diff --git a/01_query.ipynb b/01_query.ipynb index 2c5d45f..9473f71 100644 --- a/01_query.ipynb +++ b/01_query.ipynb @@ -1,82 +1,53 @@ { "cells": [ { - "cell_type": "markdown", - "metadata": {}, + "cell_type": "raw", + "metadata": { + "tags": [ + "remove-cell" + ] + }, "source": [ - "# Chapter 1" + "---\n", + "title: \"Basic queries\"\n", + "teaching: 3000\n", + "exercises: 0\n", + "questions:\n", + "- \"How can we select and download the data we want from the Gaia server?\"\n", + "objectives:\n", + "- \"Compose a basic query in ADQL/SQL.\"\n", + "- \"Use queries to explore a database and its tables.\"\n", + "- \"Use queries to download data.\"\n", + "- \"Develop, test, and debug a query incrementally.\"\n", + "keypoints:\n", + "- \"If you can't download an entire dataset (or it's not practical) use queries to select the data you need.\"\n", + "\n", + "- \"Read the metadata and the documentation to make sure you understand the tables, their columns, and what they mean.\"\n", + "\n", + "- \"Develop queries incrementally: start with something simple, test it, and add a little bit at a time.\"\n", + "\n", + "- \"Use ADQL features like `TOP` and `COUNT` to test before you run a query that might return a lot of data.\"\n", + "\n", + "- \"If you know your query will return fewer than 3000 rows, you can \n", + "run it synchronously, which might complete faster (but it doesn't seem to make much difference). If it might return more than 3000 rows, you should run it asynchronously.\"\n", + "\n", + "- \"ADQL and SQL are not case-sensitive, so you don't have to \n", + "capitalize the keywords, but you should.\"\n", + "\n", + "- \"ADQL and SQL don't require you to break a query into multiple \n", + "lines, but you should.\"\n", + "\n", + "---\n", + "FIXME\n", + "\n", + "{% include links.md %}\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "*Astronomical Data in Python* is an introduction to tools and practices for working with astronomical data. Topics covered include:\n", - "\n", - "* Writing queries that select and download data from a database.\n", - "\n", - "* Using data stored in an Astropy `Table` or Pandas `DataFrame`.\n", - "\n", - "* Working with coordinates and other quantities with units.\n", - "\n", - "* Storing data in various formats.\n", - "\n", - "* Performing database join operations that combine data from multiple tables.\n", - "\n", - "* Visualizing data and preparing publication-quality figures." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As a running example, we will replicate part of the analysis in a recent paper, \"[Off the beaten path: Gaia reveals GD-1 stars outside of the main stream](https://arxiv.org/abs/1805.00425)\" by Adrian M. Price-Whelan and Ana Bonaca.\n", - "\n", - "As the abstract explains, \"Using data from the Gaia second data release combined with Pan-STARRS photometry, we present a sample of highly-probable members of the longest cold stream in the Milky Way, GD-1.\"\n", - "\n", - "GD-1 is a [stellar stream](https://en.wikipedia.org/wiki/List_of_stellar_streams), which is \"an association of stars orbiting a galaxy that was once a globular cluster or dwarf galaxy that has now been torn apart and stretched out along its orbit by tidal forces.\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[This article in *Science* magazine](https://www.sciencemag.org/news/2018/10/streams-stars-reveal-galaxy-s-violent-history-and-perhaps-its-unseen-dark-matter) explains some of the background, including the process that led to the paper and a discussion of the scientific implications:\n", - "\n", - "* \"The streams are particularly useful for ... galactic archaeology --- rewinding the cosmic clock to reconstruct the assembly of the Milky Way.\"\n", - "\n", - "* \"They also are being used as exquisitely sensitive scales to measure the galaxy's mass.\"\n", - "\n", - "* \"... the streams are well-positioned to reveal the presence of dark matter ... because the streams are so fragile, theorists say, collisions with marauding clumps of dark matter could leave telltale scars, potential clues to its nature.\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Data\n", - "\n", - "The datasets we will work with are:\n", - " \n", - "* [Gaia](https://en.wikipedia.org/wiki/Gaia_(spacecraft)), which is \"a space observatory of the European Space Agency (ESA), launched in 2013 ... designed for astrometry: measuring the positions, distances and motions of stars with unprecedented precision\", and\n", - "\n", - "* [Pan-STARRS](https://en.wikipedia.org/wiki/Pan-STARRS), The Panoramic Survey Telescope and Rapid Response System, which is a survey designed to monitor the sky for transient objects, producing a catalog with accurate astronometry and photometry of detected sources.\n", - "\n", - "Both of these datasets are very large, which can make them challenging to work with. It might not be possible, or practical, to download the entire dataset.\n", - "One of the goals of this workshop is to provide tools for working with large datasets." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Prerequisites\n", - "\n", - "These notebooks are meant for people who are familiar with basic Python, but not necessarily the libraries we will use, like Astropy or Pandas. If you are familiar with Python lists and dictionaries, and you know how to write a function that takes parameters and returns a value, you know enough Python to get started.\n", - "\n", - "We assume that you have some familiarity with operating systems, like the ability to use a command-line interface. But we don't assume you have any prior experience with databases.\n", - "\n", - "We assume that you are familiar with astronomy at the undergraduate level, but we will not assume specialized knowledge of the datasets or analysis methods we'll use. " + "# Queries" ] }, { @@ -85,7 +56,7 @@ "source": [ "## Outline\n", "\n", - "The first lesson demonstrates the steps for selecting and downloading data from the Gaia Database:\n", + "This lesson demonstrates the steps for selecting and downloading data from the Gaia Database:\n", "\n", "1. First we'll make a connection to the Gaia server,\n", "\n", @@ -93,17 +64,7 @@ "\n", "3. We will write a query and send it to the server, and finally\n", "\n", - "4. We will download the response from the server.\n", - "\n", - "After completing this lesson, you should be able to\n", - "\n", - "* Compose a basic query in ADQL.\n", - "\n", - "* Use queries to explore a database and its tables.\n", - "\n", - "* Use queries to download data.\n", - "\n", - "* Develop, test, and debug a query incrementally." + "4. We will download the response from the server.\n" ] }, { @@ -121,6 +82,50 @@ "But you might find it easier to learn from [this ADQL Cookbook](https://www.gaia.ac.uk/data/gaia-data-release-1/adql-cookbook)." ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Using Jupyter\n", + "\n", + "If you have not worked with Jupyter notebooks before, you might start with [the tutorial on from Jupyter.org called \"Try Classic Notebook\"](https://jupyter.org/try), or [this tutorial from DataQuest](https://www.dataquest.io/blog/jupyter-notebook-tutorial/).\n", + "\n", + "There are two environments you can use to write and run notebooks: \n", + "\n", + "* \"Jupyter Notebook\" is the original, and\n", + "\n", + "* \"Jupyter Lab\" is a newer environment with more features.\n", + "\n", + "For these lessons, you can use either one." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you are too impatient for the tutorials, here's are the most important things to know:\n", + "\n", + "1. Notebooks are made up of code cells and text cells (and a few other less common kinds). Code cells contain code; text cells, like this one, contain explanatory text written in [Markdown](https://www.markdownguide.org/).\n", + "\n", + "2. To run a code cell, click the cell to select it and press Shift-Enter. The output of the code should appear below the cell.\n", + "\n", + "3. In general, notebooks only run correctly if you run every code cell in order from top to bottom. If you run cells out of order, you are likely to get errors.\n", + "\n", + "4. You can modify existing cells, but then you have to run them again to see the effect.\n", + "\n", + "5. You can add new cells, but again, you might have to be careful about the order you run them in.\n", + "\n", + "6. If you have added or modified cells and the behavior of the notebook seems strange, you can restart the \"kernel\", which clears all of the variables and functions you have defined, and run the cells again from the beginning.\n", + "\n", + "* If you are using Jupyter notebook, open the Kernel menu and select \"Restart and Run All\".\n", + "\n", + "* In Jupyter Lab...\n", + "\n", + "* In Colab, open the Runtime menu and select \"Restart and run all\"\n", + "\n", + "Before you go on, you might want to explore the other menus and the toolbar to see what else you can do." + ] + }, { "cell_type": "markdown", "metadata": { @@ -131,7 +136,7 @@ "source": [ "## Installing libraries\n", "\n", - "If you are running this notebook on Colab, you can run the following cell to install Astroquery and the other libraries we'll use.\n", + "If you are running this notebook on Colab, you can run the following cell to install [Astroquery](https://astroquery.readthedocs.io/en/latest/) and [Gala](https://gala-astro.readthedocs.io/en/latest/).\n", "\n", "If you are running this notebook on your own computer, you might have to install these libraries yourself. See the instructions in the preface." ] @@ -152,7 +157,7 @@ "IN_COLAB = 'google.colab' in sys.modules\n", "\n", "if IN_COLAB:\n", - " !pip install astroquery astro-gala pyia" + " !pip install astroquery astro-gala" ] }, { @@ -162,7 +167,6 @@ "## Connecting to Gaia\n", "\n", "The library we'll use to get Gaia data is [Astroquery](https://astroquery.readthedocs.io/en/latest/).\n", - "\n", "Astroquery provides `Gaia`, which is an [object that represents a connection to the Gaia database](https://astroquery.readthedocs.io/en/latest/gaia/gaia.html).\n", "\n", "We can connect to the Gaia database like this:" @@ -198,7 +202,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Running this import statement has the effect of creating a [TAP+](http://www.ivoa.net/documents/TAP/) connection; TAP stands for \"Table Access Protocol\". It is a network protocol for sending queries to the database and getting back the results. We're not sure why it seems to create two connections." + "Running this import statement has the effect of creating a [TAP+](http://www.ivoa.net/documents/TAP/) connection; TAP stands for \"Table Access Protocol\", which is a network protocol for sending queries to the database and getting back the results. " ] }, { @@ -213,7 +217,7 @@ "\n", "* Each table is a 2-D array with one or more named columns of data.