chain() : obj;
+  };
+
+  // A method to easily add functions to the OOP wrapper.
+  var addToWrapper = function(name, func) {
+    wrapper.prototype[name] = function() {
+      var args = slice.call(arguments);
+      unshift.call(args, this._wrapped);
+      return result(func.apply(_, args), this._chain);
+    };
+  };
+
+  // Add all of the Underscore functions to the wrapper object.
+  _.mixin(_);
+
+  // Add all mutator Array functions to the wrapper.
+  each(['pop', 'push', 'reverse', 'shift', 'sort', 'splice', 'unshift'], function(name) {
+    var method = ArrayProto[name];
+    wrapper.prototype[name] = function() {
+      var wrapped = this._wrapped;
+      method.apply(wrapped, arguments);
+      var length = wrapped.length;
+      if ((name == 'shift' || name == 'splice') && length === 0) delete wrapped[0];
+      return result(wrapped, this._chain);
+    };
+  });
+
+  // Add all accessor Array functions to the wrapper.
+  each(['concat', 'join', 'slice'], function(name) {
+    var method = ArrayProto[name];
+    wrapper.prototype[name] = function() {
+      return result(method.apply(this._wrapped, arguments), this._chain);
+    };
+  });
+
+  // Start chaining a wrapped Underscore object.
+  wrapper.prototype.chain = function() {
+    this._chain = true;
+    return this;
+  };
+
+  // Extracts the result from a wrapped and chained object.
+  wrapper.prototype.value = function() {
+    return this._wrapped;
+  };
+
+}).call(this);
diff --git a/_build/html/_static/underscore.js b/_build/html/_static/underscore.js
new file mode 100644
index 0000000..5b55f32
--- /dev/null
+++ b/_build/html/_static/underscore.js
@@ -0,0 +1,31 @@
+// Underscore.js 1.3.1
+// (c) 2009-2012 Jeremy Ashkenas, DocumentCloud Inc.
+// Underscore is freely distributable under the MIT license.
+// Portions of Underscore are inspired or borrowed from Prototype,
+// Oliver Steele's Functional, and John Resig's Micro-Templating.
+// For all details and documentation:
+// http://documentcloud.github.com/underscore
+(function(){function q(a,c,d){if(a===c)return a!==0||1/a==1/c;if(a==null||c==null)return a===c;if(a._chain)a=a._wrapped;if(c._chain)c=c._wrapped;if(a.isEqual&&b.isFunction(a.isEqual))return a.isEqual(c);if(c.isEqual&&b.isFunction(c.isEqual))return c.isEqual(a);var e=l.call(a);if(e!=l.call(c))return false;switch(e){case "[object String]":return a==String(c);case "[object Number]":return a!=+a?c!=+c:a==0?1/a==1/c:a==+c;case "[object Date]":case "[object Boolean]":return+a==+c;case "[object RegExp]":return a.source==
+c.source&&a.global==c.global&&a.multiline==c.multiline&&a.ignoreCase==c.ignoreCase}if(typeof a!="object"||typeof c!="object")return false;for(var f=d.length;f--;)if(d[f]==a)return true;d.push(a);var f=0,g=true;if(e=="[object Array]"){if(f=a.length,g=f==c.length)for(;f--;)if(!(g=f in a==f in c&&q(a[f],c[f],d)))break}else{if("constructor"in a!="constructor"in c||a.constructor!=c.constructor)return false;for(var h in a)if(b.has(a,h)&&(f++,!(g=b.has(c,h)&&q(a[h],c[h],d))))break;if(g){for(h in c)if(b.has(c,
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diff --git a/_build/html/_static/vendor/fontawesome/5.13.0/LICENSE.txt b/_build/html/_static/vendor/fontawesome/5.13.0/LICENSE.txt
new file mode 100644
index 0000000..f31bef9
--- /dev/null
+++ b/_build/html/_static/vendor/fontawesome/5.13.0/LICENSE.txt
@@ -0,0 +1,34 @@
+Font Awesome Free License
+-------------------------
+
+Font Awesome Free is free, open source, and GPL friendly. You can use it for
+commercial projects, open source projects, or really almost whatever you want.
+Full Font Awesome Free license: https://fontawesome.com/license/free.
+
+# Icons: CC BY 4.0 License (https://creativecommons.org/licenses/by/4.0/)
+In the Font Awesome Free download, the CC BY 4.0 license applies to all icons
+packaged as SVG and JS file types.
+
+# Fonts: SIL OFL 1.1 License (https://scripts.sil.org/OFL)
+In the Font Awesome Free download, the SIL OFL license applies to all icons
+packaged as web and desktop font files.
+
+# Code: MIT License (https://opensource.org/licenses/MIT)
+In the Font Awesome Free download, the MIT license applies to all non-font and
+non-icon files.
+
+# Attribution
+Attribution is required by MIT, SIL OFL, and CC BY licenses. Downloaded Font
+Awesome Free files already contain embedded comments with sufficient
+attribution, so you shouldn't need to do anything additional when using these
+files normally.
+
+We've kept attribution comments terse, so we ask that you do not actively work
+to remove them from files, especially code. They're a great way for folks to
+learn about Font Awesome.
+
+# Brand Icons
+All brand icons are trademarks of their respective owners. The use of these
+trademarks does not indicate endorsement of the trademark holder by Font
+Awesome, nor vice versa. **Please do not use brand logos for any purpose except
+to represent the company, product, or service to which they refer.**
diff --git a/_build/html/_static/vendor/fontawesome/5.13.0/css/all.min.css b/_build/html/_static/vendor/fontawesome/5.13.0/css/all.min.css
new file mode 100644
index 0000000..3d28ab2
--- /dev/null
+++ b/_build/html/_static/vendor/fontawesome/5.13.0/css/all.min.css
@@ -0,0 +1,5 @@
+/*!
+ * Font Awesome Free 5.13.0 by @fontawesome - https://fontawesome.com
+ * License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License)
+ */
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new file mode 100644
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new file mode 100644
index 0000000..7742838
--- /dev/null
+++ b/_build/html/_static/vendor/fontawesome/5.13.0/webfonts/fa-solid-900.svg
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+License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License)
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+ By Robert Madole
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diff --git a/_build/html/_static/vendor/lato_latin-ext/1.44.1/LICENSE.md b/_build/html/_static/vendor/lato_latin-ext/1.44.1/LICENSE.md
new file mode 100644
index 0000000..89bc0f2
--- /dev/null
+++ b/_build/html/_static/vendor/lato_latin-ext/1.44.1/LICENSE.md
@@ -0,0 +1,20 @@
+Copyright (c) 2019 Jan Bednar
+
+Permission is hereby granted, free of charge, to any person obtaining
+a copy of this software and associated documentation files (the
+"Software"), to deal in the Software without restriction, including
+without limitation the rights to use, copy, modify, merge, publish,
+distribute, sublicense, and/or sell copies of the Software, and to
+permit persons to whom the Software is furnished to do so, subject to
+the following conditions:
+
+The above copyright notice and this permission notice shall be
+included in all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE
+LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
+OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION
+WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
diff --git a/_build/html/_static/vendor/lato_latin-ext/1.44.1/files/lato-latin-ext-100-italic.woff b/_build/html/_static/vendor/lato_latin-ext/1.44.1/files/lato-latin-ext-100-italic.woff
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diff --git a/_build/html/_static/vendor/lato_latin-ext/1.44.1/index.css b/_build/html/_static/vendor/lato_latin-ext/1.44.1/index.css
new file mode 100644
index 0000000..d170b50
--- /dev/null
+++ b/_build/html/_static/vendor/lato_latin-ext/1.44.1/index.css
@@ -0,0 +1,120 @@
+/* lato-100normal - latin-ext */
+@font-face {
+  font-family: 'Lato';
+  font-style: normal;
+  font-display: swap;
+  font-weight: 100;
+  src:
+    local('Lato Hairline'),
+    local('Lato-Hairline'), 
+    url('./files/lato-latin-ext-100.woff2') format('woff2'), /* Chrome 26+, Opera 23+, Firefox 39+ */
+    url('./files/lato-latin-ext-100.woff') format('woff'); /* Chrome 6+, Firefox 3.6+, IE 9+, Safari 5.1+ */
+}
+/* lato-100italic - latin-ext */
+@font-face {
+  font-family: 'Lato';
+  font-style: italic;
+  font-display: swap;
+  font-weight: 100;
+  src:
+    local('Lato Hairline Italic'),
+    local('Lato-HairlineItalic'), 
+    url('./files/lato-latin-ext-100-italic.woff2') format('woff2'), /* Chrome 26+, Opera 23+, Firefox 39+ */
+    url('./files/lato-latin-ext-100-italic.woff') format('woff'); /* Chrome 6+, Firefox 3.6+, IE 9+, Safari 5.1+ */
+}
+/* lato-300normal - latin-ext */
+@font-face {
+  font-family: 'Lato';
+  font-style: normal;
+  font-display: swap;
+  font-weight: 300;
+  src:
+    local('Lato Light'),
+    local('Lato-Light'), 
+    url('./files/lato-latin-ext-300.woff2') format('woff2'), /* Chrome 26+, Opera 23+, Firefox 39+ */
+    url('./files/lato-latin-ext-300.woff') format('woff'); /* Chrome 6+, Firefox 3.6+, IE 9+, Safari 5.1+ */
+}
+/* lato-300italic - latin-ext */
+@font-face {
+  font-family: 'Lato';
+  font-style: italic;
+  font-display: swap;
+  font-weight: 300;
+  src:
+    local('Lato Light Italic'),
+    local('Lato-LightItalic'), 
+    url('./files/lato-latin-ext-300-italic.woff2') format('woff2'), /* Chrome 26+, Opera 23+, Firefox 39+ */
+    url('./files/lato-latin-ext-300-italic.woff') format('woff'); /* Chrome 6+, Firefox 3.6+, IE 9+, Safari 5.1+ */
+}
+/* lato-400normal - latin-ext */
+@font-face {
+  font-family: 'Lato';
+  font-style: normal;
+  font-display: swap;
+  font-weight: 400;
+  src:
+    local('Lato Regular'),
+    local('Lato-Regular'), 
+    url('./files/lato-latin-ext-400.woff2') format('woff2'), /* Chrome 26+, Opera 23+, Firefox 39+ */
+    url('./files/lato-latin-ext-400.woff') format('woff'); /* Chrome 6+, Firefox 3.6+, IE 9+, Safari 5.1+ */
+}
+/* lato-400italic - latin-ext */
+@font-face {
+  font-family: 'Lato';
+  font-style: italic;
+  font-display: swap;
+  font-weight: 400;
+  src:
+    local('Lato Italic'),
+    local('Lato-Italic'), 
+    url('./files/lato-latin-ext-400-italic.woff2') format('woff2'), /* Chrome 26+, Opera 23+, Firefox 39+ */
+    url('./files/lato-latin-ext-400-italic.woff') format('woff'); /* Chrome 6+, Firefox 3.6+, IE 9+, Safari 5.1+ */
+}
+/* lato-700normal - latin-ext */
+@font-face {
+  font-family: 'Lato';
+  font-style: normal;
+  font-display: swap;
+  font-weight: 700;
+  src:
+    local('Lato Bold'),
+    local('Lato-Bold'), 
+    url('./files/lato-latin-ext-700.woff2') format('woff2'), /* Chrome 26+, Opera 23+, Firefox 39+ */
+    url('./files/lato-latin-ext-700.woff') format('woff'); /* Chrome 6+, Firefox 3.6+, IE 9+, Safari 5.1+ */
+}
+/* lato-700italic - latin-ext */
+@font-face {
+  font-family: 'Lato';
+  font-style: italic;
+  font-display: swap;
+  font-weight: 700;
+  src:
+    local('Lato Bold Italic'),
+    local('Lato-BoldItalic'), 
+    url('./files/lato-latin-ext-700-italic.woff2') format('woff2'), /* Chrome 26+, Opera 23+, Firefox 39+ */
+    url('./files/lato-latin-ext-700-italic.woff') format('woff'); /* Chrome 6+, Firefox 3.6+, IE 9+, Safari 5.1+ */
+}
+/* lato-900normal - latin-ext */
+@font-face {
+  font-family: 'Lato';
+  font-style: normal;
+  font-display: swap;
+  font-weight: 900;
+  src:
+    local('Lato Black'),
+    local('Lato-Black'), 
+    url('./files/lato-latin-ext-900.woff2') format('woff2'), /* Chrome 26+, Opera 23+, Firefox 39+ */
+    url('./files/lato-latin-ext-900.woff') format('woff'); /* Chrome 6+, Firefox 3.6+, IE 9+, Safari 5.1+ */
+}
+/* lato-900italic - latin-ext */
+@font-face {
+  font-family: 'Lato';
+  font-style: italic;
+  font-display: swap;
+  font-weight: 900;
+  src:
+    local('Lato Black Italic'),
+    local('Lato-BlackItalic'), 
+    url('./files/lato-latin-ext-900-italic.woff2') format('woff2'), /* Chrome 26+, Opera 23+, Firefox 39+ */
+    url('./files/lato-latin-ext-900-italic.woff') format('woff'); /* Chrome 6+, Firefox 3.6+, IE 9+, Safari 5.1+ */
+}
diff --git a/_build/html/_static/vendor/open-sans_all/1.44.1/LICENSE.md b/_build/html/_static/vendor/open-sans_all/1.44.1/LICENSE.md
new file mode 100644
index 0000000..89bc0f2
--- /dev/null
+++ b/_build/html/_static/vendor/open-sans_all/1.44.1/LICENSE.md
@@ -0,0 +1,20 @@
+Copyright (c) 2019 Jan Bednar
+
+Permission is hereby granted, free of charge, to any person obtaining
+a copy of this software and associated documentation files (the
+"Software"), to deal in the Software without restriction, including
+without limitation the rights to use, copy, modify, merge, publish,
+distribute, sublicense, and/or sell copies of the Software, and to
+permit persons to whom the Software is furnished to do so, subject to
+the following conditions:
+
+The above copyright notice and this permission notice shall be
+included in all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE
+LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
+OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION
+WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
diff --git a/_build/html/_static/vendor/open-sans_all/1.44.1/files/open-sans-all-400-italic.woff b/_build/html/_static/vendor/open-sans_all/1.44.1/files/open-sans-all-400-italic.woff
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diff --git a/_build/html/_static/vendor/open-sans_all/1.44.1/index.css b/_build/html/_static/vendor/open-sans_all/1.44.1/index.css
new file mode 100644
index 0000000..2a1e73b
--- /dev/null
+++ b/_build/html/_static/vendor/open-sans_all/1.44.1/index.css
@@ -0,0 +1,120 @@
+/* open-sans-300normal - all */
+@font-face {
+  font-family: 'Open Sans';
+  font-style: normal;
+  font-display: swap;
+  font-weight: 300;
+  src:
+    local('Open Sans Light'),
+    local('OpenSans-Light'), 
+    url('./files/open-sans-all-300.woff2') format('woff2'), /* Chrome 26+, Opera 23+, Firefox 39+ */
+    url('./files/open-sans-all-300.woff') format('woff'); /* Chrome 6+, Firefox 3.6+, IE 9+, Safari 5.1+ */
+}
+/* open-sans-300italic - all */
+@font-face {
+  font-family: 'Open Sans';
+  font-style: italic;
+  font-display: swap;
+  font-weight: 300;
+  src:
+    local('Open Sans Light Italic'),
+    local('OpenSans-LightItalic'), 
+    url('./files/open-sans-all-300-italic.woff2') format('woff2'), /* Chrome 26+, Opera 23+, Firefox 39+ */
+    url('./files/open-sans-all-300-italic.woff') format('woff'); /* Chrome 6+, Firefox 3.6+, IE 9+, Safari 5.1+ */
+}
+/* open-sans-400normal - all */
+@font-face {
+  font-family: 'Open Sans';
+  font-style: normal;
+  font-display: swap;
+  font-weight: 400;
+  src:
+    local('Open Sans Regular'),
+    local('OpenSans-Regular'), 
+    url('./files/open-sans-all-400.woff2') format('woff2'), /* Chrome 26+, Opera 23+, Firefox 39+ */
+    url('./files/open-sans-all-400.woff') format('woff'); /* Chrome 6+, Firefox 3.6+, IE 9+, Safari 5.1+ */
+}
+/* open-sans-400italic - all */
+@font-face {
+  font-family: 'Open Sans';
+  font-style: italic;
+  font-display: swap;
+  font-weight: 400;
+  src:
+    local('Open Sans Italic'),
+    local('OpenSans-Italic'), 
+    url('./files/open-sans-all-400-italic.woff2') format('woff2'), /* Chrome 26+, Opera 23+, Firefox 39+ */
+    url('./files/open-sans-all-400-italic.woff') format('woff'); /* Chrome 6+, Firefox 3.6+, IE 9+, Safari 5.1+ */
+}
+/* open-sans-600normal - all */
+@font-face {
+  font-family: 'Open Sans';
+  font-style: normal;
+  font-display: swap;
+  font-weight: 600;
+  src:
+    local('Open Sans SemiBold'),
+    local('OpenSans-SemiBold'), 
+    url('./files/open-sans-all-600.woff2') format('woff2'), /* Chrome 26+, Opera 23+, Firefox 39+ */
+    url('./files/open-sans-all-600.woff') format('woff'); /* Chrome 6+, Firefox 3.6+, IE 9+, Safari 5.1+ */
+}
+/* open-sans-600italic - all */
+@font-face {
+  font-family: 'Open Sans';
+  font-style: italic;
+  font-display: swap;
+  font-weight: 600;
+  src:
+    local('Open Sans SemiBold Italic'),
+    local('OpenSans-SemiBoldItalic'), 
+    url('./files/open-sans-all-600-italic.woff2') format('woff2'), /* Chrome 26+, Opera 23+, Firefox 39+ */
+    url('./files/open-sans-all-600-italic.woff') format('woff'); /* Chrome 6+, Firefox 3.6+, IE 9+, Safari 5.1+ */
+}
+/* open-sans-700normal - all */
+@font-face {
+  font-family: 'Open Sans';
+  font-style: normal;
+  font-display: swap;
+  font-weight: 700;
+  src:
+    local('Open Sans Bold'),
+    local('OpenSans-Bold'), 
+    url('./files/open-sans-all-700.woff2') format('woff2'), /* Chrome 26+, Opera 23+, Firefox 39+ */
+    url('./files/open-sans-all-700.woff') format('woff'); /* Chrome 6+, Firefox 3.6+, IE 9+, Safari 5.1+ */
+}
+/* open-sans-700italic - all */
+@font-face {
+  font-family: 'Open Sans';
+  font-style: italic;
+  font-display: swap;
+  font-weight: 700;
+  src:
+    local('Open Sans Bold Italic'),
+    local('OpenSans-BoldItalic'), 
+    url('./files/open-sans-all-700-italic.woff2') format('woff2'), /* Chrome 26+, Opera 23+, Firefox 39+ */
+    url('./files/open-sans-all-700-italic.woff') format('woff'); /* Chrome 6+, Firefox 3.6+, IE 9+, Safari 5.1+ */
+}
+/* open-sans-800normal - all */
+@font-face {
+  font-family: 'Open Sans';
+  font-style: normal;
+  font-display: swap;
+  font-weight: 800;
+  src:
+    local('Open Sans ExtraBold'),
+    local('OpenSans-ExtraBold'), 
+    url('./files/open-sans-all-800.woff2') format('woff2'), /* Chrome 26+, Opera 23+, Firefox 39+ */
+    url('./files/open-sans-all-800.woff') format('woff'); /* Chrome 6+, Firefox 3.6+, IE 9+, Safari 5.1+ */
+}
+/* open-sans-800italic - all */
+@font-face {
+  font-family: 'Open Sans';
+  font-style: italic;
+  font-display: swap;
+  font-weight: 800;
+  src:
+    local('Open Sans ExtraBold Italic'),
+    local('OpenSans-ExtraBoldItalic'), 
+    url('./files/open-sans-all-800-italic.woff2') format('woff2'), /* Chrome 26+, Opera 23+, Firefox 39+ */
+    url('./files/open-sans-all-800-italic.woff') format('woff'); /* Chrome 6+, Firefox 3.6+, IE 9+, Safari 5.1+ */
+}
diff --git a/_build/html/_static/webpack-macros.html b/_build/html/_static/webpack-macros.html
new file mode 100644
index 0000000..a4b36de
--- /dev/null
+++ b/_build/html/_static/webpack-macros.html
@@ -0,0 +1,28 @@
+<!-- these macros are generated by "yarn build:production". do not edit by hand. -->
+{% macro head_pre_icons() %}
+  <link rel="stylesheet"
+    href="{{ pathto('_static/vendor/fontawesome/5.13.0/css/all.min.css', 1) }}">
+  <link rel="preload" as="font" type="font/woff2" crossorigin
+    href="{{ pathto('_static/vendor/fontawesome/5.13.0/webfonts/fa-solid-900.woff2', 1) }}">
+  <link rel="preload" as="font" type="font/woff2" crossorigin
+    href="{{ pathto('_static/vendor/fontawesome/5.13.0/webfonts/fa-brands-400.woff2', 1) }}">
+{% endmacro %}
+
+{% macro head_pre_fonts() %}
+  <link rel="stylesheet"
+    href="{{ pathto('_static/vendor/open-sans_all/1.44.1/index.css', 1) }}">
+  <link rel="stylesheet"
+    href="{{ pathto('_static/vendor/lato_latin-ext/1.44.1/index.css', 1) }}">
+{% endmacro %}
+
+{% macro head_pre_bootstrap() %}
+  <link rel="stylesheet" href="{{ pathto('_static/css/index.d431a4ee1c1efae0e38bdfebc22debff.css', 1) }}">
+{% endmacro %}
+
+{% macro head_js_preload() %}
+  <link rel="preload" as="script" href="{{ pathto('_static/js/index.30270b6e4c972e43c488.js', 1) }}">
+{% endmacro %}
+
+{% macro body_post() %}
+  <script src="{{ pathto('_static/js/index.30270b6e4c972e43c488.js', 1) }}"></script>
+{% endmacro %}
\ No newline at end of file
diff --git a/_build/html/genindex.html b/_build/html/genindex.html
new file mode 100644
index 0000000..1892aea
--- /dev/null
+++ b/_build/html/genindex.html
@@ -0,0 +1,247 @@
+
+<!DOCTYPE html>
+
+<html>
+  <head>
+    <meta charset="utf-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+    <title>Index &#8212; Astronomical Data in Python</title>
+    
+  <link rel="stylesheet" href="_static/css/index.d431a4ee1c1efae0e38bdfebc22debff.css">
+
+    
+  <link rel="stylesheet"
+    href="_static/vendor/fontawesome/5.13.0/css/all.min.css">
+  <link rel="preload" as="font" type="font/woff2" crossorigin
+    href="_static/vendor/fontawesome/5.13.0/webfonts/fa-solid-900.woff2">
+  <link rel="preload" as="font" type="font/woff2" crossorigin
+    href="_static/vendor/fontawesome/5.13.0/webfonts/fa-brands-400.woff2">
+
+    
+      
+  <link rel="stylesheet"
+    href="_static/vendor/open-sans_all/1.44.1/index.css">
+  <link rel="stylesheet"
+    href="_static/vendor/lato_latin-ext/1.44.1/index.css">
+
+    
+    <link rel="stylesheet" href="_static/sphinx-book-theme.bfb7730f9caf2ec0b46a44615585038c.css" type="text/css" />
+    <link rel="stylesheet" href="_static/pygments.css" type="text/css" />
+    <link rel="stylesheet" type="text/css" href="_static/togglebutton.css" />
+    <link rel="stylesheet" type="text/css" href="_static/copybutton.css" />
+    <link rel="stylesheet" type="text/css" href="_static/mystnb.css" />
+    <link rel="stylesheet" type="text/css" href="_static/sphinx-thebe.css" />
+    <link rel="stylesheet" type="text/css" href="_static/panels-main.c949a650a448cc0ae9fd3441c0e17fb0.css" />
+    <link rel="stylesheet" type="text/css" href="_static/panels-variables.06eb56fa6e07937060861dad626602ad.css" />
+    
+  <link rel="preload" as="script" href="_static/js/index.30270b6e4c972e43c488.js">
+
+    <script id="documentation_options" data-url_root="./" src="_static/documentation_options.js"></script>
+    <script src="_static/jquery.js"></script>
+    <script src="_static/underscore.js"></script>
+    <script src="_static/doctools.js"></script>
+    <script src="_static/language_data.js"></script>
+    <script src="_static/togglebutton.js"></script>
+    <script src="_static/clipboard.min.js"></script>
+    <script src="_static/copybutton.js"></script>
+    <script >var togglebuttonSelector = '.toggle, .admonition.dropdown, .tag_hide_input div.cell_input, .tag_hide-input div.cell_input, .tag_hide_output div.cell_output, .tag_hide-output div.cell_output, .tag_hide_cell.cell, .tag_hide-cell.cell';</script>
+    <script src="_static/sphinx-book-theme.be0a4a0c39cd630af62a2fcf693f3f06.js"></script>
+    <script async="async" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.7/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+    <script type="text/x-mathjax-config">MathJax.Hub.Config({"tex2jax": {"inlineMath": [["\\(", "\\)"]], "displayMath": [["\\[", "\\]"]], "processRefs": false, "processEnvironments": false}})</script>
+    <script async="async" src="https://unpkg.com/thebelab@latest/lib/index.js"></script>
+    <script >
+        const thebe_selector = ".thebe"
+        const thebe_selector_input = "pre"
+        const thebe_selector_output = ".output"
+    </script>
+    <script async="async" src="_static/sphinx-thebe.js"></script>
+    <link rel="index" title="Index" href="#" />
+    <link rel="search" title="Search" href="search.html" />
+
+    <meta name="viewport" content="width=device-width, initial-scale=1" />
+    <meta name="docsearch:language" content="en" />
+
+
+
+  </head>
+  <body data-spy="scroll" data-target="#bd-toc-nav" data-offset="80">
+    
+
+    <div class="container-xl">
+      <div class="row">
+          
+<div class="col-12 col-md-3 bd-sidebar site-navigation show" id="site-navigation">
+    
+        <div class="navbar-brand-box">
+<a class="navbar-brand text-wrap" href="index.html">
+  
+  
+  <h1 class="site-logo" id="site-title">Astronomical Data in Python</h1>
+  
+</a>
+</div>
+
+<form class="bd-search d-flex align-items-center" action="search.html" method="get">
+  <i class="icon fas fa-search"></i>
+  <input type="search" class="form-control" name="q" id="search-input" placeholder="Search this book..." aria-label="Search this book..." autocomplete="off" >
+</form>
+
+<nav class="bd-links" id="bd-docs-nav" aria-label="Main navigation">
+  <ul class="nav sidenav_l1">
+ <li class="toctree-l1">
+  <a class="reference internal" href="README.html">
+   Astronomical Data in Python
+  </a>
+ </li>
+</ul>
+<ul class="nav sidenav_l1">
+ <li class="toctree-l1">
+  <a class="reference internal" href="01_query.html">
+   Chapter 1
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="02_coords.html">
+   Chapter 2
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="03_motion.html">
+   Chapter 3
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="04_select.html">
+   Chapter 4
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="05_join.html">
+   Chapter 5
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="06_photo.html">
+   Chapter 6
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="07_plot.html">
+   Chapter 7
+  </a>
+ </li>
+</ul>
+
+</nav>
+
+ <!-- To handle the deprecated key -->
+
+<div class="navbar_extra_footer">
+  Powered by <a href="https://jupyterbook.org">Jupyter Book</a>
+</div>
+
+</div>
+
+
+          
+
+
+          
+<main class="col py-md-3 pl-md-4 bd-content overflow-auto" role="main">
+    
+    <div class="row topbar fixed-top container-xl">
+    <div class="col-12 col-md-3 bd-topbar-whitespace site-navigation show">
+    </div>
+    <div class="col pl-2 topbar-main">
+        
+        <button id="navbar-toggler" class="navbar-toggler ml-0" type="button" data-toggle="collapse"
+            data-toggle="tooltip" data-placement="bottom" data-target=".site-navigation" aria-controls="navbar-menu"
+            aria-expanded="true" aria-label="Toggle navigation" aria-controls="site-navigation"
+            title="Toggle navigation" data-toggle="tooltip" data-placement="left">
+            <i class="fas fa-bars"></i>
+            <i class="fas fa-arrow-left"></i>
+            <i class="fas fa-arrow-up"></i>
+        </button>
+        
+        <div class="dropdown-buttons-trigger">
+    <button id="dropdown-buttons-trigger" class="btn btn-secondary topbarbtn" aria-label="Download this page"><i
+            class="fas fa-download"></i></button>
+
+    
+</div>
+        <!-- Source interaction buttons -->
+
+<div class="dropdown-buttons-trigger">
+    <button id="dropdown-buttons-trigger" class="btn btn-secondary topbarbtn"
+        aria-label="Connect with source repository"><i class="fab fa-github"></i></button>
+    <div class="dropdown-buttons sourcebuttons">
+        <a class="repository-button"
+            href="https://github.com/AllenDowney/AstronomyDataPython"><button type="button" class="btn btn-secondary topbarbtn"
+                data-toggle="tooltip" data-placement="left" title="Source repository"><i
+                    class="fab fa-github"></i>repository</button></a>
+        
+        
+    </div>
+</div>
+
+
+        <!-- Full screen (wrap in <a> to have style consistency -->
+        <a class="full-screen-button"><button type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip"
+                data-placement="bottom" onclick="toggleFullScreen()" title="Fullscreen mode"><i
+                    class="fas fa-expand"></i></button></a>
+
+        <!-- Launch buttons -->
+
+    </div>
+
+    <!-- Table of contents -->
+    <div class="d-none d-md-block col-md-2 bd-toc show">
+        
+    </div>
+</div>
+    <div id="main-content" class="row">
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+   Astronomical Data in Python
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+ <li class="toctree-l1">
+  <a class="reference internal" href="01_query.html">
+   Chapter 1
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="02_coords.html">
+   Chapter 2
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="03_motion.html">
+   Chapter 3
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="04_select.html">
+   Chapter 4
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="05_join.html">
+   Chapter 5
+  </a>
+ </li>
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+  <a class="reference internal" href="06_photo.html">
+   Chapter 6
+  </a>
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+            <i class="fas fa-list"></i> Contents
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+            <ul class="nav section-nav flex-column">
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#prerequisites">
+   Prerequisites
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#notebook-1">
+   Notebook 1
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#notebook-2">
+   Notebook 2
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#notebook-3">
+   Notebook 3
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#notebook-4">
+   Notebook 4
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#notebook-5">
+   Notebook 5
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+  <a class="reference internal nav-link" href="#notebook-6">
+   Notebook 6
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#notebook-7">
+   Notebook 7
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#installation-instructions">
+   Installation instructions
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+                
+  <div class="section" id="astronomical-data-in-python">
+<h1>Astronomical Data in Python<a class="headerlink" href="#astronomical-data-in-python" title="Permalink to this headline">¶</a></h1>
+<p><em>Astronomical Data in Python</em> is an introduction to tools and practices for working with astronomical data.  Topics covered include:</p>
+<ul class="simple">
+<li><p>Writing queries that select and download data from a database.</p></li>
+<li><p>Using data stored in an Astropy <code class="docutils literal notranslate"><span class="pre">Table</span></code> or Pandas <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code>.</p></li>
+<li><p>Working with coordinates and other quantities with units.</p></li>
+<li><p>Storing data in various formats.</p></li>
+<li><p>Performing database join operations that combine data from multiple tables.</p></li>
+<li><p>Visualizing data and preparing publication-quality figures.</p></li>
+</ul>
+<p>As a running example, we will replicate part of the analysis in a recent paper, “<a class="reference external" href="https://arxiv.org/abs/1805.00425">Off the beaten path: Gaia reveals GD-1 stars outside of the main stream</a>� by Adrian M. Price-Whelan and Ana Bonaca.</p>
+<p>This material was developed in collaboration with <a class="reference external" href="https://carpentries.org/">The Carpentries</a> and the Astronomy Curriculum Development Committee, and supported by funding from the American Institute of Physics through the American Astronomical Society.</p>
+<p>I am grateful for contributions from the members of the committee – Azalee Bostroem, Rodolfo Montez, and Phil Rosenfield – and from Erin Becker, Brett Morris and Adrian Price-Whelan.</p>
+<p>The original format of this material is a series of Jupyter notebooks.  Using the
+links below, you can read the notebooks on NBViewer or run them on Colab.  If you
+want to run the notebooks in your own environment, you can download them from
+this repository and follow the instructions below to set up your environment.</p>
+<div class="section" id="prerequisites">
+<h2>Prerequisites<a class="headerlink" href="#prerequisites" title="Permalink to this headline">¶</a></h2>
+<p>This material should be accessible to people familiar with basic Python, but not necessarily the libraries we will use, like Astropy or Pandas.  If you are familiar with Python lists and dictionaries, and you know how to write a function that takes parameters and returns a value, that should be enough.</p>
+<p>We assume that you are familiar with astronomy at the undergraduate level, but we will not assume specialized knowledge of the datasets or analysis methods we’ll use.</p>
+</div>
+<div class="section" id="notebook-1">
+<h2>Notebook 1<a class="headerlink" href="#notebook-1" title="Permalink to this headline">¶</a></h2>
+<p>This notebook demonstrates the following steps:</p>
+<ol class="simple">
+<li><p>Making a connection to the Gaia server,</p></li>
+<li><p>Exploring information about the database and the tables it contains,</p></li>
+<li><p>Writing a query and sending it to the server, and finally</p></li>
+<li><p>Downloading the response from the server as an Astropy <code class="docutils literal notranslate"><span class="pre">Table</span></code>.</p></li>
+</ol>
+<p>Press this button to run this notebook on Colab:</p>
+<p><a class="reference external" href="https://colab.research.google.com/github/AllenDowney/AstronomicalData/blob/main/01_query.ipynb"><img src="run_on_colab_small.png"></a></p>
+<p><a class="reference external" href="https://nbviewer.jupyter.org/github/AllenDowney/AstronomicalData/blob/main/01_query.ipynb">or click here to read it on NBViewer</a></p>
+</div>
+<div class="section" id="notebook-2">
+<h2>Notebook 2<a class="headerlink" href="#notebook-2" title="Permalink to this headline">¶</a></h2>
+<p>This notebook starts with an example that does a “cone search�; that is, it selects stars that appear in a circular region of the sky.</p>
+<p>Then, to select stars in the vicinity of GD-1, we:</p>
+<ul class="simple">
+<li><p>Use <code class="docutils literal notranslate"><span class="pre">Quantity</span></code> objects to represent measurements with units.</p></li>
+<li><p>Use the <code class="docutils literal notranslate"><span class="pre">Gala</span></code> library to convert coordinates from one frame to another.</p></li>
+<li><p>Use the ADQL keywords <code class="docutils literal notranslate"><span class="pre">POLYGON</span></code>, <code class="docutils literal notranslate"><span class="pre">CONTAINS</span></code>, and <code class="docutils literal notranslate"><span class="pre">POINT</span></code> to select stars that fall within a polygonal region.</p></li>
+<li><p>Submit a query and download the results.</p></li>
+<li><p>Store the results in a FITS file.</p></li>
+</ul>
+<p>Press this button to run this notebook on Colab:</p>
+<p><a class="reference external" href="https://colab.research.google.com/github/AllenDowney/AstronomicalData/blob/main/02_coords.ipynb"><img src="run_on_colab_small.png"></a></p>
+<p><a class="reference external" href="https://nbviewer.jupyter.org/github/AllenDowney/AstronomicalData/blob/main/02_coords.ipynb">or click here to read it on NBViewer</a></p>
+</div>
+<div class="section" id="notebook-3">
+<h2>Notebook 3<a class="headerlink" href="#notebook-3" title="Permalink to this headline">¶</a></h2>
+<p>Here are the steps in this notebook:</p>
+<ol class="simple">
+<li><p>We’ll read back the results from the previous notebook, which we saved in a FITS file.</p></li>
+<li><p>Then we’ll transform the coordinates and proper motion data from ICRS back to the coordinate frame of GD-1.</p></li>
+<li><p>We’ll put those results into a Pandas <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code>, which we’ll use to select stars near the centerline of GD-1.</p></li>
+<li><p>Plotting the proper motion of those stars, we’ll identify a region of proper motion for stars that are likely to be in GD-1.</p></li>
+<li><p>Finally, we’ll select and plot the stars whose proper motion is in that region.</p></li>
+</ol>
+<p>Press this button to run this notebook on Colab:</p>
+<p><a class="reference external" href="https://colab.research.google.com/github/AllenDowney/AstronomicalData/blob/main/03_motion.ipynb"><img src="run_on_colab_small.png"></a></p>
+<p><a class="reference external" href="https://nbviewer.jupyter.org/github/AllenDowney/AstronomicalData/blob/main/03_motion.ipynb">or click here to read it on NBViewer</a></p>
+</div>
+<div class="section" id="notebook-4">
+<h2>Notebook 4<a class="headerlink" href="#notebook-4" title="Permalink to this headline">¶</a></h2>
+<p>Here are the steps in this notebook:</p>
+<ol class="simple">
+<li><p>Using data from the previous notebook, we’ll identify the values of proper motion for stars likely to be in GD-1.</p></li>
+<li><p>Then we’ll compose an ADQL query that selects stars based on proper motion, so we can download only the data we need.</p></li>
+<li><p>We’ll also see how to write the results to a CSV file.</p></li>
+</ol>
+<p>That will make it possible to search a bigger region of the sky in a single query.</p>
+<p>Press this button to run this notebook on Colab:</p>
+<p><a class="reference external" href="https://colab.research.google.com/github/AllenDowney/AstronomicalData/blob/main/04_select.ipynb"><img src="run_on_colab_small.png"></a></p>
+<p><a class="reference external" href="https://nbviewer.jupyter.org/github/AllenDowney/AstronomicalData/blob/main/04_select.ipynb">or click here to read it on NBViewer</a></p>
+</div>
+<div class="section" id="notebook-5">
+<h2>Notebook 5<a class="headerlink" href="#notebook-5" title="Permalink to this headline">¶</a></h2>
+<p>Here are the steps in this notebook:</p>
+<ol class="simple">
+<li><p>We’ll reload the candidate stars we identified in the previous notebook.</p></li>
+<li><p>Then we’ll run a query on the Gaia server that uploads the table of candidates and uses a <code class="docutils literal notranslate"><span class="pre">JOIN</span></code> operation to select photometry data for the candidate stars.</p></li>
+<li><p>We’ll write the results to a file for use in the next notebook.</p></li>
+</ol>
+<p>Press this button to run this notebook on Colab:</p>
+<p><a class="reference external" href="https://colab.research.google.com/github/AllenDowney/AstronomicalData/blob/main/05_join.ipynb"><img src="run_on_colab_small.png"></a></p>
+<p><a class="reference external" href="https://nbviewer.jupyter.org/github/AllenDowney/AstronomicalData/blob/main/05_join.ipynb">or click here to read it on NBViewer</a></p>
+</div>
+<div class="section" id="notebook-6">
+<h2>Notebook 6<a class="headerlink" href="#notebook-6" title="Permalink to this headline">¶</a></h2>
+<p>Here are the steps in this notebook:</p>
+<ol class="simple">
+<li><p>We’ll reload the data from the previous notebook and make a color-magnitude diagram.</p></li>
+<li><p>Then we’ll specify a polygon in the diagram that contains stars with the photometry we expect.</p></li>
+<li><p>Then we’ll merge the photometry data with the list of candidate stars, storing the result in a Pandas <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code>.</p></li>
+</ol>
+<p>Press this button to run this notebook on Colab:</p>
+<p><a class="reference external" href="https://colab.research.google.com/github/AllenDowney/AstronomicalData/blob/main/06_photo.ipynb"><img src="run_on_colab_small.png"></a></p>
+<p><a class="reference external" href="https://nbviewer.jupyter.org/github/AllenDowney/AstronomicalData/blob/main/06_photo.ipynb">or click here to read it on NBViewer</a></p>
+</div>
+<div class="section" id="notebook-7">
+<h2>Notebook 7<a class="headerlink" href="#notebook-7" title="Permalink to this headline">¶</a></h2>
+<p>Here are the steps in this notebook:</p>
+<ol class="simple">
+<li><p>Starting with the figure from the previous notebook, we’ll add annotations to present the results more clearly.</p></li>
+<li><p>The we’ll see several ways to customize figures to make them more appealing and effective.</p></li>
+<li><p>Finally, we’ll see how to make a figure with multiple panels or subplots.</p></li>
+</ol>
+<p>Press this button to run this notebook on Colab:</p>
+<p><a class="reference external" href="https://colab.research.google.com/github/AllenDowney/AstronomicalData/blob/main/07_plot.ipynb"><img src="run_on_colab_small.png"></a></p>
+<p><a class="reference external" href="https://nbviewer.jupyter.org/github/AllenDowney/AstronomicalData/blob/main/07_plot.ipynb">or click here to read it on NBViewer</a></p>
+</div>
+<div class="section" id="installation-instructions">
+<h2>Installation instructions<a class="headerlink" href="#installation-instructions" title="Permalink to this headline">¶</a></h2>
+<p>Coming soon.</p>
+</div>
+</div>
+
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+    <footer class="footer mt-5 mt-md-0">
+    <div class="container">
+      <p>
+        
+          By Allen B. Downey<br/>
+        
+            &copy; Copyright 2020.<br/>
+      </p>
+    </div>
+  </footer>
+</main>
+
+
+      </div>
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+
+    
+  <script src="_static/js/index.30270b6e4c972e43c488.js"></script>
+
+
+    
+  </body>
+</html>
\ No newline at end of file
diff --git a/_build/html/last_resort.html b/_build/html/last_resort.html
new file mode 100644
index 0000000..b708665
--- /dev/null
+++ b/_build/html/last_resort.html
@@ -0,0 +1,313 @@
+
+<!DOCTYPE html>
+
+<html>
+  <head>
+    <meta charset="utf-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+    <title>The Notebook of Last Resort &#8212; Astronomical Data in Python</title>
+    
+  <link rel="stylesheet" href="_static/css/index.d431a4ee1c1efae0e38bdfebc22debff.css">
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+    <script async="async" src="https://unpkg.com/thebelab@latest/lib/index.js"></script>
+    <script >
+        const thebe_selector = ".thebe"
+        const thebe_selector_input = "pre"
+        const thebe_selector_output = ".output"
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+  <body data-spy="scroll" data-target="#bd-toc-nav" data-offset="80">
+    
+
+    <div class="container-xl">
+      <div class="row">
+          
+<div class="col-12 col-md-3 bd-sidebar site-navigation show" id="site-navigation">
+    
+        <div class="navbar-brand-box">
+<a class="navbar-brand text-wrap" href="index.html">
+  
+  
+  <h1 class="site-logo" id="site-title">Astronomical Data in Python</h1>
+  
+</a>
+</div>
+
+<form class="bd-search d-flex align-items-center" action="search.html" method="get">
+  <i class="icon fas fa-search"></i>
+  <input type="search" class="form-control" name="q" id="search-input" placeholder="Search this book..." aria-label="Search this book..." autocomplete="off" >
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+<nav class="bd-links" id="bd-docs-nav" aria-label="Main navigation">
+  <ul class="nav sidenav_l1">
+ <li class="toctree-l1">
+  <a class="reference internal" href="README.html">
+   Astronomical Data in Python
+  </a>
+ </li>
+</ul>
+<ul class="nav sidenav_l1">
+ <li class="toctree-l1">
+  <a class="reference internal" href="01_query.html">
+   Chapter 1
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="02_coords.html">
+   Chapter 2
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="03_motion.html">
+   Chapter 3
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="04_select.html">
+   Chapter 4
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="05_join.html">
+   Chapter 5
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="06_photo.html">
+   Chapter 6
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="07_plot.html">
+   Chapter 7
+  </a>
+ </li>
+</ul>
+
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+        <!-- Download PDF via print -->
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+                data-placement="bottom" onclick="toggleFullScreen()" title="Fullscreen mode"><i
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+        aria-label="Launch interactive content"><i class="fas fa-rocket"></i></button>
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+        
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+                class="btn btn-secondary topbarbtn" title="Launch Binder" data-toggle="tooltip"
+                data-placement="left"><img class="binder-button-logo"
+                    src="_static/images/logo_binder.svg"
+                    alt="Interact on binder">Binder</button></a>
+        
+        
+        
+        <a class="colab-button" href="https://colab.research.google.com/github/AllenDowney/AstronomyDataPython/blob/master/last_resort.ipynb"><button type="button" class="btn btn-secondary topbarbtn"
+                title="Launch Colab" data-toggle="tooltip" data-placement="left"><img class="colab-button-logo"
+                    src="_static/images/logo_colab.png"
+                    alt="Interact on Colab">Colab</button></a>
+        
+        
+    </div>
+</div>
+
+    </div>
+
+    <!-- Table of contents -->
+    <div class="d-none d-md-block col-md-2 bd-toc show">
+        
+    </div>
+</div>
+    <div id="main-content" class="row">
+        <div class="col-12 col-md-9 pl-md-3 pr-md-0">
+        
+              <div>
+                
+  <div class="section" id="the-notebook-of-last-resort">
+<h1>The Notebook of Last Resort<a class="headerlink" href="#the-notebook-of-last-resort" title="Permalink to this headline">¶</a></h1>
+<p>If you are not able to get everything installed that we need for the workshop, you have the option of running this notebook on Colab.</p>
+<p>Before you get started, you probably want to press the Save button!</p>
+<div class="cell tag_hide-cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># If we&#39;re running on Colab, install libraries</span>
+
+<span class="kn">import</span> <span class="nn">sys</span>
+<span class="n">IN_COLAB</span> <span class="o">=</span> <span class="s1">&#39;google.colab&#39;</span> <span class="ow">in</span> <span class="n">sys</span><span class="o">.</span><span class="n">modules</span>
+
+<span class="k">if</span> <span class="n">IN_COLAB</span><span class="p">:</span>
+    <span class="o">!</span>pip install astroquery astro-gala pyia
+</pre></div>
+</div>
+</div>
+</div>
+<p>That should be everything you need.  Now you can type code and run it in the following cells.</p>
+</div>
+
+    <script type="text/x-thebe-config">
+    {
+        requestKernel: true,
+        binderOptions: {
+            repo: "binder-examples/jupyter-stacks-datascience",
+            ref: "master",
+        },
+        codeMirrorConfig: {
+            theme: "abcdef",
+            mode: "python"
+        },
+        kernelOptions: {
+            kernelName: "python3",
+            path: "./."
+        },
+        predefinedOutput: true
+    }
+    </script>
+    <script>kernelName = 'python3'</script>
+
+              </div>
+              
+        </div>
+    </div>
+    
+    
+    <div class='prev-next-bottom'>
+        
+
+    </div>
+    <footer class="footer mt-5 mt-md-0">
+    <div class="container">
+      <p>
+        
+          By Allen B. Downey<br/>
+        
+            &copy; Copyright 2020.<br/>
+      </p>
+    </div>
+  </footer>
+</main>
+
+
+      </div>
+    </div>
+
+    
+  <script src="_static/js/index.30270b6e4c972e43c488.js"></script>
+
+
+    
+  </body>
+</html>
\ No newline at end of file
diff --git a/_build/html/objects.inv b/_build/html/objects.inv
new file mode 100644
index 0000000..75fed17
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+ </li>
+</ul>
+<ul class="nav sidenav_l1">
+ <li class="toctree-l1">
+  <a class="reference internal" href="01_query.html">
+   Chapter 1
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="02_coords.html">
+   Chapter 2
+  </a>
+ </li>
+ <li class="toctree-l1">
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+   Chapter 3
+  </a>
+ </li>
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+  <a class="reference internal" href="04_select.html">
+   Chapter 4
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+ </li>
+ <li class="toctree-l1">
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+   Chapter 5
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+ </li>
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+   Chapter 6
+  </a>
+ </li>
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+   Chapter 7
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+    <div id="main-content" class="row">
+        <div class="col-12 col-md-9 pl-md-3 pr-md-0">
+        
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+                
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+  <div id="fallback" class="admonition warning">
+  <script>$('#fallback').hide();</script>
+  <p>
+    Please activate JavaScript to enable the search
+    functionality.
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+    Searching for multiple words only shows matches that contain
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+  <form action="" method="get">
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+   Chapter 1
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+   Chapter 2
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+   Chapter 3
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+   Chapter 4
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+   Chapter 5
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+   Chapter 6
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+          
+
+
+          
+<main class="col py-md-3 pl-md-4 bd-content overflow-auto" role="main">
+    
+    <div class="row topbar fixed-top container-xl">
+    <div class="col-12 col-md-3 bd-topbar-whitespace site-navigation show">
+    </div>
+    <div class="col pl-2 topbar-main">
+        
+        <button id="navbar-toggler" class="navbar-toggler ml-0" type="button" data-toggle="collapse"
+            data-toggle="tooltip" data-placement="bottom" data-target=".site-navigation" aria-controls="navbar-menu"
+            aria-expanded="true" aria-label="Toggle navigation" aria-controls="site-navigation"
+            title="Toggle navigation" data-toggle="tooltip" data-placement="left">
+            <i class="fas fa-bars"></i>
+            <i class="fas fa-arrow-left"></i>
+            <i class="fas fa-arrow-up"></i>
+        </button>
+        
+        <div class="dropdown-buttons-trigger">
+    <button id="dropdown-buttons-trigger" class="btn btn-secondary topbarbtn" aria-label="Download this page"><i
+            class="fas fa-download"></i></button>
+
+    
+    <div class="dropdown-buttons">
+        <!-- ipynb file if we had a myst markdown file -->
+        
+        <!-- Download raw file -->
+        <a class="dropdown-buttons" href="_sources/test_setup.ipynb"><button type="button"
+                class="btn btn-secondary topbarbtn" title="Download source file" data-toggle="tooltip"
+                data-placement="left">.ipynb</button></a>
+        <!-- Download PDF via print -->
+        <button type="button" id="download-print" class="btn btn-secondary topbarbtn" title="Print to PDF"
+            onClick="window.print()" data-toggle="tooltip" data-placement="left">.pdf</button>
+    </div>
+    
+</div>
+        <!-- Source interaction buttons -->
+
+<div class="dropdown-buttons-trigger">
+    <button id="dropdown-buttons-trigger" class="btn btn-secondary topbarbtn"
+        aria-label="Connect with source repository"><i class="fab fa-github"></i></button>
+    <div class="dropdown-buttons sourcebuttons">
+        <a class="repository-button"
+            href="https://github.com/AllenDowney/AstronomyDataPython"><button type="button" class="btn btn-secondary topbarbtn"
+                data-toggle="tooltip" data-placement="left" title="Source repository"><i
+                    class="fab fa-github"></i>repository</button></a>
+        
+        
+    </div>
+</div>
+
+
+        <!-- Full screen (wrap in <a> to have style consistency -->
+        <a class="full-screen-button"><button type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip"
+                data-placement="bottom" onclick="toggleFullScreen()" title="Fullscreen mode"><i
+                    class="fas fa-expand"></i></button></a>
+
+        <!-- Launch buttons -->
+
+<div class="dropdown-buttons-trigger">
+    <button id="dropdown-buttons-trigger" class="btn btn-secondary topbarbtn"
+        aria-label="Launch interactive content"><i class="fas fa-rocket"></i></button>
+    <div class="dropdown-buttons">
+        
+        <a class="binder-button" href="https://mybinder.org/v2/gh/AllenDowney/AstronomyDataPython/master?urlpath=tree/test_setup.ipynb"><button type="button"
+                class="btn btn-secondary topbarbtn" title="Launch Binder" data-toggle="tooltip"
+                data-placement="left"><img class="binder-button-logo"
+                    src="_static/images/logo_binder.svg"
+                    alt="Interact on binder">Binder</button></a>
+        
+        
+        
+        <a class="colab-button" href="https://colab.research.google.com/github/AllenDowney/AstronomyDataPython/blob/master/test_setup.ipynb"><button type="button" class="btn btn-secondary topbarbtn"
+                title="Launch Colab" data-toggle="tooltip" data-placement="left"><img class="colab-button-logo"
+                    src="_static/images/logo_colab.png"
+                    alt="Interact on Colab">Colab</button></a>
+        
+        
+    </div>
+</div>
+
+    </div>
+
+    <!-- Table of contents -->
+    <div class="d-none d-md-block col-md-2 bd-toc show">
+        
+    </div>
+</div>
+    <div id="main-content" class="row">
+        <div class="col-12 col-md-9 pl-md-3 pr-md-0">
+        
+              <div>
+                
+  <div class="section" id="astronomical-data-in-python">
+<h1>Astronomical Data in Python<a class="headerlink" href="#astronomical-data-in-python" title="Permalink to this headline">¶</a></h1>
+<p>This notebook imports the libraries we need for the workshop.</p>
+<p>If any of them are missing, you’ll get an error message.</p>
+<p>If you don’t get any error messages, you are all set.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">wget</span> <span class="kn">import</span> <span class="n">download</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib</span> <span class="k">as</span> <span class="nn">mpl</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">from</span> <span class="nn">matplotlib.path</span> <span class="kn">import</span> <span class="n">Path</span>
+<span class="kn">from</span> <span class="nn">matplotlib.patches</span> <span class="kn">import</span> <span class="n">Polygon</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">astropy.coordinates</span> <span class="k">as</span> <span class="nn">coord</span>
+<span class="kn">import</span> <span class="nn">astropy.units</span> <span class="k">as</span> <span class="nn">u</span>
+<span class="kn">from</span> <span class="nn">astropy.table</span> <span class="kn">import</span> <span class="n">Table</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">gala.coordinates</span> <span class="k">as</span> <span class="nn">gc</span>
+<span class="kn">from</span> <span class="nn">pyia</span> <span class="kn">import</span> <span class="n">GaiaData</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Note: running this import statement opens a connection</span>
+<span class="c1"># to a Gaia server, so it will fail if you are not connected</span>
+<span class="c1"># to the internet.</span>
+
+<span class="kn">from</span> <span class="nn">astroquery.gaia</span> <span class="kn">import</span> <span class="n">Gaia</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Created TAP+ (v1.2.1) - Connection:
+	Host: gea.esac.esa.int
+	Use HTTPS: True
+	Port: 443
+	SSL Port: 443
+Created TAP+ (v1.2.1) - Connection:
+	Host: geadata.esac.esa.int
+	Use HTTPS: True
+	Port: 443
+	SSL Port: 443
+</pre></div>
+</div>
+</div>
+</div>
+<p>During the workshop, we might put some code on Slack and ask you to cut and paste it into the notebook.</p>
+<p>If you are on a Mac, you might encounter a problem:</p>
+</div>
+
+    <script type="text/x-thebe-config">
+    {
+        requestKernel: true,
+        binderOptions: {
+            repo: "binder-examples/jupyter-stacks-datascience",
+            ref: "master",
+        },
+        codeMirrorConfig: {
+            theme: "abcdef",
+            mode: "python"
+        },
+        kernelOptions: {
+            kernelName: "python3",
+            path: "./."
+        },
+        predefinedOutput: true
+    }
+    </script>
+    <script>kernelName = 'python3'</script>
+
+              </div>
+              
+        </div>
+    </div>
+    
+    
+    <div class='prev-next-bottom'>
+        
+
+    </div>
+    <footer class="footer mt-5 mt-md-0">
+    <div class="container">
+      <p>
+        
+          By Allen B. Downey<br/>
+        
+            &copy; Copyright 2020.<br/>
+      </p>
+    </div>
+  </footer>
+</main>
+
+
+      </div>
+    </div>
+
+    
+  <script src="_static/js/index.30270b6e4c972e43c488.js"></script>
+
+
+    
+  </body>
+</html>
\ No newline at end of file
diff --git a/_build/jupyter_execute/01_query.ipynb b/_build/jupyter_execute/01_query.ipynb
new file mode 100644
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--- /dev/null
+++ b/_build/jupyter_execute/01_query.ipynb
@@ -0,0 +1,1675 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Chapter 1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*Astronomical Data in Python* is an introduction to tools and practices for working with astronomical data.  Topics covered include:\n",
+    "\n",
+    "* Writing queries that select and download data from a database.\n",
+    "\n",
+    "* Using data stored in an Astropy `Table` or Pandas `DataFrame`.\n",
+    "\n",
+    "* Working with coordinates and other quantities with units.\n",
+    "\n",
+    "* Storing data in various formats.\n",
+    "\n",
+    "* Performing database join operations that combine data from multiple tables.\n",
+    "\n",
+    "* Visualizing data and preparing publication-quality figures."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As a running example, we will replicate part of the analysis in a recent paper, \"[Off the beaten path: Gaia reveals GD-1 stars outside of the main stream](https://arxiv.org/abs/1805.00425)\" by Adrian M. Price-Whelan and Ana Bonaca.\n",
+    "\n",
+    "As the abstract explains, \"Using data from the Gaia second data release combined with Pan-STARRS photometry, we present a sample of highly-probable members of the longest cold stream in the Milky Way, GD-1.\"\n",
+    "\n",
+    "GD-1 is a [stellar stream](https://en.wikipedia.org/wiki/List_of_stellar_streams), which is \"an association of stars orbiting a galaxy that was once a globular cluster or dwarf galaxy that has now been torn apart and stretched out along its orbit by tidal forces.\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[This article in *Science* magazine](https://www.sciencemag.org/news/2018/10/streams-stars-reveal-galaxy-s-violent-history-and-perhaps-its-unseen-dark-matter) explains some of the background, including the process that led to the paper and an discussion of the scientific implications:\n",
+    "\n",
+    "* \"The streams are particularly useful for ... galactic archaeology --- rewinding the cosmic clock to reconstruct the assembly of the Milky Way.\"\n",
+    "\n",
+    "* \"They also are being used as exquisitely sensitive scales to measure the galaxy's mass.\"\n",
+    "\n",
+    "* \"... the streams are well-positioned to reveal the presence of dark matter ... because the streams are so fragile, theorists say, collisions with marauding clumps of dark matter could leave telltale scars, potential clues to its nature.\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Data\n",
+    "\n",
+    "The datasets we will work with are:\n",
+    " \n",
+    "* [Gaia](https://en.wikipedia.org/wiki/Gaia_(spacecraft)), which is \"a space observatory of the European Space Agency (ESA), launched in 2013 ... designed for astrometry: measuring the positions, distances and motions of stars with unprecedented precision\", and\n",
+    "\n",
+    "* [Pan-STARRS](https://en.wikipedia.org/wiki/Pan-STARRS), The Panoramic Survey Telescope and Rapid Response System, which is a survey designed to monitor the sky for transient objects, producing a catalog with accurate astronometry and photometry of detected sources.\n",
+    "\n",
+    "Both of these datasets are very large, which can make them challenging to work with.  It might not be possible, or practical, to download the entire dataset.\n",
+    "One of the goals of this workshop is to provide tools for working with large datasets."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Prerequisites\n",
+    "\n",
+    "These notebooks are meant for people who are familiar with basic Python, but not necessarily the libraries we will use, like Astropy or Pandas.  If you are familiar with Python lists and dictionaries, and you know how to write a function that takes parameters and returns a value, you know enough Python to get started.\n",
+    "\n",
+    "We assume that you have some familiarity with operating systems, like the ability to use a command-line interface.  But we don't assume you have any prior experience with databases.\n",
+    "\n",
+    "We assume that you are familiar with astronomy at the undergraduate level, but we will not assume specialized knowledge of the datasets or analysis methods we'll use. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Outline\n",
+    "\n",
+    "The first lesson demonstrates the steps for selecting and downloading data from the Gaia Database:\n",
+    "\n",
+    "1. First we'll make a connection to the Gaia server,\n",
+    "\n",
+    "2. We will explore information about the database and the tables it contains,\n",
+    "\n",
+    "3. We will write a query and send it to the server, and finally\n",
+    "\n",
+    "4. We will download the response from the server.\n",
+    "\n",
+    "After completing this lesson, you should be able to\n",
+    "\n",
+    "* Compose a basic query in ADQL.\n",
+    "\n",
+    "* Use queries to explore a database and its tables.\n",
+    "\n",
+    "* Use queries to download data.\n",
+    "\n",
+    "* Develop, test, and debug a query incrementally."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Query Language\n",
+    "\n",
+    "In order to select data from a database, you have to compose a query, which is like a program written in a \"query language\".\n",
+    "The query language we'll use is ADQL, which stands for \"Astronomical Data Query Language\".\n",
+    "\n",
+    "ADQL is a dialect of [SQL](https://en.wikipedia.org/wiki/SQL) (Structured Query Language), which is by far the most commonly used query language.  Almost everything you will learn about ADQL also works in SQL.\n",
+    "\n",
+    "[The reference manual for ADQL is here](http://www.ivoa.net/documents/ADQL/20180112/PR-ADQL-2.1-20180112.html).\n",
+    "But you might find it easier to learn from [this ADQL Cookbook](https://www.gaia.ac.uk/data/gaia-data-release-1/adql-cookbook)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Installing libraries\n",
+    "\n",
+    "The library we'll use to get Gaia data is [Astroquery](https://astroquery.readthedocs.io/en/latest/).\n",
+    "\n",
+    "If you are running this notebook on Colab, you can run the following cell to install Astroquery and the other libraries we'll use.\n",
+    "\n",
+    "If you are running this notebook on your own computer, you might have to install these libraries yourself.  \n",
+    "\n",
+    "If you are using this notebook as part of a Carpentries workshop, you should have received setup instructions.\n",
+    "\n",
+    "TODO: Add a link to the instructions.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "tags": [
+     "hide-cell"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# If we're running on Colab, install libraries\n",
+    "\n",
+    "import sys\n",
+    "IN_COLAB = 'google.colab' in sys.modules\n",
+    "\n",
+    "if IN_COLAB:\n",
+    "    !pip install astroquery astro-gala pyia"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Connecting to Gaia\n",
+    "\n",
+    "Astroquery provides `Gaia`, which is an [object that represents a connection to the Gaia database](https://astroquery.readthedocs.io/en/latest/gaia/gaia.html).\n",
+    "\n",
+    "We can connect to the Gaia database like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Created TAP+ (v1.2.1) - Connection:\n",
+      "\tHost: gea.esac.esa.int\n",
+      "\tUse HTTPS: True\n",
+      "\tPort: 443\n",
+      "\tSSL Port: 443\n",
+      "Created TAP+ (v1.2.1) - Connection:\n",
+      "\tHost: geadata.esac.esa.int\n",
+      "\tUse HTTPS: True\n",
+      "\tPort: 443\n",
+      "\tSSL Port: 443\n"
+     ]
+    }
+   ],
+   "source": [
+    "from astroquery.gaia import Gaia"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Running this import statement has the effect of creating a [TAP+](http://www.ivoa.net/documents/TAP/) connection; TAP stands for \"Table Access Protocol\".  It is a network protocol for sending queries to the database and getting back the results.  We're not sure why it seems to create two connections."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Databases and Tables\n",
+    "\n",
+    "What is a database, anyway?  Most generally, it can be any collection of data, but when we are talking about ADQL or SQL:\n",
+    "\n",
+    "* A database is a collection of one or more named tables.\n",
+    "\n",
+    "* Each table is a 2-D array with one or more named columns of data.\n",
+    "\n",
+    "We can use `Gaia.load_tables` to get the names of the tables in the Gaia database.  With the option `only_names=True`, it loads information about the tables, called the \"metadata\", not the data itself."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO: Retrieving tables... [astroquery.utils.tap.core]\n",
+      "INFO: Parsing tables... [astroquery.utils.tap.core]\n",
+      "INFO: Done. [astroquery.utils.tap.core]\n"
+     ]
+    }
+   ],
+   "source": [
+    "tables = Gaia.load_tables(only_names=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "tags": [
+     "hide-output"
+    ]
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "external.external.apassdr9\n",
+      "external.external.gaiadr2_geometric_distance\n",
+      "external.external.galex_ais\n",
+      "external.external.ravedr5_com\n",
+      "external.external.ravedr5_dr5\n",
+      "external.external.ravedr5_gra\n",
+      "external.external.ravedr5_on\n",
+      "external.external.sdssdr13_photoprimary\n",
+      "external.external.skymapperdr1_master\n",
+      "external.external.tmass_xsc\n",
+      "public.public.hipparcos\n",
+      "public.public.hipparcos_newreduction\n",
+      "public.public.hubble_sc\n",
+      "public.public.igsl_source\n",
+      "public.public.igsl_source_catalog_ids\n",
+      "public.public.tycho2\n",
+      "public.public.dual\n",
+      "tap_config.tap_config.coord_sys\n",
+      "tap_config.tap_config.properties\n",
+      "tap_schema.tap_schema.columns\n",
+      "tap_schema.tap_schema.key_columns\n",
+      "tap_schema.tap_schema.keys\n",
+      "tap_schema.tap_schema.schemas\n",
+      "tap_schema.tap_schema.tables\n",
+      "gaiadr1.gaiadr1.aux_qso_icrf2_match\n",
+      "gaiadr1.gaiadr1.ext_phot_zero_point\n",
+      "gaiadr1.gaiadr1.allwise_best_neighbour\n",
+      "gaiadr1.gaiadr1.allwise_neighbourhood\n",
+      "gaiadr1.gaiadr1.gsc23_best_neighbour\n",
+      "gaiadr1.gaiadr1.gsc23_neighbourhood\n",
+      "gaiadr1.gaiadr1.ppmxl_best_neighbour\n",
+      "gaiadr1.gaiadr1.ppmxl_neighbourhood\n",
+      "gaiadr1.gaiadr1.sdss_dr9_best_neighbour\n",
+      "gaiadr1.gaiadr1.sdss_dr9_neighbourhood\n",
+      "gaiadr1.gaiadr1.tmass_best_neighbour\n",
+      "gaiadr1.gaiadr1.tmass_neighbourhood\n",
+      "gaiadr1.gaiadr1.ucac4_best_neighbour\n",
+      "gaiadr1.gaiadr1.ucac4_neighbourhood\n",
+      "gaiadr1.gaiadr1.urat1_best_neighbour\n",
+      "gaiadr1.gaiadr1.urat1_neighbourhood\n",
+      "gaiadr1.gaiadr1.cepheid\n",
+      "gaiadr1.gaiadr1.phot_variable_time_series_gfov\n",
+      "gaiadr1.gaiadr1.phot_variable_time_series_gfov_statistical_parameters\n",
+      "gaiadr1.gaiadr1.rrlyrae\n",
+      "gaiadr1.gaiadr1.variable_summary\n",
+      "gaiadr1.gaiadr1.allwise_original_valid\n",
+      "gaiadr1.gaiadr1.gsc23_original_valid\n",
+      "gaiadr1.gaiadr1.ppmxl_original_valid\n",
+      "gaiadr1.gaiadr1.sdssdr9_original_valid\n",
+      "gaiadr1.gaiadr1.tmass_original_valid\n",
+      "gaiadr1.gaiadr1.ucac4_original_valid\n",
+      "gaiadr1.gaiadr1.urat1_original_valid\n",
+      "gaiadr1.gaiadr1.gaia_source\n",
+      "gaiadr1.gaiadr1.tgas_source\n",
+      "gaiadr2.gaiadr2.aux_allwise_agn_gdr2_cross_id\n",
+      "gaiadr2.gaiadr2.aux_iers_gdr2_cross_id\n",
+      "gaiadr2.gaiadr2.aux_sso_orbit_residuals\n",
+      "gaiadr2.gaiadr2.aux_sso_orbits\n",
+      "gaiadr2.gaiadr2.dr1_neighbourhood\n",
+      "gaiadr2.gaiadr2.allwise_best_neighbour\n",
+      "gaiadr2.gaiadr2.allwise_neighbourhood\n",
+      "gaiadr2.gaiadr2.apassdr9_best_neighbour\n",
+      "gaiadr2.gaiadr2.apassdr9_neighbourhood\n",
+      "gaiadr2.gaiadr2.gsc23_best_neighbour\n",
+      "gaiadr2.gaiadr2.gsc23_neighbourhood\n",
+      "gaiadr2.gaiadr2.hipparcos2_best_neighbour\n",
+      "gaiadr2.gaiadr2.hipparcos2_neighbourhood\n",
+      "gaiadr2.gaiadr2.panstarrs1_best_neighbour\n",
+      "gaiadr2.gaiadr2.panstarrs1_neighbourhood\n",
+      "gaiadr2.gaiadr2.ppmxl_best_neighbour\n",
+      "gaiadr2.gaiadr2.ppmxl_neighbourhood\n",
+      "gaiadr2.gaiadr2.ravedr5_best_neighbour\n",
+      "gaiadr2.gaiadr2.ravedr5_neighbourhood\n",
+      "gaiadr2.gaiadr2.sdssdr9_best_neighbour\n",
+      "gaiadr2.gaiadr2.sdssdr9_neighbourhood\n",
+      "gaiadr2.gaiadr2.tmass_best_neighbour\n",
+      "gaiadr2.gaiadr2.tmass_neighbourhood\n",
+      "gaiadr2.gaiadr2.tycho2_best_neighbour\n",
+      "gaiadr2.gaiadr2.tycho2_neighbourhood\n",
+      "gaiadr2.gaiadr2.urat1_best_neighbour\n",
+      "gaiadr2.gaiadr2.urat1_neighbourhood\n",
+      "gaiadr2.gaiadr2.sso_observation\n",
+      "gaiadr2.gaiadr2.sso_source\n",
+      "gaiadr2.gaiadr2.vari_cepheid\n",
+      "gaiadr2.gaiadr2.vari_classifier_class_definition\n",
+      "gaiadr2.gaiadr2.vari_classifier_definition\n",
+      "gaiadr2.gaiadr2.vari_classifier_result\n",
+      "gaiadr2.gaiadr2.vari_long_period_variable\n",
+      "gaiadr2.gaiadr2.vari_rotation_modulation\n",
+      "gaiadr2.gaiadr2.vari_rrlyrae\n",
+      "gaiadr2.gaiadr2.vari_short_timescale\n",
+      "gaiadr2.gaiadr2.vari_time_series_statistics\n",
+      "gaiadr2.gaiadr2.panstarrs1_original_valid\n",
+      "gaiadr2.gaiadr2.gaia_source\n",
+      "gaiadr2.gaiadr2.ruwe\n"
+     ]
+    }
+   ],
+   "source": [
+    "for table in (tables):\n",
+    "    print(table.get_qualified_name())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "So that's a lot of tables.  The ones we'll use are:\n",
+    "\n",
+    "* `gaiadr2.gaia_source`, which contains Gaia data from [data release 2](https://www.cosmos.esa.int/web/gaia/data-release-2),\n",
+    "\n",
+    "* `gaiadr2.panstarrs1_original_valid`, which contains the photometry data we'll use from PanSTARRS, and\n",
+    "\n",
+    "* `gaiadr2.panstarrs1_best_neighbour`, which we'll use to cross-match each star observed by Gaia with the same star observed by PanSTARRS.\n",
+    "\n",
+    "We can use `load_table` (not `load_tables`) to get the metadata for a single table.  The name of this function is misleading, because it only downloads metadata. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Retrieving table 'gaiadr2.gaia_source'\n",
+      "Parsing table 'gaiadr2.gaia_source'...\n",
+      "Done.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<astroquery.utils.tap.model.taptable.TapTableMeta at 0x7f922376e0a0>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "meta = Gaia.load_table('gaiadr2.gaia_source')\n",
+    "meta"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Jupyter shows that the result is an object of type `TapTableMeta`, but it does not display the contents.\n",
+    "\n",
+    "To see the metadata, we have to print the object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "TAP Table name: gaiadr2.gaiadr2.gaia_source\n",
+      "Description: This table has an entry for every Gaia observed source as listed in the\n",
+      "Main Database accumulating catalogue version from which the catalogue\n",
+      "release has been generated. It contains the basic source parameters,\n",
+      "that is only final data (no epoch data) and no spectra (neither final\n",
+      "nor epoch).\n",
+      "Num. columns: 96\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(meta)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notice one gotcha: in the list of table names, this table appears as `gaiadr2.gaiadr2.gaia_source`, but when we load the metadata, we refer to it as `gaiadr2.gaia_source`.\n",
+    "\n",
+    "**Exercise:** Go back and try\n",
+    "\n",
+    "```\n",
+    "meta = Gaia.load_table('gaiadr2.gaiadr2.gaia_source')\n",
+    "```\n",
+    "\n",
+    "What happens?  Is the error message helpful?  If you had not made this error deliberately, would you have been able to figure it out?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Columns\n",
+    "\n",
+    "The following loop prints the names of the columns in the table."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "tags": [
+     "hide-output"
+    ]
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "solution_id\n",
+      "designation\n",
+      "source_id\n",
+      "random_index\n",
+      "ref_epoch\n",
+      "ra\n",
+      "ra_error\n",
+      "dec\n",
+      "dec_error\n",
+      "parallax\n",
+      "parallax_error\n",
+      "parallax_over_error\n",
+      "pmra\n",
+      "pmra_error\n",
+      "pmdec\n",
+      "pmdec_error\n",
+      "ra_dec_corr\n",
+      "ra_parallax_corr\n",
+      "ra_pmra_corr\n",
+      "ra_pmdec_corr\n",
+      "dec_parallax_corr\n",
+      "dec_pmra_corr\n",
+      "dec_pmdec_corr\n",
+      "parallax_pmra_corr\n",
+      "parallax_pmdec_corr\n",
+      "pmra_pmdec_corr\n",
+      "astrometric_n_obs_al\n",
+      "astrometric_n_obs_ac\n",
+      "astrometric_n_good_obs_al\n",
+      "astrometric_n_bad_obs_al\n",
+      "astrometric_gof_al\n",
+      "astrometric_chi2_al\n",
+      "astrometric_excess_noise\n",
+      "astrometric_excess_noise_sig\n",
+      "astrometric_params_solved\n",
+      "astrometric_primary_flag\n",
+      "astrometric_weight_al\n",
+      "astrometric_pseudo_colour\n",
+      "astrometric_pseudo_colour_error\n",
+      "mean_varpi_factor_al\n",
+      "astrometric_matched_observations\n",
+      "visibility_periods_used\n",
+      "astrometric_sigma5d_max\n",
+      "frame_rotator_object_type\n",
+      "matched_observations\n",
+      "duplicated_source\n",
+      "phot_g_n_obs\n",
+      "phot_g_mean_flux\n",
+      "phot_g_mean_flux_error\n",
+      "phot_g_mean_flux_over_error\n",
+      "phot_g_mean_mag\n",
+      "phot_bp_n_obs\n",
+      "phot_bp_mean_flux\n",
+      "phot_bp_mean_flux_error\n",
+      "phot_bp_mean_flux_over_error\n",
+      "phot_bp_mean_mag\n",
+      "phot_rp_n_obs\n",
+      "phot_rp_mean_flux\n",
+      "phot_rp_mean_flux_error\n",
+      "phot_rp_mean_flux_over_error\n",
+      "phot_rp_mean_mag\n",
+      "phot_bp_rp_excess_factor\n",
+      "phot_proc_mode\n",
+      "bp_rp\n",
+      "bp_g\n",
+      "g_rp\n",
+      "radial_velocity\n",
+      "radial_velocity_error\n",
+      "rv_nb_transits\n",
+      "rv_template_teff\n",
+      "rv_template_logg\n",
+      "rv_template_fe_h\n",
+      "phot_variable_flag\n",
+      "l\n",
+      "b\n",
+      "ecl_lon\n",
+      "ecl_lat\n",
+      "priam_flags\n",
+      "teff_val\n",
+      "teff_percentile_lower\n",
+      "teff_percentile_upper\n",
+      "a_g_val\n",
+      "a_g_percentile_lower\n",
+      "a_g_percentile_upper\n",
+      "e_bp_min_rp_val\n",
+      "e_bp_min_rp_percentile_lower\n",
+      "e_bp_min_rp_percentile_upper\n",
+      "flame_flags\n",
+      "radius_val\n",
+      "radius_percentile_lower\n",
+      "radius_percentile_upper\n",
+      "lum_val\n",
+      "lum_percentile_lower\n",
+      "lum_percentile_upper\n",
+      "datalink_url\n",
+      "epoch_photometry_url\n"
+     ]
+    }
+   ],
+   "source": [
+    "for column in meta.columns:\n",
+    "    print(column.name)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can probably guess what many of these columns are by looking at the names, but you should resist the temptation to guess.\n",
+    "To find out what the columns mean, [read the documentation](https://gea.esac.esa.int/archive/documentation/GDR2/Gaia_archive/chap_datamodel/sec_dm_main_tables/ssec_dm_gaia_source.html).\n",
+    "\n",
+    "If you want to know what can go wrong when you don't read the documentation, [you might like this article](https://www.vox.com/future-perfect/2019/6/4/18650969/married-women-miserable-fake-paul-dolan-happiness)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** One of the other tables we'll use is `gaiadr2.gaiadr2.panstarrs1_original_valid`.  Use `load_table` to get the metadata for this table.  How many columns are there and what are their names?\n",
+    "\n",
+    "Hint: Remember the gotcha we mentioned earlier."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "tags": [
+     "remove-cell"
+    ]
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Retrieving table 'gaiadr2.panstarrs1_original_valid'\n",
+      "Parsing table 'gaiadr2.panstarrs1_original_valid'...\n",
+      "Done.\n",
+      "TAP Table name: gaiadr2.gaiadr2.panstarrs1_original_valid\n",
+      "Description: The Panoramic Survey Telescope and Rapid Response System (Pan-STARRS) is\n",
+      "a system for wide-field astronomical imaging developed and operated by\n",
+      "the Institute for Astronomy at the University of Hawaii. Pan-STARRS1\n",
+      "(PS1) is the first part of Pan-STARRS to be completed and is the basis\n",
+      "for Data Release 1 (DR1). The PS1 survey used a 1.8 meter telescope and\n",
+      "its 1.4 Gigapixel camera to image the sky in five broadband filters (g,\n",
+      "r, i, z, y).\n",
+      "\n",
+      "The current table contains a filtered subsample of the 10 723 304 629\n",
+      "entries listed in the original ObjectThin table.\n",
+      "We used only ObjectThin and MeanObject tables to extract\n",
+      "panstarrs1OriginalValid table, this means that objects detected only in\n",
+      "stack images are not included here. The main reason for us to avoid the\n",
+      "use of objects detected in stack images is that their astrometry is not\n",
+      "as good as the mean objects astrometry: “The stack positions (raStack,\n",
+      "decStack) have considerably larger systematic astrometric errors than\n",
+      "the mean epoch positions (raMean, decMean).� The astrometry for the\n",
+      "MeanObject positions uses Gaia DR1 as a reference catalog, while the\n",
+      "stack positions use 2MASS as a reference catalog.\n",
+      "\n",
+      "In details, we filtered out all objects where:\n",
+      "\n",
+      "-   nDetections = 1\n",
+      "\n",
+      "-   no good quality data in Pan-STARRS, objInfoFlag 33554432 not set\n",
+      "\n",
+      "-   mean astrometry could not be measured, objInfoFlag 524288 set\n",
+      "\n",
+      "-   stack position used for mean astrometry, objInfoFlag 1048576 set\n",
+      "\n",
+      "-   error on all magnitudes equal to 0 or to -999;\n",
+      "\n",
+      "-   all magnitudes set to -999;\n",
+      "\n",
+      "-   error on RA or DEC greater than 1 arcsec.\n",
+      "\n",
+      "The number of objects in panstarrs1OriginalValid is 2 264 263 282.\n",
+      "\n",
+      "The panstarrs1OriginalValid table contains only a subset of the columns\n",
+      "available in the combined ObjectThin and MeanObject tables. A\n",
+      "description of the original ObjectThin and MeanObjects tables can be\n",
+      "found at:\n",
+      "https://outerspace.stsci.edu/display/PANSTARRS/PS1+Database+object+and+detection+tables\n",
+      "\n",
+      "Download:\n",
+      "http://mastweb.stsci.edu/ps1casjobs/home.aspx\n",
+      "Documentation:\n",
+      "https://outerspace.stsci.edu/display/PANSTARRS\n",
+      "http://pswww.ifa.hawaii.edu/pswww/\n",
+      "References:\n",
+      "The Pan-STARRS1 Surveys, Chambers, K.C., et al. 2016, arXiv:1612.05560\n",
+      "Pan-STARRS Data Processing System, Magnier, E. A., et al. 2016,\n",
+      "arXiv:1612.05240\n",
+      "Pan-STARRS Pixel Processing: Detrending, Warping, Stacking, Waters, C.\n",
+      "Z., et al. 2016, arXiv:1612.05245\n",
+      "Pan-STARRS Pixel Analysis: Source Detection and Characterization,\n",
+      "Magnier, E. A., et al. 2016, arXiv:1612.05244\n",
+      "Pan-STARRS Photometric and Astrometric Calibration, Magnier, E. A., et\n",
+      "al. 2016, arXiv:1612.05242\n",
+      "The Pan-STARRS1 Database and Data Products, Flewelling, H. A., et al.\n",
+      "2016, arXiv:1612.05243\n",
+      "\n",
+      "Catalogue curator:\n",
+      "SSDC - ASI Space Science Data Center\n",
+      "https://www.ssdc.asi.it/\n",
+      "Num. columns: 26\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "meta2 = Gaia.load_table('gaiadr2.panstarrs1_original_valid')\n",
+    "print(meta2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "tags": [
+     "hide-cell"
+    ]
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "obj_name\n",
+      "obj_id\n",
+      "ra\n",
+      "dec\n",
+      "ra_error\n",
+      "dec_error\n",
+      "epoch_mean\n",
+      "g_mean_psf_mag\n",
+      "g_mean_psf_mag_error\n",
+      "g_flags\n",
+      "r_mean_psf_mag\n",
+      "r_mean_psf_mag_error\n",
+      "r_flags\n",
+      "i_mean_psf_mag\n",
+      "i_mean_psf_mag_error\n",
+      "i_flags\n",
+      "z_mean_psf_mag\n",
+      "z_mean_psf_mag_error\n",
+      "z_flags\n",
+      "y_mean_psf_mag\n",
+      "y_mean_psf_mag_error\n",
+      "y_flags\n",
+      "n_detections\n",
+      "zone_id\n",
+      "obj_info_flag\n",
+      "quality_flag\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "for column in meta2.columns:\n",
+    "    print(column.name)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Writing queries\n",
+    "\n",
+    "By now you might be wondering how we actually download the data.  With tables this big, you generally don't.  Instead, you use queries to select only the data you want.\n",
+    "\n",
+    "A query is a string written in a query language like SQL; for the Gaia database, the query language is a dialect of SQL called ADQL.\n",
+    "\n",
+    "Here's an example of an ADQL query."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query1 = \"\"\"SELECT \n",
+    "TOP 10\n",
+    "source_id, ref_epoch, ra, dec, parallax \n",
+    "FROM gaiadr2.gaia_source\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Python note:** We use a [triple-quoted string](https://docs.python.org/3/tutorial/introduction.html#strings) here so we can include line breaks in the query, which makes it easier to read.\n",
+    "\n",
+    "The words in uppercase are ADQL keywords:\n",
+    "\n",
+    "* `SELECT` indicates that we are selecting data (as opposed to adding or modifying data).\n",
+    "\n",
+    "* `TOP` indicates that we only want the first 10 rows of the table, which is useful for testing a query before asking for all of the data.\n",
+    "\n",
+    "* `FROM` specifies which table we want data from.\n",
+    "\n",
+    "The third line is a list of column names, indicating which columns we want.  \n",
+    "\n",
+    "In this example, the keywords are capitalized and the column names are lowercase.  This is a common style, but it is not required.  ADQL and SQL are not case-sensitive."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To run this query, we use the `Gaia` object, which represents our connection to the Gaia database, and invoke `launch_job`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<astroquery.utils.tap.model.job.Job at 0x7f9222e9cb20>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "job1 = Gaia.launch_job(query1)\n",
+    "job1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The result is an object that represents the job running on a Gaia server.\n",
+    "\n",
+    "If you print it, it displays metadata for the forthcoming table."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<Table length=10>\n",
+      "   name    dtype  unit                            description                            \n",
+      "--------- ------- ---- ------------------------------------------------------------------\n",
+      "source_id   int64      Unique source identifier (unique within a particular Data Release)\n",
+      "ref_epoch float64   yr                                                    Reference epoch\n",
+      "       ra float64  deg                                                    Right ascension\n",
+      "      dec float64  deg                                                        Declination\n",
+      " parallax float64  mas                                                           Parallax\n",
+      "Jobid: None\n",
+      "Phase: COMPLETED\n",
+      "Owner: None\n",
+      "Output file: sync_20201005090721.xml.gz\n",
+      "Results: None\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(job1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Don't worry about `Results: None`.  That does not actually mean there are no results.\n",
+    "\n",
+    "However, `Phase: COMPLETED` indicates that the job is complete, so we can get the results like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "astropy.table.table.Table"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results1 = job1.get_results()\n",
+    "type(results1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Optional detail:**  Why is `table` repeated three times?  The first is the name of the module, the second is the name of the submodule, and the third is the name of the class.  Most of the time we only care about the last one.  It's like the Linnean name for gorilla, which is *Gorilla Gorilla Gorilla*."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The result is an [Astropy Table](https://docs.astropy.org/en/stable/table/), which is similar to a table in an SQL database except:\n",
+    "\n",
+    "* SQL databases are stored on disk drives, so they are persistent; that is, they \"survive\" even if you turn off the computer.  An Astropy `Table` is stored in memory; it disappears when you turn off the computer (or shut down this Jupyter notebook).\n",
+    "\n",
+    "* SQL databases are designed to process queries.  An Astropy `Table` can perform some query-like operations, like selecting columns and rows.  But these operations use Python syntax, not SQL.\n",
+    "\n",
+    "Jupyter knows how to display the contents of a `Table`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<i>Table length=10</i>\n",
+       "<table id=\"table140265627585264\" class=\"table-striped table-bordered table-condensed\">\n",
+       "<thead><tr><th>source_id</th><th>ref_epoch</th><th>ra</th><th>dec</th><th>parallax</th></tr></thead>\n",
+       "<thead><tr><th></th><th>yr</th><th>deg</th><th>deg</th><th>mas</th></tr></thead>\n",
+       "<thead><tr><th>int64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th></tr></thead>\n",
+       "<tr><td>4530738361793769600</td><td>2015.5</td><td>281.56725362448725</td><td>20.40682117430378</td><td>0.9785380604519425</td></tr>\n",
+       "<tr><td>4530752651135081216</td><td>2015.5</td><td>281.0861565355257</td><td>20.523350496351846</td><td>0.2674800612552977</td></tr>\n",
+       "<tr><td>4530743343951405568</td><td>2015.5</td><td>281.37114418299177</td><td>20.474147574053124</td><td>-0.43911323550176806</td></tr>\n",
+       "<tr><td>4530755060627162368</td><td>2015.5</td><td>281.2676236268299</td><td>20.558523922346158</td><td>1.1422630184554958</td></tr>\n",
+       "<tr><td>4530746844341315968</td><td>2015.5</td><td>281.1370431749541</td><td>20.377852388898184</td><td>1.0092247424630945</td></tr>\n",
+       "<tr><td>4530768456615026432</td><td>2015.5</td><td>281.8720921436347</td><td>20.31829694530366</td><td>-0.06900136127674149</td></tr>\n",
+       "<tr><td>4530763513119137280</td><td>2015.5</td><td>281.9211808864116</td><td>20.20956829578524</td><td>0.1266016679823622</td></tr>\n",
+       "<tr><td>4530736364618539264</td><td>2015.5</td><td>281.4913475613274</td><td>20.346579041327693</td><td>0.3894019486060072</td></tr>\n",
+       "<tr><td>4530735952305177728</td><td>2015.5</td><td>281.4085549165704</td><td>20.311030903719928</td><td>0.2041189982608354</td></tr>\n",
+       "<tr><td>4530751281056022656</td><td>2015.5</td><td>281.0585328377638</td><td>20.460309556214753</td><td>0.10294642821734962</td></tr>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<Table length=10>\n",
+       "     source_id      ref_epoch ...        dec               parallax      \n",
+       "                        yr    ...        deg                 mas         \n",
+       "       int64         float64  ...      float64             float64       \n",
+       "------------------- --------- ... ------------------ --------------------\n",
+       "4530738361793769600    2015.5 ...  20.40682117430378   0.9785380604519425\n",
+       "4530752651135081216    2015.5 ... 20.523350496351846   0.2674800612552977\n",
+       "4530743343951405568    2015.5 ... 20.474147574053124 -0.43911323550176806\n",
+       "4530755060627162368    2015.5 ... 20.558523922346158   1.1422630184554958\n",
+       "4530746844341315968    2015.5 ... 20.377852388898184   1.0092247424630945\n",
+       "4530768456615026432    2015.5 ...  20.31829694530366 -0.06900136127674149\n",
+       "4530763513119137280    2015.5 ...  20.20956829578524   0.1266016679823622\n",
+       "4530736364618539264    2015.5 ... 20.346579041327693   0.3894019486060072\n",
+       "4530735952305177728    2015.5 ... 20.311030903719928   0.2041189982608354\n",
+       "4530751281056022656    2015.5 ... 20.460309556214753  0.10294642821734962"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Each column has a name, units, and a data type.\n",
+    "\n",
+    "For example, the units of `ra` and `dec` are degrees, and their data type is `float64`, which is a 64-bit floating-point number, used to store measurements with a fraction part.\n",
+    "\n",
+    "This information comes from the Gaia database, and has been stored in the Astropy `Table` by Astroquery."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Read [the documentation of this table](https://gea.esac.esa.int/archive/documentation/GDR2/Gaia_archive/chap_datamodel/sec_dm_main_tables/ssec_dm_gaia_source.html) and choose a column that looks interesting to you.  Add the column name to the query and run it again.  What are the units of the column you selected?  What is its data type?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Asynchronous queries\n",
+    "\n",
+    "`launch_job` asks the server to run the job \"synchronously\", which normally means it runs immediately.  But synchronous jobs are limited to 2000 rows.  For queries that return more rows, you should run \"asynchronously\", which mean they might take longer to get started.\n",
+    "\n",
+    "If you are not sure how many rows a query will return, you can use the SQL command `COUNT` to find out how many rows are in the result without actually returning them.  We'll see an example of this later.\n",
+    "\n",
+    "The results of an asynchronous query are stored in a file on the server, so you can start a query and come back later to get the results.\n",
+    "\n",
+    "For anonymous users, files are kept for three days.\n",
+    "\n",
+    "As an example, let's try a query that's similar to `query1`, with two changes:\n",
+    "\n",
+    "* It selects the first 3000 rows, so it is bigger than we should run synchronously.\n",
+    "\n",
+    "* It uses a new keyword, `WHERE`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query2 = \"\"\"SELECT TOP 3000\n",
+    "source_id, ref_epoch, ra, dec, parallax\n",
+    "FROM gaiadr2.gaia_source\n",
+    "WHERE parallax < 1\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A `WHERE` clause indicates which rows we want; in this case, the query selects only rows \"where\" `parallax` is less than 1.  This has the effect of selecting stars with relatively low parallax, which are farther away.  We'll use this clause to exclude nearby stars that are unlikely to be part of GD-1.\n",
+    "\n",
+    "`WHERE` is one of the most common clauses in ADQL/SQL, and one of the most useful, because it allows us to select only the rows we need from the database.\n",
+    "\n",
+    "We use `launch_job_async` to submit an asynchronous query."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO: Query finished. [astroquery.utils.tap.core]\n",
+      "<Table length=3000>\n",
+      "   name    dtype  unit                            description                            \n",
+      "--------- ------- ---- ------------------------------------------------------------------\n",
+      "source_id   int64      Unique source identifier (unique within a particular Data Release)\n",
+      "ref_epoch float64   yr                                                    Reference epoch\n",
+      "       ra float64  deg                                                    Right ascension\n",
+      "      dec float64  deg                                                        Declination\n",
+      " parallax float64  mas                                                           Parallax\n",
+      "Jobid: 1601903242219O\n",
+      "Phase: COMPLETED\n",
+      "Owner: None\n",
+      "Output file: async_20201005090722.vot\n",
+      "Results: None\n"
+     ]
+    }
+   ],
+   "source": [
+    "job2 = Gaia.launch_job_async(query2)\n",
+    "print(job2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And here are the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<i>Table length=3000</i>\n",
+       "<table id=\"table140265625141056\" class=\"table-striped table-bordered table-condensed\">\n",
+       "<thead><tr><th>source_id</th><th>ref_epoch</th><th>ra</th><th>dec</th><th>parallax</th></tr></thead>\n",
+       "<thead><tr><th></th><th>yr</th><th>deg</th><th>deg</th><th>mas</th></tr></thead>\n",
+       "<thead><tr><th>int64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th></tr></thead>\n",
+       "<tr><td>4530738361793769600</td><td>2015.5</td><td>281.56725362448725</td><td>20.40682117430378</td><td>0.9785380604519425</td></tr>\n",
+       "<tr><td>4530752651135081216</td><td>2015.5</td><td>281.0861565355257</td><td>20.523350496351846</td><td>0.2674800612552977</td></tr>\n",
+       "<tr><td>4530743343951405568</td><td>2015.5</td><td>281.37114418299177</td><td>20.474147574053124</td><td>-0.43911323550176806</td></tr>\n",
+       "<tr><td>4530768456615026432</td><td>2015.5</td><td>281.8720921436347</td><td>20.31829694530366</td><td>-0.06900136127674149</td></tr>\n",
+       "<tr><td>4530763513119137280</td><td>2015.5</td><td>281.9211808864116</td><td>20.20956829578524</td><td>0.1266016679823622</td></tr>\n",
+       "<tr><td>4530736364618539264</td><td>2015.5</td><td>281.4913475613274</td><td>20.346579041327693</td><td>0.3894019486060072</td></tr>\n",
+       "<tr><td>4530735952305177728</td><td>2015.5</td><td>281.4085549165704</td><td>20.311030903719928</td><td>0.2041189982608354</td></tr>\n",
+       "<tr><td>4530751281056022656</td><td>2015.5</td><td>281.0585328377638</td><td>20.460309556214753</td><td>0.10294642821734962</td></tr>\n",
+       "<tr><td>4530740938774409344</td><td>2015.5</td><td>281.3762569536416</td><td>20.436140058941206</td><td>0.9242670062090182</td></tr>\n",
+       "<tr><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr>\n",
+       "<tr><td>4467710915011802624</td><td>2015.5</td><td>269.9680969307347</td><td>1.1429085038160882</td><td>0.42361471245557913</td></tr>\n",
+       "<tr><td>4467706551328679552</td><td>2015.5</td><td>270.033164589881</td><td>1.0565747323689927</td><td>0.922888231734588</td></tr>\n",
+       "<tr><td>4467712255037300096</td><td>2015.5</td><td>270.7724717923047</td><td>0.6581664892880896</td><td>-2.669179465293931</td></tr>\n",
+       "<tr><td>4467735001181761792</td><td>2015.5</td><td>270.3628606248308</td><td>0.8947079323599124</td><td>0.6117399163086398</td></tr>\n",
+       "<tr><td>4467737101421916672</td><td>2015.5</td><td>270.5110834661444</td><td>0.9806225910160181</td><td>-0.39818224846127004</td></tr>\n",
+       "<tr><td>4467707547757327488</td><td>2015.5</td><td>269.88746280594927</td><td>1.0212759940136962</td><td>0.7741412301054209</td></tr>\n",
+       "<tr><td>4467732772094573056</td><td>2015.5</td><td>270.55997182760126</td><td>0.9037072088489417</td><td>-1.7920417800164183</td></tr>\n",
+       "<tr><td>4467732355491087744</td><td>2015.5</td><td>270.6730790702491</td><td>0.9197224705139885</td><td>-0.3464446494840354</td></tr>\n",
+       "<tr><td>4467717099766944512</td><td>2015.5</td><td>270.57667173120825</td><td>0.726277659009568</td><td>0.05443955111134051</td></tr>\n",
+       "<tr><td>4467719058265781248</td><td>2015.5</td><td>270.7248052971514</td><td>0.8205551921782785</td><td>0.3733943917490343</td></tr>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<Table length=3000>\n",
+       "     source_id      ref_epoch ...        dec               parallax      \n",
+       "                        yr    ...        deg                 mas         \n",
+       "       int64         float64  ...      float64             float64       \n",
+       "------------------- --------- ... ------------------ --------------------\n",
+       "4530738361793769600    2015.5 ...  20.40682117430378   0.9785380604519425\n",
+       "4530752651135081216    2015.5 ... 20.523350496351846   0.2674800612552977\n",
+       "4530743343951405568    2015.5 ... 20.474147574053124 -0.43911323550176806\n",
+       "4530768456615026432    2015.5 ...  20.31829694530366 -0.06900136127674149\n",
+       "4530763513119137280    2015.5 ...  20.20956829578524   0.1266016679823622\n",
+       "4530736364618539264    2015.5 ... 20.346579041327693   0.3894019486060072\n",
+       "4530735952305177728    2015.5 ... 20.311030903719928   0.2041189982608354\n",
+       "4530751281056022656    2015.5 ... 20.460309556214753  0.10294642821734962\n",
+       "4530740938774409344    2015.5 ... 20.436140058941206   0.9242670062090182\n",
+       "                ...       ... ...                ...                  ...\n",
+       "4467710915011802624    2015.5 ... 1.1429085038160882  0.42361471245557913\n",
+       "4467706551328679552    2015.5 ... 1.0565747323689927    0.922888231734588\n",
+       "4467712255037300096    2015.5 ... 0.6581664892880896   -2.669179465293931\n",
+       "4467735001181761792    2015.5 ... 0.8947079323599124   0.6117399163086398\n",
+       "4467737101421916672    2015.5 ... 0.9806225910160181 -0.39818224846127004\n",
+       "4467707547757327488    2015.5 ... 1.0212759940136962   0.7741412301054209\n",
+       "4467732772094573056    2015.5 ... 0.9037072088489417  -1.7920417800164183\n",
+       "4467732355491087744    2015.5 ... 0.9197224705139885  -0.3464446494840354\n",
+       "4467717099766944512    2015.5 ...  0.726277659009568  0.05443955111134051\n",
+       "4467719058265781248    2015.5 ... 0.8205551921782785   0.3733943917490343"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results2 = job2.get_results()\n",
+    "results2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You might notice that some values of `parallax` are negative.  As [this FAQ explains](https://www.cosmos.esa.int/web/gaia/archive-tips#negative%20parallax), \"Negative parallaxes are caused by errors in the observations.\"  Negative parallaxes have \"no physical meaning,\" but they can be a \"useful diagnostic on the quality of the astrometric solution.\"\n",
+    "\n",
+    "Later we will see an example where we use `parallax` and `parallax_error` to identify stars where the distance estimate is likely to be inaccurate."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** The clauses in a query have to be in the right order.  Go back and change the order of the clauses in `query2` and run it again.  \n",
+    "\n",
+    "The query should fail, but notice that you don't get much useful debugging information.  \n",
+    "\n",
+    "For this reason, developing and debugging ADQL queries can be really hard.  A few suggestions that might help:\n",
+    "\n",
+    "* Whenever possible, start with a working query, either an example you find online or a query you have used in the past.\n",
+    "\n",
+    "* Make small changes and test each change before you continue.\n",
+    "\n",
+    "* While you are debugging, use `TOP` to limit the number of rows in the result.  That will make each attempt run faster, which reduces your testing time.  \n",
+    "\n",
+    "* Launching test queries synchronously might make them start faster, too."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Operators\n",
+    "\n",
+    "In a `WHERE` clause, you can use any of the [SQL comparison operators](https://www.w3schools.com/sql/sql_operators.asp); here are the most common ones:\n",
+    "\n",
+    "| Symbol | Operation\n",
+    "|--------| :---\n",
+    "| `>` | greater than\n",
+    "| `<` | less than\n",
+    "| `>=` | greater than or equal\n",
+    "| `<=` | less than or equal\n",
+    "| `=` | equal\n",
+    "| `!=` or `<>` | not equal\n",
+    "\n",
+    "Most of these are the same as Python, but some are not.  In particular, notice that the equality operator is `=`, not `==`.\n",
+    "Be careful to keep your Python out of your ADQL!\n",
+    "\n",
+    "You can combine comparisons using the logical operators:\n",
+    "\n",
+    "* AND: true if both comparisons are true\n",
+    "* OR: true if either or both comparisons are true\n",
+    "\n",
+    "Finally, you can use `NOT` to invert the result of a comparison. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** [Read about SQL operators here](https://www.w3schools.com/sql/sql_operators.asp) and then modify the previous query to select rows where `bp_rp` is between `-0.75` and `2`.\n",
+    "\n",
+    "You can [read about this variable here](https://gea.esac.esa.int/archive/documentation/GDR2/Gaia_archive/chap_datamodel/sec_dm_main_tables/ssec_dm_gaia_source.html)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "tags": [
+     "hide-cell"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "# This is what most people will probably do\n",
+    "\n",
+    "query = \"\"\"SELECT TOP 10\n",
+    "source_id, ref_epoch, ra, dec, parallax\n",
+    "FROM gaiadr2.gaia_source\n",
+    "WHERE parallax < 1 \n",
+    "  AND bp_rp > -0.75 AND bp_rp < 2\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "tags": [
+     "hide-cell"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "# But if someone notices the BETWEEN operator, \n",
+    "# they might do this\n",
+    "\n",
+    "query = \"\"\"SELECT TOP 10\n",
+    "source_id, ref_epoch, ra, dec, parallax\n",
+    "FROM gaiadr2.gaia_source\n",
+    "WHERE parallax < 1 \n",
+    "  AND bp_rp BETWEEN -0.75 AND 2\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This [Hertzsprung-Russell diagram](https://sci.esa.int/web/gaia/-/60198-gaia-hertzsprung-russell-diagram) shows the BP-RP color and luminosity of stars in the Gaia catalog.\n",
+    "\n",
+    "Selecting stars with `bp-rp` less than 2 excludes many [class M dwarf stars](https://xkcd.com/2360/), which are low temperature, low luminosity.  A star like that at GD-1's distance would be hard to detect, so if it is detected, it it more likely to be in the foreground."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Cleaning up\n",
+    "\n",
+    "Asynchronous jobs have a `jobid`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(None, '1601903242219O')"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "job1.jobid, job2.jobid"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Which you can use to remove the job from the server."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Removed jobs: '['1601903242219O']'.\n"
+     ]
+    }
+   ],
+   "source": [
+    "Gaia.remove_jobs([job2.jobid])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you don't remove it job from the server, it will be removed eventually, so don't feel too bad if you don't clean up after yourself."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Formatting queries\n",
+    "\n",
+    "So far the queries have been string \"literals\", meaning that the entire string is part of the program.\n",
+    "But writing queries yourself can be slow, repetitive, and error-prone.\n",
+    "\n",
+    "It is often a good idea to write Python code that assembles a query for you.  One useful tool for that is the [string `format` method](https://www.w3schools.com/python/ref_string_format.asp).\n",
+    "\n",
+    "As an example, we'll divide the previous query into two parts; a list of column names and a \"base\" for the query that contains everything except the column names.\n",
+    "\n",
+    "Here's the list of columns we'll select.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "columns = 'source_id, ra, dec, pmra, pmdec, parallax, parallax_error, radial_velocity'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And here's the base; it's a string that contains at least one format specifier in curly brackets (braces)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query3_base = \"\"\"SELECT TOP 10 \n",
+    "{columns}\n",
+    "FROM gaiadr2.gaia_source\n",
+    "WHERE parallax < 1\n",
+    "  AND bp_rp BETWEEN -0.75 AND 2\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This base query contains one format specifier, `{columns}`, which is a placeholder for the list of column names we will provide.\n",
+    "\n",
+    "To assemble the query, we invoke `format` on the base string and provide a keyword argument that assigns a value to `columns`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query3 = query3_base.format(columns=columns)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The result is a string with line breaks.  If you display it, the line breaks appear as `\\n`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'SELECT TOP 10 \\nsource_id, ra, dec, pmra, pmdec, parallax, parallax_error, radial_velocity\\nFROM gaiadr2.gaia_source\\nWHERE parallax < 1\\n  AND bp_rp BETWEEN -0.75 AND 2\\n'"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "query3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "But if you print it, the line breaks appear as... line breaks."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "SELECT TOP 10 \n",
+      "source_id, ra, dec, pmra, pmdec, parallax, parallax_error, radial_velocity\n",
+      "FROM gaiadr2.gaia_source\n",
+      "WHERE parallax < 1\n",
+      "  AND bp_rp BETWEEN -0.75 AND 2\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(query3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notice that the format specifier has been replaced with the value of `columns`.\n",
+    "\n",
+    "Let's run it and see if it works:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<Table length=10>\n",
+      "      name       dtype    unit                              description                             n_bad\n",
+      "--------------- ------- -------- ------------------------------------------------------------------ -----\n",
+      "      source_id   int64          Unique source identifier (unique within a particular Data Release)     0\n",
+      "             ra float64      deg                                                    Right ascension     0\n",
+      "            dec float64      deg                                                        Declination     0\n",
+      "           pmra float64 mas / yr                         Proper motion in right ascension direction     0\n",
+      "          pmdec float64 mas / yr                             Proper motion in declination direction     0\n",
+      "       parallax float64      mas                                                           Parallax     0\n",
+      " parallax_error float64      mas                                         Standard error of parallax     0\n",
+      "radial_velocity float64   km / s                                                    Radial velocity    10\n",
+      "Jobid: None\n",
+      "Phase: COMPLETED\n",
+      "Owner: None\n",
+      "Output file: sync_20201005090726.xml.gz\n",
+      "Results: None\n"
+     ]
+    }
+   ],
+   "source": [
+    "job3 = Gaia.launch_job(query3)\n",
+    "print(job3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<i>Table length=10</i>\n",
+       "<table id=\"table140265627700432\" class=\"table-striped table-bordered table-condensed\">\n",
+       "<thead><tr><th>source_id</th><th>ra</th><th>dec</th><th>pmra</th><th>pmdec</th><th>parallax</th><th>parallax_error</th><th>radial_velocity</th></tr></thead>\n",
+       "<thead><tr><th></th><th>deg</th><th>deg</th><th>mas / yr</th><th>mas / yr</th><th>mas</th><th>mas</th><th>km / s</th></tr></thead>\n",
+       "<thead><tr><th>int64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th></tr></thead>\n",
+       "<tr><td>4467710915011802624</td><td>269.9680969307347</td><td>1.1429085038160882</td><td>2.0233280236600626</td><td>-2.5692427875510266</td><td>0.42361471245557913</td><td>0.470352406647465</td><td>--</td></tr>\n",
+       "<tr><td>4467706551328679552</td><td>270.033164589881</td><td>1.0565747323689927</td><td>-3.414829591355289</td><td>-3.8437215857495737</td><td>0.922888231734588</td><td>0.927008559859825</td><td>--</td></tr>\n",
+       "<tr><td>4467712255037300096</td><td>270.7724717923047</td><td>0.6581664892880896</td><td>-3.5620173752896025</td><td>-6.595792323153987</td><td>-2.669179465293931</td><td>0.9719742773203504</td><td>--</td></tr>\n",
+       "<tr><td>4467735001181761792</td><td>270.3628606248308</td><td>0.8947079323599124</td><td>2.1307079926489205</td><td>0.8826727710910712</td><td>0.6117399163086398</td><td>0.509812721702093</td><td>--</td></tr>\n",
+       "<tr><td>4467737101421916672</td><td>270.5110834661444</td><td>0.9806225910160181</td><td>0.17532366511560785</td><td>-5.113270239706202</td><td>-0.39818224846127004</td><td>0.7549581886719651</td><td>--</td></tr>\n",
+       "<tr><td>4467707547757327488</td><td>269.88746280594927</td><td>1.0212759940136962</td><td>-2.6382230817672987</td><td>-3.707776532049287</td><td>0.7741412301054209</td><td>0.3022057897812064</td><td>--</td></tr>\n",
+       "<tr><td>4467732355491087744</td><td>270.6730790702491</td><td>0.9197224705139885</td><td>-2.2735991502653037</td><td>-11.864952855984358</td><td>-0.3464446494840354</td><td>0.4937921513912002</td><td>--</td></tr>\n",
+       "<tr><td>4467717099766944512</td><td>270.57667173120825</td><td>0.726277659009568</td><td>-3.4598362614808367</td><td>-4.601426893365921</td><td>0.05443955111134051</td><td>0.8867339293525688</td><td>--</td></tr>\n",
+       "<tr><td>4467719058265781248</td><td>270.7248052971514</td><td>0.8205551921782785</td><td>-3.255079498426542</td><td>-9.249285069111085</td><td>0.3733943917490343</td><td>0.390952370410666</td><td>--</td></tr>\n",
+       "<tr><td>4467722326741572352</td><td>270.87431291888504</td><td>0.8595565975869158</td><td>0.10696398351859826</td><td>1.2035993780158853</td><td>-0.11850943432864373</td><td>0.1660452431882023</td><td>--</td></tr>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<Table length=10>\n",
+       "     source_id              ra         ...   parallax_error   radial_velocity\n",
+       "                           deg         ...        mas              km / s    \n",
+       "       int64             float64       ...      float64           float64    \n",
+       "------------------- ------------------ ... ------------------ ---------------\n",
+       "4467710915011802624  269.9680969307347 ...  0.470352406647465              --\n",
+       "4467706551328679552   270.033164589881 ...  0.927008559859825              --\n",
+       "4467712255037300096  270.7724717923047 ... 0.9719742773203504              --\n",
+       "4467735001181761792  270.3628606248308 ...  0.509812721702093              --\n",
+       "4467737101421916672  270.5110834661444 ... 0.7549581886719651              --\n",
+       "4467707547757327488 269.88746280594927 ... 0.3022057897812064              --\n",
+       "4467732355491087744  270.6730790702491 ... 0.4937921513912002              --\n",
+       "4467717099766944512 270.57667173120825 ... 0.8867339293525688              --\n",
+       "4467719058265781248  270.7248052971514 ...  0.390952370410666              --\n",
+       "4467722326741572352 270.87431291888504 ... 0.1660452431882023              --"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results3 = job3.get_results()\n",
+    "results3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Good so far."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** This query always selects sources with `parallax` less than 1.  But suppose you want to take that upper bound as an input.\n",
+    "\n",
+    "Modify `query3_base` to replace `1` with a format specifier like `{max_parallax}`.  Now, when you call `format`, add a keyword argument that assigns a value to `max_parallax`, and confirm that the format specifier gets replaced with the value you provide."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "tags": [
+     "hide-cell"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "query4_base = \"\"\"SELECT TOP 10\n",
+    "{columns}\n",
+    "FROM gaiadr2.gaia_source\n",
+    "WHERE parallax < {max_parallax} AND \n",
+    "bp_rp BETWEEN -0.75 AND 2\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "tags": [
+     "hide-cell"
+    ]
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "SELECT TOP 10\n",
+      "source_id, ra, dec, pmra, pmdec, parallax, parallax_error, radial_velocity\n",
+      "FROM gaiadr2.gaia_source\n",
+      "WHERE parallax < 0.5 AND \n",
+      "bp_rp BETWEEN -0.75 AND 2\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "query4 = query4_base.format(columns=columns,\n",
+    "                          max_parallax=0.5)\n",
+    "print(query)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Style note:**  You might notice that the variable names in this notebook are numbered, like `query1`, `query2`, etc.  \n",
+    "\n",
+    "The advantage of this style is that it isolates each section of the notebook from the others, so if you go back and run the cells out of order, it's less likely that you will get unexpected interactions.\n",
+    "\n",
+    "A drawback of this style is that it can be a nuisance to update the notebook if you add, remove, or reorder a section.\n",
+    "\n",
+    "What do you think of this choice?  Are there alternatives you prefer?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "This notebook demonstrates the following steps:\n",
+    "\n",
+    "1. Making a connection to the Gaia server,\n",
+    "\n",
+    "2. Exploring information about the database and the tables it contains,\n",
+    "\n",
+    "3. Writing a query and sending it to the server, and finally\n",
+    "\n",
+    "4. Downloading the response from the server as an Astropy `Table`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Best practices\n",
+    "\n",
+    "* If you can't download an entire dataset (or it's not practical) use queries to select the data you need.\n",
+    "\n",
+    "* Read the metadata and the documentation to make sure you understand the tables, their columns, and what they mean.\n",
+    "\n",
+    "* Develop queries incrementally: start with something simple, test it, and add a little bit at a time.\n",
+    "\n",
+    "* Use ADQL features like `TOP` and `COUNT` to test before you run a query that might return a lot of data.\n",
+    "\n",
+    "* If you know your query will return fewer than 3000 rows, you can run it synchronously, which might complete faster (but it doesn't seem to make much difference).  If it might return more than 3000 rows, you should run it asynchronously.\n",
+    "\n",
+    "* ADQL and SQL are not case-sensitive, so you don't have to capitalize the keywords, but you should.\n",
+    "\n",
+    "* ADQL and SQL don't require you to break a query into multiple lines, but you should.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Jupyter notebooks can be good for developing and testing code, but they have some drawbacks.  In particular, if you run the cells out of order, you might find that variables don't have the values you expect.\n",
+    "\n",
+    "There are a few things you can do to mitigate these problems:\n",
+    "\n",
+    "* Make each section of the notebook self-contained.  Try not to use the same variable name in more than one section.\n",
+    "\n",
+    "* Keep notebooks short.  Look for places where you can break your analysis into phases with one notebook per phase."
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "celltoolbar": "Tags",
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/_build/jupyter_execute/01_query.py b/_build/jupyter_execute/01_query.py
new file mode 100644
index 0000000..dc37d2d
--- /dev/null
+++ b/_build/jupyter_execute/01_query.py
@@ -0,0 +1,601 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# # Chapter 1
+
+# *Astronomical Data in Python* is an introduction to tools and practices for working with astronomical data.  Topics covered include:
+# 
+# * Writing queries that select and download data from a database.
+# 
+# * Using data stored in an Astropy `Table` or Pandas `DataFrame`.
+# 
+# * Working with coordinates and other quantities with units.
+# 
+# * Storing data in various formats.
+# 
+# * Performing database join operations that combine data from multiple tables.
+# 
+# * Visualizing data and preparing publication-quality figures.
+
+# As a running example, we will replicate part of the analysis in a recent paper, "[Off the beaten path: Gaia reveals GD-1 stars outside of the main stream](https://arxiv.org/abs/1805.00425)" by Adrian M. Price-Whelan and Ana Bonaca.
+# 
+# As the abstract explains, "Using data from the Gaia second data release combined with Pan-STARRS photometry, we present a sample of highly-probable members of the longest cold stream in the Milky Way, GD-1."
+# 
+# GD-1 is a [stellar stream](https://en.wikipedia.org/wiki/List_of_stellar_streams), which is "an association of stars orbiting a galaxy that was once a globular cluster or dwarf galaxy that has now been torn apart and stretched out along its orbit by tidal forces."
+
+# [This article in *Science* magazine](https://www.sciencemag.org/news/2018/10/streams-stars-reveal-galaxy-s-violent-history-and-perhaps-its-unseen-dark-matter) explains some of the background, including the process that led to the paper and an discussion of the scientific implications:
+# 
+# * "The streams are particularly useful for ... galactic archaeology --- rewinding the cosmic clock to reconstruct the assembly of the Milky Way."
+# 
+# * "They also are being used as exquisitely sensitive scales to measure the galaxy's mass."
+# 
+# * "... the streams are well-positioned to reveal the presence of dark matter ... because the streams are so fragile, theorists say, collisions with marauding clumps of dark matter could leave telltale scars, potential clues to its nature."
+
+# ## Data
+# 
+# The datasets we will work with are:
+#  
+# * [Gaia](https://en.wikipedia.org/wiki/Gaia_(spacecraft)), which is "a space observatory of the European Space Agency (ESA), launched in 2013 ... designed for astrometry: measuring the positions, distances and motions of stars with unprecedented precision", and
+# 
+# * [Pan-STARRS](https://en.wikipedia.org/wiki/Pan-STARRS), The Panoramic Survey Telescope and Rapid Response System, which is a survey designed to monitor the sky for transient objects, producing a catalog with accurate astronometry and photometry of detected sources.
+# 
+# Both of these datasets are very large, which can make them challenging to work with.  It might not be possible, or practical, to download the entire dataset.
+# One of the goals of this workshop is to provide tools for working with large datasets.
+
+# ## Prerequisites
+# 
+# These notebooks are meant for people who are familiar with basic Python, but not necessarily the libraries we will use, like Astropy or Pandas.  If you are familiar with Python lists and dictionaries, and you know how to write a function that takes parameters and returns a value, you know enough Python to get started.
+# 
+# We assume that you have some familiarity with operating systems, like the ability to use a command-line interface.  But we don't assume you have any prior experience with databases.
+# 
+# We assume that you are familiar with astronomy at the undergraduate level, but we will not assume specialized knowledge of the datasets or analysis methods we'll use. 
+
+# ## Outline
+# 
+# The first lesson demonstrates the steps for selecting and downloading data from the Gaia Database:
+# 
+# 1. First we'll make a connection to the Gaia server,
+# 
+# 2. We will explore information about the database and the tables it contains,
+# 
+# 3. We will write a query and send it to the server, and finally
+# 
+# 4. We will download the response from the server.
+# 
+# After completing this lesson, you should be able to
+# 
+# * Compose a basic query in ADQL.
+# 
+# * Use queries to explore a database and its tables.
+# 
+# * Use queries to download data.
+# 
+# * Develop, test, and debug a query incrementally.
+
+# ## Query Language
+# 
+# In order to select data from a database, you have to compose a query, which is like a program written in a "query language".
+# The query language we'll use is ADQL, which stands for "Astronomical Data Query Language".
+# 
+# ADQL is a dialect of [SQL](https://en.wikipedia.org/wiki/SQL) (Structured Query Language), which is by far the most commonly used query language.  Almost everything you will learn about ADQL also works in SQL.
+# 
+# [The reference manual for ADQL is here](http://www.ivoa.net/documents/ADQL/20180112/PR-ADQL-2.1-20180112.html).
+# But you might find it easier to learn from [this ADQL Cookbook](https://www.gaia.ac.uk/data/gaia-data-release-1/adql-cookbook).
+
+# ## Installing libraries
+# 
+# The library we'll use to get Gaia data is [Astroquery](https://astroquery.readthedocs.io/en/latest/).
+# 
+# If you are running this notebook on Colab, you can run the following cell to install Astroquery and the other libraries we'll use.
+# 
+# If you are running this notebook on your own computer, you might have to install these libraries yourself.  
+# 
+# If you are using this notebook as part of a Carpentries workshop, you should have received setup instructions.
+# 
+# TODO: Add a link to the instructions.
+# 
+
+# In[1]:
+
+
+# If we're running on Colab, install libraries
+
+import sys
+IN_COLAB = 'google.colab' in sys.modules
+
+if IN_COLAB:
+    get_ipython().system('pip install astroquery astro-gala pyia')
+
+
+# ## Connecting to Gaia
+# 
+# Astroquery provides `Gaia`, which is an [object that represents a connection to the Gaia database](https://astroquery.readthedocs.io/en/latest/gaia/gaia.html).
+# 
+# We can connect to the Gaia database like this:
+
+# In[2]:
+
+
+from astroquery.gaia import Gaia
+
+
+# Running this import statement has the effect of creating a [TAP+](http://www.ivoa.net/documents/TAP/) connection; TAP stands for "Table Access Protocol".  It is a network protocol for sending queries to the database and getting back the results.  We're not sure why it seems to create two connections.
+
+# ## Databases and Tables
+# 
+# What is a database, anyway?  Most generally, it can be any collection of data, but when we are talking about ADQL or SQL:
+# 
+# * A database is a collection of one or more named tables.
+# 
+# * Each table is a 2-D array with one or more named columns of data.
+# 
+# We can use `Gaia.load_tables` to get the names of the tables in the Gaia database.  With the option `only_names=True`, it loads information about the tables, called the "metadata", not the data itself.
+
+# In[3]:
+
+
+tables = Gaia.load_tables(only_names=True)
+
+
+# In[4]:
+
+
+for table in (tables):
+    print(table.get_qualified_name())
+
+
+# So that's a lot of tables.  The ones we'll use are:
+# 
+# * `gaiadr2.gaia_source`, which contains Gaia data from [data release 2](https://www.cosmos.esa.int/web/gaia/data-release-2),
+# 
+# * `gaiadr2.panstarrs1_original_valid`, which contains the photometry data we'll use from PanSTARRS, and
+# 
+# * `gaiadr2.panstarrs1_best_neighbour`, which we'll use to cross-match each star observed by Gaia with the same star observed by PanSTARRS.
+# 
+# We can use `load_table` (not `load_tables`) to get the metadata for a single table.  The name of this function is misleading, because it only downloads metadata. 
+
+# In[5]:
+
+
+meta = Gaia.load_table('gaiadr2.gaia_source')
+meta
+
+
+# Jupyter shows that the result is an object of type `TapTableMeta`, but it does not display the contents.
+# 
+# To see the metadata, we have to print the object.
+
+# In[6]:
+
+
+print(meta)
+
+
+# Notice one gotcha: in the list of table names, this table appears as `gaiadr2.gaiadr2.gaia_source`, but when we load the metadata, we refer to it as `gaiadr2.gaia_source`.
+# 
+# **Exercise:** Go back and try
+# 
+# ```
+# meta = Gaia.load_table('gaiadr2.gaiadr2.gaia_source')
+# ```
+# 
+# What happens?  Is the error message helpful?  If you had not made this error deliberately, would you have been able to figure it out?
+
+# ## Columns
+# 
+# The following loop prints the names of the columns in the table.
+
+# In[7]:
+
+
+for column in meta.columns:
+    print(column.name)
+
+
+# You can probably guess what many of these columns are by looking at the names, but you should resist the temptation to guess.
+# To find out what the columns mean, [read the documentation](https://gea.esac.esa.int/archive/documentation/GDR2/Gaia_archive/chap_datamodel/sec_dm_main_tables/ssec_dm_gaia_source.html).
+# 
+# If you want to know what can go wrong when you don't read the documentation, [you might like this article](https://www.vox.com/future-perfect/2019/6/4/18650969/married-women-miserable-fake-paul-dolan-happiness).
+
+# **Exercise:** One of the other tables we'll use is `gaiadr2.gaiadr2.panstarrs1_original_valid`.  Use `load_table` to get the metadata for this table.  How many columns are there and what are their names?
+# 
+# Hint: Remember the gotcha we mentioned earlier.
+
+# In[8]:
+
+
+# Solution
+
+meta2 = Gaia.load_table('gaiadr2.panstarrs1_original_valid')
+print(meta2)
+
+
+# In[9]:
+
+
+# Solution
+
+for column in meta2.columns:
+    print(column.name)
+
+
+# ## Writing queries
+# 
+# By now you might be wondering how we actually download the data.  With tables this big, you generally don't.  Instead, you use queries to select only the data you want.
+# 
+# A query is a string written in a query language like SQL; for the Gaia database, the query language is a dialect of SQL called ADQL.
+# 
+# Here's an example of an ADQL query.
+
+# In[10]:
+
+
+query1 = """SELECT 
+TOP 10
+source_id, ref_epoch, ra, dec, parallax 
+FROM gaiadr2.gaia_source"""
+
+
+# **Python note:** We use a [triple-quoted string](https://docs.python.org/3/tutorial/introduction.html#strings) here so we can include line breaks in the query, which makes it easier to read.
+# 
+# The words in uppercase are ADQL keywords:
+# 
+# * `SELECT` indicates that we are selecting data (as opposed to adding or modifying data).
+# 
+# * `TOP` indicates that we only want the first 10 rows of the table, which is useful for testing a query before asking for all of the data.
+# 
+# * `FROM` specifies which table we want data from.
+# 
+# The third line is a list of column names, indicating which columns we want.  
+# 
+# In this example, the keywords are capitalized and the column names are lowercase.  This is a common style, but it is not required.  ADQL and SQL are not case-sensitive.
+
+# To run this query, we use the `Gaia` object, which represents our connection to the Gaia database, and invoke `launch_job`:
+
+# In[11]:
+
+
+job1 = Gaia.launch_job(query1)
+job1
+
+
+# The result is an object that represents the job running on a Gaia server.
+# 
+# If you print it, it displays metadata for the forthcoming table.
+
+# In[12]:
+
+
+print(job1)
+
+
+# Don't worry about `Results: None`.  That does not actually mean there are no results.
+# 
+# However, `Phase: COMPLETED` indicates that the job is complete, so we can get the results like this:
+
+# In[13]:
+
+
+results1 = job1.get_results()
+type(results1)
+
+
+# **Optional detail:**  Why is `table` repeated three times?  The first is the name of the module, the second is the name of the submodule, and the third is the name of the class.  Most of the time we only care about the last one.  It's like the Linnean name for gorilla, which is *Gorilla Gorilla Gorilla*.
+
+# The result is an [Astropy Table](https://docs.astropy.org/en/stable/table/), which is similar to a table in an SQL database except:
+# 
+# * SQL databases are stored on disk drives, so they are persistent; that is, they "survive" even if you turn off the computer.  An Astropy `Table` is stored in memory; it disappears when you turn off the computer (or shut down this Jupyter notebook).
+# 
+# * SQL databases are designed to process queries.  An Astropy `Table` can perform some query-like operations, like selecting columns and rows.  But these operations use Python syntax, not SQL.
+# 
+# Jupyter knows how to display the contents of a `Table`.
+
+# In[14]:
+
+
+results1
+
+
+# Each column has a name, units, and a data type.
+# 
+# For example, the units of `ra` and `dec` are degrees, and their data type is `float64`, which is a 64-bit floating-point number, used to store measurements with a fraction part.
+# 
+# This information comes from the Gaia database, and has been stored in the Astropy `Table` by Astroquery.
+
+# **Exercise:** Read [the documentation of this table](https://gea.esac.esa.int/archive/documentation/GDR2/Gaia_archive/chap_datamodel/sec_dm_main_tables/ssec_dm_gaia_source.html) and choose a column that looks interesting to you.  Add the column name to the query and run it again.  What are the units of the column you selected?  What is its data type?
+
+# ## Asynchronous queries
+# 
+# `launch_job` asks the server to run the job "synchronously", which normally means it runs immediately.  But synchronous jobs are limited to 2000 rows.  For queries that return more rows, you should run "asynchronously", which mean they might take longer to get started.
+# 
+# If you are not sure how many rows a query will return, you can use the SQL command `COUNT` to find out how many rows are in the result without actually returning them.  We'll see an example of this later.
+# 
+# The results of an asynchronous query are stored in a file on the server, so you can start a query and come back later to get the results.
+# 
+# For anonymous users, files are kept for three days.
+# 
+# As an example, let's try a query that's similar to `query1`, with two changes:
+# 
+# * It selects the first 3000 rows, so it is bigger than we should run synchronously.
+# 
+# * It uses a new keyword, `WHERE`.
+
+# In[15]:
+
+
+query2 = """SELECT TOP 3000
+source_id, ref_epoch, ra, dec, parallax
+FROM gaiadr2.gaia_source
+WHERE parallax < 1
+"""
+
+
+# A `WHERE` clause indicates which rows we want; in this case, the query selects only rows "where" `parallax` is less than 1.  This has the effect of selecting stars with relatively low parallax, which are farther away.  We'll use this clause to exclude nearby stars that are unlikely to be part of GD-1.
+# 
+# `WHERE` is one of the most common clauses in ADQL/SQL, and one of the most useful, because it allows us to select only the rows we need from the database.
+# 
+# We use `launch_job_async` to submit an asynchronous query.
+
+# In[16]:
+
+
+job2 = Gaia.launch_job_async(query2)
+print(job2)
+
+
+# And here are the results.
+
+# In[17]:
+
+
+results2 = job2.get_results()
+results2
+
+
+# You might notice that some values of `parallax` are negative.  As [this FAQ explains](https://www.cosmos.esa.int/web/gaia/archive-tips#negative%20parallax), "Negative parallaxes are caused by errors in the observations."  Negative parallaxes have "no physical meaning," but they can be a "useful diagnostic on the quality of the astrometric solution."
+# 
+# Later we will see an example where we use `parallax` and `parallax_error` to identify stars where the distance estimate is likely to be inaccurate.
+
+# **Exercise:** The clauses in a query have to be in the right order.  Go back and change the order of the clauses in `query2` and run it again.  
+# 
+# The query should fail, but notice that you don't get much useful debugging information.  
+# 
+# For this reason, developing and debugging ADQL queries can be really hard.  A few suggestions that might help:
+# 
+# * Whenever possible, start with a working query, either an example you find online or a query you have used in the past.
+# 
+# * Make small changes and test each change before you continue.
+# 
+# * While you are debugging, use `TOP` to limit the number of rows in the result.  That will make each attempt run faster, which reduces your testing time.  
+# 
+# * Launching test queries synchronously might make them start faster, too.
+
+# ## Operators
+# 
+# In a `WHERE` clause, you can use any of the [SQL comparison operators](https://www.w3schools.com/sql/sql_operators.asp); here are the most common ones:
+# 
+# | Symbol | Operation
+# |--------| :---
+# | `>` | greater than
+# | `<` | less than
+# | `>=` | greater than or equal
+# | `<=` | less than or equal
+# | `=` | equal
+# | `!=` or `<>` | not equal
+# 
+# Most of these are the same as Python, but some are not.  In particular, notice that the equality operator is `=`, not `==`.
+# Be careful to keep your Python out of your ADQL!
+# 
+# You can combine comparisons using the logical operators:
+# 
+# * AND: true if both comparisons are true
+# * OR: true if either or both comparisons are true
+# 
+# Finally, you can use `NOT` to invert the result of a comparison. 
+
+# **Exercise:** [Read about SQL operators here](https://www.w3schools.com/sql/sql_operators.asp) and then modify the previous query to select rows where `bp_rp` is between `-0.75` and `2`.
+# 
+# You can [read about this variable here](https://gea.esac.esa.int/archive/documentation/GDR2/Gaia_archive/chap_datamodel/sec_dm_main_tables/ssec_dm_gaia_source.html).
+
+# In[18]:
+
+
+# Solution
+
+# This is what most people will probably do
+
+query = """SELECT TOP 10
+source_id, ref_epoch, ra, dec, parallax
+FROM gaiadr2.gaia_source
+WHERE parallax < 1 
+  AND bp_rp > -0.75 AND bp_rp < 2
+"""
+
+
+# In[19]:
+
+
+# Solution
+
+# But if someone notices the BETWEEN operator, 
+# they might do this
+
+query = """SELECT TOP 10
+source_id, ref_epoch, ra, dec, parallax
+FROM gaiadr2.gaia_source
+WHERE parallax < 1 
+  AND bp_rp BETWEEN -0.75 AND 2
+"""
+
+
+# This [Hertzsprung-Russell diagram](https://sci.esa.int/web/gaia/-/60198-gaia-hertzsprung-russell-diagram) shows the BP-RP color and luminosity of stars in the Gaia catalog.
+# 
+# Selecting stars with `bp-rp` less than 2 excludes many [class M dwarf stars](https://xkcd.com/2360/), which are low temperature, low luminosity.  A star like that at GD-1's distance would be hard to detect, so if it is detected, it it more likely to be in the foreground.
+
+# ## Cleaning up
+# 
+# Asynchronous jobs have a `jobid`.
+
+# In[20]:
+
+
+job1.jobid, job2.jobid
+
+
+# Which you can use to remove the job from the server.
+
+# In[21]:
+
+
+Gaia.remove_jobs([job2.jobid])
+
+
+# If you don't remove it job from the server, it will be removed eventually, so don't feel too bad if you don't clean up after yourself.
+
+# ## Formatting queries
+# 
+# So far the queries have been string "literals", meaning that the entire string is part of the program.
+# But writing queries yourself can be slow, repetitive, and error-prone.
+# 
+# It is often a good idea to write Python code that assembles a query for you.  One useful tool for that is the [string `format` method](https://www.w3schools.com/python/ref_string_format.asp).
+# 
+# As an example, we'll divide the previous query into two parts; a list of column names and a "base" for the query that contains everything except the column names.
+# 
+# Here's the list of columns we'll select.  
+
+# In[22]:
+
+
+columns = 'source_id, ra, dec, pmra, pmdec, parallax, parallax_error, radial_velocity'
+
+
+# And here's the base; it's a string that contains at least one format specifier in curly brackets (braces).
+
+# In[23]:
+
+
+query3_base = """SELECT TOP 10 
+{columns}
+FROM gaiadr2.gaia_source
+WHERE parallax < 1
+  AND bp_rp BETWEEN -0.75 AND 2
+"""
+
+
+# This base query contains one format specifier, `{columns}`, which is a placeholder for the list of column names we will provide.
+# 
+# To assemble the query, we invoke `format` on the base string and provide a keyword argument that assigns a value to `columns`.
+
+# In[24]:
+
+
+query3 = query3_base.format(columns=columns)
+
+
+# The result is a string with line breaks.  If you display it, the line breaks appear as `\n`.
+
+# In[25]:
+
+
+query3
+
+
+# But if you print it, the line breaks appear as... line breaks.
+
+# In[26]:
+
+
+print(query3)
+
+
+# Notice that the format specifier has been replaced with the value of `columns`.
+# 
+# Let's run it and see if it works:
+
+# In[27]:
+
+
+job3 = Gaia.launch_job(query3)
+print(job3)
+
+
+# In[28]:
+
+
+results3 = job3.get_results()
+results3
+
+
+# Good so far.
+
+# **Exercise:** This query always selects sources with `parallax` less than 1.  But suppose you want to take that upper bound as an input.
+# 
+# Modify `query3_base` to replace `1` with a format specifier like `{max_parallax}`.  Now, when you call `format`, add a keyword argument that assigns a value to `max_parallax`, and confirm that the format specifier gets replaced with the value you provide.
+
+# In[29]:
+
+
+# Solution
+
+query4_base = """SELECT TOP 10
+{columns}
+FROM gaiadr2.gaia_source
+WHERE parallax < {max_parallax} AND 
+bp_rp BETWEEN -0.75 AND 2
+"""
+
+
+# In[30]:
+
+
+# Solution
+
+query4 = query4_base.format(columns=columns,
+                          max_parallax=0.5)
+print(query)
+
+
+# **Style note:**  You might notice that the variable names in this notebook are numbered, like `query1`, `query2`, etc.  
+# 
+# The advantage of this style is that it isolates each section of the notebook from the others, so if you go back and run the cells out of order, it's less likely that you will get unexpected interactions.
+# 
+# A drawback of this style is that it can be a nuisance to update the notebook if you add, remove, or reorder a section.
+# 
+# What do you think of this choice?  Are there alternatives you prefer?
+
+# ## Summary
+# 
+# This notebook demonstrates the following steps:
+# 
+# 1. Making a connection to the Gaia server,
+# 
+# 2. Exploring information about the database and the tables it contains,
+# 
+# 3. Writing a query and sending it to the server, and finally
+# 
+# 4. Downloading the response from the server as an Astropy `Table`.
+
+# ## Best practices
+# 
+# * If you can't download an entire dataset (or it's not practical) use queries to select the data you need.
+# 
+# * Read the metadata and the documentation to make sure you understand the tables, their columns, and what they mean.
+# 
+# * Develop queries incrementally: start with something simple, test it, and add a little bit at a time.
+# 
+# * Use ADQL features like `TOP` and `COUNT` to test before you run a query that might return a lot of data.
+# 
+# * If you know your query will return fewer than 3000 rows, you can run it synchronously, which might complete faster (but it doesn't seem to make much difference).  If it might return more than 3000 rows, you should run it asynchronously.
+# 
+# * ADQL and SQL are not case-sensitive, so you don't have to capitalize the keywords, but you should.
+# 
+# * ADQL and SQL don't require you to break a query into multiple lines, but you should.
+# 
+
+# Jupyter notebooks can be good for developing and testing code, but they have some drawbacks.  In particular, if you run the cells out of order, you might find that variables don't have the values you expect.
+# 
+# There are a few things you can do to mitigate these problems:
+# 
+# * Make each section of the notebook self-contained.  Try not to use the same variable name in more than one section.
+# 
+# * Keep notebooks short.  Look for places where you can break your analysis into phases with one notebook per phase.
diff --git a/_build/jupyter_execute/02_coords.ipynb b/_build/jupyter_execute/02_coords.ipynb
new file mode 100644
index 0000000..f287bbb
--- /dev/null
+++ b/_build/jupyter_execute/02_coords.ipynb
@@ -0,0 +1,1972 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Chapter 2\n",
+    "\n",
+    "This is the second in a series of notebooks related to astronomy data.\n",
+    "\n",
+    "As a running example, we are replicating parts of the analysis in a recent paper, \"[Off the beaten path: Gaia reveals GD-1 stars outside of the main stream](https://arxiv.org/abs/1805.00425)\" by Adrian M. Price-Whelan and Ana Bonaca.\n",
+    "\n",
+    "In the first notebook, we wrote ADQL queries and used them to select and download data from the Gaia server.\n",
+    "\n",
+    "In this notebook, we'll pick up where we left off and write a query to select stars from the region of the sky where we expect GD-1 to be."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Outline\n",
+    "\n",
+    "We'll start with an example that does a \"cone search\"; that is, it selects stars that appear in a circular region of the sky.\n",
+    "\n",
+    "Then, to select stars in the vicinity of GD-1, we'll:\n",
+    "\n",
+    "* Use `Quantity` objects to represent measurements with units.\n",
+    "\n",
+    "* Use the `Gala` library to convert coordinates from one frame to another.\n",
+    "\n",
+    "* Use the ADQL keywords `POLYGON`, `CONTAINS`, and `POINT` to select stars that fall within a polygonal region.\n",
+    "\n",
+    "* Submit a query and download the results.\n",
+    "\n",
+    "* Store the results in a FITS file.\n",
+    "\n",
+    "After completing this lesson, you should be able to\n",
+    "\n",
+    "* Use Python string formatting to compose more complex ADQL queries.\n",
+    "\n",
+    "* Work with coordinates and other quantities that have units.\n",
+    "\n",
+    "* Download the results of a query and store them in a file."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Installing libraries\n",
+    "\n",
+    "If you are running this notebook on Colab, you can run the following cell to install Astroquery and a the other libraries we'll use.\n",
+    "\n",
+    "If you are running this notebook on your own computer, you might have to install these libraries yourself.  \n",
+    "\n",
+    "If you are using this notebook as part of a Carpentries workshop, you should have received setup instructions.\n",
+    "\n",
+    "TODO: Add a link to the instructions.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# If we're running on Colab, install libraries\n",
+    "\n",
+    "import sys\n",
+    "IN_COLAB = 'google.colab' in sys.modules\n",
+    "\n",
+    "if IN_COLAB:\n",
+    "    !pip install astroquery astro-gala pyia"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Selecting a region"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "One of the most common ways to restrict a query is to select stars in a particular region of the sky.\n",
+    "\n",
+    "For example, here's a query from the [Gaia archive documentation](https://gea.esac.esa.int/archive-help/adql/examples/index.html) that selects \"all the objects ... in a circular region centered at (266.41683, -29.00781) with a search radius of 5 arcmin (0.08333 deg).\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query = \"\"\"\n",
+    "SELECT \n",
+    "TOP 10 source_id\n",
+    "FROM gaiadr2.gaia_source\n",
+    "WHERE 1=CONTAINS(\n",
+    "  POINT(ra, dec),\n",
+    "  CIRCLE(266.41683, -29.00781, 0.08333333))\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This query uses three keywords that are specific to ADQL (not SQL):\n",
+    "\n",
+    "* `POINT`: a location in [ICRS coordinates](https://en.wikipedia.org/wiki/International_Celestial_Reference_System), specified in degrees of right ascension and declination.\n",
+    "\n",
+    "* `CIRCLE`: a circle where the first two values are the coordinates of the center and the third is the radius in degrees.\n",
+    "\n",
+    "* `CONTAINS`: a function that returns `1` if a `POINT` is contained in a shape and `0` otherwise.\n",
+    "\n",
+    "Here is the [documentation of `CONTAINS`](http://www.ivoa.net/documents/ADQL/20180112/PR-ADQL-2.1-20180112.html#tth_sEc4.2.12).\n",
+    "\n",
+    "A query like this is called a cone search because it selects stars in a cone.\n",
+    "\n",
+    "Here's how we run it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Created TAP+ (v1.2.1) - Connection:\n",
+      "\tHost: gea.esac.esa.int\n",
+      "\tUse HTTPS: True\n",
+      "\tPort: 443\n",
+      "\tSSL Port: 443\n",
+      "Created TAP+ (v1.2.1) - Connection:\n",
+      "\tHost: geadata.esac.esa.int\n",
+      "\tUse HTTPS: True\n",
+      "\tPort: 443\n",
+      "\tSSL Port: 443\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<i>Table length=10</i>\n",
+       "<table id=\"table139949838258672\" class=\"table-striped table-bordered table-condensed\">\n",
+       "<thead><tr><th>source_id</th></tr></thead>\n",
+       "<thead><tr><th>int64</th></tr></thead>\n",
+       "<tr><td>4057468321929794432</td></tr>\n",
+       "<tr><td>4057468287575835392</td></tr>\n",
+       "<tr><td>4057482027171038976</td></tr>\n",
+       "<tr><td>4057470349160630656</td></tr>\n",
+       "<tr><td>4057470039924301696</td></tr>\n",
+       "<tr><td>4057469868125641984</td></tr>\n",
+       "<tr><td>4057468351995073024</td></tr>\n",
+       "<tr><td>4057469661959554560</td></tr>\n",
+       "<tr><td>4057470520960672640</td></tr>\n",
+       "<tr><td>4057470555320409600</td></tr>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<Table length=10>\n",
+       "     source_id     \n",
+       "       int64       \n",
+       "-------------------\n",
+       "4057468321929794432\n",
+       "4057468287575835392\n",
+       "4057482027171038976\n",
+       "4057470349160630656\n",
+       "4057470039924301696\n",
+       "4057469868125641984\n",
+       "4057468351995073024\n",
+       "4057469661959554560\n",
+       "4057470520960672640\n",
+       "4057470555320409600"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from astroquery.gaia import Gaia\n",
+    "\n",
+    "job = Gaia.launch_job(query)\n",
+    "result = job.get_results()\n",
+    "result"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** When you are debugging queries like this, you can use `TOP` to limit the size of the results, but then you still don't know how big the results will be.\n",
+    "\n",
+    "An alternative is to use `COUNT`, which asks for the number of rows that would be selected, but it does not return them.\n",
+    "\n",
+    "In the previous query, replace `TOP 10 source_id` with `COUNT(source_id)` and run the query again.  How many stars has Gaia identified in the cone we searched?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Getting GD-1 Data\n",
+    "\n",
+    "From the Price-Whelan and Bonaca paper, we will try to reproduce Figure 1, which includes this representation of stars likely to belong to GD-1:\n",
+    "\n",
+    "<img src=\"https://github.com/datacarpentry/astronomy-python/raw/gh-pages/fig/gd1-4.png\">"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Along the axis of right ascension ($\\phi_1$) the figure extends from -100 to 20 degrees.\n",
+    "\n",
+    "Along the axis of declination ($\\phi_2$) the figure extends from about -8 to 4 degrees.\n",
+    "\n",
+    "Ideally, we would select all stars from this rectangle, but there are more than 10 million of them, so\n",
+    "\n",
+    "* That would be difficult to work with,\n",
+    "\n",
+    "* As anonymous users, we are limited to 3 million rows in a single query, and\n",
+    "\n",
+    "* While we are developing and testing code, it will be faster to work with a smaller dataset.\n",
+    "\n",
+    "So we'll start by selecting stars in a smaller rectangle, from -55 to -45 degrees right ascension and -8 to 4 degrees of declination.\n",
+    "\n",
+    "But first we let's see how to represent quantities with units like degrees."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Working with coordinates\n",
+    "\n",
+    "Coordinates are physical quantities, which means that they have two parts, a value and a unit.\n",
+    "\n",
+    "For example, the coordinate $30^{\\circ}$ has value 30 and its units are degrees.\n",
+    "\n",
+    "Until recently, most scientific computation was done with values only; units were left out of the program altogether, [often with disastrous results](https://en.wikipedia.org/wiki/Mars_Climate_Orbiter#Cause_of_failure).\n",
+    "\n",
+    "Astropy provides tools for including units explicitly in computations, which makes it possible to detect errors before they cause disasters.\n",
+    "\n",
+    "To use Astropy units, we import them like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<module 'astropy.units' from '/home/downey/anaconda3/envs/AstronomicalData/lib/python3.8/site-packages/astropy/units/__init__.py'>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import astropy.units as u\n",
+    "\n",
+    "u"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`u` is an object that contains most common units and all SI units.\n",
+    "\n",
+    "You can use `dir` to list them, but you should also [read the documentation](https://docs.astropy.org/en/stable/units/)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['A',\n",
+       " 'AA',\n",
+       " 'AB',\n",
+       " 'ABflux',\n",
+       " 'ABmag',\n",
+       " 'AU',\n",
+       " 'Angstrom',\n",
+       " 'B',\n",
+       " 'Ba',\n",
+       " 'Barye',\n",
+       " 'Bi',\n",
+       " 'Biot',\n",
+       " 'Bol',\n",
+       " 'Bq',\n",
+       " 'C',\n",
+       " 'Celsius',\n",
+       " 'Ci',\n",
+       " 'CompositeUnit',\n",
+       " 'D',\n",
+       " 'Da',\n",
+       " 'Dalton',\n",
+       " 'Debye',\n",
+       " 'Decibel',\n",
+       " 'DecibelUnit',\n",
+       " 'Dex',\n",
+       " 'DexUnit',\n",
+       " 'EA',\n",
+       " 'EAU',\n",
+       " 'EB',\n",
+       " 'EBa',\n",
+       " 'EC',\n",
+       " 'ED',\n",
+       " 'EF',\n",
+       " 'EG',\n",
+       " 'EGal',\n",
+       " 'EH',\n",
+       " 'EHz',\n",
+       " 'EJ',\n",
+       " 'EJy',\n",
+       " 'EK',\n",
+       " 'EL',\n",
+       " 'EN',\n",
+       " 'EOhm',\n",
+       " 'EP',\n",
+       " 'EPa',\n",
+       " 'ER',\n",
+       " 'ERy',\n",
+       " 'ES',\n",
+       " 'ESt',\n",
+       " 'ET',\n",
+       " 'EV',\n",
+       " 'EW',\n",
+       " 'EWb',\n",
+       " 'Ea',\n",
+       " 'Eadu',\n",
+       " 'Earcmin',\n",
+       " 'Earcsec',\n",
+       " 'Eau',\n",
+       " 'Eb',\n",
+       " 'Ebarn',\n",
+       " 'Ebeam',\n",
+       " 'Ebin',\n",
+       " 'Ebit',\n",
+       " 'Ebyte',\n",
+       " 'Ecd',\n",
+       " 'Echan',\n",
+       " 'Ecount',\n",
+       " 'Ect',\n",
+       " 'Ed',\n",
+       " 'Edeg',\n",
+       " 'Edyn',\n",
+       " 'EeV',\n",
+       " 'Eerg',\n",
+       " 'Eg',\n",
+       " 'Eh',\n",
+       " 'EiB',\n",
+       " 'Eib',\n",
+       " 'Eibit',\n",
+       " 'Eibyte',\n",
+       " 'Ek',\n",
+       " 'El',\n",
+       " 'Elm',\n",
+       " 'Elx',\n",
+       " 'Elyr',\n",
+       " 'Em',\n",
+       " 'Emag',\n",
+       " 'Emin',\n",
+       " 'Emol',\n",
+       " 'Eohm',\n",
+       " 'Epc',\n",
+       " 'Eph',\n",
+       " 'Ephoton',\n",
+       " 'Epix',\n",
+       " 'Epixel',\n",
+       " 'Erad',\n",
+       " 'Es',\n",
+       " 'Esr',\n",
+       " 'Eu',\n",
+       " 'Evox',\n",
+       " 'Evoxel',\n",
+       " 'Eyr',\n",
+       " 'F',\n",
+       " 'Farad',\n",
+       " 'Fr',\n",
+       " 'Franklin',\n",
+       " 'FunctionQuantity',\n",
+       " 'FunctionUnitBase',\n",
+       " 'G',\n",
+       " 'GA',\n",
+       " 'GAU',\n",
+       " 'GB',\n",
+       " 'GBa',\n",
+       " 'GC',\n",
+       " 'GD',\n",
+       " 'GF',\n",
+       " 'GG',\n",
+       " 'GGal',\n",
+       " 'GH',\n",
+       " 'GHz',\n",
+       " 'GJ',\n",
+       " 'GJy',\n",
+       " 'GK',\n",
+       " 'GL',\n",
+       " 'GN',\n",
+       " 'GOhm',\n",
+       " 'GP',\n",
+       " 'GPa',\n",
+       " 'GR',\n",
+       " 'GRy',\n",
+       " 'GS',\n",
+       " 'GSt',\n",
+       " 'GT',\n",
+       " 'GV',\n",
+       " 'GW',\n",
+       " 'GWb',\n",
+       " 'Ga',\n",
+       " 'Gadu',\n",
+       " 'Gal',\n",
+       " 'Garcmin',\n",
+       " 'Garcsec',\n",
+       " 'Gau',\n",
+       " 'Gauss',\n",
+       " 'Gb',\n",
+       " 'Gbarn',\n",
+       " 'Gbeam',\n",
+       " 'Gbin',\n",
+       " 'Gbit',\n",
+       " 'Gbyte',\n",
+       " 'Gcd',\n",
+       " 'Gchan',\n",
+       " 'Gcount',\n",
+       " 'Gct',\n",
+       " 'Gd',\n",
+       " 'Gdeg',\n",
+       " 'Gdyn',\n",
+       " 'GeV',\n",
+       " 'Gerg',\n",
+       " 'Gg',\n",
+       " 'Gh',\n",
+       " 'GiB',\n",
+       " 'Gib',\n",
+       " 'Gibit',\n",
+       " 'Gibyte',\n",
+       " 'Gk',\n",
+       " 'Gl',\n",
+       " 'Glm',\n",
+       " 'Glx',\n",
+       " 'Glyr',\n",
+       " 'Gm',\n",
+       " 'Gmag',\n",
+       " 'Gmin',\n",
+       " 'Gmol',\n",
+       " 'Gohm',\n",
+       " 'Gpc',\n",
+       " 'Gph',\n",
+       " 'Gphoton',\n",
+       " 'Gpix',\n",
+       " 'Gpixel',\n",
+       " 'Grad',\n",
+       " 'Gs',\n",
+       " 'Gsr',\n",
+       " 'Gu',\n",
+       " 'Gvox',\n",
+       " 'Gvoxel',\n",
+       " 'Gyr',\n",
+       " 'H',\n",
+       " 'Henry',\n",
+       " 'Hertz',\n",
+       " 'Hz',\n",
+       " 'IrreducibleUnit',\n",
+       " 'J',\n",
+       " 'Jansky',\n",
+       " 'Joule',\n",
+       " 'Jy',\n",
+       " 'K',\n",
+       " 'Kayser',\n",
+       " 'Kelvin',\n",
+       " 'KiB',\n",
+       " 'Kib',\n",
+       " 'Kibit',\n",
+       " 'Kibyte',\n",
+       " 'L',\n",
+       " 'L_bol',\n",
+       " 'L_sun',\n",
+       " 'LogQuantity',\n",
+       " 'LogUnit',\n",
+       " 'Lsun',\n",
+       " 'MA',\n",
+       " 'MAU',\n",
+       " 'MB',\n",
+       " 'MBa',\n",
+       " 'MC',\n",
+       " 'MD',\n",
+       " 'MF',\n",
+       " 'MG',\n",
+       " 'MGal',\n",
+       " 'MH',\n",
+       " 'MHz',\n",
+       " 'MJ',\n",
+       " 'MJy',\n",
+       " 'MK',\n",
+       " 'ML',\n",
+       " 'MN',\n",
+       " 'MOhm',\n",
+       " 'MP',\n",
+       " 'MPa',\n",
+       " 'MR',\n",
+       " 'MRy',\n",
+       " 'MS',\n",
+       " 'MSt',\n",
+       " 'MT',\n",
+       " 'MV',\n",
+       " 'MW',\n",
+       " 'MWb',\n",
+       " 'M_bol',\n",
+       " 'M_e',\n",
+       " 'M_earth',\n",
+       " 'M_jup',\n",
+       " 'M_jupiter',\n",
+       " 'M_p',\n",
+       " 'M_sun',\n",
+       " 'Ma',\n",
+       " 'Madu',\n",
+       " 'MagUnit',\n",
+       " 'Magnitude',\n",
+       " 'Marcmin',\n",
+       " 'Marcsec',\n",
+       " 'Mau',\n",
+       " 'Mb',\n",
+       " 'Mbarn',\n",
+       " 'Mbeam',\n",
+       " 'Mbin',\n",
+       " 'Mbit',\n",
+       " 'Mbyte',\n",
+       " 'Mcd',\n",
+       " 'Mchan',\n",
+       " 'Mcount',\n",
+       " 'Mct',\n",
+       " 'Md',\n",
+       " 'Mdeg',\n",
+       " 'Mdyn',\n",
+       " 'MeV',\n",
+       " 'Mearth',\n",
+       " 'Merg',\n",
+       " 'Mg',\n",
+       " 'Mh',\n",
+       " 'MiB',\n",
+       " 'Mib',\n",
+       " 'Mibit',\n",
+       " 'Mibyte',\n",
+       " 'Mjup',\n",
+       " 'Mjupiter',\n",
+       " 'Mk',\n",
+       " 'Ml',\n",
+       " 'Mlm',\n",
+       " 'Mlx',\n",
+       " 'Mlyr',\n",
+       " 'Mm',\n",
+       " 'Mmag',\n",
+       " 'Mmin',\n",
+       " 'Mmol',\n",
+       " 'Mohm',\n",
+       " 'Mpc',\n",
+       " 'Mph',\n",
+       " 'Mphoton',\n",
+       " 'Mpix',\n",
+       " 'Mpixel',\n",
+       " 'Mrad',\n",
+       " 'Ms',\n",
+       " 'Msr',\n",
+       " 'Msun',\n",
+       " 'Mu',\n",
+       " 'Mvox',\n",
+       " 'Mvoxel',\n",
+       " 'Myr',\n",
+       " 'N',\n",
+       " 'NamedUnit',\n",
+       " 'Newton',\n",
+       " 'Ohm',\n",
+       " 'P',\n",
+       " 'PA',\n",
+       " 'PAU',\n",
+       " 'PB',\n",
+       " 'PBa',\n",
+       " 'PC',\n",
+       " 'PD',\n",
+       " 'PF',\n",
+       " 'PG',\n",
+       " 'PGal',\n",
+       " 'PH',\n",
+       " 'PHz',\n",
+       " 'PJ',\n",
+       " 'PJy',\n",
+       " 'PK',\n",
+       " 'PL',\n",
+       " 'PN',\n",
+       " 'POhm',\n",
+       " 'PP',\n",
+       " 'PPa',\n",
+       " 'PR',\n",
+       " 'PRy',\n",
+       " 'PS',\n",
+       " 'PSt',\n",
+       " 'PT',\n",
+       " 'PV',\n",
+       " 'PW',\n",
+       " 'PWb',\n",
+       " 'Pa',\n",
+       " 'Padu',\n",
+       " 'Parcmin',\n",
+       " 'Parcsec',\n",
+       " 'Pascal',\n",
+       " 'Pau',\n",
+       " 'Pb',\n",
+       " 'Pbarn',\n",
+       " 'Pbeam',\n",
+       " 'Pbin',\n",
+       " 'Pbit',\n",
+       " 'Pbyte',\n",
+       " 'Pcd',\n",
+       " 'Pchan',\n",
+       " 'Pcount',\n",
+       " 'Pct',\n",
+       " 'Pd',\n",
+       " 'Pdeg',\n",
+       " 'Pdyn',\n",
+       " 'PeV',\n",
+       " 'Perg',\n",
+       " 'Pg',\n",
+       " 'Ph',\n",
+       " 'PiB',\n",
+       " 'Pib',\n",
+       " 'Pibit',\n",
+       " 'Pibyte',\n",
+       " 'Pk',\n",
+       " 'Pl',\n",
+       " 'Plm',\n",
+       " 'Plx',\n",
+       " 'Plyr',\n",
+       " 'Pm',\n",
+       " 'Pmag',\n",
+       " 'Pmin',\n",
+       " 'Pmol',\n",
+       " 'Pohm',\n",
+       " 'Ppc',\n",
+       " 'Pph',\n",
+       " 'Pphoton',\n",
+       " 'Ppix',\n",
+       " 'Ppixel',\n",
+       " 'Prad',\n",
+       " 'PrefixUnit',\n",
+       " 'Ps',\n",
+       " 'Psr',\n",
+       " 'Pu',\n",
+       " 'Pvox',\n",
+       " 'Pvoxel',\n",
+       " 'Pyr',\n",
+       " 'Quantity',\n",
+       " 'QuantityInfo',\n",
+       " 'QuantityInfoBase',\n",
+       " 'R',\n",
+       " 'R_earth',\n",
+       " 'R_jup',\n",
+       " 'R_jupiter',\n",
+       " 'R_sun',\n",
+       " 'Rayleigh',\n",
+       " 'Rearth',\n",
+       " 'Rjup',\n",
+       " 'Rjupiter',\n",
+       " 'Rsun',\n",
+       " 'Ry',\n",
+       " 'S',\n",
+       " 'ST',\n",
+       " 'STflux',\n",
+       " 'STmag',\n",
+       " 'Siemens',\n",
+       " 'SpecificTypeQuantity',\n",
+       " 'St',\n",
+       " 'Sun',\n",
+       " 'T',\n",
+       " 'TA',\n",
+       " 'TAU',\n",
+       " 'TB',\n",
+       " 'TBa',\n",
+       " 'TC',\n",
+       " 'TD',\n",
+       " 'TF',\n",
+       " 'TG',\n",
+       " 'TGal',\n",
+       " 'TH',\n",
+       " 'THz',\n",
+       " 'TJ',\n",
+       " 'TJy',\n",
+       " 'TK',\n",
+       " 'TL',\n",
+       " 'TN',\n",
+       " 'TOhm',\n",
+       " 'TP',\n",
+       " 'TPa',\n",
+       " 'TR',\n",
+       " 'TRy',\n",
+       " 'TS',\n",
+       " 'TSt',\n",
+       " 'TT',\n",
+       " 'TV',\n",
+       " 'TW',\n",
+       " 'TWb',\n",
+       " 'Ta',\n",
+       " 'Tadu',\n",
+       " 'Tarcmin',\n",
+       " 'Tarcsec',\n",
+       " 'Tau',\n",
+       " 'Tb',\n",
+       " 'Tbarn',\n",
+       " 'Tbeam',\n",
+       " 'Tbin',\n",
+       " 'Tbit',\n",
+       " 'Tbyte',\n",
+       " 'Tcd',\n",
+       " 'Tchan',\n",
+       " 'Tcount',\n",
+       " 'Tct',\n",
+       " 'Td',\n",
+       " 'Tdeg',\n",
+       " 'Tdyn',\n",
+       " 'TeV',\n",
+       " 'Terg',\n",
+       " 'Tesla',\n",
+       " 'Tg',\n",
+       " 'Th',\n",
+       " 'TiB',\n",
+       " 'Tib',\n",
+       " 'Tibit',\n",
+       " 'Tibyte',\n",
+       " 'Tk',\n",
+       " 'Tl',\n",
+       " 'Tlm',\n",
+       " 'Tlx',\n",
+       " 'Tlyr',\n",
+       " 'Tm',\n",
+       " 'Tmag',\n",
+       " 'Tmin',\n",
+       " 'Tmol',\n",
+       " 'Tohm',\n",
+       " 'Tpc',\n",
+       " 'Tph',\n",
+       " 'Tphoton',\n",
+       " 'Tpix',\n",
+       " 'Tpixel',\n",
+       " 'Trad',\n",
+       " 'Ts',\n",
+       " 'Tsr',\n",
+       " 'Tu',\n",
+       " 'Tvox',\n",
+       " 'Tvoxel',\n",
+       " 'Tyr',\n",
+       " 'Unit',\n",
+       " 'UnitBase',\n",
+       " 'UnitConversionError',\n",
+       " 'UnitTypeError',\n",
+       " 'UnitsError',\n",
+       " 'UnitsWarning',\n",
+       " 'UnrecognizedUnit',\n",
+       " 'V',\n",
+       " 'Volt',\n",
+       " 'W',\n",
+       " 'Watt',\n",
+       " 'Wb',\n",
+       " 'Weber',\n",
+       " 'YA',\n",
+       " 'YAU',\n",
+       " 'YB',\n",
+       " 'YBa',\n",
+       " 'YC',\n",
+       " 'YD',\n",
+       " 'YF',\n",
+       " 'YG',\n",
+       " 'YGal',\n",
+       " 'YH',\n",
+       " 'YHz',\n",
+       " 'YJ',\n",
+       " 'YJy',\n",
+       " 'YK',\n",
+       " 'YL',\n",
+       " 'YN',\n",
+       " 'YOhm',\n",
+       " 'YP',\n",
+       " 'YPa',\n",
+       " 'YR',\n",
+       " 'YRy',\n",
+       " 'YS',\n",
+       " 'YSt',\n",
+       " 'YT',\n",
+       " 'YV',\n",
+       " 'YW',\n",
+       " 'YWb',\n",
+       " 'Ya',\n",
+       " 'Yadu',\n",
+       " 'Yarcmin',\n",
+       " 'Yarcsec',\n",
+       " 'Yau',\n",
+       " 'Yb',\n",
+       " 'Ybarn',\n",
+       " 'Ybeam',\n",
+       " 'Ybin',\n",
+       " 'Ybit',\n",
+       " 'Ybyte',\n",
+       " 'Ycd',\n",
+       " 'Ychan',\n",
+       " 'Ycount',\n",
+       " 'Yct',\n",
+       " 'Yd',\n",
+       " 'Ydeg',\n",
+       " 'Ydyn',\n",
+       " 'YeV',\n",
+       " 'Yerg',\n",
+       " 'Yg',\n",
+       " 'Yh',\n",
+       " 'Yk',\n",
+       " 'Yl',\n",
+       " 'Ylm',\n",
+       " 'Ylx',\n",
+       " 'Ylyr',\n",
+       " 'Ym',\n",
+       " 'Ymag',\n",
+       " 'Ymin',\n",
+       " 'Ymol',\n",
+       " 'Yohm',\n",
+       " 'Ypc',\n",
+       " 'Yph',\n",
+       " 'Yphoton',\n",
+       " 'Ypix',\n",
+       " 'Ypixel',\n",
+       " 'Yrad',\n",
+       " 'Ys',\n",
+       " 'Ysr',\n",
+       " 'Yu',\n",
+       " 'Yvox',\n",
+       " 'Yvoxel',\n",
+       " 'Yyr',\n",
+       " 'ZA',\n",
+       " 'ZAU',\n",
+       " 'ZB',\n",
+       " 'ZBa',\n",
+       " 'ZC',\n",
+       " 'ZD',\n",
+       " 'ZF',\n",
+       " 'ZG',\n",
+       " 'ZGal',\n",
+       " 'ZH',\n",
+       " 'ZHz',\n",
+       " 'ZJ',\n",
+       " 'ZJy',\n",
+       " 'ZK',\n",
+       " 'ZL',\n",
+       " 'ZN',\n",
+       " 'ZOhm',\n",
+       " 'ZP',\n",
+       " 'ZPa',\n",
+       " 'ZR',\n",
+       " 'ZRy',\n",
+       " 'ZS',\n",
+       " 'ZSt',\n",
+       " 'ZT',\n",
+       " 'ZV',\n",
+       " 'ZW',\n",
+       " 'ZWb',\n",
+       " 'Za',\n",
+       " 'Zadu',\n",
+       " 'Zarcmin',\n",
+       " 'Zarcsec',\n",
+       " 'Zau',\n",
+       " 'Zb',\n",
+       " 'Zbarn',\n",
+       " 'Zbeam',\n",
+       " 'Zbin',\n",
+       " 'Zbit',\n",
+       " 'Zbyte',\n",
+       " 'Zcd',\n",
+       " 'Zchan',\n",
+       " 'Zcount',\n",
+       " 'Zct',\n",
+       " 'Zd',\n",
+       " 'Zdeg',\n",
+       " 'Zdyn',\n",
+       " 'ZeV',\n",
+       " 'Zerg',\n",
+       " 'Zg',\n",
+       " 'Zh',\n",
+       " 'Zk',\n",
+       " 'Zl',\n",
+       " 'Zlm',\n",
+       " 'Zlx',\n",
+       " 'Zlyr',\n",
+       " 'Zm',\n",
+       " 'Zmag',\n",
+       " 'Zmin',\n",
+       " 'Zmol',\n",
+       " 'Zohm',\n",
+       " 'Zpc',\n",
+       " 'Zph',\n",
+       " 'Zphoton',\n",
+       " 'Zpix',\n",
+       " 'Zpixel',\n",
+       " 'Zrad',\n",
+       " 'Zs',\n",
+       " 'Zsr',\n",
+       " 'Zu',\n",
+       " 'Zvox',\n",
+       " 'Zvoxel',\n",
+       " 'Zyr',\n",
+       " '__builtins__',\n",
+       " '__cached__',\n",
+       " '__doc__',\n",
+       " '__file__',\n",
+       " '__loader__',\n",
+       " '__name__',\n",
+       " '__package__',\n",
+       " '__path__',\n",
+       " '__spec__',\n",
+       " 'a',\n",
+       " 'aA',\n",
+       " 'aAU',\n",
+       " 'aB',\n",
+       " 'aBa',\n",
+       " 'aC',\n",
+       " 'aD',\n",
+       " 'aF',\n",
+       " 'aG',\n",
+       " 'aGal',\n",
+       " 'aH',\n",
+       " 'aHz',\n",
+       " 'aJ',\n",
+       " 'aJy',\n",
+       " 'aK',\n",
+       " 'aL',\n",
+       " 'aN',\n",
+       " 'aOhm',\n",
+       " 'aP',\n",
+       " 'aPa',\n",
+       " 'aR',\n",
+       " 'aRy',\n",
+       " 'aS',\n",
+       " 'aSt',\n",
+       " 'aT',\n",
+       " 'aV',\n",
+       " 'aW',\n",
+       " 'aWb',\n",
+       " 'aa',\n",
+       " 'aadu',\n",
+       " 'aarcmin',\n",
+       " 'aarcsec',\n",
+       " 'aau',\n",
+       " 'ab',\n",
+       " 'abA',\n",
+       " 'abC',\n",
+       " 'abampere',\n",
+       " 'abarn',\n",
+       " 'abcoulomb',\n",
+       " 'abeam',\n",
+       " 'abin',\n",
+       " 'abit',\n",
+       " 'abyte',\n",
+       " 'acd',\n",
+       " 'achan',\n",
+       " 'acount',\n",
+       " 'act',\n",
+       " 'ad',\n",
+       " 'add_enabled_equivalencies',\n",
+       " 'add_enabled_units',\n",
+       " 'adeg',\n",
+       " 'adu',\n",
+       " 'adyn',\n",
+       " 'aeV',\n",
+       " 'aerg',\n",
+       " 'ag',\n",
+       " 'ah',\n",
+       " 'ak',\n",
+       " 'al',\n",
+       " 'allclose',\n",
+       " 'alm',\n",
+       " 'alx',\n",
+       " 'alyr',\n",
+       " 'am',\n",
+       " 'amag',\n",
+       " 'amin',\n",
+       " 'amol',\n",
+       " 'amp',\n",
+       " 'ampere',\n",
+       " 'angstrom',\n",
+       " 'annum',\n",
+       " 'aohm',\n",
+       " 'apc',\n",
+       " 'aph',\n",
+       " 'aphoton',\n",
+       " 'apix',\n",
+       " 'apixel',\n",
+       " 'arad',\n",
+       " 'arcmin',\n",
+       " 'arcminute',\n",
+       " 'arcsec',\n",
+       " 'arcsecond',\n",
+       " 'asr',\n",
+       " 'astronomical_unit',\n",
+       " 'astrophys',\n",
+       " 'attoBarye',\n",
+       " 'attoDa',\n",
+       " 'attoDalton',\n",
+       " 'attoDebye',\n",
+       " 'attoFarad',\n",
+       " 'attoGauss',\n",
+       " 'attoHenry',\n",
+       " 'attoHertz',\n",
+       " 'attoJansky',\n",
+       " 'attoJoule',\n",
+       " 'attoKayser',\n",
+       " 'attoKelvin',\n",
+       " 'attoNewton',\n",
+       " 'attoOhm',\n",
+       " 'attoPascal',\n",
+       " 'attoRayleigh',\n",
+       " 'attoSiemens',\n",
+       " 'attoTesla',\n",
+       " 'attoVolt',\n",
+       " 'attoWatt',\n",
+       " 'attoWeber',\n",
+       " 'attoamp',\n",
+       " 'attoampere',\n",
+       " 'attoannum',\n",
+       " 'attoarcminute',\n",
+       " 'attoarcsecond',\n",
+       " 'attoastronomical_unit',\n",
+       " 'attobarn',\n",
+       " 'attobarye',\n",
+       " 'attobit',\n",
+       " 'attobyte',\n",
+       " 'attocandela',\n",
+       " 'attocoulomb',\n",
+       " 'attocount',\n",
+       " 'attoday',\n",
+       " 'attodebye',\n",
+       " 'attodegree',\n",
+       " 'attodyne',\n",
+       " 'attoelectronvolt',\n",
+       " 'attofarad',\n",
+       " 'attogal',\n",
+       " 'attogauss',\n",
+       " 'attogram',\n",
+       " 'attohenry',\n",
+       " 'attohertz',\n",
+       " 'attohour',\n",
+       " 'attohr',\n",
+       " 'attojansky',\n",
+       " 'attojoule',\n",
+       " 'attokayser',\n",
+       " 'attolightyear',\n",
+       " 'attoliter',\n",
+       " 'attolumen',\n",
+       " 'attolux',\n",
+       " 'attometer',\n",
+       " 'attominute',\n",
+       " 'attomole',\n",
+       " 'attonewton',\n",
+       " 'attoparsec',\n",
+       " 'attopascal',\n",
+       " 'attophoton',\n",
+       " 'attopixel',\n",
+       " 'attopoise',\n",
+       " 'attoradian',\n",
+       " 'attorayleigh',\n",
+       " 'attorydberg',\n",
+       " 'attosecond',\n",
+       " 'attosiemens',\n",
+       " 'attosteradian',\n",
+       " 'attostokes',\n",
+       " 'attotesla',\n",
+       " 'attovolt',\n",
+       " 'attovoxel',\n",
+       " 'attowatt',\n",
+       " 'attoweber',\n",
+       " 'attoyear',\n",
+       " 'au',\n",
+       " 'avox',\n",
+       " 'avoxel',\n",
+       " 'ayr',\n",
+       " 'b',\n",
+       " 'bar',\n",
+       " 'barn',\n",
+       " 'barye',\n",
+       " 'beam',\n",
+       " 'beam_angular_area',\n",
+       " 'becquerel',\n",
+       " 'bin',\n",
+       " 'binary_prefixes',\n",
+       " 'bit',\n",
+       " 'bol',\n",
+       " 'brightness_temperature',\n",
+       " 'byte',\n",
+       " 'cA',\n",
+       " 'cAU',\n",
+       " 'cB',\n",
+       " 'cBa',\n",
+       " 'cC',\n",
+       " 'cD',\n",
+       " 'cF',\n",
+       " 'cG',\n",
+       " 'cGal',\n",
+       " 'cH',\n",
+       " 'cHz',\n",
+       " 'cJ',\n",
+       " 'cJy',\n",
+       " 'cK',\n",
+       " 'cL',\n",
+       " 'cN',\n",
+       " 'cOhm',\n",
+       " 'cP',\n",
+       " 'cPa',\n",
+       " 'cR',\n",
+       " 'cRy',\n",
+       " 'cS',\n",
+       " 'cSt',\n",
+       " 'cT',\n",
+       " 'cV',\n",
+       " 'cW',\n",
+       " 'cWb',\n",
+       " 'ca',\n",
+       " 'cadu',\n",
+       " 'candela',\n",
+       " 'carcmin',\n",
+       " 'carcsec',\n",
+       " 'cau',\n",
+       " 'cb',\n",
+       " 'cbarn',\n",
+       " 'cbeam',\n",
+       " 'cbin',\n",
+       " 'cbit',\n",
+       " 'cbyte',\n",
+       " 'ccd',\n",
+       " 'cchan',\n",
+       " 'ccount',\n",
+       " 'cct',\n",
+       " 'cd',\n",
+       " 'cdeg',\n",
+       " 'cdyn',\n",
+       " 'ceV',\n",
+       " 'centiBarye',\n",
+       " 'centiDa',\n",
+       " 'centiDalton',\n",
+       " 'centiDebye',\n",
+       " 'centiFarad',\n",
+       " 'centiGauss',\n",
+       " 'centiHenry',\n",
+       " 'centiHertz',\n",
+       " 'centiJansky',\n",
+       " 'centiJoule',\n",
+       " 'centiKayser',\n",
+       " 'centiKelvin',\n",
+       " 'centiNewton',\n",
+       " 'centiOhm',\n",
+       " 'centiPascal',\n",
+       " 'centiRayleigh',\n",
+       " 'centiSiemens',\n",
+       " 'centiTesla',\n",
+       " 'centiVolt',\n",
+       " 'centiWatt',\n",
+       " 'centiWeber',\n",
+       " 'centiamp',\n",
+       " 'centiampere',\n",
+       " 'centiannum',\n",
+       " 'centiarcminute',\n",
+       " 'centiarcsecond',\n",
+       " 'centiastronomical_unit',\n",
+       " 'centibarn',\n",
+       " 'centibarye',\n",
+       " 'centibit',\n",
+       " 'centibyte',\n",
+       " 'centicandela',\n",
+       " 'centicoulomb',\n",
+       " 'centicount',\n",
+       " 'centiday',\n",
+       " 'centidebye',\n",
+       " 'centidegree',\n",
+       " 'centidyne',\n",
+       " 'centielectronvolt',\n",
+       " 'centifarad',\n",
+       " 'centigal',\n",
+       " 'centigauss',\n",
+       " 'centigram',\n",
+       " 'centihenry',\n",
+       " 'centihertz',\n",
+       " 'centihour',\n",
+       " 'centihr',\n",
+       " 'centijansky',\n",
+       " 'centijoule',\n",
+       " 'centikayser',\n",
+       " 'centilightyear',\n",
+       " 'centiliter',\n",
+       " 'centilumen',\n",
+       " 'centilux',\n",
+       " 'centimeter',\n",
+       " 'centiminute',\n",
+       " 'centimole',\n",
+       " 'centinewton',\n",
+       " 'centiparsec',\n",
+       " 'centipascal',\n",
+       " 'centiphoton',\n",
+       " 'centipixel',\n",
+       " 'centipoise',\n",
+       " 'centiradian',\n",
+       " 'centirayleigh',\n",
+       " 'centirydberg',\n",
+       " 'centisecond',\n",
+       " 'centisiemens',\n",
+       " 'centisteradian',\n",
+       " 'centistokes',\n",
+       " 'centitesla',\n",
+       " 'centivolt',\n",
+       " 'centivoxel',\n",
+       " 'centiwatt',\n",
+       " 'centiweber',\n",
+       " 'centiyear',\n",
+       " 'cerg',\n",
+       " 'cg',\n",
+       " 'cgs',\n",
+       " 'ch',\n",
+       " 'chan',\n",
+       " 'ck',\n",
+       " 'cl',\n",
+       " 'clm',\n",
+       " 'clx',\n",
+       " 'clyr',\n",
+       " 'cm',\n",
+       " 'cmag',\n",
+       " 'cmin',\n",
+       " 'cmol',\n",
+       " 'cohm',\n",
+       " 'core',\n",
+       " 'coulomb',\n",
+       " 'count',\n",
+       " 'cpc',\n",
+       " 'cph',\n",
+       " 'cphoton',\n",
+       " 'cpix',\n",
+       " 'cpixel',\n",
+       " 'crad',\n",
+       " 'cs',\n",
+       " 'csr',\n",
+       " 'ct',\n",
+       " 'cu',\n",
+       " 'curie',\n",
+       " 'cvox',\n",
+       " 'cvoxel',\n",
+       " 'cy',\n",
+       " 'cycle',\n",
+       " 'cyr',\n",
+       " 'd',\n",
+       " 'dA',\n",
+       " 'dAU',\n",
+       " 'dB',\n",
+       " 'dBa',\n",
+       " 'dC',\n",
+       " 'dD',\n",
+       " 'dF',\n",
+       " 'dG',\n",
+       " 'dGal',\n",
+       " 'dH',\n",
+       " 'dHz',\n",
+       " 'dJ',\n",
+       " 'dJy',\n",
+       " 'dK',\n",
+       " 'dL',\n",
+       " 'dN',\n",
+       " 'dOhm',\n",
+       " 'dP',\n",
+       " 'dPa',\n",
+       " 'dR',\n",
+       " 'dRy',\n",
+       " 'dS',\n",
+       " 'dSt',\n",
+       " 'dT',\n",
+       " ...]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dir(u)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To create a quantity, we multiply a value by a unit."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "astropy.units.quantity.Quantity"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "coord = 30 * u.deg\n",
+    "type(coord)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The result is a `Quantity` object.\n",
+    "\n",
+    "Jupyter knows how to display `Quantities` like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$30 \\; \\mathrm{{}^{\\circ}}$"
+      ],
+      "text/plain": [
+       "<Quantity 30. deg>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "coord"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Selecting a rectangle\n",
+    "\n",
+    "Now we'll select a rectangle from -55 to -45 degrees right ascension and -8 to 4 degrees of declination.\n",
+    "\n",
+    "We'll define variables to contain these limits."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "phi1_min = -55\n",
+    "phi1_max = -45\n",
+    "phi2_min = -8\n",
+    "phi2_max = 4"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To represent a rectangle, we'll use two lists of coordinates and multiply by their units."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "phi1_rect = [phi1_min, phi1_min, phi1_max, phi1_max] * u.deg\n",
+    "phi2_rect = [phi2_min, phi2_max, phi2_max, phi2_min] * u.deg"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`phi1_rect` and `phi2_rect` represent the coordinates of the corners of a rectangle.  \n",
+    "\n",
+    "But they are in \"[a Heliocentric spherical coordinate system defined by the orbit of the GD1 stream](https://gala-astro.readthedocs.io/en/latest/_modules/gala/coordinates/gd1.html)\"\n",
+    "\n",
+    "In order to use them in a Gaia query, we have to convert them to [International Celestial Reference System](https://en.wikipedia.org/wiki/International_Celestial_Reference_System) (ICRS) coordinates.  We can do that by storing the coordinates in a `GD1Koposov10` object provided by [Gala](https://gala-astro.readthedocs.io/en/latest/coordinates/)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "gala.coordinates.gd1.GD1Koposov10"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import gala.coordinates as gc\n",
+    "\n",
+    "corners = gc.GD1Koposov10(phi1=phi1_rect, phi2=phi2_rect)\n",
+    "type(corners)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can display the result like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<GD1Koposov10 Coordinate: (phi1, phi2) in deg\n",
+       "    [(-55., -8.), (-55.,  4.), (-45.,  4.), (-45., -8.)]>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "corners"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can use `transform_to` to convert to ICRS coordinates."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "astropy.coordinates.builtin_frames.icrs.ICRS"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import astropy.coordinates as coord\n",
+    "\n",
+    "corners_icrs = corners.transform_to(coord.ICRS)\n",
+    "type(corners_icrs)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The result is an `ICRS` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<ICRS Coordinate: (ra, dec) in deg\n",
+       "    [(146.27533314, 19.26190982), (135.42163944, 25.87738723),\n",
+       "     (141.60264825, 34.3048303 ), (152.81671045, 27.13611254)]>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "corners_icrs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notice that a rectangle in one coordinate system is not necessarily a rectangle in another.  In this example, the result is a polygon."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Selecting a polygon\n",
+    "\n",
+    "In order to use this polygon as part of an ADQL query, we have to convert it to a string with a comma-separated list of coordinates, as in this example:\n",
+    "\n",
+    "```\n",
+    "\"\"\"\n",
+    "POLYGON(143.65, 20.98, \n",
+    "        134.46, 26.39, \n",
+    "        140.58, 34.85, \n",
+    "        150.16, 29.01)\n",
+    "\"\"\"\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`corners_icrs` behaves like a list, so we can use a `for` loop to iterate through the points."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<ICRS Coordinate: (ra, dec) in deg\n",
+      "    (146.27533314, 19.26190982)>\n",
+      "<ICRS Coordinate: (ra, dec) in deg\n",
+      "    (135.42163944, 25.87738723)>\n",
+      "<ICRS Coordinate: (ra, dec) in deg\n",
+      "    (141.60264825, 34.3048303)>\n",
+      "<ICRS Coordinate: (ra, dec) in deg\n",
+      "    (152.81671045, 27.13611254)>\n"
+     ]
+    }
+   ],
+   "source": [
+    "for point in corners_icrs:\n",
+    "    print(point)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "From that, we can select the coordinates `ra` and `dec`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "146d16m31.1993s 19d15m42.8754s\n",
+      "135d25m17.902s 25d52m38.594s\n",
+      "141d36m09.5337s 34d18m17.3891s\n",
+      "152d49m00.1576s 27d08m10.0051s\n"
+     ]
+    }
+   ],
+   "source": [
+    "for point in corners_icrs:\n",
+    "    print(point.ra, point.dec)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The results are quantities with units, but if we select the `value` part, we get a dimensionless floating-point number."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "146.27533313607782 19.261909820533692\n",
+      "135.42163944306296 25.87738722767213\n",
+      "141.60264825107333 34.304830296257144\n",
+      "152.81671044675923 27.136112541397996\n"
+     ]
+    }
+   ],
+   "source": [
+    "for point in corners_icrs:\n",
+    "    print(point.ra.value, point.dec.value)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can use string `format` to convert these numbers to strings."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['146.27533313607782, 19.261909820533692',\n",
+       " '135.42163944306296, 25.87738722767213',\n",
+       " '141.60264825107333, 34.304830296257144',\n",
+       " '152.81671044675923, 27.136112541397996']"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "point_base = \"{point.ra.value}, {point.dec.value}\"\n",
+    "\n",
+    "t = [point_base.format(point=point)\n",
+    "     for point in corners_icrs]\n",
+    "t"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The result is a list of strings, which we can join into a single string using `join`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'146.27533313607782, 19.261909820533692, 135.42163944306296, 25.87738722767213, 141.60264825107333, 34.304830296257144, 152.81671044675923, 27.136112541397996'"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "point_list = ', '.join(t)\n",
+    "point_list"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notice that we invoke `join` on a string and pass the list as an argument.\n",
+    "\n",
+    "Before we can assemble the query, we need `columns` again (as we saw in the previous notebook)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "columns = 'source_id, ra, dec, pmra, pmdec, parallax, parallax_error, radial_velocity'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's the base for the query, with format specifiers for `columns` and `point_list`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query_base = \"\"\"SELECT {columns}\n",
+    "FROM gaiadr2.gaia_source\n",
+    "WHERE parallax < 1\n",
+    "  AND bp_rp BETWEEN -0.75 AND 2 \n",
+    "  AND 1 = CONTAINS(POINT(ra, dec), \n",
+    "                   POLYGON({point_list}))\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And here's the result:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "SELECT source_id, ra, dec, pmra, pmdec, parallax, parallax_error, radial_velocity\n",
+      "FROM gaiadr2.gaia_source\n",
+      "WHERE parallax < 1\n",
+      "  AND bp_rp BETWEEN -0.75 AND 2 \n",
+      "  AND 1 = CONTAINS(POINT(ra, dec), \n",
+      "                   POLYGON(146.27533313607782, 19.261909820533692, 135.42163944306296, 25.87738722767213, 141.60264825107333, 34.304830296257144, 152.81671044675923, 27.136112541397996))\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "query = query_base.format(columns=columns, \n",
+    "                          point_list=point_list)\n",
+    "print(query)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As always, we should take a minute to proof-read the query before we launch it.\n",
+    "\n",
+    "The result will be bigger than our previous queries, so it will take a little longer."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO: Query finished. [astroquery.utils.tap.core]\n",
+      "<Table length=140340>\n",
+      "      name       dtype    unit                              description                             n_bad \n",
+      "--------------- ------- -------- ------------------------------------------------------------------ ------\n",
+      "      source_id   int64          Unique source identifier (unique within a particular Data Release)      0\n",
+      "             ra float64      deg                                                    Right ascension      0\n",
+      "            dec float64      deg                                                        Declination      0\n",
+      "           pmra float64 mas / yr                         Proper motion in right ascension direction      0\n",
+      "          pmdec float64 mas / yr                             Proper motion in declination direction      0\n",
+      "       parallax float64      mas                                                           Parallax      0\n",
+      " parallax_error float64      mas                                         Standard error of parallax      0\n",
+      "radial_velocity float64   km / s                                                    Radial velocity 139374\n",
+      "Jobid: 1603114980658O\n",
+      "Phase: COMPLETED\n",
+      "Owner: None\n",
+      "Output file: async_20201019094300.vot\n",
+      "Results: None\n"
+     ]
+    }
+   ],
+   "source": [
+    "job = Gaia.launch_job_async(query)\n",
+    "print(job)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here are the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "140340"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results = job.get_results()\n",
+    "len(results)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There are more than 100,000 stars in this polygon, but that's a manageable size to work with."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Saving results\n",
+    "\n",
+    "This is the set of stars we'll work with in the next step.  But since we have a substantial dataset now, this is a good time to save it.\n",
+    "\n",
+    "Storing the data in a file means we can shut down this notebook and pick up where we left off without running the previous query again.\n",
+    "\n",
+    "Astropy `Table` objects provide `write`, which writes the table to disk."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "filename = 'gd1_results.fits'\n",
+    "results.write(filename, overwrite=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Because the filename ends with `fits`, the table is written in the [FITS format](https://en.wikipedia.org/wiki/FITS), which preserves the metadata associated with the table.\n",
+    "\n",
+    "If the file already exists, the `overwrite` argument causes it to be overwritten.\n",
+    "\n",
+    "To see how big the file is, we can use `ls` with the `-lh` option, which prints information about the file including its size in human-readable form."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-rw-rw-r-- 1 downey downey 8.6M Oct 19 09:43 gd1_results.fits\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!ls -lh gd1_results.fits"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The file is about 8.6 MB.  If you are using Windows, `ls` might not work; in that case, try:\n",
+    "\n",
+    "```\n",
+    "!dir gd1_results.fits\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "In this notebook, we composed more complex queries to select stars within a polygonal region of the sky.  Then we downloaded the results and saved them in a FITS file.\n",
+    "\n",
+    "In the next notebook, we'll reload the data from this file and replicate the next step in the analysis, using proper motion to identify stars likely to be in GD-1."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Best practices\n",
+    "\n",
+    "* For measurements with units, use `Quantity` objects that represent units explicitly and check for errors.\n",
+    "\n",
+    "* Use the `format` function to compose queries; it is often faster and less error-prone.\n",
+    "\n",
+    "* Develop queries incrementally: start with something simple, test it, and add a little bit at a time.\n",
+    "\n",
+    "* Once you have a query working, save the data in a local file.  If you shut down the notebook and come back to it later, you can reload the file; you don't have to run the query again."
