From 4d01b794842bed0f0703c17bc174c5e4ea024dd6 Mon Sep 17 00:00:00 2001
From: Allen Downey <downey@allendowney.com>
Date: Wed, 18 Nov 2020 19:32:36 -0500
Subject: [PATCH] Update documentation

---
 01_query.html               |  102 +--
 03_motion.html              |   10 +-
 04_select.html              |   23 +-
 05_join.html                |   14 +-
 06_photo.html               |   68 +-
 _images/04_select_57_0.png  |  Bin 40126 -> 42397 bytes
 _images/06_photo_23_0.png   |  Bin 24092 -> 24002 bytes
 _images/06_photo_61_0.png   |  Bin 32706 -> 31739 bytes
 _images/06_photo_63_0.png   |  Bin 10093 -> 9861 bytes
 _images/pilot_day3_12_0.png |  Bin 0 -> 81181 bytes
 _images/pilot_day3_15_0.png |  Bin 0 -> 81640 bytes
 _images/pilot_day3_20_0.png |  Bin 0 -> 15355 bytes
 _images/pilot_day3_45_0.png |  Bin 0 -> 83013 bytes
 _images/pilot_day3_64_0.png |  Bin 0 -> 36300 bytes
 _images/pilot_day3_67_0.png |  Bin 0 -> 32396 bytes
 _images/pilot_day3_6_0.png  |  Bin 0 -> 115356 bytes
 _images/pilot_day4_1_0.png  |  Bin 0 -> 32869 bytes
 _sources/01_query.ipynb     |  190 ++---
 _sources/03_motion.ipynb    |  126 ++--
 _sources/04_select.ipynb    |   31 +-
 _sources/05_join.ipynb      |   14 +-
 _sources/06_photo.ipynb     |   74 +-
 _sources/pilot_day1.ipynb   |  237 ++++++
 _sources/pilot_day3.ipynb   | 1423 +++++++++++++++++++++++++++++++++++
 _sources/pilot_day4.ipynb   |   74 ++
 _static/pygments.css        |    8 +-
 objects.inv                 |  Bin 418 -> 442 bytes
 pilot_day1.html             |  170 +++++
 pilot_day3.html             | 1261 +++++++++++++++++++++++++++++++
 pilot_day4.html             |  331 ++++++++
 searchindex.js              |    2 +-
 31 files changed, 3833 insertions(+), 325 deletions(-)
 create mode 100644 _images/pilot_day3_12_0.png
 create mode 100644 _images/pilot_day3_15_0.png
 create mode 100644 _images/pilot_day3_20_0.png
 create mode 100644 _images/pilot_day3_45_0.png
 create mode 100644 _images/pilot_day3_64_0.png
 create mode 100644 _images/pilot_day3_67_0.png
 create mode 100644 _images/pilot_day3_6_0.png
 create mode 100644 _images/pilot_day4_1_0.png
 create mode 100644 _sources/pilot_day3.ipynb
 create mode 100644 _sources/pilot_day4.ipynb
 create mode 100644 pilot_day3.html
 create mode 100644 pilot_day4.html

diff --git a/01_query.html b/01_query.html
index b19a66c..dfd6465 100644
--- a/01_query.html
+++ b/01_query.html
@@ -582,7 +582,7 @@ 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 0x7f10a2326820&gt;
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;astroquery.utils.tap.model.taptable.TapTableMeta at 0x7fbbf0d60a60&gt;
 </pre></div>
 </div>
 </div>
@@ -830,7 +830,7 @@ quality_flag
 </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 0x7f10a2344250&gt;
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;astroquery.utils.tap.model.job.Job at 0x7fbbf0d8f5b0&gt;
 </pre></div>
 </div>
 </div>
@@ -851,11 +851,11 @@ source_id   int64      Unique source identifier (unique within a particular Data
 ref_epoch float64   yr                                                    Reference epoch     0
        ra float64  deg                                                    Right ascension     0
       dec float64  deg                                                        Declination     0
- parallax float64  mas                                                           Parallax     2
+ parallax float64  mas                                                           Parallax     3
 Jobid: None
 Phase: COMPLETED
 Owner: None
-Output file: sync_20201117092213.xml.gz
+Output file: sync_20201117154748.xml.gz
 Results: None
 </pre></div>
 </div>
@@ -891,20 +891,20 @@ Results: None
 </div>
 <div class="cell_output docutils container">
 <div class="output text_html"><i>Table length=10</i>
-<table id="table139709417416256" class="table-striped table-bordered table-condensed">
+<table id="table140445176362752" 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>2029534160912857984</td><td>2015.5</td><td>302.1221011162626</td><td>29.464242405581903</td><td>0.26719312673637957</td></tr>
-<tr><td>2029527563843522688</td><td>2015.5</td><td>301.76635488333477</td><td>29.297302003155878</td><td>-0.5339115955037064</td></tr>
-<tr><td>2029530175194496768</td><td>2015.5</td><td>301.84979907220344</td><td>29.424817571056252</td><td>0.4993858623581147</td></tr>
-<tr><td>2029552990056965376</td><td>2015.5</td><td>302.61233151638965</td><td>29.69609949341979</td><td>--</td></tr>
-<tr><td>2029559174807808512</td><td>2015.5</td><td>302.08554269720975</td><td>29.568937428891136</td><td>0.07378124688708597</td></tr>
-<tr><td>2029534126551565440</td><td>2015.5</td><td>302.1383595452346</td><td>29.4560049110412</td><td>--</td></tr>
-<tr><td>2029534023485750400</td><td>2015.5</td><td>302.06234099159894</td><td>29.463904128839516</td><td>-0.6248984748555095</td></tr>
-<tr><td>2029552990053470592</td><td>2015.5</td><td>302.61273920007653</td><td>29.701315238258985</td><td>-0.5113424569489686</td></tr>
-<tr><td>2029545430912236672</td><td>2015.5</td><td>302.2752745129967</td><td>29.482001000231968</td><td>0.049122831032945236</td></tr>
-<tr><td>2029534435790829568</td><td>2015.5</td><td>302.08962936217654</td><td>29.490016052993543</td><td>1.0104447723028214</td></tr>
+<tr><td>5849745869473487488</td><td>2015.5</td><td>219.73560389985096</td><td>-65.20710341703752</td><td>0.5482007725782427</td></tr>
+<tr><td>5849746938973887488</td><td>2015.5</td><td>219.60817792260227</td><td>-65.1665758810783</td><td>--</td></tr>
+<tr><td>5849749412871953152</td><td>2015.5</td><td>219.47762991904543</td><td>-65.19549094160195</td><td>0.8018859767657315</td></tr>
+<tr><td>5849756177391089408</td><td>2015.5</td><td>221.04673046937168</td><td>-65.57259338863145</td><td>0.2470355479426332</td></tr>
+<tr><td>5849740479338016512</td><td>2015.5</td><td>219.48034924849668</td><td>-65.248465161243</td><td>0.7498253475529492</td></tr>
+<tr><td>5849755150954806784</td><td>2015.5</td><td>220.7783390948865</td><td>-65.59812207307075</td><td>-0.17531203854271796</td></tr>
+<tr><td>5849742231683753344</td><td>2015.5</td><td>219.12014600682343</td><td>-65.30003719122678</td><td>0.2758280660620302</td></tr>
+<tr><td>5849741853727882752</td><td>2015.5</td><td>219.08570521942724</td><td>-65.3172714882006</td><td>1.4628766254219059</td></tr>
+<tr><td>5849749138009127680</td><td>2015.5</td><td>219.7980142071744</td><td>-65.08195659745067</td><td>--</td></tr>
+<tr><td>5849741716288860672</td><td>2015.5</td><td>219.0424551498036</td><td>-65.33572466059165</td><td>--</td></tr>
 </table></div></div>
 </div>
 <p>Each column has a name, units, and a data type.</p>
@@ -957,10 +957,10 @@ ref_epoch float64   yr                                                    Refere
        ra float64  deg                                                    Right ascension
       dec float64  deg                                                        Declination
  parallax float64  mas                                                           Parallax
-Jobid: 1605622934969O
+Jobid: 1605646069281O
 Phase: COMPLETED
 Owner: None
-Output file: async_20201117092215.vot
+Output file: async_20201117154749.vot
 Results: None
 </pre></div>
 </div>
@@ -976,30 +976,30 @@ Results: None
 </div>
 <div class="cell_output docutils container">
 <div class="output text_html"><i>Table length=3000</i>
-<table id="table139709414533440" class="table-striped table-bordered table-condensed">
+<table id="table140445173361824" 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>2029534160912857984</td><td>2015.5</td><td>302.1221011162626</td><td>29.464242405581903</td><td>0.26719312673637957</td></tr>
-<tr><td>2029527563843522688</td><td>2015.5</td><td>301.76635488333477</td><td>29.297302003155878</td><td>-0.5339115955037064</td></tr>
-<tr><td>2029530175194496768</td><td>2015.5</td><td>301.84979907220344</td><td>29.424817571056252</td><td>0.4993858623581147</td></tr>
-<tr><td>2029559174807808512</td><td>2015.5</td><td>302.08554269720975</td><td>29.568937428891136</td><td>0.07378124688708597</td></tr>
-<tr><td>2029534023485750400</td><td>2015.5</td><td>302.06234099159894</td><td>29.463904128839516</td><td>-0.6248984748555095</td></tr>
-<tr><td>2029552990053470592</td><td>2015.5</td><td>302.61273920007653</td><td>29.701315238258985</td><td>-0.5113424569489686</td></tr>
-<tr><td>2029545430912236672</td><td>2015.5</td><td>302.2752745129967</td><td>29.482001000231968</td><td>0.049122831032945236</td></tr>
-<tr><td>2029551375154168192</td><td>2015.5</td><td>302.6322719484525</td><td>29.629231848937902</td><td>-0.4273623555622012</td></tr>
-<tr><td>2029537184581680000</td><td>2015.5</td><td>301.8270877829269</td><td>29.481945977423116</td><td>0.686631849492751</td></tr>
+<tr><td>5849745869473487488</td><td>2015.5</td><td>219.73560389985096</td><td>-65.20710341703752</td><td>0.5482007725782427</td></tr>
+<tr><td>5849749412871953152</td><td>2015.5</td><td>219.47762991904543</td><td>-65.19549094160195</td><td>0.8018859767657315</td></tr>
+<tr><td>5849756177391089408</td><td>2015.5</td><td>221.04673046937168</td><td>-65.57259338863145</td><td>0.2470355479426332</td></tr>
+<tr><td>5849740479338016512</td><td>2015.5</td><td>219.48034924849668</td><td>-65.248465161243</td><td>0.7498253475529492</td></tr>
+<tr><td>5849755150954806784</td><td>2015.5</td><td>220.7783390948865</td><td>-65.59812207307075</td><td>-0.17531203854271796</td></tr>
+<tr><td>5849742231683753344</td><td>2015.5</td><td>219.12014600682343</td><td>-65.30003719122678</td><td>0.2758280660620302</td></tr>
+<tr><td>5849756594002789504</td><td>2015.5</td><td>221.2361295175457</td><td>-65.52974480682217</td><td>0.29058299600659726</td></tr>
+<tr><td>5849758208912099200</td><td>2015.5</td><td>220.75442873671042</td><td>-65.56715622350804</td><td>-0.1685867513524159</td></tr>
+<tr><td>5849754768662465920</td><td>2015.5</td><td>221.0112032878186</td><td>-65.5732299894003</td><td>0.14826104583068447</td></tr>
 <tr><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr>
-<tr><td>5351023617248935296</td><td>2015.5</td><td>163.20342713669297</td><td>-57.45843664435045</td><td>-0.12712245925716417</td></tr>
-<tr><td>5351013034403261056</td><td>2015.5</td><td>162.76029338894156</td><td>-57.57725086313733</td><td>-0.04737425207265826</td></tr>
-<tr><td>6230089722024056576</td><td>2015.5</td><td>221.13188844526658</td><td>-23.856129423698576</td><td>0.40215405335632315</td></tr>
-<tr><td>6229981248329591552</td><td>2015.5</td><td>219.5757477670797</td><td>-24.520741795344115</td><td>-0.1711128865194354</td></tr>
-<tr><td>6230163114425691904</td><td>2015.5</td><td>220.67263853794475</td><td>-23.502746567943603</td><td>-0.0983885985018888</td></tr>
-<tr><td>6229932491861252352</td><td>2015.5</td><td>220.73523627555818</td><td>-24.640708681077328</td><td>0.4328661904699682</td></tr>
-<tr><td>6230016879379130240</td><td>2015.5</td><td>219.83052324579677</td><td>-24.2061244892915</td><td>0.3925985611610745</td></tr>
-<tr><td>6230073366787772800</td><td>2015.5</td><td>221.47192783117885</td><td>-23.959139666294128</td><td>0.48625897986634425</td></tr>
-<tr><td>6230145075563001728</td><td>2015.5</td><td>221.03467394339305</td><td>-23.58099620047661</td><td>0.4002784743473182</td></tr>
-<tr><td>6230167031435813760</td><td>2015.5</td><td>220.7851856662174</td><td>-23.336372421616733</td><td>-0.8736321545140885</td></tr>
+<tr><td>4687418695774232192</td><td>2015.5</td><td>16.869927634756948</td><td>-72.68242362167037</td><td>-0.48502626489731665</td></tr>
+<tr><td>4687422891933697920</td><td>2015.5</td><td>15.901327641112253</td><td>-72.80447466138797</td><td>0.2995397868127374</td></tr>
+<tr><td>4687420723006033280</td><td>2015.5</td><td>15.47314164379284</td><td>-72.89011101634064</td><td>-0.9978112875699006</td></tr>
+<tr><td>4687422715871646976</td><td>2015.5</td><td>15.99138434019087</td><td>-72.82313785245728</td><td>-2.942541578796724</td></tr>
+<tr><td>4687423815384413184</td><td>2015.5</td><td>15.944465951055934</td><td>-72.77907061819404</td><td>0.551112050315771</td></tr>
+<tr><td>4687423098092057728</td><td>2015.5</td><td>16.011069579363397</td><td>-72.77136072133104</td><td>0.5275024949652448</td></tr>
+<tr><td>4687420894804720512</td><td>2015.5</td><td>15.645744782387933</td><td>-72.86243763758765</td><td>-0.3893691578003666</td></tr>
+<tr><td>4110043733924819712</td><td>2015.5</td><td>261.33786534447233</td><td>-25.232978342149263</td><td>0.16336768694395531</td></tr>
+<tr><td>4110047449052774912</td><td>2015.5</td><td>261.1262641088297</td><td>-25.31967337502969</td><td>0.126451056290413</td></tr>
+<tr><td>4110049437580528640</td><td>2015.5</td><td>260.94992109082744</td><td>-25.32242018581512</td><td>-0.025095711306698878</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>
@@ -1106,7 +1106,7 @@ Be careful to keep your Python out of your ADQL!</p>
 </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;1605622934969O&#39;)
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>(None, &#39;1605646069281O&#39;)
 </pre></div>
 </div>
 </div>
@@ -1119,7 +1119,7 @@ Be careful to keep your Python out of your ADQL!</p>
 </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;1605622934969O&#39;]&#39;.