\n", "\n", - "We can use `Gaia.load_tables` to get the names of the tables in the Gaia database. With the option `only_names=True`, it loads information about the tables, called the \"metadata\", not the data itself." + "We can use `Gaia.load_tables` to get the names of the tables in the Gaia database. With the option `only_names=True`, it loads information about the tables, called \"metadata\", not the data itself." ] }, { @@ -240,7 +244,8 @@ "execution_count": 4, "metadata": { "tags": [ - "hide-output" + "hide-output", + "truncate-output" ] }, "outputs": [ @@ -257,6 +262,7 @@ "external.ravedr5_on\n", "external.sdssdr13_photoprimary\n", "external.skymapperdr1_master\n", + "external.skymapperdr2_master\n", "external.tmass_xsc\n", "public.hipparcos\n", "public.hipparcos_newreduction\n", @@ -272,6 +278,29 @@ "tap_schema.keys\n", "tap_schema.schemas\n", "tap_schema.tables\n", + "gaiaedr3.gaia_source\n", + "gaiaedr3.agn_cross_id\n", + "gaiaedr3.commanded_scan_law\n", + "gaiaedr3.dr2_neighbourhood\n", + "gaiaedr3.frame_rotator_source\n", + "gaiaedr3.hipparcos2_best_neighbour\n", + "gaiaedr3.hipparcos2_neighbourhood\n", + "gaiaedr3.panstarrs1_best_neighbour\n", + "gaiaedr3.panstarrs1_join\n", + "gaiaedr3.panstarrs1_neighbourhood\n", + "gaiaedr3.sdssdr13_best_neighbour\n", + "gaiaedr3.sdssdr13_join\n", + "gaiaedr3.sdssdr13_neighbourhood\n", + "gaiaedr3.skymapperdr2_best_neighbour\n", + "gaiaedr3.skymapperdr2_join\n", + "gaiaedr3.skymapperdr2_neighbourhood\n", + "gaiaedr3.tycho2tdsc_merge_best_neighbour\n", + "gaiaedr3.tycho2tdsc_merge_neighbourhood\n", + "gaiaedr3.urat1_best_neighbour\n", + "gaiaedr3.urat1_neighbourhood\n", + "gaiaedr3.gaia_source_simulation\n", + "gaiaedr3.gaia_universe_model\n", + "gaiaedr3.tycho2tdsc_merge\n", "gaiadr1.aux_qso_icrf2_match\n", "gaiadr1.ext_phot_zero_point\n", "gaiadr1.allwise_best_neighbour\n", @@ -363,12 +392,12 @@ "\n", "* `gaiadr2.panstarrs1_best_neighbour`, which we'll use to cross-match each star observed by Gaia with the same star observed by PanSTARRS.\n", "\n", - "We can use `load_table` (not `load_tables`) to get the metadata for a single table. The name of this function is misleading, because it only downloads metadata. " + "We can use `load_table` (not `load_tables`) to get the metadata for a single table. The name of this function is misleading, because it only downloads metadata, not the contents of the table." ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 63, "metadata": {}, "outputs": [ { @@ -383,10 +412,10 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 5, + "execution_count": 63, "metadata": {}, "output_type": "execute_result" } @@ -407,7 +436,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 41, "metadata": {}, "outputs": [ { @@ -428,38 +457,6 @@ "print(meta)" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In `meta`, the name of the table appears as `gaiadr2.gaiadr2.gaia_source`, which is the \"qualified name\", but when we load the metadata, we refer to it as `gaiadr2.gaia_source`." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "tags": [ - "hide-cell" - ] - }, - "outputs": [], - "source": [ - "# Solution\n", - "\n", - "# The error message, last time we tried, was\n", - "\n", - "# Retrieving table 'gaiadr2.gaiadr2.gaia_source'\n", - "# 500 Error 500:\n", - "# esavo.tap.TAPException: esavo.tap.TAPException: Schema cannot be null\n", - "\n", - "# Which is not remotely helpful.\n", - "\n", - "# The point of this exercise is to alert the participants to the difficulty\n", - "# of debugging queries with VERY limited feedback. So developing and testing\n", - "# incrementally is very important." - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -471,10 +468,11 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 42, "metadata": { "tags": [ - "hide-output" + "hide-output", + "truncate-output" ] }, "outputs": [ @@ -600,109 +598,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Exercise\n", + "### Exercise\n", "\n", "One of the other tables we'll use is `gaiadr2.panstarrs1_original_valid`. Use `load_table` to get the metadata for this table. How many columns are there and what are their names?" ] }, { "cell_type": "code", - "execution_count": 9, - "metadata": { - "scrolled": true, - "tags": [ - "hide-cell", - "remove-output" - ] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Retrieving table 'gaiadr2.panstarrs1_original_valid'\n", - "Parsing table 'gaiadr2.panstarrs1_original_valid'...\n", - "Done.\n", - "TAP Table name: gaiadr2.gaiadr2.panstarrs1_original_valid\n", - "Description: The Panoramic Survey Telescope and Rapid Response System (Pan-STARRS) is\n", - "a system for wide-field astronomical imaging developed and operated by\n", - "the Institute for Astronomy at the University of Hawaii. Pan-STARRS1\n", - "(PS1) is the first part of Pan-STARRS to be completed and is the basis\n", - "for Data Release 1 (DR1). The PS1 survey used a 1.8 meter telescope and\n", - "its 1.4 Gigapixel camera to image the sky in five broadband filters (g,\n", - "r, i, z, y).\n", - "\n", - "The current table contains a filtered subsample of the 10 723 304 629\n", - "entries listed in the original ObjectThin table.\n", - "We used only ObjectThin and MeanObject tables to extract\n", - "panstarrs1OriginalValid table, this means that objects detected only in\n", - "stack images are not included here. The main reason for us to avoid the\n", - "use of objects detected in stack images is that their astrometry is not\n", - "as good as the mean objects astrometry: “The stack positions (raStack,\n", - "decStack) have considerably larger systematic astrometric errors than\n", - "the mean epoch positions (raMean, decMean).” The astrometry for the\n", - "MeanObject positions uses Gaia DR1 as a reference catalog, while the\n", - "stack positions use 2MASS as a reference catalog.\n", - "\n", - "In details, we filtered out all objects where:\n", - "\n", - "- nDetections = 1\n", - "\n", - "- no good quality data in Pan-STARRS, objInfoFlag 33554432 not set\n", - "\n", - "- mean astrometry could not be measured, objInfoFlag 524288 set\n", - "\n", - "- stack position used for mean astrometry, objInfoFlag 1048576 set\n", - "\n", - "- error on all magnitudes equal to 0 or to -999;\n", - "\n", - "- all magnitudes set to -999;\n", - "\n", - "- error on RA or DEC greater than 1 arcsec.\n", - "\n", - "The number of objects in panstarrs1OriginalValid is 2 264 263 282.\n", - "\n", - "The panstarrs1OriginalValid table contains only a subset of the columns\n", - "available in the combined ObjectThin and MeanObject tables. A\n", - "description of the original ObjectThin and MeanObjects tables can be\n", - "found at:\n", - "https://outerspace.stsci.edu/display/PANSTARRS/PS1+Database+object+and+detection+tables\n", - "\n", - "Download:\n", - "http://mastweb.stsci.edu/ps1casjobs/home.aspx\n", - "Documentation:\n", - "https://outerspace.stsci.edu/display/PANSTARRS\n", - "http://pswww.ifa.hawaii.edu/pswww/\n", - "References:\n", - "The Pan-STARRS1 Surveys, Chambers, K.C., et al. 2016, arXiv:1612.05560\n", - "Pan-STARRS Data Processing System, Magnier, E. A., et al. 2016,\n", - "arXiv:1612.05240\n", - "Pan-STARRS Pixel Processing: Detrending, Warping, Stacking, Waters, C.\n", - "Z., et al. 2016, arXiv:1612.05245\n", - "Pan-STARRS Pixel Analysis: Source Detection and Characterization,\n", - "Magnier, E. A., et al. 2016, arXiv:1612.05244\n", - "Pan-STARRS Photometric and Astrometric Calibration, Magnier, E. A., et\n", - "al. 2016, arXiv:1612.05242\n", - "The Pan-STARRS1 Database and Data Products, Flewelling, H. A., et al.\n", - "2016, arXiv:1612.05243\n", - "\n", - "Catalogue curator:\n", - "SSDC - ASI Space Science Data Center\n", - "https://www.ssdc.asi.it/\n", - "Num. columns: 26\n" - ] - } - ], - "source": [ - "# Solution\n", - "\n", - "meta2 = Gaia.load_table('gaiadr2.panstarrs1_original_valid')\n", - "print(meta2)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, + "execution_count": 44, "metadata": { "tags": [ "hide-cell" @@ -745,6 +648,9 @@ "source": [ "# Solution\n", "\n", + "meta2 = Gaia.load_table('gaiadr2.panstarrs1_original_valid')\n", + "print(meta2)\n", + "\n", "for column in meta2.columns:\n", " print(column.name)" ] @@ -755,7 +661,7 @@ "source": [ "## Writing queries\n", "\n", - "By now you might be wondering how we actually download the data. With tables this big, you generally don't. Instead, you use queries to select only the data you want.\n", + "By now you might be wondering how we download the actual data. With tables this big, you generally don't. Instead, you use queries to select only the data you want.\n", "\n", "A query is a string written in a query language like SQL; for the Gaia database, the query language is a dialect of SQL called ADQL.\n", "\n", @@ -764,7 +670,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 45, "metadata": {}, "outputs": [], "source": [ @@ -802,16 +708,16 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 46, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 12, + "execution_count": 46, "metadata": {}, "output_type": "execute_result" } @@ -832,7 +738,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 47, "metadata": {}, "outputs": [ { @@ -846,11 +752,11 @@ "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 3\n", + " parallax float64 mas Parallax 5\n", "Jobid: None\n", "Phase: COMPLETED\n", "Owner: None\n", - "Output file: sync_20201117154748.xml.gz\n", + "Output file: sync_20201203154608.xml.gz\n", "Results: None\n" ] } @@ -870,7 +776,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 48, "metadata": {}, "outputs": [ { @@ -879,7 +785,7 @@ "astropy.table.table.Table" ] }, - "execution_count": 14, + "execution_count": 48, "metadata": {}, "output_type": "execute_result" } @@ -911,48 +817,48 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 49, "metadata": {}, "outputs": [ { "data": { "text/html": [ "Table length=10\n", - "\n", + "