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/_build/jupyter_execute/02_coords.py b/_build/jupyter_execute/02_coords.py
new file mode 100644
index 0000000..d12b02c
--- /dev/null
+++ b/_build/jupyter_execute/02_coords.py
@@ -0,0 +1,413 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# # Chapter 2
+# 
+# This is the second in a series of notebooks related to astronomy data.
+# 
+# As a running example, we are replicating parts of the analysis in a recent paper, "[Off the beaten path: Gaia reveals GD-1 stars outside of the main stream](https://arxiv.org/abs/1805.00425)" by Adrian M. Price-Whelan and Ana Bonaca.
+# 
+# In the first notebook, we wrote ADQL queries and used them to select and download data from the Gaia server.
+# 
+# In this notebook, we'll pick up where we left off and write a query to select stars from the region of the sky where we expect GD-1 to be.
+
+# ## Outline
+# 
+# We'll start with an example that does a "cone search"; that is, it selects stars that appear in a circular region of the sky.
+# 
+# Then, to select stars in the vicinity of GD-1, we'll:
+# 
+# * Use `Quantity` objects to represent measurements with units.
+# 
+# * Use the `Gala` library to convert coordinates from one frame to another.
+# 
+# * Use the ADQL keywords `POLYGON`, `CONTAINS`, and `POINT` to select stars that fall within a polygonal region.
+# 
+# * Submit a query and download the results.
+# 
+# * Store the results in a FITS file.
+# 
+# After completing this lesson, you should be able to
+# 
+# * Use Python string formatting to compose more complex ADQL queries.
+# 
+# * Work with coordinates and other quantities that have units.
+# 
+# * Download the results of a query and store them in a file.
+
+# ## Installing libraries
+# 
+# If you are running this notebook on Colab, you can run the following cell to install Astroquery and a the other libraries we'll use.
+# 
+# If you are running this notebook on your own computer, you might have to install these libraries yourself.  
+# 
+# If you are using this notebook as part of a Carpentries workshop, you should have received setup instructions.
+# 
+# TODO: Add a link to the instructions.
+# 
+
+# In[1]:
+
+
+# If we're running on Colab, install libraries
+
+import sys
+IN_COLAB = 'google.colab' in sys.modules
+
+if IN_COLAB:
+    get_ipython().system('pip install astroquery astro-gala pyia')
+
+
+# ## Selecting a region
+
+# One of the most common ways to restrict a query is to select stars in a particular region of the sky.
+# 
+# For example, here's a query from the [Gaia archive documentation](https://gea.esac.esa.int/archive-help/adql/examples/index.html) that selects "all the objects ... in a circular region centered at (266.41683, -29.00781) with a search radius of 5 arcmin (0.08333 deg)."
+
+# In[2]:
+
+
+query = """
+SELECT 
+TOP 10 source_id
+FROM gaiadr2.gaia_source
+WHERE 1=CONTAINS(
+  POINT(ra, dec),
+  CIRCLE(266.41683, -29.00781, 0.08333333))
+"""
+
+
+# This query uses three keywords that are specific to ADQL (not SQL):
+# 
+# * `POINT`: a location in [ICRS coordinates](https://en.wikipedia.org/wiki/International_Celestial_Reference_System), specified in degrees of right ascension and declination.
+# 
+# * `CIRCLE`: a circle where the first two values are the coordinates of the center and the third is the radius in degrees.
+# 
+# * `CONTAINS`: a function that returns `1` if a `POINT` is contained in a shape and `0` otherwise.
+# 
+# Here is the [documentation of `CONTAINS`](http://www.ivoa.net/documents/ADQL/20180112/PR-ADQL-2.1-20180112.html#tth_sEc4.2.12).
+# 
+# A query like this is called a cone search because it selects stars in a cone.
+# 
+# Here's how we run it.
+
+# In[3]:
+
+
+from astroquery.gaia import Gaia
+
+job = Gaia.launch_job(query)
+result = job.get_results()
+result
+
+
+# **Exercise:** When you are debugging queries like this, you can use `TOP` to limit the size of the results, but then you still don't know how big the results will be.
+# 
+# An alternative is to use `COUNT`, which asks for the number of rows that would be selected, but it does not return them.
+# 
+# In the previous query, replace `TOP 10 source_id` with `COUNT(source_id)` and run the query again.  How many stars has Gaia identified in the cone we searched?
+
+# ## Getting GD-1 Data
+# 
+# From the Price-Whelan and Bonaca paper, we will try to reproduce Figure 1, which includes this representation of stars likely to belong to GD-1:
+# 
+# <img src="https://github.com/datacarpentry/astronomy-python/raw/gh-pages/fig/gd1-4.png">
+
+# Along the axis of right ascension ($\phi_1$) the figure extends from -100 to 20 degrees.
+# 
+# Along the axis of declination ($\phi_2$) the figure extends from about -8 to 4 degrees.
+# 
+# Ideally, we would select all stars from this rectangle, but there are more than 10 million of them, so
+# 
+# * That would be difficult to work with,
+# 
+# * As anonymous users, we are limited to 3 million rows in a single query, and
+# 
+# * While we are developing and testing code, it will be faster to work with a smaller dataset.
+# 
+# So we'll start by selecting stars in a smaller rectangle, from -55 to -45 degrees right ascension and -8 to 4 degrees of declination.
+# 
+# But first we let's see how to represent quantities with units like degrees.
+
+# ## Working with coordinates
+# 
+# Coordinates are physical quantities, which means that they have two parts, a value and a unit.
+# 
+# For example, the coordinate $30^{\circ}$ has value 30 and its units are degrees.
+# 
+# Until recently, most scientific computation was done with values only; units were left out of the program altogether, [often with disastrous results](https://en.wikipedia.org/wiki/Mars_Climate_Orbiter#Cause_of_failure).
+# 
+# Astropy provides tools for including units explicitly in computations, which makes it possible to detect errors before they cause disasters.
+# 
+# To use Astropy units, we import them like this:
+
+# In[4]:
+
+
+import astropy.units as u
+
+u
+
+
+# `u` is an object that contains most common units and all SI units.
+# 
+# You can use `dir` to list them, but you should also [read the documentation](https://docs.astropy.org/en/stable/units/).
+
+# In[5]:
+
+
+dir(u)
+
+
+# To create a quantity, we multiply a value by a unit.
+
+# In[6]:
+
+
+coord = 30 * u.deg
+type(coord)
+
+
+# The result is a `Quantity` object.
+# 
+# Jupyter knows how to display `Quantities` like this:
+
+# In[7]:
+
+
+coord
+
+
+# ## Selecting a rectangle
+# 
+# Now we'll select a rectangle from -55 to -45 degrees right ascension and -8 to 4 degrees of declination.
+# 
+# We'll define variables to contain these limits.
+
+# In[8]:
+
+
+phi1_min = -55
+phi1_max = -45
+phi2_min = -8
+phi2_max = 4
+
+
+# To represent a rectangle, we'll use two lists of coordinates and multiply by their units.
+
+# In[9]:
+
+
+phi1_rect = [phi1_min, phi1_min, phi1_max, phi1_max] * u.deg
+phi2_rect = [phi2_min, phi2_max, phi2_max, phi2_min] * u.deg
+
+
+# `phi1_rect` and `phi2_rect` represent the coordinates of the corners of a rectangle.  
+# 
+# But they are in "[a Heliocentric spherical coordinate system defined by the orbit of the GD1 stream](https://gala-astro.readthedocs.io/en/latest/_modules/gala/coordinates/gd1.html)"
+# 
+# In order to use them in a Gaia query, we have to convert them to [International Celestial Reference System](https://en.wikipedia.org/wiki/International_Celestial_Reference_System) (ICRS) coordinates.  We can do that by storing the coordinates in a `GD1Koposov10` object provided by [Gala](https://gala-astro.readthedocs.io/en/latest/coordinates/).
+
+# In[10]:
+
+
+import gala.coordinates as gc
+
+corners = gc.GD1Koposov10(phi1=phi1_rect, phi2=phi2_rect)
+type(corners)
+
+
+# We can display the result like this:
+
+# In[11]:
+
+
+corners
+
+
+# Now we can use `transform_to` to convert to ICRS coordinates.
+
+# In[12]:
+
+
+import astropy.coordinates as coord
+
+corners_icrs = corners.transform_to(coord.ICRS)
+type(corners_icrs)
+
+
+# The result is an `ICRS` object.
+
+# In[13]:
+
+
+corners_icrs
+
+
+# Notice that a rectangle in one coordinate system is not necessarily a rectangle in another.  In this example, the result is a polygon.
+
+# ## Selecting a polygon
+# 
+# In order to use this polygon as part of an ADQL query, we have to convert it to a string with a comma-separated list of coordinates, as in this example:
+# 
+# ```
+# """
+# POLYGON(143.65, 20.98, 
+#         134.46, 26.39, 
+#         140.58, 34.85, 
+#         150.16, 29.01)
+# """
+# ```
+
+# `corners_icrs` behaves like a list, so we can use a `for` loop to iterate through the points.
+
+# In[14]:
+
+
+for point in corners_icrs:
+    print(point)
+
+
+# From that, we can select the coordinates `ra` and `dec`:
+
+# In[15]:
+
+
+for point in corners_icrs:
+    print(point.ra, point.dec)
+
+
+# The results are quantities with units, but if we select the `value` part, we get a dimensionless floating-point number.
+
+# In[16]:
+
+
+for point in corners_icrs:
+    print(point.ra.value, point.dec.value)
+
+
+# We can use string `format` to convert these numbers to strings.
+
+# In[17]:
+
+
+point_base = "{point.ra.value}, {point.dec.value}"
+
+t = [point_base.format(point=point)
+     for point in corners_icrs]
+t
+
+
+# The result is a list of strings, which we can join into a single string using `join`.
+
+# In[18]:
+
+
+point_list = ', '.join(t)
+point_list
+
+
+# Notice that we invoke `join` on a string and pass the list as an argument.
+# 
+# Before we can assemble the query, we need `columns` again (as we saw in the previous notebook).
+
+# In[19]:
+
+
+columns = 'source_id, ra, dec, pmra, pmdec, parallax, parallax_error, radial_velocity'
+
+
+# Here's the base for the query, with format specifiers for `columns` and `point_list`.
+
+# In[20]:
+
+
+query_base = """SELECT {columns}
+FROM gaiadr2.gaia_source
+WHERE parallax < 1
+  AND bp_rp BETWEEN -0.75 AND 2 
+  AND 1 = CONTAINS(POINT(ra, dec), 
+                   POLYGON({point_list}))
+"""
+
+
+# And here's the result:
+
+# In[21]:
+
+
+query = query_base.format(columns=columns, 
+                          point_list=point_list)
+print(query)
+
+
+# As always, we should take a minute to proof-read the query before we launch it.
+# 
+# The result will be bigger than our previous queries, so it will take a little longer.
+
+# In[22]:
+
+
+job = Gaia.launch_job_async(query)
+print(job)
+
+
+# Here are the results.
+
+# In[23]:
+
+
+results = job.get_results()
+len(results)
+
+
+# There are more than 100,000 stars in this polygon, but that's a manageable size to work with.
+
+# ## Saving results
+# 
+# This is the set of stars we'll work with in the next step.  But since we have a substantial dataset now, this is a good time to save it.
+# 
+# Storing the data in a file means we can shut down this notebook and pick up where we left off without running the previous query again.
+# 
+# Astropy `Table` objects provide `write`, which writes the table to disk.
+
+# In[24]:
+
+
+filename = 'gd1_results.fits'
+results.write(filename, overwrite=True)
+
+
+# Because the filename ends with `fits`, the table is written in the [FITS format](https://en.wikipedia.org/wiki/FITS), which preserves the metadata associated with the table.
+# 
+# If the file already exists, the `overwrite` argument causes it to be overwritten.
+# 
+# To see how big the file is, we can use `ls` with the `-lh` option, which prints information about the file including its size in human-readable form.
+
+# In[25]:
+
+
+get_ipython().system('ls -lh gd1_results.fits')
+
+
+# The file is about 8.6 MB.  If you are using Windows, `ls` might not work; in that case, try:
+# 
+# ```
+# !dir gd1_results.fits
+# ```
+
+# ## Summary
+# 
+# In this notebook, we composed more complex queries to select stars within a polygonal region of the sky.  Then we downloaded the results and saved them in a FITS file.
+# 
+# In the next notebook, we'll reload the data from this file and replicate the next step in the analysis, using proper motion to identify stars likely to be in GD-1.
+
+# ## Best practices
+# 
+# * For measurements with units, use `Quantity` objects that represent units explicitly and check for errors.
+# 
+# * Use the `format` function to compose queries; it is often faster and less error-prone.
+# 
+# * Develop queries incrementally: start with something simple, test it, and add a little bit at a time.
+# 
+# * Once you have a query working, save the data in a local file.  If you shut down the notebook and come back to it later, you can reload the file; you don't have to run the query again.
diff --git a/_build/jupyter_execute/03_motion.ipynb b/_build/jupyter_execute/03_motion.ipynb
new file mode 100644
index 0000000..1ec8f83
--- /dev/null
+++ b/_build/jupyter_execute/03_motion.ipynb
@@ -0,0 +1,1914 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Chapter 3\n",
+    "\n",
+    "This is the third in a series of notebooks related to astronomy data.\n",
+    "\n",
+    "As a running example, we are replicating parts of the analysis in a recent paper, \"[Off the beaten path: Gaia reveals GD-1 stars outside of the main stream](https://arxiv.org/abs/1805.00425)\" by Adrian M. Price-Whelan and Ana Bonaca.\n",
+    "\n",
+    "In the first lesson, we wrote ADQL queries and used them to select and download data from the Gaia server.\n",
+    "\n",
+    "In the second lesson, we wrote a query to select stars from the region of the sky where we expect GD-1 to be, and saved the results in a FITS file.\n",
+    "\n",
+    "Now we'll read that data back and implement the next step in the analysis, identifying stars with the proper motion we expect for GD-1."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Outline\n",
+    "\n",
+    "Here are the steps in this lesson:\n",
+    "\n",
+    "1. We'll read back the results from the previous lesson, which we saved in a FITS file.\n",
+    "\n",
+    "2. Then we'll transform the coordinates and proper motion data from ICRS back to the coordinate frame of GD-1.\n",
+    "\n",
+    "3. We'll put those results into a Pandas `DataFrame`, which we'll use to select stars near the centerline of GD-1.\n",
+    "\n",
+    "4. Plotting the proper motion of those stars, we'll identify a region of proper motion for stars that are likely to be in GD-1.\n",
+    "\n",
+    "5. Finally, we'll select and plot the stars whose proper motion is in that region.\n",
+    "\n",
+    "After completing this lesson, you should be able to\n",
+    "\n",
+    "* Select rows and columns from an Astropy `Table`.\n",
+    "\n",
+    "* Use Matplotlib to make a scatter plot.\n",
+    "\n",
+    "* Use Gala to transform coordinates.\n",
+    "\n",
+    "* Make a Pandas `DataFrame` and use a Boolean `Series` to select rows.\n",
+    "\n",
+    "* Save a `DataFrame` in an HDF5 file.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Installing libraries\n",
+    "\n",
+    "If you are running this notebook on Colab, you can run the following cell to install Astroquery and a the other libraries we'll use.\n",
+    "\n",
+    "If you are running this notebook on your own computer, you might have to install these libraries yourself.  \n",
+    "\n",
+    "If you are using this notebook as part of a Carpentries workshop, you should have received setup instructions.\n",
+    "\n",
+    "TODO: Add a link to the instructions.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# If we're running on Colab, install libraries\n",
+    "\n",
+    "import sys\n",
+    "IN_COLAB = 'google.colab' in sys.modules\n",
+    "\n",
+    "if IN_COLAB:\n",
+    "    !pip install astroquery astro-gala pyia python-wget"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Reload the data\n",
+    "\n",
+    "In the previous lesson, we ran a query on the Gaia server and downloaded data for roughly 100,000 stars.  We saved the data in a FITS file so that now, picking up where we left off, we can read the data from a local file rather than running the query again.\n",
+    "\n",
+    "If you ran the previous lesson successfully, you should already have a file called `gd1_results.fits` that contains the data we downloaded.\n",
+    "\n",
+    "If not, you can run the following cell, which downloads the data from our repository."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from wget import download\n",
+    "\n",
+    "filename = 'gd1_results.fits'\n",
+    "path = 'https://github.com/AllenDowney/AstronomicalData/raw/main/data/'\n",
+    "\n",
+    "if not os.path.exists(filename):\n",
+    "    print(download(path+filename))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now here's how we can read the data from the file back into an Astropy `Table`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from astropy.table import Table\n",
+    "\n",
+    "results = Table.read(filename)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The result is an Astropy `Table`.\n",
+    "\n",
+    "We can use `info` to refresh our memory of the contents."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Table length=140340>\n",
+       "      name       dtype    unit                              description                            \n",
+       "--------------- ------- -------- ------------------------------------------------------------------\n",
+       "      source_id   int64          Unique source identifier (unique within a particular Data Release)\n",
+       "             ra float64      deg                                                    Right ascension\n",
+       "            dec float64      deg                                                        Declination\n",
+       "           pmra float64 mas / yr                         Proper motion in right ascension direction\n",
+       "          pmdec float64 mas / yr                             Proper motion in declination direction\n",
+       "       parallax float64      mas                                                           Parallax\n",
+       " parallax_error float64      mas                                         Standard error of parallax\n",
+       "radial_velocity float64   km / s                                                    Radial velocity"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results.info"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Selecting rows and columns\n",
+    "\n",
+    "In this section we'll see operations for selecting columns and rows from an Astropy `Table`.  You can find more information about these operations in the [Astropy documentation](https://docs.astropy.org/en/stable/table/access_table.html).\n",
+    "\n",
+    "We can get the names of the columns like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['source_id',\n",
+       " 'ra',\n",
+       " 'dec',\n",
+       " 'pmra',\n",
+       " 'pmdec',\n",
+       " 'parallax',\n",
+       " 'parallax_error',\n",
+       " 'radial_velocity']"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results.colnames"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And select an individual column like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "&lt;Column name=&apos;ra&apos; dtype=&apos;float64&apos; unit=&apos;deg&apos; description=&apos;Right ascension&apos; length=140340&gt;\n",
+       "<table>\n",
+       "<tr><td>142.48301935991023</td></tr>\n",
+       "<tr><td>142.25452941346344</td></tr>\n",
+       "<tr><td>142.64528557468074</td></tr>\n",
+       "<tr><td>142.57739430926034</td></tr>\n",
+       "<tr><td>142.58913564478618</td></tr>\n",
+       "<tr><td>141.81762228999614</td></tr>\n",
+       "<tr><td>143.18339801317677</td></tr>\n",
+       "<tr><td>142.9347319464589</td></tr>\n",
+       "<tr><td>142.26769745823267</td></tr>\n",
+       "<tr><td>142.89551292869012</td></tr>\n",
+       "<tr><td>142.2780935768316</td></tr>\n",
+       "<tr><td>142.06138786534987</td></tr>\n",
+       "<tr><td>...</td></tr>\n",
+       "<tr><td>143.05456487172972</td></tr>\n",
+       "<tr><td>144.0436496516182</td></tr>\n",
+       "<tr><td>144.06566578919313</td></tr>\n",
+       "<tr><td>144.13177563215973</td></tr>\n",
+       "<tr><td>143.77696341662764</td></tr>\n",
+       "<tr><td>142.945956347594</td></tr>\n",
+       "<tr><td>142.97282480557786</td></tr>\n",
+       "<tr><td>143.4166017695258</td></tr>\n",
+       "<tr><td>143.64484588686904</td></tr>\n",
+       "<tr><td>143.41554585481808</td></tr>\n",
+       "<tr><td>143.6908739159247</td></tr>\n",
+       "<tr><td>143.7702681295401</td></tr>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<Column name='ra' dtype='float64' unit='deg' description='Right ascension' length=140340>\n",
+       "142.48301935991023\n",
+       "142.25452941346344\n",
+       "142.64528557468074\n",
+       "142.57739430926034\n",
+       "142.58913564478618\n",
+       "141.81762228999614\n",
+       "143.18339801317677\n",
+       " 142.9347319464589\n",
+       "142.26769745823267\n",
+       "142.89551292869012\n",
+       " 142.2780935768316\n",
+       "142.06138786534987\n",
+       "               ...\n",
+       "143.05456487172972\n",
+       " 144.0436496516182\n",
+       "144.06566578919313\n",
+       "144.13177563215973\n",
+       "143.77696341662764\n",
+       "  142.945956347594\n",
+       "142.97282480557786\n",
+       " 143.4166017695258\n",
+       "143.64484588686904\n",
+       "143.41554585481808\n",
+       " 143.6908739159247\n",
+       " 143.7702681295401"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results['ra']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The result is a `Column` object that contains the data, and also the data type, units, and name of the column."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "astropy.table.column.Column"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(results['ra'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The rows in the `Table` are numbered from 0 to `n-1`, where `n` is the number of rows.  We can select the first row like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<i>Row index=0</i>\n",
+       "<table id=\"table140510732444000\">\n",
+       "<thead><tr><th>source_id</th><th>ra</th><th>dec</th><th>pmra</th><th>pmdec</th><th>parallax</th><th>parallax_error</th><th>radial_velocity</th></tr></thead>\n",
+       "<thead><tr><th></th><th>deg</th><th>deg</th><th>mas / yr</th><th>mas / yr</th><th>mas</th><th>mas</th><th>km / s</th></tr></thead>\n",
+       "<thead><tr><th>int64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th></tr></thead>\n",
+       "<tr><td>637987125186749568</td><td>142.48301935991023</td><td>21.75771616932985</td><td>-2.5168384683875766</td><td>2.941813096629439</td><td>-0.2573448962333354</td><td>0.823720794509811</td><td>1e+20</td></tr>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<Row index=0>\n",
+       "    source_id              ra                dec                pmra              pmdec             parallax        parallax_error  radial_velocity\n",
+       "                          deg                deg              mas / yr           mas / yr             mas                mas             km / s    \n",
+       "      int64             float64            float64            float64            float64            float64            float64          float64    \n",
+       "------------------ ------------------ ----------------- ------------------- ----------------- ------------------- ----------------- ---------------\n",
+       "637987125186749568 142.48301935991023 21.75771616932985 -2.5168384683875766 2.941813096629439 -0.2573448962333354 0.823720794509811           1e+20"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As you might have guessed, the result is a `Row` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "astropy.table.row.Row"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(results[0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notice that the bracket operator selects both columns and rows.  You might wonder how it knows which to select.\n",
+    "\n",
+    "If the expression in brackets is a string, it selects a column; if the expression is an integer, it selects a row.\n",
+    "\n",
+    "If you apply the bracket operator twice, you can select a column and then an element from the column."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "142.48301935991023"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results['ra'][0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Or you can select a row and then an element from the row."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "142.48301935991023"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results[0]['ra']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You get the same result either way."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Scatter plot\n",
+    "\n",
+    "To see what the results look like, we'll use a scatter plot.  The library we'll use is [Matplotlib](https://matplotlib.org/), which is the most widely-used plotting library for Python.\n",
+    "\n",
+    "The Matplotlib interface is based on MATLAB (hence the name), so if you know MATLAB, some of it will be familiar.\n",
+    "\n",
+    "We'll import like this."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Pyplot part of the Matplotlib library.  It is conventional to import it using the shortened name `plt`.\n",
+    "\n",
+    "Pyplot provides two functions that can make scatterplots, [plt.scatter](https://matplotlib.org/3.3.0/api/_as_gen/matplotlib.pyplot.scatter.html) and [plt.plot](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.plot.html).\n",
+    "\n",
+    "* `scatter` is more versatile; for example, you can make every point in a scatter plot a different color.\n",
+    "\n",
+    "* `plot` is more limited, but for simple cases, it can be substantially faster.  \n",
+    "\n",
+    "Jake Vanderplas explains these differences in [The Python Data Science Handbook](https://jakevdp.github.io/PythonDataScienceHandbook/04.02-simple-scatter-plots.html)\n",
+    "\n",
+    "Since we are plotting more than 100,000 points and they are all the same size and color, we'll use `plot`.\n",
+    "\n",
+    "Here's a scatter plot with right ascension on the x-axis and declination on the y-axis, both ICRS coordinates in degrees."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "filenames": {
+       "image/png": "/home/downey/AstronomicalData/_build/jupyter_execute/03_motion_28_0.png"
+      },
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x = results['ra']\n",
+    "y = results['dec']\n",
+    "plt.plot(x, y, 'ko')\n",
+    "\n",
+    "plt.xlabel('ra (degree ICRS)')\n",
+    "plt.ylabel('dec (degree ICRS)');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The arguments to `plt.plot` are `x`, `y`, and a string that specifies the style.  In this case, the letters `ko` indicate that we want a black, round marker (`k` is for black because `b` is for blue).\n",
+    "\n",
+    "The functions `xlabel` and `ylabel` put labels on the axes.\n",
+    "\n",
+    "This scatter plot has a problem.  It is \"[overplotted](https://python-graph-gallery.com/134-how-to-avoid-overplotting-with-python/)\", which means that there are so many overlapping points, we can't distinguish between high and low density areas.\n",
+    "\n",
+    "To fix this, we can provide optional arguments to control the size and transparency of the points.\n",
+    "\n",
+    "**Exercise:** In the call to `plt.plot`, add the keyword argument `markersize=0.1` to make the markers smaller.\n",
+    "\n",
+    "Then add the argument `alpha=0.1` to make the markers nearly transparent.\n",
+    "\n",
+    "Adjust these arguments until you think the figure shows the data most clearly.\n",
+    "\n",
+    "Note: Once you have made these changes, you might notice that the figure shows stripes with lower density of stars.  These stripes are caused by the way Gaia scans the sky, which [you can read about here](https://www.cosmos.esa.int/web/gaia/scanning-law).  The dataset we are using, [Gaia Data Release 2](https://www.cosmos.esa.int/web/gaia/dr2), covers 22 months of observations; during this time, some parts of the sky were scanned more than others."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Transform back\n",
+    "\n",
+    "Remember that we selected data from a rectangle of coordinates in the `GD1Koposov10` frame, then transformed them to ICRS when we constructed the query.\n",
+    "The coordinates in `results` are in ICRS.\n",
+    "\n",
+    "To plot them, we will transform them back to the `GD1Koposov10` frame; that way, the axes of the figure are aligned with the GD-1, which will make it easy to select stars near the centerline of the stream.\n",
+    "\n",
+    "To do that, we'll put the results into a `GaiaData` object, provided by the [pyia library](https://pyia.readthedocs.io/en/latest/api/pyia.GaiaData.html)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "pyia.data.GaiaData"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from pyia import GaiaData\n",
+    "\n",
+    "gaia_data = GaiaData(results)\n",
+    "type(gaia_data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can extract sky coordinates from the `GaiaData` object, like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import astropy.units as u\n",
+    "\n",
+    "skycoord = gaia_data.get_skycoord(\n",
+    "                distance=8*u.kpc, \n",
+    "                radial_velocity=0*u.km/u.s)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We provide `distance` and `radial_velocity` to prepare the data for reflex correction, which we explain below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "astropy.coordinates.sky_coordinate.SkyCoord"
+      ]
+     },
+     "execution_count": 75,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(skycoord)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The result is an Astropy `SkyCoord` object ([documentation here](https://docs.astropy.org/en/stable/api/astropy.coordinates.SkyCoord.html#astropy.coordinates.SkyCoord)), which provides `transform_to`, so we can transform the coordinates to other frames."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "astropy.coordinates.sky_coordinate.SkyCoord"
+      ]
+     },
+     "execution_count": 76,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import gala.coordinates as gc\n",
+    "\n",
+    "transformed = skycoord.transform_to(gc.GD1Koposov10)\n",
+    "type(transformed)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The result is another `SkyCoord` object, now in the `GD1Koposov10` frame."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The next step is to correct the proper motion measurements from Gaia for reflex due to the motion of our solar system around the Galactic center.\n",
+    "\n",
+    "When we created `skycoord`, we provided `distance` and `radial_velocity` as arguments, which means we ignore the measurements provided by Gaia and replace them with these fixed values.\n",
+    "\n",
+    "That might seem like a strange thing to do, but here's the motivation:\n",
+    "\n",
+    "* Because the stars in GD-1 are so far away, the distance estimates we get from Gaia, which are based on parallax, are not very precise.  So we replace them with our current best estimate of the mean distance to GD-1, about 8 kpc.  See [Koposov, Rix, and Hogg, 2010](https://ui.adsabs.harvard.edu/abs/2010ApJ...712..260K/abstract).\n",
+    "\n",
+    "* For the other stars in the table, this distance estimate will be inaccurate, so reflex correction will not be correct.  But that should have only a small effect on our ability to identify stars with the proper motion we expect for GD-1.\n",
+    "\n",
+    "* The measurement of radial velocity has no effect on the correction for proper motion; the value we provide is arbitrary, but we have to provide a value to avoid errors in the reflex correction calculation.\n",
+    "\n",
+    "We are grateful to Adrian Price-Whelen for his help explaining this step in the analysis."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "With this preparation, we can use `reflex_correct` from Gala ([documentation here](https://gala-astro.readthedocs.io/en/latest/api/gala.coordinates.reflex_correct.html)) to correct for solar reflex motion."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "astropy.coordinates.sky_coordinate.SkyCoord"
+      ]
+     },
+     "execution_count": 77,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gd1_coord = gc.reflex_correct(transformed)\n",
+    "\n",
+    "type(gd1_coord)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The result is a `SkyCoord` object that contains \n",
+    "\n",
+    "* The transformed coordinates as attributes named `phi1` and `phi2`, which represent right ascension and declination in the `GD1Koposov10` frame.\n",
+    "\n",
+    "* The transformed and corrected proper motions as `pm_phi1_cosphi2` and `pm_phi2`.\n",
+    "\n",
+    "We can select the coordinates like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "phi1 = gd1_coord.phi1\n",
+    "phi2 = gd1_coord.phi2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And plot them like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "filenames": {
+       "image/png": "/home/downey/AstronomicalData/_build/jupyter_execute/03_motion_45_0.png"
+      },
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(phi1, phi2, 'ko', markersize=0.1, alpha=0.2)\n",
+    "\n",
+    "plt.xlabel('ra (degree GD1)')\n",
+    "plt.ylabel('dec (degree GD1)');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Remember that we started with a rectangle in GD-1 coordinates.  When transformed to ICRS, it's a non-rectangular polygon.  Now that we have transformed back to GD-1 coordinates, it's a rectangle again."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Pandas DataFrame\n",
+    "\n",
+    "At this point we have three objects containing different subsets of the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "astropy.table.table.Table"
+      ]
+     },
+     "execution_count": 80,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(results)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "pyia.data.GaiaData"
+      ]
+     },
+     "execution_count": 81,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(gaia_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "astropy.coordinates.sky_coordinate.SkyCoord"
+      ]
+     },
+     "execution_count": 82,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(gd1_coord)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "On one hand, this makes sense, since each object provides different capabilities.  But working with three different object types can be awkward.\n",
+    "\n",
+    "It will be more convenient to choose one object and get all of the data into it.  We'll use a Pandas DataFrame, for two reasons:\n",
+    "\n",
+    "1. It provides capabilities that are pretty much a superset of the other data structures, so it's the all-in-one solution.\n",
+    "\n",
+    "2. Pandas is a general-purpose tool that is useful in many domains, especially data science.  If you are going to develop expertise in one tool, Pandas is a good choice.\n",
+    "\n",
+    "However, compared to an Astropy `Table`, Pandas has one big drawback: it does not keep the metadata associated with the table, including the units for the columns."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It's easy to convert a `Table` to a Pandas `DataFrame`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(140340, 8)"
+      ]
+     },
+     "execution_count": 83,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "df = results.to_pandas()\n",
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`DataFrame` provides `shape`, which shows the number of rows and columns.\n",
+    "\n",
+    "It also provides `head`, which displays the first few rows.  It is useful for spot-checking large results as you go along."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>source_id</th>\n",
+       "      <th>ra</th>\n",
+       "      <th>dec</th>\n",
+       "      <th>pmra</th>\n",
+       "      <th>pmdec</th>\n",
+       "      <th>parallax</th>\n",
+       "      <th>parallax_error</th>\n",
+       "      <th>radial_velocity</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>637987125186749568</td>\n",
+       "      <td>142.483019</td>\n",
+       "      <td>21.757716</td>\n",
+       "      <td>-2.516838</td>\n",
+       "      <td>2.941813</td>\n",
+       "      <td>-0.257345</td>\n",
+       "      <td>0.823721</td>\n",
+       "      <td>1.000000e+20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>638285195917112960</td>\n",
+       "      <td>142.254529</td>\n",
+       "      <td>22.476168</td>\n",
+       "      <td>2.662702</td>\n",
+       "      <td>-12.165984</td>\n",
+       "      <td>0.422728</td>\n",
+       "      <td>0.297472</td>\n",
+       "      <td>1.000000e+20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>638073505568978688</td>\n",
+       "      <td>142.645286</td>\n",
+       "      <td>22.166932</td>\n",
+       "      <td>18.306747</td>\n",
+       "      <td>-7.950660</td>\n",
+       "      <td>0.103640</td>\n",
+       "      <td>0.544584</td>\n",
+       "      <td>1.000000e+20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>638086386175786752</td>\n",
+       "      <td>142.577394</td>\n",
+       "      <td>22.227920</td>\n",
+       "      <td>0.987786</td>\n",
+       "      <td>-2.584105</td>\n",
+       "      <td>-0.857327</td>\n",
+       "      <td>1.059607</td>\n",
+       "      <td>1.000000e+20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>638049655615392384</td>\n",
+       "      <td>142.589136</td>\n",
+       "      <td>22.110783</td>\n",
+       "      <td>0.244439</td>\n",
+       "      <td>-4.941079</td>\n",
+       "      <td>0.099625</td>\n",
+       "      <td>0.486224</td>\n",
+       "      <td>1.000000e+20</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            source_id          ra        dec       pmra      pmdec  parallax  \\\n",
+       "0  637987125186749568  142.483019  21.757716  -2.516838   2.941813 -0.257345   \n",
+       "1  638285195917112960  142.254529  22.476168   2.662702 -12.165984  0.422728   \n",
+       "2  638073505568978688  142.645286  22.166932  18.306747  -7.950660  0.103640   \n",
+       "3  638086386175786752  142.577394  22.227920   0.987786  -2.584105 -0.857327   \n",
+       "4  638049655615392384  142.589136  22.110783   0.244439  -4.941079  0.099625   \n",
+       "\n",
+       "   parallax_error  radial_velocity  \n",
+       "0        0.823721     1.000000e+20  \n",
+       "1        0.297472     1.000000e+20  \n",
+       "2        0.544584     1.000000e+20  \n",
+       "3        1.059607     1.000000e+20  \n",
+       "4        0.486224     1.000000e+20  "
+      ]
+     },
+     "execution_count": 84,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Python detail: `shape` is an attribute, so we can display it's value without calling it as a function; `head` is a function, so we need the parentheses."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can extract the columns we want from `gd1_coord` and add them as columns in the `DataFrame`.  `phi1` and `phi2` contain the transformed coordinates."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(140340, 10)"
+      ]
+     },
+     "execution_count": 85,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['phi1'] = gd1_coord.phi1\n",
+    "df['phi2'] = gd1_coord.phi2\n",
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`pm_phi1_cosphi2` and `pm_phi2` contain the components of proper motion in the transformed frame."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(140340, 12)"
+      ]
+     },
+     "execution_count": 86,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['pm_phi1'] = gd1_coord.pm_phi1_cosphi2\n",
+    "df['pm_phi2'] = gd1_coord.pm_phi2\n",
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Detail:** If you notice that `SkyCoord` has an attribute called `proper_motion`, you might wonder why we are not using it.\n",
+    "\n",
+    "We could have: `proper_motion` contains the same data as `pm_phi1_cosphi2` and `pm_phi2`, but in a different format."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plot proper motion\n",
+    "\n",
+    "Now we are ready to replicate one of the panels in Figure 1 of the Price-Whelan and Bonaca paper, the one that shows the components of proper motion as a scatter plot:\n",
+    "\n",
+    "<img width=\"300\" src=\"https://github.com/datacarpentry/astronomy-python/raw/gh-pages/fig/gd1-1.png\">\n",
+    "\n",
+    "In this figure, the shaded area is a high-density region of stars with the proper motion we expect for stars in GD-1.  \n",
+    "\n",
+    "* Due to the nature of tidal streams, we expect the proper motion for most stars to be along the axis of the stream; that is, we expect motion in the direction of `phi2` to be near 0.\n",
+    "\n",
+    "* In the direction of `phi1`, we don't have a prior expectation for proper motion, except that it should form a cluster at a non-zero value. \n",
+    "\n",
+    "To locate this cluster, we'll select stars near the centerline of GD-1 and plot their proper motion."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Selecting the centerline\n",
+    "\n",
+    "As we can see in the following figure, many stars in GD-1 are less than 1 degree of declination from the line `phi2=0`.\n",
+    "\n",
+    "<img src=\"https://github.com/datacarpentry/astronomy-python/raw/gh-pages/fig/gd1-4.png\">\n",
+    "\n",
+    "If we select stars near this line, they are more likely to be in GD-1.\n",
+    "\n",
+    "We'll start by selecting the `phi2` column from the `DataFrame`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "pandas.core.series.Series"
+      ]
+     },
+     "execution_count": 99,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "phi2 = df['phi2']\n",
+    "type(phi2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The result is a `Series`, which is the structure Pandas uses to represent columns.\n",
+    "\n",
+    "We can use a comparison operator, `>`, to compare the values in a `Series` to a constant."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "pandas.core.series.Series"
+      ]
+     },
+     "execution_count": 100,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "phi2_min = -1.0 * u.deg\n",
+    "phi2_max = 1.0 * u.deg\n",
+    "\n",
+    "mask = (df['phi2'] > phi2_min)\n",
+    "type(mask)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dtype('bool')"
+      ]
+     },
+     "execution_count": 101,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mask.dtype"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The result is a `Series` of Boolean values, that is, `True` and `False`. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    False\n",
+       "1    False\n",
+       "2    False\n",
+       "3    False\n",
+       "4    False\n",
+       "Name: phi2, dtype: bool"
+      ]
+     },
+     "execution_count": 102,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mask.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A Boolean `Series` is sometimes called a \"mask\" because we can use it to mask out some of the rows in a `DataFrame` and select the rest, like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "pandas.core.frame.DataFrame"
+      ]
+     },
+     "execution_count": 103,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "selected = df[mask]\n",
+    "type(selected)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`selected` is a `DataFrame` that contains only the rows from `df` that correspond to `True` values in `mask`.\n",
+    "\n",
+    "The previous mask selects all stars where `phi2` exceeds `phi2_min`; now we'll select stars where `phi2` falls between `phi2_min` and `phi2_max`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "phi_mask = ((df['phi2'] > phi2_min) & \n",
+    "            (df['phi2'] < phi2_max))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `&` operator computes \"logical AND\", which means the result is true where elements from both Boolean `Series` are true.\n",
+    "\n",
+    "The sum of a Boolean `Series` is the number of `True` values, so we can use `sum` to see how many stars are in the selected region."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "25084"
+      ]
+     },
+     "execution_count": 105,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "phi_mask.sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And we can use `phi1_mask` to select stars near the centerline, which are more likely to be in GD-1."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "25084"
+      ]
+     },
+     "execution_count": 106,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "centerline = df[phi_mask]\n",
+    "len(centerline)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's a scatter plot of proper motion for the selected stars."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "filenames": {
+       "image/png": "/home/downey/AstronomicalData/_build/jupyter_execute/03_motion_79_0.png"
+      },
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pm1 = centerline['pm_phi1']\n",
+    "pm2 = centerline['pm_phi2']\n",
+    "\n",
+    "plt.plot(pm1, pm2, 'ko', markersize=0.1, alpha=0.1)\n",
+    "    \n",
+    "plt.xlabel('Proper motion phi1 (GD1 frame)')\n",
+    "plt.ylabel('Proper motion phi2 (GD1 frame)');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Looking at these results, we see a large cluster around (0, 0), and a smaller cluster near (0, -10).\n",
+    "\n",
+    "We can use `xlim` and `ylim` to set the limits on the axes and zoom in on the region near (0, 0)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "filenames": {
+       "image/png": "/home/downey/AstronomicalData/_build/jupyter_execute/03_motion_81_0.png"
+      },
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pm1 = centerline['pm_phi1']\n",
+    "pm2 = centerline['pm_phi2']\n",
+    "\n",
+    "plt.plot(pm1, pm2, 'ko', markersize=0.3, alpha=0.3)\n",
+    "    \n",
+    "plt.xlabel('Proper motion phi1 (GD1 frame)')\n",
+    "plt.ylabel('Proper motion phi2 (GD1 frame)')\n",
+    "\n",
+    "plt.xlim(-12, 8)\n",
+    "plt.ylim(-10, 10);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can see the smaller cluster more clearly.\n",
+    "\n",
+    "You might notice that our figure is less dense than the one in the paper.  That's because we started with a set of stars from a relatively small region.  The figure in the paper is based on a region about 10 times bigger.\n",
+    "\n",
+    "In the next lesson we'll go back and select stars from a larger region.  But first we'll use the proper motion data to identify stars likely to be in GD-1."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Filtering based on proper motion\n",
+    "\n",
+    "The next step is to select stars in the \"overdense\" region of proper motion, which are candidates to be in GD-1.\n",
+    "\n",
+    "In the original paper, Price-Whelan and Bonaca used a polygon to cover this region, as shown in this figure.\n",
+    "\n",
+    "<img width=\"300\" src=\"https://github.com/datacarpentry/astronomy-python/raw/gh-pages/fig/gd1-1.png\">\n",
+    "\n",
+    "We'll use a simple rectangle for now, but in a later lesson we'll see how to select a polygonal region as well.\n",
+    "\n",
+    "Here are bounds on proper motion we chose by eye,"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pm1_min = -8.9\n",
+    "pm1_max = -6.9\n",
+    "pm2_min = -2.2\n",
+    "pm2_max =  1.0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To draw these bounds, we'll make two lists containing the coordinates of the corners of the rectangle."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pm1_rect = [pm1_min, pm1_min, pm1_max, pm1_max, pm1_min] * u.mas/u.yr\n",
+    "pm2_rect = [pm2_min, pm2_max, pm2_max, pm2_min, pm2_min] * u.mas/u.yr"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's what the plot looks like with the bounds we chose."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 118,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "filenames": {
+       "image/png": "/home/downey/AstronomicalData/_build/jupyter_execute/03_motion_88_0.png"
+      },
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(pm1, pm2, 'ko', markersize=0.3, alpha=0.3)\n",
+    "plt.plot(pm1_rect, pm2_rect, '-')\n",
+    "    \n",
+    "plt.xlabel('Proper motion phi1 (GD1 frame)')\n",
+    "plt.ylabel('Proper motion phi2 (GD1 frame)')\n",
+    "\n",
+    "plt.xlim(-12, 8)\n",
+    "plt.ylim(-10, 10);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To select rows that fall within these bounds, we'll use the following function, which uses Pandas operators to make a mask that selects rows where `series` falls between `low` and `high`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 119,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def between(series, low, high):\n",
+    "    \"\"\"Make a Boolean Series.\n",
+    "    \n",
+    "    series: Pandas Series\n",
+    "    low: lower bound\n",
+    "    high: upper bound\n",
+    "    \n",
+    "    returns: Boolean Series\n",
+    "    \"\"\"\n",
+    "    return (series > low) & (series < high)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The following mask select stars with proper motion in the region we chose."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 120,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pm_mask = (between(df['pm_phi1'], pm1_min, pm1_max) & \n",
+    "           between(df['pm_phi2'], pm2_min, pm2_max))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Again, the sum of a Boolean series is the number of `True` values."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 121,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1049"
+      ]
+     },
+     "execution_count": 121,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pm_mask.sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can use this mask to select rows from `df`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 122,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1049"
+      ]
+     },
+     "execution_count": 122,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "selected = df[pm_mask]\n",
+    "len(selected)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "These are the stars we think are likely to be in GD-1.  Let's see what they look like, plotting their coordinates (not their proper motion)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 123,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "filenames": {
+       "image/png": "/home/downey/AstronomicalData/_build/jupyter_execute/03_motion_98_0.png"
+      },
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "phi1 = selected['phi1']\n",
+    "phi2 = selected['phi2']\n",
+    "\n",
+    "plt.plot(phi1, phi2, 'ko', markersize=0.5, alpha=0.5)\n",
+    "\n",
+    "plt.xlabel('ra (degree GD1)')\n",
+    "plt.ylabel('dec (degree GD1)');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now that's starting to look like a tidal stream!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Saving the DataFrame\n",
+    "\n",
+    "At this point we have run a successful query and cleaned up the results; this is a good time to save the data.\n",
+    "\n",
+    "To save a Pandas `DataFrame`, one option is to convert it to an Astropy `Table`, like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 124,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "astropy.table.table.Table"
+      ]
+     },
+     "execution_count": 124,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "selected_table = Table.from_pandas(selected)\n",
+    "type(selected_table)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Then we could write the `Table` to a FITS file, as we did in the previous lesson.  \n",
+    "\n",
+    "But Pandas provides functions to write DataFrames in other formats; to see what they are [find the functions here that begin with `to_`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html).\n",
+    "\n",
+    "One of the best options is HDF5, which is Version 5 of [Hierarchical Data Format](https://en.wikipedia.org/wiki/Hierarchical_Data_Format).\n",
+    "\n",
+    "HDF5 is a binary format, so files are small and fast to read and write (like FITS, but unlike XML).\n",
+    "\n",
+    "An HDF5 file is similar to an SQL database in the sense that it can contain more than one table, although in HDF5 vocabulary, a table is called a Dataset.  ([Multi-extension FITS files](https://www.stsci.edu/itt/review/dhb_2011/Intro/intro_ch23.html) can also contain more than one table.)\n",
+    "\n",
+    "And HDF5 stores the metadata associated with the table, including column names, row labels, and data types (like FITS).\n",
+    "\n",
+    "Finally, HDF5 is a cross-language standard, so if you write an HDF5 file with Pandas, you can read it back with many other software tools (more than FITS)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Before we write the HDF5, let's delete the old one, if it exists."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!rm -f gd1_dataframe.hdf5"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can write a Pandas `DataFrame` to an HDF5 file like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "filename = 'gd1_dataframe.hdf5'\n",
+    "\n",
+    "df.to_hdf(filename, 'df')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Because an HDF5 file can contain more than one Dataset, we have to provide a name, or \"key\", that identifies the Dataset in the file.\n",
+    "\n",
+    "We could use any string as the key, but in this example I use the variable name `df`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** We're going to need `centerline` and `selected` later as well.  Write a line or two of code to add it as a second Dataset in the HDF5 file."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 127,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "centerline.to_hdf(filename, 'centerline')\n",
+    "selected.to_hdf(filename, 'selected')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Detail:** Reading and writing HDF5 tables requires a library called `PyTables` that is not always installed with Pandas.  You can install it with pip like this:\n",
+    "\n",
+    "```\n",
+    "pip install tables\n",
+    "```\n",
+    "\n",
+    "If you install it using Conda, the name of the package is `pytables`.\n",
+    "\n",
+    "```\n",
+    "conda install pytables\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can use `ls` to confirm that the file exists and check the size:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 128,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-rw-rw-r-- 1 downey downey 17M Oct 19 12:05 gd1_dataframe.hdf5\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!ls -lh gd1_dataframe.hdf5"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you are using Windows, `ls` might not work; in that case, try:\n",
+    "\n",
+    "```\n",
+    "!dir gd1_dataframe.hdf5\n",
+    "```\n",
+    "\n",
+    "We can read the file back like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 129,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(140340, 12)"
+      ]
+     },
+     "execution_count": 129,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "read_back_df = pd.read_hdf(filename, 'df')\n",
+    "read_back_df.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Pandas can write a variety of other formats, [which you can read about here](https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "In this lesson, we re-loaded the Gaia data we saved from a previous query.\n",
+    "\n",
+    "We transformed the coordinates and proper motion from ICRS to a frame aligned with GD-1, and stored the results in a Pandas `DataFrame`.\n",
+    "\n",
+    "Then we replicated the selection process from the Price-Whelan and Bonaca paper:\n",
+    "\n",
+    "* We selected stars near the centerline of GD-1 and made a scatter plot of their proper motion.\n",
+    "\n",
+    "* We identified a region of proper motion that contains stars likely to be in GD-1.\n",
+    "\n",
+    "* We used a Boolean `Series` as a mask to select stars whose proper motion is in that region.\n",
+    "\n",
+    "So far, we have used data from a relatively small region of the sky.  In the next lesson, we'll write a query that selects stars based on proper motion, which will allow us to explore a larger region."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Best practices\n",
+    "\n",
+    "* When you make a scatter plot, adjust the size of the markers and their transparency so the figure is not overplotted; otherwise it can misrepresent the data badly.\n",
+    "\n",
+    "* For simple scatter plots in Matplotlib, `plot` is faster than `scatter`.\n",
+    "\n",
+    "* An Astropy `Table` and a Pandas `DataFrame` are similar in many ways and they provide many of the same functions.  They have pros and cons, but for many projects, either one would be a reasonable choice."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
\ No newline at end of file
diff --git a/_build/jupyter_execute/03_motion.py b/_build/jupyter_execute/03_motion.py
new file mode 100644
index 0000000..6938dd4
--- /dev/null
+++ b/_build/jupyter_execute/03_motion.py
@@ -0,0 +1,776 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# # Chapter 3
+# 
+# This is the third in a series of notebooks related to astronomy data.
+# 
+# As a running example, we are replicating parts of the analysis in a recent paper, "[Off the beaten path: Gaia reveals GD-1 stars outside of the main stream](https://arxiv.org/abs/1805.00425)" by Adrian M. Price-Whelan and Ana Bonaca.
+# 
+# In the first lesson, we wrote ADQL queries and used them to select and download data from the Gaia server.
+# 
+# In the second lesson, we wrote a query to select stars from the region of the sky where we expect GD-1 to be, and saved the results in a FITS file.
+# 
+# Now we'll read that data back and implement the next step in the analysis, identifying stars with the proper motion we expect for GD-1.
+
+# ## Outline
+# 
+# Here are the steps in this lesson:
+# 
+# 1. We'll read back the results from the previous lesson, which we saved in a FITS file.
+# 
+# 2. Then we'll transform the coordinates and proper motion data from ICRS back to the coordinate frame of GD-1.
+# 
+# 3. We'll put those results into a Pandas `DataFrame`, which we'll use to select stars near the centerline of GD-1.
+# 
+# 4. Plotting the proper motion of those stars, we'll identify a region of proper motion for stars that are likely to be in GD-1.
+# 
+# 5. Finally, we'll select and plot the stars whose proper motion is in that region.
+# 
+# After completing this lesson, you should be able to
+# 
+# * Select rows and columns from an Astropy `Table`.
+# 
+# * Use Matplotlib to make a scatter plot.
+# 
+# * Use Gala to transform coordinates.
+# 
+# * Make a Pandas `DataFrame` and use a Boolean `Series` to select rows.
+# 
+# * Save a `DataFrame` in an HDF5 file.
+# 
+
+# ## Installing libraries
+# 
+# If you are running this notebook on Colab, you can run the following cell to install Astroquery and a the other libraries we'll use.
+# 
+# If you are running this notebook on your own computer, you might have to install these libraries yourself.  
+# 
+# If you are using this notebook as part of a Carpentries workshop, you should have received setup instructions.
+# 
+# TODO: Add a link to the instructions.
+# 
+
+# In[1]:
+
+
+# If we're running on Colab, install libraries
+
+import sys
+IN_COLAB = 'google.colab' in sys.modules
+
+if IN_COLAB:
+    get_ipython().system('pip install astroquery astro-gala pyia python-wget')
+
+
+# ## Reload the data
+# 
+# In the previous lesson, we ran a query on the Gaia server and downloaded data for roughly 100,000 stars.  We saved the data in a FITS file so that now, picking up where we left off, we can read the data from a local file rather than running the query again.
+# 
+# If you ran the previous lesson successfully, you should already have a file called `gd1_results.fits` that contains the data we downloaded.
+# 
+# If not, you can run the following cell, which downloads the data from our repository.
+
+# In[2]:
+
+
+import os
+from wget import download
+
+filename = 'gd1_results.fits'
+path = 'https://github.com/AllenDowney/AstronomicalData/raw/main/data/'
+
+if not os.path.exists(filename):
+    print(download(path+filename))
+
+
+# Now here's how we can read the data from the file back into an Astropy `Table`:
+
+# In[3]:
+
+
+from astropy.table import Table
+
+results = Table.read(filename)
+
+
+# The result is an Astropy `Table`.
+# 
+# We can use `info` to refresh our memory of the contents.
+
+# In[4]:
+
+
+results.info
+
+
+# ## Selecting rows and columns
+# 
+# In this section we'll see operations for selecting columns and rows from an Astropy `Table`.  You can find more information about these operations in the [Astropy documentation](https://docs.astropy.org/en/stable/table/access_table.html).
+# 
+# We can get the names of the columns like this:
+
+# In[5]:
+
+
+results.colnames
+
+
+# And select an individual column like this:
+
+# In[6]:
+
+
+results['ra']
+
+
+# The result is a `Column` object that contains the data, and also the data type, units, and name of the column.
+
+# In[7]:
+
+
+type(results['ra'])
+
+
+# The rows in the `Table` are numbered from 0 to `n-1`, where `n` is the number of rows.  We can select the first row like this:
+
+# In[8]:
+
+
+results[0]
+
+
+# As you might have guessed, the result is a `Row` object.
+
+# In[9]:
+
+
+type(results[0])
+
+
+# Notice that the bracket operator selects both columns and rows.  You might wonder how it knows which to select.
+# 
+# If the expression in brackets is a string, it selects a column; if the expression is an integer, it selects a row.
+# 
+# If you apply the bracket operator twice, you can select a column and then an element from the column.
+
+# In[10]:
+
+
+results['ra'][0]
+
+
+# Or you can select a row and then an element from the row.
+
+# In[11]:
+
+
+results[0]['ra']
+
+
+# You get the same result either way.
+
+# ## Scatter plot
+# 
+# To see what the results look like, we'll use a scatter plot.  The library we'll use is [Matplotlib](https://matplotlib.org/), which is the most widely-used plotting library for Python.
+# 
+# The Matplotlib interface is based on MATLAB (hence the name), so if you know MATLAB, some of it will be familiar.
+# 
+# We'll import like this.
+
+# In[12]:
+
+
+import matplotlib.pyplot as plt
+
+
+# Pyplot part of the Matplotlib library.  It is conventional to import it using the shortened name `plt`.
+# 
+# Pyplot provides two functions that can make scatterplots, [plt.scatter](https://matplotlib.org/3.3.0/api/_as_gen/matplotlib.pyplot.scatter.html) and [plt.plot](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.plot.html).
+# 
+# * `scatter` is more versatile; for example, you can make every point in a scatter plot a different color.
+# 
+# * `plot` is more limited, but for simple cases, it can be substantially faster.  
+# 
+# Jake Vanderplas explains these differences in [The Python Data Science Handbook](https://jakevdp.github.io/PythonDataScienceHandbook/04.02-simple-scatter-plots.html)
+# 
+# Since we are plotting more than 100,000 points and they are all the same size and color, we'll use `plot`.
+# 
+# Here's a scatter plot with right ascension on the x-axis and declination on the y-axis, both ICRS coordinates in degrees.
+
+# In[13]:
+
+
+x = results['ra']
+y = results['dec']
+plt.plot(x, y, 'ko')
+
+plt.xlabel('ra (degree ICRS)')
+plt.ylabel('dec (degree ICRS)');
+
+
+# The arguments to `plt.plot` are `x`, `y`, and a string that specifies the style.  In this case, the letters `ko` indicate that we want a black, round marker (`k` is for black because `b` is for blue).
+# 
+# The functions `xlabel` and `ylabel` put labels on the axes.
+# 
+# This scatter plot has a problem.  It is "[overplotted](https://python-graph-gallery.com/134-how-to-avoid-overplotting-with-python/)", which means that there are so many overlapping points, we can't distinguish between high and low density areas.
+# 
+# To fix this, we can provide optional arguments to control the size and transparency of the points.
+# 
+# **Exercise:** In the call to `plt.plot`, add the keyword argument `markersize=0.1` to make the markers smaller.
+# 
+# Then add the argument `alpha=0.1` to make the markers nearly transparent.
+# 
+# Adjust these arguments until you think the figure shows the data most clearly.
+# 
+# Note: Once you have made these changes, you might notice that the figure shows stripes with lower density of stars.  These stripes are caused by the way Gaia scans the sky, which [you can read about here](https://www.cosmos.esa.int/web/gaia/scanning-law).  The dataset we are using, [Gaia Data Release 2](https://www.cosmos.esa.int/web/gaia/dr2), covers 22 months of observations; during this time, some parts of the sky were scanned more than others.
+
+# ## Transform back
+# 
+# Remember that we selected data from a rectangle of coordinates in the `GD1Koposov10` frame, then transformed them to ICRS when we constructed the query.
+# The coordinates in `results` are in ICRS.
+# 
+# To plot them, we will transform them back to the `GD1Koposov10` frame; that way, the axes of the figure are aligned with the GD-1, which will make it easy to select stars near the centerline of the stream.
+# 
+# To do that, we'll put the results into a `GaiaData` object, provided by the [pyia library](https://pyia.readthedocs.io/en/latest/api/pyia.GaiaData.html).
+
+# In[14]:
+
+
+from pyia import GaiaData
+
+gaia_data = GaiaData(results)
+type(gaia_data)
+
+
+# Now we can extract sky coordinates from the `GaiaData` object, like this:
+
+# In[73]:
+
+
+import astropy.units as u
+
+skycoord = gaia_data.get_skycoord(
+                distance=8*u.kpc, 
+                radial_velocity=0*u.km/u.s)
+
+
+# We provide `distance` and `radial_velocity` to prepare the data for reflex correction, which we explain below.
+
+# In[75]:
+
+
+type(skycoord)
+
+
+# The result is an Astropy `SkyCoord` object ([documentation here](https://docs.astropy.org/en/stable/api/astropy.coordinates.SkyCoord.html#astropy.coordinates.SkyCoord)), which provides `transform_to`, so we can transform the coordinates to other frames.
+
+# In[76]:
+
+
+import gala.coordinates as gc
+
+transformed = skycoord.transform_to(gc.GD1Koposov10)
+type(transformed)
+
+
+# The result is another `SkyCoord` object, now in the `GD1Koposov10` frame.
+
+# The next step is to correct the proper motion measurements from Gaia for reflex due to the motion of our solar system around the Galactic center.
+# 
+# When we created `skycoord`, we provided `distance` and `radial_velocity` as arguments, which means we ignore the measurements provided by Gaia and replace them with these fixed values.
+# 
+# That might seem like a strange thing to do, but here's the motivation:
+# 
+# * Because the stars in GD-1 are so far away, the distance estimates we get from Gaia, which are based on parallax, are not very precise.  So we replace them with our current best estimate of the mean distance to GD-1, about 8 kpc.  See [Koposov, Rix, and Hogg, 2010](https://ui.adsabs.harvard.edu/abs/2010ApJ...712..260K/abstract).
+# 
+# * For the other stars in the table, this distance estimate will be inaccurate, so reflex correction will not be correct.  But that should have only a small effect on our ability to identify stars with the proper motion we expect for GD-1.
+# 
+# * The measurement of radial velocity has no effect on the correction for proper motion; the value we provide is arbitrary, but we have to provide a value to avoid errors in the reflex correction calculation.
+# 
+# We are grateful to Adrian Price-Whelen for his help explaining this step in the analysis.
+
+# With this preparation, we can use `reflex_correct` from Gala ([documentation here](https://gala-astro.readthedocs.io/en/latest/api/gala.coordinates.reflex_correct.html)) to correct for solar reflex motion.
+
+# In[77]:
+
+
+gd1_coord = gc.reflex_correct(transformed)
+
+type(gd1_coord)
+
+
+# The result is a `SkyCoord` object that contains 
+# 
+# * The transformed coordinates as attributes named `phi1` and `phi2`, which represent right ascension and declination in the `GD1Koposov10` frame.
+# 
+# * The transformed and corrected proper motions as `pm_phi1_cosphi2` and `pm_phi2`.
+# 
+# We can select the coordinates like this:
+
+# In[78]:
+
+
+phi1 = gd1_coord.phi1
+phi2 = gd1_coord.phi2
+
+
+# And plot them like this:
+
+# In[79]:
+
+
+plt.plot(phi1, phi2, 'ko', markersize=0.1, alpha=0.2)
+
+plt.xlabel('ra (degree GD1)')
+plt.ylabel('dec (degree GD1)');
+
+
+# Remember that we started with a rectangle in GD-1 coordinates.  When transformed to ICRS, it's a non-rectangular polygon.  Now that we have transformed back to GD-1 coordinates, it's a rectangle again.
+
+# ## Pandas DataFrame
+# 
+# At this point we have three objects containing different subsets of the data.
+
+# In[80]:
+
+
+type(results)
+
+
+# In[81]:
+
+
+type(gaia_data)
+
+
+# In[82]:
+
+
+type(gd1_coord)
+
+
+# On one hand, this makes sense, since each object provides different capabilities.  But working with three different object types can be awkward.
+# 
+# It will be more convenient to choose one object and get all of the data into it.  We'll use a Pandas DataFrame, for two reasons:
+# 
+# 1. It provides capabilities that are pretty much a superset of the other data structures, so it's the all-in-one solution.
+# 
+# 2. Pandas is a general-purpose tool that is useful in many domains, especially data science.  If you are going to develop expertise in one tool, Pandas is a good choice.
+# 
+# However, compared to an Astropy `Table`, Pandas has one big drawback: it does not keep the metadata associated with the table, including the units for the columns.
+
+# It's easy to convert a `Table` to a Pandas `DataFrame`.
+
+# In[83]:
+
+
+import pandas as pd
+
+df = results.to_pandas()
+df.shape
+
+
+# `DataFrame` provides `shape`, which shows the number of rows and columns.
+# 
+# It also provides `head`, which displays the first few rows.  It is useful for spot-checking large results as you go along.
+
+# In[84]:
+
+
+df.head()
+
+
+# Python detail: `shape` is an attribute, so we can display it's value without calling it as a function; `head` is a function, so we need the parentheses.
+
+# Now we can extract the columns we want from `gd1_coord` and add them as columns in the `DataFrame`.  `phi1` and `phi2` contain the transformed coordinates.
+
+# In[85]:
+
+
+df['phi1'] = gd1_coord.phi1
+df['phi2'] = gd1_coord.phi2
+df.shape
+
+
+# `pm_phi1_cosphi2` and `pm_phi2` contain the components of proper motion in the transformed frame.
+
+# In[86]:
+
+
+df['pm_phi1'] = gd1_coord.pm_phi1_cosphi2
+df['pm_phi2'] = gd1_coord.pm_phi2
+df.shape
+
+
+# **Detail:** If you notice that `SkyCoord` has an attribute called `proper_motion`, you might wonder why we are not using it.
+# 
+# We could have: `proper_motion` contains the same data as `pm_phi1_cosphi2` and `pm_phi2`, but in a different format.
+
+# ## Plot proper motion
+# 
+# Now we are ready to replicate one of the panels in Figure 1 of the Price-Whelan and Bonaca paper, the one that shows the components of proper motion as a scatter plot:
+# 
+# <img width="300" src="https://github.com/datacarpentry/astronomy-python/raw/gh-pages/fig/gd1-1.png">
+# 
+# In this figure, the shaded area is a high-density region of stars with the proper motion we expect for stars in GD-1.  
+# 
+# * Due to the nature of tidal streams, we expect the proper motion for most stars to be along the axis of the stream; that is, we expect motion in the direction of `phi2` to be near 0.
+# 
+# * In the direction of `phi1`, we don't have a prior expectation for proper motion, except that it should form a cluster at a non-zero value. 
+# 
+# To locate this cluster, we'll select stars near the centerline of GD-1 and plot their proper motion.
+
+# ## Selecting the centerline
+# 
+# As we can see in the following figure, many stars in GD-1 are less than 1 degree of declination from the line `phi2=0`.
+# 
+# <img src="https://github.com/datacarpentry/astronomy-python/raw/gh-pages/fig/gd1-4.png">
+# 
+# If we select stars near this line, they are more likely to be in GD-1.
+# 
+# We'll start by selecting the `phi2` column from the `DataFrame`:
+
+# In[99]:
+
+
+phi2 = df['phi2']
+type(phi2)
+
+
+# The result is a `Series`, which is the structure Pandas uses to represent columns.
+# 
+# We can use a comparison operator, `>`, to compare the values in a `Series` to a constant.
+
+# In[100]:
+
+
+phi2_min = -1.0 * u.deg
+phi2_max = 1.0 * u.deg
+
+mask = (df['phi2'] > phi2_min)
+type(mask)
+
+
+# In[101]:
+
+
+mask.dtype
+
+
+# The result is a `Series` of Boolean values, that is, `True` and `False`. 
+
+# In[102]:
+
+
+mask.head()
+
+
+# A Boolean `Series` is sometimes called a "mask" because we can use it to mask out some of the rows in a `DataFrame` and select the rest, like this:
+
+# In[103]:
+
+
+selected = df[mask]
+type(selected)
+
+
+# `selected` is a `DataFrame` that contains only the rows from `df` that correspond to `True` values in `mask`.
+# 
+# The previous mask selects all stars where `phi2` exceeds `phi2_min`; now we'll select stars where `phi2` falls between `phi2_min` and `phi2_max`.
+
+# In[104]:
+
+
+phi_mask = ((df['phi2'] > phi2_min) & 
+            (df['phi2'] < phi2_max))
+
+
+# The `&` operator computes "logical AND", which means the result is true where elements from both Boolean `Series` are true.
+# 
+# The sum of a Boolean `Series` is the number of `True` values, so we can use `sum` to see how many stars are in the selected region.
+
+# In[105]:
+
+
+phi_mask.sum()
+
+
+# And we can use `phi1_mask` to select stars near the centerline, which are more likely to be in GD-1.
+
+# In[106]:
+
+
+centerline = df[phi_mask]
+len(centerline)
+
+
+# Here's a scatter plot of proper motion for the selected stars.
+
+# In[112]:
+
+
+pm1 = centerline['pm_phi1']
+pm2 = centerline['pm_phi2']
+
+plt.plot(pm1, pm2, 'ko', markersize=0.1, alpha=0.1)
+    
+plt.xlabel('Proper motion phi1 (GD1 frame)')
+plt.ylabel('Proper motion phi2 (GD1 frame)');
+
+
+# Looking at these results, we see a large cluster around (0, 0), and a smaller cluster near (0, -10).
+# 
+# We can use `xlim` and `ylim` to set the limits on the axes and zoom in on the region near (0, 0).
+
+# In[113]:
+
+
+pm1 = centerline['pm_phi1']
+pm2 = centerline['pm_phi2']
+
+plt.plot(pm1, pm2, 'ko', markersize=0.3, alpha=0.3)
+    
+plt.xlabel('Proper motion phi1 (GD1 frame)')
+plt.ylabel('Proper motion phi2 (GD1 frame)')
+
+plt.xlim(-12, 8)
+plt.ylim(-10, 10);
+
+
+# Now we can see the smaller cluster more clearly.
+# 
+# You might notice that our figure is less dense than the one in the paper.  That's because we started with a set of stars from a relatively small region.  The figure in the paper is based on a region about 10 times bigger.
+# 
+# In the next lesson we'll go back and select stars from a larger region.  But first we'll use the proper motion data to identify stars likely to be in GD-1.
+
+# ## Filtering based on proper motion
+# 
+# The next step is to select stars in the "overdense" region of proper motion, which are candidates to be in GD-1.
+# 
+# In the original paper, Price-Whelan and Bonaca used a polygon to cover this region, as shown in this figure.
+# 
+# <img width="300" src="https://github.com/datacarpentry/astronomy-python/raw/gh-pages/fig/gd1-1.png">
+# 
+# We'll use a simple rectangle for now, but in a later lesson we'll see how to select a polygonal region as well.
+# 
+# Here are bounds on proper motion we chose by eye,
+
+# In[96]:
+
+
+pm1_min = -8.9
+pm1_max = -6.9
+pm2_min = -2.2
+pm2_max =  1.0
+
+
+# To draw these bounds, we'll make two lists containing the coordinates of the corners of the rectangle.
+
+# In[114]:
+
+
+pm1_rect = [pm1_min, pm1_min, pm1_max, pm1_max, pm1_min] * u.mas/u.yr
+pm2_rect = [pm2_min, pm2_max, pm2_max, pm2_min, pm2_min] * u.mas/u.yr
+
+
+# Here's what the plot looks like with the bounds we chose.
+
+# In[118]:
+
+
+plt.plot(pm1, pm2, 'ko', markersize=0.3, alpha=0.3)
+plt.plot(pm1_rect, pm2_rect, '-')
+    
+plt.xlabel('Proper motion phi1 (GD1 frame)')
+plt.ylabel('Proper motion phi2 (GD1 frame)')
+
+plt.xlim(-12, 8)
+plt.ylim(-10, 10);
+
+
+# To select rows that fall within these bounds, we'll use the following function, which uses Pandas operators to make a mask that selects rows where `series` falls between `low` and `high`.
+
+# In[119]:
+
+
+def between(series, low, high):
+    """Make a Boolean Series.
+    
+    series: Pandas Series
+    low: lower bound
+    high: upper bound
+    
+    returns: Boolean Series
+    """
+    return (series > low) & (series < high)
+
+
+# The following mask select stars with proper motion in the region we chose.
+
+# In[120]:
+
+
+pm_mask = (between(df['pm_phi1'], pm1_min, pm1_max) & 
+           between(df['pm_phi2'], pm2_min, pm2_max))
+
+
+# Again, the sum of a Boolean series is the number of `True` values.
+
+# In[121]:
+
+
+pm_mask.sum()
+
+
+# Now we can use this mask to select rows from `df`.
+
+# In[122]:
+
+
+selected = df[pm_mask]
+len(selected)
+
+
+# These are the stars we think are likely to be in GD-1.  Let's see what they look like, plotting their coordinates (not their proper motion).
+
+# In[123]:
+
+
+phi1 = selected['phi1']
+phi2 = selected['phi2']
+
+plt.plot(phi1, phi2, 'ko', markersize=0.5, alpha=0.5)
+
+plt.xlabel('ra (degree GD1)')
+plt.ylabel('dec (degree GD1)');
+
+
+# Now that's starting to look like a tidal stream!
+
+# ## Saving the DataFrame
+# 
+# At this point we have run a successful query and cleaned up the results; this is a good time to save the data.
+# 
+# To save a Pandas `DataFrame`, one option is to convert it to an Astropy `Table`, like this:
+
+# In[124]:
+
+
+selected_table = Table.from_pandas(selected)
+type(selected_table)
+
+
+# Then we could write the `Table` to a FITS file, as we did in the previous lesson.  
+# 
+# But Pandas provides functions to write DataFrames in other formats; to see what they are [find the functions here that begin with `to_`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html).
+# 
+# One of the best options is HDF5, which is Version 5 of [Hierarchical Data Format](https://en.wikipedia.org/wiki/Hierarchical_Data_Format).
+# 
+# HDF5 is a binary format, so files are small and fast to read and write (like FITS, but unlike XML).
+# 
+# An HDF5 file is similar to an SQL database in the sense that it can contain more than one table, although in HDF5 vocabulary, a table is called a Dataset.  ([Multi-extension FITS files](https://www.stsci.edu/itt/review/dhb_2011/Intro/intro_ch23.html) can also contain more than one table.)
+# 
+# And HDF5 stores the metadata associated with the table, including column names, row labels, and data types (like FITS).
+# 
+# Finally, HDF5 is a cross-language standard, so if you write an HDF5 file with Pandas, you can read it back with many other software tools (more than FITS).
+
+# Before we write the HDF5, let's delete the old one, if it exists.
+
+# In[125]:
+
+
+get_ipython().system('rm -f gd1_dataframe.hdf5')
+
+
+# We can write a Pandas `DataFrame` to an HDF5 file like this:
+
+# In[126]:
+
+
+filename = 'gd1_dataframe.hdf5'
+
+df.to_hdf(filename, 'df')
+
+
+# Because an HDF5 file can contain more than one Dataset, we have to provide a name, or "key", that identifies the Dataset in the file.
+# 
+# We could use any string as the key, but in this example I use the variable name `df`.
+
+# **Exercise:** We're going to need `centerline` and `selected` later as well.  Write a line or two of code to add it as a second Dataset in the HDF5 file.
+
+# In[127]:
+
+
+# Solution
+
+centerline.to_hdf(filename, 'centerline')
+selected.to_hdf(filename, 'selected')
+
+
+# **Detail:** Reading and writing HDF5 tables requires a library called `PyTables` that is not always installed with Pandas.  You can install it with pip like this:
+# 
+# ```
+# pip install tables
+# ```
+# 
+# If you install it using Conda, the name of the package is `pytables`.
+# 
+# ```
+# conda install pytables
+# ```
+
+# We can use `ls` to confirm that the file exists and check the size:
+
+# In[128]:
+
+
+get_ipython().system('ls -lh gd1_dataframe.hdf5')
+
+
+# If you are using Windows, `ls` might not work; in that case, try:
+# 
+# ```
+# !dir gd1_dataframe.hdf5
+# ```
+# 
+# We can read the file back like this:
+
+# In[129]:
+
+
+read_back_df = pd.read_hdf(filename, 'df')
+read_back_df.shape
+
+
+# Pandas can write a variety of other formats, [which you can read about here](https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html).
+
+# ## Summary
+# 
+# In this lesson, we re-loaded the Gaia data we saved from a previous query.
+# 
+# We transformed the coordinates and proper motion from ICRS to a frame aligned with GD-1, and stored the results in a Pandas `DataFrame`.
+# 
+# Then we replicated the selection process from the Price-Whelan and Bonaca paper:
+# 
+# * We selected stars near the centerline of GD-1 and made a scatter plot of their proper motion.
+# 
+# * We identified a region of proper motion that contains stars likely to be in GD-1.
+# 
+# * We used a Boolean `Series` as a mask to select stars whose proper motion is in that region.
+# 
+# So far, we have used data from a relatively small region of the sky.  In the next lesson, we'll write a query that selects stars based on proper motion, which will allow us to explore a larger region.
+
+# ## Best practices
+# 
+# * When you make a scatter plot, adjust the size of the markers and their transparency so the figure is not overplotted; otherwise it can misrepresent the data badly.
+# 
+# * For simple scatter plots in Matplotlib, `plot` is faster than `scatter`.
+# 
+# * An Astropy `Table` and a Pandas `DataFrame` are similar in many ways and they provide many of the same functions.  They have pros and cons, but for many projects, either one would be a reasonable choice.
+
+# In[ ]:
+
+
+
+
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diff --git a/_build/jupyter_execute/04_select.ipynb b/_build/jupyter_execute/04_select.ipynb
new file mode 100644
index 0000000..6c68b59
--- /dev/null
+++ b/_build/jupyter_execute/04_select.ipynb
@@ -0,0 +1,1460 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Chapter 4\n",
+    "\n",
+    "This is the fourth in a series of notebooks related to astronomy data.\n",
+    "\n",
+    "As a running example, we are replicating parts of the analysis in a recent paper, \"[Off the beaten path: Gaia reveals GD-1 stars outside of the main stream](https://arxiv.org/abs/1805.00425)\" by Adrian M. Price-Whelan and Ana Bonaca.\n",
+    "\n",
+    "In the first lesson, we wrote ADQL queries and used them to select and download data from the Gaia server.\n",
+    "\n",
+    "In the second lesson, we write a query to select stars from the region of the sky where we expect GD-1 to be, and save the results in a FITS file.\n",
+    "\n",
+    "In the third lesson, we read that data back and identified stars with the proper motion we expect for GD-1."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Outline\n",
+    "\n",
+    "Here are the steps in this lesson:\n",
+    "\n",
+    "1. Using data from the previous lesson, we'll identify the values of proper motion for stars likely to be in GD-1.\n",
+    "\n",
+    "2. Then we'll compose an ADQL query that selects stars based on proper motion, so we can download only the data we need.\n",
+    "\n",
+    "3. We'll also see how to write the results to a CSV file.\n",
+    "\n",
+    "That will make it possible to search a bigger region of the sky in a single query.\n",
+    "\n",
+    "After completing this lesson, you should be able to\n",
+    "\n",
+    "* Convert proper motion between frames.\n",
+    "\n",
+    "* Write an ADQL query that selects based on proper motion."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Installing libraries\n",
+    "\n",
+    "If you are running this notebook on Colab, you can run the following cell to install Astroquery and a the other libraries we'll use.\n",
+    "\n",
+    "If you are running this notebook on your own computer, you might have to install these libraries yourself.  \n",
+    "\n",
+    "If you are using this notebook as part of a Carpentries workshop, you should have received setup instructions.\n",
+    "\n",
+    "TODO: Add a link to the instructions.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# If we're running on Colab, install libraries\n",
+    "\n",
+    "import sys\n",
+    "IN_COLAB = 'google.colab' in sys.modules\n",
+    "\n",
+    "if IN_COLAB:\n",
+    "    !pip install astroquery astro-gala pyia python-wget"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Reload the data\n",
+    "\n",
+    "The following cells download the data from the previous lesson, if necessary, and load it into a Pandas `DataFrame`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from wget import download\n",
+    "\n",
+    "filename = 'gd1_dataframe.hdf5'\n",
+    "path = 'https://github.com/AllenDowney/AstronomicalData/raw/main/data/'\n",
+    "\n",
+    "if not os.path.exists(filename):\n",
+    "    print(download(path+filename))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "df = pd.read_hdf(filename, 'df')\n",
+    "centerline = pd.read_hdf(filename, 'centerline')\n",
+    "selected = pd.read_hdf(filename, 'selected')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Selection by proper motion\n",
+    "\n",
+    "At this point we have downloaded data for a relatively large number of stars (more than 100,000) and selected a relatively small number (around 1000).\n",
+    "\n",
+    "It would be more efficient to use ADQL to select only the stars we need.  That would also make it possible to download data covering a larger region of the sky.\n",
+    "\n",
+    "However, the selection we did was based on proper motion in the `GD1Koposov10` frame.  In order to do the same selection in ADQL, we have to work with proper motions in ICRS.\n",
+    "\n",
+    "As a reminder, here's the rectangle we selected based on proper motion in the `GD1Koposov10` frame."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pm1_min = -8.9\n",
+    "pm1_max = -6.9\n",
+    "pm2_min = -2.2\n",
+    "pm2_max =  1.0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import astropy.units as u\n",
+    "\n",
+    "pm1_rect = [pm1_min, pm1_min, pm1_max, pm1_max, pm1_min] * u.mas/u.yr\n",
+    "pm2_rect = [pm2_min, pm2_max, pm2_max, pm2_min, pm2_min] * u.mas/u.yr"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The following figure shows:\n",
+    "\n",
+    "* Proper motion for the stars we selected along the center line of GD-1,\n",
+    "\n",
+    "* The rectangle we selected, and\n",
+    "\n",
+    "* The stars inside the rectangle highlighted in green."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "filenames": {
+       "image/png": "/home/downey/AstronomicalData/_build/jupyter_execute/04_select_11_0.png"
+      },
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "pm1 = centerline['pm_phi1']\n",
+    "pm2 = centerline['pm_phi2']\n",
+    "plt.plot(pm1, pm2, 'ko', markersize=0.3, alpha=0.3)\n",
+    "\n",
+    "pm1 = selected['pm_phi1']\n",
+    "pm2 = selected['pm_phi2']\n",
+    "plt.plot(pm1, pm2, 'gx', markersize=0.3, alpha=0.3)\n",
+    "\n",
+    "plt.plot(pm1_rect, pm2_rect, '-')\n",
+    "    \n",
+    "plt.xlabel('Proper motion phi1 (GD1 frame)')\n",
+    "plt.ylabel('Proper motion phi2 (GD1 frame)')\n",
+    "\n",
+    "plt.xlim(-12, 8)\n",
+    "plt.ylim(-10, 10);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we'll make the same plot using proper motions in the ICRS frame, which are stored in columns `pmra` and `pmdec`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "filenames": {
+       "image/png": "/home/downey/AstronomicalData/_build/jupyter_execute/04_select_13_0.png"
+      },
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pm1 = centerline['pmra']\n",
+    "pm2 = centerline['pmdec']\n",
+    "plt.plot(pm1, pm2, 'ko', markersize=0.3, alpha=0.3)\n",
+    "\n",
+    "pm1 = selected['pmra']\n",
+    "pm2 = selected['pmdec']\n",
+    "plt.plot(pm1, pm2, 'gx', markersize=1, alpha=0.3)\n",
+    "    \n",
+    "plt.xlabel('Proper motion phi1 (ICRS frame)')\n",
+    "plt.ylabel('Proper motion phi2 (ICRS frame)')\n",
+    "\n",
+    "plt.xlim([-10, 5])\n",
+    "plt.ylim([-20, 5]);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The proper motions of the selected stars are more spread out in this frame, which is why it was preferable to do the selection in the GD-1 frame.\n",
+    "\n",
+    "But now we can define a polygon that encloses the proper motions of these stars in ICRS, \n",
+    "and use the polygon as a selection criterion in an ADQL query.\n",
+    "\n",
+    "SciPy provides a function that computes the [convex hull](https://en.wikipedia.org/wiki/Convex_hull) of a set of points, which is the smallest convex polygon that contains all of the points.\n",
+    "\n",
+    "To use it, I'll select columns `pmra` and `pmdec` and convert them to a NumPy array."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1049, 2)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "points = selected[['pmra','pmdec']].to_numpy()\n",
+    "points.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We'll pass the points to `ConvexHull`, which returns an object that contains the results. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<scipy.spatial.qhull.ConvexHull at 0x7f446b1e8bb0>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from scipy.spatial import ConvexHull\n",
+    "\n",
+    "hull = ConvexHull(points)\n",
+    "hull"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`hull.vertices` contains the indices of the points that fall on the perimeter of the hull."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 692,  873,  141,  303,   42,  622,   45,   83,  127,  182, 1006,\n",
+       "        971,  967, 1001,  969,  940], dtype=int32)"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hull.vertices"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can use them as an index into the original array to select the corresponding rows."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ -4.05037121, -14.75623261],\n",
+       "       [ -3.41981085, -14.72365546],\n",
+       "       [ -3.03521988, -14.44357135],\n",
+       "       [ -2.26847919, -13.7140236 ],\n",
+       "       [ -2.61172203, -13.24797471],\n",
+       "       [ -2.73471401, -13.09054471],\n",
+       "       [ -3.19923146, -12.5942653 ],\n",
+       "       [ -3.34082546, -12.47611926],\n",
+       "       [ -5.67489413, -11.16083338],\n",
+       "       [ -5.95159272, -11.10547884],\n",
+       "       [ -6.42394023, -11.05981295],\n",
+       "       [ -7.09631023, -11.95187806],\n",
+       "       [ -7.30641519, -12.24559977],\n",
+       "       [ -7.04016696, -12.88580702],\n",
+       "       [ -6.00347705, -13.75912098],\n",
+       "       [ -4.42442296, -14.74641176]])"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pm_vertices = points[hull.vertices]\n",
+    "pm_vertices"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To plot the resulting polygon, we have to pull out the x and y coordinates."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pmra_poly, pmdec_poly = np.transpose(pm_vertices)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The following figure shows proper motion in ICRS again, along with the convex hull we just computed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "filenames": {
+       "image/png": "/home/downey/AstronomicalData/_build/jupyter_execute/04_select_25_0.png"
+      },
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pm1 = centerline['pmra']\n",
+    "pm2 = centerline['pmdec']\n",
+    "plt.plot(pm1, pm2, 'ko', markersize=0.3, alpha=0.3)\n",
+    "\n",
+    "pm1 = selected['pmra']\n",
+    "pm2 = selected['pmdec']\n",
+    "plt.plot(pm1, pm2, 'gx', markersize=0.3, alpha=0.3)\n",
+    "\n",
+    "plt.plot(pmra_poly, pmdec_poly)\n",
+    "    \n",
+    "plt.xlabel('Proper motion phi1 (ICRS frame)')\n",
+    "plt.ylabel('Proper motion phi2 (ICRS frame)')\n",
+    "\n",
+    "plt.xlim([-10, 5])\n",
+    "plt.ylim([-20, 5]);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To use `pm_vertices` as part of an ADQL query, we have to convert it to a string.\n",
+    "\n",
+    "We'll use `flatten` to convert from a 2-D array to a 1-D array, and `str` to convert each element to a string."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['-4.050371212154984',\n",
+       " '-14.75623260987968',\n",
+       " '-3.4198108491382455',\n",
+       " '-14.723655456335619',\n",
+       " '-3.035219883740934',\n",
+       " '-14.443571352854612',\n",
+       " '-2.268479190206636',\n",
+       " '-13.714023598831554',\n",
+       " '-2.611722027231764',\n",
+       " '-13.247974712069263',\n",
+       " '-2.7347140078529106',\n",
+       " '-13.090544709622938',\n",
+       " '-3.199231461993783',\n",
+       " '-12.594265302440828',\n",
+       " '-3.34082545787549',\n",
+       " '-12.476119260818695',\n",
+       " '-5.674894125178565',\n",
+       " '-11.160833381392624',\n",
+       " '-5.95159272432137',\n",
+       " '-11.105478836426514',\n",
+       " '-6.423940229776128',\n",
+       " '-11.05981294804957',\n",
+       " '-7.096310230579248',\n",
+       " '-11.951878058650085',\n",
+       " '-7.306415190921692',\n",
+       " '-12.245599765990594',\n",
+       " '-7.040166963232815',\n",
+       " '-12.885807024935527',\n",
+       " '-6.0034770546523735',\n",
+       " '-13.759120984106968',\n",
+       " '-4.42442296194263',\n",
+       " '-14.7464117578883']"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "t = [str(x) for x in pm_vertices.flatten()]\n",
+    "t"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now `t` is a list of strings; we can use `join` to make a single string with commas between the elements."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'-4.050371212154984, -14.75623260987968, -3.4198108491382455, -14.723655456335619, -3.035219883740934, -14.443571352854612, -2.268479190206636, -13.714023598831554, -2.611722027231764, -13.247974712069263, -2.7347140078529106, -13.090544709622938, -3.199231461993783, -12.594265302440828, -3.34082545787549, -12.476119260818695, -5.674894125178565, -11.160833381392624, -5.95159272432137, -11.105478836426514, -6.423940229776128, -11.05981294804957, -7.096310230579248, -11.951878058650085, -7.306415190921692, -12.245599765990594, -7.040166963232815, -12.885807024935527, -6.0034770546523735, -13.759120984106968, -4.42442296194263, -14.7464117578883'"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pm_point_list = ', '.join(t)\n",
+    "pm_point_list"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Selecting the region\n",
+    "\n",
+    "Let's review how we got to this point.\n",
+    "\n",
+    "1. We made an ADQL query to the Gaia server to get data for stars in the vicinity of GD-1.\n",
+    "\n",
+    "2. We transformed to `GD1` coordinates so we could select stars along the centerline of GD-1.\n",
+    "\n",
+    "3. We plotted the proper motion of the centerline stars to identify the bounds of the overdense region.\n",
+    "\n",
+    "4. We made a mask that selects stars whose proper motion is in the overdense region.\n",
+    "\n",
+    "The problem is that we downloaded data for more than 100,000 stars and selected only about 1000 of them.\n",
+    "\n",
+    "It will be more efficient if we select on proper motion as part of the query.  That will allow us to work with a larger region of the sky in a single query, and download less unneeded data.\n",
+    "\n",
+    "This query will select on the following conditions:\n",
+    "\n",
+    "* `parallax < 1`\n",
+    "\n",
+    "* `bp_rp BETWEEN -0.75 AND 2`\n",
+    "\n",
+    "* Coordinates within a rectangle in the GD-1 frame, transformed to ICRS.\n",
+    "\n",
+    "* Proper motion with the polygon we just computed.\n",
+    "\n",
+    "The first three conditions are the same as in the previous query.  Only the last one is new.\n",
+    "\n",
+    "Here's the rectangle in the GD-1 frame we'll select."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "phi1_min = -70\n",
+    "phi1_max = -20\n",
+    "phi2_min = -5\n",
+    "phi2_max = 5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "phi1_rect = [phi1_min, phi1_min, phi1_max, phi1_max] * u.deg\n",
+    "phi2_rect = [phi2_min, phi2_max, phi2_max, phi2_min] * u.deg"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's how we transform it to ICRS, as we saw in the previous lesson."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import gala.coordinates as gc\n",
+    "import astropy.coordinates as coord\n",
+    "\n",
+    "corners = gc.GD1Koposov10(phi1=phi1_rect, phi2=phi2_rect)\n",
+    "corners_icrs = corners.transform_to(coord.ICRS)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To use `corners_icrs` as part of an ADQL query, we have to convert it to a string.  Here's how we do that, as we saw in the previous lesson."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'135.30559858565638, 8.398623940157561, 126.50951508623503, 13.44494195652069, 163.0173655836748, 54.24242734020255, 172.9328536286811, 46.47260492416258'"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "point_base = \"{point.ra.value}, {point.dec.value}\"\n",
+    "\n",
+    "t = [point_base.format(point=point)\n",
+    "     for point in corners_icrs]\n",
+    "\n",
+    "point_list = ', '.join(t)\n",
+    "point_list"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we have everything we need to assemble the query."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Assemble the query\n",
+    "\n",
+    "Here's the base string we used for the query in the previous lesson."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query_base = \"\"\"SELECT \n",
+    "{columns}\n",
+    "FROM gaiadr2.gaia_source\n",
+    "WHERE parallax < 1\n",
+    "  AND bp_rp BETWEEN -0.75 AND 2 \n",
+    "  AND 1 = CONTAINS(POINT(ra, dec), \n",
+    "                   POLYGON({point_list}))\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Modify `query_base` by adding a new clause to select stars whose coordinates of proper motion, `pmra` and `pmdec`, fall within the polygon defined by `pm_point_list`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "query_base = \"\"\"SELECT \n",
+    "{columns}\n",
+    "FROM gaiadr2.gaia_source\n",
+    "WHERE parallax < 1\n",
+    "  AND bp_rp BETWEEN -0.75 AND 2 \n",
+    "  AND 1 = CONTAINS(POINT(ra, dec), \n",
+    "                   POLYGON({point_list}))\n",
+    "  AND 1 = CONTAINS(POINT(pmra, pmdec),\n",
+    "                   POLYGON({pm_point_list}))\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here again are the columns we want to select."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "columns = 'source_id, ra, dec, pmra, pmdec, parallax, parallax_error, radial_velocity'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Use `format` to format `query_base` and define `query`, filling in the values of `columns`, `point_list`, and `pm_point_list`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "SELECT \n",
+      "source_id, ra, dec, pmra, pmdec, parallax, parallax_error, radial_velocity\n",
+      "FROM gaiadr2.gaia_source\n",
+      "WHERE parallax < 1\n",
+      "  AND bp_rp BETWEEN -0.75 AND 2 \n",
+      "  AND 1 = CONTAINS(POINT(ra, dec), \n",
+      "                   POLYGON(135.30559858565638, 8.398623940157561, 126.50951508623503, 13.44494195652069, 163.0173655836748, 54.24242734020255, 172.9328536286811, 46.47260492416258))\n",
+      "  AND 1 = CONTAINS(POINT(pmra, pmdec),\n",
+      "                   POLYGON(-4.050371212154984, -14.75623260987968, -3.4198108491382455, -14.723655456335619, -3.035219883740934, -14.443571352854612, -2.268479190206636, -13.714023598831554, -2.611722027231764, -13.247974712069263, -2.7347140078529106, -13.090544709622938, -3.199231461993783, -12.594265302440828, -3.34082545787549, -12.476119260818695, -5.674894125178565, -11.160833381392624, -5.95159272432137, -11.105478836426514, -6.423940229776128, -11.05981294804957, -7.096310230579248, -11.951878058650085, -7.306415190921692, -12.245599765990594, -7.040166963232815, -12.885807024935527, -6.0034770546523735, -13.759120984106968, -4.42442296194263, -14.7464117578883))\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "query = query_base.format(columns=columns, \n",
+    "                            point_list=point_list,\n",
+    "                            pm_point_list=pm_point_list)\n",
+    "print(query)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's how we run it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Created TAP+ (v1.2.1) - Connection:\n",
+      "\tHost: gea.esac.esa.int\n",
+      "\tUse HTTPS: True\n",
+      "\tPort: 443\n",
+      "\tSSL Port: 443\n",
+      "Created TAP+ (v1.2.1) - Connection:\n",
+      "\tHost: geadata.esac.esa.int\n",
+      "\tUse HTTPS: True\n",
+      "\tPort: 443\n",
+      "\tSSL Port: 443\n",
+      "INFO: Query finished. [astroquery.utils.tap.core]\n",
+      "<Table length=7346>\n",
+      "      name       dtype    unit                              description                             n_bad\n",
+      "--------------- ------- -------- ------------------------------------------------------------------ -----\n",
+      "      source_id   int64          Unique source identifier (unique within a particular Data Release)     0\n",
+      "             ra float64      deg                                                    Right ascension     0\n",
+      "            dec float64      deg                                                        Declination     0\n",
+      "           pmra float64 mas / yr                         Proper motion in right ascension direction     0\n",
+      "          pmdec float64 mas / yr                             Proper motion in declination direction     0\n",
+      "       parallax float64      mas                                                           Parallax     0\n",
+      " parallax_error float64      mas                                         Standard error of parallax     0\n",
+      "radial_velocity float64   km / s                                                    Radial velocity  7295\n",
+      "Jobid: 1603132746237O\n",
+      "Phase: COMPLETED\n",
+      "Owner: None\n",
+      "Output file: async_20201019143906.vot\n",
+      "Results: None\n"
+     ]
+    }
+   ],
+   "source": [
+    "from astroquery.gaia import Gaia\n",
+    "\n",
+    "job = Gaia.launch_job_async(query)\n",
+    "print(job)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And get the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "7346"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "candidate_table = job.get_results()\n",
+    "len(candidate_table)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plotting one more time\n",
+    "\n",
+    "Let's see what the results look like."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "filenames": {
+       "image/png": "/home/downey/AstronomicalData/_build/jupyter_execute/04_select_51_0.png"
+      },
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x = candidate_table['ra']\n",
+    "y = candidate_table['dec']\n",
+    "plt.plot(x, y, 'ko', markersize=0.3, alpha=0.3)\n",
+    "\n",
+    "plt.xlabel('ra (degree ICRS)')\n",
+    "plt.ylabel('dec (degree ICRS)');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here we can see why it was useful to transform these coordinates.  In ICRS, it is more difficult to identity the stars near the centerline of GD-1.\n",
+    "\n",
+    "So, before we move on to the next step, let's collect the code we used to transform the coordinates and make a Pandas `DataFrame`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pyia import GaiaData\n",
+    "\n",
+    "def make_dataframe(table):\n",
+    "    \"\"\"Transform coordinates from ICRS to GD-1 frame.\n",
+    "    \n",
+    "    table: Astropy Table\n",
+    "    \n",
+    "    returns: Pandas DataFrame\n",
+    "    \"\"\"\n",
+    "    gaia_data = GaiaData(table)\n",
+    "\n",
+    "    c_sky = gaia_data.get_skycoord(distance=8*u.kpc, \n",
+    "                                   radial_velocity=0*u.km/u.s)\n",
+    "    c_gd1 = gc.reflex_correct(\n",
+    "                c_sky.transform_to(gc.GD1Koposov10))\n",
+    "\n",
+    "    df = table.to_pandas()\n",
+    "    df['phi1'] = c_gd1.phi1\n",
+    "    df['phi2'] = c_gd1.phi2\n",
+    "    df['pm_phi1'] = c_gd1.pm_phi1_cosphi2\n",
+    "    df['pm_phi2'] = c_gd1.pm_phi2\n",
+    "    return df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's how we can use this function:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "candidate_df = make_dataframe(candidate_table)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And let's see the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 648x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "filenames": {
+       "image/png": "/home/downey/AstronomicalData/_build/jupyter_execute/04_select_57_0.png"
+      },
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x = candidate_df['phi1']\n",
+    "y = candidate_df['phi2']\n",
+    "\n",
+    "plt.plot(x, y, 'ko', markersize=0.5, alpha=0.5)\n",
+    "\n",
+    "plt.xlabel('ra (degree GD1)')\n",
+    "plt.ylabel('dec (degree GD1)');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We're starting to see GD-1 more clearly.\n",
+    "\n",
+    "We can compare this figure with one of these panels in Figure 1 from the original paper:\n",
+    "\n",
+    "<img height=\"150\" src=\"https://github.com/datacarpentry/astronomy-python/raw/gh-pages/fig/gd1-2.png\">\n",
+    "\n",
+    "<img height=\"150\" src=\"https://github.com/datacarpentry/astronomy-python/raw/gh-pages/fig/gd1-4.png\">\n",
+    "\n",
+    "The top panel shows stars selected based on proper motion only, so it is comparable to our figure (although notice that it covers a wider region).\n",
+    "\n",
+    "In the next lesson, we will use photometry data from Pan-STARRS to do a second round of filtering, and see if we can replicate the bottom panel.\n",
+    "\n",
+    "We'll also learn how to add annotations like the ones in the figure from the paper, and customize the style of the figure to present the results clearly and compellingly."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Saving the DataFrame\n",
+    "\n",
+    "Let's save this `DataFrame` so we can pick up where we left off without running this query again."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!rm -f gd1_candidates.hdf5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "filename = 'gd1_candidates.hdf5'\n",
+    "\n",
+    "candidate_df.to_hdf(filename, 'candidate_df')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can use `ls` to confirm that the file exists and check the size:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-rw-rw-r-- 1 downey downey 756K Oct 19 14:39 gd1_candidates.hdf5\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!ls -lh gd1_candidates.hdf5"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you are using Windows, `ls` might not work; in that case, try:\n",
+    "\n",
+    "```\n",
+    "!dir gd1_candidates.hdf5\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## CSV\n",
+    "\n",
+    "Pandas can write a variety of other formats, [which you can read about here](https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html).\n",
+    "\n",
+    "We won't cover all of them, but one other important one is [CSV](https://en.wikipedia.org/wiki/Comma-separated_values), which stands for \"comma-separated values\".\n",
+    "\n",
+    "CSV is a plain-text format with minimal formatting requirements, so it can be read and written by pretty much any tool that works with data.  In that sense, it is the \"least common denominator\" of data formats.\n",
+    "\n",
+    "However, it has an important limitation: some information about the data gets lost in translation, notably the data types.  If you read a CSV file from someone else, you might need some additional information to make sure you are getting it right.\n",
+    "\n",
+    "Also, CSV files tend to be big, and slow to read and write.\n",
+    "\n",
+    "With those caveats, here's how to write one:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "candidate_df.to_csv('gd1_candidates.csv')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can check the file size like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-rw-rw-r-- 1 downey downey 1.6M Oct 19 14:39 gd1_candidates.csv\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!ls -lh gd1_candidates.csv"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The CSV file about 2 times bigger than the HDF5 file (so that's not that bad, really).\n",
+    "\n",
+    "We can see the first few lines like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      ",source_id,ra,dec,pmra,pmdec,parallax,parallax_error,radial_velocity,phi1,phi2,pm_phi1,pm_phi2\r\n",
+      "0,635559124339440000,137.58671691646745,19.1965441084838,-3.770521900009566,-12.490481778113859,0.7913934419894347,0.2717538145759051,,-59.63048941944396,-1.21648525150429,-7.361362712556612,-0.5926328820420083\r\n",
+      "1,635860218726658176,138.5187065217173,19.09233926905897,-5.941679495793577,-11.346409129876392,0.30745551377348623,0.19946557779138105,,-59.247329893833296,-2.0160784008206476,-7.527126084599517,1.7487794924398758\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!head -3 gd1_candidates.csv"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The CSV file contains the names of the columns, but not the data types.\n",
+    "\n",
+    "We can read the CSV file back like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "read_back_csv = pd.read_csv('gd1_candidates.csv')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's compare the first few rows of `candidate_df` and `read_back_csv`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>source_id</th>\n",
+       "      <th>ra</th>\n",
+       "      <th>dec</th>\n",
+       "      <th>pmra</th>\n",
+       "      <th>pmdec</th>\n",
+       "      <th>parallax</th>\n",
+       "      <th>parallax_error</th>\n",
+       "      <th>radial_velocity</th>\n",
+       "      <th>phi1</th>\n",
+       "      <th>phi2</th>\n",
+       "      <th>pm_phi1</th>\n",
+       "      <th>pm_phi2</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>635559124339440000</td>\n",
+       "      <td>137.586717</td>\n",
+       "      <td>19.196544</td>\n",
+       "      <td>-3.770522</td>\n",
+       "      <td>-12.490482</td>\n",
+       "      <td>0.791393</td>\n",
+       "      <td>0.271754</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-59.630489</td>\n",
+       "      <td>-1.216485</td>\n",
+       "      <td>-7.361363</td>\n",
+       "      <td>-0.592633</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>635860218726658176</td>\n",
+       "      <td>138.518707</td>\n",
+       "      <td>19.092339</td>\n",
+       "      <td>-5.941679</td>\n",
+       "      <td>-11.346409</td>\n",
+       "      <td>0.307456</td>\n",
+       "      <td>0.199466</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-59.247330</td>\n",
+       "      <td>-2.016078</td>\n",
+       "      <td>-7.527126</td>\n",
+       "      <td>1.748779</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>635674126383965568</td>\n",
+       "      <td>138.842874</td>\n",
+       "      <td>19.031798</td>\n",
+       "      <td>-3.897001</td>\n",
+       "      <td>-12.702780</td>\n",
+       "      <td>0.779463</td>\n",
+       "      <td>0.223692</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-59.133391</td>\n",
+       "      <td>-2.306901</td>\n",
+       "      <td>-7.560608</td>\n",
+       "      <td>-0.741800</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            source_id          ra        dec      pmra      pmdec  parallax  \\\n",
+       "0  635559124339440000  137.586717  19.196544 -3.770522 -12.490482  0.791393   \n",
+       "1  635860218726658176  138.518707  19.092339 -5.941679 -11.346409  0.307456   \n",
+       "2  635674126383965568  138.842874  19.031798 -3.897001 -12.702780  0.779463   \n",
+       "\n",
+       "   parallax_error  radial_velocity       phi1      phi2   pm_phi1   pm_phi2  \n",
+       "0        0.271754              NaN -59.630489 -1.216485 -7.361363 -0.592633  \n",
+       "1        0.199466              NaN -59.247330 -2.016078 -7.527126  1.748779  \n",
+       "2        0.223692              NaN -59.133391 -2.306901 -7.560608 -0.741800  "
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "candidate_df.head(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Unnamed: 0</th>\n",
+       "      <th>source_id</th>\n",
+       "      <th>ra</th>\n",
+       "      <th>dec</th>\n",
+       "      <th>pmra</th>\n",
+       "      <th>pmdec</th>\n",
+       "      <th>parallax</th>\n",
+       "      <th>parallax_error</th>\n",
+       "      <th>radial_velocity</th>\n",
+       "      <th>phi1</th>\n",
+       "      <th>phi2</th>\n",
+       "      <th>pm_phi1</th>\n",
+       "      <th>pm_phi2</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>635559124339440000</td>\n",
+       "      <td>137.586717</td>\n",
+       "      <td>19.196544</td>\n",
+       "      <td>-3.770522</td>\n",
+       "      <td>-12.490482</td>\n",
+       "      <td>0.791393</td>\n",
+       "      <td>0.271754</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-59.630489</td>\n",
+       "      <td>-1.216485</td>\n",
+       "      <td>-7.361363</td>\n",
+       "      <td>-0.592633</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>635860218726658176</td>\n",
+       "      <td>138.518707</td>\n",
+       "      <td>19.092339</td>\n",
+       "      <td>-5.941679</td>\n",
+       "      <td>-11.346409</td>\n",
+       "      <td>0.307456</td>\n",
+       "      <td>0.199466</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-59.247330</td>\n",
+       "      <td>-2.016078</td>\n",
+       "      <td>-7.527126</td>\n",
+       "      <td>1.748779</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>635674126383965568</td>\n",
+       "      <td>138.842874</td>\n",
+       "      <td>19.031798</td>\n",
+       "      <td>-3.897001</td>\n",
+       "      <td>-12.702780</td>\n",
+       "      <td>0.779463</td>\n",
+       "      <td>0.223692</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-59.133391</td>\n",
+       "      <td>-2.306901</td>\n",
+       "      <td>-7.560608</td>\n",
+       "      <td>-0.741800</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Unnamed: 0           source_id          ra        dec      pmra      pmdec  \\\n",
+       "0           0  635559124339440000  137.586717  19.196544 -3.770522 -12.490482   \n",
+       "1           1  635860218726658176  138.518707  19.092339 -5.941679 -11.346409   \n",
+       "2           2  635674126383965568  138.842874  19.031798 -3.897001 -12.702780   \n",
+       "\n",
+       "   parallax  parallax_error  radial_velocity       phi1      phi2   pm_phi1  \\\n",
+       "0  0.791393        0.271754              NaN -59.630489 -1.216485 -7.361363   \n",
+       "1  0.307456        0.199466              NaN -59.247330 -2.016078 -7.527126   \n",
+       "2  0.779463        0.223692              NaN -59.133391 -2.306901 -7.560608   \n",
+       "\n",
+       "    pm_phi2  \n",
+       "0 -0.592633  \n",
+       "1  1.748779  \n",
+       "2 -0.741800  "
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "read_back_csv.head(3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notice that the index in `candidate_df` has become an unnamed column in `read_back_csv`.  The Pandas functions for writing and reading CSV files provide options to avoid that problem, but this is an example of the kind of thing that can go wrong with CSV files."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "In the previous lesson we downloaded data for a large number of stars and then selected a small fraction of them based on proper motion.\n",
+    "\n",
+    "In this lesson, we improved this process by writing a more complex query that uses the database to select stars based on proper motion.  This process requires more computation on the Gaia server, but then we're able to either:\n",
+    "\n",
+    "1. Search the same region and download less data, or\n",
+    "\n",
+    "2. Search a larger region while still downloading a manageable amount of data.\n",
+    "\n",
+    "In the next lesson, we'll learn about the databased `JOIN` operation and use it to download photometry data from Pan-STARRS."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Best practices\n",
+    "\n",
+    "* When possible, \"move the computation to the data\"; that is, do as much of the work as possible on the database server before downloading the data.\n",
+    "\n",
+    "* For most applications, saving data in FITS or HDF5 is better than CSV.  FITS and HDF5 are binary formats, so the files are usually smaller, and they store metadata, so you don't lose anything when you read the file back.\n",
+    "\n",
+    "* On the other hand, CSV is a \"least common denominator\" format; that is, it can be read by practically any application that works with data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
\ No newline at end of file
diff --git a/_build/jupyter_execute/04_select.py b/_build/jupyter_execute/04_select.py
new file mode 100644
index 0000000..80e3271
--- /dev/null
+++ b/_build/jupyter_execute/04_select.py
@@ -0,0 +1,611 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# # Chapter 4
+# 
+# This is the fourth in a series of notebooks related to astronomy data.
+# 
+# As a running example, we are replicating parts of the analysis in a recent paper, "[Off the beaten path: Gaia reveals GD-1 stars outside of the main stream](https://arxiv.org/abs/1805.00425)" by Adrian M. Price-Whelan and Ana Bonaca.
+# 
+# In the first lesson, we wrote ADQL queries and used them to select and download data from the Gaia server.
+# 
+# In the second lesson, we write a query to select stars from the region of the sky where we expect GD-1 to be, and save the results in a FITS file.
+# 
+# In the third lesson, we read that data back and identified stars with the proper motion we expect for GD-1.
+
+# ## Outline
+# 
+# Here are the steps in this lesson:
+# 
+# 1. Using data from the previous lesson, we'll identify the values of proper motion for stars likely to be in GD-1.
+# 
+# 2. Then we'll compose an ADQL query that selects stars based on proper motion, so we can download only the data we need.
+# 
+# 3. We'll also see how to write the results to a CSV file.
+# 
+# That will make it possible to search a bigger region of the sky in a single query.
+# 
+# After completing this lesson, you should be able to
+# 
+# * Convert proper motion between frames.
+# 
+# * Write an ADQL query that selects based on proper motion.
+
+# ## Installing libraries
+# 
+# If you are running this notebook on Colab, you can run the following cell to install Astroquery and a the other libraries we'll use.
+# 
+# If you are running this notebook on your own computer, you might have to install these libraries yourself.  
+# 
+# If you are using this notebook as part of a Carpentries workshop, you should have received setup instructions.
+# 
+# TODO: Add a link to the instructions.
+# 
+
+# In[1]:
+
+
+# If we're running on Colab, install libraries
+
+import sys
+IN_COLAB = 'google.colab' in sys.modules
+
+if IN_COLAB:
+    get_ipython().system('pip install astroquery astro-gala pyia python-wget')
+
+
+# ## Reload the data
+# 
+# The following cells download the data from the previous lesson, if necessary, and load it into a Pandas `DataFrame`.
+
+# In[2]:
+
+
+import os
+from wget import download
+
+filename = 'gd1_dataframe.hdf5'
+path = 'https://github.com/AllenDowney/AstronomicalData/raw/main/data/'
+
+if not os.path.exists(filename):
+    print(download(path+filename))
+
+
+# In[3]:
+
+
+import pandas as pd
+
+df = pd.read_hdf(filename, 'df')
+centerline = pd.read_hdf(filename, 'centerline')
+selected = pd.read_hdf(filename, 'selected')
+
+
+# ## Selection by proper motion
+# 
+# At this point we have downloaded data for a relatively large number of stars (more than 100,000) and selected a relatively small number (around 1000).
+# 
+# It would be more efficient to use ADQL to select only the stars we need.  That would also make it possible to download data covering a larger region of the sky.
+# 
+# However, the selection we did was based on proper motion in the `GD1Koposov10` frame.  In order to do the same selection in ADQL, we have to work with proper motions in ICRS.
+# 
+# As a reminder, here's the rectangle we selected based on proper motion in the `GD1Koposov10` frame.
+
+# In[4]:
+
+
+pm1_min = -8.9
+pm1_max = -6.9
+pm2_min = -2.2
+pm2_max =  1.0
+
+
+# In[5]:
+
+
+import astropy.units as u
+
+pm1_rect = [pm1_min, pm1_min, pm1_max, pm1_max, pm1_min] * u.mas/u.yr
+pm2_rect = [pm2_min, pm2_max, pm2_max, pm2_min, pm2_min] * u.mas/u.yr
+
+
+# The following figure shows:
+# 
+# * Proper motion for the stars we selected along the center line of GD-1,
+# 
+# * The rectangle we selected, and
+# 
+# * The stars inside the rectangle highlighted in green.
+
+# In[6]:
+
+
+import matplotlib.pyplot as plt
+
+pm1 = centerline['pm_phi1']
+pm2 = centerline['pm_phi2']
+plt.plot(pm1, pm2, 'ko', markersize=0.3, alpha=0.3)
+
+pm1 = selected['pm_phi1']
+pm2 = selected['pm_phi2']
+plt.plot(pm1, pm2, 'gx', markersize=0.3, alpha=0.3)
+
+plt.plot(pm1_rect, pm2_rect, '-')
+    
+plt.xlabel('Proper motion phi1 (GD1 frame)')
+plt.ylabel('Proper motion phi2 (GD1 frame)')
+
+plt.xlim(-12, 8)
+plt.ylim(-10, 10);
+
+
+# Now we'll make the same plot using proper motions in the ICRS frame, which are stored in columns `pmra` and `pmdec`.
+
+# In[7]:
+
+
+pm1 = centerline['pmra']
+pm2 = centerline['pmdec']
+plt.plot(pm1, pm2, 'ko', markersize=0.3, alpha=0.3)
+
+pm1 = selected['pmra']
+pm2 = selected['pmdec']
+plt.plot(pm1, pm2, 'gx', markersize=1, alpha=0.3)
+    
+plt.xlabel('Proper motion phi1 (ICRS frame)')
+plt.ylabel('Proper motion phi2 (ICRS frame)')
+
+plt.xlim([-10, 5])
+plt.ylim([-20, 5]);
+
+
+# The proper motions of the selected stars are more spread out in this frame, which is why it was preferable to do the selection in the GD-1 frame.
+# 
+# But now we can define a polygon that encloses the proper motions of these stars in ICRS, 
+# and use the polygon as a selection criterion in an ADQL query.
+# 
+# SciPy provides a function that computes the [convex hull](https://en.wikipedia.org/wiki/Convex_hull) of a set of points, which is the smallest convex polygon that contains all of the points.
+# 
+# To use it, I'll select columns `pmra` and `pmdec` and convert them to a NumPy array.
+
+# In[8]:
+
+
+import numpy as np
+
+points = selected[['pmra','pmdec']].to_numpy()
+points.shape
+
+
+# We'll pass the points to `ConvexHull`, which returns an object that contains the results. 
+
+# In[9]:
+
+
+from scipy.spatial import ConvexHull
+
+hull = ConvexHull(points)
+hull
+
+
+# `hull.vertices` contains the indices of the points that fall on the perimeter of the hull.
+
+# In[10]:
+
+
+hull.vertices
+
+
+# We can use them as an index into the original array to select the corresponding rows.
+
+# In[11]:
+
+
+pm_vertices = points[hull.vertices]
+pm_vertices
+
+
+# To plot the resulting polygon, we have to pull out the x and y coordinates.
+
+# In[12]:
+
+
+pmra_poly, pmdec_poly = np.transpose(pm_vertices)
+
+
+# The following figure shows proper motion in ICRS again, along with the convex hull we just computed.
+
+# In[13]:
+
+
+pm1 = centerline['pmra']
+pm2 = centerline['pmdec']
+plt.plot(pm1, pm2, 'ko', markersize=0.3, alpha=0.3)
+
+pm1 = selected['pmra']
+pm2 = selected['pmdec']
+plt.plot(pm1, pm2, 'gx', markersize=0.3, alpha=0.3)
+
+plt.plot(pmra_poly, pmdec_poly)
+    
+plt.xlabel('Proper motion phi1 (ICRS frame)')
+plt.ylabel('Proper motion phi2 (ICRS frame)')
+
+plt.xlim([-10, 5])
+plt.ylim([-20, 5]);
+
+
+# To use `pm_vertices` as part of an ADQL query, we have to convert it to a string.
+# 
+# We'll use `flatten` to convert from a 2-D array to a 1-D array, and `str` to convert each element to a string.
+
+# In[14]:
+
+
+t = [str(x) for x in pm_vertices.flatten()]
+t
+
+
+# Now `t` is a list of strings; we can use `join` to make a single string with commas between the elements.
+
+# In[15]:
+
+
+pm_point_list = ', '.join(t)
+pm_point_list
+
+
+# ## Selecting the region
+# 
+# Let's review how we got to this point.
+# 
+# 1. We made an ADQL query to the Gaia server to get data for stars in the vicinity of GD-1.
+# 
+# 2. We transformed to `GD1` coordinates so we could select stars along the centerline of GD-1.
+# 
+# 3. We plotted the proper motion of the centerline stars to identify the bounds of the overdense region.
+# 
+# 4. We made a mask that selects stars whose proper motion is in the overdense region.
+# 
+# The problem is that we downloaded data for more than 100,000 stars and selected only about 1000 of them.
+# 
+# It will be more efficient if we select on proper motion as part of the query.  That will allow us to work with a larger region of the sky in a single query, and download less unneeded data.
+# 
+# This query will select on the following conditions:
+# 
+# * `parallax < 1`
+# 
+# * `bp_rp BETWEEN -0.75 AND 2`
+# 
+# * Coordinates within a rectangle in the GD-1 frame, transformed to ICRS.
+# 
+# * Proper motion with the polygon we just computed.
+# 
+# The first three conditions are the same as in the previous query.  Only the last one is new.
+# 
+# Here's the rectangle in the GD-1 frame we'll select.
+
+# In[16]:
+
+
+phi1_min = -70
+phi1_max = -20
+phi2_min = -5
+phi2_max = 5
+
+
+# In[17]:
+
+
+phi1_rect = [phi1_min, phi1_min, phi1_max, phi1_max] * u.deg
+phi2_rect = [phi2_min, phi2_max, phi2_max, phi2_min] * u.deg
+
+
+# Here's how we transform it to ICRS, as we saw in the previous lesson.
+
+# In[18]:
+
+
+import gala.coordinates as gc
+import astropy.coordinates as coord
+
+corners = gc.GD1Koposov10(phi1=phi1_rect, phi2=phi2_rect)
+corners_icrs = corners.transform_to(coord.ICRS)
+
+
+# To use `corners_icrs` as part of an ADQL query, we have to convert it to a string.  Here's how we do that, as we saw in the previous lesson.
+
+# In[19]:
+
+
+point_base = "{point.ra.value}, {point.dec.value}"
+
+t = [point_base.format(point=point)
+     for point in corners_icrs]
+
+point_list = ', '.join(t)
+point_list
+
+
+# Now we have everything we need to assemble the query.
+
+# ## Assemble the query
+# 
+# Here's the base string we used for the query in the previous lesson.
+
+# In[20]:
+
+
+query_base = """SELECT 
+{columns}
+FROM gaiadr2.gaia_source
+WHERE parallax < 1
+  AND bp_rp BETWEEN -0.75 AND 2 
+  AND 1 = CONTAINS(POINT(ra, dec), 
+                   POLYGON({point_list}))
+"""
+
+
+# **Exercise:** Modify `query_base` by adding a new clause to select stars whose coordinates of proper motion, `pmra` and `pmdec`, fall within the polygon defined by `pm_point_list`.
+
+# In[21]:
+
+
+# Solution
+
+query_base = """SELECT 
+{columns}
+FROM gaiadr2.gaia_source
+WHERE parallax < 1
+  AND bp_rp BETWEEN -0.75 AND 2 
+  AND 1 = CONTAINS(POINT(ra, dec), 
+                   POLYGON({point_list}))
+  AND 1 = CONTAINS(POINT(pmra, pmdec),
+                   POLYGON({pm_point_list}))
+"""
+
+
+# Here again are the columns we want to select.
+
+# In[22]:
+
+
+columns = 'source_id, ra, dec, pmra, pmdec, parallax, parallax_error, radial_velocity'
+
+
+# **Exercise:** Use `format` to format `query_base` and define `query`, filling in the values of `columns`, `point_list`, and `pm_point_list`.
+
+# In[23]:
+
+
+# Solution
+
+query = query_base.format(columns=columns, 
+                            point_list=point_list,
+                            pm_point_list=pm_point_list)
+print(query)
+
+
+# Here's how we run it.
+
+# In[24]:
+
+
+from astroquery.gaia import Gaia
+
+job = Gaia.launch_job_async(query)
+print(job)
+
+
+# And get the results.
+
+# In[25]:
+
+
+candidate_table = job.get_results()
+len(candidate_table)
+
+
+# ## Plotting one more time
+# 
+# Let's see what the results look like.
+
+# In[26]:
+
+
+x = candidate_table['ra']
+y = candidate_table['dec']
+plt.plot(x, y, 'ko', markersize=0.3, alpha=0.3)
+
+plt.xlabel('ra (degree ICRS)')
+plt.ylabel('dec (degree ICRS)');
+
+
+# Here we can see why it was useful to transform these coordinates.  In ICRS, it is more difficult to identity the stars near the centerline of GD-1.
+# 
+# So, before we move on to the next step, let's collect the code we used to transform the coordinates and make a Pandas `DataFrame`:
+
+# In[27]:
+
+
+from pyia import GaiaData
+
+def make_dataframe(table):
+    """Transform coordinates from ICRS to GD-1 frame.
+    
+    table: Astropy Table
+    
+    returns: Pandas DataFrame
+    """
+    gaia_data = GaiaData(table)
+
+    c_sky = gaia_data.get_skycoord(distance=8*u.kpc, 
+                                   radial_velocity=0*u.km/u.s)
+    c_gd1 = gc.reflex_correct(
+                c_sky.transform_to(gc.GD1Koposov10))
+
+    df = table.to_pandas()
+    df['phi1'] = c_gd1.phi1
+    df['phi2'] = c_gd1.phi2
+    df['pm_phi1'] = c_gd1.pm_phi1_cosphi2
+    df['pm_phi2'] = c_gd1.pm_phi2
+    return df
+
+
+# Here's how we can use this function:
+
+# In[28]:
+
+
+candidate_df = make_dataframe(candidate_table)
+
+
+# And let's see the results.
+
+# In[44]:
+
+
+x = candidate_df['phi1']
+y = candidate_df['phi2']
+
+plt.plot(x, y, 'ko', markersize=0.5, alpha=0.5)
+
+plt.xlabel('ra (degree GD1)')
+plt.ylabel('dec (degree GD1)');
+
+
+# We're starting to see GD-1 more clearly.
+# 
+# We can compare this figure with one of these panels in Figure 1 from the original paper:
+# 
+# <img height="150" src="https://github.com/datacarpentry/astronomy-python/raw/gh-pages/fig/gd1-2.png">
+# 
+# <img height="150" src="https://github.com/datacarpentry/astronomy-python/raw/gh-pages/fig/gd1-4.png">
+# 
+# The top panel shows stars selected based on proper motion only, so it is comparable to our figure (although notice that it covers a wider region).
+# 
+# In the next lesson, we will use photometry data from Pan-STARRS to do a second round of filtering, and see if we can replicate the bottom panel.
+# 
+# We'll also learn how to add annotations like the ones in the figure from the paper, and customize the style of the figure to present the results clearly and compellingly.
+
+# ## Saving the DataFrame
+# 
+# Let's save this `DataFrame` so we can pick up where we left off without running this query again.
+
+# In[30]:
+
+
+get_ipython().system('rm -f gd1_candidates.hdf5')
+
+
+# In[31]:
+
+
+filename = 'gd1_candidates.hdf5'
+
+candidate_df.to_hdf(filename, 'candidate_df')
+
+
+# We can use `ls` to confirm that the file exists and check the size:
+
+# In[32]:
+
+
+get_ipython().system('ls -lh gd1_candidates.hdf5')
+
+
+# If you are using Windows, `ls` might not work; in that case, try:
+# 
+# ```
+# !dir gd1_candidates.hdf5
+# ```
+
+# ## CSV
+# 
+# Pandas can write a variety of other formats, [which you can read about here](https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html).
+# 
+# We won't cover all of them, but one other important one is [CSV](https://en.wikipedia.org/wiki/Comma-separated_values), which stands for "comma-separated values".
+# 
+# CSV is a plain-text format with minimal formatting requirements, so it can be read and written by pretty much any tool that works with data.  In that sense, it is the "least common denominator" of data formats.
+# 
+# However, it has an important limitation: some information about the data gets lost in translation, notably the data types.  If you read a CSV file from someone else, you might need some additional information to make sure you are getting it right.
+# 
+# Also, CSV files tend to be big, and slow to read and write.
+# 
+# With those caveats, here's how to write one:
+
+# In[33]:
+
+
+candidate_df.to_csv('gd1_candidates.csv')
+
+
+# We can check the file size like this:
+
+# In[34]:
+
+
+get_ipython().system('ls -lh gd1_candidates.csv')
+
+
+# The CSV file about 2 times bigger than the HDF5 file (so that's not that bad, really).
+# 
+# We can see the first few lines like this:
+
+# In[35]:
+
+
+get_ipython().system('head -3 gd1_candidates.csv')
+
+
+# The CSV file contains the names of the columns, but not the data types.
+# 
+# We can read the CSV file back like this:
+
+# In[36]:
+
+
+read_back_csv = pd.read_csv('gd1_candidates.csv')
+
+
+# Let's compare the first few rows of `candidate_df` and `read_back_csv`
+
+# In[37]:
+
+
+candidate_df.head(3)
+
+
+# In[38]:
+
+
+read_back_csv.head(3)
+
+
+# Notice that the index in `candidate_df` has become an unnamed column in `read_back_csv`.  The Pandas functions for writing and reading CSV files provide options to avoid that problem, but this is an example of the kind of thing that can go wrong with CSV files.
+
+# ## Summary
+# 
+# In the previous lesson we downloaded data for a large number of stars and then selected a small fraction of them based on proper motion.
+# 
+# In this lesson, we improved this process by writing a more complex query that uses the database to select stars based on proper motion.  This process requires more computation on the Gaia server, but then we're able to either:
+# 
+# 1. Search the same region and download less data, or
+# 
+# 2. Search a larger region while still downloading a manageable amount of data.
+# 
+# In the next lesson, we'll learn about the databased `JOIN` operation and use it to download photometry data from Pan-STARRS.
+
+# ## Best practices
+# 
+# * When possible, "move the computation to the data"; that is, do as much of the work as possible on the database server before downloading the data.
+# 
+# * For most applications, saving data in FITS or HDF5 is better than CSV.  FITS and HDF5 are binary formats, so the files are usually smaller, and they store metadata, so you don't lose anything when you read the file back.
+# 
+# * On the other hand, CSV is a "least common denominator" format; that is, it can be read by practically any application that works with data.
+
+# In[ ]:
+
+
+
+
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diff --git a/_build/jupyter_execute/05_join.ipynb b/_build/jupyter_execute/05_join.ipynb
new file mode 100644
index 0000000..b98b274
--- /dev/null
+++ b/_build/jupyter_execute/05_join.ipynb
@@ -0,0 +1,1304 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Chapter 5\n",
+    "\n",
+    "This is the fifth in a series of notebooks related to astronomy data.\n",
+    "\n",
+    "As a continuing example, we will replicate part of the analysis in a recent paper, \"[Off the beaten path: Gaia reveals GD-1 stars outside of the main stream](https://arxiv.org/abs/1805.00425)\" by Adrian M. Price-Whelan and Ana Bonaca.\n",
+    "\n",
+    "Picking up where we left off, the next step in the analysis is to select candidate stars based on photometry.  The following figure from the paper is a color-magnitude diagram for the stars selected based on proper motion:\n",
+    "\n",
+    "<img width=\"300\" src=\"https://github.com/datacarpentry/astronomy-python/raw/gh-pages/fig/gd1-3.png\">\n",
+    "\n",
+    "In red is a theoretical isochrone, showing where we expect the stars in GD-1 to fall based on the metallicity and age of their original globular cluster. \n",
+    "\n",
+    "By selecting stars in the shaded area, we can further distinguish the main sequence of GD-1 from younger background stars."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Outline\n",
+    "\n",
+    "Here are the steps in this notebook:\n",
+    "\n",
+    "1. We'll reload the candidate stars we identified in the previous notebook.\n",
+    "\n",
+    "2. Then we'll run a query on the Gaia server that uploads the table of candidates and uses a `JOIN` operation to select photometry data for the candidate stars.\n",
+    "\n",
+    "3. We'll write the results to a file for use in the next notebook.\n",
+    "\n",
+    "After completing this lesson, you should be able to\n",
+    "\n",
+    "* Upload a table to the Gaia server.\n",
+    "\n",
+    "* Write ADQL queries involving `JOIN` operations."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Installing libraries\n",
+    "\n",
+    "If you are running this notebook on Colab, you can run the following cell to install Astroquery and a the other libraries we'll use.\n",
+    "\n",
+    "If you are running this notebook on your own computer, you might have to install these libraries yourself.  \n",
+    "\n",
+    "If you are using this notebook as part of a Carpentries workshop, you should have received setup instructions.\n",
+    "\n",
+    "TODO: Add a link to the instructions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# If we're running on Colab, install libraries\n",
+    "\n",
+    "import sys\n",
+    "IN_COLAB = 'google.colab' in sys.modules\n",
+    "\n",
+    "if IN_COLAB:\n",
+    "    !pip install astroquery astro-gala pyia python-wget"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Reloading the data\n",
+    "\n",
+    "The following cell downloads the data from the previous notebook."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from wget import download\n",
+    "\n",
+    "filename = 'gd1_candidates.hdf5'\n",
+    "path = 'https://github.com/AllenDowney/AstronomicalData/raw/main/data/'\n",
+    "\n",
+    "if not os.path.exists(filename):\n",
+    "    print(download(path+filename))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And we can read it back."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "candidate_df = pd.read_hdf(filename, 'candidate_df')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`candidate_df` is the Pandas DataFrame that contains results from the query in the previous notebook, which selects stars likely to be in GD-1 based on proper motion.  It also includes position and proper motion transformed to the ICRS frame."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "filenames": {
+       "image/png": "/home/downey/AstronomicalData/_build/jupyter_execute/05_join_9_0.png"
+      },
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "x = candidate_df['phi1']\n",
+    "y = candidate_df['phi2']\n",
+    "\n",
+    "plt.plot(x, y, 'ko', markersize=0.3, alpha=0.3)\n",
+    "\n",
+    "plt.xlabel('ra (degree GD1)')\n",
+    "plt.ylabel('dec (degree GD1)');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is the same figure we saw at the end of the previous notebook.  GD-1 is visible against the background stars, but we will be able to see it more clearly after selecting based on photometry data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Getting photometry data\n",
+    "\n",
+    "The Gaia dataset contains some photometry data, including the variable `bp_rp`, which we used in the original query to select stars with BP - RP color between -0.75 and 2.\n",
+    "\n",
+    "Selecting stars with `bp-rp` less than 2 excludes many class M dwarf stars, which are low temperature, low luminosity.  A star like that at GD-1's distance would be hard to detect, so if it is detected, it it more likely to be in the foreground.\n",
+    "\n",
+    "Now, to select stars with the age and metal richness we expect in GD-1, we will use `g - i` color and apparent `g`-band magnitude, which are available from the Pan-STARRS survey."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Conveniently, the Gaia server provides data from Pan-STARRS as a table in the same database we have been using, so we can access it by making ADQL queries.\n",
+    "\n",
+    "In general, looking up a star from the Gaia catalog and finding the corresponding star in the Pan-STARRS catalog is not easy.  This kind of cross matching is not always possible, because a star might appear in one catalog and not the other.  And even when both stars are present, there might not be a clear one-to-one relationship between stars in the two catalogs.\n",
+    "\n",
+    "Fortunately, smart people have worked on this problem, and the Gaia database includes cross-matching tables that suggest a best neighbor in the Pan-STARRS catalog for many stars in the Gaia catalog.\n",
+    "\n",
+    "[This document describes the cross matching process](https://gea.esac.esa.int/archive/documentation/GDR2/Catalogue_consolidation/chap_cu9val_cu9val/ssec_cu9xma/sssec_cu9xma_extcat.html).  Briefly, it uses a cone search to find possible matches in approximately the right position, then uses attributes like color and magnitude to choose pairs of stars most likely to be identical."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "So the hard part of cross-matching has been done for us.  However, using the results is a little tricky.\n",
+    "\n",
+    "But, it is also an opportunity to learn about one of the most important tools for working with databases: \"joining\" tables.\n",
+    "\n",
+    "In general, a \"join\" is an operation where you match up records from one table with records from another table using as a \"key\" a piece of information that is common to both tables, usually some kind of ID code.\n",
+    "\n",
+    "In this example:\n",
+    "\n",
+    "* Stars in the Gaia dataset are identified by `source_id`.\n",
+    "\n",
+    "* Stars in the Pan-STARRS dataset are identified by `obj_id`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For each candidate star we have selected so far, we have the `source_id`; the goal is to find the `obj_id` for the same star (we hope) in the Pan-STARRS catalog.\n",
+    "\n",
+    "To do that we will:\n",
+    "\n",
+    "1. Make a table that contains the `source_id` for each candidate star and upload the table to the Gaia server;\n",
+    "\n",
+    "2. Use the `JOIN` operator to look up each `source_id` in the `gaiadr2.panstarrs1_best_neighbour` table, which contains the `obj_id` of the best match for each star in the Gaia catalog; then\n",
+    "\n",
+    "3. Use the `JOIN` operator again to look up each `obj_id` in the `panstarrs1_original_valid` table, which contains the Pan-STARRS photometry data we want.\n",
+    "\n",
+    "Let's start with the first step, uploading a table."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Preparing a table for uploading\n",
+    "\n",
+    "For each candidate star, we want to find the corresponding row in the `gaiadr2.panstarrs1_best_neighbour` table.\n",
+    "\n",
+    "In order to do that, we have to:\n",
+    "\n",
+    "1. Write the table in a local file as an XML VOTable, which is a format suitable for transmitting a table over a network.\n",
+    "\n",
+    "2. Write an ADQL query that refers to the uploaded table.\n",
+    "\n",
+    "3. Change the way we submit the job so it uploads the table before running the query."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The first step is not too difficult because Astropy provides a function called `writeto` that can write a `Table` in `XML`.\n",
+    "\n",
+    "[The documentation of this process is here](https://docs.astropy.org/en/stable/io/votable/).\n",
+    "\n",
+    "First we have to convert our Pandas `DataFrame` to an Astropy `Table`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "astropy.table.table.Table"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from astropy.table import Table\n",
+    "\n",
+    "candidate_table = Table.from_pandas(candidate_df)\n",
+    "type(candidate_table)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To write the file, we can use `Table.write` with `format='votable'`, [as described here](https://docs.astropy.org/en/stable/io/unified.html#vo-tables)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "table = candidate_table[['source_id']]\n",
+    "table.write('candidate_df.xml', format='votable', overwrite=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notice that we select a single column from the table, `source_id`.\n",
+    "We could write the entire table to a file, but that would take longer to transmit over the network, and we really only need one column.\n",
+    "\n",
+    "This process, taking a structure like a `Table` and translating it into a form that can be transmitted over a network, is called [serialization](https://en.wikipedia.org/wiki/Serialization).\n",
+    "\n",
+    "XML is one of the most common serialization formats.  One nice feature is that XML data is plain text, as opposed to binary digits, so you can read the file we just wrote:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<?xml version=\"1.0\" encoding=\"utf-8\"?>\r\n",
+      "<!-- Produced with astropy.io.votable version 4.0.1.post1\r\n",
+      "     http://www.astropy.org/ -->\r\n",
+      "<VOTABLE version=\"1.4\" xmlns=\"http://www.ivoa.net/xml/VOTable/v1.4\" xmlns:xsi=\"http://www.w3.org/2001/XMLSchema-instance\" xsi:noNamespaceSchemaLocation=\"http://www.ivoa.net/xml/VOTable/v1.4\">\r\n",
+      " <RESOURCE type=\"results\">\r\n",
+      "  <TABLE>\r\n",
+      "   <FIELD ID=\"source_id\" datatype=\"long\" name=\"source_id\"/>\r\n",
+      "   <DATA>\r\n",
+      "    <TABLEDATA>\r\n",
+      "     <TR>\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!head candidate_df.xml"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "XML is a general format, so different XML files contain different kinds of data.  In order to read an XML file, it's not enough to know that it's XML; you also have to know the data format, which is called a [schema](https://en.wikipedia.org/wiki/XML_schema).\n",
+    "\n",
+    "In this example, the schema is VOTable; notice that one of the first tags in the file specifies the schema, and even includes the URL where you can get its definition.\n",
+    "\n",
+    "So this is an example of a self-documenting format.\n",
+    "\n",
+    "A drawback of XML is that it tends to be big, which is why we wrote just the `source_id` column rather than the whole table.\n",
+    "The size of the file is about 750 KB, so that's not too bad."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-rw-rw-r-- 1 downey downey 396K Oct 19 14:48 candidate_df.xml\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!ls -lh candidate_df.xml"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you are using Windows, `ls` might not work; in that case, try:\n",
+    "\n",
+    "```\n",
+    "!dir candidate_df.xml\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** There's a gotcha here we want to warn you about.  Why do you think we used double brackets to specify the column we wanted?  What happens if you use single brackets?\n",
+    "\n",
+    "Run these cells to find out."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "astropy.table.table.Table"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "table = candidate_table[['source_id']]\n",
+    "type(table)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "astropy.table.column.Column"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "column = candidate_table['source_id']\n",
+    "type(column)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# writeto(column, 'candidate_df.xml')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Uploading a table\n",
+    "\n",
+    "The next step is to upload this table to the Gaia server and use it as part of a query.\n",
+    "\n",
+    "[Here's the documentation that explains how to run a query with an uploaded table](https://astroquery.readthedocs.io/en/latest/gaia/gaia.html#synchronous-query-on-an-on-the-fly-uploaded-table).\n",
+    "\n",
+    "In the spirit of incremental development and testing, let's start with the simplest possible query."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query = \"\"\"SELECT *\n",
+    "FROM tap_upload.candidate_df\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This query downloads all rows and all columns from the uploaded table.  The name of the table has two parts: `tap_upload` specifies a table that was uploaded using TAP+ (remember that's the name of the protocol we're using to talk to the Gaia server).\n",
+    "\n",
+    "And `candidate_df` is the name of the table, which we get to choose (unlike `tap_upload`, which we didn't get to choose).\n",
+    "\n",
+    "Here's how we run the query:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Created TAP+ (v1.2.1) - Connection:\n",
+      "\tHost: gea.esac.esa.int\n",
+      "\tUse HTTPS: True\n",
+      "\tPort: 443\n",
+      "\tSSL Port: 443\n",
+      "Created TAP+ (v1.2.1) - Connection:\n",
+      "\tHost: geadata.esac.esa.int\n",
+      "\tUse HTTPS: True\n",
+      "\tPort: 443\n",
+      "\tSSL Port: 443\n",
+      "INFO: Query finished. [astroquery.utils.tap.core]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from astroquery.gaia import Gaia\n",
+    "\n",
+    "job = Gaia.launch_job_async(query=query, \n",
+    "                            upload_resource='candidate_df.xml', \n",
+    "                            upload_table_name='candidate_df')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`upload_resource` specifies the name of the file we want to upload, which is the file we just wrote.\n",
+    "\n",
+    "`upload_table_name` is the name we assign to this table, which is the name we used in the query.\n",
+    "\n",
+    "And here are the results:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<i>Table length=7346</i>\n",
+       "<table id=\"table140665432275552\" class=\"table-striped table-bordered table-condensed\">\n",
+       "<thead><tr><th>source_id</th></tr></thead>\n",
+       "<thead><tr><th>int64</th></tr></thead>\n",
+       "<tr><td>635559124339440000</td></tr>\n",
+       "<tr><td>635860218726658176</td></tr>\n",
+       "<tr><td>635674126383965568</td></tr>\n",
+       "<tr><td>635535454774983040</td></tr>\n",
+       "<tr><td>635497276810313600</td></tr>\n",
+       "<tr><td>635614168640132864</td></tr>\n",
+       "<tr><td>635821843194387840</td></tr>\n",
+       "<tr><td>635551706931167104</td></tr>\n",
+       "<tr><td>635518889086133376</td></tr>\n",
+       "<tr><td>635580294233854464</td></tr>\n",
+       "<tr><td>...</td></tr>\n",
+       "<tr><td>612282738058264960</td></tr>\n",
+       "<tr><td>612485911486166656</td></tr>\n",
+       "<tr><td>612386332668697600</td></tr>\n",
+       "<tr><td>612296172717818624</td></tr>\n",
+       "<tr><td>612250375480101760</td></tr>\n",
+       "<tr><td>612394926899159168</td></tr>\n",
+       "<tr><td>612288854091187712</td></tr>\n",
+       "<tr><td>612428870024913152</td></tr>\n",
+       "<tr><td>612256418500423168</td></tr>\n",
+       "<tr><td>612429144902815104</td></tr>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<Table length=7346>\n",
+       "    source_id     \n",
+       "      int64       \n",
+       "------------------\n",
+       "635559124339440000\n",
+       "635860218726658176\n",
+       "635674126383965568\n",
+       "635535454774983040\n",
+       "635497276810313600\n",
+       "635614168640132864\n",
+       "635821843194387840\n",
+       "635551706931167104\n",
+       "635518889086133376\n",
+       "635580294233854464\n",
+       "               ...\n",
+       "612282738058264960\n",
+       "612485911486166656\n",
+       "612386332668697600\n",
+       "612296172717818624\n",
+       "612250375480101760\n",
+       "612394926899159168\n",
+       "612288854091187712\n",
+       "612428870024913152\n",
+       "612256418500423168\n",
+       "612429144902815104"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results = job.get_results()\n",
+    "results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If things go according to plan, the result should contain the same rows and columns as the uploaded table."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(7346, 7346)"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(candidate_table), len(results)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "set(candidate_table['source_id']) == set(results['source_id'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this example, we uploaded a table and then downloaded it again, so that's not too useful.\n",
+    "\n",
+    "But now that we can upload a table, we can join it with other tables on the Gaia server."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Joining with an uploaded table\n",
+    "\n",
+    "Here's the first example of a query that contains a `JOIN` clause."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query1 = \"\"\"SELECT *\n",
+    "FROM gaiadr2.panstarrs1_best_neighbour as best\n",
+    "JOIN tap_upload.candidate_df as candidate_df\n",
+    "ON best.source_id = candidate_df.source_id\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's break that down one clause at a time:\n",
+    "\n",
+    "* `SELECT *` means we will download all columns from both tables.\n",
+    "\n",
+    "* `FROM gaiadr2.panstarrs1_best_neighbour as best` means that we'll get the columns from the Pan-STARRS best neighbor table, which we'll refer to using the short name `best`.\n",
+    "\n",
+    "* `JOIN tap_upload.candidate_df as candidate_df` means that we'll also get columns from the uploaded table, which we'll refer to using the short name `candidate_df`.\n",
+    "\n",
+    "* `ON best.source_id = candidate_df.source_id` specifies that we will use `source_id ` to match up the rows from the two tables.\n",
+    "\n",
+    "Here's the [documentation of the best neighbor table](https://gea.esac.esa.int/archive/documentation/GDR2/Gaia_archive/chap_datamodel/sec_dm_crossmatches/ssec_dm_panstarrs1_best_neighbour.html).\n",
+    "\n",
+    "Let's run the query:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO: Query finished. [astroquery.utils.tap.core]\n"
+     ]
+    }
+   ],
+   "source": [
+    "job1 = Gaia.launch_job_async(query=query1, \n",
+    "                       upload_resource='candidate_df.xml', \n",
+    "                       upload_table_name='candidate_df')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And get the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<i>Table length=3724</i>\n",
+       "<table id=\"table140665411011344\" class=\"table-striped table-bordered table-condensed\">\n",
+       "<thead><tr><th>source_id</th><th>original_ext_source_id</th><th>angular_distance</th><th>number_of_neighbours</th><th>number_of_mates</th><th>best_neighbour_multiplicity</th><th>gaia_astrometric_params</th><th>source_id_2</th></tr></thead>\n",
+       "<thead><tr><th></th><th></th><th>arcsec</th><th></th><th></th><th></th><th></th><th></th></tr></thead>\n",
+       "<thead><tr><th>int64</th><th>int64</th><th>float64</th><th>int32</th><th>int16</th><th>int16</th><th>int16</th><th>int64</th></tr></thead>\n",
+       "<tr><td>635860218726658176</td><td>130911385187671349</td><td>0.053667035895467084</td><td>1</td><td>0</td><td>1</td><td>5</td><td>635860218726658176</td></tr>\n",
+       "<tr><td>635674126383965568</td><td>130831388428488720</td><td>0.038810268141577516</td><td>1</td><td>0</td><td>1</td><td>5</td><td>635674126383965568</td></tr>\n",
+       "<tr><td>635535454774983040</td><td>130631378377657369</td><td>0.034323028828991076</td><td>1</td><td>0</td><td>1</td><td>5</td><td>635535454774983040</td></tr>\n",
+       "<tr><td>635497276810313600</td><td>130811380445631930</td><td>0.04720255413250006</td><td>1</td><td>0</td><td>1</td><td>5</td><td>635497276810313600</td></tr>\n",
+       "<tr><td>635614168640132864</td><td>130571395922140135</td><td>0.020304189709964143</td><td>1</td><td>0</td><td>1</td><td>5</td><td>635614168640132864</td></tr>\n",
+       "<tr><td>635598607974369792</td><td>130341392091279513</td><td>0.036524626853403054</td><td>1</td><td>0</td><td>1</td><td>5</td><td>635598607974369792</td></tr>\n",
+       "<tr><td>635737661835496576</td><td>131001399333502136</td><td>0.036626827820716606</td><td>1</td><td>0</td><td>1</td><td>5</td><td>635737661835496576</td></tr>\n",
+       "<tr><td>635850945892748672</td><td>132011398654934147</td><td>0.021178742393378396</td><td>1</td><td>0</td><td>1</td><td>5</td><td>635850945892748672</td></tr>\n",
+       "<tr><td>635600532119713664</td><td>130421392285893623</td><td>0.04518820915043015</td><td>1</td><td>0</td><td>1</td><td>5</td><td>635600532119713664</td></tr>\n",
+       "<tr><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr>\n",
+       "<tr><td>612241781249124608</td><td>129751343755995561</td><td>0.04235715830001815</td><td>1</td><td>0</td><td>1</td><td>5</td><td>612241781249124608</td></tr>\n",
+       "<tr><td>612332147361443072</td><td>130141341458538777</td><td>0.02265249859012977</td><td>1</td><td>0</td><td>1</td><td>5</td><td>612332147361443072</td></tr>\n",
+       "<tr><td>612426744016802432</td><td>130521346852465656</td><td>0.03247653009961843</td><td>1</td><td>0</td><td>1</td><td>5</td><td>612426744016802432</td></tr>\n",
+       "<tr><td>612331739340341760</td><td>130111341217793839</td><td>0.036064240818025735</td><td>1</td><td>0</td><td>1</td><td>5</td><td>612331739340341760</td></tr>\n",
+       "<tr><td>612282738058264960</td><td>129741340445933519</td><td>0.025293237353496898</td><td>1</td><td>0</td><td>1</td><td>5</td><td>612282738058264960</td></tr>\n",
+       "<tr><td>612386332668697600</td><td>130351354570219774</td><td>0.02010316001403086</td><td>1</td><td>0</td><td>1</td><td>5</td><td>612386332668697600</td></tr>\n",
+       "<tr><td>612296172717818624</td><td>129691338006168780</td><td>0.051264212025836205</td><td>1</td><td>0</td><td>1</td><td>5</td><td>612296172717818624</td></tr>\n",
+       "<tr><td>612250375480101760</td><td>129741346475897464</td><td>0.031783740347530905</td><td>1</td><td>0</td><td>1</td><td>5</td><td>612250375480101760</td></tr>\n",
+       "<tr><td>612394926899159168</td><td>130581355199751795</td><td>0.04019174830546698</td><td>1</td><td>0</td><td>1</td><td>5</td><td>612394926899159168</td></tr>\n",
+       "<tr><td>612256418500423168</td><td>129931349075297310</td><td>0.009242789669513156</td><td>1</td><td>0</td><td>1</td><td>5</td><td>612256418500423168</td></tr>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<Table length=3724>\n",
+       "    source_id      original_ext_source_id ...    source_id_2    \n",
+       "                                          ...                   \n",
+       "      int64                int64          ...       int64       \n",
+       "------------------ ---------------------- ... ------------------\n",
+       "635860218726658176     130911385187671349 ... 635860218726658176\n",
+       "635674126383965568     130831388428488720 ... 635674126383965568\n",
+       "635535454774983040     130631378377657369 ... 635535454774983040\n",
+       "635497276810313600     130811380445631930 ... 635497276810313600\n",
+       "635614168640132864     130571395922140135 ... 635614168640132864\n",
+       "635598607974369792     130341392091279513 ... 635598607974369792\n",
+       "635737661835496576     131001399333502136 ... 635737661835496576\n",
+       "635850945892748672     132011398654934147 ... 635850945892748672\n",
+       "635600532119713664     130421392285893623 ... 635600532119713664\n",
+       "               ...                    ... ...                ...\n",
+       "612241781249124608     129751343755995561 ... 612241781249124608\n",
+       "612332147361443072     130141341458538777 ... 612332147361443072\n",
+       "612426744016802432     130521346852465656 ... 612426744016802432\n",
+       "612331739340341760     130111341217793839 ... 612331739340341760\n",
+       "612282738058264960     129741340445933519 ... 612282738058264960\n",
+       "612386332668697600     130351354570219774 ... 612386332668697600\n",
+       "612296172717818624     129691338006168780 ... 612296172717818624\n",
+       "612250375480101760     129741346475897464 ... 612250375480101760\n",
+       "612394926899159168     130581355199751795 ... 612394926899159168\n",
+       "612256418500423168     129931349075297310 ... 612256418500423168"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results1 = job1.get_results()\n",
+    "results1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This table contains all of the columns from the best neighbor table, plus the single column from the uploaded table."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['source_id',\n",
+       " 'original_ext_source_id',\n",
+       " 'angular_distance',\n",
+       " 'number_of_neighbours',\n",
+       " 'number_of_mates',\n",
+       " 'best_neighbour_multiplicity',\n",
+       " 'gaia_astrometric_params',\n",
+       " 'source_id_2']"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results1.colnames"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Because one of the column names appears in both tables, the second instance of `source_id` has been appended with the suffix `_2`.\n",
+    "\n",
+    "The length of the results table is about 2000, which means we were not able to find matches for all stars in the list of candidate_df."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3724"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(results1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To get more information about the matching process, we can inspect `best_neighbour_multiplicity`, which indicates for each star in Gaia how many stars in Pan-STARRS are equally likely matches.\n",
+    "\n",
+    "For this kind of data exploration, we'll convert a column from the table to a Pandas `Series` so we can use `value_counts`, which counts the number of times each value appears in a `Series`, like a histogram."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1    3724\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "nn = pd.Series(results1['best_neighbour_multiplicity'])\n",
+    "nn.value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The result shows that `1` is the only value in the `Series`, appearing xxx times.\n",
+    "\n",
+    "That means that in every case where a match was found, the matching algorithm identified a single neighbor as the most likely match.\n",
+    "\n",
+    "Similarly, `number_of_mates` indicates the number of other stars in Gaia that match with the same star in Pan-STARRS."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    3724\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nm = pd.Series(results1['number_of_mates'])\n",
+    "nm.value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For this set of candidate_df, almost all of the stars we've selected from Pan-STARRS are only matched with a single star in the Gaia catalog.\n",
+    "\n",
+    "**Detail** The table also contains `number_of_neighbors` which is the number of stars in Pan-STARRS that match in terms of position, before using other critieria to choose the most likely match."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Getting the photometry data\n",
+    "\n",
+    "The most important column in `results1` is `original_ext_source_id` which is the `obj_id` we will use to look up the likely matches in Pan-STARRS to get photometry data.\n",
+    "\n",
+    "The process is similar to what we just did to look up the matches.  We will:\n",
+    "\n",
+    "1. Make a table that contains `source_id` and `original_ext_source_id`.\n",
+    "\n",
+    "2. Write the table to an XML VOTable file.\n",
+    "\n",
+    "3. Write a query that joins the uploaded table with `gaiadr2.panstarrs1_original_valid` and selects the photometry data we want.\n",
+    "\n",
+    "4. Run the query using the uploaded table.\n",
+    "\n",
+    "Since we've done everything here before, we'll do these steps as an exercise.\n",
+    "\n",
+    "**Exercise:** Select `source_id` and `original_ext_source_id` from `results1` and write the resulting table as a file named `external.xml`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "table = results1[['source_id', 'original_ext_source_id']]\n",
+    "table.write('external.xml', format='votable', overwrite=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Use `!head` to confirm that the file exists and contains an XML VOTable."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<?xml version=\"1.0\" encoding=\"utf-8\"?>\r\n",
+      "<!-- Produced with astropy.io.votable version 4.0.1.post1\r\n",
+      "     http://www.astropy.org/ -->\r\n",
+      "<VOTABLE version=\"1.4\" xmlns=\"http://www.ivoa.net/xml/VOTable/v1.4\" xmlns:xsi=\"http://www.w3.org/2001/XMLSchema-instance\" xsi:noNamespaceSchemaLocation=\"http://www.ivoa.net/xml/VOTable/v1.4\">\r\n",
+      " <RESOURCE type=\"results\">\r\n",
+      "  <TABLE>\r\n",
+      "   <FIELD ID=\"source_id\" datatype=\"long\" name=\"source_id\" ucd=\"meta.id;meta.main\">\r\n",
+      "    <DESCRIPTION>\r\n",
+      "     Unique Gaia source identifier\r\n",
+      "    </DESCRIPTION>\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!head external.xml"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Read [the documentation of the Pan-STARRS table](https://gea.esac.esa.int/archive/documentation/GDR2/Gaia_archive/chap_datamodel/sec_dm_external_catalogues/ssec_dm_panstarrs1_original_valid.html) and make note of `obj_id`, which contains the object IDs we'll use to find the rows we want.\n",
+    "\n",
+    "Write a query that uses each value of `original_ext_source_id` from the uploaded table to find a row in `gaiadr2.panstarrs1_original_valid` with the same value in `obj_id`, and select all columns from both tables.\n",
+    "\n",
+    "Suggestion: Develop and test your query incrementally.  For example:\n",
+    "\n",
+    "1. Write a query that downloads all columns from the uploaded table.  Test to make sure we can read the uploaded table.\n",
+    "\n",
+    "2. Write a query that downloads the first 10 rows from `gaiadr2.panstarrs1_original_valid`.  Test to make sure we can access Pan-STARRS data.\n",
+    "\n",
+    "3. Write a query that joins the two tables and selects all columns.  Test that the join works as expected.\n",
+    "\n",
+    "\n",
+    "As a bonus exercise, write a query that joins the two tables and selects just the columns we need:\n",
+    "\n",
+    "* `source_id` from the uploaded table\n",
+    "\n",
+    "* `g_mean_psf_mag` from `gaiadr2.panstarrs1_original_valid`\n",
+    "\n",
+    "* `i_mean_psf_mag` from `gaiadr2.panstarrs1_original_valid`\n",
+    "\n",
+    "Hint: When you select a column from a join, you have to specify which table the column is in."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "query2 = \"\"\"SELECT *\n",
+    "FROM tap_upload.external as external\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "query2 = \"\"\"SELECT TOP 10 *\n",
+    "FROM gaiadr2.panstarrs1_original_valid\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "query2 = \"\"\"SELECT *\n",
+    "FROM gaiadr2.panstarrs1_original_valid as ps\n",
+    "JOIN tap_upload.external as external\n",
+    "ON ps.obj_id = external.original_ext_source_id\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "query2 = \"\"\"SELECT\n",
+    "external.source_id, ps.g_mean_psf_mag, ps.i_mean_psf_mag\n",
+    "FROM gaiadr2.panstarrs1_original_valid as ps\n",
+    "JOIN tap_upload.external as external\n",
+    "ON ps.obj_id = external.original_ext_source_id\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "SELECT\n",
+      "external.source_id, ps.g_mean_psf_mag, ps.i_mean_psf_mag\n",
+      "FROM gaiadr2.panstarrs1_original_valid as ps\n",
+      "JOIN tap_upload.external as external\n",
+      "ON ps.obj_id = external.original_ext_source_id\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(query2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO: Query finished. [astroquery.utils.tap.core]\n"
+     ]
+    }
+   ],
+   "source": [
+    "job2 = Gaia.launch_job_async(query=query2, \n",
+    "                       upload_resource='external.xml', \n",
+    "                       upload_table_name='external')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<i>Table length=3724</i>\n",
+       "<table id=\"table140665408248800\" class=\"table-striped table-bordered table-condensed\">\n",
+       "<thead><tr><th>source_id</th><th>g_mean_psf_mag</th><th>i_mean_psf_mag</th></tr></thead>\n",
+       "<thead><tr><th></th><th></th><th>mag</th></tr></thead>\n",
+       "<thead><tr><th>int64</th><th>float64</th><th>float64</th></tr></thead>\n",
+       "<tr><td>635860218726658176</td><td>17.8978004455566</td><td>17.5174007415771</td></tr>\n",
+       "<tr><td>635674126383965568</td><td>19.2873001098633</td><td>17.6781005859375</td></tr>\n",
+       "<tr><td>635535454774983040</td><td>16.9237995147705</td><td>16.478099822998</td></tr>\n",
+       "<tr><td>635497276810313600</td><td>19.9242000579834</td><td>18.3339996337891</td></tr>\n",
+       "<tr><td>635614168640132864</td><td>16.1515998840332</td><td>14.6662998199463</td></tr>\n",
+       "<tr><td>635598607974369792</td><td>16.5223999023438</td><td>16.1375007629395</td></tr>\n",
+       "<tr><td>635737661835496576</td><td>14.5032997131348</td><td>13.9849004745483</td></tr>\n",
+       "<tr><td>635850945892748672</td><td>16.5174999237061</td><td>16.0450000762939</td></tr>\n",
+       "<tr><td>635600532119713664</td><td>20.4505996704102</td><td>19.5177001953125</td></tr>\n",
+       "<tr><td>...</td><td>...</td><td>...</td></tr>\n",
+       "<tr><td>612241781249124608</td><td>20.2343997955322</td><td>18.6518001556396</td></tr>\n",
+       "<tr><td>612332147361443072</td><td>21.3848991394043</td><td>20.3076000213623</td></tr>\n",
+       "<tr><td>612426744016802432</td><td>17.8281002044678</td><td>17.4281005859375</td></tr>\n",
+       "<tr><td>612331739340341760</td><td>21.8656997680664</td><td>19.5223007202148</td></tr>\n",
+       "<tr><td>612282738058264960</td><td>22.5151996612549</td><td>19.9743995666504</td></tr>\n",
+       "<tr><td>612386332668697600</td><td>19.3792991638184</td><td>17.9923000335693</td></tr>\n",
+       "<tr><td>612296172717818624</td><td>17.4944000244141</td><td>16.926700592041</td></tr>\n",
+       "<tr><td>612250375480101760</td><td>15.3330001831055</td><td>14.6280002593994</td></tr>\n",
+       "<tr><td>612394926899159168</td><td>16.4414005279541</td><td>15.8212003707886</td></tr>\n",
+       "<tr><td>612256418500423168</td><td>20.8715991973877</td><td>19.9612007141113</td></tr>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<Table length=3724>\n",
+       "    source_id       g_mean_psf_mag   i_mean_psf_mag \n",
+       "                                          mag       \n",
+       "      int64            float64          float64     \n",
+       "------------------ ---------------- ----------------\n",
+       "635860218726658176 17.8978004455566 17.5174007415771\n",
+       "635674126383965568 19.2873001098633 17.6781005859375\n",
+       "635535454774983040 16.9237995147705  16.478099822998\n",
+       "635497276810313600 19.9242000579834 18.3339996337891\n",
+       "635614168640132864 16.1515998840332 14.6662998199463\n",
+       "635598607974369792 16.5223999023438 16.1375007629395\n",
+       "635737661835496576 14.5032997131348 13.9849004745483\n",
+       "635850945892748672 16.5174999237061 16.0450000762939\n",
+       "635600532119713664 20.4505996704102 19.5177001953125\n",
+       "               ...              ...              ...\n",
+       "612241781249124608 20.2343997955322 18.6518001556396\n",
+       "612332147361443072 21.3848991394043 20.3076000213623\n",
+       "612426744016802432 17.8281002044678 17.4281005859375\n",
+       "612331739340341760 21.8656997680664 19.5223007202148\n",
+       "612282738058264960 22.5151996612549 19.9743995666504\n",
+       "612386332668697600 19.3792991638184 17.9923000335693\n",
+       "612296172717818624 17.4944000244141  16.926700592041\n",
+       "612250375480101760 15.3330001831055 14.6280002593994\n",
+       "612394926899159168 16.4414005279541 15.8212003707886\n",
+       "612256418500423168 20.8715991973877 19.9612007141113"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results2 = job2.get_results()\n",
+    "results2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Challenge exercise**\n",
+    "\n",
+    "Do both joins in one query.\n",
+    "\n",
+    "There's an [example here](https://github.com/smoh/Getting-started-with-Gaia/blob/master/gaia-adql-snippets.md) you could start with."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Write the data\n",
+    "\n",
+    "Since we have the data in an Astropy `Table`, let's store it in a FITS file."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "filename = 'gd1_photo.fits'\n",
+    "results2.write(filename, overwrite=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can check that the file exists, and see how big it is."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-rw-rw-r-- 1 downey downey 96K Oct 19 14:49 gd1_photo.fits\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!ls -lh gd1_photo.fits"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "At around 175 KB, it is smaller than some of the other files we've been working with.\n",
+    "\n",
+    "If you are using Windows, `ls` might not work; in that case, try:\n",
+    "\n",
+    "```\n",
+    "!dir gd1_photo.fits\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "In this notebook, we used database `JOIN` operations to select photometry data for the stars we've identified as candidates to be in GD-1.\n",
+    "\n",
+    "In the next notebook, we'll use this data for a second round of selection, identifying stars that have photometry data consistent with GD-1."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Best practice\n",
+    "\n",
+    "* Use `JOIN` operations to combine data from multiple tables in a databased, using some kind of identifier to match up records from one table with records from another.\n",
+    "\n",
+    "* This is another example of a practice we saw in the previous notebook, moving the computation to the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
\ No newline at end of file
diff --git a/_build/jupyter_execute/05_join.py b/_build/jupyter_execute/05_join.py
new file mode 100644
index 0000000..8f23dd3
--- /dev/null
+++ b/_build/jupyter_execute/05_join.py
@@ -0,0 +1,564 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# # Chapter 5
+# 
+# This is the fifth in a series of notebooks related to astronomy data.