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Removed jobs: &#39;[&#39;1605646069281O&#39;]&#39;.
 </pre></div>
 </div>
 </div>
@@ -1216,7 +1216,7 @@ radial_velocity float64   km / s
 Jobid: None
 Phase: COMPLETED
 Owner: None
-Output file: sync_20201117092220.xml.gz
+Output file: sync_20201117154752.xml.gz
 Results: None
 </pre></div>
 </div>
@@ -1231,20 +1231,20 @@ Results: None
 </div>
 <div class="cell_output docutils container">
 <div class="output text_html"><i>Table length=10</i>
-<table id="table139709417540336" class="table-striped table-bordered table-condensed">
+<table id="table140445173436528" 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>6230144152145537152</td><td>221.00484394613122</td><td>-23.650193344617016</td><td>-3.5074596301747727</td><td>-10.224921783269979</td><td>0.4472611806640159</td><td>0.1288335146382159</td><td>--</td></tr>
-<tr><td>6230171669999288192</td><td>222.11986606839224</td><td>-24.50411126495661</td><td>-3.5454562059199444</td><td>-4.5898956770256065</td><td>0.01235077758559544</td><td>0.6798312067588314</td><td>--</td></tr>
-<tr><td>6230030795072008832</td><td>220.6858032920676</td><td>-24.529155332809893</td><td>-7.395300108444131</td><td>-2.3509644395754723</td><td>0.9192193090012722</td><td>1.011426390802751</td><td>--</td></tr>
-<tr><td>6230121401702753664</td><td>220.2681390809788</td><td>-23.90595652859401</td><td>-0.9310688845911029</td><td>-4.850975141945376</td><td>0.7892660162616956</td><td>0.612231874239708</td><td>--</td></tr>
-<tr><td>6229987570521485440</td><td>219.69736802123325</td><td>-24.463333246855726</td><td>-1.2742408484284249</td><td>-3.404899385900498</td><td>0.2215366853699605</td><td>0.21596169286289862</td><td>--</td></tr>
-<tr><td>6230018425567339520</td><td>220.1035725206795</td><td>-24.23214642244001</td><td>3.5001851583023207</td><td>-7.522436158423504</td><td>0.10694586740680614</td><td>0.8296685650840913</td><td>--</td></tr>
-<tr><td>6230130266515510528</td><td>220.4226870525988</td><td>-23.73255151335769</td><td>-2.363789876365865</td><td>-4.703438038908609</td><td>0.3047725187302132</td><td>0.2970783741597157</td><td>--</td></tr>
-<tr><td>6230061757492831616</td><td>221.17289130564805</td><td>-24.25689151102804</td><td>-2.857319870871607</td><td>-3.3752756010215696</td><td>0.5718461700819417</td><td>0.08382745553667581</td><td>--</td></tr>
-<tr><td>2003409519814774528</td><td>339.93278128584706</td><td>54.693746629826066</td><td>-4.309526744320447</td><td>-4.179418290817069</td><td>-0.03422181968448189</td><td>0.16657261834688394</td><td>--</td></tr>
-<tr><td>2003390901139218816</td><td>338.7103475641665</td><td>54.842371081247144</td><td>0.9558208039434155</td><td>-3.918955387981184</td><td>0.7072974894588037</td><td>0.4296262772904238</td><td>--</td></tr>
+<tr><td>4687420551207336960</td><td>15.645304428598138</td><td>-72.89167644508707</td><td>1.2391150617590077</td><td>-0.44283475442839</td><td>-0.6198096756639259</td><td>0.2807306692481813</td><td>--</td></tr>
+<tr><td>4687420345043286528</td><td>17.02520966291366</td><td>-72.58813710995527</td><td>0.5942325468950411</td><td>-0.9226900184931504</td><td>0.14195864820393828</td><td>0.2762606040704309</td><td>--</td></tr>
+<tr><td>4687423403067577856</td><td>16.093293953294165</td><td>-72.76190688901534</td><td>4.282221460096154</td><td>-1.151311128485597</td><td>-2.35198675239641</td><td>0.8824042279602063</td><td>--</td></tr>
+<tr><td>4687418695774232192</td><td>16.869927634756948</td><td>-72.68242362167037</td><td>3.757251484808558</td><td>-0.27830874302625963</td><td>-0.48502626489731665</td><td>0.5173500081780815</td><td>--</td></tr>
+<tr><td>4687422891933697920</td><td>15.901327641112253</td><td>-72.80447466138797</td><td>1.620291207312266</td><td>-0.7541576089819368</td><td>0.2995397868127374</td><td>0.2764244386276099</td><td>--</td></tr>
+<tr><td>4687422715871646976</td><td>15.99138434019087</td><td>-72.82313785245728</td><td>2.8954637100285368</td><td>4.214822457223685</td><td>-2.942541578796724</td><td>0.847035789335997</td><td>--</td></tr>
+<tr><td>4687423815384413184</td><td>15.944465951055934</td><td>-72.77907061819404</td><td>4.313074669192134</td><td>-1.460344295147088</td><td>0.551112050315771</td><td>0.7664351946147043</td><td>--</td></tr>
+<tr><td>4687423098092057728</td><td>16.011069579363397</td><td>-72.77136072133104</td><td>1.7413320910807104</td><td>-1.814893105748193</td><td>0.5275024949652448</td><td>0.3616348905813909</td><td>--</td></tr>
+<tr><td>4687420894804720512</td><td>15.645744782387933</td><td>-72.86243763758765</td><td>0.40415899559873986</td><td>-2.0036523797855805</td><td>-0.3893691578003666</td><td>0.35767437939443886</td><td>--</td></tr>
+<tr><td>4110047449052774912</td><td>261.1262641088297</td><td>-25.31967337502969</td><td>-2.4098547436119064</td><td>-1.301609808236016</td><td>0.126451056290413</td><td>0.7276250597861622</td><td>--</td></tr>
 </table></div></div>
 </div>
 <p>Good so far.</p>
diff --git a/03_motion.html b/03_motion.html
index d694f75..ab3a6bc 100644
--- a/03_motion.html
+++ b/03_motion.html
@@ -481,7 +481,7 @@ radial_velocity float64   km / s
 </div>
 <div class="cell_output docutils container">
 <div class="output text_html"><i>Row index=0</i>
-<table id="table140510732444000">
+<table id="table139758619539344">
 <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>
@@ -971,8 +971,8 @@ Name: phi2, dtype: bool
 <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>
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">subset</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">subset</span><span class="p">)</span>
 </pre></div>
 </div>
 </div>
@@ -982,7 +982,7 @@ Name: phi2, dtype: bool
 </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><code class="docutils literal notranslate"><span class="pre">subset</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">
@@ -1252,7 +1252,7 @@ Name: phi2, dtype: bool
 </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
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>-rw-rw-r-- 1 downey downey 17M Nov 18 19:06 gd1_dataframe.hdf5
 </pre></div>
 </div>
 </div>
diff --git a/04_select.html b/04_select.html
index 9ecacfe..83ab596 100644
--- a/04_select.html
+++ b/04_select.html
@@ -419,8 +419,8 @@
 <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">xlabel</span><span class="p">(</span><span class="s1">&#39;Proper motion ra (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 dec (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>
@@ -451,6 +451,11 @@ and use the polygon as a selection criterion in an ADQL query.</p>
 </div>
 </div>
 </div>
+<p>NOTE: If you are using an older version of Pandas, you might not have <code class="docutils literal notranslate"><span class="pre">to_numpy()</span></code>; you can use <code class="docutils literal notranslate"><span class="pre">values</span></code> instead, like this:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></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">values</span>
+
+</pre></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">
@@ -462,7 +467,7 @@ and use the polygon as a selection criterion in an ADQL query.</p>
 </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;
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;scipy.spatial.qhull.ConvexHull at 0x7fd67bffd910&gt;
 </pre></div>
 </div>
 </div>
@@ -778,10 +783,10 @@ INFO: Query finished. [astroquery.utils.tap.core]
        parallax float64      mas                                                           Parallax     0
  parallax_error float64      mas                                         Standard error of parallax     0
 radial_velocity float64   km / s                                                    Radial velocity  7295
-Jobid: 1603132746237O
+Jobid: 1605744463707O
 Phase: COMPLETED
 Owner: None
-Output file: async_20201019143906.vot
+Output file: async_20201118190743.vot
 Results: None
 </pre></div>
 </div>
@@ -910,7 +915,7 @@ Results: None
 </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
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>-rw-rw-r-- 1 downey downey 756K Nov 18 19:07 gd1_candidates.hdf5
 </pre></div>
 </div>
 </div>
@@ -943,7 +948,7 @@ Results: None
 </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
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>-rw-rw-r-- 1 downey downey 1.6M Nov 18 19:07 gd1_candidates.csv
 </pre></div>
 </div>
 </div>
@@ -958,8 +963,8 @@ Results: None
 </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
+0,635559124339440000,137.58671691646745,19.1965441084838,-3.770521900009566,-12.490481778113859,0.7913934419894347,0.2717538145759051,,-59.63048941944396,-1.21648525150429,-7.3613627125974945,-0.5926328820644919
+1,635860218726658176,138.5187065217173,19.09233926905897,-5.941679495793577,-11.346409129876392,0.30745551377348623,0.19946557779138105,,-59.247329893833296,-2.0160784008206476,-7.52712608464053,1.7487794924176672
 </pre></div>
 </div>
 </div>
diff --git a/05_join.html b/05_join.html
index e8de824..7330c09 100644
--- a/05_join.html
+++ b/05_join.html
@@ -443,7 +443,7 @@ We could write the entire table to a file, but that would take longer to transmi
 </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
+&lt;!-- Produced with astropy.io.votable version 4.0.2
      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;
@@ -468,7 +468,7 @@ The size of the file is about 750 KB, so that’s not too bad.</p>
 </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
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>-rw-rw-r-- 1 downey downey 396K Nov 18 19:21 candidate_df.xml
 </pre></div>
 </div>
 </div>
@@ -568,7 +568,7 @@ INFO: Query finished. [astroquery.utils.tap.core]
 </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">
+<table id="table140178070186304" 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>
@@ -669,7 +669,7 @@ INFO: Query finished. [astroquery.utils.tap.core]
 </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">
+<table id="table140178046436112" 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>
@@ -798,7 +798,7 @@ dtype: int64
 </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
+&lt;!-- Produced with astropy.io.votable version 4.0.2
      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;
@@ -914,7 +914,7 @@ ON ps.obj_id = external.original_ext_source_id
 </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">
+<table id="table140178044101488" 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>
@@ -963,7 +963,7 @@ ON ps.obj_id = external.original_ext_source_id
 </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
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>-rw-rw-r-- 1 downey downey 96K Nov 18 19:22 gd1_photo.fits
 </pre></div>
 </div>
 </div>
diff --git a/06_photo.html b/06_photo.html
index 7c6f0b6..bd01567 100644
--- a/06_photo.html
+++ b/06_photo.html
@@ -434,9 +434,9 @@ Before calling <code class="docutils literal notranslate"><span class="pre">plt.
 <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">coords</span> <span class="o">=</span> <span class="kc">None</span>
+
+<span class="k">if</span> <span class="ow">not</span> <span class="n">IN_COLAB</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>
@@ -454,16 +454,16 @@ Before calling <code class="docutils literal notranslate"><span class="pre">plt.
 </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)]
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[(0.2643369175627239, 17.84253127299485),
+ (0.3539426523297491, 18.799116997792495),
+ (0.47491039426523296, 19.682119205298015),
+ (0.6317204301075269, 20.454746136865342),
+ (0.7661290322580645, 20.785871964679913),
+ (0.8064516129032258, 21.41133186166299),
+ (0.5869175627240143, 21.300956585724798),
+ (0.39426523297491034, 20.565121412803535),
+ (0.22401433691756267, 19.240618101545255),
+ (0.19713261648745517, 18.02649006622517)]
 </pre></div>
 </div>
 </div>
@@ -493,10 +493,10 @@ Before calling <code class="docutils literal notranslate"><span class="pre">plt.
 </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]))
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>(array([0.26433692, 0.35394265, 0.47491039, 0.63172043, 0.76612903,
+        0.80645161, 0.58691756, 0.39426523, 0.22401434, 0.19713262]),
+ array([17.84253127, 18.799117  , 19.68211921, 20.45474614, 20.78587196,
+        21.41133186, 21.30095659, 20.56512141, 19.2406181 , 18.02649007]))
 </pre></div>
 </div>
 </div>
@@ -531,16 +531,16 @@ Before calling <code class="docutils literal notranslate"><span class="pre">plt.