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" ], "text/plain": [ "\n", - " source_id ref_epoch ... dec parallax \n", - " yr ... deg mas \n", - " int64 float64 ... float64 float64 \n", - "------------------- --------- ... ------------------ --------------------\n", - "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 --" + " source_id ref_epoch ... dec parallax \n", + " yr ... deg mas \n", + " int64 float64 ... float64 float64 \n", + "------------------- --------- ... ------------------- -------------------\n", + "5958819901197208576 2015.5 ... -42.03004488213482 -0.5002044267802102\n", + "5958821378666779136 2015.5 ... -42.0248496385989 --\n", + "5958828525443694976 2015.5 ... -43.16302120806172 --\n", + "5958821103796633472 2015.5 ... -42.036988110447425 --\n", + "5958823886946980352 2015.5 ... -41.922401166547274 --\n", + "5958822890506247552 2015.5 ... -41.95720763183602 --\n", + "5958825944161775616 2015.5 ... -43.322283668549964 0.3111803012975799\n", + "5958839726713099904 2015.5 ... -42.99642220007396 0.34188414063767153\n", + "5958828559794975744 2015.5 ... -43.24271464140307 0.19982119290900674\n", + "5958831720947299072 2015.5 ... -43.154506897707854 0.7055097280886358" ] }, - "execution_count": 15, + "execution_count": 49, "metadata": {}, "output_type": "execute_result" } @@ -976,7 +882,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Exercise\n", + "### Exercise\n", "\n", "Read [the documentation of this table](https://gea.esac.esa.int/archive/documentation/GDR2/Gaia_archive/chap_datamodel/sec_dm_main_tables/ssec_dm_gaia_source.html) and choose a column that looks interesting to you. Add the column name to the query and run it again. What are the units of the column you selected? What is its data type?" ] @@ -1004,7 +910,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 50, "metadata": {}, "outputs": [], "source": [ @@ -1044,10 +950,10 @@ " ra float64 deg Right ascension\n", " dec float64 deg Declination\n", " parallax float64 mas Parallax\n", - "Jobid: 1605646069281O\n", + "Jobid: 1607028117072O\n", "Phase: COMPLETED\n", "Owner: None\n", - "Output file: async_20201117154749.vot\n", + "Output file: async_20201203154157.vot\n", "Results: None\n" ] } @@ -1073,58 +979,58 @@ "data": { "text/html": [ "Table length=3000\n", - "
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source_idref_epochradecparallax
yrdegdegmas
int64float64float64float64float64
58497458694734874882015.5219.73560389985096-65.207103417037520.5482007725782427
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...............
46874186957742321922015.516.869927634756948-72.68242362167037-0.48502626489731665
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46874238153844131842015.515.944465951055934-72.779070618194040.551112050315771
46874230980920577282015.516.011069579363397-72.771360721331040.5275024949652448
46874208948047205122015.515.645744782387933-72.86243763758765-0.3893691578003666
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22660989323419983362015.5282.475336820247472.42703933645112-0.07096965217830103
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58448747280270863362015.5195.95342635179594-69.43994738640447-0.8998207133359908
58448751746999860482015.5195.7213758541214-69.454388432173470.04510342308702224
" ], "text/plain": [ "\n", - " source_id ref_epoch ... dec parallax \n", - " yr ... deg mas \n", - " int64 float64 ... float64 float64 \n", - "------------------- --------- ... ------------------- ---------------------\n", - "5849745869473487488 2015.5 ... -65.20710341703752 0.5482007725782427\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", - "5849756594002789504 2015.5 ... -65.52974480682217 0.29058299600659726\n", - "5849758208912099200 2015.5 ... -65.56715622350804 -0.1685867513524159\n", - "5849754768662465920 2015.5 ... -65.5732299894003 0.14826104583068447\n", - " ... ... ... ... ...\n", - "4687418695774232192 2015.5 ... -72.68242362167037 -0.48502626489731665\n", - "4687422891933697920 2015.5 ... -72.80447466138797 0.2995397868127374\n", - "4687420723006033280 2015.5 ... -72.89011101634064 -0.9978112875699006\n", - "4687422715871646976 2015.5 ... -72.82313785245728 -2.942541578796724\n", - "4687423815384413184 2015.5 ... -72.77907061819404 0.551112050315771\n", - "4687423098092057728 2015.5 ... -72.77136072133104 0.5275024949652448\n", - "4687420894804720512 2015.5 ... -72.86243763758765 -0.3893691578003666\n", - "4110043733924819712 2015.5 ... -25.232978342149263 0.16336768694395531\n", - "4110047449052774912 2015.5 ... -25.31967337502969 0.126451056290413\n", - "4110049437580528640 2015.5 ... -25.32242018581512 -0.025095711306698878" + " source_id ref_epoch ... dec parallax \n", + " yr ... deg mas \n", + " int64 float64 ... float64 float64 \n", + "------------------- --------- ... ------------------- --------------------\n", + "6101968476761520256 2015.5 ... -40.712359294192744 0.9580315447630948\n", + "6102006203756731136 2015.5 ... -40.18295977800455 0.4654078698855609\n", + "6101999537971250816 2015.5 ... -40.38078271831785 0.6324535932708285\n", + "6101989230045046912 2015.5 ... -40.30216757702745 -0.1120282976060022\n", + "6102019432256470144 2015.5 ... -40.07122353409279 0.4252570435065329\n", + "6101990020319424768 2015.5 ... -40.29644668414232 -0.5473391189616934\n", + "6102009914608095744 2015.5 ... -40.2690582632144 -0.37215204032841775\n", + "6101991463427276160 2015.5 ... -40.50754907738312 0.845560802664492\n", + "6102028571944992000 2015.5 ... -40.42670615599747 -0.6195307007338599\n", + " ... ... ... ... ...\n", + "2266125732937920640 2015.5 ... 72.58152826290214 0.611248163129301\n", + "2266098932341998336 2015.5 ... 72.42703933645112 -0.07096965217830103\n", + "5844916131519471872 2015.5 ... -69.57840395657252 0.3439372751682742\n", + "5844905857953792768 2015.5 ... -69.49181695753501 0.07481443392415044\n", + "5844885242102786688 2015.5 ... -69.47882731331919 0.34993373247988196\n", + "5844898882924614272 2015.5 ... -69.73387320199522 0.8081386490572667\n", + "5844868680708131712 2015.5 ... -69.6114926600852 0.8402524443841332\n", + "5844898814205127552 2015.5 ... -69.74069115214805 0.10499541379687834\n", + "5844874728027086336 2015.5 ... -69.43994738640447 -0.8998207133359908\n", + "5844875174699986048 2015.5 ... -69.45438843217347 0.04510342308702224" ] }, "execution_count": 18, @@ -1141,16 +1047,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "You might notice that some values of `parallax` are negative. As [this FAQ explains](https://www.cosmos.esa.int/web/gaia/archive-tips#negative%20parallax), \"Negative parallaxes are caused by errors in the observations.\" Negative parallaxes have \"no physical meaning,\" but they can be a \"useful diagnostic on the quality of the astrometric solution.\"\n", - "\n", - "Later we will see an example where we use `parallax` and `parallax_error` to identify stars where the distance estimate is likely to be inaccurate." + "You might notice that some values of `parallax` are negative. As [this FAQ explains](https://www.cosmos.esa.int/web/gaia/archive-tips#negative%20parallax), \"Negative parallaxes are caused by errors in the observations.\" Negative parallaxes have \"no physical meaning,\" but they can be a \"useful diagnostic on the quality of the astrometric solution.\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Exercise\n", + "### Exercise\n", "\n", "The clauses in a query have to be in the right order. Go back and change the order of the clauses in `query2` and run it again. \n", "\n", @@ -1162,7 +1066,7 @@ "\n", "* Make small changes and test each change before you continue.\n", "\n", - "* While you are debugging, use `TOP` to limit the number of rows in the result. That will make each attempt run faster, which reduces your testing time. \n", + "* While you are debugging, use `TOP` to limit the number of rows in the result. That will make each test run faster, which reduces your development time. \n", "\n", "* Launching test queries synchronously might make them start faster, too." ] @@ -1199,7 +1103,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Exercise\n", + "### Exercise\n", "\n", "[Read about SQL operators here](https://www.w3schools.com/sql/sql_operators.asp) and then modify the previous query to select rows where `bp_rp` is between `-0.75` and `2`.\n", "\n", @@ -1208,7 +1112,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 53, "metadata": { "tags": [ "hide-cell" @@ -1218,30 +1122,16 @@ "source": [ "# Solution\n", "\n", - "# This is what most people will probably do\n", + "# Here's a solution using > and < operators\n", "\n", "query = \"\"\"SELECT TOP 10\n", "source_id, ref_epoch, ra, dec, parallax\n", "FROM gaiadr2.gaia_source\n", "WHERE parallax < 1 \n", " AND bp_rp > -0.75 AND bp_rp < 2\n", - "\"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": { - "tags": [ - "hide-cell" - ] - }, - "outputs": [], - "source": [ - "# Solution\n", + "\"\"\"\n", "\n", - "# But if someone notices the BETWEEN operator, \n", - "# they might do this\n", + "# And here's a solution using the BETWEEN operator\n", "\n", "query = \"\"\"SELECT TOP 10\n", "source_id, ref_epoch, ra, dec, parallax\n", @@ -1255,7 +1145,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "This [Hertzsprung-Russell diagram](https://sci.esa.int/web/gaia/-/60198-gaia-hertzsprung-russell-diagram) shows the BP-RP color and luminosity of stars in the Gaia catalog.\n", + "This [Hertzsprung-Russell diagram](https://sci.esa.int/web/gaia/-/60198-gaia-hertzsprung-russell-diagram) shows the BP-RP color and luminosity of stars in the Gaia catalog (Copyright: ESA/Gaia/DPAC, CC BY-SA 3.0 IGO).\n", + "\n", + "\n", "\n", "Selecting stars with `bp-rp` less than 2 excludes many [class M dwarf stars](https://xkcd.com/2360/), which are low temperature, low luminosity. A star like that at GD-1's distance would be hard to detect, so if it is detected, it it more likely to be in the foreground." ] @@ -1277,7 +1169,7 @@ { "data": { "text/plain": [ - "(None, '1605646069281O')" + "(None, '1607028117072O')" ] }, "execution_count": 21, @@ -1307,7 +1199,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Removed jobs: '['1605646069281O']'.\n" + "Removed jobs: '['1607028117072O']'.\n" ] } ], @@ -1340,11 +1232,11 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 54, "metadata": {}, "outputs": [], "source": [ - "columns = 'source_id, ra, dec, pmra, pmdec, parallax, parallax_error, radial_velocity'" + "columns = 'source_id, ra, dec, pmra, pmdec, parallax, radial_velocity'" ] }, { @@ -1356,7 +1248,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 55, "metadata": {}, "outputs": [], "source": [ @@ -1379,7 +1271,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 56, "metadata": {}, "outputs": [], "source": [ @@ -1395,16 +1287,16 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 57, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'SELECT TOP 10 \\nsource_id, ra, dec, pmra, pmdec, parallax, parallax_error, radial_velocity\\nFROM gaiadr2.gaia_source\\nWHERE parallax < 1\\n AND bp_rp BETWEEN -0.75 AND 2\\n'" + "'SELECT TOP 10 \\nsource_id, ra, dec, pmra, pmdec, parallax, radial_velocity\\nFROM gaiadr2.gaia_source\\nWHERE parallax < 1\\n AND bp_rp BETWEEN -0.75 AND 2\\n'" ] }, - "execution_count": 26, + "execution_count": 57, "metadata": {}, "output_type": "execute_result" } @@ -1422,7 +1314,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 58, "metadata": {}, "outputs": [ { @@ -1430,7 +1322,7 @@ "output_type": "stream", "text": [ "SELECT TOP 10 \n", - "source_id, ra, dec, pmra, pmdec, parallax, parallax_error, radial_velocity\n", + "source_id, ra, dec, pmra, pmdec, parallax, radial_velocity\n", "FROM gaiadr2.gaia_source\n", "WHERE parallax < 1\n", " AND bp_rp BETWEEN -0.75 AND 2\n", @@ -1453,7 +1345,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 59, "metadata": { "scrolled": true }, @@ -1471,12 +1363,11 @@ " pmra float64 mas / yr Proper motion in right ascension direction 0\n", " pmdec float64 mas / yr Proper motion in declination direction 0\n", " parallax float64 mas Parallax 0\n", - " parallax_error float64 mas Standard error of parallax 0\n", - "radial_velocity float64 km / s Radial velocity 10\n", + "radial_velocity float64 km / s Radial velocity 9\n", "Jobid: None\n", "Phase: COMPLETED\n", "Owner: None\n", - "Output file: sync_20201117154752.xml.gz\n", + "Output file: sync_20201203155727.xml.gz\n", "Results: None\n" ] } @@ -1488,48 +1379,48 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 60, "metadata": {}, "outputs": [ { "data": { "text/html": [ "Table length=10\n", - "