+# 
+# As a continuing example, we will replicate part of the analysis in a recent paper, "[Off the beaten path: Gaia reveals GD-1 stars outside of the main stream](https://arxiv.org/abs/1805.00425)" by Adrian M. Price-Whelan and Ana Bonaca.
+# 
+# Picking up where we left off, the next step in the analysis is to select candidate stars based on photometry.  The following figure from the paper is a color-magnitude diagram for the stars selected based on proper motion:
+# 
+# <img width="300" src="https://github.com/datacarpentry/astronomy-python/raw/gh-pages/fig/gd1-3.png">
+# 
+# In red is a theoretical isochrone, showing where we expect the stars in GD-1 to fall based on the metallicity and age of their original globular cluster. 
+# 
+# By selecting stars in the shaded area, we can further distinguish the main sequence of GD-1 from younger background stars.
+
+# ## Outline
+# 
+# Here are the steps in this notebook:
+# 
+# 1. We'll reload the candidate stars we identified in the previous notebook.
+# 
+# 2. Then we'll run a query on the Gaia server that uploads the table of candidates and uses a `JOIN` operation to select photometry data for the candidate stars.
+# 
+# 3. We'll write the results to a file for use in the next notebook.
+# 
+# After completing this lesson, you should be able to
+# 
+# * Upload a table to the Gaia server.
+# 
+# * Write ADQL queries involving `JOIN` operations.
+
+# ## Installing libraries
+# 
+# If you are running this notebook on Colab, you can run the following cell to install Astroquery and a the other libraries we'll use.
+# 
+# If you are running this notebook on your own computer, you might have to install these libraries yourself.  
+# 
+# If you are using this notebook as part of a Carpentries workshop, you should have received setup instructions.
+# 
+# TODO: Add a link to the instructions.
+
+# In[1]:
+
+
+# If we're running on Colab, install libraries
+
+import sys
+IN_COLAB = 'google.colab' in sys.modules
+
+if IN_COLAB:
+    get_ipython().system('pip install astroquery astro-gala pyia python-wget')
+
+
+# ## Reloading the data
+# 
+# The following cell downloads the data from the previous notebook.
+
+# In[2]:
+
+
+import os
+from wget import download
+
+filename = 'gd1_candidates.hdf5'
+path = 'https://github.com/AllenDowney/AstronomicalData/raw/main/data/'
+
+if not os.path.exists(filename):
+    print(download(path+filename))
+
+
+# And we can read it back.
+
+# In[3]:
+
+
+import pandas as pd
+
+candidate_df = pd.read_hdf(filename, 'candidate_df')
+
+
+# `candidate_df` is the Pandas DataFrame that contains results from the query in the previous notebook, which selects stars likely to be in GD-1 based on proper motion.  It also includes position and proper motion transformed to the ICRS frame.
+
+# In[4]:
+
+
+import matplotlib.pyplot as plt
+
+x = candidate_df['phi1']
+y = candidate_df['phi2']
+
+plt.plot(x, y, 'ko', markersize=0.3, alpha=0.3)
+
+plt.xlabel('ra (degree GD1)')
+plt.ylabel('dec (degree GD1)');
+
+
+# This is the same figure we saw at the end of the previous notebook.  GD-1 is visible against the background stars, but we will be able to see it more clearly after selecting based on photometry data.
+
+# ## Getting photometry data
+# 
+# The Gaia dataset contains some photometry data, including the variable `bp_rp`, which we used in the original query to select stars with BP - RP color between -0.75 and 2.
+# 
+# Selecting stars with `bp-rp` less than 2 excludes many class M dwarf stars, which are low temperature, low luminosity.  A star like that at GD-1's distance would be hard to detect, so if it is detected, it it more likely to be in the foreground.
+# 
+# Now, to select stars with the age and metal richness we expect in GD-1, we will use `g - i` color and apparent `g`-band magnitude, which are available from the Pan-STARRS survey.
+
+# Conveniently, the Gaia server provides data from Pan-STARRS as a table in the same database we have been using, so we can access it by making ADQL queries.
+# 
+# In general, looking up a star from the Gaia catalog and finding the corresponding star in the Pan-STARRS catalog is not easy.  This kind of cross matching is not always possible, because a star might appear in one catalog and not the other.  And even when both stars are present, there might not be a clear one-to-one relationship between stars in the two catalogs.
+# 
+# Fortunately, smart people have worked on this problem, and the Gaia database includes cross-matching tables that suggest a best neighbor in the Pan-STARRS catalog for many stars in the Gaia catalog.
+# 
+# [This document describes the cross matching process](https://gea.esac.esa.int/archive/documentation/GDR2/Catalogue_consolidation/chap_cu9val_cu9val/ssec_cu9xma/sssec_cu9xma_extcat.html).  Briefly, it uses a cone search to find possible matches in approximately the right position, then uses attributes like color and magnitude to choose pairs of stars most likely to be identical.
+
+# So the hard part of cross-matching has been done for us.  However, using the results is a little tricky.
+# 
+# But, it is also an opportunity to learn about one of the most important tools for working with databases: "joining" tables.
+# 
+# In general, a "join" is an operation where you match up records from one table with records from another table using as a "key" a piece of information that is common to both tables, usually some kind of ID code.
+# 
+# In this example:
+# 
+# * Stars in the Gaia dataset are identified by `source_id`.
+# 
+# * Stars in the Pan-STARRS dataset are identified by `obj_id`.
+
+# For each candidate star we have selected so far, we have the `source_id`; the goal is to find the `obj_id` for the same star (we hope) in the Pan-STARRS catalog.
+# 
+# To do that we will:
+# 
+# 1. Make a table that contains the `source_id` for each candidate star and upload the table to the Gaia server;
+# 
+# 2. Use the `JOIN` operator to look up each `source_id` in the `gaiadr2.panstarrs1_best_neighbour` table, which contains the `obj_id` of the best match for each star in the Gaia catalog; then
+# 
+# 3. Use the `JOIN` operator again to look up each `obj_id` in the `panstarrs1_original_valid` table, which contains the Pan-STARRS photometry data we want.
+# 
+# Let's start with the first step, uploading a table.
+
+# ## Preparing a table for uploading
+# 
+# For each candidate star, we want to find the corresponding row in the `gaiadr2.panstarrs1_best_neighbour` table.
+# 
+# In order to do that, we have to:
+# 
+# 1. Write the table in a local file as an XML VOTable, which is a format suitable for transmitting a table over a network.
+# 
+# 2. Write an ADQL query that refers to the uploaded table.
+# 
+# 3. Change the way we submit the job so it uploads the table before running the query.
+
+# The first step is not too difficult because Astropy provides a function called `writeto` that can write a `Table` in `XML`.
+# 
+# [The documentation of this process is here](https://docs.astropy.org/en/stable/io/votable/).
+# 
+# First we have to convert our Pandas `DataFrame` to an Astropy `Table`.
+
+# In[5]:
+
+
+from astropy.table import Table
+
+candidate_table = Table.from_pandas(candidate_df)
+type(candidate_table)
+
+
+# To write the file, we can use `Table.write` with `format='votable'`, [as described here](https://docs.astropy.org/en/stable/io/unified.html#vo-tables).
+
+# In[6]:
+
+
+table = candidate_table[['source_id']]
+table.write('candidate_df.xml', format='votable', overwrite=True)
+
+
+# Notice that we select a single column from the table, `source_id`.
+# We could write the entire table to a file, but that would take longer to transmit over the network, and we really only need one column.
+# 
+# This process, taking a structure like a `Table` and translating it into a form that can be transmitted over a network, is called [serialization](https://en.wikipedia.org/wiki/Serialization).
+# 
+# XML is one of the most common serialization formats.  One nice feature is that XML data is plain text, as opposed to binary digits, so you can read the file we just wrote:
+
+# In[7]:
+
+
+get_ipython().system('head candidate_df.xml')
+
+
+# XML is a general format, so different XML files contain different kinds of data.  In order to read an XML file, it's not enough to know that it's XML; you also have to know the data format, which is called a [schema](https://en.wikipedia.org/wiki/XML_schema).
+# 
+# In this example, the schema is VOTable; notice that one of the first tags in the file specifies the schema, and even includes the URL where you can get its definition.
+# 
+# So this is an example of a self-documenting format.
+# 
+# A drawback of XML is that it tends to be big, which is why we wrote just the `source_id` column rather than the whole table.
+# The size of the file is about 750 KB, so that's not too bad.
+
+# In[8]:
+
+
+get_ipython().system('ls -lh candidate_df.xml')
+
+
+# If you are using Windows, `ls` might not work; in that case, try:
+# 
+# ```
+# !dir candidate_df.xml
+# ```
+
+# **Exercise:** There's a gotcha here we want to warn you about.  Why do you think we used double brackets to specify the column we wanted?  What happens if you use single brackets?
+# 
+# Run these cells to find out.
+
+# In[9]:
+
+
+table = candidate_table[['source_id']]
+type(table)
+
+
+# In[10]:
+
+
+column = candidate_table['source_id']
+type(column)
+
+
+# In[11]:
+
+
+# writeto(column, 'candidate_df.xml')
+
+
+# ## Uploading a table
+# 
+# The next step is to upload this table to the Gaia server and use it as part of a query.
+# 
+# [Here's the documentation that explains how to run a query with an uploaded table](https://astroquery.readthedocs.io/en/latest/gaia/gaia.html#synchronous-query-on-an-on-the-fly-uploaded-table).
+# 
+# In the spirit of incremental development and testing, let's start with the simplest possible query.
+
+# In[12]:
+
+
+query = """SELECT *
+FROM tap_upload.candidate_df
+"""
+
+
+# This query downloads all rows and all columns from the uploaded table.  The name of the table has two parts: `tap_upload` specifies a table that was uploaded using TAP+ (remember that's the name of the protocol we're using to talk to the Gaia server).
+# 
+# And `candidate_df` is the name of the table, which we get to choose (unlike `tap_upload`, which we didn't get to choose).
+# 
+# Here's how we run the query:
+
+# In[13]:
+
+
+from astroquery.gaia import Gaia
+
+job = Gaia.launch_job_async(query=query, 
+                            upload_resource='candidate_df.xml', 
+                            upload_table_name='candidate_df')
+
+
+# `upload_resource` specifies the name of the file we want to upload, which is the file we just wrote.
+# 
+# `upload_table_name` is the name we assign to this table, which is the name we used in the query.
+# 
+# And here are the results:
+
+# In[14]:
+
+
+results = job.get_results()
+results
+
+
+# If things go according to plan, the result should contain the same rows and columns as the uploaded table.
+
+# In[15]:
+
+
+len(candidate_table), len(results)
+
+
+# In[16]:
+
+
+set(candidate_table['source_id']) == set(results['source_id'])
+
+
+# In this example, we uploaded a table and then downloaded it again, so that's not too useful.
+# 
+# But now that we can upload a table, we can join it with other tables on the Gaia server.
+
+# ## Joining with an uploaded table
+# 
+# Here's the first example of a query that contains a `JOIN` clause.
+
+# In[17]:
+
+
+query1 = """SELECT *
+FROM gaiadr2.panstarrs1_best_neighbour as best
+JOIN tap_upload.candidate_df as candidate_df
+ON best.source_id = candidate_df.source_id
+"""
+
+
+# Let's break that down one clause at a time:
+# 
+# * `SELECT *` means we will download all columns from both tables.
+# 
+# * `FROM gaiadr2.panstarrs1_best_neighbour as best` means that we'll get the columns from the Pan-STARRS best neighbor table, which we'll refer to using the short name `best`.
+# 
+# * `JOIN tap_upload.candidate_df as candidate_df` means that we'll also get columns from the uploaded table, which we'll refer to using the short name `candidate_df`.
+# 
+# * `ON best.source_id = candidate_df.source_id` specifies that we will use `source_id ` to match up the rows from the two tables.
+# 
+# Here's the [documentation of the best neighbor table](https://gea.esac.esa.int/archive/documentation/GDR2/Gaia_archive/chap_datamodel/sec_dm_crossmatches/ssec_dm_panstarrs1_best_neighbour.html).
+# 
+# Let's run the query:
+
+# In[18]:
+
+
+job1 = Gaia.launch_job_async(query=query1, 
+                       upload_resource='candidate_df.xml', 
+                       upload_table_name='candidate_df')
+
+
+# And get the results.
+
+# In[19]:
+
+
+results1 = job1.get_results()
+results1
+
+
+# This table contains all of the columns from the best neighbor table, plus the single column from the uploaded table.
+
+# In[20]:
+
+
+results1.colnames
+
+
+# Because one of the column names appears in both tables, the second instance of `source_id` has been appended with the suffix `_2`.
+# 
+# The length of the results table is about 2000, which means we were not able to find matches for all stars in the list of candidate_df.
+
+# In[21]:
+
+
+len(results1)
+
+
+# To get more information about the matching process, we can inspect `best_neighbour_multiplicity`, which indicates for each star in Gaia how many stars in Pan-STARRS are equally likely matches.
+# 
+# For this kind of data exploration, we'll convert a column from the table to a Pandas `Series` so we can use `value_counts`, which counts the number of times each value appears in a `Series`, like a histogram.
+
+# In[22]:
+
+
+import pandas as pd
+
+nn = pd.Series(results1['best_neighbour_multiplicity'])
+nn.value_counts()
+
+
+# The result shows that `1` is the only value in the `Series`, appearing xxx times.
+# 
+# That means that in every case where a match was found, the matching algorithm identified a single neighbor as the most likely match.
+# 
+# Similarly, `number_of_mates` indicates the number of other stars in Gaia that match with the same star in Pan-STARRS.
+
+# In[23]:
+
+
+nm = pd.Series(results1['number_of_mates'])
+nm.value_counts()
+
+
+# For this set of candidate_df, almost all of the stars we've selected from Pan-STARRS are only matched with a single star in the Gaia catalog.
+# 
+# **Detail** The table also contains `number_of_neighbors` which is the number of stars in Pan-STARRS that match in terms of position, before using other critieria to choose the most likely match.
+
+# ## Getting the photometry data
+# 
+# The most important column in `results1` is `original_ext_source_id` which is the `obj_id` we will use to look up the likely matches in Pan-STARRS to get photometry data.
+# 
+# The process is similar to what we just did to look up the matches.  We will:
+# 
+# 1. Make a table that contains `source_id` and `original_ext_source_id`.
+# 
+# 2. Write the table to an XML VOTable file.
+# 
+# 3. Write a query that joins the uploaded table with `gaiadr2.panstarrs1_original_valid` and selects the photometry data we want.
+# 
+# 4. Run the query using the uploaded table.
+# 
+# Since we've done everything here before, we'll do these steps as an exercise.
+# 
+# **Exercise:** Select `source_id` and `original_ext_source_id` from `results1` and write the resulting table as a file named `external.xml`.
+
+# In[24]:
+
+
+# Solution
+
+table = results1[['source_id', 'original_ext_source_id']]
+table.write('external.xml', format='votable', overwrite=True)
+
+
+# Use `!head` to confirm that the file exists and contains an XML VOTable.
+
+# In[25]:
+
+
+get_ipython().system('head external.xml')
+
+
+# **Exercise:** Read [the documentation of the Pan-STARRS table](https://gea.esac.esa.int/archive/documentation/GDR2/Gaia_archive/chap_datamodel/sec_dm_external_catalogues/ssec_dm_panstarrs1_original_valid.html) and make note of `obj_id`, which contains the object IDs we'll use to find the rows we want.
+# 
+# Write a query that uses each value of `original_ext_source_id` from the uploaded table to find a row in `gaiadr2.panstarrs1_original_valid` with the same value in `obj_id`, and select all columns from both tables.
+# 
+# Suggestion: Develop and test your query incrementally.  For example:
+# 
+# 1. Write a query that downloads all columns from the uploaded table.  Test to make sure we can read the uploaded table.
+# 
+# 2. Write a query that downloads the first 10 rows from `gaiadr2.panstarrs1_original_valid`.  Test to make sure we can access Pan-STARRS data.
+# 
+# 3. Write a query that joins the two tables and selects all columns.  Test that the join works as expected.
+# 
+# 
+# As a bonus exercise, write a query that joins the two tables and selects just the columns we need:
+# 
+# * `source_id` from the uploaded table
+# 
+# * `g_mean_psf_mag` from `gaiadr2.panstarrs1_original_valid`
+# 
+# * `i_mean_psf_mag` from `gaiadr2.panstarrs1_original_valid`
+# 
+# Hint: When you select a column from a join, you have to specify which table the column is in.
+
+# In[26]:
+
+
+# Solution
+
+query2 = """SELECT *
+FROM tap_upload.external as external
+"""
+
+
+# In[27]:
+
+
+# Solution
+
+query2 = """SELECT TOP 10 *
+FROM gaiadr2.panstarrs1_original_valid
+"""
+
+
+# In[28]:
+
+
+# Solution
+
+query2 = """SELECT *
+FROM gaiadr2.panstarrs1_original_valid as ps
+JOIN tap_upload.external as external
+ON ps.obj_id = external.original_ext_source_id
+"""
+
+
+# In[29]:
+
+
+# Solution
+
+query2 = """SELECT
+external.source_id, ps.g_mean_psf_mag, ps.i_mean_psf_mag
+FROM gaiadr2.panstarrs1_original_valid as ps
+JOIN tap_upload.external as external
+ON ps.obj_id = external.original_ext_source_id
+"""
+
+
+# In[30]:
+
+
+print(query2)
+
+
+# In[31]:
+
+
+job2 = Gaia.launch_job_async(query=query2, 
+                       upload_resource='external.xml', 
+                       upload_table_name='external')
+
+
+# In[32]:
+
+
+results2 = job2.get_results()
+results2
+
+
+# **Challenge exercise**
+# 
+# Do both joins in one query.
+# 
+# There's an [example here](https://github.com/smoh/Getting-started-with-Gaia/blob/master/gaia-adql-snippets.md) you could start with.
+
+# ## Write the data
+# 
+# Since we have the data in an Astropy `Table`, let's store it in a FITS file.
+
+# In[33]:
+
+
+filename = 'gd1_photo.fits'
+results2.write(filename, overwrite=True)
+
+
+# We can check that the file exists, and see how big it is.
+
+# In[34]:
+
+
+get_ipython().system('ls -lh gd1_photo.fits')
+
+
+# At around 175 KB, it is smaller than some of the other files we've been working with.
+# 
+# If you are using Windows, `ls` might not work; in that case, try:
+# 
+# ```
+# !dir gd1_photo.fits
+# ```
+
+# ## Summary
+# 
+# In this notebook, we used database `JOIN` operations to select photometry data for the stars we've identified as candidates to be in GD-1.
+# 
+# In the next notebook, we'll use this data for a second round of selection, identifying stars that have photometry data consistent with GD-1.
+
+# ## Best practice
+# 
+# * Use `JOIN` operations to combine data from multiple tables in a databased, using some kind of identifier to match up records from one table with records from another.
+# 
+# * This is another example of a practice we saw in the previous notebook, moving the computation to the data.
+
+# In[ ]:
+
+
+
+
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diff --git a/_build/jupyter_execute/06_photo.ipynb b/_build/jupyter_execute/06_photo.ipynb
new file mode 100644
index 0000000..960a67a
--- /dev/null
+++ b/_build/jupyter_execute/06_photo.ipynb
@@ -0,0 +1,1384 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Chapter 6\n",
+    "\n",
+    "This is the sixth in a series of notebooks related to astronomy data.\n",
+    "\n",
+    "As a continuing example, we will replicate part of the analysis in a recent paper, \"[Off the beaten path: Gaia reveals GD-1 stars outside of the main stream](https://arxiv.org/abs/1805.00425)\" by Adrian M. Price-Whelan and Ana Bonaca.\n",
+    "\n",
+    "In the previous lesson we downloaded photometry data from Pan-STARRS, which is available from the same server we've been using to get Gaia data. \n",
+    "\n",
+    "The next step in the analysis is to select candidate stars based on the photometry data.  The following figure from the paper is a color-magnitude diagram for the stars selected based on proper motion:\n",
+    "\n",
+    "<img width=\"300\" src=\"https://github.com/datacarpentry/astronomy-python/raw/gh-pages/fig/gd1-3.png\">\n",
+    "\n",
+    "In red is a theoretical isochrone, showing where we expect the stars in GD-1 to fall based on the metallicity and age of their original globular cluster. \n",
+    "\n",
+    "By selecting stars in the shaded area, we can further distinguish the main sequence of GD-1 from younger background stars."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Outline\n",
+    "\n",
+    "Here are the steps in this notebook:\n",
+    "\n",
+    "1. We'll reload the data from the previous notebook and make a color-magnitude diagram.\n",
+    "\n",
+    "2. Then we'll specify a polygon in the diagram that contains stars with the photometry we expect.\n",
+    "\n",
+    "3. Then we'll merge the photometry data with the list of candidate stars, storing the result in a Pandas `DataFrame`.\n",
+    "\n",
+    "After completing this lesson, you should be able to\n",
+    "\n",
+    "* Use Matplotlib to specify a `Polygon` and determine which points fall inside it.\n",
+    "\n",
+    "* Use Pandas to merge data from multiple `DataFrames`, much like a database `JOIN` operation."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Installing libraries\n",
+    "\n",
+    "If you are running this notebook on Colab, you can run the following cell to install Astroquery and a the other libraries we'll use.\n",
+    "\n",
+    "If you are running this notebook on your own computer, you might have to install these libraries yourself.  \n",
+    "\n",
+    "If you are using this notebook as part of a Carpentries workshop, you should have received setup instructions.\n",
+    "\n",
+    "TODO: Add a link to the instructions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# If we're running on Colab, install libraries\n",
+    "\n",
+    "import sys\n",
+    "IN_COLAB = 'google.colab' in sys.modules\n",
+    "\n",
+    "if IN_COLAB:\n",
+    "    !pip install astroquery astro-gala pyia python-wget"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Reload the data\n",
+    "\n",
+    "The following cell downloads the photometry data we created in the previous notebook."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from wget import download\n",
+    "\n",
+    "filename = 'gd1_photo.fits'\n",
+    "filepath = 'https://github.com/AllenDowney/AstronomicalData/raw/main/data/'\n",
+    "\n",
+    "if not os.path.exists(filename):\n",
+    "    print(download(filepath+filename))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can read the data back into an Astropy `Table`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from astropy.table import Table\n",
+    "\n",
+    "photo_table = Table.read(filename)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plotting photometry data\n",
+    "\n",
+    "Now that we have photometry data from Pan-STARRS, we can replicate the [color-magnitude diagram](https://en.wikipedia.org/wiki/Galaxy_color%E2%80%93magnitude_diagram) from the original paper:\n",
+    "\n",
+    "<img width=\"300\" src=\"https://github.com/datacarpentry/astronomy-python/raw/gh-pages/fig/gd1-3.png\">\n",
+    "\n",
+    "The y-axis shows the apparent magnitude of each source with the [g filter](https://en.wikipedia.org/wiki/Photometric_system).\n",
+    "\n",
+    "The x-axis shows the difference in apparent magnitude between the g and i filters, which indicates color.\n",
+    "\n",
+    "Stars with lower values of (g-i) are brighter in g-band than in i-band, compared to other stars, which means they are bluer.\n",
+    "\n",
+    "Stars in the lower-left quadrant of this diagram are less bright and less metallic than the others, which means they are [likely to be older](http://spiff.rit.edu/classes/ladder/lectures/ordinary_stars/ordinary.html).\n",
+    "\n",
+    "Since we expect the stars in GD-1 to be older than the background stars, the stars in the lower-left are more likely to be in GD-1."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "def plot_cmd(table):\n",
+    "    \"\"\"Plot a color magnitude diagram.\n",
+    "    \n",
+    "    table: Table or DataFrame with photometry data\n",
+    "    \"\"\"\n",
+    "    y = table['g_mean_psf_mag']\n",
+    "    x = table['g_mean_psf_mag'] - table['i_mean_psf_mag']\n",
+    "\n",
+    "    plt.plot(x, y, 'ko', markersize=0.3, alpha=0.3)\n",
+    "\n",
+    "    plt.xlim([0, 1.5])\n",
+    "    plt.ylim([14, 22])\n",
+    "    plt.gca().invert_yaxis()\n",
+    "\n",
+    "    plt.ylabel('$g_0$')\n",
+    "    plt.xlabel('$(g-i)_0$')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`plot_cmd` uses a new function, `invert_yaxis`, to invert the `y` axis, which is conventional when plotting magnitudes, since lower magnitude indicates higher brightness.\n",
+    "\n",
+    "`invert_yaxis` is a little different from the other functions we've used.  You can't call it like this:\n",
+    "\n",
+    "```\n",
+    "plt.invert_yaxis()          # doesn't work\n",
+    "```\n",
+    "\n",
+    "You have to call it like this:\n",
+    "\n",
+    "```\n",
+    "plt.gca().invert_yaxis()          # works\n",
+    "```\n",
+    "\n",
+    "`gca` stands for \"get current axis\".  It returns an object that represents the axes of the current figure, and that object provides `invert_yaxis`.\n",
+    "\n",
+    "**In case anyone asks:** The most likely reason for this inconsistency in the interface is that `invert_yaxis` is a lesser-used function, so it's not made available at the top level of the interface."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's what the results look like."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "filenames": {
+       "image/png": "/home/downey/AstronomicalData/_build/jupyter_execute/06_photo_12_0.png"
+      },
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_cmd(photo_table)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Our figure does not look exactly like the one in the paper because we are working with a smaller region of the sky, so we don't have as many stars.  But we can see an overdense region in the lower left that contains stars with the photometry we expect for GD-1.\n",
+    "\n",
+    "The authors of the original paper derive a detailed polygon that defines a boundary between stars that are likely to be in GD-1 or not.\n",
+    "\n",
+    "As a simplification, we'll choose a boundary by eye that seems to contain the overdense region."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Drawing a polygon\n",
+    "\n",
+    "Matplotlib provides a function called `ginput` that lets us click on the figure and make a list of coordinates.\n",
+    "\n",
+    "It's a little tricky to use `ginput` in a Jupyter notebook.  \n",
+    "Before calling `plt.ginput` we have to tell Matplotlib to use `TkAgg` to draw the figure in a new window.\n",
+    "\n",
+    "When you run the following cell, a figure should appear in a new window.  Click on it 10 times to draw a polygon around the overdense area.  A red cross should appear where you click."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib as mpl\n",
+    "\n",
+    "if IN_COLAB:\n",
+    "    coords = None\n",
+    "else:\n",
+    "    mpl.use('TkAgg')\n",
+    "    plot_cmd(photo_table)\n",
+    "    coords = plt.ginput(10)\n",
+    "    mpl.use('agg')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The argument to `ginput` is the number of times the user has to click on the figure.\n",
+    "\n",
+    "The result from `ginput` is a list of coordinate pairs."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[(0.2150537634408602, 17.548197203826344),\n",
+       " (0.3897849462365591, 18.94628403237675),\n",
+       " (0.5376344086021505, 19.902869757174393),\n",
+       " (0.7034050179211468, 20.601913171449596),\n",
+       " (0.8288530465949819, 21.300956585724798),\n",
+       " (0.6630824372759856, 21.52170713760118),\n",
+       " (0.4301075268817204, 20.785871964679913),\n",
+       " (0.27329749103942647, 19.71891096394408),\n",
+       " (0.17473118279569888, 18.688741721854306),\n",
+       " (0.17473118279569888, 17.95290654893304)]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "coords"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If `ginput` doesn't work for you, you could use the following coordinates."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if coords is None:\n",
+    "    coords = [(0.2, 17.5), \n",
+    "              (0.2, 19.5), \n",
+    "              (0.65, 22),\n",
+    "              (0.75, 21),\n",
+    "              (0.4, 19),\n",
+    "              (0.4, 17.5)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The next step is to convert the coordinates to a format we can use to plot them, which is a sequence of `x` coordinates and a sequence of `y` coordinates.  The NumPy function `transpose` does what we want. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([0.21505376, 0.38978495, 0.53763441, 0.70340502, 0.82885305,\n",
+       "        0.66308244, 0.43010753, 0.27329749, 0.17473118, 0.17473118]),\n",
+       " array([17.5481972 , 18.94628403, 19.90286976, 20.60191317, 21.30095659,\n",
+       "        21.52170714, 20.78587196, 19.71891096, 18.68874172, 17.95290655]))"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "xs, ys = np.transpose(coords)\n",
+    "xs, ys"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To display the polygon, we'll draw the figure again and use `plt.plot` to draw the polygon."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "filenames": {
+       "image/png": "/home/downey/AstronomicalData/_build/jupyter_execute/06_photo_23_0.png"
+      },
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_cmd(photo_table)\n",
+    "plt.plot(xs, ys);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If it looks like your polygon does a good job surrounding the overdense area, go on to the next section.  Otherwise you can try again.\n",
+    "\n",
+    "If you want a polygon with more points (or fewer), you can change the argument to `ginput`.\n",
+    "\n",
+    "The polygon does not have to be \"closed\".  When we use this polygon in the next section, the last and first points will be connected by a straight line.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Which points are in the polygon?\n",
+    "\n",
+    "Matplotlib provides a `Path` object that we can use to check which points fall in the polygon we selected.\n",
+    "\n",
+    "Here's how we make a `Path` using a list of coordinates."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Path(array([[ 0.21505376, 17.5481972 ],\n",
+       "       [ 0.38978495, 18.94628403],\n",
+       "       [ 0.53763441, 19.90286976],\n",
+       "       [ 0.70340502, 20.60191317],\n",
+       "       [ 0.82885305, 21.30095659],\n",
+       "       [ 0.66308244, 21.52170714],\n",
+       "       [ 0.43010753, 20.78587196],\n",
+       "       [ 0.27329749, 19.71891096],\n",
+       "       [ 0.17473118, 18.68874172],\n",
+       "       [ 0.17473118, 17.95290655]]), None)"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from matplotlib.path import Path\n",
+    "\n",
+    "path = Path(coords)\n",
+    "path"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`Path` provides `contains_points`, which figures out which points are inside the polygon.\n",
+    "\n",
+    "To test it, we'll create a list with two points, one inside the polygon and one outside."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "points = [(0.4, 20), \n",
+    "          (0.4, 30)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can make sure `contains_points` does what we expect."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ True, False])"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "inside = path.contains_points(points)\n",
+    "inside"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The result is an array of Boolean values.\n",
+    "\n",
+    "We are almost ready to select stars whose photometry data falls in this polygon.  But first we need to do some data cleaning."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Reloading the data\n",
+    "\n",
+    "Now we need to combine the photometry data with the list of candidate stars we identified in a previous notebook.  The following cell downloads it:\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from wget import download\n",
+    "\n",
+    "filename = 'gd1_candidates.hdf5'\n",
+    "filepath = 'https://github.com/AllenDowney/AstronomicalData/raw/main/data/'\n",
+    "\n",
+    "if not os.path.exists(filename):\n",
+    "    print(download(filepath+filename))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "candidate_df = pd.read_hdf(filename, 'candidate_df')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`candidate_df` is the Pandas DataFrame that contains the results from Notebook XX, which selects stars likely to be in GD-1 based on proper motion.  It also includes position and proper motion transformed to the ICRS frame."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Merging photometry data\n",
+    "\n",
+    "Before we select stars based on photometry data, we have to solve two problems:\n",
+    "\n",
+    "1. We only have Pan-STARRS data for some stars in `candidate_df`.\n",
+    "\n",
+    "2. Even for the stars where we have Pan-STARRS data in `photo_table`, some photometry data is missing.\n",
+    "\n",
+    "We will solve these problems in two step:\n",
+    "\n",
+    "1. We'll merge the data from `candidate_df` and `photo_table` into a single Pandas `DataFrame`.\n",
+    "\n",
+    "2. We'll use Pandas functions to deal with missing data.\n",
+    "\n",
+    "`candidate_df` is already a `DataFrame`, but `results` is an Astropy `Table`.  Let's convert it to Pandas:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "source_id\n",
+      "g_mean_psf_mag\n",
+      "i_mean_psf_mag\n"
+     ]
+    }
+   ],
+   "source": [
+    "photo_df = photo_table.to_pandas()\n",
+    "\n",
+    "for colname in photo_df.columns:\n",
+    "    print(colname)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we want to combine `candidate_df` and `photo_df` into a single table, using `source_id` to match up the rows.\n",
+    "\n",
+    "You might recognize this task; it's the same as the JOIN operation in ADQL/SQL.\n",
+    "\n",
+    "Pandas provides a function called `merge` that does what we want.  Here's how we use it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>source_id</th>\n",
+       "      <th>ra</th>\n",
+       "      <th>dec</th>\n",
+       "      <th>pmra</th>\n",
+       "      <th>pmdec</th>\n",
+       "      <th>parallax</th>\n",
+       "      <th>parallax_error</th>\n",
+       "      <th>radial_velocity</th>\n",
+       "      <th>phi1</th>\n",
+       "      <th>phi2</th>\n",
+       "      <th>pm_phi1</th>\n",
+       "      <th>pm_phi2</th>\n",
+       "      <th>g_mean_psf_mag</th>\n",
+       "      <th>i_mean_psf_mag</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>635559124339440000</td>\n",
+       "      <td>137.586717</td>\n",
+       "      <td>19.196544</td>\n",
+       "      <td>-3.770522</td>\n",
+       "      <td>-12.490482</td>\n",
+       "      <td>0.791393</td>\n",
+       "      <td>0.271754</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-59.630489</td>\n",
+       "      <td>-1.216485</td>\n",
+       "      <td>-7.361363</td>\n",
+       "      <td>-0.592633</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>635860218726658176</td>\n",
+       "      <td>138.518707</td>\n",
+       "      <td>19.092339</td>\n",
+       "      <td>-5.941679</td>\n",
+       "      <td>-11.346409</td>\n",
+       "      <td>0.307456</td>\n",
+       "      <td>0.199466</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-59.247330</td>\n",
+       "      <td>-2.016078</td>\n",
+       "      <td>-7.527126</td>\n",
+       "      <td>1.748779</td>\n",
+       "      <td>17.8978</td>\n",
+       "      <td>17.517401</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>635674126383965568</td>\n",
+       "      <td>138.842874</td>\n",
+       "      <td>19.031798</td>\n",
+       "      <td>-3.897001</td>\n",
+       "      <td>-12.702780</td>\n",
+       "      <td>0.779463</td>\n",
+       "      <td>0.223692</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-59.133391</td>\n",
+       "      <td>-2.306901</td>\n",
+       "      <td>-7.560608</td>\n",
+       "      <td>-0.741800</td>\n",
+       "      <td>19.2873</td>\n",
+       "      <td>17.678101</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>635535454774983040</td>\n",
+       "      <td>137.837752</td>\n",
+       "      <td>18.864007</td>\n",
+       "      <td>-4.335041</td>\n",
+       "      <td>-14.492309</td>\n",
+       "      <td>0.314514</td>\n",
+       "      <td>0.102775</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-59.785300</td>\n",
+       "      <td>-1.594569</td>\n",
+       "      <td>-9.357536</td>\n",
+       "      <td>-1.218492</td>\n",
+       "      <td>16.9238</td>\n",
+       "      <td>16.478100</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>635497276810313600</td>\n",
+       "      <td>138.044516</td>\n",
+       "      <td>19.009471</td>\n",
+       "      <td>-7.172931</td>\n",
+       "      <td>-12.291499</td>\n",
+       "      <td>0.425404</td>\n",
+       "      <td>0.337689</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-59.557744</td>\n",
+       "      <td>-1.682147</td>\n",
+       "      <td>-9.000831</td>\n",
+       "      <td>2.334407</td>\n",
+       "      <td>19.9242</td>\n",
+       "      <td>18.334000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            source_id          ra        dec      pmra      pmdec  parallax  \\\n",
+       "0  635559124339440000  137.586717  19.196544 -3.770522 -12.490482  0.791393   \n",
+       "1  635860218726658176  138.518707  19.092339 -5.941679 -11.346409  0.307456   \n",
+       "2  635674126383965568  138.842874  19.031798 -3.897001 -12.702780  0.779463   \n",
+       "3  635535454774983040  137.837752  18.864007 -4.335041 -14.492309  0.314514   \n",
+       "4  635497276810313600  138.044516  19.009471 -7.172931 -12.291499  0.425404   \n",
+       "\n",
+       "   parallax_error  radial_velocity       phi1      phi2   pm_phi1   pm_phi2  \\\n",
+       "0        0.271754              NaN -59.630489 -1.216485 -7.361363 -0.592633   \n",
+       "1        0.199466              NaN -59.247330 -2.016078 -7.527126  1.748779   \n",
+       "2        0.223692              NaN -59.133391 -2.306901 -7.560608 -0.741800   \n",
+       "3        0.102775              NaN -59.785300 -1.594569 -9.357536 -1.218492   \n",
+       "4        0.337689              NaN -59.557744 -1.682147 -9.000831  2.334407   \n",
+       "\n",
+       "   g_mean_psf_mag  i_mean_psf_mag  \n",
+       "0             NaN             NaN  \n",
+       "1         17.8978       17.517401  \n",
+       "2         19.2873       17.678101  \n",
+       "3         16.9238       16.478100  \n",
+       "4         19.9242       18.334000  "
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "merged = pd.merge(candidate_df, \n",
+    "                  photo_df, \n",
+    "                  on='source_id', \n",
+    "                  how='left')\n",
+    "merged.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The first argument is the \"left\" table, the second argument is the \"right\" table, and the keyword argument `on='source_id'` specifies a column to use to match up the rows.\n",
+    "\n",
+    "The argument `how='left'` means that the result should have all rows from the left table, even if some of them don't match up with a row in the right table.\n",
+    "\n",
+    "If you are interested in the other options for `how`, you can [read the documentation of `merge`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.merge.html).\n",
+    "\n",
+    "You can also do different types of join in ADQL/SQL; [you can read about that here](https://www.w3schools.com/sql/sql_join.asp).\n",
+    "\n",
+    "The result is a `DataFrame` that contains the same number of rows as `candidate_df`. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(7346, 3724, 7346)"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(candidate_df), len(photo_df), len(merged)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And all columns from both tables."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "source_id\n",
+      "ra\n",
+      "dec\n",
+      "pmra\n",
+      "pmdec\n",
+      "parallax\n",
+      "parallax_error\n",
+      "radial_velocity\n",
+      "phi1\n",
+      "phi2\n",
+      "pm_phi1\n",
+      "pm_phi2\n",
+      "g_mean_psf_mag\n",
+      "i_mean_psf_mag\n"
+     ]
+    }
+   ],
+   "source": [
+    "for colname in merged.columns:\n",
+    "    print(colname)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Detail** You might notice that Pandas also provides a function called `join`; it does almost the same thing, but the interface is slightly different.  We think `merge` is a little easier to use, so that's what we chose.  It's also more consistent with JOIN in SQL, so if you learn how to use `pd.merge`, you are also learning how to use SQL JOIN.\n",
+    "\n",
+    "Also, someone might ask why we have to use Pandas to do this join; why didn't we do it in ADQL.  The answer is that we could have done that, but since we already have the data we need, we should probably do the computation locally rather than make another round trip to the Gaia server."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Missing data\n",
+    "\n",
+    "Let's add columns to the merged table for magnitude and color."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "merged['mag'] = merged['g_mean_psf_mag']\n",
+    "merged['color'] = merged['g_mean_psf_mag'] - merged['i_mean_psf_mag']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "These columns contain the special value `NaN` where we are missing data.\n",
+    "\n",
+    "We can use `notnull` to see which rows contain value data, that is, not null values."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0       False\n",
+       "1        True\n",
+       "2        True\n",
+       "3        True\n",
+       "4        True\n",
+       "        ...  \n",
+       "7341     True\n",
+       "7342    False\n",
+       "7343    False\n",
+       "7344     True\n",
+       "7345    False\n",
+       "Name: color, Length: 7346, dtype: bool"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "merged['color'].notnull()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And `sum` to count the number of valid values."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3724"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "merged['color'].notnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For scientific purposes, it's not obvious what we should do with candidate stars if we don't have photometry data.  Should we give them the benefit of the doubt or leave them out?\n",
+    "\n",
+    "In part the answer depends on the goal: are we trying to identify more stars that might be in GD-1, or a smaller set of stars that have higher probability?\n",
+    "\n",
+    "In the next section, we'll leave them out, but you can experiment with the alternative."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Selecting based on photometry\n",
+    "\n",
+    "Now let's see how many of these points are inside the polygon we chose.\n",
+    "\n",
+    "We can use a list of column names to select `color` and `mag`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>color</th>\n",
+       "      <th>mag</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.3804</td>\n",
+       "      <td>17.8978</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.6092</td>\n",
+       "      <td>19.2873</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.4457</td>\n",
+       "      <td>16.9238</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1.5902</td>\n",
+       "      <td>19.9242</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    color      mag\n",
+       "0     NaN      NaN\n",
+       "1  0.3804  17.8978\n",
+       "2  1.6092  19.2873\n",
+       "3  0.4457  16.9238\n",
+       "4  1.5902  19.9242"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "points = merged[['color', 'mag']]\n",
+    "points.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The result is a `DataFrame` that can be treated as a sequence of coordinates, so we can pass it to `contains_points`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([False, False, False, ..., False, False, False])"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "inside = path.contains_points(points)\n",
+    "inside"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The result is a Boolean array.  We can use `sum` to see how many stars fall in the polygon."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "496"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "inside.sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can use `inside` as a mask to select stars that fall inside the polygon."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "selected = merged[inside]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's make a color-magnitude plot one more time, highlighting the selected stars with green `x` marks."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "filenames": {
+       "image/png": "/home/downey/AstronomicalData/_build/jupyter_execute/06_photo_61_0.png"
+      },
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_cmd(photo_table)\n",
+    "plt.plot(xs, ys)\n",
+    "\n",
+    "plt.plot(selected['color'], selected['mag'], 'gx');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It looks like the selected stars are, in fact, inside the polygon, which means they have photometry data consistent with GD-1.\n",
+    "\n",
+    "Finally, we can plot the coordinates of the selected stars:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x180 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "filenames": {
+       "image/png": "/home/downey/AstronomicalData/_build/jupyter_execute/06_photo_63_0.png"
+      },
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10,2.5))\n",
+    "\n",
+    "x = selected['phi1']\n",
+    "y = selected['phi2']\n",
+    "\n",
+    "plt.plot(x, y, 'ko', markersize=0.7, alpha=0.9)\n",
+    "\n",
+    "plt.xlabel('ra (degree GD1)')\n",
+    "plt.ylabel('dec (degree GD1)')\n",
+    "\n",
+    "plt.axis('equal');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This example includes two new Matplotlib commands:\n",
+    "\n",
+    "* `figure` creates the figure.  In previous examples, we didn't have to use this function; the figure was created automatically.  But when we call it explicitly, we can provide arguments like `figsize`, which sets the size of the figure.\n",
+    "\n",
+    "* `axis` with the parameter `equal` sets up the axes so a unit is the same size along the `x` and `y` axes.\n",
+    "\n",
+    "In an example like this, where `x` and `y` represent coordinates in space, equal axes ensures that the distance between points is represented accurately.   "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Write the data\n",
+    "\n",
+    "Let's write the merged DataFrame to a file."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "filename = 'gd1_merged.hdf5'\n",
+    "\n",
+    "merged.to_hdf(filename, 'merged')\n",
+    "selected.to_hdf(filename, 'selected')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-rw-rw-r-- 1 downey downey 2.0M Oct 19 17:21 gd1_merged.hdf5\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!ls -lh gd1_merged.hdf5"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you are using Windows, `ls` might not work; in that case, try:\n",
+    "\n",
+    "```\n",
+    "!dir gd1_merged.hdf5\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Save the polygon\n",
+    "\n",
+    "[Reproducibile research](https://en.wikipedia.org/wiki/Reproducibility#Reproducible_research) is \"the idea that ... the full computational environment used to produce the results in the paper such as the code, data, etc. can be used to reproduce the results and create new work based on the research.\"\n",
+    "\n",
+    "This Jupyter notebook is an example of reproducible research because it contains all of the code needed to reproduce the results, including the database queries that download the data and and analysis.\n",
+    "\n",
+    "However, when we used `ginput` to define a polygon by hand, we introduced a non-reproducible element to the analysis.  If someone running this notebook chooses a different polygon, they will get different results.  So it is important to record the polygon we chose as part of the data analysis pipeline.\n",
+    "\n",
+    "Since `coords` is a NumPy array, we can't use `to_hdf` to save it in a file.  But we can convert it to a Pandas `DataFrame` and save that.\n",
+    "\n",
+    "As an alternative, we could use [PyTables](http://www.pytables.org/index.html), which is the library Pandas uses to read and write files.  It is a powerful library, but not easy to use directly.  So let's take advantage of Pandas."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "coords_df = pd.DataFrame(coords)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "filename = 'gd1_polygon.hdf5'\n",
+    "coords_df.to_hdf(filename, 'coords_df')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can read it back like this."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "coords2_df = pd.read_hdf(filename, 'coords_df')\n",
+    "coords2 = coords2_df.to_numpy()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And verify that the data we read back is the same."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.all(coords2 == coords)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "In this notebook, we worked with two datasets: the list of candidate stars from Gaia and the photometry data from Pan-STARRS.\n",
+    "\n",
+    "We drew a color-magnitude diagram and used it to identify stars we think are likely to be in GD-1.\n",
+    "\n",
+    "Then we used a Pandas `merge` operation to combine the data into a single `DataFrame`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Best practices\n",
+    "\n",
+    "* If you want to perform something like a database `JOIN` operation with data that is in a Pandas `DataFrame`, you can use the `join` or `merge` function.  In many cases, `merge` is easier to use because the arguments are more like SQL.\n",
+    "\n",
+    "* Use Matplotlib options to control the size and aspect ratio of figures to make them easier to interpret.  In this example, we scaled the axes so the size of a degree is equal along both axes.\n",
+    "\n",
+    "* Matplotlib also provides operations for working with points, polygons, and other geometric entities, so it's not just for making figures.\n",
+    "\n",
+    "* Be sure to record every element of the data analysis pipeline that would be needed to replicate the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/_build/jupyter_execute/06_photo.py b/_build/jupyter_execute/06_photo.py
new file mode 100644
index 0000000..ad2d8a7
--- /dev/null
+++ b/_build/jupyter_execute/06_photo.py
@@ -0,0 +1,569 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# # Chapter 6
+# 
+# This is the sixth in a series of notebooks related to astronomy data.
+# 
+# As a continuing example, we will replicate part of the analysis in a recent paper, "[Off the beaten path: Gaia reveals GD-1 stars outside of the main stream](https://arxiv.org/abs/1805.00425)" by Adrian M. Price-Whelan and Ana Bonaca.
+# 
+# In the previous lesson we downloaded photometry data from Pan-STARRS, which is available from the same server we've been using to get Gaia data. 
+# 
+# The next step in the analysis is to select candidate stars based on the photometry data.  The following figure from the paper is a color-magnitude diagram for the stars selected based on proper motion:
+# 
+# <img width="300" src="https://github.com/datacarpentry/astronomy-python/raw/gh-pages/fig/gd1-3.png">
+# 
+# In red is a theoretical isochrone, showing where we expect the stars in GD-1 to fall based on the metallicity and age of their original globular cluster. 
+# 
+# By selecting stars in the shaded area, we can further distinguish the main sequence of GD-1 from younger background stars.
+
+# ## Outline
+# 
+# Here are the steps in this notebook:
+# 
+# 1. We'll reload the data from the previous notebook and make a color-magnitude diagram.
+# 
+# 2. Then we'll specify a polygon in the diagram that contains stars with the photometry we expect.
+# 
+# 3. Then we'll merge the photometry data with the list of candidate stars, storing the result in a Pandas `DataFrame`.
+# 
+# After completing this lesson, you should be able to
+# 
+# * Use Matplotlib to specify a `Polygon` and determine which points fall inside it.
+# 
+# * Use Pandas to merge data from multiple `DataFrames`, much like a database `JOIN` operation.
+
+# ## Installing libraries
+# 
+# If you are running this notebook on Colab, you can run the following cell to install Astroquery and a the other libraries we'll use.
+# 
+# If you are running this notebook on your own computer, you might have to install these libraries yourself.  
+# 
+# If you are using this notebook as part of a Carpentries workshop, you should have received setup instructions.
+# 
+# TODO: Add a link to the instructions.
+
+# In[1]:
+
+
+# If we're running on Colab, install libraries
+
+import sys
+IN_COLAB = 'google.colab' in sys.modules
+
+if IN_COLAB:
+    get_ipython().system('pip install astroquery astro-gala pyia python-wget')
+
+
+# ## Reload the data
+# 
+# The following cell downloads the photometry data we created in the previous notebook.
+
+# In[2]:
+
+
+import os
+from wget import download
+
+filename = 'gd1_photo.fits'
+filepath = 'https://github.com/AllenDowney/AstronomicalData/raw/main/data/'
+
+if not os.path.exists(filename):
+    print(download(filepath+filename))
+
+
+# Now we can read the data back into an Astropy `Table`.
+
+# In[3]:
+
+
+from astropy.table import Table
+
+photo_table = Table.read(filename)
+
+
+# ## Plotting photometry data
+# 
+# Now that we have photometry data from Pan-STARRS, we can replicate the [color-magnitude diagram](https://en.wikipedia.org/wiki/Galaxy_color%E2%80%93magnitude_diagram) from the original paper:
+# 
+# <img width="300" src="https://github.com/datacarpentry/astronomy-python/raw/gh-pages/fig/gd1-3.png">
+# 
+# The y-axis shows the apparent magnitude of each source with the [g filter](https://en.wikipedia.org/wiki/Photometric_system).
+# 
+# The x-axis shows the difference in apparent magnitude between the g and i filters, which indicates color.
+# 
+# Stars with lower values of (g-i) are brighter in g-band than in i-band, compared to other stars, which means they are bluer.
+# 
+# Stars in the lower-left quadrant of this diagram are less bright and less metallic than the others, which means they are [likely to be older](http://spiff.rit.edu/classes/ladder/lectures/ordinary_stars/ordinary.html).
+# 
+# Since we expect the stars in GD-1 to be older than the background stars, the stars in the lower-left are more likely to be in GD-1.
+
+# In[4]:
+
+
+import matplotlib.pyplot as plt
+
+def plot_cmd(table):
+    """Plot a color magnitude diagram.
+    
+    table: Table or DataFrame with photometry data
+    """
+    y = table['g_mean_psf_mag']
+    x = table['g_mean_psf_mag'] - table['i_mean_psf_mag']
+
+    plt.plot(x, y, 'ko', markersize=0.3, alpha=0.3)
+
+    plt.xlim([0, 1.5])
+    plt.ylim([14, 22])
+    plt.gca().invert_yaxis()
+
+    plt.ylabel('$g_0$')
+    plt.xlabel('$(g-i)_0$')
+
+
+# `plot_cmd` uses a new function, `invert_yaxis`, to invert the `y` axis, which is conventional when plotting magnitudes, since lower magnitude indicates higher brightness.
+# 
+# `invert_yaxis` is a little different from the other functions we've used.  You can't call it like this:
+# 
+# ```
+# plt.invert_yaxis()          # doesn't work
+# ```
+# 
+# You have to call it like this:
+# 
+# ```
+# plt.gca().invert_yaxis()          # works
+# ```
+# 
+# `gca` stands for "get current axis".  It returns an object that represents the axes of the current figure, and that object provides `invert_yaxis`.
+# 
+# **In case anyone asks:** The most likely reason for this inconsistency in the interface is that `invert_yaxis` is a lesser-used function, so it's not made available at the top level of the interface.
+
+# Here's what the results look like.
+
+# In[5]:
+
+
+plot_cmd(photo_table)
+
+
+# Our figure does not look exactly like the one in the paper because we are working with a smaller region of the sky, so we don't have as many stars.  But we can see an overdense region in the lower left that contains stars with the photometry we expect for GD-1.
+# 
+# The authors of the original paper derive a detailed polygon that defines a boundary between stars that are likely to be in GD-1 or not.
+# 
+# As a simplification, we'll choose a boundary by eye that seems to contain the overdense region.
+
+# ## Drawing a polygon
+# 
+# Matplotlib provides a function called `ginput` that lets us click on the figure and make a list of coordinates.
+# 
+# It's a little tricky to use `ginput` in a Jupyter notebook.  
+# Before calling `plt.ginput` we have to tell Matplotlib to use `TkAgg` to draw the figure in a new window.
+# 
+# When you run the following cell, a figure should appear in a new window.  Click on it 10 times to draw a polygon around the overdense area.  A red cross should appear where you click.
+
+# In[6]:
+
+
+import matplotlib as mpl
+
+if IN_COLAB:
+    coords = None
+else:
+    mpl.use('TkAgg')
+    plot_cmd(photo_table)
+    coords = plt.ginput(10)
+    mpl.use('agg')
+
+
+# The argument to `ginput` is the number of times the user has to click on the figure.
+# 
+# The result from `ginput` is a list of coordinate pairs.
+
+# In[7]:
+
+
+coords
+
+
+# If `ginput` doesn't work for you, you could use the following coordinates.
+
+# In[8]:
+
+
+if coords is None:
+    coords = [(0.2, 17.5), 
+              (0.2, 19.5), 
+              (0.65, 22),
+              (0.75, 21),
+              (0.4, 19),
+              (0.4, 17.5)]
+
+
+# The next step is to convert the coordinates to a format we can use to plot them, which is a sequence of `x` coordinates and a sequence of `y` coordinates.  The NumPy function `transpose` does what we want. 
+
+# In[9]:
+
+
+import numpy as np
+
+xs, ys = np.transpose(coords)
+xs, ys
+
+
+# To display the polygon, we'll draw the figure again and use `plt.plot` to draw the polygon.
+
+# In[10]:
+
+
+plot_cmd(photo_table)
+plt.plot(xs, ys);
+
+
+# If it looks like your polygon does a good job surrounding the overdense area, go on to the next section.  Otherwise you can try again.
+# 
+# If you want a polygon with more points (or fewer), you can change the argument to `ginput`.
+# 
+# The polygon does not have to be "closed".  When we use this polygon in the next section, the last and first points will be connected by a straight line.
+# 
+
+# ## Which points are in the polygon?
+# 
+# Matplotlib provides a `Path` object that we can use to check which points fall in the polygon we selected.
+# 
+# Here's how we make a `Path` using a list of coordinates.
+
+# In[11]:
+
+
+from matplotlib.path import Path
+
+path = Path(coords)
+path
+
+
+# `Path` provides `contains_points`, which figures out which points are inside the polygon.
+# 
+# To test it, we'll create a list with two points, one inside the polygon and one outside.
+
+# In[12]:
+
+
+points = [(0.4, 20), 
+          (0.4, 30)]
+
+
+# Now we can make sure `contains_points` does what we expect.
+
+# In[13]:
+
+
+inside = path.contains_points(points)
+inside
+
+
+# The result is an array of Boolean values.
+# 
+# We are almost ready to select stars whose photometry data falls in this polygon.  But first we need to do some data cleaning.
+
+# ## Reloading the data
+# 
+# Now we need to combine the photometry data with the list of candidate stars we identified in a previous notebook.  The following cell downloads it:
+# 
+# 
+
+# In[14]:
+
+
+import os
+from wget import download
+
+filename = 'gd1_candidates.hdf5'
+filepath = 'https://github.com/AllenDowney/AstronomicalData/raw/main/data/'
+
+if not os.path.exists(filename):
+    print(download(filepath+filename))
+
+
+# In[15]:
+
+
+import pandas as pd
+
+candidate_df = pd.read_hdf(filename, 'candidate_df')
+
+
+# `candidate_df` is the Pandas DataFrame that contains the results from Notebook XX, which selects stars likely to be in GD-1 based on proper motion.  It also includes position and proper motion transformed to the ICRS frame.
+
+# ## Merging photometry data
+# 
+# Before we select stars based on photometry data, we have to solve two problems:
+# 
+# 1. We only have Pan-STARRS data for some stars in `candidate_df`.
+# 
+# 2. Even for the stars where we have Pan-STARRS data in `photo_table`, some photometry data is missing.
+# 
+# We will solve these problems in two step:
+# 
+# 1. We'll merge the data from `candidate_df` and `photo_table` into a single Pandas `DataFrame`.
+# 
+# 2. We'll use Pandas functions to deal with missing data.
+# 
+# `candidate_df` is already a `DataFrame`, but `results` is an Astropy `Table`.  Let's convert it to Pandas:
+
+# In[16]:
+
+
+photo_df = photo_table.to_pandas()
+
+for colname in photo_df.columns:
+    print(colname)
+
+
+# Now we want to combine `candidate_df` and `photo_df` into a single table, using `source_id` to match up the rows.
+# 
+# You might recognize this task; it's the same as the JOIN operation in ADQL/SQL.
+# 
+# Pandas provides a function called `merge` that does what we want.  Here's how we use it.
+
+# In[17]:
+
+
+merged = pd.merge(candidate_df, 
+                  photo_df, 
+                  on='source_id', 
+                  how='left')
+merged.head()
+
+
+# The first argument is the "left" table, the second argument is the "right" table, and the keyword argument `on='source_id'` specifies a column to use to match up the rows.
+# 
+# The argument `how='left'` means that the result should have all rows from the left table, even if some of them don't match up with a row in the right table.
+# 
+# If you are interested in the other options for `how`, you can [read the documentation of `merge`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.merge.html).
+# 
+# You can also do different types of join in ADQL/SQL; [you can read about that here](https://www.w3schools.com/sql/sql_join.asp).
+# 
+# The result is a `DataFrame` that contains the same number of rows as `candidate_df`. 
+
+# In[18]:
+
+
+len(candidate_df), len(photo_df), len(merged)
+
+
+# And all columns from both tables.
+
+# In[19]:
+
+
+for colname in merged.columns:
+    print(colname)
+
+
+# **Detail** You might notice that Pandas also provides a function called `join`; it does almost the same thing, but the interface is slightly different.  We think `merge` is a little easier to use, so that's what we chose.  It's also more consistent with JOIN in SQL, so if you learn how to use `pd.merge`, you are also learning how to use SQL JOIN.
+# 
+# Also, someone might ask why we have to use Pandas to do this join; why didn't we do it in ADQL.  The answer is that we could have done that, but since we already have the data we need, we should probably do the computation locally rather than make another round trip to the Gaia server.
+
+# ## Missing data
+# 
+# Let's add columns to the merged table for magnitude and color.
+
+# In[20]:
+
+
+merged['mag'] = merged['g_mean_psf_mag']
+merged['color'] = merged['g_mean_psf_mag'] - merged['i_mean_psf_mag']
+
+
+# These columns contain the special value `NaN` where we are missing data.
+# 
+# We can use `notnull` to see which rows contain value data, that is, not null values.
+
+# In[21]:
+
+
+merged['color'].notnull()
+
+
+# And `sum` to count the number of valid values.
+
+# In[22]:
+
+
+merged['color'].notnull().sum()
+
+
+# For scientific purposes, it's not obvious what we should do with candidate stars if we don't have photometry data.  Should we give them the benefit of the doubt or leave them out?
+# 
+# In part the answer depends on the goal: are we trying to identify more stars that might be in GD-1, or a smaller set of stars that have higher probability?
+# 
+# In the next section, we'll leave them out, but you can experiment with the alternative.
+
+# ## Selecting based on photometry
+# 
+# Now let's see how many of these points are inside the polygon we chose.
+# 
+# We can use a list of column names to select `color` and `mag`.
+
+# In[23]:
+
+
+points = merged[['color', 'mag']]
+points.head()
+
+
+# The result is a `DataFrame` that can be treated as a sequence of coordinates, so we can pass it to `contains_points`:
+
+# In[24]:
+
+
+inside = path.contains_points(points)
+inside
+
+
+# The result is a Boolean array.  We can use `sum` to see how many stars fall in the polygon.
+
+# In[25]:
+
+
+inside.sum()
+
+
+# Now we can use `inside` as a mask to select stars that fall inside the polygon.
+
+# In[26]:
+
+
+selected = merged[inside]
+
+
+# Let's make a color-magnitude plot one more time, highlighting the selected stars with green `x` marks.
+
+# In[27]:
+
+
+plot_cmd(photo_table)
+plt.plot(xs, ys)
+
+plt.plot(selected['color'], selected['mag'], 'gx');
+
+
+# It looks like the selected stars are, in fact, inside the polygon, which means they have photometry data consistent with GD-1.
+# 
+# Finally, we can plot the coordinates of the selected stars:
+
+# In[28]:
+
+
+plt.figure(figsize=(10,2.5))
+
+x = selected['phi1']
+y = selected['phi2']
+
+plt.plot(x, y, 'ko', markersize=0.7, alpha=0.9)
+
+plt.xlabel('ra (degree GD1)')
+plt.ylabel('dec (degree GD1)')
+
+plt.axis('equal');
+
+
+# This example includes two new Matplotlib commands:
+# 
+# * `figure` creates the figure.  In previous examples, we didn't have to use this function; the figure was created automatically.  But when we call it explicitly, we can provide arguments like `figsize`, which sets the size of the figure.
+# 
+# * `axis` with the parameter `equal` sets up the axes so a unit is the same size along the `x` and `y` axes.
+# 
+# In an example like this, where `x` and `y` represent coordinates in space, equal axes ensures that the distance between points is represented accurately.   
+
+# ## Write the data
+# 
+# Let's write the merged DataFrame to a file.
+
+# In[29]:
+
+
+filename = 'gd1_merged.hdf5'
+
+merged.to_hdf(filename, 'merged')
+selected.to_hdf(filename, 'selected')
+
+
+# In[30]:
+
+
+get_ipython().system('ls -lh gd1_merged.hdf5')
+
+
+# If you are using Windows, `ls` might not work; in that case, try:
+# 
+# ```
+# !dir gd1_merged.hdf5
+# ```
+
+# ## Save the polygon
+# 
+# [Reproducibile research](https://en.wikipedia.org/wiki/Reproducibility#Reproducible_research) is "the idea that ... the full computational environment used to produce the results in the paper such as the code, data, etc. can be used to reproduce the results and create new work based on the research."
+# 
+# This Jupyter notebook is an example of reproducible research because it contains all of the code needed to reproduce the results, including the database queries that download the data and and analysis.
+# 
+# However, when we used `ginput` to define a polygon by hand, we introduced a non-reproducible element to the analysis.  If someone running this notebook chooses a different polygon, they will get different results.  So it is important to record the polygon we chose as part of the data analysis pipeline.
+# 
+# Since `coords` is a NumPy array, we can't use `to_hdf` to save it in a file.  But we can convert it to a Pandas `DataFrame` and save that.
+# 
+# As an alternative, we could use [PyTables](http://www.pytables.org/index.html), which is the library Pandas uses to read and write files.  It is a powerful library, but not easy to use directly.  So let's take advantage of Pandas.
+
+# In[31]:
+
+
+coords_df = pd.DataFrame(coords)
+
+
+# In[32]:
+
+
+filename = 'gd1_polygon.hdf5'
+coords_df.to_hdf(filename, 'coords_df')
+
+
+# We can read it back like this.
+
+# In[33]:
+
+
+coords2_df = pd.read_hdf(filename, 'coords_df')
+coords2 = coords2_df.to_numpy()
+
+
+# And verify that the data we read back is the same.
+
+# In[34]:
+
+
+np.all(coords2 == coords)
+
+
+# ## Summary
+# 
+# In this notebook, we worked with two datasets: the list of candidate stars from Gaia and the photometry data from Pan-STARRS.
+# 
+# We drew a color-magnitude diagram and used it to identify stars we think are likely to be in GD-1.
+# 
+# Then we used a Pandas `merge` operation to combine the data into a single `DataFrame`.
+
+# ## Best practices
+# 
+# * If you want to perform something like a database `JOIN` operation with data that is in a Pandas `DataFrame`, you can use the `join` or `merge` function.  In many cases, `merge` is easier to use because the arguments are more like SQL.
+# 
+# * Use Matplotlib options to control the size and aspect ratio of figures to make them easier to interpret.  In this example, we scaled the axes so the size of a degree is equal along both axes.
+# 
+# * Matplotlib also provides operations for working with points, polygons, and other geometric entities, so it's not just for making figures.
+# 
+# * Be sure to record every element of the data analysis pipeline that would be needed to replicate the results.
+
+# In[ ]:
+
+
+
+
diff --git a/_build/jupyter_execute/06_photo_12_0.png b/_build/jupyter_execute/06_photo_12_0.png
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diff --git a/_build/jupyter_execute/07_plot.ipynb b/_build/jupyter_execute/07_plot.ipynb
new file mode 100644
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@@ -0,0 +1,1197 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Chapter 7\n",
+    "\n",
+    "This is the seventh in a series of notebooks related to astronomy data.\n",
+    "\n",
+    "As a continuing example, we will replicate part of the analysis in a recent paper, \"[Off the beaten path: Gaia reveals GD-1 stars outside of the main stream](https://arxiv.org/abs/1805.00425)\" by Adrian M. Price-Whelan and Ana Bonaca.\n",
+    "\n",
+    "In the previous notebook we selected photometry data from Pan-STARRS and used it to identify stars we think are likely to be in GD-1\n",
+    "\n",
+    "In this notebook, we'll take the results from previous lessons and use them to make a figure that tells a compelling scientific story."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Outline\n",
+    "\n",
+    "Here are the steps in this notebook:\n",
+    "\n",
+    "1. Starting with the figure from the previous notebook, we'll add annotations to present the results more clearly.\n",
+    "\n",
+    "2. The we'll see several ways to customize figures to make them more appealing and effective.\n",
+    "\n",
+    "3. Finally, we'll see how to make a figure with multiple panels or subplots.\n",
+    "\n",
+    "After completing this lesson, you should be able to\n",
+    "\n",
+    "* Design a figure that tells a compelling story.\n",
+    "\n",
+    "* Use Matplotlib features to customize the appearance of figures.\n",
+    "\n",
+    "* Generate a figure with multiple subplots."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Installing libraries\n",
+    "\n",
+    "If you are running this notebook on Colab, you can run the following cell to install Astroquery and a the other libraries we'll use.\n",
+    "\n",
+    "If you are running this notebook on your own computer, you might have to install these libraries yourself.  \n",
+    "\n",
+    "If you are using this notebook as part of a Carpentries workshop, you should have received setup instructions.\n",
+    "\n",
+    "TODO: Add a link to the instructions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# If we're running on Colab, install libraries\n",
+    "\n",
+    "import sys\n",
+    "IN_COLAB = 'google.colab' in sys.modules\n",
+    "\n",
+    "if IN_COLAB:\n",
+    "    !pip install astroquery astro-gala pyia python-wget"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Making Figures That Tell a Story\n",
+    "\n",
+    "So far the figure we've made have been \"quick and dirty\".  Mostly we have used Matplotlib's default style, although we have adjusted a few parameters, like `markersize` and `alpha`, to improve legibility.\n",
+    "\n",
+    "Now that the analysis is done, it's time to think more about:\n",
+    "\n",
+    "1. Making professional-looking figures that are ready for publication, and\n",
+    "\n",
+    "2. Making figures that communicate a scientific result clearly and compellingly.\n",
+    "\n",
+    "Not necessarily in that order."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's start by reviewing Figure 1 from the original paper.  We've seen the individual panels, but now let's look at the whole thing, along with the caption:\n",
+    "\n",
+    "<img width=\"500\" src=\"https://github.com/datacarpentry/astronomy-python/raw/gh-pages/fig/gd1-5.png\">"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Think about the following questions:\n",
+    "\n",
+    "1. What is the primary scientific result of this work?\n",
+    "\n",
+    "2. What story is this figure telling?\n",
+    "\n",
+    "3. In the design of this figure, can you identify 1-2 choices the authors made that you think are effective?  Think about big-picture elements, like the number of panels and how they are arranged, as well as details like the choice of typeface.\n",
+    "\n",
+    "4. Can you identify 1-2 elements that could be improved, or that you might have done differently?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Some topics that might come up in this discussion:\n",
+    "\n",
+    "1. The primary result is that the multiple stages of selection make it possible to separate likely candidates from the background more effectively than in previous work, which makes it possible to see the structure of GD-1 in \"unprecedented detail\".\n",
+    "\n",
+    "2. The figure documents the selection process as a sequence of steps.  Reading right-to-left, top-to-bottom, we see selection based on proper motion, the results of the first selection, selection based on color and magnitude, and the results of the second selection.  So this figure documents the methodology and presents the primary result.\n",
+    "\n",
+    "3. It's mostly black and white, with minimal use of color, so it will work well in print.  The annotations in the bottom left panel guide the reader to the most important results.  It contains enough technical detail for a professional audience, but most of it is also comprehensible to a more general audience.  The two left panels have the same dimensions and their axes are aligned.\n",
+    "\n",
+    "4. Since the panels represent a sequence, it might be better to arrange them left-to-right.  The placement and size of the axis labels could be tweaked.  The entire figure could be a little bigger to match the width and proportion of the caption.  The top left panel has unnused white space (but that leaves space for the annotations in the bottom left)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plotting GD-1\n",
+    "\n",
+    "Let's start with the panel in the lower left.  The following cell reloads the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from wget import download\n",
+    "\n",
+    "filename = 'gd1_merged.hdf5'\n",
+    "path = 'https://github.com/AllenDowney/AstronomicalData/raw/main/data/'\n",
+    "\n",
+    "if not os.path.exists(filename):\n",
+    "    print(download(path+filename))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "selected = pd.read_hdf(filename, 'selected')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "def plot_second_selection(df):\n",
+    "    x = df['phi1']\n",
+    "    y = df['phi2']\n",
+    "\n",
+    "    plt.plot(x, y, 'ko', markersize=0.7, alpha=0.9)\n",
+    "\n",
+    "    plt.xlabel('$\\phi_1$ [deg]')\n",
+    "    plt.ylabel('$\\phi_2$ [deg]')\n",
+    "    plt.title('Proper motion + photometry selection', fontsize='medium')\n",
+    "\n",
+    "    plt.axis('equal')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And here's what it looks like."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x180 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "filenames": {
+       "image/png": "/home/downey/AstronomicalData/_build/jupyter_execute/07_plot_13_0.png"
+      },
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10,2.5))\n",
+    "plot_second_selection(selected)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Annotations\n",
+    "\n",
+    "The figure in the paper uses three other features to present the results more clearly and compellingly:\n",
+    "\n",
+    "* A vertical dashed line to distinguish the previously undetected region of GD-1,\n",
+    "\n",
+    "* A label that identifies the new region, and\n",
+    "\n",
+    "* Several annotations that combine text and arrows to identify features of GD-1.\n",
+    "\n",
+    "As an exercise, choose any or all of these features and add them to the figure:\n",
+    "\n",
+    "* To draw vertical lines, see [`plt.vlines`](https://matplotlib.org/3.3.1/api/_as_gen/matplotlib.pyplot.vlines.html) and [`plt.axvline`](https://matplotlib.org/3.3.1/api/_as_gen/matplotlib.pyplot.axvline.html#matplotlib.pyplot.axvline).\n",
+    "\n",
+    "* To add text, see [`plt.text`](https://matplotlib.org/3.3.1/api/_as_gen/matplotlib.pyplot.text.html).\n",
+    "\n",
+    "* To add an annotation with text and an arrow, see [plt.annotate]().\n",
+    "\n",
+    "And here is some [additional information about text and arrows](https://matplotlib.org/3.3.1/tutorials/text/annotations.html#plotting-guide-annotation)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "# plt.axvline(-55, ls='--', color='gray', \n",
+    "#             alpha=0.4, dashes=(6,4), lw=2)\n",
+    "# plt.text(-60, 5.5, 'Previously\\nundetected', \n",
+    "#          fontsize='small', ha='right', va='top');\n",
+    "\n",
+    "# arrowprops=dict(color='gray', shrink=0.05, width=1.5, \n",
+    "#                 headwidth=6, headlength=8, alpha=0.4)\n",
+    "\n",
+    "# plt.annotate('Spur', xy=(-33, 2), xytext=(-35, 5.5),\n",
+    "#              arrowprops=arrowprops,\n",
+    "#              fontsize='small')\n",
+    "\n",
+    "# plt.annotate('Gap', xy=(-22, -1), xytext=(-25, -5.5),\n",
+    "#              arrowprops=arrowprops,\n",
+    "#              fontsize='small')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Customization\n",
+    "\n",
+    "Matplotlib provides a default style that determines things like the colors of lines, the placement of labels and ticks on the axes, and many other properties.\n",
+    "\n",
+    "There are several ways to override these defaults and customize your figures:\n",
+    "\n",
+    "* To customize only the current figure, you can call functions like `tick_params`, which we'll demonstrate below.\n",
+    "\n",
+    "* To customize all figures in a notebook, you use `rcParams`.\n",
+    "\n",
+    "* To override more than a few defaults at the same time, you can use a style sheet."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As a simple example, notice that Matplotlib puts ticks on the outside of the figures by default, and only on the left and bottom sides of the axes.\n",
+    "\n",
+    "To change this behavior, you can use `gca()` to get the current axes and `tick_params` to change the settings.\n",
+    "\n",
+    "Here's how you can put the ticks on the inside of the figure:\n",
+    "\n",
+    "```\n",
+    "plt.gca().tick_params(direction='in')\n",
+    "```\n",
+    "\n",
+    "**Exercise:** Read the documentation of [`tick_params`](https://matplotlib.org/3.1.1/api/_as_gen/matplotlib.axes.Axes.tick_params.html) and use it to put ticks on the top and right sides of the axes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "# plt.gca().tick_params(top=True, right=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## rcParams\n",
+    "\n",
+    "If you want to make a customization that applies to all figures in a notebook, you can use `rcParams`.\n",
+    "\n",
+    "Here's an example that reads the current font size from `rcParams`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "10.0"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "plt.rcParams['font.size']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And sets it to a new value:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.rcParams['font.size'] = 14"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Plot the previous figure again, and see what font sizes have changed.  Look up any other element of `rcParams`, change its value, and check the effect on the figure."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you find yourself making the same customizations in several notebooks, you can put changes to `rcParams` in a `matplotlibrc` file, [which you can read about here](https://matplotlib.org/3.3.1/tutorials/introductory/customizing.html#customizing-with-matplotlibrc-files)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Style sheets\n",
+    "\n",
+    "The `matplotlibrc` file is read when you import Matplotlib, so it is not easy to switch from one set of options to another.\n",
+    "\n",
+    "The solution to this problem is style sheets, [which you can read about here](https://matplotlib.org/3.1.1/tutorials/introductory/customizing.html).\n",
+    "\n",
+    "Matplotlib provides a set of predefined style sheets, or you can make your own.\n",
+    "\n",
+    "The following cell displays a list of style sheets installed on your system."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Solarize_Light2',\n",
+       " '_classic_test_patch',\n",
+       " 'bmh',\n",
+       " 'classic',\n",
+       " 'dark_background',\n",
+       " 'fast',\n",
+       " 'fivethirtyeight',\n",
+       " 'ggplot',\n",
+       " 'grayscale',\n",
+       " 'seaborn',\n",
+       " 'seaborn-bright',\n",
+       " 'seaborn-colorblind',\n",
+       " 'seaborn-dark',\n",
+       " 'seaborn-dark-palette',\n",
+       " 'seaborn-darkgrid',\n",
+       " 'seaborn-deep',\n",
+       " 'seaborn-muted',\n",
+       " 'seaborn-notebook',\n",
+       " 'seaborn-paper',\n",
+       " 'seaborn-pastel',\n",
+       " 'seaborn-poster',\n",
+       " 'seaborn-talk',\n",
+       " 'seaborn-ticks',\n",
+       " 'seaborn-white',\n",
+       " 'seaborn-whitegrid',\n",
+       " 'tableau-colorblind10']"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "plt.style.available"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that `seaborn-paper`, `seaborn-talk` and `seaborn-poster` are particularly intended to prepare versions of a figure with text sizes and other features that work well in papers, talks, and posters.\n",
+    "\n",
+    "To use any of these style sheets, run `plt.style.use` like this:\n",
+    "\n",
+    "```\n",
+    "plt.style.use('fivethirtyeight')\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The style sheet you choose will affect the appearance of all figures you plot after calling `use`, unless you override any of the options or call `use` again.\n",
+    "\n",
+    "**Exercise:** Choose one of the styles on the list and select it by calling `use`.  Then go back and plot one of the figures above and see what effect it has."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you can't find a style sheet that's exactly what you want, you can make your own.  This repository includes a style sheet called `az-paper-twocol.mplstyle`, with customizations chosen by Azalee Bostroem for publication in astronomy journals.\n",
+    "\n",
+    "The following cell downloads the style sheet."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "filename = 'az-paper-twocol.mplstyle'\n",
+    "path = 'https://github.com/AllenDowney/AstronomicalData/raw/main/data/'\n",
+    "\n",
+    "if not os.path.exists(filename):\n",
+    "    print(download(path+filename))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can use it like this:\n",
+    "\n",
+    "```\n",
+    "plt.style.use('./az-paper-twocol.mplstyle')\n",
+    "```\n",
+    "\n",
+    "The prefix `./` tells Matplotlib to look for the file in the current directory."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As an alternative, you can install a style sheet for your own use by putting it in your configuration directory.  To find out where that is, you can run the following command:\n",
+    "\n",
+    "```\n",
+    "import matplotlib as mpl\n",
+    "\n",
+    "mpl.get_configdir()\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## LaTeX fonts\n",
+    "\n",
+    "When you include mathematical expressions in titles, labels, and annotations, Matplotlib uses [`mathtext`](https://matplotlib.org/3.1.0/tutorials/text/mathtext.html) to typeset them.  `mathtext` uses the same syntax as LaTeX, but it provides only a subset of its features.\n",
+    "\n",
+    "If you need features that are not provided by `mathtext`, or you prefer the way LaTeX typesets mathematical expressions, you can customize Matplotlib to use LaTeX.\n",
+    "\n",
+    "In `matplotlibrc` or in a style sheet, you can add the following line:\n",
+    "\n",
+    "```\n",
+    "text.usetex        : true\n",
+    "```\n",
+    "\n",
+    "Or in a notebook you can run the following code.\n",
+    "\n",
+    "```\n",
+    "plt.rcParams['text.usetex'] = True\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.rcParams['text.usetex'] = True"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you go back and draw the figure again, you should see the difference.\n",
+    "\n",
+    "If you get an error message like\n",
+    "\n",
+    "```\n",
+    "LaTeX Error: File `type1cm.sty' not found.\n",
+    "```\n",
+    "\n",
+    "You might have to install a package that contains the fonts LaTeX needs.  On some systems, the packages `texlive-latex-extra` or `cm-super` might be what you need.  [See here for more help with this](https://stackoverflow.com/questions/11354149/python-unable-to-render-tex-in-matplotlib).\n",
+    "\n",
+    "In case you are curious, `cm` stands for [Computer Modern](https://en.wikipedia.org/wiki/Computer_Modern), the font LaTeX uses to typeset math."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Multiple panels\n",
+    "\n",
+    "So far we've been working with one figure at a time, but the figure we are replicating contains multiple panels, also known as \"subplots\".\n",
+    "\n",
+    "Confusingly, Matplotlib provides *three* functions for making figures like this: `subplot`, `subplots`, and `subplot2grid`.\n",
+    "\n",
+    "* [`subplot`](https://matplotlib.org/3.3.1/api/_as_gen/matplotlib.pyplot.subplot.html) is simple and similar to MATLAB, so if you are familiar with that interface, you might like `subplot`\n",
+    "\n",
+    "* [`subplots`](https://matplotlib.org/3.3.1/api/_as_gen/matplotlib.pyplot.subplots.html) is more object-oriented, which some people prefer.\n",
+    "\n",
+    "* [`subplot2grid`](https://matplotlib.org/3.3.1/api/_as_gen/matplotlib.pyplot.subplot2grid.html) is most convenient if you want to control the relative sizes of the subplots. \n",
+    "\n",
+    "So we'll use `subplot2grid`.\n",
+    "\n",
+    "All of these functions are easier to use if we put the code that generates each panel in a function."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Upper right\n",
+    "\n",
+    "To make the panel in the upper right, we have to reload `centerline`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "filename = 'gd1_dataframe.hdf5'\n",
+    "path = 'https://github.com/AllenDowney/AstronomicalData/raw/main/data/'\n",
+    "\n",
+    "if not os.path.exists(filename):\n",
+    "    print(download(path+filename))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "centerline = pd.read_hdf(filename, 'centerline')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And define the coordinates of the rectangle we selected."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pm1_min = -8.9\n",
+    "pm1_max = -6.9\n",
+    "pm2_min = -2.2\n",
+    "pm2_max =  1.0\n",
+    "\n",
+    "pm1_rect = [pm1_min, pm1_min, pm1_max, pm1_max]\n",
+    "pm2_rect = [pm2_min, pm2_max, pm2_max, pm2_min]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To plot this rectangle, we'll use a feature we have not seen before: `Polygon`, which is provided by Matplotlib.\n",
+    "\n",
+    "To create a `Polygon`, we have to put the coordinates in an array with `x` values in the first column and `y` values in the second column. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[-8.9, -2.2],\n",
+       "       [-8.9,  1. ],\n",
+       "       [-6.9,  1. ],\n",
+       "       [-6.9, -2.2]])"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "vertices = np.transpose([pm1_rect, pm2_rect])\n",
+    "vertices"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The following function takes a `DataFrame` as a parameter, plots the proper motion for each star, and adds a shaded `Polygon` to show the region we selected."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from matplotlib.patches import Polygon\n",
+    "\n",
+    "def plot_proper_motion(df):\n",
+    "    pm1 = df['pm_phi1']\n",
+    "    pm2 = df['pm_phi2']\n",
+    "\n",
+    "    plt.plot(pm1, pm2, 'ko', markersize=0.3, alpha=0.3)\n",
+    "    \n",
+    "    poly = Polygon(vertices, closed=True, \n",
+    "                   facecolor='C1', alpha=0.4)\n",
+    "    plt.gca().add_patch(poly)\n",
+    "    \n",
+    "    plt.xlabel('$\\mu_{\\phi_1} [\\mathrm{mas~yr}^{-1}]$')\n",
+    "    plt.ylabel('$\\mu_{\\phi_2} [\\mathrm{mas~yr}^{-1}]$')\n",
+    "\n",
+    "    plt.xlim(-12, 8)\n",
+    "    plt.ylim(-10, 10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notice that `add_patch` is like `invert_yaxis`; in order to call it, we have to use `gca` to get the current axes.\n",
+    "\n",
+    "Here's what the new version of the figure looks like.  We've changed the labels on the axes to be consistent with the paper."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "filenames": {
+       "image/png": "/home/downey/AstronomicalData/_build/jupyter_execute/07_plot_50_0.png"
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.rcParams['text.usetex'] = False\n",
+    "plt.style.use('default')\n",
+    "\n",
+    "plot_proper_motion(centerline)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Upper left\n",
+    "\n",
+    "Now let's work on the panel in the upper left. We have to reload `candidates`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "filename = 'gd1_candidates.hdf5'\n",
+    "path = 'https://github.com/AllenDowney/AstronomicalData/raw/main/data/'\n",
+    "\n",
+    "if not os.path.exists(filename):\n",
+    "    print(download(path+filename))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "filename = 'gd1_candidates.hdf5'\n",
+    "\n",
+    "candidate_df = pd.read_hdf(filename, 'candidate_df')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's a function that takes a `DataFrame` of candidate stars and plots their positions in GD-1 coordindates. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_first_selection(df):\n",
+    "    x = df['phi1']\n",
+    "    y = df['phi2']\n",
+    "\n",
+    "    plt.plot(x, y, 'ko', markersize=0.3, alpha=0.3)\n",
+    "\n",
+    "    plt.xlabel('$\\phi_1$ [deg]')\n",
+    "    plt.ylabel('$\\phi_2$ [deg]')\n",
+    "    plt.title('Proper motion selection', fontsize='medium')\n",
+    "\n",
+    "    plt.axis('equal')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And here's what it looks like."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "filenames": {
+       "image/png": "/home/downey/AstronomicalData/_build/jupyter_execute/07_plot_57_0.png"
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_first_selection(candidate_df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Lower right\n",
+    "\n",
+    "For the figure in the lower right, we need to reload the merged `DataFrame`, which contains data from Gaia and photometry data from Pan-STARRS."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "filename = 'gd1_merged.hdf5'\n",
+    "\n",
+    "merged = pd.read_hdf(filename, 'merged')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "From the previous notebook, here's the function that plots the color-magnitude diagram."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "def plot_cmd(table):\n",
+    "    \"\"\"Plot a color magnitude diagram.\n",
+    "    \n",
+    "    table: Table or DataFrame with photometry data\n",
+    "    \"\"\"\n",
+    "    y = table['g_mean_psf_mag']\n",
+    "    x = table['g_mean_psf_mag'] - table['i_mean_psf_mag']\n",
+    "\n",
+    "    plt.plot(x, y, 'ko', markersize=0.3, alpha=0.3)\n",
+    "\n",
+    "    plt.xlim([0, 1.5])\n",
+    "    plt.ylim([14, 22])\n",
+    "    plt.gca().invert_yaxis()\n",
+    "\n",
+    "    plt.ylabel('$g_0$')\n",
+    "    plt.xlabel('$(g-i)_0$')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And here's what it looks like."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "filenames": {
+       "image/png": "/home/downey/AstronomicalData/_build/jupyter_execute/07_plot_63_0.png"
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_cmd(merged)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Add a few lines to `plot_cmd` to show the Polygon we selected as a shaded area.  \n",
+    "\n",
+    "Run these cells to get the polygon coordinates we saved in the previous notebook."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "filename = 'gd1_polygon.hdf5'\n",
+    "path = 'https://github.com/AllenDowney/AstronomicalData/raw/main/data/'\n",
+    "\n",
+    "if not os.path.exists(filename):\n",
+    "    print(download(path+filename))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 0.21505376, 17.5481972 ],\n",
+       "       [ 0.38978495, 18.94628403],\n",
+       "       [ 0.53763441, 19.90286976],\n",
+       "       [ 0.70340502, 20.60191317],\n",
+       "       [ 0.82885305, 21.30095659],\n",
+       "       [ 0.66308244, 21.52170714],\n",
+       "       [ 0.43010753, 20.78587196],\n",
+       "       [ 0.27329749, 19.71891096],\n",
+       "       [ 0.17473118, 18.68874172],\n",
+       "       [ 0.17473118, 17.95290655]])"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "coords_df = pd.read_hdf(filename, 'coords_df')\n",
+    "coords = coords_df.to_numpy()\n",
+    "coords"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "#poly = Polygon(coords, closed=True, \n",
+    "#               facecolor='C1', alpha=0.4)\n",
+    "#plt.gca().add_patch(poly)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Subplots\n",
+    "\n",
+    "Now we're ready to put it all together.  To make a figure with four subplots, we'll use `subplot2grid`, [which requires two arguments](https://matplotlib.org/3.3.1/api/_as_gen/matplotlib.pyplot.subplot2grid.html):\n",
+    "\n",
+    "* `shape`, which is a tuple with the number of rows and columns in the grid, and\n",
+    "\n",
+    "* `loc`, which is a tuple identifying the location in the grid we're about to fill.\n",
+    "\n",
+    "In this example, `shape` is `(2, 2)` to create two rows and two columns.\n",
+    "\n",
+    "For the first panel, `loc` is `(0, 0)`, which indicates row 0 and column 0, which is the upper-left panel.\n",
+    "\n",
+    "Here's how we use it to draw the four panels."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "filenames": {
+       "image/png": "/home/downey/AstronomicalData/_build/jupyter_execute/07_plot_69_0.png"
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "shape = (2, 2)\n",
+    "plt.subplot2grid(shape, (0, 0))\n",
+    "plot_first_selection(candidate_df)\n",
+    "\n",
+    "plt.subplot2grid(shape, (0, 1))\n",
+    "plot_proper_motion(centerline)\n",
+    "\n",
+    "plt.subplot2grid(shape, (1, 0))\n",
+    "plot_second_selection(selected)\n",
+    "\n",
+    "plt.subplot2grid(shape, (1, 1))\n",
+    "plot_cmd(merged)\n",
+    "poly = Polygon(coords, closed=True, \n",
+    "               facecolor='C1', alpha=0.4)\n",
+    "plt.gca().add_patch(poly)\n",
+    "\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We use [`plt.tight_layout`](https://matplotlib.org/3.3.1/tutorials/intermediate/tight_layout_guide.html) at the end, which adjusts the sizes of the panels to make sure the titles and axis labels don't overlap.\n",
+    "\n",
+    "**Exercise:** See what happens if you leave out `tight_layout`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Adjusting proportions\n",
+    "\n",
+    "In the previous figure, the panels are all the same size.  To get a better view of GD-1, we'd like to stretch the panels on the left and compress the ones on the right.\n",
+    "\n",
+    "To do that, we'll use the `colspan` argument to make a panel that spans multiple columns in the grid.\n",
+    "\n",
+    "In the following example, `shape` is `(2, 4)`, which means 2 rows and 4 columns.\n",
+    "\n",
+    "The panels on the left span three columns, so they are three times wider than the panels on the right.\n",
+    "\n",
+    "At the same time, we use `figsize` to adjust the aspect ratio of the whole figure."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 900x450 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "filenames": {
+       "image/png": "/home/downey/AstronomicalData/_build/jupyter_execute/07_plot_72_0.png"
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(9, 4.5))\n",
+    "\n",
+    "shape = (2, 4)\n",
+    "plt.subplot2grid(shape, (0, 0), colspan=3)\n",
+    "plot_first_selection(candidate_df)\n",
+    "\n",
+    "plt.subplot2grid(shape, (0, 3))\n",
+    "plot_proper_motion(centerline)\n",
+    "\n",
+    "plt.subplot2grid(shape, (1, 0), colspan=3)\n",
+    "plot_second_selection(selected)\n",
+    "\n",
+    "plt.subplot2grid(shape, (1, 3))\n",
+    "plot_cmd(merged)\n",
+    "poly = Polygon(coords, closed=True, \n",
+    "               facecolor='C1', alpha=0.4)\n",
+    "plt.gca().add_patch(poly)\n",
+    "\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is looking more and more like the figure in the paper.\n",
+    "\n",
+    "**Exercise:** In this example, the ratio of the widths of the panels is 3:1.  How would you adjust it if you wanted the ratio to be 3:2?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "In this notebook, we reverse-engineered the figure we've been replicating, identifying elements that seem effective and others that could be improved.\n",
+    "\n",
+    "We explored features Matplotlib provides for adding annotations to figures -- including text, lines, arrows, and polygons -- and several ways to customize the appearance of figures.  And we learned how to create figures that contain multiple panels."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Best practices\n",
+    "\n",
+    "* The most effective figures focus on telling a single story clearly and compellingly.\n",
+    "\n",
+    "* Consider using annotations to guide the readers attention to the most important elements of a figure.\n",
+    "\n",
+    "* The default Matplotlib style generates good quality figures, but there are several ways you can override the defaults.\n",
+    "\n",
+    "* If you find yourself making the same customizations on several projects, you might want to create your own style sheet."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/_build/jupyter_execute/07_plot.py b/_build/jupyter_execute/07_plot.py
new file mode 100644
index 0000000..f8a7447
--- /dev/null
+++ b/_build/jupyter_execute/07_plot.py
@@ -0,0 +1,691 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# # Chapter 7
+# 
+# This is the seventh in a series of notebooks related to astronomy data.
+# 
+# As a continuing example, we will replicate part of the analysis in a recent paper, "[Off the beaten path: Gaia reveals GD-1 stars outside of the main stream](https://arxiv.org/abs/1805.00425)" by Adrian M. Price-Whelan and Ana Bonaca.
+# 
+# In the previous notebook we selected photometry data from Pan-STARRS and used it to identify stars we think are likely to be in GD-1
+# 
+# In this notebook, we'll take the results from previous lessons and use them to make a figure that tells a compelling scientific story.
+
+# ## Outline
+# 
+# Here are the steps in this notebook:
+# 
+# 1. Starting with the figure from the previous notebook, we'll add annotations to present the results more clearly.
+# 
+# 2. The we'll see several ways to customize figures to make them more appealing and effective.
+# 
+# 3. Finally, we'll see how to make a figure with multiple panels or subplots.
+# 
+# After completing this lesson, you should be able to
+# 
+# * Design a figure that tells a compelling story.
+# 
+# * Use Matplotlib features to customize the appearance of figures.
+# 
+# * Generate a figure with multiple subplots.
+
+# ## Installing libraries
+# 
+# If you are running this notebook on Colab, you can run the following cell to install Astroquery and a the other libraries we'll use.
+# 
+# If you are running this notebook on your own computer, you might have to install these libraries yourself.  
+# 
+# If you are using this notebook as part of a Carpentries workshop, you should have received setup instructions.
+# 
+# TODO: Add a link to the instructions.
+
+# In[1]:
+
+
+# If we're running on Colab, install libraries
+
+import sys
+IN_COLAB = 'google.colab' in sys.modules
+
+if IN_COLAB:
+    get_ipython().system('pip install astroquery astro-gala pyia python-wget')
+
+
+# ## Making Figures That Tell a Story
+# 
+# So far the figure we've made have been "quick and dirty".  Mostly we have used Matplotlib's default style, although we have adjusted a few parameters, like `markersize` and `alpha`, to improve legibility.
+# 
+# Now that the analysis is done, it's time to think more about:
+# 
+# 1. Making professional-looking figures that are ready for publication, and
+# 
+# 2. Making figures that communicate a scientific result clearly and compellingly.
+# 
+# Not necessarily in that order.
+
+# Let's start by reviewing Figure 1 from the original paper.  We've seen the individual panels, but now let's look at the whole thing, along with the caption:
+# 
+# <img width="500" src="https://github.com/datacarpentry/astronomy-python/raw/gh-pages/fig/gd1-5.png">
+
+# **Exercise:** Think about the following questions:
+# 
+# 1. What is the primary scientific result of this work?
+# 
+# 2. What story is this figure telling?
+# 
+# 3. In the design of this figure, can you identify 1-2 choices the authors made that you think are effective?  Think about big-picture elements, like the number of panels and how they are arranged, as well as details like the choice of typeface.
+# 
+# 4. Can you identify 1-2 elements that could be improved, or that you might have done differently?
+
+# Some topics that might come up in this discussion:
+# 
+# 1. The primary result is that the multiple stages of selection make it possible to separate likely candidates from the background more effectively than in previous work, which makes it possible to see the structure of GD-1 in "unprecedented detail".
+# 
+# 2. The figure documents the selection process as a sequence of steps.  Reading right-to-left, top-to-bottom, we see selection based on proper motion, the results of the first selection, selection based on color and magnitude, and the results of the second selection.  So this figure documents the methodology and presents the primary result.
+# 
+# 3. It's mostly black and white, with minimal use of color, so it will work well in print.  The annotations in the bottom left panel guide the reader to the most important results.  It contains enough technical detail for a professional audience, but most of it is also comprehensible to a more general audience.  The two left panels have the same dimensions and their axes are aligned.
+# 
+# 4. Since the panels represent a sequence, it might be better to arrange them left-to-right.  The placement and size of the axis labels could be tweaked.  The entire figure could be a little bigger to match the width and proportion of the caption.  The top left panel has unnused white space (but that leaves space for the annotations in the bottom left).
+
+# ## Plotting GD-1
+# 
+# Let's start with the panel in the lower left.  The following cell reloads the data.
+
+# In[2]:
+
+
+import os
+from wget import download
+
+filename = 'gd1_merged.hdf5'
+path = 'https://github.com/AllenDowney/AstronomicalData/raw/main/data/'
+
+if not os.path.exists(filename):
+    print(download(path+filename))
+
+
+# In[3]:
+
+
+import pandas as pd
+
+selected = pd.read_hdf(filename, 'selected')
+
+
+# In[4]:
+
+
+import matplotlib.pyplot as plt
+
+def plot_second_selection(df):
+    x = df['phi1']
+    y = df['phi2']
+
+    plt.plot(x, y, 'ko', markersize=0.7, alpha=0.9)
+
+    plt.xlabel('$\phi_1$ [deg]')
+    plt.ylabel('$\phi_2$ [deg]')
+    plt.title('Proper motion + photometry selection', fontsize='medium')
+
+    plt.axis('equal')
+
+
+# And here's what it looks like.
+
+# In[5]:
+
+
+plt.figure(figsize=(10,2.5))
+plot_second_selection(selected)
+
+
+# ## Annotations
+# 
+# The figure in the paper uses three other features to present the results more clearly and compellingly:
+# 
+# * A vertical dashed line to distinguish the previously undetected region of GD-1,
+# 
+# * A label that identifies the new region, and
+# 
+# * Several annotations that combine text and arrows to identify features of GD-1.
+# 
+# As an exercise, choose any or all of these features and add them to the figure:
+# 
+# * To draw vertical lines, see [`plt.vlines`](https://matplotlib.org/3.3.1/api/_as_gen/matplotlib.pyplot.vlines.html) and [`plt.axvline`](https://matplotlib.org/3.3.1/api/_as_gen/matplotlib.pyplot.axvline.html#matplotlib.pyplot.axvline).
+# 
+# * To add text, see [`plt.text`](https://matplotlib.org/3.3.1/api/_as_gen/matplotlib.pyplot.text.html).
+# 
+# * To add an annotation with text and an arrow, see [plt.annotate]().
+# 
+# And here is some [additional information about text and arrows](https://matplotlib.org/3.3.1/tutorials/text/annotations.html#plotting-guide-annotation).
+
+# In[6]:
+
+
+# Solution
+
+# plt.axvline(-55, ls='--', color='gray', 
+#             alpha=0.4, dashes=(6,4), lw=2)
+# plt.text(-60, 5.5, 'Previously\nundetected', 
+#          fontsize='small', ha='right', va='top');
+
+# arrowprops=dict(color='gray', shrink=0.05, width=1.5, 
+#                 headwidth=6, headlength=8, alpha=0.4)
+
+# plt.annotate('Spur', xy=(-33, 2), xytext=(-35, 5.5),
+#              arrowprops=arrowprops,
+#              fontsize='small')
+
+# plt.annotate('Gap', xy=(-22, -1), xytext=(-25, -5.5),
+#              arrowprops=arrowprops,
+#              fontsize='small')
+
+
+# ## Customization
+# 
+# Matplotlib provides a default style that determines things like the colors of lines, the placement of labels and ticks on the axes, and many other properties.
+# 
+# There are several ways to override these defaults and customize your figures:
+# 
+# * To customize only the current figure, you can call functions like `tick_params`, which we'll demonstrate below.
+# 
+# * To customize all figures in a notebook, you use `rcParams`.
+# 
+# * To override more than a few defaults at the same time, you can use a style sheet.
+
+# As a simple example, notice that Matplotlib puts ticks on the outside of the figures by default, and only on the left and bottom sides of the axes.
+# 
+# To change this behavior, you can use `gca()` to get the current axes and `tick_params` to change the settings.
+# 
+# Here's how you can put the ticks on the inside of the figure:
+# 
+# ```
+# plt.gca().tick_params(direction='in')
+# ```
+# 
+# **Exercise:** Read the documentation of [`tick_params`](https://matplotlib.org/3.1.1/api/_as_gen/matplotlib.axes.Axes.tick_params.html) and use it to put ticks on the top and right sides of the axes.
+
+# In[7]:
+
+
+# Solution
+
+# plt.gca().tick_params(top=True, right=True)
+
+
+# ## rcParams
+# 
+# If you want to make a customization that applies to all figures in a notebook, you can use `rcParams`.
+# 
+# Here's an example that reads the current font size from `rcParams`:
+
+# In[8]:
+
+
+plt.rcParams['font.size']
+
+
+# And sets it to a new value:
+
+# In[9]:
+
+
+plt.rcParams['font.size'] = 14
+
+
+# **Exercise:** Plot the previous figure again, and see what font sizes have changed.  Look up any other element of `rcParams`, change its value, and check the effect on the figure.
+
+# If you find yourself making the same customizations in several notebooks, you can put changes to `rcParams` in a `matplotlibrc` file, [which you can read about here](https://matplotlib.org/3.3.1/tutorials/introductory/customizing.html#customizing-with-matplotlibrc-files).
+
+# ## Style sheets
+# 
+# The `matplotlibrc` file is read when you import Matplotlib, so it is not easy to switch from one set of options to another.
+# 
+# The solution to this problem is style sheets, [which you can read about here](https://matplotlib.org/3.1.1/tutorials/introductory/customizing.html).
+# 
+# Matplotlib provides a set of predefined style sheets, or you can make your own.
+# 
+# The following cell displays a list of style sheets installed on your system.
+
+# In[10]:
+
+
+plt.style.available
+
+
+# Note that `seaborn-paper`, `seaborn-talk` and `seaborn-poster` are particularly intended to prepare versions of a figure with text sizes and other features that work well in papers, talks, and posters.
+# 
+# To use any of these style sheets, run `plt.style.use` like this:
+# 
+# ```
+# plt.style.use('fivethirtyeight')
+# ```
+
+# In[ ]:
+
+
+
+
+
+# The style sheet you choose will affect the appearance of all figures you plot after calling `use`, unless you override any of the options or call `use` again.
+# 
+# **Exercise:** Choose one of the styles on the list and select it by calling `use`.  Then go back and plot one of the figures above and see what effect it has.
+
+# If you can't find a style sheet that's exactly what you want, you can make your own.  This repository includes a style sheet called `az-paper-twocol.mplstyle`, with customizations chosen by Azalee Bostroem for publication in astronomy journals.
+# 
+# The following cell downloads the style sheet.
+
+# In[11]:
+
+
+import os
+
+filename = 'az-paper-twocol.mplstyle'
+path = 'https://github.com/AllenDowney/AstronomicalData/raw/main/data/'
+
+if not os.path.exists(filename):
+    print(download(path+filename))
+
+
+# You can use it like this:
+# 
+# ```
+# plt.style.use('./az-paper-twocol.mplstyle')
+# ```
+# 
+# The prefix `./` tells Matplotlib to look for the file in the current directory.
+
+# In[ ]:
+
+
+
+
+
+# As an alternative, you can install a style sheet for your own use by putting it in your configuration directory.  To find out where that is, you can run the following command:
+# 
+# ```
+# import matplotlib as mpl
+# 
+# mpl.get_configdir()
+# ```
+
+# In[ ]:
+
+
+
+
+
+# ## LaTeX fonts
+# 
+# When you include mathematical expressions in titles, labels, and annotations, Matplotlib uses [`mathtext`](https://matplotlib.org/3.1.0/tutorials/text/mathtext.html) to typeset them.  `mathtext` uses the same syntax as LaTeX, but it provides only a subset of its features.
+# 
+# If you need features that are not provided by `mathtext`, or you prefer the way LaTeX typesets mathematical expressions, you can customize Matplotlib to use LaTeX.
+# 
+# In `matplotlibrc` or in a style sheet, you can add the following line:
+# 
+# ```
+# text.usetex        : true
+# ```
+# 
+# Or in a notebook you can run the following code.
+# 
+# ```
+# plt.rcParams['text.usetex'] = True
+# ```
+
+# In[12]:
+
+
+plt.rcParams['text.usetex'] = True
+
+
+# If you go back and draw the figure again, you should see the difference.
+# 
+# If you get an error message like
+# 
+# ```
+# LaTeX Error: File `type1cm.sty' not found.
+# ```
+# 
+# You might have to install a package that contains the fonts LaTeX needs.  On some systems, the packages `texlive-latex-extra` or `cm-super` might be what you need.  [See here for more help with this](https://stackoverflow.com/questions/11354149/python-unable-to-render-tex-in-matplotlib).
+# 
+# In case you are curious, `cm` stands for [Computer Modern](https://en.wikipedia.org/wiki/Computer_Modern), the font LaTeX uses to typeset math.
+
+# ## Multiple panels
+# 
+# So far we've been working with one figure at a time, but the figure we are replicating contains multiple panels, also known as "subplots".
+# 
+# Confusingly, Matplotlib provides *three* functions for making figures like this: `subplot`, `subplots`, and `subplot2grid`.
+# 
+# * [`subplot`](https://matplotlib.org/3.3.1/api/_as_gen/matplotlib.pyplot.subplot.html) is simple and similar to MATLAB, so if you are familiar with that interface, you might like `subplot`
+# 
+# * [`subplots`](https://matplotlib.org/3.3.1/api/_as_gen/matplotlib.pyplot.subplots.html) is more object-oriented, which some people prefer.
+# 
+# * [`subplot2grid`](https://matplotlib.org/3.3.1/api/_as_gen/matplotlib.pyplot.subplot2grid.html) is most convenient if you want to control the relative sizes of the subplots. 
+# 
+# So we'll use `subplot2grid`.
+# 
+# All of these functions are easier to use if we put the code that generates each panel in a function.
+
+# ## Upper right
+# 
+# To make the panel in the upper right, we have to reload `centerline`.
+
+# In[13]:
+
+
+import os
+
+filename = 'gd1_dataframe.hdf5'
+path = 'https://github.com/AllenDowney/AstronomicalData/raw/main/data/'
+
+if not os.path.exists(filename):
+    print(download(path+filename))
+
+
+# In[14]:
+
+
+import pandas as pd
+
+centerline = pd.read_hdf(filename, 'centerline')
+
+
+# And define the coordinates of the rectangle we selected.
+
+# In[15]:
+
+
+pm1_min = -8.9
+pm1_max = -6.9
+pm2_min = -2.2
+pm2_max =  1.0
+
+pm1_rect = [pm1_min, pm1_min, pm1_max, pm1_max]
+pm2_rect = [pm2_min, pm2_max, pm2_max, pm2_min]
+
+
+# To plot this rectangle, we'll use a feature we have not seen before: `Polygon`, which is provided by Matplotlib.
+# 
+# To create a `Polygon`, we have to put the coordinates in an array with `x` values in the first column and `y` values in the second column. 
+
+# In[16]:
+
+
+import numpy as np
+
+vertices = np.transpose([pm1_rect, pm2_rect])
+vertices
+
+
+# The following function takes a `DataFrame` as a parameter, plots the proper motion for each star, and adds a shaded `Polygon` to show the region we selected.
+
+# In[17]:
+
+
+from matplotlib.patches import Polygon
+
+def plot_proper_motion(df):
+    pm1 = df['pm_phi1']
+    pm2 = df['pm_phi2']
+
+    plt.plot(pm1, pm2, 'ko', markersize=0.3, alpha=0.3)
+    
+    poly = Polygon(vertices, closed=True, 
+                   facecolor='C1', alpha=0.4)
+    plt.gca().add_patch(poly)
+    
+    plt.xlabel('$\mu_{\phi_1} [\mathrm{mas~yr}^{-1}]$')
+    plt.ylabel('$\mu_{\phi_2} [\mathrm{mas~yr}^{-1}]$')
+
+    plt.xlim(-12, 8)
+    plt.ylim(-10, 10)
+
+
+# Notice that `add_patch` is like `invert_yaxis`; in order to call it, we have to use `gca` to get the current axes.
+# 
+# Here's what the new version of the figure looks like.  We've changed the labels on the axes to be consistent with the paper.
+
+# In[18]:
+
+
+plt.rcParams['text.usetex'] = False
+plt.style.use('default')
+
+plot_proper_motion(centerline)
+
+
+# ## Upper left
+# 
+# Now let's work on the panel in the upper left. We have to reload `candidates`.
+
+# In[19]:
+
+
+import os
+
+filename = 'gd1_candidates.hdf5'
+path = 'https://github.com/AllenDowney/AstronomicalData/raw/main/data/'
+
+if not os.path.exists(filename):
+    print(download(path+filename))
+
+
+# In[20]:
+
+
+import pandas as pd
+
+filename = 'gd1_candidates.hdf5'
+
+candidate_df = pd.read_hdf(filename, 'candidate_df')
+
+
+# Here's a function that takes a `DataFrame` of candidate stars and plots their positions in GD-1 coordindates. 
+
+# In[21]:
+
+
+def plot_first_selection(df):
+    x = df['phi1']
+    y = df['phi2']
+
+    plt.plot(x, y, 'ko', markersize=0.3, alpha=0.3)
+
+    plt.xlabel('$\phi_1$ [deg]')
+    plt.ylabel('$\phi_2$ [deg]')
+    plt.title('Proper motion selection', fontsize='medium')
+
+    plt.axis('equal')
+
+
+# And here's what it looks like.
+
+# In[22]:
+
+
+plot_first_selection(candidate_df)
+
+
+# ## Lower right
+# 
+# For the figure in the lower right, we need to reload the merged `DataFrame`, which contains data from Gaia and photometry data from Pan-STARRS.
+
+# In[23]:
+
+
+import pandas as pd
+
+filename = 'gd1_merged.hdf5'
+
+merged = pd.read_hdf(filename, 'merged')
+
+
+# From the previous notebook, here's the function that plots the color-magnitude diagram.
+
+# In[24]:
+
+
+import matplotlib.pyplot as plt
+
+def plot_cmd(table):
+    """Plot a color magnitude diagram.
+    
+    table: Table or DataFrame with photometry data
+    """
+    y = table['g_mean_psf_mag']
+    x = table['g_mean_psf_mag'] - table['i_mean_psf_mag']
+
+    plt.plot(x, y, 'ko', markersize=0.3, alpha=0.3)
+
+    plt.xlim([0, 1.5])
+    plt.ylim([14, 22])
+    plt.gca().invert_yaxis()
+
+    plt.ylabel('$g_0$')
+    plt.xlabel('$(g-i)_0$')
+
+
+# And here's what it looks like.
+
+# In[25]:
+
+
+plot_cmd(merged)
+
+
+# **Exercise:** Add a few lines to `plot_cmd` to show the Polygon we selected as a shaded area.  
+# 
+# Run these cells to get the polygon coordinates we saved in the previous notebook.
+
+# In[26]:
+
+
+import os
+
+filename = 'gd1_polygon.hdf5'
+path = 'https://github.com/AllenDowney/AstronomicalData/raw/main/data/'
+
+if not os.path.exists(filename):
+    print(download(path+filename))
+
+
+# In[27]:
+
+
+coords_df = pd.read_hdf(filename, 'coords_df')
+coords = coords_df.to_numpy()
+coords
+
+
+# In[28]:
+
+
+# Solution
+
+#poly = Polygon(coords, closed=True, 
+#               facecolor='C1', alpha=0.4)
+#plt.gca().add_patch(poly)
+
+
+# ## Subplots
+# 
+# Now we're ready to put it all together.  To make a figure with four subplots, we'll use `subplot2grid`, [which requires two arguments](https://matplotlib.org/3.3.1/api/_as_gen/matplotlib.pyplot.subplot2grid.html):
+# 
+# * `shape`, which is a tuple with the number of rows and columns in the grid, and
+# 
+# * `loc`, which is a tuple identifying the location in the grid we're about to fill.
+# 
+# In this example, `shape` is `(2, 2)` to create two rows and two columns.
+# 
+# For the first panel, `loc` is `(0, 0)`, which indicates row 0 and column 0, which is the upper-left panel.
+# 
+# Here's how we use it to draw the four panels.
+
+# In[29]:
+
+
+shape = (2, 2)
+plt.subplot2grid(shape, (0, 0))
+plot_first_selection(candidate_df)
+
+plt.subplot2grid(shape, (0, 1))
+plot_proper_motion(centerline)
+
+plt.subplot2grid(shape, (1, 0))
+plot_second_selection(selected)
+
+plt.subplot2grid(shape, (1, 1))
+plot_cmd(merged)
+poly = Polygon(coords, closed=True, 
+               facecolor='C1', alpha=0.4)
+plt.gca().add_patch(poly)
+
+plt.tight_layout()
+
+
+# We use [`plt.tight_layout`](https://matplotlib.org/3.3.1/tutorials/intermediate/tight_layout_guide.html) at the end, which adjusts the sizes of the panels to make sure the titles and axis labels don't overlap.
+# 
+# **Exercise:** See what happens if you leave out `tight_layout`.
+
+# ## Adjusting proportions
+# 
+# In the previous figure, the panels are all the same size.  To get a better view of GD-1, we'd like to stretch the panels on the left and compress the ones on the right.
+# 
+# To do that, we'll use the `colspan` argument to make a panel that spans multiple columns in the grid.
+# 
+# In the following example, `shape` is `(2, 4)`, which means 2 rows and 4 columns.
+# 
+# The panels on the left span three columns, so they are three times wider than the panels on the right.
+# 
+# At the same time, we use `figsize` to adjust the aspect ratio of the whole figure.
+
+# In[30]:
+
+
+plt.figure(figsize=(9, 4.5))
+
+shape = (2, 4)
+plt.subplot2grid(shape, (0, 0), colspan=3)
+plot_first_selection(candidate_df)
+
+plt.subplot2grid(shape, (0, 3))
+plot_proper_motion(centerline)
+
+plt.subplot2grid(shape, (1, 0), colspan=3)
+plot_second_selection(selected)
+
+plt.subplot2grid(shape, (1, 3))
+plot_cmd(merged)
+poly = Polygon(coords, closed=True, 
+               facecolor='C1', alpha=0.4)
+plt.gca().add_patch(poly)
+
+plt.tight_layout()
+
+
+# This is looking more and more like the figure in the paper.
+# 
+# **Exercise:** In this example, the ratio of the widths of the panels is 3:1.  How would you adjust it if you wanted the ratio to be 3:2?
+
+# ## Summary
+# 
+# In this notebook, we reverse-engineered the figure we've been replicating, identifying elements that seem effective and others that could be improved.
+# 
+# We explored features Matplotlib provides for adding annotations to figures -- including text, lines, arrows, and polygons -- and several ways to customize the appearance of figures.  And we learned how to create figures that contain multiple panels.
+
+# ## Best practices
+# 
+# * The most effective figures focus on telling a single story clearly and compellingly.
+# 
+# * Consider using annotations to guide the readers attention to the most important elements of a figure.
+# 
+# * The default Matplotlib style generates good quality figures, but there are several ways you can override the defaults.
+# 
+# * If you find yourself making the same customizations on several projects, you might want to create your own style sheet.
+
+# In[ ]:
+
+
+
+
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Lesson 1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Introduction\n",
+    "\n",
+    "This workshop is an introduction to tools and practices for working with astronomical data.  Topics covered include:\n",
+    "\n",
+    "* Writing queries that select and download data from a database.\n",
+    "\n",
+    "* Using data stored in an Astropy `Table` or Pandas `DataFrame`.\n",
+    "\n",
+    "* Working with coordinates and other quantities with units.\n",
+    "\n",
+    "* Storing data in various formats.\n",
+    "\n",
+    "* Performing database join operations that combine data from multiple tables.\n",
+    "\n",
+    "* Visualizing data and preparing publication-quality figures."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As a running example, we will replicate part of the analysis in a recent paper, \"[Off the beaten path: Gaia reveals GD-1 stars outside of the main stream](https://arxiv.org/abs/1805.00425)\" by Adrian M. Price-Whelan and Ana Bonaca.\n",
+    "\n",
+    "As the abstract explains, \"Using data from the Gaia second data release combined with Pan-STARRS photometry, we present a sample of highly-probable members of the longest cold stream in the Milky Way, GD-1.\"\n",
+    "\n",
+    "GD-1 is a [stellar stream](https://en.wikipedia.org/wiki/List_of_stellar_streams), which is \"an association of stars orbiting a galaxy that was once a globular cluster or dwarf galaxy that has now been torn apart and stretched out along its orbit by tidal forces.\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[This article in *Science* magazine](https://www.sciencemag.org/news/2018/10/streams-stars-reveal-galaxy-s-violent-history-and-perhaps-its-unseen-dark-matter) explains some of the background, including the process that led to the paper and an discussion of the scientific implications:\n",
+    "\n",
+    "* \"The streams are particularly useful for ... galactic archaeology --- rewinding the cosmic clock to reconstruct the assembly of the Milky Way.\"\n",
+    "\n",
+    "* \"They also are being used as exquisitely sensitive scales to measure the galaxy's mass.\"\n",
+    "\n",
+    "* \"... the streams are well-positioned to reveal the presence of dark matter ... because the streams are so fragile, theorists say, collisions with marauding clumps of dark matter could leave telltale scars, potential clues to its nature.\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Prerequisites\n",
+    "\n",
+    "This workshop is meant for people who are familiar with basic Python, but not necessarily the libraries we will use, like Astropy or Pandas.  If you are familiar with Python lists and dictionaries, and you know how to write a function that takes parameters and returns a value, you know enough Python for this workshop.\n",
+    "\n",
+    "We assume that you have some familiarity with operating systems, like the ability to use a command-line interface.  But we don't assume you have any prior experience with databases.\n",
+    "\n",
+    "We assume that you are familiar with astronomy at the undergraduate level, but we will not assume specialized knowledge of the datasets or analysis methods we'll use. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Data\n",
+    "\n",
+    "The datasets we will work with are:\n",
+    " \n",
+    "* [Gaia](https://en.wikipedia.org/wiki/Gaia_(spacecraft)), which is \"a space observatory of the European Space Agency (ESA), launched in 2013 ... designed for astrometry: measuring the positions, distances and motions of stars with unprecedented precision\", and\n",
+    "\n",
+    "* [Pan-STARRS](https://en.wikipedia.org/wiki/Pan-STARRS), The Panoramic Survey Telescope and Rapid Response System, which is a survey designed to monitor the sky for transient objects, producing a catalog with accurate astronometry and photometry of detected sources.\n",
+    "\n",
+    "Both of these datasets are very large, which can make them challenging to work with.  It might not be possible, or practical, to download the entire dataset.\n",
+    "One of the goals of this workshop is to provide tools for working with large datasets."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Lesson 1\n",
+    "\n",
+    "The first lesson demonstrates the steps for selecting and downloading data from the Gaia Database:\n",
+    "\n",
+    "1. First we'll make a connection to the Gaia server,\n",
+    "\n",
+    "2. We will explore information about the database and the tables it contains,\n",
+    "\n",
+    "3. We will write a query and send it to the server, and finally\n",
+    "\n",
+    "4. We will download the response from the server.\n",
+    "\n",
+    "After completing this lesson, you should be able to\n",
+    "\n",
+    "* Compose a basic query in ADQL.\n",
+    "\n",
+    "* Use queries to explore a database and its tables.\n",
+    "\n",
+    "* Use queries to download data.\n",
+    "\n",
+    "* Develop, test, and debug a query incrementally."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Query Language\n",
+    "\n",
+    "In order to select data from a database, you have to compose a query, which is like a program written in a \"query language\".\n",
+    "The query language we'll use is ADQL, which stands for \"Astronomical Data Query Language\".\n",
+    "\n",
+    "ADQL is a dialect of [SQL](https://en.wikipedia.org/wiki/SQL) (Structured Query Language), which is by far the most commonly used query language.  Almost everything you will learn about ADQL also works in SQL.\n",
+    "\n",
+    "[The reference manual for ADQL is here](http://www.ivoa.net/documents/ADQL/20180112/PR-ADQL-2.1-20180112.html).\n",
+    "But you might find it easier to learn from [this ADQL Cookbook](https://www.gaia.ac.uk/data/gaia-data-release-1/adql-cookbook)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Installing libraries\n",
+    "\n",
+    "The library we'll use to get Gaia data is [Astroquery](https://astroquery.readthedocs.io/en/latest/).\n",
+    "\n",
+    "If you are running this notebook on Colab, you can run the following cell to install Astroquery and the other libraries we'll use.\n",
+    "\n",
+    "If you are running this notebook on your own computer, you might have to install these libraries yourself.  \n",
+    "\n",
+    "If you are using this notebook as part of a Carpentries workshop, you should have received setup instructions.\n",
+    "\n",
+    "TODO: Add a link to the instructions.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# If we're running on Colab, install libraries\n",
+    "\n",
+    "import sys\n",
+    "IN_COLAB = 'google.colab' in sys.modules\n",
+    "\n",
+    "if IN_COLAB:\n",
+    "    !pip install astroquery astro-gala pyia"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Connecting to Gaia\n",
+    "\n",
+    "Astroquery provides `Gaia`, which is an [object that represents a connection to the Gaia database](https://astroquery.readthedocs.io/en/latest/gaia/gaia.html).\n",
+    "\n",
+    "We can connect to the Gaia database like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Created TAP+ (v1.2.1) - Connection:\n",
+      "\tHost: gea.esac.esa.int\n",
+      "\tUse HTTPS: True\n",
+      "\tPort: 443\n",
+      "\tSSL Port: 443\n",
+      "Created TAP+ (v1.2.1) - Connection:\n",
+      "\tHost: geadata.esac.esa.int\n",
+      "\tUse HTTPS: True\n",
+      "\tPort: 443\n",
+      "\tSSL Port: 443\n"
+     ]
+    }
+   ],
+   "source": [
+    "from astroquery.gaia import Gaia"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Optional detail \n",
+    "\n",
+    "> Running this import statement has the effect of creating a [TAP+](http://www.ivoa.net/documents/TAP/) connection; TAP stands for \"Table Access Protocol\".  It is a network protocol for sending queries to the database and getting back the results.  We're not sure why it seems to create two connections."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Databases and Tables\n",
+    "\n",
+    "What is a database, anyway?  Most generally, it can be any collection of data, but when we are talking about ADQL or SQL:\n",
+    "\n",
+    "* A database is a collection of one or more named tables.\n",
+    "\n",
+    "* Each table is a 2-D array with one or more named columns of data.\n",
+    "\n",
+    "We can use `Gaia.load_tables` to get the names of the tables in the Gaia database.  With the option `only_names=True`, it loads information about the tables, called the \"metadata\", not the data itself."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO: Retrieving tables... [astroquery.utils.tap.core]\n",
+      "INFO: Parsing tables... [astroquery.utils.tap.core]\n",
+      "INFO: Done. [astroquery.utils.tap.core]\n"
+     ]
+    }
+   ],
+   "source": [
+    "tables = Gaia.load_tables(only_names=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "external.external.apassdr9\n",
+      "external.external.gaiadr2_geometric_distance\n",
+      "external.external.galex_ais\n",
+      "external.external.ravedr5_com\n",
+      "external.external.ravedr5_dr5\n",
+      "external.external.ravedr5_gra\n",
+      "external.external.ravedr5_on\n",
+      "external.external.sdssdr13_photoprimary\n",
+      "external.external.skymapperdr1_master\n",
+      "external.external.tmass_xsc\n",
+      "public.public.hipparcos\n",
+      "public.public.hipparcos_newreduction\n",
+      "public.public.hubble_sc\n",
+      "public.public.igsl_source\n",
+      "public.public.igsl_source_catalog_ids\n",
+      "public.public.tycho2\n",
+      "public.public.dual\n",
+      "tap_config.tap_config.coord_sys\n",
+      "tap_config.tap_config.properties\n",
+      "tap_schema.tap_schema.columns\n",
+      "tap_schema.tap_schema.key_columns\n",
+      "tap_schema.tap_schema.keys\n",
+      "tap_schema.tap_schema.schemas\n",
+      "tap_schema.tap_schema.tables\n",
+      "gaiadr1.gaiadr1.aux_qso_icrf2_match\n",
+      "gaiadr1.gaiadr1.ext_phot_zero_point\n",
+      "gaiadr1.gaiadr1.allwise_best_neighbour\n",
+      "gaiadr1.gaiadr1.allwise_neighbourhood\n",
+      "gaiadr1.gaiadr1.gsc23_best_neighbour\n",
+      "gaiadr1.gaiadr1.gsc23_neighbourhood\n",
+      "gaiadr1.gaiadr1.ppmxl_best_neighbour\n",
+      "gaiadr1.gaiadr1.ppmxl_neighbourhood\n",
+      "gaiadr1.gaiadr1.sdss_dr9_best_neighbour\n",
+      "gaiadr1.gaiadr1.sdss_dr9_neighbourhood\n",
+      "gaiadr1.gaiadr1.tmass_best_neighbour\n",
+      "gaiadr1.gaiadr1.tmass_neighbourhood\n",
+      "gaiadr1.gaiadr1.ucac4_best_neighbour\n",
+      "gaiadr1.gaiadr1.ucac4_neighbourhood\n",
+      "gaiadr1.gaiadr1.urat1_best_neighbour\n",
+      "gaiadr1.gaiadr1.urat1_neighbourhood\n",
+      "gaiadr1.gaiadr1.cepheid\n",
+      "gaiadr1.gaiadr1.phot_variable_time_series_gfov\n",
+      "gaiadr1.gaiadr1.phot_variable_time_series_gfov_statistical_parameters\n",
+      "gaiadr1.gaiadr1.rrlyrae\n",
+      "gaiadr1.gaiadr1.variable_summary\n",
+      "gaiadr1.gaiadr1.allwise_original_valid\n",
+      "gaiadr1.gaiadr1.gsc23_original_valid\n",
+      "gaiadr1.gaiadr1.ppmxl_original_valid\n",
+      "gaiadr1.gaiadr1.sdssdr9_original_valid\n",
+      "gaiadr1.gaiadr1.tmass_original_valid\n",
+      "gaiadr1.gaiadr1.ucac4_original_valid\n",
+      "gaiadr1.gaiadr1.urat1_original_valid\n",
+      "gaiadr1.gaiadr1.gaia_source\n",
+      "gaiadr1.gaiadr1.tgas_source\n",
+      "gaiadr2.gaiadr2.aux_allwise_agn_gdr2_cross_id\n",
+      "gaiadr2.gaiadr2.aux_iers_gdr2_cross_id\n",
+      "gaiadr2.gaiadr2.aux_sso_orbit_residuals\n",
+      "gaiadr2.gaiadr2.aux_sso_orbits\n",
+      "gaiadr2.gaiadr2.dr1_neighbourhood\n",
+      "gaiadr2.gaiadr2.allwise_best_neighbour\n",
+      "gaiadr2.gaiadr2.allwise_neighbourhood\n",
+      "gaiadr2.gaiadr2.apassdr9_best_neighbour\n",
+      "gaiadr2.gaiadr2.apassdr9_neighbourhood\n",
+      "gaiadr2.gaiadr2.gsc23_best_neighbour\n",
+      "gaiadr2.gaiadr2.gsc23_neighbourhood\n",
+      "gaiadr2.gaiadr2.hipparcos2_best_neighbour\n",
+      "gaiadr2.gaiadr2.hipparcos2_neighbourhood\n",
+      "gaiadr2.gaiadr2.panstarrs1_best_neighbour\n",
+      "gaiadr2.gaiadr2.panstarrs1_neighbourhood\n",
+      "gaiadr2.gaiadr2.ppmxl_best_neighbour\n",
+      "gaiadr2.gaiadr2.ppmxl_neighbourhood\n",
+      "gaiadr2.gaiadr2.ravedr5_best_neighbour\n",
+      "gaiadr2.gaiadr2.ravedr5_neighbourhood\n",
+      "gaiadr2.gaiadr2.sdssdr9_best_neighbour\n",
+      "gaiadr2.gaiadr2.sdssdr9_neighbourhood\n",
+      "gaiadr2.gaiadr2.tmass_best_neighbour\n",
+      "gaiadr2.gaiadr2.tmass_neighbourhood\n",
+      "gaiadr2.gaiadr2.tycho2_best_neighbour\n",
+      "gaiadr2.gaiadr2.tycho2_neighbourhood\n",
+      "gaiadr2.gaiadr2.urat1_best_neighbour\n",
+      "gaiadr2.gaiadr2.urat1_neighbourhood\n",
+      "gaiadr2.gaiadr2.sso_observation\n",
+      "gaiadr2.gaiadr2.sso_source\n",
+      "gaiadr2.gaiadr2.vari_cepheid\n",
+      "gaiadr2.gaiadr2.vari_classifier_class_definition\n",
+      "gaiadr2.gaiadr2.vari_classifier_definition\n",
+      "gaiadr2.gaiadr2.vari_classifier_result\n",
+      "gaiadr2.gaiadr2.vari_long_period_variable\n",
+      "gaiadr2.gaiadr2.vari_rotation_modulation\n",
+      "gaiadr2.gaiadr2.vari_rrlyrae\n",
+      "gaiadr2.gaiadr2.vari_short_timescale\n",
+      "gaiadr2.gaiadr2.vari_time_series_statistics\n",
+      "gaiadr2.gaiadr2.panstarrs1_original_valid\n",
+      "gaiadr2.gaiadr2.gaia_source\n",
+      "gaiadr2.gaiadr2.ruwe\n"
+     ]
+    }
+   ],
+   "source": [
+    "for table in (tables):\n",
+    "    print(table.get_qualified_name())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "So that's a lot of tables.  The ones we'll use are:\n",
+    "\n",
+    "* `gaiadr2.gaia_source`, which contains Gaia data from [data release 2](https://www.cosmos.esa.int/web/gaia/data-release-2),\n",
+    "\n",
+    "* `gaiadr2.panstarrs1_original_valid`, which contains the photometry data we'll use from PanSTARRS, and\n",
+    "\n",
+    "* `gaiadr2.panstarrs1_best_neighbour`, which we'll use to cross-match each star observed by Gaia with the same star observed by PanSTARRS.\n",
+    "\n",
+    "We can use `load_table` (not `load_tables`) to get the metadata for a single table.  The name of this function is misleading, because it only downloads metadata. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Retrieving table 'gaiadr2.gaia_source'\n",
+      "Parsing table 'gaiadr2.gaia_source'...\n",
+      "Done.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<astroquery.utils.tap.model.taptable.TapTableMeta at 0x7f922376e0a0>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "meta = Gaia.load_table('gaiadr2.gaia_source')\n",
+    "meta"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Jupyter shows that the result is an object of type `TapTableMeta`, but it does not display the contents.\n",
+    "\n",
+    "To see the metadata, we have to print the object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "TAP Table name: gaiadr2.gaiadr2.gaia_source\n",
+      "Description: This table has an entry for every Gaia observed source as listed in the\n",
+      "Main Database accumulating catalogue version from which the catalogue\n",
+      "release has been generated. It contains the basic source parameters,\n",
+      "that is only final data (no epoch data) and no spectra (neither final\n",
+      "nor epoch).\n",
+      "Num. columns: 96\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(meta)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notice one gotcha: in the list of table names, this table appears as `gaiadr2.gaiadr2.gaia_source`, but when we load the metadata, we refer to it as `gaiadr2.gaia_source`.\n",
+    "\n",
+    "**Exercise:** Go back and try\n",
+    "\n",
+    "```\n",
+    "meta = Gaia.load_table('gaiadr2.gaiadr2.gaia_source')\n",
+    "```\n",
+    "\n",
+    "What happens?  Is the error message helpful?  If you had not made this error deliberately, would you have been able to figure it out?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Columns\n",
+    "\n",
+    "The following loop prints the names of the columns in the table."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "solution_id\n",
+      "designation\n",
+      "source_id\n",
+      "random_index\n",
+      "ref_epoch\n",
+      "ra\n",
+      "ra_error\n",
+      "dec\n",
+      "dec_error\n",
+      "parallax\n",
+      "parallax_error\n",
+      "parallax_over_error\n",
+      "pmra\n",
+      "pmra_error\n",
+      "pmdec\n",
+      "pmdec_error\n",
+      "ra_dec_corr\n",
+      "ra_parallax_corr\n",
+      "ra_pmra_corr\n",
+      "ra_pmdec_corr\n",
+      "dec_parallax_corr\n",
+      "dec_pmra_corr\n",
+      "dec_pmdec_corr\n",
+      "parallax_pmra_corr\n",
+      "parallax_pmdec_corr\n",
+      "pmra_pmdec_corr\n",
+      "astrometric_n_obs_al\n",
+      "astrometric_n_obs_ac\n",
+      "astrometric_n_good_obs_al\n",
+      "astrometric_n_bad_obs_al\n",
+      "astrometric_gof_al\n",
+      "astrometric_chi2_al\n",
+      "astrometric_excess_noise\n",
+      "astrometric_excess_noise_sig\n",
+      "astrometric_params_solved\n",
+      "astrometric_primary_flag\n",
+      "astrometric_weight_al\n",
+      "astrometric_pseudo_colour\n",
+      "astrometric_pseudo_colour_error\n",
+      "mean_varpi_factor_al\n",
+      "astrometric_matched_observations\n",
+      "visibility_periods_used\n",
+      "astrometric_sigma5d_max\n",
+      "frame_rotator_object_type\n",
+      "matched_observations\n",
+      "duplicated_source\n",
+      "phot_g_n_obs\n",
+      "phot_g_mean_flux\n",
+      "phot_g_mean_flux_error\n",
+      "phot_g_mean_flux_over_error\n",
+      "phot_g_mean_mag\n",
+      "phot_bp_n_obs\n",
+      "phot_bp_mean_flux\n",
+      "phot_bp_mean_flux_error\n",
+      "phot_bp_mean_flux_over_error\n",
+      "phot_bp_mean_mag\n",
+      "phot_rp_n_obs\n",
+      "phot_rp_mean_flux\n",
+      "phot_rp_mean_flux_error\n",
+      "phot_rp_mean_flux_over_error\n",
+      "phot_rp_mean_mag\n",
+      "phot_bp_rp_excess_factor\n",
+      "phot_proc_mode\n",
+      "bp_rp\n",
+      "bp_g\n",
+      "g_rp\n",
+      "radial_velocity\n",
+      "radial_velocity_error\n",
+      "rv_nb_transits\n",
+      "rv_template_teff\n",
+      "rv_template_logg\n",
+      "rv_template_fe_h\n",
+      "phot_variable_flag\n",
+      "l\n",
+      "b\n",
+      "ecl_lon\n",
+      "ecl_lat\n",
+      "priam_flags\n",
+      "teff_val\n",
+      "teff_percentile_lower\n",
+      "teff_percentile_upper\n",
+      "a_g_val\n",
+      "a_g_percentile_lower\n",
+      "a_g_percentile_upper\n",
+      "e_bp_min_rp_val\n",
+      "e_bp_min_rp_percentile_lower\n",
+      "e_bp_min_rp_percentile_upper\n",
+      "flame_flags\n",
+      "radius_val\n",
+      "radius_percentile_lower\n",
+      "radius_percentile_upper\n",
+      "lum_val\n",
+      "lum_percentile_lower\n",
+      "lum_percentile_upper\n",
+      "datalink_url\n",
+      "epoch_photometry_url\n"
+     ]
+    }
+   ],
+   "source": [
+    "for column in meta.columns:\n",
+    "    print(column.name)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can probably guess what many of these columns are by looking at the names, but you should resist the temptation to guess.\n",
+    "To find out what the columns mean, [read the documentation](https://gea.esac.esa.int/archive/documentation/GDR2/Gaia_archive/chap_datamodel/sec_dm_main_tables/ssec_dm_gaia_source.html).\n",
+    "\n",
+    "If you want to know what can go wrong when you don't read the documentation, [you might like this article](https://www.vox.com/future-perfect/2019/6/4/18650969/married-women-miserable-fake-paul-dolan-happiness)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** One of the other tables we'll use is `gaiadr2.gaiadr2.panstarrs1_original_valid`.  Use `load_table` to get the metadata for this table.  How many columns are there and what are their names?\n",
+    "\n",
+    "Hint: Remember the gotcha we mentioned earlier."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Retrieving table 'gaiadr2.panstarrs1_original_valid'\n",
+      "Parsing table 'gaiadr2.panstarrs1_original_valid'...\n",
+      "Done.\n",
+      "TAP Table name: gaiadr2.gaiadr2.panstarrs1_original_valid\n",
+      "Description: The Panoramic Survey Telescope and Rapid Response System (Pan-STARRS) is\n",
+      "a system for wide-field astronomical imaging developed and operated by\n",
+      "the Institute for Astronomy at the University of Hawaii. Pan-STARRS1\n",
+      "(PS1) is the first part of Pan-STARRS to be completed and is the basis\n",
+      "for Data Release 1 (DR1). The PS1 survey used a 1.8 meter telescope and\n",
+      "its 1.4 Gigapixel camera to image the sky in five broadband filters (g,\n",
+      "r, i, z, y).\n",
+      "\n",
+      "The current table contains a filtered subsample of the 10 723 304 629\n",
+      "entries listed in the original ObjectThin table.\n",
+      "We used only ObjectThin and MeanObject tables to extract\n",
+      "panstarrs1OriginalValid table, this means that objects detected only in\n",
+      "stack images are not included here. The main reason for us to avoid the\n",
+      "use of objects detected in stack images is that their astrometry is not\n",
+      "as good as the mean objects astrometry: “The stack positions (raStack,\n",
+      "decStack) have considerably larger systematic astrometric errors than\n",
+      "the mean epoch positions (raMean, decMean).� The astrometry for the\n",
+      "MeanObject positions uses Gaia DR1 as a reference catalog, while the\n",
+      "stack positions use 2MASS as a reference catalog.\n",
+      "\n",
+      "In details, we filtered out all objects where:\n",
+      "\n",
+      "-   nDetections = 1\n",
+      "\n",
+      "-   no good quality data in Pan-STARRS, objInfoFlag 33554432 not set\n",
+      "\n",
+      "-   mean astrometry could not be measured, objInfoFlag 524288 set\n",
+      "\n",
+      "-   stack position used for mean astrometry, objInfoFlag 1048576 set\n",
+      "\n",
+      "-   error on all magnitudes equal to 0 or to -999;\n",
+      "\n",
+      "-   all magnitudes set to -999;\n",
+      "\n",
+      "-   error on RA or DEC greater than 1 arcsec.\n",
+      "\n",
+      "The number of objects in panstarrs1OriginalValid is 2 264 263 282.\n",
+      "\n",
+      "The panstarrs1OriginalValid table contains only a subset of the columns\n",
+      "available in the combined ObjectThin and MeanObject tables. A\n",
+      "description of the original ObjectThin and MeanObjects tables can be\n",
+      "found at:\n",
+      "https://outerspace.stsci.edu/display/PANSTARRS/PS1+Database+object+and+detection+tables\n",
+      "\n",
+      "Download:\n",
+      "http://mastweb.stsci.edu/ps1casjobs/home.aspx\n",
+      "Documentation:\n",
+      "https://outerspace.stsci.edu/display/PANSTARRS\n",
+      "http://pswww.ifa.hawaii.edu/pswww/\n",
+      "References:\n",
+      "The Pan-STARRS1 Surveys, Chambers, K.C., et al. 2016, arXiv:1612.05560\n",
+      "Pan-STARRS Data Processing System, Magnier, E. A., et al. 2016,\n",
+      "arXiv:1612.05240\n",
+      "Pan-STARRS Pixel Processing: Detrending, Warping, Stacking, Waters, C.\n",
+      "Z., et al. 2016, arXiv:1612.05245\n",
+      "Pan-STARRS Pixel Analysis: Source Detection and Characterization,\n",
+      "Magnier, E. A., et al. 2016, arXiv:1612.05244\n",
+      "Pan-STARRS Photometric and Astrometric Calibration, Magnier, E. A., et\n",
+      "al. 2016, arXiv:1612.05242\n",
+      "The Pan-STARRS1 Database and Data Products, Flewelling, H. A., et al.\n",
+      "2016, arXiv:1612.05243\n",
+      "\n",
+      "Catalogue curator:\n",
+      "SSDC - ASI Space Science Data Center\n",
+      "https://www.ssdc.asi.it/\n",
+      "Num. columns: 26\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "meta2 = Gaia.load_table('gaiadr2.panstarrs1_original_valid')\n",
+    "print(meta2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "obj_name\n",
+      "obj_id\n",
+      "ra\n",
+      "dec\n",
+      "ra_error\n",
+      "dec_error\n",
+      "epoch_mean\n",
+      "g_mean_psf_mag\n",
+      "g_mean_psf_mag_error\n",
+      "g_flags\n",
+      "r_mean_psf_mag\n",
+      "r_mean_psf_mag_error\n",
+      "r_flags\n",
+      "i_mean_psf_mag\n",
+      "i_mean_psf_mag_error\n",
+      "i_flags\n",
+      "z_mean_psf_mag\n",
+      "z_mean_psf_mag_error\n",
+      "z_flags\n",
+      "y_mean_psf_mag\n",
+      "y_mean_psf_mag_error\n",
+      "y_flags\n",
+      "n_detections\n",
+      "zone_id\n",
+      "obj_info_flag\n",
+      "quality_flag\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "for column in meta2.columns:\n",
+    "    print(column.name)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Writing queries\n",
+    "\n",
+    "By now you might be wondering how we actually download the data.  With tables this big, you generally don't.  Instead, you use queries to select only the data you want.\n",
+    "\n",
+    "A query is a string written in a query language like SQL; for the Gaia database, the query language is a dialect of SQL called ADQL.\n",
+    "\n",
+    "Here's an example of an ADQL query."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query1 = \"\"\"SELECT \n",
+    "TOP 10\n",
+    "source_id, ref_epoch, ra, dec, parallax \n",
+    "FROM gaiadr2.gaia_source\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Python note:** We use a [triple-quoted string](https://docs.python.org/3/tutorial/introduction.html#strings) here so we can include line breaks in the query, which makes it easier to read.\n",
+    "\n",
+    "The words in uppercase are ADQL keywords:\n",
+    "\n",
+    "* `SELECT` indicates that we are selecting data (as opposed to adding or modifying data).\n",
+    "\n",
+    "* `TOP` indicates that we only want the first 10 rows of the table, which is useful for testing a query before asking for all of the data.\n",
+    "\n",
+    "* `FROM` specifies which table we want data from.\n",
+    "\n",
+    "The third line is a list of column names, indicating which columns we want.  \n",
+    "\n",
+    "In this example, the keywords are capitalized and the column names are lowercase.  This is a common style, but it is not required.  ADQL and SQL are not case-sensitive."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To run this query, we use the `Gaia` object, which represents our connection to the Gaia database, and invoke `launch_job`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<astroquery.utils.tap.model.job.Job at 0x7f9222e9cb20>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "job1 = Gaia.launch_job(query1)\n",
+    "job1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The result is an object that represents the job running on a Gaia server.\n",
+    "\n",
+    "If you print it, it displays metadata for the forthcoming table."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<Table length=10>\n",
+      "   name    dtype  unit                            description                            \n",
+      "--------- ------- ---- ------------------------------------------------------------------\n",
+      "source_id   int64      Unique source identifier (unique within a particular Data Release)\n",
+      "ref_epoch float64   yr                                                    Reference epoch\n",
+      "       ra float64  deg                                                    Right ascension\n",
+      "      dec float64  deg                                                        Declination\n",
+      " parallax float64  mas                                                           Parallax\n",
+      "Jobid: None\n",
+      "Phase: COMPLETED\n",
+      "Owner: None\n",
+      "Output file: sync_20201005090721.xml.gz\n",
+      "Results: None\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(job1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Don't worry about `Results: None`.  That does not actually mean there are no results.\n",
+    "\n",
+    "However, `Phase: COMPLETED` indicates that the job is complete, so we can get the results like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "astropy.table.table.Table"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results1 = job1.get_results()\n",
+    "type(results1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Optional detail:**  Why is `table` repeated three times?  The first is the name of the module, the second is the name of the submodule, and the third is the name of the class.  Most of the time we only care about the last one.  It's like the Linnean name for gorilla, which is *Gorilla Gorilla Gorilla*."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The result is an [Astropy Table](https://docs.astropy.org/en/stable/table/), which is similar to a table in an SQL database except:\n",
+    "\n",
+    "* SQL databases are stored on disk drives, so they are persistent; that is, they \"survive\" even if you turn off the computer.  An Astropy `Table` is stored in memory; it disappears when you turn off the computer (or shut down this Jupyter notebook).\n",
+    "\n",
+    "* SQL databases are designed to process queries.  An Astropy `Table` can perform some query-like operations, like selecting columns and rows.  But these operations use Python syntax, not SQL.\n",
+    "\n",
+    "Jupyter knows how to display the contents of a `Table`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<i>Table length=10</i>\n",
+       "<table id=\"table140265627585264\" class=\"table-striped table-bordered table-condensed\">\n",
+       "<thead><tr><th>source_id</th><th>ref_epoch</th><th>ra</th><th>dec</th><th>parallax</th></tr></thead>\n",
+       "<thead><tr><th></th><th>yr</th><th>deg</th><th>deg</th><th>mas</th></tr></thead>\n",
+       "<thead><tr><th>int64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th></tr></thead>\n",
+       "<tr><td>4530738361793769600</td><td>2015.5</td><td>281.56725362448725</td><td>20.40682117430378</td><td>0.9785380604519425</td></tr>\n",
+       "<tr><td>4530752651135081216</td><td>2015.5</td><td>281.0861565355257</td><td>20.523350496351846</td><td>0.2674800612552977</td></tr>\n",
+       "<tr><td>4530743343951405568</td><td>2015.5</td><td>281.37114418299177</td><td>20.474147574053124</td><td>-0.43911323550176806</td></tr>\n",
+       "<tr><td>4530755060627162368</td><td>2015.5</td><td>281.2676236268299</td><td>20.558523922346158</td><td>1.1422630184554958</td></tr>\n",
+       "<tr><td>4530746844341315968</td><td>2015.5</td><td>281.1370431749541</td><td>20.377852388898184</td><td>1.0092247424630945</td></tr>\n",
+       "<tr><td>4530768456615026432</td><td>2015.5</td><td>281.8720921436347</td><td>20.31829694530366</td><td>-0.06900136127674149</td></tr>\n",
+       "<tr><td>4530763513119137280</td><td>2015.5</td><td>281.9211808864116</td><td>20.20956829578524</td><td>0.1266016679823622</td></tr>\n",
+       "<tr><td>4530736364618539264</td><td>2015.5</td><td>281.4913475613274</td><td>20.346579041327693</td><td>0.3894019486060072</td></tr>\n",
+       "<tr><td>4530735952305177728</td><td>2015.5</td><td>281.4085549165704</td><td>20.311030903719928</td><td>0.2041189982608354</td></tr>\n",
+       "<tr><td>4530751281056022656</td><td>2015.5</td><td>281.0585328377638</td><td>20.460309556214753</td><td>0.10294642821734962</td></tr>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<Table length=10>\n",
+       "     source_id      ref_epoch ...        dec               parallax      \n",
+       "                        yr    ...        deg                 mas         \n",
+       "       int64         float64  ...      float64             float64       \n",
+       "------------------- --------- ... ------------------ --------------------\n",
+       "4530738361793769600    2015.5 ...  20.40682117430378   0.9785380604519425\n",
+       "4530752651135081216    2015.5 ... 20.523350496351846   0.2674800612552977\n",
+       "4530743343951405568    2015.5 ... 20.474147574053124 -0.43911323550176806\n",
+       "4530755060627162368    2015.5 ... 20.558523922346158   1.1422630184554958\n",
+       "4530746844341315968    2015.5 ... 20.377852388898184   1.0092247424630945\n",
+       "4530768456615026432    2015.5 ...  20.31829694530366 -0.06900136127674149\n",
+       "4530763513119137280    2015.5 ...  20.20956829578524   0.1266016679823622\n",
+       "4530736364618539264    2015.5 ... 20.346579041327693   0.3894019486060072\n",
+       "4530735952305177728    2015.5 ... 20.311030903719928   0.2041189982608354\n",
+       "4530751281056022656    2015.5 ... 20.460309556214753  0.10294642821734962"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Each column has a name, units, and a data type.\n",
+    "\n",
+    "For example, the units of `ra` and `dec` are degrees, and their data type is `float64`, which is a 64-bit floating-point number, used to store measurements with a fraction part.\n",
+    "\n",
+    "This information comes from the Gaia database, and has been stored in the Astropy `Table` by Astroquery."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Read [the documentation of this table](https://gea.esac.esa.int/archive/documentation/GDR2/Gaia_archive/chap_datamodel/sec_dm_main_tables/ssec_dm_gaia_source.html) and choose a column that looks interesting to you.  Add the column name to the query and run it again.  What are the units of the column you selected?  What is its data type?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Asynchronous queries\n",
+    "\n",
+    "`launch_job` asks the server to run the job \"synchronously\", which normally means it runs immediately.  But synchronous jobs are limited to 2000 rows.  For queries that return more rows, you should run \"asynchronously\", which mean they might take longer to get started.\n",
+    "\n",
+    "If you are not sure how many rows a query will return, you can use the SQL command `COUNT` to find out how many rows are in the result without actually returning them.  We'll see an example of this later.\n",
+    "\n",
+    "The results of an asynchronous query are stored in a file on the server, so you can start a query and come back later to get the results.\n",
+    "\n",
+    "For anonymous users, files are kept for three days.\n",
+    "\n",
+    "As an example, let's try a query that's similar to `query1`, with two changes:\n",
+    "\n",
+    "* It selects the first 3000 rows, so it is bigger than we should run synchronously.\n",
+    "\n",
+    "* It uses a new keyword, `WHERE`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query2 = \"\"\"SELECT TOP 3000\n",
+    "source_id, ref_epoch, ra, dec, parallax\n",
+    "FROM gaiadr2.gaia_source\n",
+    "WHERE parallax < 1\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A `WHERE` clause indicates which rows we want; in this case, the query selects only rows \"where\" `parallax` is less than 1.  This has the effect of selecting stars with relatively low parallax, which are farther away.  We'll use this clause to exclude nearby stars that are unlikely to be part of GD-1.\n",
+    "\n",
+    "`WHERE` is one of the most common clauses in ADQL/SQL, and one of the most useful, because it allows us to select only the rows we need from the database.\n",
+    "\n",
+    "We use `launch_job_async` to submit an asynchronous query."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO: Query finished. [astroquery.utils.tap.core]\n",
+      "<Table length=3000>\n",
+      "   name    dtype  unit                            description                            \n",
+      "--------- ------- ---- ------------------------------------------------------------------\n",
+      "source_id   int64      Unique source identifier (unique within a particular Data Release)\n",
+      "ref_epoch float64   yr                                                    Reference epoch\n",
+      "       ra float64  deg                                                    Right ascension\n",
+      "      dec float64  deg                                                        Declination\n",
+      " parallax float64  mas                                                           Parallax\n",
+      "Jobid: 1601903242219O\n",
+      "Phase: COMPLETED\n",
+      "Owner: None\n",
+      "Output file: async_20201005090722.vot\n",
+      "Results: None\n"
+     ]
+    }
+   ],
+   "source": [
+    "job2 = Gaia.launch_job_async(query2)\n",
+    "print(job2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And here are the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<i>Table length=3000</i>\n",
+       "<table id=\"table140265625141056\" class=\"table-striped table-bordered table-condensed\">\n",
+       "<thead><tr><th>source_id</th><th>ref_epoch</th><th>ra</th><th>dec</th><th>parallax</th></tr></thead>\n",
+       "<thead><tr><th></th><th>yr</th><th>deg</th><th>deg</th><th>mas</th></tr></thead>\n",
+       "<thead><tr><th>int64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th></tr></thead>\n",
+       "<tr><td>4530738361793769600</td><td>2015.5</td><td>281.56725362448725</td><td>20.40682117430378</td><td>0.9785380604519425</td></tr>\n",
+       "<tr><td>4530752651135081216</td><td>2015.5</td><td>281.0861565355257</td><td>20.523350496351846</td><td>0.2674800612552977</td></tr>\n",
+       "<tr><td>4530743343951405568</td><td>2015.5</td><td>281.37114418299177</td><td>20.474147574053124</td><td>-0.43911323550176806</td></tr>\n",
+       "<tr><td>4530768456615026432</td><td>2015.5</td><td>281.8720921436347</td><td>20.31829694530366</td><td>-0.06900136127674149</td></tr>\n",
+       "<tr><td>4530763513119137280</td><td>2015.5</td><td>281.9211808864116</td><td>20.20956829578524</td><td>0.1266016679823622</td></tr>\n",
+       "<tr><td>4530736364618539264</td><td>2015.5</td><td>281.4913475613274</td><td>20.346579041327693</td><td>0.3894019486060072</td></tr>\n",
+       "<tr><td>4530735952305177728</td><td>2015.5</td><td>281.4085549165704</td><td>20.311030903719928</td><td>0.2041189982608354</td></tr>\n",
+       "<tr><td>4530751281056022656</td><td>2015.5</td><td>281.0585328377638</td><td>20.460309556214753</td><td>0.10294642821734962</td></tr>\n",
+       "<tr><td>4530740938774409344</td><td>2015.5</td><td>281.3762569536416</td><td>20.436140058941206</td><td>0.9242670062090182</td></tr>\n",
+       "<tr><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr>\n",
+       "<tr><td>4467710915011802624</td><td>2015.5</td><td>269.9680969307347</td><td>1.1429085038160882</td><td>0.42361471245557913</td></tr>\n",
+       "<tr><td>4467706551328679552</td><td>2015.5</td><td>270.033164589881</td><td>1.0565747323689927</td><td>0.922888231734588</td></tr>\n",
+       "<tr><td>4467712255037300096</td><td>2015.5</td><td>270.7724717923047</td><td>0.6581664892880896</td><td>-2.669179465293931</td></tr>\n",
+       "<tr><td>4467735001181761792</td><td>2015.5</td><td>270.3628606248308</td><td>0.8947079323599124</td><td>0.6117399163086398</td></tr>\n",
+       "<tr><td>4467737101421916672</td><td>2015.5</td><td>270.5110834661444</td><td>0.9806225910160181</td><td>-0.39818224846127004</td></tr>\n",
+       "<tr><td>4467707547757327488</td><td>2015.5</td><td>269.88746280594927</td><td>1.0212759940136962</td><td>0.7741412301054209</td></tr>\n",
+       "<tr><td>4467732772094573056</td><td>2015.5</td><td>270.55997182760126</td><td>0.9037072088489417</td><td>-1.7920417800164183</td></tr>\n",
+       "<tr><td>4467732355491087744</td><td>2015.5</td><td>270.6730790702491</td><td>0.9197224705139885</td><td>-0.3464446494840354</td></tr>\n",
+       "<tr><td>4467717099766944512</td><td>2015.5</td><td>270.57667173120825</td><td>0.726277659009568</td><td>0.05443955111134051</td></tr>\n",
+       "<tr><td>4467719058265781248</td><td>2015.5</td><td>270.7248052971514</td><td>0.8205551921782785</td><td>0.3733943917490343</td></tr>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<Table length=3000>\n",
+       "     source_id      ref_epoch ...        dec               parallax      \n",
+       "                        yr    ...        deg                 mas         \n",
+       "       int64         float64  ...      float64             float64       \n",
+       "------------------- --------- ... ------------------ --------------------\n",
+       "4530738361793769600    2015.5 ...  20.40682117430378   0.9785380604519425\n",
+       "4530752651135081216    2015.5 ... 20.523350496351846   0.2674800612552977\n",
+       "4530743343951405568    2015.5 ... 20.474147574053124 -0.43911323550176806\n",
+       "4530768456615026432    2015.5 ...  20.31829694530366 -0.06900136127674149\n",
+       "4530763513119137280    2015.5 ...  20.20956829578524   0.1266016679823622\n",
+       "4530736364618539264    2015.5 ... 20.346579041327693   0.3894019486060072\n",
+       "4530735952305177728    2015.5 ... 20.311030903719928   0.2041189982608354\n",
+       "4530751281056022656    2015.5 ... 20.460309556214753  0.10294642821734962\n",
+       "4530740938774409344    2015.5 ... 20.436140058941206   0.9242670062090182\n",
+       "                ...       ... ...                ...                  ...\n",
+       "4467710915011802624    2015.5 ... 1.1429085038160882  0.42361471245557913\n",
+       "4467706551328679552    2015.5 ... 1.0565747323689927    0.922888231734588\n",
+       "4467712255037300096    2015.5 ... 0.6581664892880896   -2.669179465293931\n",
+       "4467735001181761792    2015.5 ... 0.8947079323599124   0.6117399163086398\n",
+       "4467737101421916672    2015.5 ... 0.9806225910160181 -0.39818224846127004\n",
+       "4467707547757327488    2015.5 ... 1.0212759940136962   0.7741412301054209\n",
+       "4467732772094573056    2015.5 ... 0.9037072088489417  -1.7920417800164183\n",
+       "4467732355491087744    2015.5 ... 0.9197224705139885  -0.3464446494840354\n",
+       "4467717099766944512    2015.5 ...  0.726277659009568  0.05443955111134051\n",
+       "4467719058265781248    2015.5 ... 0.8205551921782785   0.3733943917490343"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results2 = job2.get_results()\n",
+    "results2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You might notice that some values of `parallax` are negative.  As [this FAQ explains](https://www.cosmos.esa.int/web/gaia/archive-tips#negative%20parallax), \"Negative parallaxes are caused by errors in the observations.\"  Negative parallaxes have \"no physical meaning,\" but they can be a \"useful diagnostic on the quality of the astrometric solution.\"\n",
+    "\n",
+    "Later we will see an example where we use `parallax` and `parallax_error` to identify stars where the distance estimate is likely to be inaccurate."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** The clauses in a query have to be in the right order.  Go back and change the order of the clauses in `query2` and run it again.  \n",
+    "\n",
+    "The query should fail, but notice that you don't get much useful debugging information.  \n",
+    "\n",
+    "For this reason, developing and debugging ADQL queries can be really hard.  A few suggestions that might help:\n",
+    "\n",
+    "* Whenever possible, start with a working query, either an example you find online or a query you have used in the past.\n",
+    "\n",
+    "* Make small changes and test each change before you continue.\n",
+    "\n",
+    "* While you are debugging, use `TOP` to limit the number of rows in the result.  That will make each attempt run faster, which reduces your testing time.  \n",
+    "\n",
+    "* Launching test queries synchronously might make them start faster, too."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Operators\n",
+    "\n",
+    "In a `WHERE` clause, you can use any of the [SQL comparison operators](https://www.w3schools.com/sql/sql_operators.asp); here are the most common ones:\n",
+    "\n",
+    "| Symbol | Operation\n",
+    "|--------| :---\n",
+    "| `>` | greater than\n",
+    "| `<` | less than\n",
+    "| `>=` | greater than or equal\n",
+    "| `<=` | less than or equal\n",
+    "| `=` | equal\n",
+    "| `!=` or `<>` | not equal\n",
+    "\n",
+    "Most of these are the same as Python, but some are not.  In particular, notice that the equality operator is `=`, not `==`.\n",
+    "Be careful to keep your Python out of your ADQL!\n",
+    "\n",
+    "You can combine comparisons using the logical operators:\n",
+    "\n",
+    "* AND: true if both comparisons are true\n",
+    "* OR: true if either or both comparisons are true\n",
+    "\n",
+    "Finally, you can use `NOT` to invert the result of a comparison. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** [Read about SQL operators here](https://www.w3schools.com/sql/sql_operators.asp) and then modify the previous query to select rows where `bp_rp` is between `-0.75` and `2`.\n",
+    "\n",
+    "You can [read about this variable here](https://gea.esac.esa.int/archive/documentation/GDR2/Gaia_archive/chap_datamodel/sec_dm_main_tables/ssec_dm_gaia_source.html)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "# This is what most people will probably do\n",
+    "\n",
+    "query = \"\"\"SELECT TOP 10\n",
+    "source_id, ref_epoch, ra, dec, parallax\n",
+    "FROM gaiadr2.gaia_source\n",
+    "WHERE parallax < 1 \n",
+    "  AND bp_rp > -0.75 AND bp_rp < 2\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "# But if someone notices the BETWEEN operator, \n",
+    "# they might do this\n",
+    "\n",
+    "query = \"\"\"SELECT TOP 10\n",
+    "source_id, ref_epoch, ra, dec, parallax\n",
+    "FROM gaiadr2.gaia_source\n",
+    "WHERE parallax < 1 \n",
+    "  AND bp_rp BETWEEN -0.75 AND 2\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This [Hertzsprung-Russell diagram](https://sci.esa.int/web/gaia/-/60198-gaia-hertzsprung-russell-diagram) shows the BP-RP color and luminosity of stars in the Gaia catalog.\n",
+    "\n",
+    "Selecting stars with `bp-rp` less than 2 excludes many [class M dwarf stars](https://xkcd.com/2360/), which are low temperature, low luminosity.  A star like that at GD-1's distance would be hard to detect, so if it is detected, it it more likely to be in the foreground."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Cleaning up\n",
+    "\n",
+    "Asynchronous jobs have a `jobid`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(None, '1601903242219O')"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "job1.jobid, job2.jobid"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Which you can use to remove the job from the server."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Removed jobs: '['1601903242219O']'.\n"
+     ]
+    }
+   ],
+   "source": [
+    "Gaia.remove_jobs([job2.jobid])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you don't remove it job from the server, it will be removed eventually, so don't feel too bad if you don't clean up after yourself."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Formatting queries\n",
+    "\n",
+    "So far the queries have been string \"literals\", meaning that the entire string is part of the program.\n",
+    "But writing queries yourself can be slow, repetitive, and error-prone.\n",
+    "\n",
+    "It is often a good idea to write Python code that assembles a query for you.  One useful tool for that is the [string `format` method](https://www.w3schools.com/python/ref_string_format.asp).\n",
+    "\n",
+    "As an example, we'll divide the previous query into two parts; a list of column names and a \"base\" for the query that contains everything except the column names.\n",
+    "\n",
+    "Here's the list of columns we'll select.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "columns = 'source_id, ra, dec, pmra, pmdec, parallax, parallax_error, radial_velocity'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And here's the base; it's a string that contains at least one format specifier in curly brackets (braces)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query3_base = \"\"\"SELECT TOP 10 \n",
+    "{columns}\n",
+    "FROM gaiadr2.gaia_source\n",
+    "WHERE parallax < 1\n",
+    "  AND bp_rp BETWEEN -0.75 AND 2\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This base query contains one format specifier, `{columns}`, which is a placeholder for the list of column names we will provide.\n",
+    "\n",
+    "To assemble the query, we invoke `format` on the base string and provide a keyword argument that assigns a value to `columns`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query3 = query3_base.format(columns=columns)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The result is a string with line breaks.  If you display it, the line breaks appear as `\\n`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'SELECT TOP 10 \\nsource_id, ra, dec, pmra, pmdec, parallax, parallax_error, radial_velocity\\nFROM gaiadr2.gaia_source\\nWHERE parallax < 1\\n  AND bp_rp BETWEEN -0.75 AND 2\\n'"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "query3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "But if you print it, the line breaks appear as... line breaks."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "SELECT TOP 10 \n",
+      "source_id, ra, dec, pmra, pmdec, parallax, parallax_error, radial_velocity\n",
+      "FROM gaiadr2.gaia_source\n",
+      "WHERE parallax < 1\n",
+      "  AND bp_rp BETWEEN -0.75 AND 2\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(query3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notice that the format specifier has been replaced with the value of `columns`.\n",
+    "\n",
+    "Let's run it and see if it works:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<Table length=10>\n",
+      "      name       dtype    unit                              description                             n_bad\n",
+      "--------------- ------- -------- ------------------------------------------------------------------ -----\n",
+      "      source_id   int64          Unique source identifier (unique within a particular Data Release)     0\n",
+      "             ra float64      deg                                                    Right ascension     0\n",
+      "            dec float64      deg                                                        Declination     0\n",
+      "           pmra float64 mas / yr                         Proper motion in right ascension direction     0\n",
+      "          pmdec float64 mas / yr                             Proper motion in declination direction     0\n",
+      "       parallax float64      mas                                                           Parallax     0\n",
+      " parallax_error float64      mas                                         Standard error of parallax     0\n",
+      "radial_velocity float64   km / s                                                    Radial velocity    10\n",
+      "Jobid: None\n",
+      "Phase: COMPLETED\n",
+      "Owner: None\n",
+      "Output file: sync_20201005090726.xml.gz\n",
+      "Results: None\n"
+     ]
+    }
+   ],
+   "source": [
+    "job3 = Gaia.launch_job(query3)\n",
+    "print(job3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<i>Table length=10</i>\n",
+       "<table id=\"table140265627700432\" class=\"table-striped table-bordered table-condensed\">\n",
+       "<thead><tr><th>source_id</th><th>ra</th><th>dec</th><th>pmra</th><th>pmdec</th><th>parallax</th><th>parallax_error</th><th>radial_velocity</th></tr></thead>\n",
+       "<thead><tr><th></th><th>deg</th><th>deg</th><th>mas / yr</th><th>mas / yr</th><th>mas</th><th>mas</th><th>km / s</th></tr></thead>\n",
+       "<thead><tr><th>int64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th></tr></thead>\n",
+       "<tr><td>4467710915011802624</td><td>269.9680969307347</td><td>1.1429085038160882</td><td>2.0233280236600626</td><td>-2.5692427875510266</td><td>0.42361471245557913</td><td>0.470352406647465</td><td>--</td></tr>\n",
+       "<tr><td>4467706551328679552</td><td>270.033164589881</td><td>1.0565747323689927</td><td>-3.414829591355289</td><td>-3.8437215857495737</td><td>0.922888231734588</td><td>0.927008559859825</td><td>--</td></tr>\n",
+       "<tr><td>4467712255037300096</td><td>270.7724717923047</td><td>0.6581664892880896</td><td>-3.5620173752896025</td><td>-6.595792323153987</td><td>-2.669179465293931</td><td>0.9719742773203504</td><td>--</td></tr>\n",
+       "<tr><td>4467735001181761792</td><td>270.3628606248308</td><td>0.8947079323599124</td><td>2.1307079926489205</td><td>0.8826727710910712</td><td>0.6117399163086398</td><td>0.509812721702093</td><td>--</td></tr>\n",
+       "<tr><td>4467737101421916672</td><td>270.5110834661444</td><td>0.9806225910160181</td><td>0.17532366511560785</td><td>-5.113270239706202</td><td>-0.39818224846127004</td><td>0.7549581886719651</td><td>--</td></tr>\n",
+       "<tr><td>4467707547757327488</td><td>269.88746280594927</td><td>1.0212759940136962</td><td>-2.6382230817672987</td><td>-3.707776532049287</td><td>0.7741412301054209</td><td>0.3022057897812064</td><td>--</td></tr>\n",
+       "<tr><td>4467732355491087744</td><td>270.6730790702491</td><td>0.9197224705139885</td><td>-2.2735991502653037</td><td>-11.864952855984358</td><td>-0.3464446494840354</td><td>0.4937921513912002</td><td>--</td></tr>\n",
+       "<tr><td>4467717099766944512</td><td>270.57667173120825</td><td>0.726277659009568</td><td>-3.4598362614808367</td><td>-4.601426893365921</td><td>0.05443955111134051</td><td>0.8867339293525688</td><td>--</td></tr>\n",
+       "<tr><td>4467719058265781248</td><td>270.7248052971514</td><td>0.8205551921782785</td><td>-3.255079498426542</td><td>-9.249285069111085</td><td>0.3733943917490343</td><td>0.390952370410666</td><td>--</td></tr>\n",
+       "<tr><td>4467722326741572352</td><td>270.87431291888504</td><td>0.8595565975869158</td><td>0.10696398351859826</td><td>1.2035993780158853</td><td>-0.11850943432864373</td><td>0.1660452431882023</td><td>--</td></tr>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<Table length=10>\n",
+       "     source_id              ra         ...   parallax_error   radial_velocity\n",
+       "                           deg         ...        mas              km / s    \n",
+       "       int64             float64       ...      float64           float64    \n",
+       "------------------- ------------------ ... ------------------ ---------------\n",
+       "4467710915011802624  269.9680969307347 ...  0.470352406647465              --\n",
+       "4467706551328679552   270.033164589881 ...  0.927008559859825              --\n",
+       "4467712255037300096  270.7724717923047 ... 0.9719742773203504              --\n",
+       "4467735001181761792  270.3628606248308 ...  0.509812721702093              --\n",
+       "4467737101421916672  270.5110834661444 ... 0.7549581886719651              --\n",
+       "4467707547757327488 269.88746280594927 ... 0.3022057897812064              --\n",
+       "4467732355491087744  270.6730790702491 ... 0.4937921513912002              --\n",
+       "4467717099766944512 270.57667173120825 ... 0.8867339293525688              --\n",
+       "4467719058265781248  270.7248052971514 ...  0.390952370410666              --\n",
+       "4467722326741572352 270.87431291888504 ... 0.1660452431882023              --"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results3 = job3.get_results()\n",
+    "results3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Good so far."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** This query always selects sources with `parallax` less than 1.  But suppose you want to take that upper bound as an input.\n",
+    "\n",
+    "Modify `query3_base` to replace `1` with a format specifier like `{max_parallax}`.  Now, when you call `format`, add a keyword argument that assigns a value to `max_parallax`, and confirm that the format specifier gets replaced with the value you provide."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "query4_base = \"\"\"SELECT TOP 10\n",
+    "{columns}\n",
+    "FROM gaiadr2.gaia_source\n",
+    "WHERE parallax < {max_parallax} AND \n",
+    "bp_rp BETWEEN -0.75 AND 2\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "SELECT TOP 10\n",
+      "source_id, ra, dec, pmra, pmdec, parallax, parallax_error, radial_velocity\n",
+      "FROM gaiadr2.gaia_source\n",
+      "WHERE parallax < 0.5 AND \n",
+      "bp_rp BETWEEN -0.75 AND 2\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "query4 = query4_base.format(columns=columns,\n",
+    "                          max_parallax=0.5)\n",
+    "print(query)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Style note:**  You might notice that the variable names in this notebook are numbered, like `query1`, `query2`, etc.  \n",
+    "\n",
+    "The advantage of this style is that it isolates each section of the notebook from the others, so if you go back and run the cells out of order, it's less likely that you will get unexpected interactions.\n",
+    "\n",
+    "A drawback of this style is that it can be a nuisance to update the notebook if you add, remove, or reorder a section.\n",
+    "\n",
+    "What do you think of this choice?  Are there alternatives you prefer?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "This notebook demonstrates the following steps:\n",
+    "\n",
+    "1. Making a connection to the Gaia server,\n",
+    "\n",
+    "2. Exploring information about the database and the tables it contains,\n",
+    "\n",
+    "3. Writing a query and sending it to the server, and finally\n",
+    "\n",
+    "4. Downloading the response from the server as an Astropy `Table`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Best practices\n",
+    "\n",
+    "* If you can't download an entire dataset (or it's not practical) use queries to select the data you need.\n",
+    "\n",
+    "* Read the metadata and the documentation to make sure you understand the tables, their columns, and what they mean.\n",
+    "\n",
+    "* Develop queries incrementally: start with something simple, test it, and add a little bit at a time.\n",
+    "\n",
+    "* Use ADQL features like `TOP` and `COUNT` to test before you run a query that might return a lot of data.\n",
+    "\n",
+    "* If you know your query will return fewer than 3000 rows, you can run it synchronously, which might complete faster (but it doesn't seem to make much difference).  If it might return more than 3000 rows, you should run it asynchronously.\n",
+    "\n",
+    "* ADQL and SQL are not case-sensitive, so you don't have to capitalize the keywords, but you should.\n",
+    "\n",
+    "* ADQL and SQL don't require you to break a query into multiple lines, but you should.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Jupyter notebooks can be good for developing and testing code, but they have some drawbacks.  In particular, if you run the cells out of order, you might find that variables don't have the values you expect.\n",
+    "\n",
+    "There are a few things you can do to mitigate these problems:\n",
+    "\n",
+    "* Make each section of the notebook self-contained.  Try not to use the same variable name in more than one section.\n",
+    "\n",
+    "* Keep notebooks short.  Look for places where you can break your analysis into phases with one notebook per phase."