 </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)
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Path(array([[ 0.26433692, 17.84253127],
+       [ 0.35394265, 18.799117  ],
+       [ 0.47491039, 19.68211921],
+       [ 0.63172043, 20.45474614],
+       [ 0.76612903, 20.78587196],
+       [ 0.80645161, 21.41133186],
+       [ 0.58691756, 21.30095659],
+       [ 0.39426523, 20.56512141],
+       [ 0.22401434, 19.2406181 ],
+       [ 0.19713262, 18.02649007]]), None)
 </pre></div>
 </div>
 </div>
@@ -954,7 +954,7 @@ Name: color, Length: 7346, dtype: bool
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>496
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>481
 </pre></div>
 </div>
 </div>
@@ -962,7 +962,7 @@ Name: color, Length: 7346, dtype: bool
 <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>
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">selected2</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>
@@ -973,7 +973,7 @@ Name: color, Length: 7346, dtype: bool
 <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>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">selected2</span><span class="p">[</span><span class="s1">&#39;color&#39;</span><span class="p">],</span> <span class="n">selected2</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>
@@ -987,8 +987,8 @@ Name: color, Length: 7346, dtype: bool
 <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">x</span> <span class="o">=</span> <span class="n">selected2</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">selected2</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>
 
@@ -1018,7 +1018,7 @@ Name: color, Length: 7346, dtype: bool
 <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>
+<span class="n">selected2</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;selected2&#39;</span><span class="p">)</span>
 </pre></div>
 </div>
 </div>
@@ -1030,7 +1030,7 @@ Name: color, Length: 7346, dtype: bool
 </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
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>-rw-rw-r-- 1 downey downey 2.0M Nov 18 19:28 gd1_merged.hdf5
 </pre></div>
 </div>
 </div>
diff --git a/_images/04_select_57_0.png b/_images/04_select_57_0.png
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diff --git a/_sources/01_query.ipynb b/_sources/01_query.ipynb
index c559bdb..2c5d45f 100644
--- a/_sources/01_query.ipynb
+++ b/_sources/01_query.ipynb
@@ -383,7 +383,7 @@
     {
      "data": {
       "text/plain": [
-       "<astroquery.utils.tap.model.taptable.TapTableMeta at 0x7f10a2326820>"
+       "<astroquery.utils.tap.model.taptable.TapTableMeta at 0x7fbbf0d60a60>"
       ]
      },
      "execution_count": 5,
@@ -808,7 +808,7 @@
     {
      "data": {
       "text/plain": [
-       "<astroquery.utils.tap.model.job.Job at 0x7f10a2344250>"
+       "<astroquery.utils.tap.model.job.Job at 0x7fbbf0d8f5b0>"
       ]
      },
      "execution_count": 12,
@@ -846,11 +846,11 @@
       "ref_epoch float64   yr                                                    Reference epoch     0\n",
       "       ra float64  deg                                                    Right ascension     0\n",
       "      dec float64  deg                                                        Declination     0\n",
-      " parallax float64  mas                                                           Parallax     2\n",
+      " parallax float64  mas                                                           Parallax     3\n",
       "Jobid: None\n",
       "Phase: COMPLETED\n",
       "Owner: None\n",
-      "Output file: sync_20201117092213.xml.gz\n",
+      "Output file: sync_20201117154748.xml.gz\n",
       "Results: None\n"
      ]
     }
@@ -918,20 +918,20 @@
      "data": {
       "text/html": [
        "<i>Table length=10</i>\n",
-       "<table id=\"table139709417416256\" class=\"table-striped table-bordered table-condensed\">\n",
+       "<table id=\"table140445176362752\" 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>2029534160912857984</td><td>2015.5</td><td>302.1221011162626</td><td>29.464242405581903</td><td>0.26719312673637957</td></tr>\n",
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-       "<tr><td>2029552990056965376</td><td>2015.5</td><td>302.61233151638965</td><td>29.69609949341979</td><td>--</td></tr>\n",
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+       "<tr><td>5849746938973887488</td><td>2015.5</td><td>219.60817792260227</td><td>-65.1665758810783</td><td>--</td></tr>\n",
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+       "<tr><td>5849749138009127680</td><td>2015.5</td><td>219.7980142071744</td><td>-65.08195659745067</td><td>--</td></tr>\n",
+       "<tr><td>5849741716288860672</td><td>2015.5</td><td>219.0424551498036</td><td>-65.33572466059165</td><td>--</td></tr>\n",
        "</table>"
       ],
       "text/plain": [
@@ -940,16 +940,16 @@
        "                        yr    ...        deg                 mas         \n",
        "       int64         float64  ...      float64             float64       \n",
        "------------------- --------- ... ------------------ --------------------\n",
-       "2029534160912857984    2015.5 ... 29.464242405581903  0.26719312673637957\n",
-       "2029527563843522688    2015.5 ... 29.297302003155878  -0.5339115955037064\n",
-       "2029530175194496768    2015.5 ... 29.424817571056252   0.4993858623581147\n",
-       "2029552990056965376    2015.5 ...  29.69609949341979                   --\n",
-       "2029559174807808512    2015.5 ... 29.568937428891136  0.07378124688708597\n",
-       "2029534126551565440    2015.5 ...   29.4560049110412                   --\n",
-       "2029534023485750400    2015.5 ... 29.463904128839516  -0.6248984748555095\n",
-       "2029552990053470592    2015.5 ... 29.701315238258985  -0.5113424569489686\n",
-       "2029545430912236672    2015.5 ... 29.482001000231968 0.049122831032945236\n",
-       "2029534435790829568    2015.5 ... 29.490016052993543   1.0104447723028214"
+       "5849745869473487488    2015.5 ... -65.20710341703752   0.5482007725782427\n",
+       "5849746938973887488    2015.5 ...  -65.1665758810783                   --\n",
+       "5849749412871953152    2015.5 ... -65.19549094160195   0.8018859767657315\n",
+       "5849756177391089408    2015.5 ... -65.57259338863145   0.2470355479426332\n",
+       "5849740479338016512    2015.5 ...   -65.248465161243   0.7498253475529492\n",
+       "5849755150954806784    2015.5 ... -65.59812207307075 -0.17531203854271796\n",
+       "5849742231683753344    2015.5 ... -65.30003719122678   0.2758280660620302\n",
+       "5849741853727882752    2015.5 ...  -65.3172714882006   1.4628766254219059\n",
+       "5849749138009127680    2015.5 ... -65.08195659745067                   --\n",
+       "5849741716288860672    2015.5 ... -65.33572466059165                   --"
       ]
      },
      "execution_count": 15,
@@ -1044,10 +1044,10 @@
       "       ra float64  deg                                                    Right ascension\n",
       "      dec float64  deg                                                        Declination\n",
       " parallax float64  mas                                                           Parallax\n",
-      "Jobid: 1605622934969O\n",
+      "Jobid: 1605646069281O\n",
       "Phase: COMPLETED\n",
       "Owner: None\n",
-      "Output file: async_20201117092215.vot\n",
+      "Output file: async_20201117154749.vot\n",
       "Results: None\n"
      ]
     }
@@ -1073,58 +1073,58 @@
      "data": {
       "text/html": [
        "<i>Table length=3000</i>\n",
-       "<table id=\"table139709414533440\" class=\"table-striped table-bordered table-condensed\">\n",
+       "<table id=\"table140445173361824\" 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",
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        "<thead><tr><th>int64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th></tr></thead>\n",
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        "<Table length=3000>\n",
-       "     source_id      ref_epoch ...         dec               parallax      \n",
-       "                        yr    ...         deg                 mas         \n",
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-       "------------------- --------- ... ------------------- --------------------\n",
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-       "                ...       ... ...                 ...                  ...\n",
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-       "6230167031435813760    2015.5 ... -23.336372421616733  -0.8736321545140885"
+       "     source_id      ref_epoch ...         dec                parallax      \n",
+       "                        yr    ...         deg                  mas         \n",
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+       "                ...       ... ...                 ...                   ...\n",
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       ]
      },
      "execution_count": 18,
@@ -1277,7 +1277,7 @@
     {
      "data": {
       "text/plain": [
-       "(None, '1605622934969O')"
+       "(None, '1605646069281O')"
       ]
      },
      "execution_count": 21,
@@ -1307,7 +1307,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Removed jobs: '['1605622934969O']'.\n"
+      "Removed jobs: '['1605646069281O']'.\n"
      ]
     }
    ],
@@ -1476,7 +1476,7 @@
       "Jobid: None\n",
       "Phase: COMPLETED\n",
       "Owner: None\n",
-      "Output file: sync_20201117092220.xml.gz\n",
+      "Output file: sync_20201117154752.xml.gz\n",
       "Results: None\n"
      ]
     }
@@ -1495,20 +1495,20 @@
      "data": {
       "text/html": [
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        "</table>"
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       "text/plain": [
@@ -1517,16 +1517,16 @@
        "                           deg         ...         mas              km / s    \n",
        "       int64             float64       ...       float64           float64    \n",
        "------------------- ------------------ ... ------------------- ---------------\n",
-       "6230144152145537152 221.00484394613122 ...  0.1288335146382159              --\n",
-       "6230171669999288192 222.11986606839224 ...  0.6798312067588314              --\n",
-       "6230030795072008832  220.6858032920676 ...   1.011426390802751              --\n",
-       "6230121401702753664  220.2681390809788 ...   0.612231874239708              --\n",
-       "6229987570521485440 219.69736802123325 ... 0.21596169286289862              --\n",
-       "6230018425567339520  220.1035725206795 ...  0.8296685650840913              --\n",
-       "6230130266515510528  220.4226870525988 ...  0.2970783741597157              --\n",
-       "6230061757492831616 221.17289130564805 ... 0.08382745553667581              --\n",
-       "2003409519814774528 339.93278128584706 ... 0.16657261834688394              --\n",
-       "2003390901139218816  338.7103475641665 ...  0.4296262772904238              --"
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+       "4687422891933697920 15.901327641112253 ...  0.2764244386276099              --\n",
+       "4687422715871646976  15.99138434019087 ...   0.847035789335997              --\n",
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+       "4687420894804720512 15.645744782387933 ... 0.35767437939443886              --\n",
+       "4110047449052774912  261.1262641088297 ...  0.7276250597861622              --"
       ]
      },
      "execution_count": 29,
diff --git a/_sources/03_motion.ipynb b/_sources/03_motion.ipynb
index a2089c9..dfa0b32 100644
--- a/_sources/03_motion.ipynb
+++ b/_sources/03_motion.ipynb
@@ -331,7 +331,7 @@
      "data": {
       "text/html": [
        "<i>Row index=0</i>\n",
-       "<table id=\"table140510732444000\">\n",
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        "<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",
@@ -592,7 +592,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -612,7 +612,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -621,7 +621,7 @@
        "astropy.coordinates.sky_coordinate.SkyCoord"
       ]
      },
-     "execution_count": 75,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -639,7 +639,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -648,7 +648,7 @@
        "astropy.coordinates.sky_coordinate.SkyCoord"
       ]
      },
-     "execution_count": 76,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -695,7 +695,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -704,7 +704,7 @@
        "astropy.coordinates.sky_coordinate.SkyCoord"
       ]
      },
-     "execution_count": 77,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -730,7 +730,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": 19,
    "metadata": {
     "scrolled": true
    },
@@ -749,7 +749,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -790,7 +790,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -799,7 +799,7 @@
        "astropy.table.table.Table"
       ]
      },
-     "execution_count": 80,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -810,7 +810,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
@@ -819,7 +819,7 @@
        "pyia.data.GaiaData"
       ]
      },
-     "execution_count": 81,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -830,7 +830,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
@@ -839,7 +839,7 @@
        "astropy.coordinates.sky_coordinate.SkyCoord"
       ]
      },
-     "execution_count": 82,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -872,7 +872,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