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source_idradecpmrapmdecparallaxparallax_errorradial_velocity
degdegmas / yrmas / yrmasmaskm / s
int64float64float64float64float64float64float64float64
468742055120733696015.645304428598138-72.891676445087071.2391150617590077-0.44283475442839-0.61980967566392590.2807306692481813--
468742034504328652817.02520966291366-72.588137109955270.5942325468950411-0.92269001849315040.141958648203938280.2762606040704309--
468742340306757785616.093293953294165-72.761906889015344.282221460096154-1.151311128485597-2.351986752396410.8824042279602063--
468741869577423219216.869927634756948-72.682423621670373.757251484808558-0.27830874302625963-0.485026264897316650.5173500081780815--
468742289193369792015.901327641112253-72.804474661387971.620291207312266-0.75415760898193680.29953978681273740.2764244386276099--
468742271587164697615.99138434019087-72.823137852457282.89546371002853684.214822457223685-2.9425415787967240.847035789335997--
468742381538441318415.944465951055934-72.779070618194044.313074669192134-1.4603442951470880.5511120503157710.7664351946147043--
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468742089480472051215.645744782387933-72.862437637587650.40415899559873986-2.0036523797855805-0.38936915780036660.35767437939443886--
4110047449052774912261.1262641088297-25.31967337502969-2.4098547436119064-1.3016098082360160.1264510562904130.7276250597861622--
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source_idradecpmrapmdecparallaxradial_velocity
degdegmas / yrmas / yrmaskm / s
int64float64float64float64float64float64float64
5933096203842096896243.78205426182762-53.059795438840304-2.424817873751699-4.4665222485478650.5068567863750518-42.33885843170957
5933101877528899712243.97057838444422-52.986714478669484-2.1222196983029993-3.07549274891692640.23373208336686707--
5933092909682644736243.79077939157932-53.210873915640164-3.247331739283727-3.07214728762429120.22141430054832106--
5933099403674089344244.10992316666963-52.99682662964451-7.422374687042380.9808177052973058-0.5694816136281146--
5933099712864927488244.14352482183628-53.00718804864607-1.8469803654970902-2.061311622119096-0.15119359534047053--
5933100430094343296244.05583528078185-52.97056623530601-2.4175778689836607-1.77410061845385010.6126293653016116--
5933090195214793088244.0040474187424-53.2398318316935-1.9355723547012178-3.2456307745304516-0.5994721050902905--
5933092359874146816243.77339485386585-53.257886140965721.205055772227777-3.1082961813495587-1.5130414932866274--
5933093974783111808243.7622231489827-53.164944966614065-4.665032988958056-1.8667474849003503-0.0273302633914116--
4118427651760743808264.39377925009563-20.392385972037322.5862935758007417-1.78872098645511230.4865048585491827--
" ], "text/plain": [ "\n", - " source_id ra ... parallax_error radial_velocity\n", - " deg ... mas km / s \n", - " int64 float64 ... float64 float64 \n", - "------------------- ------------------ ... ------------------- ---------------\n", - "4687420551207336960 15.645304428598138 ... 0.2807306692481813 --\n", - "4687420345043286528 17.02520966291366 ... 0.2762606040704309 --\n", - "4687423403067577856 16.093293953294165 ... 0.8824042279602063 --\n", - "4687418695774232192 16.869927634756948 ... 0.5173500081780815 --\n", - "4687422891933697920 15.901327641112253 ... 0.2764244386276099 --\n", - "4687422715871646976 15.99138434019087 ... 0.847035789335997 --\n", - "4687423815384413184 15.944465951055934 ... 0.7664351946147043 --\n", - "4687423098092057728 16.011069579363397 ... 0.3616348905813909 --\n", - "4687420894804720512 15.645744782387933 ... 0.35767437939443886 --\n", - "4110047449052774912 261.1262641088297 ... 0.7276250597861622 --" + " source_id ra ... radial_velocity \n", + " deg ... km / s \n", + " int64 float64 ... float64 \n", + "------------------- ------------------ ... ------------------\n", + "5933096203842096896 243.78205426182762 ... -42.33885843170957\n", + "5933101877528899712 243.97057838444422 ... --\n", + "5933092909682644736 243.79077939157932 ... --\n", + "5933099403674089344 244.10992316666963 ... --\n", + "5933099712864927488 244.14352482183628 ... --\n", + "5933100430094343296 244.05583528078185 ... --\n", + "5933090195214793088 244.0040474187424 ... --\n", + "5933092359874146816 243.77339485386585 ... --\n", + "5933093974783111808 243.7622231489827 ... --\n", + "4118427651760743808 264.39377925009563 ... --" ] }, - "execution_count": 29, + "execution_count": 60, "metadata": {}, "output_type": "execute_result" } @@ -1550,7 +1441,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Exercise\n", + "### Exercise\n", "\n", "This query always selects sources with `parallax` less than 1. But suppose you want to take that upper bound as an input.\n", "\n", @@ -1559,7 +1450,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 61, "metadata": { "tags": [ "hide-cell" @@ -1574,52 +1465,13 @@ "FROM gaiadr2.gaia_source\n", "WHERE parallax < {max_parallax} AND \n", "bp_rp BETWEEN -0.75 AND 2\n", - "\"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": { - "tags": [ - "hide-cell" - ] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "SELECT TOP 10\n", - "source_id, ref_epoch, ra, dec, parallax\n", - "FROM gaiadr2.gaia_source\n", - "WHERE parallax < 1 \n", - " AND bp_rp BETWEEN -0.75 AND 2\n", - "\n" - ] - } - ], - "source": [ - "# Solution\n", + "\"\"\"\n", "\n", "query4 = query4_base.format(columns=columns,\n", - " max_parallax=0.5)\n", + " max_parallax=0.5)\n", "print(query)" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Style note:** You might notice that the variable names in this notebook are numbered, like `query1`, `query2`, etc. \n", - "\n", - "The advantage of this style is that it isolates each section of the notebook from the others, so if you go back and run the cells out of order, it's less likely that you will get unexpected interactions.\n", - "\n", - "A drawback of this style is that it can be a nuisance to update the notebook if you add, remove, or reorder a section.\n", - "\n", - "What do you think of this choice? Are there alternatives you prefer?" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -1634,7 +1486,9 @@ "\n", "3. Writing a query and sending it to the server, and finally\n", "\n", - "4. Downloading the response from the server as an Astropy `Table`." + "4. Downloading the response from the server as an Astropy `Table`.\n", + "\n", + "In the next lesson we will extend these queries to select a particular region of the sky." ] }, { diff --git a/02_coords.ipynb b/02_coords.ipynb index 3e684b0..1d02ecf 100644 --- a/02_coords.ipynb +++ b/02_coords.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Chapter 2\n", + "# Coordinates and units\n", "\n", "This is the second in a series of notebooks related to astronomy data.\n", "\n", @@ -75,7 +75,7 @@ "IN_COLAB = 'google.colab' in sys.modules\n", "\n", "if IN_COLAB:\n", - " !pip install astroquery astro-gala pyia" + " !pip install astroquery astro-gala" ] }, { diff --git a/03_motion.ipynb b/03_motion.ipynb index e76189e..ac31939 100644 --- a/03_motion.ipynb +++ b/03_motion.ipynb @@ -79,7 +79,7 @@ "IN_COLAB = 'google.colab' in sys.modules\n", "\n", "if IN_COLAB:\n", - " !pip install astroquery astro-gala pyia python-wget" + " !pip install astroquery astro-gala python-wget" ] }, { @@ -331,7 +331,7 @@ "data": { "text/html": [ "Row index=0\n", - "
\n", + "
\n", "\n", "\n", "\n", @@ -555,64 +555,49 @@ "\n", "To plot them, we will transform them back to the `GD1Koposov10` frame; that way, the axes of the figure are aligned with the GD-1, which will make it easy to select stars near the centerline of the stream.\n", "\n", - "To do that, we'll put the results into a `GaiaData` object, provided by the [pyia library](https://pyia.readthedocs.io/en/latest/api/pyia.GaiaData.html).\n", - "\n", - "TODO: Do we need pyia, or could we do this with astropy and gala?" + "To do that, we'll put the results into a `SkyCoord` object, \n", + "which is an \"interface for celestial coordinate representation, manipulation, and transformation between systems\", provided by Astropy." ] }, { "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "pyia.data.GaiaData" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from pyia import GaiaData\n", - "\n", - "gaia_data = GaiaData(results)\n", - "type(gaia_data)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we can extract sky coordinates from the `GaiaData` object, like this:" - ] - }, - { - "cell_type": "code", - "execution_count": 15, + "execution_count": 53, "metadata": {}, "outputs": [], "source": [ + "import astropy.coordinates as coord\n", "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)" + "skycoord = coord.SkyCoord(\n", + " ra=results['ra'], \n", + " dec=results['dec'],\n", + " pm_ra_cosdec=results['pmra'],\n", + " pm_dec=results['pmdec'], \n", + " distance=8*u.kpc, \n", + " radial_velocity=0*u.km/u.s)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "We provide `distance` and `radial_velocity` to prepare the data for reflex correction, which we explain below." + "Most of the arguments we send to `SkyCoord` come directly from `results`.\n", + "\n", + "We provide `distance` and `radial_velocity` to prepare the data for reflex correction, which we explain below.\n", + "\n", + "The result is a `SkyCoord` object ([documentation here](https://docs.astropy.org/en/stable/api/astropy.coordinates.SkyCoord.html#astropy.coordinates.SkyCoord))." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The result is an Astropy `SkyCoord` object." ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 59, "metadata": {}, "outputs": [ { @@ -621,7 +606,7 @@ "astropy.coordinates.sky_coordinate.SkyCoord" ] }, - "execution_count": 16, + "execution_count": 59, "metadata": {}, "output_type": "execute_result" } @@ -634,12 +619,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The result is an Astropy `SkyCoord` object ([documentation here](https://docs.astropy.org/en/stable/api/astropy.coordinates.SkyCoord.html#astropy.coordinates.SkyCoord)), which provides `transform_to`, so we can transform the coordinates to other frames." + "`SkyCoord` provides `transform_to`, so we can transform the coordinates to other frames." ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 60, "metadata": {}, "outputs": [ { @@ -648,7 +633,7 @@ "astropy.coordinates.sky_coordinate.SkyCoord" ] }, - "execution_count": 17, + "execution_count": 60, "metadata": {}, "output_type": "execute_result" } @@ -695,7 +680,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 61, "metadata": {}, "outputs": [ { @@ -704,7 +689,7 @@ "astropy.coordinates.sky_coordinate.SkyCoord" ] }, - "execution_count": 18, + "execution_count": 61, "metadata": {}, "output_type": "execute_result" } @@ -730,7 +715,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 57, "metadata": { "scrolled": true }, @@ -749,7 +734,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 58, "metadata": {}, "outputs": [ { @@ -785,7 +770,7 @@ "source": [ "## Pandas DataFrame\n", "\n", - "At this point we have three objects containing different subsets of the data." + "At this point we have two objects containing different subsets of the data. `results` is the Astropy `Table` we downloaded from Gaia." ] }, { @@ -809,28 +794,15 @@ ] }, { - "cell_type": "code", - "execution_count": 22, + "cell_type": "markdown", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "pyia.data.GaiaData" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ - "type(gaia_data)" + "And `gd1_coord` is a `SkyCoord` object that contains the transformed coordinates and proper motions." ] }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 63, "metadata": {}, "outputs": [ { @@ -839,7 +811,7 @@ "astropy.coordinates.sky_coordinate.SkyCoord" ] }, - "execution_count": 23, + "execution_count": 63, "metadata": {}, "output_type": "execute_result" } @@ -852,7 +824,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "On one hand, this makes sense, since each object provides different capabilities. But working with three different object types can be awkward.\n", + "On one hand, this division of labor makes sense because each object provides different capabilities. But working with multiple object types can be awkward.\n", "\n", "It will be more convenient to choose one object and get all of the data into it. We'll use a Pandas DataFrame, for two reasons:\n", "\n", @@ -1027,7 +999,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Python detail: `shape` is an attribute, so we can display it's value without calling it as a function; `head` is a function, so we need the parentheses." + "Python detail: `shape` is an attribute, so we display its value without calling it as a function; `head` is a function, so we need the parentheses." ] }, { @@ -1039,16 +1011,16 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 64, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(140340, 10)" + "(140340, 12)" ] }, - "execution_count": 26, + "execution_count": 64, "metadata": {}, "output_type": "execute_result" } @@ -1068,7 +1040,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 65, "metadata": {}, "outputs": [ { @@ -1077,7 +1049,7 @@ "(140340, 12)" ] }, - "execution_count": 27, + "execution_count": 65, "metadata": {}, "output_type": "execute_result" } @@ -1097,6 +1069,256 @@ "We could have: `proper_motion` contains the same data as `pm_phi1_cosphi2` and `pm_phi2`, but in a different format." ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exploring data\n", + "\n", + "One benefit of using Pandas is that it provides function for exploring the data and checking for problems.\n", + "\n", + "One of the most useful of these functions is `describe`, which computes summary statistics for each column." + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "
source_idradecpmrapmdecparallaxparallax_errorradial_velocity
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source_idradecpmrapmdecparallaxparallax_errorradial_velocityphi1phi2pm_phi1pm_phi2
count1.403400e+05140340.000000140340.000000140340.000000140340.000000140340.000000140340.0000001.403400e+05140340.000000140340.000000140340.000000140340.000000
mean6.792378e+17143.82297126.780161-2.484410-6.1007840.1794740.5180689.931167e+19-50.091337-1.803264-0.8689801.409215
std3.792015e+163.6978243.0526395.9139237.2020130.7596220.5055588.267982e+182.8923213.4444396.6577006.518573
min6.214900e+17135.42569919.286617-106.755260-138.065163-15.2876020.020824-1.792684e+02-54.999989-8.029159-115.275637-161.150142
25%6.443515e+17140.96780724.592348-5.038746-8.341641-0.0359830.1411081.000000e+20-52.603097-4.750410-2.948851-1.107074
50%6.888056e+17143.73418326.746169-1.834971-4.6895700.3627050.3361031.000000e+20-50.147567-1.6714970.5850381.987196
75%6.976578e+17146.60718028.9904900.452995-1.9378330.6576360.7511711.000000e+20-47.5934661.1606323.0017614.628859
max7.974418e+17152.77739334.285481104.31992320.9810700.9999574.1712211.000000e+20-45.0000864.01479439.80247179.275199
\n", + "" + ], + "text/plain": [ + " source_id ra dec pmra \\\n", + "count 1.403400e+05 140340.000000 140340.000000 140340.000000 \n", + "mean 6.792378e+17 143.822971 26.780161 -2.484410 \n", + "std 3.792015e+16 3.697824 3.052639 5.913923 \n", + "min 6.214900e+17 135.425699 19.286617 -106.755260 \n", + "25% 6.443515e+17 140.967807 24.592348 -5.038746 \n", + "50% 6.888056e+17 143.734183 26.746169 -1.834971 \n", + "75% 6.976578e+17 146.607180 28.990490 0.452995 \n", + "max 7.974418e+17 152.777393 34.285481 104.319923 \n", + "\n", + " pmdec parallax parallax_error radial_velocity \\\n", + "count 140340.000000 140340.000000 140340.000000 1.403400e+05 \n", + "mean -6.100784 0.179474 0.518068 9.931167e+19 \n", + "std 7.202013 0.759622 0.505558 8.267982e+18 \n", + "min -138.065163 -15.287602 0.020824 -1.792684e+02 \n", + "25% -8.341641 -0.035983 0.141108 1.000000e+20 \n", + "50% -4.689570 0.362705 0.336103 1.000000e+20 \n", + "75% -1.937833 0.657636 0.751171 1.000000e+20 \n", + "max 20.981070 0.999957 4.171221 1.000000e+20 \n", + "\n", + " phi1 phi2 pm_phi1 pm_phi2 \n", + "count 140340.000000 140340.000000 140340.000000 140340.000000 \n", + "mean -50.091337 -1.803264 -0.868980 1.409215 \n", + "std 2.892321 3.444439 6.657700 6.518573 \n", + "min -54.999989 -8.029159 -115.275637 -161.150142 \n", + "25% -52.603097 -4.750410 -2.948851 -1.107074 \n", + "50% -50.147567 -1.671497 0.585038 1.987196 \n", + "75% -47.593466 1.160632 3.001761 4.628859 \n", + "max -45.000086 4.014794 39.802471 79.275199 " + ] + }, + "execution_count": 66, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.describe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Exercise\n", + "\n", + "Review the summary statistics in this table.\n", + "\n", + "* Do the values makes senses based on what you know about the context?\n", + "\n", + "* Do you see any values that seem problematic, or evidence of other data issues?" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [], + "source": [ + "# Solution\n", + "\n", + "# A few issues that are likely to come up:\n", + "\n", + "# 1. Why are some of the parallax values negative?\n", + "# Some parallax measurements are inaccurate, especially\n", + "# stars that are far away.\n", + "\n", + "# 2. Why are some of the radial velocities 1e20?\n", + "# It seems like this value is used to indicate invalid data.\n", + "# Notice that the 25th percentile is 1e20, which indicates\n", + "# that at least 75% of these values are invalid." + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -1441,12 +1663,12 @@ "\n", "We'll use a simple rectangle for now, but in a later lesson we'll see how to select a polygonal region as well.\n", "\n", - "Here are bounds on proper motion we chose by eye," + "Here are bounds on proper motion we chose by eye:" ] }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 44, "metadata": {}, "outputs": [], "source": [ @@ -1465,7 +1687,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 45, "metadata": {}, "outputs": [], "source": [ @@ -1482,7 +1704,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 46, "metadata": {}, "outputs": [ { diff --git a/04_select.ipynb b/04_select.ipynb index c5b1cbd..331f0a5 100644 --- a/04_select.ipynb +++ b/04_select.ipynb @@ -71,7 +71,7 @@ "IN_COLAB = 'google.colab' in sys.modules\n", "\n", "if IN_COLAB:\n", - " !pip install astroquery astro-gala pyia python-wget" + " !pip install astroquery astro-gala python-wget" ] }, { @@ -216,7 +216,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -302,7 +302,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 9, @@ -794,24 +794,19 @@ "\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", - "\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: 1605744463707O\n", - "Phase: COMPLETED\n", - "Owner: None\n", - "Output file: async_20201118190743.vot\n", - "Results: None\n" + "\tSSL Port: 443\n" + ] + }, + { + "ename": "HTTPError", + "evalue": "OK", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mHTTPError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mastroquery\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgaia\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mGaia\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mjob\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mGaia\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlaunch_job_async\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mquery\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjob\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/anaconda3/envs/AstronomicalData/lib/python3.8/site-packages/astroquery/utils/tap/core.py\u001b[0m in \u001b[0;36mlaunch_job_async\u001b[0;34m(self, query, name, output_file, output_format, verbose, dump_to_file, background, upload_resource, upload_table_name, autorun)\u001b[0m\n\u001b[1;32m 422\u001b[0m self.__connHandler.dump_to_file(suitableOutputFile,\n\u001b[1;32m 423\u001b[0m response)\n\u001b[0;32m--> 424\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mrequests\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexceptions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mHTTPError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresponse\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreason\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 425\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 426\u001b[0m location = self.__connHandler.find_header(\n", + "\u001b[0;31mHTTPError\u001b[0m: OK" ] } ], @@ -831,20 +826,9 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "7346" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "candidate_table = job.get_results()\n", "len(candidate_table)" @@ -861,22 +845,9 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "x = candidate_table['ra']\n", "y = candidate_table['dec']\n", @@ -897,12 +868,10 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "from pyia import GaiaData\n", - "\n", "def make_dataframe(table):\n", " \"\"\"Transform coordinates from ICRS to GD-1 frame.