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/_build/jupyter_execute/AstronomicalData/01_query.py b/_build/jupyter_execute/AstronomicalData/01_query.py
new file mode 100644
index 0000000..e9d667c
--- /dev/null
+++ b/_build/jupyter_execute/AstronomicalData/01_query.py
@@ -0,0 +1,605 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# # Lesson 1
+
+# ## Introduction
+# 
+# This workshop is an introduction to tools and practices for working with astronomical data.  Topics covered include:
+# 
+# * Writing queries that select and download data from a database.
+# 
+# * Using data stored in an Astropy `Table` or Pandas `DataFrame`.
+# 
+# * Working with coordinates and other quantities with units.
+# 
+# * Storing data in various formats.
+# 
+# * Performing database join operations that combine data from multiple tables.
+# 
+# * Visualizing data and preparing publication-quality figures.
+
+# As a running example, we will replicate part of the analysis in a recent paper, "[Off the beaten path: Gaia reveals GD-1 stars outside of the main stream](https://arxiv.org/abs/1805.00425)" by Adrian M. Price-Whelan and Ana Bonaca.
+# 
+# As the abstract explains, "Using data from the Gaia second data release combined with Pan-STARRS photometry, we present a sample of highly-probable members of the longest cold stream in the Milky Way, GD-1."
+# 
+# GD-1 is a [stellar stream](https://en.wikipedia.org/wiki/List_of_stellar_streams), which is "an association of stars orbiting a galaxy that was once a globular cluster or dwarf galaxy that has now been torn apart and stretched out along its orbit by tidal forces."
+
+# [This article in *Science* magazine](https://www.sciencemag.org/news/2018/10/streams-stars-reveal-galaxy-s-violent-history-and-perhaps-its-unseen-dark-matter) explains some of the background, including the process that led to the paper and an discussion of the scientific implications:
+# 
+# * "The streams are particularly useful for ... galactic archaeology --- rewinding the cosmic clock to reconstruct the assembly of the Milky Way."
+# 
+# * "They also are being used as exquisitely sensitive scales to measure the galaxy's mass."
+# 
+# * "... the streams are well-positioned to reveal the presence of dark matter ... because the streams are so fragile, theorists say, collisions with marauding clumps of dark matter could leave telltale scars, potential clues to its nature."
+
+# ## Prerequisites
+# 
+# This workshop is meant for people who are familiar with basic Python, but not necessarily the libraries we will use, like Astropy or Pandas.  If you are familiar with Python lists and dictionaries, and you know how to write a function that takes parameters and returns a value, you know enough Python for this workshop.
+# 
+# We assume that you have some familiarity with operating systems, like the ability to use a command-line interface.  But we don't assume you have any prior experience with databases.
+# 
+# We assume that you are familiar with astronomy at the undergraduate level, but we will not assume specialized knowledge of the datasets or analysis methods we'll use. 
+
+# ## Data
+# 
+# The datasets we will work with are:
+#  
+# * [Gaia](https://en.wikipedia.org/wiki/Gaia_(spacecraft)), which is "a space observatory of the European Space Agency (ESA), launched in 2013 ... designed for astrometry: measuring the positions, distances and motions of stars with unprecedented precision", and
+# 
+# * [Pan-STARRS](https://en.wikipedia.org/wiki/Pan-STARRS), The Panoramic Survey Telescope and Rapid Response System, which is a survey designed to monitor the sky for transient objects, producing a catalog with accurate astronometry and photometry of detected sources.
+# 
+# Both of these datasets are very large, which can make them challenging to work with.  It might not be possible, or practical, to download the entire dataset.
+# One of the goals of this workshop is to provide tools for working with large datasets.
+
+# ## Lesson 1
+# 
+# The first lesson demonstrates the steps for selecting and downloading data from the Gaia Database:
+# 
+# 1. First we'll make a connection to the Gaia server,
+# 
+# 2. We will explore information about the database and the tables it contains,
+# 
+# 3. We will write a query and send it to the server, and finally
+# 
+# 4. We will download the response from the server.
+# 
+# After completing this lesson, you should be able to
+# 
+# * Compose a basic query in ADQL.
+# 
+# * Use queries to explore a database and its tables.
+# 
+# * Use queries to download data.
+# 
+# * Develop, test, and debug a query incrementally.
+
+# ## Query Language
+# 
+# In order to select data from a database, you have to compose a query, which is like a program written in a "query language".
+# The query language we'll use is ADQL, which stands for "Astronomical Data Query Language".
+# 
+# ADQL is a dialect of [SQL](https://en.wikipedia.org/wiki/SQL) (Structured Query Language), which is by far the most commonly used query language.  Almost everything you will learn about ADQL also works in SQL.
+# 
+# [The reference manual for ADQL is here](http://www.ivoa.net/documents/ADQL/20180112/PR-ADQL-2.1-20180112.html).
+# But you might find it easier to learn from [this ADQL Cookbook](https://www.gaia.ac.uk/data/gaia-data-release-1/adql-cookbook).
+
+# ## Installing libraries
+# 
+# The library we'll use to get Gaia data is [Astroquery](https://astroquery.readthedocs.io/en/latest/).
+# 
+# If you are running this notebook on Colab, you can run the following cell to install Astroquery and the other libraries we'll use.
+# 
+# If you are running this notebook on your own computer, you might have to install these libraries yourself.  
+# 
+# If you are using this notebook as part of a Carpentries workshop, you should have received setup instructions.
+# 
+# TODO: Add a link to the instructions.
+# 
+
+# In[1]:
+
+
+# If we're running on Colab, install libraries
+
+import sys
+IN_COLAB = 'google.colab' in sys.modules
+
+if IN_COLAB:
+    get_ipython().system('pip install astroquery astro-gala pyia')
+
+
+# ## Connecting to Gaia
+# 
+# Astroquery provides `Gaia`, which is an [object that represents a connection to the Gaia database](https://astroquery.readthedocs.io/en/latest/gaia/gaia.html).
+# 
+# We can connect to the Gaia database like this:
+
+# In[2]:
+
+
+from astroquery.gaia import Gaia
+
+
+# #### Optional detail 
+# 
+# > Running this import statement has the effect of creating a [TAP+](http://www.ivoa.net/documents/TAP/) connection; TAP stands for "Table Access Protocol".  It is a network protocol for sending queries to the database and getting back the results.  We're not sure why it seems to create two connections.
+
+# ## Databases and Tables
+# 
+# What is a database, anyway?  Most generally, it can be any collection of data, but when we are talking about ADQL or SQL:
+# 
+# * A database is a collection of one or more named tables.
+# 
+# * Each table is a 2-D array with one or more named columns of data.
+# 
+# We can use `Gaia.load_tables` to get the names of the tables in the Gaia database.  With the option `only_names=True`, it loads information about the tables, called the "metadata", not the data itself.
+
+# In[3]:
+
+
+tables = Gaia.load_tables(only_names=True)
+
+
+# In[4]:
+
+
+for table in (tables):
+    print(table.get_qualified_name())
+
+
+# So that's a lot of tables.  The ones we'll use are:
+# 
+# * `gaiadr2.gaia_source`, which contains Gaia data from [data release 2](https://www.cosmos.esa.int/web/gaia/data-release-2),
+# 
+# * `gaiadr2.panstarrs1_original_valid`, which contains the photometry data we'll use from PanSTARRS, and
+# 
+# * `gaiadr2.panstarrs1_best_neighbour`, which we'll use to cross-match each star observed by Gaia with the same star observed by PanSTARRS.
+# 
+# We can use `load_table` (not `load_tables`) to get the metadata for a single table.  The name of this function is misleading, because it only downloads metadata. 
+
+# In[5]:
+
+
+meta = Gaia.load_table('gaiadr2.gaia_source')
+meta
+
+
+# Jupyter shows that the result is an object of type `TapTableMeta`, but it does not display the contents.
+# 
+# To see the metadata, we have to print the object.
+
+# In[6]:
+
+
+print(meta)
+
+
+# Notice one gotcha: in the list of table names, this table appears as `gaiadr2.gaiadr2.gaia_source`, but when we load the metadata, we refer to it as `gaiadr2.gaia_source`.
+# 
+# **Exercise:** Go back and try
+# 
+# ```
+# meta = Gaia.load_table('gaiadr2.gaiadr2.gaia_source')
+# ```
+# 
+# What happens?  Is the error message helpful?  If you had not made this error deliberately, would you have been able to figure it out?
+
+# ## Columns
+# 
+# The following loop prints the names of the columns in the table.
+
+# In[7]:
+
+
+for column in meta.columns:
+    print(column.name)
+
+
+# You can probably guess what many of these columns are by looking at the names, but you should resist the temptation to guess.
+# To find out what the columns mean, [read the documentation](https://gea.esac.esa.int/archive/documentation/GDR2/Gaia_archive/chap_datamodel/sec_dm_main_tables/ssec_dm_gaia_source.html).
+# 
+# If you want to know what can go wrong when you don't read the documentation, [you might like this article](https://www.vox.com/future-perfect/2019/6/4/18650969/married-women-miserable-fake-paul-dolan-happiness).
+
+# **Exercise:** One of the other tables we'll use is `gaiadr2.gaiadr2.panstarrs1_original_valid`.  Use `load_table` to get the metadata for this table.  How many columns are there and what are their names?
+# 
+# Hint: Remember the gotcha we mentioned earlier.
+
+# In[8]:
+
+
+# Solution
+
+meta2 = Gaia.load_table('gaiadr2.panstarrs1_original_valid')
+print(meta2)
+
+
+# In[9]:
+
+
+# Solution
+
+for column in meta2.columns:
+    print(column.name)
+
+
+# ## Writing queries
+# 
+# By now you might be wondering how we actually download the data.  With tables this big, you generally don't.  Instead, you use queries to select only the data you want.
+# 
+# A query is a string written in a query language like SQL; for the Gaia database, the query language is a dialect of SQL called ADQL.
+# 
+# Here's an example of an ADQL query.
+
+# In[10]:
+
+
+query1 = """SELECT 
+TOP 10
+source_id, ref_epoch, ra, dec, parallax 
+FROM gaiadr2.gaia_source"""
+
+
+# **Python note:** We use a [triple-quoted string](https://docs.python.org/3/tutorial/introduction.html#strings) here so we can include line breaks in the query, which makes it easier to read.
+# 
+# The words in uppercase are ADQL keywords:
+# 
+# * `SELECT` indicates that we are selecting data (as opposed to adding or modifying data).
+# 
+# * `TOP` indicates that we only want the first 10 rows of the table, which is useful for testing a query before asking for all of the data.
+# 
+# * `FROM` specifies which table we want data from.
+# 
+# The third line is a list of column names, indicating which columns we want.  
+# 
+# In this example, the keywords are capitalized and the column names are lowercase.  This is a common style, but it is not required.  ADQL and SQL are not case-sensitive.
+
+# To run this query, we use the `Gaia` object, which represents our connection to the Gaia database, and invoke `launch_job`:
+
+# In[11]:
+
+
+job1 = Gaia.launch_job(query1)
+job1
+
+
+# The result is an object that represents the job running on a Gaia server.
+# 
+# If you print it, it displays metadata for the forthcoming table.
+
+# In[12]:
+
+
+print(job1)
+
+
+# Don't worry about `Results: None`.  That does not actually mean there are no results.
+# 
+# However, `Phase: COMPLETED` indicates that the job is complete, so we can get the results like this:
+
+# In[13]:
+
+
+results1 = job1.get_results()
+type(results1)
+
+
+# **Optional detail:**  Why is `table` repeated three times?  The first is the name of the module, the second is the name of the submodule, and the third is the name of the class.  Most of the time we only care about the last one.  It's like the Linnean name for gorilla, which is *Gorilla Gorilla Gorilla*.
+
+# The result is an [Astropy Table](https://docs.astropy.org/en/stable/table/), which is similar to a table in an SQL database except:
+# 
+# * SQL databases are stored on disk drives, so they are persistent; that is, they "survive" even if you turn off the computer.  An Astropy `Table` is stored in memory; it disappears when you turn off the computer (or shut down this Jupyter notebook).
+# 
+# * SQL databases are designed to process queries.  An Astropy `Table` can perform some query-like operations, like selecting columns and rows.  But these operations use Python syntax, not SQL.
+# 
+# Jupyter knows how to display the contents of a `Table`.
+
+# In[14]:
+
+
+results1
+
+
+# Each column has a name, units, and a data type.
+# 
+# For example, the units of `ra` and `dec` are degrees, and their data type is `float64`, which is a 64-bit floating-point number, used to store measurements with a fraction part.
+# 
+# This information comes from the Gaia database, and has been stored in the Astropy `Table` by Astroquery.
+
+# **Exercise:** Read [the documentation of this table](https://gea.esac.esa.int/archive/documentation/GDR2/Gaia_archive/chap_datamodel/sec_dm_main_tables/ssec_dm_gaia_source.html) and choose a column that looks interesting to you.  Add the column name to the query and run it again.  What are the units of the column you selected?  What is its data type?
+
+# ## Asynchronous queries
+# 
+# `launch_job` asks the server to run the job "synchronously", which normally means it runs immediately.  But synchronous jobs are limited to 2000 rows.  For queries that return more rows, you should run "asynchronously", which mean they might take longer to get started.
+# 
+# If you are not sure how many rows a query will return, you can use the SQL command `COUNT` to find out how many rows are in the result without actually returning them.  We'll see an example of this later.
+# 
+# The results of an asynchronous query are stored in a file on the server, so you can start a query and come back later to get the results.
+# 
+# For anonymous users, files are kept for three days.
+# 
+# As an example, let's try a query that's similar to `query1`, with two changes:
+# 
+# * It selects the first 3000 rows, so it is bigger than we should run synchronously.
+# 
+# * It uses a new keyword, `WHERE`.
+
+# In[15]:
+
+
+query2 = """SELECT TOP 3000
+source_id, ref_epoch, ra, dec, parallax
+FROM gaiadr2.gaia_source
+WHERE parallax < 1
+"""
+
+
+# A `WHERE` clause indicates which rows we want; in this case, the query selects only rows "where" `parallax` is less than 1.  This has the effect of selecting stars with relatively low parallax, which are farther away.  We'll use this clause to exclude nearby stars that are unlikely to be part of GD-1.
+# 
+# `WHERE` is one of the most common clauses in ADQL/SQL, and one of the most useful, because it allows us to select only the rows we need from the database.
+# 
+# We use `launch_job_async` to submit an asynchronous query.
+
+# In[16]:
+
+
+job2 = Gaia.launch_job_async(query2)
+print(job2)
+
+
+# And here are the results.
+
+# In[17]:
+
+
+results2 = job2.get_results()
+results2
+
+
+# You might notice that some values of `parallax` are negative.  As [this FAQ explains](https://www.cosmos.esa.int/web/gaia/archive-tips#negative%20parallax), "Negative parallaxes are caused by errors in the observations."  Negative parallaxes have "no physical meaning," but they can be a "useful diagnostic on the quality of the astrometric solution."
+# 
+# Later we will see an example where we use `parallax` and `parallax_error` to identify stars where the distance estimate is likely to be inaccurate.
+
+# **Exercise:** The clauses in a query have to be in the right order.  Go back and change the order of the clauses in `query2` and run it again.  
+# 
+# The query should fail, but notice that you don't get much useful debugging information.  
+# 
+# For this reason, developing and debugging ADQL queries can be really hard.  A few suggestions that might help:
+# 
+# * Whenever possible, start with a working query, either an example you find online or a query you have used in the past.
+# 
+# * Make small changes and test each change before you continue.
+# 
+# * While you are debugging, use `TOP` to limit the number of rows in the result.  That will make each attempt run faster, which reduces your testing time.  
+# 
+# * Launching test queries synchronously might make them start faster, too.
+
+# ## Operators
+# 
+# In a `WHERE` clause, you can use any of the [SQL comparison operators](https://www.w3schools.com/sql/sql_operators.asp); here are the most common ones:
+# 
+# | Symbol | Operation
+# |--------| :---
+# | `>` | greater than
+# | `<` | less than
+# | `>=` | greater than or equal
+# | `<=` | less than or equal
+# | `=` | equal
+# | `!=` or `<>` | not equal
+# 
+# Most of these are the same as Python, but some are not.  In particular, notice that the equality operator is `=`, not `==`.
+# Be careful to keep your Python out of your ADQL!
+# 
+# You can combine comparisons using the logical operators:
+# 
+# * AND: true if both comparisons are true
+# * OR: true if either or both comparisons are true
+# 
+# Finally, you can use `NOT` to invert the result of a comparison. 
+
+# **Exercise:** [Read about SQL operators here](https://www.w3schools.com/sql/sql_operators.asp) and then modify the previous query to select rows where `bp_rp` is between `-0.75` and `2`.
+# 
+# You can [read about this variable here](https://gea.esac.esa.int/archive/documentation/GDR2/Gaia_archive/chap_datamodel/sec_dm_main_tables/ssec_dm_gaia_source.html).
+
+# In[18]:
+
+
+# Solution
+
+# This is what most people will probably do
+
+query = """SELECT TOP 10
+source_id, ref_epoch, ra, dec, parallax
+FROM gaiadr2.gaia_source
+WHERE parallax < 1 
+  AND bp_rp > -0.75 AND bp_rp < 2
+"""
+
+
+# In[19]:
+
+
+# Solution
+
+# But if someone notices the BETWEEN operator, 
+# they might do this
+
+query = """SELECT TOP 10
+source_id, ref_epoch, ra, dec, parallax
+FROM gaiadr2.gaia_source
+WHERE parallax < 1 
+  AND bp_rp BETWEEN -0.75 AND 2
+"""
+
+
+# This [Hertzsprung-Russell diagram](https://sci.esa.int/web/gaia/-/60198-gaia-hertzsprung-russell-diagram) shows the BP-RP color and luminosity of stars in the Gaia catalog.
+# 
+# Selecting stars with `bp-rp` less than 2 excludes many [class M dwarf stars](https://xkcd.com/2360/), which are low temperature, low luminosity.  A star like that at GD-1's distance would be hard to detect, so if it is detected, it it more likely to be in the foreground.
+
+# ## Cleaning up
+# 
+# Asynchronous jobs have a `jobid`.
+
+# In[20]:
+
+
+job1.jobid, job2.jobid
+
+
+# Which you can use to remove the job from the server.
+
+# In[21]:
+
+
+Gaia.remove_jobs([job2.jobid])
+
+
+# If you don't remove it job from the server, it will be removed eventually, so don't feel too bad if you don't clean up after yourself.
+
+# ## Formatting queries
+# 
+# So far the queries have been string "literals", meaning that the entire string is part of the program.
+# But writing queries yourself can be slow, repetitive, and error-prone.
+# 
+# It is often a good idea to write Python code that assembles a query for you.  One useful tool for that is the [string `format` method](https://www.w3schools.com/python/ref_string_format.asp).
+# 
+# As an example, we'll divide the previous query into two parts; a list of column names and a "base" for the query that contains everything except the column names.
+# 
+# Here's the list of columns we'll select.  
+
+# In[22]:
+
+
+columns = 'source_id, ra, dec, pmra, pmdec, parallax, parallax_error, radial_velocity'
+
+
+# And here's the base; it's a string that contains at least one format specifier in curly brackets (braces).
+
+# In[23]:
+
+
+query3_base = """SELECT TOP 10 
+{columns}
+FROM gaiadr2.gaia_source
+WHERE parallax < 1
+  AND bp_rp BETWEEN -0.75 AND 2
+"""
+
+
+# This base query contains one format specifier, `{columns}`, which is a placeholder for the list of column names we will provide.
+# 
+# To assemble the query, we invoke `format` on the base string and provide a keyword argument that assigns a value to `columns`.
+
+# In[24]:
+
+
+query3 = query3_base.format(columns=columns)
+
+
+# The result is a string with line breaks.  If you display it, the line breaks appear as `\n`.
+
+# In[25]:
+
+
+query3
+
+
+# But if you print it, the line breaks appear as... line breaks.
+
+# In[26]:
+
+
+print(query3)
+
+
+# Notice that the format specifier has been replaced with the value of `columns`.
+# 
+# Let's run it and see if it works:
+
+# In[27]:
+
+
+job3 = Gaia.launch_job(query3)
+print(job3)
+
+
+# In[28]:
+
+
+results3 = job3.get_results()
+results3
+
+
+# Good so far.
+
+# **Exercise:** This query always selects sources with `parallax` less than 1.  But suppose you want to take that upper bound as an input.
+# 
+# Modify `query3_base` to replace `1` with a format specifier like `{max_parallax}`.  Now, when you call `format`, add a keyword argument that assigns a value to `max_parallax`, and confirm that the format specifier gets replaced with the value you provide.
+
+# In[29]:
+
+
+# Solution
+
+query4_base = """SELECT TOP 10
+{columns}
+FROM gaiadr2.gaia_source
+WHERE parallax < {max_parallax} AND 
+bp_rp BETWEEN -0.75 AND 2
+"""
+
+
+# In[30]:
+
+
+# Solution
+
+query4 = query4_base.format(columns=columns,
+                          max_parallax=0.5)
+print(query)
+
+
+# **Style note:**  You might notice that the variable names in this notebook are numbered, like `query1`, `query2`, etc.  
+# 
+# The advantage of this style is that it isolates each section of the notebook from the others, so if you go back and run the cells out of order, it's less likely that you will get unexpected interactions.
+# 
+# A drawback of this style is that it can be a nuisance to update the notebook if you add, remove, or reorder a section.
+# 
+# What do you think of this choice?  Are there alternatives you prefer?
+
+# ## Summary
+# 
+# This notebook demonstrates the following steps:
+# 
+# 1. Making a connection to the Gaia server,
+# 
+# 2. Exploring information about the database and the tables it contains,
+# 
+# 3. Writing a query and sending it to the server, and finally
+# 
+# 4. Downloading the response from the server as an Astropy `Table`.
+
+# ## Best practices
+# 
+# * If you can't download an entire dataset (or it's not practical) use queries to select the data you need.
+# 
+# * Read the metadata and the documentation to make sure you understand the tables, their columns, and what they mean.
+# 
+# * Develop queries incrementally: start with something simple, test it, and add a little bit at a time.
+# 
+# * Use ADQL features like `TOP` and `COUNT` to test before you run a query that might return a lot of data.
+# 
+# * If you know your query will return fewer than 3000 rows, you can run it synchronously, which might complete faster (but it doesn't seem to make much difference).  If it might return more than 3000 rows, you should run it asynchronously.
+# 
+# * ADQL and SQL are not case-sensitive, so you don't have to capitalize the keywords, but you should.
+# 
+# * ADQL and SQL don't require you to break a query into multiple lines, but you should.
+# 
+
+# Jupyter notebooks can be good for developing and testing code, but they have some drawbacks.  In particular, if you run the cells out of order, you might find that variables don't have the values you expect.
+# 
+# There are a few things you can do to mitigate these problems:
+# 
+# * Make each section of the notebook self-contained.  Try not to use the same variable name in more than one section.
+# 
+# * Keep notebooks short.  Look for places where you can break your analysis into phases with one notebook per phase.
diff --git a/_build/jupyter_execute/AstronomicalData/02_coords.ipynb b/_build/jupyter_execute/AstronomicalData/02_coords.ipynb
new file mode 100644
index 0000000..40537ed
--- /dev/null
+++ b/_build/jupyter_execute/AstronomicalData/02_coords.ipynb
@@ -0,0 +1,1970 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Lesson 2\n",
+    "\n",
+    "This is the second in a series of lessons related to astronomy data.\n",
+    "\n",
+    "As a running example, we are replicating parts of the analysis in a recent paper, \"[Off the beaten path: Gaia reveals GD-1 stars outside of the main stream](https://arxiv.org/abs/1805.00425)\" by Adrian M. Price-Whelan and Ana Bonaca.\n",
+    "\n",
+    "In the first notebook, we wrote ADQL queries and used them to select and download data from the Gaia server.\n",
+    "\n",
+    "In this notebook, we'll pick up where we left off and write a query to select stars from the region of the sky where we expect GD-1 to be."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We'll start with an example that does a \"cone search\"; that is, it selects stars that appear in a circular region of the sky.\n",
+    "\n",
+    "Then, to select stars in the vicinity of GD-1, we'll:\n",
+    "\n",
+    "* Use `Quantity` objects to represent measurements with units.\n",
+    "\n",
+    "* Use the `Gala` library to convert coordinates from one frame to another.\n",
+    "\n",
+    "* Use the ADQL keywords `POLYGON`, `CONTAINS`, and `POINT` to select stars that fall within a polygonal region.\n",
+    "\n",
+    "* Submit a query and download the results.\n",
+    "\n",
+    "* Store the results in a FITS file.\n",
+    "\n",
+    "After completing this lesson, you should be able to\n",
+    "\n",
+    "* Use Python string formatting to compose more complex ADQL queries.\n",
+    "\n",
+    "* Work with coordinates and other quantities that have units.\n",
+    "\n",
+    "* Download the results of a query and store them in a file."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Installing libraries\n",
+    "\n",
+    "If you are running this notebook on Colab, you can run the following cell to install Astroquery and a the other libraries we'll use.\n",
+    "\n",
+    "If you are running this notebook on your own computer, you might have to install these libraries yourself.  \n",
+    "\n",
+    "If you are using this notebook as part of a Carpentries workshop, you should have received setup instructions.\n",
+    "\n",
+    "TODO: Add a link to the instructions.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# If we're running on Colab, install libraries\n",
+    "\n",
+    "import sys\n",
+    "IN_COLAB = 'google.colab' in sys.modules\n",
+    "\n",
+    "if IN_COLAB:\n",
+    "    !pip install astroquery astro-gala pyia"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Selecting a region"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "One of the most common ways to restrict a query is to select stars in a particular region of the sky.\n",
+    "\n",
+    "For example, here's a query from the [Gaia archive documentation](https://gea.esac.esa.int/archive-help/adql/examples/index.html) that selects \"all the objects ... in a circular region centered at (266.41683, -29.00781) with a search radius of 5 arcmin (0.08333 deg).\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query = \"\"\"\n",
+    "SELECT \n",
+    "TOP 10 source_id\n",
+    "FROM gaiadr2.gaia_source\n",
+    "WHERE 1=CONTAINS(\n",
+    "  POINT(ra, dec),\n",
+    "  CIRCLE(266.41683, -29.00781, 0.08333333))\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This query uses three keywords that are specific to ADQL (not SQL):\n",
+    "\n",
+    "* `POINT`: a location in [ICRS coordinates](https://en.wikipedia.org/wiki/International_Celestial_Reference_System), specified in degrees of right ascension and declination.\n",
+    "\n",
+    "* `CIRCLE`: a circle where the first two values are the coordinates of the center and the third is the radius in degrees.\n",
+    "\n",
+    "* `CONTAINS`: a function that returns `1` if a `POINT` is contained in a shape and `0` otherwise.\n",
+    "\n",
+    "Here is the [documentation of `CONTAINS`](http://www.ivoa.net/documents/ADQL/20180112/PR-ADQL-2.1-20180112.html#tth_sEc4.2.12).\n",
+    "\n",
+    "A query like this is called a cone search because it selects stars in a cone.\n",
+    "\n",
+    "Here's how we run it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Created TAP+ (v1.2.1) - Connection:\n",
+      "\tHost: gea.esac.esa.int\n",
+      "\tUse HTTPS: True\n",
+      "\tPort: 443\n",
+      "\tSSL Port: 443\n",
+      "Created TAP+ (v1.2.1) - Connection:\n",
+      "\tHost: geadata.esac.esa.int\n",
+      "\tUse HTTPS: True\n",
+      "\tPort: 443\n",
+      "\tSSL Port: 443\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<i>Table length=10</i>\n",
+       "<table id=\"table139949838258672\" class=\"table-striped table-bordered table-condensed\">\n",
+       "<thead><tr><th>source_id</th></tr></thead>\n",
+       "<thead><tr><th>int64</th></tr></thead>\n",
+       "<tr><td>4057468321929794432</td></tr>\n",
+       "<tr><td>4057468287575835392</td></tr>\n",
+       "<tr><td>4057482027171038976</td></tr>\n",
+       "<tr><td>4057470349160630656</td></tr>\n",
+       "<tr><td>4057470039924301696</td></tr>\n",
+       "<tr><td>4057469868125641984</td></tr>\n",
+       "<tr><td>4057468351995073024</td></tr>\n",
+       "<tr><td>4057469661959554560</td></tr>\n",
+       "<tr><td>4057470520960672640</td></tr>\n",
+       "<tr><td>4057470555320409600</td></tr>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<Table length=10>\n",
+       "     source_id     \n",
+       "       int64       \n",
+       "-------------------\n",
+       "4057468321929794432\n",
+       "4057468287575835392\n",
+       "4057482027171038976\n",
+       "4057470349160630656\n",
+       "4057470039924301696\n",
+       "4057469868125641984\n",
+       "4057468351995073024\n",
+       "4057469661959554560\n",
+       "4057470520960672640\n",
+       "4057470555320409600"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from astroquery.gaia import Gaia\n",
+    "\n",
+    "job = Gaia.launch_job(query)\n",
+    "result = job.get_results()\n",
+    "result"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** When you are debugging queries like this, you can use `TOP` to limit the size of the results, but then you still don't know how big the results will be.\n",
+    "\n",
+    "An alternative is to use `COUNT`, which asks for the number of rows that would be selected, but it does not return them.\n",
+    "\n",
+    "In the previous query, replace `TOP 10 source_id` with `COUNT(source_id)` and run the query again.  How many stars has Gaia identified in the cone we searched?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Getting GD-1 Data\n",
+    "\n",
+    "From the Price-Whelan and Bonaca paper, we will try to reproduce Figure 1, which includes this representation of stars likely to belong to GD-1:\n",
+    "\n",
+    "<img src=\"https://github.com/datacarpentry/astronomy-python/raw/gh-pages/fig/gd1-4.png\">"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Along the axis of right ascension ($\\phi_1$) the figure extends from -100 to 20 degrees.\n",
+    "\n",
+    "Along the axis of declination ($\\phi_2$) the figure extends from about -8 to 4 degrees.\n",
+    "\n",
+    "Ideally, we would select all stars from this rectangle, but there are more than 10 million of them, so\n",
+    "\n",
+    "* That would be difficult to work with,\n",
+    "\n",
+    "* As anonymous users, we are limited to 3 million rows in a single query, and\n",
+    "\n",
+    "* While we are developing and testing code, it will be faster to work with a smaller dataset.\n",
+    "\n",
+    "So we'll start by selecting stars in a smaller rectangle, from -55 to -45 degrees right ascension and -8 to 4 degrees of declination.\n",
+    "\n",
+    "But first we let's see how to represent quantities with units like degrees."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Working with coordinates\n",
+    "\n",
+    "Coordinates are physical quantities, which means that they have two parts, a value and a unit.\n",
+    "\n",
+    "For example, the coordinate $30^{\\circ}$ has value 30 and its units are degrees.\n",
+    "\n",
+    "Until recently, most scientific computation was done with values only; units were left out of the program altogether, [often with disastrous results](https://en.wikipedia.org/wiki/Mars_Climate_Orbiter#Cause_of_failure).\n",
+    "\n",
+    "Astropy provides tools for including units explicitly in computations, which makes it possible to detect errors before they cause disasters.\n",
+    "\n",
+    "To use Astropy units, we import them like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<module 'astropy.units' from '/home/downey/anaconda3/envs/AstronomicalData/lib/python3.8/site-packages/astropy/units/__init__.py'>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import astropy.units as u\n",
+    "\n",
+    "u"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`u` is an object that contains most common units and all SI units.\n",
+    "\n",
+    "You can use `dir` to list them, but you should also [read the documentation](https://docs.astropy.org/en/stable/units/)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['A',\n",
+       " 'AA',\n",
+       " 'AB',\n",
+       " 'ABflux',\n",
+       " 'ABmag',\n",
+       " 'AU',\n",
+       " 'Angstrom',\n",
+       " 'B',\n",
+       " 'Ba',\n",
+       " 'Barye',\n",
+       " 'Bi',\n",
+       " 'Biot',\n",
+       " 'Bol',\n",
+       " 'Bq',\n",
+       " 'C',\n",
+       " 'Celsius',\n",
+       " 'Ci',\n",
+       " 'CompositeUnit',\n",
+       " 'D',\n",
+       " 'Da',\n",
+       " 'Dalton',\n",
+       " 'Debye',\n",
+       " 'Decibel',\n",
+       " 'DecibelUnit',\n",
+       " 'Dex',\n",
+       " 'DexUnit',\n",
+       " 'EA',\n",
+       " 'EAU',\n",
+       " 'EB',\n",
+       " 'EBa',\n",
+       " 'EC',\n",
+       " 'ED',\n",
+       " 'EF',\n",
+       " 'EG',\n",
+       " 'EGal',\n",
+       " 'EH',\n",
+       " 'EHz',\n",
+       " 'EJ',\n",
+       " 'EJy',\n",
+       " 'EK',\n",
+       " 'EL',\n",
+       " 'EN',\n",
+       " 'EOhm',\n",
+       " 'EP',\n",
+       " 'EPa',\n",
+       " 'ER',\n",
+       " 'ERy',\n",
+       " 'ES',\n",
+       " 'ESt',\n",
+       " 'ET',\n",
+       " 'EV',\n",
+       " 'EW',\n",
+       " 'EWb',\n",
+       " 'Ea',\n",
+       " 'Eadu',\n",
+       " 'Earcmin',\n",
+       " 'Earcsec',\n",
+       " 'Eau',\n",
+       " 'Eb',\n",
+       " 'Ebarn',\n",
+       " 'Ebeam',\n",
+       " 'Ebin',\n",
+       " 'Ebit',\n",
+       " 'Ebyte',\n",
+       " 'Ecd',\n",
+       " 'Echan',\n",
+       " 'Ecount',\n",
+       " 'Ect',\n",
+       " 'Ed',\n",
+       " 'Edeg',\n",
+       " 'Edyn',\n",
+       " 'EeV',\n",
+       " 'Eerg',\n",
+       " 'Eg',\n",
+       " 'Eh',\n",
+       " 'EiB',\n",
+       " 'Eib',\n",
+       " 'Eibit',\n",
+       " 'Eibyte',\n",
+       " 'Ek',\n",
+       " 'El',\n",
+       " 'Elm',\n",
+       " 'Elx',\n",
+       " 'Elyr',\n",
+       " 'Em',\n",
+       " 'Emag',\n",
+       " 'Emin',\n",
+       " 'Emol',\n",
+       " 'Eohm',\n",
+       " 'Epc',\n",
+       " 'Eph',\n",
+       " 'Ephoton',\n",
+       " 'Epix',\n",
+       " 'Epixel',\n",
+       " 'Erad',\n",
+       " 'Es',\n",
+       " 'Esr',\n",
+       " 'Eu',\n",
+       " 'Evox',\n",
+       " 'Evoxel',\n",
+       " 'Eyr',\n",
+       " 'F',\n",
+       " 'Farad',\n",
+       " 'Fr',\n",
+       " 'Franklin',\n",
+       " 'FunctionQuantity',\n",
+       " 'FunctionUnitBase',\n",
+       " 'G',\n",
+       " 'GA',\n",
+       " 'GAU',\n",
+       " 'GB',\n",
+       " 'GBa',\n",
+       " 'GC',\n",
+       " 'GD',\n",
+       " 'GF',\n",
+       " 'GG',\n",
+       " 'GGal',\n",
+       " 'GH',\n",
+       " 'GHz',\n",
+       " 'GJ',\n",
+       " 'GJy',\n",
+       " 'GK',\n",
+       " 'GL',\n",
+       " 'GN',\n",
+       " 'GOhm',\n",
+       " 'GP',\n",
+       " 'GPa',\n",
+       " 'GR',\n",
+       " 'GRy',\n",
+       " 'GS',\n",
+       " 'GSt',\n",
+       " 'GT',\n",
+       " 'GV',\n",
+       " 'GW',\n",
+       " 'GWb',\n",
+       " 'Ga',\n",
+       " 'Gadu',\n",
+       " 'Gal',\n",
+       " 'Garcmin',\n",
+       " 'Garcsec',\n",
+       " 'Gau',\n",
+       " 'Gauss',\n",
+       " 'Gb',\n",
+       " 'Gbarn',\n",
+       " 'Gbeam',\n",
+       " 'Gbin',\n",
+       " 'Gbit',\n",
+       " 'Gbyte',\n",
+       " 'Gcd',\n",
+       " 'Gchan',\n",
+       " 'Gcount',\n",
+       " 'Gct',\n",
+       " 'Gd',\n",
+       " 'Gdeg',\n",
+       " 'Gdyn',\n",
+       " 'GeV',\n",
+       " 'Gerg',\n",
+       " 'Gg',\n",
+       " 'Gh',\n",
+       " 'GiB',\n",
+       " 'Gib',\n",
+       " 'Gibit',\n",
+       " 'Gibyte',\n",
+       " 'Gk',\n",
+       " 'Gl',\n",
+       " 'Glm',\n",
+       " 'Glx',\n",
+       " 'Glyr',\n",
+       " 'Gm',\n",
+       " 'Gmag',\n",
+       " 'Gmin',\n",
+       " 'Gmol',\n",
+       " 'Gohm',\n",
+       " 'Gpc',\n",
+       " 'Gph',\n",
+       " 'Gphoton',\n",
+       " 'Gpix',\n",
+       " 'Gpixel',\n",
+       " 'Grad',\n",
+       " 'Gs',\n",
+       " 'Gsr',\n",
+       " 'Gu',\n",
+       " 'Gvox',\n",
+       " 'Gvoxel',\n",
+       " 'Gyr',\n",
+       " 'H',\n",
+       " 'Henry',\n",
+       " 'Hertz',\n",
+       " 'Hz',\n",
+       " 'IrreducibleUnit',\n",
+       " 'J',\n",
+       " 'Jansky',\n",
+       " 'Joule',\n",
+       " 'Jy',\n",
+       " 'K',\n",
+       " 'Kayser',\n",
+       " 'Kelvin',\n",
+       " 'KiB',\n",
+       " 'Kib',\n",
+       " 'Kibit',\n",
+       " 'Kibyte',\n",
+       " 'L',\n",
+       " 'L_bol',\n",
+       " 'L_sun',\n",
+       " 'LogQuantity',\n",
+       " 'LogUnit',\n",
+       " 'Lsun',\n",
+       " 'MA',\n",
+       " 'MAU',\n",
+       " 'MB',\n",
+       " 'MBa',\n",
+       " 'MC',\n",
+       " 'MD',\n",
+       " 'MF',\n",
+       " 'MG',\n",
+       " 'MGal',\n",
+       " 'MH',\n",
+       " 'MHz',\n",
+       " 'MJ',\n",
+       " 'MJy',\n",
+       " 'MK',\n",
+       " 'ML',\n",
+       " 'MN',\n",
+       " 'MOhm',\n",
+       " 'MP',\n",
+       " 'MPa',\n",
+       " 'MR',\n",
+       " 'MRy',\n",
+       " 'MS',\n",
+       " 'MSt',\n",
+       " 'MT',\n",
+       " 'MV',\n",
+       " 'MW',\n",
+       " 'MWb',\n",
+       " 'M_bol',\n",
+       " 'M_e',\n",
+       " 'M_earth',\n",
+       " 'M_jup',\n",
+       " 'M_jupiter',\n",
+       " 'M_p',\n",
+       " 'M_sun',\n",
+       " 'Ma',\n",
+       " 'Madu',\n",
+       " 'MagUnit',\n",
+       " 'Magnitude',\n",
+       " 'Marcmin',\n",
+       " 'Marcsec',\n",
+       " 'Mau',\n",
+       " 'Mb',\n",
+       " 'Mbarn',\n",
+       " 'Mbeam',\n",
+       " 'Mbin',\n",
+       " 'Mbit',\n",
+       " 'Mbyte',\n",
+       " 'Mcd',\n",
+       " 'Mchan',\n",
+       " 'Mcount',\n",
+       " 'Mct',\n",
+       " 'Md',\n",
+       " 'Mdeg',\n",
+       " 'Mdyn',\n",
+       " 'MeV',\n",
+       " 'Mearth',\n",
+       " 'Merg',\n",
+       " 'Mg',\n",
+       " 'Mh',\n",
+       " 'MiB',\n",
+       " 'Mib',\n",
+       " 'Mibit',\n",
+       " 'Mibyte',\n",
+       " 'Mjup',\n",
+       " 'Mjupiter',\n",
+       " 'Mk',\n",
+       " 'Ml',\n",
+       " 'Mlm',\n",
+       " 'Mlx',\n",
+       " 'Mlyr',\n",
+       " 'Mm',\n",
+       " 'Mmag',\n",
+       " 'Mmin',\n",
+       " 'Mmol',\n",
+       " 'Mohm',\n",
+       " 'Mpc',\n",
+       " 'Mph',\n",
+       " 'Mphoton',\n",
+       " 'Mpix',\n",
+       " 'Mpixel',\n",
+       " 'Mrad',\n",
+       " 'Ms',\n",
+       " 'Msr',\n",
+       " 'Msun',\n",
+       " 'Mu',\n",
+       " 'Mvox',\n",
+       " 'Mvoxel',\n",
+       " 'Myr',\n",
+       " 'N',\n",
+       " 'NamedUnit',\n",
+       " 'Newton',\n",
+       " 'Ohm',\n",
+       " 'P',\n",
+       " 'PA',\n",
+       " 'PAU',\n",
+       " 'PB',\n",
+       " 'PBa',\n",
+       " 'PC',\n",
+       " 'PD',\n",
+       " 'PF',\n",
+       " 'PG',\n",
+       " 'PGal',\n",
+       " 'PH',\n",
+       " 'PHz',\n",
+       " 'PJ',\n",
+       " 'PJy',\n",
+       " 'PK',\n",
+       " 'PL',\n",
+       " 'PN',\n",
+       " 'POhm',\n",
+       " 'PP',\n",
+       " 'PPa',\n",
+       " 'PR',\n",
+       " 'PRy',\n",
+       " 'PS',\n",
+       " 'PSt',\n",
+       " 'PT',\n",
+       " 'PV',\n",
+       " 'PW',\n",
+       " 'PWb',\n",
+       " 'Pa',\n",
+       " 'Padu',\n",
+       " 'Parcmin',\n",
+       " 'Parcsec',\n",
+       " 'Pascal',\n",
+       " 'Pau',\n",
+       " 'Pb',\n",
+       " 'Pbarn',\n",
+       " 'Pbeam',\n",
+       " 'Pbin',\n",
+       " 'Pbit',\n",
+       " 'Pbyte',\n",
+       " 'Pcd',\n",
+       " 'Pchan',\n",
+       " 'Pcount',\n",
+       " 'Pct',\n",
+       " 'Pd',\n",
+       " 'Pdeg',\n",
+       " 'Pdyn',\n",
+       " 'PeV',\n",
+       " 'Perg',\n",
+       " 'Pg',\n",
+       " 'Ph',\n",
+       " 'PiB',\n",
+       " 'Pib',\n",
+       " 'Pibit',\n",
+       " 'Pibyte',\n",
+       " 'Pk',\n",
+       " 'Pl',\n",
+       " 'Plm',\n",
+       " 'Plx',\n",
+       " 'Plyr',\n",
+       " 'Pm',\n",
+       " 'Pmag',\n",
+       " 'Pmin',\n",
+       " 'Pmol',\n",
+       " 'Pohm',\n",
+       " 'Ppc',\n",
+       " 'Pph',\n",
+       " 'Pphoton',\n",
+       " 'Ppix',\n",
+       " 'Ppixel',\n",
+       " 'Prad',\n",
+       " 'PrefixUnit',\n",
+       " 'Ps',\n",
+       " 'Psr',\n",
+       " 'Pu',\n",
+       " 'Pvox',\n",
+       " 'Pvoxel',\n",
+       " 'Pyr',\n",
+       " 'Quantity',\n",
+       " 'QuantityInfo',\n",
+       " 'QuantityInfoBase',\n",
+       " 'R',\n",
+       " 'R_earth',\n",
+       " 'R_jup',\n",
+       " 'R_jupiter',\n",
+       " 'R_sun',\n",
+       " 'Rayleigh',\n",
+       " 'Rearth',\n",
+       " 'Rjup',\n",
+       " 'Rjupiter',\n",
+       " 'Rsun',\n",
+       " 'Ry',\n",
+       " 'S',\n",
+       " 'ST',\n",
+       " 'STflux',\n",
+       " 'STmag',\n",
+       " 'Siemens',\n",
+       " 'SpecificTypeQuantity',\n",
+       " 'St',\n",
+       " 'Sun',\n",
+       " 'T',\n",
+       " 'TA',\n",
+       " 'TAU',\n",
+       " 'TB',\n",
+       " 'TBa',\n",
+       " 'TC',\n",
+       " 'TD',\n",
+       " 'TF',\n",
+       " 'TG',\n",
+       " 'TGal',\n",
+       " 'TH',\n",
+       " 'THz',\n",
+       " 'TJ',\n",
+       " 'TJy',\n",
+       " 'TK',\n",
+       " 'TL',\n",
+       " 'TN',\n",
+       " 'TOhm',\n",
+       " 'TP',\n",
+       " 'TPa',\n",
+       " 'TR',\n",
+       " 'TRy',\n",
+       " 'TS',\n",
+       " 'TSt',\n",
+       " 'TT',\n",
+       " 'TV',\n",
+       " 'TW',\n",
+       " 'TWb',\n",
+       " 'Ta',\n",
+       " 'Tadu',\n",
+       " 'Tarcmin',\n",
+       " 'Tarcsec',\n",
+       " 'Tau',\n",
+       " 'Tb',\n",
+       " 'Tbarn',\n",
+       " 'Tbeam',\n",
+       " 'Tbin',\n",
+       " 'Tbit',\n",
+       " 'Tbyte',\n",
+       " 'Tcd',\n",
+       " 'Tchan',\n",
+       " 'Tcount',\n",
+       " 'Tct',\n",
+       " 'Td',\n",
+       " 'Tdeg',\n",
+       " 'Tdyn',\n",
+       " 'TeV',\n",
+       " 'Terg',\n",
+       " 'Tesla',\n",
+       " 'Tg',\n",
+       " 'Th',\n",
+       " 'TiB',\n",
+       " 'Tib',\n",
+       " 'Tibit',\n",
+       " 'Tibyte',\n",
+       " 'Tk',\n",
+       " 'Tl',\n",
+       " 'Tlm',\n",
+       " 'Tlx',\n",
+       " 'Tlyr',\n",
+       " 'Tm',\n",
+       " 'Tmag',\n",
+       " 'Tmin',\n",
+       " 'Tmol',\n",
+       " 'Tohm',\n",
+       " 'Tpc',\n",
+       " 'Tph',\n",
+       " 'Tphoton',\n",
+       " 'Tpix',\n",
+       " 'Tpixel',\n",
+       " 'Trad',\n",
+       " 'Ts',\n",
+       " 'Tsr',\n",
+       " 'Tu',\n",
+       " 'Tvox',\n",
+       " 'Tvoxel',\n",
+       " 'Tyr',\n",
+       " 'Unit',\n",
+       " 'UnitBase',\n",
+       " 'UnitConversionError',\n",
+       " 'UnitTypeError',\n",
+       " 'UnitsError',\n",
+       " 'UnitsWarning',\n",
+       " 'UnrecognizedUnit',\n",
+       " 'V',\n",
+       " 'Volt',\n",
+       " 'W',\n",
+       " 'Watt',\n",
+       " 'Wb',\n",
+       " 'Weber',\n",
+       " 'YA',\n",
+       " 'YAU',\n",
+       " 'YB',\n",
+       " 'YBa',\n",
+       " 'YC',\n",
+       " 'YD',\n",
+       " 'YF',\n",
+       " 'YG',\n",
+       " 'YGal',\n",
+       " 'YH',\n",
+       " 'YHz',\n",
+       " 'YJ',\n",
+       " 'YJy',\n",
+       " 'YK',\n",
+       " 'YL',\n",
+       " 'YN',\n",
+       " 'YOhm',\n",
+       " 'YP',\n",
+       " 'YPa',\n",
+       " 'YR',\n",
+       " 'YRy',\n",
+       " 'YS',\n",
+       " 'YSt',\n",
+       " 'YT',\n",
+       " 'YV',\n",
+       " 'YW',\n",
+       " 'YWb',\n",
+       " 'Ya',\n",
+       " 'Yadu',\n",
+       " 'Yarcmin',\n",
+       " 'Yarcsec',\n",
+       " 'Yau',\n",
+       " 'Yb',\n",
+       " 'Ybarn',\n",
+       " 'Ybeam',\n",
+       " 'Ybin',\n",
+       " 'Ybit',\n",
+       " 'Ybyte',\n",
+       " 'Ycd',\n",
+       " 'Ychan',\n",
+       " 'Ycount',\n",
+       " 'Yct',\n",
+       " 'Yd',\n",
+       " 'Ydeg',\n",
+       " 'Ydyn',\n",
+       " 'YeV',\n",
+       " 'Yerg',\n",
+       " 'Yg',\n",
+       " 'Yh',\n",
+       " 'Yk',\n",
+       " 'Yl',\n",
+       " 'Ylm',\n",
+       " 'Ylx',\n",
+       " 'Ylyr',\n",
+       " 'Ym',\n",
+       " 'Ymag',\n",
+       " 'Ymin',\n",
+       " 'Ymol',\n",
+       " 'Yohm',\n",
+       " 'Ypc',\n",
+       " 'Yph',\n",
+       " 'Yphoton',\n",
+       " 'Ypix',\n",
+       " 'Ypixel',\n",
+       " 'Yrad',\n",
+       " 'Ys',\n",
+       " 'Ysr',\n",
+       " 'Yu',\n",
+       " 'Yvox',\n",
+       " 'Yvoxel',\n",
+       " 'Yyr',\n",
+       " 'ZA',\n",
+       " 'ZAU',\n",
+       " 'ZB',\n",
+       " 'ZBa',\n",
+       " 'ZC',\n",
+       " 'ZD',\n",
+       " 'ZF',\n",
+       " 'ZG',\n",
+       " 'ZGal',\n",
+       " 'ZH',\n",
+       " 'ZHz',\n",
+       " 'ZJ',\n",
+       " 'ZJy',\n",
+       " 'ZK',\n",
+       " 'ZL',\n",
+       " 'ZN',\n",
+       " 'ZOhm',\n",
+       " 'ZP',\n",
+       " 'ZPa',\n",
+       " 'ZR',\n",
+       " 'ZRy',\n",
+       " 'ZS',\n",
+       " 'ZSt',\n",
+       " 'ZT',\n",
+       " 'ZV',\n",
+       " 'ZW',\n",
+       " 'ZWb',\n",
+       " 'Za',\n",
+       " 'Zadu',\n",
+       " 'Zarcmin',\n",
+       " 'Zarcsec',\n",
+       " 'Zau',\n",
+       " 'Zb',\n",
+       " 'Zbarn',\n",
+       " 'Zbeam',\n",
+       " 'Zbin',\n",
+       " 'Zbit',\n",
+       " 'Zbyte',\n",
+       " 'Zcd',\n",
+       " 'Zchan',\n",
+       " 'Zcount',\n",
+       " 'Zct',\n",
+       " 'Zd',\n",
+       " 'Zdeg',\n",
+       " 'Zdyn',\n",
+       " 'ZeV',\n",
+       " 'Zerg',\n",
+       " 'Zg',\n",
+       " 'Zh',\n",
+       " 'Zk',\n",
+       " 'Zl',\n",
+       " 'Zlm',\n",
+       " 'Zlx',\n",
+       " 'Zlyr',\n",
+       " 'Zm',\n",
+       " 'Zmag',\n",
+       " 'Zmin',\n",
+       " 'Zmol',\n",
+       " 'Zohm',\n",
+       " 'Zpc',\n",
+       " 'Zph',\n",
+       " 'Zphoton',\n",
+       " 'Zpix',\n",
+       " 'Zpixel',\n",
+       " 'Zrad',\n",
+       " 'Zs',\n",
+       " 'Zsr',\n",
+       " 'Zu',\n",
+       " 'Zvox',\n",
+       " 'Zvoxel',\n",
+       " 'Zyr',\n",
+       " '__builtins__',\n",
+       " '__cached__',\n",
+       " '__doc__',\n",
+       " '__file__',\n",
+       " '__loader__',\n",
+       " '__name__',\n",
+       " '__package__',\n",
+       " '__path__',\n",
+       " '__spec__',\n",
+       " 'a',\n",
+       " 'aA',\n",
+       " 'aAU',\n",
+       " 'aB',\n",
+       " 'aBa',\n",
+       " 'aC',\n",
+       " 'aD',\n",
+       " 'aF',\n",
+       " 'aG',\n",
+       " 'aGal',\n",
+       " 'aH',\n",
+       " 'aHz',\n",
+       " 'aJ',\n",
+       " 'aJy',\n",
+       " 'aK',\n",
+       " 'aL',\n",
+       " 'aN',\n",
+       " 'aOhm',\n",
+       " 'aP',\n",
+       " 'aPa',\n",
+       " 'aR',\n",
+       " 'aRy',\n",
+       " 'aS',\n",
+       " 'aSt',\n",
+       " 'aT',\n",
+       " 'aV',\n",
+       " 'aW',\n",
+       " 'aWb',\n",
+       " 'aa',\n",
+       " 'aadu',\n",
+       " 'aarcmin',\n",
+       " 'aarcsec',\n",
+       " 'aau',\n",
+       " 'ab',\n",
+       " 'abA',\n",
+       " 'abC',\n",
+       " 'abampere',\n",
+       " 'abarn',\n",
+       " 'abcoulomb',\n",
+       " 'abeam',\n",
+       " 'abin',\n",
+       " 'abit',\n",
+       " 'abyte',\n",
+       " 'acd',\n",
+       " 'achan',\n",
+       " 'acount',\n",
+       " 'act',\n",
+       " 'ad',\n",
+       " 'add_enabled_equivalencies',\n",
+       " 'add_enabled_units',\n",
+       " 'adeg',\n",
+       " 'adu',\n",
+       " 'adyn',\n",
+       " 'aeV',\n",
+       " 'aerg',\n",
+       " 'ag',\n",
+       " 'ah',\n",
+       " 'ak',\n",
+       " 'al',\n",
+       " 'allclose',\n",
+       " 'alm',\n",
+       " 'alx',\n",
+       " 'alyr',\n",
+       " 'am',\n",
+       " 'amag',\n",
+       " 'amin',\n",
+       " 'amol',\n",
+       " 'amp',\n",
+       " 'ampere',\n",
+       " 'angstrom',\n",
+       " 'annum',\n",
+       " 'aohm',\n",
+       " 'apc',\n",
+       " 'aph',\n",
+       " 'aphoton',\n",
+       " 'apix',\n",
+       " 'apixel',\n",
+       " 'arad',\n",
+       " 'arcmin',\n",
+       " 'arcminute',\n",
+       " 'arcsec',\n",
+       " 'arcsecond',\n",
+       " 'asr',\n",
+       " 'astronomical_unit',\n",
+       " 'astrophys',\n",
+       " 'attoBarye',\n",
+       " 'attoDa',\n",
+       " 'attoDalton',\n",
+       " 'attoDebye',\n",
+       " 'attoFarad',\n",
+       " 'attoGauss',\n",
+       " 'attoHenry',\n",
+       " 'attoHertz',\n",
+       " 'attoJansky',\n",
+       " 'attoJoule',\n",
+       " 'attoKayser',\n",
+       " 'attoKelvin',\n",
+       " 'attoNewton',\n",
+       " 'attoOhm',\n",
+       " 'attoPascal',\n",
+       " 'attoRayleigh',\n",
+       " 'attoSiemens',\n",
+       " 'attoTesla',\n",
+       " 'attoVolt',\n",
+       " 'attoWatt',\n",
+       " 'attoWeber',\n",
+       " 'attoamp',\n",
+       " 'attoampere',\n",
+       " 'attoannum',\n",
+       " 'attoarcminute',\n",
+       " 'attoarcsecond',\n",
+       " 'attoastronomical_unit',\n",
+       " 'attobarn',\n",
+       " 'attobarye',\n",
+       " 'attobit',\n",
+       " 'attobyte',\n",
+       " 'attocandela',\n",
+       " 'attocoulomb',\n",
+       " 'attocount',\n",
+       " 'attoday',\n",
+       " 'attodebye',\n",
+       " 'attodegree',\n",
+       " 'attodyne',\n",
+       " 'attoelectronvolt',\n",
+       " 'attofarad',\n",
+       " 'attogal',\n",
+       " 'attogauss',\n",
+       " 'attogram',\n",
+       " 'attohenry',\n",
+       " 'attohertz',\n",
+       " 'attohour',\n",
+       " 'attohr',\n",
+       " 'attojansky',\n",
+       " 'attojoule',\n",
+       " 'attokayser',\n",
+       " 'attolightyear',\n",
+       " 'attoliter',\n",
+       " 'attolumen',\n",
+       " 'attolux',\n",
+       " 'attometer',\n",
+       " 'attominute',\n",
+       " 'attomole',\n",
+       " 'attonewton',\n",
+       " 'attoparsec',\n",
+       " 'attopascal',\n",
+       " 'attophoton',\n",
+       " 'attopixel',\n",
+       " 'attopoise',\n",
+       " 'attoradian',\n",
+       " 'attorayleigh',\n",
+       " 'attorydberg',\n",
+       " 'attosecond',\n",
+       " 'attosiemens',\n",
+       " 'attosteradian',\n",
+       " 'attostokes',\n",
+       " 'attotesla',\n",
+       " 'attovolt',\n",
+       " 'attovoxel',\n",
+       " 'attowatt',\n",
+       " 'attoweber',\n",
+       " 'attoyear',\n",
+       " 'au',\n",
+       " 'avox',\n",
+       " 'avoxel',\n",
+       " 'ayr',\n",
+       " 'b',\n",
+       " 'bar',\n",
+       " 'barn',\n",
+       " 'barye',\n",
+       " 'beam',\n",
+       " 'beam_angular_area',\n",
+       " 'becquerel',\n",
+       " 'bin',\n",
+       " 'binary_prefixes',\n",
+       " 'bit',\n",
+       " 'bol',\n",
+       " 'brightness_temperature',\n",
+       " 'byte',\n",
+       " 'cA',\n",
+       " 'cAU',\n",
+       " 'cB',\n",
+       " 'cBa',\n",
+       " 'cC',\n",
+       " 'cD',\n",
+       " 'cF',\n",
+       " 'cG',\n",
+       " 'cGal',\n",
+       " 'cH',\n",
+       " 'cHz',\n",
+       " 'cJ',\n",
+       " 'cJy',\n",
+       " 'cK',\n",
+       " 'cL',\n",
+       " 'cN',\n",
+       " 'cOhm',\n",
+       " 'cP',\n",
+       " 'cPa',\n",
+       " 'cR',\n",
+       " 'cRy',\n",
+       " 'cS',\n",
+       " 'cSt',\n",
+       " 'cT',\n",
+       " 'cV',\n",
+       " 'cW',\n",
+       " 'cWb',\n",
+       " 'ca',\n",
+       " 'cadu',\n",
+       " 'candela',\n",
+       " 'carcmin',\n",
+       " 'carcsec',\n",
+       " 'cau',\n",
+       " 'cb',\n",
+       " 'cbarn',\n",
+       " 'cbeam',\n",
+       " 'cbin',\n",
+       " 'cbit',\n",
+       " 'cbyte',\n",
+       " 'ccd',\n",
+       " 'cchan',\n",
+       " 'ccount',\n",
+       " 'cct',\n",
+       " 'cd',\n",
+       " 'cdeg',\n",
+       " 'cdyn',\n",
+       " 'ceV',\n",
+       " 'centiBarye',\n",
+       " 'centiDa',\n",
+       " 'centiDalton',\n",
+       " 'centiDebye',\n",
+       " 'centiFarad',\n",
+       " 'centiGauss',\n",
+       " 'centiHenry',\n",
+       " 'centiHertz',\n",
+       " 'centiJansky',\n",
+       " 'centiJoule',\n",
+       " 'centiKayser',\n",
+       " 'centiKelvin',\n",
+       " 'centiNewton',\n",
+       " 'centiOhm',\n",
+       " 'centiPascal',\n",
+       " 'centiRayleigh',\n",
+       " 'centiSiemens',\n",
+       " 'centiTesla',\n",
+       " 'centiVolt',\n",
+       " 'centiWatt',\n",
+       " 'centiWeber',\n",
+       " 'centiamp',\n",
+       " 'centiampere',\n",
+       " 'centiannum',\n",
+       " 'centiarcminute',\n",
+       " 'centiarcsecond',\n",
+       " 'centiastronomical_unit',\n",
+       " 'centibarn',\n",
+       " 'centibarye',\n",
+       " 'centibit',\n",
+       " 'centibyte',\n",
+       " 'centicandela',\n",
+       " 'centicoulomb',\n",
+       " 'centicount',\n",
+       " 'centiday',\n",
+       " 'centidebye',\n",
+       " 'centidegree',\n",
+       " 'centidyne',\n",
+       " 'centielectronvolt',\n",
+       " 'centifarad',\n",
+       " 'centigal',\n",
+       " 'centigauss',\n",
+       " 'centigram',\n",
+       " 'centihenry',\n",
+       " 'centihertz',\n",
+       " 'centihour',\n",
+       " 'centihr',\n",
+       " 'centijansky',\n",
+       " 'centijoule',\n",
+       " 'centikayser',\n",
+       " 'centilightyear',\n",
+       " 'centiliter',\n",
+       " 'centilumen',\n",
+       " 'centilux',\n",
+       " 'centimeter',\n",
+       " 'centiminute',\n",
+       " 'centimole',\n",
+       " 'centinewton',\n",
+       " 'centiparsec',\n",
+       " 'centipascal',\n",
+       " 'centiphoton',\n",
+       " 'centipixel',\n",
+       " 'centipoise',\n",
+       " 'centiradian',\n",
+       " 'centirayleigh',\n",
+       " 'centirydberg',\n",
+       " 'centisecond',\n",
+       " 'centisiemens',\n",
+       " 'centisteradian',\n",
+       " 'centistokes',\n",
+       " 'centitesla',\n",
+       " 'centivolt',\n",
+       " 'centivoxel',\n",
+       " 'centiwatt',\n",
+       " 'centiweber',\n",
+       " 'centiyear',\n",
+       " 'cerg',\n",
+       " 'cg',\n",
+       " 'cgs',\n",
+       " 'ch',\n",
+       " 'chan',\n",
+       " 'ck',\n",
+       " 'cl',\n",
+       " 'clm',\n",
+       " 'clx',\n",
+       " 'clyr',\n",
+       " 'cm',\n",
+       " 'cmag',\n",
+       " 'cmin',\n",
+       " 'cmol',\n",
+       " 'cohm',\n",
+       " 'core',\n",
+       " 'coulomb',\n",
+       " 'count',\n",
+       " 'cpc',\n",
+       " 'cph',\n",
+       " 'cphoton',\n",
+       " 'cpix',\n",
+       " 'cpixel',\n",
+       " 'crad',\n",
+       " 'cs',\n",
+       " 'csr',\n",
+       " 'ct',\n",
+       " 'cu',\n",
+       " 'curie',\n",
+       " 'cvox',\n",
+       " 'cvoxel',\n",
+       " 'cy',\n",
+       " 'cycle',\n",
+       " 'cyr',\n",
+       " 'd',\n",
+       " 'dA',\n",
+       " 'dAU',\n",
+       " 'dB',\n",
+       " 'dBa',\n",
+       " 'dC',\n",
+       " 'dD',\n",
+       " 'dF',\n",
+       " 'dG',\n",
+       " 'dGal',\n",
+       " 'dH',\n",
+       " 'dHz',\n",
+       " 'dJ',\n",
+       " 'dJy',\n",
+       " 'dK',\n",
+       " 'dL',\n",
+       " 'dN',\n",
+       " 'dOhm',\n",
+       " 'dP',\n",
+       " 'dPa',\n",
+       " 'dR',\n",
+       " 'dRy',\n",
+       " 'dS',\n",
+       " 'dSt',\n",
+       " 'dT',\n",
+       " ...]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dir(u)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To create a quantity, we multiply a value by a unit."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "astropy.units.quantity.Quantity"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "coord = 30 * u.deg\n",
+    "type(coord)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The result is a `Quantity` object.\n",
+    "\n",
+    "Jupyter knows how to display `Quantities` like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$30 \\; \\mathrm{{}^{\\circ}}$"
+      ],
+      "text/plain": [
+       "<Quantity 30. deg>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "coord"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Selecting a rectangle\n",
+    "\n",
+    "Now we'll select a rectangle from -55 to -45 degrees right ascension and -8 to 4 degrees of declination.\n",
+    "\n",
+    "We'll define variables to contain these limits."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "phi1_min = -55\n",
+    "phi1_max = -45\n",
+    "phi2_min = -8\n",
+    "phi2_max = 4"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To represent a rectangle, we'll use two lists of coordinates and multiply by their units."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "phi1_rect = [phi1_min, phi1_min, phi1_max, phi1_max] * u.deg\n",
+    "phi2_rect = [phi2_min, phi2_max, phi2_max, phi2_min] * u.deg"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`phi1_rect` and `phi2_rect` represent the coordinates of the corners of a rectangle.  \n",
+    "\n",
+    "But they are in \"[a Heliocentric spherical coordinate system defined by the orbit of the GD1 stream](https://gala-astro.readthedocs.io/en/latest/_modules/gala/coordinates/gd1.html)\"\n",
+    "\n",
+    "In order to use them in a Gaia query, we have to convert them to [International Celestial Reference System](https://en.wikipedia.org/wiki/International_Celestial_Reference_System) (ICRS) coordinates.  We can do that by storing the coordinates in a `GD1Koposov10` object provided by [Gala](https://gala-astro.readthedocs.io/en/latest/coordinates/)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "gala.coordinates.gd1.GD1Koposov10"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import gala.coordinates as gc\n",
+    "\n",
+    "corners = gc.GD1Koposov10(phi1=phi1_rect, phi2=phi2_rect)\n",
+    "type(corners)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can display the result like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<GD1Koposov10 Coordinate: (phi1, phi2) in deg\n",
+       "    [(-55., -8.), (-55.,  4.), (-45.,  4.), (-45., -8.)]>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "corners"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can use `transform_to` to convert to ICRS coordinates."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "astropy.coordinates.builtin_frames.icrs.ICRS"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import astropy.coordinates as coord\n",
+    "\n",
+    "corners_icrs = corners.transform_to(coord.ICRS)\n",
+    "type(corners_icrs)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The result is an `ICRS` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<ICRS Coordinate: (ra, dec) in deg\n",
+       "    [(146.27533314, 19.26190982), (135.42163944, 25.87738723),\n",
+       "     (141.60264825, 34.3048303 ), (152.81671045, 27.13611254)]>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "corners_icrs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notice that a rectangle in one coordinate system is not necessarily a rectangle in another.  In this example, the result is a polygon."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Selecting a polygon\n",
+    "\n",
+    "In order to use this polygon as part of an ADQL query, we have to convert it to a string with a comma-separated list of coordinates, as in this example:\n",
+    "\n",
+    "```\n",
+    "\"\"\"\n",
+    "POLYGON(143.65, 20.98, \n",
+    "        134.46, 26.39, \n",
+    "        140.58, 34.85, \n",
+    "        150.16, 29.01)\n",
+    "\"\"\"\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`corners_icrs` behaves like a list, so we can use a `for` loop to iterate through the points."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<ICRS Coordinate: (ra, dec) in deg\n",
+      "    (146.27533314, 19.26190982)>\n",
+      "<ICRS Coordinate: (ra, dec) in deg\n",
+      "    (135.42163944, 25.87738723)>\n",
+      "<ICRS Coordinate: (ra, dec) in deg\n",
+      "    (141.60264825, 34.3048303)>\n",
+      "<ICRS Coordinate: (ra, dec) in deg\n",
+      "    (152.81671045, 27.13611254)>\n"
+     ]
+    }
+   ],
+   "source": [
+    "for point in corners_icrs:\n",
+    "    print(point)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "From that, we can select the coordinates `ra` and `dec`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "146d16m31.1993s 19d15m42.8754s\n",
+      "135d25m17.902s 25d52m38.594s\n",
+      "141d36m09.5337s 34d18m17.3891s\n",
+      "152d49m00.1576s 27d08m10.0051s\n"
+     ]
+    }
+   ],
+   "source": [
+    "for point in corners_icrs:\n",
+    "    print(point.ra, point.dec)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The results are quantities with units, but if we select the `value` part, we get a dimensionless floating-point number."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "146.27533313607782 19.261909820533692\n",
+      "135.42163944306296 25.87738722767213\n",
+      "141.60264825107333 34.304830296257144\n",
+      "152.81671044675923 27.136112541397996\n"
+     ]
+    }
+   ],
+   "source": [
+    "for point in corners_icrs:\n",
+    "    print(point.ra.value, point.dec.value)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can use string `format` to convert these numbers to strings."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['146.27533313607782, 19.261909820533692',\n",
+       " '135.42163944306296, 25.87738722767213',\n",
+       " '141.60264825107333, 34.304830296257144',\n",
+       " '152.81671044675923, 27.136112541397996']"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "point_base = \"{point.ra.value}, {point.dec.value}\"\n",
+    "\n",
+    "t = [point_base.format(point=point)\n",
+    "     for point in corners_icrs]\n",
+    "t"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The result is a list of strings, which we can join into a single string using `join`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'146.27533313607782, 19.261909820533692, 135.42163944306296, 25.87738722767213, 141.60264825107333, 34.304830296257144, 152.81671044675923, 27.136112541397996'"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "point_list = ', '.join(t)\n",
+    "point_list"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notice that we invoke `join` on a string and pass the list as an argument.\n",
+    "\n",
+    "Before we can assemble the query, we need `columns` again (as we saw in the previous notebook)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "columns = 'source_id, ra, dec, pmra, pmdec, parallax, parallax_error, radial_velocity'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's the base for the query, with format specifiers for `columns` and `point_list`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query_base = \"\"\"SELECT {columns}\n",
+    "FROM gaiadr2.gaia_source\n",
+    "WHERE parallax < 1\n",
+    "  AND bp_rp BETWEEN -0.75 AND 2 \n",
+    "  AND 1 = CONTAINS(POINT(ra, dec), \n",
+    "                   POLYGON({point_list}))\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And here's the result:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "SELECT source_id, ra, dec, pmra, pmdec, parallax, parallax_error, radial_velocity\n",
+      "FROM gaiadr2.gaia_source\n",
+      "WHERE parallax < 1\n",
+      "  AND bp_rp BETWEEN -0.75 AND 2 \n",
+      "  AND 1 = CONTAINS(POINT(ra, dec), \n",
+      "                   POLYGON(146.27533313607782, 19.261909820533692, 135.42163944306296, 25.87738722767213, 141.60264825107333, 34.304830296257144, 152.81671044675923, 27.136112541397996))\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "query = query_base.format(columns=columns, \n",
+    "                          point_list=point_list)\n",
+    "print(query)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As always, we should take a minute to proof-read the query before we launch it.\n",
+    "\n",
+    "The result will be bigger than our previous queries, so it will take a little longer."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO: Query finished. [astroquery.utils.tap.core]\n",
+      "<Table length=140340>\n",
+      "      name       dtype    unit                              description                             n_bad \n",
+      "--------------- ------- -------- ------------------------------------------------------------------ ------\n",
+      "      source_id   int64          Unique source identifier (unique within a particular Data Release)      0\n",
+      "             ra float64      deg                                                    Right ascension      0\n",
+      "            dec float64      deg                                                        Declination      0\n",
+      "           pmra float64 mas / yr                         Proper motion in right ascension direction      0\n",
+      "          pmdec float64 mas / yr                             Proper motion in declination direction      0\n",
+      "       parallax float64      mas                                                           Parallax      0\n",
+      " parallax_error float64      mas                                         Standard error of parallax      0\n",
+      "radial_velocity float64   km / s                                                    Radial velocity 139374\n",
+      "Jobid: 1603114980658O\n",
+      "Phase: COMPLETED\n",
+      "Owner: None\n",
+      "Output file: async_20201019094300.vot\n",
+      "Results: None\n"
+     ]
+    }
+   ],
+   "source": [
+    "job = Gaia.launch_job_async(query)\n",
+    "print(job)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here are the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "140340"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results = job.get_results()\n",
+    "len(results)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There are more than 100,000 stars in this polygon, but that's a manageable size to work with."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Saving results\n",
+    "\n",
+    "This is the set of stars we'll work with in the next step.  But since we have a substantial dataset now, this is a good time to save it.\n",
+    "\n",
+    "Storing the data in a file means we can shut down this notebook and pick up where we left off without running the previous query again.\n",
+    "\n",
+    "Astropy `Table` objects provide `write`, which writes the table to disk."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "filename = 'gd1_results.fits'\n",
+    "results.write(filename, overwrite=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Because the filename ends with `fits`, the table is written in the [FITS format](https://en.wikipedia.org/wiki/FITS), which preserves the metadata associated with the table.\n",
+    "\n",
+    "If the file already exists, the `overwrite` argument causes it to be overwritten.\n",
+    "\n",
+    "To see how big the file is, we can use `ls` with the `-lh` option, which prints information about the file including its size in human-readable form."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-rw-rw-r-- 1 downey downey 8.6M Oct 19 09:43 gd1_results.fits\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!ls -lh gd1_results.fits"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The file is about 8.6 MB.  If you are using Windows, `ls` might not work; in that case, try:\n",
+    "\n",
+    "```\n",
+    "!dir gd1_results.fits\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "In this notebook, we composed more complex queries to select stars within a polygonal region of the sky.  Then we downloaded the results and saved them in a FITS file.\n",
+    "\n",
+    "In the next notebook, we'll reload the data from this file and replicate the next step in the analysis, using proper motion to identify stars likely to be in GD-1."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Best practices\n",
+    "\n",
+    "* For measurements with units, use `Quantity` objects that represent units explicitly and check for errors.\n",
+    "\n",
+    "* Use the `format` function to compose queries; it is often faster and less error-prone.\n",
+    "\n",
+    "* Develop queries incrementally: start with something simple, test it, and add a little bit at a time.\n",
+    "\n",
+    "* Once you have a query working, save the data in a local file.  If you shut down the notebook and come back to it later, you can reload the file; you don't have to run the query again."
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/_build/jupyter_execute/AstronomicalData/02_coords.py b/_build/jupyter_execute/AstronomicalData/02_coords.py
new file mode 100644
index 0000000..090c014
--- /dev/null
+++ b/_build/jupyter_execute/AstronomicalData/02_coords.py
@@ -0,0 +1,411 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# # Lesson 2
+# 
+# This is the second in a series of lessons related to astronomy data.
+# 
+# As a running example, we are replicating parts of the analysis in a recent paper, "[Off the beaten path: Gaia reveals GD-1 stars outside of the main stream](https://arxiv.org/abs/1805.00425)" by Adrian M. Price-Whelan and Ana Bonaca.
+# 
+# In the first notebook, we wrote ADQL queries and used them to select and download data from the Gaia server.
+# 
+# In this notebook, we'll pick up where we left off and write a query to select stars from the region of the sky where we expect GD-1 to be.
+
+# We'll start with an example that does a "cone search"; that is, it selects stars that appear in a circular region of the sky.
+# 
+# Then, to select stars in the vicinity of GD-1, we'll:
+# 
+# * Use `Quantity` objects to represent measurements with units.
+# 
+# * Use the `Gala` library to convert coordinates from one frame to another.
+# 
+# * Use the ADQL keywords `POLYGON`, `CONTAINS`, and `POINT` to select stars that fall within a polygonal region.
+# 
+# * Submit a query and download the results.
+# 
+# * Store the results in a FITS file.
+# 
+# After completing this lesson, you should be able to
+# 
+# * Use Python string formatting to compose more complex ADQL queries.
+# 
+# * Work with coordinates and other quantities that have units.
+# 
+# * Download the results of a query and store them in a file.
+
+# ## Installing libraries
+# 
+# If you are running this notebook on Colab, you can run the following cell to install Astroquery and a the other libraries we'll use.
+# 
+# If you are running this notebook on your own computer, you might have to install these libraries yourself.  
+# 
+# If you are using this notebook as part of a Carpentries workshop, you should have received setup instructions.
+# 
+# TODO: Add a link to the instructions.
+# 
+
+# In[1]:
+
+
+# If we're running on Colab, install libraries
+
+import sys
+IN_COLAB = 'google.colab' in sys.modules
+
+if IN_COLAB:
+    get_ipython().system('pip install astroquery astro-gala pyia')
+
+
+# ## Selecting a region
+
+# One of the most common ways to restrict a query is to select stars in a particular region of the sky.
+# 
+# For example, here's a query from the [Gaia archive documentation](https://gea.esac.esa.int/archive-help/adql/examples/index.html) that selects "all the objects ... in a circular region centered at (266.41683, -29.00781) with a search radius of 5 arcmin (0.08333 deg)."
+
+# In[2]:
+
+
+query = """
+SELECT 
+TOP 10 source_id
+FROM gaiadr2.gaia_source
+WHERE 1=CONTAINS(
+  POINT(ra, dec),
+  CIRCLE(266.41683, -29.00781, 0.08333333))
+"""
+
+
+# This query uses three keywords that are specific to ADQL (not SQL):
+# 
+# * `POINT`: a location in [ICRS coordinates](https://en.wikipedia.org/wiki/International_Celestial_Reference_System), specified in degrees of right ascension and declination.
+# 
+# * `CIRCLE`: a circle where the first two values are the coordinates of the center and the third is the radius in degrees.
+# 
+# * `CONTAINS`: a function that returns `1` if a `POINT` is contained in a shape and `0` otherwise.
+# 
+# Here is the [documentation of `CONTAINS`](http://www.ivoa.net/documents/ADQL/20180112/PR-ADQL-2.1-20180112.html#tth_sEc4.2.12).
+# 
+# A query like this is called a cone search because it selects stars in a cone.
+# 
+# Here's how we run it.
+
+# In[3]:
+
+
+from astroquery.gaia import Gaia
+
+job = Gaia.launch_job(query)
+result = job.get_results()
+result
+
+
+# **Exercise:** When you are debugging queries like this, you can use `TOP` to limit the size of the results, but then you still don't know how big the results will be.
+# 
+# An alternative is to use `COUNT`, which asks for the number of rows that would be selected, but it does not return them.
+# 
+# In the previous query, replace `TOP 10 source_id` with `COUNT(source_id)` and run the query again.  How many stars has Gaia identified in the cone we searched?
+
+# ## Getting GD-1 Data
+# 
+# From the Price-Whelan and Bonaca paper, we will try to reproduce Figure 1, which includes this representation of stars likely to belong to GD-1:
+# 
+# <img src="https://github.com/datacarpentry/astronomy-python/raw/gh-pages/fig/gd1-4.png">
+
+# Along the axis of right ascension ($\phi_1$) the figure extends from -100 to 20 degrees.
+# 
+# Along the axis of declination ($\phi_2$) the figure extends from about -8 to 4 degrees.
+# 
+# Ideally, we would select all stars from this rectangle, but there are more than 10 million of them, so
+# 
+# * That would be difficult to work with,
+# 
+# * As anonymous users, we are limited to 3 million rows in a single query, and
+# 
+# * While we are developing and testing code, it will be faster to work with a smaller dataset.
+# 
+# So we'll start by selecting stars in a smaller rectangle, from -55 to -45 degrees right ascension and -8 to 4 degrees of declination.
+# 
+# But first we let's see how to represent quantities with units like degrees.
+
+# ## Working with coordinates
+# 
+# Coordinates are physical quantities, which means that they have two parts, a value and a unit.
+# 
+# For example, the coordinate $30^{\circ}$ has value 30 and its units are degrees.
+# 
+# Until recently, most scientific computation was done with values only; units were left out of the program altogether, [often with disastrous results](https://en.wikipedia.org/wiki/Mars_Climate_Orbiter#Cause_of_failure).
+# 
+# Astropy provides tools for including units explicitly in computations, which makes it possible to detect errors before they cause disasters.
+# 
+# To use Astropy units, we import them like this:
+
+# In[4]:
+
+
+import astropy.units as u
+
+u
+
+
+# `u` is an object that contains most common units and all SI units.
+# 
+# You can use `dir` to list them, but you should also [read the documentation](https://docs.astropy.org/en/stable/units/).
+
+# In[5]:
+
+
+dir(u)
+
+
+# To create a quantity, we multiply a value by a unit.
+
+# In[6]:
+
+
+coord = 30 * u.deg
+type(coord)
+
+
+# The result is a `Quantity` object.
+# 
+# Jupyter knows how to display `Quantities` like this:
+
+# In[7]:
+
+
+coord
+
+
+# ## Selecting a rectangle
+# 
+# Now we'll select a rectangle from -55 to -45 degrees right ascension and -8 to 4 degrees of declination.
+# 
+# We'll define variables to contain these limits.
+
+# In[8]:
+
+
+phi1_min = -55
+phi1_max = -45
+phi2_min = -8
+phi2_max = 4
+
+
+# To represent a rectangle, we'll use two lists of coordinates and multiply by their units.
+
+# In[9]:
+
+
+phi1_rect = [phi1_min, phi1_min, phi1_max, phi1_max] * u.deg
+phi2_rect = [phi2_min, phi2_max, phi2_max, phi2_min] * u.deg
+
+
+# `phi1_rect` and `phi2_rect` represent the coordinates of the corners of a rectangle.  
+# 
+# But they are in "[a Heliocentric spherical coordinate system defined by the orbit of the GD1 stream](https://gala-astro.readthedocs.io/en/latest/_modules/gala/coordinates/gd1.html)"
+# 
+# In order to use them in a Gaia query, we have to convert them to [International Celestial Reference System](https://en.wikipedia.org/wiki/International_Celestial_Reference_System) (ICRS) coordinates.  We can do that by storing the coordinates in a `GD1Koposov10` object provided by [Gala](https://gala-astro.readthedocs.io/en/latest/coordinates/).
+
+# In[10]:
+
+
+import gala.coordinates as gc
+
+corners = gc.GD1Koposov10(phi1=phi1_rect, phi2=phi2_rect)
+type(corners)
+
+
+# We can display the result like this:
+
+# In[11]:
+
+
+corners
+
+
+# Now we can use `transform_to` to convert to ICRS coordinates.
+
+# In[12]:
+
+
+import astropy.coordinates as coord
+
+corners_icrs = corners.transform_to(coord.ICRS)
+type(corners_icrs)
+
+
+# The result is an `ICRS` object.
+
+# In[13]:
+
+
+corners_icrs
+
+
+# Notice that a rectangle in one coordinate system is not necessarily a rectangle in another.  In this example, the result is a polygon.
+
+# ## Selecting a polygon
+# 
+# In order to use this polygon as part of an ADQL query, we have to convert it to a string with a comma-separated list of coordinates, as in this example:
+# 
+# ```
+# """
+# POLYGON(143.65, 20.98, 
+#         134.46, 26.39, 
+#         140.58, 34.85, 
+#         150.16, 29.01)
+# """
+# ```
+
+# `corners_icrs` behaves like a list, so we can use a `for` loop to iterate through the points.
+
+# In[14]:
+
+
+for point in corners_icrs:
+    print(point)
+
+
+# From that, we can select the coordinates `ra` and `dec`:
+
+# In[15]:
+
+
+for point in corners_icrs:
+    print(point.ra, point.dec)
+
+
+# The results are quantities with units, but if we select the `value` part, we get a dimensionless floating-point number.
+
+# In[16]:
+
+
+for point in corners_icrs:
+    print(point.ra.value, point.dec.value)
+
+
+# We can use string `format` to convert these numbers to strings.
+
+# In[17]:
+
+
+point_base = "{point.ra.value}, {point.dec.value}"
+
+t = [point_base.format(point=point)
+     for point in corners_icrs]
+t
+
+
+# The result is a list of strings, which we can join into a single string using `join`.
+
+# In[18]:
+
+
+point_list = ', '.join(t)
+point_list
+
+
+# Notice that we invoke `join` on a string and pass the list as an argument.
+# 
+# Before we can assemble the query, we need `columns` again (as we saw in the previous notebook).
+
+# In[19]:
+
+
+columns = 'source_id, ra, dec, pmra, pmdec, parallax, parallax_error, radial_velocity'
+
+
+# Here's the base for the query, with format specifiers for `columns` and `point_list`.
+
+# In[20]:
+
+
+query_base = """SELECT {columns}
+FROM gaiadr2.gaia_source
+WHERE parallax < 1
+  AND bp_rp BETWEEN -0.75 AND 2 
+  AND 1 = CONTAINS(POINT(ra, dec), 
+                   POLYGON({point_list}))
+"""
+
+
+# And here's the result:
+
+# In[21]:
+
+
+query = query_base.format(columns=columns, 
+                          point_list=point_list)
+print(query)
+
+
+# As always, we should take a minute to proof-read the query before we launch it.
+# 
+# The result will be bigger than our previous queries, so it will take a little longer.
+
+# In[22]:
+
+
+job = Gaia.launch_job_async(query)
+print(job)
+
+
+# Here are the results.
+
+# In[23]:
+
+
+results = job.get_results()
+len(results)
+
+
+# There are more than 100,000 stars in this polygon, but that's a manageable size to work with.
+
+# ## Saving results
+# 
+# This is the set of stars we'll work with in the next step.  But since we have a substantial dataset now, this is a good time to save it.
+# 
+# Storing the data in a file means we can shut down this notebook and pick up where we left off without running the previous query again.
+# 
+# Astropy `Table` objects provide `write`, which writes the table to disk.
+
+# In[24]:
+
+
+filename = 'gd1_results.fits'
+results.write(filename, overwrite=True)
+
+
+# Because the filename ends with `fits`, the table is written in the [FITS format](https://en.wikipedia.org/wiki/FITS), which preserves the metadata associated with the table.
+# 
+# If the file already exists, the `overwrite` argument causes it to be overwritten.
+# 
+# To see how big the file is, we can use `ls` with the `-lh` option, which prints information about the file including its size in human-readable form.
+
+# In[25]:
+
+
+get_ipython().system('ls -lh gd1_results.fits')
+
+
+# The file is about 8.6 MB.  If you are using Windows, `ls` might not work; in that case, try:
+# 
+# ```
+# !dir gd1_results.fits
+# ```
+
+# ## Summary
+# 
+# In this notebook, we composed more complex queries to select stars within a polygonal region of the sky.  Then we downloaded the results and saved them in a FITS file.
+# 
+# In the next notebook, we'll reload the data from this file and replicate the next step in the analysis, using proper motion to identify stars likely to be in GD-1.
+
+# ## Best practices
+# 
+# * For measurements with units, use `Quantity` objects that represent units explicitly and check for errors.
+# 
+# * Use the `format` function to compose queries; it is often faster and less error-prone.
+# 
+# * Develop queries incrementally: start with something simple, test it, and add a little bit at a time.
+# 
+# * Once you have a query working, save the data in a local file.  If you shut down the notebook and come back to it later, you can reload the file; you don't have to run the query again.
diff --git a/_build/jupyter_execute/AstronomicalData/_build/jupyter_execute/01_query.ipynb b/_build/jupyter_execute/AstronomicalData/_build/jupyter_execute/01_query.ipynb
new file mode 100644
index 0000000..94813e9
--- /dev/null
+++ b/_build/jupyter_execute/AstronomicalData/_build/jupyter_execute/01_query.ipynb
@@ -0,0 +1,1640 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Lesson 1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Introduction\n",
+    "\n",
+    "This workshop is an introduction to tools and practices for working with astronomical data.  Topics covered include:\n",
+    "\n",
+    "* Writing queries that select and download data from a database.\n",
+    "\n",
+    "* Using data stored in an Astropy `Table` or Pandas `DataFrame`.\n",
+    "\n",
+    "* Working with coordinates and other quantities with units.\n",
+    "\n",
+    "* Storing data in various formats.\n",
+    "\n",
+    "* Performing database join operations that combine data from multiple tables.\n",
+    "\n",
+    "* Visualizing data and preparing publication-quality figures."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As a running example, we will replicate part of the analysis in a recent paper, \"[Off the beaten path: Gaia reveals GD-1 stars outside of the main stream](https://arxiv.org/abs/1805.00425)\" by Adrian M. Price-Whelan and Ana Bonaca.\n",
+    "\n",
+    "As the abstract explains, \"Using data from the Gaia second data release combined with Pan-STARRS photometry, we present a sample of highly-probable members of the longest cold stream in the Milky Way, GD-1.\"\n",
+    "\n",
+    "GD-1 is a [stellar stream](https://en.wikipedia.org/wiki/List_of_stellar_streams), which is \"an association of stars orbiting a galaxy that was once a globular cluster or dwarf galaxy that has now been torn apart and stretched out along its orbit by tidal forces.\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[This article in *Science* magazine](https://www.sciencemag.org/news/2018/10/streams-stars-reveal-galaxy-s-violent-history-and-perhaps-its-unseen-dark-matter) explains some of the background, including the process that led to the paper and an discussion of the scientific implications:\n",
+    "\n",
+    "* \"The streams are particularly useful for ... galactic archaeology --- rewinding the cosmic clock to reconstruct the assembly of the Milky Way.\"\n",
+    "\n",
+    "* \"They also are being used as exquisitely sensitive scales to measure the galaxy's mass.\"\n",
+    "\n",
+    "* \"... the streams are well-positioned to reveal the presence of dark matter ... because the streams are so fragile, theorists say, collisions with marauding clumps of dark matter could leave telltale scars, potential clues to its nature.\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Prerequisites\n",
+    "\n",
+    "This workshop is meant for people who are familiar with basic Python, but not necessarily the libraries we will use, like Astropy or Pandas.  If you are familiar with Python lists and dictionaries, and you know how to write a function that takes parameters and returns a value, you know enough Python for this workshop.\n",
+    "\n",
+    "We assume that you have some familiarity with operating systems, like the ability to use a command-line interface.  But we don't assume you have any prior experience with databases.\n",
+    "\n",
+    "We assume that you are familiar with astronomy at the undergraduate level, but we will not assume specialized knowledge of the datasets or analysis methods we'll use. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Data\n",
+    "\n",
+    "The datasets we will work with are:\n",
+    " \n",
+    "* [Gaia](https://en.wikipedia.org/wiki/Gaia_(spacecraft)), which is \"a space observatory of the European Space Agency (ESA), launched in 2013 ... designed for astrometry: measuring the positions, distances and motions of stars with unprecedented precision\", and\n",
+    "\n",
+    "* [Pan-STARRS](https://en.wikipedia.org/wiki/Pan-STARRS), The Panoramic Survey Telescope and Rapid Response System, which is a survey designed to monitor the sky for transient objects, producing a catalog with accurate astronometry and photometry of detected sources.\n",
+    "\n",
+    "Both of these datasets are very large, which can make them challenging to work with.  It might not be possible, or practical, to download the entire dataset.\n",
+    "One of the goals of this workshop is to provide tools for working with large datasets."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Lesson 1\n",
+    "\n",
+    "The first lesson demonstrates the steps for selecting and downloading data from the Gaia Database:\n",
+    "\n",
+    "1. First we'll make a connection to the Gaia server,\n",
+    "\n",
+    "2. We will explore information about the database and the tables it contains,\n",
+    "\n",
+    "3. We will write a query and send it to the server, and finally\n",
+    "\n",
+    "4. We will download the response from the server.\n",
+    "\n",
+    "After completing this lesson, you should be able to\n",
+    "\n",
+    "* Compose a basic query in ADQL.\n",
+    "\n",
+    "* Use queries to explore a database and its tables.\n",
+    "\n",
+    "* Use queries to download data.\n",
+    "\n",
+    "* Develop, test, and debug a query incrementally."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Query Language\n",
+    "\n",
+    "In order to select data from a database, you have to compose a query, which is like a program written in a \"query language\".\n",
+    "The query language we'll use is ADQL, which stands for \"Astronomical Data Query Language\".\n",
+    "\n",
+    "ADQL is a dialect of [SQL](https://en.wikipedia.org/wiki/SQL) (Structured Query Language), which is by far the most commonly used query language.  Almost everything you will learn about ADQL also works in SQL.\n",
+    "\n",
+    "[The reference manual for ADQL is here](http://www.ivoa.net/documents/ADQL/20180112/PR-ADQL-2.1-20180112.html).\n",
+    "But you might find it easier to learn from [this ADQL Cookbook](https://www.gaia.ac.uk/data/gaia-data-release-1/adql-cookbook)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Installing libraries\n",
+    "\n",
+    "The library we'll use to get Gaia data is [Astroquery](https://astroquery.readthedocs.io/en/latest/).\n",
+    "\n",
+    "If you are running this notebook on Colab, you can run the following cell to install Astroquery and the other libraries we'll use.\n",
+    "\n",
+    "If you are running this notebook on your own computer, you might have to install these libraries yourself.  \n",
+    "\n",
+    "If you are using this notebook as part of a Carpentries workshop, you should have received setup instructions.\n",
+    "\n",
+    "TODO: Add a link to the instructions.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# If we're running on Colab, install libraries\n",
+    "\n",
+    "import sys\n",
+    "IN_COLAB = 'google.colab' in sys.modules\n",
+    "\n",
+    "if IN_COLAB:\n",
+    "    !pip install astroquery astro-gala pyia"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Connecting to Gaia\n",
+    "\n",
+    "Astroquery provides `Gaia`, which is an [object that represents a connection to the Gaia database](https://astroquery.readthedocs.io/en/latest/gaia/gaia.html).\n",
+    "\n",
+    "We can connect to the Gaia database like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Created TAP+ (v1.2.1) - Connection:\n",
+      "\tHost: gea.esac.esa.int\n",
+      "\tUse HTTPS: True\n",
+      "\tPort: 443\n",
+      "\tSSL Port: 443\n",
+      "Created TAP+ (v1.2.1) - Connection:\n",
+      "\tHost: geadata.esac.esa.int\n",
+      "\tUse HTTPS: True\n",
+      "\tPort: 443\n",
+      "\tSSL Port: 443\n"
+     ]
+    }
+   ],
+   "source": [
+    "from astroquery.gaia import Gaia"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Optional detail \n",
+    "\n",
+    "> Running this import statement has the effect of creating a [TAP+](http://www.ivoa.net/documents/TAP/) connection; TAP stands for \"Table Access Protocol\".  It is a network protocol for sending queries to the database and getting back the results.  We're not sure why it seems to create two connections."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Databases and Tables\n",
+    "\n",
+    "What is a database, anyway?  Most generally, it can be any collection of data, but when we are talking about ADQL or SQL:\n",
+    "\n",
+    "* A database is a collection of one or more named tables.\n",
+    "\n",
+    "* Each table is a 2-D array with one or more named columns of data.\n",
+    "\n",
+    "We can use `Gaia.load_tables` to get the names of the tables in the Gaia database.  With the option `only_names=True`, it loads information about the tables, called the \"metadata\", not the data itself."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO: Retrieving tables... [astroquery.utils.tap.core]\n",
+      "INFO: Parsing tables... [astroquery.utils.tap.core]\n",
+      "INFO: Done. [astroquery.utils.tap.core]\n"
+     ]
+    }
+   ],
+   "source": [
+    "tables = Gaia.load_tables(only_names=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "external.external.apassdr9\n",
+      "external.external.gaiadr2_geometric_distance\n",
+      "external.external.galex_ais\n",
+      "external.external.ravedr5_com\n",
+      "external.external.ravedr5_dr5\n",
+      "external.external.ravedr5_gra\n",
+      "external.external.ravedr5_on\n",
+      "external.external.sdssdr13_photoprimary\n",
+      "external.external.skymapperdr1_master\n",
+      "external.external.tmass_xsc\n",
+      "public.public.hipparcos\n",
+      "public.public.hipparcos_newreduction\n",
+      "public.public.hubble_sc\n",
+      "public.public.igsl_source\n",
+      "public.public.igsl_source_catalog_ids\n",
+      "public.public.tycho2\n",
+      "public.public.dual\n",
+      "tap_config.tap_config.coord_sys\n",
+      "tap_config.tap_config.properties\n",
+      "tap_schema.tap_schema.columns\n",
+      "tap_schema.tap_schema.key_columns\n",
+      "tap_schema.tap_schema.keys\n",
+      "tap_schema.tap_schema.schemas\n",
+      "tap_schema.tap_schema.tables\n",
+      "gaiadr1.gaiadr1.aux_qso_icrf2_match\n",
+      "gaiadr1.gaiadr1.ext_phot_zero_point\n",
+      "gaiadr1.gaiadr1.allwise_best_neighbour\n",
+      "gaiadr1.gaiadr1.allwise_neighbourhood\n",
+      "gaiadr1.gaiadr1.gsc23_best_neighbour\n",
+      "gaiadr1.gaiadr1.gsc23_neighbourhood\n",
+      "gaiadr1.gaiadr1.ppmxl_best_neighbour\n",
+      "gaiadr1.gaiadr1.ppmxl_neighbourhood\n",
+      "gaiadr1.gaiadr1.sdss_dr9_best_neighbour\n",
+      "gaiadr1.gaiadr1.sdss_dr9_neighbourhood\n",
+      "gaiadr1.gaiadr1.tmass_best_neighbour\n",
+      "gaiadr1.gaiadr1.tmass_neighbourhood\n",
+      "gaiadr1.gaiadr1.ucac4_best_neighbour\n",
+      "gaiadr1.gaiadr1.ucac4_neighbourhood\n",
+      "gaiadr1.gaiadr1.urat1_best_neighbour\n",
+      "gaiadr1.gaiadr1.urat1_neighbourhood\n",
+      "gaiadr1.gaiadr1.cepheid\n",
+      "gaiadr1.gaiadr1.phot_variable_time_series_gfov\n",
+      "gaiadr1.gaiadr1.phot_variable_time_series_gfov_statistical_parameters\n",
+      "gaiadr1.gaiadr1.rrlyrae\n",
+      "gaiadr1.gaiadr1.variable_summary\n",
+      "gaiadr1.gaiadr1.allwise_original_valid\n",
+      "gaiadr1.gaiadr1.gsc23_original_valid\n",
+      "gaiadr1.gaiadr1.ppmxl_original_valid\n",
+      "gaiadr1.gaiadr1.sdssdr9_original_valid\n",
+      "gaiadr1.gaiadr1.tmass_original_valid\n",
+      "gaiadr1.gaiadr1.ucac4_original_valid\n",
+      "gaiadr1.gaiadr1.urat1_original_valid\n",
+      "gaiadr1.gaiadr1.gaia_source\n",
+      "gaiadr1.gaiadr1.tgas_source\n",
+      "gaiadr2.gaiadr2.aux_allwise_agn_gdr2_cross_id\n",
+      "gaiadr2.gaiadr2.aux_iers_gdr2_cross_id\n",
+      "gaiadr2.gaiadr2.aux_sso_orbit_residuals\n",
+      "gaiadr2.gaiadr2.aux_sso_orbits\n",
+      "gaiadr2.gaiadr2.dr1_neighbourhood\n",
+      "gaiadr2.gaiadr2.allwise_best_neighbour\n",
+      "gaiadr2.gaiadr2.allwise_neighbourhood\n",
+      "gaiadr2.gaiadr2.apassdr9_best_neighbour\n",
+      "gaiadr2.gaiadr2.apassdr9_neighbourhood\n",
+      "gaiadr2.gaiadr2.gsc23_best_neighbour\n",
+      "gaiadr2.gaiadr2.gsc23_neighbourhood\n",
+      "gaiadr2.gaiadr2.hipparcos2_best_neighbour\n",
+      "gaiadr2.gaiadr2.hipparcos2_neighbourhood\n",
+      "gaiadr2.gaiadr2.panstarrs1_best_neighbour\n",
+      "gaiadr2.gaiadr2.panstarrs1_neighbourhood\n",
+      "gaiadr2.gaiadr2.ppmxl_best_neighbour\n",
+      "gaiadr2.gaiadr2.ppmxl_neighbourhood\n",
+      "gaiadr2.gaiadr2.ravedr5_best_neighbour\n",
+      "gaiadr2.gaiadr2.ravedr5_neighbourhood\n",
+      "gaiadr2.gaiadr2.sdssdr9_best_neighbour\n",
+      "gaiadr2.gaiadr2.sdssdr9_neighbourhood\n",
+      "gaiadr2.gaiadr2.tmass_best_neighbour\n",
+      "gaiadr2.gaiadr2.tmass_neighbourhood\n",
+      "gaiadr2.gaiadr2.tycho2_best_neighbour\n",
+      "gaiadr2.gaiadr2.tycho2_neighbourhood\n",
+      "gaiadr2.gaiadr2.urat1_best_neighbour\n",
+      "gaiadr2.gaiadr2.urat1_neighbourhood\n",
+      "gaiadr2.gaiadr2.sso_observation\n",
+      "gaiadr2.gaiadr2.sso_source\n",
+      "gaiadr2.gaiadr2.vari_cepheid\n",
+      "gaiadr2.gaiadr2.vari_classifier_class_definition\n",
+      "gaiadr2.gaiadr2.vari_classifier_definition\n",
+      "gaiadr2.gaiadr2.vari_classifier_result\n",
+      "gaiadr2.gaiadr2.vari_long_period_variable\n",
+      "gaiadr2.gaiadr2.vari_rotation_modulation\n",
+      "gaiadr2.gaiadr2.vari_rrlyrae\n",
+      "gaiadr2.gaiadr2.vari_short_timescale\n",
+      "gaiadr2.gaiadr2.vari_time_series_statistics\n",
+      "gaiadr2.gaiadr2.panstarrs1_original_valid\n",
+      "gaiadr2.gaiadr2.gaia_source\n",
+      "gaiadr2.gaiadr2.ruwe\n"
+     ]
+    }
+   ],
+   "source": [
+    "for table in (tables):\n",
+    "    print(table.get_qualified_name())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "So that's a lot of tables.  The ones we'll use are:\n",
+    "\n",
+    "* `gaiadr2.gaia_source`, which contains Gaia data from [data release 2](https://www.cosmos.esa.int/web/gaia/data-release-2),\n",
+    "\n",
+    "* `gaiadr2.panstarrs1_original_valid`, which contains the photometry data we'll use from PanSTARRS, and\n",
+    "\n",
+    "* `gaiadr2.panstarrs1_best_neighbour`, which we'll use to cross-match each star observed by Gaia with the same star observed by PanSTARRS.\n",
+    "\n",
+    "We can use `load_table` (not `load_tables`) to get the metadata for a single table.  The name of this function is misleading, because it only downloads metadata. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Retrieving table 'gaiadr2.gaia_source'\n",
+      "Parsing table 'gaiadr2.gaia_source'...\n",
+      "Done.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<astroquery.utils.tap.model.taptable.TapTableMeta at 0x7f922376e0a0>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "meta = Gaia.load_table('gaiadr2.gaia_source')\n",
+    "meta"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Jupyter shows that the result is an object of type `TapTableMeta`, but it does not display the contents.\n",
+    "\n",
+    "To see the metadata, we have to print the object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "TAP Table name: gaiadr2.gaiadr2.gaia_source\n",
+      "Description: This table has an entry for every Gaia observed source as listed in the\n",
+      "Main Database accumulating catalogue version from which the catalogue\n",
+      "release has been generated. It contains the basic source parameters,\n",
+      "that is only final data (no epoch data) and no spectra (neither final\n",
+      "nor epoch).\n",
+      "Num. columns: 96\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(meta)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notice one gotcha: in the list of table names, this table appears as `gaiadr2.gaiadr2.gaia_source`, but when we load the metadata, we refer to it as `gaiadr2.gaia_source`.\n",
+    "\n",
+    "**Exercise:** Go back and try\n",
+    "\n",
+    "```\n",
+    "meta = Gaia.load_table('gaiadr2.gaiadr2.gaia_source')\n",
+    "```\n",
+    "\n",
+    "What happens?  Is the error message helpful?  If you had not made this error deliberately, would you have been able to figure it out?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Columns\n",
+    "\n",
+    "The following loop prints the names of the columns in the table."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "solution_id\n",
+      "designation\n",
+      "source_id\n",
+      "random_index\n",
+      "ref_epoch\n",
+      "ra\n",
+      "ra_error\n",
+      "dec\n",
+      "dec_error\n",
+      "parallax\n",
+      "parallax_error\n",
+      "parallax_over_error\n",
+      "pmra\n",
+      "pmra_error\n",
+      "pmdec\n",
+      "pmdec_error\n",
+      "ra_dec_corr\n",
+      "ra_parallax_corr\n",
+      "ra_pmra_corr\n",
+      "ra_pmdec_corr\n",
+      "dec_parallax_corr\n",
+      "dec_pmra_corr\n",
+      "dec_pmdec_corr\n",
+      "parallax_pmra_corr\n",
+      "parallax_pmdec_corr\n",
+      "pmra_pmdec_corr\n",
+      "astrometric_n_obs_al\n",
+      "astrometric_n_obs_ac\n",
+      "astrometric_n_good_obs_al\n",
+      "astrometric_n_bad_obs_al\n",
+      "astrometric_gof_al\n",
+      "astrometric_chi2_al\n",
+      "astrometric_excess_noise\n",
+      "astrometric_excess_noise_sig\n",
+      "astrometric_params_solved\n",
+      "astrometric_primary_flag\n",
+      "astrometric_weight_al\n",
+      "astrometric_pseudo_colour\n",
+      "astrometric_pseudo_colour_error\n",
+      "mean_varpi_factor_al\n",
+      "astrometric_matched_observations\n",
+      "visibility_periods_used\n",
+      "astrometric_sigma5d_max\n",
+      "frame_rotator_object_type\n",
+      "matched_observations\n",
+      "duplicated_source\n",
+      "phot_g_n_obs\n",
+      "phot_g_mean_flux\n",
+      "phot_g_mean_flux_error\n",
+      "phot_g_mean_flux_over_error\n",
+      "phot_g_mean_mag\n",
+      "phot_bp_n_obs\n",
+      "phot_bp_mean_flux\n",
+      "phot_bp_mean_flux_error\n",
+      "phot_bp_mean_flux_over_error\n",
+      "phot_bp_mean_mag\n",
+      "phot_rp_n_obs\n",
+      "phot_rp_mean_flux\n",
+      "phot_rp_mean_flux_error\n",
+      "phot_rp_mean_flux_over_error\n",
+      "phot_rp_mean_mag\n",
+      "phot_bp_rp_excess_factor\n",
+      "phot_proc_mode\n",
+      "bp_rp\n",
+      "bp_g\n",
+      "g_rp\n",
+      "radial_velocity\n",
+      "radial_velocity_error\n",
+      "rv_nb_transits\n",
+      "rv_template_teff\n",
+      "rv_template_logg\n",
+      "rv_template_fe_h\n",
+      "phot_variable_flag\n",
+      "l\n",
+      "b\n",
+      "ecl_lon\n",
+      "ecl_lat\n",
+      "priam_flags\n",
+      "teff_val\n",
+      "teff_percentile_lower\n",
+      "teff_percentile_upper\n",
+      "a_g_val\n",
+      "a_g_percentile_lower\n",
+      "a_g_percentile_upper\n",
+      "e_bp_min_rp_val\n",
+      "e_bp_min_rp_percentile_lower\n",
+      "e_bp_min_rp_percentile_upper\n",
+      "flame_flags\n",
+      "radius_val\n",
+      "radius_percentile_lower\n",
+      "radius_percentile_upper\n",
+      "lum_val\n",
+      "lum_percentile_lower\n",
+      "lum_percentile_upper\n",
+      "datalink_url\n",
+      "epoch_photometry_url\n"
+     ]
+    }
+   ],
+   "source": [
+    "for column in meta.columns:\n",
+    "    print(column.name)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can probably guess what many of these columns are by looking at the names, but you should resist the temptation to guess.\n",
+    "To find out what the columns mean, [read the documentation](https://gea.esac.esa.int/archive/documentation/GDR2/Gaia_archive/chap_datamodel/sec_dm_main_tables/ssec_dm_gaia_source.html).\n",
+    "\n",
+    "If you want to know what can go wrong when you don't read the documentation, [you might like this article](https://www.vox.com/future-perfect/2019/6/4/18650969/married-women-miserable-fake-paul-dolan-happiness)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** One of the other tables we'll use is `gaiadr2.gaiadr2.panstarrs1_original_valid`.  Use `load_table` to get the metadata for this table.  How many columns are there and what are their names?\n",
+    "\n",
+    "Hint: Remember the gotcha we mentioned earlier."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Retrieving table 'gaiadr2.panstarrs1_original_valid'\n",
+      "Parsing table 'gaiadr2.panstarrs1_original_valid'...\n",
+      "Done.\n",
+      "TAP Table name: gaiadr2.gaiadr2.panstarrs1_original_valid\n",
+      "Description: The Panoramic Survey Telescope and Rapid Response System (Pan-STARRS) is\n",
+      "a system for wide-field astronomical imaging developed and operated by\n",
+      "the Institute for Astronomy at the University of Hawaii. Pan-STARRS1\n",
+      "(PS1) is the first part of Pan-STARRS to be completed and is the basis\n",
+      "for Data Release 1 (DR1). The PS1 survey used a 1.8 meter telescope and\n",
+      "its 1.4 Gigapixel camera to image the sky in five broadband filters (g,\n",
+      "r, i, z, y).\n",
+      "\n",
+      "The current table contains a filtered subsample of the 10 723 304 629\n",
+      "entries listed in the original ObjectThin table.\n",
+      "We used only ObjectThin and MeanObject tables to extract\n",
+      "panstarrs1OriginalValid table, this means that objects detected only in\n",
+      "stack images are not included here. The main reason for us to avoid the\n",
+      "use of objects detected in stack images is that their astrometry is not\n",
+      "as good as the mean objects astrometry: “The stack positions (raStack,\n",
+      "decStack) have considerably larger systematic astrometric errors than\n",
+      "the mean epoch positions (raMean, decMean).� The astrometry for the\n",
+      "MeanObject positions uses Gaia DR1 as a reference catalog, while the\n",
+      "stack positions use 2MASS as a reference catalog.\n",
+      "\n",
+      "In details, we filtered out all objects where:\n",
+      "\n",
+      "-   nDetections = 1\n",
+      "\n",
+      "-   no good quality data in Pan-STARRS, objInfoFlag 33554432 not set\n",
+      "\n",
+      "-   mean astrometry could not be measured, objInfoFlag 524288 set\n",
+      "\n",
+      "-   stack position used for mean astrometry, objInfoFlag 1048576 set\n",
+      "\n",
+      "-   error on all magnitudes equal to 0 or to -999;\n",
+      "\n",
+      "-   all magnitudes set to -999;\n",
+      "\n",
+      "-   error on RA or DEC greater than 1 arcsec.\n",
+      "\n",
+      "The number of objects in panstarrs1OriginalValid is 2 264 263 282.\n",
+      "\n",
+      "The panstarrs1OriginalValid table contains only a subset of the columns\n",
+      "available in the combined ObjectThin and MeanObject tables. A\n",
+      "description of the original ObjectThin and MeanObjects tables can be\n",
+      "found at:\n",
+      "https://outerspace.stsci.edu/display/PANSTARRS/PS1+Database+object+and+detection+tables\n",
+      "\n",
+      "Download:\n",
+      "http://mastweb.stsci.edu/ps1casjobs/home.aspx\n",
+      "Documentation:\n",
+      "https://outerspace.stsci.edu/display/PANSTARRS\n",
+      "http://pswww.ifa.hawaii.edu/pswww/\n",
+      "References:\n",
+      "The Pan-STARRS1 Surveys, Chambers, K.C., et al. 2016, arXiv:1612.05560\n",
+      "Pan-STARRS Data Processing System, Magnier, E. A., et al. 2016,\n",
+      "arXiv:1612.05240\n",
+      "Pan-STARRS Pixel Processing: Detrending, Warping, Stacking, Waters, C.\n",
+      "Z., et al. 2016, arXiv:1612.05245\n",
+      "Pan-STARRS Pixel Analysis: Source Detection and Characterization,\n",
+      "Magnier, E. A., et al. 2016, arXiv:1612.05244\n",
+      "Pan-STARRS Photometric and Astrometric Calibration, Magnier, E. A., et\n",
+      "al. 2016, arXiv:1612.05242\n",
+      "The Pan-STARRS1 Database and Data Products, Flewelling, H. A., et al.\n",
+      "2016, arXiv:1612.05243\n",
+      "\n",
+      "Catalogue curator:\n",
+      "SSDC - ASI Space Science Data Center\n",
+      "https://www.ssdc.asi.it/\n",
+      "Num. columns: 26\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "meta2 = Gaia.load_table('gaiadr2.panstarrs1_original_valid')\n",
+    "print(meta2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "obj_name\n",
+      "obj_id\n",
+      "ra\n",
+      "dec\n",
+      "ra_error\n",
+      "dec_error\n",
+      "epoch_mean\n",
+      "g_mean_psf_mag\n",
+      "g_mean_psf_mag_error\n",
+      "g_flags\n",
+      "r_mean_psf_mag\n",
+      "r_mean_psf_mag_error\n",
+      "r_flags\n",
+      "i_mean_psf_mag\n",
+      "i_mean_psf_mag_error\n",
+      "i_flags\n",
+      "z_mean_psf_mag\n",
+      "z_mean_psf_mag_error\n",
+      "z_flags\n",
+      "y_mean_psf_mag\n",
+      "y_mean_psf_mag_error\n",
+      "y_flags\n",
+      "n_detections\n",
+      "zone_id\n",
+      "obj_info_flag\n",
+      "quality_flag\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "for column in meta2.columns:\n",
+    "    print(column.name)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Writing queries\n",
+    "\n",
+    "By now you might be wondering how we actually download the data.  With tables this big, you generally don't.  Instead, you use queries to select only the data you want.\n",
+    "\n",
+    "A query is a string written in a query language like SQL; for the Gaia database, the query language is a dialect of SQL called ADQL.\n",
+    "\n",
+    "Here's an example of an ADQL query."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query1 = \"\"\"SELECT \n",
+    "TOP 10\n",
+    "source_id, ref_epoch, ra, dec, parallax \n",
+    "FROM gaiadr2.gaia_source\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Python note:** We use a [triple-quoted string](https://docs.python.org/3/tutorial/introduction.html#strings) here so we can include line breaks in the query, which makes it easier to read.\n",
+    "\n",
+    "The words in uppercase are ADQL keywords:\n",
+    "\n",
+    "* `SELECT` indicates that we are selecting data (as opposed to adding or modifying data).\n",
+    "\n",
+    "* `TOP` indicates that we only want the first 10 rows of the table, which is useful for testing a query before asking for all of the data.\n",
+    "\n",
+    "* `FROM` specifies which table we want data from.\n",
+    "\n",
+    "The third line is a list of column names, indicating which columns we want.  \n",
+    "\n",
+    "In this example, the keywords are capitalized and the column names are lowercase.  This is a common style, but it is not required.  ADQL and SQL are not case-sensitive."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To run this query, we use the `Gaia` object, which represents our connection to the Gaia database, and invoke `launch_job`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<astroquery.utils.tap.model.job.Job at 0x7f9222e9cb20>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "job1 = Gaia.launch_job(query1)\n",
+    "job1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The result is an object that represents the job running on a Gaia server.\n",
+    "\n",
+    "If you print it, it displays metadata for the forthcoming table."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<Table length=10>\n",
+      "   name    dtype  unit                            description                            \n",
+      "--------- ------- ---- ------------------------------------------------------------------\n",
+      "source_id   int64      Unique source identifier (unique within a particular Data Release)\n",
+      "ref_epoch float64   yr                                                    Reference epoch\n",
+      "       ra float64  deg                                                    Right ascension\n",
+      "      dec float64  deg                                                        Declination\n",
+      " parallax float64  mas                                                           Parallax\n",
+      "Jobid: None\n",
+      "Phase: COMPLETED\n",
+      "Owner: None\n",
+      "Output file: sync_20201005090721.xml.gz\n",
+      "Results: None\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(job1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Don't worry about `Results: None`.  That does not actually mean there are no results.\n",
+    "\n",
+    "However, `Phase: COMPLETED` indicates that the job is complete, so we can get the results like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "astropy.table.table.Table"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results1 = job1.get_results()\n",
+    "type(results1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Optional detail:**  Why is `table` repeated three times?  The first is the name of the module, the second is the name of the submodule, and the third is the name of the class.  Most of the time we only care about the last one.  It's like the Linnean name for gorilla, which is *Gorilla Gorilla Gorilla*."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The result is an [Astropy Table](https://docs.astropy.org/en/stable/table/), which is similar to a table in an SQL database except:\n",
+    "\n",
+    "* SQL databases are stored on disk drives, so they are persistent; that is, they \"survive\" even if you turn off the computer.  An Astropy `Table` is stored in memory; it disappears when you turn off the computer (or shut down this Jupyter notebook).\n",
+    "\n",
+    "* SQL databases are designed to process queries.  An Astropy `Table` can perform some query-like operations, like selecting columns and rows.  But these operations use Python syntax, not SQL.\n",
+    "\n",
+    "Jupyter knows how to display the contents of a `Table`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<i>Table length=10</i>\n",
+       "<table id=\"table140265627585264\" class=\"table-striped table-bordered table-condensed\">\n",
+       "<thead><tr><th>source_id</th><th>ref_epoch</th><th>ra</th><th>dec</th><th>parallax</th></tr></thead>\n",
+       "<thead><tr><th></th><th>yr</th><th>deg</th><th>deg</th><th>mas</th></tr></thead>\n",
+       "<thead><tr><th>int64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th></tr></thead>\n",
+       "<tr><td>4530738361793769600</td><td>2015.5</td><td>281.56725362448725</td><td>20.40682117430378</td><td>0.9785380604519425</td></tr>\n",
+       "<tr><td>4530752651135081216</td><td>2015.5</td><td>281.0861565355257</td><td>20.523350496351846</td><td>0.2674800612552977</td></tr>\n",
+       "<tr><td>4530743343951405568</td><td>2015.5</td><td>281.37114418299177</td><td>20.474147574053124</td><td>-0.43911323550176806</td></tr>\n",
+       "<tr><td>4530755060627162368</td><td>2015.5</td><td>281.2676236268299</td><td>20.558523922346158</td><td>1.1422630184554958</td></tr>\n",
+       "<tr><td>4530746844341315968</td><td>2015.5</td><td>281.1370431749541</td><td>20.377852388898184</td><td>1.0092247424630945</td></tr>\n",
+       "<tr><td>4530768456615026432</td><td>2015.5</td><td>281.8720921436347</td><td>20.31829694530366</td><td>-0.06900136127674149</td></tr>\n",
+       "<tr><td>4530763513119137280</td><td>2015.5</td><td>281.9211808864116</td><td>20.20956829578524</td><td>0.1266016679823622</td></tr>\n",
+       "<tr><td>4530736364618539264</td><td>2015.5</td><td>281.4913475613274</td><td>20.346579041327693</td><td>0.3894019486060072</td></tr>\n",
+       "<tr><td>4530735952305177728</td><td>2015.5</td><td>281.4085549165704</td><td>20.311030903719928</td><td>0.2041189982608354</td></tr>\n",
+       "<tr><td>4530751281056022656</td><td>2015.5</td><td>281.0585328377638</td><td>20.460309556214753</td><td>0.10294642821734962</td></tr>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<Table length=10>\n",
+       "     source_id      ref_epoch ...        dec               parallax      \n",
+       "                        yr    ...        deg                 mas         \n",
+       "       int64         float64  ...      float64             float64       \n",
+       "------------------- --------- ... ------------------ --------------------\n",
+       "4530738361793769600    2015.5 ...  20.40682117430378   0.9785380604519425\n",
+       "4530752651135081216    2015.5 ... 20.523350496351846   0.2674800612552977\n",
+       "4530743343951405568    2015.5 ... 20.474147574053124 -0.43911323550176806\n",
+       "4530755060627162368    2015.5 ... 20.558523922346158   1.1422630184554958\n",
+       "4530746844341315968    2015.5 ... 20.377852388898184   1.0092247424630945\n",
+       "4530768456615026432    2015.5 ...  20.31829694530366 -0.06900136127674149\n",
+       "4530763513119137280    2015.5 ...  20.20956829578524   0.1266016679823622\n",
+       "4530736364618539264    2015.5 ... 20.346579041327693   0.3894019486060072\n",
+       "4530735952305177728    2015.5 ... 20.311030903719928   0.2041189982608354\n",
+       "4530751281056022656    2015.5 ... 20.460309556214753  0.10294642821734962"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Each column has a name, units, and a data type.\n",
+    "\n",
+    "For example, the units of `ra` and `dec` are degrees, and their data type is `float64`, which is a 64-bit floating-point number, used to store measurements with a fraction part.\n",
+    "\n",
+    "This information comes from the Gaia database, and has been stored in the Astropy `Table` by Astroquery."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Read [the documentation of this table](https://gea.esac.esa.int/archive/documentation/GDR2/Gaia_archive/chap_datamodel/sec_dm_main_tables/ssec_dm_gaia_source.html) and choose a column that looks interesting to you.  Add the column name to the query and run it again.  What are the units of the column you selected?  What is its data type?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Asynchronous queries\n",
+    "\n",
+    "`launch_job` asks the server to run the job \"synchronously\", which normally means it runs immediately.  But synchronous jobs are limited to 2000 rows.  For queries that return more rows, you should run \"asynchronously\", which mean they might take longer to get started.\n",
+    "\n",
+    "If you are not sure how many rows a query will return, you can use the SQL command `COUNT` to find out how many rows are in the result without actually returning them.  We'll see an example of this later.\n",
+    "\n",
+    "The results of an asynchronous query are stored in a file on the server, so you can start a query and come back later to get the results.\n",
+    "\n",
+    "For anonymous users, files are kept for three days.\n",
+    "\n",
+    "As an example, let's try a query that's similar to `query1`, with two changes:\n",
+    "\n",
+    "* It selects the first 3000 rows, so it is bigger than we should run synchronously.\n",
+    "\n",
+    "* It uses a new keyword, `WHERE`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query2 = \"\"\"SELECT TOP 3000\n",
+    "source_id, ref_epoch, ra, dec, parallax\n",
+    "FROM gaiadr2.gaia_source\n",
+    "WHERE parallax < 1\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A `WHERE` clause indicates which rows we want; in this case, the query selects only rows \"where\" `parallax` is less than 1.  This has the effect of selecting stars with relatively low parallax, which are farther away.  We'll use this clause to exclude nearby stars that are unlikely to be part of GD-1.\n",
+    "\n",
+    "`WHERE` is one of the most common clauses in ADQL/SQL, and one of the most useful, because it allows us to select only the rows we need from the database.\n",
+    "\n",
+    "We use `launch_job_async` to submit an asynchronous query."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO: Query finished. [astroquery.utils.tap.core]\n",
+      "<Table length=3000>\n",
+      "   name    dtype  unit                            description                            \n",
+      "--------- ------- ---- ------------------------------------------------------------------\n",
+      "source_id   int64      Unique source identifier (unique within a particular Data Release)\n",
+      "ref_epoch float64   yr                                                    Reference epoch\n",
+      "       ra float64  deg                                                    Right ascension\n",
+      "      dec float64  deg                                                        Declination\n",
+      " parallax float64  mas                                                           Parallax\n",
+      "Jobid: 1601903242219O\n",
+      "Phase: COMPLETED\n",
+      "Owner: None\n",
+      "Output file: async_20201005090722.vot\n",
+      "Results: None\n"
+     ]
+    }
+   ],
+   "source": [
+    "job2 = Gaia.launch_job_async(query2)\n",
+    "print(job2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And here are the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<i>Table length=3000</i>\n",
+       "<table id=\"table140265625141056\" class=\"table-striped table-bordered table-condensed\">\n",
+       "<thead><tr><th>source_id</th><th>ref_epoch</th><th>ra</th><th>dec</th><th>parallax</th></tr></thead>\n",
+       "<thead><tr><th></th><th>yr</th><th>deg</th><th>deg</th><th>mas</th></tr></thead>\n",
+       "<thead><tr><th>int64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th></tr></thead>\n",
+       "<tr><td>4530738361793769600</td><td>2015.5</td><td>281.56725362448725</td><td>20.40682117430378</td><td>0.9785380604519425</td></tr>\n",
+       "<tr><td>4530752651135081216</td><td>2015.5</td><td>281.0861565355257</td><td>20.523350496351846</td><td>0.2674800612552977</td></tr>\n",
+       "<tr><td>4530743343951405568</td><td>2015.5</td><td>281.37114418299177</td><td>20.474147574053124</td><td>-0.43911323550176806</td></tr>\n",
+       "<tr><td>4530768456615026432</td><td>2015.5</td><td>281.8720921436347</td><td>20.31829694530366</td><td>-0.06900136127674149</td></tr>\n",
+       "<tr><td>4530763513119137280</td><td>2015.5</td><td>281.9211808864116</td><td>20.20956829578524</td><td>0.1266016679823622</td></tr>\n",
+       "<tr><td>4530736364618539264</td><td>2015.5</td><td>281.4913475613274</td><td>20.346579041327693</td><td>0.3894019486060072</td></tr>\n",
+       "<tr><td>4530735952305177728</td><td>2015.5</td><td>281.4085549165704</td><td>20.311030903719928</td><td>0.2041189982608354</td></tr>\n",
+       "<tr><td>4530751281056022656</td><td>2015.5</td><td>281.0585328377638</td><td>20.460309556214753</td><td>0.10294642821734962</td></tr>\n",
+       "<tr><td>4530740938774409344</td><td>2015.5</td><td>281.3762569536416</td><td>20.436140058941206</td><td>0.9242670062090182</td></tr>\n",
+       "<tr><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr>\n",
+       "<tr><td>4467710915011802624</td><td>2015.5</td><td>269.9680969307347</td><td>1.1429085038160882</td><td>0.42361471245557913</td></tr>\n",
+       "<tr><td>4467706551328679552</td><td>2015.5</td><td>270.033164589881</td><td>1.0565747323689927</td><td>0.922888231734588</td></tr>\n",
+       "<tr><td>4467712255037300096</td><td>2015.5</td><td>270.7724717923047</td><td>0.6581664892880896</td><td>-2.669179465293931</td></tr>\n",
+       "<tr><td>4467735001181761792</td><td>2015.5</td><td>270.3628606248308</td><td>0.8947079323599124</td><td>0.6117399163086398</td></tr>\n",
+       "<tr><td>4467737101421916672</td><td>2015.5</td><td>270.5110834661444</td><td>0.9806225910160181</td><td>-0.39818224846127004</td></tr>\n",
+       "<tr><td>4467707547757327488</td><td>2015.5</td><td>269.88746280594927</td><td>1.0212759940136962</td><td>0.7741412301054209</td></tr>\n",
+       "<tr><td>4467732772094573056</td><td>2015.5</td><td>270.55997182760126</td><td>0.9037072088489417</td><td>-1.7920417800164183</td></tr>\n",
+       "<tr><td>4467732355491087744</td><td>2015.5</td><td>270.6730790702491</td><td>0.9197224705139885</td><td>-0.3464446494840354</td></tr>\n",
+       "<tr><td>4467717099766944512</td><td>2015.5</td><td>270.57667173120825</td><td>0.726277659009568</td><td>0.05443955111134051</td></tr>\n",
+       "<tr><td>4467719058265781248</td><td>2015.5</td><td>270.7248052971514</td><td>0.8205551921782785</td><td>0.3733943917490343</td></tr>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<Table length=3000>\n",
+       "     source_id      ref_epoch ...        dec               parallax      \n",
+       "                        yr    ...        deg                 mas         \n",
+       "       int64         float64  ...      float64             float64       \n",
+       "------------------- --------- ... ------------------ --------------------\n",
+       "4530738361793769600    2015.5 ...  20.40682117430378   0.9785380604519425\n",
+       "4530752651135081216    2015.5 ... 20.523350496351846   0.2674800612552977\n",
+       "4530743343951405568    2015.5 ... 20.474147574053124 -0.43911323550176806\n",
+       "4530768456615026432    2015.5 ...  20.31829694530366 -0.06900136127674149\n",
+       "4530763513119137280    2015.5 ...  20.20956829578524   0.1266016679823622\n",
+       "4530736364618539264    2015.5 ... 20.346579041327693   0.3894019486060072\n",
+       "4530735952305177728    2015.5 ... 20.311030903719928   0.2041189982608354\n",
+       "4530751281056022656    2015.5 ... 20.460309556214753  0.10294642821734962\n",
+       "4530740938774409344    2015.5 ... 20.436140058941206   0.9242670062090182\n",
+       "                ...       ... ...                ...                  ...\n",
+       "4467710915011802624    2015.5 ... 1.1429085038160882  0.42361471245557913\n",
+       "4467706551328679552    2015.5 ... 1.0565747323689927    0.922888231734588\n",
+       "4467712255037300096    2015.5 ... 0.6581664892880896   -2.669179465293931\n",
+       "4467735001181761792    2015.5 ... 0.8947079323599124   0.6117399163086398\n",
+       "4467737101421916672    2015.5 ... 0.9806225910160181 -0.39818224846127004\n",
+       "4467707547757327488    2015.5 ... 1.0212759940136962   0.7741412301054209\n",
+       "4467732772094573056    2015.5 ... 0.9037072088489417  -1.7920417800164183\n",
+       "4467732355491087744    2015.5 ... 0.9197224705139885  -0.3464446494840354\n",
+       "4467717099766944512    2015.5 ...  0.726277659009568  0.05443955111134051\n",
+       "4467719058265781248    2015.5 ... 0.8205551921782785   0.3733943917490343"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results2 = job2.get_results()\n",
+    "results2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You might notice that some values of `parallax` are negative.  As [this FAQ explains](https://www.cosmos.esa.int/web/gaia/archive-tips#negative%20parallax), \"Negative parallaxes are caused by errors in the observations.\"  Negative parallaxes have \"no physical meaning,\" but they can be a \"useful diagnostic on the quality of the astrometric solution.\"\n",
+    "\n",
+    "Later we will see an example where we use `parallax` and `parallax_error` to identify stars where the distance estimate is likely to be inaccurate."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** The clauses in a query have to be in the right order.  Go back and change the order of the clauses in `query2` and run it again.  \n",
+    "\n",
+    "The query should fail, but notice that you don't get much useful debugging information.  \n",
+    "\n",
+    "For this reason, developing and debugging ADQL queries can be really hard.  A few suggestions that might help:\n",
+    "\n",
+    "* Whenever possible, start with a working query, either an example you find online or a query you have used in the past.\n",
+    "\n",
+    "* Make small changes and test each change before you continue.\n",
+    "\n",
+    "* While you are debugging, use `TOP` to limit the number of rows in the result.  That will make each attempt run faster, which reduces your testing time.  \n",
+    "\n",
+    "* Launching test queries synchronously might make them start faster, too."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Operators\n",
+    "\n",
+    "In a `WHERE` clause, you can use any of the [SQL comparison operators](https://www.w3schools.com/sql/sql_operators.asp):\n",
+    "\n",
+    "* `>`: greater than\n",
+    "* `<`: less than\n",
+    "* `>=`: greater than or equal\n",
+    "* `<=`: less than or equal\n",
+    "* `=`: equal\n",
+    "* `!=` or `<>`: not equal\n",
+    "\n",
+    "Most of these are the same as Python, but some are not.  In particular, notice that the equality operator is `=`, not `==`.\n",
+    "Be careful to keep your Python out of your ADQL!\n",
+    "\n",
+    "You can combine comparisons using the logical operators:\n",
+    "\n",
+    "* AND: true if both comparisons are true\n",
+    "* OR: true if either or both comparisons are true\n",
+    "\n",
+    "Finally, you can use `NOT` to invert the result of a comparison. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** [Read about SQL operators here](https://www.w3schools.com/sql/sql_operators.asp) and then modify the previous query to select rows where `bp_rp` is between `-0.75` and `2`.\n",
+    "\n",
+    "You can [read about this variable here](https://gea.esac.esa.int/archive/documentation/GDR2/Gaia_archive/chap_datamodel/sec_dm_main_tables/ssec_dm_gaia_source.html)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "# This is what most people will probably do\n",
+    "\n",
+    "query = \"\"\"SELECT TOP 10\n",
+    "source_id, ref_epoch, ra, dec, parallax\n",
+    "FROM gaiadr2.gaia_source\n",
+    "WHERE parallax < 1 \n",
+    "  AND bp_rp > -0.75 AND bp_rp < 2\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "# But if someone notices the BETWEEN operator, \n",
+    "# they might do this\n",
+    "\n",
+    "query = \"\"\"SELECT TOP 10\n",
+    "source_id, ref_epoch, ra, dec, parallax\n",
+    "FROM gaiadr2.gaia_source\n",
+    "WHERE parallax < 1 \n",
+    "  AND bp_rp BETWEEN -0.75 AND 2\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This [Hertzsprung-Russell diagram](https://sci.esa.int/web/gaia/-/60198-gaia-hertzsprung-russell-diagram) shows the BP-RP color and luminosity of stars in the Gaia catalog.\n",
+    "\n",
+    "Selecting stars with `bp-rp` less than 2 excludes many [class M dwarf stars](https://xkcd.com/2360/), which are low temperature, low luminosity.  A star like that at GD-1's distance would be hard to detect, so if it is detected, it it more likely to be in the foreground."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Cleaning up\n",
+    "\n",
+    "Asynchronous jobs have a `jobid`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(None, '1601903242219O')"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "job1.jobid, job2.jobid"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Which you can use to remove the job from the server."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Removed jobs: '['1601903242219O']'.\n"
+     ]
+    }
+   ],
+   "source": [
+    "Gaia.remove_jobs([job2.jobid])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you don't remove it job from the server, it will be removed eventually, so don't feel too bad if you don't clean up after yourself."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Formatting queries\n",
+    "\n",
+    "So far the queries have been string \"literals\", meaning that the entire string is part of the program.\n",
+    "But writing queries yourself can be slow, repetitive, and error-prone.\n",
+    "\n",
+    "It is often a good idea to write Python code that assembles a query for you.  One useful tool for that is the [string `format` method](https://www.w3schools.com/python/ref_string_format.asp).\n",
+    "\n",
+    "As an example, we'll divide the previous query into two parts; a list of column names and a \"base\" for the query that contains everything except the column names.\n",
+    "\n",
+    "Here's the list of columns we'll select.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "columns = 'source_id, ra, dec, pmra, pmdec, parallax, parallax_error, radial_velocity'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And here's the base; it's a string that contains at least one format specifier in curly brackets (braces)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query3_base = \"\"\"SELECT TOP 10 \n",
+    "{columns}\n",
+    "FROM gaiadr2.gaia_source\n",
+    "WHERE parallax < 1\n",
+    "  AND bp_rp BETWEEN -0.75 AND 2\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This base query contains one format specifier, `{columns}`, which is a placeholder for the list of column names we will provide.\n",
+    "\n",
+    "To assemble the query, we invoke `format` on the base string and provide a keyword argument that assigns a value to `columns`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query3 = query3_base.format(columns=columns)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The result is a string with line breaks.  If you display it, the line breaks appear as `\\n`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'SELECT TOP 10 \\nsource_id, ra, dec, pmra, pmdec, parallax, parallax_error, radial_velocity\\nFROM gaiadr2.gaia_source\\nWHERE parallax < 1\\n  AND bp_rp BETWEEN -0.75 AND 2\\n'"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "query3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "But if you print it, the line breaks appear as... line breaks."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "SELECT TOP 10 \n",
+      "source_id, ra, dec, pmra, pmdec, parallax, parallax_error, radial_velocity\n",
+      "FROM gaiadr2.gaia_source\n",
+      "WHERE parallax < 1\n",
+      "  AND bp_rp BETWEEN -0.75 AND 2\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(query3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notice that the format specifier has been replaced with the value of `columns`.\n",
+    "\n",
+    "Let's run it and see if it works:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<Table length=10>\n",
+      "      name       dtype    unit                              description                             n_bad\n",
+      "--------------- ------- -------- ------------------------------------------------------------------ -----\n",
+      "      source_id   int64          Unique source identifier (unique within a particular Data Release)     0\n",
+      "             ra float64      deg                                                    Right ascension     0\n",
+      "            dec float64      deg                                                        Declination     0\n",
+      "           pmra float64 mas / yr                         Proper motion in right ascension direction     0\n",
+      "          pmdec float64 mas / yr                             Proper motion in declination direction     0\n",
+      "       parallax float64      mas                                                           Parallax     0\n",
+      " parallax_error float64      mas                                         Standard error of parallax     0\n",
+      "radial_velocity float64   km / s                                                    Radial velocity    10\n",
+      "Jobid: None\n",
+      "Phase: COMPLETED\n",
+      "Owner: None\n",
+      "Output file: sync_20201005090726.xml.gz\n",
+      "Results: None\n"
+     ]
+    }
+   ],
+   "source": [
+    "job3 = Gaia.launch_job(query3)\n",
+    "print(job3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<i>Table length=10</i>\n",
+       "<table id=\"table140265627700432\" class=\"table-striped table-bordered table-condensed\">\n",
+       "<thead><tr><th>source_id</th><th>ra</th><th>dec</th><th>pmra</th><th>pmdec</th><th>parallax</th><th>parallax_error</th><th>radial_velocity</th></tr></thead>\n",
+       "<thead><tr><th></th><th>deg</th><th>deg</th><th>mas / yr</th><th>mas / yr</th><th>mas</th><th>mas</th><th>km / s</th></tr></thead>\n",
+       "<thead><tr><th>int64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th></tr></thead>\n",
+       "<tr><td>4467710915011802624</td><td>269.9680969307347</td><td>1.1429085038160882</td><td>2.0233280236600626</td><td>-2.5692427875510266</td><td>0.42361471245557913</td><td>0.470352406647465</td><td>--</td></tr>\n",
+       "<tr><td>4467706551328679552</td><td>270.033164589881</td><td>1.0565747323689927</td><td>-3.414829591355289</td><td>-3.8437215857495737</td><td>0.922888231734588</td><td>0.927008559859825</td><td>--</td></tr>\n",
+       "<tr><td>4467712255037300096</td><td>270.7724717923047</td><td>0.6581664892880896</td><td>-3.5620173752896025</td><td>-6.595792323153987</td><td>-2.669179465293931</td><td>0.9719742773203504</td><td>--</td></tr>\n",
+       "<tr><td>4467735001181761792</td><td>270.3628606248308</td><td>0.8947079323599124</td><td>2.1307079926489205</td><td>0.8826727710910712</td><td>0.6117399163086398</td><td>0.509812721702093</td><td>--</td></tr>\n",
+       "<tr><td>4467737101421916672</td><td>270.5110834661444</td><td>0.9806225910160181</td><td>0.17532366511560785</td><td>-5.113270239706202</td><td>-0.39818224846127004</td><td>0.7549581886719651</td><td>--</td></tr>\n",
+       "<tr><td>4467707547757327488</td><td>269.88746280594927</td><td>1.0212759940136962</td><td>-2.6382230817672987</td><td>-3.707776532049287</td><td>0.7741412301054209</td><td>0.3022057897812064</td><td>--</td></tr>\n",
+       "<tr><td>4467732355491087744</td><td>270.6730790702491</td><td>0.9197224705139885</td><td>-2.2735991502653037</td><td>-11.864952855984358</td><td>-0.3464446494840354</td><td>0.4937921513912002</td><td>--</td></tr>\n",
+       "<tr><td>4467717099766944512</td><td>270.57667173120825</td><td>0.726277659009568</td><td>-3.4598362614808367</td><td>-4.601426893365921</td><td>0.05443955111134051</td><td>0.8867339293525688</td><td>--</td></tr>\n",
+       "<tr><td>4467719058265781248</td><td>270.7248052971514</td><td>0.8205551921782785</td><td>-3.255079498426542</td><td>-9.249285069111085</td><td>0.3733943917490343</td><td>0.390952370410666</td><td>--</td></tr>\n",
+       "<tr><td>4467722326741572352</td><td>270.87431291888504</td><td>0.8595565975869158</td><td>0.10696398351859826</td><td>1.2035993780158853</td><td>-0.11850943432864373</td><td>0.1660452431882023</td><td>--</td></tr>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<Table length=10>\n",
+       "     source_id              ra         ...   parallax_error   radial_velocity\n",
+       "                           deg         ...        mas              km / s    \n",
+       "       int64             float64       ...      float64           float64    \n",
+       "------------------- ------------------ ... ------------------ ---------------\n",
+       "4467710915011802624  269.9680969307347 ...  0.470352406647465              --\n",
+       "4467706551328679552   270.033164589881 ...  0.927008559859825              --\n",
+       "4467712255037300096  270.7724717923047 ... 0.9719742773203504              --\n",
+       "4467735001181761792  270.3628606248308 ...  0.509812721702093              --\n",
+       "4467737101421916672  270.5110834661444 ... 0.7549581886719651              --\n",
+       "4467707547757327488 269.88746280594927 ... 0.3022057897812064              --\n",
+       "4467732355491087744  270.6730790702491 ... 0.4937921513912002              --\n",
+       "4467717099766944512 270.57667173120825 ... 0.8867339293525688              --\n",
+       "4467719058265781248  270.7248052971514 ...  0.390952370410666              --\n",
+       "4467722326741572352 270.87431291888504 ... 0.1660452431882023              --"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results3 = job3.get_results()\n",
+    "results3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Good so far."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** This query always selects sources with `parallax` less than 1.  But suppose you want to take that upper bound as an input.\n",
+    "\n",
+    "Modify `query3_base` to replace `1` with a format specifier like `{max_parallax}`.  Now, when you call `format`, add a keyword argument that assigns a value to `max_parallax`, and confirm that the format specifier gets replaced with the value you provide."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "query4_base = \"\"\"SELECT TOP 10\n",
+    "{columns}\n",
+    "FROM gaiadr2.gaia_source\n",
+    "WHERE parallax < {max_parallax} AND \n",
+    "bp_rp BETWEEN -0.75 AND 2\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "SELECT TOP 10\n",
+      "source_id, ra, dec, pmra, pmdec, parallax, parallax_error, radial_velocity\n",
+      "FROM gaiadr2.gaia_source\n",
+      "WHERE parallax < 0.5 AND \n",
+      "bp_rp BETWEEN -0.75 AND 2\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "\n",
+    "query4 = query4_base.format(columns=columns,\n",
+    "                          max_parallax=0.5)\n",
+    "print(query)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Style note:**  You might notice that the variable names in this notebook are numbered, like `query1`, `query2`, etc.  \n",
+    "\n",
+    "The advantage of this style is that it isolates each section of the notebook from the others, so if you go back and run the cells out of order, it's less likely that you will get unexpected interactions.\n",
+    "\n",
+    "A drawback of this style is that it can be a nuisance to update the notebook if you add, remove, or reorder a section.\n",
+    "\n",
+    "What do you think of this choice?  Are there alternatives you prefer?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "This notebook demonstrates the following steps:\n",
+    "\n",
+    "1. Making a connection to the Gaia server,\n",
+    "\n",
+    "2. Exploring information about the database and the tables it contains,\n",
+    "\n",
+    "3. Writing a query and sending it to the server, and finally\n",
+    "\n",
+    "4. Downloading the response from the server as an Astropy `Table`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Best practices\n",
+    "\n",
+    "* If you can't download an entire dataset (or it's not practical) use queries to select the data you need.\n",
+    "\n",
+    "* Read the metadata and the documentation to make sure you understand the tables, their columns, and what they mean.\n",
+    "\n",
+    "* Develop queries incrementally: start with something simple, test it, and add a little bit at a time.\n",
+    "\n",
+    "* Use ADQL features like `TOP` and `COUNT` to test before you run a query that might return a lot of data.\n",
+    "\n",
+    "* If you know your query will return fewer than 3000 rows, you can run it synchronously, which might complete faster (but it doesn't seem to make much difference).  If it might return more than 3000 rows, you should run it asynchronously.\n",
+    "\n",
+    "* ADQL and SQL are not case-sensitive, so you don't have to capitalize the keywords, but you should.\n",
+    "\n",
+    "* ADQL and SQL don't require you to break a query into multiple lines, but you should.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Jupyter notebooks can be good for developing and testing code, but they have some drawbacks.  In particular, if you run the cells out of order, you might find that variables don't have the values you expect.\n",
+    "\n",
+    "There are a few things you can do to mitigate these problems:\n",
+    "\n",
+    "* Make each section of the notebook self-contained.  Try not to use the same variable name in more than one section.\n",
+    "\n",
+    "* Keep notebooks short.  Look for places where you can break your analysis into phases with one notebook per phase."
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/_build/jupyter_execute/AstronomicalData/_build/jupyter_execute/01_query.py b/_build/jupyter_execute/AstronomicalData/_build/jupyter_execute/01_query.py
new file mode 100644
index 0000000..4d0736c
--- /dev/null
+++ b/_build/jupyter_execute/AstronomicalData/_build/jupyter_execute/01_query.py
@@ -0,0 +1,603 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# # Lesson 1
+
+# ## Introduction
+# 
+# This workshop is an introduction to tools and practices for working with astronomical data.  Topics covered include:
+# 
+# * Writing queries that select and download data from a database.
+# 
+# * Using data stored in an Astropy `Table` or Pandas `DataFrame`.
+# 
+# * Working with coordinates and other quantities with units.
+# 
+# * Storing data in various formats.
+# 
+# * Performing database join operations that combine data from multiple tables.
+# 
+# * Visualizing data and preparing publication-quality figures.
+
+# As a running example, we will replicate part of the analysis in a recent paper, "[Off the beaten path: Gaia reveals GD-1 stars outside of the main stream](https://arxiv.org/abs/1805.00425)" by Adrian M. Price-Whelan and Ana Bonaca.
+# 
+# As the abstract explains, "Using data from the Gaia second data release combined with Pan-STARRS photometry, we present a sample of highly-probable members of the longest cold stream in the Milky Way, GD-1."
+# 
+# GD-1 is a [stellar stream](https://en.wikipedia.org/wiki/List_of_stellar_streams), which is "an association of stars orbiting a galaxy that was once a globular cluster or dwarf galaxy that has now been torn apart and stretched out along its orbit by tidal forces."
+
+# [This article in *Science* magazine](https://www.sciencemag.org/news/2018/10/streams-stars-reveal-galaxy-s-violent-history-and-perhaps-its-unseen-dark-matter) explains some of the background, including the process that led to the paper and an discussion of the scientific implications:
+# 
+# * "The streams are particularly useful for ... galactic archaeology --- rewinding the cosmic clock to reconstruct the assembly of the Milky Way."
+# 
+# * "They also are being used as exquisitely sensitive scales to measure the galaxy's mass."
+# 
+# * "... the streams are well-positioned to reveal the presence of dark matter ... because the streams are so fragile, theorists say, collisions with marauding clumps of dark matter could leave telltale scars, potential clues to its nature."
+
+# ## Prerequisites
+# 
+# This workshop is meant for people who are familiar with basic Python, but not necessarily the libraries we will use, like Astropy or Pandas.  If you are familiar with Python lists and dictionaries, and you know how to write a function that takes parameters and returns a value, you know enough Python for this workshop.
+# 
+# We assume that you have some familiarity with operating systems, like the ability to use a command-line interface.  But we don't assume you have any prior experience with databases.
+# 
+# We assume that you are familiar with astronomy at the undergraduate level, but we will not assume specialized knowledge of the datasets or analysis methods we'll use. 
+
+# ## Data
+# 
+# The datasets we will work with are:
+#  
+# * [Gaia](https://en.wikipedia.org/wiki/Gaia_(spacecraft)), which is "a space observatory of the European Space Agency (ESA), launched in 2013 ... designed for astrometry: measuring the positions, distances and motions of stars with unprecedented precision", and
+# 
+# * [Pan-STARRS](https://en.wikipedia.org/wiki/Pan-STARRS), The Panoramic Survey Telescope and Rapid Response System, which is a survey designed to monitor the sky for transient objects, producing a catalog with accurate astronometry and photometry of detected sources.
+# 
+# Both of these datasets are very large, which can make them challenging to work with.  It might not be possible, or practical, to download the entire dataset.
+# One of the goals of this workshop is to provide tools for working with large datasets.
+
+# ## Lesson 1
+# 
+# The first lesson demonstrates the steps for selecting and downloading data from the Gaia Database:
+# 
+# 1. First we'll make a connection to the Gaia server,
+# 
+# 2. We will explore information about the database and the tables it contains,
+# 
+# 3. We will write a query and send it to the server, and finally
+# 
+# 4. We will download the response from the server.
+# 
+# After completing this lesson, you should be able to
+# 
+# * Compose a basic query in ADQL.
+# 
+# * Use queries to explore a database and its tables.
+# 
+# * Use queries to download data.
+# 
+# * Develop, test, and debug a query incrementally.
+
+# ## Query Language
+# 
+# In order to select data from a database, you have to compose a query, which is like a program written in a "query language".
+# The query language we'll use is ADQL, which stands for "Astronomical Data Query Language".
+# 
+# ADQL is a dialect of [SQL](https://en.wikipedia.org/wiki/SQL) (Structured Query Language), which is by far the most commonly used query language.  Almost everything you will learn about ADQL also works in SQL.
+# 
+# [The reference manual for ADQL is here](http://www.ivoa.net/documents/ADQL/20180112/PR-ADQL-2.1-20180112.html).
+# But you might find it easier to learn from [this ADQL Cookbook](https://www.gaia.ac.uk/data/gaia-data-release-1/adql-cookbook).
+
+# ## Installing libraries
+# 
+# The library we'll use to get Gaia data is [Astroquery](https://astroquery.readthedocs.io/en/latest/).
+# 
+# If you are running this notebook on Colab, you can run the following cell to install Astroquery and the other libraries we'll use.
+# 
+# If you are running this notebook on your own computer, you might have to install these libraries yourself.  
+# 
+# If you are using this notebook as part of a Carpentries workshop, you should have received setup instructions.
+# 
+# TODO: Add a link to the instructions.
+# 
+
+# In[1]:
+
+
+# If we're running on Colab, install libraries
+
+import sys
+IN_COLAB = 'google.colab' in sys.modules
+
+if IN_COLAB:
+    get_ipython().system('pip install astroquery astro-gala pyia')
+
+
+# ## Connecting to Gaia
+# 
+# Astroquery provides `Gaia`, which is an [object that represents a connection to the Gaia database](https://astroquery.readthedocs.io/en/latest/gaia/gaia.html).
+# 
+# We can connect to the Gaia database like this:
+
+# In[2]:
+
+
+from astroquery.gaia import Gaia
+
+
+# #### Optional detail 
+# 
+# > Running this import statement has the effect of creating a [TAP+](http://www.ivoa.net/documents/TAP/) connection; TAP stands for "Table Access Protocol".  It is a network protocol for sending queries to the database and getting back the results.  We're not sure why it seems to create two connections.
+
+# ## Databases and Tables
+# 
+# What is a database, anyway?  Most generally, it can be any collection of data, but when we are talking about ADQL or SQL:
+# 
+# * A database is a collection of one or more named tables.
+# 
+# * Each table is a 2-D array with one or more named columns of data.
+# 
+# We can use `Gaia.load_tables` to get the names of the tables in the Gaia database.  With the option `only_names=True`, it loads information about the tables, called the "metadata", not the data itself.
+
+# In[3]:
+
+
+tables = Gaia.load_tables(only_names=True)
+
+
+# In[4]:
+
+
+for table in (tables):
+    print(table.get_qualified_name())
+
+
+# So that's a lot of tables.  The ones we'll use are:
+# 
+# * `gaiadr2.gaia_source`, which contains Gaia data from [data release 2](https://www.cosmos.esa.int/web/gaia/data-release-2),
+# 
+# * `gaiadr2.panstarrs1_original_valid`, which contains the photometry data we'll use from PanSTARRS, and
+# 
+# * `gaiadr2.panstarrs1_best_neighbour`, which we'll use to cross-match each star observed by Gaia with the same star observed by PanSTARRS.
+# 
+# We can use `load_table` (not `load_tables`) to get the metadata for a single table.  The name of this function is misleading, because it only downloads metadata. 
+
+# In[5]:
+
+
+meta = Gaia.load_table('gaiadr2.gaia_source')
+meta
+
+
+# Jupyter shows that the result is an object of type `TapTableMeta`, but it does not display the contents.
+# 
+# To see the metadata, we have to print the object.
+
+# In[6]:
+
+
+print(meta)
+
+
+# Notice one gotcha: in the list of table names, this table appears as `gaiadr2.gaiadr2.gaia_source`, but when we load the metadata, we refer to it as `gaiadr2.gaia_source`.
+# 
+# **Exercise:** Go back and try
+# 
+# ```
+# meta = Gaia.load_table('gaiadr2.gaiadr2.gaia_source')
+# ```
+# 
+# What happens?  Is the error message helpful?  If you had not made this error deliberately, would you have been able to figure it out?
+
+# ## Columns
+# 
+# The following loop prints the names of the columns in the table.
+
+# In[7]:
+
+
+for column in meta.columns:
+    print(column.name)
+
+
+# You can probably guess what many of these columns are by looking at the names, but you should resist the temptation to guess.
+# To find out what the columns mean, [read the documentation](https://gea.esac.esa.int/archive/documentation/GDR2/Gaia_archive/chap_datamodel/sec_dm_main_tables/ssec_dm_gaia_source.html).
+# 
+# If you want to know what can go wrong when you don't read the documentation, [you might like this article](https://www.vox.com/future-perfect/2019/6/4/18650969/married-women-miserable-fake-paul-dolan-happiness).
+
+# **Exercise:** One of the other tables we'll use is `gaiadr2.gaiadr2.panstarrs1_original_valid`.  Use `load_table` to get the metadata for this table.  How many columns are there and what are their names?
+# 
+# Hint: Remember the gotcha we mentioned earlier.
+
+# In[8]:
+
+
+# Solution
+
+meta2 = Gaia.load_table('gaiadr2.panstarrs1_original_valid')
+print(meta2)
+
+
+# In[9]:
+
+
+# Solution
+
+for column in meta2.columns:
+    print(column.name)
+
+
+# ## Writing queries
+# 
+# By now you might be wondering how we actually download the data.  With tables this big, you generally don't.  Instead, you use queries to select only the data you want.
+# 
+# A query is a string written in a query language like SQL; for the Gaia database, the query language is a dialect of SQL called ADQL.
+# 
+# Here's an example of an ADQL query.
+
+# In[10]:
+
+
+query1 = """SELECT 
+TOP 10
+source_id, ref_epoch, ra, dec, parallax 
+FROM gaiadr2.gaia_source"""
+
+
+# **Python note:** We use a [triple-quoted string](https://docs.python.org/3/tutorial/introduction.html#strings) here so we can include line breaks in the query, which makes it easier to read.
+# 
+# The words in uppercase are ADQL keywords:
+# 
+# * `SELECT` indicates that we are selecting data (as opposed to adding or modifying data).
+# 
+# * `TOP` indicates that we only want the first 10 rows of the table, which is useful for testing a query before asking for all of the data.
+# 
+# * `FROM` specifies which table we want data from.
+# 
+# The third line is a list of column names, indicating which columns we want.  
+# 
+# In this example, the keywords are capitalized and the column names are lowercase.  This is a common style, but it is not required.  ADQL and SQL are not case-sensitive.
+
+# To run this query, we use the `Gaia` object, which represents our connection to the Gaia database, and invoke `launch_job`:
+
+# In[11]:
+
+
+job1 = Gaia.launch_job(query1)
+job1
+
+
+# The result is an object that represents the job running on a Gaia server.
+# 
+# If you print it, it displays metadata for the forthcoming table.
+
+# In[12]:
+
+
+print(job1)
+
+
+# Don't worry about `Results: None`.  That does not actually mean there are no results.
+# 
+# However, `Phase: COMPLETED` indicates that the job is complete, so we can get the results like this:
+
+# In[13]:
+
+
+results1 = job1.get_results()
+type(results1)
+
+
+# **Optional detail:**  Why is `table` repeated three times?  The first is the name of the module, the second is the name of the submodule, and the third is the name of the class.  Most of the time we only care about the last one.  It's like the Linnean name for gorilla, which is *Gorilla Gorilla Gorilla*.
+
+# The result is an [Astropy Table](https://docs.astropy.org/en/stable/table/), which is similar to a table in an SQL database except:
+# 
+# * SQL databases are stored on disk drives, so they are persistent; that is, they "survive" even if you turn off the computer.  An Astropy `Table` is stored in memory; it disappears when you turn off the computer (or shut down this Jupyter notebook).
+# 
+# * SQL databases are designed to process queries.  An Astropy `Table` can perform some query-like operations, like selecting columns and rows.  But these operations use Python syntax, not SQL.
+# 
+# Jupyter knows how to display the contents of a `Table`.
+
+# In[14]:
+
+
+results1
+
+
+# Each column has a name, units, and a data type.
+# 
+# For example, the units of `ra` and `dec` are degrees, and their data type is `float64`, which is a 64-bit floating-point number, used to store measurements with a fraction part.
+# 
+# This information comes from the Gaia database, and has been stored in the Astropy `Table` by Astroquery.
+
+# **Exercise:** Read [the documentation of this table](https://gea.esac.esa.int/archive/documentation/GDR2/Gaia_archive/chap_datamodel/sec_dm_main_tables/ssec_dm_gaia_source.html) and choose a column that looks interesting to you.  Add the column name to the query and run it again.  What are the units of the column you selected?  What is its data type?
+
+# ## Asynchronous queries
+# 
+# `launch_job` asks the server to run the job "synchronously", which normally means it runs immediately.  But synchronous jobs are limited to 2000 rows.  For queries that return more rows, you should run "asynchronously", which mean they might take longer to get started.
+# 
+# If you are not sure how many rows a query will return, you can use the SQL command `COUNT` to find out how many rows are in the result without actually returning them.  We'll see an example of this later.
+# 
+# The results of an asynchronous query are stored in a file on the server, so you can start a query and come back later to get the results.
+# 
+# For anonymous users, files are kept for three days.
+# 
+# As an example, let's try a query that's similar to `query1`, with two changes:
+# 
+# * It selects the first 3000 rows, so it is bigger than we should run synchronously.
+# 
+# * It uses a new keyword, `WHERE`.
+
+# In[15]:
+
+
+query2 = """SELECT TOP 3000
+source_id, ref_epoch, ra, dec, parallax
+FROM gaiadr2.gaia_source
+WHERE parallax < 1
+"""
+
+
+# A `WHERE` clause indicates which rows we want; in this case, the query selects only rows "where" `parallax` is less than 1.  This has the effect of selecting stars with relatively low parallax, which are farther away.  We'll use this clause to exclude nearby stars that are unlikely to be part of GD-1.
+# 
+# `WHERE` is one of the most common clauses in ADQL/SQL, and one of the most useful, because it allows us to select only the rows we need from the database.
+# 
+# We use `launch_job_async` to submit an asynchronous query.
+
+# In[16]:
+
+
+job2 = Gaia.launch_job_async(query2)
+print(job2)
+
+
+# And here are the results.
+
+# In[17]:
+
+
+results2 = job2.get_results()
+results2
+
+
+# You might notice that some values of `parallax` are negative.  As [this FAQ explains](https://www.cosmos.esa.int/web/gaia/archive-tips#negative%20parallax), "Negative parallaxes are caused by errors in the observations."  Negative parallaxes have "no physical meaning," but they can be a "useful diagnostic on the quality of the astrometric solution."
+# 
+# Later we will see an example where we use `parallax` and `parallax_error` to identify stars where the distance estimate is likely to be inaccurate.
+
+# **Exercise:** The clauses in a query have to be in the right order.  Go back and change the order of the clauses in `query2` and run it again.  
+# 
+# The query should fail, but notice that you don't get much useful debugging information.  
+# 
+# For this reason, developing and debugging ADQL queries can be really hard.  A few suggestions that might help:
+# 
+# * Whenever possible, start with a working query, either an example you find online or a query you have used in the past.
+# 
+# * Make small changes and test each change before you continue.
+# 
+# * While you are debugging, use `TOP` to limit the number of rows in the result.  That will make each attempt run faster, which reduces your testing time.  
+# 
+# * Launching test queries synchronously might make them start faster, too.
+
+# ## Operators
+# 
+# In a `WHERE` clause, you can use any of the [SQL comparison operators](https://www.w3schools.com/sql/sql_operators.asp):
+# 
+# * `>`: greater than
+# * `<`: less than
+# * `>=`: greater than or equal
+# * `<=`: less than or equal
+# * `=`: equal
+# * `!=` or `<>`: not equal
+# 
+# Most of these are the same as Python, but some are not.  In particular, notice that the equality operator is `=`, not `==`.
+# Be careful to keep your Python out of your ADQL!
+# 
+# You can combine comparisons using the logical operators:
+# 
+# * AND: true if both comparisons are true
+# * OR: true if either or both comparisons are true
+# 
+# Finally, you can use `NOT` to invert the result of a comparison. 