@@ -881,7 +881,7 @@
        "(140340, 8)"
       ]
      },
-     "execution_count": 83,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -904,7 +904,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 84,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
@@ -1014,7 +1014,7 @@
        "4        0.486224     1.000000e+20  "
       ]
      },
-     "execution_count": 84,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1039,7 +1039,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 85,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
@@ -1048,7 +1048,7 @@
        "(140340, 10)"
       ]
      },
-     "execution_count": 85,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1068,7 +1068,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 86,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
@@ -1077,7 +1077,7 @@
        "(140340, 12)"
       ]
      },
-     "execution_count": 86,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1133,7 +1133,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 99,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
@@ -1142,7 +1142,7 @@
        "pandas.core.series.Series"
       ]
      },
-     "execution_count": 99,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1163,7 +1163,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 100,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
@@ -1172,7 +1172,7 @@
        "pandas.core.series.Series"
       ]
      },
-     "execution_count": 100,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1187,7 +1187,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 101,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
@@ -1196,7 +1196,7 @@
        "dtype('bool')"
       ]
      },
-     "execution_count": 101,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1214,7 +1214,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 102,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
@@ -1228,7 +1228,7 @@
        "Name: phi2, dtype: bool"
       ]
      },
-     "execution_count": 102,
+     "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1246,7 +1246,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 103,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
     {
@@ -1255,28 +1255,28 @@
        "pandas.core.frame.DataFrame"
       ]
      },
-     "execution_count": 103,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "selected = df[mask]\n",
-    "type(selected)"
+    "subset = df[mask]\n",
+    "type(subset)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "`selected` is a `DataFrame` that contains only the rows from `df` that correspond to `True` values in `mask`.\n",
+    "`subset` 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,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1295,7 +1295,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 105,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [
     {
@@ -1304,7 +1304,7 @@
        "25084"
       ]
      },
-     "execution_count": 105,
+     "execution_count": 34,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1322,7 +1322,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 106,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [
     {
@@ -1331,7 +1331,7 @@
        "25084"
       ]
      },
-     "execution_count": 106,
+     "execution_count": 35,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1350,7 +1350,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 112,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [
     {
@@ -1387,7 +1387,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 113,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [
     {
@@ -1446,7 +1446,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 96,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1465,7 +1465,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 114,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1482,7 +1482,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 118,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [
     {
@@ -1518,7 +1518,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 119,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1543,7 +1543,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 120,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1560,7 +1560,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 121,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [
     {
@@ -1569,7 +1569,7 @@
        "1049"
       ]
      },
-     "execution_count": 121,
+     "execution_count": 43,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1587,7 +1587,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 122,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [
     {
@@ -1596,7 +1596,7 @@
        "1049"
       ]
      },
-     "execution_count": 122,
+     "execution_count": 44,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1615,7 +1615,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 123,
+   "execution_count": 45,
    "metadata": {},
    "outputs": [
     {
@@ -1661,7 +1661,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 124,
+   "execution_count": 46,
    "metadata": {},
    "outputs": [
     {
@@ -1670,7 +1670,7 @@
        "astropy.table.table.Table"
       ]
      },
-     "execution_count": 124,
+     "execution_count": 46,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1708,7 +1708,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 125,
+   "execution_count": 47,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1724,7 +1724,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 126,
+   "execution_count": 48,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1753,7 +1753,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 127,
+   "execution_count": 49,
    "metadata": {
     "tags": [
      "hide-cell"
@@ -1793,14 +1793,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 128,
+   "execution_count": 50,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "-rw-rw-r-- 1 downey downey 17M Oct 19 12:05 gd1_dataframe.hdf5\r\n"
+      "-rw-rw-r-- 1 downey downey 17M Nov 18 19:06 gd1_dataframe.hdf5\r\n"
      ]
     }
    ],
@@ -1823,7 +1823,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 129,
+   "execution_count": 51,
    "metadata": {},
    "outputs": [
     {
@@ -1832,7 +1832,7 @@
        "(140340, 12)"
       ]
      },
-     "execution_count": 129,
+     "execution_count": 51,
      "metadata": {},
      "output_type": "execute_result"
     }
diff --git a/_sources/04_select.ipynb b/_sources/04_select.ipynb
index 691c30a..892243f 100644
--- a/_sources/04_select.ipynb
+++ b/_sources/04_select.ipynb
@@ -236,8 +236,8 @@
     "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",
+    "plt.xlabel('Proper motion ra (ICRS frame)')\n",
+    "plt.ylabel('Proper motion dec (ICRS frame)')\n",
     "\n",
     "plt.xlim([-10, 5])\n",
     "plt.ylim([-20, 5]);"
@@ -284,6 +284,13 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "NOTE: If you are using an older version of Pandas, you might not have `to_numpy()`; you can use `values` instead, like this:\n",
+    "\n",
+    "```\n",
+    "points = selected[['pmra','pmdec']].values\n",
+    "\n",
+    "```\n",
+    "\n",
     "We'll pass the points to `ConvexHull`, which returns an object that contains the results. "
    ]
   },
@@ -295,7 +302,7 @@
     {
      "data": {
       "text/plain": [
-       "<scipy.spatial.qhull.ConvexHull at 0x7f446b1e8bb0>"
+       "<scipy.spatial.qhull.ConvexHull at 0x7fd67bffd910>"
       ]
      },
      "execution_count": 9,
@@ -800,10 +807,10 @@
       "       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",
+      "Jobid: 1605744463707O\n",
       "Phase: COMPLETED\n",
       "Owner: None\n",
-      "Output file: async_20201019143906.vot\n",
+      "Output file: async_20201118190743.vot\n",
       "Results: None\n"
      ]
     }
@@ -943,14 +950,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<Figure size 648x216 with 1 Axes>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {
@@ -1033,7 +1040,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "-rw-rw-r-- 1 downey downey 756K Oct 19 14:39 gd1_candidates.hdf5\r\n"
+      "-rw-rw-r-- 1 downey downey 756K Nov 18 19:07 gd1_candidates.hdf5\r\n"
      ]
     }
    ],
@@ -1096,7 +1103,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "-rw-rw-r-- 1 downey downey 1.6M Oct 19 14:39 gd1_candidates.csv\r\n"
+      "-rw-rw-r-- 1 downey downey 1.6M Nov 18 19:07 gd1_candidates.csv\r\n"
      ]
     }
    ],
@@ -1123,8 +1130,8 @@
      "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"
+      "0,635559124339440000,137.58671691646745,19.1965441084838,-3.770521900009566,-12.490481778113859,0.7913934419894347,0.2717538145759051,,-59.63048941944396,-1.21648525150429,-7.3613627125974945,-0.5926328820644919\r\n",
+      "1,635860218726658176,138.5187065217173,19.09233926905897,-5.941679495793577,-11.346409129876392,0.30745551377348623,0.19946557779138105,,-59.247329893833296,-2.0160784008206476,-7.52712608464053,1.7487794924176672\r\n"
      ]
     }
    ],
diff --git a/_sources/05_join.ipynb b/_sources/05_join.ipynb
index caa9d2b..a95ea7e 100644
--- a/_sources/05_join.ipynb
+++ b/_sources/05_join.ipynb
@@ -311,7 +311,7 @@
      "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",
+      "<!-- Produced with astropy.io.votable version 4.0.2\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",
@@ -350,7 +350,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "-rw-rw-r-- 1 downey downey 396K Oct 19 14:48 candidate_df.xml\r\n"
+      "-rw-rw-r-- 1 downey downey 396K Nov 18 19:21 candidate_df.xml\r\n"
      ]
     }
    ],
@@ -517,7 +517,7 @@
      "data": {
       "text/html": [
        "<i>Table length=7346</i>\n",
-       "<table id=\"table140665432275552\" class=\"table-striped table-bordered table-condensed\">\n",
+       "<table id=\"table140178070186304\" 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",
@@ -713,7 +713,7 @@
      "data": {
       "text/html": [
        "<i>Table length=3724</i>\n",
-       "<table id=\"table140665411011344\" class=\"table-striped table-bordered table-condensed\">\n",
+       "<table id=\"table140178046436112\" 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",
@@ -973,7 +973,7 @@
      "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",
+      "<!-- Produced with astropy.io.votable version 4.0.2\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",
@@ -1140,7 +1140,7 @@
      "data": {
       "text/html": [
        "<i>Table length=3724</i>\n",
-       "<table id=\"table140665408248800\" class=\"table-striped table-bordered table-condensed\">\n",
+       "<table id=\"table140178044101488\" 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",
@@ -1250,7 +1250,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "-rw-rw-r-- 1 downey downey 96K Oct 19 14:49 gd1_photo.fits\r\n"
+      "-rw-rw-r-- 1 downey downey 96K Nov 18 19:22 gd1_photo.fits\r\n"
      ]
     }
    ],
diff --git a/_sources/06_photo.ipynb b/_sources/06_photo.ipynb
index d04371b..98b9991 100644
--- a/_sources/06_photo.ipynb
+++ b/_sources/06_photo.ipynb
@@ -251,9 +251,9 @@
    "source": [
     "import matplotlib as mpl\n",
     "\n",
-    "if IN_COLAB:\n",
-    "    coords = None\n",
-    "else:\n",
+    "coords = None\n",
+    "\n",
+    "if not IN_COLAB:\n",
     "    mpl.use('TkAgg')\n",
     "    plot_cmd(photo_table)\n",
     "    coords = plt.ginput(10)\n",
@@ -277,16 +277,16 @@
     {
      "data": {
       "text/plain": [
-       "[(0.2150537634408602, 17.548197203826344),\n",
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-       " (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)]"
+       "[(0.2643369175627239, 17.84253127299485),\n",
+       " (0.3539426523297491, 18.799116997792495),\n",
+       " (0.47491039426523296, 19.682119205298015),\n",
+       " (0.6317204301075269, 20.454746136865342),\n",
+       " (0.7661290322580645, 20.785871964679913),\n",
+       " (0.8064516129032258, 21.41133186166299),\n",
+       " (0.5869175627240143, 21.300956585724798),\n",
+       " (0.39426523297491034, 20.565121412803535),\n",
+       " (0.22401433691756267, 19.240618101545255),\n",
+       " (0.19713261648745517, 18.02649006622517)]"
       ]
      },
      "execution_count": 7,
@@ -335,10 +335,10 @@
     {
      "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]))"
+       "(array([0.26433692, 0.35394265, 0.47491039, 0.63172043, 0.76612903,\n",
+       "        0.80645161, 0.58691756, 0.39426523, 0.22401434, 0.19713262]),\n",
+       " array([17.84253127, 18.799117  , 19.68211921, 20.45474614, 20.78587196,\n",
+       "        21.41133186, 21.30095659, 20.56512141, 19.2406181 , 18.02649007]))"
       ]
      },
      "execution_count": 9,
@@ -367,7 +367,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -413,16 +413,16 @@
     {
      "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)"
+       "Path(array([[ 0.26433692, 17.84253127],\n",
+       "       [ 0.35394265, 18.799117  ],\n",
+       "       [ 0.47491039, 19.68211921],\n",
+       "       [ 0.63172043, 20.45474614],\n",
+       "       [ 0.76612903, 20.78587196],\n",
+       "       [ 0.80645161, 21.41133186],\n",
+       "       [ 0.58691756, 21.30095659],\n",
+       "       [ 0.39426523, 20.56512141],\n",
+       "       [ 0.22401434, 19.2406181 ],\n",
+       "       [ 0.19713262, 18.02649007]]), None)"
       ]
      },
      "execution_count": 11,
@@ -1073,7 +1073,7 @@
     {
      "data": {
       "text/plain": [
-       "496"
+       "481"
       ]
      },
      "execution_count": 25,
@@ -1098,7 +1098,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "selected = merged[inside]"
+    "selected2 = merged[inside]"