\n", " \n", @@ -910,18 +879,22 @@ " \n", " returns: Pandas DataFrame\n", " \"\"\"\n", - " gaia_data = GaiaData(table)\n", + " skycoord = coord.SkyCoord(\n", + " ra=results['ra'], \n", + " dec=results['dec'],\n", + " pm_ra_cosdec=results['pmra'],\n", + " pm_dec=results['pmdec'], \n", + " distance=8*u.kpc, \n", + " radial_velocity=0*u.km/u.s)\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", + " transformed = skycoord.transform_to(gc.GD1Koposov10)\n", + " gd1_coord = gc.reflex_correct(transformed)\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", + " df['phi1'] = gd1_coord.phi1\n", + " df['phi2'] = gd1_coord.phi2\n", + " df['pm_phi1'] = gd1_coord.pm_phi1_cosphi2\n", + " df['pm_phi2'] = gd1_coord.pm_phi2\n", " return df" ] }, @@ -934,7 +907,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -950,22 +923,9 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ], - "text/plain": [ - " Unnamed: 0 source_id ra dec pmra pmdec \\\n", - "0 0 635559124339440000 137.586717 19.196544 -3.770522 -12.490482 \n", - "1 1 635860218726658176 138.518707 19.092339 -5.941679 -11.346409 \n", - "2 2 635674126383965568 138.842874 19.031798 -3.897001 -12.702780 \n", - "\n", - " parallax parallax_error radial_velocity phi1 phi2 pm_phi1 \\\n", - "0 0.791393 0.271754 NaN -59.630489 -1.216485 -7.361363 \n", - "1 0.307456 0.199466 NaN -59.247330 -2.016078 -7.527126 \n", - "2 0.779463 0.223692 NaN -59.133391 -2.306901 -7.560608 \n", - "\n", - " pm_phi2 \n", - "0 -0.592633 \n", - "1 1.748779 \n", - "2 -0.741800 " - ] - }, - "execution_count": 38, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "read_back_csv.head(3)" ] diff --git a/05_join.ipynb b/05_join.ipynb index a95ea7e..fab853e 100644 --- a/05_join.ipynb +++ b/05_join.ipynb @@ -71,7 +71,7 @@ "IN_COLAB = 'google.colab' in sys.modules\n", "\n", "if IN_COLAB:\n", - " !pip install astroquery astro-gala pyia python-wget" + " !pip install astroquery astro-gala python-wget" ] }, { @@ -184,16 +184,16 @@ "\n", "Fortunately, smart people have worked on this problem, and the Gaia database includes cross-matching tables that suggest a best neighbor in the Pan-STARRS catalog for many stars in the Gaia catalog.\n", "\n", - "[This document describes the cross matching process](https://gea.esac.esa.int/archive/documentation/GDR2/Catalogue_consolidation/chap_cu9val_cu9val/ssec_cu9xma/sssec_cu9xma_extcat.html). Briefly, it uses a cone search to find possible matches in approximately the right position, then uses attributes like color and magnitude to choose pairs of stars most likely to be identical." + "[This document describes the cross matching process](https://gea.esac.esa.int/archive/documentation/GDR2/Catalogue_consolidation/chap_cu9val_cu9val/ssec_cu9xma/sssec_cu9xma_extcat.html). Briefly, it uses a cone search to find possible matches in approximately the right position, then uses attributes like color and magnitude to choose pairs of observations most likely to be the same star." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "So the hard part of cross-matching has been done for us. However, using the results is a little tricky.\n", + "## Joining tables\n", "\n", - "But, it is also an opportunity to learn about one of the most important tools for working with databases: \"joining\" tables.\n", + "So the hard part of cross-matching has been done for us. Using the results is a little tricky, but it gives us a chance to learn about one of the most important tools for working with databases: \"joining\" tables.\n", "\n", "In general, a \"join\" is an operation where you match up records from one table with records from another table using as a \"key\" a piece of information that is common to both tables, usually some kind of ID code.\n", "\n", @@ -285,8 +285,8 @@ "metadata": {}, "outputs": [], "source": [ - "table = candidate_table[['source_id']]\n", - "table.write('candidate_df.xml', format='votable', overwrite=True)" + "table_id = candidate_table[['source_id']]\n", + "table_id.write('candidate_df.xml', format='votable', overwrite=True)" ] }, { @@ -395,8 +395,8 @@ } ], "source": [ - "table = candidate_table[['source_id']]\n", - "type(table)" + "table_id = candidate_table[['source_id']]\n", + "type(table_id)" ] }, { @@ -952,8 +952,8 @@ "source": [ "# Solution\n", "\n", - "table = results1[['source_id', 'original_ext_source_id']]\n", - "table.write('external.xml', format='votable', overwrite=True)" + "table_ext = results1[['source_id', 'original_ext_source_id']]\n", + "table_ext.write('external.xml', format='votable', overwrite=True)" ] }, { diff --git a/06_photo.ipynb b/06_photo.ipynb index 98b9991..8b74810 100644 --- a/06_photo.ipynb +++ b/06_photo.ipynb @@ -59,7 +59,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 25, "metadata": { "tags": [ "remove-cell" @@ -73,7 +73,7 @@ "IN_COLAB = 'google.colab' in sys.modules\n", "\n", "if IN_COLAB:\n", - " !pip install astroquery astro-gala pyia python-wget" + " !pip install astroquery astro-gala python-wget" ] }, { @@ -87,7 +87,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ @@ -110,7 +110,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ @@ -142,7 +142,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ @@ -198,7 +198,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 29, "metadata": {}, "outputs": [ { @@ -229,6 +229,509 @@ "As a simplification, we'll choose a boundary by eye that seems to contain the overdense region." ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Isochrone\n", + "\n", + "http://waps.cfa.harvard.edu/MIST/interp_isos.html\n", + " \n", + "MIST Version 1.2\n", + "\n", + "Rotation initial v/v_crit = 0.4\n", + "\n", + "Single age, log10 scale = 10.079\n", + "\n", + "Composition [Fe/H] = -1.35\n", + "\n", + "Synthetic Photometry, PanStarrs\n", + "\n", + "Extinction av = 0\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 132, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "10.079181246047625" + ] + }, + "execution_count": 132, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "\n", + "log_age = np.log10(12e9)\n", + "log_age" + ] + }, + { + "cell_type": "code", + "execution_count": 182, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "10.176091259055681" + ] + }, + "execution_count": 182, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "\n", + "log_age = np.log10(15e9)\n", + "log_age" + ] + }, + { + "cell_type": "code", + "execution_count": 147, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "from wget import download\n", + "\n", + "filename = 'read_mist_models.py'\n", + "filepath = 'https://github.com/jieunchoi/MIST_codes/raw/master/scripts/'\n", + "\n", + "if not os.path.exists(filename):\n", + " print(download(filepath+filename))" + ] + }, + { + "cell_type": "code", + "execution_count": 149, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reading in: mist_iso_12.0_-1.35.cmd\n" + ] + } + ], + "source": [ + "import read_mist_models\n", + "\n", + "filename = 'mist_iso_12.0_-1.35.cmd'\n", + "iso = read_mist_models.ISOCMD(filename)" + ] + }, + { + "cell_type": "code", + "execution_count": 150, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "read_mist_models.ISOCMD" + ] + }, + "execution_count": 150, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "type(iso)" + ] + }, + { + "cell_type": "code", + "execution_count": 151, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "list" + ] + }, + "execution_count": 151, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "type(iso.isocmds)" + ] + }, + { + "cell_type": "code", + "execution_count": 152, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1" + ] + }, + "execution_count": 152, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(iso.isocmds)" + ] + }, + { + "cell_type": "code", + "execution_count": 153, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "numpy.ndarray" + ] + }, + "execution_count": 153, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "type(iso.isocmds[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 154, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "dtype([('EEP', 'Table length=5\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
EEPlog10_isochrone_age_yrinitial_massstar_masslog_Tefflog_glog_L[Fe/H]_init[Fe/H]PS_gPS_rPS_iPS_zPS_yPS_wPS_openphase
int32float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64
25110.0790.105870850108208960.105869705589364823.54036068292639565.321292252841703-2.7463861921790302-1.35-1.30902413.83327812.39528311.63853511.28630911.09987712.30464511.9700720.0
25210.0790.108809974798175170.108808758217223443.54258290625209735.312318300317242-2.7181724188486394-1.35-1.30888713.72806512.30235811.56230511.21656411.03057212.22069511.8916950.0
25310.0790.112652468448451230.112651153162689773.54551901495962075.300571015130943-2.6811483213239216-1.35-1.308713.59011312.18069911.46111311.12465910.93905112.11006911.7887120.0
25410.0790.116427328711895660.116425909285919643.54840384284145665.288998772212527-2.644742589781073-1.35-1.30852713.45454412.06139811.36112811.03319410.84876912.00072911.6869940.0
25510.0790.120222397889611750.120220866480104383.5513078778592485.277331450307816-2.608093791390564-1.35-1.30828513.3183711.9418211.26008710.94010610.75770811.8903111.5842020.0
" + ], + "text/plain": [ + "\n", + " EEP log10_isochrone_age_yr initial_mass ... PS_w PS_open phase \n", + "int32 float64 float64 ... float64 float64 float64\n", + "----- ---------------------- ------------------- ... --------- --------- -------\n", + " 251 10.079 0.10587085010820896 ... 12.304645 11.970072 0.0\n", + " 252 10.079 0.10880997479817517 ... 12.220695 11.891695 0.0\n", + " 253 10.079 0.11265246844845123 ... 12.110069 11.788712 0.0\n", + " 254 10.079 0.11642732871189566 ... 12.000729 11.686994 0.0\n", + " 255 10.079 0.12022239788961175 ... 