+
+# **Exercise:** [Read about SQL operators here](https://www.w3schools.com/sql/sql_operators.asp) and then modify the previous query to select rows where `bp_rp` is between `-0.75` and `2`.
+# 
+# You can [read about this variable here](https://gea.esac.esa.int/archive/documentation/GDR2/Gaia_archive/chap_datamodel/sec_dm_main_tables/ssec_dm_gaia_source.html).
+
+# In[18]:
+
+
+# Solution
+
+# This is what most people will probably do
+
+query = """SELECT TOP 10
+source_id, ref_epoch, ra, dec, parallax
+FROM gaiadr2.gaia_source
+WHERE parallax < 1 
+  AND bp_rp > -0.75 AND bp_rp < 2
+"""
+
+
+# In[19]:
+
+
+# Solution
+
+# But if someone notices the BETWEEN operator, 
+# they might do this
+
+query = """SELECT TOP 10
+source_id, ref_epoch, ra, dec, parallax
+FROM gaiadr2.gaia_source
+WHERE parallax < 1 
+  AND bp_rp BETWEEN -0.75 AND 2
+"""
+
+
+# This [Hertzsprung-Russell diagram](https://sci.esa.int/web/gaia/-/60198-gaia-hertzsprung-russell-diagram) shows the BP-RP color and luminosity of stars in the Gaia catalog.
+# 
+# Selecting stars with `bp-rp` less than 2 excludes many [class M dwarf stars](https://xkcd.com/2360/), which are low temperature, low luminosity.  A star like that at GD-1's distance would be hard to detect, so if it is detected, it it more likely to be in the foreground.
+
+# ## Cleaning up
+# 
+# Asynchronous jobs have a `jobid`.
+
+# In[20]:
+
+
+job1.jobid, job2.jobid
+
+
+# Which you can use to remove the job from the server.
+
+# In[21]:
+
+
+Gaia.remove_jobs([job2.jobid])
+
+
+# If you don't remove it job from the server, it will be removed eventually, so don't feel too bad if you don't clean up after yourself.
+
+# ## Formatting queries
+# 
+# So far the queries have been string "literals", meaning that the entire string is part of the program.
+# But writing queries yourself can be slow, repetitive, and error-prone.
+# 
+# It is often a good idea to write Python code that assembles a query for you.  One useful tool for that is the [string `format` method](https://www.w3schools.com/python/ref_string_format.asp).
+# 
+# As an example, we'll divide the previous query into two parts; a list of column names and a "base" for the query that contains everything except the column names.
+# 
+# Here's the list of columns we'll select.  
+
+# In[22]:
+
+
+columns = 'source_id, ra, dec, pmra, pmdec, parallax, parallax_error, radial_velocity'
+
+
+# And here's the base; it's a string that contains at least one format specifier in curly brackets (braces).
+
+# In[23]:
+
+
+query3_base = """SELECT TOP 10 
+{columns}
+FROM gaiadr2.gaia_source
+WHERE parallax < 1
+  AND bp_rp BETWEEN -0.75 AND 2
+"""
+
+
+# This base query contains one format specifier, `{columns}`, which is a placeholder for the list of column names we will provide.
+# 
+# To assemble the query, we invoke `format` on the base string and provide a keyword argument that assigns a value to `columns`.
+
+# In[24]:
+
+
+query3 = query3_base.format(columns=columns)
+
+
+# The result is a string with line breaks.  If you display it, the line breaks appear as `\n`.
+
+# In[25]:
+
+
+query3
+
+
+# But if you print it, the line breaks appear as... line breaks.
+
+# In[26]:
+
+
+print(query3)
+
+
+# Notice that the format specifier has been replaced with the value of `columns`.
+# 
+# Let's run it and see if it works:
+
+# In[27]:
+
+
+job3 = Gaia.launch_job(query3)
+print(job3)
+
+
+# In[28]:
+
+
+results3 = job3.get_results()
+results3
+
+
+# Good so far.
+
+# **Exercise:** This query always selects sources with `parallax` less than 1.  But suppose you want to take that upper bound as an input.
+# 
+# Modify `query3_base` to replace `1` with a format specifier like `{max_parallax}`.  Now, when you call `format`, add a keyword argument that assigns a value to `max_parallax`, and confirm that the format specifier gets replaced with the value you provide.
+
+# In[29]:
+
+
+# Solution
+
+query4_base = """SELECT TOP 10
+{columns}
+FROM gaiadr2.gaia_source
+WHERE parallax < {max_parallax} AND 
+bp_rp BETWEEN -0.75 AND 2
+"""
+
+
+# In[30]:
+
+
+# Solution
+
+query4 = query4_base.format(columns=columns,
+                          max_parallax=0.5)
+print(query)
+
+
+# **Style note:**  You might notice that the variable names in this notebook are numbered, like `query1`, `query2`, etc.  
+# 
+# The advantage of this style is that it isolates each section of the notebook from the others, so if you go back and run the cells out of order, it's less likely that you will get unexpected interactions.
+# 
+# A drawback of this style is that it can be a nuisance to update the notebook if you add, remove, or reorder a section.
+# 
+# What do you think of this choice?  Are there alternatives you prefer?
+
+# ## Summary
+# 
+# This notebook demonstrates the following steps:
+# 
+# 1. Making a connection to the Gaia server,
+# 
+# 2. Exploring information about the database and the tables it contains,
+# 
+# 3. Writing a query and sending it to the server, and finally
+# 
+# 4. Downloading the response from the server as an Astropy `Table`.
+
+# ## Best practices
+# 
+# * If you can't download an entire dataset (or it's not practical) use queries to select the data you need.
+# 
+# * Read the metadata and the documentation to make sure you understand the tables, their columns, and what they mean.
+# 
+# * Develop queries incrementally: start with something simple, test it, and add a little bit at a time.
+# 
+# * Use ADQL features like `TOP` and `COUNT` to test before you run a query that might return a lot of data.
+# 
+# * If you know your query will return fewer than 3000 rows, you can run it synchronously, which might complete faster (but it doesn't seem to make much difference).  If it might return more than 3000 rows, you should run it asynchronously.
+# 
+# * ADQL and SQL are not case-sensitive, so you don't have to capitalize the keywords, but you should.
+# 
+# * ADQL and SQL don't require you to break a query into multiple lines, but you should.
+# 
+
+# Jupyter notebooks can be good for developing and testing code, but they have some drawbacks.  In particular, if you run the cells out of order, you might find that variables don't have the values you expect.
+# 
+# There are a few things you can do to mitigate these problems:
+# 
+# * Make each section of the notebook self-contained.  Try not to use the same variable name in more than one section.
+# 
+# * Keep notebooks short.  Look for places where you can break your analysis into phases with one notebook per phase.
diff --git a/_build/jupyter_execute/AstronomicalData/_build/jupyter_execute/02_coords.ipynb b/_build/jupyter_execute/AstronomicalData/_build/jupyter_execute/02_coords.ipynb
new file mode 100644
index 0000000..7707176
--- /dev/null
+++ b/_build/jupyter_execute/AstronomicalData/_build/jupyter_execute/02_coords.ipynb
@@ -0,0 +1,1966 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Lesson 2\n",
+    "\n",
+    "This is the second in a series of lessons related to astronomy data.\n",
+    "\n",
+    "As a running example, we are replicating parts of the analysis in a recent paper, \"[Off the beaten path: Gaia reveals GD-1 stars outside of the main stream](https://arxiv.org/abs/1805.00425)\" by Adrian M. Price-Whelan and Ana Bonaca.\n",
+    "\n",
+    "In the first notebook, we wrote ADQL queries and used them to select and download data from the Gaia server.\n",
+    "\n",
+    "In this notebook, we'll pick up where we left off and write a query to select stars from the region of the sky where we expect GD-1 to be."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We'll start with an example that does a \"cone search\"; that is, it selects stars that appear in a circular region of the sky.\n",
+    "\n",
+    "Then, to select stars in the vicinity of GD-1, we'll:\n",
+    "\n",
+    "* Use `Quantity` objects to represent measurements with units.\n",
+    "\n",
+    "* Use the `Gala` library to convert coordinates from one frame to another.\n",
+    "\n",
+    "* Use the ADQL keywords `POLYGON`, `CONTAINS`, and `POINT` to select stars that fall within a polygonal region.\n",
+    "\n",
+    "* Submit a query and download the results.\n",
+    "\n",
+    "* Store the results in a FITS file.\n",
+    "\n",
+    "After completing this lesson, you should be able to\n",
+    "\n",
+    "* Use Python string formatting to compose more complex ADQL queries.\n",
+    "\n",
+    "* Work with coordinates and other quantities that have units.\n",
+    "\n",
+    "* Download the results of a query and store them in a file."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Installing libraries\n",
+    "\n",
+    "If you are running this notebook on Colab, you can run the following cell to install Astroquery and a the other libraries we'll use.\n",
+    "\n",
+    "If you are running this notebook on your own computer, you might have to install these libraries yourself.  \n",
+    "\n",
+    "If you are using this notebook as part of a Carpentries workshop, you should have received setup instructions.\n",
+    "\n",
+    "TODO: Add a link to the instructions.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# If we're running on Colab, install libraries\n",
+    "\n",
+    "import sys\n",
+    "IN_COLAB = 'google.colab' in sys.modules\n",
+    "\n",
+    "if IN_COLAB:\n",
+    "    !pip install astroquery astro-gala pyia"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Selecting a region"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "One of the most common ways to restrict a query is to select stars in a particular region of the sky.\n",
+    "\n",
+    "For example, here's a query from the [Gaia archive documentation](https://gea.esac.esa.int/archive-help/adql/examples/index.html) that selects \"all the objects ... in a circular region centered at (266.41683, -29.00781) with a search radius of 5 arcmin (0.08333 deg).\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query = \"\"\"\n",
+    "SELECT \n",
+    "TOP 10 source_id\n",
+    "FROM gaiadr2.gaia_source\n",
+    "WHERE 1=CONTAINS(\n",
+    "  POINT(ra, dec),\n",
+    "  CIRCLE(266.41683, -29.00781, 0.08333333))\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This query uses three keywords that are specific to ADQL (not SQL):\n",
+    "\n",
+    "* `POINT`: a location in [ICRS coordinates](https://en.wikipedia.org/wiki/International_Celestial_Reference_System), specified in degrees of right ascension and declination.\n",
+    "\n",
+    "* `CIRCLE`: a circle where the first two values are the coordinates of the center and the third is the radius in degrees.\n",
+    "\n",
+    "* `CONTAINS`: a function that returns `1` if a `POINT` is contained in a shape and `0` otherwise.\n",
+    "\n",
+    "Here is the [documentation of `CONTAINS`](http://www.ivoa.net/documents/ADQL/20180112/PR-ADQL-2.1-20180112.html#tth_sEc4.2.12).\n",
+    "\n",
+    "A query like this is called a cone search because it selects stars in a cone.\n",
+    "\n",
+    "Here's how we run it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Created TAP+ (v1.2.1) - Connection:\n",
+      "\tHost: gea.esac.esa.int\n",
+      "\tUse HTTPS: True\n",
+      "\tPort: 443\n",
+      "\tSSL Port: 443\n",
+      "Created TAP+ (v1.2.1) - Connection:\n",
+      "\tHost: geadata.esac.esa.int\n",
+      "\tUse HTTPS: True\n",
+      "\tPort: 443\n",
+      "\tSSL Port: 443\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<i>Table length=10</i>\n",
+       "<table id=\"table140683453947328\" class=\"table-striped table-bordered table-condensed\">\n",
+       "<thead><tr><th>source_id</th></tr></thead>\n",
+       "<thead><tr><th>int64</th></tr></thead>\n",
+       "<tr><td>4057468321929794432</td></tr>\n",
+       "<tr><td>4057468287575835392</td></tr>\n",
+       "<tr><td>4057482027171038976</td></tr>\n",
+       "<tr><td>4057470349160630656</td></tr>\n",
+       "<tr><td>4057470039924301696</td></tr>\n",
+       "<tr><td>4057469868125641984</td></tr>\n",
+       "<tr><td>4057468351995073024</td></tr>\n",
+       "<tr><td>4057469661959554560</td></tr>\n",
+       "<tr><td>4057470520960672640</td></tr>\n",
+       "<tr><td>4057470555320409600</td></tr>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<Table length=10>\n",
+       "     source_id     \n",
+       "       int64       \n",
+       "-------------------\n",
+       "4057468321929794432\n",
+       "4057468287575835392\n",
+       "4057482027171038976\n",
+       "4057470349160630656\n",
+       "4057470039924301696\n",
+       "4057469868125641984\n",
+       "4057468351995073024\n",
+       "4057469661959554560\n",
+       "4057470520960672640\n",
+       "4057470555320409600"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from astroquery.gaia import Gaia\n",
+    "\n",
+    "job = Gaia.launch_job(query)\n",
+    "result = job.get_results()\n",
+    "result"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** When you are debugging queries like this, you can use `TOP` to limit the size of the results, but then you still don't know how big the results will be.\n",
+    "\n",
+    "An alternative is to use `COUNT`, which asks for the number of rows that would be selected, but it does not return them.\n",
+    "\n",
+    "In the previous query, replace `TOP 10 source_id` with `COUNT(source_id)` and run the query again.  How many stars has Gaia identified in the cone we searched?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Getting GD-1 Data\n",
+    "\n",
+    "From the Price-Whelan and Bonaca paper, we will try to reproduce Figure 1, which includes this representation of stars likely to belong to GD-1:\n",
+    "\n",
+    "<img src=\"https://github.com/datacarpentry/astronomy-python/raw/gh-pages/fig/gd1-4.png\">"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Along the axis of right ascension ($\\phi_1$) the figure extends from -100 to 20 degrees.\n",
+    "\n",
+    "Along the axis of declination ($\\phi_2$) the figure extends from about -8 to 4 degrees.\n",
+    "\n",
+    "Ideally, we would select all stars from this rectangle, but there are more than 10 million of them, so\n",
+    "\n",
+    "* That would be difficult to work with,\n",
+    "\n",
+    "* As anonymous users, we are limited to 3 million rows in a single query, and\n",
+    "\n",
+    "* While we are developing and testing code, it will be faster to work with a smaller dataset.\n",
+    "\n",
+    "So we'll start by selecting stars in a smaller rectangle, from -55 to -45 degrees right ascension and -8 to 4 degrees of declination.\n",
+    "\n",
+    "But first we let's see how to represent quantities with units like degrees."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Working with coordinates\n",
+    "\n",
+    "Coordinates are physical quantities, which means that they have two parts, a value and a unit.\n",
+    "\n",
+    "For example, the coordinate $30^{\\circ}$ has value 30 and its units are degrees.\n",
+    "\n",
+    "Until recently, most scientific computation was done with values only; units were left out of the program altogether, [often with disastrous results](https://en.wikipedia.org/wiki/Mars_Climate_Orbiter#Cause_of_failure).\n",
+    "\n",
+    "Astropy provides tools for including units explicitly in computations, which makes it possible to detect errors before they cause disasters.\n",
+    "\n",
+    "To use Astropy units, we import them like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<module 'astropy.units' from '/home/downey/anaconda3/envs/AstronomicalData/lib/python3.8/site-packages/astropy/units/__init__.py'>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import astropy.units as u\n",
+    "\n",
+    "u"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`u` is an object that contains most common units and all SI units.\n",
+    "\n",
+    "You can use `dir` to list them, but you should also [read the documentation](https://docs.astropy.org/en/stable/units/)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['A',\n",
+       " 'AA',\n",
+       " 'AB',\n",
+       " 'ABflux',\n",
+       " 'ABmag',\n",
+       " 'AU',\n",
+       " 'Angstrom',\n",
+       " 'B',\n",
+       " 'Ba',\n",
+       " 'Barye',\n",
+       " 'Bi',\n",
+       " 'Biot',\n",
+       " 'Bol',\n",
+       " 'Bq',\n",
+       " 'C',\n",
+       " 'Celsius',\n",
+       " 'Ci',\n",
+       " 'CompositeUnit',\n",
+       " 'D',\n",
+       " 'Da',\n",
+       " 'Dalton',\n",
+       " 'Debye',\n",
+       " 'Decibel',\n",
+       " 'DecibelUnit',\n",
+       " 'Dex',\n",
+       " 'DexUnit',\n",
+       " 'EA',\n",
+       " 'EAU',\n",
+       " 'EB',\n",
+       " 'EBa',\n",
+       " 'EC',\n",
+       " 'ED',\n",
+       " 'EF',\n",
+       " 'EG',\n",
+       " 'EGal',\n",
+       " 'EH',\n",
+       " 'EHz',\n",
+       " 'EJ',\n",
+       " 'EJy',\n",
+       " 'EK',\n",
+       " 'EL',\n",
+       " 'EN',\n",
+       " 'EOhm',\n",
+       " 'EP',\n",
+       " 'EPa',\n",
+       " 'ER',\n",
+       " 'ERy',\n",
+       " 'ES',\n",
+       " 'ESt',\n",
+       " 'ET',\n",
+       " 'EV',\n",
+       " 'EW',\n",
+       " 'EWb',\n",
+       " 'Ea',\n",
+       " 'Eadu',\n",
+       " 'Earcmin',\n",
+       " 'Earcsec',\n",
+       " 'Eau',\n",
+       " 'Eb',\n",
+       " 'Ebarn',\n",
+       " 'Ebeam',\n",
+       " 'Ebin',\n",
+       " 'Ebit',\n",
+       " 'Ebyte',\n",
+       " 'Ecd',\n",
+       " 'Echan',\n",
+       " 'Ecount',\n",
+       " 'Ect',\n",
+       " 'Ed',\n",
+       " 'Edeg',\n",
+       " 'Edyn',\n",
+       " 'EeV',\n",
+       " 'Eerg',\n",
+       " 'Eg',\n",
+       " 'Eh',\n",
+       " 'EiB',\n",
+       " 'Eib',\n",
+       " 'Eibit',\n",
+       " 'Eibyte',\n",
+       " 'Ek',\n",
+       " 'El',\n",
+       " 'Elm',\n",
+       " 'Elx',\n",
+       " 'Elyr',\n",
+       " 'Em',\n",
+       " 'Emag',\n",
+       " 'Emin',\n",
+       " 'Emol',\n",
+       " 'Eohm',\n",
+       " 'Epc',\n",
+       " 'Eph',\n",
+       " 'Ephoton',\n",
+       " 'Epix',\n",
+       " 'Epixel',\n",
+       " 'Erad',\n",
+       " 'Es',\n",
+       " 'Esr',\n",
+       " 'Eu',\n",
+       " 'Evox',\n",
+       " 'Evoxel',\n",
+       " 'Eyr',\n",
+       " 'F',\n",
+       " 'Farad',\n",
+       " 'Fr',\n",
+       " 'Franklin',\n",
+       " 'FunctionQuantity',\n",
+       " 'FunctionUnitBase',\n",
+       " 'G',\n",
+       " 'GA',\n",
+       " 'GAU',\n",
+       " 'GB',\n",
+       " 'GBa',\n",
+       " 'GC',\n",
+       " 'GD',\n",
+       " 'GF',\n",
+       " 'GG',\n",
+       " 'GGal',\n",
+       " 'GH',\n",
+       " 'GHz',\n",
+       " 'GJ',\n",
+       " 'GJy',\n",
+       " 'GK',\n",
+       " 'GL',\n",
+       " 'GN',\n",
+       " 'GOhm',\n",
+       " 'GP',\n",
+       " 'GPa',\n",
+       " 'GR',\n",
+       " 'GRy',\n",
+       " 'GS',\n",
+       " 'GSt',\n",
+       " 'GT',\n",
+       " 'GV',\n",
+       " 'GW',\n",
+       " 'GWb',\n",
+       " 'Ga',\n",
+       " 'Gadu',\n",
+       " 'Gal',\n",
+       " 'Garcmin',\n",
+       " 'Garcsec',\n",
+       " 'Gau',\n",
+       " 'Gauss',\n",
+       " 'Gb',\n",
+       " 'Gbarn',\n",
+       " 'Gbeam',\n",
+       " 'Gbin',\n",
+       " 'Gbit',\n",
+       " 'Gbyte',\n",
+       " 'Gcd',\n",
+       " 'Gchan',\n",
+       " 'Gcount',\n",
+       " 'Gct',\n",
+       " 'Gd',\n",
+       " 'Gdeg',\n",
+       " 'Gdyn',\n",
+       " 'GeV',\n",
+       " 'Gerg',\n",
+       " 'Gg',\n",
+       " 'Gh',\n",
+       " 'GiB',\n",
+       " 'Gib',\n",
+       " 'Gibit',\n",
+       " 'Gibyte',\n",
+       " 'Gk',\n",
+       " 'Gl',\n",
+       " 'Glm',\n",
+       " 'Glx',\n",
+       " 'Glyr',\n",
+       " 'Gm',\n",
+       " 'Gmag',\n",
+       " 'Gmin',\n",
+       " 'Gmol',\n",
+       " 'Gohm',\n",
+       " 'Gpc',\n",
+       " 'Gph',\n",
+       " 'Gphoton',\n",
+       " 'Gpix',\n",
+       " 'Gpixel',\n",
+       " 'Grad',\n",
+       " 'Gs',\n",
+       " 'Gsr',\n",
+       " 'Gu',\n",
+       " 'Gvox',\n",
+       " 'Gvoxel',\n",
+       " 'Gyr',\n",
+       " 'H',\n",
+       " 'Henry',\n",
+       " 'Hertz',\n",
+       " 'Hz',\n",
+       " 'IrreducibleUnit',\n",
+       " 'J',\n",
+       " 'Jansky',\n",
+       " 'Joule',\n",
+       " 'Jy',\n",
+       " 'K',\n",
+       " 'Kayser',\n",
+       " 'Kelvin',\n",
+       " 'KiB',\n",
+       " 'Kib',\n",
+       " 'Kibit',\n",
+       " 'Kibyte',\n",
+       " 'L',\n",
+       " 'L_bol',\n",
+       " 'L_sun',\n",
+       " 'LogQuantity',\n",
+       " 'LogUnit',\n",
+       " 'Lsun',\n",
+       " 'MA',\n",
+       " 'MAU',\n",
+       " 'MB',\n",
+       " 'MBa',\n",
+       " 'MC',\n",
+       " 'MD',\n",
+       " 'MF',\n",
+       " 'MG',\n",
+       " 'MGal',\n",
+       " 'MH',\n",
+       " 'MHz',\n",
+       " 'MJ',\n",
+       " 'MJy',\n",
+       " 'MK',\n",
+       " 'ML',\n",
+       " 'MN',\n",
+       " 'MOhm',\n",
+       " 'MP',\n",
+       " 'MPa',\n",
+       " 'MR',\n",
+       " 'MRy',\n",
+       " 'MS',\n",
+       " 'MSt',\n",
+       " 'MT',\n",
+       " 'MV',\n",
+       " 'MW',\n",
+       " 'MWb',\n",
+       " 'M_bol',\n",
+       " 'M_e',\n",
+       " 'M_earth',\n",
+       " 'M_jup',\n",
+       " 'M_jupiter',\n",
+       " 'M_p',\n",
+       " 'M_sun',\n",
+       " 'Ma',\n",
+       " 'Madu',\n",
+       " 'MagUnit',\n",
+       " 'Magnitude',\n",
+       " 'Marcmin',\n",
+       " 'Marcsec',\n",
+       " 'Mau',\n",
+       " 'Mb',\n",
+       " 'Mbarn',\n",
+       " 'Mbeam',\n",
+       " 'Mbin',\n",
+       " 'Mbit',\n",
+       " 'Mbyte',\n",
+       " 'Mcd',\n",
+       " 'Mchan',\n",
+       " 'Mcount',\n",
+       " 'Mct',\n",
+       " 'Md',\n",
+       " 'Mdeg',\n",
+       " 'Mdyn',\n",
+       " 'MeV',\n",
+       " 'Mearth',\n",
+       " 'Merg',\n",
+       " 'Mg',\n",
+       " 'Mh',\n",
+       " 'MiB',\n",
+       " 'Mib',\n",
+       " 'Mibit',\n",
+       " 'Mibyte',\n",
+       " 'Mjup',\n",
+       " 'Mjupiter',\n",
+       " 'Mk',\n",
+       " 'Ml',\n",
+       " 'Mlm',\n",
+       " 'Mlx',\n",
+       " 'Mlyr',\n",
+       " 'Mm',\n",
+       " 'Mmag',\n",
+       " 'Mmin',\n",
+       " 'Mmol',\n",
+       " 'Mohm',\n",
+       " 'Mpc',\n",
+       " 'Mph',\n",
+       " 'Mphoton',\n",
+       " 'Mpix',\n",
+       " 'Mpixel',\n",
+       " 'Mrad',\n",
+       " 'Ms',\n",
+       " 'Msr',\n",
+       " 'Msun',\n",
+       " 'Mu',\n",
+       " 'Mvox',\n",
+       " 'Mvoxel',\n",
+       " 'Myr',\n",
+       " 'N',\n",
+       " 'NamedUnit',\n",
+       " 'Newton',\n",
+       " 'Ohm',\n",
+       " 'P',\n",
+       " 'PA',\n",
+       " 'PAU',\n",
+       " 'PB',\n",
+       " 'PBa',\n",
+       " 'PC',\n",
+       " 'PD',\n",
+       " 'PF',\n",
+       " 'PG',\n",
+       " 'PGal',\n",
+       " 'PH',\n",
+       " 'PHz',\n",
+       " 'PJ',\n",
+       " 'PJy',\n",
+       " 'PK',\n",
+       " 'PL',\n",
+       " 'PN',\n",
+       " 'POhm',\n",
+       " 'PP',\n",
+       " 'PPa',\n",
+       " 'PR',\n",
+       " 'PRy',\n",
+       " 'PS',\n",
+       " 'PSt',\n",
+       " 'PT',\n",
+       " 'PV',\n",
+       " 'PW',\n",
+       " 'PWb',\n",
+       " 'Pa',\n",
+       " 'Padu',\n",
+       " 'Parcmin',\n",
+       " 'Parcsec',\n",
+       " 'Pascal',\n",
+       " 'Pau',\n",
+       " 'Pb',\n",
+       " 'Pbarn',\n",
+       " 'Pbeam',\n",
+       " 'Pbin',\n",
+       " 'Pbit',\n",
+       " 'Pbyte',\n",
+       " 'Pcd',\n",
+       " 'Pchan',\n",
+       " 'Pcount',\n",
+       " 'Pct',\n",
+       " 'Pd',\n",
+       " 'Pdeg',\n",
+       " 'Pdyn',\n",
+       " 'PeV',\n",
+       " 'Perg',\n",
+       " 'Pg',\n",
+       " 'Ph',\n",
+       " 'PiB',\n",
+       " 'Pib',\n",
+       " 'Pibit',\n",
+       " 'Pibyte',\n",
+       " 'Pk',\n",
+       " 'Pl',\n",
+       " 'Plm',\n",
+       " 'Plx',\n",
+       " 'Plyr',\n",
+       " 'Pm',\n",
+       " 'Pmag',\n",
+       " 'Pmin',\n",
+       " 'Pmol',\n",
+       " 'Pohm',\n",
+       " 'Ppc',\n",
+       " 'Pph',\n",
+       " 'Pphoton',\n",
+       " 'Ppix',\n",
+       " 'Ppixel',\n",
+       " 'Prad',\n",
+       " 'PrefixUnit',\n",
+       " 'Ps',\n",
+       " 'Psr',\n",
+       " 'Pu',\n",
+       " 'Pvox',\n",
+       " 'Pvoxel',\n",
+       " 'Pyr',\n",
+       " 'Quantity',\n",
+       " 'QuantityInfo',\n",
+       " 'QuantityInfoBase',\n",
+       " 'R',\n",
+       " 'R_earth',\n",
+       " 'R_jup',\n",
+       " 'R_jupiter',\n",
+       " 'R_sun',\n",
+       " 'Rayleigh',\n",
+       " 'Rearth',\n",
+       " 'Rjup',\n",
+       " 'Rjupiter',\n",
+       " 'Rsun',\n",
+       " 'Ry',\n",
+       " 'S',\n",
+       " 'ST',\n",
+       " 'STflux',\n",
+       " 'STmag',\n",
+       " 'Siemens',\n",
+       " 'SpecificTypeQuantity',\n",
+       " 'St',\n",
+       " 'Sun',\n",
+       " 'T',\n",
+       " 'TA',\n",
+       " 'TAU',\n",
+       " 'TB',\n",
+       " 'TBa',\n",
+       " 'TC',\n",
+       " 'TD',\n",
+       " 'TF',\n",
+       " 'TG',\n",
+       " 'TGal',\n",
+       " 'TH',\n",
+       " 'THz',\n",
+       " 'TJ',\n",
+       " 'TJy',\n",
+       " 'TK',\n",
+       " 'TL',\n",
+       " 'TN',\n",
+       " 'TOhm',\n",
+       " 'TP',\n",
+       " 'TPa',\n",
+       " 'TR',\n",
+       " 'TRy',\n",
+       " 'TS',\n",
+       " 'TSt',\n",
+       " 'TT',\n",
+       " 'TV',\n",
+       " 'TW',\n",
+       " 'TWb',\n",
+       " 'Ta',\n",
+       " 'Tadu',\n",
+       " 'Tarcmin',\n",
+       " 'Tarcsec',\n",
+       " 'Tau',\n",
+       " 'Tb',\n",
+       " 'Tbarn',\n",
+       " 'Tbeam',\n",
+       " 'Tbin',\n",
+       " 'Tbit',\n",
+       " 'Tbyte',\n",
+       " 'Tcd',\n",
+       " 'Tchan',\n",
+       " 'Tcount',\n",
+       " 'Tct',\n",
+       " 'Td',\n",
+       " 'Tdeg',\n",
+       " 'Tdyn',\n",
+       " 'TeV',\n",
+       " 'Terg',\n",
+       " 'Tesla',\n",
+       " 'Tg',\n",
+       " 'Th',\n",
+       " 'TiB',\n",
+       " 'Tib',\n",
+       " 'Tibit',\n",
+       " 'Tibyte',\n",
+       " 'Tk',\n",
+       " 'Tl',\n",
+       " 'Tlm',\n",
+       " 'Tlx',\n",
+       " 'Tlyr',\n",
+       " 'Tm',\n",
+       " 'Tmag',\n",
+       " 'Tmin',\n",
+       " 'Tmol',\n",
+       " 'Tohm',\n",
+       " 'Tpc',\n",
+       " 'Tph',\n",
+       " 'Tphoton',\n",
+       " 'Tpix',\n",
+       " 'Tpixel',\n",
+       " 'Trad',\n",
+       " 'Ts',\n",
+       " 'Tsr',\n",
+       " 'Tu',\n",
+       " 'Tvox',\n",
+       " 'Tvoxel',\n",
+       " 'Tyr',\n",
+       " 'Unit',\n",
+       " 'UnitBase',\n",
+       " 'UnitConversionError',\n",
+       " 'UnitTypeError',\n",
+       " 'UnitsError',\n",
+       " 'UnitsWarning',\n",
+       " 'UnrecognizedUnit',\n",
+       " 'V',\n",
+       " 'Volt',\n",
+       " 'W',\n",
+       " 'Watt',\n",
+       " 'Wb',\n",
+       " 'Weber',\n",
+       " 'YA',\n",
+       " 'YAU',\n",
+       " 'YB',\n",
+       " 'YBa',\n",
+       " 'YC',\n",
+       " 'YD',\n",
+       " 'YF',\n",
+       " 'YG',\n",
+       " 'YGal',\n",
+       " 'YH',\n",
+       " 'YHz',\n",
+       " 'YJ',\n",
+       " 'YJy',\n",
+       " 'YK',\n",
+       " 'YL',\n",
+       " 'YN',\n",
+       " 'YOhm',\n",
+       " 'YP',\n",
+       " 'YPa',\n",
+       " 'YR',\n",
+       " 'YRy',\n",
+       " 'YS',\n",
+       " 'YSt',\n",
+       " 'YT',\n",
+       " 'YV',\n",
+       " 'YW',\n",
+       " 'YWb',\n",
+       " 'Ya',\n",
+       " 'Yadu',\n",
+       " 'Yarcmin',\n",
+       " 'Yarcsec',\n",
+       " 'Yau',\n",
+       " 'Yb',\n",
+       " 'Ybarn',\n",
+       " 'Ybeam',\n",
+       " 'Ybin',\n",
+       " 'Ybit',\n",
+       " 'Ybyte',\n",
+       " 'Ycd',\n",
+       " 'Ychan',\n",
+       " 'Ycount',\n",
+       " 'Yct',\n",
+       " 'Yd',\n",
+       " 'Ydeg',\n",
+       " 'Ydyn',\n",
+       " 'YeV',\n",
+       " 'Yerg',\n",
+       " 'Yg',\n",
+       " 'Yh',\n",
+       " 'Yk',\n",
+       " 'Yl',\n",
+       " 'Ylm',\n",
+       " 'Ylx',\n",
+       " 'Ylyr',\n",
+       " 'Ym',\n",
+       " 'Ymag',\n",
+       " 'Ymin',\n",
+       " 'Ymol',\n",
+       " 'Yohm',\n",
+       " 'Ypc',\n",
+       " 'Yph',\n",
+       " 'Yphoton',\n",
+       " 'Ypix',\n",
+       " 'Ypixel',\n",
+       " 'Yrad',\n",
+       " 'Ys',\n",
+       " 'Ysr',\n",
+       " 'Yu',\n",
+       " 'Yvox',\n",
+       " 'Yvoxel',\n",
+       " 'Yyr',\n",
+       " 'ZA',\n",
+       " 'ZAU',\n",
+       " 'ZB',\n",
+       " 'ZBa',\n",
+       " 'ZC',\n",
+       " 'ZD',\n",
+       " 'ZF',\n",
+       " 'ZG',\n",
+       " 'ZGal',\n",
+       " 'ZH',\n",
+       " 'ZHz',\n",
+       " 'ZJ',\n",
+       " 'ZJy',\n",
+       " 'ZK',\n",
+       " 'ZL',\n",
+       " 'ZN',\n",
+       " 'ZOhm',\n",
+       " 'ZP',\n",
+       " 'ZPa',\n",
+       " 'ZR',\n",
+       " 'ZRy',\n",
+       " 'ZS',\n",
+       " 'ZSt',\n",
+       " 'ZT',\n",
+       " 'ZV',\n",
+       " 'ZW',\n",
+       " 'ZWb',\n",
+       " 'Za',\n",
+       " 'Zadu',\n",
+       " 'Zarcmin',\n",
+       " 'Zarcsec',\n",
+       " 'Zau',\n",
+       " 'Zb',\n",
+       " 'Zbarn',\n",
+       " 'Zbeam',\n",
+       " 'Zbin',\n",
+       " 'Zbit',\n",
+       " 'Zbyte',\n",
+       " 'Zcd',\n",
+       " 'Zchan',\n",
+       " 'Zcount',\n",
+       " 'Zct',\n",
+       " 'Zd',\n",
+       " 'Zdeg',\n",
+       " 'Zdyn',\n",
+       " 'ZeV',\n",
+       " 'Zerg',\n",
+       " 'Zg',\n",
+       " 'Zh',\n",
+       " 'Zk',\n",
+       " 'Zl',\n",
+       " 'Zlm',\n",
+       " 'Zlx',\n",
+       " 'Zlyr',\n",
+       " 'Zm',\n",
+       " 'Zmag',\n",
+       " 'Zmin',\n",
+       " 'Zmol',\n",
+       " 'Zohm',\n",
+       " 'Zpc',\n",
+       " 'Zph',\n",
+       " 'Zphoton',\n",
+       " 'Zpix',\n",
+       " 'Zpixel',\n",
+       " 'Zrad',\n",
+       " 'Zs',\n",
+       " 'Zsr',\n",
+       " 'Zu',\n",
+       " 'Zvox',\n",
+       " 'Zvoxel',\n",
+       " 'Zyr',\n",
+       " '__builtins__',\n",
+       " '__cached__',\n",
+       " '__doc__',\n",
+       " '__file__',\n",
+       " '__loader__',\n",
+       " '__name__',\n",
+       " '__package__',\n",
+       " '__path__',\n",
+       " '__spec__',\n",
+       " 'a',\n",
+       " 'aA',\n",
+       " 'aAU',\n",
+       " 'aB',\n",
+       " 'aBa',\n",
+       " 'aC',\n",
+       " 'aD',\n",
+       " 'aF',\n",
+       " 'aG',\n",
+       " 'aGal',\n",
+       " 'aH',\n",
+       " 'aHz',\n",
+       " 'aJ',\n",
+       " 'aJy',\n",
+       " 'aK',\n",
+       " 'aL',\n",
+       " 'aN',\n",
+       " 'aOhm',\n",
+       " 'aP',\n",
+       " 'aPa',\n",
+       " 'aR',\n",
+       " 'aRy',\n",
+       " 'aS',\n",
+       " 'aSt',\n",
+       " 'aT',\n",
+       " 'aV',\n",
+       " 'aW',\n",
+       " 'aWb',\n",
+       " 'aa',\n",
+       " 'aadu',\n",
+       " 'aarcmin',\n",
+       " 'aarcsec',\n",
+       " 'aau',\n",
+       " 'ab',\n",
+       " 'abA',\n",
+       " 'abC',\n",
+       " 'abampere',\n",
+       " 'abarn',\n",
+       " 'abcoulomb',\n",
+       " 'abeam',\n",
+       " 'abin',\n",
+       " 'abit',\n",
+       " 'abyte',\n",
+       " 'acd',\n",
+       " 'achan',\n",
+       " 'acount',\n",
+       " 'act',\n",
+       " 'ad',\n",
+       " 'add_enabled_equivalencies',\n",
+       " 'add_enabled_units',\n",
+       " 'adeg',\n",
+       " 'adu',\n",
+       " 'adyn',\n",
+       " 'aeV',\n",
+       " 'aerg',\n",
+       " 'ag',\n",
+       " 'ah',\n",
+       " 'ak',\n",
+       " 'al',\n",
+       " 'allclose',\n",
+       " 'alm',\n",
+       " 'alx',\n",
+       " 'alyr',\n",
+       " 'am',\n",
+       " 'amag',\n",
+       " 'amin',\n",
+       " 'amol',\n",
+       " 'amp',\n",
+       " 'ampere',\n",
+       " 'angstrom',\n",
+       " 'annum',\n",
+       " 'aohm',\n",
+       " 'apc',\n",
+       " 'aph',\n",
+       " 'aphoton',\n",
+       " 'apix',\n",
+       " 'apixel',\n",
+       " 'arad',\n",
+       " 'arcmin',\n",
+       " 'arcminute',\n",
+       " 'arcsec',\n",
+       " 'arcsecond',\n",
+       " 'asr',\n",
+       " 'astronomical_unit',\n",
+       " 'astrophys',\n",
+       " 'attoBarye',\n",
+       " 'attoDa',\n",
+       " 'attoDalton',\n",
+       " 'attoDebye',\n",
+       " 'attoFarad',\n",
+       " 'attoGauss',\n",
+       " 'attoHenry',\n",
+       " 'attoHertz',\n",
+       " 'attoJansky',\n",
+       " 'attoJoule',\n",
+       " 'attoKayser',\n",
+       " 'attoKelvin',\n",
+       " 'attoNewton',\n",
+       " 'attoOhm',\n",
+       " 'attoPascal',\n",
+       " 'attoRayleigh',\n",
+       " 'attoSiemens',\n",
+       " 'attoTesla',\n",
+       " 'attoVolt',\n",
+       " 'attoWatt',\n",
+       " 'attoWeber',\n",
+       " 'attoamp',\n",
+       " 'attoampere',\n",
+       " 'attoannum',\n",
+       " 'attoarcminute',\n",
+       " 'attoarcsecond',\n",
+       " 'attoastronomical_unit',\n",
+       " 'attobarn',\n",
+       " 'attobarye',\n",
+       " 'attobit',\n",
+       " 'attobyte',\n",
+       " 'attocandela',\n",
+       " 'attocoulomb',\n",
+       " 'attocount',\n",
+       " 'attoday',\n",
+       " 'attodebye',\n",
+       " 'attodegree',\n",
+       " 'attodyne',\n",
+       " 'attoelectronvolt',\n",
+       " 'attofarad',\n",
+       " 'attogal',\n",
+       " 'attogauss',\n",
+       " 'attogram',\n",
+       " 'attohenry',\n",
+       " 'attohertz',\n",
+       " 'attohour',\n",
+       " 'attohr',\n",
+       " 'attojansky',\n",
+       " 'attojoule',\n",
+       " 'attokayser',\n",
+       " 'attolightyear',\n",
+       " 'attoliter',\n",
+       " 'attolumen',\n",
+       " 'attolux',\n",
+       " 'attometer',\n",
+       " 'attominute',\n",
+       " 'attomole',\n",
+       " 'attonewton',\n",
+       " 'attoparsec',\n",
+       " 'attopascal',\n",
+       " 'attophoton',\n",
+       " 'attopixel',\n",
+       " 'attopoise',\n",
+       " 'attoradian',\n",
+       " 'attorayleigh',\n",
+       " 'attorydberg',\n",
+       " 'attosecond',\n",
+       " 'attosiemens',\n",
+       " 'attosteradian',\n",
+       " 'attostokes',\n",
+       " 'attotesla',\n",
+       " 'attovolt',\n",
+       " 'attovoxel',\n",
+       " 'attowatt',\n",
+       " 'attoweber',\n",
+       " 'attoyear',\n",
+       " 'au',\n",
+       " 'avox',\n",
+       " 'avoxel',\n",
+       " 'ayr',\n",
+       " 'b',\n",
+       " 'bar',\n",
+       " 'barn',\n",
+       " 'barye',\n",
+       " 'beam',\n",
+       " 'beam_angular_area',\n",
+       " 'becquerel',\n",
+       " 'bin',\n",
+       " 'binary_prefixes',\n",
+       " 'bit',\n",
+       " 'bol',\n",
+       " 'brightness_temperature',\n",
+       " 'byte',\n",
+       " 'cA',\n",
+       " 'cAU',\n",
+       " 'cB',\n",
+       " 'cBa',\n",
+       " 'cC',\n",
+       " 'cD',\n",
+       " 'cF',\n",
+       " 'cG',\n",
+       " 'cGal',\n",
+       " 'cH',\n",
+       " 'cHz',\n",
+       " 'cJ',\n",
+       " 'cJy',\n",
+       " 'cK',\n",
+       " 'cL',\n",
+       " 'cN',\n",
+       " 'cOhm',\n",
+       " 'cP',\n",
+       " 'cPa',\n",
+       " 'cR',\n",
+       " 'cRy',\n",
+       " 'cS',\n",
+       " 'cSt',\n",
+       " 'cT',\n",
+       " 'cV',\n",
+       " 'cW',\n",
+       " 'cWb',\n",
+       " 'ca',\n",
+       " 'cadu',\n",
+       " 'candela',\n",
+       " 'carcmin',\n",
+       " 'carcsec',\n",
+       " 'cau',\n",
+       " 'cb',\n",
+       " 'cbarn',\n",
+       " 'cbeam',\n",
+       " 'cbin',\n",
+       " 'cbit',\n",
+       " 'cbyte',\n",
+       " 'ccd',\n",
+       " 'cchan',\n",
+       " 'ccount',\n",
+       " 'cct',\n",
+       " 'cd',\n",
+       " 'cdeg',\n",
+       " 'cdyn',\n",
+       " 'ceV',\n",
+       " 'centiBarye',\n",
+       " 'centiDa',\n",
+       " 'centiDalton',\n",
+       " 'centiDebye',\n",
+       " 'centiFarad',\n",
+       " 'centiGauss',\n",
+       " 'centiHenry',\n",
+       " 'centiHertz',\n",
+       " 'centiJansky',\n",
+       " 'centiJoule',\n",
+       " 'centiKayser',\n",
+       " 'centiKelvin',\n",
+       " 'centiNewton',\n",
+       " 'centiOhm',\n",
+       " 'centiPascal',\n",
+       " 'centiRayleigh',\n",
+       " 'centiSiemens',\n",
+       " 'centiTesla',\n",
+       " 'centiVolt',\n",
+       " 'centiWatt',\n",
+       " 'centiWeber',\n",
+       " 'centiamp',\n",
+       " 'centiampere',\n",
+       " 'centiannum',\n",
+       " 'centiarcminute',\n",
+       " 'centiarcsecond',\n",
+       " 'centiastronomical_unit',\n",
+       " 'centibarn',\n",
+       " 'centibarye',\n",
+       " 'centibit',\n",
+       " 'centibyte',\n",
+       " 'centicandela',\n",
+       " 'centicoulomb',\n",
+       " 'centicount',\n",
+       " 'centiday',\n",
+       " 'centidebye',\n",
+       " 'centidegree',\n",
+       " 'centidyne',\n",
+       " 'centielectronvolt',\n",
+       " 'centifarad',\n",
+       " 'centigal',\n",
+       " 'centigauss',\n",
+       " 'centigram',\n",
+       " 'centihenry',\n",
+       " 'centihertz',\n",
+       " 'centihour',\n",
+       " 'centihr',\n",
+       " 'centijansky',\n",
+       " 'centijoule',\n",
+       " 'centikayser',\n",
+       " 'centilightyear',\n",
+       " 'centiliter',\n",
+       " 'centilumen',\n",
+       " 'centilux',\n",
+       " 'centimeter',\n",
+       " 'centiminute',\n",
+       " 'centimole',\n",
+       " 'centinewton',\n",
+       " 'centiparsec',\n",
+       " 'centipascal',\n",
+       " 'centiphoton',\n",
+       " 'centipixel',\n",
+       " 'centipoise',\n",
+       " 'centiradian',\n",
+       " 'centirayleigh',\n",
+       " 'centirydberg',\n",
+       " 'centisecond',\n",
+       " 'centisiemens',\n",
+       " 'centisteradian',\n",
+       " 'centistokes',\n",
+       " 'centitesla',\n",
+       " 'centivolt',\n",
+       " 'centivoxel',\n",
+       " 'centiwatt',\n",
+       " 'centiweber',\n",
+       " 'centiyear',\n",
+       " 'cerg',\n",
+       " 'cg',\n",
+       " 'cgs',\n",
+       " 'ch',\n",
+       " 'chan',\n",
+       " 'ck',\n",
+       " 'cl',\n",
+       " 'clm',\n",
+       " 'clx',\n",
+       " 'clyr',\n",
+       " 'cm',\n",
+       " 'cmag',\n",
+       " 'cmin',\n",
+       " 'cmol',\n",
+       " 'cohm',\n",
+       " 'core',\n",
+       " 'coulomb',\n",
+       " 'count',\n",
+       " 'cpc',\n",
+       " 'cph',\n",
+       " 'cphoton',\n",
+       " 'cpix',\n",
+       " 'cpixel',\n",
+       " 'crad',\n",
+       " 'cs',\n",
+       " 'csr',\n",
+       " 'ct',\n",
+       " 'cu',\n",
+       " 'curie',\n",
+       " 'cvox',\n",
+       " 'cvoxel',\n",
+       " 'cy',\n",
+       " 'cycle',\n",
+       " 'cyr',\n",
+       " 'd',\n",
+       " 'dA',\n",
+       " 'dAU',\n",
+       " 'dB',\n",
+       " 'dBa',\n",
+       " 'dC',\n",
+       " 'dD',\n",
+       " 'dF',\n",
+       " 'dG',\n",
+       " 'dGal',\n",
+       " 'dH',\n",
+       " 'dHz',\n",
+       " 'dJ',\n",
+       " 'dJy',\n",
+       " 'dK',\n",
+       " 'dL',\n",
+       " 'dN',\n",
+       " 'dOhm',\n",
+       " 'dP',\n",
+       " 'dPa',\n",
+       " 'dR',\n",
+       " 'dRy',\n",
+       " 'dS',\n",
+       " 'dSt',\n",
+       " 'dT',\n",
+       " ...]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dir(u)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To create a quantity, we multiply a value by a unit."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "astropy.units.quantity.Quantity"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "coord = 30 * u.deg\n",
+    "type(coord)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The result is a `Quantity` object.\n",
+    "\n",
+    "Jupyter knows how to display `Quantities` like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$30 \\; \\mathrm{{}^{\\circ}}$"
+      ],
+      "text/plain": [
+       "<Quantity 30. deg>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "coord"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Selecting a rectangle\n",
+    "\n",
+    "Now we'll select a rectangle from -55 to -45 degrees right ascension and -8 to 4 degrees of declination.\n",
+    "\n",
+    "We'll define variables to contain these limits."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "phi1_min = -55\n",
+    "phi1_max = -45\n",
+    "phi2_min = -8\n",
+    "phi2_max = 4"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To represent a rectangle, we'll use two lists of coordinates and multiply by their units."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "phi1_rect = [phi1_min, phi1_min, phi1_max, phi1_max] * u.deg\n",
+    "phi2_rect = [phi2_min, phi2_max, phi2_max, phi2_min] * u.deg"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`phi1_rect` and `phi2_rect` represent the coordinates of the corners of a rectangle.  \n",
+    "\n",
+    "But they are in \"[a Heliocentric spherical coordinate system defined by the orbit of the GD1 stream](https://gala-astro.readthedocs.io/en/latest/_modules/gala/coordinates/gd1.html)\"\n",
+    "\n",
+    "In order to use them in a Gaia query, we have to convert them to [International Celestial Reference System](https://en.wikipedia.org/wiki/International_Celestial_Reference_System) (ICRS) coordinates.  We can do that by storing the coordinates in a `GD1Koposov10` object provided by [Gala](https://gala-astro.readthedocs.io/en/latest/coordinates/)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "gala.coordinates.gd1.GD1Koposov10"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import gala.coordinates as gc\n",
+    "\n",
+    "corners = gc.GD1Koposov10(phi1=phi1_rect, phi2=phi2_rect)\n",
+    "type(corners)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can display the result like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<GD1Koposov10 Coordinate: (phi1, phi2) in deg\n",
+       "    [(-55., -8.), (-55.,  4.), (-45.,  4.), (-45., -8.)]>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "corners"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can use `transform_to` to convert to ICRS coordinates."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "astropy.coordinates.builtin_frames.icrs.ICRS"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import astropy.coordinates as coord\n",
+    "\n",
+    "corners_icrs = corners.transform_to(coord.ICRS)\n",
+    "type(corners_icrs)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The result is an `ICRS` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<ICRS Coordinate: (ra, dec) in deg\n",
+       "    [(146.27533314, 19.26190982), (135.42163944, 25.87738723),\n",
+       "     (141.60264825, 34.3048303 ), (152.81671045, 27.13611254)]>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "corners_icrs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notice that a rectangle in one coordinate system is not necessarily a rectangle in another.  In this example, the result is a polygon."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Selecting a polygon\n",
+    "\n",
+    "In order to use this polygon as part of an ADQL query, we have to convert it to a string with a comma-separated list of coordinates, as in this example:\n",
+    "\n",
+    "```\n",
+    "\"\"\"\n",
+    "POLYGON(143.65, 20.98, \n",
+    "        134.46, 26.39, \n",
+    "        140.58, 34.85, \n",
+    "        150.16, 29.01)\n",
+    "\"\"\"\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`corners_icrs` behaves like a list, so we can use a `for` loop to iterate through the points."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<ICRS Coordinate: (ra, dec) in deg\n",
+      "    (146.27533314, 19.26190982)>\n",
+      "<ICRS Coordinate: (ra, dec) in deg\n",
+      "    (135.42163944, 25.87738723)>\n",
+      "<ICRS Coordinate: (ra, dec) in deg\n",
+      "    (141.60264825, 34.3048303)>\n",
+      "<ICRS Coordinate: (ra, dec) in deg\n",
+      "    (152.81671045, 27.13611254)>\n"
+     ]
+    }
+   ],
+   "source": [
+    "for point in corners_icrs:\n",
+    "    print(point)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "From that, we can select the coordinates `ra` and `dec`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "146d16m31.1993s 19d15m42.8754s\n",
+      "135d25m17.902s 25d52m38.594s\n",
+      "141d36m09.5337s 34d18m17.3891s\n",
+      "152d49m00.1576s 27d08m10.0051s\n"
+     ]
+    }
+   ],
+   "source": [
+    "for point in corners_icrs:\n",
+    "    print(point.ra, point.dec)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The results are quantities with units, but if we select the `value` part, we get a dimensionless floating-point number."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "146.27533313607782 19.261909820533692\n",
+      "135.42163944306296 25.87738722767213\n",
+      "141.60264825107333 34.304830296257144\n",
+      "152.81671044675923 27.136112541397996\n"
+     ]
+    }
+   ],
+   "source": [
+    "for point in corners_icrs:\n",
+    "    print(point.ra.value, point.dec.value)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can use string `format` to convert these numbers to strings."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['146.27533313607782, 19.261909820533692',\n",
+       " '135.42163944306296, 25.87738722767213',\n",
+       " '141.60264825107333, 34.304830296257144',\n",
+       " '152.81671044675923, 27.136112541397996']"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "point_base = \"{point.ra.value}, {point.dec.value}\"\n",
+    "\n",
+    "t = [point_base.format(point=point)\n",
+    "     for point in corners_icrs]\n",
+    "t"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The result is a list of strings, which we can join into a single string using `join`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'146.27533313607782, 19.261909820533692, 135.42163944306296, 25.87738722767213, 141.60264825107333, 34.304830296257144, 152.81671044675923, 27.136112541397996'"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "point_list = ', '.join(t)\n",
+    "point_list"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notice that we invoke `join` on a string and pass the list as an argument.\n",
+    "\n",
+    "Before we can assemble the query, we need `columns` again (as we saw in the previous notebook)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "columns = 'source_id, ra, dec, pmra, pmdec, parallax, parallax_error, radial_velocity'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's the base for the query, with format specifiers for `columns` and `point_list`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query_base = \"\"\"SELECT {columns}\n",
+    "FROM gaiadr2.gaia_source\n",
+    "WHERE parallax < 1\n",
+    "  AND bp_rp BETWEEN -0.75 AND 2 \n",
+    "  AND 1 = CONTAINS(POINT(ra, dec), \n",
+    "                   POLYGON({point_list}))\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And here's the result:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "SELECT source_id, ra, dec, pmra, pmdec, parallax, parallax_error, radial_velocity\n",
+      "FROM gaiadr2.gaia_source\n",
+      "WHERE parallax < 1\n",
+      "  AND bp_rp BETWEEN -0.75 AND 2 \n",
+      "  AND 1 = CONTAINS(POINT(ra, dec), \n",
+      "                   POLYGON(146.27533313607782, 19.261909820533692, 135.42163944306296, 25.87738722767213, 141.60264825107333, 34.304830296257144, 152.81671044675923, 27.136112541397996))\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "query = query_base.format(columns=columns, \n",
+    "                          point_list=point_list)\n",
+    "print(query)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As always, we should take a minute to proof-read the query before we launch it.\n",
+    "\n",
+    "The result will be bigger than our previous queries, so it will take a little longer."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO: Query finished. [astroquery.utils.tap.core]\n",
+      "<Table length=140340>\n",
+      "      name       dtype    unit                              description                             n_bad \n",
+      "--------------- ------- -------- ------------------------------------------------------------------ ------\n",
+      "      source_id   int64          Unique source identifier (unique within a particular Data Release)      0\n",
+      "             ra float64      deg                                                    Right ascension      0\n",
+      "            dec float64      deg                                                        Declination      0\n",
+      "           pmra float64 mas / yr                         Proper motion in right ascension direction      0\n",
+      "          pmdec float64 mas / yr                             Proper motion in declination direction      0\n",
+      "       parallax float64      mas                                                           Parallax      0\n",
+      " parallax_error float64      mas                                         Standard error of parallax      0\n",
+      "radial_velocity float64   km / s                                                    Radial velocity 139374\n",
+      "Jobid: 1601903357321O\n",
+      "Phase: COMPLETED\n",
+      "Owner: None\n",
+      "Output file: async_20201005090917.vot\n",
+      "Results: None\n"
+     ]
+    }
+   ],
+   "source": [
+    "job = Gaia.launch_job_async(query)\n",
+    "print(job)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here are the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "140340"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results = job.get_results()\n",
+    "len(results)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There are more than 100,000 stars in this polygon, but that's a manageable size to work with."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Saving results\n",
+    "\n",
+    "This is the set of stars we'll work with in the next step.  But since we have a substantial dataset now, this is a good time to save it.\n",
+    "\n",
+    "Storing the data in a file means we can shut down this notebook and pick up where we left off without running the previous query again.\n",
+    "\n",
+    "Astropy `Table` objects provide `write`, which writes the table to disk."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "filename = 'gd1_results.fits'\n",
+    "results.write(filename, overwrite=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Because the filename ends with `fits`, the table is written in the [FITS format](https://en.wikipedia.org/wiki/FITS), which preserves the metadata associated with the table.\n",
+    "\n",
+    "If the file already exists, the `overwrite` argument causes it to be overwritten.\n",
+    "\n",
+    "To see how big the file is, we can use `ls` with the `-lh` option, which prints information about the file including its size in human-readable form."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-rw-rw-r-- 1 downey downey 8.6M Oct  5 09:09 gd1_results.fits\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!ls -lh gd1_results.fits"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The file is about 8.6 MB."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "In this notebook, we composed more complex queries to select stars within a polygonal region of the sky.  Then we downloaded the results and saved them in a FITS file.\n",
+    "\n",
+    "In the next notebook, we'll reload the data from this file and replicate the next step in the analysis, using proper motion to identify stars likely to be in GD-1."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Best practices\n",
+    "\n",
+    "* For measurements with units, use `Quantity` objects that represent units explicitly and check for errors.\n",
+    "\n",
+    "* Use the `format` function to compose queries; it is often faster and less error-prone.\n",
+    "\n",
+    "* Develop queries incrementally: start with something simple, test it, and add a little bit at a time.\n",
+    "\n",
+    "* Once you have a query working, save the data in a local file.  If you shut down the notebook and come back to it later, you can reload the file; you don't have to run the query again."
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/_build/jupyter_execute/AstronomicalData/_build/jupyter_execute/02_coords.py b/_build/jupyter_execute/AstronomicalData/_build/jupyter_execute/02_coords.py
new file mode 100644
index 0000000..7e0d02e
--- /dev/null
+++ b/_build/jupyter_execute/AstronomicalData/_build/jupyter_execute/02_coords.py
@@ -0,0 +1,407 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# # Lesson 2
+# 
+# This is the second in a series of lessons related to astronomy data.
+# 
+# As a running example, we are replicating parts of the analysis in a recent paper, "[Off the beaten path: Gaia reveals GD-1 stars outside of the main stream](https://arxiv.org/abs/1805.00425)" by Adrian M. Price-Whelan and Ana Bonaca.
+# 
+# In the first notebook, we wrote ADQL queries and used them to select and download data from the Gaia server.
+# 
+# In this notebook, we'll pick up where we left off and write a query to select stars from the region of the sky where we expect GD-1 to be.
+
+# We'll start with an example that does a "cone search"; that is, it selects stars that appear in a circular region of the sky.
+# 
+# Then, to select stars in the vicinity of GD-1, we'll:
+# 
+# * Use `Quantity` objects to represent measurements with units.
+# 
+# * Use the `Gala` library to convert coordinates from one frame to another.
+# 
+# * Use the ADQL keywords `POLYGON`, `CONTAINS`, and `POINT` to select stars that fall within a polygonal region.
+# 
+# * Submit a query and download the results.
+# 
+# * Store the results in a FITS file.
+# 
+# After completing this lesson, you should be able to
+# 
+# * Use Python string formatting to compose more complex ADQL queries.
+# 
+# * Work with coordinates and other quantities that have units.
+# 
+# * Download the results of a query and store them in a file.
+
+# ## Installing libraries
+# 
+# If you are running this notebook on Colab, you can run the following cell to install Astroquery and a the other libraries we'll use.
+# 
+# If you are running this notebook on your own computer, you might have to install these libraries yourself.  
+# 
+# If you are using this notebook as part of a Carpentries workshop, you should have received setup instructions.
+# 
+# TODO: Add a link to the instructions.
+# 
+
+# In[1]:
+
+
+# If we're running on Colab, install libraries
+
+import sys
+IN_COLAB = 'google.colab' in sys.modules
+
+if IN_COLAB:
+    get_ipython().system('pip install astroquery astro-gala pyia')
+
+
+# ## Selecting a region
+
+# One of the most common ways to restrict a query is to select stars in a particular region of the sky.
+# 
+# For example, here's a query from the [Gaia archive documentation](https://gea.esac.esa.int/archive-help/adql/examples/index.html) that selects "all the objects ... in a circular region centered at (266.41683, -29.00781) with a search radius of 5 arcmin (0.08333 deg)."
+
+# In[2]:
+
+
+query = """
+SELECT 
+TOP 10 source_id
+FROM gaiadr2.gaia_source
+WHERE 1=CONTAINS(
+  POINT(ra, dec),
+  CIRCLE(266.41683, -29.00781, 0.08333333))
+"""
+
+
+# This query uses three keywords that are specific to ADQL (not SQL):
+# 
+# * `POINT`: a location in [ICRS coordinates](https://en.wikipedia.org/wiki/International_Celestial_Reference_System), specified in degrees of right ascension and declination.
+# 
+# * `CIRCLE`: a circle where the first two values are the coordinates of the center and the third is the radius in degrees.
+# 
+# * `CONTAINS`: a function that returns `1` if a `POINT` is contained in a shape and `0` otherwise.
+# 
+# Here is the [documentation of `CONTAINS`](http://www.ivoa.net/documents/ADQL/20180112/PR-ADQL-2.1-20180112.html#tth_sEc4.2.12).
+# 
+# A query like this is called a cone search because it selects stars in a cone.
+# 
+# Here's how we run it.
+
+# In[3]:
+
+
+from astroquery.gaia import Gaia
+
+job = Gaia.launch_job(query)
+result = job.get_results()
+result
+
+
+# **Exercise:** When you are debugging queries like this, you can use `TOP` to limit the size of the results, but then you still don't know how big the results will be.
+# 
+# An alternative is to use `COUNT`, which asks for the number of rows that would be selected, but it does not return them.
+# 
+# In the previous query, replace `TOP 10 source_id` with `COUNT(source_id)` and run the query again.  How many stars has Gaia identified in the cone we searched?
+
+# ## Getting GD-1 Data
+# 
+# From the Price-Whelan and Bonaca paper, we will try to reproduce Figure 1, which includes this representation of stars likely to belong to GD-1:
+# 
+# <img src="https://github.com/datacarpentry/astronomy-python/raw/gh-pages/fig/gd1-4.png">
+
+# Along the axis of right ascension ($\phi_1$) the figure extends from -100 to 20 degrees.
+# 
+# Along the axis of declination ($\phi_2$) the figure extends from about -8 to 4 degrees.
+# 
+# Ideally, we would select all stars from this rectangle, but there are more than 10 million of them, so
+# 
+# * That would be difficult to work with,
+# 
+# * As anonymous users, we are limited to 3 million rows in a single query, and
+# 
+# * While we are developing and testing code, it will be faster to work with a smaller dataset.
+# 
+# So we'll start by selecting stars in a smaller rectangle, from -55 to -45 degrees right ascension and -8 to 4 degrees of declination.
+# 
+# But first we let's see how to represent quantities with units like degrees.
+
+# ## Working with coordinates
+# 
+# Coordinates are physical quantities, which means that they have two parts, a value and a unit.
+# 
+# For example, the coordinate $30^{\circ}$ has value 30 and its units are degrees.
+# 
+# Until recently, most scientific computation was done with values only; units were left out of the program altogether, [often with disastrous results](https://en.wikipedia.org/wiki/Mars_Climate_Orbiter#Cause_of_failure).
+# 
+# Astropy provides tools for including units explicitly in computations, which makes it possible to detect errors before they cause disasters.
+# 
+# To use Astropy units, we import them like this:
+
+# In[4]:
+
+
+import astropy.units as u
+
+u
+
+
+# `u` is an object that contains most common units and all SI units.
+# 
+# You can use `dir` to list them, but you should also [read the documentation](https://docs.astropy.org/en/stable/units/).
+
+# In[5]:
+
+
+dir(u)
+
+
+# To create a quantity, we multiply a value by a unit.
+
+# In[6]:
+
+
+coord = 30 * u.deg
+type(coord)
+
+
+# The result is a `Quantity` object.
+# 
+# Jupyter knows how to display `Quantities` like this:
+
+# In[7]:
+
+
+coord
+
+
+# ## Selecting a rectangle
+# 
+# Now we'll select a rectangle from -55 to -45 degrees right ascension and -8 to 4 degrees of declination.
+# 
+# We'll define variables to contain these limits.
+
+# In[8]:
+
+
+phi1_min = -55
+phi1_max = -45
+phi2_min = -8
+phi2_max = 4
+
+
+# To represent a rectangle, we'll use two lists of coordinates and multiply by their units.
+
+# In[9]:
+
+
+phi1_rect = [phi1_min, phi1_min, phi1_max, phi1_max] * u.deg
+phi2_rect = [phi2_min, phi2_max, phi2_max, phi2_min] * u.deg
+
+
+# `phi1_rect` and `phi2_rect` represent the coordinates of the corners of a rectangle.  
+# 
+# But they are in "[a Heliocentric spherical coordinate system defined by the orbit of the GD1 stream](https://gala-astro.readthedocs.io/en/latest/_modules/gala/coordinates/gd1.html)"
+# 
+# In order to use them in a Gaia query, we have to convert them to [International Celestial Reference System](https://en.wikipedia.org/wiki/International_Celestial_Reference_System) (ICRS) coordinates.  We can do that by storing the coordinates in a `GD1Koposov10` object provided by [Gala](https://gala-astro.readthedocs.io/en/latest/coordinates/).
+
+# In[10]:
+
+
+import gala.coordinates as gc
+
+corners = gc.GD1Koposov10(phi1=phi1_rect, phi2=phi2_rect)
+type(corners)
+
+
+# We can display the result like this:
+
+# In[11]:
+
+
+corners
+
+
+# Now we can use `transform_to` to convert to ICRS coordinates.
+
+# In[12]:
+
+
+import astropy.coordinates as coord
+
+corners_icrs = corners.transform_to(coord.ICRS)
+type(corners_icrs)
+
+
+# The result is an `ICRS` object.
+
+# In[13]:
+
+
+corners_icrs
+
+
+# Notice that a rectangle in one coordinate system is not necessarily a rectangle in another.  In this example, the result is a polygon.
+
+# ## Selecting a polygon
+# 
+# In order to use this polygon as part of an ADQL query, we have to convert it to a string with a comma-separated list of coordinates, as in this example:
+# 
+# ```
+# """
+# POLYGON(143.65, 20.98, 
+#         134.46, 26.39, 
+#         140.58, 34.85, 
+#         150.16, 29.01)
+# """
+# ```
+
+# `corners_icrs` behaves like a list, so we can use a `for` loop to iterate through the points.
+
+# In[14]:
+
+
+for point in corners_icrs:
+    print(point)
+
+
+# From that, we can select the coordinates `ra` and `dec`:
+
+# In[15]:
+
+
+for point in corners_icrs:
+    print(point.ra, point.dec)
+
+
+# The results are quantities with units, but if we select the `value` part, we get a dimensionless floating-point number.
+
+# In[16]:
+
+
+for point in corners_icrs:
+    print(point.ra.value, point.dec.value)
+
+
+# We can use string `format` to convert these numbers to strings.
+
+# In[17]:
+
+
+point_base = "{point.ra.value}, {point.dec.value}"
+
+t = [point_base.format(point=point)
+     for point in corners_icrs]
+t
+
+
+# The result is a list of strings, which we can join into a single string using `join`.
+
+# In[18]:
+
+
+point_list = ', '.join(t)
+point_list
+
+
+# Notice that we invoke `join` on a string and pass the list as an argument.
+# 
+# Before we can assemble the query, we need `columns` again (as we saw in the previous notebook).
+
+# In[19]:
+
+
+columns = 'source_id, ra, dec, pmra, pmdec, parallax, parallax_error, radial_velocity'
+
+
+# Here's the base for the query, with format specifiers for `columns` and `point_list`.
+
+# In[20]:
+
+
+query_base = """SELECT {columns}
+FROM gaiadr2.gaia_source
+WHERE parallax < 1
+  AND bp_rp BETWEEN -0.75 AND 2 
+  AND 1 = CONTAINS(POINT(ra, dec), 
+                   POLYGON({point_list}))
+"""
+
+
+# And here's the result:
+
+# In[21]:
+
+
+query = query_base.format(columns=columns, 
+                          point_list=point_list)
+print(query)
+
+
+# As always, we should take a minute to proof-read the query before we launch it.
+# 
+# The result will be bigger than our previous queries, so it will take a little longer.
+
+# In[22]:
+
+
+job = Gaia.launch_job_async(query)
+print(job)
+
+
+# Here are the results.
+
+# In[23]:
+
+
+results = job.get_results()
+len(results)
+
+
+# There are more than 100,000 stars in this polygon, but that's a manageable size to work with.
+
+# ## Saving results
+# 
+# This is the set of stars we'll work with in the next step.  But since we have a substantial dataset now, this is a good time to save it.
+# 
+# Storing the data in a file means we can shut down this notebook and pick up where we left off without running the previous query again.
+# 
+# Astropy `Table` objects provide `write`, which writes the table to disk.
+
+# In[24]:
+
+
+filename = 'gd1_results.fits'
+results.write(filename, overwrite=True)
+
+
+# Because the filename ends with `fits`, the table is written in the [FITS format](https://en.wikipedia.org/wiki/FITS), which preserves the metadata associated with the table.
+# 
+# If the file already exists, the `overwrite` argument causes it to be overwritten.
+# 
+# To see how big the file is, we can use `ls` with the `-lh` option, which prints information about the file including its size in human-readable form.
+
+# In[25]:
+
+
+get_ipython().system('ls -lh gd1_results.fits')
+
+
+# The file is about 8.6 MB.
+
+# ## Summary
+# 
+# In this notebook, we composed more complex queries to select stars within a polygonal region of the sky.  Then we downloaded the results and saved them in a FITS file.
+# 
+# In the next notebook, we'll reload the data from this file and replicate the next step in the analysis, using proper motion to identify stars likely to be in GD-1.
+
+# ## Best practices
+# 
+# * For measurements with units, use `Quantity` objects that represent units explicitly and check for errors.
+# 
+# * Use the `format` function to compose queries; it is often faster and less error-prone.
+# 
+# * Develop queries incrementally: start with something simple, test it, and add a little bit at a time.
+# 
+# * Once you have a query working, save the data in a local file.  If you shut down the notebook and come back to it later, you can reload the file; you don't have to run the query again.
diff --git a/_build/jupyter_execute/last_resort.ipynb b/_build/jupyter_execute/last_resort.ipynb
new file mode 100644
index 0000000..bcd74a2
--- /dev/null
+++ b/_build/jupyter_execute/last_resort.ipynb
@@ -0,0 +1,72 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# The Notebook of Last Resort"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you are not able to get everything installed that we need for the workshop, you have the option of running this notebook on Colab.\n",
+    "\n",
+    "Before you get started, you probably want to press the Save button!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "tags": [
+     "hide-cell"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# If we're running on Colab, install libraries\n",
+    "\n",
+    "import sys\n",
+    "IN_COLAB = 'google.colab' in sys.modules\n",
+    "\n",
+    "if IN_COLAB:\n",
+    "    !pip install astroquery astro-gala pyia"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "That should be everything you need.  Now you can type code and run it in the following cells."
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/_build/jupyter_execute/last_resort.py b/_build/jupyter_execute/last_resort.py
new file mode 100644
index 0000000..936977a
--- /dev/null
+++ b/_build/jupyter_execute/last_resort.py
@@ -0,0 +1,22 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# # The Notebook of Last Resort
+
+# If you are not able to get everything installed that we need for the workshop, you have the option of running this notebook on Colab.
+# 
+# Before you get started, you probably want to press the Save button!
+
+# In[1]:
+
+
+# If we're running on Colab, install libraries
+
+import sys
+IN_COLAB = 'google.colab' in sys.modules
+
+if IN_COLAB:
+    get_ipython().system('pip install astroquery astro-gala pyia')
+
+
+# That should be everything you need.  Now you can type code and run it in the following cells.
diff --git a/_build/jupyter_execute/test_setup.ipynb b/_build/jupyter_execute/test_setup.ipynb
new file mode 100644
index 0000000..088e685
--- /dev/null
+++ b/_build/jupyter_execute/test_setup.ipynb
@@ -0,0 +1,136 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Astronomical Data in Python\n",
+    "\n",
+    "This notebook imports the libraries we need for the workshop.\n",
+    "\n",
+    "If any of them are missing, you'll get an error message.\n",
+    "\n",
+    "If you don't get any error messages, you are all set."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from wget import download"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib as mpl\n",
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib.path import Path\n",
+    "from matplotlib.patches import Polygon"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import astropy.coordinates as coord\n",
+    "import astropy.units as u\n",
+    "from astropy.table import Table"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import gala.coordinates as gc\n",
+    "from pyia import GaiaData"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Created TAP+ (v1.2.1) - Connection:\n",
+      "\tHost: gea.esac.esa.int\n",
+      "\tUse HTTPS: True\n",
+      "\tPort: 443\n",
+      "\tSSL Port: 443\n",
+      "Created TAP+ (v1.2.1) - Connection:\n",
+      "\tHost: geadata.esac.esa.int\n",
+      "\tUse HTTPS: True\n",
+      "\tPort: 443\n",
+      "\tSSL Port: 443\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Note: running this import statement opens a connection\n",
+    "# to a Gaia server, so it will fail if you are not connected\n",
+    "# to the internet.\n",
+    "\n",
+    "from astroquery.gaia import Gaia"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "During the workshop, we might put some code on Slack and ask you to cut and paste it into the notebook.\n",
+    "\n",
+    "If you are on a Mac, you might encounter a problem: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
\ No newline at end of file
diff --git a/_build/jupyter_execute/test_setup.py b/_build/jupyter_execute/test_setup.py
new file mode 100644
index 0000000..89b6bfb
--- /dev/null
+++ b/_build/jupyter_execute/test_setup.py
@@ -0,0 +1,67 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# # Astronomical Data in Python
+# 
+# This notebook imports the libraries we need for the workshop.
+# 
+# If any of them are missing, you'll get an error message.
+# 
+# If you don't get any error messages, you are all set.
+
+# In[7]:
+
+
+from wget import download
+
+
+# In[1]:
+
+
+import pandas as pd
+import numpy as np
+
+
+# In[2]:
+
+
+import matplotlib as mpl
+import matplotlib.pyplot as plt
+from matplotlib.path import Path
+from matplotlib.patches import Polygon
+
+
+# In[3]:
+
+
+import astropy.coordinates as coord
+import astropy.units as u
+from astropy.table import Table
+
+
+# In[4]:
+
+
+import gala.coordinates as gc
+from pyia import GaiaData
+
+
+# In[5]:
+
+
+# Note: running this import statement opens a connection
+# to a Gaia server, so it will fail if you are not connected
+# to the internet.
+
+from astroquery.gaia import Gaia
+
+
+# During the workshop, we might put some code on Slack and ask you to cut and paste it into the notebook.
+# 
+# If you are on a Mac, you might encounter a problem: 
+
+# In[ ]:
+
+
+
+
diff --git a/_build/latex/03_motion_28_0.png b/_build/latex/03_motion_28_0.png
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new file mode 100644
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diff --git a/_build/latex/LICRcyr2utf8.xdy b/_build/latex/LICRcyr2utf8.xdy
new file mode 100644
index 0000000..a9ca1c8
--- /dev/null
+++ b/_build/latex/LICRcyr2utf8.xdy
@@ -0,0 +1,101 @@
+;; -*- coding: utf-8; mode: Lisp; -*-
+;; style file for xindy
+;; filename: LICRcyr2utf8.xdy
+;; description: style file for xindy which maps back LaTeX Internal
+;;   Character Representation of Cyrillic to utf-8
+;; usage: for use with pdflatex produced .idx files.
+;; Contributed by the Sphinx team, July 2018.
+(merge-rule "\IeC {\'\CYRG }" "Ѓ" :string)
+(merge-rule "\IeC {\'\CYRK }" "Ќ" :string)
+(merge-rule "\IeC {\'\cyrg }" "Ñ“" :string)
+(merge-rule "\IeC {\'\cyrk }" "ќ" :string)
+(merge-rule "\IeC {\CYRA }" "�" :string)
+(merge-rule "\IeC {\CYRB }" "Б" :string)
+(merge-rule "\IeC {\CYRC }" "Ц" :string)
+(merge-rule "\IeC {\CYRCH }" "Ч" :string)
+(merge-rule "\IeC {\CYRD }" "Д" :string)
+(merge-rule "\IeC {\CYRDJE }" "Ђ" :string)
+(merge-rule "\IeC {\CYRDZE }" "Ð…" :string)
+(merge-rule "\IeC {\CYRDZHE }" "�" :string)
+(merge-rule "\IeC {\CYRE }" "Е" :string)
+(merge-rule "\IeC {\CYREREV }" "Э" :string)
+(merge-rule "\IeC {\CYRERY }" "Ы" :string)
+(merge-rule "\IeC {\CYRF }" "Ф" :string)
+(merge-rule "\IeC {\CYRG }" "Г" :string)
+(merge-rule "\IeC {\CYRGUP }" "Ò�" :string)
+(merge-rule "\IeC {\CYRH }" "Ð¥" :string)
+(merge-rule "\IeC {\CYRHRDSN }" "Ъ" :string)
+(merge-rule "\IeC {\CYRI }" "И" :string)
+(merge-rule "\IeC {\CYRIE }" "Є" :string)
+(merge-rule "\IeC {\CYRII }" "І" :string)
+(merge-rule "\IeC {\CYRISHRT }" "Й" :string)
+(merge-rule "\IeC {\CYRJE }" "Ј" :string)
+(merge-rule "\IeC {\CYRK }" "К" :string)
+(merge-rule "\IeC {\CYRL }" "Л" :string)
+(merge-rule "\IeC {\CYRLJE }" "Љ" :string)
+(merge-rule "\IeC {\CYRM }" "М" :string)
+(merge-rule "\IeC {\CYRN }" "�" :string)
+(merge-rule "\IeC {\CYRNJE }" "Њ" :string)
+(merge-rule "\IeC {\CYRO }" "О" :string)
+(merge-rule "\IeC {\CYRP }" "П" :string)
+(merge-rule "\IeC {\CYRR }" "Р" :string)
+(merge-rule "\IeC {\CYRS }" "С" :string)
+(merge-rule "\IeC {\CYRSFTSN }" "Ь" :string)
+(merge-rule "\IeC {\CYRSH }" "Ш" :string)
+(merge-rule "\IeC {\CYRSHCH }" "Щ" :string)
+(merge-rule "\IeC {\CYRT }" "Т" :string)
+(merge-rule "\IeC {\CYRTSHE }" "Ћ" :string)
+(merge-rule "\IeC {\CYRU }" "У" :string)
+(merge-rule "\IeC {\CYRUSHRT }" "ÐŽ" :string)
+(merge-rule "\IeC {\CYRV }" "Ð’" :string)
+(merge-rule "\IeC {\CYRYA }" "Я" :string)
+(merge-rule "\IeC {\CYRYI }" "Ї" :string)
+(merge-rule "\IeC {\CYRYO }" "�" :string)
+(merge-rule "\IeC {\CYRYU }" "Ю" :string)
+(merge-rule "\IeC {\CYRZ }" "З" :string)
+(merge-rule "\IeC {\CYRZH }" "Ж" :string)
+(merge-rule "\IeC {\cyra }" "а" :string)
+(merge-rule "\IeC {\cyrb }" "б" :string)
+(merge-rule "\IeC {\cyrc }" "ц" :string)
+(merge-rule "\IeC {\cyrch }" "ч" :string)
+(merge-rule "\IeC {\cyrd }" "д" :string)
+(merge-rule "\IeC {\cyrdje }" "Ñ’" :string)
+(merge-rule "\IeC {\cyrdze }" "Ñ•" :string)
+(merge-rule "\IeC {\cyrdzhe }" "ÑŸ" :string)
+(merge-rule "\IeC {\cyre }" "е" :string)
+(merge-rule "\IeC {\cyrerev }" "�" :string)
+(merge-rule "\IeC {\cyrery }" "Ñ‹" :string)
+(merge-rule "\IeC {\cyrf }" "Ñ„" :string)
+(merge-rule "\IeC {\cyrg }" "г" :string)
+(merge-rule "\IeC {\cyrgup }" "Ò‘" :string)
+(merge-rule "\IeC {\cyrh }" "Ñ…" :string)
+(merge-rule "\IeC {\cyrhrdsn }" "ÑŠ" :string)
+(merge-rule "\IeC {\cyri }" "и" :string)
+(merge-rule "\IeC {\cyrie }" "Ñ”" :string)
+(merge-rule "\IeC {\cyrii }" "Ñ–" :string)
+(merge-rule "\IeC {\cyrishrt }" "й" :string)
+(merge-rule "\IeC {\cyrje }" "ј" :string)
+(merge-rule "\IeC {\cyrk }" "к" :string)
+(merge-rule "\IeC {\cyrl }" "л" :string)
+(merge-rule "\IeC {\cyrlje }" "Ñ™" :string)
+(merge-rule "\IeC {\cyrm }" "м" :string)
+(merge-rule "\IeC {\cyrn }" "н" :string)
+(merge-rule "\IeC {\cyrnje }" "Ñš" :string)
+(merge-rule "\IeC {\cyro }" "о" :string)
+(merge-rule "\IeC {\cyrp }" "п" :string)
+(merge-rule "\IeC {\cyrr }" "Ñ€" :string)
+(merge-rule "\IeC {\cyrs }" "�" :string)
+(merge-rule "\IeC {\cyrsftsn }" "ь" :string)
+(merge-rule "\IeC {\cyrsh }" "ш" :string)
+(merge-rule "\IeC {\cyrshch }" "щ" :string)
+(merge-rule "\IeC {\cyrt }" "Ñ‚" :string)
+(merge-rule "\IeC {\cyrtshe }" "Ñ›" :string)
+(merge-rule "\IeC {\cyru }" "у" :string)
+(merge-rule "\IeC {\cyrushrt }" "Ñž" :string)
+(merge-rule "\IeC {\cyrv }" "в" :string)
+(merge-rule "\IeC {\cyrya }" "�" :string)
+(merge-rule "\IeC {\cyryi }" "Ñ—" :string)
+(merge-rule "\IeC {\cyryo }" "Ñ‘" :string)
+(merge-rule "\IeC {\cyryu }" "ÑŽ" :string)
+(merge-rule "\IeC {\cyrz }" "з" :string)
+(merge-rule "\IeC {\cyrzh }" "ж" :string)
diff --git a/_build/latex/LICRlatin2utf8.xdy b/_build/latex/LICRlatin2utf8.xdy
new file mode 100644
index 0000000..31c80f9
--- /dev/null
+++ b/_build/latex/LICRlatin2utf8.xdy
@@ -0,0 +1,239 @@
+;; style file for xindy
+;; filename: LICRlatin2utf8.xdy
+;; description: style file for xindy which maps back LaTeX Internal
+;;   Character Representation of letters (as arising in .idx index
+;;   file) to UTF-8 encoding for correct sorting by xindy.
+;; usage: for use with the pdflatex engine,
+;;        *not* for use with xelatex or lualatex.
+;;
+;; This is based upon xindy's distributed file tex/inputenc/utf8.xdy.
+;; The modifications include:
+;;
+;; - Updates for compatibility with current LaTeX macro encoding.
+;;
+;; - Systematic usage of the \IeC {...} mark-up, because mark-up in
+;;   tex/inputenc/utf8.xdy was using it on seemingly random basis, and
+;;   Sphinx coercing of xindy usability for both Latin and Cyrillic scripts
+;;   with pdflatex requires its systematic presence here.
+;;
+;; - Support for some extra letters: Ÿ, Ŋ, ŋ, Œ, œ, IJ, ij, ȷ and ẞ.
+;;
+;;   Indeed Sphinx needs to support for pdflatex engine all Unicode letters
+;;   available in TeX T1 font encoding.  The above letters are found in
+;;   that encoding but not in the Latin1, 2, 3 charsets which are those
+;;   covered by original tex/inputenc/utf8.xdy.
+;;
+;; - There is a problem that È· is not supported out-of-the box by LaTeX
+;;   with inputenc, one must add explicitely
+;;       \DeclareUnicodeCharacter{0237}{\j}
+;;   to preamble of LaTeX document.  However this character is not supported
+;;   by the TeX "times" font used by default by Sphinx for pdflatex engine.
+;;
+;;     **Update**: since LaTeX 2018/12/01, the \j as well as \SS, \k{} and
+;;                  \.{} need no extra user declaration anymore.
+;;
+;; - ẞ needs \DeclareUnicodeCharacter{1E9E}{\SS} (but ß needs no extra set-up).
+;;
+;; - U+02DB (Ë›) and U+02D9 (Ë™) are also not supported by inputenc
+;;   out of the box and require
+;;       \DeclareUnicodeCharacter{02DB}{\k{}}
+;;       \DeclareUnicodeCharacter{02D9}{\.{}}
+;;   to be added to preamble.
+;;
+;; - U+0127 ħ and U+0126 Ħ are absent from TeX T1+TS1 font encodings.
+;;
+;; - Characters ÅŠ and Å‹ are not supported by TeX font "times" used by
+;;   default by Sphinx for pdflatex engine but they are supported by
+;;   some TeX fonts, in particular by the default LaTeX font for T1
+;;   encoding.
+;;
+;; - " and ~ must be escaped as ~" and resp. ~~ in xindy merge rules.
+;;
+;; Contributed by the Sphinx team, July 2018.
+;;
+;; See sphinx.xdy for superior figures, as they are escaped by LaTeX writer.
+(merge-rule "\IeC {\textonesuperior }" "¹" :string)
+(merge-rule "\IeC {\texttwosuperior }" "²" :string)
+(merge-rule "\IeC {\textthreesuperior }" "³" :string)
+(merge-rule "\IeC {\'a}" "á" :string)
+(merge-rule "\IeC {\'A}" "�" :string)
+(merge-rule "\IeC {\`a}" "à" :string)
+(merge-rule "\IeC {\`A}" "À" :string)
+(merge-rule "\IeC {\^a}" "â" :string)
+(merge-rule "\IeC {\^A}" "Â" :string)
+(merge-rule "\IeC {\~"a}" "ä" :string)
+(merge-rule "\IeC {\~"A}" "Ä" :string)
+(merge-rule "\IeC {\~~a}" "ã" :string)
+(merge-rule "\IeC {\~~A}" "Ã" :string)
+(merge-rule "\IeC {\c c}" "ç" :string)
+(merge-rule "\IeC {\c C}" "Ç" :string)
+(merge-rule "\IeC {\'c}" "ć" :string)
+(merge-rule "\IeC {\'C}" "Ć" :string)
+(merge-rule "\IeC {\^c}" "ĉ" :string)
+(merge-rule "\IeC {\^C}" "Ĉ" :string)
+(merge-rule "\IeC {\.c}" "Ä‹" :string)
+(merge-rule "\IeC {\.C}" "ÄŠ" :string)
+(merge-rule "\IeC {\c s}" "ÅŸ" :string)
+(merge-rule "\IeC {\c S}" "Åž" :string)
+(merge-rule "\IeC {\c t}" "Å£" :string)
+(merge-rule "\IeC {\c T}" "Å¢" :string)
+(merge-rule "\IeC {\-}" "­" :string); soft hyphen
+(merge-rule "\IeC {\textdiv }" "÷" :string)
+(merge-rule "\IeC {\'e}" "é" :string)
+(merge-rule "\IeC {\'E}" "É" :string)
+(merge-rule "\IeC {\`e}" "è" :string)
+(merge-rule "\IeC {\`E}" "È" :string)
+(merge-rule "\IeC {\^e}" "ê" :string)
+(merge-rule "\IeC {\^E}" "Ê" :string)
+(merge-rule "\IeC {\~"e}" "ë" :string)
+(merge-rule "\IeC {\~"E}" "Ë" :string)
+(merge-rule "\IeC {\^g}" "�" :string)
+(merge-rule "\IeC {\^G}" "Ĝ" :string)
+(merge-rule "\IeC {\.g}" "Ä¡" :string)
+(merge-rule "\IeC {\.G}" "Ä " :string)
+(merge-rule "\IeC {\^h}" "Ä¥" :string)
+(merge-rule "\IeC {\^H}" "Ĥ" :string)
+(merge-rule "\IeC {\H o}" "Å‘" :string)
+(merge-rule "\IeC {\H O}" "�" :string)
+(merge-rule "\IeC {\textacutedbl }" "�" :string)
+(merge-rule "\IeC {\H u}" "ű" :string)
+(merge-rule "\IeC {\H U}" "Å°" :string)
+(merge-rule "\IeC {\ae }" "æ" :string)
+(merge-rule "\IeC {\AE }" "Æ" :string)
+(merge-rule "\IeC {\textcopyright }" "©" :string)
+(merge-rule "\IeC {\c \ }" "¸" :string)
+(merge-rule "\IeC {\dh }" "ð" :string)
+(merge-rule "\IeC {\DH }" "�" :string)
+(merge-rule "\IeC {\dj }" "Ä‘" :string)
+(merge-rule "\IeC {\DJ }" "�" :string)
+(merge-rule "\IeC {\guillemotleft }" "«" :string)
+(merge-rule "\IeC {\guillemotright }" "»" :string)
+(merge-rule "\IeC {\'\i }" "í" :string)
+(merge-rule "\IeC {\`\i }" "ì" :string)
+(merge-rule "\IeC {\^\i }" "î" :string)
+(merge-rule "\IeC {\~"\i }" "ï" :string)
+(merge-rule "\IeC {\i }" "ı" :string)
+(merge-rule "\IeC {\^\j }" "ĵ" :string)
+(merge-rule "\IeC {\k {}}" "Ë›" :string)
+(merge-rule "\IeC {\l }" "Å‚" :string)
+(merge-rule "\IeC {\L }" "�" :string)
+(merge-rule "\IeC {\nobreakspace }" " " :string)
+(merge-rule "\IeC {\o }" "ø" :string)
+(merge-rule "\IeC {\O }" "Ø" :string)
+(merge-rule "\IeC {\textsterling }" "£" :string)
+(merge-rule "\IeC {\textparagraph }" "¶" :string)
+(merge-rule "\IeC {\ss }" "ß" :string)
+(merge-rule "\IeC {\textsection }" "§" :string)
+(merge-rule "\IeC {\textbrokenbar }" "¦" :string)
+(merge-rule "\IeC {\textcent }" "¢" :string)
+(merge-rule "\IeC {\textcurrency }" "¤" :string)
+(merge-rule "\IeC {\textdegree }" "°" :string)
+(merge-rule "\IeC {\textexclamdown }" "¡" :string)
+(merge-rule "\IeC {\texthbar }" "ħ" :string)
+(merge-rule "\IeC {\textHbar }" "Ħ" :string)
+(merge-rule "\IeC {\textonehalf }" "½" :string)
+(merge-rule "\IeC {\textonequarter }" "¼" :string)
+(merge-rule "\IeC {\textordfeminine }" "ª" :string)
+(merge-rule "\IeC {\textordmasculine }" "º" :string)
+(merge-rule "\IeC {\textperiodcentered }" "·" :string)
+(merge-rule "\IeC {\textquestiondown }" "¿" :string)
+(merge-rule "\IeC {\textregistered }" "®" :string)
+(merge-rule "\IeC {\textthreequarters }" "¾" :string)
+(merge-rule "\IeC {\textyen }" "Â¥" :string)
+(merge-rule "\IeC {\th }" "þ" :string)
+(merge-rule "\IeC {\TH }" "Þ" :string)
+(merge-rule "\IeC {\'I}" "�" :string)
+(merge-rule "\IeC {\`I}" "Ì" :string)
+(merge-rule "\IeC {\^I}" "ÃŽ" :string)
+(merge-rule "\IeC {\~"I}" "�" :string)
+(merge-rule "\IeC {\.I}" "Ä°" :string)
+(merge-rule "\IeC {\^J}" "Ä´" :string)
+(merge-rule "\IeC {\k a}" "Ä…" :string)
+(merge-rule "\IeC {\k A}" "Ä„" :string)
+(merge-rule "\IeC {\k e}" "Ä™" :string)
+(merge-rule "\IeC {\k E}" "Ę" :string)
+(merge-rule "\IeC {\'l}" "ĺ" :string)
+(merge-rule "\IeC {\'L}" "Ĺ" :string)
+(merge-rule "\IeC {\textlnot }" "¬" :string)
+(merge-rule "\IeC {\textmu }" "µ" :string)
+(merge-rule "\IeC {\'n}" "Å„" :string)
+(merge-rule "\IeC {\'N}" "Ń" :string)
+(merge-rule "\IeC {\~~n}" "ñ" :string)
+(merge-rule "\IeC {\~~N}" "Ñ" :string)
+(merge-rule "\IeC {\'o}" "ó" :string)
+(merge-rule "\IeC {\'O}" "Ó" :string)
+(merge-rule "\IeC {\`o}" "ò" :string)
+(merge-rule "\IeC {\`O}" "Ã’" :string)
+(merge-rule "\IeC {\^o}" "ô" :string)
+(merge-rule "\IeC {\^O}" "Ô" :string)
+(merge-rule "\IeC {\~"o}" "ö" :string)
+(merge-rule "\IeC {\~"O}" "Ö" :string)
+(merge-rule "\IeC {\~~o}" "õ" :string)
+(merge-rule "\IeC {\~~O}" "Õ" :string)
+(merge-rule "\IeC {\textpm }" "±" :string)
+(merge-rule "\IeC {\r a}" "Ã¥" :string)
+(merge-rule "\IeC {\r A}" "Ã…" :string)
+(merge-rule "\IeC {\'r}" "Å•" :string)
+(merge-rule "\IeC {\'R}" "Å”" :string)
+(merge-rule "\IeC {\r u}" "ů" :string)
+(merge-rule "\IeC {\r U}" "Å®" :string)
+(merge-rule "\IeC {\'s}" "Å›" :string)
+(merge-rule "\IeC {\'S}" "Åš" :string)
+(merge-rule "\IeC {\^s}" "�" :string)
+(merge-rule "\IeC {\^S}" "Ŝ" :string)
+(merge-rule "\IeC {\textasciidieresis }" "¨" :string)
+(merge-rule "\IeC {\textasciimacron }" "¯" :string)
+(merge-rule "\IeC {\.{}}" "Ë™" :string)
+(merge-rule "\IeC {\textasciiacute }" "´" :string)
+(merge-rule "\IeC {\texttimes }" "×" :string)
+(merge-rule "\IeC {\u a}" "ă" :string)
+(merge-rule "\IeC {\u A}" "Ä‚" :string)
+(merge-rule "\IeC {\u g}" "ÄŸ" :string)
+(merge-rule "\IeC {\u G}" "Äž" :string)
+(merge-rule "\IeC {\textasciibreve }" "˘" :string)
+(merge-rule "\IeC {\'u}" "ú" :string)
+(merge-rule "\IeC {\'U}" "Ú" :string)
+(merge-rule "\IeC {\`u}" "ù" :string)
+(merge-rule "\IeC {\`U}" "Ù" :string)
+(merge-rule "\IeC {\^u}" "û" :string)
+(merge-rule "\IeC {\^U}" "Û" :string)
+(merge-rule "\IeC {\~"u}" "ü" :string)
+(merge-rule "\IeC {\~"U}" "Ü" :string)
+(merge-rule "\IeC {\u u}" "Å­" :string)
+(merge-rule "\IeC {\u U}" "Ŭ" :string)
+(merge-rule "\IeC {\v c}" "�" :string)
+(merge-rule "\IeC {\v C}" "Č" :string)
+(merge-rule "\IeC {\v d}" "�" :string)
+(merge-rule "\IeC {\v D}" "ÄŽ" :string)
+(merge-rule "\IeC {\v e}" "Ä›" :string)
+(merge-rule "\IeC {\v E}" "Äš" :string)
+(merge-rule "\IeC {\v l}" "ľ" :string)
+(merge-rule "\IeC {\v L}" "Ľ" :string)
+(merge-rule "\IeC {\v n}" "ň" :string)
+(merge-rule "\IeC {\v N}" "Ň" :string)
+(merge-rule "\IeC {\v r}" "Å™" :string)
+(merge-rule "\IeC {\v R}" "Ř" :string)
+(merge-rule "\IeC {\v s}" "Å¡" :string)
+(merge-rule "\IeC {\v S}" "Å " :string)
+(merge-rule "\IeC {\textasciicaron }" "ˇ" :string)
+(merge-rule "\IeC {\v t}" "Å¥" :string)
+(merge-rule "\IeC {\v T}" "Ť" :string)
+(merge-rule "\IeC {\v z}" "ž" :string)
+(merge-rule "\IeC {\v Z}" "Ž" :string)
+(merge-rule "\IeC {\'y}" "ý" :string)
+(merge-rule "\IeC {\'Y}" "�" :string)
+(merge-rule "\IeC {\~"y}" "ÿ" :string)
+(merge-rule "\IeC {\'z}" "ź" :string)
+(merge-rule "\IeC {\'Z}" "Ź" :string)
+(merge-rule "\IeC {\.z}" "ż" :string)
+(merge-rule "\IeC {\.Z}" "Å»" :string)
+;; letters not in Latin1, 2, 3 but available in TeX T1 font encoding
+(merge-rule "\IeC {\~"Y}" "Ÿ" :string)
+(merge-rule "\IeC {\NG }" "ÅŠ" :string)
+(merge-rule "\IeC {\ng }" "Å‹" :string)
+(merge-rule "\IeC {\OE }" "Å’" :string)
+(merge-rule "\IeC {\oe }" "Å“" :string)
+(merge-rule "\IeC {\IJ }" "IJ" :string)
+(merge-rule "\IeC {\ij }" "ij" :string)
+(merge-rule "\IeC {\j }" "È·" :string)
+(merge-rule "\IeC {\SS }" "ẞ" :string)
diff --git a/_build/latex/LatinRules.xdy b/_build/latex/LatinRules.xdy
new file mode 100644
index 0000000..99f14a2
--- /dev/null
+++ b/_build/latex/LatinRules.xdy
@@ -0,0 +1,607 @@
+;; style file for xindy
+;; filename: LatinRules.xdy
+;;
+;; It is based upon xindy's files lang/general/utf8.xdy and
+;; lang/general/utf8-lang.xdy which implement
+;; "a general sorting order for Western European languages"
+;;
+;; The aim for Sphinx is to be able to index in a Cyrillic document
+;; also terms using the Latin alphabets, inclusive of letters
+;; with diacritics.  To this effect the xindy rules from lang/general
+;; got manually re-coded to avoid collisions with the encoding
+;; done by xindy for sorting words in Cyrillic languages, which was
+;; observed not to use bytes with octal encoding 0o266 or higher.
+;;
+;; So here we use only 0o266 or higher bytes.
+;; (Ŋ, ŋ, IJ, and ij are absent from
+;; lang/general/utf8.xdy and not included here)
+;; Contributed by the Sphinx team, 2018.
+
+(define-letter-group "A" :prefixes ("¶"))
+(define-letter-group "B" :after "A" :prefixes ("·"))
+(define-letter-group "C" :after "B" :prefixes ("¸"))
+(define-letter-group "D" :after "C" :prefixes ("¹"))
+(define-letter-group "E" :after "D" :prefixes ("º"))
+(define-letter-group "F" :after "E" :prefixes ("»"))
+(define-letter-group "G" :after "F" :prefixes ("¼"))
+(define-letter-group "H" :after "G" :prefixes ("½"))
+(define-letter-group "I" :after "H" :prefixes ("¾"))
+(define-letter-group "J" :after "I" :prefixes ("¿"))
+(define-letter-group "K" :after "J" :prefixes ("À"))
+(define-letter-group "L" :after "K" :prefixes ("Á"))
+(define-letter-group "M" :after "L" :prefixes ("Â"))
+(define-letter-group "N" :after "M" :prefixes ("Ã"))
+(define-letter-group "O" :after "N" :prefixes ("Ä"))
+(define-letter-group "P" :after "O" :prefixes ("È"))
+(define-letter-group "Q" :after "P" :prefixes ("Ê"))
+(define-letter-group "R" :after "Q" :prefixes ("Ë"))
+(define-letter-group "S" :after "R" :prefixes ("Ð"))
+(define-letter-group "T" :after "S" :prefixes ("Ú"))
+(define-letter-group "U" :after "T" :prefixes ("à"))
+(define-letter-group "V" :after "U" :prefixes ("å"))
+(define-letter-group "W" :after "V" :prefixes ("æ"))
+(define-letter-group "X" :after "W" :prefixes ("ë"))
+(define-letter-group "Y" :after "X" :prefixes ("í"))
+(define-letter-group "Z" :after "Y" :prefixes ("ð"))
+
+(define-rule-set "sphinx-xy-alphabetize"
+
+  :rules  (("À" "¶" :string)
+           ("Ä‚" "¶" :string)
+           ("â" "¶" :string)
+           ("Ä" "¶" :string)
+           ("à" "¶" :string)
+           ("Ã…" "¶" :string)
+           ("Ã" "¶" :string)
+           ("Ã�" "¶" :string)
+           ("á" "¶" :string)
+           ("ã" "¶" :string)
+           ("Â" "¶" :string)
+           ("ă" "¶" :string)
+           ("Ã¥" "¶" :string)
+           ("Ä…" "¶" :string)
+           ("ä" "¶" :string)
+           ("Ä„" "¶" :string)
+           ("æ" "¶º" :string)
+           ("Æ" "¶º" :string)
+           ("ć" "¸" :string)
+           ("ĉ" "¸" :string)
+           ("ç" "¸" :string)
+           ("ÄŒ" "¸" :string)
+           ("Ä�" "¸" :string)
+           ("Ĉ" "¸" :string)
+           ("Ç" "¸" :string)
+           ("Ć" "¸" :string)
+           ("Ä�" "¹" :string)
+           ("Ä�" "¹" :string)
+           ("ÄŽ" "¹" :string)
+           ("Ä‘" "¹" :string)
+           ("ê" "º" :string)
+           ("Ę" "º" :string)
+           ("Äš" "º" :string)
+           ("ë" "º" :string)
+           ("Ä›" "º" :string)
+           ("é" "º" :string)
+           ("È" "º" :string)
+           ("Ë" "º" :string)
+           ("É" "º" :string)
+           ("è" "º" :string)
+           ("Ê" "º" :string)
+           ("Ä™" "º" :string)
+           ("Ä�" "¼" :string)
+           ("ÄŸ" "¼" :string)
+           ("Äž" "¼" :string)
+           ("Äœ" "¼" :string)
+           ("Ä¥" "½" :string)
+           ("Ĥ" "½" :string)
+           ("Ã�" "¾" :string)
+           ("Ã�" "¾" :string)
+           ("ï" "¾" :string)
+           ("ÃŽ" "¾" :string)
+           ("î" "¾" :string)
+           ("ı" "¾" :string)
+           ("Ä°" "¾" :string)
+           ("í" "¾" :string)
+           ("ÃŒ" "¾" :string)
+           ("ì" "¾" :string)
+           ("Ä´" "¿" :string)
+           ("ĵ" "¿" :string)
+           ("Å‚" "Á" :string)
+           ("Å�" "Á" :string)
+           ("ľ" "Á" :string)
+           ("Ľ" "Á" :string)
+           ("Å„" "Ã" :string)
+           ("Ń" "Ã" :string)
+           ("ñ" "Ã" :string)
+           ("ň" "Ã" :string)
+           ("Ñ" "Ã" :string)
+           ("Ň" "Ã" :string)
+           ("Õ" "Ä" :string)
+           ("Å�" "Ä" :string)
+           ("ó" "Ä" :string)
+           ("ö" "Ä" :string)
+           ("ô" "Ä" :string)
+           ("Å‘" "Ä" :string)
+           ("Ø" "Ä" :string)
+           ("Ö" "Ä" :string)
+           ("õ" "Ä" :string)
+           ("Ô" "Ä" :string)
+           ("ø" "Ä" :string)
+           ("Ó" "Ä" :string)
+           ("Ã’" "Ä" :string)
+           ("ò" "Ä" :string)
+           ("œ" "ĺ" :string)
+           ("Œ" "ĺ" :string)
+           ("Ř" "Ë" :string)
+           ("Å™" "Ë" :string)
+           ("Å”" "Ë" :string)
+           ("Å•" "Ë" :string)
+           ("Å�" "Ð" :string)
+           ("Åš" "Ð" :string)
+           ("È™" "Ð" :string)
+           ("ÅŸ" "Ð" :string)
+           ("Åœ" "Ð" :string)
+           ("Å›" "Ð" :string)
+           ("Ș" "Ð" :string)
+           ("Å¡" "Ð" :string)
+           ("Åž" "Ð" :string)
+           ("Å " "Ð" :string)
+           ("ß" "ÐÐ" :string)
+           ("Èš" "Ú" :string)
+           ("Ť" "Ú" :string)
+           ("È›" "Ú" :string)
+           ("Å¥" "Ú" :string)
+           ("û" "à" :string)
+           ("Å­" "à" :string)
+           ("ů" "à" :string)
+           ("ű" "à" :string)
+           ("ù" "à" :string)
+           ("Ŭ" "à" :string)
+           ("Ù" "à" :string)
+           ("Å°" "à" :string)
+           ("Ãœ" "à" :string)
+           ("Å®" "à" :string)
+           ("ú" "à" :string)
+           ("Ú" "à" :string)
+           ("Û" "à" :string)
+           ("ü" "à" :string)
+           ("ÿ" "í" :string)
+           ("Ã�" "í" :string)
+           ("Ÿ" "í" :string)
+           ("ý" "í" :string)
+           ("Å»" "ð" :string)
+           ("Ž" "ð" :string)
+           ("Ź" "ð" :string)
+           ("ž" "ð" :string)
+           ("ż" "ð" :string)
+           ("ź" "ð" :string)
+           ("a" "¶" :string)
+           ("A" "¶" :string)
+           ("b" "·" :string)
+           ("B" "·" :string)
+           ("c" "¸" :string)
+           ("C" "¸" :string)
+           ("d" "¹" :string)
+           ("D" "¹" :string)
+           ("e" "º" :string)
+           ("E" "º" :string)
+           ("F" "»" :string)
+           ("f" "»" :string)
+           ("G" "¼" :string)
+           ("g" "¼" :string)
+           ("H" "½" :string)
+           ("h" "½" :string)
+           ("i" "¾" :string)
+           ("I" "¾" :string)
+           ("J" "¿" :string)
+           ("j" "¿" :string)
+           ("K" "À" :string)
+           ("k" "À" :string)
+           ("L" "Á" :string)
+           ("l" "Á" :string)
+           ("M" "Â" :string)
+           ("m" "Â" :string)
+           ("n" "Ã" :string)
+           ("N" "Ã" :string)
+           ("O" "Ä" :string)
+           ("o" "Ä" :string)
+           ("p" "È" :string)
+           ("P" "È" :string)
+           ("Q" "Ê" :string)
+           ("q" "Ê" :string)
+           ("r" "Ë" :string)
+           ("R" "Ë" :string)
+           ("S" "Ð" :string)
+           ("s" "Ð" :string)
+           ("t" "Ú" :string)
+           ("T" "Ú" :string)
+           ("u" "à" :string)
+           ("U" "à" :string)
+           ("v" "å" :string)
+           ("V" "å" :string)
+           ("W" "æ" :string)
+           ("w" "æ" :string)
+           ("x" "ë" :string)
+           ("X" "ë" :string)
+           ("Y" "í" :string)
+           ("y" "í" :string)
+           ("z" "ð" :string)
+           ("Z" "ð" :string)
+           ))
+
+(define-rule-set "sphinx-xy-resolve-diacritics"
+
+  :rules  (("Ĥ" "£" :string)
+           ("ó" "£" :string)
+           ("ľ" "£" :string)
+           ("Ř" "£" :string)
+           ("Ä�" "£" :string)
+           ("Ä�" "£" :string)
+           ("Äš" "£" :string)
+           ("Ä¥" "£" :string)
+           ("ÄŒ" "£" :string)
+           ("Ä´" "£" :string)
+           ("Ä›" "£" :string)
+           ("ž" "£" :string)
+           ("ÄŽ" "£" :string)
+           ("Å™" "£" :string)
+           ("Ž" "£" :string)
+           ("ı" "£" :string)
+           ("Ť" "£" :string)
+           ("á" "£" :string)
+           ("Ä�" "£" :string)
+           ("Ã�" "£" :string)
+           ("ň" "£" :string)
+           ("Å " "£" :string)
+           ("Ň" "£" :string)
+           ("ĵ" "£" :string)
+           ("Å¥" "£" :string)
+           ("Ó" "£" :string)
+           ("ý" "£" :string)
+           ("Äœ" "£" :string)
+           ("Ú" "£" :string)
+           ("Ľ" "£" :string)
+           ("Å¡" "£" :string)
+           ("Ã�" "£" :string)
+           ("ú" "£" :string)
+           ("Åš" "¤" :string)
+           ("ć" "¤" :string)
+           ("Å�" "¤" :string)
+           ("Å‚" "¤" :string)
+           ("Å„" "¤" :string)
+           ("À" "¤" :string)
+           ("Ź" "¤" :string)
+           ("à" "¤" :string)
+           ("Ń" "¤" :string)
+           ("Ä�" "¤" :string)
+           ("ÿ" "¤" :string)
+           ("Å›" "¤" :string)
+           ("Äž" "¤" :string)
+           ("ÄŸ" "¤" :string)
+           ("Ù" "¤" :string)
+           ("Ä°" "¤" :string)
+           ("Ä‘" "¤" :string)
+           ("ù" "¤" :string)
+           ("Èš" "¤" :string)
+           ("é" "¤" :string)
+           ("Å•" "¤" :string)
+           ("Ć" "¤" :string)
+           ("È›" "¤" :string)
+           ("ò" "¤" :string)
+           ("ź" "¤" :string)
+           ("Ã’" "¤" :string)
+           ("Ÿ" "¤" :string)
+           ("Å”" "¤" :string)
+           ("É" "¤" :string)
+           ("ĉ" "¥" :string)
+           ("ô" "¥" :string)
+           ("Ã�" "¥" :string)
+           ("Å�" "¥" :string)
+           ("Å»" "¥" :string)
+           ("Ä‚" "¥" :string)
+           ("Åœ" "¥" :string)
+           ("ñ" "¥" :string)
+           ("Å­" "¥" :string)
+           ("í" "¥" :string)
+           ("È" "¥" :string)
+           ("Ô" "¥" :string)
+           ("Ŭ" "¥" :string)
+           ("ż" "¥" :string)
+           ("Ñ" "¥" :string)
+           ("è" "¥" :string)
+           ("Ĉ" "¥" :string)
+           ("ă" "¥" :string)
+           ("â" "¦" :string)
+           ("û" "¦" :string)
+           ("ê" "¦" :string)
+           ("Õ" "¦" :string)
+           ("õ" "¦" :string)
+           ("È™" "¦" :string)
+           ("ç" "¦" :string)
+           ("Â" "¦" :string)
+           ("Ê" "¦" :string)
+           ("Û" "¦" :string)
+           ("Ç" "¦" :string)
+           ("ì" "¦" :string)
+           ("ÃŒ" "¦" :string)
+           ("Ș" "¦" :string)
+           ("ö" "§" :string)
+           ("Ö" "§" :string)
+           ("ÅŸ" "§" :string)
+           ("ů" "§" :string)
+           ("ë" "§" :string)
+           ("ã" "§" :string)
+           ("î" "§" :string)
+           ("ÃŽ" "§" :string)
+           ("Ã" "§" :string)
+           ("Åž" "§" :string)
+           ("Å®" "§" :string)
+           ("Ë" "§" :string)
+           ("ï" "¨" :string)
+           ("Å�" "¨" :string)
+           ("Ã�" "¨" :string)
+           ("Ę" "¨" :string)
+           ("Å‘" "¨" :string)
+           ("Ãœ" "¨" :string)
+           ("Ã…" "¨" :string)
+           ("ü" "¨" :string)
+           ("Ä™" "¨" :string)
+           ("Ã¥" "¨" :string)
+           ("Ä" "©" :string)
+           ("ű" "©" :string)
+           ("Ø" "©" :string)
+           ("ø" "©" :string)
+           ("Å°" "©" :string)
+           ("ä" "©" :string)
+           ("Ä„" "ª" :string)
+           ("Ä…" "ª" :string)
+           ("Å“" "ÿ" :string)
+           ("ß" "ÿ" :string)
+           ("Æ" "ÿ" :string)
+           ("Å’" "ÿ" :string)
+           ("æ" "ÿ" :string)
+           ("e" "¢" :string)
+           ("t" "¢" :string)
+           ("L" "¢" :string)
+           ("Y" "¢" :string)
+           ("J" "¢" :string)
+           ("a" "¢" :string)
+           ("p" "¢" :string)
+           ("u" "¢" :string)
+           ("j" "¢" :string)
+           ("b" "¢" :string)
+           ("G" "¢" :string)
+           ("U" "¢" :string)
+           ("F" "¢" :string)
+           ("H" "¢" :string)
+           ("i" "¢" :string)
+           ("z" "¢" :string)
+           ("c" "¢" :string)
+           ("l" "¢" :string)
+           ("A" "¢" :string)
+           ("Q" "¢" :string)
+           ("w" "¢" :string)
+           ("D" "¢" :string)
+           ("R" "¢" :string)
+           ("d" "¢" :string)
+           ("s" "¢" :string)
+           ("r" "¢" :string)
+           ("k" "¢" :string)
+           ("v" "¢" :string)
+           ("m" "¢" :string)
+           ("P" "¢" :string)
+           ("y" "¢" :string)
+           ("K" "¢" :string)
+           ("q" "¢" :string)
+           ("S" "¢" :string)
+           ("I" "¢" :string)
+           ("C" "¢" :string)
+           ("M" "¢" :string)
+           ("Z" "¢" :string)
+           ("T" "¢" :string)
+           ("W" "¢" :string)
+           ("B" "¢" :string)
+           ("h" "¢" :string)
+           ("x" "¢" :string)
+           ("X" "¢" :string)
+           ("f" "¢" :string)
+           ("E" "¢" :string)
+           ("V" "¢" :string)
+           ("N" "¢" :string)
+           ("O" "¢" :string)
+           ("o" "¢" :string)
+           ("g" "¢" :string)
+           ("n" "¢" :string)
+           ))
+
+(define-rule-set "sphinx-xy-resolve-case"
+
+  :rules  (("Ú" "8" :string)
+           ("Ÿ" "8" :string)
+           ("Ç" "8" :string)
+           ("Ĉ" "8" :string)
+           ("Å”" "8" :string)
+           ("Ľ" "8" :string)
+           ("Å®" "8" :string)
+           ("�" "8" :string)
+           ("É" "8" :string)
+           ("Ë" "8" :string)
+           ("Ș" "8" :string)
+           ("Ì" "8" :string)
+           ("Ê" "8" :string)
+           ("Ň" "8" :string)
+           ("Ä„" "8" :string)
+           ("Å " "8" :string)
+           ("Û" "8" :string)
+           ("Åž" "8" :string)
+           ("Ć" "8" :string)
+           ("Ã’" "8" :string)
+           ("Ĝ" "8" :string)
+           ("Ñ" "8" :string)
+           ("Ó" "8" :string)
+           ("ÃŽ" "8" :string)
+           ("�" "8" :string)
+           ("Ã" "8" :string)
+           ("Èš" "8" :string)
+           ("Ã…" "8" :string)
+           ("Äž" "8" :string)
+           ("Ü" "8" :string)
+           ("È" "8" :string)
+           ("Ô" "8" :string)
+           ("Ä°" "8" :string)
+           ("Å°" "8" :string)
+           ("Ù" "8" :string)
+           ("Ŭ" "8" :string)
+           ("Â" "8" :string)
+           ("Ť" "8" :string)
+           ("Ń" "8" :string)
+           ("ÄŽ" "8" :string)
+           ("Ź" "8" :string)
+           ("Ž" "8" :string)
+           ("�" "8" :string)
+           ("Ŝ" "8" :string)
+           ("Č" "8" :string)
+           ("Ä´" "8" :string)
+           ("Ö" "8" :string)
+           ("Ø" "8" :string)
+           ("Å»" "8" :string)
+           ("�" "8" :string)
+           ("Ä‚" "8" :string)
+           ("Äš" "8" :string)
+           ("�" "8" :string)
+           ("Õ" "8" :string)
+           ("Ę" "8" :string)
+           ("�" "8" :string)
+           ("À" "8" :string)
+           ("Ĥ" "8" :string)
+           ("Ä" "8" :string)
+           ("Åš" "8" :string)
+           ("Ř" "8" :string)
+           ("�" "8" :string)
+           ("Å’" "89" :string)
+           ("Æ" "89" :string)
+           ("ì" "9" :string)
+           ("è" "9" :string)
+           ("Ä…" "9" :string)
+           ("Å¡" "9" :string)
+           ("ú" "9" :string)
+           ("Ã¥" "9" :string)
+           ("ă" "9" :string)
+           ("Ä™" "9" :string)
+           ("ü" "9" :string)
+           ("ź" "9" :string)
+           ("ò" "9" :string)
+           ("Å¥" "9" :string)
+           ("È›" "9" :string)
+           ("ĵ" "9" :string)
+           ("Å•" "9" :string)
+           ("ż" "9" :string)
+           ("ä" "9" :string)
+           ("ý" "9" :string)
+           ("ù" "9" :string)
+           ("á" "9" :string)
+           ("é" "9" :string)
+           ("�" "9" :string)
+           ("ň" "9" :string)
+           ("Å›" "9" :string)
+           ("ø" "9" :string)
+           ("í" "9" :string)
+           ("Ä‘" "9" :string)
+           ("ı" "9" :string)
+           ("ÄŸ" "9" :string)
+           ("î" "9" :string)
+           ("ã" "9" :string)
+           ("à" "9" :string)
+           ("Å™" "9" :string)
+           ("Å‘" "9" :string)
+           ("ů" "9" :string)
+           ("È™" "9" :string)
+           ("ÿ" "9" :string)
+           ("ë" "9" :string)
+           ("Å­" "9" :string)
+           ("ç" "9" :string)
+           ("ű" "9" :string)
+           ("ñ" "9" :string)
+           ("õ" "9" :string)
+           ("Ä›" "9" :string)
+           ("ÅŸ" "9" :string)
+           ("ž" "9" :string)
+           ("�" "9" :string)
+           ("�" "9" :string)
+           ("Å„" "9" :string)
+           ("û" "9" :string)
+           ("Å‚" "9" :string)
+           ("�" "9" :string)
+           ("Ä¥" "9" :string)
+           ("ê" "9" :string)
+           ("ô" "9" :string)
+           ("ĉ" "9" :string)
+           ("â" "9" :string)
+           ("ć" "9" :string)
+           ("ï" "9" :string)
+           ("ö" "9" :string)
+           ("ľ" "9" :string)
+           ("ó" "9" :string)
+           ("æ" "99" :string)
+           ("ß" "99" :string)
+           ("Å“" "99" :string)
+           ("N" "8" :string)
+           ("V" "8" :string)
+           ("O" "8" :string)
+           ("X" "8" :string)
+           ("E" "8" :string)
+           ("P" "8" :string)
+           ("K" "8" :string)
+           ("T" "8" :string)
+           ("Z" "8" :string)
+           ("M" "8" :string)
+           ("C" "8" :string)
+           ("I" "8" :string)
+           ("S" "8" :string)
+           ("B" "8" :string)
+           ("W" "8" :string)
+           ("D" "8" :string)
+           ("R" "8" :string)
+           ("H" "8" :string)
+           ("F" "8" :string)
+           ("Q" "8" :string)
+           ("A" "8" :string)
+           ("G" "8" :string)
+           ("U" "8" :string)
+           ("J" "8" :string)
+           ("Y" "8" :string)
+           ("L" "8" :string)
+           ("o" "9" :string)
+           ("n" "9" :string)
+           ("g" "9" :string)
+           ("x" "9" :string)
+           ("f" "9" :string)
+           ("y" "9" :string)
+           ("q" "9" :string)
+           ("h" "9" :string)
+           ("w" "9" :string)
+           ("s" "9" :string)
+           ("d" "9" :string)
+           ("v" "9" :string)
+           ("k" "9" :string)
+           ("r" "9" :string)
+           ("m" "9" :string)
+           ("z" "9" :string)
+           ("c" "9" :string)
+           ("i" "9" :string)
+           ("l" "9" :string)
+           ("b" "9" :string)
+           ("j" "9" :string)
+           ("a" "9" :string)
+           ("p" "9" :string)
+           ("u" "9" :string)
+           ("t" "9" :string)
+           ("e" "9" :string)
+           ))
+
+(use-rule-set :run 0
+	      :rule-set ("sphinx-xy-alphabetize"))
+(use-rule-set :run 1
+	      :rule-set ("sphinx-xy-resolve-diacritics"))
+(use-rule-set :run 2
+	      :rule-set ("sphinx-xy-resolve-case"))
diff --git a/_build/latex/Makefile b/_build/latex/Makefile
new file mode 100644
index 0000000..e4653f2
--- /dev/null
+++ b/_build/latex/Makefile
@@ -0,0 +1,64 @@
+# Makefile for Sphinx LaTeX output
+
+ALLDOCS = $(basename $(wildcard *.tex))
+ALLPDF = $(addsuffix .pdf,$(ALLDOCS))
+ALLDVI = $(addsuffix .dvi,$(ALLDOCS))
+ALLXDV =
+ALLPS  = $(addsuffix .ps,$(ALLDOCS))
+
+# Prefix for archive names
+ARCHIVEPREFIX =
+# Additional LaTeX options (passed via variables in latexmkrc/latexmkjarc file)
+export LATEXOPTS ?=
+# Additional latexmk options
+LATEXMKOPTS ?=
+# format: pdf or dvi (used only by archive targets)
+FMT = pdf
+
+LATEX = latexmk -dvi
+PDFLATEX = latexmk -pdf -dvi- -ps-
+
+
+%.dvi: %.tex FORCE_MAKE
+	$(LATEX) $(LATEXMKOPTS) '$<'
+
+%.ps: %.dvi
+	dvips '$<'
+
+%.pdf: %.tex FORCE_MAKE
+	$(PDFLATEX) $(LATEXMKOPTS) '$<'
+
+all: $(ALLPDF)
+
+all-dvi: $(ALLDVI)
+
+all-ps: $(ALLPS)
+
+all-pdf: $(ALLPDF)
+
+zip: all-$(FMT)
+	mkdir $(ARCHIVEPREFIX)docs-$(FMT)
+	cp $(ALLPDF) $(ARCHIVEPREFIX)docs-$(FMT)
+	zip -q -r -9 $(ARCHIVEPREFIX)docs-$(FMT).zip $(ARCHIVEPREFIX)docs-$(FMT)
+	rm -r $(ARCHIVEPREFIX)docs-$(FMT)
+
+tar: all-$(FMT)
+	mkdir $(ARCHIVEPREFIX)docs-$(FMT)
+	cp $(ALLPDF) $(ARCHIVEPREFIX)docs-$(FMT)
+	tar cf $(ARCHIVEPREFIX)docs-$(FMT).tar $(ARCHIVEPREFIX)docs-$(FMT)
+	rm -r $(ARCHIVEPREFIX)docs-$(FMT)
+
+gz: tar
+	gzip -9 < $(ARCHIVEPREFIX)docs-$(FMT).tar > $(ARCHIVEPREFIX)docs-$(FMT).tar.gz
+
+bz2: tar
+	bzip2 -9 -k $(ARCHIVEPREFIX)docs-$(FMT).tar
+
+xz: tar
+	xz -9 -k $(ARCHIVEPREFIX)docs-$(FMT).tar
+
+clean:
+	rm -f *.log *.ind *.aux *.toc *.syn *.idx *.out *.ilg *.pla *.ps *.tar *.tar.gz *.tar.bz2 *.tar.xz $(ALLPDF) $(ALLDVI) $(ALLXDV) *.fls *.fdb_latexmk
+
+.PHONY: all all-pdf all-dvi all-ps clean zip tar gz bz2 xz
+.PHONY: FORCE_MAKE
\ No newline at end of file
diff --git a/_build/latex/_panels_static/panels-main.c949a650a448cc0ae9fd3441c0e17fb0.css b/_build/latex/_panels_static/panels-main.c949a650a448cc0ae9fd3441c0e17fb0.css
new file mode 100644
index 0000000..fc14abc
--- /dev/null
+++ b/_build/latex/_panels_static/panels-main.c949a650a448cc0ae9fd3441c0e17fb0.css
@@ -0,0 +1 @@
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+\@writefile{toc}{\contentsline {section}{\numberline {7.11}Upper right}{114}{section.7.11}}
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+\@writefile{toc}{\contentsline {section}{\numberline {7.12}Upper left}{116}{section.7.12}}
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+\@writefile{toc}{\contentsline {section}{\numberline {7.13}Lower right}{117}{section.7.13}}
+\newlabel{07_plot:lower-right}{{7.13}{117}{Lower right}{section.7.13}{}}
+\@writefile{toc}{\contentsline {section}{\numberline {7.14}Subplots}{119}{section.7.14}}
+\newlabel{07_plot:subplots}{{7.14}{119}{Subplots}{section.7.14}{}}
+\@writefile{toc}{\contentsline {section}{\numberline {7.15}Adjusting proportions}{120}{section.7.15}}
+\newlabel{07_plot:adjusting-proportions}{{7.15}{120}{Adjusting proportions}{section.7.15}{}}
+\@writefile{toc}{\contentsline {section}{\numberline {7.16}Summary}{121}{section.7.16}}
+\newlabel{07_plot:summary}{{7.16}{121}{Summary}{section.7.16}{}}
+\@writefile{toc}{\contentsline {section}{\numberline {7.17}Best practices}{122}{section.7.17}}
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diff --git a/_build/latex/book.fdb_latexmk b/_build/latex/book.fdb_latexmk
new file mode 100644
index 0000000..d6bc1a0
--- /dev/null
+++ b/_build/latex/book.fdb_latexmk
@@ -0,0 +1,203 @@
+# Fdb version 3
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+  (generated)
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diff --git a/_build/latex/book.tex b/_build/latex/book.tex
new file mode 100644
index 0000000..3bcb107
--- /dev/null
+++ b/_build/latex/book.tex
@@ -0,0 +1,6779 @@
+%% Generated by Sphinx.
+\def\sphinxdocclass{report}
+\documentclass[letterpaper,10pt,english]{sphinxmanual}
+\ifdefined\pdfpxdimen
+   \let\sphinxpxdimen\pdfpxdimen\else\newdimen\sphinxpxdimen
+\fi \sphinxpxdimen=.75bp\relax
+
+\PassOptionsToPackage{warn}{textcomp}
+\usepackage[utf8]{inputenc}
+\ifdefined\DeclareUnicodeCharacter
+% support both utf8 and utf8x syntaxes
+  \ifdefined\DeclareUnicodeCharacterAsOptional
+    \def\sphinxDUC#1{\DeclareUnicodeCharacter{"#1}}
+  \else
+    \let\sphinxDUC\DeclareUnicodeCharacter
+  \fi
+  \sphinxDUC{00A0}{\nobreakspace}
+  \sphinxDUC{2500}{\sphinxunichar{2500}}
+  \sphinxDUC{2502}{\sphinxunichar{2502}}
+  \sphinxDUC{2514}{\sphinxunichar{2514}}
+  \sphinxDUC{251C}{\sphinxunichar{251C}}
+  \sphinxDUC{2572}{\textbackslash}
+\fi
+\usepackage{cmap}
+\usepackage[T1]{fontenc}
+\usepackage{amsmath,amssymb,amstext}
+\usepackage{babel}
+
+
+
+\usepackage{times}
+\expandafter\ifx\csname T@LGR\endcsname\relax
+\else
+% LGR was declared as font encoding
+  \substitutefont{LGR}{\rmdefault}{cmr}
+  \substitutefont{LGR}{\sfdefault}{cmss}
+  \substitutefont{LGR}{\ttdefault}{cmtt}
+\fi
+\expandafter\ifx\csname T@X2\endcsname\relax
+  \expandafter\ifx\csname T@T2A\endcsname\relax
+  \else
+  % T2A was declared as font encoding
+    \substitutefont{T2A}{\rmdefault}{cmr}
+    \substitutefont{T2A}{\sfdefault}{cmss}
+    \substitutefont{T2A}{\ttdefault}{cmtt}
+  \fi
+\else
+% X2 was declared as font encoding
+  \substitutefont{X2}{\rmdefault}{cmr}
+  \substitutefont{X2}{\sfdefault}{cmss}
+  \substitutefont{X2}{\ttdefault}{cmtt}
+\fi
+
+
+\usepackage[Bjarne]{fncychap}
+\usepackage[,numfigreset=1,mathnumfig]{sphinx}
+
+\fvset{fontsize=\small}
+\usepackage{geometry}
+
+
+% Include hyperref last.
+\usepackage{hyperref}
+% Fix anchor placement for figures with captions.
+\usepackage{hypcap}% it must be loaded after hyperref.
+% Set up styles of URL: it should be placed after hyperref.
+\urlstyle{same}
+
+
+\usepackage{sphinxmessages}
+
+
+
+
+\title{Astronomical Data in Python}
+\date{Nov 04, 2020}
+\release{}
+\author{Allen B.\@{} Downey}
+\newcommand{\sphinxlogo}{\vbox{}}
+\renewcommand{\releasename}{}
+\makeindex
+\begin{document}
+
+\pagestyle{empty}
+\sphinxmaketitle
+\pagestyle{plain}
+\sphinxtableofcontents
+\pagestyle{normal}
+\phantomsection\label{\detokenize{README::doc}}
+
+
+\sphinxstyleemphasis{Astronomical Data in Python} is an introduction to tools and practices for working with astronomical data.  Topics covered include:
+\begin{itemize}
+\item {} 
+Writing queries that select and download data from a database.
+
+\item {} 
+Using data stored in an Astropy \sphinxcode{\sphinxupquote{Table}} or Pandas \sphinxcode{\sphinxupquote{DataFrame}}.
+
+\item {} 
+Working with coordinates and other quantities with units.
+
+\item {} 
+Storing data in various formats.
+
+\item {} 
+Performing database join operations that combine data from multiple tables.
+
+\item {} 
+Visualizing data and preparing publication\sphinxhyphen{}quality figures.
+
+\end{itemize}
+
+As a running example, we will replicate part of the analysis in a recent paper, “\sphinxhref{https://arxiv.org/abs/1805.00425}{Off the beaten path: Gaia reveals GD\sphinxhyphen{}1 stars outside of the main stream}� by Adrian M. Price\sphinxhyphen{}Whelan and Ana Bonaca.
+
+This material was developed in collaboration with \sphinxhref{https://carpentries.org/}{The Carpentries} and the Astronomy Curriculum Development Committee, and supported by funding from the American Institute of Physics through the American Astronomical Society.
+
+I am grateful for contributions from the members of the committee \textendash{} Azalee Bostroem, Rodolfo Montez, and Phil Rosenfield \textendash{} and from Erin Becker, Brett Morris and Adrian Price\sphinxhyphen{}Whelan.
+
+The original format of this material is a series of Jupyter notebooks.  Using the
+links below, you can read the notebooks on NBViewer or run them on Colab.  If you
+want to run the notebooks in your own environment, you can download them from
+this repository and follow the instructions below to set up your environment.
+
+This material is also available in the form of \sphinxhref{https://datacarpentry.github.io/astronomy-python}{Carpentries lessons}, but you should be
+aware that these versions might diverge in the future.
+
+\sphinxstylestrong{Prerequisites}
+
+This material should be accessible to people familiar with basic Python, but not necessarily the libraries we will use, like Astropy or Pandas.  If you are familiar with Python lists and dictionaries, and you know how to write a function that takes parameters and returns a value, that should be enough.
+
+We assume that you are familiar with astronomy at the undergraduate level, but we will not assume specialized knowledge of the datasets or analysis methods we’ll use.
+
+\sphinxstylestrong{Notebook 1}
+
+This notebook demonstrates the following steps:
+\begin{enumerate}
+\sphinxsetlistlabels{\arabic}{enumi}{enumii}{}{.}%
+\item {} 
+Making a connection to the Gaia server,
+
+\item {} 
+Exploring information about the database and the tables it contains,
+
+\item {} 
+Writing a query and sending it to the server, and finally
+
+\item {} 
+Downloading the response from the server as an Astropy \sphinxcode{\sphinxupquote{Table}}.
+
+\end{enumerate}
+
+Press this button to run this notebook on Colab:
+
+\sphinxhref{https://colab.research.google.com/github/AllenDowney/AstronomicalData/blob/main/01\_query.ipynb}{}
+
+\sphinxhref{https://nbviewer.jupyter.org/github/AllenDowney/AstronomicalData/blob/main/01\_query.ipynb}{or click here to read it on NBViewer}
+
+\sphinxstylestrong{Notebook 2}
+
+This notebook starts with an example that does a “cone search�; that is, it selects stars that appear in a circular region of the sky.
+
+Then, to select stars in the vicinity of GD\sphinxhyphen{}1, we:
+\begin{itemize}
+\item {} 
+Use \sphinxcode{\sphinxupquote{Quantity}} objects to represent measurements with units.
+
+\item {} 
+Use the \sphinxcode{\sphinxupquote{Gala}} library to convert coordinates from one frame to another.
+
+\item {} 
+Use the ADQL keywords \sphinxcode{\sphinxupquote{POLYGON}}, \sphinxcode{\sphinxupquote{CONTAINS}}, and \sphinxcode{\sphinxupquote{POINT}} to select stars that fall within a polygonal region.
+
+\item {} 
+Submit a query and download the results.
+
+\item {} 
+Store the results in a FITS file.
+
+\end{itemize}
+
+Press this button to run this notebook on Colab:
+
+\sphinxhref{https://colab.research.google.com/github/AllenDowney/AstronomicalData/blob/main/02\_coords.ipynb}{}
+
+\sphinxhref{https://nbviewer.jupyter.org/github/AllenDowney/AstronomicalData/blob/main/02\_coords.ipynb}{or click here to read it on NBViewer}
+
+\sphinxstylestrong{Notebook 3}
+
+Here are the steps in this notebook:
+\begin{enumerate}
+\sphinxsetlistlabels{\arabic}{enumi}{enumii}{}{.}%
+\item {} 
+We’ll read back the results from the previous notebook, which we saved in a FITS file.
+
+\item {} 
+Then we’ll transform the coordinates and proper motion data from ICRS back to the coordinate frame of GD\sphinxhyphen{}1.
+
+\item {} 
+We’ll put those results into a Pandas \sphinxcode{\sphinxupquote{DataFrame}}, which we’ll use to select stars near the centerline of GD\sphinxhyphen{}1.
+
+\item {} 
+Plotting the proper motion of those stars, we’ll identify a region of proper motion for stars that are likely to be in GD\sphinxhyphen{}1.
+
+\item {} 
+Finally, we’ll select and plot the stars whose proper motion is in that region.
+
+\end{enumerate}
+
+Press this button to run this notebook on Colab:
+
+\sphinxhref{https://colab.research.google.com/github/AllenDowney/AstronomicalData/blob/main/03\_motion.ipynb}{}
+
+\sphinxhref{https://nbviewer.jupyter.org/github/AllenDowney/AstronomicalData/blob/main/03\_motion.ipynb}{or click here to read it on NBViewer}
+
+\sphinxstylestrong{Notebook 4}
+
+Here are the steps in this notebook:
+\begin{enumerate}
+\sphinxsetlistlabels{\arabic}{enumi}{enumii}{}{.}%
+\item {} 
+Using data from the previous notebook, we’ll identify the values of proper motion for stars likely to be in GD\sphinxhyphen{}1.
+
+\item {} 
+Then we’ll compose an ADQL query that selects stars based on proper motion, so we can download only the data we need.
+
+\item {} 
+We’ll also see how to write the results to a CSV file.
+
+\end{enumerate}
+
+That will make it possible to search a bigger region of the sky in a single query.
+
+Press this button to run this notebook on Colab:
+
+\sphinxhref{https://colab.research.google.com/github/AllenDowney/AstronomicalData/blob/main/04\_select.ipynb}{}
+
+\sphinxhref{https://nbviewer.jupyter.org/github/AllenDowney/AstronomicalData/blob/main/04\_select.ipynb}{or click here to read it on NBViewer}
+
+\sphinxstylestrong{Notebook 5}
+
+Here are the steps in this notebook:
+\begin{enumerate}
+\sphinxsetlistlabels{\arabic}{enumi}{enumii}{}{.}%
+\item {} 
+We’ll reload the candidate stars we identified in the previous notebook.
+
+\item {} 
+Then we’ll run a query on the Gaia server that uploads the table of candidates and uses a \sphinxcode{\sphinxupquote{JOIN}} operation to select photometry data for the candidate stars.
+
+\item {} 
+We’ll write the results to a file for use in the next notebook.
+
+\end{enumerate}
+
+Press this button to run this notebook on Colab:
+
+\sphinxhref{https://colab.research.google.com/github/AllenDowney/AstronomicalData/blob/main/05\_join.ipynb}{}
+
+\sphinxhref{https://nbviewer.jupyter.org/github/AllenDowney/AstronomicalData/blob/main/05\_join.ipynb}{or click here to read it on NBViewer}
+
+\sphinxstylestrong{Notebook 6}
+
+Here are the steps in this notebook:
+\begin{enumerate}
+\sphinxsetlistlabels{\arabic}{enumi}{enumii}{}{.}%
+\item {} 
+We’ll reload the data from the previous notebook and make a color\sphinxhyphen{}magnitude diagram.
+
+\item {} 
+Then we’ll specify a polygon in the diagram that contains stars with the photometry we expect.
+
+\item {} 
+Then we’ll merge the photometry data with the list of candidate stars, storing the result in a Pandas \sphinxcode{\sphinxupquote{DataFrame}}.
+
+\end{enumerate}
+
+Press this button to run this notebook on Colab:
+
+\sphinxhref{https://colab.research.google.com/github/AllenDowney/AstronomicalData/blob/main/06\_photo.ipynb}{}
+
+\sphinxhref{https://nbviewer.jupyter.org/github/AllenDowney/AstronomicalData/blob/main/06\_photo.ipynb}{or click here to read it on NBViewer}
+
+\sphinxstylestrong{Notebook 7}
+
+Here are the steps in this notebook:
+\begin{enumerate}
+\sphinxsetlistlabels{\arabic}{enumi}{enumii}{}{.}%
+\item {} 
+Starting with the figure from the previous notebook, we’ll add annotations to present the results more clearly.
+
+\item {} 
+The we’ll see several ways to customize figures to make them more appealing and effective.
+
+\item {} 
+Finally, we’ll see how to make a figure with multiple panels or subplots.
+
+\end{enumerate}
+
+Press this button to run this notebook on Colab:
+
+\sphinxhref{https://colab.research.google.com/github/AllenDowney/AstronomicalData/blob/main/07\_plot.ipynb}{}
+
+\sphinxhref{https://nbviewer.jupyter.org/github/AllenDowney/AstronomicalData/blob/main/07\_plot.ipynb}{or click here to read it on NBViewer}
+
+\sphinxstylestrong{Installation instructions}
+
+Coming soon.
+
+
+\chapter{Chapter 1}
+\label{\detokenize{01_query:chapter-1}}\label{\detokenize{01_query::doc}}
+\sphinxstyleemphasis{Astronomical Data in Python} is an introduction to tools and practices for working with astronomical data.  Topics covered include:
+\begin{itemize}
+\item {} 
+Writing queries that select and download data from a database.
+
+\item {} 
+Using data stored in an Astropy \sphinxcode{\sphinxupquote{Table}} or Pandas \sphinxcode{\sphinxupquote{DataFrame}}.
+
+\item {} 
+Working with coordinates and other quantities with units.
+
+\item {} 
+Storing data in various formats.
+
+\item {} 
+Performing database join operations that combine data from multiple tables.
+
+\item {} 
+Visualizing data and preparing publication\sphinxhyphen{}quality figures.
+
+\end{itemize}
+
+As a running example, we will replicate part of the analysis in a recent paper, “\sphinxhref{https://arxiv.org/abs/1805.00425}{Off the beaten path: Gaia reveals GD\sphinxhyphen{}1 stars outside of the main stream}� by Adrian M. Price\sphinxhyphen{}Whelan and Ana Bonaca.
+
+As the abstract explains, “Using data from the Gaia second data release combined with Pan\sphinxhyphen{}STARRS photometry, we present a sample of highly\sphinxhyphen{}probable members of the longest cold stream in the Milky Way, GD\sphinxhyphen{}1.�
+
+GD\sphinxhyphen{}1 is a \sphinxhref{https://en.wikipedia.org/wiki/List\_of\_stellar\_streams}{stellar stream}, which is “an association of stars orbiting a galaxy that was once a globular cluster or dwarf galaxy that has now been torn apart and stretched out along its orbit by tidal forces.�
+
+\sphinxhref{https://www.sciencemag.org/news/2018/10/streams-stars-reveal-galaxy-s-violent-history-and-perhaps-its-unseen-dark-matter}{This article in \sphinxstyleemphasis{Science} magazine} explains some of the background, including the process that led to the paper and an discussion of the scientific implications:
+\begin{itemize}
+\item {} 
+“The streams are particularly useful for … galactic archaeology — rewinding the cosmic clock to reconstruct the assembly of the Milky Way.�
+
+\item {} 
+“They also are being used as exquisitely sensitive scales to measure the galaxy’s mass.�
+
+\item {} 
+“… the streams are well\sphinxhyphen{}positioned to reveal the presence of dark matter … because the streams are so fragile, theorists say, collisions with marauding clumps of dark matter could leave telltale scars, potential clues to its nature.�
+
+\end{itemize}
+
+
+\section{Data}
+\label{\detokenize{01_query:data}}
+The datasets we will work with are:
+\begin{itemize}
+\item {} 
+\sphinxhref{https://en.wikipedia.org/wiki/Gaia\_(spacecraft)}{Gaia}, which is “a space observatory of the European Space Agency (ESA), launched in 2013 … designed for astrometry: measuring the positions, distances and motions of stars with unprecedented precision�, and
+
+\item {} 
+\sphinxhref{https://en.wikipedia.org/wiki/Pan-STARRS}{Pan\sphinxhyphen{}STARRS}, The Panoramic Survey Telescope and Rapid Response System, which is a survey designed to monitor the sky for transient objects, producing a catalog with accurate astronometry and photometry of detected sources.
+
+\end{itemize}
+
+Both of these datasets are very large, which can make them challenging to work with.  It might not be possible, or practical, to download the entire dataset.
+One of the goals of this workshop is to provide tools for working with large datasets.
+
+
+\section{Prerequisites}
+\label{\detokenize{01_query:prerequisites}}
+These notebooks are meant for people who are familiar with basic Python, but not necessarily the libraries we will use, like Astropy or Pandas.  If you are familiar with Python lists and dictionaries, and you know how to write a function that takes parameters and returns a value, you know enough Python to get started.
+
+We assume that you have some familiarity with operating systems, like the ability to use a command\sphinxhyphen{}line interface.  But we don’t assume you have any prior experience with databases.
+
+We assume that you are familiar with astronomy at the undergraduate level, but we will not assume specialized knowledge of the datasets or analysis methods we’ll use.
+
+
+\section{Outline}
+\label{\detokenize{01_query:outline}}
+The first lesson demonstrates the steps for selecting and downloading data from the Gaia Database:
+\begin{enumerate}
+\sphinxsetlistlabels{\arabic}{enumi}{enumii}{}{.}%
+\item {} 
+First we’ll make a connection to the Gaia server,
+
+\item {} 
+We will explore information about the database and the tables it contains,
+
+\item {} 
+We will write a query and send it to the server, and finally
+
+\item {} 
+We will download the response from the server.
+
+\end{enumerate}
+
+After completing this lesson, you should be able to
+\begin{itemize}
+\item {} 
+Compose a basic query in ADQL.
+
+\item {} 
+Use queries to explore a database and its tables.
+
+\item {} 
+Use queries to download data.
+
+\item {} 
+Develop, test, and debug a query incrementally.
+
+\end{itemize}
+
+
+\section{Query Language}
+\label{\detokenize{01_query:query-language}}
+In order to select data from a database, you have to compose a query, which is like a program written in a “query language�.
+The query language we’ll use is ADQL, which stands for “Astronomical Data Query Language�.
+
+ADQL is a dialect of \sphinxhref{https://en.wikipedia.org/wiki/SQL}{SQL} (Structured Query Language), which is by far the most commonly used query language.  Almost everything you will learn about ADQL also works in SQL.
+
+\sphinxhref{http://www.ivoa.net/documents/ADQL/20180112/PR-ADQL-2.1-20180112.html}{The reference manual for ADQL is here}.
+But you might find it easier to learn from \sphinxhref{https://www.gaia.ac.uk/data/gaia-data-release-1/adql-cookbook}{this ADQL Cookbook}.
+
+
+\section{Installing libraries}
+\label{\detokenize{01_query:installing-libraries}}
+The library we’ll use to get Gaia data is \sphinxhref{https://astroquery.readthedocs.io/en/latest/}{Astroquery}.
+
+If you are running this notebook on Colab, you can run the following cell to install Astroquery and the other libraries we’ll use.
+
+If you are running this notebook on your own computer, you might have to install these libraries yourself.
+
+If you are using this notebook as part of a Carpentries workshop, you should have received setup instructions.
+
+TODO: Add a link to the instructions.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{c+c1}{\PYGZsh{} If we\PYGZsq{}re running on Colab, install libraries}
+
+\PYG{k+kn}{import} \PYG{n+nn}{sys}
+\PYG{n}{IN\PYGZus{}COLAB} \PYG{o}{=} \PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{google.colab}\PYG{l+s+s1}{\PYGZsq{}} \PYG{o+ow}{in} \PYG{n}{sys}\PYG{o}{.}\PYG{n}{modules}
+
+\PYG{k}{if} \PYG{n}{IN\PYGZus{}COLAB}\PYG{p}{:}
+    \PYG{o}{!}pip install astroquery astro\PYGZhy{}gala pyia
+\end{sphinxVerbatim}
+
+
+\section{Connecting to Gaia}
+\label{\detokenize{01_query:connecting-to-gaia}}
+Astroquery provides \sphinxcode{\sphinxupquote{Gaia}}, which is an \sphinxhref{https://astroquery.readthedocs.io/en/latest/gaia/gaia.html}{object that represents a connection to the Gaia database}.
+
+We can connect to the Gaia database like this:
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{k+kn}{from} \PYG{n+nn}{astroquery}\PYG{n+nn}{.}\PYG{n+nn}{gaia} \PYG{k+kn}{import} \PYG{n}{Gaia}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+Created TAP+ (v1.2.1) \PYGZhy{} Connection:
+	Host: gea.esac.esa.int
+	Use HTTPS: True
+	Port: 443
+	SSL Port: 443
+Created TAP+ (v1.2.1) \PYGZhy{} Connection:
+	Host: geadata.esac.esa.int
+	Use HTTPS: True
+	Port: 443
+	SSL Port: 443
+\end{sphinxVerbatim}
+
+Running this import statement has the effect of creating a \sphinxhref{http://www.ivoa.net/documents/TAP/}{TAP+} connection; TAP stands for “Table Access Protocol�.  It is a network protocol for sending queries to the database and getting back the results.  We’re not sure why it seems to create two connections.
+
+
+\section{Databases and Tables}
+\label{\detokenize{01_query:databases-and-tables}}
+What is a database, anyway?  Most generally, it can be any collection of data, but when we are talking about ADQL or SQL:
+\begin{itemize}
+\item {} 
+A database is a collection of one or more named tables.
+
+\item {} 
+Each table is a 2\sphinxhyphen{}D array with one or more named columns of data.
+
+\end{itemize}
+
+We can use \sphinxcode{\sphinxupquote{Gaia.load\_tables}} to get the names of the tables in the Gaia database.  With the option \sphinxcode{\sphinxupquote{only\_names=True}}, it loads information about the tables, called the “metadata�, not the data itself.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{tables} \PYG{o}{=} \PYG{n}{Gaia}\PYG{o}{.}\PYG{n}{load\PYGZus{}tables}\PYG{p}{(}\PYG{n}{only\PYGZus{}names}\PYG{o}{=}\PYG{k+kc}{True}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+INFO: Retrieving tables... [astroquery.utils.tap.core]
+INFO: Parsing tables... [astroquery.utils.tap.core]
+INFO: Done. [astroquery.utils.tap.core]
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{k}{for} \PYG{n}{table} \PYG{o+ow}{in} \PYG{p}{(}\PYG{n}{tables}\PYG{p}{)}\PYG{p}{:}
+    \PYG{n+nb}{print}\PYG{p}{(}\PYG{n}{table}\PYG{o}{.}\PYG{n}{get\PYGZus{}qualified\PYGZus{}name}\PYG{p}{(}\PYG{p}{)}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+external.external.apassdr9
+external.external.gaiadr2\PYGZus{}geometric\PYGZus{}distance
+external.external.galex\PYGZus{}ais
+external.external.ravedr5\PYGZus{}com
+external.external.ravedr5\PYGZus{}dr5
+external.external.ravedr5\PYGZus{}gra
+external.external.ravedr5\PYGZus{}on
+external.external.sdssdr13\PYGZus{}photoprimary
+external.external.skymapperdr1\PYGZus{}master
+external.external.tmass\PYGZus{}xsc
+public.public.hipparcos
+public.public.hipparcos\PYGZus{}newreduction
+public.public.hubble\PYGZus{}sc
+public.public.igsl\PYGZus{}source
+public.public.igsl\PYGZus{}source\PYGZus{}catalog\PYGZus{}ids
+public.public.tycho2
+public.public.dual
+tap\PYGZus{}config.tap\PYGZus{}config.coord\PYGZus{}sys
+tap\PYGZus{}config.tap\PYGZus{}config.properties
+tap\PYGZus{}schema.tap\PYGZus{}schema.columns
+tap\PYGZus{}schema.tap\PYGZus{}schema.key\PYGZus{}columns
+tap\PYGZus{}schema.tap\PYGZus{}schema.keys
+tap\PYGZus{}schema.tap\PYGZus{}schema.schemas
+tap\PYGZus{}schema.tap\PYGZus{}schema.tables
+gaiadr1.gaiadr1.aux\PYGZus{}qso\PYGZus{}icrf2\PYGZus{}match
+gaiadr1.gaiadr1.ext\PYGZus{}phot\PYGZus{}zero\PYGZus{}point
+gaiadr1.gaiadr1.allwise\PYGZus{}best\PYGZus{}neighbour
+gaiadr1.gaiadr1.allwise\PYGZus{}neighbourhood
+gaiadr1.gaiadr1.gsc23\PYGZus{}best\PYGZus{}neighbour
+gaiadr1.gaiadr1.gsc23\PYGZus{}neighbourhood
+gaiadr1.gaiadr1.ppmxl\PYGZus{}best\PYGZus{}neighbour
+gaiadr1.gaiadr1.ppmxl\PYGZus{}neighbourhood
+gaiadr1.gaiadr1.sdss\PYGZus{}dr9\PYGZus{}best\PYGZus{}neighbour
+gaiadr1.gaiadr1.sdss\PYGZus{}dr9\PYGZus{}neighbourhood
+gaiadr1.gaiadr1.tmass\PYGZus{}best\PYGZus{}neighbour
+gaiadr1.gaiadr1.tmass\PYGZus{}neighbourhood
+gaiadr1.gaiadr1.ucac4\PYGZus{}best\PYGZus{}neighbour
+gaiadr1.gaiadr1.ucac4\PYGZus{}neighbourhood
+gaiadr1.gaiadr1.urat1\PYGZus{}best\PYGZus{}neighbour
+gaiadr1.gaiadr1.urat1\PYGZus{}neighbourhood
+gaiadr1.gaiadr1.cepheid
+gaiadr1.gaiadr1.phot\PYGZus{}variable\PYGZus{}time\PYGZus{}series\PYGZus{}gfov
+gaiadr1.gaiadr1.phot\PYGZus{}variable\PYGZus{}time\PYGZus{}series\PYGZus{}gfov\PYGZus{}statistical\PYGZus{}parameters
+gaiadr1.gaiadr1.rrlyrae
+gaiadr1.gaiadr1.variable\PYGZus{}summary
+gaiadr1.gaiadr1.allwise\PYGZus{}original\PYGZus{}valid
+gaiadr1.gaiadr1.gsc23\PYGZus{}original\PYGZus{}valid
+gaiadr1.gaiadr1.ppmxl\PYGZus{}original\PYGZus{}valid
+gaiadr1.gaiadr1.sdssdr9\PYGZus{}original\PYGZus{}valid
+gaiadr1.gaiadr1.tmass\PYGZus{}original\PYGZus{}valid
+gaiadr1.gaiadr1.ucac4\PYGZus{}original\PYGZus{}valid
+gaiadr1.gaiadr1.urat1\PYGZus{}original\PYGZus{}valid
+gaiadr1.gaiadr1.gaia\PYGZus{}source
+gaiadr1.gaiadr1.tgas\PYGZus{}source
+gaiadr2.gaiadr2.aux\PYGZus{}allwise\PYGZus{}agn\PYGZus{}gdr2\PYGZus{}cross\PYGZus{}id
+gaiadr2.gaiadr2.aux\PYGZus{}iers\PYGZus{}gdr2\PYGZus{}cross\PYGZus{}id
+gaiadr2.gaiadr2.aux\PYGZus{}sso\PYGZus{}orbit\PYGZus{}residuals
+gaiadr2.gaiadr2.aux\PYGZus{}sso\PYGZus{}orbits
+gaiadr2.gaiadr2.dr1\PYGZus{}neighbourhood
+gaiadr2.gaiadr2.allwise\PYGZus{}best\PYGZus{}neighbour
+gaiadr2.gaiadr2.allwise\PYGZus{}neighbourhood
+gaiadr2.gaiadr2.apassdr9\PYGZus{}best\PYGZus{}neighbour
+gaiadr2.gaiadr2.apassdr9\PYGZus{}neighbourhood
+gaiadr2.gaiadr2.gsc23\PYGZus{}best\PYGZus{}neighbour
+gaiadr2.gaiadr2.gsc23\PYGZus{}neighbourhood
+gaiadr2.gaiadr2.hipparcos2\PYGZus{}best\PYGZus{}neighbour
+gaiadr2.gaiadr2.hipparcos2\PYGZus{}neighbourhood
+gaiadr2.gaiadr2.panstarrs1\PYGZus{}best\PYGZus{}neighbour
+gaiadr2.gaiadr2.panstarrs1\PYGZus{}neighbourhood
+gaiadr2.gaiadr2.ppmxl\PYGZus{}best\PYGZus{}neighbour
+gaiadr2.gaiadr2.ppmxl\PYGZus{}neighbourhood
+gaiadr2.gaiadr2.ravedr5\PYGZus{}best\PYGZus{}neighbour
+gaiadr2.gaiadr2.ravedr5\PYGZus{}neighbourhood
+gaiadr2.gaiadr2.sdssdr9\PYGZus{}best\PYGZus{}neighbour
+gaiadr2.gaiadr2.sdssdr9\PYGZus{}neighbourhood
+gaiadr2.gaiadr2.tmass\PYGZus{}best\PYGZus{}neighbour
+gaiadr2.gaiadr2.tmass\PYGZus{}neighbourhood
+gaiadr2.gaiadr2.tycho2\PYGZus{}best\PYGZus{}neighbour
+gaiadr2.gaiadr2.tycho2\PYGZus{}neighbourhood
+gaiadr2.gaiadr2.urat1\PYGZus{}best\PYGZus{}neighbour
+gaiadr2.gaiadr2.urat1\PYGZus{}neighbourhood
+gaiadr2.gaiadr2.sso\PYGZus{}observation
+gaiadr2.gaiadr2.sso\PYGZus{}source
+gaiadr2.gaiadr2.vari\PYGZus{}cepheid
+gaiadr2.gaiadr2.vari\PYGZus{}classifier\PYGZus{}class\PYGZus{}definition
+gaiadr2.gaiadr2.vari\PYGZus{}classifier\PYGZus{}definition
+gaiadr2.gaiadr2.vari\PYGZus{}classifier\PYGZus{}result
+gaiadr2.gaiadr2.vari\PYGZus{}long\PYGZus{}period\PYGZus{}variable
+gaiadr2.gaiadr2.vari\PYGZus{}rotation\PYGZus{}modulation
+gaiadr2.gaiadr2.vari\PYGZus{}rrlyrae
+gaiadr2.gaiadr2.vari\PYGZus{}short\PYGZus{}timescale
+gaiadr2.gaiadr2.vari\PYGZus{}time\PYGZus{}series\PYGZus{}statistics
+gaiadr2.gaiadr2.panstarrs1\PYGZus{}original\PYGZus{}valid
+gaiadr2.gaiadr2.gaia\PYGZus{}source
+gaiadr2.gaiadr2.ruwe
+\end{sphinxVerbatim}
+
+So that’s a lot of tables.  The ones we’ll use are:
+\begin{itemize}
+\item {} 
+\sphinxcode{\sphinxupquote{gaiadr2.gaia\_source}}, which contains Gaia data from \sphinxhref{https://www.cosmos.esa.int/web/gaia/data-release-2}{data release 2},
+
+\item {} 
+\sphinxcode{\sphinxupquote{gaiadr2.panstarrs1\_original\_valid}}, which contains the photometry data we’ll use from PanSTARRS, and
+
+\item {} 
+\sphinxcode{\sphinxupquote{gaiadr2.panstarrs1\_best\_neighbour}}, which we’ll use to cross\sphinxhyphen{}match each star observed by Gaia with the same star observed by PanSTARRS.
+
+\end{itemize}
+
+We can use \sphinxcode{\sphinxupquote{load\_table}} (not \sphinxcode{\sphinxupquote{load\_tables}}) to get the metadata for a single table.  The name of this function is misleading, because it only downloads metadata.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{meta} \PYG{o}{=} \PYG{n}{Gaia}\PYG{o}{.}\PYG{n}{load\PYGZus{}table}\PYG{p}{(}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{gaiadr2.gaia\PYGZus{}source}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{)}
+\PYG{n}{meta}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+Retrieving table \PYGZsq{}gaiadr2.gaia\PYGZus{}source\PYGZsq{}
+Parsing table \PYGZsq{}gaiadr2.gaia\PYGZus{}source\PYGZsq{}...
+Done.
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYGZlt{}astroquery.utils.tap.model.taptable.TapTableMeta at 0x7f922376e0a0\PYGZgt{}
+\end{sphinxVerbatim}
+
+Jupyter shows that the result is an object of type \sphinxcode{\sphinxupquote{TapTableMeta}}, but it does not display the contents.
+
+To see the metadata, we have to print the object.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n+nb}{print}\PYG{p}{(}\PYG{n}{meta}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+TAP Table name: gaiadr2.gaiadr2.gaia\PYGZus{}source
+Description: This table has an entry for every Gaia observed source as listed in the
+Main Database accumulating catalogue version from which the catalogue
+release has been generated. It contains the basic source parameters,
+that is only final data (no epoch data) and no spectra (neither final
+nor epoch).
+Num. columns: 96
+\end{sphinxVerbatim}
+
+Notice one gotcha: in the list of table names, this table appears as \sphinxcode{\sphinxupquote{gaiadr2.gaiadr2.gaia\_source}}, but when we load the metadata, we refer to it as \sphinxcode{\sphinxupquote{gaiadr2.gaia\_source}}.
+
+\sphinxstylestrong{Exercise:} Go back and try
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{meta} \PYG{o}{=} \PYG{n}{Gaia}\PYG{o}{.}\PYG{n}{load\PYGZus{}table}\PYG{p}{(}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{gaiadr2.gaiadr2.gaia\PYGZus{}source}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+What happens?  Is the error message helpful?  If you had not made this error deliberately, would you have been able to figure it out?
+
+
+\section{Columns}
+\label{\detokenize{01_query:columns}}
+The following loop prints the names of the columns in the table.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{k}{for} \PYG{n}{column} \PYG{o+ow}{in} \PYG{n}{meta}\PYG{o}{.}\PYG{n}{columns}\PYG{p}{:}
+    \PYG{n+nb}{print}\PYG{p}{(}\PYG{n}{column}\PYG{o}{.}\PYG{n}{name}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+solution\PYGZus{}id
+designation
+source\PYGZus{}id
+random\PYGZus{}index
+ref\PYGZus{}epoch
+ra
+ra\PYGZus{}error
+dec
+dec\PYGZus{}error
+parallax
+parallax\PYGZus{}error
+parallax\PYGZus{}over\PYGZus{}error
+pmra
+pmra\PYGZus{}error
+pmdec
+pmdec\PYGZus{}error
+ra\PYGZus{}dec\PYGZus{}corr
+ra\PYGZus{}parallax\PYGZus{}corr
+ra\PYGZus{}pmra\PYGZus{}corr
+ra\PYGZus{}pmdec\PYGZus{}corr
+dec\PYGZus{}parallax\PYGZus{}corr
+dec\PYGZus{}pmra\PYGZus{}corr
+dec\PYGZus{}pmdec\PYGZus{}corr
+parallax\PYGZus{}pmra\PYGZus{}corr
+parallax\PYGZus{}pmdec\PYGZus{}corr
+pmra\PYGZus{}pmdec\PYGZus{}corr
+astrometric\PYGZus{}n\PYGZus{}obs\PYGZus{}al
+astrometric\PYGZus{}n\PYGZus{}obs\PYGZus{}ac
+astrometric\PYGZus{}n\PYGZus{}good\PYGZus{}obs\PYGZus{}al
+astrometric\PYGZus{}n\PYGZus{}bad\PYGZus{}obs\PYGZus{}al
+astrometric\PYGZus{}gof\PYGZus{}al
+astrometric\PYGZus{}chi2\PYGZus{}al
+astrometric\PYGZus{}excess\PYGZus{}noise
+astrometric\PYGZus{}excess\PYGZus{}noise\PYGZus{}sig
+astrometric\PYGZus{}params\PYGZus{}solved
+astrometric\PYGZus{}primary\PYGZus{}flag
+astrometric\PYGZus{}weight\PYGZus{}al
+astrometric\PYGZus{}pseudo\PYGZus{}colour
+astrometric\PYGZus{}pseudo\PYGZus{}colour\PYGZus{}error
+mean\PYGZus{}varpi\PYGZus{}factor\PYGZus{}al
+astrometric\PYGZus{}matched\PYGZus{}observations
+visibility\PYGZus{}periods\PYGZus{}used
+astrometric\PYGZus{}sigma5d\PYGZus{}max
+frame\PYGZus{}rotator\PYGZus{}object\PYGZus{}type
+matched\PYGZus{}observations
+duplicated\PYGZus{}source
+phot\PYGZus{}g\PYGZus{}n\PYGZus{}obs
+phot\PYGZus{}g\PYGZus{}mean\PYGZus{}flux
+phot\PYGZus{}g\PYGZus{}mean\PYGZus{}flux\PYGZus{}error
+phot\PYGZus{}g\PYGZus{}mean\PYGZus{}flux\PYGZus{}over\PYGZus{}error
+phot\PYGZus{}g\PYGZus{}mean\PYGZus{}mag
+phot\PYGZus{}bp\PYGZus{}n\PYGZus{}obs
+phot\PYGZus{}bp\PYGZus{}mean\PYGZus{}flux
+phot\PYGZus{}bp\PYGZus{}mean\PYGZus{}flux\PYGZus{}error
+phot\PYGZus{}bp\PYGZus{}mean\PYGZus{}flux\PYGZus{}over\PYGZus{}error
+phot\PYGZus{}bp\PYGZus{}mean\PYGZus{}mag
+phot\PYGZus{}rp\PYGZus{}n\PYGZus{}obs
+phot\PYGZus{}rp\PYGZus{}mean\PYGZus{}flux
+phot\PYGZus{}rp\PYGZus{}mean\PYGZus{}flux\PYGZus{}error
+phot\PYGZus{}rp\PYGZus{}mean\PYGZus{}flux\PYGZus{}over\PYGZus{}error
+phot\PYGZus{}rp\PYGZus{}mean\PYGZus{}mag
+phot\PYGZus{}bp\PYGZus{}rp\PYGZus{}excess\PYGZus{}factor
+phot\PYGZus{}proc\PYGZus{}mode
+bp\PYGZus{}rp
+bp\PYGZus{}g
+g\PYGZus{}rp
+radial\PYGZus{}velocity
+radial\PYGZus{}velocity\PYGZus{}error
+rv\PYGZus{}nb\PYGZus{}transits
+rv\PYGZus{}template\PYGZus{}teff
+rv\PYGZus{}template\PYGZus{}logg
+rv\PYGZus{}template\PYGZus{}fe\PYGZus{}h
+phot\PYGZus{}variable\PYGZus{}flag
+l
+b
+ecl\PYGZus{}lon
+ecl\PYGZus{}lat
+priam\PYGZus{}flags
+teff\PYGZus{}val
+teff\PYGZus{}percentile\PYGZus{}lower
+teff\PYGZus{}percentile\PYGZus{}upper
+a\PYGZus{}g\PYGZus{}val
+a\PYGZus{}g\PYGZus{}percentile\PYGZus{}lower
+a\PYGZus{}g\PYGZus{}percentile\PYGZus{}upper
+e\PYGZus{}bp\PYGZus{}min\PYGZus{}rp\PYGZus{}val
+e\PYGZus{}bp\PYGZus{}min\PYGZus{}rp\PYGZus{}percentile\PYGZus{}lower
+e\PYGZus{}bp\PYGZus{}min\PYGZus{}rp\PYGZus{}percentile\PYGZus{}upper
+flame\PYGZus{}flags
+radius\PYGZus{}val
+radius\PYGZus{}percentile\PYGZus{}lower
+radius\PYGZus{}percentile\PYGZus{}upper
+lum\PYGZus{}val
+lum\PYGZus{}percentile\PYGZus{}lower
+lum\PYGZus{}percentile\PYGZus{}upper
+datalink\PYGZus{}url
+epoch\PYGZus{}photometry\PYGZus{}url
+\end{sphinxVerbatim}
+
+You can probably guess what many of these columns are by looking at the names, but you should resist the temptation to guess.
+To find out what the columns mean, \sphinxhref{https://gea.esac.esa.int/archive/documentation/GDR2/Gaia\_archive/chap\_datamodel/sec\_dm\_main\_tables/ssec\_dm\_gaia\_source.html}{read the documentation}.
+
+If you want to know what can go wrong when you don’t read the documentation, \sphinxhref{https://www.vox.com/future-perfect/2019/6/4/18650969/married-women-miserable-fake-paul-dolan-happiness}{you might like this article}.
+
+\sphinxstylestrong{Exercise:} One of the other tables we’ll use is \sphinxcode{\sphinxupquote{gaiadr2.gaiadr2.panstarrs1\_original\_valid}}.  Use \sphinxcode{\sphinxupquote{load\_table}} to get the metadata for this table.  How many columns are there and what are their names?
+
+Hint: Remember the gotcha we mentioned earlier.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{c+c1}{\PYGZsh{} Solution}
+
+\PYG{k}{for} \PYG{n}{column} \PYG{o+ow}{in} \PYG{n}{meta2}\PYG{o}{.}\PYG{n}{columns}\PYG{p}{:}
+    \PYG{n+nb}{print}\PYG{p}{(}\PYG{n}{column}\PYG{o}{.}\PYG{n}{name}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+obj\PYGZus{}name
+obj\PYGZus{}id
+ra
+dec
+ra\PYGZus{}error
+dec\PYGZus{}error
+epoch\PYGZus{}mean
+g\PYGZus{}mean\PYGZus{}psf\PYGZus{}mag
+g\PYGZus{}mean\PYGZus{}psf\PYGZus{}mag\PYGZus{}error
+g\PYGZus{}flags
+r\PYGZus{}mean\PYGZus{}psf\PYGZus{}mag
+r\PYGZus{}mean\PYGZus{}psf\PYGZus{}mag\PYGZus{}error
+r\PYGZus{}flags
+i\PYGZus{}mean\PYGZus{}psf\PYGZus{}mag
+i\PYGZus{}mean\PYGZus{}psf\PYGZus{}mag\PYGZus{}error
+i\PYGZus{}flags
+z\PYGZus{}mean\PYGZus{}psf\PYGZus{}mag
+z\PYGZus{}mean\PYGZus{}psf\PYGZus{}mag\PYGZus{}error
+z\PYGZus{}flags
+y\PYGZus{}mean\PYGZus{}psf\PYGZus{}mag
+y\PYGZus{}mean\PYGZus{}psf\PYGZus{}mag\PYGZus{}error
+y\PYGZus{}flags
+n\PYGZus{}detections
+zone\PYGZus{}id
+obj\PYGZus{}info\PYGZus{}flag
+quality\PYGZus{}flag
+\end{sphinxVerbatim}
+
+
+\section{Writing queries}
+\label{\detokenize{01_query:writing-queries}}
+By now you might be wondering how we actually download the data.  With tables this big, you generally don’t.  Instead, you use queries to select only the data you want.
+
+A query is a string written in a query language like SQL; for the Gaia database, the query language is a dialect of SQL called ADQL.
+
+Here’s an example of an ADQL query.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{query1} \PYG{o}{=} \PYG{l+s+s2}{\PYGZdq{}\PYGZdq{}\PYGZdq{}}\PYG{l+s+s2}{SELECT }
+\PYG{l+s+s2}{TOP 10}
+\PYG{l+s+s2}{source\PYGZus{}id, ref\PYGZus{}epoch, ra, dec, parallax }
+\PYG{l+s+s2}{FROM gaiadr2.gaia\PYGZus{}source}\PYG{l+s+s2}{\PYGZdq{}\PYGZdq{}\PYGZdq{}}
+\end{sphinxVerbatim}
+
+\sphinxstylestrong{Python note:} We use a \sphinxhref{https://docs.python.org/3/tutorial/introduction.html\#strings}{triple\sphinxhyphen{}quoted string} here so we can include line breaks in the query, which makes it easier to read.