    ]
   },
   {
@@ -1115,7 +1115,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1130,7 +1130,7 @@
     "plot_cmd(photo_table)\n",
     "plt.plot(xs, ys)\n",
     "\n",
-    "plt.plot(selected['color'], selected['mag'], 'gx');"
+    "plt.plot(selected2['color'], selected2['mag'], 'gx');"
    ]
   },
   {
@@ -1149,7 +1149,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 720x180 with 1 Axes>"
       ]
@@ -1163,8 +1163,8 @@
    "source": [
     "plt.figure(figsize=(10,2.5))\n",
     "\n",
-    "x = selected['phi1']\n",
-    "y = selected['phi2']\n",
+    "x = selected2['phi1']\n",
+    "y = selected2['phi2']\n",
     "\n",
     "plt.plot(x, y, 'ko', markersize=0.7, alpha=0.9)\n",
     "\n",
@@ -1205,7 +1205,7 @@
     "filename = 'gd1_merged.hdf5'\n",
     "\n",
     "merged.to_hdf(filename, 'merged')\n",
-    "selected.to_hdf(filename, 'selected')"
+    "selected2.to_hdf(filename, 'selected2')"
    ]
   },
   {
@@ -1217,7 +1217,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "-rw-rw-r-- 1 downey downey 2.0M Oct 19 17:21 gd1_merged.hdf5\r\n"
+      "-rw-rw-r-- 1 downey downey 2.0M Nov 18 19:28 gd1_merged.hdf5\r\n"
      ]
     }
    ],
diff --git a/_sources/pilot_day1.ipynb b/_sources/pilot_day1.ipynb
index 2990576..437b249 100644
--- a/_sources/pilot_day1.ipynb
+++ b/_sources/pilot_day1.ipynb
@@ -736,6 +736,243 @@
     "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": 104,
+   "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>a</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>a</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>a</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>a</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   a\n",
+       "0  a\n",
+       "1  a\n",
+       "2  a"
+      ]
+     },
+     "execution_count": 104,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "df1 = pd.DataFrame(index=[0, 1, 2])\n",
+    "df1['a'] = 'a'\n",
+    "df1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "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>b</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>b</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>b</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>b</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   b\n",
+       "0  b\n",
+       "2  b\n",
+       "4  b"
+      ]
+     },
+     "execution_count": 105,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2 = pd.DataFrame(index=[0, 2, 4])\n",
+    "df2['b'] = 'b'\n",
+    "df2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "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>a</th>\n",
+       "      <th>b</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>a</td>\n",
+       "      <td>b</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>a</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>a</td>\n",
+       "      <td>b</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   a    b\n",
+       "0  a    b\n",
+       "1  a  NaN\n",
+       "2  a    b"
+      ]
+     },
+     "execution_count": 108,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1['b'] = df2['b']\n",
+    "df1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(3, 2)"
+      ]
+     },
+     "execution_count": 109,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 111,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1 is df2"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/_sources/pilot_day3.ipynb b/_sources/pilot_day3.ipynb
new file mode 100644
index 0000000..72d64a4
--- /dev/null
+++ b/_sources/pilot_day3.ipynb
@@ -0,0 +1,1423 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "filename = 'gd1_results.fits'\n",
+    "from astropy.table import Table\n",
+    "\n",
+    "results = Table.read(filename)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "pyia.data.GaiaData"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from pyia import GaiaData\n",
+    "\n",
+    "gaia_data = GaiaData(results)\n",
+    "type(gaia_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "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": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "astropy.coordinates.sky_coordinate.SkyCoord"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import gala.coordinates as gc\n",
+    "\n",
+    "transformed = skycoord.transform_to(gc.GD1Koposov10)\n",
+    "type(transformed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "astropy.coordinates.sky_coordinate.SkyCoord"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gd1_coord = gc.reflex_correct(transformed)\n",
+    "\n",
+    "type(gd1_coord)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "phi1 = gd1_coord.phi1\n",
+    "phi2 = gd1_coord.phi2"
+   ]
+  },
+  {
+   "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": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "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": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(140340, 8)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "df = results.to_pandas()\n",
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(140340, 10)"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['phi1'] = gd1_coord.phi1\n",
+    "df['phi2'] = gd1_coord.phi2\n",
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(140340, 12)"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "df['pm_phi1'] = gd1_coord.pm_phi1_cosphi2\n",
+    "df['pm_phi2'] = gd1_coord.pm_phi2\n",
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "25084"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "phi2_min = -1.0 * u.deg\n",
+    "phi2_max = 1.0 * u.deg\n",
+    "\n",
+    "phi_mask = ((df['phi2'] > phi2_min) & \n",
+    "            (df['phi2'] < phi2_max))\n",
+    "\n",
+    "phi_mask.sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "centerline = df[phi_mask]\n"
+   ]
+  },
+  {
+   "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['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": "code",
+   "execution_count": 14,
+   "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": 15,
+   "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": "code",
+   "execution_count": 16,
+   "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": "code",
+   "execution_count": 17,
+   "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": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pm_mask = (between(df['pm_phi1'], pm1_min, pm1_max) & \n",
+    "           between(df['pm_phi2'], pm2_min, pm2_max))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1049"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pm_mask.sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "selected = df[pm_mask]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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W0UnV+CQrhCJjoiPdRMLQWcjT7zN9ylaW7GVhF/2eFV5V/YgaRVlZbeD6/aVk+YWihDo77zeZe3RePs8CqoNPvAyfZi4VIbsmQfc70+geB5CVp8qMEp3jP3mzw1xNMU3e62o8wpd6AbJjAMz8r3m/EdG5Zff4Bhvuinlv6QVw7eoCit078WcAm61kW72zovR10UpPxjv5nqJ8niWPyv5FeTrPO+e5ar5Kx0lmL6T43n6/r7V8itYnMj3IgeTJaFrIsgplF4C/U3mu6lWlB2B6KwBfLL1usuJlMq7JdKqynYNuWVVaxbrmepfFW3RVad570s8UrQGpEg/V+0XSPWuhna5ekqpMIuGm10yY6oFDdiEYgDfmXNcB+Ie850xeVQzAYDDYXKBTVkh1Je4okKWLdGGr0r1Op0uyy55OLxU963Q/lBVikfeouGFk4r2xscGtVqtwX/8ymcoMgK78LpI2eeU26761tTU+fvw433TTTZkNiDLyXFjJ38vSRNTIpr+P0yyrPOnQd54BKBoEfjuAJwM4L3WdC027iBLRy4no80T0BSKa1xFmwbsyd2rM6xbLdFlt7Wip+3cR0rqIM87s7CwACJ/pmpYl/ry+vo6FhQUsLCyg1+tt6bKnj+y0OVspS97V1VUcPnwYq6urW3QaL3yamZkpfY9ofkveJxPvfr+PpaUl9Pv9M+IxGAxw6tSpzWMf49/Te/lPTU1hbm5u8/d0ON1uF29/+9vR7XZL5SmiKG2S+SPv6NakTESEhx56CO94xzvw/Oc/H41Go9C9lVU28lxYyd/L0iSvLsiL6+TkJGZmZtBsNjenzabdVUbdy1lWIVLK/wdwac5vX857TvQC8BgAXwTwDACPBdACcFHRM1UHgU25M9ItA5EWi0h4ZdsGlLVwTAzqJsOUGWDLc+fEJ1Gtra1ZczMlEW2xdbtdPnjwIJ84ceKMFqLKoGRZy1Elr2blvWQ8Wq0W79mzh0+cOJH5e1n8488rKyv8whe+kFdWVqTiKEqyR1W0d1Jaxo2Njc28FOfJTqezZUuKrLKVzsd5+bnovvi3Rx555IweWJE+4nyV3DJDJC/IAgUX0PcA2JbzWyPvOdELwPMAHEt8fjOANxc94/MsoJhut8vz8/M8Pz+vXPFm+TOzUPFLV6UoTJFCkC486QJqmyyDNhgMznClyBg7Fcr2XkpWWnn5osyVtLGxcYYBE3WNFukmTZ5REdVhulLMe1+ROy1pQJI+9iwdFhm7onTI2uwvyxVY1BArch/qaEzGSBsA0xeAVwH47cTn1wL4zYz7rgKwDGB5+/btlZRgg2SiqVYWJloAqu+WIc7oJ06c4D179nCr1cq9J1kBqQ5+iRijsngk74srnpWVFV5ZWanU0pel0+nw9ddfzysrK7kVe1xp5RkCEfmy7lEd9BV5R1q/ZTuxZskY91xWVlZKDUiWkUn69rOMp2h5S6dDVg+gbOxKpmenozEZo2QAALwOwD0AvhldywB+rugZ0QvAT2YYgPcWPWOiB2CyUBdZftlwRFrJOuJSReZ0D+CRRx4RyuxlBURU1qzKJq8Vl1d5tFqtzUKX7par7oAqWjEXtQSzKijZnUXz5MgyAHnpJHJ4fDpOsc6KeohFxD2ABx98kOfn53llZaXybDCR1rVqQ6yoDJWVr3RZcNYDAPBzAE4CeBGAJwJ4EoAXA7hbhxHwxQWkq5JOI5N4Iq4ckVbTysoKX3/99by2tqbsGtJpEPNcWUWuAJH0yHs+z52TfCbL95uu5LJacFW2NxY1RrK6l70/Tz9Z4WTJ3O12ef/+/Zv+/7J3J1v8eWHLxCE2VGX5W4ROp8PXXHMNX3/99bl5TbZuKMrX6XvyZNfdG4tRMQB3ANiR8f0OAHfkPSd6ATgLwH0Anp4YBL646BlVA6Ba4alUhkWts7KupUilV5Rx9u/fz/v37y9tIWVVzCJdU1l9xC23tbW1TB9r1kCfiOEUqVDThTEeazh+/PjmgLNoYRPtgeVRJd11vCfr93TlLJLWyR5AnKZF41R5YSYrSBkdFMkomzfjtE+73IrykGzLX6X+8MkAfEblN5kLwCsA/E00G+iGsvtVDYBqQVNpqeS1euPfsioSmYxS1I2Pfch5XeysSjFv0VU6/qL6SFbgZb7qLDmy3iuqgyRZRubYsWPcbDZ5eXmZu90ur62tbbYoZf22OtAdrmxlmvSLy47DFDV0ZOSrooNkeKqtdRGjXBR2UT4RlamK0RFFxQDcrfKbyUtHD0C1shU1BmW/ZRU0mZa2SOs3r6Cl312mlyK58uTodh8duMprHabDzZtxU6VXFhui5Klct912G5977rl86623bnElxK2u5GBbkSH3lSo9NNkejkpFpbJILe+9g8HW2UG6jKlIOUgiahyKKDI6umbHqRiAfwJwKuNaBfDNvOdMXjrGAFR7A8k5xlUG37IyRVFLOx2GSIHLe4dMIZdxJ6S/L3LhpA1R8rNIZSuTful7v/zlL/NrXvMaXl5e3tLNzpJZpoUrW/mI3G+q15FE1cglew8y5Ui17OWFoSO8quhIpzyjo/PYVRUD8LSiK+85k5cOA6Ba+ERatnnIujSyWkpVWxqyFbqpwpU0RHE8b7rppi1ztHUdNp/Xg5FptYq8T1ZXIvfb6IGoVl5FrVMdvTdRmW0Yybx3p78ry1MqsuqMn3frAFQuWwvB8lrgqlOyZBOyqAeQlfnW1tZy55CLkn6nag+gjCw3UnIg0vZh8zoKmaxrQ8Zg2zxEXhRVl4hrqvbUylw1IhMvknlfVwNEBJUewD4Av5T4vAbgawC+DuD1ec+ZvGwZANstC9X3x5lqZWWF9+/fX2nBiA4jpeOdOguHida7Shgqcqr0VkTeZRrR9+uSUyacqj21KumUbCRUcWOqomIA7gLwnYnPJ6O/jwfwl3nPmbxsuYB0PmeSZObTsWBEpmXqw4HfZYVDpPBUNTgiPcOk20u01axa8E23wHWVA11yyoRTtQdQBdUegI6Bc2Y1A3B36vNbEv/flfecycvlILBqAopS1Y+vQyZffNN55Pn0dbup8lCtqGPXlswYjq8NFV0Vt4segEtU5XTZA/hCzvcTAO7Le87k5UsPwEQrS8TvX/ReUy6ZvHtc+KZFuuQmqVJR16WiKmNU4lEXTI8B0PC3MyGi92F48MtbU98fBHA+M1+d+aBBdu/ezcvLy7ZfewbMZ+6hbiLM9fV1HDlyBFdeeSWmpqYK32tCJll5bb8zrR+XsgUCPkNEdzPz7jO+LzAA5wD4bQDPwXCbBgD4fgw3hPvPzPwNQ7Lm4osBsEWoyIrJ00+eYQgExhVpA5B48BkALo4+foaZv2hAPiHGzQCMA7Z6U7oJxjlQJ/IMQOnRjsx8HzMvRpezyj8wmpg47k7l6EhZjB7TF7AOc/XjU3WGYwstZ/sGAqqontnrmrrKPa4wD88y7na7mZWzLoNet4ZBqQvIJ1y5gEJ3P1A3xtkNljeh4pZbbgEAXHvttWeMDemKi686UXYBRQ//IBHNRv9vI6Kn6xbQJrLdNF1WvW7dQ9+oq/7y5E5+rztuplqiSTl9be1mydVoNDA3N4drrrkmOaV9E11uw2Q4dcivpQaAiH4FwPUYntgFAGcD+LBJoUwjm3GrdPfrUGDqQp30J5Luye91x82Uiyopp69usCy5iAjT09OYmJjA0aNHreShWuTXrMUByQvACgBCtBVE9N2psudMXLr2ArK5mMX0CuK6UWU5fp30J5LudYxbXeRkVl+4N4qrlJGzEEzEBfRwFAADm+sDao2NWSIxydaIzff6imyrKHl/rD8A2rvWLNhdz7ov6zuRdE9+X5e8URc5gey8JiK/rpZ7HXQlYgA+QkTvB/AkIvp5AMcB/JZZsUYHnzOBaKVX9OxgMJAKQ9ZtkHV/lQKaF2fRMLPuU61oAmZRdVH56toygdAsICLaA+BlGLqCjjHzJ0wLloWtWUDs6Ui+bqqsmI2fnZmZwdLSkrZVtyK6r5I+VbePyLpvXPKLbwS9i1NpFhCGB7cfY+YDAD5FROdplc4zajF4o4EqLZ342WazqbW1JKL7Kq3rZJyTvYGsMLN6C1n3qbimuGReuk1Ue4JVepBVYWasrq7iyJEjWF9fryRHMh4u41QklylEZgH9PIA/BPD+6KunAPgTYxJ5QF26gFUzSJWKNH52YmJiSxhVZTKt+2Scy4xNlfEKkXsXFhawsLDgvKGh2uBx2VDq9XpYXFzEzMwMAFSSQ3U2lukK2op+s0aGkxeGs4Aei62zgFbLnjNx2ToRzFdktod2hY8y5aH7PAHZw2V0HOKjA1/PHhB9t45DfVTCqsvhO8z5s4BENoO7k5mfS0QnmfnZRHQWgHuYeZc5s5TNuG8GJ7M9tCt8lCkQiNG5U6ypvG4i3CpjALcT0VsAfFs0GPwHABa1SBXYApd0KdPuER9nmojIVBZPUXSFY4uq8voe31g+2ZlhNlFxMebp3VT5s+laEzEA1wM4DWAVwC8A+BiAtxY+UQIRvYuIPkdEp4joj4noSVXCc4GJwliW8C4r/CrxTT87rhtvVZXX9/jG8rXbbW/llC1DHA02Hz582Fp8rI5BZvmF4gtDA9EuukflwnBK6VnR/+8A8A6R53waAzDh/3PlUxV5b5X4mjrK0aS+XJ37LPu8rkPDdcgSf7exsaHtjGPXxHm31WrVTvYkkD0TmB+trH8XwPay+1QvAD8G4HdF7jVlAFQyZ10zdBaiB5urxldkKwTfqMtgdqvV4j179nCr1bL6Xln91EWfaXzOozLkGQCRQeA/w/BYyE8D+Gai5/AjOnogRLQI4PeZOXODOSK6CsBVALB9+/ZLH3jgAR2v3cK4HyHIjgZuXehdNK6udCLLYDBAu91Gs9nExITZ4z2SOgEgpZ88fdZFz3WnypGQV2R9z8y3lzx3HEBWqb6Bmf80uucGALsB/DiXCQJzs4BCJnSDiN51p42M0Qn5YisqBrtMh7JhhjRRI88AGHHriFwAXgfgrwE8QfQZ3S6gUenejTK6XQc+zfOuGyrlpUyHsmGGNFEDFVxAX0e0E2iCrwJYBnAdM9+nYI1eDuDdAK5g5tOiz+nuAfjs+uHQ0gHgVg8hDaqjW4chTdSosg7g3QB+CcMtIJ4K4ACGu4HeBuCIojy/CeA8AJ8gohUi+p+K4VTC5y0ffJ/yJ8tgMMCpU6cwGAyknnM59ZWI0Gg00Ov1UNZQKoPZ7zn8ptCdfj6ufSnC93QXMQAvZ+b3M/PXmflrzPwBAK9g5t8H8GSVlzLzdzHzBcz8rOi6WiWcqvicmXw2Tiq0220cOHAA7XbbtShSxIY4veGYbMHOMuhFYfhecQTE8L0hJ2IABkT0aiKaiK5XJ34LudMQosZJpKKwUZmUvaPZbOLQoUNoNpvGZDBBbIiBrRuO6ThWtCgMnyoO1fzD7M+Op67wvSEnYgB+BsBrAfQB9KL/f5aIvg3AfoOyBQQQqShsHGpf9o6JiQns2rVL61TFqoZN5PnYEE9NTW0pyLIFO8ugF4XhsuJI66XKbqG+7HjqiipeBiu9wKyRYV8vn1YC+4LqGae6Z3S4mFFVZUbIYDDgVqvFBw8eDDNKUhSt3M5a7Vu00M+XHU/rQlKXOmc8QfVMYCK6kIhOEFE7+ryLiCrtBVQn2LEvlku60SItjKx7ZFt1cYaZnZ3NbJW6GE+p0kru9XpYWlrC3r17ve2e60I2DxdtOpi1309eXiIiTE9PY3p6unb7V7kiqUsrvcAsq5C8ANwO4DJsPQ9A+/5AIpeLHoDrecfdbpfn5+d5fn7eyb5D8X2dTkdKD76vsfBdviqYPDdCpgfgGtdlV4W0LnXpFhX2Aror+nsy8d1K2XMmLhcGwHXmdt2NjgtRp9MZuQU7rtO2Knnym9p8r274Hm/TmzAmyTMAIiNyDxHRMxHN+CGiVwHoau6IGIcFuoNZ97gexHHdjY67ofFAqKgMMt1XHXpSwaeZNirkyV+HcyN0o7vs2iBOv9XV1dy8b9oNJGIA3oDhecDfS0RrAH4RwOuNSGMQ0dkyhw8fLkwQ3e/0HdVCJPOcKz2ZKFw2jVme/L5XfCaomodcNEIajQZmZmawuLjo7AyQ0q0gEoKcA2CCmb9uRBIBqmwFwSy28djq6iqWlpaMHxkX/zY5OYl+vz/WS9tF0qYu+Ly9yChTNQ+5SjdbeV96N1AiemNRgMz8bk2yCWPjTGCdCZIXVtLQzMzMaDM4rjCRietqFOoq97gz6ummshfQedG1G0OXz1Oi62oAF5kQ0iZ5Xb68/V9Uuoh53dJer4fFxUXMzMyg2Wx6vVJQhKLut2rXuq7uMxvuF5vuClfjM7bfnZduLuNvg1wDwMw3MvONAM4HcAkzX8fM1wG4FMNN4WqN7DJ8lQopz0fbaDSwb98+7Ny5ExMTE7X31ybjmS4wqhW5iH9epnCOUkG2aRxdGmId7y5L97Lf69oQESZralDyAvA5AI9LfH4cgM+VPWfi0jkNtGgKlq6VszbxQb6s1bUm5ZKZIufrtFTZfFj2jE356vDusnTXfV6Br6DCeQA3AHg1gD/GcCroj2F4hOOvGbRLmdgYA6grPgw+xjLMzMxg586dxns1LOG3lbnXJkXp5kOaAv7qrgyOWvcAcnvZdY2bLMpHQkYPXwLgBdHHv2Tmk5rlEyIYgHx8yMg+yFA3inRm0sDJ3O/DMZoq4QYD+ijSg8BEdG78PzPfw8y3RNfJrHtGnbg1IWIwXeDD3G+TMviuf1WKdGZyLYXM/Y1GA7Ozs0m3sDY5RNE5Bmcbn8cRimYB/SkR/ToRvTBaAwAAIKJnENE+IjoG4OXmRfQDnxNxHKib/k0YrKIwZSs7mfuJCESEo0ePluo/DndyclJr/FUqcx8aRYCa7LYaPEWzgF4C4ASAXwBwLxF9jYj+HsCHAUwBeB0z/6FR6TzCl9bEuKJL/7YKlgmDlQwzHQ/Zyk72flH9x+H2+32t8felMldBRXZbDR7hlcA+EMYAAlWx5RcW9fuq+vnjCsK1fzsPH/zedUa3/lQWggUKGAWf9CjEQRZTLoo0oq0+mZZeMkzfe6R1brH7gC39jbUBGAwGOHXqFAaDgfSzsod8+4itbqZPejHhoqgSP9cVuU9pY4pxiKMqY20A2u02Dhw4gHa7Lf2s7CHfPmKr8jGhl7xCLVrYdcZ9fX0dt9xyy+acc5kKR7Wlp0unrvKszUq5buXSJiJHQl5OROclPp9HRM81K5Ydms0mDh06hGazKf1sVsF13ZqTxVY304ReivZZEinsJuNuo8LRpVNXedZmpVy3cmkTkZXAJzHcCyg+EGYCw2XFl1iQbwu2B4FtDGSFwTI1HeQ940Kf6XeOS5pWiadPC8bGgSqDwMQJK8HMAwBnaRLqABExEZ2vI7wiVLqcNvz8oXuqpoO8FryLwcf0O10NgNr2dVfJu6Z0FMqTHCIG4D4imiOis6PrWgD3VX0xEV0AYA+Av6salggqGWNychIzMzPYtm3bZsHSncFC93Q0deBi4NF25edjuvkok8+IGICrATwfwBqABwE8F8BVGt79HgBvQnTWsGlUMka/38fS0hLuvffezYKlO4OJtoRGeSbDKE4ZLKuMZdNT5H5bU1xjfEw3H2Uqw2XZLjUAzNxn5p9i5klmbjDzTzNzv8pLiehHAKwxc0vg3quIaJmIlk+fPl3lndIZIy5QyUNbXGWwqq27UTIguuNiQjdl++eY2LvH1CpcnajoepTybhZO3VZxBs27AFyI4ZYQ7ejzLgBvFXjuOIB2xvVKAHcCeGJ03/0Azi8Lj1nveQBFpPcA92FP8Koy+LofvgoqcSnSnyndFIUrm54y9/uQX/NQ0XW32+WDBw9yq9XiwWDgdfxUsBEf5JwHIGIAbgdwGYCTie/aZc8VhLcTQD+q+O8H8AiG4wBTZc/aMgDpTDoKlWfdC01SfpW46KyMRam7zk2gcshNfNBQnH7ptAx6LqeKAbgr+nsy8d1K2XOiV+gBBESoaoTrmoZVDZ/Ke1wgczJXWtZRaKCZJs8AiAwCP0REz0Q0WEtErwLQlfAy1Q7RaX084r7JNC7jW3XwXcfYjYv4J/3Dqr5iEbldT58sS99k+qXTUjRv2E6/OtQPIgbgDQDeD+B7iWgNwC8CeL0uAZh5BzM/pCs8m7guNLZxGV8fZneIxF93oU9WbqpGUERuk9MnRXRSJX1NbLyngzrUD8LbQUeHwkww89fNipRP1ZXAzHpXCeoOzwQ6ZaxDfE0iEn/T202rpIHrdDOhkzrowbXek+StBM41AET0xqIAmfndmmQTpqoB8OWMUJuMY5xdYrrQ1zE9TTS8VldXsbi4iH379m2ej+BDResrKltBnBdduzF0+Twluq4GcJEJIU0zjqsE49XMk5OTrkUZC0y7quqYh5M60eEi6/V6WFpawt69e7ccjlPF1ZKWqw7+ex0UHQl5IzPfCOB8DDeDu46ZrwNwKYCn2hJQJz74kW0Tr2bu9yut3Qt4Qt3zsI7KOjaCO3fu1HY4TlouGTnrbCxEBoG3A3g48flhADuMSFNDfE/8OrYYAb16LQvL9zT0AV06ksmPee80sfleUq54iuTs7KyQnHUY7M1DxAD8DoBPE9GvEtGvYLiK90NmxbKDru6oz4lf1xajTr2WheV7GuqiSn4vOpBeBpn8aDNdknL1ej0cPXp0c8ppGXVtZAEoXwgWJfIlAK6NrmeLPGPi0r0QTMcCEtcLaGxhO54631cWlsrq1DpSJb8ndWFrUZ6rdPEl3XXKgQoLwcDM9zDzLdF10pw5souO3RPr2sKWpUprjBVajDr1WhZW3u+j1jOo0lJN6qhqi7fqqW3pIzhVKMqTvpRpG/lvrM8ErsPuib5QpdDXtSIti7OKYXOJroqtajiuF50B9TgL2YZraawNQJwYk5OTyor2pRKoKkfZ81UKvQ8FXiWcsjinKxFf8oIqJuQv06vKO6empnDttddmroMQrdhdn4W8vr5udHW0KGNtAOLE6Pf7yorW3ZKIC8RgMJAqGEVyiBQyky0ikxlZl9wq4aQrEZM6tGFcTMynNzEAX5SfRCt23WsTRInlA+BHrzhrYMDXS/cgsI5BFt0DRvEA28rKCs/Pz3On06ksh8ignS8DX7LoktvHvJDExo6XOuSX3arZh3znYjdR2/FGziCw8F5APlB1K4g6wDxcNj8YDPDe974Xc3NzmJ6e1hJmWCpfX/LSUDZtTecFlfBd50/X77eBylYQAQFYc/cx7ppOT0/n+jlVwxzVzD0O6JqpZHrwUyWvuZ4kMM7lI/QAKlLHzbkCo4NvPQAVfJQJ8FcuFUIPwBC1XgWYQndvpq7USQ95g5mDwQCtVgudTmfT3xvPm/ettetrC9x1z8QGwQAIklcp+Jp5VdCxwGYUqGvBT8rdbrcxNzeHm2+++YzTxEwZuKxw62RM04xS4y6PYAAEqWulEHgU0cqorgU/KXez2cTCwgLm5+fPOE3MVF7OMjKxizT5rrJ0qLPRqB1ZU4N8vWwdCp/FOOw/4oMMJgmHhw8xlc5Z+wV1Op0z3lWWDr5MebWZX0yXPYRpoH4TBpMfhQ0NvqmEa0qWUadIb2U6taFzkfJmM+2L5GEN4zdhENhzRN0OcWaok+GWxZSLIh6vASCsw+D6KyadH0UqK9XN+YreJ1sebLv5yuQskqfX62FhYQELCwva82EwAJ4gOpgsWyH5ajCK5DJdOGV0WNfxAFtUOUnL5ftEypvNMynKtreYm5vD3Nyc9nwYXEA1Q7Zb6qtryaVcMjpM3xt/npycRL/fH3vXUJ5+bK00Nvk+nWG7diUGF9CIIDvttNFoYHZ2dnPQxxdUW9Y6ejRVTqWKP7fbbWeuIV96dVmVmulp0VWOg5TVm80zKVzhzAAQ0TVE9HkiupeI3ulCBpEM4UthUyEuoABw9OhRr/zYqgXCtk8+bajiz81m05lryJdxCRO7h5rEF735hBMDQEQvAvBKALuY+WIAh1zIIZIh6pxpYtkBjIwf27ZPPs9QVWnRiVZ6eff5Mi6h48AcU9upuxhb0oktw+iqB/B6ADcz878AADP3XQghkiF8yTQqGSKWfWpqymn3U2dmdt2V1lFhiYaRd59rHaTlALJnVYnEU3f5KnqnL3oTwVrDM2txgOkLwAqAGwHcCeB2AM8puPcqAMsAlrdv337mCgdF0gsvfF8EVedFTHWWPY3NcwNc5UnZ9+alb1Y447CgUge644Eqh8KrQETHiaidcb0SwFkAngzgcgC/BOAjlGOWmfkDzLybmXdv27ZNm3y2p69VRbalxB6NXfjSi6oKa5rJIdoSddVilS0LeembJb+P21H7iK14OJkGSkQfx9AF9BfR5y8CuJyZTxc9V3UaaLIAA7A6fc02yWmW8f4voxI30+TlBV+n1OrGZFnQFfaolVfT+DYN9E8AvBgAiOhCAI8F8JDplyZbH1Wmk9UBG5t/2cRmjyZPX6PSkynDZFnQFXbVPJ2Xn5gZ3W4X3W7Xi96zaVz1AB4L4AiAZwF4GMABZv6zsud09gBGpaIXoSzeddCLzda3DX3UQec+U1V/efkp3hIdwOaJfKOQVnk9gLASuAK+ZwxR+UxUrrp147uuZRkld1Kd0iaWNW8ld9wzAB7dy0j3xnEu9OWbC2gk8N21InoIiInVwrp1o+I68GkgPI0Od5Jo/EzrQTWtXaRPLGu/389d3zE9PY3p6enN3+K0mpyczJVXRgde1RtZU4N8vVyeB5CF71POsvZnt7UHuw+6ScfJB5l0Ippm3W6XDx48yK1Wy8iUTNVwXOz7XyXORfLKTHl1kQ+RMw3UeaUuc/lmAOpEWabzKbPqIi37KK1HYN4av6J0GgwG3Gq1zoi7a33YyFs646hrfYQL8gxAGAMIFJL2f3KN/L1p6ix7GWV+6qy4m9KHT3rOk8WGjLreoSOcMAYQKIRz/LFpX7VX/ktJRm2qL/Bouk1OThaOKWTF3ZQ+TJxZIXJPFnlxtJGPfZnyWkQwAGNEUSES3XdmXObCZ6FaCZmkbFDTBbJ5JJ33svSsuxKsUz42KWswAB5iqqIpKkSimWzUWtEyurbd+xGRzceKTOXMirJepo6dR6vI6BKTsgYD4CGmptUVFaI6FQidiE6VBexXtiL5oCjdTDQkTIQp0sssy586jbOPPT1TBAOgGR2ZR7WiKSsE41rJFyGzZYZt/VXdANBEj8VX37lO41zncS5Zwiwgzehc4Sk7+u/T7Is6olt/ttPDxowt13msTrN3XIWfRZgFZAmXLRFdLVTVXkzdu866W/i2W5LpvGeix0JEaDQaWF9fd7JhmqjLrkpeNN3T86mHEQyAJGUZS2fmseVz1uU68Cljq6LTiNlOPwBWXFS9Xg8LCwtYWFiwntaiLjuf8+Lk5CRmZmYwOTnpWpRgAGQpyli6W8C2fM7pOKnOuPBxRoosOgfgXaWfaRqNBubm5jA3N2csrfPyWKzT+J7Z2dlMGXzOi/1+H0tLS+j3nZyEu5Ws5cG+Xj5sBVG0t4hPS79lkN0Goq7xFMHnfW3yGMVjFsuOmex0OrXNg7LbsujQP2wfCTmqpFt1ydaXSquDPfCby66W9Ll1BZjx/5aF6VInJnoayfi6cKfk6TOWBUDm7z6UpzLKpu6urq7i8OHDm/o2qv8sq+Dr5UMPIE1R67nT6XCn0ym03D63pss2GPN1kzgTOtURpqrOXLfAfUrrMll8Lk8ixPInd2412QNwXqnLXKYMgIkM3u12eX5+nufn5wszo0+FSwafC5oJneoIU1Vnos/pjLfrfOnKWNY13mWMtQFw0WoQ6QG4zmwymPBL6sInWYqQ2b5ZRdeujLKJrcRdxcXnhk0V8gzAWIwBlPnQTPhvs04WEpGL2U8fZlpWn1YV+zzlL0lSZzJTGEV17WocIm9ufpV0cRUX1+Nbtsv/WKwEjjOjbytks+Ty9axYX3UI+C1bHkUyV4mPC10k3xlX+sm5+nVKF9eYKv/hUPiakFeAXVdyrt/vGy71MRgM0G63cfHFF+P06dNeNSBCPqmGKf2FrSBqgqkDLKp0LZnPnJqm+x0iMvjkGnPpdmq32zhw4ABuv/320m2TbadJOv/6lm6+Y9u1GgxATajqm6xSYfV6PSwtLWHv3r2F7zdZKcqEbaPScekrbjabOHToEK644orSbZNdp0ldxmfGleACGhNs+JVNdv9lwnbtBvEJ12lSB5eQax3ZwCsXEBE9i4juIKIVIlomostcyDFOVOlaij5rsvsqE7brmRw+4TpNRO6Je2yDwcCJu8jk/l7psH1ziblyAb0TwI3M/CwA/y36XFt8S9RAObrSbBzS3nQc40qy3W5bcRel41PUYKjqwhI57tIlrgwAA/j26P8nAug4kkMLviXquGPTN+1j2uuusE3HMa4km82mtp5bkQ5k1llU7U2KHHfpEidjAET0fQCOASAMjdDzmfmBnHuvAnAVAGzfvv3SBx7IvM0pvvj5AkNs+qZ9THvdYyA+xrGMIh3UMT5Vsb4OgIiOA8jKfTcAeAmA25n5j4jo1QCuYuaXloUZBoEDgXLGsYJLE3SwFa8WghHRVwE8iZmZhqnzVWb+9rLnggEIBAIBebyaBYShz/+K6P8XA/hbR3IEAoHA2HKWo/f+PIBbiOgsAN9C5OMPBAKBgD2cGABm/isAl7p4dyAQCASGhK0gAoFAYEwJBiAQCATGlGAAAoFAYEwJBiAQCATGlFrtBkpEpwH4txS4mPMBPORaCMuEOI8HIc714WnMvC39Za0MQB0houWsBRijTIjzeBDiXH+CCygQCATGlGAAAoFAYEwJBsA8H3AtgANCnMeDEOeaE8YAAoFAYEwJPYBAIBAYU4IBCAQCgTElGABDENGvEtFadPD9ChG9IvX7diL6BhEdcCWjbvLiTER7iOhuIlqN/r7Ytay6KEpnInozEX2BiD5PRD/sUk4TENEBImIiOj/6fDYRfShK588S0Ztdy6ibdJyj73YR0V8T0b1R3B/vUkYZXG0HPS68h5kP5f0G4P/aFMYSWXF+CMBeZu4QURPD40CfYl80Y5wRZyK6CMBPAbgYwL8DcJyILmTmDRcC6oaILgCwB8DfJb7+SQCPY+adRPQEAJ8hot9j5vtdyKibrDhHW9p/GMBrmblFRN8J4F8diShN6AE4gIh+FMB9AO51LIoVmPkkM3eij/cCeDwRPc6lTBZ4JYDbmPlfmPlLAL4A4DLHMunkPQDeBCA5i4QBnBNVit8G4GEAX3Mgmymy4vwyAKeYuQUAzPz3dTLywQCYZT8RnSKiI0T0ZAAgonMAXA/gRreiGeOMOKf4CQAnmflfbAtmkKw4PwXAlxP3PIgR6fUQ0Y8AWIsrvQR/COCbALoYtpIPMfM/2JbPBAVxvhAAE9ExIrqHiN7kQDxlgguoAiUH398K4CYMWws3Afh1AFdiWPG/h5m/UcfDqhXjHD97MYB3YNhqqg2Kcc5K3NrMuS6J81uQnYaXAdjA0OX1ZACfJKLjzHyfMUE1ohjnswD8IIDnAPgnACei83dPGBNUI8EAVICZXypyHxH9FoCl6ONzAbyKiN4J4EkABkT0LWb+TTNS6kUxziCipwL4YwA/x8xfNCSeERTj/CCACxI/PxXDs7BrQV6ciWgngKcDaEUNmKcCuIeILgPw0wA+zsz/CqBPRJ8CsBtDd6f3KMb5QQC3M/ND0b0fA3AJgFoYgOACMgQRTSc+/hiANgAw8wuYeQcz7wDwGwDeXpfKv4y8OBPRkwB8FMCbmflTDkQzRl6cAfwfAD9FRI8joqcD+G4An7Ytn26YeZWZJxN5+EEAlzDzOoZunxfTkHMAXA7gcw7F1UJJnI8B2EVET4jGPq4A8BmH4koRegDmeCcRPQvDbv/9AH7BqTR2yIvzfgDfBeCXieiXo+9exsx96xLqJzPOzHwvEX0Ew8rgEQBvqNPgoCL/A8BRDI0gATjKzKfcimQWZv5HIno3gLswzAMfY+aPOhZLmLAVRCAQCIwpwQUUCAQCY0owAIFAIDCmBAMQCAQCY0owAIFAIDCmBAMQCAQCY0owAIGRh4h+g4hemPH9DxHRUtYzLiCiNxLR56IdJVtE9G4iOjv67f7o+1Ui+gwRHUzup0REHyeir6TjQ0S3EdF3245LoB4EAxCoHdFCI6G8S0TfAeByZv5LwzI9puLzV2O41cDlzLwTw60F+hhuqhbzoui3ywA8A1uPJ3wXgNdmBH0rhhuYBQJnEAxAoBYQ0Y5oj/n3AbgHwAVEdCsRLUf7sOdtrvcqAB9PhPPyqJX9VwB+PPH9OdFmbncR0UkiemX0/ROI6CPRZm+/T0R3EtHu6LdvENF/J6I7ATyPiH6WiD5Nw3MB3h8bBSJ6GQ33i7+HiP6AiM7NkPMGAK9n5q8AADM/zMw3M/MZu2ky8zcAXA3gRyMDh2jvma9nhPtJAC+NVqkGAlsIBiBQJ74HwP9i5mcz8wMAbmDm3QB2AbiCiHZlPPMDAO4GABoe1PFbAPYCeAG2bvx1A4A/Y+bnAHgRgHdF2xn8FwD/yMy7MNzs7dLEM+cAaDPzcwH8PYD/COAHmPlZGG6K9jM0PDjkrQBeysyXAFgG8MakgER0HoBzo22jhYgMw5cw3GKi6L4BhltRf79o2IHxIRiAQJ14gJnvSHx+NRHdA+AkhgevXJTxzDSA09H/3wvgS8z8tzxcAv/hxH0vAzBPRCsA/gLA4wFsx3Cnx9sAgJnbAJJbG2wA+KPo/5dgaBzuisJ4CYZumssjuT4Vff86AE9LyUhI7BRKRD8c9SLuJ6LnF+hDdDvZPoY7dAYCWwjdwkCd+Gb8T7TB2gEAz4n2Y/kghpV2mn9OfZ+39wkB+Alm/vyWL4v37P5WYn8fAvAhZt5yDCIR7QXwCWZ+TV4gzPw1IvomET2dmb/EzMcAHIsGdB+bKeyw17ADwN8UyBfzeAz1EAhsIfQAAnXl2zE0CF8logaAf59z32cx3IgOGO5M+XQiemb0OVkpHwNwTVzhE9Gzo+//CsCro+8uArAz5z0nMNzmezK69zuI6GkA7gDwA0T0XdH3TyCiCzOe/zUAt0Y7p8aGJ/Ns2WgM4X0A/oSZ/zFHniQXYkxOnwvIEQxAoJZEJzOdxLBiOwIgb5vpjwL4oeiZbwG4CsBHo0HgBxL33QTgbACniKgdfQaGFe02IjqF4UlupwB8NUOez2Do6/9/0b2fADDNzKcB/CcAvxd9fweGrqg0twI4DuDO6L5PRfE7mbjnzyPZPo3h1subO8wS0ScB/AGAlxDRgxQdQh8Zx39m5m6OfgJjTNgNNDDyRJX9TDzDRvLZxwA4m5m/FfUcTgC4kJkf1iymEYjovwL4GjMfdi1LwD/CGEBgHLgOwwHdryg8+wQMW95nY+jnf31dKv+IrwD4HddCBPwk9AACgUBgTAljAIFAIDCmBAMQCAQCY0owAIFAIDCmBAMQCAQCY0owAIFAIDCm/BtCuHWkhq1WlwAAAABJRU5ErkJggg==\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": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "filename = 'gd1_dataframe.hdf5'\n",
+    "\n",
+    "df.to_hdf(filename, 'df')\n",
+    "centerline.to_hdf(filename, 'centerline')\n",
+    "selected.to_hdf(filename, 'selected')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-rw-rw-r-- 1 downey downey 17M Nov 18 19:06 gd1_dataframe.hdf5\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!ls -lh gd1_dataframe.hdf5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!rm gd1_dataframe.hdf5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--2020-11-18 19:31:21--  https://github.com/AllenDowney/AstronomicalData/raw/main/data/gd1_dataframe.hdf5\n",
+      "Resolving github.com... 140.82.114.3\n",
+      "Connecting to github.com|140.82.114.3|:443... connected.\n",
+      "HTTP request sent, awaiting response... 302 Found\n",
+      "Location: https://raw.githubusercontent.com/AllenDowney/AstronomicalData/main/data/gd1_dataframe.hdf5 [following]\n",
+      "--2020-11-18 19:31:21--  https://raw.githubusercontent.com/AllenDowney/AstronomicalData/main/data/gd1_dataframe.hdf5\n",
+      "Resolving raw.githubusercontent.com... 151.101.116.133\n",
+      "Connecting to raw.githubusercontent.com|151.101.116.133|:443... connected.\n",
+      "HTTP request sent, awaiting response... 200 OK\n",
+      "Length: 17335888 (17M) [application/octet-stream]\n",
+      "Saving to: ‘gd1_dataframe.hdf5’\n",
+      "\n",
+      "gd1_dataframe.hdf5  100%[===================>]  16.53M  8.77MB/s    in 1.9s    \n",
+      "\n",
+      "2020-11-18 19:31:24 (8.77 MB/s) - ‘gd1_dataframe.hdf5’ saved [17335888/17335888]\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "!wget https://github.com/AllenDowney/AstronomicalData/raw/main/data/gd1_dataframe.hdf5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "filename = 'gd1_dataframe.hdf5'\n",
+    "df = pd.read_hdf(filename, 'df')\n",
+    "centerline = pd.read_hdf(filename, 'centerline')\n",
+    "selected = pd.read_hdf(filename, 'selected')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(140340, 12)"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(25084, 12)"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "centerline.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1049, 12)"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "selected.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "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": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "points = selected[['pmra', 'pmdec']].to_numpy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ -3.65859609, -12.48641977],\n",
+       "       [ -3.57235105, -13.19584836],\n",
+       "       [ -4.52678061, -12.74505387],\n",
+       "       ...,\n",
+       "       [ -3.7313674 , -13.06684162],\n",
+       "       [ -3.63378135, -12.93358913],\n",
+       "       [ -3.44742277, -13.22808211]])"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "points"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1049, 2)"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "points.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scipy.spatial import ConvexHull"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "hull = ConvexHull(points)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<scipy.spatial.qhull.ConvexHull at 0x7fac07b1b8e0>"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hull"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "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": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hull.vertices"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pm_vertices = points[hull.vertices]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "numpy.ndarray"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(pm_vertices)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(16, 2)"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pm_vertices.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "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": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pm_vertices"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "xs, ys = np.transpose(pm_vertices)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([-4.05037121, -3.41981085, -3.03521988, -2.26847919, -2.61172203,\n",
+       "       -2.73471401, -3.19923146, -3.34082546, -5.67489413, -5.95159272,\n",
+       "       -6.42394023, -7.09631023, -7.30641519, -7.04016696, -6.00347705,\n",
+       "       -4.42442296])"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "xs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([-14.75623261, -14.72365546, -14.44357135, -13.7140236 ,\n",
+       "       -13.24797471, -13.09054471, -12.5942653 , -12.47611926,\n",
+       "       -11.16083338, -11.10547884, -11.05981295, -11.95187806,\n",
+       "       -12.24559977, -12.88580702, -13.75912098, -14.74641176])"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ys"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "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.plot(xs, ys)\n",
+    "    \n",
+    "plt.xlabel('Proper motion ra (ICRS frame)')\n",
+    "plt.ylabel('Proper motion dec (ICRS frame)')\n",
+    "\n",
+    "plt.xlim([-10, 5])\n",
+    "plt.ylim([-20, 5]);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "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": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pm_vertices"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "32"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "t = [str(x) for x in pm_vertices.flatten()]\n",
+    "len(t)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "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": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pm_point_list = ', '.join(t)\n",
+    "pm_point_list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "phi1_min = -70\n",
+    "phi1_max = -20\n",
+    "phi2_min = -5\n",
+    "phi2_max = 5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import astropy.units as u\n",
+    "\n",
+    "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": "code",
+   "execution_count": 108,
+   "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": "code",
+   "execution_count": 109,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'135.30559858565638, 8.398623940157561, 126.50951508623503, 13.44494195652069, 163.0173655836748, 54.24242734020255, 172.9328536286811, 46.47260492416258'"
+      ]
+     },
+     "execution_count": 109,
+     "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": "code",
+   "execution_count": 74,
+   "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": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "columns = 'source_id, ra, dec, pmra, pmdec, parallax, parallax_error, radial_velocity'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise 1:** Use `format` to format `query_base` and define `query`, filling in the values of `columns` and `point_list`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query = query_base.format(columns=columns, point_list=point_list)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "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",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(query)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise 2:** 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": 79,
+   "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",
+    "  AND 1 = CONTAINS(POINT(pmra, pmdec), \n",
+    "                   POLYGON({pm_point_list}))\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query = query_base.format(columns=columns, \n",
+    "                          point_list=point_list, \n",
+    "                          pm_point_list=pm_point_list)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "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": [
+    "print(query)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "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",
+      "<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: 1605731697505O\n",
+      "Phase: COMPLETED\n",
+      "Owner: None\n",
+      "Output file: async_20201118153457.vot\n",
+      "Results: None\n"
+     ]
+    }
+   ],
+   "source": [
+    "from astroquery.gaia import Gaia\n",
+    "\n",
+    "job = Gaia.launch_job_async(query)\n",
+    "print(job)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "astropy.table.table.Table"
+      ]
+     },
+     "execution_count": 87,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "candidate_table = job.get_results()\n",
+    "type(candidate_table)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "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",
+    "\n",
+    "plt.plot(x, y, 'ko', markersize=0.3, alpha=0.3)\n",
+    "\n",
+    "plt.xlabel('ra (deg ICRS)')\n",
+    "plt.ylabel('dec (deg ICRS)');"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "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": "code",
+   "execution_count": 90,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(7346, 12)"
+      ]
+     },
+     "execution_count": 90,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "candidate_df = make_dataframe(candidate_table)\n",
+    "candidate_df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "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_df['phi1']\n",
+    "y = candidate_df['phi2']\n",
+    "\n",
+    "plt.plot(x, y, 'ko', markersize=0.3, alpha=0.3)\n",
+    "\n",
+    "plt.xlabel('phi1 (deg GD1)')\n",
+    "plt.ylabel('phi2 (deg GD1)');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise**  Write `candidate_df` to an HDF file named `gd1_candidates`.\n",
+    "\n",
+    "Save it as a dataset named `candidate_df`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "candidate_df.to_hdf('gd1_candidates.hdf', 'candidate_df')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-rw-rw-r-- 1 downey downey 756K Nov 18 15:54 gd1_candidates.hdf\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!ls -lh gd1_candidates.hdf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "candidate_df.to_csv('gd1_candidates.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-rw-rw-r-- 1 downey downey 1.6M Nov 18 15:51 gd1_candidates.csv\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!ls -lh gd1_candidates.csv"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "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,-7.3613627125974945,-0.5926328820644919\r\n",
+      "1,635860218726658176,138.5187065217173,19.09233926905897,-5.941679495793577,-11.346409129876392,0.30745551377348623,0.19946557779138105,,-59.247329893833296,-2.0160784008206476,-7.52712608464053,1.7487794924176672\r\n",
+      "2,635674126383965568,138.8428741026386,19.031798198627634,-3.8970011609340207,-12.702779525389634,0.7794631695848825,0.22369151932421705,,-59.13339098769217,-2.3069007451798287,-7.560607655557421,-0.7417999555980245\r\n",
+      "3,635535454774983040,137.8377518255436,18.864006786112604,-4.335040664412791,-14.492308604905652,0.31451429349160354,0.10277456217796248,,-59.78529963350303,-1.5945692849622026,-9.35753561702402,-1.2184922339592716\r\n",
+      "4,635497276810313600,138.0445160213759,19.00947118796605,-7.1729306406216615,-12.291499169815987,0.42540354480291037,0.3376892347379532,,-59.55774434692722,-1.6821467937242753,-9.000831056712947,2.3344066165829673\r\n",
+      "5,635614168640132864,139.59219748145836,18.807955539071433,-3.309602916796381,-13.708904908478631,0.5833821573394844,0.5746175895352217,,-58.938113464510934,-3.0241923218278397,-8.062761531706451,-1.8690819375362338\r\n",
+      "6,635821843194387840,139.88094034815086,19.62185456718988,-6.544201177153814,-12.55978220563274,0.3788707393484704,0.2134264091820104,,-58.10569437830526,-2.8105428528649257,-8.814581159026405,1.451032881430119\r\n",
+      "7,635551706931167104,138.04665586038192,19.248909662830798,-6.224595114220405,-12.224246333795001,0.7832805254544403,0.12699359319614956,,-59.354285349557074,-1.5557649698784075,-8.427716582116249,1.5738026079151446\r\n",
+      "8,635518889086133376,137.2374229207837,18.7428630711791,-3.3186800714801046,-12.710314902969365,0.17932476739336733,0.7503378631682915,,-60.19048364007102,-1.1779749872253444,-7.3401827273605385,-1.064062501817137\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!head gd1_candidates.csv"
+   ]
+  },
+  {
+   "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/_sources/pilot_day4.ipynb b/_sources/pilot_day4.ipynb
new file mode 100644
index 0000000..7d41f4b
--- /dev/null
+++ b/_sources/pilot_day4.ipynb
@@ -0,0 +1,74 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "filename = 'gd1_candidates.hdf5'\n",
+    "candidate_df = pd.read_hdf(filename, 'candidate_df')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "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": "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/_static/pygments.css b/_static/pygments.css
index d14395e..f346859 100644
--- a/_static/pygments.css
+++ b/_static/pygments.css
@@ -1,8 +1,8 @@
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-span.linenos.special { color: #000000; background-color: #ffffc0; padding: 0 5px 0 5px; }
+td.linenos pre { color: #000000; background-color: #f0f0f0; padding-left: 5px; padding-right: 5px; }
+span.linenos { color: #000000; background-color: #f0f0f0; padding-left: 5px; padding-right: 5px; }
+td.linenos pre.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
+span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
 .highlight .hll { background-color: #ffffcc }
 .highlight { background: #eeffcc; }
 .highlight .c { color: #408090; font-style: italic } /* Comment */
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Hv-lW2?(Lf5

diff --git a/pilot_day1.html b/pilot_day1.html
index 5de51c4..3d3e86a 100644
--- a/pilot_day1.html
+++ b/pilot_day1.html
@@ -813,6 +813,176 @@ Results: None
 <code class="docutils literal notranslate"><span class="pre">bp_rp</span></code> is between -0.75 and 2.</p>
 <p>You can read about this variable at
 https://gea.esac.esa.int/archive/documentation/GDR2/Gaia_archive/chap_datamodel/sec_dm_main_tables/ssec_dm_gaia_source.html</p>
+<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">df1</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">index</span><span class="o">=</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="mi">2</span><span class="p">])</span>
+<span class="n">df1</span><span class="p">[</span><span class="s1">&#39;a&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;a&#39;</span>
+<span class="n">df1</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>a</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>a</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>a</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>a</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">df2</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">index</span><span class="o">=</span><span class="p">[</span><span class="mi">0</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">df2</span><span class="p">[</span><span class="s1">&#39;b&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;b&#39;</span>
+<span class="n">df2</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>b</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>b</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>b</td>
+    </tr>
+    <tr>
+      <th>4</th>
+      <td>b</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">df1</span><span class="p">[</span><span class="s1">&#39;b&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df2</span><span class="p">[</span><span class="s1">&#39;b&#39;</span><span class="p">]</span>
+<span class="n">df1</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>a</th>
+      <th>b</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>a</td>
+      <td>b</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>a</td>
+      <td>NaN</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>a</td>
+      <td>b</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">df1</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>(3, 2)
+</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">df1</span> <span class="ow">is</span> <span class="n">df2</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>False
+</pre></div>
+</div>
+</div>
+</div>
 </div>
 
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new file mode 100644
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@@ -0,0 +1,1261 @@
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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">
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+ <li class="toctree-l1">
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+   Astronomical Data in Python
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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">
+  <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>
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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/pilot_day3.ipynb"><button type="button" class="btn btn-secondary topbarbtn"
+                title="Launch Colab" data-toggle="tooltip" data-placement="left"><img class="colab-button-logo"
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+                    alt="Interact on Colab">Colab</button></a>
+        
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+    </div>
+</div>
+
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+
+    <!-- Table of contents -->
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+            <i class="fas fa-list"></i> Contents
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+
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+</div>
+    <div id="main-content" class="row">
+        <div class="col-12 col-md-9 pl-md-3 pr-md-0">
+        
+              <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_results.fits&#39;</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>
+<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>
+<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>
+<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>
+<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>
+<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>
+<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">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/pilot_day3_6_0.png" src="_images/pilot_day3_6_0.png" />
+</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">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>
+<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>
+<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>
+<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">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>
+
+<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>