+
+The words in uppercase are ADQL keywords:
+\begin{itemize}
+\item {} 
+\sphinxcode{\sphinxupquote{SELECT}} indicates that we are selecting data (as opposed to adding or modifying data).
+
+\item {} 
+\sphinxcode{\sphinxupquote{TOP}} indicates that we only want the first 10 rows of the table, which is useful for testing a query before asking for all of the data.
+
+\item {} 
+\sphinxcode{\sphinxupquote{FROM}} specifies which table we want data from.
+
+\end{itemize}
+
+The third line is a list of column names, indicating which columns we want.
+
+In this example, the keywords are capitalized and the column names are lowercase.  This is a common style, but it is not required.  ADQL and SQL are not case\sphinxhyphen{}sensitive.
+
+To run this query, we use the \sphinxcode{\sphinxupquote{Gaia}} object, which represents our connection to the Gaia database, and invoke \sphinxcode{\sphinxupquote{launch\_job}}:
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{job1} \PYG{o}{=} \PYG{n}{Gaia}\PYG{o}{.}\PYG{n}{launch\PYGZus{}job}\PYG{p}{(}\PYG{n}{query1}\PYG{p}{)}
+\PYG{n}{job1}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYGZlt{}astroquery.utils.tap.model.job.Job at 0x7f9222e9cb20\PYGZgt{}
+\end{sphinxVerbatim}
+
+The result is an object that represents the job running on a Gaia server.
+
+If you print it, it displays metadata for the forthcoming table.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n+nb}{print}\PYG{p}{(}\PYG{n}{job1}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYGZlt{}Table length=10\PYGZgt{}
+   name    dtype  unit                            description                            
+\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{} \PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{} \PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{} \PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}
+source\PYGZus{}id   int64      Unique source identifier (unique within a particular Data Release)
+ref\PYGZus{}epoch float64   yr                                                    Reference epoch
+       ra float64  deg                                                    Right ascension
+      dec float64  deg                                                        Declination
+ parallax float64  mas                                                           Parallax
+Jobid: None
+Phase: COMPLETED
+Owner: None
+Output file: sync\PYGZus{}20201005090721.xml.gz
+Results: None
+\end{sphinxVerbatim}
+
+Don’t worry about \sphinxcode{\sphinxupquote{Results: None}}.  That does not actually mean there are no results.
+
+However, \sphinxcode{\sphinxupquote{Phase: COMPLETED}} indicates that the job is complete, so we can get the results like this:
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{results1} \PYG{o}{=} \PYG{n}{job1}\PYG{o}{.}\PYG{n}{get\PYGZus{}results}\PYG{p}{(}\PYG{p}{)}
+\PYG{n+nb}{type}\PYG{p}{(}\PYG{n}{results1}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+astropy.table.table.Table
+\end{sphinxVerbatim}
+
+\sphinxstylestrong{Optional detail:}  Why is \sphinxcode{\sphinxupquote{table}} repeated three times?  The first is the name of the module, the second is the name of the submodule, and the third is the name of the class.  Most of the time we only care about the last one.  It’s like the Linnean name for gorilla, which is \sphinxstyleemphasis{Gorilla Gorilla Gorilla}.
+
+The result is an \sphinxhref{https://docs.astropy.org/en/stable/table/}{Astropy Table}, which is similar to a table in an SQL database except:
+\begin{itemize}
+\item {} 
+SQL databases are stored on disk drives, so they are persistent; that is, they “survive� even if you turn off the computer.  An Astropy \sphinxcode{\sphinxupquote{Table}} is stored in memory; it disappears when you turn off the computer (or shut down this Jupyter notebook).
+
+\item {} 
+SQL databases are designed to process queries.  An Astropy \sphinxcode{\sphinxupquote{Table}} can perform some query\sphinxhyphen{}like operations, like selecting columns and rows.  But these operations use Python syntax, not SQL.
+
+\end{itemize}
+
+Jupyter knows how to display the contents of a \sphinxcode{\sphinxupquote{Table}}.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{results1}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYGZlt{}Table length=10\PYGZgt{}
+     source\PYGZus{}id      ref\PYGZus{}epoch ...        dec               parallax      
+                        yr    ...        deg                 mas         
+       int64         float64  ...      float64             float64       
+\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{} \PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{} ... \PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{} \PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}
+4530738361793769600    2015.5 ...  20.40682117430378   0.9785380604519425
+4530752651135081216    2015.5 ... 20.523350496351846   0.2674800612552977
+4530743343951405568    2015.5 ... 20.474147574053124 \PYGZhy{}0.43911323550176806
+4530755060627162368    2015.5 ... 20.558523922346158   1.1422630184554958
+4530746844341315968    2015.5 ... 20.377852388898184   1.0092247424630945
+4530768456615026432    2015.5 ...  20.31829694530366 \PYGZhy{}0.06900136127674149
+4530763513119137280    2015.5 ...  20.20956829578524   0.1266016679823622
+4530736364618539264    2015.5 ... 20.346579041327693   0.3894019486060072
+4530735952305177728    2015.5 ... 20.311030903719928   0.2041189982608354
+4530751281056022656    2015.5 ... 20.460309556214753  0.10294642821734962
+\end{sphinxVerbatim}
+
+Each column has a name, units, and a data type.
+
+For example, the units of \sphinxcode{\sphinxupquote{ra}} and \sphinxcode{\sphinxupquote{dec}} are degrees, and their data type is \sphinxcode{\sphinxupquote{float64}}, which is a 64\sphinxhyphen{}bit floating\sphinxhyphen{}point number, used to store measurements with a fraction part.
+
+This information comes from the Gaia database, and has been stored in the Astropy \sphinxcode{\sphinxupquote{Table}} by Astroquery.
+
+\sphinxstylestrong{Exercise:} Read \sphinxhref{https://gea.esac.esa.int/archive/documentation/GDR2/Gaia\_archive/chap\_datamodel/sec\_dm\_main\_tables/ssec\_dm\_gaia\_source.html}{the documentation of this table} and choose a column that looks interesting to you.  Add the column name to the query and run it again.  What are the units of the column you selected?  What is its data type?
+
+
+\section{Asynchronous queries}
+\label{\detokenize{01_query:asynchronous-queries}}
+\sphinxcode{\sphinxupquote{launch\_job}} asks the server to run the job “synchronously�, which normally means it runs immediately.  But synchronous jobs are limited to 2000 rows.  For queries that return more rows, you should run “asynchronously�, which mean they might take longer to get started.
+
+If you are not sure how many rows a query will return, you can use the SQL command \sphinxcode{\sphinxupquote{COUNT}} to find out how many rows are in the result without actually returning them.  We’ll see an example of this later.
+
+The results of an asynchronous query are stored in a file on the server, so you can start a query and come back later to get the results.
+
+For anonymous users, files are kept for three days.
+
+As an example, let’s try a query that’s similar to \sphinxcode{\sphinxupquote{query1}}, with two changes:
+\begin{itemize}
+\item {} 
+It selects the first 3000 rows, so it is bigger than we should run synchronously.
+
+\item {} 
+It uses a new keyword, \sphinxcode{\sphinxupquote{WHERE}}.
+
+\end{itemize}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{query2} \PYG{o}{=} \PYG{l+s+s2}{\PYGZdq{}\PYGZdq{}\PYGZdq{}}\PYG{l+s+s2}{SELECT TOP 3000}
+\PYG{l+s+s2}{source\PYGZus{}id, ref\PYGZus{}epoch, ra, dec, parallax}
+\PYG{l+s+s2}{FROM gaiadr2.gaia\PYGZus{}source}
+\PYG{l+s+s2}{WHERE parallax \PYGZlt{} 1}
+\PYG{l+s+s2}{\PYGZdq{}\PYGZdq{}\PYGZdq{}}
+\end{sphinxVerbatim}
+
+A \sphinxcode{\sphinxupquote{WHERE}} clause indicates which rows we want; in this case, the query selects only rows “where� \sphinxcode{\sphinxupquote{parallax}} is less than 1.  This has the effect of selecting stars with relatively low parallax, which are farther away.  We’ll use this clause to exclude nearby stars that are unlikely to be part of GD\sphinxhyphen{}1.
+
+\sphinxcode{\sphinxupquote{WHERE}} is one of the most common clauses in ADQL/SQL, and one of the most useful, because it allows us to select only the rows we need from the database.
+
+We use \sphinxcode{\sphinxupquote{launch\_job\_async}} to submit an asynchronous query.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{job2} \PYG{o}{=} \PYG{n}{Gaia}\PYG{o}{.}\PYG{n}{launch\PYGZus{}job\PYGZus{}async}\PYG{p}{(}\PYG{n}{query2}\PYG{p}{)}
+\PYG{n+nb}{print}\PYG{p}{(}\PYG{n}{job2}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+INFO: Query finished. [astroquery.utils.tap.core]
+\PYGZlt{}Table length=3000\PYGZgt{}
+   name    dtype  unit                            description                            
+\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{} \PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{} \PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{} \PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}
+source\PYGZus{}id   int64      Unique source identifier (unique within a particular Data Release)
+ref\PYGZus{}epoch float64   yr                                                    Reference epoch
+       ra float64  deg                                                    Right ascension
+      dec float64  deg                                                        Declination
+ parallax float64  mas                                                           Parallax
+Jobid: 1601903242219O
+Phase: COMPLETED
+Owner: None
+Output file: async\PYGZus{}20201005090722.vot
+Results: None
+\end{sphinxVerbatim}
+
+And here are the results.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{results2} \PYG{o}{=} \PYG{n}{job2}\PYG{o}{.}\PYG{n}{get\PYGZus{}results}\PYG{p}{(}\PYG{p}{)}
+\PYG{n}{results2}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYGZlt{}Table length=3000\PYGZgt{}
+     source\PYGZus{}id      ref\PYGZus{}epoch ...        dec               parallax      
+                        yr    ...        deg                 mas         
+       int64         float64  ...      float64             float64       
+\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{} \PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{} ... \PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{} \PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}
+4530738361793769600    2015.5 ...  20.40682117430378   0.9785380604519425
+4530752651135081216    2015.5 ... 20.523350496351846   0.2674800612552977
+4530743343951405568    2015.5 ... 20.474147574053124 \PYGZhy{}0.43911323550176806
+4530768456615026432    2015.5 ...  20.31829694530366 \PYGZhy{}0.06900136127674149
+4530763513119137280    2015.5 ...  20.20956829578524   0.1266016679823622
+4530736364618539264    2015.5 ... 20.346579041327693   0.3894019486060072
+4530735952305177728    2015.5 ... 20.311030903719928   0.2041189982608354
+4530751281056022656    2015.5 ... 20.460309556214753  0.10294642821734962
+4530740938774409344    2015.5 ... 20.436140058941206   0.9242670062090182
+                ...       ... ...                ...                  ...
+4467710915011802624    2015.5 ... 1.1429085038160882  0.42361471245557913
+4467706551328679552    2015.5 ... 1.0565747323689927    0.922888231734588
+4467712255037300096    2015.5 ... 0.6581664892880896   \PYGZhy{}2.669179465293931
+4467735001181761792    2015.5 ... 0.8947079323599124   0.6117399163086398
+4467737101421916672    2015.5 ... 0.9806225910160181 \PYGZhy{}0.39818224846127004
+4467707547757327488    2015.5 ... 1.0212759940136962   0.7741412301054209
+4467732772094573056    2015.5 ... 0.9037072088489417  \PYGZhy{}1.7920417800164183
+4467732355491087744    2015.5 ... 0.9197224705139885  \PYGZhy{}0.3464446494840354
+4467717099766944512    2015.5 ...  0.726277659009568  0.05443955111134051
+4467719058265781248    2015.5 ... 0.8205551921782785   0.3733943917490343
+\end{sphinxVerbatim}
+
+You might notice that some values of \sphinxcode{\sphinxupquote{parallax}} are negative.  As \sphinxhref{https://www.cosmos.esa.int/web/gaia/archive-tips\#negative\%20parallax}{this FAQ explains}, “Negative parallaxes are caused by errors in the observations.�  Negative parallaxes have “no physical meaning,� but they can be a “useful diagnostic on the quality of the astrometric solution.�
+
+Later we will see an example where we use \sphinxcode{\sphinxupquote{parallax}} and \sphinxcode{\sphinxupquote{parallax\_error}} to identify stars where the distance estimate is likely to be inaccurate.
+
+\sphinxstylestrong{Exercise:} The clauses in a query have to be in the right order.  Go back and change the order of the clauses in \sphinxcode{\sphinxupquote{query2}} and run it again.
+
+The query should fail, but notice that you don’t get much useful debugging information.
+
+For this reason, developing and debugging ADQL queries can be really hard.  A few suggestions that might help:
+\begin{itemize}
+\item {} 
+Whenever possible, start with a working query, either an example you find online or a query you have used in the past.
+
+\item {} 
+Make small changes and test each change before you continue.
+
+\item {} 
+While you are debugging, use \sphinxcode{\sphinxupquote{TOP}} to limit the number of rows in the result.  That will make each attempt run faster, which reduces your testing time.
+
+\item {} 
+Launching test queries synchronously might make them start faster, too.
+
+\end{itemize}
+
+
+\section{Operators}
+\label{\detokenize{01_query:operators}}
+In a \sphinxcode{\sphinxupquote{WHERE}} clause, you can use any of the \sphinxhref{https://www.w3schools.com/sql/sql\_operators.asp}{SQL comparison operators}; here are the most common ones:
+
+
+\begin{savenotes}\sphinxattablestart
+\centering
+\begin{tabulary}{\linewidth}[t]{|T|T|}
+\hline
+\sphinxstyletheadfamily 
+Symbol
+&\sphinxstyletheadfamily 
+Operation
+\\
+\hline
+\sphinxcode{\sphinxupquote{\textgreater{}}}
+&
+greater than
+\\
+\hline
+\sphinxcode{\sphinxupquote{\textless{}}}
+&
+less than
+\\
+\hline
+\sphinxcode{\sphinxupquote{\textgreater{}=}}
+&
+greater than or equal
+\\
+\hline
+\sphinxcode{\sphinxupquote{\textless{}=}}
+&
+less than or equal
+\\
+\hline
+\sphinxcode{\sphinxupquote{=}}
+&
+equal
+\\
+\hline
+\sphinxcode{\sphinxupquote{!=}} or \sphinxcode{\sphinxupquote{\textless{}\textgreater{}}}
+&
+not equal
+\\
+\hline
+\end{tabulary}
+\par
+\sphinxattableend\end{savenotes}
+
+Most of these are the same as Python, but some are not.  In particular, notice that the equality operator is \sphinxcode{\sphinxupquote{=}}, not \sphinxcode{\sphinxupquote{==}}.
+Be careful to keep your Python out of your ADQL!
+
+You can combine comparisons using the logical operators:
+\begin{itemize}
+\item {} 
+AND: true if both comparisons are true
+
+\item {} 
+OR: true if either or both comparisons are true
+
+\end{itemize}
+
+Finally, you can use \sphinxcode{\sphinxupquote{NOT}} to invert the result of a comparison.
+
+\sphinxstylestrong{Exercise:} \sphinxhref{https://www.w3schools.com/sql/sql\_operators.asp}{Read about SQL operators here} and then modify the previous query to select rows where \sphinxcode{\sphinxupquote{bp\_rp}} is between \sphinxcode{\sphinxupquote{\sphinxhyphen{}0.75}} and \sphinxcode{\sphinxupquote{2}}.
+
+You can \sphinxhref{https://gea.esac.esa.int/archive/documentation/GDR2/Gaia\_archive/chap\_datamodel/sec\_dm\_main\_tables/ssec\_dm\_gaia\_source.html}{read about this variable here}.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{c+c1}{\PYGZsh{} Solution}
+
+\PYG{c+c1}{\PYGZsh{} This is what most people will probably do}
+
+\PYG{n}{query} \PYG{o}{=} \PYG{l+s+s2}{\PYGZdq{}\PYGZdq{}\PYGZdq{}}\PYG{l+s+s2}{SELECT TOP 10}
+\PYG{l+s+s2}{source\PYGZus{}id, ref\PYGZus{}epoch, ra, dec, parallax}
+\PYG{l+s+s2}{FROM gaiadr2.gaia\PYGZus{}source}
+\PYG{l+s+s2}{WHERE parallax \PYGZlt{} 1 }
+\PYG{l+s+s2}{  AND bp\PYGZus{}rp \PYGZgt{} \PYGZhy{}0.75 AND bp\PYGZus{}rp \PYGZlt{} 2}
+\PYG{l+s+s2}{\PYGZdq{}\PYGZdq{}\PYGZdq{}}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{c+c1}{\PYGZsh{} Solution}
+
+\PYG{c+c1}{\PYGZsh{} But if someone notices the BETWEEN operator, }
+\PYG{c+c1}{\PYGZsh{} they might do this}
+
+\PYG{n}{query} \PYG{o}{=} \PYG{l+s+s2}{\PYGZdq{}\PYGZdq{}\PYGZdq{}}\PYG{l+s+s2}{SELECT TOP 10}
+\PYG{l+s+s2}{source\PYGZus{}id, ref\PYGZus{}epoch, ra, dec, parallax}
+\PYG{l+s+s2}{FROM gaiadr2.gaia\PYGZus{}source}
+\PYG{l+s+s2}{WHERE parallax \PYGZlt{} 1 }
+\PYG{l+s+s2}{  AND bp\PYGZus{}rp BETWEEN \PYGZhy{}0.75 AND 2}
+\PYG{l+s+s2}{\PYGZdq{}\PYGZdq{}\PYGZdq{}}
+\end{sphinxVerbatim}
+
+This \sphinxhref{https://sci.esa.int/web/gaia/-/60198-gaia-hertzsprung-russell-diagram}{Hertzsprung\sphinxhyphen{}Russell diagram} shows the BP\sphinxhyphen{}RP color and luminosity of stars in the Gaia catalog.
+
+Selecting stars with \sphinxcode{\sphinxupquote{bp\sphinxhyphen{}rp}} less than 2 excludes many \sphinxhref{https://xkcd.com/2360/}{class M dwarf stars}, which are low temperature, low luminosity.  A star like that at GD\sphinxhyphen{}1’s distance would be hard to detect, so if it is detected, it it more likely to be in the foreground.
+
+
+\section{Cleaning up}
+\label{\detokenize{01_query:cleaning-up}}
+Asynchronous jobs have a \sphinxcode{\sphinxupquote{jobid}}.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{job1}\PYG{o}{.}\PYG{n}{jobid}\PYG{p}{,} \PYG{n}{job2}\PYG{o}{.}\PYG{n}{jobid}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+(None, \PYGZsq{}1601903242219O\PYGZsq{})
+\end{sphinxVerbatim}
+
+Which you can use to remove the job from the server.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{Gaia}\PYG{o}{.}\PYG{n}{remove\PYGZus{}jobs}\PYG{p}{(}\PYG{p}{[}\PYG{n}{job2}\PYG{o}{.}\PYG{n}{jobid}\PYG{p}{]}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+Removed jobs: \PYGZsq{}[\PYGZsq{}1601903242219O\PYGZsq{}]\PYGZsq{}.
+\end{sphinxVerbatim}
+
+If you don’t remove it job from the server, it will be removed eventually, so don’t feel too bad if you don’t clean up after yourself.
+
+
+\section{Formatting queries}
+\label{\detokenize{01_query:formatting-queries}}
+So far the queries have been string “literals�, meaning that the entire string is part of the program.
+But writing queries yourself can be slow, repetitive, and error\sphinxhyphen{}prone.
+
+It is often a good idea to write Python code that assembles a query for you.  One useful tool for that is the \sphinxhref{https://www.w3schools.com/python/ref\_string\_format.asp}{string \sphinxcode{\sphinxupquote{format}} method}.
+
+As an example, we’ll divide the previous query into two parts; a list of column names and a “base� for the query that contains everything except the column names.
+
+Here’s the list of columns we’ll select.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{columns} \PYG{o}{=} \PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{source\PYGZus{}id, ra, dec, pmra, pmdec, parallax, parallax\PYGZus{}error, radial\PYGZus{}velocity}\PYG{l+s+s1}{\PYGZsq{}}
+\end{sphinxVerbatim}
+
+And here’s the base; it’s a string that contains at least one format specifier in curly brackets (braces).
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{query3\PYGZus{}base} \PYG{o}{=} \PYG{l+s+s2}{\PYGZdq{}\PYGZdq{}\PYGZdq{}}\PYG{l+s+s2}{SELECT TOP 10 }
+\PYG{l+s+si}{\PYGZob{}columns\PYGZcb{}}
+\PYG{l+s+s2}{FROM gaiadr2.gaia\PYGZus{}source}
+\PYG{l+s+s2}{WHERE parallax \PYGZlt{} 1}
+\PYG{l+s+s2}{  AND bp\PYGZus{}rp BETWEEN \PYGZhy{}0.75 AND 2}
+\PYG{l+s+s2}{\PYGZdq{}\PYGZdq{}\PYGZdq{}}
+\end{sphinxVerbatim}
+
+This base query contains one format specifier, \sphinxcode{\sphinxupquote{\{columns\}}}, which is a placeholder for the list of column names we will provide.
+
+To assemble the query, we invoke \sphinxcode{\sphinxupquote{format}} on the base string and provide a keyword argument that assigns a value to \sphinxcode{\sphinxupquote{columns}}.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{query3} \PYG{o}{=} \PYG{n}{query3\PYGZus{}base}\PYG{o}{.}\PYG{n}{format}\PYG{p}{(}\PYG{n}{columns}\PYG{o}{=}\PYG{n}{columns}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+The result is a string with line breaks.  If you display it, the line breaks appear as \sphinxcode{\sphinxupquote{\textbackslash{}n}}.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{query3}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYGZsq{}SELECT TOP 10 \PYGZbs{}nsource\PYGZus{}id, ra, dec, pmra, pmdec, parallax, parallax\PYGZus{}error, radial\PYGZus{}velocity\PYGZbs{}nFROM gaiadr2.gaia\PYGZus{}source\PYGZbs{}nWHERE parallax \PYGZlt{} 1\PYGZbs{}n  AND bp\PYGZus{}rp BETWEEN \PYGZhy{}0.75 AND 2\PYGZbs{}n\PYGZsq{}
+\end{sphinxVerbatim}
+
+But if you print it, the line breaks appear as… line breaks.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n+nb}{print}\PYG{p}{(}\PYG{n}{query3}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+SELECT TOP 10 
+source\PYGZus{}id, ra, dec, pmra, pmdec, parallax, parallax\PYGZus{}error, radial\PYGZus{}velocity
+FROM gaiadr2.gaia\PYGZus{}source
+WHERE parallax \PYGZlt{} 1
+  AND bp\PYGZus{}rp BETWEEN \PYGZhy{}0.75 AND 2
+\end{sphinxVerbatim}
+
+Notice that the format specifier has been replaced with the value of \sphinxcode{\sphinxupquote{columns}}.
+
+Let’s run it and see if it works:
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{job3} \PYG{o}{=} \PYG{n}{Gaia}\PYG{o}{.}\PYG{n}{launch\PYGZus{}job}\PYG{p}{(}\PYG{n}{query3}\PYG{p}{)}
+\PYG{n+nb}{print}\PYG{p}{(}\PYG{n}{job3}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYGZlt{}Table length=10\PYGZgt{}
+      name       dtype    unit                              description                             n\PYGZus{}bad
+\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{} \PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{} \PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{} \PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{} \PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}
+      source\PYGZus{}id   int64          Unique source identifier (unique within a particular Data Release)     0
+             ra float64      deg                                                    Right ascension     0
+            dec float64      deg                                                        Declination     0
+           pmra float64 mas / yr                         Proper motion in right ascension direction     0
+          pmdec float64 mas / yr                             Proper motion in declination direction     0
+       parallax float64      mas                                                           Parallax     0
+ parallax\PYGZus{}error float64      mas                                         Standard error of parallax     0
+radial\PYGZus{}velocity float64   km / s                                                    Radial velocity    10
+Jobid: None
+Phase: COMPLETED
+Owner: None
+Output file: sync\PYGZus{}20201005090726.xml.gz
+Results: None
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{results3} \PYG{o}{=} \PYG{n}{job3}\PYG{o}{.}\PYG{n}{get\PYGZus{}results}\PYG{p}{(}\PYG{p}{)}
+\PYG{n}{results3}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYGZlt{}Table length=10\PYGZgt{}
+     source\PYGZus{}id              ra         ...   parallax\PYGZus{}error   radial\PYGZus{}velocity
+                           deg         ...        mas              km / s    
+       int64             float64       ...      float64           float64    
+\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{} \PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{} ... \PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{} \PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}
+4467710915011802624  269.9680969307347 ...  0.470352406647465              \PYGZhy{}\PYGZhy{}
+4467706551328679552   270.033164589881 ...  0.927008559859825              \PYGZhy{}\PYGZhy{}
+4467712255037300096  270.7724717923047 ... 0.9719742773203504              \PYGZhy{}\PYGZhy{}
+4467735001181761792  270.3628606248308 ...  0.509812721702093              \PYGZhy{}\PYGZhy{}
+4467737101421916672  270.5110834661444 ... 0.7549581886719651              \PYGZhy{}\PYGZhy{}
+4467707547757327488 269.88746280594927 ... 0.3022057897812064              \PYGZhy{}\PYGZhy{}
+4467732355491087744  270.6730790702491 ... 0.4937921513912002              \PYGZhy{}\PYGZhy{}
+4467717099766944512 270.57667173120825 ... 0.8867339293525688              \PYGZhy{}\PYGZhy{}
+4467719058265781248  270.7248052971514 ...  0.390952370410666              \PYGZhy{}\PYGZhy{}
+4467722326741572352 270.87431291888504 ... 0.1660452431882023              \PYGZhy{}\PYGZhy{}
+\end{sphinxVerbatim}
+
+Good so far.
+
+\sphinxstylestrong{Exercise:} This query always selects sources with \sphinxcode{\sphinxupquote{parallax}} less than 1.  But suppose you want to take that upper bound as an input.
+
+Modify \sphinxcode{\sphinxupquote{query3\_base}} to replace \sphinxcode{\sphinxupquote{1}} with a format specifier like \sphinxcode{\sphinxupquote{\{max\_parallax\}}}.  Now, when you call \sphinxcode{\sphinxupquote{format}}, add a keyword argument that assigns a value to \sphinxcode{\sphinxupquote{max\_parallax}}, and confirm that the format specifier gets replaced with the value you provide.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{c+c1}{\PYGZsh{} Solution}
+
+\PYG{n}{query4\PYGZus{}base} \PYG{o}{=} \PYG{l+s+s2}{\PYGZdq{}\PYGZdq{}\PYGZdq{}}\PYG{l+s+s2}{SELECT TOP 10}
+\PYG{l+s+si}{\PYGZob{}columns\PYGZcb{}}
+\PYG{l+s+s2}{FROM gaiadr2.gaia\PYGZus{}source}
+\PYG{l+s+s2}{WHERE parallax \PYGZlt{} }\PYG{l+s+si}{\PYGZob{}max\PYGZus{}parallax\PYGZcb{}}\PYG{l+s+s2}{ AND }
+\PYG{l+s+s2}{bp\PYGZus{}rp BETWEEN \PYGZhy{}0.75 AND 2}
+\PYG{l+s+s2}{\PYGZdq{}\PYGZdq{}\PYGZdq{}}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{c+c1}{\PYGZsh{} Solution}
+
+\PYG{n}{query4} \PYG{o}{=} \PYG{n}{query4\PYGZus{}base}\PYG{o}{.}\PYG{n}{format}\PYG{p}{(}\PYG{n}{columns}\PYG{o}{=}\PYG{n}{columns}\PYG{p}{,}
+                          \PYG{n}{max\PYGZus{}parallax}\PYG{o}{=}\PYG{l+m+mf}{0.5}\PYG{p}{)}
+\PYG{n+nb}{print}\PYG{p}{(}\PYG{n}{query}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+SELECT TOP 10
+source\PYGZus{}id, ra, dec, pmra, pmdec, parallax, parallax\PYGZus{}error, radial\PYGZus{}velocity
+FROM gaiadr2.gaia\PYGZus{}source
+WHERE parallax \PYGZlt{} 0.5 AND 
+bp\PYGZus{}rp BETWEEN \PYGZhy{}0.75 AND 2
+\end{sphinxVerbatim}
+
+\sphinxstylestrong{Style note:}  You might notice that the variable names in this notebook are numbered, like \sphinxcode{\sphinxupquote{query1}}, \sphinxcode{\sphinxupquote{query2}}, etc.
+
+The advantage of this style is that it isolates each section of the notebook from the others, so if you go back and run the cells out of order, it’s less likely that you will get unexpected interactions.
+
+A drawback of this style is that it can be a nuisance to update the notebook if you add, remove, or reorder a section.
+
+What do you think of this choice?  Are there alternatives you prefer?
+
+
+\section{Summary}
+\label{\detokenize{01_query:summary}}
+This notebook demonstrates the following steps:
+\begin{enumerate}
+\sphinxsetlistlabels{\arabic}{enumi}{enumii}{}{.}%
+\item {} 
+Making a connection to the Gaia server,
+
+\item {} 
+Exploring information about the database and the tables it contains,
+
+\item {} 
+Writing a query and sending it to the server, and finally
+
+\item {} 
+Downloading the response from the server as an Astropy \sphinxcode{\sphinxupquote{Table}}.
+
+\end{enumerate}
+
+
+\section{Best practices}
+\label{\detokenize{01_query:best-practices}}\begin{itemize}
+\item {} 
+If you can’t download an entire dataset (or it’s not practical) use queries to select the data you need.
+
+\item {} 
+Read the metadata and the documentation to make sure you understand the tables, their columns, and what they mean.
+
+\item {} 
+Develop queries incrementally: start with something simple, test it, and add a little bit at a time.
+
+\item {} 
+Use ADQL features like \sphinxcode{\sphinxupquote{TOP}} and \sphinxcode{\sphinxupquote{COUNT}} to test before you run a query that might return a lot of data.
+
+\item {} 
+If you know your query will return fewer than 3000 rows, you can run it synchronously, which might complete faster (but it doesn’t seem to make much difference).  If it might return more than 3000 rows, you should run it asynchronously.
+
+\item {} 
+ADQL and SQL are not case\sphinxhyphen{}sensitive, so you don’t have to capitalize the keywords, but you should.
+
+\item {} 
+ADQL and SQL don’t require you to break a query into multiple lines, but you should.
+
+\end{itemize}
+
+Jupyter notebooks can be good for developing and testing code, but they have some drawbacks.  In particular, if you run the cells out of order, you might find that variables don’t have the values you expect.
+
+There are a few things you can do to mitigate these problems:
+\begin{itemize}
+\item {} 
+Make each section of the notebook self\sphinxhyphen{}contained.  Try not to use the same variable name in more than one section.
+
+\item {} 
+Keep notebooks short.  Look for places where you can break your analysis into phases with one notebook per phase.
+
+\end{itemize}
+
+
+\chapter{Chapter 2}
+\label{\detokenize{02_coords:chapter-2}}\label{\detokenize{02_coords::doc}}
+This is the second in a series of notebooks related to astronomy data.
+
+As a running example, we are replicating parts of the analysis in a recent paper, “\sphinxhref{https://arxiv.org/abs/1805.00425}{Off the beaten path: Gaia reveals GD\sphinxhyphen{}1 stars outside of the main stream}� by Adrian M. Price\sphinxhyphen{}Whelan and Ana Bonaca.
+
+In the first notebook, we wrote ADQL queries and used them to select and download data from the Gaia server.
+
+In this notebook, we’ll pick up where we left off and write a query to select stars from the region of the sky where we expect GD\sphinxhyphen{}1 to be.
+
+
+\section{Outline}
+\label{\detokenize{02_coords:outline}}
+We’ll start with an example that does a “cone search�; that is, it selects stars that appear in a circular region of the sky.
+
+Then, to select stars in the vicinity of GD\sphinxhyphen{}1, we’ll:
+\begin{itemize}
+\item {} 
+Use \sphinxcode{\sphinxupquote{Quantity}} objects to represent measurements with units.
+
+\item {} 
+Use the \sphinxcode{\sphinxupquote{Gala}} library to convert coordinates from one frame to another.
+
+\item {} 
+Use the ADQL keywords \sphinxcode{\sphinxupquote{POLYGON}}, \sphinxcode{\sphinxupquote{CONTAINS}}, and \sphinxcode{\sphinxupquote{POINT}} to select stars that fall within a polygonal region.
+
+\item {} 
+Submit a query and download the results.
+
+\item {} 
+Store the results in a FITS file.
+
+\end{itemize}
+
+After completing this lesson, you should be able to
+\begin{itemize}
+\item {} 
+Use Python string formatting to compose more complex ADQL queries.
+
+\item {} 
+Work with coordinates and other quantities that have units.
+
+\item {} 
+Download the results of a query and store them in a file.
+
+\end{itemize}
+
+
+\section{Installing libraries}
+\label{\detokenize{02_coords:installing-libraries}}
+If you are running this notebook on Colab, you can run the following cell to install Astroquery and a the other libraries we’ll use.
+
+If you are running this notebook on your own computer, you might have to install these libraries yourself.
+
+If you are using this notebook as part of a Carpentries workshop, you should have received setup instructions.
+
+TODO: Add a link to the instructions.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{c+c1}{\PYGZsh{} If we\PYGZsq{}re running on Colab, install libraries}
+
+\PYG{k+kn}{import} \PYG{n+nn}{sys}
+\PYG{n}{IN\PYGZus{}COLAB} \PYG{o}{=} \PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{google.colab}\PYG{l+s+s1}{\PYGZsq{}} \PYG{o+ow}{in} \PYG{n}{sys}\PYG{o}{.}\PYG{n}{modules}
+
+\PYG{k}{if} \PYG{n}{IN\PYGZus{}COLAB}\PYG{p}{:}
+    \PYG{o}{!}pip install astroquery astro\PYGZhy{}gala pyia
+\end{sphinxVerbatim}
+
+
+\section{Selecting a region}
+\label{\detokenize{02_coords:selecting-a-region}}
+One of the most common ways to restrict a query is to select stars in a particular region of the sky.
+
+For example, here’s a query from the \sphinxhref{https://gea.esac.esa.int/archive-help/adql/examples/index.html}{Gaia archive documentation} that selects “all the objects … in a circular region centered at (266.41683, \sphinxhyphen{}29.00781) with a search radius of 5 arcmin (0.08333 deg).�
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{query} \PYG{o}{=} \PYG{l+s+s2}{\PYGZdq{}\PYGZdq{}\PYGZdq{}}
+\PYG{l+s+s2}{SELECT }
+\PYG{l+s+s2}{TOP 10 source\PYGZus{}id}
+\PYG{l+s+s2}{FROM gaiadr2.gaia\PYGZus{}source}
+\PYG{l+s+s2}{WHERE 1=CONTAINS(}
+\PYG{l+s+s2}{  POINT(ra, dec),}
+\PYG{l+s+s2}{  CIRCLE(266.41683, \PYGZhy{}29.00781, 0.08333333))}
+\PYG{l+s+s2}{\PYGZdq{}\PYGZdq{}\PYGZdq{}}
+\end{sphinxVerbatim}
+
+This query uses three keywords that are specific to ADQL (not SQL):
+\begin{itemize}
+\item {} 
+\sphinxcode{\sphinxupquote{POINT}}: a location in \sphinxhref{https://en.wikipedia.org/wiki/International\_Celestial\_Reference\_System}{ICRS coordinates}, specified in degrees of right ascension and declination.
+
+\item {} 
+\sphinxcode{\sphinxupquote{CIRCLE}}: a circle where the first two values are the coordinates of the center and the third is the radius in degrees.
+
+\item {} 
+\sphinxcode{\sphinxupquote{CONTAINS}}: a function that returns \sphinxcode{\sphinxupquote{1}} if a \sphinxcode{\sphinxupquote{POINT}} is contained in a shape and \sphinxcode{\sphinxupquote{0}} otherwise.
+
+\end{itemize}
+
+Here is the \sphinxhref{http://www.ivoa.net/documents/ADQL/20180112/PR-ADQL-2.1-20180112.html\#tth\_sEc4.2.12}{documentation of \sphinxcode{\sphinxupquote{CONTAINS}}}.
+
+A query like this is called a cone search because it selects stars in a cone.
+
+Here’s how we run it.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{k+kn}{from} \PYG{n+nn}{astroquery}\PYG{n+nn}{.}\PYG{n+nn}{gaia} \PYG{k+kn}{import} \PYG{n}{Gaia}
+
+\PYG{n}{job} \PYG{o}{=} \PYG{n}{Gaia}\PYG{o}{.}\PYG{n}{launch\PYGZus{}job}\PYG{p}{(}\PYG{n}{query}\PYG{p}{)}
+\PYG{n}{result} \PYG{o}{=} \PYG{n}{job}\PYG{o}{.}\PYG{n}{get\PYGZus{}results}\PYG{p}{(}\PYG{p}{)}
+\PYG{n}{result}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+Created TAP+ (v1.2.1) \PYGZhy{} Connection:
+	Host: gea.esac.esa.int
+	Use HTTPS: True
+	Port: 443
+	SSL Port: 443
+Created TAP+ (v1.2.1) \PYGZhy{} Connection:
+	Host: geadata.esac.esa.int
+	Use HTTPS: True
+	Port: 443
+	SSL Port: 443
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYGZlt{}Table length=10\PYGZgt{}
+     source\PYGZus{}id     
+       int64       
+\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}
+4057468321929794432
+4057468287575835392
+4057482027171038976
+4057470349160630656
+4057470039924301696
+4057469868125641984
+4057468351995073024
+4057469661959554560
+4057470520960672640
+4057470555320409600
+\end{sphinxVerbatim}
+
+\sphinxstylestrong{Exercise:} When you are debugging queries like this, you can use \sphinxcode{\sphinxupquote{TOP}} to limit the size of the results, but then you still don’t know how big the results will be.
+
+An alternative is to use \sphinxcode{\sphinxupquote{COUNT}}, which asks for the number of rows that would be selected, but it does not return them.
+
+In the previous query, replace \sphinxcode{\sphinxupquote{TOP 10 source\_id}} with \sphinxcode{\sphinxupquote{COUNT(source\_id)}} and run the query again.  How many stars has Gaia identified in the cone we searched?
+
+
+\section{Getting GD\sphinxhyphen{}1 Data}
+\label{\detokenize{02_coords:getting-gd-1-data}}
+From the Price\sphinxhyphen{}Whelan and Bonaca paper, we will try to reproduce Figure 1, which includes this representation of stars likely to belong to GD\sphinxhyphen{}1:
+
+
+
+Along the axis of right ascension (\(\phi_1\)) the figure extends from \sphinxhyphen{}100 to 20 degrees.
+
+Along the axis of declination (\(\phi_2\)) the figure extends from about \sphinxhyphen{}8 to 4 degrees.
+
+Ideally, we would select all stars from this rectangle, but there are more than 10 million of them, so
+\begin{itemize}
+\item {} 
+That would be difficult to work with,
+
+\item {} 
+As anonymous users, we are limited to 3 million rows in a single query, and
+
+\item {} 
+While we are developing and testing code, it will be faster to work with a smaller dataset.
+
+\end{itemize}
+
+So we’ll start by selecting stars in a smaller rectangle, from \sphinxhyphen{}55 to \sphinxhyphen{}45 degrees right ascension and \sphinxhyphen{}8 to 4 degrees of declination.
+
+But first we let’s see how to represent quantities with units like degrees.
+
+
+\section{Working with coordinates}
+\label{\detokenize{02_coords:working-with-coordinates}}
+Coordinates are physical quantities, which means that they have two parts, a value and a unit.
+
+For example, the coordinate \(30^{\circ}\) has value 30 and its units are degrees.
+
+Until recently, most scientific computation was done with values only; units were left out of the program altogether, \sphinxhref{https://en.wikipedia.org/wiki/Mars\_Climate\_Orbiter\#Cause\_of\_failure}{often with disastrous results}.
+
+Astropy provides tools for including units explicitly in computations, which makes it possible to detect errors before they cause disasters.
+
+To use Astropy units, we import them like this:
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{k+kn}{import} \PYG{n+nn}{astropy}\PYG{n+nn}{.}\PYG{n+nn}{units} \PYG{k}{as} \PYG{n+nn}{u}
+
+\PYG{n}{u}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYGZlt{}module \PYGZsq{}astropy.units\PYGZsq{} from \PYGZsq{}/home/downey/anaconda3/envs/AstronomicalData/lib/python3.8/site\PYGZhy{}packages/astropy/units/\PYGZus{}\PYGZus{}init\PYGZus{}\PYGZus{}.py\PYGZsq{}\PYGZgt{}
+\end{sphinxVerbatim}
+
+\sphinxcode{\sphinxupquote{u}} is an object that contains most common units and all SI units.
+
+You can use \sphinxcode{\sphinxupquote{dir}} to list them, but you should also \sphinxhref{https://docs.astropy.org/en/stable/units/}{read the documentation}.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n+nb}{dir}\PYG{p}{(}\PYG{n}{u}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+[\PYGZsq{}A\PYGZsq{},
+ \PYGZsq{}AA\PYGZsq{},
+ \PYGZsq{}AB\PYGZsq{},
+ \PYGZsq{}ABflux\PYGZsq{},
+ \PYGZsq{}ABmag\PYGZsq{},
+ \PYGZsq{}AU\PYGZsq{},
+ \PYGZsq{}Angstrom\PYGZsq{},
+ \PYGZsq{}B\PYGZsq{},
+ \PYGZsq{}Ba\PYGZsq{},
+ \PYGZsq{}Barye\PYGZsq{},
+ \PYGZsq{}Bi\PYGZsq{},
+ \PYGZsq{}Biot\PYGZsq{},
+ \PYGZsq{}Bol\PYGZsq{},
+ \PYGZsq{}Bq\PYGZsq{},
+ \PYGZsq{}C\PYGZsq{},
+ \PYGZsq{}Celsius\PYGZsq{},
+ \PYGZsq{}Ci\PYGZsq{},
+ \PYGZsq{}CompositeUnit\PYGZsq{},
+ \PYGZsq{}D\PYGZsq{},
+ \PYGZsq{}Da\PYGZsq{},
+ \PYGZsq{}Dalton\PYGZsq{},
+ \PYGZsq{}Debye\PYGZsq{},
+ \PYGZsq{}Decibel\PYGZsq{},
+ \PYGZsq{}DecibelUnit\PYGZsq{},
+ \PYGZsq{}Dex\PYGZsq{},
+ \PYGZsq{}DexUnit\PYGZsq{},
+ \PYGZsq{}EA\PYGZsq{},
+ \PYGZsq{}EAU\PYGZsq{},
+ \PYGZsq{}EB\PYGZsq{},
+ \PYGZsq{}EBa\PYGZsq{},
+ \PYGZsq{}EC\PYGZsq{},
+ \PYGZsq{}ED\PYGZsq{},
+ \PYGZsq{}EF\PYGZsq{},
+ \PYGZsq{}EG\PYGZsq{},
+ \PYGZsq{}EGal\PYGZsq{},
+ \PYGZsq{}EH\PYGZsq{},
+ \PYGZsq{}EHz\PYGZsq{},
+ \PYGZsq{}EJ\PYGZsq{},
+ \PYGZsq{}EJy\PYGZsq{},
+ \PYGZsq{}EK\PYGZsq{},
+ \PYGZsq{}EL\PYGZsq{},
+ \PYGZsq{}EN\PYGZsq{},
+ \PYGZsq{}EOhm\PYGZsq{},
+ \PYGZsq{}EP\PYGZsq{},
+ \PYGZsq{}EPa\PYGZsq{},
+ \PYGZsq{}ER\PYGZsq{},
+ \PYGZsq{}ERy\PYGZsq{},
+ \PYGZsq{}ES\PYGZsq{},
+ \PYGZsq{}ESt\PYGZsq{},
+ \PYGZsq{}ET\PYGZsq{},
+ \PYGZsq{}EV\PYGZsq{},
+ \PYGZsq{}EW\PYGZsq{},
+ \PYGZsq{}EWb\PYGZsq{},
+ \PYGZsq{}Ea\PYGZsq{},
+ \PYGZsq{}Eadu\PYGZsq{},
+ \PYGZsq{}Earcmin\PYGZsq{},
+ \PYGZsq{}Earcsec\PYGZsq{},
+ \PYGZsq{}Eau\PYGZsq{},
+ \PYGZsq{}Eb\PYGZsq{},
+ \PYGZsq{}Ebarn\PYGZsq{},
+ \PYGZsq{}Ebeam\PYGZsq{},
+ \PYGZsq{}Ebin\PYGZsq{},
+ \PYGZsq{}Ebit\PYGZsq{},
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+ \PYGZsq{}dL\PYGZsq{},
+ \PYGZsq{}dN\PYGZsq{},
+ \PYGZsq{}dOhm\PYGZsq{},
+ \PYGZsq{}dP\PYGZsq{},
+ \PYGZsq{}dPa\PYGZsq{},
+ \PYGZsq{}dR\PYGZsq{},
+ \PYGZsq{}dRy\PYGZsq{},
+ \PYGZsq{}dS\PYGZsq{},
+ \PYGZsq{}dSt\PYGZsq{},
+ \PYGZsq{}dT\PYGZsq{},
+ ...]
+\end{sphinxVerbatim}
+
+To create a quantity, we multiply a value by a unit.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{coord} \PYG{o}{=} \PYG{l+m+mi}{30} \PYG{o}{*} \PYG{n}{u}\PYG{o}{.}\PYG{n}{deg}
+\PYG{n+nb}{type}\PYG{p}{(}\PYG{n}{coord}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+astropy.units.quantity.Quantity
+\end{sphinxVerbatim}
+
+The result is a \sphinxcode{\sphinxupquote{Quantity}} object.
+
+Jupyter knows how to display \sphinxcode{\sphinxupquote{Quantities}} like this:
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{coord}
+\end{sphinxVerbatim}
+\begin{equation*}
+\begin{split}30 \; \mathrm{{}^{\circ}}\end{split}
+\end{equation*}
+
+\section{Selecting a rectangle}
+\label{\detokenize{02_coords:selecting-a-rectangle}}
+Now we’ll select a rectangle from \sphinxhyphen{}55 to \sphinxhyphen{}45 degrees right ascension and \sphinxhyphen{}8 to 4 degrees of declination.
+
+We’ll define variables to contain these limits.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{phi1\PYGZus{}min} \PYG{o}{=} \PYG{o}{\PYGZhy{}}\PYG{l+m+mi}{55}
+\PYG{n}{phi1\PYGZus{}max} \PYG{o}{=} \PYG{o}{\PYGZhy{}}\PYG{l+m+mi}{45}
+\PYG{n}{phi2\PYGZus{}min} \PYG{o}{=} \PYG{o}{\PYGZhy{}}\PYG{l+m+mi}{8}
+\PYG{n}{phi2\PYGZus{}max} \PYG{o}{=} \PYG{l+m+mi}{4}
+\end{sphinxVerbatim}
+
+To represent a rectangle, we’ll use two lists of coordinates and multiply by their units.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{phi1\PYGZus{}rect} \PYG{o}{=} \PYG{p}{[}\PYG{n}{phi1\PYGZus{}min}\PYG{p}{,} \PYG{n}{phi1\PYGZus{}min}\PYG{p}{,} \PYG{n}{phi1\PYGZus{}max}\PYG{p}{,} \PYG{n}{phi1\PYGZus{}max}\PYG{p}{]} \PYG{o}{*} \PYG{n}{u}\PYG{o}{.}\PYG{n}{deg}
+\PYG{n}{phi2\PYGZus{}rect} \PYG{o}{=} \PYG{p}{[}\PYG{n}{phi2\PYGZus{}min}\PYG{p}{,} \PYG{n}{phi2\PYGZus{}max}\PYG{p}{,} \PYG{n}{phi2\PYGZus{}max}\PYG{p}{,} \PYG{n}{phi2\PYGZus{}min}\PYG{p}{]} \PYG{o}{*} \PYG{n}{u}\PYG{o}{.}\PYG{n}{deg}
+\end{sphinxVerbatim}
+
+\sphinxcode{\sphinxupquote{phi1\_rect}} and \sphinxcode{\sphinxupquote{phi2\_rect}} represent the coordinates of the corners of a rectangle.
+
+But they are in “\sphinxhref{https://gala-astro.readthedocs.io/en/latest/\_modules/gala/coordinates/gd1.html}{a Heliocentric spherical coordinate system defined by the orbit of the GD1 stream}�
+
+In order to use them in a Gaia query, we have to convert them to \sphinxhref{https://en.wikipedia.org/wiki/International\_Celestial\_Reference\_System}{International Celestial Reference System} (ICRS) coordinates.  We can do that by storing the coordinates in a \sphinxcode{\sphinxupquote{GD1Koposov10}} object provided by \sphinxhref{https://gala-astro.readthedocs.io/en/latest/coordinates/}{Gala}.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{k+kn}{import} \PYG{n+nn}{gala}\PYG{n+nn}{.}\PYG{n+nn}{coordinates} \PYG{k}{as} \PYG{n+nn}{gc}
+
+\PYG{n}{corners} \PYG{o}{=} \PYG{n}{gc}\PYG{o}{.}\PYG{n}{GD1Koposov10}\PYG{p}{(}\PYG{n}{phi1}\PYG{o}{=}\PYG{n}{phi1\PYGZus{}rect}\PYG{p}{,} \PYG{n}{phi2}\PYG{o}{=}\PYG{n}{phi2\PYGZus{}rect}\PYG{p}{)}
+\PYG{n+nb}{type}\PYG{p}{(}\PYG{n}{corners}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+gala.coordinates.gd1.GD1Koposov10
+\end{sphinxVerbatim}
+
+We can display the result like this:
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{corners}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYGZlt{}GD1Koposov10 Coordinate: (phi1, phi2) in deg
+    [(\PYGZhy{}55., \PYGZhy{}8.), (\PYGZhy{}55.,  4.), (\PYGZhy{}45.,  4.), (\PYGZhy{}45., \PYGZhy{}8.)]\PYGZgt{}
+\end{sphinxVerbatim}
+
+Now we can use \sphinxcode{\sphinxupquote{transform\_to}} to convert to ICRS coordinates.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{k+kn}{import} \PYG{n+nn}{astropy}\PYG{n+nn}{.}\PYG{n+nn}{coordinates} \PYG{k}{as} \PYG{n+nn}{coord}
+
+\PYG{n}{corners\PYGZus{}icrs} \PYG{o}{=} \PYG{n}{corners}\PYG{o}{.}\PYG{n}{transform\PYGZus{}to}\PYG{p}{(}\PYG{n}{coord}\PYG{o}{.}\PYG{n}{ICRS}\PYG{p}{)}
+\PYG{n+nb}{type}\PYG{p}{(}\PYG{n}{corners\PYGZus{}icrs}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+astropy.coordinates.builtin\PYGZus{}frames.icrs.ICRS
+\end{sphinxVerbatim}
+
+The result is an \sphinxcode{\sphinxupquote{ICRS}} object.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{corners\PYGZus{}icrs}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYGZlt{}ICRS Coordinate: (ra, dec) in deg
+    [(146.27533314, 19.26190982), (135.42163944, 25.87738723),
+     (141.60264825, 34.3048303 ), (152.81671045, 27.13611254)]\PYGZgt{}
+\end{sphinxVerbatim}
+
+Notice that a rectangle in one coordinate system is not necessarily a rectangle in another.  In this example, the result is a polygon.
+
+
+\section{Selecting a polygon}
+\label{\detokenize{02_coords:selecting-a-polygon}}
+In order to use this polygon as part of an ADQL query, we have to convert it to a string with a comma\sphinxhyphen{}separated list of coordinates, as in this example:
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{l+s+sd}{\PYGZdq{}\PYGZdq{}\PYGZdq{}}
+\PYG{l+s+sd}{POLYGON(143.65, 20.98, }
+\PYG{l+s+sd}{        134.46, 26.39, }
+\PYG{l+s+sd}{        140.58, 34.85, }
+\PYG{l+s+sd}{        150.16, 29.01)}
+\PYG{l+s+sd}{\PYGZdq{}\PYGZdq{}\PYGZdq{}}
+\end{sphinxVerbatim}
+
+\sphinxcode{\sphinxupquote{corners\_icrs}} behaves like a list, so we can use a \sphinxcode{\sphinxupquote{for}} loop to iterate through the points.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{k}{for} \PYG{n}{point} \PYG{o+ow}{in} \PYG{n}{corners\PYGZus{}icrs}\PYG{p}{:}
+    \PYG{n+nb}{print}\PYG{p}{(}\PYG{n}{point}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYGZlt{}ICRS Coordinate: (ra, dec) in deg
+    (146.27533314, 19.26190982)\PYGZgt{}
+\PYGZlt{}ICRS Coordinate: (ra, dec) in deg
+    (135.42163944, 25.87738723)\PYGZgt{}
+\PYGZlt{}ICRS Coordinate: (ra, dec) in deg
+    (141.60264825, 34.3048303)\PYGZgt{}
+\PYGZlt{}ICRS Coordinate: (ra, dec) in deg
+    (152.81671045, 27.13611254)\PYGZgt{}
+\end{sphinxVerbatim}
+
+From that, we can select the coordinates \sphinxcode{\sphinxupquote{ra}} and \sphinxcode{\sphinxupquote{dec}}:
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{k}{for} \PYG{n}{point} \PYG{o+ow}{in} \PYG{n}{corners\PYGZus{}icrs}\PYG{p}{:}
+    \PYG{n+nb}{print}\PYG{p}{(}\PYG{n}{point}\PYG{o}{.}\PYG{n}{ra}\PYG{p}{,} \PYG{n}{point}\PYG{o}{.}\PYG{n}{dec}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+146d16m31.1993s 19d15m42.8754s
+135d25m17.902s 25d52m38.594s
+141d36m09.5337s 34d18m17.3891s
+152d49m00.1576s 27d08m10.0051s
+\end{sphinxVerbatim}
+
+The results are quantities with units, but if we select the \sphinxcode{\sphinxupquote{value}} part, we get a dimensionless floating\sphinxhyphen{}point number.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{k}{for} \PYG{n}{point} \PYG{o+ow}{in} \PYG{n}{corners\PYGZus{}icrs}\PYG{p}{:}
+    \PYG{n+nb}{print}\PYG{p}{(}\PYG{n}{point}\PYG{o}{.}\PYG{n}{ra}\PYG{o}{.}\PYG{n}{value}\PYG{p}{,} \PYG{n}{point}\PYG{o}{.}\PYG{n}{dec}\PYG{o}{.}\PYG{n}{value}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+146.27533313607782 19.261909820533692
+135.42163944306296 25.87738722767213
+141.60264825107333 34.304830296257144
+152.81671044675923 27.136112541397996
+\end{sphinxVerbatim}
+
+We can use string \sphinxcode{\sphinxupquote{format}} to convert these numbers to strings.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{point\PYGZus{}base} \PYG{o}{=} \PYG{l+s+s2}{\PYGZdq{}}\PYG{l+s+si}{\PYGZob{}point.ra.value\PYGZcb{}}\PYG{l+s+s2}{, }\PYG{l+s+si}{\PYGZob{}point.dec.value\PYGZcb{}}\PYG{l+s+s2}{\PYGZdq{}}
+
+\PYG{n}{t} \PYG{o}{=} \PYG{p}{[}\PYG{n}{point\PYGZus{}base}\PYG{o}{.}\PYG{n}{format}\PYG{p}{(}\PYG{n}{point}\PYG{o}{=}\PYG{n}{point}\PYG{p}{)}
+     \PYG{k}{for} \PYG{n}{point} \PYG{o+ow}{in} \PYG{n}{corners\PYGZus{}icrs}\PYG{p}{]}
+\PYG{n}{t}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+[\PYGZsq{}146.27533313607782, 19.261909820533692\PYGZsq{},
+ \PYGZsq{}135.42163944306296, 25.87738722767213\PYGZsq{},
+ \PYGZsq{}141.60264825107333, 34.304830296257144\PYGZsq{},
+ \PYGZsq{}152.81671044675923, 27.136112541397996\PYGZsq{}]
+\end{sphinxVerbatim}
+
+The result is a list of strings, which we can join into a single string using \sphinxcode{\sphinxupquote{join}}.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{point\PYGZus{}list} \PYG{o}{=} \PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{, }\PYG{l+s+s1}{\PYGZsq{}}\PYG{o}{.}\PYG{n}{join}\PYG{p}{(}\PYG{n}{t}\PYG{p}{)}
+\PYG{n}{point\PYGZus{}list}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYGZsq{}146.27533313607782, 19.261909820533692, 135.42163944306296, 25.87738722767213, 141.60264825107333, 34.304830296257144, 152.81671044675923, 27.136112541397996\PYGZsq{}
+\end{sphinxVerbatim}
+
+Notice that we invoke \sphinxcode{\sphinxupquote{join}} on a string and pass the list as an argument.
+
+Before we can assemble the query, we need \sphinxcode{\sphinxupquote{columns}} again (as we saw in the previous notebook).
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{columns} \PYG{o}{=} \PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{source\PYGZus{}id, ra, dec, pmra, pmdec, parallax, parallax\PYGZus{}error, radial\PYGZus{}velocity}\PYG{l+s+s1}{\PYGZsq{}}
+\end{sphinxVerbatim}
+
+Here’s the base for the query, with format specifiers for \sphinxcode{\sphinxupquote{columns}} and \sphinxcode{\sphinxupquote{point\_list}}.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{query\PYGZus{}base} \PYG{o}{=} \PYG{l+s+s2}{\PYGZdq{}\PYGZdq{}\PYGZdq{}}\PYG{l+s+s2}{SELECT }\PYG{l+s+si}{\PYGZob{}columns\PYGZcb{}}
+\PYG{l+s+s2}{FROM gaiadr2.gaia\PYGZus{}source}
+\PYG{l+s+s2}{WHERE parallax \PYGZlt{} 1}
+\PYG{l+s+s2}{  AND bp\PYGZus{}rp BETWEEN \PYGZhy{}0.75 AND 2 }
+\PYG{l+s+s2}{  AND 1 = CONTAINS(POINT(ra, dec), }
+\PYG{l+s+s2}{                   POLYGON(}\PYG{l+s+si}{\PYGZob{}point\PYGZus{}list\PYGZcb{}}\PYG{l+s+s2}{))}
+\PYG{l+s+s2}{\PYGZdq{}\PYGZdq{}\PYGZdq{}}
+\end{sphinxVerbatim}
+
+And here’s the result:
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{query} \PYG{o}{=} \PYG{n}{query\PYGZus{}base}\PYG{o}{.}\PYG{n}{format}\PYG{p}{(}\PYG{n}{columns}\PYG{o}{=}\PYG{n}{columns}\PYG{p}{,} 
+                          \PYG{n}{point\PYGZus{}list}\PYG{o}{=}\PYG{n}{point\PYGZus{}list}\PYG{p}{)}
+\PYG{n+nb}{print}\PYG{p}{(}\PYG{n}{query}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+SELECT source\PYGZus{}id, ra, dec, pmra, pmdec, parallax, parallax\PYGZus{}error, radial\PYGZus{}velocity
+FROM gaiadr2.gaia\PYGZus{}source
+WHERE parallax \PYGZlt{} 1
+  AND bp\PYGZus{}rp BETWEEN \PYGZhy{}0.75 AND 2 
+  AND 1 = CONTAINS(POINT(ra, dec), 
+                   POLYGON(146.27533313607782, 19.261909820533692, 135.42163944306296, 25.87738722767213, 141.60264825107333, 34.304830296257144, 152.81671044675923, 27.136112541397996))
+\end{sphinxVerbatim}
+
+As always, we should take a minute to proof\sphinxhyphen{}read the query before we launch it.
+
+The result will be bigger than our previous queries, so it will take a little longer.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{job} \PYG{o}{=} \PYG{n}{Gaia}\PYG{o}{.}\PYG{n}{launch\PYGZus{}job\PYGZus{}async}\PYG{p}{(}\PYG{n}{query}\PYG{p}{)}
+\PYG{n+nb}{print}\PYG{p}{(}\PYG{n}{job}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+INFO: Query finished. [astroquery.utils.tap.core]
+\PYGZlt{}Table length=140340\PYGZgt{}
+      name       dtype    unit                              description                             n\PYGZus{}bad 
+\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{} \PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{} \PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{} \PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{} \PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}
+      source\PYGZus{}id   int64          Unique source identifier (unique within a particular Data Release)      0
+             ra float64      deg                                                    Right ascension      0
+            dec float64      deg                                                        Declination      0
+           pmra float64 mas / yr                         Proper motion in right ascension direction      0
+          pmdec float64 mas / yr                             Proper motion in declination direction      0
+       parallax float64      mas                                                           Parallax      0
+ parallax\PYGZus{}error float64      mas                                         Standard error of parallax      0
+radial\PYGZus{}velocity float64   km / s                                                    Radial velocity 139374
+Jobid: 1603114980658O
+Phase: COMPLETED
+Owner: None
+Output file: async\PYGZus{}20201019094300.vot
+Results: None
+\end{sphinxVerbatim}
+
+Here are the results.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{results} \PYG{o}{=} \PYG{n}{job}\PYG{o}{.}\PYG{n}{get\PYGZus{}results}\PYG{p}{(}\PYG{p}{)}
+\PYG{n+nb}{len}\PYG{p}{(}\PYG{n}{results}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+140340
+\end{sphinxVerbatim}
+
+There are more than 100,000 stars in this polygon, but that’s a manageable size to work with.
+
+
+\section{Saving results}
+\label{\detokenize{02_coords:saving-results}}
+This is the set of stars we’ll work with in the next step.  But since we have a substantial dataset now, this is a good time to save it.
+
+Storing the data in a file means we can shut down this notebook and pick up where we left off without running the previous query again.
+
+Astropy \sphinxcode{\sphinxupquote{Table}} objects provide \sphinxcode{\sphinxupquote{write}}, which writes the table to disk.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{filename} \PYG{o}{=} \PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{gd1\PYGZus{}results.fits}\PYG{l+s+s1}{\PYGZsq{}}
+\PYG{n}{results}\PYG{o}{.}\PYG{n}{write}\PYG{p}{(}\PYG{n}{filename}\PYG{p}{,} \PYG{n}{overwrite}\PYG{o}{=}\PYG{k+kc}{True}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+Because the filename ends with \sphinxcode{\sphinxupquote{fits}}, the table is written in the \sphinxhref{https://en.wikipedia.org/wiki/FITS}{FITS format}, which preserves the metadata associated with the table.
+
+If the file already exists, the \sphinxcode{\sphinxupquote{overwrite}} argument causes it to be overwritten.
+
+To see how big the file is, we can use \sphinxcode{\sphinxupquote{ls}} with the \sphinxcode{\sphinxupquote{\sphinxhyphen{}lh}} option, which prints information about the file including its size in human\sphinxhyphen{}readable form.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{o}{!}ls \PYGZhy{}lh gd1\PYGZus{}results.fits
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYGZhy{}rw\PYGZhy{}rw\PYGZhy{}r\PYGZhy{}\PYGZhy{} 1 downey downey 8.6M Oct 19 09:43 gd1\PYGZus{}results.fits
+\end{sphinxVerbatim}
+
+The file is about 8.6 MB.  If you are using Windows, \sphinxcode{\sphinxupquote{ls}} might not work; in that case, try:
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+!dir gd1\PYGZus{}results.fits
+\end{sphinxVerbatim}
+
+
+\section{Summary}
+\label{\detokenize{02_coords:summary}}
+In this notebook, we composed more complex queries to select stars within a polygonal region of the sky.  Then we downloaded the results and saved them in a FITS file.
+
+In the next notebook, we’ll reload the data from this file and replicate the next step in the analysis, using proper motion to identify stars likely to be in GD\sphinxhyphen{}1.
+
+
+\section{Best practices}
+\label{\detokenize{02_coords:best-practices}}\begin{itemize}
+\item {} 
+For measurements with units, use \sphinxcode{\sphinxupquote{Quantity}} objects that represent units explicitly and check for errors.
+
+\item {} 
+Use the \sphinxcode{\sphinxupquote{format}} function to compose queries; it is often faster and less error\sphinxhyphen{}prone.
+
+\item {} 
+Develop queries incrementally: start with something simple, test it, and add a little bit at a time.
+
+\item {} 
+Once you have a query working, save the data in a local file.  If you shut down the notebook and come back to it later, you can reload the file; you don’t have to run the query again.
+
+\end{itemize}
+
+
+\chapter{Chapter 3}
+\label{\detokenize{03_motion:chapter-3}}\label{\detokenize{03_motion::doc}}
+This is the third in a series of notebooks related to astronomy data.
+
+As a running example, we are replicating parts of the analysis in a recent paper, “\sphinxhref{https://arxiv.org/abs/1805.00425}{Off the beaten path: Gaia reveals GD\sphinxhyphen{}1 stars outside of the main stream}� by Adrian M. Price\sphinxhyphen{}Whelan and Ana Bonaca.
+
+In the first lesson, we wrote ADQL queries and used them to select and download data from the Gaia server.
+
+In the second lesson, we wrote a query to select stars from the region of the sky where we expect GD\sphinxhyphen{}1 to be, and saved the results in a FITS file.
+
+Now we’ll read that data back and implement the next step in the analysis, identifying stars with the proper motion we expect for GD\sphinxhyphen{}1.
+
+
+\section{Outline}
+\label{\detokenize{03_motion:outline}}
+Here are the steps in this lesson:
+\begin{enumerate}
+\sphinxsetlistlabels{\arabic}{enumi}{enumii}{}{.}%
+\item {} 
+We’ll read back the results from the previous lesson, which we saved in a FITS file.
+
+\item {} 
+Then we’ll transform the coordinates and proper motion data from ICRS back to the coordinate frame of GD\sphinxhyphen{}1.
+
+\item {} 
+We’ll put those results into a Pandas \sphinxcode{\sphinxupquote{DataFrame}}, which we’ll use to select stars near the centerline of GD\sphinxhyphen{}1.
+
+\item {} 
+Plotting the proper motion of those stars, we’ll identify a region of proper motion for stars that are likely to be in GD\sphinxhyphen{}1.
+
+\item {} 
+Finally, we’ll select and plot the stars whose proper motion is in that region.
+
+\end{enumerate}
+
+After completing this lesson, you should be able to
+\begin{itemize}
+\item {} 
+Select rows and columns from an Astropy \sphinxcode{\sphinxupquote{Table}}.
+
+\item {} 
+Use Matplotlib to make a scatter plot.
+
+\item {} 
+Use Gala to transform coordinates.
+
+\item {} 
+Make a Pandas \sphinxcode{\sphinxupquote{DataFrame}} and use a Boolean \sphinxcode{\sphinxupquote{Series}} to select rows.
+
+\item {} 
+Save a \sphinxcode{\sphinxupquote{DataFrame}} in an HDF5 file.
+
+\end{itemize}
+
+
+\section{Installing libraries}
+\label{\detokenize{03_motion:installing-libraries}}
+If you are running this notebook on Colab, you can run the following cell to install Astroquery and a the other libraries we’ll use.
+
+If you are running this notebook on your own computer, you might have to install these libraries yourself.
+
+If you are using this notebook as part of a Carpentries workshop, you should have received setup instructions.
+
+TODO: Add a link to the instructions.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{c+c1}{\PYGZsh{} If we\PYGZsq{}re running on Colab, install libraries}
+
+\PYG{k+kn}{import} \PYG{n+nn}{sys}
+\PYG{n}{IN\PYGZus{}COLAB} \PYG{o}{=} \PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{google.colab}\PYG{l+s+s1}{\PYGZsq{}} \PYG{o+ow}{in} \PYG{n}{sys}\PYG{o}{.}\PYG{n}{modules}
+
+\PYG{k}{if} \PYG{n}{IN\PYGZus{}COLAB}\PYG{p}{:}
+    \PYG{o}{!}pip install astroquery astro\PYGZhy{}gala pyia python\PYGZhy{}wget
+\end{sphinxVerbatim}
+
+
+\section{Reload the data}
+\label{\detokenize{03_motion:reload-the-data}}
+In the previous lesson, we ran a query on the Gaia server and downloaded data for roughly 100,000 stars.  We saved the data in a FITS file so that now, picking up where we left off, we can read the data from a local file rather than running the query again.
+
+If you ran the previous lesson successfully, you should already have a file called \sphinxcode{\sphinxupquote{gd1\_results.fits}} that contains the data we downloaded.
+
+If not, you can run the following cell, which downloads the data from our repository.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{k+kn}{import} \PYG{n+nn}{os}
+\PYG{k+kn}{from} \PYG{n+nn}{wget} \PYG{k+kn}{import} \PYG{n}{download}
+
+\PYG{n}{filename} \PYG{o}{=} \PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{gd1\PYGZus{}results.fits}\PYG{l+s+s1}{\PYGZsq{}}
+\PYG{n}{path} \PYG{o}{=} \PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{https://github.com/AllenDowney/AstronomicalData/raw/main/data/}\PYG{l+s+s1}{\PYGZsq{}}
+
+\PYG{k}{if} \PYG{o+ow}{not} \PYG{n}{os}\PYG{o}{.}\PYG{n}{path}\PYG{o}{.}\PYG{n}{exists}\PYG{p}{(}\PYG{n}{filename}\PYG{p}{)}\PYG{p}{:}
+    \PYG{n+nb}{print}\PYG{p}{(}\PYG{n}{download}\PYG{p}{(}\PYG{n}{path}\PYG{o}{+}\PYG{n}{filename}\PYG{p}{)}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+Now here’s how we can read the data from the file back into an Astropy \sphinxcode{\sphinxupquote{Table}}:
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{k+kn}{from} \PYG{n+nn}{astropy}\PYG{n+nn}{.}\PYG{n+nn}{table} \PYG{k+kn}{import} \PYG{n}{Table}
+
+\PYG{n}{results} \PYG{o}{=} \PYG{n}{Table}\PYG{o}{.}\PYG{n}{read}\PYG{p}{(}\PYG{n}{filename}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+The result is an Astropy \sphinxcode{\sphinxupquote{Table}}.
+
+We can use \sphinxcode{\sphinxupquote{info}} to refresh our memory of the contents.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{results}\PYG{o}{.}\PYG{n}{info}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYGZlt{}Table length=140340\PYGZgt{}
+      name       dtype    unit                              description                            
+\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{} \PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{} \PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{} \PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}
+      source\PYGZus{}id   int64          Unique source identifier (unique within a particular Data Release)
+             ra float64      deg                                                    Right ascension
+            dec float64      deg                                                        Declination
+           pmra float64 mas / yr                         Proper motion in right ascension direction
+          pmdec float64 mas / yr                             Proper motion in declination direction
+       parallax float64      mas                                                           Parallax
+ parallax\PYGZus{}error float64      mas                                         Standard error of parallax
+radial\PYGZus{}velocity float64   km / s                                                    Radial velocity
+\end{sphinxVerbatim}
+
+
+\section{Selecting rows and columns}
+\label{\detokenize{03_motion:selecting-rows-and-columns}}
+In this section we’ll see operations for selecting columns and rows from an Astropy \sphinxcode{\sphinxupquote{Table}}.  You can find more information about these operations in the \sphinxhref{https://docs.astropy.org/en/stable/table/access\_table.html}{Astropy documentation}.
+
+We can get the names of the columns like this:
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{results}\PYG{o}{.}\PYG{n}{colnames}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+[\PYGZsq{}source\PYGZus{}id\PYGZsq{},
+ \PYGZsq{}ra\PYGZsq{},
+ \PYGZsq{}dec\PYGZsq{},
+ \PYGZsq{}pmra\PYGZsq{},
+ \PYGZsq{}pmdec\PYGZsq{},
+ \PYGZsq{}parallax\PYGZsq{},
+ \PYGZsq{}parallax\PYGZus{}error\PYGZsq{},
+ \PYGZsq{}radial\PYGZus{}velocity\PYGZsq{}]
+\end{sphinxVerbatim}
+
+And select an individual column like this:
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{results}\PYG{p}{[}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{ra}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{]}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYGZlt{}Column name=\PYGZsq{}ra\PYGZsq{} dtype=\PYGZsq{}float64\PYGZsq{} unit=\PYGZsq{}deg\PYGZsq{} description=\PYGZsq{}Right ascension\PYGZsq{} length=140340\PYGZgt{}
+142.48301935991023
+142.25452941346344
+142.64528557468074
+142.57739430926034
+142.58913564478618
+141.81762228999614
+143.18339801317677
+ 142.9347319464589
+142.26769745823267
+142.89551292869012
+ 142.2780935768316
+142.06138786534987
+               ...
+143.05456487172972
+ 144.0436496516182
+144.06566578919313
+144.13177563215973
+143.77696341662764
+  142.945956347594
+142.97282480557786
+ 143.4166017695258
+143.64484588686904
+143.41554585481808
+ 143.6908739159247
+ 143.7702681295401
+\end{sphinxVerbatim}
+
+The result is a \sphinxcode{\sphinxupquote{Column}} object that contains the data, and also the data type, units, and name of the column.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n+nb}{type}\PYG{p}{(}\PYG{n}{results}\PYG{p}{[}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{ra}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{]}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+astropy.table.column.Column
+\end{sphinxVerbatim}
+
+The rows in the \sphinxcode{\sphinxupquote{Table}} are numbered from 0 to \sphinxcode{\sphinxupquote{n\sphinxhyphen{}1}}, where \sphinxcode{\sphinxupquote{n}} is the number of rows.  We can select the first row like this:
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{results}\PYG{p}{[}\PYG{l+m+mi}{0}\PYG{p}{]}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYGZlt{}Row index=0\PYGZgt{}
+    source\PYGZus{}id              ra                dec                pmra              pmdec             parallax        parallax\PYGZus{}error  radial\PYGZus{}velocity
+                          deg                deg              mas / yr           mas / yr             mas                mas             km / s    
+      int64             float64            float64            float64            float64            float64            float64          float64    
+\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{} \PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{} \PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{} \PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{} \PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{} \PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{} \PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{} \PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}
+637987125186749568 142.48301935991023 21.75771616932985 \PYGZhy{}2.5168384683875766 2.941813096629439 \PYGZhy{}0.2573448962333354 0.823720794509811           1e+20
+\end{sphinxVerbatim}
+
+As you might have guessed, the result is a \sphinxcode{\sphinxupquote{Row}} object.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n+nb}{type}\PYG{p}{(}\PYG{n}{results}\PYG{p}{[}\PYG{l+m+mi}{0}\PYG{p}{]}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+astropy.table.row.Row
+\end{sphinxVerbatim}
+
+Notice that the bracket operator selects both columns and rows.  You might wonder how it knows which to select.
+
+If the expression in brackets is a string, it selects a column; if the expression is an integer, it selects a row.
+
+If you apply the bracket operator twice, you can select a column and then an element from the column.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{results}\PYG{p}{[}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{ra}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{]}\PYG{p}{[}\PYG{l+m+mi}{0}\PYG{p}{]}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+142.48301935991023
+\end{sphinxVerbatim}
+
+Or you can select a row and then an element from the row.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{results}\PYG{p}{[}\PYG{l+m+mi}{0}\PYG{p}{]}\PYG{p}{[}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{ra}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{]}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+142.48301935991023
+\end{sphinxVerbatim}
+
+You get the same result either way.
+
+
+\section{Scatter plot}
+\label{\detokenize{03_motion:scatter-plot}}
+To see what the results look like, we’ll use a scatter plot.  The library we’ll use is \sphinxhref{https://matplotlib.org/}{Matplotlib}, which is the most widely\sphinxhyphen{}used plotting library for Python.
+
+The Matplotlib interface is based on MATLAB (hence the name), so if you know MATLAB, some of it will be familiar.
+
+We’ll import like this.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{k+kn}{import} \PYG{n+nn}{matplotlib}\PYG{n+nn}{.}\PYG{n+nn}{pyplot} \PYG{k}{as} \PYG{n+nn}{plt}
+\end{sphinxVerbatim}
+
+Pyplot part of the Matplotlib library.  It is conventional to import it using the shortened name \sphinxcode{\sphinxupquote{plt}}.
+
+Pyplot provides two functions that can make scatterplots, \sphinxhref{https://matplotlib.org/3.3.0/api/\_as\_gen/matplotlib.pyplot.scatter.html}{plt.scatter} and \sphinxhref{https://matplotlib.org/api/\_as\_gen/matplotlib.pyplot.plot.html}{plt.plot}.
+\begin{itemize}
+\item {} 
+\sphinxcode{\sphinxupquote{scatter}} is more versatile; for example, you can make every point in a scatter plot a different color.
+
+\item {} 
+\sphinxcode{\sphinxupquote{plot}} is more limited, but for simple cases, it can be substantially faster.
+
+\end{itemize}
+
+Jake Vanderplas explains these differences in \sphinxhref{https://jakevdp.github.io/PythonDataScienceHandbook/04.02-simple-scatter-plots.html}{The Python Data Science Handbook}
+
+Since we are plotting more than 100,000 points and they are all the same size and color, we’ll use \sphinxcode{\sphinxupquote{plot}}.
+
+Here’s a scatter plot with right ascension on the x\sphinxhyphen{}axis and declination on the y\sphinxhyphen{}axis, both ICRS coordinates in degrees.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{x} \PYG{o}{=} \PYG{n}{results}\PYG{p}{[}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{ra}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{]}
+\PYG{n}{y} \PYG{o}{=} \PYG{n}{results}\PYG{p}{[}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{dec}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{]}
+\PYG{n}{plt}\PYG{o}{.}\PYG{n}{plot}\PYG{p}{(}\PYG{n}{x}\PYG{p}{,} \PYG{n}{y}\PYG{p}{,} \PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{ko}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{)}
+
+\PYG{n}{plt}\PYG{o}{.}\PYG{n}{xlabel}\PYG{p}{(}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{ra (degree ICRS)}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{)}
+\PYG{n}{plt}\PYG{o}{.}\PYG{n}{ylabel}\PYG{p}{(}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{dec (degree ICRS)}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{)}\PYG{p}{;}
+\end{sphinxVerbatim}
+
+\noindent\sphinxincludegraphics{{03_motion_28_0}.png}
+
+The arguments to \sphinxcode{\sphinxupquote{plt.plot}} are \sphinxcode{\sphinxupquote{x}}, \sphinxcode{\sphinxupquote{y}}, and a string that specifies the style.  In this case, the letters \sphinxcode{\sphinxupquote{ko}} indicate that we want a black, round marker (\sphinxcode{\sphinxupquote{k}} is for black because \sphinxcode{\sphinxupquote{b}} is for blue).
+
+The functions \sphinxcode{\sphinxupquote{xlabel}} and \sphinxcode{\sphinxupquote{ylabel}} put labels on the axes.
+
+This scatter plot has a problem.  It is “\sphinxhref{https://python-graph-gallery.com/134-how-to-avoid-overplotting-with-python/}{overplotted}�, which means that there are so many overlapping points, we can’t distinguish between high and low density areas.
+
+To fix this, we can provide optional arguments to control the size and transparency of the points.
+
+\sphinxstylestrong{Exercise:} In the call to \sphinxcode{\sphinxupquote{plt.plot}}, add the keyword argument \sphinxcode{\sphinxupquote{markersize=0.1}} to make the markers smaller.
+
+Then add the argument \sphinxcode{\sphinxupquote{alpha=0.1}} to make the markers nearly transparent.
+
+Adjust these arguments until you think the figure shows the data most clearly.
+
+Note: Once you have made these changes, you might notice that the figure shows stripes with lower density of stars.  These stripes are caused by the way Gaia scans the sky, which \sphinxhref{https://www.cosmos.esa.int/web/gaia/scanning-law}{you can read about here}.  The dataset we are using, \sphinxhref{https://www.cosmos.esa.int/web/gaia/dr2}{Gaia Data Release 2}, covers 22 months of observations; during this time, some parts of the sky were scanned more than others.
+
+
+\section{Transform back}
+\label{\detokenize{03_motion:transform-back}}
+Remember that we selected data from a rectangle of coordinates in the \sphinxcode{\sphinxupquote{GD1Koposov10}} frame, then transformed them to ICRS when we constructed the query.
+The coordinates in \sphinxcode{\sphinxupquote{results}} are in ICRS.
+
+To plot them, we will transform them back to the \sphinxcode{\sphinxupquote{GD1Koposov10}} frame; that way, the axes of the figure are aligned with the GD\sphinxhyphen{}1, which will make it easy to select stars near the centerline of the stream.
+
+To do that, we’ll put the results into a \sphinxcode{\sphinxupquote{GaiaData}} object, provided by the \sphinxhref{https://pyia.readthedocs.io/en/latest/api/pyia.GaiaData.html}{pyia library}.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{k+kn}{from} \PYG{n+nn}{pyia} \PYG{k+kn}{import} \PYG{n}{GaiaData}
+
+\PYG{n}{gaia\PYGZus{}data} \PYG{o}{=} \PYG{n}{GaiaData}\PYG{p}{(}\PYG{n}{results}\PYG{p}{)}
+\PYG{n+nb}{type}\PYG{p}{(}\PYG{n}{gaia\PYGZus{}data}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+pyia.data.GaiaData
+\end{sphinxVerbatim}
+
+Now we can extract sky coordinates from the \sphinxcode{\sphinxupquote{GaiaData}} object, like this:
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{k+kn}{import} \PYG{n+nn}{astropy}\PYG{n+nn}{.}\PYG{n+nn}{units} \PYG{k}{as} \PYG{n+nn}{u}
+
+\PYG{n}{skycoord} \PYG{o}{=} \PYG{n}{gaia\PYGZus{}data}\PYG{o}{.}\PYG{n}{get\PYGZus{}skycoord}\PYG{p}{(}
+                \PYG{n}{distance}\PYG{o}{=}\PYG{l+m+mi}{8}\PYG{o}{*}\PYG{n}{u}\PYG{o}{.}\PYG{n}{kpc}\PYG{p}{,} 
+                \PYG{n}{radial\PYGZus{}velocity}\PYG{o}{=}\PYG{l+m+mi}{0}\PYG{o}{*}\PYG{n}{u}\PYG{o}{.}\PYG{n}{km}\PYG{o}{/}\PYG{n}{u}\PYG{o}{.}\PYG{n}{s}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+We provide \sphinxcode{\sphinxupquote{distance}} and \sphinxcode{\sphinxupquote{radial\_velocity}} to prepare the data for reflex correction, which we explain below.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n+nb}{type}\PYG{p}{(}\PYG{n}{skycoord}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+astropy.coordinates.sky\PYGZus{}coordinate.SkyCoord
+\end{sphinxVerbatim}
+
+The result is an Astropy \sphinxcode{\sphinxupquote{SkyCoord}} object (\sphinxhref{https://docs.astropy.org/en/stable/api/astropy.coordinates.SkyCoord.html\#astropy.coordinates.SkyCoord}{documentation here}), which provides \sphinxcode{\sphinxupquote{transform\_to}}, so we can transform the coordinates to other frames.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{k+kn}{import} \PYG{n+nn}{gala}\PYG{n+nn}{.}\PYG{n+nn}{coordinates} \PYG{k}{as} \PYG{n+nn}{gc}
+
+\PYG{n}{transformed} \PYG{o}{=} \PYG{n}{skycoord}\PYG{o}{.}\PYG{n}{transform\PYGZus{}to}\PYG{p}{(}\PYG{n}{gc}\PYG{o}{.}\PYG{n}{GD1Koposov10}\PYG{p}{)}
+\PYG{n+nb}{type}\PYG{p}{(}\PYG{n}{transformed}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+astropy.coordinates.sky\PYGZus{}coordinate.SkyCoord
+\end{sphinxVerbatim}
+
+The result is another \sphinxcode{\sphinxupquote{SkyCoord}} object, now in the \sphinxcode{\sphinxupquote{GD1Koposov10}} frame.
+
+The next step is to correct the proper motion measurements from Gaia for reflex due to the motion of our solar system around the Galactic center.
+
+When we created \sphinxcode{\sphinxupquote{skycoord}}, we provided \sphinxcode{\sphinxupquote{distance}} and \sphinxcode{\sphinxupquote{radial\_velocity}} as arguments, which means we ignore the measurements provided by Gaia and replace them with these fixed values.
+
+That might seem like a strange thing to do, but here’s the motivation:
+\begin{itemize}
+\item {} 
+Because the stars in GD\sphinxhyphen{}1 are so far away, the distance estimates we get from Gaia, which are based on parallax, are not very precise.  So we replace them with our current best estimate of the mean distance to GD\sphinxhyphen{}1, about 8 kpc.  See \sphinxhref{https://ui.adsabs.harvard.edu/abs/2010ApJ...712..260K/abstract}{Koposov, Rix, and Hogg, 2010}.
+
+\item {} 
+For the other stars in the table, this distance estimate will be inaccurate, so reflex correction will not be correct.  But that should have only a small effect on our ability to identify stars with the proper motion we expect for GD\sphinxhyphen{}1.
+
+\item {} 
+The measurement of radial velocity has no effect on the correction for proper motion; the value we provide is arbitrary, but we have to provide a value to avoid errors in the reflex correction calculation.
+
+\end{itemize}
+
+We are grateful to Adrian Price\sphinxhyphen{}Whelen for his help explaining this step in the analysis.
+
+With this preparation, we can use \sphinxcode{\sphinxupquote{reflex\_correct}} from Gala (\sphinxhref{https://gala-astro.readthedocs.io/en/latest/api/gala.coordinates.reflex\_correct.html}{documentation here}) to correct for solar reflex motion.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{gd1\PYGZus{}coord} \PYG{o}{=} \PYG{n}{gc}\PYG{o}{.}\PYG{n}{reflex\PYGZus{}correct}\PYG{p}{(}\PYG{n}{transformed}\PYG{p}{)}
+
+\PYG{n+nb}{type}\PYG{p}{(}\PYG{n}{gd1\PYGZus{}coord}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+astropy.coordinates.sky\PYGZus{}coordinate.SkyCoord
+\end{sphinxVerbatim}
+
+The result is a \sphinxcode{\sphinxupquote{SkyCoord}} object that contains
+\begin{itemize}
+\item {} 
+The transformed coordinates as attributes named \sphinxcode{\sphinxupquote{phi1}} and \sphinxcode{\sphinxupquote{phi2}}, which represent right ascension and declination in the \sphinxcode{\sphinxupquote{GD1Koposov10}} frame.
+
+\item {} 
+The transformed and corrected proper motions as \sphinxcode{\sphinxupquote{pm\_phi1\_cosphi2}} and \sphinxcode{\sphinxupquote{pm\_phi2}}.
+
+\end{itemize}
+
+We can select the coordinates like this:
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{phi1} \PYG{o}{=} \PYG{n}{gd1\PYGZus{}coord}\PYG{o}{.}\PYG{n}{phi1}
+\PYG{n}{phi2} \PYG{o}{=} \PYG{n}{gd1\PYGZus{}coord}\PYG{o}{.}\PYG{n}{phi2}
+\end{sphinxVerbatim}
+
+And plot them like this:
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{plt}\PYG{o}{.}\PYG{n}{plot}\PYG{p}{(}\PYG{n}{phi1}\PYG{p}{,} \PYG{n}{phi2}\PYG{p}{,} \PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{ko}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{,} \PYG{n}{markersize}\PYG{o}{=}\PYG{l+m+mf}{0.1}\PYG{p}{,} \PYG{n}{alpha}\PYG{o}{=}\PYG{l+m+mf}{0.2}\PYG{p}{)}
+
+\PYG{n}{plt}\PYG{o}{.}\PYG{n}{xlabel}\PYG{p}{(}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{ra (degree GD1)}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{)}
+\PYG{n}{plt}\PYG{o}{.}\PYG{n}{ylabel}\PYG{p}{(}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{dec (degree GD1)}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{)}\PYG{p}{;}
+\end{sphinxVerbatim}
+
+\noindent\sphinxincludegraphics{{03_motion_45_0}.png}
+
+Remember that we started with a rectangle in GD\sphinxhyphen{}1 coordinates.  When transformed to ICRS, it’s a non\sphinxhyphen{}rectangular polygon.  Now that we have transformed back to GD\sphinxhyphen{}1 coordinates, it’s a rectangle again.
+
+
+\section{Pandas DataFrame}
+\label{\detokenize{03_motion:pandas-dataframe}}
+At this point we have three objects containing different subsets of the data.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n+nb}{type}\PYG{p}{(}\PYG{n}{results}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+astropy.table.table.Table
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n+nb}{type}\PYG{p}{(}\PYG{n}{gaia\PYGZus{}data}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+pyia.data.GaiaData
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n+nb}{type}\PYG{p}{(}\PYG{n}{gd1\PYGZus{}coord}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+astropy.coordinates.sky\PYGZus{}coordinate.SkyCoord
+\end{sphinxVerbatim}
+
+On one hand, this makes sense, since each object provides different capabilities.  But working with three different object types can be awkward.
+
+It will be more convenient to choose one object and get all of the data into it.  We’ll use a Pandas DataFrame, for two reasons:
+\begin{enumerate}
+\sphinxsetlistlabels{\arabic}{enumi}{enumii}{}{.}%
+\item {} 
+It provides capabilities that are pretty much a superset of the other data structures, so it’s the all\sphinxhyphen{}in\sphinxhyphen{}one solution.
+
+\item {} 
+Pandas is a general\sphinxhyphen{}purpose tool that is useful in many domains, especially data science.  If you are going to develop expertise in one tool, Pandas is a good choice.
+
+\end{enumerate}
+
+However, compared to an Astropy \sphinxcode{\sphinxupquote{Table}}, Pandas has one big drawback: it does not keep the metadata associated with the table, including the units for the columns.
+
+It’s easy to convert a \sphinxcode{\sphinxupquote{Table}} to a Pandas \sphinxcode{\sphinxupquote{DataFrame}}.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{k+kn}{import} \PYG{n+nn}{pandas} \PYG{k}{as} \PYG{n+nn}{pd}
+
+\PYG{n}{df} \PYG{o}{=} \PYG{n}{results}\PYG{o}{.}\PYG{n}{to\PYGZus{}pandas}\PYG{p}{(}\PYG{p}{)}
+\PYG{n}{df}\PYG{o}{.}\PYG{n}{shape}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+(140340, 8)
+\end{sphinxVerbatim}
+
+\sphinxcode{\sphinxupquote{DataFrame}} provides \sphinxcode{\sphinxupquote{shape}}, which shows the number of rows and columns.
+
+It also provides \sphinxcode{\sphinxupquote{head}}, which displays the first few rows.  It is useful for spot\sphinxhyphen{}checking large results as you go along.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{df}\PYG{o}{.}\PYG{n}{head}\PYG{p}{(}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+            source\PYGZus{}id          ra        dec       pmra      pmdec  parallax  \PYGZbs{}
+0  637987125186749568  142.483019  21.757716  \PYGZhy{}2.516838   2.941813 \PYGZhy{}0.257345   
+1  638285195917112960  142.254529  22.476168   2.662702 \PYGZhy{}12.165984  0.422728   
+2  638073505568978688  142.645286  22.166932  18.306747  \PYGZhy{}7.950660  0.103640   
+3  638086386175786752  142.577394  22.227920   0.987786  \PYGZhy{}2.584105 \PYGZhy{}0.857327   
+4  638049655615392384  142.589136  22.110783   0.244439  \PYGZhy{}4.941079  0.099625   
+
+   parallax\PYGZus{}error  radial\PYGZus{}velocity  
+0        0.823721     1.000000e+20  
+1        0.297472     1.000000e+20  
+2        0.544584     1.000000e+20  
+3        1.059607     1.000000e+20  
+4        0.486224     1.000000e+20  
+\end{sphinxVerbatim}
+
+Python detail: \sphinxcode{\sphinxupquote{shape}} is an attribute, so we can display it’s value without calling it as a function; \sphinxcode{\sphinxupquote{head}} is a function, so we need the parentheses.
+
+Now we can extract the columns we want from \sphinxcode{\sphinxupquote{gd1\_coord}} and add them as columns in the \sphinxcode{\sphinxupquote{DataFrame}}.  \sphinxcode{\sphinxupquote{phi1}} and \sphinxcode{\sphinxupquote{phi2}} contain the transformed coordinates.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{df}\PYG{p}{[}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{phi1}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{]} \PYG{o}{=} \PYG{n}{gd1\PYGZus{}coord}\PYG{o}{.}\PYG{n}{phi1}
+\PYG{n}{df}\PYG{p}{[}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{phi2}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{]} \PYG{o}{=} \PYG{n}{gd1\PYGZus{}coord}\PYG{o}{.}\PYG{n}{phi2}
+\PYG{n}{df}\PYG{o}{.}\PYG{n}{shape}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+(140340, 10)
+\end{sphinxVerbatim}
+
+\sphinxcode{\sphinxupquote{pm\_phi1\_cosphi2}} and \sphinxcode{\sphinxupquote{pm\_phi2}} contain the components of proper motion in the transformed frame.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{df}\PYG{p}{[}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{pm\PYGZus{}phi1}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{]} \PYG{o}{=} \PYG{n}{gd1\PYGZus{}coord}\PYG{o}{.}\PYG{n}{pm\PYGZus{}phi1\PYGZus{}cosphi2}
+\PYG{n}{df}\PYG{p}{[}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{pm\PYGZus{}phi2}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{]} \PYG{o}{=} \PYG{n}{gd1\PYGZus{}coord}\PYG{o}{.}\PYG{n}{pm\PYGZus{}phi2}
+\PYG{n}{df}\PYG{o}{.}\PYG{n}{shape}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+(140340, 12)
+\end{sphinxVerbatim}
+
+\sphinxstylestrong{Detail:} If you notice that \sphinxcode{\sphinxupquote{SkyCoord}} has an attribute called \sphinxcode{\sphinxupquote{proper\_motion}}, you might wonder why we are not using it.
+
+We could have: \sphinxcode{\sphinxupquote{proper\_motion}} contains the same data as \sphinxcode{\sphinxupquote{pm\_phi1\_cosphi2}} and \sphinxcode{\sphinxupquote{pm\_phi2}}, but in a different format.
+
+
+\section{Plot proper motion}
+\label{\detokenize{03_motion:plot-proper-motion}}
+Now we are ready to replicate one of the panels in Figure 1 of the Price\sphinxhyphen{}Whelan and Bonaca paper, the one that shows the components of proper motion as a scatter plot:
+
+
+
+In this figure, the shaded area is a high\sphinxhyphen{}density region of stars with the proper motion we expect for stars in GD\sphinxhyphen{}1.
+\begin{itemize}
+\item {} 
+Due to the nature of tidal streams, we expect the proper motion for most stars to be along the axis of the stream; that is, we expect motion in the direction of \sphinxcode{\sphinxupquote{phi2}} to be near 0.
+
+\item {} 
+In the direction of \sphinxcode{\sphinxupquote{phi1}}, we don’t have a prior expectation for proper motion, except that it should form a cluster at a non\sphinxhyphen{}zero value.
+
+\end{itemize}
+
+To locate this cluster, we’ll select stars near the centerline of GD\sphinxhyphen{}1 and plot their proper motion.
+
+
+\section{Selecting the centerline}
+\label{\detokenize{03_motion:selecting-the-centerline}}
+As we can see in the following figure, many stars in GD\sphinxhyphen{}1 are less than 1 degree of declination from the line \sphinxcode{\sphinxupquote{phi2=0}}.
+
+
+
+If we select stars near this line, they are more likely to be in GD\sphinxhyphen{}1.
+
+We’ll start by selecting the \sphinxcode{\sphinxupquote{phi2}} column from the \sphinxcode{\sphinxupquote{DataFrame}}:
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{phi2} \PYG{o}{=} \PYG{n}{df}\PYG{p}{[}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{phi2}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{]}
+\PYG{n+nb}{type}\PYG{p}{(}\PYG{n}{phi2}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+pandas.core.series.Series
+\end{sphinxVerbatim}
+
+The result is a \sphinxcode{\sphinxupquote{Series}}, which is the structure Pandas uses to represent columns.
+
+We can use a comparison operator, \sphinxcode{\sphinxupquote{\textgreater{}}}, to compare the values in a \sphinxcode{\sphinxupquote{Series}} to a constant.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{phi2\PYGZus{}min} \PYG{o}{=} \PYG{o}{\PYGZhy{}}\PYG{l+m+mf}{1.0} \PYG{o}{*} \PYG{n}{u}\PYG{o}{.}\PYG{n}{deg}
+\PYG{n}{phi2\PYGZus{}max} \PYG{o}{=} \PYG{l+m+mf}{1.0} \PYG{o}{*} \PYG{n}{u}\PYG{o}{.}\PYG{n}{deg}
+
+\PYG{n}{mask} \PYG{o}{=} \PYG{p}{(}\PYG{n}{df}\PYG{p}{[}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{phi2}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{]} \PYG{o}{\PYGZgt{}} \PYG{n}{phi2\PYGZus{}min}\PYG{p}{)}
+\PYG{n+nb}{type}\PYG{p}{(}\PYG{n}{mask}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+pandas.core.series.Series
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{mask}\PYG{o}{.}\PYG{n}{dtype}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+dtype(\PYGZsq{}bool\PYGZsq{})
+\end{sphinxVerbatim}
+
+The result is a \sphinxcode{\sphinxupquote{Series}} of Boolean values, that is, \sphinxcode{\sphinxupquote{True}} and \sphinxcode{\sphinxupquote{False}}.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{mask}\PYG{o}{.}\PYG{n}{head}\PYG{p}{(}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+0    False
+1    False
+2    False
+3    False
+4    False
+Name: phi2, dtype: bool
+\end{sphinxVerbatim}
+
+A Boolean \sphinxcode{\sphinxupquote{Series}} is sometimes called a “mask� because we can use it to mask out some of the rows in a \sphinxcode{\sphinxupquote{DataFrame}} and select the rest, like this:
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{selected} \PYG{o}{=} \PYG{n}{df}\PYG{p}{[}\PYG{n}{mask}\PYG{p}{]}
+\PYG{n+nb}{type}\PYG{p}{(}\PYG{n}{selected}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+pandas.core.frame.DataFrame
+\end{sphinxVerbatim}
+
+\sphinxcode{\sphinxupquote{selected}} is a \sphinxcode{\sphinxupquote{DataFrame}} that contains only the rows from \sphinxcode{\sphinxupquote{df}} that correspond to \sphinxcode{\sphinxupquote{True}} values in \sphinxcode{\sphinxupquote{mask}}.
+
+The previous mask selects all stars where \sphinxcode{\sphinxupquote{phi2}} exceeds \sphinxcode{\sphinxupquote{phi2\_min}}; now we’ll select stars where \sphinxcode{\sphinxupquote{phi2}} falls between \sphinxcode{\sphinxupquote{phi2\_min}} and \sphinxcode{\sphinxupquote{phi2\_max}}.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{phi\PYGZus{}mask} \PYG{o}{=} \PYG{p}{(}\PYG{p}{(}\PYG{n}{df}\PYG{p}{[}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{phi2}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{]} \PYG{o}{\PYGZgt{}} \PYG{n}{phi2\PYGZus{}min}\PYG{p}{)} \PYG{o}{\PYGZam{}} 
+            \PYG{p}{(}\PYG{n}{df}\PYG{p}{[}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{phi2}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{]} \PYG{o}{\PYGZlt{}} \PYG{n}{phi2\PYGZus{}max}\PYG{p}{)}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+The \sphinxcode{\sphinxupquote{\&}} operator computes “logical AND�, which means the result is true where elements from both Boolean \sphinxcode{\sphinxupquote{Series}} are true.
+
+The sum of a Boolean \sphinxcode{\sphinxupquote{Series}} is the number of \sphinxcode{\sphinxupquote{True}} values, so we can use \sphinxcode{\sphinxupquote{sum}} to see how many stars are in the selected region.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{phi\PYGZus{}mask}\PYG{o}{.}\PYG{n}{sum}\PYG{p}{(}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+25084
+\end{sphinxVerbatim}
+
+And we can use \sphinxcode{\sphinxupquote{phi1\_mask}} to select stars near the centerline, which are more likely to be in GD\sphinxhyphen{}1.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{centerline} \PYG{o}{=} \PYG{n}{df}\PYG{p}{[}\PYG{n}{phi\PYGZus{}mask}\PYG{p}{]}
+\PYG{n+nb}{len}\PYG{p}{(}\PYG{n}{centerline}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+25084
+\end{sphinxVerbatim}
+
+Here’s a scatter plot of proper motion for the selected stars.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{pm1} \PYG{o}{=} \PYG{n}{centerline}\PYG{p}{[}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{pm\PYGZus{}phi1}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{]}
+\PYG{n}{pm2} \PYG{o}{=} \PYG{n}{centerline}\PYG{p}{[}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{pm\PYGZus{}phi2}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{]}
+
+\PYG{n}{plt}\PYG{o}{.}\PYG{n}{plot}\PYG{p}{(}\PYG{n}{pm1}\PYG{p}{,} \PYG{n}{pm2}\PYG{p}{,} \PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{ko}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{,} \PYG{n}{markersize}\PYG{o}{=}\PYG{l+m+mf}{0.1}\PYG{p}{,} \PYG{n}{alpha}\PYG{o}{=}\PYG{l+m+mf}{0.1}\PYG{p}{)}
+    
+\PYG{n}{plt}\PYG{o}{.}\PYG{n}{xlabel}\PYG{p}{(}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{Proper motion phi1 (GD1 frame)}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{)}
+\PYG{n}{plt}\PYG{o}{.}\PYG{n}{ylabel}\PYG{p}{(}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{Proper motion phi2 (GD1 frame)}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{)}\PYG{p}{;}
+\end{sphinxVerbatim}
+
+\noindent\sphinxincludegraphics{{03_motion_79_0}.png}
+
+Looking at these results, we see a large cluster around (0, 0), and a smaller cluster near (0, \sphinxhyphen{}10).
+
+We can use \sphinxcode{\sphinxupquote{xlim}} and \sphinxcode{\sphinxupquote{ylim}} to set the limits on the axes and zoom in on the region near (0, 0).
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{pm1} \PYG{o}{=} \PYG{n}{centerline}\PYG{p}{[}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{pm\PYGZus{}phi1}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{]}
+\PYG{n}{pm2} \PYG{o}{=} \PYG{n}{centerline}\PYG{p}{[}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{pm\PYGZus{}phi2}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{]}
+
+\PYG{n}{plt}\PYG{o}{.}\PYG{n}{plot}\PYG{p}{(}\PYG{n}{pm1}\PYG{p}{,} \PYG{n}{pm2}\PYG{p}{,} \PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{ko}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{,} \PYG{n}{markersize}\PYG{o}{=}\PYG{l+m+mf}{0.3}\PYG{p}{,} \PYG{n}{alpha}\PYG{o}{=}\PYG{l+m+mf}{0.3}\PYG{p}{)}
+    
+\PYG{n}{plt}\PYG{o}{.}\PYG{n}{xlabel}\PYG{p}{(}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{Proper motion phi1 (GD1 frame)}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{)}
+\PYG{n}{plt}\PYG{o}{.}\PYG{n}{ylabel}\PYG{p}{(}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{Proper motion phi2 (GD1 frame)}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{)}
+
+\PYG{n}{plt}\PYG{o}{.}\PYG{n}{xlim}\PYG{p}{(}\PYG{o}{\PYGZhy{}}\PYG{l+m+mi}{12}\PYG{p}{,} \PYG{l+m+mi}{8}\PYG{p}{)}
+\PYG{n}{plt}\PYG{o}{.}\PYG{n}{ylim}\PYG{p}{(}\PYG{o}{\PYGZhy{}}\PYG{l+m+mi}{10}\PYG{p}{,} \PYG{l+m+mi}{10}\PYG{p}{)}\PYG{p}{;}
+\end{sphinxVerbatim}
+
+\noindent\sphinxincludegraphics{{03_motion_81_0}.png}
+
+Now we can see the smaller cluster more clearly.
+
+You might notice that our figure is less dense than the one in the paper.  That’s because we started with a set of stars from a relatively small region.  The figure in the paper is based on a region about 10 times bigger.
+
+In the next lesson we’ll go back and select stars from a larger region.  But first we’ll use the proper motion data to identify stars likely to be in GD\sphinxhyphen{}1.
+
+
+\section{Filtering based on proper motion}
+\label{\detokenize{03_motion:filtering-based-on-proper-motion}}
+The next step is to select stars in the “overdense� region of proper motion, which are candidates to be in GD\sphinxhyphen{}1.
+
+In the original paper, Price\sphinxhyphen{}Whelan and Bonaca used a polygon to cover this region, as shown in this figure.
+
+
+
+We’ll use a simple rectangle for now, but in a later lesson we’ll see how to select a polygonal region as well.
+
+Here are bounds on proper motion we chose by eye,
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{pm1\PYGZus{}min} \PYG{o}{=} \PYG{o}{\PYGZhy{}}\PYG{l+m+mf}{8.9}
+\PYG{n}{pm1\PYGZus{}max} \PYG{o}{=} \PYG{o}{\PYGZhy{}}\PYG{l+m+mf}{6.9}
+\PYG{n}{pm2\PYGZus{}min} \PYG{o}{=} \PYG{o}{\PYGZhy{}}\PYG{l+m+mf}{2.2}
+\PYG{n}{pm2\PYGZus{}max} \PYG{o}{=}  \PYG{l+m+mf}{1.0}
+\end{sphinxVerbatim}
+
+To draw these bounds, we’ll make two lists containing the coordinates of the corners of the rectangle.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{pm1\PYGZus{}rect} \PYG{o}{=} \PYG{p}{[}\PYG{n}{pm1\PYGZus{}min}\PYG{p}{,} \PYG{n}{pm1\PYGZus{}min}\PYG{p}{,} \PYG{n}{pm1\PYGZus{}max}\PYG{p}{,} \PYG{n}{pm1\PYGZus{}max}\PYG{p}{,} \PYG{n}{pm1\PYGZus{}min}\PYG{p}{]} \PYG{o}{*} \PYG{n}{u}\PYG{o}{.}\PYG{n}{mas}\PYG{o}{/}\PYG{n}{u}\PYG{o}{.}\PYG{n}{yr}
+\PYG{n}{pm2\PYGZus{}rect} \PYG{o}{=} \PYG{p}{[}\PYG{n}{pm2\PYGZus{}min}\PYG{p}{,} \PYG{n}{pm2\PYGZus{}max}\PYG{p}{,} \PYG{n}{pm2\PYGZus{}max}\PYG{p}{,} \PYG{n}{pm2\PYGZus{}min}\PYG{p}{,} \PYG{n}{pm2\PYGZus{}min}\PYG{p}{]} \PYG{o}{*} \PYG{n}{u}\PYG{o}{.}\PYG{n}{mas}\PYG{o}{/}\PYG{n}{u}\PYG{o}{.}\PYG{n}{yr}
+\end{sphinxVerbatim}
+
+Here’s what the plot looks like with the bounds we chose.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{plt}\PYG{o}{.}\PYG{n}{plot}\PYG{p}{(}\PYG{n}{pm1}\PYG{p}{,} \PYG{n}{pm2}\PYG{p}{,} \PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{ko}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{,} \PYG{n}{markersize}\PYG{o}{=}\PYG{l+m+mf}{0.3}\PYG{p}{,} \PYG{n}{alpha}\PYG{o}{=}\PYG{l+m+mf}{0.3}\PYG{p}{)}
+\PYG{n}{plt}\PYG{o}{.}\PYG{n}{plot}\PYG{p}{(}\PYG{n}{pm1\PYGZus{}rect}\PYG{p}{,} \PYG{n}{pm2\PYGZus{}rect}\PYG{p}{,} \PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{\PYGZhy{}}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{)}
+    
+\PYG{n}{plt}\PYG{o}{.}\PYG{n}{xlabel}\PYG{p}{(}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{Proper motion phi1 (GD1 frame)}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{)}
+\PYG{n}{plt}\PYG{o}{.}\PYG{n}{ylabel}\PYG{p}{(}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{Proper motion phi2 (GD1 frame)}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{)}
+
+\PYG{n}{plt}\PYG{o}{.}\PYG{n}{xlim}\PYG{p}{(}\PYG{o}{\PYGZhy{}}\PYG{l+m+mi}{12}\PYG{p}{,} \PYG{l+m+mi}{8}\PYG{p}{)}
+\PYG{n}{plt}\PYG{o}{.}\PYG{n}{ylim}\PYG{p}{(}\PYG{o}{\PYGZhy{}}\PYG{l+m+mi}{10}\PYG{p}{,} \PYG{l+m+mi}{10}\PYG{p}{)}\PYG{p}{;}
+\end{sphinxVerbatim}
+
+\noindent\sphinxincludegraphics{{03_motion_88_0}.png}
+
+To select rows that fall within these bounds, we’ll use the following function, which uses Pandas operators to make a mask that selects rows where \sphinxcode{\sphinxupquote{series}} falls between \sphinxcode{\sphinxupquote{low}} and \sphinxcode{\sphinxupquote{high}}.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{k}{def} \PYG{n+nf}{between}\PYG{p}{(}\PYG{n}{series}\PYG{p}{,} \PYG{n}{low}\PYG{p}{,} \PYG{n}{high}\PYG{p}{)}\PYG{p}{:}
+    \PYG{l+s+sd}{\PYGZdq{}\PYGZdq{}\PYGZdq{}Make a Boolean Series.}
+\PYG{l+s+sd}{    }
+\PYG{l+s+sd}{    series: Pandas Series}
+\PYG{l+s+sd}{    low: lower bound}
+\PYG{l+s+sd}{    high: upper bound}
+\PYG{l+s+sd}{    }
+\PYG{l+s+sd}{    returns: Boolean Series}
+\PYG{l+s+sd}{    \PYGZdq{}\PYGZdq{}\PYGZdq{}}
+    \PYG{k}{return} \PYG{p}{(}\PYG{n}{series} \PYG{o}{\PYGZgt{}} \PYG{n}{low}\PYG{p}{)} \PYG{o}{\PYGZam{}} \PYG{p}{(}\PYG{n}{series} \PYG{o}{\PYGZlt{}} \PYG{n}{high}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+The following mask select stars with proper motion in the region we chose.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{pm\PYGZus{}mask} \PYG{o}{=} \PYG{p}{(}\PYG{n}{between}\PYG{p}{(}\PYG{n}{df}\PYG{p}{[}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{pm\PYGZus{}phi1}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{]}\PYG{p}{,} \PYG{n}{pm1\PYGZus{}min}\PYG{p}{,} \PYG{n}{pm1\PYGZus{}max}\PYG{p}{)} \PYG{o}{\PYGZam{}} 
+           \PYG{n}{between}\PYG{p}{(}\PYG{n}{df}\PYG{p}{[}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{pm\PYGZus{}phi2}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{]}\PYG{p}{,} \PYG{n}{pm2\PYGZus{}min}\PYG{p}{,} \PYG{n}{pm2\PYGZus{}max}\PYG{p}{)}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+Again, the sum of a Boolean series is the number of \sphinxcode{\sphinxupquote{True}} values.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{pm\PYGZus{}mask}\PYG{o}{.}\PYG{n}{sum}\PYG{p}{(}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+1049
+\end{sphinxVerbatim}
+
+Now we can use this mask to select rows from \sphinxcode{\sphinxupquote{df}}.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{selected} \PYG{o}{=} \PYG{n}{df}\PYG{p}{[}\PYG{n}{pm\PYGZus{}mask}\PYG{p}{]}
+\PYG{n+nb}{len}\PYG{p}{(}\PYG{n}{selected}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+1049
+\end{sphinxVerbatim}
+
+These are the stars we think are likely to be in GD\sphinxhyphen{}1.  Let’s see what they look like, plotting their coordinates (not their proper motion).
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{phi1} \PYG{o}{=} \PYG{n}{selected}\PYG{p}{[}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{phi1}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{]}
+\PYG{n}{phi2} \PYG{o}{=} \PYG{n}{selected}\PYG{p}{[}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{phi2}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{]}
+
+\PYG{n}{plt}\PYG{o}{.}\PYG{n}{plot}\PYG{p}{(}\PYG{n}{phi1}\PYG{p}{,} \PYG{n}{phi2}\PYG{p}{,} \PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{ko}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{,} \PYG{n}{markersize}\PYG{o}{=}\PYG{l+m+mf}{0.5}\PYG{p}{,} \PYG{n}{alpha}\PYG{o}{=}\PYG{l+m+mf}{0.5}\PYG{p}{)}
+
+\PYG{n}{plt}\PYG{o}{.}\PYG{n}{xlabel}\PYG{p}{(}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{ra (degree GD1)}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{)}
+\PYG{n}{plt}\PYG{o}{.}\PYG{n}{ylabel}\PYG{p}{(}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{dec (degree GD1)}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{)}\PYG{p}{;}
+\end{sphinxVerbatim}
+
+\noindent\sphinxincludegraphics{{03_motion_98_0}.png}
+
+Now that’s starting to look like a tidal stream!
+
+
+\section{Saving the DataFrame}
+\label{\detokenize{03_motion:saving-the-dataframe}}
+At this point we have run a successful query and cleaned up the results; this is a good time to save the data.
+
+To save a Pandas \sphinxcode{\sphinxupquote{DataFrame}}, one option is to convert it to an Astropy \sphinxcode{\sphinxupquote{Table}}, like this:
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{selected\PYGZus{}table} \PYG{o}{=} \PYG{n}{Table}\PYG{o}{.}\PYG{n}{from\PYGZus{}pandas}\PYG{p}{(}\PYG{n}{selected}\PYG{p}{)}
+\PYG{n+nb}{type}\PYG{p}{(}\PYG{n}{selected\PYGZus{}table}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+astropy.table.table.Table
+\end{sphinxVerbatim}
+
+Then we could write the \sphinxcode{\sphinxupquote{Table}} to a FITS file, as we did in the previous lesson.
+
+But Pandas provides functions to write DataFrames in other formats; to see what they are \sphinxhref{https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html}{find the functions here that begin with \sphinxcode{\sphinxupquote{to\_}}}.
+
+One of the best options is HDF5, which is Version 5 of \sphinxhref{https://en.wikipedia.org/wiki/Hierarchical\_Data\_Format}{Hierarchical Data Format}.
+
+HDF5 is a binary format, so files are small and fast to read and write (like FITS, but unlike XML).
+
+An HDF5 file is similar to an SQL database in the sense that it can contain more than one table, although in HDF5 vocabulary, a table is called a Dataset.  (\sphinxhref{https://www.stsci.edu/itt/review/dhb\_2011/Intro/intro\_ch23.html}{Multi\sphinxhyphen{}extension FITS files} can also contain more than one table.)
+
+And HDF5 stores the metadata associated with the table, including column names, row labels, and data types (like FITS).
+
+Finally, HDF5 is a cross\sphinxhyphen{}language standard, so if you write an HDF5 file with Pandas, you can read it back with many other software tools (more than FITS).
+
+Before we write the HDF5, let’s delete the old one, if it exists.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{o}{!}rm \PYGZhy{}f gd1\PYGZus{}dataframe.hdf5
+\end{sphinxVerbatim}
+
+We can write a Pandas \sphinxcode{\sphinxupquote{DataFrame}} to an HDF5 file like this:
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{filename} \PYG{o}{=} \PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{gd1\PYGZus{}dataframe.hdf5}\PYG{l+s+s1}{\PYGZsq{}}
+
+\PYG{n}{df}\PYG{o}{.}\PYG{n}{to\PYGZus{}hdf}\PYG{p}{(}\PYG{n}{filename}\PYG{p}{,} \PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{df}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+Because an HDF5 file can contain more than one Dataset, we have to provide a name, or “key�, that identifies the Dataset in the file.
+
+We could use any string as the key, but in this example I use the variable name \sphinxcode{\sphinxupquote{df}}.
+
+\sphinxstylestrong{Exercise:} We’re going to need \sphinxcode{\sphinxupquote{centerline}} and \sphinxcode{\sphinxupquote{selected}} later as well.  Write a line or two of code to add it as a second Dataset in the HDF5 file.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{c+c1}{\PYGZsh{} Solution}
+
+\PYG{n}{centerline}\PYG{o}{.}\PYG{n}{to\PYGZus{}hdf}\PYG{p}{(}\PYG{n}{filename}\PYG{p}{,} \PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{centerline}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{)}
+\PYG{n}{selected}\PYG{o}{.}\PYG{n}{to\PYGZus{}hdf}\PYG{p}{(}\PYG{n}{filename}\PYG{p}{,} \PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{selected}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+\sphinxstylestrong{Detail:} Reading and writing HDF5 tables requires a library called \sphinxcode{\sphinxupquote{PyTables}} that is not always installed with Pandas.  You can install it with pip like this:
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{pip} \PYG{n}{install} \PYG{n}{tables}
+\end{sphinxVerbatim}
+
+If you install it using Conda, the name of the package is \sphinxcode{\sphinxupquote{pytables}}.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{conda} \PYG{n}{install} \PYG{n}{pytables}
+\end{sphinxVerbatim}
+
+We can use \sphinxcode{\sphinxupquote{ls}} to confirm that the file exists and check the size:
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{o}{!}ls \PYGZhy{}lh gd1\PYGZus{}dataframe.hdf5
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYGZhy{}rw\PYGZhy{}rw\PYGZhy{}r\PYGZhy{}\PYGZhy{} 1 downey downey 17M Oct 19 12:05 gd1\PYGZus{}dataframe.hdf5
+\end{sphinxVerbatim}
+
+If you are using Windows, \sphinxcode{\sphinxupquote{ls}} might not work; in that case, try:
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+!dir gd1\PYGZus{}dataframe.hdf5
+\end{sphinxVerbatim}
+
+We can read the file back like this:
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{read\PYGZus{}back\PYGZus{}df} \PYG{o}{=} \PYG{n}{pd}\PYG{o}{.}\PYG{n}{read\PYGZus{}hdf}\PYG{p}{(}\PYG{n}{filename}\PYG{p}{,} \PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{df}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{)}
+\PYG{n}{read\PYGZus{}back\PYGZus{}df}\PYG{o}{.}\PYG{n}{shape}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+(140340, 12)
+\end{sphinxVerbatim}
+
+Pandas can write a variety of other formats, \sphinxhref{https://pandas.pydata.org/pandas-docs/stable/user\_guide/io.html}{which you can read about here}.
+
+
+\section{Summary}
+\label{\detokenize{03_motion:summary}}
+In this lesson, we re\sphinxhyphen{}loaded the Gaia data we saved from a previous query.
+
+We transformed the coordinates and proper motion from ICRS to a frame aligned with GD\sphinxhyphen{}1, and stored the results in a Pandas \sphinxcode{\sphinxupquote{DataFrame}}.
+
+Then we replicated the selection process from the Price\sphinxhyphen{}Whelan and Bonaca paper:
+\begin{itemize}
+\item {} 
+We selected stars near the centerline of GD\sphinxhyphen{}1 and made a scatter plot of their proper motion.
+
+\item {} 
+We identified a region of proper motion that contains stars likely to be in GD\sphinxhyphen{}1.
+
+\item {} 
+We used a Boolean \sphinxcode{\sphinxupquote{Series}} as a mask to select stars whose proper motion is in that region.
+
+\end{itemize}
+
+So far, we have used data from a relatively small region of the sky.  In the next lesson, we’ll write a query that selects stars based on proper motion, which will allow us to explore a larger region.
+
+
+\section{Best practices}
+\label{\detokenize{03_motion:best-practices}}\begin{itemize}
+\item {} 
+When you make a scatter plot, adjust the size of the markers and their transparency so the figure is not overplotted; otherwise it can misrepresent the data badly.
+
+\item {} 
+For simple scatter plots in Matplotlib, \sphinxcode{\sphinxupquote{plot}} is faster than \sphinxcode{\sphinxupquote{scatter}}.
+
+\item {} 
+An Astropy \sphinxcode{\sphinxupquote{Table}} and a Pandas \sphinxcode{\sphinxupquote{DataFrame}} are similar in many ways and they provide many of the same functions.  They have pros and cons, but for many projects, either one would be a reasonable choice.
+
+\end{itemize}
+
+
+\chapter{Chapter 4}
+\label{\detokenize{04_select:chapter-4}}\label{\detokenize{04_select::doc}}
+This is the fourth in a series of notebooks related to astronomy data.
+
+As a running example, we are replicating parts of the analysis in a recent paper, “\sphinxhref{https://arxiv.org/abs/1805.00425}{Off the beaten path: Gaia reveals GD\sphinxhyphen{}1 stars outside of the main stream}� by Adrian M. Price\sphinxhyphen{}Whelan and Ana Bonaca.
+
+In the first lesson, we wrote ADQL queries and used them to select and download data from the Gaia server.
+
+In the second lesson, we write a query to select stars from the region of the sky where we expect GD\sphinxhyphen{}1 to be, and save the results in a FITS file.
+
+In the third lesson, we read that data back and identified stars with the proper motion we expect for GD\sphinxhyphen{}1.
+
+
+\section{Outline}
+\label{\detokenize{04_select:outline}}
+Here are the steps in this lesson:
+\begin{enumerate}
+\sphinxsetlistlabels{\arabic}{enumi}{enumii}{}{.}%
+\item {} 
+Using data from the previous lesson, we’ll identify the values of proper motion for stars likely to be in GD\sphinxhyphen{}1.
+
+\item {} 
+Then we’ll compose an ADQL query that selects stars based on proper motion, so we can download only the data we need.
+
+\item {} 
+We’ll also see how to write the results to a CSV file.
+
+\end{enumerate}
+
+That will make it possible to search a bigger region of the sky in a single query.
+
+After completing this lesson, you should be able to
+\begin{itemize}
+\item {} 
+Convert proper motion between frames.
+
+\item {} 
+Write an ADQL query that selects based on proper motion.
+
+\end{itemize}
+
+
+\section{Installing libraries}
+\label{\detokenize{04_select:installing-libraries}}
+If you are running this notebook on Colab, you can run the following cell to install Astroquery and a the other libraries we’ll use.
+
+If you are running this notebook on your own computer, you might have to install these libraries yourself.
+
+If you are using this notebook as part of a Carpentries workshop, you should have received setup instructions.
+
+TODO: Add a link to the instructions.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{c+c1}{\PYGZsh{} If we\PYGZsq{}re running on Colab, install libraries}
+
+\PYG{k+kn}{import} \PYG{n+nn}{sys}
+\PYG{n}{IN\PYGZus{}COLAB} \PYG{o}{=} \PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{google.colab}\PYG{l+s+s1}{\PYGZsq{}} \PYG{o+ow}{in} \PYG{n}{sys}\PYG{o}{.}\PYG{n}{modules}
+
+\PYG{k}{if} \PYG{n}{IN\PYGZus{}COLAB}\PYG{p}{:}
+    \PYG{o}{!}pip install astroquery astro\PYGZhy{}gala pyia python\PYGZhy{}wget
+\end{sphinxVerbatim}
+
+
+\section{Reload the data}
+\label{\detokenize{04_select:reload-the-data}}
+The following cells download the data from the previous lesson, if necessary, and load it into a Pandas \sphinxcode{\sphinxupquote{DataFrame}}.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{k+kn}{import} \PYG{n+nn}{os}
+\PYG{k+kn}{from} \PYG{n+nn}{wget} \PYG{k+kn}{import} \PYG{n}{download}
+
+\PYG{n}{filename} \PYG{o}{=} \PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{gd1\PYGZus{}dataframe.hdf5}\PYG{l+s+s1}{\PYGZsq{}}
+\PYG{n}{path} \PYG{o}{=} \PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{https://github.com/AllenDowney/AstronomicalData/raw/main/data/}\PYG{l+s+s1}{\PYGZsq{}}
+
+\PYG{k}{if} \PYG{o+ow}{not} \PYG{n}{os}\PYG{o}{.}\PYG{n}{path}\PYG{o}{.}\PYG{n}{exists}\PYG{p}{(}\PYG{n}{filename}\PYG{p}{)}\PYG{p}{:}
+    \PYG{n+nb}{print}\PYG{p}{(}\PYG{n}{download}\PYG{p}{(}\PYG{n}{path}\PYG{o}{+}\PYG{n}{filename}\PYG{p}{)}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{k+kn}{import} \PYG{n+nn}{pandas} \PYG{k}{as} \PYG{n+nn}{pd}
+
+\PYG{n}{df} \PYG{o}{=} \PYG{n}{pd}\PYG{o}{.}\PYG{n}{read\PYGZus{}hdf}\PYG{p}{(}\PYG{n}{filename}\PYG{p}{,} \PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{df}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{)}
+\PYG{n}{centerline} \PYG{o}{=} \PYG{n}{pd}\PYG{o}{.}\PYG{n}{read\PYGZus{}hdf}\PYG{p}{(}\PYG{n}{filename}\PYG{p}{,} \PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{centerline}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{)}
+\PYG{n}{selected} \PYG{o}{=} \PYG{n}{pd}\PYG{o}{.}\PYG{n}{read\PYGZus{}hdf}\PYG{p}{(}\PYG{n}{filename}\PYG{p}{,} \PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{selected}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+
+\section{Selection by proper motion}
+\label{\detokenize{04_select:selection-by-proper-motion}}
+At this point we have downloaded data for a relatively large number of stars (more than 100,000) and selected a relatively small number (around 1000).
+
+It would be more efficient to use ADQL to select only the stars we need.  That would also make it possible to download data covering a larger region of the sky.
+
+However, the selection we did was based on proper motion in the \sphinxcode{\sphinxupquote{GD1Koposov10}} frame.  In order to do the same selection in ADQL, we have to work with proper motions in ICRS.
+
+As a reminder, here’s the rectangle we selected based on proper motion in the \sphinxcode{\sphinxupquote{GD1Koposov10}} frame.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{pm1\PYGZus{}min} \PYG{o}{=} \PYG{o}{\PYGZhy{}}\PYG{l+m+mf}{8.9}
+\PYG{n}{pm1\PYGZus{}max} \PYG{o}{=} \PYG{o}{\PYGZhy{}}\PYG{l+m+mf}{6.9}
+\PYG{n}{pm2\PYGZus{}min} \PYG{o}{=} \PYG{o}{\PYGZhy{}}\PYG{l+m+mf}{2.2}
+\PYG{n}{pm2\PYGZus{}max} \PYG{o}{=}  \PYG{l+m+mf}{1.0}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{k+kn}{import} \PYG{n+nn}{astropy}\PYG{n+nn}{.}\PYG{n+nn}{units} \PYG{k}{as} \PYG{n+nn}{u}
+
+\PYG{n}{pm1\PYGZus{}rect} \PYG{o}{=} \PYG{p}{[}\PYG{n}{pm1\PYGZus{}min}\PYG{p}{,} \PYG{n}{pm1\PYGZus{}min}\PYG{p}{,} \PYG{n}{pm1\PYGZus{}max}\PYG{p}{,} \PYG{n}{pm1\PYGZus{}max}\PYG{p}{,} \PYG{n}{pm1\PYGZus{}min}\PYG{p}{]} \PYG{o}{*} \PYG{n}{u}\PYG{o}{.}\PYG{n}{mas}\PYG{o}{/}\PYG{n}{u}\PYG{o}{.}\PYG{n}{yr}
+\PYG{n}{pm2\PYGZus{}rect} \PYG{o}{=} \PYG{p}{[}\PYG{n}{pm2\PYGZus{}min}\PYG{p}{,} \PYG{n}{pm2\PYGZus{}max}\PYG{p}{,} \PYG{n}{pm2\PYGZus{}max}\PYG{p}{,} \PYG{n}{pm2\PYGZus{}min}\PYG{p}{,} \PYG{n}{pm2\PYGZus{}min}\PYG{p}{]} \PYG{o}{*} \PYG{n}{u}\PYG{o}{.}\PYG{n}{mas}\PYG{o}{/}\PYG{n}{u}\PYG{o}{.}\PYG{n}{yr}
+\end{sphinxVerbatim}
+
+The following figure shows:
+\begin{itemize}
+\item {} 
+Proper motion for the stars we selected along the center line of GD\sphinxhyphen{}1,
+
+\item {} 
+The rectangle we selected, and
+
+\item {} 
+The stars inside the rectangle highlighted in green.
+
+\end{itemize}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{k+kn}{import} \PYG{n+nn}{matplotlib}\PYG{n+nn}{.}\PYG{n+nn}{pyplot} \PYG{k}{as} \PYG{n+nn}{plt}
+
+\PYG{n}{pm1} \PYG{o}{=} \PYG{n}{centerline}\PYG{p}{[}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{pm\PYGZus{}phi1}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{]}
+\PYG{n}{pm2} \PYG{o}{=} \PYG{n}{centerline}\PYG{p}{[}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{pm\PYGZus{}phi2}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{]}
+\PYG{n}{plt}\PYG{o}{.}\PYG{n}{plot}\PYG{p}{(}\PYG{n}{pm1}\PYG{p}{,} \PYG{n}{pm2}\PYG{p}{,} \PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{ko}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{,} \PYG{n}{markersize}\PYG{o}{=}\PYG{l+m+mf}{0.3}\PYG{p}{,} \PYG{n}{alpha}\PYG{o}{=}\PYG{l+m+mf}{0.3}\PYG{p}{)}
+
+\PYG{n}{pm1} \PYG{o}{=} \PYG{n}{selected}\PYG{p}{[}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{pm\PYGZus{}phi1}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{]}
+\PYG{n}{pm2} \PYG{o}{=} \PYG{n}{selected}\PYG{p}{[}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{pm\PYGZus{}phi2}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{]}
+\PYG{n}{plt}\PYG{o}{.}\PYG{n}{plot}\PYG{p}{(}\PYG{n}{pm1}\PYG{p}{,} \PYG{n}{pm2}\PYG{p}{,} \PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{gx}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{,} \PYG{n}{markersize}\PYG{o}{=}\PYG{l+m+mf}{0.3}\PYG{p}{,} \PYG{n}{alpha}\PYG{o}{=}\PYG{l+m+mf}{0.3}\PYG{p}{)}
+
+\PYG{n}{plt}\PYG{o}{.}\PYG{n}{plot}\PYG{p}{(}\PYG{n}{pm1\PYGZus{}rect}\PYG{p}{,} \PYG{n}{pm2\PYGZus{}rect}\PYG{p}{,} \PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{\PYGZhy{}}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{)}
+    
+\PYG{n}{plt}\PYG{o}{.}\PYG{n}{xlabel}\PYG{p}{(}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{Proper motion phi1 (GD1 frame)}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{)}
+\PYG{n}{plt}\PYG{o}{.}\PYG{n}{ylabel}\PYG{p}{(}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{Proper motion phi2 (GD1 frame)}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{)}
+
+\PYG{n}{plt}\PYG{o}{.}\PYG{n}{xlim}\PYG{p}{(}\PYG{o}{\PYGZhy{}}\PYG{l+m+mi}{12}\PYG{p}{,} \PYG{l+m+mi}{8}\PYG{p}{)}
+\PYG{n}{plt}\PYG{o}{.}\PYG{n}{ylim}\PYG{p}{(}\PYG{o}{\PYGZhy{}}\PYG{l+m+mi}{10}\PYG{p}{,} \PYG{l+m+mi}{10}\PYG{p}{)}\PYG{p}{;}
+\end{sphinxVerbatim}
+
+\noindent\sphinxincludegraphics{{04_select_11_0}.png}
+
+Now we’ll make the same plot using proper motions in the ICRS frame, which are stored in columns \sphinxcode{\sphinxupquote{pmra}} and \sphinxcode{\sphinxupquote{pmdec}}.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{pm1} \PYG{o}{=} \PYG{n}{centerline}\PYG{p}{[}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{pmra}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{]}
+\PYG{n}{pm2} \PYG{o}{=} \PYG{n}{centerline}\PYG{p}{[}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{pmdec}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{]}
+\PYG{n}{plt}\PYG{o}{.}\PYG{n}{plot}\PYG{p}{(}\PYG{n}{pm1}\PYG{p}{,} \PYG{n}{pm2}\PYG{p}{,} \PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{ko}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{,} \PYG{n}{markersize}\PYG{o}{=}\PYG{l+m+mf}{0.3}\PYG{p}{,} \PYG{n}{alpha}\PYG{o}{=}\PYG{l+m+mf}{0.3}\PYG{p}{)}
+
+\PYG{n}{pm1} \PYG{o}{=} \PYG{n}{selected}\PYG{p}{[}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{pmra}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{]}
+\PYG{n}{pm2} \PYG{o}{=} \PYG{n}{selected}\PYG{p}{[}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{pmdec}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{]}
+\PYG{n}{plt}\PYG{o}{.}\PYG{n}{plot}\PYG{p}{(}\PYG{n}{pm1}\PYG{p}{,} \PYG{n}{pm2}\PYG{p}{,} \PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{gx}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{,} \PYG{n}{markersize}\PYG{o}{=}\PYG{l+m+mi}{1}\PYG{p}{,} \PYG{n}{alpha}\PYG{o}{=}\PYG{l+m+mf}{0.3}\PYG{p}{)}
+    
+\PYG{n}{plt}\PYG{o}{.}\PYG{n}{xlabel}\PYG{p}{(}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{Proper motion phi1 (ICRS frame)}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{)}
+\PYG{n}{plt}\PYG{o}{.}\PYG{n}{ylabel}\PYG{p}{(}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{Proper motion phi2 (ICRS frame)}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{)}
+
+\PYG{n}{plt}\PYG{o}{.}\PYG{n}{xlim}\PYG{p}{(}\PYG{p}{[}\PYG{o}{\PYGZhy{}}\PYG{l+m+mi}{10}\PYG{p}{,} \PYG{l+m+mi}{5}\PYG{p}{]}\PYG{p}{)}
+\PYG{n}{plt}\PYG{o}{.}\PYG{n}{ylim}\PYG{p}{(}\PYG{p}{[}\PYG{o}{\PYGZhy{}}\PYG{l+m+mi}{20}\PYG{p}{,} \PYG{l+m+mi}{5}\PYG{p}{]}\PYG{p}{)}\PYG{p}{;}
+\end{sphinxVerbatim}
+
+\noindent\sphinxincludegraphics{{04_select_13_0}.png}
+
+The proper motions of the selected stars are more spread out in this frame, which is why it was preferable to do the selection in the GD\sphinxhyphen{}1 frame.
+
+But now we can define a polygon that encloses the proper motions of these stars in ICRS,
+and use the polygon as a selection criterion in an ADQL query.
+
+SciPy provides a function that computes the \sphinxhref{https://en.wikipedia.org/wiki/Convex\_hull}{convex hull} of a set of points, which is the smallest convex polygon that contains all of the points.
+
+To use it, I’ll select columns \sphinxcode{\sphinxupquote{pmra}} and \sphinxcode{\sphinxupquote{pmdec}} and convert them to a NumPy array.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{k+kn}{import} \PYG{n+nn}{numpy} \PYG{k}{as} \PYG{n+nn}{np}
+
+\PYG{n}{points} \PYG{o}{=} \PYG{n}{selected}\PYG{p}{[}\PYG{p}{[}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{pmra}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{,}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{pmdec}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{]}\PYG{p}{]}\PYG{o}{.}\PYG{n}{to\PYGZus{}numpy}\PYG{p}{(}\PYG{p}{)}
+\PYG{n}{points}\PYG{o}{.}\PYG{n}{shape}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+(1049, 2)
+\end{sphinxVerbatim}
+
+We’ll pass the points to \sphinxcode{\sphinxupquote{ConvexHull}}, which returns an object that contains the results.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{k+kn}{from} \PYG{n+nn}{scipy}\PYG{n+nn}{.}\PYG{n+nn}{spatial} \PYG{k+kn}{import} \PYG{n}{ConvexHull}
+
+\PYG{n}{hull} \PYG{o}{=} \PYG{n}{ConvexHull}\PYG{p}{(}\PYG{n}{points}\PYG{p}{)}
+\PYG{n}{hull}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYGZlt{}scipy.spatial.qhull.ConvexHull at 0x7f446b1e8bb0\PYGZgt{}
+\end{sphinxVerbatim}
+
+\sphinxcode{\sphinxupquote{hull.vertices}} contains the indices of the points that fall on the perimeter of the hull.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{hull}\PYG{o}{.}\PYG{n}{vertices}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+array([ 692,  873,  141,  303,   42,  622,   45,   83,  127,  182, 1006,
+        971,  967, 1001,  969,  940], dtype=int32)
+\end{sphinxVerbatim}
+
+We can use them as an index into the original array to select the corresponding rows.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{pm\PYGZus{}vertices} \PYG{o}{=} \PYG{n}{points}\PYG{p}{[}\PYG{n}{hull}\PYG{o}{.}\PYG{n}{vertices}\PYG{p}{]}
+\PYG{n}{pm\PYGZus{}vertices}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+array([[ \PYGZhy{}4.05037121, \PYGZhy{}14.75623261],
+       [ \PYGZhy{}3.41981085, \PYGZhy{}14.72365546],
+       [ \PYGZhy{}3.03521988, \PYGZhy{}14.44357135],
+       [ \PYGZhy{}2.26847919, \PYGZhy{}13.7140236 ],
+       [ \PYGZhy{}2.61172203, \PYGZhy{}13.24797471],
+       [ \PYGZhy{}2.73471401, \PYGZhy{}13.09054471],
+       [ \PYGZhy{}3.19923146, \PYGZhy{}12.5942653 ],
+       [ \PYGZhy{}3.34082546, \PYGZhy{}12.47611926],
+       [ \PYGZhy{}5.67489413, \PYGZhy{}11.16083338],
+       [ \PYGZhy{}5.95159272, \PYGZhy{}11.10547884],
+       [ \PYGZhy{}6.42394023, \PYGZhy{}11.05981295],
+       [ \PYGZhy{}7.09631023, \PYGZhy{}11.95187806],
+       [ \PYGZhy{}7.30641519, \PYGZhy{}12.24559977],
+       [ \PYGZhy{}7.04016696, \PYGZhy{}12.88580702],
+       [ \PYGZhy{}6.00347705, \PYGZhy{}13.75912098],
+       [ \PYGZhy{}4.42442296, \PYGZhy{}14.74641176]])
+\end{sphinxVerbatim}
+
+To plot the resulting polygon, we have to pull out the x and y coordinates.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{pmra\PYGZus{}poly}\PYG{p}{,} \PYG{n}{pmdec\PYGZus{}poly} \PYG{o}{=} \PYG{n}{np}\PYG{o}{.}\PYG{n}{transpose}\PYG{p}{(}\PYG{n}{pm\PYGZus{}vertices}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+The following figure shows proper motion in ICRS again, along with the convex hull we just computed.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{pm1} \PYG{o}{=} \PYG{n}{centerline}\PYG{p}{[}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{pmra}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{]}
+\PYG{n}{pm2} \PYG{o}{=} \PYG{n}{centerline}\PYG{p}{[}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{pmdec}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{]}
+\PYG{n}{plt}\PYG{o}{.}\PYG{n}{plot}\PYG{p}{(}\PYG{n}{pm1}\PYG{p}{,} \PYG{n}{pm2}\PYG{p}{,} \PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{ko}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{,} \PYG{n}{markersize}\PYG{o}{=}\PYG{l+m+mf}{0.3}\PYG{p}{,} \PYG{n}{alpha}\PYG{o}{=}\PYG{l+m+mf}{0.3}\PYG{p}{)}
+
+\PYG{n}{pm1} \PYG{o}{=} \PYG{n}{selected}\PYG{p}{[}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{pmra}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{]}
+\PYG{n}{pm2} \PYG{o}{=} \PYG{n}{selected}\PYG{p}{[}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{pmdec}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{]}
+\PYG{n}{plt}\PYG{o}{.}\PYG{n}{plot}\PYG{p}{(}\PYG{n}{pm1}\PYG{p}{,} \PYG{n}{pm2}\PYG{p}{,} \PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{gx}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{,} \PYG{n}{markersize}\PYG{o}{=}\PYG{l+m+mf}{0.3}\PYG{p}{,} \PYG{n}{alpha}\PYG{o}{=}\PYG{l+m+mf}{0.3}\PYG{p}{)}
+
+\PYG{n}{plt}\PYG{o}{.}\PYG{n}{plot}\PYG{p}{(}\PYG{n}{pmra\PYGZus{}poly}\PYG{p}{,} \PYG{n}{pmdec\PYGZus{}poly}\PYG{p}{)}
+    
+\PYG{n}{plt}\PYG{o}{.}\PYG{n}{xlabel}\PYG{p}{(}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{Proper motion phi1 (ICRS frame)}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{)}
+\PYG{n}{plt}\PYG{o}{.}\PYG{n}{ylabel}\PYG{p}{(}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{Proper motion phi2 (ICRS frame)}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{)}
+
+\PYG{n}{plt}\PYG{o}{.}\PYG{n}{xlim}\PYG{p}{(}\PYG{p}{[}\PYG{o}{\PYGZhy{}}\PYG{l+m+mi}{10}\PYG{p}{,} \PYG{l+m+mi}{5}\PYG{p}{]}\PYG{p}{)}
+\PYG{n}{plt}\PYG{o}{.}\PYG{n}{ylim}\PYG{p}{(}\PYG{p}{[}\PYG{o}{\PYGZhy{}}\PYG{l+m+mi}{20}\PYG{p}{,} \PYG{l+m+mi}{5}\PYG{p}{]}\PYG{p}{)}\PYG{p}{;}
+\end{sphinxVerbatim}
+
+\noindent\sphinxincludegraphics{{04_select_25_0}.png}
+
+To use \sphinxcode{\sphinxupquote{pm\_vertices}} as part of an ADQL query, we have to convert it to a string.
+
+We’ll use \sphinxcode{\sphinxupquote{flatten}} to convert from a 2\sphinxhyphen{}D array to a 1\sphinxhyphen{}D array, and \sphinxcode{\sphinxupquote{str}} to convert each element to a string.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{t} \PYG{o}{=} \PYG{p}{[}\PYG{n+nb}{str}\PYG{p}{(}\PYG{n}{x}\PYG{p}{)} \PYG{k}{for} \PYG{n}{x} \PYG{o+ow}{in} \PYG{n}{pm\PYGZus{}vertices}\PYG{o}{.}\PYG{n}{flatten}\PYG{p}{(}\PYG{p}{)}\PYG{p}{]}
+\PYG{n}{t}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+[\PYGZsq{}\PYGZhy{}4.050371212154984\PYGZsq{},
+ \PYGZsq{}\PYGZhy{}14.75623260987968\PYGZsq{},
+ \PYGZsq{}\PYGZhy{}3.4198108491382455\PYGZsq{},
+ \PYGZsq{}\PYGZhy{}14.723655456335619\PYGZsq{},
+ \PYGZsq{}\PYGZhy{}3.035219883740934\PYGZsq{},
+ \PYGZsq{}\PYGZhy{}14.443571352854612\PYGZsq{},
+ \PYGZsq{}\PYGZhy{}2.268479190206636\PYGZsq{},
+ \PYGZsq{}\PYGZhy{}13.714023598831554\PYGZsq{},
+ \PYGZsq{}\PYGZhy{}2.611722027231764\PYGZsq{},
+ \PYGZsq{}\PYGZhy{}13.247974712069263\PYGZsq{},
+ \PYGZsq{}\PYGZhy{}2.7347140078529106\PYGZsq{},
+ \PYGZsq{}\PYGZhy{}13.090544709622938\PYGZsq{},
+ \PYGZsq{}\PYGZhy{}3.199231461993783\PYGZsq{},
+ \PYGZsq{}\PYGZhy{}12.594265302440828\PYGZsq{},
+ \PYGZsq{}\PYGZhy{}3.34082545787549\PYGZsq{},
+ \PYGZsq{}\PYGZhy{}12.476119260818695\PYGZsq{},
+ \PYGZsq{}\PYGZhy{}5.674894125178565\PYGZsq{},
+ \PYGZsq{}\PYGZhy{}11.160833381392624\PYGZsq{},
+ \PYGZsq{}\PYGZhy{}5.95159272432137\PYGZsq{},
+ \PYGZsq{}\PYGZhy{}11.105478836426514\PYGZsq{},
+ \PYGZsq{}\PYGZhy{}6.423940229776128\PYGZsq{},
+ \PYGZsq{}\PYGZhy{}11.05981294804957\PYGZsq{},
+ \PYGZsq{}\PYGZhy{}7.096310230579248\PYGZsq{},
+ \PYGZsq{}\PYGZhy{}11.951878058650085\PYGZsq{},
+ \PYGZsq{}\PYGZhy{}7.306415190921692\PYGZsq{},
+ \PYGZsq{}\PYGZhy{}12.245599765990594\PYGZsq{},
+ \PYGZsq{}\PYGZhy{}7.040166963232815\PYGZsq{},
+ \PYGZsq{}\PYGZhy{}12.885807024935527\PYGZsq{},
+ \PYGZsq{}\PYGZhy{}6.0034770546523735\PYGZsq{},
+ \PYGZsq{}\PYGZhy{}13.759120984106968\PYGZsq{},
+ \PYGZsq{}\PYGZhy{}4.42442296194263\PYGZsq{},
+ \PYGZsq{}\PYGZhy{}14.7464117578883\PYGZsq{}]
+\end{sphinxVerbatim}
+
+Now \sphinxcode{\sphinxupquote{t}} is a list of strings; we can use \sphinxcode{\sphinxupquote{join}} to make a single string with commas between the elements.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{pm\PYGZus{}point\PYGZus{}list} \PYG{o}{=} \PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{, }\PYG{l+s+s1}{\PYGZsq{}}\PYG{o}{.}\PYG{n}{join}\PYG{p}{(}\PYG{n}{t}\PYG{p}{)}
+\PYG{n}{pm\PYGZus{}point\PYGZus{}list}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYGZsq{}\PYGZhy{}4.050371212154984, \PYGZhy{}14.75623260987968, \PYGZhy{}3.4198108491382455, \PYGZhy{}14.723655456335619, \PYGZhy{}3.035219883740934, \PYGZhy{}14.443571352854612, \PYGZhy{}2.268479190206636, \PYGZhy{}13.714023598831554, \PYGZhy{}2.611722027231764, \PYGZhy{}13.247974712069263, \PYGZhy{}2.7347140078529106, \PYGZhy{}13.090544709622938, \PYGZhy{}3.199231461993783, \PYGZhy{}12.594265302440828, \PYGZhy{}3.34082545787549, \PYGZhy{}12.476119260818695, \PYGZhy{}5.674894125178565, \PYGZhy{}11.160833381392624, \PYGZhy{}5.95159272432137, \PYGZhy{}11.105478836426514, \PYGZhy{}6.423940229776128, \PYGZhy{}11.05981294804957, \PYGZhy{}7.096310230579248, \PYGZhy{}11.951878058650085, \PYGZhy{}7.306415190921692, \PYGZhy{}12.245599765990594, \PYGZhy{}7.040166963232815, \PYGZhy{}12.885807024935527, \PYGZhy{}6.0034770546523735, \PYGZhy{}13.759120984106968, \PYGZhy{}4.42442296194263, \PYGZhy{}14.7464117578883\PYGZsq{}
+\end{sphinxVerbatim}
+
+
+\section{Selecting the region}
+\label{\detokenize{04_select:selecting-the-region}}
+Let’s review how we got to this point.
+\begin{enumerate}
+\sphinxsetlistlabels{\arabic}{enumi}{enumii}{}{.}%
+\item {} 
+We made an ADQL query to the Gaia server to get data for stars in the vicinity of GD\sphinxhyphen{}1.
+
+\item {} 
+We transformed to \sphinxcode{\sphinxupquote{GD1}} coordinates so we could select stars along the centerline of GD\sphinxhyphen{}1.
+
+\item {} 
+We plotted the proper motion of the centerline stars to identify the bounds of the overdense region.
+
+\item {} 
+We made a mask that selects stars whose proper motion is in the overdense region.
+
+\end{enumerate}
+
+The problem is that we downloaded data for more than 100,000 stars and selected only about 1000 of them.
+
+It will be more efficient if we select on proper motion as part of the query.  That will allow us to work with a larger region of the sky in a single query, and download less unneeded data.
+
+This query will select on the following conditions:
+\begin{itemize}
+\item {} 
+\sphinxcode{\sphinxupquote{parallax \textless{} 1}}
+
+\item {} 
+\sphinxcode{\sphinxupquote{bp\_rp BETWEEN \sphinxhyphen{}0.75 AND 2}}
+
+\item {} 
+Coordinates within a rectangle in the GD\sphinxhyphen{}1 frame, transformed to ICRS.
+
+\item {} 
+Proper motion with the polygon we just computed.
+
+\end{itemize}
+
+The first three conditions are the same as in the previous query.  Only the last one is new.
+
+Here’s the rectangle in the GD\sphinxhyphen{}1 frame we’ll select.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{phi1\PYGZus{}min} \PYG{o}{=} \PYG{o}{\PYGZhy{}}\PYG{l+m+mi}{70}
+\PYG{n}{phi1\PYGZus{}max} \PYG{o}{=} \PYG{o}{\PYGZhy{}}\PYG{l+m+mi}{20}
+\PYG{n}{phi2\PYGZus{}min} \PYG{o}{=} \PYG{o}{\PYGZhy{}}\PYG{l+m+mi}{5}
+\PYG{n}{phi2\PYGZus{}max} \PYG{o}{=} \PYG{l+m+mi}{5}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{phi1\PYGZus{}rect} \PYG{o}{=} \PYG{p}{[}\PYG{n}{phi1\PYGZus{}min}\PYG{p}{,} \PYG{n}{phi1\PYGZus{}min}\PYG{p}{,} \PYG{n}{phi1\PYGZus{}max}\PYG{p}{,} \PYG{n}{phi1\PYGZus{}max}\PYG{p}{]} \PYG{o}{*} \PYG{n}{u}\PYG{o}{.}\PYG{n}{deg}
+\PYG{n}{phi2\PYGZus{}rect} \PYG{o}{=} \PYG{p}{[}\PYG{n}{phi2\PYGZus{}min}\PYG{p}{,} \PYG{n}{phi2\PYGZus{}max}\PYG{p}{,} \PYG{n}{phi2\PYGZus{}max}\PYG{p}{,} \PYG{n}{phi2\PYGZus{}min}\PYG{p}{]} \PYG{o}{*} \PYG{n}{u}\PYG{o}{.}\PYG{n}{deg}
+\end{sphinxVerbatim}
+
+Here’s how we transform it to ICRS, as we saw in the previous lesson.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{k+kn}{import} \PYG{n+nn}{gala}\PYG{n+nn}{.}\PYG{n+nn}{coordinates} \PYG{k}{as} \PYG{n+nn}{gc}
+\PYG{k+kn}{import} \PYG{n+nn}{astropy}\PYG{n+nn}{.}\PYG{n+nn}{coordinates} \PYG{k}{as} \PYG{n+nn}{coord}
+
+\PYG{n}{corners} \PYG{o}{=} \PYG{n}{gc}\PYG{o}{.}\PYG{n}{GD1Koposov10}\PYG{p}{(}\PYG{n}{phi1}\PYG{o}{=}\PYG{n}{phi1\PYGZus{}rect}\PYG{p}{,} \PYG{n}{phi2}\PYG{o}{=}\PYG{n}{phi2\PYGZus{}rect}\PYG{p}{)}
+\PYG{n}{corners\PYGZus{}icrs} \PYG{o}{=} \PYG{n}{corners}\PYG{o}{.}\PYG{n}{transform\PYGZus{}to}\PYG{p}{(}\PYG{n}{coord}\PYG{o}{.}\PYG{n}{ICRS}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+To use \sphinxcode{\sphinxupquote{corners\_icrs}} as part of an ADQL query, we have to convert it to a string.  Here’s how we do that, as we saw in the previous lesson.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{point\PYGZus{}base} \PYG{o}{=} \PYG{l+s+s2}{\PYGZdq{}}\PYG{l+s+si}{\PYGZob{}point.ra.value\PYGZcb{}}\PYG{l+s+s2}{, }\PYG{l+s+si}{\PYGZob{}point.dec.value\PYGZcb{}}\PYG{l+s+s2}{\PYGZdq{}}
+
+\PYG{n}{t} \PYG{o}{=} \PYG{p}{[}\PYG{n}{point\PYGZus{}base}\PYG{o}{.}\PYG{n}{format}\PYG{p}{(}\PYG{n}{point}\PYG{o}{=}\PYG{n}{point}\PYG{p}{)}
+     \PYG{k}{for} \PYG{n}{point} \PYG{o+ow}{in} \PYG{n}{corners\PYGZus{}icrs}\PYG{p}{]}
+
+\PYG{n}{point\PYGZus{}list} \PYG{o}{=} \PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{, }\PYG{l+s+s1}{\PYGZsq{}}\PYG{o}{.}\PYG{n}{join}\PYG{p}{(}\PYG{n}{t}\PYG{p}{)}
+\PYG{n}{point\PYGZus{}list}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYGZsq{}135.30559858565638, 8.398623940157561, 126.50951508623503, 13.44494195652069, 163.0173655836748, 54.24242734020255, 172.9328536286811, 46.47260492416258\PYGZsq{}
+\end{sphinxVerbatim}
+
+Now we have everything we need to assemble the query.
+
+
+\section{Assemble the query}
+\label{\detokenize{04_select:assemble-the-query}}
+Here’s the base string we used for the query in the previous lesson.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{query\PYGZus{}base} \PYG{o}{=} \PYG{l+s+s2}{\PYGZdq{}\PYGZdq{}\PYGZdq{}}\PYG{l+s+s2}{SELECT }
+\PYG{l+s+si}{\PYGZob{}columns\PYGZcb{}}
+\PYG{l+s+s2}{FROM gaiadr2.gaia\PYGZus{}source}
+\PYG{l+s+s2}{WHERE parallax \PYGZlt{} 1}
+\PYG{l+s+s2}{  AND bp\PYGZus{}rp BETWEEN \PYGZhy{}0.75 AND 2 }
+\PYG{l+s+s2}{  AND 1 = CONTAINS(POINT(ra, dec), }
+\PYG{l+s+s2}{                   POLYGON(}\PYG{l+s+si}{\PYGZob{}point\PYGZus{}list\PYGZcb{}}\PYG{l+s+s2}{))}
+\PYG{l+s+s2}{\PYGZdq{}\PYGZdq{}\PYGZdq{}}
+\end{sphinxVerbatim}
+
+\sphinxstylestrong{Exercise:} Modify \sphinxcode{\sphinxupquote{query\_base}} by adding a new clause to select stars whose coordinates of proper motion, \sphinxcode{\sphinxupquote{pmra}} and \sphinxcode{\sphinxupquote{pmdec}}, fall within the polygon defined by \sphinxcode{\sphinxupquote{pm\_point\_list}}.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{c+c1}{\PYGZsh{} Solution}
+
+\PYG{n}{query\PYGZus{}base} \PYG{o}{=} \PYG{l+s+s2}{\PYGZdq{}\PYGZdq{}\PYGZdq{}}\PYG{l+s+s2}{SELECT }
+\PYG{l+s+si}{\PYGZob{}columns\PYGZcb{}}
+\PYG{l+s+s2}{FROM gaiadr2.gaia\PYGZus{}source}
+\PYG{l+s+s2}{WHERE parallax \PYGZlt{} 1}
+\PYG{l+s+s2}{  AND bp\PYGZus{}rp BETWEEN \PYGZhy{}0.75 AND 2 }
+\PYG{l+s+s2}{  AND 1 = CONTAINS(POINT(ra, dec), }
+\PYG{l+s+s2}{                   POLYGON(}\PYG{l+s+si}{\PYGZob{}point\PYGZus{}list\PYGZcb{}}\PYG{l+s+s2}{))}
+\PYG{l+s+s2}{  AND 1 = CONTAINS(POINT(pmra, pmdec),}
+\PYG{l+s+s2}{                   POLYGON(}\PYG{l+s+si}{\PYGZob{}pm\PYGZus{}point\PYGZus{}list\PYGZcb{}}\PYG{l+s+s2}{))}
+\PYG{l+s+s2}{\PYGZdq{}\PYGZdq{}\PYGZdq{}}
+\end{sphinxVerbatim}
+
+Here again are the columns we want to select.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{columns} \PYG{o}{=} \PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{source\PYGZus{}id, ra, dec, pmra, pmdec, parallax, parallax\PYGZus{}error, radial\PYGZus{}velocity}\PYG{l+s+s1}{\PYGZsq{}}
+\end{sphinxVerbatim}
+
+\sphinxstylestrong{Exercise:} Use \sphinxcode{\sphinxupquote{format}} to format \sphinxcode{\sphinxupquote{query\_base}} and define \sphinxcode{\sphinxupquote{query}}, filling in the values of \sphinxcode{\sphinxupquote{columns}}, \sphinxcode{\sphinxupquote{point\_list}}, and \sphinxcode{\sphinxupquote{pm\_point\_list}}.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{c+c1}{\PYGZsh{} Solution}
+
+\PYG{n}{query} \PYG{o}{=} \PYG{n}{query\PYGZus{}base}\PYG{o}{.}\PYG{n}{format}\PYG{p}{(}\PYG{n}{columns}\PYG{o}{=}\PYG{n}{columns}\PYG{p}{,} 
+                            \PYG{n}{point\PYGZus{}list}\PYG{o}{=}\PYG{n}{point\PYGZus{}list}\PYG{p}{,}
+                            \PYG{n}{pm\PYGZus{}point\PYGZus{}list}\PYG{o}{=}\PYG{n}{pm\PYGZus{}point\PYGZus{}list}\PYG{p}{)}
+\PYG{n+nb}{print}\PYG{p}{(}\PYG{n}{query}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+SELECT 
+source\PYGZus{}id, ra, dec, pmra, pmdec, parallax, parallax\PYGZus{}error, radial\PYGZus{}velocity
+FROM gaiadr2.gaia\PYGZus{}source
+WHERE parallax \PYGZlt{} 1
+  AND bp\PYGZus{}rp BETWEEN \PYGZhy{}0.75 AND 2 
+  AND 1 = CONTAINS(POINT(ra, dec), 
+                   POLYGON(135.30559858565638, 8.398623940157561, 126.50951508623503, 13.44494195652069, 163.0173655836748, 54.24242734020255, 172.9328536286811, 46.47260492416258))
+  AND 1 = CONTAINS(POINT(pmra, pmdec),
+                   POLYGON(\PYGZhy{}4.050371212154984, \PYGZhy{}14.75623260987968, \PYGZhy{}3.4198108491382455, \PYGZhy{}14.723655456335619, \PYGZhy{}3.035219883740934, \PYGZhy{}14.443571352854612, \PYGZhy{}2.268479190206636, \PYGZhy{}13.714023598831554, \PYGZhy{}2.611722027231764, \PYGZhy{}13.247974712069263, \PYGZhy{}2.7347140078529106, \PYGZhy{}13.090544709622938, \PYGZhy{}3.199231461993783, \PYGZhy{}12.594265302440828, \PYGZhy{}3.34082545787549, \PYGZhy{}12.476119260818695, \PYGZhy{}5.674894125178565, \PYGZhy{}11.160833381392624, \PYGZhy{}5.95159272432137, \PYGZhy{}11.105478836426514, \PYGZhy{}6.423940229776128, \PYGZhy{}11.05981294804957, \PYGZhy{}7.096310230579248, \PYGZhy{}11.951878058650085, \PYGZhy{}7.306415190921692, \PYGZhy{}12.245599765990594, \PYGZhy{}7.040166963232815, \PYGZhy{}12.885807024935527, \PYGZhy{}6.0034770546523735, \PYGZhy{}13.759120984106968, \PYGZhy{}4.42442296194263, \PYGZhy{}14.7464117578883))
+\end{sphinxVerbatim}
+
+Here’s how we run it.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{k+kn}{from} \PYG{n+nn}{astroquery}\PYG{n+nn}{.}\PYG{n+nn}{gaia} \PYG{k+kn}{import} \PYG{n}{Gaia}
+
+\PYG{n}{job} \PYG{o}{=} \PYG{n}{Gaia}\PYG{o}{.}\PYG{n}{launch\PYGZus{}job\PYGZus{}async}\PYG{p}{(}\PYG{n}{query}\PYG{p}{)}
+\PYG{n+nb}{print}\PYG{p}{(}\PYG{n}{job}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+Created TAP+ (v1.2.1) \PYGZhy{} Connection:
+	Host: gea.esac.esa.int
+	Use HTTPS: True
+	Port: 443
+	SSL Port: 443
+Created TAP+ (v1.2.1) \PYGZhy{} Connection:
+	Host: geadata.esac.esa.int
+	Use HTTPS: True
+	Port: 443
+	SSL Port: 443
+INFO: Query finished. [astroquery.utils.tap.core]
+\PYGZlt{}Table length=7346\PYGZgt{}
+      name       dtype    unit                              description                             n\PYGZus{}bad
+\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{} \PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{} \PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{} \PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{} \PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}
+      source\PYGZus{}id   int64          Unique source identifier (unique within a particular Data Release)     0
+             ra float64      deg                                                    Right ascension     0
+            dec float64      deg                                                        Declination     0
+           pmra float64 mas / yr                         Proper motion in right ascension direction     0
+          pmdec float64 mas / yr                             Proper motion in declination direction     0
+       parallax float64      mas                                                           Parallax     0
+ parallax\PYGZus{}error float64      mas                                         Standard error of parallax     0
+radial\PYGZus{}velocity float64   km / s                                                    Radial velocity  7295
+Jobid: 1603132746237O
+Phase: COMPLETED
+Owner: None
+Output file: async\PYGZus{}20201019143906.vot
+Results: None
+\end{sphinxVerbatim}
+
+And get the results.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{candidate\PYGZus{}table} \PYG{o}{=} \PYG{n}{job}\PYG{o}{.}\PYG{n}{get\PYGZus{}results}\PYG{p}{(}\PYG{p}{)}
+\PYG{n+nb}{len}\PYG{p}{(}\PYG{n}{candidate\PYGZus{}table}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+7346
+\end{sphinxVerbatim}
+
+
+\section{Plotting one more time}
+\label{\detokenize{04_select:plotting-one-more-time}}
+Let’s see what the results look like.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{x} \PYG{o}{=} \PYG{n}{candidate\PYGZus{}table}\PYG{p}{[}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{ra}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{]}
+\PYG{n}{y} \PYG{o}{=} \PYG{n}{candidate\PYGZus{}table}\PYG{p}{[}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{dec}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{]}
+\PYG{n}{plt}\PYG{o}{.}\PYG{n}{plot}\PYG{p}{(}\PYG{n}{x}\PYG{p}{,} \PYG{n}{y}\PYG{p}{,} \PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{ko}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{,} \PYG{n}{markersize}\PYG{o}{=}\PYG{l+m+mf}{0.3}\PYG{p}{,} \PYG{n}{alpha}\PYG{o}{=}\PYG{l+m+mf}{0.3}\PYG{p}{)}
+
+\PYG{n}{plt}\PYG{o}{.}\PYG{n}{xlabel}\PYG{p}{(}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{ra (degree ICRS)}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{)}
+\PYG{n}{plt}\PYG{o}{.}\PYG{n}{ylabel}\PYG{p}{(}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{dec (degree ICRS)}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{)}\PYG{p}{;}
+\end{sphinxVerbatim}
+
+\noindent\sphinxincludegraphics{{04_select_51_0}.png}
+
+Here we can see why it was useful to transform these coordinates.  In ICRS, it is more difficult to identity the stars near the centerline of GD\sphinxhyphen{}1.
+
+So, before we move on to the next step, let’s collect the code we used to transform the coordinates and make a Pandas \sphinxcode{\sphinxupquote{DataFrame}}:
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{k+kn}{from} \PYG{n+nn}{pyia} \PYG{k+kn}{import} \PYG{n}{GaiaData}
+
+\PYG{k}{def} \PYG{n+nf}{make\PYGZus{}dataframe}\PYG{p}{(}\PYG{n}{table}\PYG{p}{)}\PYG{p}{:}
+    \PYG{l+s+sd}{\PYGZdq{}\PYGZdq{}\PYGZdq{}Transform coordinates from ICRS to GD\PYGZhy{}1 frame.}
+\PYG{l+s+sd}{    }
+\PYG{l+s+sd}{    table: Astropy Table}
+\PYG{l+s+sd}{    }
+\PYG{l+s+sd}{    returns: Pandas DataFrame}
+\PYG{l+s+sd}{    \PYGZdq{}\PYGZdq{}\PYGZdq{}}
+    \PYG{n}{gaia\PYGZus{}data} \PYG{o}{=} \PYG{n}{GaiaData}\PYG{p}{(}\PYG{n}{table}\PYG{p}{)}
+
+    \PYG{n}{c\PYGZus{}sky} \PYG{o}{=} \PYG{n}{gaia\PYGZus{}data}\PYG{o}{.}\PYG{n}{get\PYGZus{}skycoord}\PYG{p}{(}\PYG{n}{distance}\PYG{o}{=}\PYG{l+m+mi}{8}\PYG{o}{*}\PYG{n}{u}\PYG{o}{.}\PYG{n}{kpc}\PYG{p}{,} 
+                                   \PYG{n}{radial\PYGZus{}velocity}\PYG{o}{=}\PYG{l+m+mi}{0}\PYG{o}{*}\PYG{n}{u}\PYG{o}{.}\PYG{n}{km}\PYG{o}{/}\PYG{n}{u}\PYG{o}{.}\PYG{n}{s}\PYG{p}{)}
+    \PYG{n}{c\PYGZus{}gd1} \PYG{o}{=} \PYG{n}{gc}\PYG{o}{.}\PYG{n}{reflex\PYGZus{}correct}\PYG{p}{(}
+                \PYG{n}{c\PYGZus{}sky}\PYG{o}{.}\PYG{n}{transform\PYGZus{}to}\PYG{p}{(}\PYG{n}{gc}\PYG{o}{.}\PYG{n}{GD1Koposov10}\PYG{p}{)}\PYG{p}{)}
+
+    \PYG{n}{df} \PYG{o}{=} \PYG{n}{table}\PYG{o}{.}\PYG{n}{to\PYGZus{}pandas}\PYG{p}{(}\PYG{p}{)}
+    \PYG{n}{df}\PYG{p}{[}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{phi1}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{]} \PYG{o}{=} \PYG{n}{c\PYGZus{}gd1}\PYG{o}{.}\PYG{n}{phi1}
+    \PYG{n}{df}\PYG{p}{[}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{phi2}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{]} \PYG{o}{=} \PYG{n}{c\PYGZus{}gd1}\PYG{o}{.}\PYG{n}{phi2}
+    \PYG{n}{df}\PYG{p}{[}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{pm\PYGZus{}phi1}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{]} \PYG{o}{=} \PYG{n}{c\PYGZus{}gd1}\PYG{o}{.}\PYG{n}{pm\PYGZus{}phi1\PYGZus{}cosphi2}
+    \PYG{n}{df}\PYG{p}{[}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{pm\PYGZus{}phi2}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{]} \PYG{o}{=} \PYG{n}{c\PYGZus{}gd1}\PYG{o}{.}\PYG{n}{pm\PYGZus{}phi2}
+    \PYG{k}{return} \PYG{n}{df}
+\end{sphinxVerbatim}
+
+Here’s how we can use this function:
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{candidate\PYGZus{}df} \PYG{o}{=} \PYG{n}{make\PYGZus{}dataframe}\PYG{p}{(}\PYG{n}{candidate\PYGZus{}table}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+And let’s see the results.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{x} \PYG{o}{=} \PYG{n}{candidate\PYGZus{}df}\PYG{p}{[}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{phi1}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{]}
+\PYG{n}{y} \PYG{o}{=} \PYG{n}{candidate\PYGZus{}df}\PYG{p}{[}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{phi2}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{]}
+
+\PYG{n}{plt}\PYG{o}{.}\PYG{n}{plot}\PYG{p}{(}\PYG{n}{x}\PYG{p}{,} \PYG{n}{y}\PYG{p}{,} \PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{ko}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{,} \PYG{n}{markersize}\PYG{o}{=}\PYG{l+m+mf}{0.5}\PYG{p}{,} \PYG{n}{alpha}\PYG{o}{=}\PYG{l+m+mf}{0.5}\PYG{p}{)}
+
+\PYG{n}{plt}\PYG{o}{.}\PYG{n}{xlabel}\PYG{p}{(}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{ra (degree GD1)}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{)}
+\PYG{n}{plt}\PYG{o}{.}\PYG{n}{ylabel}\PYG{p}{(}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{dec (degree GD1)}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{)}\PYG{p}{;}
+\end{sphinxVerbatim}
+
+\noindent\sphinxincludegraphics{{04_select_57_0}.png}
+
+We’re starting to see GD\sphinxhyphen{}1 more clearly.
+
+We can compare this figure with one of these panels in Figure 1 from the original paper:
+
+
+
+
+
+The top panel shows stars selected based on proper motion only, so it is comparable to our figure (although notice that it covers a wider region).
+
+In the next lesson, we will use photometry data from Pan\sphinxhyphen{}STARRS to do a second round of filtering, and see if we can replicate the bottom panel.
+
+We’ll also learn how to add annotations like the ones in the figure from the paper, and customize the style of the figure to present the results clearly and compellingly.
+
+
+\section{Saving the DataFrame}
+\label{\detokenize{04_select:saving-the-dataframe}}
+Let’s save this \sphinxcode{\sphinxupquote{DataFrame}} so we can pick up where we left off without running this query again.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{o}{!}rm \PYGZhy{}f gd1\PYGZus{}candidates.hdf5
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{filename} \PYG{o}{=} \PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{gd1\PYGZus{}candidates.hdf5}\PYG{l+s+s1}{\PYGZsq{}}
+
+\PYG{n}{candidate\PYGZus{}df}\PYG{o}{.}\PYG{n}{to\PYGZus{}hdf}\PYG{p}{(}\PYG{n}{filename}\PYG{p}{,} \PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{candidate\PYGZus{}df}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+We can use \sphinxcode{\sphinxupquote{ls}} to confirm that the file exists and check the size:
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{o}{!}ls \PYGZhy{}lh gd1\PYGZus{}candidates.hdf5
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYGZhy{}rw\PYGZhy{}rw\PYGZhy{}r\PYGZhy{}\PYGZhy{} 1 downey downey 756K Oct 19 14:39 gd1\PYGZus{}candidates.hdf5
+\end{sphinxVerbatim}
+
+If you are using Windows, \sphinxcode{\sphinxupquote{ls}} might not work; in that case, try:
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+!dir gd1\PYGZus{}candidates.hdf5
+\end{sphinxVerbatim}
+
+
+\section{CSV}
+\label{\detokenize{04_select:csv}}
+Pandas can write a variety of other formats, \sphinxhref{https://pandas.pydata.org/pandas-docs/stable/user\_guide/io.html}{which you can read about here}.