+<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>
+</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">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/pilot_day3_12_0.png" src="_images/pilot_day3_12_0.png" />
+</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">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="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>
+<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/pilot_day3_15_0.png" src="_images/pilot_day3_15_0.png" />
+</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">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>
+<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>
+<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>
+<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>
+</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</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/pilot_day3_20_0.png" src="_images/pilot_day3_20_0.png" />
+</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_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>
+<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>
+<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 Nov 18 19:06 gd1_dataframe.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="o">!</span>rm gd1_dataframe.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="o">!</span>wget https://github.com/AllenDowney/AstronomicalData/raw/main/data/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>--2020-11-18 19:31:21--  https://github.com/AllenDowney/AstronomicalData/raw/main/data/gd1_dataframe.hdf5
+Resolving github.com... 140.82.114.3
+Connecting to github.com|140.82.114.3|:443... connected.
+HTTP request sent, awaiting response... 302 Found
+Location: https://raw.githubusercontent.com/AllenDowney/AstronomicalData/main/data/gd1_dataframe.hdf5 [following]
+--2020-11-18 19:31:21--  https://raw.githubusercontent.com/AllenDowney/AstronomicalData/main/data/gd1_dataframe.hdf5
+Resolving raw.githubusercontent.com... 151.101.116.133
+Connecting to raw.githubusercontent.com|151.101.116.133|:443... connected.
+HTTP request sent, awaiting response... 200 OK
+Length: 17335888 (17M) [application/octet-stream]
+Saving to: ‘gd1_dataframe.hdf5’
+
+gd1_dataframe.hdf5  100%[===================&gt;]  16.53M  8.77MB/s    in 1.9s    
+
+2020-11-18 19:31:24 (8.77 MB/s) - ‘gd1_dataframe.hdf5’ saved [17335888/17335888]
+</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_dataframe.hdf5&#39;</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 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">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>
+<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">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>(25084, 12)
+</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">selected</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, 12)
+</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">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="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>
+</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">points</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([[ -3.65859609, -12.48641977],
+       [ -3.57235105, -13.19584836],
+       [ -4.52678061, -12.74505387],
+       ...,
+       [ -3.7313674 , -13.06684162],
+       [ -3.63378135, -12.93358913],
+       [ -3.44742277, -13.22808211]])
+</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">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>
+<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>
+</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">hull</span> <span class="o">=</span> <span class="n">ConvexHull</span><span class="p">(</span><span class="n">points</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">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 0x7fac07b1b8e0&gt;
+</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">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>
+<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>
+</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">pm_vertices</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>numpy.ndarray
+</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">pm_vertices</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>(16, 2)
+</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">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>
+<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">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">pm_vertices</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">xs</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, -3.41981085, -3.03521988, -2.26847919, -2.61172203,
+       -2.73471401, -3.19923146, -3.34082546, -5.67489413, -5.95159272,
+       -6.42394023, -7.09631023, -7.30641519, -7.04016696, -6.00347705,
+       -4.42442296])
+</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">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([-14.75623261, -14.72365546, -14.44357135, -13.7140236 ,
+       -13.24797471, -13.09054471, -12.5942653 , -12.47611926,
+       -11.16083338, -11.10547884, -11.05981295, -11.95187806,
+       -12.24559977, -12.88580702, -13.75912098, -14.74641176])
+</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">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">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">xlabel</span><span class="p">(</span><span class="s1">&#39;Proper motion ra (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 dec (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/pilot_day3_45_0.png" src="_images/pilot_day3_45_0.png" />
+</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">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>
+<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="nb">len</span><span class="p">(</span><span class="n">t</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>32
+</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">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 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="kn">import</span> <span class="nn">astropy.units</span> <span class="k">as</span> <span class="nn">u</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>
+<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>
+<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>
+<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>
+<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 1:</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> 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</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>
+</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">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))
+</pre></div>
+</div>
+</div>
+</div>
+<p><strong>Exercise 2:</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="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>
+<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="n">pm_point_list</span><span class="o">=</span><span class="n">pm_point_list</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="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>
+<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: 1605731697505O
+Phase: COMPLETED
+Owner: None
+Output file: async_20201118153457.vot
+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">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">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>
+<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 (deg 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 (deg ICRS)&#39;</span><span class="p">);</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<img alt="_images/pilot_day3_64_0.png" src="_images/pilot_day3_64_0.png" />
+</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">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>
+<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>
+<span class="n">candidate_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>(7346, 12)
+</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">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;phi1 (deg 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;phi2 (deg GD1)&#39;</span><span class="p">);</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<img alt="_images/pilot_day3_67_0.png" src="_images/pilot_day3_67_0.png" />
+</div>
+</div>
+<p><strong>Exercise</strong>  Write <code class="docutils literal notranslate"><span class="pre">candidate_df</span></code> to an HDF file named <code class="docutils literal notranslate"><span class="pre">gd1_candidates</span></code>.</p>
+<p>Save it as a dataset named <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="n">candidate_df</span><span class="o">.</span><span class="n">to_hdf</span><span class="p">(</span><span class="s1">&#39;gd1_candidates.hdf&#39;</span><span class="p">,</span> <span class="s1">&#39;candidate_df&#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_candidates.hdf
+</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 Nov 18 15:54 gd1_candidates.hdf
+</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">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>
+<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 Nov 18 15:51 gd1_candidates.csv
+</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>head 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,-7.3613627125974945,-0.5926328820644919
+1,635860218726658176,138.5187065217173,19.09233926905897,-5.941679495793577,-11.346409129876392,0.30745551377348623,0.19946557779138105,,-59.247329893833296,-2.0160784008206476,-7.52712608464053,1.7487794924176672
+2,635674126383965568,138.8428741026386,19.031798198627634,-3.8970011609340207,-12.702779525389634,0.7794631695848825,0.22369151932421705,,-59.13339098769217,-2.3069007451798287,-7.560607655557421,-0.7417999555980245
+3,635535454774983040,137.8377518255436,18.864006786112604,-4.335040664412791,-14.492308604905652,0.31451429349160354,0.10277456217796248,,-59.78529963350303,-1.5945692849622026,-9.35753561702402,-1.2184922339592716
+4,635497276810313600,138.0445160213759,19.00947118796605,-7.1729306406216615,-12.291499169815987,0.42540354480291037,0.3376892347379532,,-59.55774434692722,-1.6821467937242753,-9.000831056712947,2.3344066165829673
+5,635614168640132864,139.59219748145836,18.807955539071433,-3.309602916796381,-13.708904908478631,0.5833821573394844,0.5746175895352217,,-58.938113464510934,-3.0241923218278397,-8.062761531706451,-1.8690819375362338
+6,635821843194387840,139.88094034815086,19.62185456718988,-6.544201177153814,-12.55978220563274,0.3788707393484704,0.2134264091820104,,-58.10569437830526,-2.8105428528649257,-8.814581159026405,1.451032881430119
+7,635551706931167104,138.04665586038192,19.248909662830798,-6.224595114220405,-12.224246333795001,0.7832805254544403,0.12699359319614956,,-59.354285349557074,-1.5557649698784075,-8.427716582116249,1.5738026079151446
+8,635518889086133376,137.2374229207837,18.7428630711791,-3.3186800714801046,-12.710314902969365,0.17932476739336733,0.7503378631682915,,-60.19048364007102,-1.1779749872253444,-7.3401827273605385,-1.064062501817137
+</pre></div>
+</div>
+</div>
+</div>
+
+    <script type="text/x-thebe-config">
+    {
+        requestKernel: true,
+        binderOptions: {
+            repo: "binder-examples/jupyter-stacks-datascience",
+            ref: "master",
+        },
+        codeMirrorConfig: {
+            theme: "abcdef",
+            mode: "python"
+        },
+        kernelOptions: {
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+    }
+    </script>
+    <script>kernelName = 'python3'</script>
+
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diff --git a/pilot_day4.html b/pilot_day4.html
new file mode 100644
index 0000000..c83f2bd
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+++ b/pilot_day4.html
@@ -0,0 +1,331 @@
+
+<!DOCTYPE html>
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+  <h1 class="site-logo" id="site-title">Astronomical Data in Python</h1>
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+   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>
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+ <!-- To handle the deprecated key -->
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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/pilot_day4.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">
+        
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+            <i class="fas fa-list"></i> Contents
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+        <div class="col-12 col-md-9 pl-md-3 pr-md-0">
+        
+              <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>
+<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/pilot_day4_1_0.png" src="_images/pilot_day4_1_0.png" />
+</div>
+</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: "./."
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+    }
+    </script>
+    <script>kernelName = 'python3'</script>
+
+              </div>
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+        
+
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+    <footer class="footer mt-5 mt-md-0">
+    <div class="container">
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+        
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+
+
+      </div>
+    </div>
+
+    
+  <script src="_static/js/index.30270b6e4c972e43c488.js"></script>
+
+
+    
+  </body>
+</html>
\ No newline at end of file
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index d9fd53b..8bcbe78 100644
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