+
+We won’t cover all of them, but one other important one is \sphinxhref{https://en.wikipedia.org/wiki/Comma-separated\_values}{CSV}, which stands for “comma\sphinxhyphen{}separated values�.
+
+CSV is a plain\sphinxhyphen{}text format with minimal formatting requirements, so it can be read and written by pretty much any tool that works with data.  In that sense, it is the “least common denominator� of data formats.
+
+However, it has an important limitation: some information about the data gets lost in translation, notably the data types.  If you read a CSV file from someone else, you might need some additional information to make sure you are getting it right.
+
+Also, CSV files tend to be big, and slow to read and write.
+
+With those caveats, here’s how to write one:
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{candidate\PYGZus{}df}\PYG{o}{.}\PYG{n}{to\PYGZus{}csv}\PYG{p}{(}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{gd1\PYGZus{}candidates.csv}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+We can check the file size like this:
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{o}{!}ls \PYGZhy{}lh gd1\PYGZus{}candidates.csv
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYGZhy{}rw\PYGZhy{}rw\PYGZhy{}r\PYGZhy{}\PYGZhy{} 1 downey downey 1.6M Oct 19 14:39 gd1\PYGZus{}candidates.csv
+\end{sphinxVerbatim}
+
+The CSV file about 2 times bigger than the HDF5 file (so that’s not that bad, really).
+
+We can see the first few lines like this:
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{o}{!}head \PYGZhy{}3 gd1\PYGZus{}candidates.csv
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+,source\PYGZus{}id,ra,dec,pmra,pmdec,parallax,parallax\PYGZus{}error,radial\PYGZus{}velocity,phi1,phi2,pm\PYGZus{}phi1,pm\PYGZus{}phi2
+0,635559124339440000,137.58671691646745,19.1965441084838,\PYGZhy{}3.770521900009566,\PYGZhy{}12.490481778113859,0.7913934419894347,0.2717538145759051,,\PYGZhy{}59.63048941944396,\PYGZhy{}1.21648525150429,\PYGZhy{}7.361362712556612,\PYGZhy{}0.5926328820420083
+1,635860218726658176,138.5187065217173,19.09233926905897,\PYGZhy{}5.941679495793577,\PYGZhy{}11.346409129876392,0.30745551377348623,0.19946557779138105,,\PYGZhy{}59.247329893833296,\PYGZhy{}2.0160784008206476,\PYGZhy{}7.527126084599517,1.7487794924398758
+\end{sphinxVerbatim}
+
+The CSV file contains the names of the columns, but not the data types.
+
+We can read the CSV file back like this:
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{read\PYGZus{}back\PYGZus{}csv} \PYG{o}{=} \PYG{n}{pd}\PYG{o}{.}\PYG{n}{read\PYGZus{}csv}\PYG{p}{(}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{gd1\PYGZus{}candidates.csv}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+Let’s compare the first few rows of \sphinxcode{\sphinxupquote{candidate\_df}} and \sphinxcode{\sphinxupquote{read\_back\_csv}}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{candidate\PYGZus{}df}\PYG{o}{.}\PYG{n}{head}\PYG{p}{(}\PYG{l+m+mi}{3}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+            source\PYGZus{}id          ra        dec      pmra      pmdec  parallax  \PYGZbs{}
+0  635559124339440000  137.586717  19.196544 \PYGZhy{}3.770522 \PYGZhy{}12.490482  0.791393   
+1  635860218726658176  138.518707  19.092339 \PYGZhy{}5.941679 \PYGZhy{}11.346409  0.307456   
+2  635674126383965568  138.842874  19.031798 \PYGZhy{}3.897001 \PYGZhy{}12.702780  0.779463   
+
+   parallax\PYGZus{}error  radial\PYGZus{}velocity       phi1      phi2   pm\PYGZus{}phi1   pm\PYGZus{}phi2  
+0        0.271754              NaN \PYGZhy{}59.630489 \PYGZhy{}1.216485 \PYGZhy{}7.361363 \PYGZhy{}0.592633  
+1        0.199466              NaN \PYGZhy{}59.247330 \PYGZhy{}2.016078 \PYGZhy{}7.527126  1.748779  
+2        0.223692              NaN \PYGZhy{}59.133391 \PYGZhy{}2.306901 \PYGZhy{}7.560608 \PYGZhy{}0.741800  
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{read\PYGZus{}back\PYGZus{}csv}\PYG{o}{.}\PYG{n}{head}\PYG{p}{(}\PYG{l+m+mi}{3}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+   Unnamed: 0           source\PYGZus{}id          ra        dec      pmra      pmdec  \PYGZbs{}
+0           0  635559124339440000  137.586717  19.196544 \PYGZhy{}3.770522 \PYGZhy{}12.490482   
+1           1  635860218726658176  138.518707  19.092339 \PYGZhy{}5.941679 \PYGZhy{}11.346409   
+2           2  635674126383965568  138.842874  19.031798 \PYGZhy{}3.897001 \PYGZhy{}12.702780   
+
+   parallax  parallax\PYGZus{}error  radial\PYGZus{}velocity       phi1      phi2   pm\PYGZus{}phi1  \PYGZbs{}
+0  0.791393        0.271754              NaN \PYGZhy{}59.630489 \PYGZhy{}1.216485 \PYGZhy{}7.361363   
+1  0.307456        0.199466              NaN \PYGZhy{}59.247330 \PYGZhy{}2.016078 \PYGZhy{}7.527126   
+2  0.779463        0.223692              NaN \PYGZhy{}59.133391 \PYGZhy{}2.306901 \PYGZhy{}7.560608   
+
+    pm\PYGZus{}phi2  
+0 \PYGZhy{}0.592633  
+1  1.748779  
+2 \PYGZhy{}0.741800  
+\end{sphinxVerbatim}
+
+Notice that the index in \sphinxcode{\sphinxupquote{candidate\_df}} has become an unnamed column in \sphinxcode{\sphinxupquote{read\_back\_csv}}.  The Pandas functions for writing and reading CSV files provide options to avoid that problem, but this is an example of the kind of thing that can go wrong with CSV files.
+
+
+\section{Summary}
+\label{\detokenize{04_select:summary}}
+In the previous lesson we downloaded data for a large number of stars and then selected a small fraction of them based on proper motion.
+
+In this lesson, we improved this process by writing a more complex query that uses the database to select stars based on proper motion.  This process requires more computation on the Gaia server, but then we’re able to either:
+\begin{enumerate}
+\sphinxsetlistlabels{\arabic}{enumi}{enumii}{}{.}%
+\item {} 
+Search the same region and download less data, or
+
+\item {} 
+Search a larger region while still downloading a manageable amount of data.
+
+\end{enumerate}
+
+In the next lesson, we’ll learn about the databased \sphinxcode{\sphinxupquote{JOIN}} operation and use it to download photometry data from Pan\sphinxhyphen{}STARRS.
+
+
+\section{Best practices}
+\label{\detokenize{04_select:best-practices}}\begin{itemize}
+\item {} 
+When possible, “move the computation to the data�; that is, do as much of the work as possible on the database server before downloading the data.
+
+\item {} 
+For most applications, saving data in FITS or HDF5 is better than CSV.  FITS and HDF5 are binary formats, so the files are usually smaller, and they store metadata, so you don’t lose anything when you read the file back.
+
+\item {} 
+On the other hand, CSV is a “least common denominator� format; that is, it can be read by practically any application that works with data.
+
+\end{itemize}
+
+
+\chapter{Chapter 5}
+\label{\detokenize{05_join:chapter-5}}\label{\detokenize{05_join::doc}}
+This is the fifth in a series of notebooks related to astronomy data.
+
+As a continuing example, we will replicate part of the analysis in a recent paper, “\sphinxhref{https://arxiv.org/abs/1805.00425}{Off the beaten path: Gaia reveals GD\sphinxhyphen{}1 stars outside of the main stream}� by Adrian M. Price\sphinxhyphen{}Whelan and Ana Bonaca.
+
+Picking up where we left off, the next step in the analysis is to select candidate stars based on photometry.  The following figure from the paper is a color\sphinxhyphen{}magnitude diagram for the stars selected based on proper motion:
+
+
+
+In red is a theoretical isochrone, showing where we expect the stars in GD\sphinxhyphen{}1 to fall based on the metallicity and age of their original globular cluster.
+
+By selecting stars in the shaded area, we can further distinguish the main sequence of GD\sphinxhyphen{}1 from younger background stars.
+
+
+\section{Outline}
+\label{\detokenize{05_join:outline}}
+Here are the steps in this notebook:
+\begin{enumerate}
+\sphinxsetlistlabels{\arabic}{enumi}{enumii}{}{.}%
+\item {} 
+We’ll reload the candidate stars we identified in the previous notebook.
+
+\item {} 
+Then we’ll run a query on the Gaia server that uploads the table of candidates and uses a \sphinxcode{\sphinxupquote{JOIN}} operation to select photometry data for the candidate stars.
+
+\item {} 
+We’ll write the results to a file for use in the next notebook.
+
+\end{enumerate}
+
+After completing this lesson, you should be able to
+\begin{itemize}
+\item {} 
+Upload a table to the Gaia server.
+
+\item {} 
+Write ADQL queries involving \sphinxcode{\sphinxupquote{JOIN}} operations.
+
+\end{itemize}
+
+
+\section{Installing libraries}
+\label{\detokenize{05_join:installing-libraries}}
+If you are running this notebook on Colab, you can run the following cell to install Astroquery and a the other libraries we’ll use.
+
+If you are running this notebook on your own computer, you might have to install these libraries yourself.
+
+If you are using this notebook as part of a Carpentries workshop, you should have received setup instructions.
+
+TODO: Add a link to the instructions.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{c+c1}{\PYGZsh{} If we\PYGZsq{}re running on Colab, install libraries}
+
+\PYG{k+kn}{import} \PYG{n+nn}{sys}
+\PYG{n}{IN\PYGZus{}COLAB} \PYG{o}{=} \PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{google.colab}\PYG{l+s+s1}{\PYGZsq{}} \PYG{o+ow}{in} \PYG{n}{sys}\PYG{o}{.}\PYG{n}{modules}
+
+\PYG{k}{if} \PYG{n}{IN\PYGZus{}COLAB}\PYG{p}{:}
+    \PYG{o}{!}pip install astroquery astro\PYGZhy{}gala pyia python\PYGZhy{}wget
+\end{sphinxVerbatim}
+
+
+\section{Reloading the data}
+\label{\detokenize{05_join:reloading-the-data}}
+The following cell downloads the data from the previous notebook.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{k+kn}{import} \PYG{n+nn}{os}
+\PYG{k+kn}{from} \PYG{n+nn}{wget} \PYG{k+kn}{import} \PYG{n}{download}
+
+\PYG{n}{filename} \PYG{o}{=} \PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{gd1\PYGZus{}candidates.hdf5}\PYG{l+s+s1}{\PYGZsq{}}
+\PYG{n}{path} \PYG{o}{=} \PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{https://github.com/AllenDowney/AstronomicalData/raw/main/data/}\PYG{l+s+s1}{\PYGZsq{}}
+
+\PYG{k}{if} \PYG{o+ow}{not} \PYG{n}{os}\PYG{o}{.}\PYG{n}{path}\PYG{o}{.}\PYG{n}{exists}\PYG{p}{(}\PYG{n}{filename}\PYG{p}{)}\PYG{p}{:}
+    \PYG{n+nb}{print}\PYG{p}{(}\PYG{n}{download}\PYG{p}{(}\PYG{n}{path}\PYG{o}{+}\PYG{n}{filename}\PYG{p}{)}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+And we can read it back.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{k+kn}{import} \PYG{n+nn}{pandas} \PYG{k}{as} \PYG{n+nn}{pd}
+
+\PYG{n}{candidate\PYGZus{}df} \PYG{o}{=} \PYG{n}{pd}\PYG{o}{.}\PYG{n}{read\PYGZus{}hdf}\PYG{p}{(}\PYG{n}{filename}\PYG{p}{,} \PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{candidate\PYGZus{}df}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+\sphinxcode{\sphinxupquote{candidate\_df}} is the Pandas DataFrame that contains results from the query in the previous notebook, which selects stars likely to be in GD\sphinxhyphen{}1 based on proper motion.  It also includes position and proper motion transformed to the ICRS frame.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{k+kn}{import} \PYG{n+nn}{matplotlib}\PYG{n+nn}{.}\PYG{n+nn}{pyplot} \PYG{k}{as} \PYG{n+nn}{plt}
+
+\PYG{n}{x} \PYG{o}{=} \PYG{n}{candidate\PYGZus{}df}\PYG{p}{[}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{phi1}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{]}
+\PYG{n}{y} \PYG{o}{=} \PYG{n}{candidate\PYGZus{}df}\PYG{p}{[}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{phi2}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{]}
+
+\PYG{n}{plt}\PYG{o}{.}\PYG{n}{plot}\PYG{p}{(}\PYG{n}{x}\PYG{p}{,} \PYG{n}{y}\PYG{p}{,} \PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{ko}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{,} \PYG{n}{markersize}\PYG{o}{=}\PYG{l+m+mf}{0.3}\PYG{p}{,} \PYG{n}{alpha}\PYG{o}{=}\PYG{l+m+mf}{0.3}\PYG{p}{)}
+
+\PYG{n}{plt}\PYG{o}{.}\PYG{n}{xlabel}\PYG{p}{(}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{ra (degree GD1)}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{)}
+\PYG{n}{plt}\PYG{o}{.}\PYG{n}{ylabel}\PYG{p}{(}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{dec (degree GD1)}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{)}\PYG{p}{;}
+\end{sphinxVerbatim}
+
+\noindent\sphinxincludegraphics{{05_join_9_0}.png}
+
+This is the same figure we saw at the end of the previous notebook.  GD\sphinxhyphen{}1 is visible against the background stars, but we will be able to see it more clearly after selecting based on photometry data.
+
+
+\section{Getting photometry data}
+\label{\detokenize{05_join:getting-photometry-data}}
+The Gaia dataset contains some photometry data, including the variable \sphinxcode{\sphinxupquote{bp\_rp}}, which we used in the original query to select stars with BP \sphinxhyphen{} RP color between \sphinxhyphen{}0.75 and 2.
+
+Selecting stars with \sphinxcode{\sphinxupquote{bp\sphinxhyphen{}rp}} less than 2 excludes many class M dwarf stars, which are low temperature, low luminosity.  A star like that at GD\sphinxhyphen{}1’s distance would be hard to detect, so if it is detected, it it more likely to be in the foreground.
+
+Now, to select stars with the age and metal richness we expect in GD\sphinxhyphen{}1, we will use \sphinxcode{\sphinxupquote{g \sphinxhyphen{} i}} color and apparent \sphinxcode{\sphinxupquote{g}}\sphinxhyphen{}band magnitude, which are available from the Pan\sphinxhyphen{}STARRS survey.
+
+Conveniently, the Gaia server provides data from Pan\sphinxhyphen{}STARRS as a table in the same database we have been using, so we can access it by making ADQL queries.
+
+In general, looking up a star from the Gaia catalog and finding the corresponding star in the Pan\sphinxhyphen{}STARRS catalog is not easy.  This kind of cross matching is not always possible, because a star might appear in one catalog and not the other.  And even when both stars are present, there might not be a clear one\sphinxhyphen{}to\sphinxhyphen{}one relationship between stars in the two catalogs.
+
+Fortunately, smart people have worked on this problem, and the Gaia database includes cross\sphinxhyphen{}matching tables that suggest a best neighbor in the Pan\sphinxhyphen{}STARRS catalog for many stars in the Gaia catalog.
+
+\sphinxhref{https://gea.esac.esa.int/archive/documentation/GDR2/Catalogue\_consolidation/chap\_cu9val\_cu9val/ssec\_cu9xma/sssec\_cu9xma\_extcat.html}{This document describes the cross matching process}.  Briefly, it uses a cone search to find possible matches in approximately the right position, then uses attributes like color and magnitude to choose pairs of stars most likely to be identical.
+
+So the hard part of cross\sphinxhyphen{}matching has been done for us.  However, using the results is a little tricky.
+
+But, it is also an opportunity to learn about one of the most important tools for working with databases: “joining� tables.
+
+In general, a “join� is an operation where you match up records from one table with records from another table using as a “key� a piece of information that is common to both tables, usually some kind of ID code.
+
+In this example:
+\begin{itemize}
+\item {} 
+Stars in the Gaia dataset are identified by \sphinxcode{\sphinxupquote{source\_id}}.
+
+\item {} 
+Stars in the Pan\sphinxhyphen{}STARRS dataset are identified by \sphinxcode{\sphinxupquote{obj\_id}}.
+
+\end{itemize}
+
+For each candidate star we have selected so far, we have the \sphinxcode{\sphinxupquote{source\_id}}; the goal is to find the \sphinxcode{\sphinxupquote{obj\_id}} for the same star (we hope) in the Pan\sphinxhyphen{}STARRS catalog.
+
+To do that we will:
+\begin{enumerate}
+\sphinxsetlistlabels{\arabic}{enumi}{enumii}{}{.}%
+\item {} 
+Make a table that contains the \sphinxcode{\sphinxupquote{source\_id}} for each candidate star and upload the table to the Gaia server;
+
+\item {} 
+Use the \sphinxcode{\sphinxupquote{JOIN}} operator to look up each \sphinxcode{\sphinxupquote{source\_id}} in the \sphinxcode{\sphinxupquote{gaiadr2.panstarrs1\_best\_neighbour}} table, which contains the \sphinxcode{\sphinxupquote{obj\_id}} of the best match for each star in the Gaia catalog; then
+
+\item {} 
+Use the \sphinxcode{\sphinxupquote{JOIN}} operator again to look up each \sphinxcode{\sphinxupquote{obj\_id}} in the \sphinxcode{\sphinxupquote{panstarrs1\_original\_valid}} table, which contains the Pan\sphinxhyphen{}STARRS photometry data we want.
+
+\end{enumerate}
+
+Let’s start with the first step, uploading a table.
+
+
+\section{Preparing a table for uploading}
+\label{\detokenize{05_join:preparing-a-table-for-uploading}}
+For each candidate star, we want to find the corresponding row in the \sphinxcode{\sphinxupquote{gaiadr2.panstarrs1\_best\_neighbour}} table.
+
+In order to do that, we have to:
+\begin{enumerate}
+\sphinxsetlistlabels{\arabic}{enumi}{enumii}{}{.}%
+\item {} 
+Write the table in a local file as an XML VOTable, which is a format suitable for transmitting a table over a network.
+
+\item {} 
+Write an ADQL query that refers to the uploaded table.
+
+\item {} 
+Change the way we submit the job so it uploads the table before running the query.
+
+\end{enumerate}
+
+The first step is not too difficult because Astropy provides a function called \sphinxcode{\sphinxupquote{writeto}} that can write a \sphinxcode{\sphinxupquote{Table}} in \sphinxcode{\sphinxupquote{XML}}.
+
+\sphinxhref{https://docs.astropy.org/en/stable/io/votable/}{The documentation of this process is here}.
+
+First we have to convert our Pandas \sphinxcode{\sphinxupquote{DataFrame}} to an Astropy \sphinxcode{\sphinxupquote{Table}}.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{k+kn}{from} \PYG{n+nn}{astropy}\PYG{n+nn}{.}\PYG{n+nn}{table} \PYG{k+kn}{import} \PYG{n}{Table}
+
+\PYG{n}{candidate\PYGZus{}table} \PYG{o}{=} \PYG{n}{Table}\PYG{o}{.}\PYG{n}{from\PYGZus{}pandas}\PYG{p}{(}\PYG{n}{candidate\PYGZus{}df}\PYG{p}{)}
+\PYG{n+nb}{type}\PYG{p}{(}\PYG{n}{candidate\PYGZus{}table}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+astropy.table.table.Table
+\end{sphinxVerbatim}
+
+To write the file, we can use \sphinxcode{\sphinxupquote{Table.write}} with \sphinxcode{\sphinxupquote{format=\textquotesingle{}votable\textquotesingle{}}}, \sphinxhref{https://docs.astropy.org/en/stable/io/unified.html\#vo-tables}{as described here}.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{table} \PYG{o}{=} \PYG{n}{candidate\PYGZus{}table}\PYG{p}{[}\PYG{p}{[}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{source\PYGZus{}id}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{]}\PYG{p}{]}
+\PYG{n}{table}\PYG{o}{.}\PYG{n}{write}\PYG{p}{(}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{candidate\PYGZus{}df.xml}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{,} \PYG{n+nb}{format}\PYG{o}{=}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{votable}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{,} \PYG{n}{overwrite}\PYG{o}{=}\PYG{k+kc}{True}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+Notice that we select a single column from the table, \sphinxcode{\sphinxupquote{source\_id}}.
+We could write the entire table to a file, but that would take longer to transmit over the network, and we really only need one column.
+
+This process, taking a structure like a \sphinxcode{\sphinxupquote{Table}} and translating it into a form that can be transmitted over a network, is called \sphinxhref{https://en.wikipedia.org/wiki/Serialization}{serialization}.
+
+XML is one of the most common serialization formats.  One nice feature is that XML data is plain text, as opposed to binary digits, so you can read the file we just wrote:
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{o}{!}head candidate\PYGZus{}df.xml
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYGZlt{}?xml version=\PYGZdq{}1.0\PYGZdq{} encoding=\PYGZdq{}utf\PYGZhy{}8\PYGZdq{}?\PYGZgt{}
+\PYGZlt{}!\PYGZhy{}\PYGZhy{} Produced with astropy.io.votable version 4.0.1.post1
+     http://www.astropy.org/ \PYGZhy{}\PYGZhy{}\PYGZgt{}
+\PYGZlt{}VOTABLE version=\PYGZdq{}1.4\PYGZdq{} xmlns=\PYGZdq{}http://www.ivoa.net/xml/VOTable/v1.4\PYGZdq{} xmlns:xsi=\PYGZdq{}http://www.w3.org/2001/XMLSchema\PYGZhy{}instance\PYGZdq{} xsi:noNamespaceSchemaLocation=\PYGZdq{}http://www.ivoa.net/xml/VOTable/v1.4\PYGZdq{}\PYGZgt{}
+ \PYGZlt{}RESOURCE type=\PYGZdq{}results\PYGZdq{}\PYGZgt{}
+  \PYGZlt{}TABLE\PYGZgt{}
+   \PYGZlt{}FIELD ID=\PYGZdq{}source\PYGZus{}id\PYGZdq{} datatype=\PYGZdq{}long\PYGZdq{} name=\PYGZdq{}source\PYGZus{}id\PYGZdq{}/\PYGZgt{}
+   \PYGZlt{}DATA\PYGZgt{}
+    \PYGZlt{}TABLEDATA\PYGZgt{}
+     \PYGZlt{}TR\PYGZgt{}
+\end{sphinxVerbatim}
+
+XML is a general format, so different XML files contain different kinds of data.  In order to read an XML file, it’s not enough to know that it’s XML; you also have to know the data format, which is called a \sphinxhref{https://en.wikipedia.org/wiki/XML\_schema}{schema}.
+
+In this example, the schema is VOTable; notice that one of the first tags in the file specifies the schema, and even includes the URL where you can get its definition.
+
+So this is an example of a self\sphinxhyphen{}documenting format.
+
+A drawback of XML is that it tends to be big, which is why we wrote just the \sphinxcode{\sphinxupquote{source\_id}} column rather than the whole table.
+The size of the file is about 750 KB, so that’s not too bad.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{o}{!}ls \PYGZhy{}lh candidate\PYGZus{}df.xml
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYGZhy{}rw\PYGZhy{}rw\PYGZhy{}r\PYGZhy{}\PYGZhy{} 1 downey downey 396K Oct 19 14:48 candidate\PYGZus{}df.xml
+\end{sphinxVerbatim}
+
+If you are using Windows, \sphinxcode{\sphinxupquote{ls}} might not work; in that case, try:
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+!dir candidate\PYGZus{}df.xml
+\end{sphinxVerbatim}
+
+\sphinxstylestrong{Exercise:} There’s a gotcha here we want to warn you about.  Why do you think we used double brackets to specify the column we wanted?  What happens if you use single brackets?
+
+Run these cells to find out.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{table} \PYG{o}{=} \PYG{n}{candidate\PYGZus{}table}\PYG{p}{[}\PYG{p}{[}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{source\PYGZus{}id}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{]}\PYG{p}{]}
+\PYG{n+nb}{type}\PYG{p}{(}\PYG{n}{table}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+astropy.table.table.Table
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{column} \PYG{o}{=} \PYG{n}{candidate\PYGZus{}table}\PYG{p}{[}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{source\PYGZus{}id}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{]}
+\PYG{n+nb}{type}\PYG{p}{(}\PYG{n}{column}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+astropy.table.column.Column
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{c+c1}{\PYGZsh{} writeto(column, \PYGZsq{}candidate\PYGZus{}df.xml\PYGZsq{})}
+\end{sphinxVerbatim}
+
+
+\section{Uploading a table}
+\label{\detokenize{05_join:uploading-a-table}}
+The next step is to upload this table to the Gaia server and use it as part of a query.
+
+\sphinxhref{https://astroquery.readthedocs.io/en/latest/gaia/gaia.html\#synchronous-query-on-an-on-the-fly-uploaded-table}{Here’s the documentation that explains how to run a query with an uploaded table}.
+
+In the spirit of incremental development and testing, let’s start with the simplest possible query.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{query} \PYG{o}{=} \PYG{l+s+s2}{\PYGZdq{}\PYGZdq{}\PYGZdq{}}\PYG{l+s+s2}{SELECT *}
+\PYG{l+s+s2}{FROM tap\PYGZus{}upload.candidate\PYGZus{}df}
+\PYG{l+s+s2}{\PYGZdq{}\PYGZdq{}\PYGZdq{}}
+\end{sphinxVerbatim}
+
+This query downloads all rows and all columns from the uploaded table.  The name of the table has two parts: \sphinxcode{\sphinxupquote{tap\_upload}} specifies a table that was uploaded using TAP+ (remember that’s the name of the protocol we’re using to talk to the Gaia server).
+
+And \sphinxcode{\sphinxupquote{candidate\_df}} is the name of the table, which we get to choose (unlike \sphinxcode{\sphinxupquote{tap\_upload}}, which we didn’t get to choose).
+
+Here’s how we run the query:
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{k+kn}{from} \PYG{n+nn}{astroquery}\PYG{n+nn}{.}\PYG{n+nn}{gaia} \PYG{k+kn}{import} \PYG{n}{Gaia}
+
+\PYG{n}{job} \PYG{o}{=} \PYG{n}{Gaia}\PYG{o}{.}\PYG{n}{launch\PYGZus{}job\PYGZus{}async}\PYG{p}{(}\PYG{n}{query}\PYG{o}{=}\PYG{n}{query}\PYG{p}{,} 
+                            \PYG{n}{upload\PYGZus{}resource}\PYG{o}{=}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{candidate\PYGZus{}df.xml}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{,} 
+                            \PYG{n}{upload\PYGZus{}table\PYGZus{}name}\PYG{o}{=}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{candidate\PYGZus{}df}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+Created TAP+ (v1.2.1) \PYGZhy{} Connection:
+	Host: gea.esac.esa.int
+	Use HTTPS: True
+	Port: 443
+	SSL Port: 443
+Created TAP+ (v1.2.1) \PYGZhy{} Connection:
+	Host: geadata.esac.esa.int
+	Use HTTPS: True
+	Port: 443
+	SSL Port: 443
+INFO: Query finished. [astroquery.utils.tap.core]
+\end{sphinxVerbatim}
+
+\sphinxcode{\sphinxupquote{upload\_resource}} specifies the name of the file we want to upload, which is the file we just wrote.
+
+\sphinxcode{\sphinxupquote{upload\_table\_name}} is the name we assign to this table, which is the name we used in the query.
+
+And here are the results:
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{results} \PYG{o}{=} \PYG{n}{job}\PYG{o}{.}\PYG{n}{get\PYGZus{}results}\PYG{p}{(}\PYG{p}{)}
+\PYG{n}{results}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYGZlt{}Table length=7346\PYGZgt{}
+    source\PYGZus{}id     
+      int64       
+\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}
+635559124339440000
+635860218726658176
+635674126383965568
+635535454774983040
+635497276810313600
+635614168640132864
+635821843194387840
+635551706931167104
+635518889086133376
+635580294233854464
+               ...
+612282738058264960
+612485911486166656
+612386332668697600
+612296172717818624
+612250375480101760
+612394926899159168
+612288854091187712
+612428870024913152
+612256418500423168
+612429144902815104
+\end{sphinxVerbatim}
+
+If things go according to plan, the result should contain the same rows and columns as the uploaded table.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n+nb}{len}\PYG{p}{(}\PYG{n}{candidate\PYGZus{}table}\PYG{p}{)}\PYG{p}{,} \PYG{n+nb}{len}\PYG{p}{(}\PYG{n}{results}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+(7346, 7346)
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n+nb}{set}\PYG{p}{(}\PYG{n}{candidate\PYGZus{}table}\PYG{p}{[}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{source\PYGZus{}id}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{]}\PYG{p}{)} \PYG{o}{==} \PYG{n+nb}{set}\PYG{p}{(}\PYG{n}{results}\PYG{p}{[}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{source\PYGZus{}id}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{]}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+True
+\end{sphinxVerbatim}
+
+In this example, we uploaded a table and then downloaded it again, so that’s not too useful.
+
+But now that we can upload a table, we can join it with other tables on the Gaia server.
+
+
+\section{Joining with an uploaded table}
+\label{\detokenize{05_join:joining-with-an-uploaded-table}}
+Here’s the first example of a query that contains a \sphinxcode{\sphinxupquote{JOIN}} clause.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{query1} \PYG{o}{=} \PYG{l+s+s2}{\PYGZdq{}\PYGZdq{}\PYGZdq{}}\PYG{l+s+s2}{SELECT *}
+\PYG{l+s+s2}{FROM gaiadr2.panstarrs1\PYGZus{}best\PYGZus{}neighbour as best}
+\PYG{l+s+s2}{JOIN tap\PYGZus{}upload.candidate\PYGZus{}df as candidate\PYGZus{}df}
+\PYG{l+s+s2}{ON best.source\PYGZus{}id = candidate\PYGZus{}df.source\PYGZus{}id}
+\PYG{l+s+s2}{\PYGZdq{}\PYGZdq{}\PYGZdq{}}
+\end{sphinxVerbatim}
+
+Let’s break that down one clause at a time:
+\begin{itemize}
+\item {} 
+\sphinxcode{\sphinxupquote{SELECT *}} means we will download all columns from both tables.
+
+\item {} 
+\sphinxcode{\sphinxupquote{FROM gaiadr2.panstarrs1\_best\_neighbour as best}} means that we’ll get the columns from the Pan\sphinxhyphen{}STARRS best neighbor table, which we’ll refer to using the short name \sphinxcode{\sphinxupquote{best}}.
+
+\item {} 
+\sphinxcode{\sphinxupquote{JOIN tap\_upload.candidate\_df as candidate\_df}} means that we’ll also get columns from the uploaded table, which we’ll refer to using the short name \sphinxcode{\sphinxupquote{candidate\_df}}.
+
+\item {} 
+\sphinxcode{\sphinxupquote{ON best.source\_id = candidate\_df.source\_id}} specifies that we will use \sphinxcode{\sphinxupquote{source\_id }} to match up the rows from the two tables.
+
+\end{itemize}
+
+Here’s the \sphinxhref{https://gea.esac.esa.int/archive/documentation/GDR2/Gaia\_archive/chap\_datamodel/sec\_dm\_crossmatches/ssec\_dm\_panstarrs1\_best\_neighbour.html}{documentation of the best neighbor table}.
+
+Let’s run the query:
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{job1} \PYG{o}{=} \PYG{n}{Gaia}\PYG{o}{.}\PYG{n}{launch\PYGZus{}job\PYGZus{}async}\PYG{p}{(}\PYG{n}{query}\PYG{o}{=}\PYG{n}{query1}\PYG{p}{,} 
+                       \PYG{n}{upload\PYGZus{}resource}\PYG{o}{=}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{candidate\PYGZus{}df.xml}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{,} 
+                       \PYG{n}{upload\PYGZus{}table\PYGZus{}name}\PYG{o}{=}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{candidate\PYGZus{}df}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+INFO: Query finished. [astroquery.utils.tap.core]
+\end{sphinxVerbatim}
+
+And get the results.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{results1} \PYG{o}{=} \PYG{n}{job1}\PYG{o}{.}\PYG{n}{get\PYGZus{}results}\PYG{p}{(}\PYG{p}{)}
+\PYG{n}{results1}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYGZlt{}Table length=3724\PYGZgt{}
+    source\PYGZus{}id      original\PYGZus{}ext\PYGZus{}source\PYGZus{}id ...    source\PYGZus{}id\PYGZus{}2    
+                                          ...                   
+      int64                int64          ...       int64       
+\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{} \PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{} ... \PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}
+635860218726658176     130911385187671349 ... 635860218726658176
+635674126383965568     130831388428488720 ... 635674126383965568
+635535454774983040     130631378377657369 ... 635535454774983040
+635497276810313600     130811380445631930 ... 635497276810313600
+635614168640132864     130571395922140135 ... 635614168640132864
+635598607974369792     130341392091279513 ... 635598607974369792
+635737661835496576     131001399333502136 ... 635737661835496576
+635850945892748672     132011398654934147 ... 635850945892748672
+635600532119713664     130421392285893623 ... 635600532119713664
+               ...                    ... ...                ...
+612241781249124608     129751343755995561 ... 612241781249124608
+612332147361443072     130141341458538777 ... 612332147361443072
+612426744016802432     130521346852465656 ... 612426744016802432
+612331739340341760     130111341217793839 ... 612331739340341760
+612282738058264960     129741340445933519 ... 612282738058264960
+612386332668697600     130351354570219774 ... 612386332668697600
+612296172717818624     129691338006168780 ... 612296172717818624
+612250375480101760     129741346475897464 ... 612250375480101760
+612394926899159168     130581355199751795 ... 612394926899159168
+612256418500423168     129931349075297310 ... 612256418500423168
+\end{sphinxVerbatim}
+
+This table contains all of the columns from the best neighbor table, plus the single column from the uploaded table.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{results1}\PYG{o}{.}\PYG{n}{colnames}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+[\PYGZsq{}source\PYGZus{}id\PYGZsq{},
+ \PYGZsq{}original\PYGZus{}ext\PYGZus{}source\PYGZus{}id\PYGZsq{},
+ \PYGZsq{}angular\PYGZus{}distance\PYGZsq{},
+ \PYGZsq{}number\PYGZus{}of\PYGZus{}neighbours\PYGZsq{},
+ \PYGZsq{}number\PYGZus{}of\PYGZus{}mates\PYGZsq{},
+ \PYGZsq{}best\PYGZus{}neighbour\PYGZus{}multiplicity\PYGZsq{},
+ \PYGZsq{}gaia\PYGZus{}astrometric\PYGZus{}params\PYGZsq{},
+ \PYGZsq{}source\PYGZus{}id\PYGZus{}2\PYGZsq{}]
+\end{sphinxVerbatim}
+
+Because one of the column names appears in both tables, the second instance of \sphinxcode{\sphinxupquote{source\_id}} has been appended with the suffix \sphinxcode{\sphinxupquote{\_2}}.
+
+The length of the results table is about 2000, which means we were not able to find matches for all stars in the list of candidate\_df.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n+nb}{len}\PYG{p}{(}\PYG{n}{results1}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+3724
+\end{sphinxVerbatim}
+
+To get more information about the matching process, we can inspect \sphinxcode{\sphinxupquote{best\_neighbour\_multiplicity}}, which indicates for each star in Gaia how many stars in Pan\sphinxhyphen{}STARRS are equally likely matches.
+
+For this kind of data exploration, we’ll convert a column from the table to a Pandas \sphinxcode{\sphinxupquote{Series}} so we can use \sphinxcode{\sphinxupquote{value\_counts}}, which counts the number of times each value appears in a \sphinxcode{\sphinxupquote{Series}}, like a histogram.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{k+kn}{import} \PYG{n+nn}{pandas} \PYG{k}{as} \PYG{n+nn}{pd}
+
+\PYG{n}{nn} \PYG{o}{=} \PYG{n}{pd}\PYG{o}{.}\PYG{n}{Series}\PYG{p}{(}\PYG{n}{results1}\PYG{p}{[}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{best\PYGZus{}neighbour\PYGZus{}multiplicity}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{]}\PYG{p}{)}
+\PYG{n}{nn}\PYG{o}{.}\PYG{n}{value\PYGZus{}counts}\PYG{p}{(}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+1    3724
+dtype: int64
+\end{sphinxVerbatim}
+
+The result shows that \sphinxcode{\sphinxupquote{1}} is the only value in the \sphinxcode{\sphinxupquote{Series}}, appearing xxx times.
+
+That means that in every case where a match was found, the matching algorithm identified a single neighbor as the most likely match.
+
+Similarly, \sphinxcode{\sphinxupquote{number\_of\_mates}} indicates the number of other stars in Gaia that match with the same star in Pan\sphinxhyphen{}STARRS.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{nm} \PYG{o}{=} \PYG{n}{pd}\PYG{o}{.}\PYG{n}{Series}\PYG{p}{(}\PYG{n}{results1}\PYG{p}{[}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{number\PYGZus{}of\PYGZus{}mates}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{]}\PYG{p}{)}
+\PYG{n}{nm}\PYG{o}{.}\PYG{n}{value\PYGZus{}counts}\PYG{p}{(}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+0    3724
+dtype: int64
+\end{sphinxVerbatim}
+
+For this set of candidate\_df, almost all of the stars we’ve selected from Pan\sphinxhyphen{}STARRS are only matched with a single star in the Gaia catalog.
+
+\sphinxstylestrong{Detail} The table also contains \sphinxcode{\sphinxupquote{number\_of\_neighbors}} which is the number of stars in Pan\sphinxhyphen{}STARRS that match in terms of position, before using other critieria to choose the most likely match.
+
+
+\section{Getting the photometry data}
+\label{\detokenize{05_join:getting-the-photometry-data}}
+The most important column in \sphinxcode{\sphinxupquote{results1}} is \sphinxcode{\sphinxupquote{original\_ext\_source\_id}} which is the \sphinxcode{\sphinxupquote{obj\_id}} we will use to look up the likely matches in Pan\sphinxhyphen{}STARRS to get photometry data.
+
+The process is similar to what we just did to look up the matches.  We will:
+\begin{enumerate}
+\sphinxsetlistlabels{\arabic}{enumi}{enumii}{}{.}%
+\item {} 
+Make a table that contains \sphinxcode{\sphinxupquote{source\_id}} and \sphinxcode{\sphinxupquote{original\_ext\_source\_id}}.
+
+\item {} 
+Write the table to an XML VOTable file.
+
+\item {} 
+Write a query that joins the uploaded table with \sphinxcode{\sphinxupquote{gaiadr2.panstarrs1\_original\_valid}} and selects the photometry data we want.
+
+\item {} 
+Run the query using the uploaded table.
+
+\end{enumerate}
+
+Since we’ve done everything here before, we’ll do these steps as an exercise.
+
+\sphinxstylestrong{Exercise:} Select \sphinxcode{\sphinxupquote{source\_id}} and \sphinxcode{\sphinxupquote{original\_ext\_source\_id}} from \sphinxcode{\sphinxupquote{results1}} and write the resulting table as a file named \sphinxcode{\sphinxupquote{external.xml}}.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{c+c1}{\PYGZsh{} Solution}
+
+\PYG{n}{table} \PYG{o}{=} \PYG{n}{results1}\PYG{p}{[}\PYG{p}{[}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{source\PYGZus{}id}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{,} \PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{original\PYGZus{}ext\PYGZus{}source\PYGZus{}id}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{]}\PYG{p}{]}
+\PYG{n}{table}\PYG{o}{.}\PYG{n}{write}\PYG{p}{(}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{external.xml}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{,} \PYG{n+nb}{format}\PYG{o}{=}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{votable}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{,} \PYG{n}{overwrite}\PYG{o}{=}\PYG{k+kc}{True}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+Use \sphinxcode{\sphinxupquote{!head}} to confirm that the file exists and contains an XML VOTable.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{o}{!}head external.xml
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYGZlt{}?xml version=\PYGZdq{}1.0\PYGZdq{} encoding=\PYGZdq{}utf\PYGZhy{}8\PYGZdq{}?\PYGZgt{}
+\PYGZlt{}!\PYGZhy{}\PYGZhy{} Produced with astropy.io.votable version 4.0.1.post1
+     http://www.astropy.org/ \PYGZhy{}\PYGZhy{}\PYGZgt{}
+\PYGZlt{}VOTABLE version=\PYGZdq{}1.4\PYGZdq{} xmlns=\PYGZdq{}http://www.ivoa.net/xml/VOTable/v1.4\PYGZdq{} xmlns:xsi=\PYGZdq{}http://www.w3.org/2001/XMLSchema\PYGZhy{}instance\PYGZdq{} xsi:noNamespaceSchemaLocation=\PYGZdq{}http://www.ivoa.net/xml/VOTable/v1.4\PYGZdq{}\PYGZgt{}
+ \PYGZlt{}RESOURCE type=\PYGZdq{}results\PYGZdq{}\PYGZgt{}
+  \PYGZlt{}TABLE\PYGZgt{}
+   \PYGZlt{}FIELD ID=\PYGZdq{}source\PYGZus{}id\PYGZdq{} datatype=\PYGZdq{}long\PYGZdq{} name=\PYGZdq{}source\PYGZus{}id\PYGZdq{} ucd=\PYGZdq{}meta.id;meta.main\PYGZdq{}\PYGZgt{}
+    \PYGZlt{}DESCRIPTION\PYGZgt{}
+     Unique Gaia source identifier
+    \PYGZlt{}/DESCRIPTION\PYGZgt{}
+\end{sphinxVerbatim}
+
+\sphinxstylestrong{Exercise:} Read \sphinxhref{https://gea.esac.esa.int/archive/documentation/GDR2/Gaia\_archive/chap\_datamodel/sec\_dm\_external\_catalogues/ssec\_dm\_panstarrs1\_original\_valid.html}{the documentation of the Pan\sphinxhyphen{}STARRS table} and make note of \sphinxcode{\sphinxupquote{obj\_id}}, which contains the object IDs we’ll use to find the rows we want.
+
+Write a query that uses each value of \sphinxcode{\sphinxupquote{original\_ext\_source\_id}} from the uploaded table to find a row in \sphinxcode{\sphinxupquote{gaiadr2.panstarrs1\_original\_valid}} with the same value in \sphinxcode{\sphinxupquote{obj\_id}}, and select all columns from both tables.
+
+Suggestion: Develop and test your query incrementally.  For example:
+\begin{enumerate}
+\sphinxsetlistlabels{\arabic}{enumi}{enumii}{}{.}%
+\item {} 
+Write a query that downloads all columns from the uploaded table.  Test to make sure we can read the uploaded table.
+
+\item {} 
+Write a query that downloads the first 10 rows from \sphinxcode{\sphinxupquote{gaiadr2.panstarrs1\_original\_valid}}.  Test to make sure we can access Pan\sphinxhyphen{}STARRS data.
+
+\item {} 
+Write a query that joins the two tables and selects all columns.  Test that the join works as expected.
+
+\end{enumerate}
+
+As a bonus exercise, write a query that joins the two tables and selects just the columns we need:
+\begin{itemize}
+\item {} 
+\sphinxcode{\sphinxupquote{source\_id}} from the uploaded table
+
+\item {} 
+\sphinxcode{\sphinxupquote{g\_mean\_psf\_mag}} from \sphinxcode{\sphinxupquote{gaiadr2.panstarrs1\_original\_valid}}
+
+\item {} 
+\sphinxcode{\sphinxupquote{i\_mean\_psf\_mag}} from \sphinxcode{\sphinxupquote{gaiadr2.panstarrs1\_original\_valid}}
+
+\end{itemize}
+
+Hint: When you select a column from a join, you have to specify which table the column is in.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{c+c1}{\PYGZsh{} Solution}
+
+\PYG{n}{query2} \PYG{o}{=} \PYG{l+s+s2}{\PYGZdq{}\PYGZdq{}\PYGZdq{}}\PYG{l+s+s2}{SELECT *}
+\PYG{l+s+s2}{FROM tap\PYGZus{}upload.external as external}
+\PYG{l+s+s2}{\PYGZdq{}\PYGZdq{}\PYGZdq{}}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{c+c1}{\PYGZsh{} Solution}
+
+\PYG{n}{query2} \PYG{o}{=} \PYG{l+s+s2}{\PYGZdq{}\PYGZdq{}\PYGZdq{}}\PYG{l+s+s2}{SELECT TOP 10 *}
+\PYG{l+s+s2}{FROM gaiadr2.panstarrs1\PYGZus{}original\PYGZus{}valid}
+\PYG{l+s+s2}{\PYGZdq{}\PYGZdq{}\PYGZdq{}}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{c+c1}{\PYGZsh{} Solution}
+
+\PYG{n}{query2} \PYG{o}{=} \PYG{l+s+s2}{\PYGZdq{}\PYGZdq{}\PYGZdq{}}\PYG{l+s+s2}{SELECT *}
+\PYG{l+s+s2}{FROM gaiadr2.panstarrs1\PYGZus{}original\PYGZus{}valid as ps}
+\PYG{l+s+s2}{JOIN tap\PYGZus{}upload.external as external}
+\PYG{l+s+s2}{ON ps.obj\PYGZus{}id = external.original\PYGZus{}ext\PYGZus{}source\PYGZus{}id}
+\PYG{l+s+s2}{\PYGZdq{}\PYGZdq{}\PYGZdq{}}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{c+c1}{\PYGZsh{} Solution}
+
+\PYG{n}{query2} \PYG{o}{=} \PYG{l+s+s2}{\PYGZdq{}\PYGZdq{}\PYGZdq{}}\PYG{l+s+s2}{SELECT}
+\PYG{l+s+s2}{external.source\PYGZus{}id, ps.g\PYGZus{}mean\PYGZus{}psf\PYGZus{}mag, ps.i\PYGZus{}mean\PYGZus{}psf\PYGZus{}mag}
+\PYG{l+s+s2}{FROM gaiadr2.panstarrs1\PYGZus{}original\PYGZus{}valid as ps}
+\PYG{l+s+s2}{JOIN tap\PYGZus{}upload.external as external}
+\PYG{l+s+s2}{ON ps.obj\PYGZus{}id = external.original\PYGZus{}ext\PYGZus{}source\PYGZus{}id}
+\PYG{l+s+s2}{\PYGZdq{}\PYGZdq{}\PYGZdq{}}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n+nb}{print}\PYG{p}{(}\PYG{n}{query2}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+SELECT
+external.source\PYGZus{}id, ps.g\PYGZus{}mean\PYGZus{}psf\PYGZus{}mag, ps.i\PYGZus{}mean\PYGZus{}psf\PYGZus{}mag
+FROM gaiadr2.panstarrs1\PYGZus{}original\PYGZus{}valid as ps
+JOIN tap\PYGZus{}upload.external as external
+ON ps.obj\PYGZus{}id = external.original\PYGZus{}ext\PYGZus{}source\PYGZus{}id
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{job2} \PYG{o}{=} \PYG{n}{Gaia}\PYG{o}{.}\PYG{n}{launch\PYGZus{}job\PYGZus{}async}\PYG{p}{(}\PYG{n}{query}\PYG{o}{=}\PYG{n}{query2}\PYG{p}{,} 
+                       \PYG{n}{upload\PYGZus{}resource}\PYG{o}{=}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{external.xml}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{,} 
+                       \PYG{n}{upload\PYGZus{}table\PYGZus{}name}\PYG{o}{=}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{external}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+INFO: Query finished. [astroquery.utils.tap.core]
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{results2} \PYG{o}{=} \PYG{n}{job2}\PYG{o}{.}\PYG{n}{get\PYGZus{}results}\PYG{p}{(}\PYG{p}{)}
+\PYG{n}{results2}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYGZlt{}Table length=3724\PYGZgt{}
+    source\PYGZus{}id       g\PYGZus{}mean\PYGZus{}psf\PYGZus{}mag   i\PYGZus{}mean\PYGZus{}psf\PYGZus{}mag 
+                                          mag       
+      int64            float64          float64     
+\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{} \PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{} \PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}\PYGZhy{}
+635860218726658176 17.8978004455566 17.5174007415771
+635674126383965568 19.2873001098633 17.6781005859375
+635535454774983040 16.9237995147705  16.478099822998
+635497276810313600 19.9242000579834 18.3339996337891
+635614168640132864 16.1515998840332 14.6662998199463
+635598607974369792 16.5223999023438 16.1375007629395
+635737661835496576 14.5032997131348 13.9849004745483
+635850945892748672 16.5174999237061 16.0450000762939
+635600532119713664 20.4505996704102 19.5177001953125
+               ...              ...              ...
+612241781249124608 20.2343997955322 18.6518001556396
+612332147361443072 21.3848991394043 20.3076000213623
+612426744016802432 17.8281002044678 17.4281005859375
+612331739340341760 21.8656997680664 19.5223007202148
+612282738058264960 22.5151996612549 19.9743995666504
+612386332668697600 19.3792991638184 17.9923000335693
+612296172717818624 17.4944000244141  16.926700592041
+612250375480101760 15.3330001831055 14.6280002593994
+612394926899159168 16.4414005279541 15.8212003707886
+612256418500423168 20.8715991973877 19.9612007141113
+\end{sphinxVerbatim}
+
+\sphinxstylestrong{Challenge exercise}
+
+Do both joins in one query.
+
+There’s an \sphinxhref{https://github.com/smoh/Getting-started-with-Gaia/blob/master/gaia-adql-snippets.md}{example here} you could start with.
+
+
+\section{Write the data}
+\label{\detokenize{05_join:write-the-data}}
+Since we have the data in an Astropy \sphinxcode{\sphinxupquote{Table}}, let’s store it in a FITS file.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{filename} \PYG{o}{=} \PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{gd1\PYGZus{}photo.fits}\PYG{l+s+s1}{\PYGZsq{}}
+\PYG{n}{results2}\PYG{o}{.}\PYG{n}{write}\PYG{p}{(}\PYG{n}{filename}\PYG{p}{,} \PYG{n}{overwrite}\PYG{o}{=}\PYG{k+kc}{True}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+We can check that the file exists, and see how big it is.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{o}{!}ls \PYGZhy{}lh gd1\PYGZus{}photo.fits
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYGZhy{}rw\PYGZhy{}rw\PYGZhy{}r\PYGZhy{}\PYGZhy{} 1 downey downey 96K Oct 19 14:49 gd1\PYGZus{}photo.fits
+\end{sphinxVerbatim}
+
+At around 175 KB, it is smaller than some of the other files we’ve been working with.
+
+If you are using Windows, \sphinxcode{\sphinxupquote{ls}} might not work; in that case, try:
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+!dir gd1\PYGZus{}photo.fits
+\end{sphinxVerbatim}
+
+
+\section{Summary}
+\label{\detokenize{05_join:summary}}
+In this notebook, we used database \sphinxcode{\sphinxupquote{JOIN}} operations to select photometry data for the stars we’ve identified as candidates to be in GD\sphinxhyphen{}1.
+
+In the next notebook, we’ll use this data for a second round of selection, identifying stars that have photometry data consistent with GD\sphinxhyphen{}1.
+
+
+\section{Best practice}
+\label{\detokenize{05_join:best-practice}}\begin{itemize}
+\item {} 
+Use \sphinxcode{\sphinxupquote{JOIN}} operations to combine data from multiple tables in a databased, using some kind of identifier to match up records from one table with records from another.
+
+\item {} 
+This is another example of a practice we saw in the previous notebook, moving the computation to the data.
+
+\end{itemize}
+
+
+\chapter{Chapter 6}
+\label{\detokenize{06_photo:chapter-6}}\label{\detokenize{06_photo::doc}}
+This is the sixth in a series of notebooks related to astronomy data.
+
+As a continuing example, we will replicate part of the analysis in a recent paper, “\sphinxhref{https://arxiv.org/abs/1805.00425}{Off the beaten path: Gaia reveals GD\sphinxhyphen{}1 stars outside of the main stream}� by Adrian M. Price\sphinxhyphen{}Whelan and Ana Bonaca.
+
+In the previous lesson we downloaded photometry data from Pan\sphinxhyphen{}STARRS, which is available from the same server we’ve been using to get Gaia data.
+
+The next step in the analysis is to select candidate stars based on the photometry data.  The following figure from the paper is a color\sphinxhyphen{}magnitude diagram for the stars selected based on proper motion:
+
+
+
+In red is a theoretical isochrone, showing where we expect the stars in GD\sphinxhyphen{}1 to fall based on the metallicity and age of their original globular cluster.
+
+By selecting stars in the shaded area, we can further distinguish the main sequence of GD\sphinxhyphen{}1 from younger background stars.
+
+
+\section{Outline}
+\label{\detokenize{06_photo:outline}}
+Here are the steps in this notebook:
+\begin{enumerate}
+\sphinxsetlistlabels{\arabic}{enumi}{enumii}{}{.}%
+\item {} 
+We’ll reload the data from the previous notebook and make a color\sphinxhyphen{}magnitude diagram.
+
+\item {} 
+Then we’ll specify a polygon in the diagram that contains stars with the photometry we expect.
+
+\item {} 
+Then we’ll merge the photometry data with the list of candidate stars, storing the result in a Pandas \sphinxcode{\sphinxupquote{DataFrame}}.
+
+\end{enumerate}
+
+After completing this lesson, you should be able to
+\begin{itemize}
+\item {} 
+Use Matplotlib to specify a \sphinxcode{\sphinxupquote{Polygon}} and determine which points fall inside it.
+
+\item {} 
+Use Pandas to merge data from multiple \sphinxcode{\sphinxupquote{DataFrames}}, much like a database \sphinxcode{\sphinxupquote{JOIN}} operation.
+
+\end{itemize}
+
+
+\section{Installing libraries}
+\label{\detokenize{06_photo:installing-libraries}}
+If you are running this notebook on Colab, you can run the following cell to install Astroquery and a the other libraries we’ll use.
+
+If you are running this notebook on your own computer, you might have to install these libraries yourself.
+
+If you are using this notebook as part of a Carpentries workshop, you should have received setup instructions.
+
+TODO: Add a link to the instructions.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{c+c1}{\PYGZsh{} If we\PYGZsq{}re running on Colab, install libraries}
+
+\PYG{k+kn}{import} \PYG{n+nn}{sys}
+\PYG{n}{IN\PYGZus{}COLAB} \PYG{o}{=} \PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{google.colab}\PYG{l+s+s1}{\PYGZsq{}} \PYG{o+ow}{in} \PYG{n}{sys}\PYG{o}{.}\PYG{n}{modules}
+
+\PYG{k}{if} \PYG{n}{IN\PYGZus{}COLAB}\PYG{p}{:}
+    \PYG{o}{!}pip install astroquery astro\PYGZhy{}gala pyia python\PYGZhy{}wget
+\end{sphinxVerbatim}
+
+
+\section{Reload the data}
+\label{\detokenize{06_photo:reload-the-data}}
+The following cell downloads the photometry data we created in the previous notebook.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{k+kn}{import} \PYG{n+nn}{os}
+\PYG{k+kn}{from} \PYG{n+nn}{wget} \PYG{k+kn}{import} \PYG{n}{download}
+
+\PYG{n}{filename} \PYG{o}{=} \PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{gd1\PYGZus{}photo.fits}\PYG{l+s+s1}{\PYGZsq{}}
+\PYG{n}{filepath} \PYG{o}{=} \PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{https://github.com/AllenDowney/AstronomicalData/raw/main/data/}\PYG{l+s+s1}{\PYGZsq{}}
+
+\PYG{k}{if} \PYG{o+ow}{not} \PYG{n}{os}\PYG{o}{.}\PYG{n}{path}\PYG{o}{.}\PYG{n}{exists}\PYG{p}{(}\PYG{n}{filename}\PYG{p}{)}\PYG{p}{:}
+    \PYG{n+nb}{print}\PYG{p}{(}\PYG{n}{download}\PYG{p}{(}\PYG{n}{filepath}\PYG{o}{+}\PYG{n}{filename}\PYG{p}{)}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+Now we can read the data back into an Astropy \sphinxcode{\sphinxupquote{Table}}.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{k+kn}{from} \PYG{n+nn}{astropy}\PYG{n+nn}{.}\PYG{n+nn}{table} \PYG{k+kn}{import} \PYG{n}{Table}
+
+\PYG{n}{photo\PYGZus{}table} \PYG{o}{=} \PYG{n}{Table}\PYG{o}{.}\PYG{n}{read}\PYG{p}{(}\PYG{n}{filename}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+
+\section{Plotting photometry data}
+\label{\detokenize{06_photo:plotting-photometry-data}}
+Now that we have photometry data from Pan\sphinxhyphen{}STARRS, we can replicate the \sphinxhref{https://en.wikipedia.org/wiki/Galaxy\_color\%E2\%80\%93magnitude\_diagram}{color\sphinxhyphen{}magnitude diagram} from the original paper:
+
+
+
+The y\sphinxhyphen{}axis shows the apparent magnitude of each source with the \sphinxhref{https://en.wikipedia.org/wiki/Photometric\_system}{g filter}.
+
+The x\sphinxhyphen{}axis shows the difference in apparent magnitude between the g and i filters, which indicates color.
+
+Stars with lower values of (g\sphinxhyphen{}i) are brighter in g\sphinxhyphen{}band than in i\sphinxhyphen{}band, compared to other stars, which means they are bluer.
+
+Stars in the lower\sphinxhyphen{}left quadrant of this diagram are less bright and less metallic than the others, which means they are \sphinxhref{http://spiff.rit.edu/classes/ladder/lectures/ordinary\_stars/ordinary.html}{likely to be older}.
+
+Since we expect the stars in GD\sphinxhyphen{}1 to be older than the background stars, the stars in the lower\sphinxhyphen{}left are more likely to be in GD\sphinxhyphen{}1.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{k+kn}{import} \PYG{n+nn}{matplotlib}\PYG{n+nn}{.}\PYG{n+nn}{pyplot} \PYG{k}{as} \PYG{n+nn}{plt}
+
+\PYG{k}{def} \PYG{n+nf}{plot\PYGZus{}cmd}\PYG{p}{(}\PYG{n}{table}\PYG{p}{)}\PYG{p}{:}
+    \PYG{l+s+sd}{\PYGZdq{}\PYGZdq{}\PYGZdq{}Plot a color magnitude diagram.}
+\PYG{l+s+sd}{    }
+\PYG{l+s+sd}{    table: Table or DataFrame with photometry data}
+\PYG{l+s+sd}{    \PYGZdq{}\PYGZdq{}\PYGZdq{}}
+    \PYG{n}{y} \PYG{o}{=} \PYG{n}{table}\PYG{p}{[}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{g\PYGZus{}mean\PYGZus{}psf\PYGZus{}mag}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{]}
+    \PYG{n}{x} \PYG{o}{=} \PYG{n}{table}\PYG{p}{[}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{g\PYGZus{}mean\PYGZus{}psf\PYGZus{}mag}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{]} \PYG{o}{\PYGZhy{}} \PYG{n}{table}\PYG{p}{[}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{i\PYGZus{}mean\PYGZus{}psf\PYGZus{}mag}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{]}
+
+    \PYG{n}{plt}\PYG{o}{.}\PYG{n}{plot}\PYG{p}{(}\PYG{n}{x}\PYG{p}{,} \PYG{n}{y}\PYG{p}{,} \PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{ko}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{,} \PYG{n}{markersize}\PYG{o}{=}\PYG{l+m+mf}{0.3}\PYG{p}{,} \PYG{n}{alpha}\PYG{o}{=}\PYG{l+m+mf}{0.3}\PYG{p}{)}
+
+    \PYG{n}{plt}\PYG{o}{.}\PYG{n}{xlim}\PYG{p}{(}\PYG{p}{[}\PYG{l+m+mi}{0}\PYG{p}{,} \PYG{l+m+mf}{1.5}\PYG{p}{]}\PYG{p}{)}
+    \PYG{n}{plt}\PYG{o}{.}\PYG{n}{ylim}\PYG{p}{(}\PYG{p}{[}\PYG{l+m+mi}{14}\PYG{p}{,} \PYG{l+m+mi}{22}\PYG{p}{]}\PYG{p}{)}
+    \PYG{n}{plt}\PYG{o}{.}\PYG{n}{gca}\PYG{p}{(}\PYG{p}{)}\PYG{o}{.}\PYG{n}{invert\PYGZus{}yaxis}\PYG{p}{(}\PYG{p}{)}
+
+    \PYG{n}{plt}\PYG{o}{.}\PYG{n}{ylabel}\PYG{p}{(}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{\PYGZdl{}g\PYGZus{}0\PYGZdl{}}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{)}
+    \PYG{n}{plt}\PYG{o}{.}\PYG{n}{xlabel}\PYG{p}{(}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{\PYGZdl{}(g\PYGZhy{}i)\PYGZus{}0\PYGZdl{}}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+\sphinxcode{\sphinxupquote{plot\_cmd}} uses a new function, \sphinxcode{\sphinxupquote{invert\_yaxis}}, to invert the \sphinxcode{\sphinxupquote{y}} axis, which is conventional when plotting magnitudes, since lower magnitude indicates higher brightness.
+
+\sphinxcode{\sphinxupquote{invert\_yaxis}} is a little different from the other functions we’ve used.  You can’t call it like this:
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{plt}\PYG{o}{.}\PYG{n}{invert\PYGZus{}yaxis}\PYG{p}{(}\PYG{p}{)}          \PYG{c+c1}{\PYGZsh{} doesn\PYGZsq{}t work}
+\end{sphinxVerbatim}
+
+You have to call it like this:
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{plt}\PYG{o}{.}\PYG{n}{gca}\PYG{p}{(}\PYG{p}{)}\PYG{o}{.}\PYG{n}{invert\PYGZus{}yaxis}\PYG{p}{(}\PYG{p}{)}          \PYG{c+c1}{\PYGZsh{} works}
+\end{sphinxVerbatim}
+
+\sphinxcode{\sphinxupquote{gca}} stands for “get current axis�.  It returns an object that represents the axes of the current figure, and that object provides \sphinxcode{\sphinxupquote{invert\_yaxis}}.
+
+\sphinxstylestrong{In case anyone asks:} The most likely reason for this inconsistency in the interface is that \sphinxcode{\sphinxupquote{invert\_yaxis}} is a lesser\sphinxhyphen{}used function, so it’s not made available at the top level of the interface.
+
+Here’s what the results look like.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{plot\PYGZus{}cmd}\PYG{p}{(}\PYG{n}{photo\PYGZus{}table}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+\noindent\sphinxincludegraphics{{06_photo_12_0}.png}
+
+Our figure does not look exactly like the one in the paper because we are working with a smaller region of the sky, so we don’t have as many stars.  But we can see an overdense region in the lower left that contains stars with the photometry we expect for GD\sphinxhyphen{}1.
+
+The authors of the original paper derive a detailed polygon that defines a boundary between stars that are likely to be in GD\sphinxhyphen{}1 or not.
+
+As a simplification, we’ll choose a boundary by eye that seems to contain the overdense region.
+
+
+\section{Drawing a polygon}
+\label{\detokenize{06_photo:drawing-a-polygon}}
+Matplotlib provides a function called \sphinxcode{\sphinxupquote{ginput}} that lets us click on the figure and make a list of coordinates.
+
+It’s a little tricky to use \sphinxcode{\sphinxupquote{ginput}} in a Jupyter notebook.Before calling \sphinxcode{\sphinxupquote{plt.ginput}} we have to tell Matplotlib to use \sphinxcode{\sphinxupquote{TkAgg}} to draw the figure in a new window.
+
+When you run the following cell, a figure should appear in a new window.  Click on it 10 times to draw a polygon around the overdense area.  A red cross should appear where you click.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{k+kn}{import} \PYG{n+nn}{matplotlib} \PYG{k}{as} \PYG{n+nn}{mpl}
+
+\PYG{k}{if} \PYG{n}{IN\PYGZus{}COLAB}\PYG{p}{:}
+    \PYG{n}{coords} \PYG{o}{=} \PYG{k+kc}{None}
+\PYG{k}{else}\PYG{p}{:}
+    \PYG{n}{mpl}\PYG{o}{.}\PYG{n}{use}\PYG{p}{(}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{TkAgg}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{)}
+    \PYG{n}{plot\PYGZus{}cmd}\PYG{p}{(}\PYG{n}{photo\PYGZus{}table}\PYG{p}{)}
+    \PYG{n}{coords} \PYG{o}{=} \PYG{n}{plt}\PYG{o}{.}\PYG{n}{ginput}\PYG{p}{(}\PYG{l+m+mi}{10}\PYG{p}{)}
+    \PYG{n}{mpl}\PYG{o}{.}\PYG{n}{use}\PYG{p}{(}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{agg}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+The argument to \sphinxcode{\sphinxupquote{ginput}} is the number of times the user has to click on the figure.
+
+The result from \sphinxcode{\sphinxupquote{ginput}} is a list of coordinate pairs.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{coords}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+[(0.2150537634408602, 17.548197203826344),
+ (0.3897849462365591, 18.94628403237675),
+ (0.5376344086021505, 19.902869757174393),
+ (0.7034050179211468, 20.601913171449596),
+ (0.8288530465949819, 21.300956585724798),
+ (0.6630824372759856, 21.52170713760118),
+ (0.4301075268817204, 20.785871964679913),
+ (0.27329749103942647, 19.71891096394408),
+ (0.17473118279569888, 18.688741721854306),
+ (0.17473118279569888, 17.95290654893304)]
+\end{sphinxVerbatim}
+
+If \sphinxcode{\sphinxupquote{ginput}} doesn’t work for you, you could use the following coordinates.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{k}{if} \PYG{n}{coords} \PYG{o+ow}{is} \PYG{k+kc}{None}\PYG{p}{:}
+    \PYG{n}{coords} \PYG{o}{=} \PYG{p}{[}\PYG{p}{(}\PYG{l+m+mf}{0.2}\PYG{p}{,} \PYG{l+m+mf}{17.5}\PYG{p}{)}\PYG{p}{,} 
+              \PYG{p}{(}\PYG{l+m+mf}{0.2}\PYG{p}{,} \PYG{l+m+mf}{19.5}\PYG{p}{)}\PYG{p}{,} 
+              \PYG{p}{(}\PYG{l+m+mf}{0.65}\PYG{p}{,} \PYG{l+m+mi}{22}\PYG{p}{)}\PYG{p}{,}
+              \PYG{p}{(}\PYG{l+m+mf}{0.75}\PYG{p}{,} \PYG{l+m+mi}{21}\PYG{p}{)}\PYG{p}{,}
+              \PYG{p}{(}\PYG{l+m+mf}{0.4}\PYG{p}{,} \PYG{l+m+mi}{19}\PYG{p}{)}\PYG{p}{,}
+              \PYG{p}{(}\PYG{l+m+mf}{0.4}\PYG{p}{,} \PYG{l+m+mf}{17.5}\PYG{p}{)}\PYG{p}{]}
+\end{sphinxVerbatim}
+
+The next step is to convert the coordinates to a format we can use to plot them, which is a sequence of \sphinxcode{\sphinxupquote{x}} coordinates and a sequence of \sphinxcode{\sphinxupquote{y}} coordinates.  The NumPy function \sphinxcode{\sphinxupquote{transpose}} does what we want.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{k+kn}{import} \PYG{n+nn}{numpy} \PYG{k}{as} \PYG{n+nn}{np}
+
+\PYG{n}{xs}\PYG{p}{,} \PYG{n}{ys} \PYG{o}{=} \PYG{n}{np}\PYG{o}{.}\PYG{n}{transpose}\PYG{p}{(}\PYG{n}{coords}\PYG{p}{)}
+\PYG{n}{xs}\PYG{p}{,} \PYG{n}{ys}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+(array([0.21505376, 0.38978495, 0.53763441, 0.70340502, 0.82885305,
+        0.66308244, 0.43010753, 0.27329749, 0.17473118, 0.17473118]),
+ array([17.5481972 , 18.94628403, 19.90286976, 20.60191317, 21.30095659,
+        21.52170714, 20.78587196, 19.71891096, 18.68874172, 17.95290655]))
+\end{sphinxVerbatim}
+
+To display the polygon, we’ll draw the figure again and use \sphinxcode{\sphinxupquote{plt.plot}} to draw the polygon.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{plot\PYGZus{}cmd}\PYG{p}{(}\PYG{n}{photo\PYGZus{}table}\PYG{p}{)}
+\PYG{n}{plt}\PYG{o}{.}\PYG{n}{plot}\PYG{p}{(}\PYG{n}{xs}\PYG{p}{,} \PYG{n}{ys}\PYG{p}{)}\PYG{p}{;}
+\end{sphinxVerbatim}
+
+\noindent\sphinxincludegraphics{{06_photo_23_0}.png}
+
+If it looks like your polygon does a good job surrounding the overdense area, go on to the next section.  Otherwise you can try again.
+
+If you want a polygon with more points (or fewer), you can change the argument to \sphinxcode{\sphinxupquote{ginput}}.
+
+The polygon does not have to be “closed�.  When we use this polygon in the next section, the last and first points will be connected by a straight line.
+
+
+\section{Which points are in the polygon?}
+\label{\detokenize{06_photo:which-points-are-in-the-polygon}}
+Matplotlib provides a \sphinxcode{\sphinxupquote{Path}} object that we can use to check which points fall in the polygon we selected.
+
+Here’s how we make a \sphinxcode{\sphinxupquote{Path}} using a list of coordinates.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{k+kn}{from} \PYG{n+nn}{matplotlib}\PYG{n+nn}{.}\PYG{n+nn}{path} \PYG{k+kn}{import} \PYG{n}{Path}
+
+\PYG{n}{path} \PYG{o}{=} \PYG{n}{Path}\PYG{p}{(}\PYG{n}{coords}\PYG{p}{)}
+\PYG{n}{path}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+Path(array([[ 0.21505376, 17.5481972 ],
+       [ 0.38978495, 18.94628403],
+       [ 0.53763441, 19.90286976],
+       [ 0.70340502, 20.60191317],
+       [ 0.82885305, 21.30095659],
+       [ 0.66308244, 21.52170714],
+       [ 0.43010753, 20.78587196],
+       [ 0.27329749, 19.71891096],
+       [ 0.17473118, 18.68874172],
+       [ 0.17473118, 17.95290655]]), None)
+\end{sphinxVerbatim}
+
+\sphinxcode{\sphinxupquote{Path}} provides \sphinxcode{\sphinxupquote{contains\_points}}, which figures out which points are inside the polygon.
+
+To test it, we’ll create a list with two points, one inside the polygon and one outside.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{points} \PYG{o}{=} \PYG{p}{[}\PYG{p}{(}\PYG{l+m+mf}{0.4}\PYG{p}{,} \PYG{l+m+mi}{20}\PYG{p}{)}\PYG{p}{,} 
+          \PYG{p}{(}\PYG{l+m+mf}{0.4}\PYG{p}{,} \PYG{l+m+mi}{30}\PYG{p}{)}\PYG{p}{]}
+\end{sphinxVerbatim}
+
+Now we can make sure \sphinxcode{\sphinxupquote{contains\_points}} does what we expect.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{inside} \PYG{o}{=} \PYG{n}{path}\PYG{o}{.}\PYG{n}{contains\PYGZus{}points}\PYG{p}{(}\PYG{n}{points}\PYG{p}{)}
+\PYG{n}{inside}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+array([ True, False])
+\end{sphinxVerbatim}
+
+The result is an array of Boolean values.
+
+We are almost ready to select stars whose photometry data falls in this polygon.  But first we need to do some data cleaning.
+
+
+\section{Reloading the data}
+\label{\detokenize{06_photo:reloading-the-data}}
+Now we need to combine the photometry data with the list of candidate stars we identified in a previous notebook.  The following cell downloads it:
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{k+kn}{import} \PYG{n+nn}{os}
+\PYG{k+kn}{from} \PYG{n+nn}{wget} \PYG{k+kn}{import} \PYG{n}{download}
+
+\PYG{n}{filename} \PYG{o}{=} \PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{gd1\PYGZus{}candidates.hdf5}\PYG{l+s+s1}{\PYGZsq{}}
+\PYG{n}{filepath} \PYG{o}{=} \PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{https://github.com/AllenDowney/AstronomicalData/raw/main/data/}\PYG{l+s+s1}{\PYGZsq{}}
+
+\PYG{k}{if} \PYG{o+ow}{not} \PYG{n}{os}\PYG{o}{.}\PYG{n}{path}\PYG{o}{.}\PYG{n}{exists}\PYG{p}{(}\PYG{n}{filename}\PYG{p}{)}\PYG{p}{:}
+    \PYG{n+nb}{print}\PYG{p}{(}\PYG{n}{download}\PYG{p}{(}\PYG{n}{filepath}\PYG{o}{+}\PYG{n}{filename}\PYG{p}{)}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{k+kn}{import} \PYG{n+nn}{pandas} \PYG{k}{as} \PYG{n+nn}{pd}
+
+\PYG{n}{candidate\PYGZus{}df} \PYG{o}{=} \PYG{n}{pd}\PYG{o}{.}\PYG{n}{read\PYGZus{}hdf}\PYG{p}{(}\PYG{n}{filename}\PYG{p}{,} \PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{candidate\PYGZus{}df}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+\sphinxcode{\sphinxupquote{candidate\_df}} is the Pandas DataFrame that contains the results from Notebook XX, which selects stars likely to be in GD\sphinxhyphen{}1 based on proper motion.  It also includes position and proper motion transformed to the ICRS frame.
+
+
+\section{Merging photometry data}
+\label{\detokenize{06_photo:merging-photometry-data}}
+Before we select stars based on photometry data, we have to solve two problems:
+\begin{enumerate}
+\sphinxsetlistlabels{\arabic}{enumi}{enumii}{}{.}%
+\item {} 
+We only have Pan\sphinxhyphen{}STARRS data for some stars in \sphinxcode{\sphinxupquote{candidate\_df}}.
+
+\item {} 
+Even for the stars where we have Pan\sphinxhyphen{}STARRS data in \sphinxcode{\sphinxupquote{photo\_table}}, some photometry data is missing.
+
+\end{enumerate}
+
+We will solve these problems in two step:
+\begin{enumerate}
+\sphinxsetlistlabels{\arabic}{enumi}{enumii}{}{.}%
+\item {} 
+We’ll merge the data from \sphinxcode{\sphinxupquote{candidate\_df}} and \sphinxcode{\sphinxupquote{photo\_table}} into a single Pandas \sphinxcode{\sphinxupquote{DataFrame}}.
+
+\item {} 
+We’ll use Pandas functions to deal with missing data.
+
+\end{enumerate}
+
+\sphinxcode{\sphinxupquote{candidate\_df}} is already a \sphinxcode{\sphinxupquote{DataFrame}}, but \sphinxcode{\sphinxupquote{results}} is an Astropy \sphinxcode{\sphinxupquote{Table}}.  Let’s convert it to Pandas:
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{photo\PYGZus{}df} \PYG{o}{=} \PYG{n}{photo\PYGZus{}table}\PYG{o}{.}\PYG{n}{to\PYGZus{}pandas}\PYG{p}{(}\PYG{p}{)}
+
+\PYG{k}{for} \PYG{n}{colname} \PYG{o+ow}{in} \PYG{n}{photo\PYGZus{}df}\PYG{o}{.}\PYG{n}{columns}\PYG{p}{:}
+    \PYG{n+nb}{print}\PYG{p}{(}\PYG{n}{colname}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+source\PYGZus{}id
+g\PYGZus{}mean\PYGZus{}psf\PYGZus{}mag
+i\PYGZus{}mean\PYGZus{}psf\PYGZus{}mag
+\end{sphinxVerbatim}
+
+Now we want to combine \sphinxcode{\sphinxupquote{candidate\_df}} and \sphinxcode{\sphinxupquote{photo\_df}} into a single table, using \sphinxcode{\sphinxupquote{source\_id}} to match up the rows.
+
+You might recognize this task; it’s the same as the JOIN operation in ADQL/SQL.
+
+Pandas provides a function called \sphinxcode{\sphinxupquote{merge}} that does what we want.  Here’s how we use it.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{merged} \PYG{o}{=} \PYG{n}{pd}\PYG{o}{.}\PYG{n}{merge}\PYG{p}{(}\PYG{n}{candidate\PYGZus{}df}\PYG{p}{,} 
+                  \PYG{n}{photo\PYGZus{}df}\PYG{p}{,} 
+                  \PYG{n}{on}\PYG{o}{=}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{source\PYGZus{}id}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{,} 
+                  \PYG{n}{how}\PYG{o}{=}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{left}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{)}
+\PYG{n}{merged}\PYG{o}{.}\PYG{n}{head}\PYG{p}{(}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+            source\PYGZus{}id          ra        dec      pmra      pmdec  parallax  \PYGZbs{}
+0  635559124339440000  137.586717  19.196544 \PYGZhy{}3.770522 \PYGZhy{}12.490482  0.791393   
+1  635860218726658176  138.518707  19.092339 \PYGZhy{}5.941679 \PYGZhy{}11.346409  0.307456   
+2  635674126383965568  138.842874  19.031798 \PYGZhy{}3.897001 \PYGZhy{}12.702780  0.779463   
+3  635535454774983040  137.837752  18.864007 \PYGZhy{}4.335041 \PYGZhy{}14.492309  0.314514   
+4  635497276810313600  138.044516  19.009471 \PYGZhy{}7.172931 \PYGZhy{}12.291499  0.425404   
+
+   parallax\PYGZus{}error  radial\PYGZus{}velocity       phi1      phi2   pm\PYGZus{}phi1   pm\PYGZus{}phi2  \PYGZbs{}
+0        0.271754              NaN \PYGZhy{}59.630489 \PYGZhy{}1.216485 \PYGZhy{}7.361363 \PYGZhy{}0.592633   
+1        0.199466              NaN \PYGZhy{}59.247330 \PYGZhy{}2.016078 \PYGZhy{}7.527126  1.748779   
+2        0.223692              NaN \PYGZhy{}59.133391 \PYGZhy{}2.306901 \PYGZhy{}7.560608 \PYGZhy{}0.741800   
+3        0.102775              NaN \PYGZhy{}59.785300 \PYGZhy{}1.594569 \PYGZhy{}9.357536 \PYGZhy{}1.218492   
+4        0.337689              NaN \PYGZhy{}59.557744 \PYGZhy{}1.682147 \PYGZhy{}9.000831  2.334407   
+
+   g\PYGZus{}mean\PYGZus{}psf\PYGZus{}mag  i\PYGZus{}mean\PYGZus{}psf\PYGZus{}mag  
+0             NaN             NaN  
+1         17.8978       17.517401  
+2         19.2873       17.678101  
+3         16.9238       16.478100  
+4         19.9242       18.334000  
+\end{sphinxVerbatim}
+
+The first argument is the “left� table, the second argument is the “right� table, and the keyword argument \sphinxcode{\sphinxupquote{on=\textquotesingle{}source\_id\textquotesingle{}}} specifies a column to use to match up the rows.
+
+The argument \sphinxcode{\sphinxupquote{how=\textquotesingle{}left\textquotesingle{}}} means that the result should have all rows from the left table, even if some of them don’t match up with a row in the right table.
+
+If you are interested in the other options for \sphinxcode{\sphinxupquote{how}}, you can \sphinxhref{https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.merge.html}{read the documentation of \sphinxcode{\sphinxupquote{merge}}}.
+
+You can also do different types of join in ADQL/SQL; \sphinxhref{https://www.w3schools.com/sql/sql\_join.asp}{you can read about that here}.
+
+The result is a \sphinxcode{\sphinxupquote{DataFrame}} that contains the same number of rows as \sphinxcode{\sphinxupquote{candidate\_df}}.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n+nb}{len}\PYG{p}{(}\PYG{n}{candidate\PYGZus{}df}\PYG{p}{)}\PYG{p}{,} \PYG{n+nb}{len}\PYG{p}{(}\PYG{n}{photo\PYGZus{}df}\PYG{p}{)}\PYG{p}{,} \PYG{n+nb}{len}\PYG{p}{(}\PYG{n}{merged}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+(7346, 3724, 7346)
+\end{sphinxVerbatim}
+
+And all columns from both tables.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{k}{for} \PYG{n}{colname} \PYG{o+ow}{in} \PYG{n}{merged}\PYG{o}{.}\PYG{n}{columns}\PYG{p}{:}
+    \PYG{n+nb}{print}\PYG{p}{(}\PYG{n}{colname}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+source\PYGZus{}id
+ra
+dec
+pmra
+pmdec
+parallax
+parallax\PYGZus{}error
+radial\PYGZus{}velocity
+phi1
+phi2
+pm\PYGZus{}phi1
+pm\PYGZus{}phi2
+g\PYGZus{}mean\PYGZus{}psf\PYGZus{}mag
+i\PYGZus{}mean\PYGZus{}psf\PYGZus{}mag
+\end{sphinxVerbatim}
+
+\sphinxstylestrong{Detail} You might notice that Pandas also provides a function called \sphinxcode{\sphinxupquote{join}}; it does almost the same thing, but the interface is slightly different.  We think \sphinxcode{\sphinxupquote{merge}} is a little easier to use, so that’s what we chose.  It’s also more consistent with JOIN in SQL, so if you learn how to use \sphinxcode{\sphinxupquote{pd.merge}}, you are also learning how to use SQL JOIN.
+
+Also, someone might ask why we have to use Pandas to do this join; why didn’t we do it in ADQL.  The answer is that we could have done that, but since we already have the data we need, we should probably do the computation locally rather than make another round trip to the Gaia server.
+
+
+\section{Missing data}
+\label{\detokenize{06_photo:missing-data}}
+Let’s add columns to the merged table for magnitude and color.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{merged}\PYG{p}{[}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{mag}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{]} \PYG{o}{=} \PYG{n}{merged}\PYG{p}{[}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{g\PYGZus{}mean\PYGZus{}psf\PYGZus{}mag}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{]}
+\PYG{n}{merged}\PYG{p}{[}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{color}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{]} \PYG{o}{=} \PYG{n}{merged}\PYG{p}{[}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{g\PYGZus{}mean\PYGZus{}psf\PYGZus{}mag}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{]} \PYG{o}{\PYGZhy{}} \PYG{n}{merged}\PYG{p}{[}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{i\PYGZus{}mean\PYGZus{}psf\PYGZus{}mag}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{]}
+\end{sphinxVerbatim}
+
+These columns contain the special value \sphinxcode{\sphinxupquote{NaN}} where we are missing data.
+
+We can use \sphinxcode{\sphinxupquote{notnull}} to see which rows contain value data, that is, not null values.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{merged}\PYG{p}{[}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{color}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{]}\PYG{o}{.}\PYG{n}{notnull}\PYG{p}{(}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+0       False
+1        True
+2        True
+3        True
+4        True
+        ...  
+7341     True
+7342    False
+7343    False
+7344     True
+7345    False
+Name: color, Length: 7346, dtype: bool
+\end{sphinxVerbatim}
+
+And \sphinxcode{\sphinxupquote{sum}} to count the number of valid values.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{merged}\PYG{p}{[}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{color}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{]}\PYG{o}{.}\PYG{n}{notnull}\PYG{p}{(}\PYG{p}{)}\PYG{o}{.}\PYG{n}{sum}\PYG{p}{(}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+3724
+\end{sphinxVerbatim}
+
+For scientific purposes, it’s not obvious what we should do with candidate stars if we don’t have photometry data.  Should we give them the benefit of the doubt or leave them out?
+
+In part the answer depends on the goal: are we trying to identify more stars that might be in GD\sphinxhyphen{}1, or a smaller set of stars that have higher probability?
+
+In the next section, we’ll leave them out, but you can experiment with the alternative.
+
+
+\section{Selecting based on photometry}
+\label{\detokenize{06_photo:selecting-based-on-photometry}}
+Now let’s see how many of these points are inside the polygon we chose.
+
+We can use a list of column names to select \sphinxcode{\sphinxupquote{color}} and \sphinxcode{\sphinxupquote{mag}}.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{points} \PYG{o}{=} \PYG{n}{merged}\PYG{p}{[}\PYG{p}{[}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{color}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{,} \PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{mag}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{]}\PYG{p}{]}
+\PYG{n}{points}\PYG{o}{.}\PYG{n}{head}\PYG{p}{(}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+    color      mag
+0     NaN      NaN
+1  0.3804  17.8978
+2  1.6092  19.2873
+3  0.4457  16.9238
+4  1.5902  19.9242
+\end{sphinxVerbatim}
+
+The result is a \sphinxcode{\sphinxupquote{DataFrame}} that can be treated as a sequence of coordinates, so we can pass it to \sphinxcode{\sphinxupquote{contains\_points}}:
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{inside} \PYG{o}{=} \PYG{n}{path}\PYG{o}{.}\PYG{n}{contains\PYGZus{}points}\PYG{p}{(}\PYG{n}{points}\PYG{p}{)}
+\PYG{n}{inside}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+array([False, False, False, ..., False, False, False])
+\end{sphinxVerbatim}
+
+The result is a Boolean array.  We can use \sphinxcode{\sphinxupquote{sum}} to see how many stars fall in the polygon.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{inside}\PYG{o}{.}\PYG{n}{sum}\PYG{p}{(}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+496
+\end{sphinxVerbatim}
+
+Now we can use \sphinxcode{\sphinxupquote{inside}} as a mask to select stars that fall inside the polygon.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{selected} \PYG{o}{=} \PYG{n}{merged}\PYG{p}{[}\PYG{n}{inside}\PYG{p}{]}
+\end{sphinxVerbatim}
+
+Let’s make a color\sphinxhyphen{}magnitude plot one more time, highlighting the selected stars with green \sphinxcode{\sphinxupquote{x}} marks.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{plot\PYGZus{}cmd}\PYG{p}{(}\PYG{n}{photo\PYGZus{}table}\PYG{p}{)}
+\PYG{n}{plt}\PYG{o}{.}\PYG{n}{plot}\PYG{p}{(}\PYG{n}{xs}\PYG{p}{,} \PYG{n}{ys}\PYG{p}{)}
+
+\PYG{n}{plt}\PYG{o}{.}\PYG{n}{plot}\PYG{p}{(}\PYG{n}{selected}\PYG{p}{[}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{color}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{]}\PYG{p}{,} \PYG{n}{selected}\PYG{p}{[}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{mag}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{]}\PYG{p}{,} \PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{gx}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{)}\PYG{p}{;}
+\end{sphinxVerbatim}
+
+\noindent\sphinxincludegraphics{{06_photo_61_0}.png}
+
+It looks like the selected stars are, in fact, inside the polygon, which means they have photometry data consistent with GD\sphinxhyphen{}1.
+
+Finally, we can plot the coordinates of the selected stars:
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{plt}\PYG{o}{.}\PYG{n}{figure}\PYG{p}{(}\PYG{n}{figsize}\PYG{o}{=}\PYG{p}{(}\PYG{l+m+mi}{10}\PYG{p}{,}\PYG{l+m+mf}{2.5}\PYG{p}{)}\PYG{p}{)}
+
+\PYG{n}{x} \PYG{o}{=} \PYG{n}{selected}\PYG{p}{[}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{phi1}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{]}
+\PYG{n}{y} \PYG{o}{=} \PYG{n}{selected}\PYG{p}{[}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{phi2}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{]}
+
+\PYG{n}{plt}\PYG{o}{.}\PYG{n}{plot}\PYG{p}{(}\PYG{n}{x}\PYG{p}{,} \PYG{n}{y}\PYG{p}{,} \PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{ko}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{,} \PYG{n}{markersize}\PYG{o}{=}\PYG{l+m+mf}{0.7}\PYG{p}{,} \PYG{n}{alpha}\PYG{o}{=}\PYG{l+m+mf}{0.9}\PYG{p}{)}
+
+\PYG{n}{plt}\PYG{o}{.}\PYG{n}{xlabel}\PYG{p}{(}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{ra (degree GD1)}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{)}
+\PYG{n}{plt}\PYG{o}{.}\PYG{n}{ylabel}\PYG{p}{(}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{dec (degree GD1)}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{)}
+
+\PYG{n}{plt}\PYG{o}{.}\PYG{n}{axis}\PYG{p}{(}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{equal}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{)}\PYG{p}{;}
+\end{sphinxVerbatim}
+
+\noindent\sphinxincludegraphics{{06_photo_63_0}.png}
+
+This example includes two new Matplotlib commands:
+\begin{itemize}
+\item {} 
+\sphinxcode{\sphinxupquote{figure}} creates the figure.  In previous examples, we didn’t have to use this function; the figure was created automatically.  But when we call it explicitly, we can provide arguments like \sphinxcode{\sphinxupquote{figsize}}, which sets the size of the figure.
+
+\item {} 
+\sphinxcode{\sphinxupquote{axis}} with the parameter \sphinxcode{\sphinxupquote{equal}} sets up the axes so a unit is the same size along the \sphinxcode{\sphinxupquote{x}} and \sphinxcode{\sphinxupquote{y}} axes.
+
+\end{itemize}
+
+In an example like this, where \sphinxcode{\sphinxupquote{x}} and \sphinxcode{\sphinxupquote{y}} represent coordinates in space, equal axes ensures that the distance between points is represented accurately.
+
+
+\section{Write the data}
+\label{\detokenize{06_photo:write-the-data}}
+Let’s write the merged DataFrame to a file.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{filename} \PYG{o}{=} \PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{gd1\PYGZus{}merged.hdf5}\PYG{l+s+s1}{\PYGZsq{}}
+
+\PYG{n}{merged}\PYG{o}{.}\PYG{n}{to\PYGZus{}hdf}\PYG{p}{(}\PYG{n}{filename}\PYG{p}{,} \PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{merged}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{)}
+\PYG{n}{selected}\PYG{o}{.}\PYG{n}{to\PYGZus{}hdf}\PYG{p}{(}\PYG{n}{filename}\PYG{p}{,} \PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{selected}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{o}{!}ls \PYGZhy{}lh gd1\PYGZus{}merged.hdf5
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYGZhy{}rw\PYGZhy{}rw\PYGZhy{}r\PYGZhy{}\PYGZhy{} 1 downey downey 2.0M Oct 19 17:21 gd1\PYGZus{}merged.hdf5
+\end{sphinxVerbatim}
+
+If you are using Windows, \sphinxcode{\sphinxupquote{ls}} might not work; in that case, try:
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+!dir gd1\PYGZus{}merged.hdf5
+\end{sphinxVerbatim}
+
+
+\section{Save the polygon}
+\label{\detokenize{06_photo:save-the-polygon}}
+\sphinxhref{https://en.wikipedia.org/wiki/Reproducibility\#Reproducible\_research}{Reproducibile research} is “the idea that … the full computational environment used to produce the results in the paper such as the code, data, etc. can be used to reproduce the results and create new work based on the research.�
+
+This Jupyter notebook is an example of reproducible research because it contains all of the code needed to reproduce the results, including the database queries that download the data and and analysis.
+
+However, when we used \sphinxcode{\sphinxupquote{ginput}} to define a polygon by hand, we introduced a non\sphinxhyphen{}reproducible element to the analysis.  If someone running this notebook chooses a different polygon, they will get different results.  So it is important to record the polygon we chose as part of the data analysis pipeline.
+
+Since \sphinxcode{\sphinxupquote{coords}} is a NumPy array, we can’t use \sphinxcode{\sphinxupquote{to\_hdf}} to save it in a file.  But we can convert it to a Pandas \sphinxcode{\sphinxupquote{DataFrame}} and save that.
+
+As an alternative, we could use \sphinxhref{http://www.pytables.org/index.html}{PyTables}, which is the library Pandas uses to read and write files.  It is a powerful library, but not easy to use directly.  So let’s take advantage of Pandas.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{coords\PYGZus{}df} \PYG{o}{=} \PYG{n}{pd}\PYG{o}{.}\PYG{n}{DataFrame}\PYG{p}{(}\PYG{n}{coords}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{filename} \PYG{o}{=} \PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{gd1\PYGZus{}polygon.hdf5}\PYG{l+s+s1}{\PYGZsq{}}
+\PYG{n}{coords\PYGZus{}df}\PYG{o}{.}\PYG{n}{to\PYGZus{}hdf}\PYG{p}{(}\PYG{n}{filename}\PYG{p}{,} \PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{coords\PYGZus{}df}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+We can read it back like this.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{coords2\PYGZus{}df} \PYG{o}{=} \PYG{n}{pd}\PYG{o}{.}\PYG{n}{read\PYGZus{}hdf}\PYG{p}{(}\PYG{n}{filename}\PYG{p}{,} \PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{coords\PYGZus{}df}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{)}
+\PYG{n}{coords2} \PYG{o}{=} \PYG{n}{coords2\PYGZus{}df}\PYG{o}{.}\PYG{n}{to\PYGZus{}numpy}\PYG{p}{(}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+And verify that the data we read back is the same.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{np}\PYG{o}{.}\PYG{n}{all}\PYG{p}{(}\PYG{n}{coords2} \PYG{o}{==} \PYG{n}{coords}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+True
+\end{sphinxVerbatim}
+
+
+\section{Summary}
+\label{\detokenize{06_photo:summary}}
+In this notebook, we worked with two datasets: the list of candidate stars from Gaia and the photometry data from Pan\sphinxhyphen{}STARRS.
+
+We drew a color\sphinxhyphen{}magnitude diagram and used it to identify stars we think are likely to be in GD\sphinxhyphen{}1.
+
+Then we used a Pandas \sphinxcode{\sphinxupquote{merge}} operation to combine the data into a single \sphinxcode{\sphinxupquote{DataFrame}}.
+
+
+\section{Best practices}
+\label{\detokenize{06_photo:best-practices}}\begin{itemize}
+\item {} 
+If you want to perform something like a database \sphinxcode{\sphinxupquote{JOIN}} operation with data that is in a Pandas \sphinxcode{\sphinxupquote{DataFrame}}, you can use the \sphinxcode{\sphinxupquote{join}} or \sphinxcode{\sphinxupquote{merge}} function.  In many cases, \sphinxcode{\sphinxupquote{merge}} is easier to use because the arguments are more like SQL.
+
+\item {} 
+Use Matplotlib options to control the size and aspect ratio of figures to make them easier to interpret.  In this example, we scaled the axes so the size of a degree is equal along both axes.
+
+\item {} 
+Matplotlib also provides operations for working with points, polygons, and other geometric entities, so it’s not just for making figures.
+
+\item {} 
+Be sure to record every element of the data analysis pipeline that would be needed to replicate the results.
+
+\end{itemize}
+
+
+\chapter{Chapter 7}
+\label{\detokenize{07_plot:chapter-7}}\label{\detokenize{07_plot::doc}}
+This is the seventh in a series of notebooks related to astronomy data.
+
+As a continuing example, we will replicate part of the analysis in a recent paper, “\sphinxhref{https://arxiv.org/abs/1805.00425}{Off the beaten path: Gaia reveals GD\sphinxhyphen{}1 stars outside of the main stream}� by Adrian M. Price\sphinxhyphen{}Whelan and Ana Bonaca.
+
+In the previous notebook we selected photometry data from Pan\sphinxhyphen{}STARRS and used it to identify stars we think are likely to be in GD\sphinxhyphen{}1
+
+In this notebook, we’ll take the results from previous lessons and use them to make a figure that tells a compelling scientific story.
+
+
+\section{Outline}
+\label{\detokenize{07_plot:outline}}
+Here are the steps in this notebook:
+\begin{enumerate}
+\sphinxsetlistlabels{\arabic}{enumi}{enumii}{}{.}%
+\item {} 
+Starting with the figure from the previous notebook, we’ll add annotations to present the results more clearly.
+
+\item {} 
+The we’ll see several ways to customize figures to make them more appealing and effective.
+
+\item {} 
+Finally, we’ll see how to make a figure with multiple panels or subplots.
+
+\end{enumerate}
+
+After completing this lesson, you should be able to
+\begin{itemize}
+\item {} 
+Design a figure that tells a compelling story.
+
+\item {} 
+Use Matplotlib features to customize the appearance of figures.
+
+\item {} 
+Generate a figure with multiple subplots.
+
+\end{itemize}
+
+
+\section{Installing libraries}
+\label{\detokenize{07_plot:installing-libraries}}
+If you are running this notebook on Colab, you can run the following cell to install Astroquery and a the other libraries we’ll use.
+
+If you are running this notebook on your own computer, you might have to install these libraries yourself.
+
+If you are using this notebook as part of a Carpentries workshop, you should have received setup instructions.
+
+TODO: Add a link to the instructions.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{c+c1}{\PYGZsh{} If we\PYGZsq{}re running on Colab, install libraries}
+
+\PYG{k+kn}{import} \PYG{n+nn}{sys}
+\PYG{n}{IN\PYGZus{}COLAB} \PYG{o}{=} \PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{google.colab}\PYG{l+s+s1}{\PYGZsq{}} \PYG{o+ow}{in} \PYG{n}{sys}\PYG{o}{.}\PYG{n}{modules}
+
+\PYG{k}{if} \PYG{n}{IN\PYGZus{}COLAB}\PYG{p}{:}
+    \PYG{o}{!}pip install astroquery astro\PYGZhy{}gala pyia python\PYGZhy{}wget
+\end{sphinxVerbatim}
+
+
+\section{Making Figures That Tell a Story}
+\label{\detokenize{07_plot:making-figures-that-tell-a-story}}
+So far the figure we’ve made have been “quick and dirty�.  Mostly we have used Matplotlib’s default style, although we have adjusted a few parameters, like \sphinxcode{\sphinxupquote{markersize}} and \sphinxcode{\sphinxupquote{alpha}}, to improve legibility.
+
+Now that the analysis is done, it’s time to think more about:
+\begin{enumerate}
+\sphinxsetlistlabels{\arabic}{enumi}{enumii}{}{.}%
+\item {} 
+Making professional\sphinxhyphen{}looking figures that are ready for publication, and
+
+\item {} 
+Making figures that communicate a scientific result clearly and compellingly.
+
+\end{enumerate}
+
+Not necessarily in that order.
+
+Let’s start by reviewing Figure 1 from the original paper.  We’ve seen the individual panels, but now let’s look at the whole thing, along with the caption:
+
+
+
+\sphinxstylestrong{Exercise:} Think about the following questions:
+\begin{enumerate}
+\sphinxsetlistlabels{\arabic}{enumi}{enumii}{}{.}%
+\item {} 
+What is the primary scientific result of this work?
+
+\item {} 
+What story is this figure telling?
+
+\item {} 
+In the design of this figure, can you identify 1\sphinxhyphen{}2 choices the authors made that you think are effective?  Think about big\sphinxhyphen{}picture elements, like the number of panels and how they are arranged, as well as details like the choice of typeface.
+
+\item {} 
+Can you identify 1\sphinxhyphen{}2 elements that could be improved, or that you might have done differently?
+
+\end{enumerate}
+
+Some topics that might come up in this discussion:
+\begin{enumerate}
+\sphinxsetlistlabels{\arabic}{enumi}{enumii}{}{.}%
+\item {} 
+The primary result is that the multiple stages of selection make it possible to separate likely candidates from the background more effectively than in previous work, which makes it possible to see the structure of GD\sphinxhyphen{}1 in “unprecedented detail�.
+
+\item {} 
+The figure documents the selection process as a sequence of steps.  Reading right\sphinxhyphen{}to\sphinxhyphen{}left, top\sphinxhyphen{}to\sphinxhyphen{}bottom, we see selection based on proper motion, the results of the first selection, selection based on color and magnitude, and the results of the second selection.  So this figure documents the methodology and presents the primary result.
+
+\item {} 
+It’s mostly black and white, with minimal use of color, so it will work well in print.  The annotations in the bottom left panel guide the reader to the most important results.  It contains enough technical detail for a professional audience, but most of it is also comprehensible to a more general audience.  The two left panels have the same dimensions and their axes are aligned.
+
+\item {} 
+Since the panels represent a sequence, it might be better to arrange them left\sphinxhyphen{}to\sphinxhyphen{}right.  The placement and size of the axis labels could be tweaked.  The entire figure could be a little bigger to match the width and proportion of the caption.  The top left panel has unnused white space (but that leaves space for the annotations in the bottom left).
+
+\end{enumerate}
+
+
+\section{Plotting GD\sphinxhyphen{}1}
+\label{\detokenize{07_plot:plotting-gd-1}}
+Let’s start with the panel in the lower left.  The following cell reloads the data.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{k+kn}{import} \PYG{n+nn}{os}
+\PYG{k+kn}{from} \PYG{n+nn}{wget} \PYG{k+kn}{import} \PYG{n}{download}
+
+\PYG{n}{filename} \PYG{o}{=} \PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{gd1\PYGZus{}merged.hdf5}\PYG{l+s+s1}{\PYGZsq{}}
+\PYG{n}{path} \PYG{o}{=} \PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{https://github.com/AllenDowney/AstronomicalData/raw/main/data/}\PYG{l+s+s1}{\PYGZsq{}}
+
+\PYG{k}{if} \PYG{o+ow}{not} \PYG{n}{os}\PYG{o}{.}\PYG{n}{path}\PYG{o}{.}\PYG{n}{exists}\PYG{p}{(}\PYG{n}{filename}\PYG{p}{)}\PYG{p}{:}
+    \PYG{n+nb}{print}\PYG{p}{(}\PYG{n}{download}\PYG{p}{(}\PYG{n}{path}\PYG{o}{+}\PYG{n}{filename}\PYG{p}{)}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{k+kn}{import} \PYG{n+nn}{pandas} \PYG{k}{as} \PYG{n+nn}{pd}
+
+\PYG{n}{selected} \PYG{o}{=} \PYG{n}{pd}\PYG{o}{.}\PYG{n}{read\PYGZus{}hdf}\PYG{p}{(}\PYG{n}{filename}\PYG{p}{,} \PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{selected}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{k+kn}{import} \PYG{n+nn}{matplotlib}\PYG{n+nn}{.}\PYG{n+nn}{pyplot} \PYG{k}{as} \PYG{n+nn}{plt}
+
+\PYG{k}{def} \PYG{n+nf}{plot\PYGZus{}second\PYGZus{}selection}\PYG{p}{(}\PYG{n}{df}\PYG{p}{)}\PYG{p}{:}
+    \PYG{n}{x} \PYG{o}{=} \PYG{n}{df}\PYG{p}{[}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{phi1}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{]}
+    \PYG{n}{y} \PYG{o}{=} \PYG{n}{df}\PYG{p}{[}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{phi2}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{]}
+
+    \PYG{n}{plt}\PYG{o}{.}\PYG{n}{plot}\PYG{p}{(}\PYG{n}{x}\PYG{p}{,} \PYG{n}{y}\PYG{p}{,} \PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{ko}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{,} \PYG{n}{markersize}\PYG{o}{=}\PYG{l+m+mf}{0.7}\PYG{p}{,} \PYG{n}{alpha}\PYG{o}{=}\PYG{l+m+mf}{0.9}\PYG{p}{)}
+
+    \PYG{n}{plt}\PYG{o}{.}\PYG{n}{xlabel}\PYG{p}{(}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{\PYGZdl{}}\PYG{l+s+s1}{\PYGZbs{}}\PYG{l+s+s1}{phi\PYGZus{}1\PYGZdl{} [deg]}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{)}
+    \PYG{n}{plt}\PYG{o}{.}\PYG{n}{ylabel}\PYG{p}{(}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{\PYGZdl{}}\PYG{l+s+s1}{\PYGZbs{}}\PYG{l+s+s1}{phi\PYGZus{}2\PYGZdl{} [deg]}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{)}
+    \PYG{n}{plt}\PYG{o}{.}\PYG{n}{title}\PYG{p}{(}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{Proper motion + photometry selection}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{,} \PYG{n}{fontsize}\PYG{o}{=}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{medium}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{)}
+
+    \PYG{n}{plt}\PYG{o}{.}\PYG{n}{axis}\PYG{p}{(}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{equal}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+And here’s what it looks like.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{plt}\PYG{o}{.}\PYG{n}{figure}\PYG{p}{(}\PYG{n}{figsize}\PYG{o}{=}\PYG{p}{(}\PYG{l+m+mi}{10}\PYG{p}{,}\PYG{l+m+mf}{2.5}\PYG{p}{)}\PYG{p}{)}
+\PYG{n}{plot\PYGZus{}second\PYGZus{}selection}\PYG{p}{(}\PYG{n}{selected}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+\noindent\sphinxincludegraphics{{07_plot_13_0}.png}
+
+
+\section{Annotations}
+\label{\detokenize{07_plot:annotations}}
+The figure in the paper uses three other features to present the results more clearly and compellingly:
+\begin{itemize}
+\item {} 
+A vertical dashed line to distinguish the previously undetected region of GD\sphinxhyphen{}1,
+
+\item {} 
+A label that identifies the new region, and
+
+\item {} 
+Several annotations that combine text and arrows to identify features of GD\sphinxhyphen{}1.
+
+\end{itemize}
+
+As an exercise, choose any or all of these features and add them to the figure:
+\begin{itemize}
+\item {} 
+To draw vertical lines, see \sphinxhref{https://matplotlib.org/3.3.1/api/\_as\_gen/matplotlib.pyplot.vlines.html}{\sphinxcode{\sphinxupquote{plt.vlines}}} and \sphinxhref{https://matplotlib.org/3.3.1/api/\_as\_gen/matplotlib.pyplot.axvline.html\#matplotlib.pyplot.axvline}{\sphinxcode{\sphinxupquote{plt.axvline}}}.
+
+\item {} 
+To add text, see \sphinxhref{https://matplotlib.org/3.3.1/api/\_as\_gen/matplotlib.pyplot.text.html}{\sphinxcode{\sphinxupquote{plt.text}}}.
+
+\item {} 
+To add an annotation with text and an arrow, see \DUrole{xref,myst}{plt.annotate}.
+
+\end{itemize}
+
+And here is some \sphinxhref{https://matplotlib.org/3.3.1/tutorials/text/annotations.html\#plotting-guide-annotation}{additional information about text and arrows}.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{c+c1}{\PYGZsh{} Solution}
+
+\PYG{c+c1}{\PYGZsh{} plt.axvline(\PYGZhy{}55, ls=\PYGZsq{}\PYGZhy{}\PYGZhy{}\PYGZsq{}, color=\PYGZsq{}gray\PYGZsq{}, }
+\PYG{c+c1}{\PYGZsh{}             alpha=0.4, dashes=(6,4), lw=2)}
+\PYG{c+c1}{\PYGZsh{} plt.text(\PYGZhy{}60, 5.5, \PYGZsq{}Previously\PYGZbs{}nundetected\PYGZsq{}, }
+\PYG{c+c1}{\PYGZsh{}          fontsize=\PYGZsq{}small\PYGZsq{}, ha=\PYGZsq{}right\PYGZsq{}, va=\PYGZsq{}top\PYGZsq{});}
+
+\PYG{c+c1}{\PYGZsh{} arrowprops=dict(color=\PYGZsq{}gray\PYGZsq{}, shrink=0.05, width=1.5, }
+\PYG{c+c1}{\PYGZsh{}                 headwidth=6, headlength=8, alpha=0.4)}
+
+\PYG{c+c1}{\PYGZsh{} plt.annotate(\PYGZsq{}Spur\PYGZsq{}, xy=(\PYGZhy{}33, 2), xytext=(\PYGZhy{}35, 5.5),}
+\PYG{c+c1}{\PYGZsh{}              arrowprops=arrowprops,}
+\PYG{c+c1}{\PYGZsh{}              fontsize=\PYGZsq{}small\PYGZsq{})}
+
+\PYG{c+c1}{\PYGZsh{} plt.annotate(\PYGZsq{}Gap\PYGZsq{}, xy=(\PYGZhy{}22, \PYGZhy{}1), xytext=(\PYGZhy{}25, \PYGZhy{}5.5),}
+\PYG{c+c1}{\PYGZsh{}              arrowprops=arrowprops,}
+\PYG{c+c1}{\PYGZsh{}              fontsize=\PYGZsq{}small\PYGZsq{})}
+\end{sphinxVerbatim}
+
+
+\section{Customization}
+\label{\detokenize{07_plot:customization}}
+Matplotlib provides a default style that determines things like the colors of lines, the placement of labels and ticks on the axes, and many other properties.
+
+There are several ways to override these defaults and customize your figures:
+\begin{itemize}
+\item {} 
+To customize only the current figure, you can call functions like \sphinxcode{\sphinxupquote{tick\_params}}, which we’ll demonstrate below.
+
+\item {} 
+To customize all figures in a notebook, you use \sphinxcode{\sphinxupquote{rcParams}}.
+
+\item {} 
+To override more than a few defaults at the same time, you can use a style sheet.
+
+\end{itemize}
+
+As a simple example, notice that Matplotlib puts ticks on the outside of the figures by default, and only on the left and bottom sides of the axes.
+
+To change this behavior, you can use \sphinxcode{\sphinxupquote{gca()}} to get the current axes and \sphinxcode{\sphinxupquote{tick\_params}} to change the settings.
+
+Here’s how you can put the ticks on the inside of the figure:
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{plt}\PYG{o}{.}\PYG{n}{gca}\PYG{p}{(}\PYG{p}{)}\PYG{o}{.}\PYG{n}{tick\PYGZus{}params}\PYG{p}{(}\PYG{n}{direction}\PYG{o}{=}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{in}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+\sphinxstylestrong{Exercise:} Read the documentation of \sphinxhref{https://matplotlib.org/3.1.1/api/\_as\_gen/matplotlib.axes.Axes.tick\_params.html}{\sphinxcode{\sphinxupquote{tick\_params}}} and use it to put ticks on the top and right sides of the axes.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{c+c1}{\PYGZsh{} Solution}
+
+\PYG{c+c1}{\PYGZsh{} plt.gca().tick\PYGZus{}params(top=True, right=True)}
+\end{sphinxVerbatim}
+
+
+\section{rcParams}
+\label{\detokenize{07_plot:rcparams}}
+If you want to make a customization that applies to all figures in a notebook, you can use \sphinxcode{\sphinxupquote{rcParams}}.
+
+Here’s an example that reads the current font size from \sphinxcode{\sphinxupquote{rcParams}}:
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{plt}\PYG{o}{.}\PYG{n}{rcParams}\PYG{p}{[}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{font.size}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{]}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+10.0
+\end{sphinxVerbatim}
+
+And sets it to a new value:
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{plt}\PYG{o}{.}\PYG{n}{rcParams}\PYG{p}{[}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{font.size}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{]} \PYG{o}{=} \PYG{l+m+mi}{14}
+\end{sphinxVerbatim}
+
+\sphinxstylestrong{Exercise:} Plot the previous figure again, and see what font sizes have changed.  Look up any other element of \sphinxcode{\sphinxupquote{rcParams}}, change its value, and check the effect on the figure.
+
+If you find yourself making the same customizations in several notebooks, you can put changes to \sphinxcode{\sphinxupquote{rcParams}} in a \sphinxcode{\sphinxupquote{matplotlibrc}} file, \sphinxhref{https://matplotlib.org/3.3.1/tutorials/introductory/customizing.html\#customizing-with-matplotlibrc-files}{which you can read about here}.
+
+
+\section{Style sheets}
+\label{\detokenize{07_plot:style-sheets}}
+The \sphinxcode{\sphinxupquote{matplotlibrc}} file is read when you import Matplotlib, so it is not easy to switch from one set of options to another.
+
+The solution to this problem is style sheets, \sphinxhref{https://matplotlib.org/3.1.1/tutorials/introductory/customizing.html}{which you can read about here}.
+
+Matplotlib provides a set of predefined style sheets, or you can make your own.
+
+The following cell displays a list of style sheets installed on your system.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{plt}\PYG{o}{.}\PYG{n}{style}\PYG{o}{.}\PYG{n}{available}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+[\PYGZsq{}Solarize\PYGZus{}Light2\PYGZsq{},
+ \PYGZsq{}\PYGZus{}classic\PYGZus{}test\PYGZus{}patch\PYGZsq{},
+ \PYGZsq{}bmh\PYGZsq{},
+ \PYGZsq{}classic\PYGZsq{},
+ \PYGZsq{}dark\PYGZus{}background\PYGZsq{},
+ \PYGZsq{}fast\PYGZsq{},
+ \PYGZsq{}fivethirtyeight\PYGZsq{},
+ \PYGZsq{}ggplot\PYGZsq{},
+ \PYGZsq{}grayscale\PYGZsq{},
+ \PYGZsq{}seaborn\PYGZsq{},
+ \PYGZsq{}seaborn\PYGZhy{}bright\PYGZsq{},
+ \PYGZsq{}seaborn\PYGZhy{}colorblind\PYGZsq{},
+ \PYGZsq{}seaborn\PYGZhy{}dark\PYGZsq{},
+ \PYGZsq{}seaborn\PYGZhy{}dark\PYGZhy{}palette\PYGZsq{},
+ \PYGZsq{}seaborn\PYGZhy{}darkgrid\PYGZsq{},
+ \PYGZsq{}seaborn\PYGZhy{}deep\PYGZsq{},
+ \PYGZsq{}seaborn\PYGZhy{}muted\PYGZsq{},
+ \PYGZsq{}seaborn\PYGZhy{}notebook\PYGZsq{},
+ \PYGZsq{}seaborn\PYGZhy{}paper\PYGZsq{},
+ \PYGZsq{}seaborn\PYGZhy{}pastel\PYGZsq{},
+ \PYGZsq{}seaborn\PYGZhy{}poster\PYGZsq{},
+ \PYGZsq{}seaborn\PYGZhy{}talk\PYGZsq{},
+ \PYGZsq{}seaborn\PYGZhy{}ticks\PYGZsq{},
+ \PYGZsq{}seaborn\PYGZhy{}white\PYGZsq{},
+ \PYGZsq{}seaborn\PYGZhy{}whitegrid\PYGZsq{},
+ \PYGZsq{}tableau\PYGZhy{}colorblind10\PYGZsq{}]
+\end{sphinxVerbatim}
+
+Note that \sphinxcode{\sphinxupquote{seaborn\sphinxhyphen{}paper}}, \sphinxcode{\sphinxupquote{seaborn\sphinxhyphen{}talk}} and \sphinxcode{\sphinxupquote{seaborn\sphinxhyphen{}poster}} are particularly intended to prepare versions of a figure with text sizes and other features that work well in papers, talks, and posters.
+
+To use any of these style sheets, run \sphinxcode{\sphinxupquote{plt.style.use}} like this:
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{plt}\PYG{o}{.}\PYG{n}{style}\PYG{o}{.}\PYG{n}{use}\PYG{p}{(}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{fivethirtyeight}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+The style sheet you choose will affect the appearance of all figures you plot after calling \sphinxcode{\sphinxupquote{use}}, unless you override any of the options or call \sphinxcode{\sphinxupquote{use}} again.
+
+\sphinxstylestrong{Exercise:} Choose one of the styles on the list and select it by calling \sphinxcode{\sphinxupquote{use}}.  Then go back and plot one of the figures above and see what effect it has.
+
+If you can’t find a style sheet that’s exactly what you want, you can make your own.  This repository includes a style sheet called \sphinxcode{\sphinxupquote{az\sphinxhyphen{}paper\sphinxhyphen{}twocol.mplstyle}}, with customizations chosen by Azalee Bostroem for publication in astronomy journals.
+
+The following cell downloads the style sheet.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{k+kn}{import} \PYG{n+nn}{os}
+
+\PYG{n}{filename} \PYG{o}{=} \PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{az\PYGZhy{}paper\PYGZhy{}twocol.mplstyle}\PYG{l+s+s1}{\PYGZsq{}}
+\PYG{n}{path} \PYG{o}{=} \PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{https://github.com/AllenDowney/AstronomicalData/raw/main/data/}\PYG{l+s+s1}{\PYGZsq{}}
+
+\PYG{k}{if} \PYG{o+ow}{not} \PYG{n}{os}\PYG{o}{.}\PYG{n}{path}\PYG{o}{.}\PYG{n}{exists}\PYG{p}{(}\PYG{n}{filename}\PYG{p}{)}\PYG{p}{:}
+    \PYG{n+nb}{print}\PYG{p}{(}\PYG{n}{download}\PYG{p}{(}\PYG{n}{path}\PYG{o}{+}\PYG{n}{filename}\PYG{p}{)}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+You can use it like this:
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{plt}\PYG{o}{.}\PYG{n}{style}\PYG{o}{.}\PYG{n}{use}\PYG{p}{(}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{./az\PYGZhy{}paper\PYGZhy{}twocol.mplstyle}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+The prefix \sphinxcode{\sphinxupquote{./}} tells Matplotlib to look for the file in the current directory.
+
+As an alternative, you can install a style sheet for your own use by putting it in your configuration directory.  To find out where that is, you can run the following command:
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{k+kn}{import} \PYG{n+nn}{matplotlib} \PYG{k}{as} \PYG{n+nn}{mpl}
+
+\PYG{n}{mpl}\PYG{o}{.}\PYG{n}{get\PYGZus{}configdir}\PYG{p}{(}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+
+\section{LaTeX fonts}
+\label{\detokenize{07_plot:latex-fonts}}
+When you include mathematical expressions in titles, labels, and annotations, Matplotlib uses \sphinxhref{https://matplotlib.org/3.1.0/tutorials/text/mathtext.html}{\sphinxcode{\sphinxupquote{mathtext}}} to typeset them.  \sphinxcode{\sphinxupquote{mathtext}} uses the same syntax as LaTeX, but it provides only a subset of its features.
+
+If you need features that are not provided by \sphinxcode{\sphinxupquote{mathtext}}, or you prefer the way LaTeX typesets mathematical expressions, you can customize Matplotlib to use LaTeX.
+
+In \sphinxcode{\sphinxupquote{matplotlibrc}} or in a style sheet, you can add the following line:
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{text}\PYG{o}{.}\PYG{n}{usetex}        \PYG{p}{:} \PYG{n}{true}
+\end{sphinxVerbatim}
+
+Or in a notebook you can run the following code.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{plt}\PYG{o}{.}\PYG{n}{rcParams}\PYG{p}{[}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{text.usetex}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{]} \PYG{o}{=} \PYG{k+kc}{True}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{plt}\PYG{o}{.}\PYG{n}{rcParams}\PYG{p}{[}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{text.usetex}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{]} \PYG{o}{=} \PYG{k+kc}{True}
+\end{sphinxVerbatim}
+
+If you go back and draw the figure again, you should see the difference.
+
+If you get an error message like
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+LaTeX Error: File `type1cm.sty\PYGZsq{} not found.
+\end{sphinxVerbatim}
+
+You might have to install a package that contains the fonts LaTeX needs.  On some systems, the packages \sphinxcode{\sphinxupquote{texlive\sphinxhyphen{}latex\sphinxhyphen{}extra}} or \sphinxcode{\sphinxupquote{cm\sphinxhyphen{}super}} might be what you need.  \sphinxhref{https://stackoverflow.com/questions/11354149/python-unable-to-render-tex-in-matplotlib}{See here for more help with this}.
+
+In case you are curious, \sphinxcode{\sphinxupquote{cm}} stands for \sphinxhref{https://en.wikipedia.org/wiki/Computer\_Modern}{Computer Modern}, the font LaTeX uses to typeset math.
+
+
+\section{Multiple panels}
+\label{\detokenize{07_plot:multiple-panels}}
+So far we’ve been working with one figure at a time, but the figure we are replicating contains multiple panels, also known as “subplots�.
+
+Confusingly, Matplotlib provides \sphinxstyleemphasis{three} functions for making figures like this: \sphinxcode{\sphinxupquote{subplot}}, \sphinxcode{\sphinxupquote{subplots}}, and \sphinxcode{\sphinxupquote{subplot2grid}}.
+\begin{itemize}
+\item {} 
+\sphinxhref{https://matplotlib.org/3.3.1/api/\_as\_gen/matplotlib.pyplot.subplot.html}{\sphinxcode{\sphinxupquote{subplot}}} is simple and similar to MATLAB, so if you are familiar with that interface, you might like \sphinxcode{\sphinxupquote{subplot}}
+
+\item {} 
+\sphinxhref{https://matplotlib.org/3.3.1/api/\_as\_gen/matplotlib.pyplot.subplots.html}{\sphinxcode{\sphinxupquote{subplots}}} is more object\sphinxhyphen{}oriented, which some people prefer.
+
+\item {} 
+\sphinxhref{https://matplotlib.org/3.3.1/api/\_as\_gen/matplotlib.pyplot.subplot2grid.html}{\sphinxcode{\sphinxupquote{subplot2grid}}} is most convenient if you want to control the relative sizes of the subplots.
+
+\end{itemize}
+
+So we’ll use \sphinxcode{\sphinxupquote{subplot2grid}}.
+
+All of these functions are easier to use if we put the code that generates each panel in a function.
+
+
+\section{Upper right}
+\label{\detokenize{07_plot:upper-right}}
+To make the panel in the upper right, we have to reload \sphinxcode{\sphinxupquote{centerline}}.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{k+kn}{import} \PYG{n+nn}{os}
+
+\PYG{n}{filename} \PYG{o}{=} \PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{gd1\PYGZus{}dataframe.hdf5}\PYG{l+s+s1}{\PYGZsq{}}
+\PYG{n}{path} \PYG{o}{=} \PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{https://github.com/AllenDowney/AstronomicalData/raw/main/data/}\PYG{l+s+s1}{\PYGZsq{}}
+
+\PYG{k}{if} \PYG{o+ow}{not} \PYG{n}{os}\PYG{o}{.}\PYG{n}{path}\PYG{o}{.}\PYG{n}{exists}\PYG{p}{(}\PYG{n}{filename}\PYG{p}{)}\PYG{p}{:}
+    \PYG{n+nb}{print}\PYG{p}{(}\PYG{n}{download}\PYG{p}{(}\PYG{n}{path}\PYG{o}{+}\PYG{n}{filename}\PYG{p}{)}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{k+kn}{import} \PYG{n+nn}{pandas} \PYG{k}{as} \PYG{n+nn}{pd}
+
+\PYG{n}{centerline} \PYG{o}{=} \PYG{n}{pd}\PYG{o}{.}\PYG{n}{read\PYGZus{}hdf}\PYG{p}{(}\PYG{n}{filename}\PYG{p}{,} \PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{centerline}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+And define the coordinates of the rectangle we selected.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{pm1\PYGZus{}min} \PYG{o}{=} \PYG{o}{\PYGZhy{}}\PYG{l+m+mf}{8.9}
+\PYG{n}{pm1\PYGZus{}max} \PYG{o}{=} \PYG{o}{\PYGZhy{}}\PYG{l+m+mf}{6.9}
+\PYG{n}{pm2\PYGZus{}min} \PYG{o}{=} \PYG{o}{\PYGZhy{}}\PYG{l+m+mf}{2.2}
+\PYG{n}{pm2\PYGZus{}max} \PYG{o}{=}  \PYG{l+m+mf}{1.0}
+
+\PYG{n}{pm1\PYGZus{}rect} \PYG{o}{=} \PYG{p}{[}\PYG{n}{pm1\PYGZus{}min}\PYG{p}{,} \PYG{n}{pm1\PYGZus{}min}\PYG{p}{,} \PYG{n}{pm1\PYGZus{}max}\PYG{p}{,} \PYG{n}{pm1\PYGZus{}max}\PYG{p}{]}
+\PYG{n}{pm2\PYGZus{}rect} \PYG{o}{=} \PYG{p}{[}\PYG{n}{pm2\PYGZus{}min}\PYG{p}{,} \PYG{n}{pm2\PYGZus{}max}\PYG{p}{,} \PYG{n}{pm2\PYGZus{}max}\PYG{p}{,} \PYG{n}{pm2\PYGZus{}min}\PYG{p}{]}
+\end{sphinxVerbatim}
+
+To plot this rectangle, we’ll use a feature we have not seen before: \sphinxcode{\sphinxupquote{Polygon}}, which is provided by Matplotlib.
+
+To create a \sphinxcode{\sphinxupquote{Polygon}}, we have to put the coordinates in an array with \sphinxcode{\sphinxupquote{x}} values in the first column and \sphinxcode{\sphinxupquote{y}} values in the second column.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{k+kn}{import} \PYG{n+nn}{numpy} \PYG{k}{as} \PYG{n+nn}{np}
+
+\PYG{n}{vertices} \PYG{o}{=} \PYG{n}{np}\PYG{o}{.}\PYG{n}{transpose}\PYG{p}{(}\PYG{p}{[}\PYG{n}{pm1\PYGZus{}rect}\PYG{p}{,} \PYG{n}{pm2\PYGZus{}rect}\PYG{p}{]}\PYG{p}{)}
+\PYG{n}{vertices}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+array([[\PYGZhy{}8.9, \PYGZhy{}2.2],
+       [\PYGZhy{}8.9,  1. ],
+       [\PYGZhy{}6.9,  1. ],
+       [\PYGZhy{}6.9, \PYGZhy{}2.2]])
+\end{sphinxVerbatim}
+
+The following function takes a \sphinxcode{\sphinxupquote{DataFrame}} as a parameter, plots the proper motion for each star, and adds a shaded \sphinxcode{\sphinxupquote{Polygon}} to show the region we selected.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{k+kn}{from} \PYG{n+nn}{matplotlib}\PYG{n+nn}{.}\PYG{n+nn}{patches} \PYG{k+kn}{import} \PYG{n}{Polygon}
+
+\PYG{k}{def} \PYG{n+nf}{plot\PYGZus{}proper\PYGZus{}motion}\PYG{p}{(}\PYG{n}{df}\PYG{p}{)}\PYG{p}{:}
+    \PYG{n}{pm1} \PYG{o}{=} \PYG{n}{df}\PYG{p}{[}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{pm\PYGZus{}phi1}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{]}
+    \PYG{n}{pm2} \PYG{o}{=} \PYG{n}{df}\PYG{p}{[}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{pm\PYGZus{}phi2}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{]}
+
+    \PYG{n}{plt}\PYG{o}{.}\PYG{n}{plot}\PYG{p}{(}\PYG{n}{pm1}\PYG{p}{,} \PYG{n}{pm2}\PYG{p}{,} \PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{ko}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{,} \PYG{n}{markersize}\PYG{o}{=}\PYG{l+m+mf}{0.3}\PYG{p}{,} \PYG{n}{alpha}\PYG{o}{=}\PYG{l+m+mf}{0.3}\PYG{p}{)}
+    
+    \PYG{n}{poly} \PYG{o}{=} \PYG{n}{Polygon}\PYG{p}{(}\PYG{n}{vertices}\PYG{p}{,} \PYG{n}{closed}\PYG{o}{=}\PYG{k+kc}{True}\PYG{p}{,} 
+                   \PYG{n}{facecolor}\PYG{o}{=}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{C1}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{,} \PYG{n}{alpha}\PYG{o}{=}\PYG{l+m+mf}{0.4}\PYG{p}{)}
+    \PYG{n}{plt}\PYG{o}{.}\PYG{n}{gca}\PYG{p}{(}\PYG{p}{)}\PYG{o}{.}\PYG{n}{add\PYGZus{}patch}\PYG{p}{(}\PYG{n}{poly}\PYG{p}{)}
+    
+    \PYG{n}{plt}\PYG{o}{.}\PYG{n}{xlabel}\PYG{p}{(}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{\PYGZdl{}}\PYG{l+s+s1}{\PYGZbs{}}\PYG{l+s+s1}{mu\PYGZus{}}\PYG{l+s+s1}{\PYGZob{}}\PYG{l+s+s1}{\PYGZbs{}}\PYG{l+s+s1}{phi\PYGZus{}1\PYGZcb{} [}\PYG{l+s+s1}{\PYGZbs{}}\PYG{l+s+s1}{mathrm}\PYG{l+s+s1}{\PYGZob{}}\PYG{l+s+s1}{mas\PYGZti{}yr\PYGZcb{}\PYGZca{}}\PYG{l+s+s1}{\PYGZob{}}\PYG{l+s+s1}{\PYGZhy{}1\PYGZcb{}]\PYGZdl{}}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{)}
+    \PYG{n}{plt}\PYG{o}{.}\PYG{n}{ylabel}\PYG{p}{(}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{\PYGZdl{}}\PYG{l+s+s1}{\PYGZbs{}}\PYG{l+s+s1}{mu\PYGZus{}}\PYG{l+s+s1}{\PYGZob{}}\PYG{l+s+s1}{\PYGZbs{}}\PYG{l+s+s1}{phi\PYGZus{}2\PYGZcb{} [}\PYG{l+s+s1}{\PYGZbs{}}\PYG{l+s+s1}{mathrm}\PYG{l+s+s1}{\PYGZob{}}\PYG{l+s+s1}{mas\PYGZti{}yr\PYGZcb{}\PYGZca{}}\PYG{l+s+s1}{\PYGZob{}}\PYG{l+s+s1}{\PYGZhy{}1\PYGZcb{}]\PYGZdl{}}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{)}
+
+    \PYG{n}{plt}\PYG{o}{.}\PYG{n}{xlim}\PYG{p}{(}\PYG{o}{\PYGZhy{}}\PYG{l+m+mi}{12}\PYG{p}{,} \PYG{l+m+mi}{8}\PYG{p}{)}
+    \PYG{n}{plt}\PYG{o}{.}\PYG{n}{ylim}\PYG{p}{(}\PYG{o}{\PYGZhy{}}\PYG{l+m+mi}{10}\PYG{p}{,} \PYG{l+m+mi}{10}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+Notice that \sphinxcode{\sphinxupquote{add\_patch}} is like \sphinxcode{\sphinxupquote{invert\_yaxis}}; in order to call it, we have to use \sphinxcode{\sphinxupquote{gca}} to get the current axes.
+
+Here’s what the new version of the figure looks like.  We’ve changed the labels on the axes to be consistent with the paper.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{plt}\PYG{o}{.}\PYG{n}{rcParams}\PYG{p}{[}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{text.usetex}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{]} \PYG{o}{=} \PYG{k+kc}{False}
+\PYG{n}{plt}\PYG{o}{.}\PYG{n}{style}\PYG{o}{.}\PYG{n}{use}\PYG{p}{(}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{default}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{)}
+
+\PYG{n}{plot\PYGZus{}proper\PYGZus{}motion}\PYG{p}{(}\PYG{n}{centerline}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+\noindent\sphinxincludegraphics{{07_plot_50_0}.png}
+
+
+\section{Upper left}
+\label{\detokenize{07_plot:upper-left}}
+Now let’s work on the panel in the upper left. We have to reload \sphinxcode{\sphinxupquote{candidates}}.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{k+kn}{import} \PYG{n+nn}{os}
+
+\PYG{n}{filename} \PYG{o}{=} \PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{gd1\PYGZus{}candidates.hdf5}\PYG{l+s+s1}{\PYGZsq{}}
+\PYG{n}{path} \PYG{o}{=} \PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{https://github.com/AllenDowney/AstronomicalData/raw/main/data/}\PYG{l+s+s1}{\PYGZsq{}}
+
+\PYG{k}{if} \PYG{o+ow}{not} \PYG{n}{os}\PYG{o}{.}\PYG{n}{path}\PYG{o}{.}\PYG{n}{exists}\PYG{p}{(}\PYG{n}{filename}\PYG{p}{)}\PYG{p}{:}
+    \PYG{n+nb}{print}\PYG{p}{(}\PYG{n}{download}\PYG{p}{(}\PYG{n}{path}\PYG{o}{+}\PYG{n}{filename}\PYG{p}{)}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{k+kn}{import} \PYG{n+nn}{pandas} \PYG{k}{as} \PYG{n+nn}{pd}
+
+\PYG{n}{filename} \PYG{o}{=} \PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{gd1\PYGZus{}candidates.hdf5}\PYG{l+s+s1}{\PYGZsq{}}
+
+\PYG{n}{candidate\PYGZus{}df} \PYG{o}{=} \PYG{n}{pd}\PYG{o}{.}\PYG{n}{read\PYGZus{}hdf}\PYG{p}{(}\PYG{n}{filename}\PYG{p}{,} \PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{candidate\PYGZus{}df}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+Here’s a function that takes a \sphinxcode{\sphinxupquote{DataFrame}} of candidate stars and plots their positions in GD\sphinxhyphen{}1 coordindates.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{k}{def} \PYG{n+nf}{plot\PYGZus{}first\PYGZus{}selection}\PYG{p}{(}\PYG{n}{df}\PYG{p}{)}\PYG{p}{:}
+    \PYG{n}{x} \PYG{o}{=} \PYG{n}{df}\PYG{p}{[}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{phi1}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{]}
+    \PYG{n}{y} \PYG{o}{=} \PYG{n}{df}\PYG{p}{[}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{phi2}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{]}
+
+    \PYG{n}{plt}\PYG{o}{.}\PYG{n}{plot}\PYG{p}{(}\PYG{n}{x}\PYG{p}{,} \PYG{n}{y}\PYG{p}{,} \PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{ko}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{,} \PYG{n}{markersize}\PYG{o}{=}\PYG{l+m+mf}{0.3}\PYG{p}{,} \PYG{n}{alpha}\PYG{o}{=}\PYG{l+m+mf}{0.3}\PYG{p}{)}
+
+    \PYG{n}{plt}\PYG{o}{.}\PYG{n}{xlabel}\PYG{p}{(}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{\PYGZdl{}}\PYG{l+s+s1}{\PYGZbs{}}\PYG{l+s+s1}{phi\PYGZus{}1\PYGZdl{} [deg]}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{)}
+    \PYG{n}{plt}\PYG{o}{.}\PYG{n}{ylabel}\PYG{p}{(}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{\PYGZdl{}}\PYG{l+s+s1}{\PYGZbs{}}\PYG{l+s+s1}{phi\PYGZus{}2\PYGZdl{} [deg]}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{)}
+    \PYG{n}{plt}\PYG{o}{.}\PYG{n}{title}\PYG{p}{(}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{Proper motion selection}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{,} \PYG{n}{fontsize}\PYG{o}{=}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{medium}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{)}
+
+    \PYG{n}{plt}\PYG{o}{.}\PYG{n}{axis}\PYG{p}{(}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{equal}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+And here’s what it looks like.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{plot\PYGZus{}first\PYGZus{}selection}\PYG{p}{(}\PYG{n}{candidate\PYGZus{}df}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+\noindent\sphinxincludegraphics{{07_plot_57_0}.png}
+
+
+\section{Lower right}
+\label{\detokenize{07_plot:lower-right}}
+For the figure in the lower right, we need to reload the merged \sphinxcode{\sphinxupquote{DataFrame}}, which contains data from Gaia and photometry data from Pan\sphinxhyphen{}STARRS.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{k+kn}{import} \PYG{n+nn}{pandas} \PYG{k}{as} \PYG{n+nn}{pd}
+
+\PYG{n}{filename} \PYG{o}{=} \PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{gd1\PYGZus{}merged.hdf5}\PYG{l+s+s1}{\PYGZsq{}}
+
+\PYG{n}{merged} \PYG{o}{=} \PYG{n}{pd}\PYG{o}{.}\PYG{n}{read\PYGZus{}hdf}\PYG{p}{(}\PYG{n}{filename}\PYG{p}{,} \PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{merged}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+From the previous notebook, here’s the function that plots the color\sphinxhyphen{}magnitude diagram.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{k+kn}{import} \PYG{n+nn}{matplotlib}\PYG{n+nn}{.}\PYG{n+nn}{pyplot} \PYG{k}{as} \PYG{n+nn}{plt}
+
+\PYG{k}{def} \PYG{n+nf}{plot\PYGZus{}cmd}\PYG{p}{(}\PYG{n}{table}\PYG{p}{)}\PYG{p}{:}
+    \PYG{l+s+sd}{\PYGZdq{}\PYGZdq{}\PYGZdq{}Plot a color magnitude diagram.}
+\PYG{l+s+sd}{    }
+\PYG{l+s+sd}{    table: Table or DataFrame with photometry data}
+\PYG{l+s+sd}{    \PYGZdq{}\PYGZdq{}\PYGZdq{}}
+    \PYG{n}{y} \PYG{o}{=} \PYG{n}{table}\PYG{p}{[}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{g\PYGZus{}mean\PYGZus{}psf\PYGZus{}mag}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{]}
+    \PYG{n}{x} \PYG{o}{=} \PYG{n}{table}\PYG{p}{[}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{g\PYGZus{}mean\PYGZus{}psf\PYGZus{}mag}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{]} \PYG{o}{\PYGZhy{}} \PYG{n}{table}\PYG{p}{[}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{i\PYGZus{}mean\PYGZus{}psf\PYGZus{}mag}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{]}
+
+    \PYG{n}{plt}\PYG{o}{.}\PYG{n}{plot}\PYG{p}{(}\PYG{n}{x}\PYG{p}{,} \PYG{n}{y}\PYG{p}{,} \PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{ko}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{,} \PYG{n}{markersize}\PYG{o}{=}\PYG{l+m+mf}{0.3}\PYG{p}{,} \PYG{n}{alpha}\PYG{o}{=}\PYG{l+m+mf}{0.3}\PYG{p}{)}
+
+    \PYG{n}{plt}\PYG{o}{.}\PYG{n}{xlim}\PYG{p}{(}\PYG{p}{[}\PYG{l+m+mi}{0}\PYG{p}{,} \PYG{l+m+mf}{1.5}\PYG{p}{]}\PYG{p}{)}
+    \PYG{n}{plt}\PYG{o}{.}\PYG{n}{ylim}\PYG{p}{(}\PYG{p}{[}\PYG{l+m+mi}{14}\PYG{p}{,} \PYG{l+m+mi}{22}\PYG{p}{]}\PYG{p}{)}
+    \PYG{n}{plt}\PYG{o}{.}\PYG{n}{gca}\PYG{p}{(}\PYG{p}{)}\PYG{o}{.}\PYG{n}{invert\PYGZus{}yaxis}\PYG{p}{(}\PYG{p}{)}
+
+    \PYG{n}{plt}\PYG{o}{.}\PYG{n}{ylabel}\PYG{p}{(}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{\PYGZdl{}g\PYGZus{}0\PYGZdl{}}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{)}
+    \PYG{n}{plt}\PYG{o}{.}\PYG{n}{xlabel}\PYG{p}{(}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{\PYGZdl{}(g\PYGZhy{}i)\PYGZus{}0\PYGZdl{}}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+And here’s what it looks like.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{plot\PYGZus{}cmd}\PYG{p}{(}\PYG{n}{merged}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+\noindent\sphinxincludegraphics{{07_plot_63_0}.png}
+
+\sphinxstylestrong{Exercise:} Add a few lines to \sphinxcode{\sphinxupquote{plot\_cmd}} to show the Polygon we selected as a shaded area.
+
+Run these cells to get the polygon coordinates we saved in the previous notebook.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{k+kn}{import} \PYG{n+nn}{os}
+
+\PYG{n}{filename} \PYG{o}{=} \PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{gd1\PYGZus{}polygon.hdf5}\PYG{l+s+s1}{\PYGZsq{}}
+\PYG{n}{path} \PYG{o}{=} \PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{https://github.com/AllenDowney/AstronomicalData/raw/main/data/}\PYG{l+s+s1}{\PYGZsq{}}
+
+\PYG{k}{if} \PYG{o+ow}{not} \PYG{n}{os}\PYG{o}{.}\PYG{n}{path}\PYG{o}{.}\PYG{n}{exists}\PYG{p}{(}\PYG{n}{filename}\PYG{p}{)}\PYG{p}{:}
+    \PYG{n+nb}{print}\PYG{p}{(}\PYG{n}{download}\PYG{p}{(}\PYG{n}{path}\PYG{o}{+}\PYG{n}{filename}\PYG{p}{)}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{coords\PYGZus{}df} \PYG{o}{=} \PYG{n}{pd}\PYG{o}{.}\PYG{n}{read\PYGZus{}hdf}\PYG{p}{(}\PYG{n}{filename}\PYG{p}{,} \PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{coords\PYGZus{}df}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{)}
+\PYG{n}{coords} \PYG{o}{=} \PYG{n}{coords\PYGZus{}df}\PYG{o}{.}\PYG{n}{to\PYGZus{}numpy}\PYG{p}{(}\PYG{p}{)}
+\PYG{n}{coords}
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+array([[ 0.21505376, 17.5481972 ],
+       [ 0.38978495, 18.94628403],
+       [ 0.53763441, 19.90286976],
+       [ 0.70340502, 20.60191317],
+       [ 0.82885305, 21.30095659],
+       [ 0.66308244, 21.52170714],
+       [ 0.43010753, 20.78587196],
+       [ 0.27329749, 19.71891096],
+       [ 0.17473118, 18.68874172],
+       [ 0.17473118, 17.95290655]])
+\end{sphinxVerbatim}
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{c+c1}{\PYGZsh{} Solution}
+
+\PYG{c+c1}{\PYGZsh{}poly = Polygon(coords, closed=True, }
+\PYG{c+c1}{\PYGZsh{}               facecolor=\PYGZsq{}C1\PYGZsq{}, alpha=0.4)}
+\PYG{c+c1}{\PYGZsh{}plt.gca().add\PYGZus{}patch(poly)}
+\end{sphinxVerbatim}
+
+
+\section{Subplots}
+\label{\detokenize{07_plot:subplots}}
+Now we’re ready to put it all together.  To make a figure with four subplots, we’ll use \sphinxcode{\sphinxupquote{subplot2grid}}, \sphinxhref{https://matplotlib.org/3.3.1/api/\_as\_gen/matplotlib.pyplot.subplot2grid.html}{which requires two arguments}:
+\begin{itemize}
+\item {} 
+\sphinxcode{\sphinxupquote{shape}}, which is a tuple with the number of rows and columns in the grid, and
+
+\item {} 
+\sphinxcode{\sphinxupquote{loc}}, which is a tuple identifying the location in the grid we’re about to fill.
+
+\end{itemize}
+
+In this example, \sphinxcode{\sphinxupquote{shape}} is \sphinxcode{\sphinxupquote{(2, 2)}} to create two rows and two columns.
+
+For the first panel, \sphinxcode{\sphinxupquote{loc}} is \sphinxcode{\sphinxupquote{(0, 0)}}, which indicates row 0 and column 0, which is the upper\sphinxhyphen{}left panel.
+
+Here’s how we use it to draw the four panels.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{shape} \PYG{o}{=} \PYG{p}{(}\PYG{l+m+mi}{2}\PYG{p}{,} \PYG{l+m+mi}{2}\PYG{p}{)}
+\PYG{n}{plt}\PYG{o}{.}\PYG{n}{subplot2grid}\PYG{p}{(}\PYG{n}{shape}\PYG{p}{,} \PYG{p}{(}\PYG{l+m+mi}{0}\PYG{p}{,} \PYG{l+m+mi}{0}\PYG{p}{)}\PYG{p}{)}
+\PYG{n}{plot\PYGZus{}first\PYGZus{}selection}\PYG{p}{(}\PYG{n}{candidate\PYGZus{}df}\PYG{p}{)}
+
+\PYG{n}{plt}\PYG{o}{.}\PYG{n}{subplot2grid}\PYG{p}{(}\PYG{n}{shape}\PYG{p}{,} \PYG{p}{(}\PYG{l+m+mi}{0}\PYG{p}{,} \PYG{l+m+mi}{1}\PYG{p}{)}\PYG{p}{)}
+\PYG{n}{plot\PYGZus{}proper\PYGZus{}motion}\PYG{p}{(}\PYG{n}{centerline}\PYG{p}{)}
+
+\PYG{n}{plt}\PYG{o}{.}\PYG{n}{subplot2grid}\PYG{p}{(}\PYG{n}{shape}\PYG{p}{,} \PYG{p}{(}\PYG{l+m+mi}{1}\PYG{p}{,} \PYG{l+m+mi}{0}\PYG{p}{)}\PYG{p}{)}
+\PYG{n}{plot\PYGZus{}second\PYGZus{}selection}\PYG{p}{(}\PYG{n}{selected}\PYG{p}{)}
+
+\PYG{n}{plt}\PYG{o}{.}\PYG{n}{subplot2grid}\PYG{p}{(}\PYG{n}{shape}\PYG{p}{,} \PYG{p}{(}\PYG{l+m+mi}{1}\PYG{p}{,} \PYG{l+m+mi}{1}\PYG{p}{)}\PYG{p}{)}
+\PYG{n}{plot\PYGZus{}cmd}\PYG{p}{(}\PYG{n}{merged}\PYG{p}{)}
+\PYG{n}{poly} \PYG{o}{=} \PYG{n}{Polygon}\PYG{p}{(}\PYG{n}{coords}\PYG{p}{,} \PYG{n}{closed}\PYG{o}{=}\PYG{k+kc}{True}\PYG{p}{,} 
+               \PYG{n}{facecolor}\PYG{o}{=}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{C1}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{,} \PYG{n}{alpha}\PYG{o}{=}\PYG{l+m+mf}{0.4}\PYG{p}{)}
+\PYG{n}{plt}\PYG{o}{.}\PYG{n}{gca}\PYG{p}{(}\PYG{p}{)}\PYG{o}{.}\PYG{n}{add\PYGZus{}patch}\PYG{p}{(}\PYG{n}{poly}\PYG{p}{)}
+
+\PYG{n}{plt}\PYG{o}{.}\PYG{n}{tight\PYGZus{}layout}\PYG{p}{(}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+\noindent\sphinxincludegraphics{{07_plot_69_0}.png}
+
+We use \sphinxhref{https://matplotlib.org/3.3.1/tutorials/intermediate/tight\_layout\_guide.html}{\sphinxcode{\sphinxupquote{plt.tight\_layout}}} at the end, which adjusts the sizes of the panels to make sure the titles and axis labels don’t overlap.
+
+\sphinxstylestrong{Exercise:} See what happens if you leave out \sphinxcode{\sphinxupquote{tight\_layout}}.
+
+
+\section{Adjusting proportions}
+\label{\detokenize{07_plot:adjusting-proportions}}
+In the previous figure, the panels are all the same size.  To get a better view of GD\sphinxhyphen{}1, we’d like to stretch the panels on the left and compress the ones on the right.
+
+To do that, we’ll use the \sphinxcode{\sphinxupquote{colspan}} argument to make a panel that spans multiple columns in the grid.
+
+In the following example, \sphinxcode{\sphinxupquote{shape}} is \sphinxcode{\sphinxupquote{(2, 4)}}, which means 2 rows and 4 columns.
+
+The panels on the left span three columns, so they are three times wider than the panels on the right.
+
+At the same time, we use \sphinxcode{\sphinxupquote{figsize}} to adjust the aspect ratio of the whole figure.
+
+\begin{sphinxVerbatim}[commandchars=\\\{\}]
+\PYG{n}{plt}\PYG{o}{.}\PYG{n}{figure}\PYG{p}{(}\PYG{n}{figsize}\PYG{o}{=}\PYG{p}{(}\PYG{l+m+mi}{9}\PYG{p}{,} \PYG{l+m+mf}{4.5}\PYG{p}{)}\PYG{p}{)}
+
+\PYG{n}{shape} \PYG{o}{=} \PYG{p}{(}\PYG{l+m+mi}{2}\PYG{p}{,} \PYG{l+m+mi}{4}\PYG{p}{)}
+\PYG{n}{plt}\PYG{o}{.}\PYG{n}{subplot2grid}\PYG{p}{(}\PYG{n}{shape}\PYG{p}{,} \PYG{p}{(}\PYG{l+m+mi}{0}\PYG{p}{,} \PYG{l+m+mi}{0}\PYG{p}{)}\PYG{p}{,} \PYG{n}{colspan}\PYG{o}{=}\PYG{l+m+mi}{3}\PYG{p}{)}
+\PYG{n}{plot\PYGZus{}first\PYGZus{}selection}\PYG{p}{(}\PYG{n}{candidate\PYGZus{}df}\PYG{p}{)}
+
+\PYG{n}{plt}\PYG{o}{.}\PYG{n}{subplot2grid}\PYG{p}{(}\PYG{n}{shape}\PYG{p}{,} \PYG{p}{(}\PYG{l+m+mi}{0}\PYG{p}{,} \PYG{l+m+mi}{3}\PYG{p}{)}\PYG{p}{)}
+\PYG{n}{plot\PYGZus{}proper\PYGZus{}motion}\PYG{p}{(}\PYG{n}{centerline}\PYG{p}{)}
+
+\PYG{n}{plt}\PYG{o}{.}\PYG{n}{subplot2grid}\PYG{p}{(}\PYG{n}{shape}\PYG{p}{,} \PYG{p}{(}\PYG{l+m+mi}{1}\PYG{p}{,} \PYG{l+m+mi}{0}\PYG{p}{)}\PYG{p}{,} \PYG{n}{colspan}\PYG{o}{=}\PYG{l+m+mi}{3}\PYG{p}{)}
+\PYG{n}{plot\PYGZus{}second\PYGZus{}selection}\PYG{p}{(}\PYG{n}{selected}\PYG{p}{)}
+
+\PYG{n}{plt}\PYG{o}{.}\PYG{n}{subplot2grid}\PYG{p}{(}\PYG{n}{shape}\PYG{p}{,} \PYG{p}{(}\PYG{l+m+mi}{1}\PYG{p}{,} \PYG{l+m+mi}{3}\PYG{p}{)}\PYG{p}{)}
+\PYG{n}{plot\PYGZus{}cmd}\PYG{p}{(}\PYG{n}{merged}\PYG{p}{)}
+\PYG{n}{poly} \PYG{o}{=} \PYG{n}{Polygon}\PYG{p}{(}\PYG{n}{coords}\PYG{p}{,} \PYG{n}{closed}\PYG{o}{=}\PYG{k+kc}{True}\PYG{p}{,} 
+               \PYG{n}{facecolor}\PYG{o}{=}\PYG{l+s+s1}{\PYGZsq{}}\PYG{l+s+s1}{C1}\PYG{l+s+s1}{\PYGZsq{}}\PYG{p}{,} \PYG{n}{alpha}\PYG{o}{=}\PYG{l+m+mf}{0.4}\PYG{p}{)}
+\PYG{n}{plt}\PYG{o}{.}\PYG{n}{gca}\PYG{p}{(}\PYG{p}{)}\PYG{o}{.}\PYG{n}{add\PYGZus{}patch}\PYG{p}{(}\PYG{n}{poly}\PYG{p}{)}
+
+\PYG{n}{plt}\PYG{o}{.}\PYG{n}{tight\PYGZus{}layout}\PYG{p}{(}\PYG{p}{)}
+\end{sphinxVerbatim}
+
+\noindent\sphinxincludegraphics{{07_plot_72_0}.png}
+
+This is looking more and more like the figure in the paper.
+
+\sphinxstylestrong{Exercise:} In this example, the ratio of the widths of the panels is 3:1.  How would you adjust it if you wanted the ratio to be 3:2?
+
+
+\section{Summary}
+\label{\detokenize{07_plot:summary}}
+In this notebook, we reverse\sphinxhyphen{}engineered the figure we’ve been replicating, identifying elements that seem effective and others that could be improved.
+
+We explored features Matplotlib provides for adding annotations to figures \textendash{} including text, lines, arrows, and polygons \textendash{} and several ways to customize the appearance of figures.  And we learned how to create figures that contain multiple panels.
+
+
+\section{Best practices}
+\label{\detokenize{07_plot:best-practices}}\begin{itemize}
+\item {} 
+The most effective figures focus on telling a single story clearly and compellingly.
+
+\item {} 
+Consider using annotations to guide the readers attention to the most important elements of a figure.
+
+\item {} 
+The default Matplotlib style generates good quality figures, but there are several ways you can override the defaults.
+
+\item {} 
+If you find yourself making the same customizations on several projects, you might want to create your own style sheet.
+
+\end{itemize}
+
+
+
+
+
+
+
+\renewcommand{\indexname}{Index}
+\printindex
+\end{document}
\ No newline at end of file
diff --git a/_build/latex/book.toc b/_build/latex/book.toc
new file mode 100644
index 0000000..9a5dd64
--- /dev/null
+++ b/_build/latex/book.toc
@@ -0,0 +1,99 @@
+\select@language {english}
+\contentsline {chapter}{\numberline {1}Chapter 1}{5}{chapter.1}
+\contentsline {section}{\numberline {1.1}Data}{5}{section.1.1}
+\contentsline {section}{\numberline {1.2}Prerequisites}{6}{section.1.2}
+\contentsline {section}{\numberline {1.3}Outline}{6}{section.1.3}
+\contentsline {section}{\numberline {1.4}Query Language}{6}{section.1.4}
+\contentsline {section}{\numberline {1.5}Installing libraries}{7}{section.1.5}
+\contentsline {section}{\numberline {1.6}Connecting to Gaia}{7}{section.1.6}
+\contentsline {section}{\numberline {1.7}Databases and Tables}{7}{section.1.7}
+\contentsline {section}{\numberline {1.8}Columns}{10}{section.1.8}
+\contentsline {section}{\numberline {1.9}Writing queries}{13}{section.1.9}
+\contentsline {section}{\numberline {1.10}Asynchronous queries}{15}{section.1.10}
+\contentsline {section}{\numberline {1.11}Operators}{17}{section.1.11}
+\contentsline {section}{\numberline {1.12}Cleaning up}{18}{section.1.12}
+\contentsline {section}{\numberline {1.13}Formatting queries}{18}{section.1.13}
+\contentsline {section}{\numberline {1.14}Summary}{21}{section.1.14}
+\contentsline {section}{\numberline {1.15}Best practices}{21}{section.1.15}
+\contentsline {chapter}{\numberline {2}Chapter 2}{23}{chapter.2}
+\contentsline {section}{\numberline {2.1}Outline}{23}{section.2.1}
+\contentsline {section}{\numberline {2.2}Installing libraries}{23}{section.2.2}
+\contentsline {section}{\numberline {2.3}Selecting a region}{24}{section.2.3}
+\contentsline {section}{\numberline {2.4}Getting GD\sphinxhyphen {}1 Data}{25}{section.2.4}
+\contentsline {section}{\numberline {2.5}Working with coordinates}{25}{section.2.5}
+\contentsline {section}{\numberline {2.6}Selecting a rectangle}{44}{section.2.6}
+\contentsline {section}{\numberline {2.7}Selecting a polygon}{45}{section.2.7}
+\contentsline {section}{\numberline {2.8}Saving results}{47}{section.2.8}
+\contentsline {section}{\numberline {2.9}Summary}{48}{section.2.9}
+\contentsline {section}{\numberline {2.10}Best practices}{48}{section.2.10}
+\contentsline {chapter}{\numberline {3}Chapter 3}{49}{chapter.3}
+\contentsline {section}{\numberline {3.1}Outline}{49}{section.3.1}
+\contentsline {section}{\numberline {3.2}Installing libraries}{50}{section.3.2}
+\contentsline {section}{\numberline {3.3}Reload the data}{50}{section.3.3}
+\contentsline {section}{\numberline {3.4}Selecting rows and columns}{51}{section.3.4}
+\contentsline {section}{\numberline {3.5}Scatter plot}{53}{section.3.5}
+\contentsline {section}{\numberline {3.6}Transform back}{54}{section.3.6}
+\contentsline {section}{\numberline {3.7}Pandas DataFrame}{57}{section.3.7}
+\contentsline {section}{\numberline {3.8}Plot proper motion}{58}{section.3.8}
+\contentsline {section}{\numberline {3.9}Selecting the centerline}{58}{section.3.9}
+\contentsline {section}{\numberline {3.10}Filtering based on proper motion}{61}{section.3.10}
+\contentsline {section}{\numberline {3.11}Saving the DataFrame}{63}{section.3.11}
+\contentsline {section}{\numberline {3.12}Summary}{65}{section.3.12}
+\contentsline {section}{\numberline {3.13}Best practices}{65}{section.3.13}
+\contentsline {chapter}{\numberline {4}Chapter 4}{67}{chapter.4}
+\contentsline {section}{\numberline {4.1}Outline}{67}{section.4.1}
+\contentsline {section}{\numberline {4.2}Installing libraries}{67}{section.4.2}
+\contentsline {section}{\numberline {4.3}Reload the data}{68}{section.4.3}
+\contentsline {section}{\numberline {4.4}Selection by proper motion}{68}{section.4.4}
+\contentsline {section}{\numberline {4.5}Selecting the region}{73}{section.4.5}
+\contentsline {section}{\numberline {4.6}Assemble the query}{74}{section.4.6}
+\contentsline {section}{\numberline {4.7}Plotting one more time}{76}{section.4.7}
+\contentsline {section}{\numberline {4.8}Saving the DataFrame}{78}{section.4.8}
+\contentsline {section}{\numberline {4.9}CSV}{79}{section.4.9}
+\contentsline {section}{\numberline {4.10}Summary}{80}{section.4.10}
+\contentsline {section}{\numberline {4.11}Best practices}{80}{section.4.11}
+\contentsline {chapter}{\numberline {5}Chapter 5}{81}{chapter.5}
+\contentsline {section}{\numberline {5.1}Outline}{81}{section.5.1}
+\contentsline {section}{\numberline {5.2}Installing libraries}{81}{section.5.2}
+\contentsline {section}{\numberline {5.3}Reloading the data}{82}{section.5.3}
+\contentsline {section}{\numberline {5.4}Getting photometry data}{83}{section.5.4}
+\contentsline {section}{\numberline {5.5}Preparing a table for uploading}{84}{section.5.5}
+\contentsline {section}{\numberline {5.6}Uploading a table}{86}{section.5.6}
+\contentsline {section}{\numberline {5.7}Joining with an uploaded table}{87}{section.5.7}
+\contentsline {section}{\numberline {5.8}Getting the photometry data}{89}{section.5.8}
+\contentsline {section}{\numberline {5.9}Write the data}{92}{section.5.9}
+\contentsline {section}{\numberline {5.10}Summary}{92}{section.5.10}
+\contentsline {section}{\numberline {5.11}Best practice}{92}{section.5.11}
+\contentsline {chapter}{\numberline {6}Chapter 6}{93}{chapter.6}
+\contentsline {section}{\numberline {6.1}Outline}{93}{section.6.1}
+\contentsline {section}{\numberline {6.2}Installing libraries}{93}{section.6.2}
+\contentsline {section}{\numberline {6.3}Reload the data}{94}{section.6.3}
+\contentsline {section}{\numberline {6.4}Plotting photometry data}{94}{section.6.4}
+\contentsline {section}{\numberline {6.5}Drawing a polygon}{96}{section.6.5}
+\contentsline {section}{\numberline {6.6}Which points are in the polygon?}{98}{section.6.6}
+\contentsline {section}{\numberline {6.7}Reloading the data}{98}{section.6.7}
+\contentsline {section}{\numberline {6.8}Merging photometry data}{99}{section.6.8}
+\contentsline {section}{\numberline {6.9}Missing data}{101}{section.6.9}
+\contentsline {section}{\numberline {6.10}Selecting based on photometry}{101}{section.6.10}
+\contentsline {section}{\numberline {6.11}Write the data}{103}{section.6.11}
+\contentsline {section}{\numberline {6.12}Save the polygon}{104}{section.6.12}
+\contentsline {section}{\numberline {6.13}Summary}{104}{section.6.13}
+\contentsline {section}{\numberline {6.14}Best practices}{104}{section.6.14}
+\contentsline {chapter}{\numberline {7}Chapter 7}{107}{chapter.7}
+\contentsline {section}{\numberline {7.1}Outline}{107}{section.7.1}
+\contentsline {section}{\numberline {7.2}Installing libraries}{107}{section.7.2}
+\contentsline {section}{\numberline {7.3}Making Figures That Tell a Story}{108}{section.7.3}
+\contentsline {section}{\numberline {7.4}Plotting GD\sphinxhyphen {}1}{109}{section.7.4}
+\contentsline {section}{\numberline {7.5}Annotations}{110}{section.7.5}
+\contentsline {section}{\numberline {7.6}Customization}{110}{section.7.6}
+\contentsline {section}{\numberline {7.7}rcParams}{111}{section.7.7}
+\contentsline {section}{\numberline {7.8}Style sheets}{111}{section.7.8}
+\contentsline {section}{\numberline {7.9}LaTeX fonts}{113}{section.7.9}
+\contentsline {section}{\numberline {7.10}Multiple panels}{113}{section.7.10}
+\contentsline {section}{\numberline {7.11}Upper right}{114}{section.7.11}
+\contentsline {section}{\numberline {7.12}Upper left}{116}{section.7.12}
+\contentsline {section}{\numberline {7.13}Lower right}{117}{section.7.13}
+\contentsline {section}{\numberline {7.14}Subplots}{119}{section.7.14}
+\contentsline {section}{\numberline {7.15}Adjusting proportions}{120}{section.7.15}
+\contentsline {section}{\numberline {7.16}Summary}{121}{section.7.16}
+\contentsline {section}{\numberline {7.17}Best practices}{122}{section.7.17}
diff --git a/_build/latex/footnotehyper-sphinx.sty b/_build/latex/footnotehyper-sphinx.sty
new file mode 100644
index 0000000..b6692cf
--- /dev/null
+++ b/_build/latex/footnotehyper-sphinx.sty
@@ -0,0 +1,269 @@
+\NeedsTeXFormat{LaTeX2e}
+\ProvidesPackage{footnotehyper-sphinx}%
+ [2017/10/27 v1.7 hyperref aware footnote.sty for sphinx (JFB)]
+%%
+%% Package: footnotehyper-sphinx
+%% Version: based on footnotehyper.sty 2017/03/07 v1.0
+%% as available at https://www.ctan.org/pkg/footnotehyper
+%% License: the one applying to Sphinx
+%%
+%% Refer to the PDF documentation  at https://www.ctan.org/pkg/footnotehyper for
+%% the code comments.
+%%
+%% Differences:
+%% 1. a partial tabulary compatibility layer added (enough for Sphinx mark-up),
+%% 2. use of \spx@opt@BeforeFootnote from sphinx.sty,
+%% 3. use of \sphinxunactivateextrasandspace from sphinx.sty,
+%% 4. macro definition \sphinxfootnotemark,
+%% 5. macro definition \sphinxlongtablepatch
+%% 6. replaced an \undefined by \@undefined
+\DeclareOption*{\PackageWarning{footnotehyper-sphinx}{Option `\CurrentOption' is unknown}}%
+\ProcessOptions\relax
+\newbox\FNH@notes
+\newdimen\FNH@width
+\let\FNH@colwidth\columnwidth
+\newif\ifFNH@savingnotes
+\AtBeginDocument {%
+    \let\FNH@latex@footnote    \footnote
+    \let\FNH@latex@footnotetext\footnotetext
+    \let\FNH@H@@footnotetext   \@footnotetext
+    \newenvironment{savenotes}
+        {\FNH@savenotes\ignorespaces}{\FNH@spewnotes\ignorespacesafterend}%
+    \let\spewnotes      \FNH@spewnotes
+    \let\footnote       \FNH@footnote
+    \let\footnotetext   \FNH@footnotetext
+    \let\endfootnote    \FNH@endfntext
+    \let\endfootnotetext\FNH@endfntext
+    \@ifpackageloaded{hyperref}
+     {\ifHy@hyperfootnotes
+         \let\FNH@H@@footnotetext\H@@footnotetext
+      \else
+         \let\FNH@hyper@fntext\FNH@nohyp@fntext
+      \fi}%
+     {\let\FNH@hyper@fntext\FNH@nohyp@fntext}%
+}%
+\def\FNH@hyper@fntext{\FNH@fntext\FNH@hyper@fntext@i}%
+\def\FNH@nohyp@fntext{\FNH@fntext\FNH@nohyp@fntext@i}%
+\def\FNH@fntext #1{%
+  \ifx\ifmeasuring@\@undefined
+    \expandafter\@secondoftwo\else\expandafter\@firstofone\fi
+% these two lines modified for Sphinx (tabulary compatibility):
+    {\ifmeasuring@\expandafter\@gobbletwo\else\expandafter\@firstofone\fi}%
+    {\ifx\equation$\expandafter\@gobbletwo\fi #1}%$
+}%
+\long\def\FNH@hyper@fntext@i#1{%
+  \global\setbox\FNH@notes\vbox
+  {\unvbox\FNH@notes
+   \FNH@startnote
+   \@makefntext
+    {\rule\z@\footnotesep\ignorespaces
+     \ifHy@nesting\expandafter\ltx@firstoftwo
+             \else\expandafter\ltx@secondoftwo
+     \fi
+     {\expandafter\hyper@@anchor\expandafter{\Hy@footnote@currentHref}{#1}}%
+     {\Hy@raisedlink
+       {\expandafter\hyper@@anchor\expandafter{\Hy@footnote@currentHref}%
+       {\relax}}%
+      \let\@currentHref\Hy@footnote@currentHref
+      \let\@currentlabelname\@empty
+      #1}%
+     \@finalstrut\strutbox
+    }%
+   \FNH@endnote
+  }%
+}%
+\long\def\FNH@nohyp@fntext@i#1{%
+  \global\setbox\FNH@notes\vbox
+  {\unvbox\FNH@notes
+   \FNH@startnote
+   \@makefntext{\rule\z@\footnotesep\ignorespaces#1\@finalstrut\strutbox}%
+   \FNH@endnote
+  }%
+}%
+\def\FNH@startnote{%
+  \hsize\FNH@colwidth
+  \interlinepenalty\interfootnotelinepenalty
+  \reset@font\footnotesize
+  \floatingpenalty\@MM
+  \@parboxrestore
+  \protected@edef\@currentlabel{\csname p@\@mpfn\endcsname\@thefnmark}%
+  \color@begingroup
+}%
+\def\FNH@endnote{\color@endgroup}%
+\def\FNH@savenotes{%
+  \begingroup
+  \ifFNH@savingnotes\else
+    \FNH@savingnotestrue
+    \let\@footnotetext    \FNH@hyper@fntext
+    \let\@mpfootnotetext  \FNH@hyper@fntext
+    \let\H@@mpfootnotetext\FNH@nohyp@fntext
+    \FNH@width\columnwidth
+    \let\FNH@colwidth\FNH@width
+    \global\setbox\FNH@notes\box\voidb@x
+    \let\FNH@thempfn\thempfn
+    \let\FNH@mpfn\@mpfn
+    \ifx\@minipagerestore\relax\let\@minipagerestore\@empty\fi
+    \expandafter\def\expandafter\@minipagerestore\expandafter{%
+      \@minipagerestore
+      \let\thempfn\FNH@thempfn
+      \let\@mpfn\FNH@mpfn
+    }%
+  \fi
+}%
+\def\FNH@spewnotes {%
+  \endgroup
+  \ifFNH@savingnotes\else
+   \ifvoid\FNH@notes\else
+    \begingroup
+     \let\@makefntext\@empty
+     \let\@finalstrut\@gobble
+     \let\rule\@gobbletwo
+     \FNH@H@@footnotetext{\unvbox\FNH@notes}%
+    \endgroup
+   \fi
+  \fi
+}%
+\def\FNH@footnote@envname    {footnote}%
+\def\FNH@footnotetext@envname{footnotetext}%
+\def\FNH@footnote{%
+% this line added for Sphinx:
+    \spx@opt@BeforeFootnote
+    \ifx\@currenvir\FNH@footnote@envname
+        \expandafter\FNH@footnoteenv
+    \else
+        \expandafter\FNH@latex@footnote
+    \fi
+}%
+\def\FNH@footnoteenv{%
+% this line added for Sphinx (footnotes in parsed literal blocks):
+    \catcode13=5 \sphinxunactivateextrasandspace
+    \@ifnextchar[%
+      \FNH@footnoteenv@i %]
+      {\stepcounter\@mpfn
+       \protected@xdef\@thefnmark{\thempfn}%
+       \@footnotemark
+       \def\FNH@endfntext@fntext{\@footnotetext}%
+       \FNH@startfntext}%
+}%
+\def\FNH@footnoteenv@i[#1]{%
+    \begingroup
+     \csname c@\@mpfn\endcsname #1\relax
+     \unrestored@protected@xdef\@thefnmark{\thempfn}%
+    \endgroup
+    \@footnotemark
+    \def\FNH@endfntext@fntext{\@footnotetext}%
+    \FNH@startfntext
+}%
+\def\FNH@footnotetext{%
+    \ifx\@currenvir\FNH@footnotetext@envname
+        \expandafter\FNH@footnotetextenv
+    \else
+        \expandafter\FNH@latex@footnotetext
+    \fi
+}%
+\def\FNH@footnotetextenv{%
+    \@ifnextchar[%
+      \FNH@footnotetextenv@i %]
+      {\protected@xdef\@thefnmark{\thempfn}%
+       \def\FNH@endfntext@fntext{\@footnotetext}%
+       \FNH@startfntext}%
+}%
+\def\FNH@footnotetextenv@i[#1]{%
+    \begingroup
+     \csname c@\@mpfn\endcsname #1\relax
+     \unrestored@protected@xdef\@thefnmark{\thempfn}%
+    \endgroup
+    \ifFNH@savingnotes
+      \def\FNH@endfntext@fntext{\FNH@nohyp@fntext}%
+    \else
+      \def\FNH@endfntext@fntext{\FNH@H@@footnotetext}%
+    \fi
+    \FNH@startfntext
+}%
+\def\FNH@startfntext{%
+  \setbox\z@\vbox\bgroup
+    \FNH@startnote
+    \FNH@prefntext
+    \rule\z@\footnotesep\ignorespaces
+}%
+\def\FNH@endfntext {%
+    \@finalstrut\strutbox
+    \FNH@postfntext
+    \FNH@endnote
+    \egroup
+  \begingroup
+    \let\@makefntext\@empty\let\@finalstrut\@gobble\let\rule\@gobbletwo
+    \FNH@endfntext@fntext {\unvbox\z@}%
+  \endgroup
+}%
+\AtBeginDocument{%
+   \let\FNH@@makefntext\@makefntext
+   \ifx\@makefntextFB\@undefined
+   \expandafter\@gobble\else\expandafter\@firstofone\fi
+   {\ifFBFrenchFootnotes \let\FNH@@makefntext\@makefntextFB \else
+                         \let\FNH@@makefntext\@makefntextORI\fi}%
+   \expandafter\FNH@check@a\FNH@@makefntext{1.2!3?4,}%
+               \FNH@@@1.2!3?4,\FNH@@@\relax
+}%
+\long\def\FNH@check@a #11.2!3?4,#2\FNH@@@#3{%
+    \ifx\relax#3\expandafter\@firstoftwo\else\expandafter\@secondoftwo\fi
+    \FNH@bad@makefntext@alert
+    {\def\FNH@prefntext{#1}\def\FNH@postfntext{#2}\FNH@check@b}%
+}%
+\def\FNH@check@b #1\relax{%
+    \expandafter\expandafter\expandafter\FNH@check@c
+    \expandafter\meaning\expandafter\FNH@prefntext
+    \meaning\FNH@postfntext1.2!3?4,\FNH@check@c\relax
+}%
+\def\FNH@check@c #11.2!3?4,#2#3\relax{%
+    \ifx\FNH@check@c#2\expandafter\@gobble\fi\FNH@bad@makefntext@alert
+}%
+% slight reformulation for Sphinx
+\def\FNH@bad@makefntext@alert{%
+  \PackageWarningNoLine{footnotehyper-sphinx}%
+    {Footnotes will be sub-optimal, sorry. This is due to the document class or^^J
+  some package modifying macro \string\@makefntext.^^J
+  You can try to report this incompatibility at^^J
+  https://github.com/sphinx-doc/sphinx with this info:}%
+    \typeout{\meaning\@makefntext}%
+    \let\FNH@prefntext\@empty\let\FNH@postfntext\@empty
+}%
+% this macro from original footnote.sty is not used anymore by Sphinx
+% but for simplicity sake let's just keep it as is
+\def\makesavenoteenv{\@ifnextchar[\FNH@msne@ii\FNH@msne@i}%]
+\def\FNH@msne@i #1{%
+  \expandafter\let\csname FNH$#1\expandafter\endcsname %$
+                  \csname #1\endcsname
+  \expandafter\let\csname endFNH$#1\expandafter\endcsname %$
+                  \csname end#1\endcsname
+  \FNH@msne@ii[#1]{FNH$#1}%$
+}%
+\def\FNH@msne@ii[#1]#2{%
+  \expandafter\edef\csname#1\endcsname{%
+    \noexpand\savenotes
+    \expandafter\noexpand\csname#2\endcsname
+  }%
+  \expandafter\edef\csname end#1\endcsname{%
+    \expandafter\noexpand\csname end#2\endcsname
+    \noexpand\expandafter
+    \noexpand\spewnotes
+    \noexpand\if@endpe\noexpand\@endpetrue\noexpand\fi
+  }%
+}%
+% end of footnotehyper 2017/02/16 v0.99
+% some extras for Sphinx :
+% \sphinxfootnotemark: usable in section titles and silently removed from TOCs.
+\def\sphinxfootnotemark [#1]%
+   {\ifx\thepage\relax\else\protect\spx@opt@BeforeFootnote
+                             \protect\footnotemark[#1]\fi}%
+\AtBeginDocument{%
+   % let hyperref less complain
+   \pdfstringdefDisableCommands{\def\sphinxfootnotemark [#1]{}}%
+   % to obtain hyperlinked footnotes in longtable environment we must replace
+   % hyperref's patch of longtable's patch of \@footnotetext by our own
+   \let\LT@p@ftntext\FNH@hyper@fntext
+   % this *requires* longtable to be used always wrapped in savenotes environment
+}%
+\endinput
+%%
+%% End of file `footnotehyper-sphinx.sty'.
diff --git a/_build/latex/index.html b/_build/latex/index.html
new file mode 100644
index 0000000..fd27117
--- /dev/null
+++ b/_build/latex/index.html
@@ -0,0 +1,2 @@
+
+<meta http-equiv="Refresh" content="0; url=README.html" />
diff --git a/_build/latex/latexmkjarc b/_build/latex/latexmkjarc
new file mode 100644
index 0000000..6e36b19
--- /dev/null
+++ b/_build/latex/latexmkjarc
@@ -0,0 +1,22 @@
+$latex = 'pdflatex ' . $ENV{'LATEXOPTS'} . ' -kanji=utf8 %O %S';
+$dvipdf = 'dvipdfmx %O -o %D %S';
+$makeindex = 'internal mendex %S %B %D';
+sub mendex {
+  my ($source, $basename, $destination) = @_;
+  my $dictfile = $basename . ".dic";
+  unlink($destination);
+  system("mendex", "-U", "-f", "-d", $dictfile, "-s", "python.ist", $source);
+  if ($? > 0) {
+    print("mendex exited with error code $? (ignored)\n");
+  }
+  if (!-e $destination) {
+    # create an empty .ind file if nothing
+    open(FH, ">" . $destination);
+    close(FH);
+  }
+  return 0;
+}
+add_cus_dep( "glo", "gls", 0, "makeglo" );
+sub makeglo {
+ return system( "mendex -J -f -s gglo.ist -o '$_[0].gls' '$_[0].glo'" );
+}
\ No newline at end of file
diff --git a/_build/latex/latexmkrc b/_build/latex/latexmkrc
new file mode 100644
index 0000000..bba17fa
--- /dev/null
+++ b/_build/latex/latexmkrc
@@ -0,0 +1,9 @@
+$latex = 'latex ' . $ENV{'LATEXOPTS'} . ' %O %S';
+$pdflatex = 'pdflatex ' . $ENV{'LATEXOPTS'} . ' %O %S';
+$lualatex = 'lualatex ' . $ENV{'LATEXOPTS'} . ' %O %S';
+$xelatex = 'xelatex --no-pdf ' . $ENV{'LATEXOPTS'} . ' %O %S';
+$makeindex = 'makeindex -s python.ist %O -o %D %S';
+add_cus_dep( "glo", "gls", 0, "makeglo" );
+sub makeglo {
+ return system( "makeindex -s gglo.ist -o '$_[0].gls' '$_[0].glo'" );
+}
\ No newline at end of file
diff --git a/_build/latex/make.bat b/_build/latex/make.bat
new file mode 100644
index 0000000..94bda21
--- /dev/null
+++ b/_build/latex/make.bat
@@ -0,0 +1,31 @@
+@ECHO OFF
+
+REM Command file for Sphinx documentation
+
+pushd %~dp0
+
+set PDFLATEX=latexmk -pdf -dvi- -ps-
+
+set "LATEXOPTS= "
+
+if "%1" == "" goto all-pdf
+
+if "%1" == "all-pdf" (
+	:all-pdf
+	for %%i in (*.tex) do (
+		%PDFLATEX% %LATEXMKOPTS% %%i
+	)
+	goto end
+)
+
+if "%1" == "all-pdf-ja" (
+	goto all-pdf
+)
+
+if "%1" == "clean" (
+	del /q /s *.dvi *.log *.ind *.aux *.toc *.syn *.idx *.out *.ilg *.pla *.ps *.tar *.tar.gz *.tar.bz2 *.tar.xz *.fls *.fdb_latexmk
+	goto end
+)
+
+:end
+popd
\ No newline at end of file
diff --git a/_build/latex/python.ist b/_build/latex/python.ist
new file mode 100644
index 0000000..70536a6
--- /dev/null
+++ b/_build/latex/python.ist
@@ -0,0 +1,16 @@
+line_max 100
+headings_flag 1
+heading_prefix "  \\bigletter "
+
+preamble "\\begin{sphinxtheindex}
+\\let\\bigletter\\sphinxstyleindexlettergroup
+\\let\\spxpagem \\sphinxstyleindexpagemain
+\\let\\spxentry \\sphinxstyleindexentry
+\\let\\spxextra \\sphinxstyleindexextra
+
+"
+
+postamble "\n\n\\end{sphinxtheindex}\n"
+
+symhead_positive "{\\sphinxsymbolsname}"
+numhead_positive "{\\sphinxnumbersname}"
diff --git a/_build/latex/sphinx.sty b/_build/latex/sphinx.sty
new file mode 100644
index 0000000..a3e91ad
--- /dev/null
+++ b/_build/latex/sphinx.sty
@@ -0,0 +1,1957 @@
+%
+% sphinx.sty
+%
+% Adapted from the old python.sty, mostly written by Fred Drake,
+% by Georg Brandl.
+%
+
+\NeedsTeXFormat{LaTeX2e}[1995/12/01]
+\ProvidesPackage{sphinx}[2019/09/02 v2.3.0 LaTeX package (Sphinx markup)]
+
+% provides \ltx@ifundefined
+% (many packages load ltxcmds: graphicx does for pdftex and lualatex but
+% not xelatex, and anyhow kvoptions does, but it may be needed in future to
+% use \sphinxdeprecationwarning earlier, and it needs \ltx@ifundefined)
+\RequirePackage{ltxcmds}
+
+%% for deprecation warnings
+\newcommand\sphinxdeprecationwarning[4]{% #1 the deprecated macro or name,
+% #2 = when deprecated, #3 = when removed, #4 = additional info
+  \edef\spx@tempa{\detokenize{#1}}%
+  \ltx@ifundefined{sphinx_depr_\spx@tempa}{%
+     \global\expandafter\let\csname sphinx_depr_\spx@tempa\endcsname\spx@tempa
+     \expandafter\AtEndDocument\expandafter{\expandafter\let\expandafter
+         \sphinxdeprecatedmacro\csname sphinx_depr_\spx@tempa\endcsname
+         \PackageWarningNoLine{sphinx}{^^J**** SPHINX DEPRECATION WARNING:^^J
+         \sphinxdeprecatedmacro^^J
+         \@spaces- is deprecated at Sphinx #2^^J
+         \@spaces- and removed at Sphinx #3.^^J
+         #4^^J****}}%
+  }{% warning already emitted (at end of latex log), don't repeat
+  }}
+
+
+%% PACKAGES
+%
+% we delay handling of options to after having loaded packages, because
+% of the need to use \definecolor.
+\RequirePackage{graphicx}
+\@ifclassloaded{memoir}{}{\RequirePackage{fancyhdr}}
+% for \text macro and \iffirstchoice@ conditional even if amsmath not loaded
+\RequirePackage{amstext}
+\RequirePackage{textcomp}% "warn" option issued from template
+\RequirePackage[nobottomtitles*]{titlesec}
+\@ifpackagelater{titlesec}{2016/03/15}%
+ {\@ifpackagelater{titlesec}{2016/03/21}%
+  {}%
+  {\newif\ifsphinx@ttlpatch@ok
+   \IfFileExists{etoolbox.sty}{%
+     \RequirePackage{etoolbox}%
+     \patchcmd{\ttlh@hang}{\parindent\z@}{\parindent\z@\leavevmode}%
+                                {\sphinx@ttlpatch@oktrue}{}%
+     \ifsphinx@ttlpatch@ok
+     \patchcmd{\ttlh@hang}{\noindent}{}{}{\sphinx@ttlpatch@okfalse}%
+     \fi
+   }{}%
+   \ifsphinx@ttlpatch@ok
+     \typeout{^^J Package Sphinx Info: ^^J
+              **** titlesec 2.10.1 successfully patched for bugfix ****^^J}%
+   \else
+     \AtEndDocument{\PackageWarningNoLine{sphinx}{^^J%
+******** titlesec 2.10.1 has a bug, (section numbers disappear) ......|^^J%
+******** and Sphinx could not patch it, perhaps because your local ...|^^J%
+******** copy is already fixed without a changed release date. .......|^^J%
+******** If not, you must update titlesec! ...........................|}}%
+   \fi
+  }%
+ }{}
+\RequirePackage{tabulary}
+% tabulary has a bug with its re-definition of \multicolumn in its first pass
+% which is not \long. But now Sphinx does not use LaTeX's \multicolumn but its
+% own macro. Hence we don't even need to patch tabulary. See sphinxmulticell.sty
+% X or S (Sphinx) may have meanings if some table package is loaded hence
+% \X was chosen to avoid possibility of conflict
+\newcolumntype{\X}[2]{p{\dimexpr
+      (\linewidth-\arrayrulewidth)*#1/#2-\tw@\tabcolsep-\arrayrulewidth\relax}}
+\newcolumntype{\Y}[1]{p{\dimexpr
+      #1\dimexpr\linewidth-\arrayrulewidth\relax-\tw@\tabcolsep-\arrayrulewidth\relax}}
+% using here T (for Tabulary) feels less of a problem than the X could be
+\newcolumntype{T}{J}%
+% For tables allowing pagebreaks
+\RequirePackage{longtable}
+% User interface to set-up whitespace before and after tables:
+\newcommand*\sphinxtablepre {0pt}%
+\newcommand*\sphinxtablepost{\medskipamount}%
+% Space from caption baseline to top of table or frame of literal-block
+\newcommand*\sphinxbelowcaptionspace{.5\sphinxbaselineskip}%
+% as one can not use \baselineskip from inside longtable (it is zero there)
+% we need \sphinxbaselineskip, which defaults to \baselineskip
+\def\sphinxbaselineskip{\baselineskip}%
+% The following is to ensure that, whether tabular(y) or longtable:
+% - if a caption is on top of table:
+%   a) the space between its last baseline and the top rule of table is
+%      exactly \sphinxbelowcaptionspace
+%   b) the space from last baseline of previous text to first baseline of
+%      caption is exactly \parskip+\baselineskip+ height of a strut.
+%   c) the caption text will wrap at width \LTcapwidth (4in)
+% - make sure this works also if "caption" package is loaded by user
+%   (with its width or margin option taking place of \LTcapwidth role)
+% TODO: obtain same for caption of literal block: a) & c) DONE, b) TO BE DONE
+%
+% To modify space below such top caption, adjust \sphinxbelowcaptionspace
+% To add or remove space above such top caption, adjust \sphinxtablepre:
+%   notice that \abovecaptionskip, \belowcaptionskip, \LTpre are **ignored**
+% A. Table with longtable
+\def\sphinxatlongtablestart
+   {\par
+    \vskip\parskip
+    \vskip\dimexpr\sphinxtablepre\relax % adjust vertical position
+    \vbox{}% get correct baseline from above
+    \LTpre\z@skip\LTpost\z@skip % set to zero longtable's own skips
+    \edef\sphinxbaselineskip{\dimexpr\the\dimexpr\baselineskip\relax\relax}%
+   }%
+% Compatibility with caption package
+\def\sphinxthelongtablecaptionisattop{%
+    \spx@ifcaptionpackage{\noalign{\vskip-\belowcaptionskip}}{}%
+}%
+% Achieves exactly \sphinxbelowcaptionspace below longtable caption
+\def\sphinxlongtablecapskipadjust
+   {\dimexpr-\dp\strutbox
+            -\spx@ifcaptionpackage{\abovecaptionskip}{\sphinxbaselineskip}%
+            +\sphinxbelowcaptionspace\relax}%
+\def\sphinxatlongtableend{\@nobreakfalse % latex3/latex2e#173
+    \prevdepth\z@\vskip\sphinxtablepost\relax}%
+% B. Table with tabular or tabulary
+\def\sphinxattablestart{\par\vskip\dimexpr\sphinxtablepre\relax}%
+\let\sphinxattableend\sphinxatlongtableend
+% This is used by tabular and tabulary templates
+\newcommand*\sphinxcapstartof[1]{%
+   \vskip\parskip
+   \vbox{}% force baselineskip for good positioning by capstart of hyperanchor
+   % hyperref puts the anchor 6pt above this baseline; in case of caption
+   % this baseline will be \ht\strutbox above first baseline of caption
+   \def\@captype{#1}%
+   \capstart
+% move back vertically, as tabular (or its caption) will compensate
+   \vskip-\baselineskip\vskip-\parskip
+}%
+\def\sphinxthecaptionisattop{% locate it after \sphinxcapstartof
+    \spx@ifcaptionpackage
+      {\caption@setposition{t}%
+       \vskip\baselineskip\vskip\parskip % undo those from \sphinxcapstartof
+       \vskip-\belowcaptionskip          % anticipate caption package skip
+       % caption package uses a \vbox, not a \vtop, so "single line" case
+       % gives different result from "multi-line" without this:
+       \nointerlineskip
+      }%
+      {}%
+}%
+\def\sphinxthecaptionisatbottom{% (not finalized; for template usage)
+    \spx@ifcaptionpackage{\caption@setposition{b}}{}%
+}%
+% The aim of \sphinxcaption is to apply to tabular(y) the maximal width
+% of caption as done by longtable
+\def\sphinxtablecapwidth{\LTcapwidth}%
+\newcommand\sphinxcaption{\@dblarg\spx@caption}%
+\long\def\spx@caption[#1]#2{%
+   \noindent\hb@xt@\linewidth{\hss
+      \vtop{\@tempdima\dimexpr\sphinxtablecapwidth\relax
+% don't exceed linewidth for the caption width
+            \ifdim\@tempdima>\linewidth\hsize\linewidth\else\hsize\@tempdima\fi
+% longtable ignores \abovecaptionskip/\belowcaptionskip, so do the same here
+            \abovecaptionskip\sphinxabovecaptionskip % \z@skip
+            \belowcaptionskip\sphinxbelowcaptionskip % \z@skip
+            \caption[{#1}]%
+               {\strut\ignorespaces#2\ifhmode\unskip\@finalstrut\strutbox\fi}%
+           }\hss}%
+   \par\prevdepth\dp\strutbox
+}%
+\def\sphinxabovecaptionskip{\z@skip}% Do not use! Flagged for removal
+\def\sphinxbelowcaptionskip{\z@skip}% Do not use! Flagged for removal
+% This wrapper of \abovecaptionskip is used in sphinxVerbatim for top
+% caption, and with another value in sphinxVerbatimintable
+% TODO: To unify space above caption of a code-block with the one above
+%       caption of a table/longtable, \abovecaptionskip must not be used
+%       This auxiliary will get renamed and receive a different meaning
+%       in future.
+\def\spx@abovecaptionskip{\abovecaptionskip}%
+% Achieve \sphinxbelowcaptionspace below a caption located above a tabular
+% or a tabulary
+\newcommand\sphinxaftertopcaption
+{%
+   \spx@ifcaptionpackage
+     {\par\prevdepth\dp\strutbox\nobreak\vskip-\abovecaptionskip}{\nobreak}%
+   \vskip\dimexpr\sphinxbelowcaptionspace\relax
+   \vskip-\baselineskip\vskip-\parskip
+}%
+% varwidth is crucial for our handling of general contents in merged cells
+\RequirePackage{varwidth}
+% but addition of a compatibility patch with hyperref is needed
+% (tested with varwidth v 0.92  Mar 2009)
+\AtBeginDocument {%
+    \let\@@vwid@Hy@raisedlink\Hy@raisedlink
+    \long\def\@vwid@Hy@raisedlink#1{\@vwid@wrap{\@@vwid@Hy@raisedlink{#1}}}%
+    \edef\@vwid@setup{%
+      \let\noexpand\Hy@raisedlink\noexpand\@vwid@Hy@raisedlink % HYPERREF !
+      \unexpanded\expandafter{\@vwid@setup}}%
+}%
+% Homemade package to handle merged cells
+\RequirePackage{sphinxmulticell}
+\RequirePackage{makeidx}
+% For framing code-blocks and warning type notices, and shadowing topics
+\RequirePackage{framed}
+% The xcolor package draws better fcolorboxes around verbatim code
+\IfFileExists{xcolor.sty}{
+    \RequirePackage{xcolor}
+}{
+    \RequirePackage{color}
+}
+% For highlighted code.
+\RequirePackage{fancyvrb}
+\define@key{FV}{hllines}{\def\sphinx@verbatim@checkifhl##1{\in@{, ##1,}{#1}}}
+% sphinxVerbatim must be usable by third party without requiring hllines set-up
+\def\sphinxresetverbatimhllines{\def\sphinx@verbatim@checkifhl##1{\in@false}}
+\sphinxresetverbatimhllines
+% For hyperlinked footnotes in tables; also for gathering footnotes from
+% topic and warning blocks. Also to allow code-blocks in footnotes.
+\RequirePackage{footnotehyper-sphinx}
+% For the H specifier. Do not \restylefloat{figure}, it breaks Sphinx code
+% for allowing figures in tables.
+\RequirePackage{float}
+% For floating figures in the text. Better to load after float.
+\RequirePackage{wrapfig}
+% Separate paragraphs by space by default.
+\IfFileExists{parskip-2001-04-09.sty}% since September 2018 TeXLive update
+% new parskip.sty, but let it rollback to old one.
+% hopefully TeX installation not broken and LaTeX kernel not too old
+   {\RequirePackage{parskip}[=v1]}
+% standard one from 1989. Admittedly \section of article/book gives possibly
+% anomalous spacing, but we can't require September 2018 release for some time.
+   {\RequirePackage{parskip}}
+% For parsed-literal blocks.
+\RequirePackage{alltt}
+% Display "real" single quotes in literal blocks.
+\RequirePackage{upquote}
+% control caption around literal-block
+\RequirePackage{capt-of}
+\RequirePackage{needspace}
+% LaTeX 2018-04-01 and later provides \@removefromreset
+\ltx@ifundefined{@removefromreset}
+    {\RequirePackage{remreset}}
+    {}% avoid warning
+% to make pdf with correct encoded bookmarks in Japanese
+% this should precede the hyperref package
+\ifx\kanjiskip\@undefined
+% for non-Japanese: make sure bookmarks are ok also with lualatex
+  \PassOptionsToPackage{pdfencoding=unicode}{hyperref}
+\else
+  \RequirePackage{atbegshi}
+  \ifx\ucs\@undefined
+    \ifnum 42146=\euc"A4A2
+      \AtBeginShipoutFirst{\special{pdf:tounicode EUC-UCS2}}
+    \else
+      \AtBeginShipoutFirst{\special{pdf:tounicode 90ms-RKSJ-UCS2}}
+    \fi
+  \else
+    \AtBeginShipoutFirst{\special{pdf:tounicode UTF8-UCS2}}
+  \fi
+\fi
+
+\ifx\@jsc@uplatextrue\@undefined\else
+  \PassOptionsToPackage{setpagesize=false}{hyperref}
+\fi
+
+% These options can be overriden inside  'hyperref' key
+% or by later use of \hypersetup.
+\PassOptionsToPackage{colorlinks,breaklinks,%
+ linkcolor=InnerLinkColor,filecolor=OuterLinkColor,%
+ menucolor=OuterLinkColor,urlcolor=OuterLinkColor,%
+ citecolor=InnerLinkColor}{hyperref}
+
+% stylesheet for highlighting with pygments
+\RequirePackage{sphinxhighlight}
+% fix baseline increase from Pygments latex formatter in case of error tokens
+% and keep \fboxsep's scope local via added braces
+\def\PYG@tok@err{%
+    \def\PYG@bc##1{{\setlength{\fboxsep}{-\fboxrule}%
+                    \fcolorbox[rgb]{1.00,0.00,0.00}{1,1,1}{\strut ##1}}}%
+}
+\def\PYG@tok@cs{%
+    \def\PYG@tc##1{\textcolor[rgb]{0.25,0.50,0.56}{##1}}%
+    \def\PYG@bc##1{{\setlength{\fboxsep}{0pt}%
+                    \colorbox[rgb]{1.00,0.94,0.94}{\strut ##1}}}%
+}%
+
+
+%% OPTIONS
+%
+% Handle options via "kvoptions" (later loaded by hyperref anyhow)
+\RequirePackage{kvoptions}
+\SetupKeyvalOptions{prefix=spx@opt@} % use \spx@opt@ prefix
+
+% Sphinx legacy text layout: 1in margins on all four sides
+\ifx\@jsc@uplatextrue\@undefined
+\DeclareStringOption[1in]{hmargin}
+\DeclareStringOption[1in]{vmargin}
+\DeclareStringOption[.5in]{marginpar}
+\else
+% Japanese standard document classes handle \mag in a special way
+\DeclareStringOption[\inv@mag in]{hmargin}
+\DeclareStringOption[\inv@mag in]{vmargin}
+\DeclareStringOption[.5\dimexpr\inv@mag in\relax]{marginpar}
+\fi
+
+\DeclareStringOption[0]{maxlistdepth}% \newcommand*\spx@opt@maxlistdepth{0}
+\DeclareStringOption[-1]{numfigreset}
+\DeclareBoolOption[false]{nonumfigreset}
+\DeclareBoolOption[false]{mathnumfig}
+% \DeclareBoolOption[false]{usespart}% not used
+% dimensions, we declare the \dimen registers here.
+\newdimen\sphinxverbatimsep
+\newdimen\sphinxverbatimborder
+\newdimen\sphinxshadowsep
+\newdimen\sphinxshadowsize
+\newdimen\sphinxshadowrule
+% \DeclareStringOption is not convenient for the handling of these dimensions
+% because we want to assign the values to the corresponding registers. Even if
+% we added the code to the key handler it would be too late for the initial
+% set-up and we would need to do initial assignments explicitely. We end up
+% using \define@key directly.
+% verbatim
+\sphinxverbatimsep=\fboxsep
+  \define@key{sphinx}{verbatimsep}{\sphinxverbatimsep\dimexpr #1\relax}
+\sphinxverbatimborder=\fboxrule
+  \define@key{sphinx}{verbatimborder}{\sphinxverbatimborder\dimexpr #1\relax}
+% topic boxes
+\sphinxshadowsep =5pt
+  \define@key{sphinx}{shadowsep}{\sphinxshadowsep\dimexpr #1\relax}
+\sphinxshadowsize=4pt
+  \define@key{sphinx}{shadowsize}{\sphinxshadowsize\dimexpr #1\relax}
+\sphinxshadowrule=\fboxrule
+  \define@key{sphinx}{shadowrule}{\sphinxshadowrule\dimexpr #1\relax}
+% verbatim
+\DeclareBoolOption[true]{verbatimwithframe}
+\DeclareBoolOption[true]{verbatimwrapslines}
+\DeclareBoolOption[true]{verbatimhintsturnover}
+\DeclareBoolOption[true]{inlineliteralwraps}
+\DeclareStringOption[t]{literalblockcappos}
+\DeclareStringOption[r]{verbatimcontinuedalign}
+\DeclareStringOption[r]{verbatimcontinuesalign}
+% parsed literal
+\DeclareBoolOption[true]{parsedliteralwraps}
+% \textvisiblespace for compatibility with fontspec+XeTeX/LuaTeX
+\DeclareStringOption[\textcolor{red}{\textvisiblespace}]{verbatimvisiblespace}
+\DeclareStringOption % must use braces to hide the brackets
+  [{\makebox[2\fontcharwd\font`\x][r]{\textcolor{red}{\tiny$\m@th\hookrightarrow$}}}]%
+  {verbatimcontinued}
+% notices/admonitions
+% the dimensions for notices/admonitions are kept as macros and assigned to
+% \spx@notice@border at time of use, hence \DeclareStringOption is ok for this
+\newdimen\spx@notice@border
+\DeclareStringOption[0.5pt]{noteborder}
+\DeclareStringOption[0.5pt]{hintborder}
+\DeclareStringOption[0.5pt]{importantborder}
+\DeclareStringOption[0.5pt]{tipborder}
+\DeclareStringOption[1pt]{warningborder}
+\DeclareStringOption[1pt]{cautionborder}
+\DeclareStringOption[1pt]{attentionborder}
+\DeclareStringOption[1pt]{dangerborder}
+\DeclareStringOption[1pt]{errorborder}
+% footnotes
+\DeclareStringOption[\mbox{ }]{AtStartFootnote}
+% we need a public macro name for direct use in latex file
+\newcommand*{\sphinxAtStartFootnote}{\spx@opt@AtStartFootnote}
+% no such need for this one, as it is used inside other macros
+\DeclareStringOption[\leavevmode\unskip]{BeforeFootnote}
+% some font styling.
+\DeclareStringOption[\sffamily\bfseries]{HeaderFamily}
+% colours
+% same problems as for dimensions: we want the key handler to use \definecolor.
+% first, some colours with no prefix, for backwards compatibility
+\newcommand*{\sphinxDeclareColorOption}[2]{%
+   \definecolor{#1}#2%
+   \define@key{sphinx}{#1}{\definecolor{#1}##1}%
+}%
+\sphinxDeclareColorOption{TitleColor}{{rgb}{0.126,0.263,0.361}}
+\sphinxDeclareColorOption{InnerLinkColor}{{rgb}{0.208,0.374,0.486}}
+\sphinxDeclareColorOption{OuterLinkColor}{{rgb}{0.216,0.439,0.388}}
+\sphinxDeclareColorOption{VerbatimColor}{{rgb}{1,1,1}}
+\sphinxDeclareColorOption{VerbatimBorderColor}{{rgb}{0,0,0}}
+% now the colours defined with "sphinx" prefix in their names
+\newcommand*{\sphinxDeclareSphinxColorOption}[2]{%
+   % set the initial default
+   \definecolor{sphinx#1}#2%
+   % set the key handler. The "value" ##1 must be acceptable by \definecolor.
+   \define@key{sphinx}{#1}{\definecolor{sphinx#1}##1}%
+}%
+% Default color chosen to be as in minted.sty LaTeX package!
+\sphinxDeclareSphinxColorOption{VerbatimHighlightColor}{{rgb}{0.878,1,1}}
+% admonition boxes, "light" style
+\sphinxDeclareSphinxColorOption{noteBorderColor}{{rgb}{0,0,0}}
+\sphinxDeclareSphinxColorOption{hintBorderColor}{{rgb}{0,0,0}}
+\sphinxDeclareSphinxColorOption{importantBorderColor}{{rgb}{0,0,0}}
+\sphinxDeclareSphinxColorOption{tipBorderColor}{{rgb}{0,0,0}}
+% admonition boxes, "heavy" style
+\sphinxDeclareSphinxColorOption{warningBorderColor}{{rgb}{0,0,0}}
+\sphinxDeclareSphinxColorOption{cautionBorderColor}{{rgb}{0,0,0}}
+\sphinxDeclareSphinxColorOption{attentionBorderColor}{{rgb}{0,0,0}}
+\sphinxDeclareSphinxColorOption{dangerBorderColor}{{rgb}{0,0,0}}
+\sphinxDeclareSphinxColorOption{errorBorderColor}{{rgb}{0,0,0}}
+\sphinxDeclareSphinxColorOption{warningBgColor}{{rgb}{1,1,1}}
+\sphinxDeclareSphinxColorOption{cautionBgColor}{{rgb}{1,1,1}}
+\sphinxDeclareSphinxColorOption{attentionBgColor}{{rgb}{1,1,1}}
+\sphinxDeclareSphinxColorOption{dangerBgColor}{{rgb}{1,1,1}}
+\sphinxDeclareSphinxColorOption{errorBgColor}{{rgb}{1,1,1}}
+
+\DeclareDefaultOption{\@unknownoptionerror}
+\ProcessKeyvalOptions*
+% don't allow use of maxlistdepth via \sphinxsetup.
+\DisableKeyvalOption{sphinx}{maxlistdepth}
+\DisableKeyvalOption{sphinx}{numfigreset}
+\DisableKeyvalOption{sphinx}{nonumfigreset}
+\DisableKeyvalOption{sphinx}{mathnumfig}
+% user interface: options can be changed midway in a document!
+\newcommand\sphinxsetup[1]{\setkeys{sphinx}{#1}}
+
+
+%% ALPHANUMERIC LIST ITEMS
+\newcommand\sphinxsetlistlabels[5]
+{% #1 = style, #2 = enum, #3 = enumnext, #4 = prefix, #5 = suffix
+ % #2 and #3 are counters used by enumerate environement e.g. enumi, enumii.
+ % #1 is a macro such as \arabic or \alph
+ % prefix and suffix are strings (by default empty and a dot).
+ \@namedef{the#2}{#1{#2}}%
+ \@namedef{label#2}{#4\@nameuse{the#2}#5}%
+ \@namedef{p@#3}{\@nameuse{p@#2}#4\@nameuse{the#2}#5}%
+}%
+
+
+%% MAXLISTDEPTH
+%
+% remove LaTeX's cap on nesting depth if 'maxlistdepth' key used.
+% This is a hack, which works with the standard classes: it assumes \@toodeep
+% is always used in "true" branches: "\if ... \@toodeep \else .. \fi."
+
+% will force use the "false" branch (if there is one)
+\def\spx@toodeep@hack{\fi\iffalse}
+
+% do nothing if 'maxlistdepth' key not used or if package enumitem loaded.
+\ifnum\spx@opt@maxlistdepth=\z@\expandafter\@gobbletwo\fi
+\AtBeginDocument{%
+\@ifpackageloaded{enumitem}{\remove@to@nnil}{}%
+  \let\spx@toodeepORI\@toodeep
+  \def\@toodeep{%
+    \ifnum\@listdepth<\spx@opt@maxlistdepth\relax
+      \expandafter\spx@toodeep@hack
+    \else
+      \expandafter\spx@toodeepORI
+    \fi}%
+% define all missing \@list... macros
+  \count@\@ne
+  \loop
+     \ltx@ifundefined{@list\romannumeral\the\count@}
+       {\iffalse}{\iftrue\advance\count@\@ne}%
+  \repeat
+  \loop
+     \ifnum\count@>\spx@opt@maxlistdepth\relax\else
+       \expandafter\let
+         \csname @list\romannumeral\the\count@\expandafter\endcsname
+         \csname @list\romannumeral\the\numexpr\count@-\@ne\endcsname
+       % workaround 2.6--3.2d babel-french issue (fixed in 3.2e; no change needed)
+       \ltx@ifundefined{leftmargin\romannumeral\the\count@}
+       {\expandafter\let
+         \csname leftmargin\romannumeral\the\count@\expandafter\endcsname
+         \csname leftmargin\romannumeral\the\numexpr\count@-\@ne\endcsname}{}%
+     \advance\count@\@ne
+  \repeat
+% define all missing enum... counters and \labelenum... macros and \p@enum..
+  \count@\@ne
+  \loop
+     \ltx@ifundefined{c@enum\romannumeral\the\count@}
+       {\iffalse}{\iftrue\advance\count@\@ne}%
+  \repeat
+  \loop
+     \ifnum\count@>\spx@opt@maxlistdepth\relax\else
+       \newcounter{enum\romannumeral\the\count@}%
+       \expandafter\def
+         \csname labelenum\romannumeral\the\count@\expandafter\endcsname
+         \expandafter
+         {\csname theenum\romannumeral\the\numexpr\count@\endcsname.}%
+       \expandafter\def
+         \csname p@enum\romannumeral\the\count@\expandafter\endcsname
+         \expandafter
+         {\csname p@enum\romannumeral\the\numexpr\count@-\@ne\expandafter
+          \endcsname\csname theenum\romannumeral\the\numexpr\count@-\@ne\endcsname.}%
+     \advance\count@\@ne
+  \repeat
+% define all missing labelitem... macros
+  \count@\@ne
+  \loop
+     \ltx@ifundefined{labelitem\romannumeral\the\count@}
+     {\iffalse}{\iftrue\advance\count@\@ne}%
+  \repeat
+  \loop
+     \ifnum\count@>\spx@opt@maxlistdepth\relax\else
+       \expandafter\let
+         \csname labelitem\romannumeral\the\count@\expandafter\endcsname
+         \csname labelitem\romannumeral\the\numexpr\count@-\@ne\endcsname
+     \advance\count@\@ne
+  \repeat
+  \PackageInfo{sphinx}{maximal list depth extended to \spx@opt@maxlistdepth}%
+\@gobble\@nnil
+}
+
+
+%% INDEX, BIBLIOGRAPHY, APPENDIX, TABLE OF CONTENTS
+%
+% fix the double index and bibliography on the table of contents
+% in jsclasses (Japanese standard document classes)
+\ifx\@jsc@uplatextrue\@undefined\else
+  \renewenvironment{sphinxtheindex}
+    {\cleardoublepage\phantomsection
+     \begin{theindex}}
+    {\end{theindex}}
+
+  \renewenvironment{sphinxthebibliography}[1]
+    {\cleardoublepage% \phantomsection % not needed here since TeXLive 2010's hyperref
+     \begin{thebibliography}{#1}}
+    {\end{thebibliography}}
+\fi
+
+% disable \@chappos in Appendix in pTeX
+\ifx\kanjiskip\@undefined\else
+  \let\py@OldAppendix=\appendix
+  \renewcommand{\appendix}{
+    \py@OldAppendix
+    \gdef\@chappos{}
+  }
+\fi
+
+% make commands known to non-Sphinx document classes
+\providecommand*{\sphinxmaketitle}{\maketitle}
+\providecommand*{\sphinxtableofcontents}{\tableofcontents}
+\ltx@ifundefined{sphinxthebibliography}
+ {\newenvironment
+  {sphinxthebibliography}{\begin{thebibliography}}{\end{thebibliography}}%
+ }
+ {}% else clause of \ltx@ifundefined
+\ltx@ifundefined{sphinxtheindex}
+ {\newenvironment{sphinxtheindex}{\begin{theindex}}{\end{theindex}}}%
+ {}% else clause of \ltx@ifundefined
+
+% for usage with xindy: this string gets internationalized in preamble
+\newcommand*{\sphinxnonalphabeticalgroupname}{}
+% redefined in preamble, headings for makeindex produced index
+\newcommand*{\sphinxsymbolsname}{}
+\newcommand*{\sphinxnumbersname}{}
+
+%% COLOR (general)
+%
+% FIXME: \normalcolor should probably be used in place of \py@NormalColor
+% elsewhere, and \py@NormalColor should never be defined. \normalcolor
+% switches to the colour from last \color call in preamble.
+\def\py@NormalColor{\color{black}}
+% FIXME: it is probably better to use \color{TitleColor}, as TitleColor
+% can be customized from 'sphinxsetup', and drop usage of \py@TitleColor
+\def\py@TitleColor{\color{TitleColor}}
+% FIXME: this line should be dropped, as "9" is default anyhow.
+\ifdefined\pdfcompresslevel\pdfcompresslevel = 9 \fi
+
+
+%% PAGE STYLING
+%
+% Style parameters and macros used by most documents here
+\raggedbottom
+\sloppy
+\hbadness = 5000                % don't print trivial gripes
+
+% Use \pagestyle{normal} as the primary pagestyle for text.
+% Redefine the 'normal' header/footer style when using "fancyhdr" package:
+\@ifpackageloaded{fancyhdr}{%
+  \ltx@ifundefined{c@chapter}
+  {% no \chapter, "howto" (non-Japanese) docclass
+   \fancypagestyle{plain}{
+    \fancyhf{}
+    \fancyfoot[C]{{\py@HeaderFamily\thepage}}
+    \renewcommand{\headrulewidth}{0pt}
+    \renewcommand{\footrulewidth}{0pt}
+   }
+   % Same as 'plain', this way we can use it in template
+   % FIXME: shouldn't this have a running header with Name and Release like 'manual'?
+   \fancypagestyle{normal}{
+    \fancyhf{}
+    \fancyfoot[C]{{\py@HeaderFamily\thepage}}
+    \renewcommand{\headrulewidth}{0pt}
+    \renewcommand{\footrulewidth}{0pt}
+   }
+  }%
+  {% classes with \chapter command
+   \fancypagestyle{normal}{
+    \fancyhf{}
+   % FIXME: this presupposes "twoside".
+   % If "oneside" class option, there are warnings in LaTeX log.
+    \fancyfoot[LE,RO]{{\py@HeaderFamily\thepage}}
+    \fancyfoot[LO]{{\py@HeaderFamily\nouppercase{\rightmark}}}
+    \fancyfoot[RE]{{\py@HeaderFamily\nouppercase{\leftmark}}}
+    \fancyhead[LE,RO]{{\py@HeaderFamily \@title\sphinxheadercomma\py@release}}
+    \renewcommand{\headrulewidth}{0.4pt}
+    \renewcommand{\footrulewidth}{0.4pt}
+    % define chaptermark with \@chappos when \@chappos is available for Japanese
+    \ltx@ifundefined{@chappos}{}
+      {\def\chaptermark##1{\markboth{\@chapapp\space\thechapter\space\@chappos\space ##1}{}}}
+    }
+  % Update the plain style so we get the page number & footer line,
+  % but not a chapter or section title.  This is to keep the first
+  % page of a chapter `clean.'
+   \fancypagestyle{plain}{
+    \fancyhf{}
+    \fancyfoot[LE,RO]{{\py@HeaderFamily\thepage}}
+    \renewcommand{\headrulewidth}{0pt}
+    \renewcommand{\footrulewidth}{0.4pt}
+    }
+   }
+  }
+  {% no fancyhdr: memoir class
+   % Provide default for 'normal' style simply as an alias of 'plain' style
+   % This way we can use \pagestyle{normal} in LaTeX template
+   \def\ps@normal{\ps@plain}
+   % Users of memoir class are invited to redefine 'normal' style in preamble
+  }
+
+% geometry
+\ifx\kanjiskip\@undefined
+  \PassOptionsToPackage{%
+     hmargin={\unexpanded{\spx@opt@hmargin}},%
+     vmargin={\unexpanded{\spx@opt@vmargin}},%
+     marginpar=\unexpanded{\spx@opt@marginpar}}
+  {geometry}
+\else
+ % set text width for Japanese documents to be integer multiple of 1zw
+ % and text height to be integer multiple of \baselineskip
+ % the execution is delayed to \sphinxsetup then geometry.sty
+ \normalsize\normalfont
+ \newcommand*\sphinxtextwidthja[1]{%
+    \if@twocolumn\tw@\fi
+    \dimexpr
+       \numexpr\dimexpr\paperwidth-\tw@\dimexpr#1\relax\relax/
+               \dimexpr\if@twocolumn\tw@\else\@ne\fi zw\relax
+    zw\relax}%
+ \newcommand*\sphinxmarginparwidthja[1]{%
+    \dimexpr\numexpr\dimexpr#1\relax/\dimexpr1zw\relax zw\relax}%
+ \newcommand*\sphinxtextlinesja[1]{%
+    \numexpr\@ne+\dimexpr\paperheight-\topskip-\tw@\dimexpr#1\relax\relax/
+                 \baselineskip\relax}%
+ \ifx\@jsc@uplatextrue\@undefined\else
+ % the way we found in order for the papersize special written by
+ % geometry in the dvi file to be correct in case of jsbook class
+   \ifnum\mag=\@m\else % do nothing special if nomag class option or 10pt
+     \PassOptionsToPackage{truedimen}{geometry}%
+   \fi
+ \fi
+ \PassOptionsToPackage{%
+    hmarginratio={1:1},%
+    textwidth=\unexpanded{\sphinxtextwidthja{\spx@opt@hmargin}},%
+    vmarginratio={1:1},%
+    lines=\unexpanded{\sphinxtextlinesja{\spx@opt@vmargin}},%
+    marginpar=\unexpanded{\sphinxmarginparwidthja{\spx@opt@marginpar}},%
+    footskip=2\baselineskip,%
+  }{geometry}%
+ \AtBeginDocument
+ {% update a dimension used by the jsclasses
+  \ifx\@jsc@uplatextrue\@undefined\else\fullwidth\textwidth\fi
+  % for some reason, jreport normalizes all dimensions with \@settopoint
+  \@ifclassloaded{jreport}
+    {\@settopoint\textwidth\@settopoint\textheight\@settopoint\marginparwidth}
+    {}% <-- "false" clause of \@ifclassloaded
+  }%
+\fi
+
+% fix fncychap's bug which uses prematurely the \textwidth value
+\@ifpackagewith{fncychap}{Bjornstrup}
+ {\AtBeginDocument{\mylen\textwidth\advance\mylen-2\myhi}}%
+ {}% <-- "false" clause of \@ifpackagewith
+
+
+%% TITLES
+%
+% Since Sphinx 1.5, users should use HeaderFamily key to 'sphinxsetup' rather
+% than defining their own \py@HeaderFamily command (which is still possible).
+% Memo: \py@HeaderFamily is also used by \maketitle as defined in
+% sphinxmanual.cls/sphinxhowto.cls
+\newcommand{\py@HeaderFamily}{\spx@opt@HeaderFamily}
+
+% This sets up the fancy chapter headings that make the documents look
+% at least a little better than the usual LaTeX output.
+\@ifpackagewith{fncychap}{Bjarne}{
+  \ChNameVar {\raggedleft\normalsize \py@HeaderFamily}
+  \ChNumVar  {\raggedleft\Large      \py@HeaderFamily}
+  \ChTitleVar{\raggedleft\Large      \py@HeaderFamily}
+  % This creates (numbered) chapter heads without the leading \vspace*{}:
+  \def\@makechapterhead#1{%
+    {\parindent \z@ \raggedright \normalfont
+      \ifnum \c@secnumdepth >\m@ne
+        \if@mainmatter
+          \DOCH
+        \fi
+      \fi
+      \interlinepenalty\@M
+      \if@mainmatter
+        \DOTI{#1}%
+      \else%
+        \DOTIS{#1}%
+      \fi
+    }}
+}{}% <-- "false" clause of \@ifpackagewith
+
+% Augment the sectioning commands used to get our own font family in place,
+% and reset some internal data items (\titleformat from titlesec package)
+\titleformat{\section}{\Large\py@HeaderFamily}%
+            {\py@TitleColor\thesection}{0.5em}{\py@TitleColor}{\py@NormalColor}
+\titleformat{\subsection}{\large\py@HeaderFamily}%
+            {\py@TitleColor\thesubsection}{0.5em}{\py@TitleColor}{\py@NormalColor}
+\titleformat{\subsubsection}{\py@HeaderFamily}%
+            {\py@TitleColor\thesubsubsection}{0.5em}{\py@TitleColor}{\py@NormalColor}
+% By default paragraphs (and subsubsections) will not be numbered because
+% sphinxmanual.cls and sphinxhowto.cls set secnumdepth to 2
+\titleformat{\paragraph}{\py@HeaderFamily}%
+            {\py@TitleColor\theparagraph}{0.5em}{\py@TitleColor}{\py@NormalColor}
+\titleformat{\subparagraph}{\py@HeaderFamily}%
+            {\py@TitleColor\thesubparagraph}{0.5em}{\py@TitleColor}{\py@NormalColor}
+
+
+%% GRAPHICS
+%
+% \sphinxincludegraphics resizes images larger than the TeX \linewidth (which
+% is adjusted in indented environments), or taller than a certain maximal
+% height (usually \textheight and this is reduced in the environments which use
+% framed.sty to avoid infinite loop if image too tall).
+%
+% In case height or width options are present the rescaling is done
+% (since 2.0), in a way keeping the width:height ratio either native from
+% image or from the width and height options if both were present.
+%
+\newdimen\spx@image@maxheight
+\AtBeginDocument{\spx@image@maxheight\textheight}
+
+% box scratch register
+\newdimen\spx@image@box
+\newcommand*{\sphinxsafeincludegraphics}[2][]{%
+    % #1 contains possibly width=, height=, but no scale= since 1.8.4
+    \setbox\spx@image@box\hbox{\includegraphics[#1,draft]{#2}}%
+    \in@false % use some handy boolean flag
+    \ifdim \wd\spx@image@box>\linewidth
+      \in@true % flag to remember to adjust options and set box dimensions
+      % compute height which results from rescaling width to \linewidth
+      % and keep current aspect ratio. multiply-divide in \numexpr uses
+      % temporarily doubled precision, hence no overflow. (of course we
+      % assume \ht is not a few sp's below \maxdimen...(about 16384pt).
+      \edef\spx@image@rescaledheight % with sp units
+           {\the\numexpr\ht\spx@image@box
+                        *\linewidth/\wd\spx@image@box sp}%
+      \ifdim\spx@image@rescaledheight>\spx@image@maxheight
+        % the rescaled height will be too big, so it is height which decides
+        % the rescaling factor
+        \def\spx@image@requiredheight{\spx@image@maxheight}% dimen register
+        \edef\spx@image@requiredwidth % with sp units
+         {\the\numexpr\wd\spx@image@box
+                      *\spx@image@maxheight/\ht\spx@image@box sp}%
+        % TODO: decide if this commented-out block could be needed due to
+        % rounding in numexpr operations going up
+        % \ifdim\spx@image@requiredwidth>\linewidth
+        %     \def\spx@image@requiredwidth{\linewidth}% dimen register
+        % \fi
+      \else
+        \def\spx@image@requiredwidth{\linewidth}% dimen register
+        \let\spx@image@requiredheight\spx@image@rescaledheight% sp units
+      \fi
+    \else
+      % width is ok, let's check height
+      \ifdim\ht\spx@image@box>\spx@image@maxheight
+        \in@true
+        \edef\spx@image@requiredwidth % with sp units
+            {\the\numexpr\wd\spx@image@box
+                         *\spx@image@maxheight/\ht\spx@image@box sp}%
+        \def\spx@image@requiredheight{\spx@image@maxheight}% dimen register
+      \fi
+    \fi % end of check of width and height
+    \ifin@
+      \setbox\spx@image@box
+      \hbox{\includegraphics
+            [%#1,% contained only width and/or height and overruled anyhow
+            width=\spx@image@requiredwidth,height=\spx@image@requiredheight]%
+            {#2}}%
+      % \includegraphics does not set box dimensions to the exactly
+      % requested ones, see https://github.com/latex3/latex2e/issues/112
+      \wd\spx@image@box\spx@image@requiredwidth
+      \ht\spx@image@box\spx@image@requiredheight
+      \leavevmode\box\spx@image@box
+    \else
+      % here we do not modify the options, no need to adjust width and height
+      % on output, they will be computed exactly as with "draft" option
+      \setbox\spx@image@box\box\voidb@x % clear memory
+      \includegraphics[#1]{#2}%
+    \fi
+}%
+% Use the "safe" one by default (2.0)
+\def\sphinxincludegraphics{\sphinxsafeincludegraphics}
+
+
+%% FIGURE IN TABLE
+%
+\newenvironment{sphinxfigure-in-table}[1][\linewidth]{%
+  \def\@captype{figure}%
+  \sphinxsetvskipsforfigintablecaption
+  \begin{minipage}{#1}%
+}{\end{minipage}}
+% store the original \caption macro for usage with figures inside longtable
+% and tabulary cells. Make sure we get the final \caption in presence of
+% caption package, whether the latter was loaded before or after sphinx.
+\AtBeginDocument{%
+    \let\spx@originalcaption\caption
+    \@ifpackageloaded{caption}
+      {\let\spx@ifcaptionpackage\@firstoftwo
+       \caption@AtBeginDocument*{\let\spx@originalcaption\caption}%
+% in presence of caption package, drop our own \sphinxcaption whose aim was to
+% ensure same width of caption to all kinds of tables (tabular(y), longtable),
+% because caption package has its own width (or margin) option
+       \def\sphinxcaption{\caption}%
+      }%
+      {\let\spx@ifcaptionpackage\@secondoftwo}%
+}
+% tabulary expands twice contents, we need to prevent double counter stepping
+\newcommand*\sphinxfigcaption
+  {\ifx\equation$%$% this is trick to identify tabulary first pass
+       \firstchoice@false\else\firstchoice@true\fi
+   \spx@originalcaption }
+\newcommand*\sphinxsetvskipsforfigintablecaption
+  {\abovecaptionskip\smallskipamount
+   \belowcaptionskip\smallskipamount}
+
+
+%% CITATIONS
+%
+\protected\def\sphinxcite{\cite}
+
+%% FOOTNOTES
+%
+% Support large numbered footnotes in minipage
+% But now obsolete due to systematic use of \savenotes/\spewnotes
+% when minipages are in use in the various macro definitions next.
+\def\thempfootnote{\arabic{mpfootnote}}
+
+
+%% NUMBERING OF FIGURES, TABLES, AND LITERAL BLOCKS
+\ltx@ifundefined{c@chapter}
+   {\newcounter{literalblock}}%
+   {\newcounter{literalblock}[chapter]%
+    \def\theliteralblock{\ifnum\c@chapter>\z@\arabic{chapter}.\fi
+                         \arabic{literalblock}}%
+    }%
+\ifspx@opt@nonumfigreset
+    \ltx@ifundefined{c@chapter}{}{%
+      \@removefromreset{figure}{chapter}%
+      \@removefromreset{table}{chapter}%
+      \@removefromreset{literalblock}{chapter}%
+      \ifspx@opt@mathnumfig
+        \@removefromreset{equation}{chapter}%
+      \fi
+    }%
+    \def\thefigure{\arabic{figure}}%
+    \def\thetable {\arabic{table}}%
+    \def\theliteralblock{\arabic{literalblock}}%
+    \ifspx@opt@mathnumfig
+      \def\theequation{\arabic{equation}}%
+    \fi
+\else
+\let\spx@preAthefigure\@empty
+\let\spx@preBthefigure\@empty
+% \ifspx@opt@usespart  % <-- LaTeX writer could pass such a 'usespart' boolean
+%                      %     as sphinx.sty package option
+% If document uses \part, (triggered in Sphinx by latex_toplevel_sectioning)
+% LaTeX core per default does not reset chapter or section
+% counters at each part.
+% But if we modify this, we need to redefine \thechapter, \thesection to
+% include the part number and this will cause problems in table of contents
+% because of too wide numbering. Simplest is to do nothing.
+% \fi
+\ifnum\spx@opt@numfigreset>0
+    \ltx@ifundefined{c@chapter}
+      {}
+      {\g@addto@macro\spx@preAthefigure{\ifnum\c@chapter>\z@\arabic{chapter}.}%
+       \g@addto@macro\spx@preBthefigure{\fi}}%
+\fi
+\ifnum\spx@opt@numfigreset>1
+  \AtBeginDocument{%
+    \@addtoreset{figure}{section}%
+    \@addtoreset{table}{section}%
+    \@addtoreset{literalblock}{section}%
+    \ifspx@opt@mathnumfig
+      \@addtoreset{equation}{section}%
+    \fi%
+  }%
+    \g@addto@macro\spx@preAthefigure{\ifnum\c@section>\z@\arabic{section}.}%
+    \g@addto@macro\spx@preBthefigure{\fi}%
+\fi
+\ifnum\spx@opt@numfigreset>2
+  \AtBeginDocument{%
+    \@addtoreset{figure}{subsection}%
+    \@addtoreset{table}{subsection}%
+    \@addtoreset{literalblock}{subsection}%
+    \ifspx@opt@mathnumfig
+      \@addtoreset{equation}{subsection}%
+    \fi%
+  }%
+    \g@addto@macro\spx@preAthefigure{\ifnum\c@subsection>\z@\arabic{subsection}.}%
+    \g@addto@macro\spx@preBthefigure{\fi}%
+\fi
+\ifnum\spx@opt@numfigreset>3
+  \AtBeginDocument{%
+    \@addtoreset{figure}{subsubsection}%
+    \@addtoreset{table}{subsubsection}%
+    \@addtoreset{literalblock}{subsubsection}%
+    \ifspx@opt@mathnumfig
+      \@addtoreset{equation}{subsubsection}%
+    \fi%
+  }%
+    \g@addto@macro\spx@preAthefigure{\ifnum\c@subsubsection>\z@\arabic{subsubsection}.}%
+    \g@addto@macro\spx@preBthefigure{\fi}%
+\fi
+\ifnum\spx@opt@numfigreset>4
+  \AtBeginDocument{%
+    \@addtoreset{figure}{paragraph}%
+    \@addtoreset{table}{paragraph}%
+    \@addtoreset{literalblock}{paragraph}%
+    \ifspx@opt@mathnumfig
+      \@addtoreset{equation}{paragraph}%
+    \fi%
+  }%
+    \g@addto@macro\spx@preAthefigure{\ifnum\c@subparagraph>\z@\arabic{subparagraph}.}%
+    \g@addto@macro\spx@preBthefigure{\fi}%
+\fi
+\ifnum\spx@opt@numfigreset>5
+  \AtBeginDocument{%
+    \@addtoreset{figure}{subparagraph}%
+    \@addtoreset{table}{subparagraph}%
+    \@addtoreset{literalblock}{subparagraph}%
+    \ifspx@opt@mathnumfig
+      \@addtoreset{equation}{subparagraph}%
+    \fi%
+  }%
+    \g@addto@macro\spx@preAthefigure{\ifnum\c@subsubparagraph>\z@\arabic{subsubparagraph}.}%
+    \g@addto@macro\spx@preBthefigure{\fi}%
+\fi
+\expandafter\g@addto@macro
+\expandafter\spx@preAthefigure\expandafter{\spx@preBthefigure}%
+\let\thefigure\spx@preAthefigure
+\let\thetable\spx@preAthefigure
+\let\theliteralblock\spx@preAthefigure
+\g@addto@macro\thefigure{\arabic{figure}}%
+\g@addto@macro\thetable{\arabic{table}}%
+\g@addto@macro\theliteralblock{\arabic{literalblock}}%
+  \ifspx@opt@mathnumfig
+    \let\theequation\spx@preAthefigure
+    \g@addto@macro\theequation{\arabic{equation}}%
+  \fi
+\fi
+
+
+%% LITERAL BLOCKS
+%
+% Based on use of "fancyvrb.sty"'s Verbatim.
+% - with framing allowing page breaks ("framed.sty")
+% - with breaking of long lines (exploits Pygments mark-up),
+% - with possibly of a top caption, non-separable by pagebreak.
+% - and usable inside tables or footnotes ("footnotehyper-sphinx").
+
+% For extensions which use \OriginalVerbatim and compatibility with Sphinx <
+% 1.5, we define and use these when (unmodified) Verbatim will be needed. But
+% Sphinx >= 1.5 does not modify the \Verbatim macro anymore.
+\let\OriginalVerbatim   \Verbatim
+\let\endOriginalVerbatim\endVerbatim
+
+% for captions of literal blocks
+% at start of caption title
+\newcommand*{\fnum@literalblock}{\literalblockname\nobreakspace\theliteralblock}
+% this will be overwritten in document preamble by Babel translation
+\newcommand*{\literalblockname}{Listing }
+% file extension needed for \caption's good functioning, the file is created
+% only if a \listof{literalblock}{foo} command is encountered, which is
+% analogous to \listoffigures, but for the code listings (foo = chosen title.)
+\newcommand*{\ext@literalblock}{lol}
+
+\newif\ifspx@inframed % flag set if we are already in a framed environment
+% if forced use of minipage encapsulation is needed (e.g. table cells)
+\newif\ifsphinxverbatimwithminipage   \sphinxverbatimwithminipagefalse
+
+% Framing macro for use with framed.sty's \FrameCommand
+%  - it obeys current indentation,
+%  - frame is \fboxsep separated from the contents,
+%  - the contents use the full available text width,
+%  - #1 = color of frame, #2 = color of background,
+%  - #3 = above frame, #4 = below frame, #5 = within frame,
+%  - #3 and #4 must be already typeset boxes; they must issue \normalcolor
+%    or similar, else, they are under scope of color #1
+\long\def\spx@fcolorbox #1#2#3#4#5{%
+  \hskip\@totalleftmargin
+  \hskip-\fboxsep\hskip-\fboxrule
+  % use of \color@b@x here is compatible with both xcolor.sty and color.sty
+  \color@b@x {\color{#1}\spx@CustomFBox{#3}{#4}}{\color{#2}}{#5}%
+  \hskip-\fboxsep\hskip-\fboxrule
+  \hskip-\linewidth \hskip-\@totalleftmargin \hskip\columnwidth
+}%
+% #1 = for material above frame, such as a caption or a "continued" hint
+% #2 = for material below frame, such as a caption or "continues on next page"
+% #3 = actual contents, which will be typeset with a background color
+\long\def\spx@CustomFBox#1#2#3{%
+  \begingroup
+  \setbox\@tempboxa\hbox{{#3}}% inner braces to avoid color leaks
+  \vbox{#1% above frame
+            % draw frame border _latest_ to avoid pdf viewer issue
+            \kern\fboxrule
+            \hbox{\kern\fboxrule
+                  \copy\@tempboxa
+                  \kern-\wd\@tempboxa\kern-\fboxrule
+                  \vrule\@width\fboxrule
+                  \kern\wd\@tempboxa
+                  \vrule\@width\fboxrule}%
+            \kern-\dimexpr\ht\@tempboxa+\dp\@tempboxa+\fboxrule\relax
+            \hrule\@height\fboxrule
+            \kern\dimexpr\ht\@tempboxa+\dp\@tempboxa\relax
+            \hrule\@height\fboxrule
+        #2% below frame
+       }%
+  \endgroup
+}%
+\def\spx@fcolorbox@put@c#1{% hide width from framed.sty measuring
+    \moveright\dimexpr\fboxrule+.5\wd\@tempboxa\hb@xt@\z@{\hss#1\hss}%
+}%
+\def\spx@fcolorbox@put@r#1{% right align with contents, width hidden
+    \moveright\dimexpr\fboxrule+\wd\@tempboxa-\fboxsep\hb@xt@\z@{\hss#1}%
+}%
+\def\spx@fcolorbox@put@l#1{% left align with contents, width hidden
+    \moveright\dimexpr\fboxrule+\fboxsep\hb@xt@\z@{#1\hss}%
+}%
+%
+\def\sphinxVerbatim@Continued
+   {\csname spx@fcolorbox@put@\spx@opt@verbatimcontinuedalign\endcsname
+    {\normalcolor\sphinxstylecodecontinued\literalblockcontinuedname}}%
+\def\sphinxVerbatim@Continues
+   {\csname spx@fcolorbox@put@\spx@opt@verbatimcontinuesalign\endcsname
+    {\normalcolor\sphinxstylecodecontinues\literalblockcontinuesname}}%
+\def\sphinxVerbatim@Title
+   {\spx@fcolorbox@put@c{\unhcopy\sphinxVerbatim@TitleBox}}%
+\let\sphinxVerbatim@Before\@empty
+\let\sphinxVerbatim@After\@empty
+% Defaults are redefined in document preamble according to language
+\newcommand*\literalblockcontinuedname{continued from previous page}%
+\newcommand*\literalblockcontinuesname{continues on next page}%
+%
+\def\spx@verbatimfcolorbox{\spx@fcolorbox{VerbatimBorderColor}{VerbatimColor}}%
+\def\sphinxVerbatim@FrameCommand
+   {\spx@verbatimfcolorbox\sphinxVerbatim@Before\sphinxVerbatim@After}%
+\def\sphinxVerbatim@FirstFrameCommand
+   {\spx@verbatimfcolorbox\sphinxVerbatim@Before\sphinxVerbatim@Continues}%
+\def\sphinxVerbatim@MidFrameCommand
+   {\spx@verbatimfcolorbox\sphinxVerbatim@Continued\sphinxVerbatim@Continues}%
+\def\sphinxVerbatim@LastFrameCommand
+   {\spx@verbatimfcolorbox\sphinxVerbatim@Continued\sphinxVerbatim@After}%
+
+% For linebreaks inside Verbatim environment from package fancyvrb.
+\newbox\sphinxcontinuationbox
+\newbox\sphinxvisiblespacebox
+\newcommand*\sphinxafterbreak {\copy\sphinxcontinuationbox}
+
+% Take advantage of the already applied Pygments mark-up to insert
+% potential linebreaks for TeX processing.
+%        {, <, #, %, $, ' and ": go to next line.
+%        _, }, ^, &, >, -, ~, and \: stay at end of broken line.
+% Use of \textquotesingle for straight quote.
+% FIXME: convert this to package options ?
+\newcommand*\sphinxbreaksbeforelist {%
+    \do\PYGZob\{\do\PYGZlt\<\do\PYGZsh\#\do\PYGZpc\%% {, <, #, %,
+    \do\PYGZdl\$\do\PYGZdq\"% $, "
+    \def\PYGZsq
+     {\discretionary{}{\sphinxafterbreak\textquotesingle}{\textquotesingle}}% '
+}
+\newcommand*\sphinxbreaksafterlist {%
+    \do\PYGZus\_\do\PYGZcb\}\do\PYGZca\^\do\PYGZam\&% _, }, ^, &,
+    \do\PYGZgt\>\do\PYGZhy\-\do\PYGZti\~% >, -, ~
+    \do\PYGZbs\\% \
+}
+\newcommand*\sphinxbreaksatspecials {%
+  \def\do##1##2%
+     {\def##1{\discretionary{}{\sphinxafterbreak\char`##2}{\char`##2}}}%
+  \sphinxbreaksbeforelist
+  \def\do##1##2%
+     {\def##1{\discretionary{\char`##2}{\sphinxafterbreak}{\char`##2}}}%
+  \sphinxbreaksafterlist
+}
+
+\def\sphinx@verbatim@nolig@list {\do \`}%
+% Some characters . , ; ? ! / are neither pygmentized nor "tex-escaped".
+% This macro makes them "active" and they will insert potential linebreaks.
+% Not compatible with math mode (cf \sphinxunactivateextras).
+\newcommand*\sphinxbreaksbeforeactivelist {}% none
+\newcommand*\sphinxbreaksafteractivelist  {\do\.\do\,\do\;\do\?\do\!\do\/}
+\newcommand*\sphinxbreaksviaactive {%
+   \def\do##1{\lccode`\~`##1%
+    \lowercase{\def~}{\discretionary{}{\sphinxafterbreak\char`##1}{\char`##1}}%
+    \catcode`##1\active}%
+   \sphinxbreaksbeforeactivelist
+   \def\do##1{\lccode`\~`##1%
+    \lowercase{\def~}{\discretionary{\char`##1}{\sphinxafterbreak}{\char`##1}}%
+    \catcode`##1\active}%
+   \sphinxbreaksafteractivelist
+   \lccode`\~`\~
+}
+
+% If the linebreak is at a space, the latter will be displayed as visible
+% space at end of first line, and a continuation symbol starts next line.
+\def\spx@verbatim@space {%
+     \nobreak\hskip\z@skip
+     \discretionary{\copy\sphinxvisiblespacebox}{\sphinxafterbreak}
+                   {\kern\fontdimen2\font}%
+}%
+
+% if the available space on page is less than \literalblockneedspace, insert pagebreak
+\newcommand{\sphinxliteralblockneedspace}{5\baselineskip}
+\newcommand{\sphinxliteralblockwithoutcaptionneedspace}{1.5\baselineskip}
+% The title (caption) is specified from outside as macro \sphinxVerbatimTitle.
+% \sphinxVerbatimTitle is reset to empty after each use of Verbatim.
+\newcommand*\sphinxVerbatimTitle {}
+% This box to typeset the caption before framed.sty multiple passes for framing.
+\newbox\sphinxVerbatim@TitleBox
+% This box to measure contents if nested as inner \MakeFramed requires then
+% minipage encapsulation but too long contents then break outer \MakeFramed
+\newbox\sphinxVerbatim@ContentsBox
+% This is a workaround to a "feature" of French lists, when literal block
+% follows immediately; usable generally (does only \par then), a priori...
+\newcommand*\sphinxvspacefixafterfrenchlists{%
+   \ifvmode\ifdim\lastskip<\z@ \vskip\parskip\fi\else\par\fi
+}
+% Holder macro for labels of literal blocks. Set-up by LaTeX writer.
+\newcommand*\sphinxLiteralBlockLabel {}
+\newcommand*\sphinxSetupCaptionForVerbatim [1]
+{%
+    \sphinxvspacefixafterfrenchlists
+    \needspace{\sphinxliteralblockneedspace}%
+% insert a \label via \sphinxLiteralBlockLabel
+% reset to normal the color for the literal block caption
+    \def\sphinxVerbatimTitle
+       {\py@NormalColor\sphinxcaption{\sphinxLiteralBlockLabel #1}}%
+}
+\newcommand*\sphinxSetupCodeBlockInFootnote {%
+  \fvset{fontsize=\footnotesize}\let\caption\sphinxfigcaption
+  \sphinxverbatimwithminipagetrue % reduces vertical spaces
+  % we counteract (this is in a group) the \@normalsize from \caption
+  \let\normalsize\footnotesize\let\@parboxrestore\relax
+  \def\spx@abovecaptionskip{\sphinxverbatimsmallskipamount}%
+}
+\newcommand*{\sphinxverbatimsmallskipamount}{\smallskipamount}
+% serves to implement line highlighting and line wrapping
+\newcommand\sphinxFancyVerbFormatLine[1]{%
+  \expandafter\sphinx@verbatim@checkifhl\expandafter{\the\FV@CodeLineNo}%
+  \ifin@
+     \sphinxVerbatimHighlightLine{#1}%
+  \else
+     \sphinxVerbatimFormatLine{#1}%
+  \fi
+}%
+\newcommand\sphinxVerbatimHighlightLine[1]{%
+  \edef\sphinxrestorefboxsep{\fboxsep\the\fboxsep\relax}%
+  \fboxsep0pt\relax % cf LaTeX bug graphics/4524
+  \colorbox{sphinxVerbatimHighlightColor}%
+           {\sphinxrestorefboxsep\sphinxVerbatimFormatLine{#1}}%
+  % no need to restore \fboxsep here, as this ends up in a \hbox from fancyvrb
+}%
+% \sphinxVerbatimFormatLine will be set locally to one of those two:
+\newcommand\sphinxVerbatimFormatLineWrap[1]{%
+    \hsize\linewidth
+    \vtop{\raggedright\hyphenpenalty\z@\exhyphenpenalty\z@
+          \doublehyphendemerits\z@\finalhyphendemerits\z@
+          \strut #1\strut}%
+}%
+\newcommand\sphinxVerbatimFormatLineNoWrap[1]{\hb@xt@\linewidth{\strut #1\hss}}%
+\g@addto@macro\FV@SetupFont{%
+    \sbox\sphinxcontinuationbox {\spx@opt@verbatimcontinued}%
+    \sbox\sphinxvisiblespacebox {\spx@opt@verbatimvisiblespace}%
+}%
+\newenvironment{sphinxVerbatim}{%
+  % first, let's check if there is a caption
+  \ifx\sphinxVerbatimTitle\empty
+      \sphinxvspacefixafterfrenchlists
+      \parskip\z@skip
+      \vskip\sphinxverbatimsmallskipamount
+      % there was no caption. Check if nevertheless a label was set.
+      \ifx\sphinxLiteralBlockLabel\empty\else
+      % we require some space to be sure hyperlink target from \phantomsection
+      % will not be separated from upcoming verbatim by a page break
+          \needspace{\sphinxliteralblockwithoutcaptionneedspace}%
+          \phantomsection\sphinxLiteralBlockLabel
+      \fi
+  \else
+     \parskip\z@skip
+     \if t\spx@opt@literalblockcappos
+       \vskip\spx@abovecaptionskip
+       \def\sphinxVerbatim@Before
+           {\sphinxVerbatim@Title\nointerlineskip
+            \kern\dimexpr-\dp\strutbox+\sphinxbelowcaptionspace
+                 % if no frame (code-blocks inside table cells), remove
+                 % the "verbatimsep" whitespace from the top (better visually)
+                 \ifspx@opt@verbatimwithframe\else-\sphinxverbatimsep\fi
+                 % caption package adds \abovecaptionskip vspace, remove it
+                 \spx@ifcaptionpackage{-\abovecaptionskip}{}\relax}%
+     \else
+       \vskip\sphinxverbatimsmallskipamount
+       \def\sphinxVerbatim@After
+          {\nointerlineskip\kern\dimexpr\dp\strutbox
+            \ifspx@opt@verbatimwithframe\else-\sphinxverbatimsep\fi
+            \spx@ifcaptionpackage{-\abovecaptionskip}{}\relax
+           \sphinxVerbatim@Title}%
+     \fi
+     \def\@captype{literalblock}%
+     \capstart
+     % \sphinxVerbatimTitle must reset color
+     \setbox\sphinxVerbatim@TitleBox
+            \hbox{\begin{minipage}{\linewidth}%
+     % caption package may detect wrongly if top or bottom, so we help it
+                    \spx@ifcaptionpackage
+                      {\caption@setposition{\spx@opt@literalblockcappos}}{}%
+                    \sphinxVerbatimTitle
+                  \end{minipage}}%
+  \fi
+  \global\let\sphinxLiteralBlockLabel\empty
+  \global\let\sphinxVerbatimTitle\empty
+  \fboxsep\sphinxverbatimsep \fboxrule\sphinxverbatimborder
+  \ifspx@opt@verbatimwithframe\else\fboxrule\z@\fi
+  \let\FrameCommand     \sphinxVerbatim@FrameCommand
+  \let\FirstFrameCommand\sphinxVerbatim@FirstFrameCommand
+  \let\MidFrameCommand  \sphinxVerbatim@MidFrameCommand
+  \let\LastFrameCommand \sphinxVerbatim@LastFrameCommand
+  \ifspx@opt@verbatimhintsturnover\else
+      \let\sphinxVerbatim@Continued\@empty
+      \let\sphinxVerbatim@Continues\@empty
+  \fi
+  \ifspx@opt@verbatimwrapslines
+  % fancyvrb's Verbatim puts each input line in (unbreakable) horizontal boxes.
+  % This customization wraps each line from the input in a \vtop, thus
+  % allowing it to wrap and display on two or more lines in the latex output.
+  %     - The codeline counter will be increased only once.
+  %     - The wrapped material will not break across pages, it is impossible
+  %       to achieve this without extensive rewrite of fancyvrb.
+  %     - The (not used in sphinx) obeytabs option to Verbatim is
+  %       broken by this change (showtabs and tabspace work).
+    \let\sphinxVerbatimFormatLine\sphinxVerbatimFormatLineWrap
+    \let\FV@Space\spx@verbatim@space
+  % Allow breaks at special characters using \PYG... macros.
+    \sphinxbreaksatspecials
+  % Breaks at punctuation characters . , ; ? ! and / (needs catcode activation)
+    \fvset{codes*=\sphinxbreaksviaactive}%
+  \else % end of conditional code for wrapping long code lines
+    \let\sphinxVerbatimFormatLine\sphinxVerbatimFormatLineNoWrap
+  \fi
+  \let\FancyVerbFormatLine\sphinxFancyVerbFormatLine
+  \VerbatimEnvironment
+  % workaround to fancyvrb's check of current list depth
+  \def\@toodeep {\advance\@listdepth\@ne}%
+  % The list environment is needed to control perfectly the vertical space.
+  % Note: \OuterFrameSep used by framed.sty is later set to \topsep hence 0pt.
+  % - if caption: distance from last text baseline to caption baseline is
+  %   A+(B-F)+\ht\strutbox, A = \abovecaptionskip (default 10pt), B =
+  %   \baselineskip, F is the framed.sty \FrameHeightAdjust macro, default 6pt.
+  %   Formula valid for F < 10pt.
+  % - distance of baseline of caption to top of frame is like for tables:
+  %   \sphinxbelowcaptionspace (=0.5\baselineskip)
+  % - if no caption: distance of last text baseline to code frame is S+(B-F),
+  %   with S = \sphinxverbatimtopskip (=\smallskip)
+  % - and distance from bottom of frame to next text baseline is
+  %   \baselineskip+\parskip.
+  % The \trivlist is used to avoid possible "too deeply nested" error.
+  \itemsep   \z@skip
+  \topsep    \z@skip
+  \partopsep \z@skip
+  % trivlist will set \parsep to \parskip (which itself is set to zero above)
+  % \leftmargin will be set to zero by trivlist
+  \rightmargin\z@
+  \parindent  \z@% becomes \itemindent. Default zero, but perhaps overwritten.
+  \trivlist\item\relax
+     \ifspx@inframed\setbox\sphinxVerbatim@ContentsBox\vbox\bgroup
+        \@setminipage\hsize\linewidth
+        % use bulk of minipage paragraph shape restores (this is needed
+        % in indented contexts, at least for some)
+        \textwidth\hsize \columnwidth\hsize \@totalleftmargin\z@
+        \leftskip\z@skip \rightskip\z@skip  \@rightskip\z@skip
+     \else
+       \ifsphinxverbatimwithminipage\noindent\begin{minipage}{\linewidth}\fi
+       \MakeFramed {% adapted over from framed.sty's snugshade environment
+        \advance\hsize-\width\@totalleftmargin\z@\linewidth\hsize\@setminipage
+        }%
+     \fi
+     % For grid placement from \strut's in \FancyVerbFormatLine
+     \lineskip\z@skip
+     % active comma should not be overwritten by \@noligs
+     \ifspx@opt@verbatimwrapslines
+       \let\verbatim@nolig@list \sphinx@verbatim@nolig@list
+     \fi
+     % will fetch its optional arguments if any
+     \OriginalVerbatim
+}
+{%
+  \endOriginalVerbatim
+  \ifspx@inframed
+   \egroup % finish \sphinxVerbatim@ContentsBox vbox
+   \nobreak % update page totals
+   \ifdim\dimexpr\ht\sphinxVerbatim@ContentsBox+
+         \dp\sphinxVerbatim@ContentsBox+
+         \ht\sphinxVerbatim@TitleBox+
+         \dp\sphinxVerbatim@TitleBox+
+         2\fboxsep+2\fboxrule+
+         % try to account for external frame parameters
+         \FrameSep+\FrameRule+
+         % Usage here of 2 baseline distances is empirical.
+         % In border case where code-block fits barely in remaining space,
+         % it gets framed and looks good but the outer frame may continue
+         % on top of next page and give (if no contents after code-block)
+         % an empty framed line, as testing showed.
+         2\baselineskip+
+         % now add all to accumulated page totals and compare to \pagegoal
+         \pagetotal+\pagedepth>\pagegoal
+    % long contents: do not \MakeFramed. Do make a caption (either before or
+    % after) if title exists. Continuation hints across pagebreaks dropped.
+    % FIXME? a bottom caption may end up isolated at top of next page
+    %        (no problem with a top caption, which is default)
+    \spx@opt@verbatimwithframefalse
+    \def\sphinxVerbatim@Title{\noindent\box\sphinxVerbatim@TitleBox\par}%
+    \sphinxVerbatim@Before
+    \noindent\unvbox\sphinxVerbatim@ContentsBox\par
+    \sphinxVerbatim@After
+   \else
+    % short enough contents: use \MakeFramed. As it is nested, this requires
+    % minipage encapsulation.
+    \noindent\begin{minipage}{\linewidth}%
+     \MakeFramed {% Use it now with the fetched contents
+      \advance\hsize-\width\@totalleftmargin\z@\linewidth\hsize\@setminipage
+      }%
+     \unvbox\sphinxVerbatim@ContentsBox
+     % some of this may be superfluous:
+     \par\unskip\@minipagefalse\endMakeFramed
+    \end{minipage}%
+   \fi
+  \else % non-nested \MakeFramed
+    \par\unskip\@minipagefalse\endMakeFramed % from framed.sty snugshade
+    \ifsphinxverbatimwithminipage\end{minipage}\fi
+  \fi
+  \endtrivlist
+}
+\newenvironment {sphinxVerbatimNoFrame}
+  {\spx@opt@verbatimwithframefalse
+   \VerbatimEnvironment
+   \begin{sphinxVerbatim}}
+  {\end{sphinxVerbatim}}
+\newenvironment {sphinxVerbatimintable}
+  {% don't use a frame if in a table cell
+   \spx@opt@verbatimwithframefalse
+   \sphinxverbatimwithminipagetrue
+   % the literal block caption uses \sphinxcaption which is wrapper of \caption,
+   % but \caption must be modified because longtable redefines it to work only
+   % for the own table caption, and tabulary has multiple passes
+   \let\caption\sphinxfigcaption
+   % reduce above caption skip
+   \def\spx@abovecaptionskip{\sphinxverbatimsmallskipamount}%
+   \VerbatimEnvironment
+   \begin{sphinxVerbatim}}
+  {\end{sphinxVerbatim}}
+
+
+%% PARSED LITERALS
+% allow long lines to wrap like they do in code-blocks
+
+% this should be kept in sync with definitions in sphinx.util.texescape
+\newcommand*\sphinxbreaksattexescapedchars{%
+  \def\do##1##2% put potential break point before character
+     {\def##1{\discretionary{}{\sphinxafterbreak\char`##2}{\char`##2}}}%
+  \do\{\{\do\textless\<\do\#\#\do\%\%\do\$\$% {, <, #, %, $
+  \def\do##1##2% put potential break point after character
+     {\def##1{\discretionary{\char`##2}{\sphinxafterbreak}{\char`##2}}}%
+  \do\_\_\do\}\}\do\textasciicircum\^\do\&\&% _, }, ^, &,
+  \do\textgreater\>\do\textasciitilde\~% >, ~
+  \do\textbackslash\\% \
+}
+\newcommand*\sphinxbreaksviaactiveinparsedliteral{%
+  \sphinxbreaksviaactive % by default handles . , ; ? ! /
+  \lccode`\~`\~ %
+  % update \dospecials as it is used by \url
+  % but deactivation will already have been done hence this is unneeded:
+  %  \expandafter\def\expandafter\dospecials\expandafter{\dospecials
+  %   \sphinxbreaksbeforeactivelist\sphinxbreaksafteractivelist\do\-}%
+}
+\newcommand*\sphinxbreaksatspaceinparsedliteral{%
+  \lccode`~32 \lowercase{\let~}\spx@verbatim@space\lccode`\~`\~
+}
+\newcommand*{\sphinxunactivateextras}{\let\do\@makeother
+      \sphinxbreaksbeforeactivelist\sphinxbreaksafteractivelist}%
+% the \catcode13=5\relax (deactivate end of input lines) is left to callers
+\newcommand*{\sphinxunactivateextrasandspace}{\catcode32=10\relax
+      \sphinxunactivateextras}%
+% now for the modified alltt environment
+\newenvironment{sphinxalltt}
+{% at start of next line to workaround Emacs/AUCTeX issue with this file
+\begin{alltt}%
+ \ifspx@opt@parsedliteralwraps
+   \sbox\sphinxcontinuationbox {\spx@opt@verbatimcontinued}%
+   \sbox\sphinxvisiblespacebox {\spx@opt@verbatimvisiblespace}%
+   \sphinxbreaksattexescapedchars
+   \sphinxbreaksviaactiveinparsedliteral
+   \sphinxbreaksatspaceinparsedliteral
+% alltt takes care of the ' as derivative ("prime") in math mode
+   \everymath\expandafter{\the\everymath\sphinxunactivateextrasandspace
+             \catcode`\<=12\catcode`\>=12\catcode`\^=7\catcode`\_=8 }%
+% not sure if displayed math (align,...) can end up in parsed-literal, anyway
+   \everydisplay\expandafter{\the\everydisplay
+             \catcode13=5 \sphinxunactivateextrasandspace
+             \catcode`\<=12\catcode`\>=12\catcode`\^=7\catcode`\_=8 }%
+ \fi }
+{\end{alltt}}
+
+% Protect \href's first argument in contexts such as sphinxalltt (or
+% \sphinxcode). Sphinx uses \#, \%, \& ... always inside \sphinxhref.
+\protected\def\sphinxhref#1#2{{%
+    \sphinxunactivateextrasandspace % never do \scantokens with active space!
+% for the \endlinechar business, https://github.com/latex3/latex2e/issues/286
+    \endlinechar\m@ne\everyeof{{\endlinechar13 #2}}% keep catcode regime for #2
+    \scantokens{\href{#1}}% normalise it for #1 during \href expansion
+}}
+% Same for \url. And also \nolinkurl for coherence.
+\protected\def\sphinxurl#1{{%
+    \sphinxunactivateextrasandspace\everyeof{}% (<- precaution for \scantokens)
+    \endlinechar\m@ne\scantokens{\url{#1}}%
+}}
+\protected\def\sphinxnolinkurl#1{{%
+    \sphinxunactivateextrasandspace\everyeof{}%
+    \endlinechar\m@ne\scantokens{\nolinkurl{#1}}%
+}}
+
+
+%% TOPIC AND CONTENTS BOXES
+%
+% Again based on use of "framed.sty", this allows breakable framed boxes.
+\long\def\spx@ShadowFBox#1{%
+  \leavevmode\begingroup
+  % first we frame the box #1
+  \setbox\@tempboxa
+   \hbox{\vrule\@width\sphinxshadowrule
+         \vbox{\hrule\@height\sphinxshadowrule
+               \kern\sphinxshadowsep
+               \hbox{\kern\sphinxshadowsep #1\kern\sphinxshadowsep}%
+               \kern\sphinxshadowsep
+               \hrule\@height\sphinxshadowrule}%
+         \vrule\@width\sphinxshadowrule}%
+  % Now we add the shadow, like \shadowbox from fancybox.sty would do
+  \dimen@\dimexpr.5\sphinxshadowrule+\sphinxshadowsize\relax
+  \hbox{\vbox{\offinterlineskip
+              \hbox{\copy\@tempboxa\kern-.5\sphinxshadowrule
+                    % add shadow on right side
+                    \lower\sphinxshadowsize
+                          \hbox{\vrule\@height\ht\@tempboxa \@width\dimen@}%
+                    }%
+              \kern-\dimen@ % shift back vertically to bottom of frame
+              % and add shadow at bottom
+              \moveright\sphinxshadowsize
+                    \vbox{\hrule\@width\wd\@tempboxa \@height\dimen@}%
+              }%
+        % move left by the size of right shadow so shadow adds no width
+        \kern-\sphinxshadowsize
+       }%
+  \endgroup
+}
+
+% use framed.sty to allow page breaks in frame+shadow
+% works well inside Lists and Quote-like environments
+% produced by ``topic'' directive (or local contents)
+% could nest if LaTeX writer authorized it
+\newenvironment{sphinxShadowBox}
+  {\def\FrameCommand {\spx@ShadowFBox }%
+   \advance\spx@image@maxheight
+   -\dimexpr2\sphinxshadowrule
+           +2\sphinxshadowsep
+           +\sphinxshadowsize
+           +\baselineskip\relax
+   % configure framed.sty not to add extra vertical spacing
+   \ltx@ifundefined{OuterFrameSep}{}{\OuterFrameSep\z@skip}%
+   % the \trivlist will add the vertical spacing on top and bottom which is
+   % typical of center environment as used in Sphinx <= 1.4.1
+   % the \noindent has the effet of an extra blank line on top, to
+   % imitate closely the layout from Sphinx <= 1.4.1; the \FrameHeightAdjust
+   % will put top part of frame on this baseline.
+   \def\FrameHeightAdjust {\baselineskip}%
+   % use package footnote to handle footnotes
+   \savenotes
+   \trivlist\item\noindent
+    % use a minipage if we are already inside a framed environment
+    \ifspx@inframed\begin{minipage}{\linewidth}\fi
+    \MakeFramed {\spx@inframedtrue
+    % framed.sty puts into "\width" the added width (=2shadowsep+2shadowrule)
+    % adjust \hsize to what the contents must use
+    \advance\hsize-\width
+    % adjust LaTeX parameters to behave properly in indented/quoted contexts
+    \FrameRestore
+    % typeset the contents as in a minipage (Sphinx <= 1.4.1 used a minipage and
+    % itemize/enumerate are therein typeset more tightly, we want to keep
+    % that). We copy-paste from LaTeX source code but don't do a real minipage.
+    \@pboxswfalse
+    \let\@listdepth\@mplistdepth \@mplistdepth\z@
+    \@minipagerestore
+    \@setminipage
+    }%
+  }%
+  {% insert the "endminipage" code
+    \par\unskip
+    \@minipagefalse
+   \endMakeFramed
+     \ifspx@inframed\end{minipage}\fi
+   \endtrivlist
+   % output the stored footnotes
+   \spewnotes
+  }
+
+
+%% NOTICES AND ADMONITIONS
+%
+% Some are quite plain
+% the spx@notice@bordercolor etc are set in the sphinxadmonition environment
+\newenvironment{sphinxlightbox}{%
+  \par
+  \noindent{\color{spx@notice@bordercolor}%
+            \rule{\linewidth}{\spx@notice@border}}\par\nobreak
+  {\parskip\z@skip\noindent}%
+  }
+  {%
+  % counteract previous possible negative skip (French lists!):
+  % (we can't cancel that any earlier \vskip introduced a potential pagebreak)
+  \sphinxvspacefixafterfrenchlists
+  \nobreak\vbox{\noindent\kern\@totalleftmargin
+      {\color{spx@notice@bordercolor}%
+       \rule[\dimexpr.4\baselineskip-\spx@notice@border\relax]
+             {\linewidth}{\spx@notice@border}}\hss}\allowbreak
+  }% end of sphinxlightbox environment definition
+% may be renewenvironment'd by user for complete customization
+\newenvironment{sphinxnote}[1]
+  {\begin{sphinxlightbox}\sphinxstrong{#1} }{\end{sphinxlightbox}}
+\newenvironment{sphinxhint}[1]
+  {\begin{sphinxlightbox}\sphinxstrong{#1} }{\end{sphinxlightbox}}
+\newenvironment{sphinximportant}[1]
+  {\begin{sphinxlightbox}\sphinxstrong{#1} }{\end{sphinxlightbox}}
+\newenvironment{sphinxtip}[1]
+  {\begin{sphinxlightbox}\sphinxstrong{#1} }{\end{sphinxlightbox}}
+% or just use the package options
+% these are needed for common handling by notice environment of lightbox
+% and heavybox but they are  currently not used by lightbox environment
+% and there is consequently no corresponding package option
+\definecolor{sphinxnoteBgColor}{rgb}{1,1,1}
+\definecolor{sphinxhintBgColor}{rgb}{1,1,1}
+\definecolor{sphinximportantBgColor}{rgb}{1,1,1}
+\definecolor{sphinxtipBgColor}{rgb}{1,1,1}
+
+% Others get more distinction
+% Code adapted from framed.sty's "snugshade" environment.
+% Nesting works (inner frames do not allow page breaks).
+\newenvironment{sphinxheavybox}{\par
+   \setlength{\FrameRule}{\spx@notice@border}%
+   \setlength{\FrameSep}{\dimexpr.6\baselineskip-\FrameRule\relax}
+   \advance\spx@image@maxheight
+   -\dimexpr2\FrameRule
+           +2\FrameSep
+           +\baselineskip\relax % will happen again if nested, needed indeed!
+   % configure framed.sty's parameters to obtain same vertical spacing
+   % as for "light" boxes. We need for this to manually insert parskip glue and
+   % revert a skip done by framed before the frame.
+    \ltx@ifundefined{OuterFrameSep}{}{\OuterFrameSep\z@skip}%
+    \vspace{\FrameHeightAdjust}
+   % copied/adapted from framed.sty's snugshade
+   \def\FrameCommand##1{\hskip\@totalleftmargin
+       \fboxsep\FrameSep \fboxrule\FrameRule
+       \fcolorbox{spx@notice@bordercolor}{spx@notice@bgcolor}{##1}%
+       \hskip-\linewidth \hskip-\@totalleftmargin \hskip\columnwidth}%
+   \savenotes
+   % use a minipage if we are already inside a framed environment
+   \ifspx@inframed
+       \noindent\begin{minipage}{\linewidth}
+   \else
+       % handle case where notice is first thing in a list item (or is quoted)
+       \if@inlabel
+        \noindent\par\vspace{-\baselineskip}
+       \else
+        \vspace{\parskip}
+       \fi
+   \fi
+   \MakeFramed {\spx@inframedtrue
+     \advance\hsize-\width \@totalleftmargin\z@ \linewidth\hsize
+     % minipage initialization copied from LaTeX source code.
+     \@pboxswfalse
+     \let\@listdepth\@mplistdepth \@mplistdepth\z@
+     \@minipagerestore
+     \@setminipage }%
+   }
+   {%
+   \par\unskip
+   \@minipagefalse
+   \endMakeFramed
+   \ifspx@inframed\end{minipage}\fi
+   % set footnotes at bottom of page
+   \spewnotes
+   % arrange for similar spacing below frame as for "light" boxes.
+   \vskip .4\baselineskip
+   }% end of sphinxheavybox environment definition
+% may be renewenvironment'd by user for complete customization
+\newenvironment{sphinxwarning}[1]
+  {\begin{sphinxheavybox}\sphinxstrong{#1} }{\end{sphinxheavybox}}
+\newenvironment{sphinxcaution}[1]
+  {\begin{sphinxheavybox}\sphinxstrong{#1} }{\end{sphinxheavybox}}
+\newenvironment{sphinxattention}[1]
+  {\begin{sphinxheavybox}\sphinxstrong{#1} }{\end{sphinxheavybox}}
+\newenvironment{sphinxdanger}[1]
+  {\begin{sphinxheavybox}\sphinxstrong{#1} }{\end{sphinxheavybox}}
+\newenvironment{sphinxerror}[1]
+  {\begin{sphinxheavybox}\sphinxstrong{#1} }{\end{sphinxheavybox}}
+% or just use package options
+
+% the \colorlet of xcolor (if at all loaded) is overkill for our use case
+\newcommand{\sphinxcolorlet}[2]
+    {\expandafter\let\csname\@backslashchar color@#1\expandafter\endcsname
+                     \csname\@backslashchar color@#2\endcsname }
+
+% the main dispatch for all types of notices
+\newenvironment{sphinxadmonition}[2]{% #1=type, #2=heading
+  % can't use #1 directly in definition of end part
+  \def\spx@noticetype {#1}%
+  % set parameters of heavybox/lightbox
+  \sphinxcolorlet{spx@notice@bordercolor}{sphinx#1BorderColor}%
+  \sphinxcolorlet{spx@notice@bgcolor}{sphinx#1BgColor}%
+  \spx@notice@border \dimexpr\csname spx@opt@#1border\endcsname\relax
+  % start specific environment, passing the heading as argument
+  \begin{sphinx#1}{#2}}
+  % workaround some LaTeX "feature" of \end command
+ {\edef\spx@temp{\noexpand\end{sphinx\spx@noticetype}}\spx@temp}
+
+
+%% PYTHON DOCS MACROS AND ENVIRONMENTS
+%  (some macros here used by \maketitle in sphinxmanual.cls and sphinxhowto.cls)
+
+% \moduleauthor{name}{email}
+\newcommand{\moduleauthor}[2]{}
+
+% \sectionauthor{name}{email}
+\newcommand{\sectionauthor}[2]{}
+
+% Allow the release number to be specified independently of the
+% \date{}.  This allows the date to reflect the document's date and
+% release to specify the release that is documented.
+%
+\newcommand{\py@release}{\releasename\space\version}
+\newcommand{\version}{}% part of \py@release, used by title page and headers
+% \releaseinfo is used on titlepage (sphinxmanual.cls, sphinxhowto.cls)
+\newcommand{\releaseinfo}{}
+\newcommand{\setreleaseinfo}[1]{\renewcommand{\releaseinfo}{#1}}
+% this is inserted via template and #1=release config variable
+\newcommand{\release}[1]{\renewcommand{\version}{#1}}
+% this is defined by template to 'releasename' latex_elements key
+\newcommand{\releasename}{}
+% Fix issue in case release and releasename deliberately left blank
+\newcommand{\sphinxheadercomma}{, }% used in fancyhdr header definition
+\newcommand{\sphinxifemptyorblank}[1]{%
+% test after one expansion of macro #1 if contents is empty or spaces
+     \if&\expandafter\@firstofone\detokenize\expandafter{#1}&%
+     \expandafter\@firstoftwo\else\expandafter\@secondoftwo\fi}%
+\AtBeginDocument {%
+   \sphinxifemptyorblank{\releasename}
+     {\sphinxifemptyorblank{\version}{\let\sphinxheadercomma\empty}{}}
+     {}%
+}%
+
+% Allow specification of the author's address separately from the
+% author's name.  This can be used to format them differently, which
+% is a good thing.
+%
+\newcommand{\py@authoraddress}{}
+\newcommand{\authoraddress}[1]{\renewcommand{\py@authoraddress}{#1}}
+
+% {fulllineitems} is the main environment for object descriptions.
+%
+\newcommand{\py@itemnewline}[1]{%
+  \kern\labelsep
+  \@tempdima\linewidth
+  \advance\@tempdima \labelwidth\makebox[\@tempdima][l]{#1}%
+  \kern-\labelsep
+}
+
+\newenvironment{fulllineitems}{%
+  \begin{list}{}{\labelwidth \leftmargin
+                 \rightmargin \z@ \topsep -\parskip \partopsep \parskip
+                 \itemsep -\parsep
+                 \let\makelabel=\py@itemnewline}%
+}{\end{list}}
+
+% Signatures, possibly multi-line
+%
+\newlength{\py@argswidth}
+\newcommand{\py@sigparams}[2]{%
+  \parbox[t]{\py@argswidth}{#1\sphinxcode{)}#2}}
+\newcommand{\pysigline}[1]{\item[{#1}]}
+\newcommand{\pysiglinewithargsret}[3]{%
+  \settowidth{\py@argswidth}{#1\sphinxcode{(}}%
+  \addtolength{\py@argswidth}{-2\py@argswidth}%
+  \addtolength{\py@argswidth}{\linewidth}%
+  \item[{#1\sphinxcode{(}\py@sigparams{#2}{#3}}]}
+\newcommand{\pysigstartmultiline}{%
+ \def\pysigstartmultiline{\vskip\smallskipamount\parskip\z@skip\itemsep\z@skip}%
+ \edef\pysigstopmultiline
+     {\noexpand\leavevmode\parskip\the\parskip\relax\itemsep\the\itemsep\relax}%
+ \parskip\z@skip\itemsep\z@skip
+}
+
+% Production lists
+%
+\newenvironment{productionlist}{%
+%  \def\sphinxoptional##1{{\Large[}##1{\Large]}}
+  \def\production##1##2{\\\sphinxcode{\sphinxupquote{##1}}&::=&\sphinxcode{\sphinxupquote{##2}}}%
+  \def\productioncont##1{\\& &\sphinxcode{\sphinxupquote{##1}}}%
+  \parindent=2em
+  \indent
+  \setlength{\LTpre}{0pt}%
+  \setlength{\LTpost}{0pt}%
+  \begin{longtable}[l]{lcl}
+}{%
+  \end{longtable}
+}
+
+% Definition lists; requested by AMK for HOWTO documents.  Probably useful
+% elsewhere as well, so keep in in the general style support.
+%
+\newenvironment{definitions}{%
+  \begin{description}%
+  \def\term##1{\item[{##1}]\mbox{}\\*[0mm]}%
+}{%
+  \end{description}%
+}
+
+%% FROM DOCTUTILS LATEX WRITER
+%
+% The following is stuff copied from docutils' latex writer.
+%
+\newcommand{\optionlistlabel}[1]{\normalfont\bfseries #1 \hfill}% \bf deprecated
+\newenvironment{optionlist}[1]
+{\begin{list}{}
+  {\setlength{\labelwidth}{#1}
+   \setlength{\rightmargin}{1cm}
+   \setlength{\leftmargin}{\rightmargin}
+   \addtolength{\leftmargin}{\labelwidth}
+   \addtolength{\leftmargin}{\labelsep}
+   \renewcommand{\makelabel}{\optionlistlabel}}
+}{\end{list}}
+
+\newlength{\lineblockindentation}
+\setlength{\lineblockindentation}{2.5em}
+\newenvironment{lineblock}[1]
+{\begin{list}{}
+  {\setlength{\partopsep}{\parskip}
+   \addtolength{\partopsep}{\baselineskip}
+   \topsep0pt\itemsep0.15\baselineskip\parsep0pt
+   \leftmargin#1\relax}
+ \raggedright}
+{\end{list}}
+
+% From docutils.writers.latex2e
+% inline markup (custom roles)
+% \DUrole{#1}{#2} tries \DUrole#1{#2}
+\providecommand*{\DUrole}[2]{%
+  \ifcsname DUrole\detokenize{#1}\endcsname
+    \csname DUrole\detokenize{#1}\endcsname{#2}%
+  \else% backwards compatibility: try \docutilsrole#1{#2}
+    \ifcsname docutilsrole\detokenize{#1}\endcsname
+      \csname docutilsrole\detokenize{#1}\endcsname{#2}%
+    \else
+      #2%
+    \fi
+  \fi
+}
+
+\providecommand*{\DUprovidelength}[2]{%
+  \ifdefined#1\else\newlength{#1}\setlength{#1}{#2}\fi
+}
+
+\DUprovidelength{\DUlineblockindent}{2.5em}
+\ifdefined\DUlineblock\else
+  \newenvironment{DUlineblock}[1]{%
+    \list{}{\setlength{\partopsep}{\parskip}
+            \addtolength{\partopsep}{\baselineskip}
+            \setlength{\topsep}{0pt}
+            \setlength{\itemsep}{0.15\baselineskip}
+            \setlength{\parsep}{0pt}
+            \setlength{\leftmargin}{#1}}
+    \raggedright
+  }
+  {\endlist}
+\fi
+
+%% TEXT STYLING
+%
+% to obtain straight quotes we execute \@noligs as patched by upquote, and
+% \scantokens is needed in cases where it would be too late for the macro to
+% first set catcodes and then fetch its argument. We also make the contents
+% breakable at non-escaped . , ; ? ! / using \sphinxbreaksviaactive,
+% and also at \ character (which is escaped to \textbackslash{}).
+\protected\def\sphinxtextbackslashbreakbefore
+ {\discretionary{}{\sphinxafterbreak\sphinx@textbackslash}{\sphinx@textbackslash}}
+\protected\def\sphinxtextbackslashbreakafter
+ {\discretionary{\sphinx@textbackslash}{\sphinxafterbreak}{\sphinx@textbackslash}}
+\let\sphinxtextbackslash\sphinxtextbackslashbreakafter
+% the macro must be protected if it ends up used in moving arguments,
+% in 'alltt' \@noligs is done already, and the \scantokens must be avoided.
+\protected\def\sphinxupquote#1{{\def\@tempa{alltt}%
+  \ifx\@tempa\@currenvir\else
+   \ifspx@opt@inlineliteralwraps
+      % break at . , ; ? ! /
+      \sphinxbreaksviaactive
+      % break also at \
+      \let\sphinx@textbackslash\textbackslash
+      \let\textbackslash\sphinxtextbackslash
+      % by default, no continuation symbol on next line but may be added
+      \let\sphinxafterbreak\sphinxafterbreakofinlineliteral
+      % do not overwrite the comma set-up
+      \let\verbatim@nolig@list\sphinx@literal@nolig@list
+   \fi
+   % fix a space-gobbling issue due to LaTeX's original \do@noligs
+% TODO: using \@noligs as patched by upquote.sty is now unneeded because
+% either ` and ' are escaped (non-unicode engines) or they don't build
+% ligatures (unicode engines). Thus remove this and unify handling of `, <, >, 
+% ' and - with the characters . , ; ? ! / as handled via
+% \sphinxbreaksviaactive.
+% Hence \sphinx@do@noligs will be removed, or rather replaced  with code
+% inserting discretionaries, as they allow a continuation symbol on start of
+% next line to achieve common design with code-blocks.
+   \let\do@noligs\sphinx@do@noligs
+   \@noligs\endlinechar\m@ne\everyeof{}% (<- in case inside \sphinxhref)
+   \expandafter\scantokens
+  \fi {{#1}}}}% extra brace pair to fix end-space gobbling issue...
+\def\sphinx@do@noligs #1{\catcode`#1\active\begingroup\lccode`\~`#1\relax
+    \lowercase{\endgroup\def~{\leavevmode\kern\z@\char`#1 }}}
+\def\sphinx@literal@nolig@list {\do\`\do\<\do\>\do\'\do\-}%
+\let\sphinxafterbreakofinlineliteral\empty
+
+% Some custom font markup commands.
+\protected\def\sphinxstrong#1{\textbf{#1}}
+\protected\def\sphinxcode#1{\texttt{#1}}
+\protected\def\sphinxbfcode#1{\textbf{\sphinxcode{#1}}}
+\protected\def\sphinxemail#1{\textsf{#1}}
+\protected\def\sphinxtablecontinued#1{\textsf{#1}}
+\protected\def\sphinxtitleref#1{\emph{#1}}
+\protected\def\sphinxmenuselection#1{\emph{#1}}
+\protected\def\sphinxguilabel#1{\emph{#1}}
+\protected\def\sphinxkeyboard#1{\sphinxcode{#1}}
+\protected\def\sphinxaccelerator#1{\underline{#1}}
+\protected\def\sphinxcrossref#1{\emph{#1}}
+\protected\def\sphinxtermref#1{\emph{#1}}
+% \optional is used for ``[, arg]``, i.e. desc_optional nodes.
+\long\protected\def\sphinxoptional#1{%
+  {\textnormal{\Large[}}{#1}\hspace{0.5mm}{\textnormal{\Large]}}}
+
+% additional customizable styling
+\def\sphinxstyleindexentry   #1{\texttt{#1}}
+\def\sphinxstyleindexextra   #1{ (\emph{#1})}
+\def\sphinxstyleindexpageref #1{, \pageref{#1}}
+\def\sphinxstyleindexpagemain#1{\textbf{#1}}
+\protected\def\spxentry#1{#1}% will get \let to \sphinxstyleindexentry in index
+\protected\def\spxextra#1{#1}% will get \let to \sphinxstyleindexextra in index
+\def\sphinxstyleindexlettergroup #1%
+    {{\Large\sffamily#1}\nopagebreak\vspace{1mm}}
+\def\sphinxstyleindexlettergroupDefault #1%
+    {{\Large\sffamily\sphinxnonalphabeticalgroupname}\nopagebreak\vspace{1mm}}
+\protected\def\sphinxstyletopictitle   #1{\textbf{#1}\par\medskip}
+\let\sphinxstylesidebartitle\sphinxstyletopictitle
+\protected\def\sphinxstyleothertitle   #1{\textbf{#1}}
+\protected\def\sphinxstylesidebarsubtitle #1{~\\\textbf{#1} \smallskip}
+% \text.. commands do not allow multiple paragraphs
+\protected\def\sphinxstyletheadfamily    {\sffamily}
+\protected\def\sphinxstyleemphasis     #1{\emph{#1}}
+\protected\def\sphinxstyleliteralemphasis#1{\emph{\sphinxcode{#1}}}
+\protected\def\sphinxstylestrong       #1{\textbf{#1}}
+\protected\def\sphinxstyleliteralstrong#1{\sphinxbfcode{#1}}
+\protected\def\sphinxstyleabbreviation #1{\textsc{#1}}
+\protected\def\sphinxstyleliteralintitle#1{\sphinxcode{#1}}
+\newcommand*\sphinxstylecodecontinued[1]{\footnotesize(#1)}%
+\newcommand*\sphinxstylecodecontinues[1]{\footnotesize(#1)}%
+% figure legend comes after caption and may contain arbitrary body elements
+\newenvironment{sphinxlegend}{\par\small}{\par}
+% reduce hyperref "Token not allowed in a PDF string" warnings on PDF builds
+\AtBeginDocument{\pdfstringdefDisableCommands{%
+% all "protected" macros possibly ending up in section titles should be here
+% TODO: examine if \sphinxhref, \sphinxurl, \sphinnolinkurl should be handled
+    \let\sphinxstyleemphasis        \@firstofone
+    \let\sphinxstyleliteralemphasis \@firstofone
+    \let\sphinxstylestrong          \@firstofone
+    \let\sphinxstyleliteralstrong   \@firstofone
+    \let\sphinxstyleabbreviation    \@firstofone
+    \let\sphinxstyleliteralintitle  \@firstofone
+    \let\sphinxupquote  \@firstofone
+    \let\sphinxstrong   \@firstofone
+    \let\sphinxcode     \@firstofone
+    \let\sphinxbfcode   \@firstofone
+    \let\sphinxemail    \@firstofone
+    \let\sphinxcrossref \@firstofone
+    \let\sphinxtermref  \@firstofone
+    \let\sphinxhyphen\sphinxhyphenforbookmarks
+}}
+
+% Special characters
+%
+% This definition prevents en-dash and em-dash TeX ligatures.
+% 
+% It inserts a potential breakpoint after the hyphen. This is to keep in sync
+% with behavior in code-blocks, parsed and inline literals. For a breakpoint
+% before the hyphen use \leavevmode\kern\z@- (within \makeatletter/\makeatother)
+\protected\def\sphinxhyphen#1{-\kern\z@}
+% The {} from texescape mark-up is kept, else -- gives en-dash in PDF bookmark
+\def\sphinxhyphenforbookmarks{-}
+
+% For curly braces inside \index macro
+\def\sphinxleftcurlybrace{\{}
+\def\sphinxrightcurlybrace{\}}
+
+% Declare Unicode characters used by linux tree command to pdflatex utf8/utf8x
+\def\spx@bd#1#2{%
+  \leavevmode
+  \begingroup
+  \ifx\spx@bd@height   \@undefined\def\spx@bd@height{\baselineskip}\fi
+  \ifx\spx@bd@width    \@undefined\setbox0\hbox{0}\def\spx@bd@width{\wd0 }\fi
+  \ifx\spx@bd@thickness\@undefined\def\spx@bd@thickness{.6\p@}\fi
+  \ifx\spx@bd@lower    \@undefined\def\spx@bd@lower{\dp\strutbox}\fi
+  \lower\spx@bd@lower#1{#2}%
+  \endgroup
+}%
+\@namedef{sphinx@u2500}% BOX DRAWINGS LIGHT HORIZONTAL
+  {\spx@bd{\vbox to\spx@bd@height}
+          {\vss\hrule\@height\spx@bd@thickness
+                     \@width\spx@bd@width\vss}}%
+\@namedef{sphinx@u2502}% BOX DRAWINGS LIGHT VERTICAL
+  {\spx@bd{\hb@xt@\spx@bd@width}
+          {\hss\vrule\@height\spx@bd@height
+                     \@width \spx@bd@thickness\hss}}%
+\@namedef{sphinx@u2514}% BOX DRAWINGS LIGHT UP AND RIGHT
+  {\spx@bd{\hb@xt@\spx@bd@width}
+          {\hss\raise.5\spx@bd@height
+           \hb@xt@\z@{\hss\vrule\@height.5\spx@bd@height
+                                \@width \spx@bd@thickness\hss}%
+           \vbox to\spx@bd@height{\vss\hrule\@height\spx@bd@thickness
+                                            \@width.5\spx@bd@width\vss}}}%
+\@namedef{sphinx@u251C}% BOX DRAWINGS LIGHT VERTICAL AND RIGHT
+  {\spx@bd{\hb@xt@\spx@bd@width}
+          {\hss
+           \hb@xt@\z@{\hss\vrule\@height\spx@bd@height
+                                \@width \spx@bd@thickness\hss}%
+           \vbox to\spx@bd@height{\vss\hrule\@height\spx@bd@thickness
+                                            \@width.5\spx@bd@width\vss}}}%
+\protected\def\sphinxunichar#1{\@nameuse{sphinx@u#1}}%
+
+% Tell TeX about pathological hyphenation cases:
+\hyphenation{Base-HTTP-Re-quest-Hand-ler}
+\endinput
diff --git a/_build/latex/sphinx.xdy b/_build/latex/sphinx.xdy
new file mode 100644
index 0000000..edca178
--- /dev/null
+++ b/_build/latex/sphinx.xdy
@@ -0,0 +1,230 @@
+;;; -*- mode: lisp; coding: utf-8; -*-
+
+;; Unfortunately xindy is out-of-the-box hyperref-incompatible.  This
+;; configuration is a workaround, which requires to pass option
+;; hyperindex=false to hyperref.
+;; textit and emph not currently used, spxpagem replaces former textbf
+(define-attributes (("textbf" "textit" "emph" "spxpagem" "default")))
+(markup-locref :open "\textbf{\hyperpage{" :close "}}" :attr "textbf")
+(markup-locref :open "\textit{\hyperpage{" :close "}}" :attr "textit")
+(markup-locref :open "\emph{\hyperpage{" :close "}}" :attr "emph")
+(markup-locref :open "\spxpagem{\hyperpage{" :close "}}" :attr "spxpagem")
+(markup-locref :open "\hyperpage{" :close "}" :attr "default")
+
+(require "numeric-sort.xdy")
+
+;; xindy base module latex.xdy loads tex.xdy and the latter instructs
+;; xindy to ignore **all** TeX macros in .idx entries, except those
+;; explicitely described in merge rule.  But when after applying all
+;; merge rules an empty string results, xindy raises an error:
+
+;;   ERROR: CHAR: index 0 should be less than the length of the string
+
+;; For example when using pdflatex with utf-8 characters the index
+;; file will contain \IeC macros and they will get ignored except if
+;; suitable merge rules are loaded early.  The texindy script coming
+;; with xindy provides this, but only for Latin scripts.  The texindy
+;; man page says to use rather xelatex or lualatex in case of Cyrillic
+;; scripts.
+
+;; Sphinx contributes LICRcyr2utf8.xdy to provide support for Cyrillic
+;; scripts for the pdflatex engine.
+
+;; Another issue caused by xindy ignoring all TeX macros except those
+;; explicitely declared reveals itself when attempting to index ">>>",
+;; as the ">" is converted to "\textgreater{}" by Sphinx's LaTeX
+;; escaping.
+
+;; To fix this, Sphinx does **not** use texindy, and does not even
+;; load the xindy latex.xdy base module.
+
+;(require "latex.xdy")
+
+;; Rather it incorporates some suitable extracts from latex.xdy and
+;; tex.xdy with additional Sphinx contributed rules.
+
+;; But, this means for pdflatex and Latin scripts that the xindy file
+;; tex/inputenc/uf8.xdy is not usable because it refers to the macro
+;; \IeC only sporadically, and as tex.xdy is not loaded, a rule such as
+;;     (merge-rule "\'e" "é" :string)
+;; does not work, it must be
+;;     (merge-rule "\IeC {\'e}" "é" :string)
+;; So Sphinx contributes LICRlatin2utf8.xdy to mitigate that problem.
+
+;;;;;;;; extracts from tex.xdy (discarding most original comments):
+
+;;;
+;;; TeX conventions
+;;;
+
+;; Discard leading and trailing white space. Collapse multiple white
+;; space characters to blank.
+
+(merge-rule "^ +" "" :eregexp)
+(merge-rule " +$" "" :eregexp)
+(merge-rule "  +" " " :eregexp)
+
+;; Handle TeX markup
+
+(merge-rule "\\([{}$%&#])" "\1" :eregexp)
+
+;;;;;;;; end of extracts from xindy's tex.xdy
+
+;;;;;;;; extracts from latex.xdy:
+
+;; Standard location classes: arabic and roman numbers, and alphabets.
+
+(define-location-class "arabic-page-numbers" ("arabic-numbers"))
+(define-location-class "roman-page-numbers"  ("roman-numbers-lowercase"))
+(define-location-class "Roman-page-numbers"  ("roman-numbers-uppercase"))
+(define-location-class "alpha-page-numbers"  ("alpha"))
+(define-location-class "Alpha-page-numbers"  ("ALPHA"))
+
+;; Output Markup
+
+(markup-letter-group-list :sep "~n~n  \indexspace~n")
+
+(markup-indexentry :open "~n  \item "           :depth 0)
+(markup-indexentry :open "~n    \subitem "      :depth 1)
+(markup-indexentry :open "~n      \subsubitem " :depth 2)
+
+(markup-locclass-list :open ", " :sep ", ")
+(markup-locref-list   :sep ", ")
+
+;;;;;;;; end of extracts from latex.xdy
+
+;; The LaTeX \index command turns \ into normal character so the TeX macros
+;; written to .idx files are not followed by a blank. This is different
+;; from non-ascii letters which end up (with pdflatex) as \IeC macros in .idx
+;; file, with a blank space after \IeC
+
+;; Details of the syntax are explained at
+;;     http://xindy.sourceforge.net/doc/manual-3.html
+;; In absence of :string, "xindy uses an auto-detection mechanism to decide,
+;; if the pattern is a regular expression or not". But it is not obvious to
+;; guess, for example "\\_" is not detected as RE but "\\P\{\}" is, so for
+;; being sure we apply the :string switch everywhere and do not use \\ etc...
+
+;; Go back from sphinx.util.texescape TeX macros to UTF-8
+
+(merge-rule "\sphinxleftcurlybrace{}" "{" :string)
+(merge-rule "\sphinxrightcurlybrace{}" "}" :string)
+(merge-rule "\_" "_" :string)
+(merge-rule "{[}" "[" :string)
+(merge-rule "{]}" "]" :string)
+(merge-rule "\textbackslash{}" "\" :string)  ; " for Emacs syntax highlighting
+(merge-rule "\textasciitilde{}" "~~" :string); the ~~ escape is needed here
+(merge-rule "\textasciicircum{}" "^" :string)
+(merge-rule "\sphinxhyphen{}" "-" :string)
+(merge-rule "\textquotesingle{}" "'" :string)
+(merge-rule "\textasciigrave{}" "`" :string)
+(merge-rule "\textless{}" "<" :string)
+(merge-rule "\textgreater{}" ">" :string)
+(merge-rule "\P{}" "¶" :string)
+(merge-rule "\S{}" "§" :string)
+(merge-rule "\texteuro{}" "€" :string)
+(merge-rule "\(\infty\)" "∞" :string)
+(merge-rule "\(\pm\)" "±" :string)
+(merge-rule "\(\rightarrow\)" "→" :string)
+(merge-rule "\(\checkmark\)" "✓" :string)
+(merge-rule "\textendash{}" "–" :string)
+(merge-rule "\textbar{}" "|" :string)
+(merge-rule "\(\sp{\text{0}}\)" "�" :string)
+(merge-rule "\(\sp{\text{1}}\)" "¹" :string)
+(merge-rule "\(\sp{\text{2}}\)" "²" :string)
+(merge-rule "\(\sp{\text{3}}\)" "³" :string)
+(merge-rule "\(\sp{\text{4}}\)" "�" :string)
+(merge-rule "\(\sp{\text{5}}\)" "�" :string)
+(merge-rule "\(\sp{\text{6}}\)" "�" :string)
+(merge-rule "\(\sp{\text{7}}\)" "�" :string)
+(merge-rule "\(\sp{\text{8}}\)" "�" :string)
+(merge-rule "\(\sp{\text{9}}\)" "�" :string)
+(merge-rule "\(\sb{\text{0}}\)" "â‚€" :string)
+(merge-rule "\(\sb{\text{1}}\)" "�" :string)
+(merge-rule "\(\sb{\text{2}}\)" "â‚‚" :string)
+(merge-rule "\(\sb{\text{3}}\)" "₃" :string)
+(merge-rule "\(\sb{\text{4}}\)" "â‚„" :string)
+(merge-rule "\(\sb{\text{5}}\)" "â‚…" :string)
+(merge-rule "\(\sb{\text{6}}\)" "₆" :string)
+(merge-rule "\(\sb{\text{7}}\)" "₇" :string)
+(merge-rule "\(\sb{\text{8}}\)" "₈" :string)
+(merge-rule "\(\sb{\text{9}}\)" "₉" :string)
+(merge-rule "\IeC {\textalpha }" "α" :string)
+(merge-rule "\IeC {\textbeta }" "β" :string)
+(merge-rule "\IeC {\textgamma }" "γ" :string)
+(merge-rule "\IeC {\textdelta }" "δ" :string)
+(merge-rule "\IeC {\textepsilon }" "ε" :string)
+(merge-rule "\IeC {\textzeta }" "ζ" :string)
+(merge-rule "\IeC {\texteta }" "η" :string)
+(merge-rule "\IeC {\texttheta }" "θ" :string)
+(merge-rule "\IeC {\textiota }" "ι" :string)
+(merge-rule "\IeC {\textkappa }" "κ" :string)
+(merge-rule "\IeC {\textlambda }" "λ" :string)
+(merge-rule "\IeC {\textmu }" "μ" :string)
+(merge-rule "\IeC {\textnu }" "ν" :string)
+(merge-rule "\IeC {\textxi }" "ξ" :string)
+(merge-rule "\IeC {\textomicron }" "ο" :string)
+(merge-rule "\IeC {\textpi }" "Ï€" :string)
+(merge-rule "\IeC {\textrho }" "�" :string)
+(merge-rule "\IeC {\textsigma }" "σ" :string)
+(merge-rule "\IeC {\texttau }" "Ï„" :string)
+(merge-rule "\IeC {\textupsilon }" "Ï…" :string)
+(merge-rule "\IeC {\textphi }" "φ" :string)
+(merge-rule "\IeC {\textchi }" "χ" :string)
+(merge-rule "\IeC {\textpsi }" "ψ" :string)
+(merge-rule "\IeC {\textomega }" "ω" :string)
+(merge-rule "\IeC {\textAlpha }" "Α" :string)
+(merge-rule "\IeC {\textBeta }" "Î’" :string)
+(merge-rule "\IeC {\textGamma }" "Γ" :string)
+(merge-rule "\IeC {\textDelta }" "Δ" :string)
+(merge-rule "\IeC {\textEpsilon }" "Ε" :string)
+(merge-rule "\IeC {\textZeta }" "Ζ" :string)
+(merge-rule "\IeC {\textEta }" "Η" :string)
+(merge-rule "\IeC {\textTheta }" "Θ" :string)
+(merge-rule "\IeC {\textIota }" "Ι" :string)
+(merge-rule "\IeC {\textKappa }" "Κ" :string)
+(merge-rule "\IeC {\textLambda }" "Λ" :string)
+(merge-rule "\IeC {\textMu }" "Μ" :string)
+(merge-rule "\IeC {\textNu }" "�" :string)
+(merge-rule "\IeC {\textTheta }" "Θ" :string)
+(merge-rule "\IeC {\textIota }" "Ι" :string)
+(merge-rule "\IeC {\textKappa }" "Κ" :string)
+(merge-rule "\IeC {\textLambda }" "Λ" :string)
+(merge-rule "\IeC {\textMu }" "Μ" :string)
+(merge-rule "\IeC {\textNu }" "�" :string)
+(merge-rule "\IeC {\textXi }" "Ξ" :string)
+(merge-rule "\IeC {\textOmicron }" "Ο" :string)
+(merge-rule "\IeC {\textPi }" "Π" :string)
+(merge-rule "\IeC {\textRho }" "Ρ" :string)
+(merge-rule "\IeC {\textSigma }" "Σ" :string)
+(merge-rule "\IeC {\textTau }" "Τ" :string)
+(merge-rule "\IeC {\textUpsilon }" "Î¥" :string)
+(merge-rule "\IeC {\textPhi }" "Φ" :string)
+(merge-rule "\IeC {\textChi }" "Χ" :string)
+(merge-rule "\IeC {\textPsi }" "Ψ" :string)
+(merge-rule "\IeC {\textOmega }" "Ω" :string)
+(merge-rule "\IeC {\textohm }" "Ω" :string)
+
+;; This xindy module provides some basic support for "see"
+(require "makeindex.xdy")
+
+;; This creates one-letter headings and works fine with utf-8 letters.
+;; For Cyrillic with pdflatex works thanks to LICRcyr2utf8.xdy
+(require "latin-lettergroups.xdy")
+
+;; currently we don't (know how to easily) separate "Numbers" from
+;; "Symbols" with xindy as is the case with makeindex.
+(markup-index :open  "\begin{sphinxtheindex}
+\let\lettergroup\sphinxstyleindexlettergroup
+\let\lettergroupDefault\sphinxstyleindexlettergroupDefault
+\let\spxpagem\sphinxstyleindexpagemain
+\let\spxentry\sphinxstyleindexentry
+\let\spxextra\sphinxstyleindexextra
+
+"
+	      :close "
+
+\end{sphinxtheindex}
+"
+	      :tree)
+
diff --git a/_build/latex/sphinxcyrillic.sty b/_build/latex/sphinxcyrillic.sty
new file mode 100644
index 0000000..482b4e3
--- /dev/null
+++ b/_build/latex/sphinxcyrillic.sty
@@ -0,0 +1,55 @@
+%% CYRILLIC IN NON-CYRILLIC DOCUMENTS (pdflatex only)
+%
+% refs: https://tex.stackexchange.com/q/460271/
+\ProvidesPackage{sphinxcyrillic}%
+  [2018/11/21 v2.0 support for Cyrillic in non-Cyrillic documents]
+\RequirePackage{kvoptions}
+\SetupKeyvalOptions{prefix=spx@cyropt@} % use \spx@cyropt@ prefix
+\DeclareBoolOption[false]{Xtwo}
+\DeclareBoolOption[false]{TtwoA}
+\DeclareDefaultOption{\@unknownoptionerror}
+\ProcessLocalKeyvalOptions* % ignore class options
+
+\ifspx@cyropt@Xtwo
+% original code by tex.sx user egreg (updated 2019/10/28):
+%   https://tex.stackexchange.com/a/460325/
+% 159 Cyrillic glyphs as available in X2 TeX 8bit font encoding
+% This assumes inputenc loaded with utf8 option, or LaTeX release
+% as recent as 2018/04/01 which does it automatically. 
+  \@tfor\next:=%
+    {�}{Ђ}{Є}{Ѕ}{І}{Ј}{Љ}{Њ}{Ћ}{Ў}{�}{�}{Б}{В}{Г}{Д}{Е}{Ж}{З}{И}{Й}%
+    {К}{Л}{М}{�}{О}{П}{Р}{С}{Т}{У}{Ф}{Х}{Ц}{Ч}{Ш}{Щ}{Ъ}{Ы}{Ь}{Э}{Ю}%
+    {Я}{а}{б}{в}{г}{д}{е}{ж}{з}{и}{й}{к}{л}{м}{н}{о}{п}{р}{�}{т}{у}%
+    {ф}{х}{ц}{ч}{ш}{щ}{ъ}{ы}{ь}{�}{ю}{�}{ё}{ђ}{є}{ѕ}{і}{ј}{љ}{њ}{ћ}%
+    {ў}{џ}{Ѣ}{ѣ}{Ѫ}{ѫ}{Ѵ}{ѵ}{�}{ґ}{Ғ}{ғ}{Ҕ}{ҕ}{Җ}{җ}{Ҙ}{ҙ}{Қ}{қ}{Ҝ}{�}%
+    {Òž}{ÒŸ}{Ò }{Ò¡}{Ò¢}{Ò£}{Ò¤}{Ò¥}{Ò¦}{Ò§}{Ò¨}{Ò©}{Òª}{Ò«}{Ò¬}{Ò­}{Ò®}{Ò¯}{Ò°}{Ò±}{Ò²}{Ò³}%
+    {Ҵ}{ҵ}{Ҷ}{ҷ}{Ҹ}{ҹ}{Һ}{һ}{Ҽ}{ҽ}{Ҿ}{ҿ}{Ӏ}{Ӄ}{ӄ}{Ӆ}{ӆ}{Ӈ}{ӈ}{Ӌ}{ӌ}%
+    {Ó�}{ÓŽ}{Ó”}{Ó•}{Ó˜}{Ó™}{Ó }{Ó¡}{Ó¨}{Ó©}\do
+  {%
+    \begingroup\def\IeC{\protect\DeclareTextSymbolDefault}%
+    \protected@edef\@temp{\endgroup
+    \@ifl@t@r{\fmtversion}{2019/10/01}{\csname u8:\next\endcsname}{\next}}%
+    \@temp{X2}%
+  }%
+\else
+\ifspx@cyropt@TtwoA
+% original code by tex.sx user jfbu:
+%   https://tex.stackexchange.com/a/460305/
+% 63*2+1=127 Cyrillic glyphs as found in T2A 8bit TeX font-encoding
+  \@tfor\@tempa:=%
+    {ae}{a}{b}{chrdsc}{chvcrs}{ch}{c}{dje}{dze}{dzhe}{d}{erev}{ery}{e}%
+    {f}{ghcrs}{gup}{g}{hdsc}{hrdsn}{h}{ie}{ii}{ishrt}{i}{je}%
+    {kbeak}{kdsc}{kvcrs}{k}{lje}{l}{m}{ndsc}{ng}{nje}{n}{otld}{o}{p}{r}%
+    {schwa}{sdsc}{sftsn}{shch}{shha}{sh}{s}{tshe}{t}{ushrt}{u}{v}%
+    {ya}{yhcrs}{yi}{yo}{yu}{y}{zdsc}{zhdsc}{zh}{z}\do
+  {%
+    \expandafter\DeclareTextSymbolDefault\expandafter
+               {\csname cyr\@tempa\endcsname}{T2A}%
+    \expandafter\uppercase\expandafter{\expandafter
+                 \def\expandafter\@tempa\expandafter{\@tempa}}%
+    \expandafter\DeclareTextSymbolDefault\expandafter
+      {\csname CYR\@tempa\endcsname}{T2A}%
+  }%
+  \DeclareTextSymbolDefault{\CYRpalochka}{T2A}%
+\fi\fi
+\endinput
diff --git a/_build/latex/sphinxhighlight.sty b/_build/latex/sphinxhighlight.sty
new file mode 100644
index 0000000..1557ce6
--- /dev/null
+++ b/_build/latex/sphinxhighlight.sty
@@ -0,0 +1,105 @@
+\NeedsTeXFormat{LaTeX2e}[1995/12/01]
+\ProvidesPackage{sphinxhighlight}[2016/05/29 stylesheet for highlighting with pygments]
+
+
+\makeatletter
+\def\PYG@reset{\let\PYG@it=\relax \let\PYG@bf=\relax%
+    \let\PYG@ul=\relax \let\PYG@tc=\relax%
+    \let\PYG@bc=\relax \let\PYG@ff=\relax}
+\def\PYG@tok#1{\csname PYG@tok@#1\endcsname}
+\def\PYG@toks#1+{\ifx\relax#1\empty\else%
+    \PYG@tok{#1}\expandafter\PYG@toks\fi}
+\def\PYG@do#1{\PYG@bc{\PYG@tc{\PYG@ul{%
+    \PYG@it{\PYG@bf{\PYG@ff{#1}}}}}}}
+\def\PYG#1#2{\PYG@reset\PYG@toks#1+\relax+\PYG@do{#2}}
+
+\expandafter\def\csname PYG@tok@w\endcsname{\def\PYG@tc##1{\textcolor[rgb]{0.73,0.73,0.73}{##1}}}
+\expandafter\def\csname PYG@tok@c\endcsname{\let\PYG@it=\textit\def\PYG@tc##1{\textcolor[rgb]{0.25,0.50,0.56}{##1}}}
+\expandafter\def\csname PYG@tok@cp\endcsname{\def\PYG@tc##1{\textcolor[rgb]{0.00,0.44,0.13}{##1}}}
+\expandafter\def\csname PYG@tok@cs\endcsname{\def\PYG@tc##1{\textcolor[rgb]{0.25,0.50,0.56}{##1}}\def\PYG@bc##1{\setlength{\fboxsep}{0pt}\colorbox[rgb]{1.00,0.94,0.94}{\strut ##1}}}
+\expandafter\def\csname PYG@tok@k\endcsname{\let\PYG@bf=\textbf\def\PYG@tc##1{\textcolor[rgb]{0.00,0.44,0.13}{##1}}}
+\expandafter\def\csname PYG@tok@kp\endcsname{\def\PYG@tc##1{\textcolor[rgb]{0.00,0.44,0.13}{##1}}}
+\expandafter\def\csname PYG@tok@kt\endcsname{\def\PYG@tc##1{\textcolor[rgb]{0.56,0.13,0.00}{##1}}}
+\expandafter\def\csname PYG@tok@o\endcsname{\def\PYG@tc##1{\textcolor[rgb]{0.40,0.40,0.40}{##1}}}
+\expandafter\def\csname PYG@tok@ow\endcsname{\let\PYG@bf=\textbf\def\PYG@tc##1{\textcolor[rgb]{0.00,0.44,0.13}{##1}}}
+\expandafter\def\csname PYG@tok@nb\endcsname{\def\PYG@tc##1{\textcolor[rgb]{0.00,0.44,0.13}{##1}}}
+\expandafter\def\csname PYG@tok@nf\endcsname{\def\PYG@tc##1{\textcolor[rgb]{0.02,0.16,0.49}{##1}}}
+\expandafter\def\csname PYG@tok@nc\endcsname{\let\PYG@bf=\textbf\def\PYG@tc##1{\textcolor[rgb]{0.05,0.52,0.71}{##1}}}
+\expandafter\def\csname PYG@tok@nn\endcsname{\let\PYG@bf=\textbf\def\PYG@tc##1{\textcolor[rgb]{0.05,0.52,0.71}{##1}}}
+\expandafter\def\csname PYG@tok@ne\endcsname{\def\PYG@tc##1{\textcolor[rgb]{0.00,0.44,0.13}{##1}}}
+\expandafter\def\csname PYG@tok@nv\endcsname{\def\PYG@tc##1{\textcolor[rgb]{0.73,0.38,0.84}{##1}}}
+\expandafter\def\csname PYG@tok@no\endcsname{\def\PYG@tc##1{\textcolor[rgb]{0.38,0.68,0.84}{##1}}}
+\expandafter\def\csname PYG@tok@nl\endcsname{\let\PYG@bf=\textbf\def\PYG@tc##1{\textcolor[rgb]{0.00,0.13,0.44}{##1}}}
+\expandafter\def\csname PYG@tok@ni\endcsname{\let\PYG@bf=\textbf\def\PYG@tc##1{\textcolor[rgb]{0.84,0.33,0.22}{##1}}}
+\expandafter\def\csname PYG@tok@na\endcsname{\def\PYG@tc##1{\textcolor[rgb]{0.25,0.44,0.63}{##1}}}
+\expandafter\def\csname PYG@tok@nt\endcsname{\let\PYG@bf=\textbf\def\PYG@tc##1{\textcolor[rgb]{0.02,0.16,0.45}{##1}}}
+\expandafter\def\csname PYG@tok@nd\endcsname{\let\PYG@bf=\textbf\def\PYG@tc##1{\textcolor[rgb]{0.33,0.33,0.33}{##1}}}
+\expandafter\def\csname PYG@tok@s\endcsname{\def\PYG@tc##1{\textcolor[rgb]{0.25,0.44,0.63}{##1}}}
+\expandafter\def\csname PYG@tok@sd\endcsname{\let\PYG@it=\textit\def\PYG@tc##1{\textcolor[rgb]{0.25,0.44,0.63}{##1}}}
+\expandafter\def\csname PYG@tok@si\endcsname{\let\PYG@it=\textit\def\PYG@tc##1{\textcolor[rgb]{0.44,0.63,0.82}{##1}}}
+\expandafter\def\csname PYG@tok@se\endcsname{\let\PYG@bf=\textbf\def\PYG@tc##1{\textcolor[rgb]{0.25,0.44,0.63}{##1}}}
+\expandafter\def\csname PYG@tok@sr\endcsname{\def\PYG@tc##1{\textcolor[rgb]{0.14,0.33,0.53}{##1}}}
+\expandafter\def\csname PYG@tok@ss\endcsname{\def\PYG@tc##1{\textcolor[rgb]{0.32,0.47,0.09}{##1}}}
+\expandafter\def\csname PYG@tok@sx\endcsname{\def\PYG@tc##1{\textcolor[rgb]{0.78,0.36,0.04}{##1}}}
+\expandafter\def\csname PYG@tok@m\endcsname{\def\PYG@tc##1{\textcolor[rgb]{0.13,0.50,0.31}{##1}}}
+\expandafter\def\csname PYG@tok@gh\endcsname{\let\PYG@bf=\textbf\def\PYG@tc##1{\textcolor[rgb]{0.00,0.00,0.50}{##1}}}
+\expandafter\def\csname PYG@tok@gu\endcsname{\let\PYG@bf=\textbf\def\PYG@tc##1{\textcolor[rgb]{0.50,0.00,0.50}{##1}}}
+\expandafter\def\csname PYG@tok@gd\endcsname{\def\PYG@tc##1{\textcolor[rgb]{0.63,0.00,0.00}{##1}}}
+\expandafter\def\csname PYG@tok@gi\endcsname{\def\PYG@tc##1{\textcolor[rgb]{0.00,0.63,0.00}{##1}}}
+\expandafter\def\csname PYG@tok@gr\endcsname{\def\PYG@tc##1{\textcolor[rgb]{1.00,0.00,0.00}{##1}}}
+\expandafter\def\csname PYG@tok@ge\endcsname{\let\PYG@it=\textit}
+\expandafter\def\csname PYG@tok@gs\endcsname{\let\PYG@bf=\textbf}
+\expandafter\def\csname PYG@tok@gp\endcsname{\let\PYG@bf=\textbf\def\PYG@tc##1{\textcolor[rgb]{0.78,0.36,0.04}{##1}}}
+\expandafter\def\csname PYG@tok@go\endcsname{\def\PYG@tc##1{\textcolor[rgb]{0.20,0.20,0.20}{##1}}}
+\expandafter\def\csname PYG@tok@gt\endcsname{\def\PYG@tc##1{\textcolor[rgb]{0.00,0.27,0.87}{##1}}}
+\expandafter\def\csname PYG@tok@err\endcsname{\def\PYG@bc##1{\setlength{\fboxsep}{0pt}\fcolorbox[rgb]{1.00,0.00,0.00}{1,1,1}{\strut ##1}}}
+\expandafter\def\csname PYG@tok@kc\endcsname{\let\PYG@bf=\textbf\def\PYG@tc##1{\textcolor[rgb]{0.00,0.44,0.13}{##1}}}
+\expandafter\def\csname PYG@tok@kd\endcsname{\let\PYG@bf=\textbf\def\PYG@tc##1{\textcolor[rgb]{0.00,0.44,0.13}{##1}}}
+\expandafter\def\csname PYG@tok@kn\endcsname{\let\PYG@bf=\textbf\def\PYG@tc##1{\textcolor[rgb]{0.00,0.44,0.13}{##1}}}
+\expandafter\def\csname PYG@tok@kr\endcsname{\let\PYG@bf=\textbf\def\PYG@tc##1{\textcolor[rgb]{0.00,0.44,0.13}{##1}}}
+\expandafter\def\csname PYG@tok@bp\endcsname{\def\PYG@tc##1{\textcolor[rgb]{0.00,0.44,0.13}{##1}}}
+\expandafter\def\csname PYG@tok@fm\endcsname{\def\PYG@tc##1{\textcolor[rgb]{0.02,0.16,0.49}{##1}}}
+\expandafter\def\csname PYG@tok@vc\endcsname{\def\PYG@tc##1{\textcolor[rgb]{0.73,0.38,0.84}{##1}}}
+\expandafter\def\csname PYG@tok@vg\endcsname{\def\PYG@tc##1{\textcolor[rgb]{0.73,0.38,0.84}{##1}}}
+\expandafter\def\csname PYG@tok@vi\endcsname{\def\PYG@tc##1{\textcolor[rgb]{0.73,0.38,0.84}{##1}}}
+\expandafter\def\csname PYG@tok@vm\endcsname{\def\PYG@tc##1{\textcolor[rgb]{0.73,0.38,0.84}{##1}}}
+\expandafter\def\csname PYG@tok@sa\endcsname{\def\PYG@tc##1{\textcolor[rgb]{0.25,0.44,0.63}{##1}}}
+\expandafter\def\csname PYG@tok@sb\endcsname{\def\PYG@tc##1{\textcolor[rgb]{0.25,0.44,0.63}{##1}}}
+\expandafter\def\csname PYG@tok@sc\endcsname{\def\PYG@tc##1{\textcolor[rgb]{0.25,0.44,0.63}{##1}}}
+\expandafter\def\csname PYG@tok@dl\endcsname{\def\PYG@tc##1{\textcolor[rgb]{0.25,0.44,0.63}{##1}}}
+\expandafter\def\csname PYG@tok@s2\endcsname{\def\PYG@tc##1{\textcolor[rgb]{0.25,0.44,0.63}{##1}}}
+\expandafter\def\csname PYG@tok@sh\endcsname{\def\PYG@tc##1{\textcolor[rgb]{0.25,0.44,0.63}{##1}}}
+\expandafter\def\csname PYG@tok@s1\endcsname{\def\PYG@tc##1{\textcolor[rgb]{0.25,0.44,0.63}{##1}}}
+\expandafter\def\csname PYG@tok@mb\endcsname{\def\PYG@tc##1{\textcolor[rgb]{0.13,0.50,0.31}{##1}}}
+\expandafter\def\csname PYG@tok@mf\endcsname{\def\PYG@tc##1{\textcolor[rgb]{0.13,0.50,0.31}{##1}}}
+\expandafter\def\csname PYG@tok@mh\endcsname{\def\PYG@tc##1{\textcolor[rgb]{0.13,0.50,0.31}{##1}}}
+\expandafter\def\csname PYG@tok@mi\endcsname{\def\PYG@tc##1{\textcolor[rgb]{0.13,0.50,0.31}{##1}}}
+\expandafter\def\csname PYG@tok@il\endcsname{\def\PYG@tc##1{\textcolor[rgb]{0.13,0.50,0.31}{##1}}}
+\expandafter\def\csname PYG@tok@mo\endcsname{\def\PYG@tc##1{\textcolor[rgb]{0.13,0.50,0.31}{##1}}}
+\expandafter\def\csname PYG@tok@ch\endcsname{\let\PYG@it=\textit\def\PYG@tc##1{\textcolor[rgb]{0.25,0.50,0.56}{##1}}}
+\expandafter\def\csname PYG@tok@cm\endcsname{\let\PYG@it=\textit\def\PYG@tc##1{\textcolor[rgb]{0.25,0.50,0.56}{##1}}}
+\expandafter\def\csname PYG@tok@cpf\endcsname{\let\PYG@it=\textit\def\PYG@tc##1{\textcolor[rgb]{0.25,0.50,0.56}{##1}}}
+\expandafter\def\csname PYG@tok@c1\endcsname{\let\PYG@it=\textit\def\PYG@tc##1{\textcolor[rgb]{0.25,0.50,0.56}{##1}}}
+
+\def\PYGZbs{\char`\\}
+\def\PYGZus{\char`\_}
+\def\PYGZob{\char`\{}
+\def\PYGZcb{\char`\}}
+\def\PYGZca{\char`\^}
+\def\PYGZam{\char`\&}
+\def\PYGZlt{\char`\<}
+\def\PYGZgt{\char`\>}
+\def\PYGZsh{\char`\#}
+\def\PYGZpc{\char`\%}
+\def\PYGZdl{\char`\$}
+\def\PYGZhy{\char`\-}
+\def\PYGZsq{\char`\'}
+\def\PYGZdq{\char`\"}
+\def\PYGZti{\char`\~}
+% for compatibility with earlier versions
+\def\PYGZat{@}
+\def\PYGZlb{[}
+\def\PYGZrb{]}
+\makeatother
+
+\renewcommand\PYGZsq{\textquotesingle}
diff --git a/_build/latex/sphinxhowto.cls b/_build/latex/sphinxhowto.cls
new file mode 100644
index 0000000..57d73ce
--- /dev/null
+++ b/_build/latex/sphinxhowto.cls
@@ -0,0 +1,102 @@
+%
+% sphinxhowto.cls for Sphinx (http://sphinx-doc.org/)
+%
+
+\NeedsTeXFormat{LaTeX2e}[1995/12/01]
+\ProvidesClass{sphinxhowto}[2019/12/01 v2.3.0 Document class (Sphinx howto)]
+
+% 'oneside' option overriding the 'twoside' default
+\newif\if@oneside
+\DeclareOption{oneside}{\@onesidetrue}
+% Pass remaining document options to the parent class.
+\DeclareOption*{\PassOptionsToClass{\CurrentOption}{\sphinxdocclass}}
+\ProcessOptions\relax
+
+% Default to two-side document
+\if@oneside
+% nothing to do (oneside is the default)
+\else
+\PassOptionsToClass{twoside}{\sphinxdocclass}
+\fi
+
+\LoadClass{\sphinxdocclass}
+
+% Set some sane defaults for section numbering depth and TOC depth.  You can
+% reset these counters in your preamble.
+%
+\setcounter{secnumdepth}{2}
+\setcounter{tocdepth}{2}% i.e. section and subsection
+
+% Adapt \and command to the flushright context of \sphinxmaketitle, to
+% avoid ragged line endings if author names do not fit all on one single line
+\DeclareRobustCommand{\and}{%
+    \end{tabular}\kern-\tabcolsep
+    \allowbreak
+    \hskip\dimexpr1em+\tabcolsep\@plus.17fil\begin{tabular}[t]{c}%
+}%
+% If it is desired that each author name be on its own line, use in preamble:
+%\DeclareRobustCommand{\and}{%
+%   \end{tabular}\kern-\tabcolsep\\\begin{tabular}[t]{c}%
+%}%
+% Change the title page to look a bit better, and fit in with the fncychap
+% ``Bjarne'' style a bit better.
+%
+\newcommand{\sphinxmaketitle}{%
+  \noindent\rule{\textwidth}{1pt}\par
+    \begingroup % for PDF information dictionary
+       \def\endgraf{ }\def\and{\& }%
+       \pdfstringdefDisableCommands{\def\\{, }}% overwrite hyperref setup
+       \hypersetup{pdfauthor={\@author}, pdftitle={\@title}}%
+    \endgroup
+  \begin{flushright}
+    \sphinxlogo
+    \py@HeaderFamily
+    {\Huge \@title }\par
+    {\itshape\large \py@release \releaseinfo}\par
+    \vspace{25pt}
+    {\Large
+      \begin{tabular}[t]{c}
+        \@author
+      \end{tabular}\kern-\tabcolsep}\par
+    \vspace{25pt}
+    \@date \par
+    \py@authoraddress \par
+  \end{flushright}
+  \@thanks
+  \setcounter{footnote}{0}
+  \let\thanks\relax\let\maketitle\relax
+  %\gdef\@thanks{}\gdef\@author{}\gdef\@title{}
+}
+
+\newcommand{\sphinxtableofcontents}{%
+  \begingroup
+    \parskip \z@skip
+    \sphinxtableofcontentshook
+    \tableofcontents
+  \endgroup
+  \noindent\rule{\textwidth}{1pt}\par
+  \vspace{12pt}%
+}  
+\newcommand\sphinxtableofcontentshook{}
+\pagenumbering{arabic}
+
+% Fix the bibliography environment to add an entry to the Table of
+% Contents.
+% For an article document class this environment is a section,
+% so no page break before it.
+%
+\newenvironment{sphinxthebibliography}[1]{%
+  % \phantomsection % not needed here since TeXLive 2010's hyperref
+  \begin{thebibliography}{#1}%
+  \addcontentsline{toc}{section}{\ifdefined\refname\refname\else\ifdefined\bibname\bibname\fi\fi}}{\end{thebibliography}}
+
+
+% Same for the indices.
+% The memoir class already does this, so we don't duplicate it in that case.
+%
+\@ifclassloaded{memoir}
+ {\newenvironment{sphinxtheindex}{\begin{theindex}}{\end{theindex}}}
+ {\newenvironment{sphinxtheindex}{%
+    \phantomsection % needed because no chapter, section, ... is created by theindex
+    \begin{theindex}%
+    \addcontentsline{toc}{section}{\indexname}}{\end{theindex}}}
diff --git a/_build/latex/sphinxmanual.cls b/_build/latex/sphinxmanual.cls
new file mode 100644
index 0000000..718189d
--- /dev/null
+++ b/_build/latex/sphinxmanual.cls
@@ -0,0 +1,128 @@
+%
+% sphinxmanual.cls for Sphinx (http://sphinx-doc.org/)
+%
+
+\NeedsTeXFormat{LaTeX2e}[1995/12/01]
+\ProvidesClass{sphinxmanual}[2019/12/01 v2.3.0 Document class (Sphinx manual)]
+
+% chapters starting at odd pages (overridden by 'openany' document option)
+\PassOptionsToClass{openright}{\sphinxdocclass}
+
+% 'oneside' option overriding the 'twoside' default
+\newif\if@oneside
+\DeclareOption{oneside}{\@onesidetrue}
+% Pass remaining document options to the parent class.
+\DeclareOption*{\PassOptionsToClass{\CurrentOption}{\sphinxdocclass}}
+\ProcessOptions\relax
+
+% Defaults two-side document
+\if@oneside
+% nothing to do (oneside is the default)
+\else
+\PassOptionsToClass{twoside}{\sphinxdocclass}
+\fi
+
+\LoadClass{\sphinxdocclass}
+
+% Set some sane defaults for section numbering depth and TOC depth.  You can
+% reset these counters in your preamble.
+%
+\setcounter{secnumdepth}{2}
+\setcounter{tocdepth}{1}
+
+% Adapt \and command to the flushright context of \sphinxmaketitle, to
+% avoid ragged line endings if author names do not fit all on one single line
+\DeclareRobustCommand{\and}{%
+    \end{tabular}\kern-\tabcolsep
+    \allowbreak
+    \hskip\dimexpr1em+\tabcolsep\@plus.17fil\begin{tabular}[t]{c}%
+}%
+% If it is desired that each author name be on its own line, use in preamble:
+%\DeclareRobustCommand{\and}{%
+%   \end{tabular}\kern-\tabcolsep\\\begin{tabular}[t]{c}%
+%}%
+% Change the title page to look a bit better, and fit in with the fncychap
+% ``Bjarne'' style a bit better.
+%
+\newcommand{\sphinxmaketitle}{%
+  \let\sphinxrestorepageanchorsetting\relax
+  \ifHy@pageanchor\def\sphinxrestorepageanchorsetting{\Hy@pageanchortrue}\fi
+  \hypersetup{pageanchor=false}% avoid duplicate destination warnings
+  \begin{titlepage}%
+    \let\footnotesize\small
+    \let\footnoterule\relax
+    \noindent\rule{\textwidth}{1pt}\par
+      \begingroup % for PDF information dictionary
+       \def\endgraf{ }\def\and{\& }%
+       \pdfstringdefDisableCommands{\def\\{, }}% overwrite hyperref setup
+       \hypersetup{pdfauthor={\@author}, pdftitle={\@title}}%
+      \endgroup
+    \begin{flushright}%
+      \sphinxlogo
+      \py@HeaderFamily
+      {\Huge \@title \par}
+      {\itshape\LARGE \py@release\releaseinfo \par}
+      \vfill
+      {\LARGE
+        \begin{tabular}[t]{c}
+          \@author
+        \end{tabular}\kern-\tabcolsep
+        \par}
+      \vfill\vfill
+      {\large
+       \@date \par
+       \vfill
+       \py@authoraddress \par
+      }%
+    \end{flushright}%\par
+    \@thanks
+  \end{titlepage}%
+  \setcounter{footnote}{0}%
+  \let\thanks\relax\let\maketitle\relax
+  %\gdef\@thanks{}\gdef\@author{}\gdef\@title{}
+  \clearpage
+  \ifdefined\sphinxbackoftitlepage\sphinxbackoftitlepage\fi
+  \if@openright\cleardoublepage\else\clearpage\fi
+  \sphinxrestorepageanchorsetting
+}
+
+\newcommand{\sphinxtableofcontents}{%
+  \pagenumbering{roman}%
+  \begingroup
+    \parskip \z@skip
+    \sphinxtableofcontentshook
+    \tableofcontents
+  \endgroup
+  % before resetting page counter, let's do the right thing.
+  \if@openright\cleardoublepage\else\clearpage\fi
+  \pagenumbering{arabic}%
+}
+
+% This is needed to get the width of the section # area wide enough in the
+% library reference.  Doing it here keeps it the same for all the manuals.
+%
+\newcommand{\sphinxtableofcontentshook}{%
+  \renewcommand*\l@section{\@dottedtocline{1}{1.5em}{2.6em}}%
+  \renewcommand*\l@subsection{\@dottedtocline{2}{4.1em}{3.5em}}%
+}
+
+% Fix the bibliography environment to add an entry to the Table of
+% Contents.
+% For a report document class this environment is a chapter.
+%
+\newenvironment{sphinxthebibliography}[1]{%
+  \if@openright\cleardoublepage\else\clearpage\fi
+  % \phantomsection % not needed here since TeXLive 2010's hyperref
+  \begin{thebibliography}{#1}%
+  \addcontentsline{toc}{chapter}{\bibname}}{\end{thebibliography}}
+
+% Same for the indices.
+% The memoir class already does this, so we don't duplicate it in that case.
+%
+\@ifclassloaded{memoir}
+ {\newenvironment{sphinxtheindex}{\begin{theindex}}{\end{theindex}}}
+ {\newenvironment{sphinxtheindex}{%
+    \if@openright\cleardoublepage\else\clearpage\fi
+    \phantomsection % needed as no chapter, section, ... created
+    \begin{theindex}%
+    \addcontentsline{toc}{chapter}{\indexname}}{\end{theindex}}}
diff --git a/_build/latex/sphinxmessages.sty b/_build/latex/sphinxmessages.sty
new file mode 100644
index 0000000..68ebffa
--- /dev/null
+++ b/_build/latex/sphinxmessages.sty
@@ -0,0 +1,21 @@
+%
+% sphinxmessages.sty
+%
+% message resources for Sphinx
+%
+\ProvidesPackage{sphinxmessages}[2019/01/04 v2.0 Localized LaTeX macros (Sphinx team)]
+
+\renewcommand{\literalblockcontinuedname}{continued from previous page}
+\renewcommand{\literalblockcontinuesname}{continues on next page}
+\renewcommand{\sphinxnonalphabeticalgroupname}{Non\sphinxhyphen{}alphabetical}
+\renewcommand{\sphinxsymbolsname}{Symbols}
+\renewcommand{\sphinxnumbersname}{Numbers}
+\def\pageautorefname{page}
+
+\addto\captionsenglish{\renewcommand{\figurename}{Fig.\@{} }}
+\def\fnum@figure{\figurename\thefigure{}}
+
+\addto\captionsenglish{\renewcommand{\tablename}{Table }}
+\def\fnum@table{\tablename\thetable{}}
+
+\addto\captionsenglish{\renewcommand{\literalblockname}{Listing}}
\ No newline at end of file
diff --git a/_build/latex/sphinxmulticell.sty b/_build/latex/sphinxmulticell.sty
new file mode 100644
index 0000000..a645491
--- /dev/null
+++ b/_build/latex/sphinxmulticell.sty
@@ -0,0 +1,317 @@
+\NeedsTeXFormat{LaTeX2e}
+\ProvidesPackage{sphinxmulticell}%
+    [2017/02/23 v1.6 better span rows and columns of a table (Sphinx team)]%
+\DeclareOption*{\PackageWarning{sphinxmulticell}{Option `\CurrentOption' is unknown}}%
+\ProcessOptions\relax
+%
+% --- MULTICOLUMN ---
+% standard LaTeX's \multicolumn
+% 1. does not allow verbatim contents,
+% 2. interacts very poorly with tabulary.
+%
+% It is needed to write own macros for Sphinx: to allow code-blocks in merged
+% cells rendered by tabular/longtable, and to allow multi-column cells with
+% paragraphs to be taken into account sanely by tabulary algorithm for column
+% widths.
+%
+% This requires quite a bit of hacking. First, in Sphinx, the multi-column
+% contents will *always* be wrapped in a varwidth environment. The issue
+% becomes to pass it the correct target width. We must trick tabulary into
+% believing the multicolumn is simply separate columns, else tabulary does not
+% incorporate the contents in its algorithm. But then we must clear the
+% vertical rules...
+%
+% configuration of tabulary
+\setlength{\tymin}{3\fontcharwd\font`0 }% minimal width of "squeezed" columns
+\setlength{\tymax}{10000pt}% allow enough room for paragraphs to "compete"
+% we need access to tabulary's final computed width. \@tempdima is too volatile
+% to hope it has kept tabulary's value when \sphinxcolwidth needs it.
+\newdimen\sphinx@TY@tablewidth
+\def\tabulary{%
+  \def\TY@final{\sphinx@TY@tablewidth\@tempdima\tabular}%
+  \let\endTY@final\endtabular
+  \TY@tabular}%
+% next hack is needed only if user has set latex_use_latex_multicolumn to True:
+% it fixes tabulary's bug with \multicolumn defined "short" in first pass. (if
+% upstream tabulary adds a \long, our extra one causes no harm)
+\def\sphinx@tempa #1\def\multicolumn#2#3#4#5#6#7#8#9\sphinx@tempa
+   {\def\TY@tab{#1\long\def\multicolumn####1####2####3{\multispan####1\relax}#9}}%
+\expandafter\sphinx@tempa\TY@tab\sphinx@tempa
+%
+% TN. 1: as \omit is never executed, Sphinx multicolumn does not need to worry
+% like standard multicolumn about |l| vs l|. On the other hand it assumes
+% columns are separated by a | ... (if not it will add extraneous
+% \arrayrulewidth space for each column separation in its estimate of available
+% width).
+%
+% TN. 1b: as Sphinx multicolumn uses neither \omit nor \span, it can not
+% (easily) get rid of extra macros from >{...} or <{...} between columns. At
+% least, it has been made compatible with colortbl's \columncolor.
+%
+% TN. 2: tabulary's second pass is handled like tabular/longtable's single
+% pass, with the difference that we hacked \TY@final to set in
+% \sphinx@TY@tablewidth the final target width as computed by tabulary. This is
+% needed only to handle columns with a "horizontal" specifier: "p" type columns
+% (inclusive of tabulary's LJRC) holds the target column width in the
+% \linewidth dimension.
+%
+% TN. 3: use of \begin{sphinxmulticolumn}...\end{sphinxmulticolumn} mark-up
+% would need some hacking around the fact that groups can not span across table
+% cells (the code does inserts & tokens, see TN1b). It was decided to keep it
+% simple with \sphinxstartmulticolumn...\sphinxstopmulticolumn.
+%
+% MEMO about nesting: if sphinxmulticolumn is encountered in a nested tabular
+% inside a tabulary it will think to be at top level in the tabulary. But
+% Sphinx generates no nested tables, and if some LaTeX macro uses internally a
+% tabular this will not have a \sphinxstartmulticolumn within it!
+%
+\def\sphinxstartmulticolumn{%
+    \ifx\equation$% $ tabulary's first pass
+        \expandafter\sphinx@TYI@start@multicolumn
+    \else % either not tabulary or tabulary's second pass
+        \expandafter\sphinx@start@multicolumn
+    \fi
+}%
+\def\sphinxstopmulticolumn{%
+    \ifx\equation$% $ tabulary's first pass
+        \expandafter\sphinx@TYI@stop@multicolumn
+    \else % either not tabulary or tabulary's second pass
+        \ignorespaces
+    \fi
+}%
+\def\sphinx@TYI@start@multicolumn#1{%
+    % use \gdef always to avoid stack space build up
+    \gdef\sphinx@tempa{#1}\begingroup\setbox\z@\hbox\bgroup
+}%
+\def\sphinx@TYI@stop@multicolumn{\egroup % varwidth was used with \tymax
+    \xdef\sphinx@tempb{\the\dimexpr\wd\z@/\sphinx@tempa}% per column width
+    \endgroup
+    \expandafter\sphinx@TYI@multispan\expandafter{\sphinx@tempa}%
+}%
+\def\sphinx@TYI@multispan #1{%
+    \kern\sphinx@tempb\ignorespaces % the per column occupied width
+    \ifnum#1>\@ne % repeat, taking into account subtleties of TeX's & ...
+      \expandafter\sphinx@TYI@multispan@next\expandafter{\the\numexpr#1-\@ne\expandafter}%
+    \fi
+}%
+\def\sphinx@TYI@multispan@next{&\relax\sphinx@TYI@multispan}%
+%
+% Now the branch handling either the second pass of tabulary or the single pass
+% of tabular/longtable. This is the delicate part where we gather the
+% dimensions from the p columns either set-up by tabulary or by user p column
+% or Sphinx \X, \Y columns. The difficulty is that to get the said width, the
+% template must be inserted (other hacks would be horribly complicated except
+% if we rewrote crucial parts of LaTeX's \@array !) and we can not do
+% \omit\span like standard \multicolumn's easy approach. Thus we must cancel
+% the \vrule separators. Also, perhaps the column specifier is of the l, c, r
+% type, then we attempt an ad hoc rescue to give varwidth a reasonable target
+% width.
+\def\sphinx@start@multicolumn#1{%
+    \gdef\sphinx@multiwidth{0pt}\gdef\sphinx@tempa{#1}\sphinx@multispan{#1}%
+}%
+\def\sphinx@multispan #1{%
+    \ifnum#1=\@ne\expandafter\sphinx@multispan@end
+            \else\expandafter\sphinx@multispan@next
+    \fi {#1}%
+}%
+\def\sphinx@multispan@next #1{%
+    % trick to recognize L, C, R, J or p, m, b type columns
+    \ifdim\baselineskip>\z@
+       \gdef\sphinx@tempb{\linewidth}%
+    \else
+    % if in an l, r, c type column, try and hope for the best
+       \xdef\sphinx@tempb{\the\dimexpr(\ifx\TY@final\@undefined\linewidth\else
+            \sphinx@TY@tablewidth\fi-\arrayrulewidth)/\sphinx@tempa
+            -\tw@\tabcolsep-\arrayrulewidth\relax}%
+    \fi
+    \noindent\kern\sphinx@tempb\relax
+    \xdef\sphinx@multiwidth
+           {\the\dimexpr\sphinx@multiwidth+\sphinx@tempb+\tw@\tabcolsep+\arrayrulewidth}%
+    % hack the \vline and the colortbl macros
+    \sphinx@hack@vline\sphinx@hack@CT&\relax
+    % repeat
+    \expandafter\sphinx@multispan\expandafter{\the\numexpr#1-\@ne}%
+}%
+% packages like colortbl add group levels, we need to "climb back up" to be
+% able to hack the \vline and also the colortbl inserted tokens. This creates
+% empty space whether or not the columns were | separated:
+\def\sphinx@hack@vline{\ifnum\currentgrouptype=6\relax
+    \kern\arrayrulewidth\arrayrulewidth\z@\else\aftergroup\sphinx@hack@vline\fi}%
+\def\sphinx@hack@CT{\ifnum\currentgrouptype=6\relax
+    \let\CT@setup\sphinx@CT@setup\else\aftergroup\sphinx@hack@CT\fi}%
+% It turns out \CT@row@color is not expanded contrarily to \CT@column@color
+% during LaTeX+colortbl preamble preparation, hence it would be possible for
+% \sphinx@CT@setup to discard only the column color and choose to obey or not
+% row color and cell color. It would even be possible to propagate cell color
+% to row color for the duration of the Sphinx multicolumn... the (provisional?)
+% choice has been made to cancel the colortbl colours for the multicolumn
+% duration.
+\def\sphinx@CT@setup #1\endgroup{\endgroup}% hack to remove colour commands
+\def\sphinx@multispan@end#1{%
+    % first, trace back our steps horizontally
+    \noindent\kern-\dimexpr\sphinx@multiwidth\relax
+    % and now we set the final computed width for the varwidth environment
+    \ifdim\baselineskip>\z@
+       \xdef\sphinx@multiwidth{\the\dimexpr\sphinx@multiwidth+\linewidth}%
+    \else
+       \xdef\sphinx@multiwidth{\the\dimexpr\sphinx@multiwidth+
+         (\ifx\TY@final\@undefined\linewidth\else
+          \sphinx@TY@tablewidth\fi-\arrayrulewidth)/\sphinx@tempa
+          -\tw@\tabcolsep-\arrayrulewidth\relax}%
+    \fi
+    % we need to remove colour set-up also for last cell of the multi-column
+    \aftergroup\sphinx@hack@CT
+}%
+\newcommand*\sphinxcolwidth[2]{%
+  % this dimension will always be used for varwidth, and serves as maximum
+  % width when cells are merged either via multirow or multicolumn or both,
+  % as always their contents is wrapped in varwidth environment.
+  \ifnum#1>\@ne % multi-column (and possibly also multi-row)
+  % we wrote our own multicolumn code especially to handle that (and allow
+  % verbatim contents)
+   \ifx\equation$%$
+       \tymax % first pass of tabulary (cf MEMO above regarding nesting)
+   \else % the \@gobble thing is for compatibility with standard \multicolumn
+       \sphinx@multiwidth\@gobble{#1/#2}%
+   \fi
+  \else % single column multirow
+   \ifx\TY@final\@undefined % not a tabulary.
+    \ifdim\baselineskip>\z@
+        % in a p{..} type column, \linewidth is the target box width
+        \linewidth
+    \else
+        % l, c, r columns. Do our best.
+        \dimexpr(\linewidth-\arrayrulewidth)/#2-
+                \tw@\tabcolsep-\arrayrulewidth\relax
+    \fi
+   \else % in tabulary
+    \ifx\equation$%$% first pass
+        \tymax % it is set to a big value so that paragraphs can express themselves
+    \else
+     % second pass.
+     \ifdim\baselineskip>\z@
+         \linewidth % in a L, R, C, J column or a p, \X, \Y ...
+     \else
+         % we have hacked \TY@final to put in \sphinx@TY@tablewidth the table width
+         \dimexpr(\sphinx@TY@tablewidth-\arrayrulewidth)/#2-
+                 \tw@\tabcolsep-\arrayrulewidth\relax
+     \fi
+    \fi
+   \fi
+  \fi
+}%
+% fallback default in case user has set latex_use_latex_multicolumn to True:
+% \sphinxcolwidth will use this only inside LaTeX's standard \multicolumn
+\def\sphinx@multiwidth #1#2{\dimexpr % #1 to gobble the \@gobble (!)
+    (\ifx\TY@final\@undefined\linewidth\else\sphinx@TY@tablewidth\fi
+     -\arrayrulewidth)*#2-\tw@\tabcolsep-\arrayrulewidth\relax}%
+%
+% --- MULTIROW ---
+% standard \multirow
+% 1. does not allow verbatim contents,
+% 2. does not allow blank lines in its argument,
+% 3. its * specifier means to typeset "horizontally" which is very
+%    bad for paragraph content. 2016 version has = specifier but it
+%    must be used with p type columns only, else results are bad,
+% 4. it requires manual intervention if the contents is too long to fit
+%    in the asked-for number of rows.
+% 5. colour panels (either from \rowcolor or \columncolor) will hide
+%    the bottom part of multirow text, hence manual tuning is needed
+%    to put the multirow insertion at the _bottom_.
+%
+% The Sphinx solution consists in always having contents wrapped
+% in a varwidth environment so that it makes sense to estimate how many
+% lines it will occupy, and then ensure by insertion of suitable struts
+% that the table rows have the needed height. The needed mark-up is done
+% by LaTeX writer, which has its own id for the merged cells.
+%
+% The colour issue is solved by clearing colour panels in all cells,
+% whether or not the multirow is single-column or multi-column.
+%
+% In passing we obtain baseline alignements across rows (only if
+% \arraystretch is 1, as LaTeX's does not obey \arraystretch in "p"
+% multi-line contents, only first and last line...)
+%
+% TODO: examine the situation with \arraystretch > 1. The \extrarowheight
+% is hopeless for multirow anyhow, it makes baseline alignment strictly
+% impossible.
+\newcommand\sphinxmultirow[2]{\begingroup
+  % #1 = nb of spanned rows, #2 = Sphinx id of "cell", #3 = contents
+  % but let's fetch #3 in a way allowing verbatim contents !
+  \def\sphinx@nbofrows{#1}\def\sphinx@cellid{#2}%
+  \afterassignment\sphinx@multirow\let\next=
+}%
+\def\sphinx@multirow {%
+  \setbox\z@\hbox\bgroup\aftergroup\sphinx@@multirow\strut
+}%
+\def\sphinx@@multirow {%
+  % The contents, which is a varwidth environment, has been captured in
+  % \box0 (a \hbox).
+  % We have with \sphinx@cellid an assigned unique id. The goal is to give
+  % about the same height to all the involved rows.
+  % For this Sphinx will insert a \sphinxtablestrut{cell_id} mark-up
+  % in LaTeX file and the expansion of the latter will do the suitable thing.
+  \dimen@\dp\z@
+  \dimen\tw@\ht\@arstrutbox
+  \advance\dimen@\dimen\tw@
+  \advance\dimen\tw@\dp\@arstrutbox
+  \count@=\dimen@       % type conversion dim -> int
+  \count\tw@=\dimen\tw@
+  \divide\count@\count\tw@ % TeX division truncates
+  \advance\dimen@-\count@\dimen\tw@
+  % 1300sp is about 0.02pt. For comparison a rule default width is 0.4pt.
+  % (note that if \count@ holds 0, surely \dimen@>1300sp)
+  \ifdim\dimen@>1300sp \advance\count@\@ne \fi
+  % now \count@ holds the count L of needed "lines"
+  % and \sphinx@nbofrows holds the number N of rows
+  % we have L >= 1 and N >= 1
+  % if L is a multiple of N, ... clear what to do !
+  % else write L = qN + r, 1 <= r < N and we will
+  % arrange for each row to have enough space for:
+  %     q+1 "lines" in each of the first r rows
+  %       q "lines" in each of the (N-r) bottom rows
+  % for a total of (q+1) * r + q * (N-r) = q * N + r = L
+  % It is possible that q == 0.
+  \count\tw@\count@
+  % the TeX division truncates
+  \divide\count\tw@\sphinx@nbofrows\relax
+  \count4\count\tw@ % q
+  \multiply\count\tw@\sphinx@nbofrows\relax
+  \advance\count@-\count\tw@ % r
+  \expandafter\xdef\csname sphinx@tablestrut_\sphinx@cellid\endcsname
+     {\noexpand\sphinx@tablestrut{\the\count4}{\the\count@}{\sphinx@cellid}}%
+  \dp\z@\z@
+  % this will use the real height if it is >\ht\@arstrutbox
+  \sphinxtablestrut{\sphinx@cellid}\box\z@
+  \endgroup % group was opened in \sphinxmultirow
+}%
+\newcommand*\sphinxtablestrut[1]{%
+  % #1 is a "cell_id", i.e. the id of a merged group of table cells
+  \csname sphinx@tablestrut_#1\endcsname
+}%
+% LaTeX typesets the table row by row, hence each execution can do
+% an update for the next row.
+\newcommand*\sphinx@tablestrut[3]{\begingroup
+  % #1 = q, #2 = (initially) r, #3 = cell_id, q+1 lines in first r rows
+  % if #2 = 0, create space for max(q,1) table lines
+  % if #2 > 0, create space for q+1 lines and decrement #2
+  \leavevmode
+  \count@#1\relax
+  \ifnum#2=\z@
+    \ifnum\count@=\z@\count@\@ne\fi
+  \else
+    % next row will be with a #2 decremented by one
+    \expandafter\xdef\csname sphinx@tablestrut_#3\endcsname
+      {\noexpand\sphinx@tablestrut{#1}{\the\numexpr#2-\@ne}{#3}}%
+   \advance\count@\@ne
+  \fi
+  \vrule\@height\ht\@arstrutbox
+        \@depth\dimexpr\count@\ht\@arstrutbox+\count@\dp\@arstrutbox-\ht\@arstrutbox\relax
+        \@width\z@
+  \endgroup
+  % we need this to avoid colour panels hiding bottom parts of multirow text
+  \sphinx@hack@CT
+}%
+\endinput
+%%
+%% End of file `sphinxmulticell.sty'.
diff --git a/_build/pdf/book.pdf b/_build/pdf/book.pdf
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