11.89031 11.584202 0.0" + ] + }, + "execution_count": 155, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from astropy.table import Table \n", + "\n", + "iso_table = Table(iso.isocmds[0])\n", + "iso_table[:5]" + ] + }, + { + "cell_type": "code", + "execution_count": 156, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['EEP',\n", + " 'log10_isochrone_age_yr',\n", + " 'initial_mass',\n", + " 'star_mass',\n", + " 'log_Teff',\n", + " 'log_g',\n", + " 'log_L',\n", + " '[Fe/H]_init',\n", + " '[Fe/H]',\n", + " 'PS_g',\n", + " 'PS_r',\n", + " 'PS_i',\n", + " 'PS_z',\n", + " 'PS_y',\n", + " 'PS_w',\n", + " 'PS_open',\n", + " 'phase']" + ] + }, + "execution_count": 156, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "iso_table.colnames" + ] + }, + { + "cell_type": "code", + "execution_count": 157, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "14.4604730134524" + ] + }, + "execution_count": 157, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import astropy.coordinates as coord\n", + "import astropy.units as u\n", + "\n", + "distance = 7.8 * u.kpc\n", + "dm = coord.Distance(distance).distmod.value\n", + "dm" + ] + }, + { + "cell_type": "code", + "execution_count": 158, + "metadata": {}, + "outputs": [], + "source": [ + "g = iso_table['PS_g'] + dm\n", + "gi = iso_table['PS_g'] - iso_table['PS_i']" + ] + }, + { + "cell_type": "code", + "execution_count": 159, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 159, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "plot_cmd(photo_table)\n", + "plt.plot(gi, g)" + ] + }, + { + "cell_type": "code", + "execution_count": 169, + "metadata": {}, + "outputs": [], + "source": [ + "def read_and_clean_cmd(filename, distance):\n", + " iso = read_mist_models.ISOCMD(filename)\n", + " iso_table = Table(iso.isocmds[0])\n", + "\n", + " phase_mask = (iso_table['phase'] >= 0) & (iso_table['phase'] < 3)\n", + " table = iso_table[phase_mask]\n", + " \n", + " dm = coord.Distance(distance).distmod.value\n", + " g = iso_table['PS_g'] + dm\n", + " gi = iso_table['PS_g'] - iso_table['PS_i']\n", + " \n", + " return gi, g" + ] + }, + { + "cell_type": "code", + "execution_count": 170, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reading in: mist_iso_12.0_-1.35.cmd\n" + ] + } + ], + "source": [ + "filename = 'mist_iso_12.0_-1.35.cmd'\n", + "\n", + "gi1, g1 = read_and_clean_cmd(filename, distance)" + ] + }, + { + "cell_type": "code", + "execution_count": 183, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reading in: mist_iso_15.0_-1.35.cmd\n" + ] + } + ], + "source": [ + "filename = 'mist_iso_15.0_-1.35.cmd'\n", + "\n", + "gi2, g2 = read_and_clean_cmd(filename, distance)" + ] + }, + { + "cell_type": "code", + "execution_count": 184, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 184, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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9CicWlABQ8Aw32rtRIy8pKaGwsDAxG8c01kcWLh3YBNWb4EAl4QMbSaveDM0mHIBAVh67fSfxeWgQy2v78nnTIDbJQfTM68HkIT04dWgPCofmMbpPDmkH1keY/K6SCNPfvwGQgCDU8+SIMNFhZXg0/wpdxPvhQtKQzMx4gRsyPkSeNB0x4znIzCZY8S5bl7zE4OqldBJBjmQPIPuS3+ObOCMmB6+G3TW13PHmOj7euJ/zRvXmoWkB8t6/NTIWk78LF/4RsmNbJaxwYqHdooCEEM8BVwD7pJTjm4/dA9wE7G8udqeU8h3zGhTaC25CMI0avlGhsDUDNTXCkV007t9C5tHdcGgHHNoOh3fAwW2Rb47VF8rtz6FOw9je/QI+D/Rh2ZHelNT150BDN3wZaUwc2I3JE3rw4yE9OHVod/qI2matfh68XxzxAwT9kcpyeiIHFrJ78BUsbxjGf6t6U7nrIJ9m/wiAFT2upnHyjeQNP4V3HltKX2p4sevjjAyug9NuRgw+Hfn2rYQ3vIevqYEeshslPS5j7IXfoeeYc+NOqPbxxv388tVSahtC3HvlyXzD/y/Ey/+AboPgW29EonwUFOJEm88AhBDnAH7gBYMA8Esp/+qlLjUDSA3Y2elbrCVorCdYvZUtn35Mfq9MMvxVEQZ/aAfy0DZEbRXIsK5mAV0HILsNxp/dn51pA1jX2JdVtXl8tL8r+xoi+kpmuuDkvl0YP6Ab4wd2ZfzAbozr2wnf/s+PmXF2FsOhbZFq0zKQ/SYQ6Hsqe7tOoCJ9FAt2ZfPxpgMcqmskTcDkoT2YPqoPl3XdwrCxU5BZ3XmhaCv3L9jAwLRq3vbdTlbjERBpkJ4FoXoOpfVgXrCQtd3PZ8Y1X+W0Eb0tx8vtLEhKyROLN/Pgexs4uW8uT1w9iPxFP4EdKyILuS76I2R18XS/FBTabQYgpVwihBjW1u0oJBeTRg1DVJWSWX8AanfDkSqaDu+kafcmwhwlzV8FDYfxAdG1rSINug4k3HUQNV3G0mPC9TR0GcwXwR5sauzJpzWdqDgQpGLrEeobm8ggRLeMEBP7+Lh+TCZjemZwcs8MBncBX7gOGqpgdyl8XgxVZdAUiLTTdSChAZOpHHI9i/xDWHioH5t2N1G7RQsrPUjPzj7OH92H80b14eyRvXRhnvl8ceAot/1rBau21nD+yB48t+Nr0Bg5G87pxeqcs/jb7nGsyxzHLy4fw19OH2qZm9+LHyQclvxhfgXPL9/K1ZMG8MAZAbJeuxwaDsOM52D8V2K/YQoKJmjPMNCfCiG+DZQAv5RSHmxHWhSMaApBXbXucwDqqmk6uJ3g5+/R+fAGhM48g0gjPbcvXXL7kdYtH046K+KkzO6GTMvkUCDMwYYwhw9UUV+9E1m7l/DuxaSH6ukkgowmwGQRJDe9kZzMAL6MAGmymWHXNH/MkJEdccKefjMHuk9kkX8ob20VFK05QGOTpEtWBqcM7co1Q3MYkhf5DO3ZmZF9ckkzMO2msGT2si948L0N+DLSeHDGRGaM7wb/moSUTSwediu/WdWZAzVNfP20IfzjolHkdbZn6m7Deg/XN/LLV8v4YN1ebjr7JO7oW0zav38VGcPvvw/9JjjcMAUF70iKE7h5BjBfZwLqCxwgYuD9I9BfSnmjxbU3AzcDDBkyZPK2bdvanN6URjgMoYbIp7H+2HfgSGQP16MRRs3RA5H/dQeafx+I/G5hcgFOak5BEDwasY8Hj0bqajjcum0AkUZ4wGTShk4llN2DjIxMQEA4RKh2H4f37yR0aDcZdXvJCRygk6xrVUW99HEoPY+QrxsZWTlkdcolO6cz/roAPfsNIj0rN7JIKjOn+Vv/+9h3KCOH4sNd+HiLn4/W72Xj3oh9/6ReOYzrIfnaWWM5I78PsinkygTznedW8fHG/Vwwpg/3XjOBvl0ja3tLm/PslO04xOShPfj9VeMYP7Cb61vmhHVVR/jRi6vZdbCemZeN5FuHnoCS5yJ2/q/8E3LyEtaWwomJlEoFIaXcq/0WQswC5tuUfQZ4BiI+gLanro3w4R/hk4ciURtZ3cDXORJn3ndchCk3NkRCGEMNUL05Es+eDFRXRlaPZneDrv0BQTjgj2jfTSEIN0actU2NyHAjTY0BxL51sKu41cPTKH34ZXf20oMDDKA+q4Bwbj8yuvUjq/sAeg4YRv9Bw+jTI4/+nbKjtvH6YBOrttawaN0eDtWHuP3ckfTrYZ6W4nBdI4s37mPR+n0s3rCDQ/WNZKQJTh+ex9emDOH80X04qVfnmBalNTaF6ZSZzn1fmUiv3CwO+AM8sGA9r5bspHeXLB6+roBrThmIECJh6Tr+V7qL214rp2t2Jq99czgFRf8HO1bCmT+HL92tdudSaFO0iwAQQvSXUlY1/70GWNsedCQVnzT7uxsOt9SudxbHV292t8iipZxekRWmnXtFfvs6w45VkXDHo/tbXiPSInu/du4FDYfg6D6obqYrHMIYuCgzOiG69GVXU3dKD+axT45hr+zBvmZmv1f2oHvvgQwfNJCzR/Vh8tAeTMhOo1N2Vot6Dh4N8uH6ffztheXsOlRv2aWR2X5uuux0fD4fUko27/fz4bp9fLh+H6u3HaQpLMnr7ONLY/py7sg8po/pdyw/TzNi2XryD1eP49JHP+EPb1UwaXB3/vbBRuqDTdx8znB+dn4+XZrbSMRK56aw5P4F63lmyRZOG5bHU+eFyHvr6sjsa8ZsGH9tTPUqKHhBMqKAXgamA72AvcDM5v+TiJiAtgI/1AkES3ToKCApI5p0XXWE0QaOQG0VVMyD7SsACdndj2niJ18S2bjDYvrvyITe/x0s/3vr4xnZkTa0trK7QSfd7+xuhLJ7ktFjEHTpTzArj9J1W5h0yims2naEb/5zpavupgm4qqA/uw41UNsQYs+RBg7VNTpe9/XTBnPR6N6kZWSwaP0+Plq/j+01ETPSmP5d+dLoPpw/pg8Fg7rHtCm6Ex5euJHHPtwEwNkjezHzynHk92k9G4lnBnC4rpGfzfmMJRv3860zhjJz4Coy3v1NJMTz+pcis0IXUJk2FdxCLQQ7DmGbPqCuBnZ92oKxk90tpn1fje0EAgHqQoIjDY0cqQ9R29BItb+eR9+roPLgsQRuA7plM7B7Nt1ysujTNYvhvTpzUq/OZGWk0xAMsnLNJsKde7Ktpo4dNQ1srT5KIHTMR5GVkcaZ+b04f3Qfzh/dhwHdO9HWaGhs4v4F6zljeE8uGts3kuPfBLEy3017a7nphRJ2HarnT1eO4mv7HoVP/xUR9l951nXa5hM9aaCCNygBcAIgGUzBro01O6r549sbOXdUb66fMpguPmFLj3ExWUZGJvtqA2ytPkowFGbKsDw6+VLPBh7rOC+s2Mutr5SSnZnOszOGMano/8G2ZXDWL+D833m296sZgIJbKAFwgqA99gi2O6dfGGaX48dLyolUgNfFXU99vIX7F6xn4qBu/POyXHq/9R04UgVffgImzGhjahVOdFgJgNgTlSikJJLBJO2Yf2lpaVSzDwaDVFRUEAwGTc/Z1ac/b7w2FeB2nBubwtzx+hruX7CeKwsGMPeCo/R+5UoI1sH33lHMX6FdoQSAQsJgjLjR/9f/dmLoxvPtuUdCPELnSEMj35tdzJziHfzsvBE8dtJKfK9cD92Hwk0fwaBWCpmCQlKhBICCI4xM0I4p6rOA6v/rf/t8PsaOHWu71aOR4bcX84915rGjpo6vPLGcFVuq+eu1Y/hl49OIBbfByZfCjQug++A2oFhBwRuUAFCwhZnpxokputHwNdOQFVLB3u8kqKxQtuMQ1zyxnL1HGnjpm6OZse7nkZW9Z/48sl1jVmL2XlZQiBfKCazgCKPD08zRq5l5rMqY1QmpweitEEu0z6IN+/jJi5/Sq4uPF2YM5KQF343sZ3Dlo3DKDW1LsIKCBZQT+DhGWztHjcxPs+NDhPnPmTOHoqIiW8eu5gjWw8opbIVkO4G9+h7e/GwXN/2rhOG9O/O/63px0pvXRvY4+OZcxfwVUhJKAHRwtGWEjFWdfr8/2mZubi7XX389U6dObWX/19dTXFxMSUlJi3OaecWqD8mMBLKq1zirscLsZV/w81dKKRzWg/9eJsibcyU0BeF7b8Pw6YkmV0EhIVACoIOjrSJk7JhyRUVFC9t4bm5uq32AjbOBKVOmRLeK1ARCeXl51FRk7EMyI4HsQlStymiQUvLX9zbw+7cquHhcX16YdoCcOV+BnJ7wg4XQvyDh9CooJArKB6BgiVgXbtmmqND9B+dtJ734FeKBm+yhxvabwpK7/reW/6zczvVTBvPnoZ+S9vat0H8S3PDfSLI9BYUUgPIBKHiGXZiml+s0jd84K7AyGZmhrU1AbrKHGpn/r/9bxn9Wbucn00fwlwGfkDb/lkgO/+/OV8xfoUNACQCFpMAqqZrenwC0+C4tLaWmJrIVWKJNQPGEoDaFJb98tZTXP9vFLy88md/kzEe8dyeMuQqufzmSiltBoQNACQAFV9BH8XjVwn0+X9T+b6yzvLzc1Bns8/nIz89n3rx5+P3+hPVDazfW2USoKcwvXi3lzdLd/Pqik/kZc+CjP8KE6yJ5/DNSN6xVQcEIJQAUTGGMwCkpKaG4uJiampqYmKeVVi2ltDS/5ObmMmPGDHJzc+M2ARnNT06zCbN2msKSX/63jP+V7uY3F5/M/4X+Fdno59RvwzVPQXp7brGtoOAdSgAotIJZBE5hYSEFBQVUVla20Ni91quHFh2kr8sYTRTLzl5O/dG3Y0aXWXkpJXf/b22U+f+k6T+RDXem3ARXPqa2blTokFACQKEVrHLx5ObmMnbs2KhGbozrt4OVBm+1DiCR+YDshIfbtv+2cCMvrdzOj84dwU/Ea7D0YZj8XbjsQbDwbygopDqUAFAwhRWz1Kd3tgshNmP0dtE1ZuficfhqNJrNLOzaNgtPfW7pFzz2USVfKxzMbT0Ww+K/wKQb4PK/Keav0KGhBICCa2jMEqCiooKCggLXWrVTDL/TngBeoM1OioqKWoSf2s1CrM7PK9vNH+ZXcMm4fvx5VCViwe0w+gq46u+Q5v71cepLstNc2MHLrE6hY6PNBYAQ4jkhxD4hxFrD8Z8JITYIIT4XQjzQ1nQoJAb63P7aTl9mZYxatVcHbjxOX81nMXXq1KiPwYwuJ7pXfVHDr14t47ST8nhsqp/0N38Ig0+P7N3rwebvJjtqqmx445aWVKJZIXa0+UpgIcQ5gB94QUo5vvnYecBvgcullAEhRB8p5T6nuhKxErgtV5MqHIObVbyxrChORLtusHm/n2ufWE7PXB9vfqUbXV++Crr0hxsXEMzIjZsur+eTCbe0pBLNCvZot5XAUsolQI3h8I+B+6SUgeYyjsw/EVBaizXijegxnrOLujErYxbr78RczKJ39I7pWO/3AX+A780uJjNd8OKMAXR97evgy4VvvkYwI5fi4uIovWY0mMHr6un2hFtaUolmhdjQXj6Ak4GzhRArhRAfCyGmWBUUQtwshCgRQpTs378/rkbbMqFYR4ZXRulUXu8rsIL+Xvj9fubOneu44MvoU7BKzmbWhps6ARoam/jBv0rYV9vAc18fw4C3v4MMHo2kdG7exSsUClFWVma6ilkpGAodCUlJBieEGAbM15mA1gIfAbcAU4BXgOHSgRiVDK5tYJeYTT/Nt/ptVtbv91NRUeFa4Pr9/hY+BTNTjjFJWyxmJWNZfZ1SSn76n894Z20VT90wiYvLf4HctJANU/7M8AtuNI0U0tozfisopBJSLRncTuB1GcEqIAyo7FntAI0J2p3TwimNi8OMZbWIGy1cND8/35IZGrVkI/M3MyEZt2d0StjmBOMM4YnFm3l7TRW3XzKai3c/BRsXIC69vwXz167TO5aNKSwUFDoK2ksAvAmcDyCEOBnwAQfaiZYTGm7j892YU7SEbxqzrqysNDWHuDUh6TVu/RoEt3BTVmvjo/V7+ev7G7iqYAA3dy2C5Y9B4ffhtJtsVw0b6VXmH4VUgNvnMBlhoC8DRcAoIcROIcT3geeA4c2moDnAd5zMPwptB7fx+U7l9AnfcnNzPS38corNBzxt0G60z9th834/t7xcytj+XXnwtDrE/FvhpHPh0vtd12m1ollBIdnw8hyqDWEU2hR2ZhG93dxu83XtQXa7QbtWn5a2wg61DY18+fFlHKxrZP53RzBgzkWQ1QV+8CHk5HmuU5mBFFIBxucw1XwACicA7DQR4zknxu4lgkszQTkxfyklv3y1jK3VdTxx/QQGLPwJBOsiOf11zF/fvlOdZr4RBYVkw60SogSAQpvBjX8BaOWEtgr3dONQdusvCAaDPLdsK+9X7OWOS0czpfJR2F4EVz0GfUZb9scLlElIIdWhBIBC0qGPJHJKG+HkYDWLVAJs1yEEg0HmLFzBX95ZxwVj+vKdbp+RvvIJmibfCBNmWNLrFXYCUAkFhVSAEgAKbQYzDdhplbAZ07RzsOpnEsZrrbTvI/WNPFbip1sW3H9OFplv/5zwgFNJv/Q+yz7EuiuZ22R5CgrtASUAFNoMVszcjb3fTV16GM1EVuWllPx23joOBeCpr0+g54IfQ1oGade9QDDcOrWz5k/wGoJqh2SuSFdCRsEOSgB0YHSEl9vrYi3jbMHNdVbrBszK/3vFNt77fC+3XzqaKV88BXvXwDVPEczpS0lJiammbxfSakanE5IVKaRmGgpOUAKgg+J4fLn1ffK697Ce+Wv7FxuvXbfrIPe+vY7po3rz/f5boegfkS0dT74YgMbGRsrKyizj/PV0mtHsJjFcMu+b00zDDQ3H0/Ol0BpKAHRQxGpGsGJKiUKstnI41qdgMMi8efNsU0nY1VFYWNhiHwAAf10D//fvVXTypfPg5YPgzR9Dr1Fw0R+j1xn3DzCDlZMaWvoc7HwWXha0xQs75u8kiI5HJUOhJZQA6MBIRFhiIl9yfVbPeCJncnNzmTFjhmPMvV0dxrF5euk2thwKcd814+n5wa+QRw/QePWTkNmpVVSSU91mfg3jcStGH0tKi7aAm9lBMn0VCu0DJQBOIMTqlDXCinlpjNvn87neVcqO1kQJptXbDvL4okqmD8nigob3Sdv4NuHzfkfm4MkxCUA7X4QGK0afSkzVzewgFehUaDuoVBAKnpColAj6dNFgn4o6HkZUH2zikkeXEGqSzP/WUHq8MB36TYTvvBXd09dN/bHQ0F4M1KndtuqvQupCpYJQSAjchkU6MaCKigrGjh0LWMfra8xfv8uXVzzywUa2Vdfx1xkT6fHxb6GpEa6ObOiuN/3Ywcp05oT2Yv52Mxq3Mx7F/E8MnNACoL3tsBpShQ4j7Ew98Zgx9Hl13JhEYp2lrt11mGeXfsH1UwYztX4xbHwXzv8d5A137QTV02u1WjmVYDWeVn1ROLFxwgqAVHmJU4UOI6xSH9tpll5g9ENY1ePz+Rwjc8zoaApL7nh9DT1yfNx5Ti9459cwsBDO+HG0XrtoHLNoHz1NTums2xNmzN9uMx+FExcnrABw0pTam462hpPmq5lojExOS4sQ7164ZuYUu9BJt3Vq1z+/fCtrdh1m5pVj6frx7yDoh6sfh7T0aFmrmH+tTbv7YjYubmYTsSDeZ1Jp/QpWOGEFADhrSu1FR6IQqx1Yb6LRX6M/bgx59MJgjIun9BEniTAt7Tsa4qH3N3DeqN5ckbsR1r4GZ/+qVZZPbQczu/q8tBtPzL1WLtZr3dDYnkilGZLCMZzQAsCI40lTMjIO/QsYD8O2isqxiuIxg759s/h5tzSZHZNScvf/PkdK+OOVoxALbocew+DMW1rRoN/BLF7Y1eNmvO1mPx39mUxVM6eCEgCt0NFfNA36FapmL2Asq1015u9Wm7WLnNE7VL0ID33d2qpjbSZRUlLCo69/wkfr9/HzL41gUOXLsH8dXPxnyMxuVUesNvxYGFksC8zcXhsPksGUjwchdrwiGXsCPyeE2Ne8/6927BUhRGnzZ6sQorSt6TjeYTSraNDnx3cT7qjBbMGY9q3PtW+liZuVM6ZVthISdkJGq1sLRdX8EQBDR47hpfVBxvbPZWLTRuTiP8Pw8wiedEGrOux8GFbtgre9hr0i2U7leENsvUAx/9REMmYAzwOX6A9IKb8mpZwkpZwEvAa8ngQ6jmvoGa6ZTd1trLueodrBzBmsr0c7pqfFbP2AWXil8bhZ3bm5uVEntVb2H5/swN8If/7yOE764t8QPErwgj9RanD2mpmdtP92fdIWr7V1Lp9Em0zs6ulIC0EVEo+krAQWQgwD5kspxxuOC2A7cL6UcpNTPWolsDtYmVXcXufmeruy+mPG0EP9daUmm7w7ta2/vri4GCEEhYWFlO7yc93TRdw4bSh3F4aQz0xHnPETuOTPjjTqj1mtco5lbNzAzgQWzz3U/y8pKbH0d8TajkLHQqquBD4b2GvH/IUQNwshSoQQJfv3708iaR0X8TION9fblTUeM4tBt7ILu7GVa99TpkyhsLAQmZbOHa+XM6h7NtN7+gm/82tETk849zemdTo5XM1SXOjbdeOfcKO9OznqvcKqbTslTzH/ExvtLQC+DrxsV0BK+YyUslBKWdi7d+8kkZWaiNUkEIvDNlE0eHFuGhmhXVt+vz9q2npy8WY27z/KPVeOYVrOF6TtXEVo+m+hU/e4aIolLNPo+7CDG9OT2beb+vTHvCykS2WoKKLEo90EgBAiA7gWeKW9aOhIiNUubOZ81Z8D91EaXmkwav36Y8ZyRh+E3++3dFDq005X7vPz+KJKpg7MpPOBCtI/vIdwvwI+lWNt6XTTV7NNZdyOldtx0mYURt+CfhxKS0tdb5ATTxRRKjPYRPtFFCJozxnABcB6KeXOdqShw8DIeJy0Qo3h+3w+8vPzWzlfzUIpvdJgB6sIH6tjQKtFZprpwlheSzudk9OZO99YQ6fMdB66YSqnBT5B+PeQdtmDTDrl1FZjZUajHfQLxbyaZ7yEPRpNT3qBoDm7Kysr29T53BYMNpF1qVDStkEywkBfBoqAUUKInUKI7zefuh4H849CSxijZaxCEo0bs1gxDy0qp6amhrlz51JTU+OaBjflzF5Yq/0IjD4IzXQBx7RpYxTQf1fvYNUXNfzm4pEMYD/pK5+AiV+DIae3GiujENEfN2NUPt+xhWKx2PW9wmxMNIGgCUWn9NvxIFYGaydcrWaesUIx/zaAlLLDfCZPniwVIggEAi2+jaitrW1VVv9/5cqVMhAIRM9VV1dHjyWKNqs2zWidPXt2C5r11+np1L53HqyT4+9eIGc8sVQWrVghm168Tso/9Zfy8G5begKBgFy2bFmLeq3oMl7ntr+JGEMvsOuDsVwiUVtba9uu03mF5AEokSY8tb2dwAoxwi4KR9OQjWX1/42LtPLy8hIyxXaKsDEzy1htAamvS7+eIByW/PLVUsJS8vDXTmFy12rSNi2Ac34FXfu3osnYJ9lsWtJmGmb9Ns6urMbFrL/J1lTdaO9tsbbAaU1EvGnDFdoeakewDgC3sdray20WX29WVnOy6s0dyaDXag2AXV36Omct2cK976zjgRkTuW5SH3jiDBBp8OMiyHA3Tm7o02zwbtcmuEF7xt0nuu1EjotC2yJV1wEoGGDU0Kxs2Prfeps2uHNA+nw+Jk6cyLRp0xLK/LW6nc57Yf76Oit2H+HB9zZw0di+fHXyIFj+GNRsgUsfcMX8nejT2tRs7onQnPUO+0Tbxb3Aqd+Jri8Vonbau/1Ux3EhAI6Xm2xlTjBLl6Ax/eLiYrRZkdcMnxUVFdE2ko1YQk4P1zXyoxdX0z0nk79cOwFxeAcseQjGXAn5X4qbJqOpSaMzllTPRqav1elmO02rdtoKbcGsUyFqJ1WEUCqjw5uAvJgTkoV4pr5urtWX0S+I0s5ZjYex7kRM0WOtw8y0Y3cuHJZ8d/ZKirbU8OL3Cjk9vw+88k3Y9AH8tBi6D46rH27oclPWzAzndtztjrf1M94RzDWx0NgR+pUMHLcmoGRrGk7aRLxah5t+6JmNpsVr/63Gw42zMhatNN7FaXZrBTT6pJT84a21LNlUzW0X5SNqttJY/jqse4vQmbcmjPnDsYVZTv0yo9PKDGfmhLerz4ymRDnorZDqTDLWZy3V+9Xe6PAzgGTCrSaWTK1Dr4VqtGkwap3GY8Z6nPpmpfF66auxDrsZgIYnFlfywIINfPuMwfz+6gk0HqrCN+tswl36s/qUByg4NbE+DDMazM6DuRB1Gr9Y24wHsc4i4ulPLHU6XQeKqccCIcSnUsrJxuMdfgaQTLjVxJL5gOrb0ph/SUkJxcXF0YVgbrQnu5mD9m2WksArMzGzsRvL6I+9ULSVBxZs4KzBWdx5ySiEEPg+uBMaDpN27dNtwvzd9KO4uNj0nFdfgZtrtetjhd3M0Ok6K5rjnf3F2p9UXq2cqmjuYyezc0oAeER7MBunc3rTQzAYpLCwkIKCAsrLy6OMSp8OwqpOO7OR5sCMJyWBxoTs+qO1J6Xkr+9t4O7/fc4FY/ry1PfOIjs7C9a+Dp+/AdNvg77j2uR+uNn0xWw/4VgErBsYmaYXpqU3t3kJJXai2Y1z3AzxjEOizb1ODnynYx0FzeNVb3ZOCYAUhpMGpiUr016MYDDI3LlzCQYjC8EKCwuj6RQqKyvJz88Hjm3m4gQjw3aTkgBwrNuqfa09mZbOr14t5R+LKvla4WCe+uap5OZkw4FKmPf/YOBkOPNWR/rtYPXSa34VOyHn87VeNxGvhm8HPePzqkF7iTxy4ycy1u22HjfXuUEiBb6ZQDGjPd5ZS4rA3NZvtjw4VT8nYioIq2X0+pQGepilU9COa8vyvSzRd5sqQUN1dbVlWgczWoz4Yr9fXv7ox3LobfPlrf/8QDY0NDQ3dlTKx6dKed8wGdi32ZFuOxj7ZPY/1noTcb3Vf6fjbut2k9Yi3vQNHTn9g9tnvSMBlQqiY8KLFgq00M71Goumvet/u404chtVFAxGEs9dddVVLTJbGq8za7+xKczTH2/mkkeXsL2mnse/XsB93zybrKwsCDfBGzfDvgoar36S0i8OxKWNGftk9t8MTqaCWDR0fV1GM4/dTMlNfWbX2NUbbx+s2ksEkql9W5m8jkcoAdCB4eQ01C8Y06C36RuZt/7bqR0rwTBp0iTy8vJa0WD8r13XFJa8VbabCx/+mL+8u54zR/TkvVvP4fKCQZEyUsLbv4B1b8ElfyFz9CUJsQMbGZQdwzJzpFsxyVjt1JqpRi+E4lk0ZhdS6lSvk6/GTftOdHitry1MMB3cpJMQKAFwnMCKERmdgNqLrT+u/23nFLPSKvX1mdFgZGo+n4/D9Y28ULSVLz20mJ+9/BlZGek8fcMkbh4j6dkpPVKBlPDBPbD6eTjrF3DGj1u1Gw/cMHX9MbNZgxlidfQamXKsydSchJBdvWbKghcYhXyihHWi1/o4+ddOFKh1ACkCvVYcy7X6NQDG6X5+fj6VlZXR81pZjenohYLP5zNl7GPHjqWsrMx2e0GnPtQcqePFD1ezzt+JDzfsJxgKM2lwd246eziXjO9Hepo4VkcoCG/dAmX/gcIb4fKHwRB5E8+YWdVhVqff74+Ok9nYJIo5OfVHf14/i0u0QLRaweyWduOsyisNyTK3mLXl9p4mk85EwGolsBIAKYBEMBLNTGFkVGaMwmgC8vl80U1kNPu9ZqvXykNkfYFZ9IsVzXXBEKXbD1G89SArtlRTsq2GxiZJj5xMrp40kGtPHcjEQd1b9cMXrodXvw1bFsP0OyObuxuYvxVTthqbeJmlFVNLFiMIBoPR8QeiQtntGOjrsetDLP0xExyxLjpLpEA11h3PGBnPtxWdXmlxW14JgBRHvA+oXlM3C9M0MkE9Q9H+19TUsGXLFo4ePcq2bdu4/vrrW8wIzLTl0tJSxoybQJU/xJb9R9m838/nuw6xYa+fLw4cJSwjvHtU3y6cO6o354zII61mK5NPPcW0D5WLXmJMxcNQuxtx5WNwyg2mfbHrq7FsSUkJo0ePjs6CYtVM7V76thYEwWAk7LegoKBFimqvz42xD4liZm5mUrHUkwi0BcNOhuD3SrddeSUAjgNoWqjVTbYSDBDR3idOnNgixl1jKD6fj5KSEqSUFBQURK/1+XyUfPoZ/YeP5kBdE1WHG9hzuJ6qww1UHWqg6kgDVYfq2e8PoH+MeuekMXFIT0b360Ln+n1cd34hvbp1tqWT+oPw4R+RJc9B9yFUjPkFI8/7RszmJn255cuX4/NF0l87CQynuqyYfzI0QuOsJ1Ztva1mMalsFrG7d6lAc6Loa7MZgBDiG8BVQBMggLeklI57+gohngOuAPZJKcc3H5sEPAVkAyHgJ1LKVU51ncgCwMrO7+aa/Px81q1bx5QpUwgGg5SXl1NYWIi/roFln31O3sDh7DnSwJ7DAfYcaWDDjr3Ukc3e2gAHagOtVpB09qXTv3sn+nfLbv50YnjvzgzvlcuwXjlkpckogyouLrb1GxDww6qnYdljEDgCp/0QzruTYFp2Qh587ZzZmLl5ubwImkT6I5wYgpXQaSsN2ok5JUvLTrTPw/H5TAKSoUAkQgA8LaX8oe7/41LK/3Nx3TmAH3hBJwDeB/4mpXxXCHEZ8Bsp5XSnuk5kAQDeH34pJTv2HeTjT9fRlNOTffWwZX8t63dWcySURs3RxlbXdMnOoF/XLPp1zaZv1yxE/SEmnTyMwT1z6d8tm37dsumSnRk/zQe3QfGz8Om/oOEwjLwYvnQX9Jvgum6tfjCfFTkJBaeZVDJt0nrt3qo/TnXE48S1a8NqbM3MSW5npU70tYW5ymy8zPxaVnS3Jdq6vUQIgOeB/wI7gEHADCnljS6vHQbM1wmA94DnpJSvCCG+DlwppfyGUz3HqwDw+/1xmSYADh2pZU+dZO2uI6zddZjPdx9mw55ajjSEomUy0gRD8nIY2C2LrKY6Rg/tR1NtNaeNH8ngXl3o1y2b3KyMmGYbruDfDxvegfJXYdtSEOmRzVym/hQGT2lZVDcmbkwvGp1WzNsLc3LDRON1KJs5TfU+Da+2fbDvf6ywmj3pafRiGtP31c6BnWinu5cZUzKdvLHC6zgkQgDkANcSYf47gdellHUurx1GSwEwBniPiCkpDZgmpdxmce3NwM0AQ4YMmbxtm2mxDgst+sZsU3QrBINB6oJNrN52kFXbD1NUuZ+K3UcIyUikTI4vnbH9u3Jyn86M6NuVwd19+Hdv4bzTC+jRrWu0DruXt6ampsWCLojhZWysh50lsL0IKj+AHasACT3zYeL1UHC9aS5//Zj4fD7Xzlcr5h2LM81J805E1JYdI/JieiouLkYI4SlCy00ZN4pALM5xL4LDqt1Y4NRX4zOTDCevlxme/rjX5y9mASCE2ASsAcqAUqBMSrnVVavH6hhGSwHwGPCxlPI1IcR1wM1Syguc6jnRZwAb99by5uptfLh2F5UHQzTJiFZ/ypDujO3XmVOH9WLcgG6c1KszTaFGioqKmDp1Kj5fJMzTKY5fQ01NDa+//no0CkhjMHonaquHUErw74Pdn8H25bCtKPI73AgI6D8RRl0Goy6FfhNbhXVq0B56NzMAu+uNx8A+xUMinKmJQiwCC+y1dKtrnNpxU87N8xuLM1Nr1020Vzztup2VJBJuBGcio87iEQC/AoYDFcB44JvAF8AbwB+llK0Nya3rGEZLAXAY6C6llCKSV/ewlLKrUz0dVQDEwyy+OHCU+WW7eat8Nxv3+hHAuAFdOH1Yd04b2p1OR6s4o7B1SKXf72fOnDlcf/310bBBfRy53QtfXFxMfX0955xzTlQb0juPfSIM+9cT2l1GxoENsHct7P0c6g5EKknLJNx/EmnDpsGQaTDkdOjUw3GMwJ3t2wvifdES0X4i7NWxtu1kvwf3AQWxjl88Y+xlvUc87SZa8/cy27Aqn0glIx4BUCqlnKT7Pwn4HhFfwFAp5c9cND6MlgJgHfBjKeViIcSXgAekyW41RnREARDLwy+l5JNNB3hmyRaWVkaY6uQh3blq0kCmDspmQF5uC4eh1UOkaWb6Bw5wjHzQGH5u585waDvsqyC0u5yD65fSK7QHUbMZZFOkcEYn6DMG+o6DvuOh33iCvcdT+vlG105MK4YU7wvg9qVuK22+PW3JbswssZic7NqyqytR9Xv1lyXbmau1aRdg0FZrMewQjwD4GPiZlLJcd2yllPL05m3GTnW4/mVgOtAL2AvMBDYAjwIZQAORMNDVTp3oiAIA3D+E4bDkrfLdPLl4M+v31NKnSxbfmTaMK8b3Ye8X68nPz+f1118nPz+fwsLCVit13URr6GcCLWiqq4F9FbC3gqaqNdRtLSG3bjsieCxrpOw2BNFvPE29RrOlrjNDT7scX9+TIS3dss9GLQdoxfStyrnRLJ20znjMB/HAKwO2Oua2HeOxRC0gMmvHrd8gHv+DGez8Zalgs3dbtq21fTPEIwBGAy8Rsf+XAqOAyVLKqUKItZpWnwx0VAHgBmU7DnH3vM8p23GIk/vmctPZw7lq0gCyMtJbME4tqZuZ9qCVMaKVhuavwXdgHexaTdOOYtKrPoPDO45dkN2dpl6jSe8/AfqOozHvZDIHTIDsri3qNLZnpgnqaTM6K52Yl1V/jOWsaIjHfGAFt1N7Ow0QWgs3M4HlxnfhFMrqtV9u7kk8gtkN7XYwmwEkQ4O20tyTPbvQ05MUH0DzxelEIoAmAtXAv4lo7rdKKf/kmoo4cTwKACklsz7Zwv0LNtCzs4/bLhnNNacMJBRqtGQmnqfvDUdg27JIbp0vPkHuX4eQ4cipTv3IHHYG6YNOhT7joO9Yglk9KS0rM33hNaakHdcYltXMwmh+AudoIrcPt53AsRsrMxrcwM3UXj8eboWbVlYTWPqV2sbonkSabrz2L9GML9G0J8Jn4tZUmAihE49/yE3b+vpVKogUhJSS3725lpdWbufS8f24f8ZEumZnOppHHDWwQADfwU2wbl6E6e8sidjsM7IJDzqdXWkD6VVwMeuO5HDyKdNscwfpfweDEQdxKBRi6tSpBIMts4la+Rbi7Y8XOGnobmYhbuo3u07PwO2EgJ3Q0kdraWWTaStuT63WDMmiJ5axjdevEa+J04n56+u3EgDtvs2jl09H3RLSaju5v7yzTg69bb78yzvrZDgcblHWzbZ9GqLbLx6olI0f/lkefXCClDO7SnlPdymfOU/KD34v5ZaPpQzWy0AgIJcuXSpra2tlIBDwvP1dbW1ti60oY9lq0LgdZFttt2fVNzta3NZrdZ223aXXbTf1v5ctW2Z5fVuMVXtvd2i39WUs9ycRdLhtMx7anN6XeLcp1fhCIBCw3BKy3Zm6l09HFAD6G6m/iZ9s3C+H3jZf3v5aeQvmv3LlSlldXd2qHqs9dqsP7JcLH/+VbHz24gjTn9lVNj17kZQrn5Gydp9pPdXV1ZZMys1Lp38oY9m3Nt49bd2Wd8M8ErV/r9m5RO8tHO8ewVYCMRH7IcdKl9a+1fPtVYDGCq/vgJdysdJoVFT0DN0K+jLaNcuWLZPAaqn2BE4+fL5jO0eVNu9AFA5L/jD/c07q1Zm7rxiLaF4U5fP5yM/PZ968edTU1ETrqKmpYe7cuS33cW1qJFQ8m06zz+OCfc+QcXgHXHAP3Po5oW+9BafdBLm9W9CirQ349NNPo2YK4xRUo9dueqmPJiouLm5h3w8GW2+fqD+nXW923ApW9dmVd+pHPL4Ap+u0c279GFY0mZmKzMbUzdhYldOPk5u6vNyD0tJjO85ZweeLb+tLr8+GHa368XdjCnJbLlYa9c+BNkZWu/bBsSgp/e5/Pp8voulbwUwqpOqnI84A9NAk90fr9sqht82Xb36207ScpqFrGnarWcGmhVL+fUpE239impRlr0gZCkbbMJtx6OteunRp3Fqj9m3UnPTmJeM1ZrONRGnqVtqSVVk3ZqhEzx7sxsQNTW0xA3BTJlaTmaZ9mj1vbvpm157Tc+4ViZixJnrWa1WHmxmAsTzKBJQ6uOP1cjn2rndloLEpepOMDMuUEdQfknLu9yOmnkcKpKx4SwYaGkxfJjsbtJcHWv+SmTEq/QsYCATk4sWLLQWM8ZpEvcBGmtzY352m/E7MzisztCpvJhBjqb+tEK+JyOz+Jsp04laIxIO2MAclA0Z6rASAigJqB1z26Cf06pLFs9+cRGlpKUOGDGHBggW2CeGCezfim/NV5KEdiHNvg7N+TjAsKCoqIiMjo0X0jVlMuRtYRa0A0Q1jpkxpmbXT2L6X2HWzcrGGORrjw2ON7nE65uW81/LGsYHEZcOMB/GEK8azVsBrW221wjaWsGS7aLFkwU0YqPIBtAP21QYY0C07avPfvn07V111lemDEgwGObprPXL2Zcj6Q6w74yGC026FjCwAMjMzo7t6adDsf16Zv5WN2OfzUVhY2ELIaGUzMjJatG8MWzS2odkz9eWMNGi+jpqaGlMfgxFa+KVmE9bo8AKz8lb9sLrGycbr1qasjYMRxvFJFuKJVXc7dvHCzB7v1kZvhNk74FRGX05/n+L1T8QKt4JHCYB2ghCCYDBIZWVlVFM3PizBYJCSlUWEX/02PhlAfHc++efd0ILZ6lNC6BGLZqrXPM3q09epCZgpU6ZYriMoKSmJMiv9S2F0+AWDwajjSnN2aRlJ6+vrW5TTj5HG8LXYeyCuF87sunidrG5oMZbRLwbTw4vD1KovyYDWTrJzINkJcbdw6wh3Em7a+2E1Bm15Lzw5nc3sQqn6cesDSBU7nBUueGix/P7zK6WUzrHHDR89GLH5r33dtK62dkSaXa/Z+O3s94FAQL7//vumawaMfV68eLGcNWuWrKqqanHeypls5kOIx0ZtHAerMm7r0v93M55e10W4tUfrxy8WG3UsNvpAIGDp+E0kYvENeLmP8d4Dp2vb2l9grBsLH8BxNwNIRFhYWyO/T2fKtlU724SPHiat6O+ET5oO4645dlynAXsNjzSW10+TzabMZnVrs5fly5dTVFTUwvSiv0bbQ8AY1micpk+ePJkrrriC+fPnR2cCALm5uabjo5lH9DMWbXZhp23ZhVDamQvcapFmGrubkFoz04WXdszOjx07lvLy8qgJzas5JFZN2MxcaHVtrLAzrZjNEmMxxziVjWdmE6tpyi0s+IrpBhzHnQBo68FNBKYM68n+ujBVtce2UjB9mbZ/QmbwEGln3WJazmtfrcpbMR+7F9zn8yGEQErZKua7uLgYv9/P1q1bW7RjFBTasfLycnJzcxk5cmQLR7KbPmjCwMo0YsUAzMavLZ4ZN3WaldHb+WNhlrm5ua0YsZNPw0iT0aFqvNbueXLj7I6lX3qTn9mzYnZPncwxRsQiLL2ireq2GVvTaJ/jTgBA27zIicRF4/oCML+8KnpM7xyN3rwti8DXBYadDSSGaXkp7/SCT506lWnTIrmE8vPzowxYW9im7Sim0a5fOKZnKFLKaH0QiTgyltFgjK7Q6LN6wfVlzBY8OQmc9oB+QU88zNLO0e5WCOivSZRTMx4lTc/QrfpipeB4ffbdoC0tDmazGSd4HdvjUgCkOgb1yOG0k/L4z8pthJoiWTmDwSBlZWUtX7KaLdBrJKRntmJaZkgGIzNqgBoD1TuzR48ezbx581o9wBMnToyGkeodwloUkdYvbVahOZH1swvttyYk9Azc7qHXZgpGB2WqmAz17efm5kZDgmNhll5mUG5gpUXHK5y80K0/b9Tu20qItwXD9QIrpcXNdSY4MUxAHQXfPWMwuw41MO+zY3n4hRAtH6j6gy22UtRHhtjZsxMFI/O107j0Yad5eXlcddVV5OXlRe30xcXFlJeXtyhvvE6DZrbQZgaaeQeOMW7ZvH7FSksyhkpqdBjTV7h9gdsy9NJsXPVj4pX5t4X92sp/45b5uWHuGt1ubPt6GhL17BtnmnEy3ITATNh5RXMfOpmeNPMMp+rneFkJLKWUTU1hedHDi+VZ938o64MhKeWx6IPoquDnLpNNz14UjaxYtGhRNLLDuIo2UUviNWiraaurq+WiRYui0TxuI0OMUQ7GiB63q4z15Y1tGH9r11dVVcnZs2e3GiutjJdMnRrtWn1thXijShJVV6Khv69O4+cUieYl8icWOpOVpTbRcEMzKhlcaiEtTfC7K8ayo6aeZ5dURo8XFRUxZ84c/H4/Tbn9aNy3iWAwyMSJExFCRLVoTaM12mbNZgZmsNNojXntfT5fNJrHzXTbzIGoafDGMhCZHWgrvK38G2Z2Xb0Wr5mDxo4dG11Yp2nRxjq9xmdrJplkaHpWsBpzK0e93TXJghYQANZOej205ysWTTfee2PWbqr7EqH1zMlm1mKe8sFMKiTyAzwH7APW6o4VAEXAGuAtoKubuo6nGYCG7z23Qo767dtyy95DUspjGqqUUjYueUTKmV3lqg/ntdKUNehj5pcuXRrNJa8dM9OmjBqtmWau19aMCaaccuQ4HbeK7XcqbzeDMJs5eNXqnLTPWOLoY12f4KXtRNHrpl6v1+ufx1SPnW9vxNo3lzOA9kkGB5wDnGoQAMXAuc2/bwT+6Kau40UA6G/S9uqjcuxd78pvzCqSTU3hlgX3rJVyZlfZWPRM9JDdRiG1tbWtNiSxSjJnzBtu9RB5YaKxLnyyK6MXSFZ06De3cRIk+oVpZrBjVl4XH2kLotyanBJt4kgFhuvUdy8ml/YUZm2NthZw7SYAIm0zzCAAjnBsO8rBQIWbeo4HAWCmrb64Yqscett8+cLyL1qUqz1yRB784wgZmnVhi2uNTF2v/dtpnVYPmZsXzy1DdLOxhxvt3qwfZmVra2vlrFmzZHV1teMK1EAg4Hje625e+uvsZjZOgikVNdxE0GL17GjntD57FZJeZ2LxZJ5ti3vi9D4luk0rAdBePoC1wFXNv7/aLARMIYS4WQhRIoQo2b9/f1KIa0uYhXbNmNSPs0f24t531rF+z5HocYRg36BLSd+5EnZ/Zho1o9nXCwoKogu07OKenRaCWdlojbZFs5hwjRanCCVjGGFNTU005FNfVltlrLfxm9U1cuRI04VPxj75fD7bFapuFw0ZI47M7McavWb3wsxuC8nPneOEeGnR982q/knNye+s8h+Z1ed2PYLm59L7m7zG1bdFdJ3T+oWkhiabSYVEf2g9AxgNvA+sBmYC1W7qOR5mAEZokn7vkXo55U8L5fQHF8kj9cGotrJi8fsyfO8AKV/+hmMddsfiNc/oNS+rmYgVLcbr9ceqqqrksmXL5Pvvvy8XLlwYrV+v2Rs1d2NEkdPmL4sWLXKl/bnRurS+GzftcTKb2bWVaNOPWyRjtuHmebDaBtXpOjtYRW4Z23V7z2NBPPfV67VO9ZFKMwAp5Xop5UVSysnAy8Dm9qCjLeFWemtSv0+XbP7+9VPYXlPHba+Vk5mZic/n45Sp5yLO/Dmsnw/bWufd0dehb9ssMiSWvDT6c0Zt1SkLqbF9o+a7fPly3nzzTUaPHs3kyZPZunVrVHPOzc3l2muvpbKykmDw2OpibcGcfrWw1axD+66srHTUGp3O67X9sWPHUllZSX5+PnAsisnN2gKzRWt2Mw19vVZw+6zFGuceD6zGQL+oURtPLzH3TrMT/WI64/Vau2apLtz2wQlO4+tUp90zYTbzNmvL1b01kwqJ/tB6BtCn+TsNeAG40U09HWUGEI899+mPK+XQ2+bLh9/foKvwqJR/HS2bnpgmly1ZLBcvXuxKA7L7Hyvc1qOfJVhpu7W1tXLx4sUtopasbOXGGYjVbEDftr6MWV1mtJr1w+x+6m3Wbm3Ldu2YQfPtxDprcyoX7wwgnuu9+qG8ws4X5XX3t1jRVjOsWGb8tGMU0MtAFdAI7AS+D9wCbGz+3EezQ9jp01EEgJSx3/xwOCx/9WqpHHrbfPnGp8f2DA6Wvy7lzK6yYeG9pouy9EzOjAEmYiFTLAzMbItI/W/NlGK2aMuqfb2ZyNgvJxOBVRtWL5Wd8zCWe+zF2el2rN3SkUiG1FZMMxFwet6t7mW8JsJUGws9Pe0mABL56UgCIB4EGpvk155eLkfe+Y5c9UV1VBPc+49LZfgPvWRgZ3mLF1B74I12aT0SMQMIBAIt2nCCRrfZOgJ9yKq2Ub2ZNm9s3yjg7ASfVR2x+C7s4PXFTwX7fyKQyvR5fd69KB9259tytXg8sBIAak/gFMWhuiDXPrGcmqNBfjctl/Mnj2Zz2QoKV/0U0bU//OADgjI9aivUImiMOfSNe+VaQbOL2p0vLi6O5vh3qlOzS+bn55OXl9finN/vp6ysrMV+AUuWLKFTp06WUTpaffoIKr39tqSkhMLCyJanxvNaGWi5t7Ex6iKWKBxtXJzy31tda9Wu0/1ob6Q6fbHAGHFmdd4K2gp6pwiy9hg3tSdwB0P3HB/Pf+80fBlp3L/yKEfCWRSceQHi2qdh71qa3rm9FYN76KGHooIAIg+kllbCziHkxiGohVBabUFpVt7KuadtJamvq1OnTq32Njarz+oFNaaFMIb7af/NQkXjSbQFEAqFYrrOKnw0qWGAMaA96WvLNp0Sy7lxPDsxf7fjlqyxVQIghTGkZw4v/uB0QmHJDc+uoLq+ieDQc9k1bAbpnz1P6LNXomVzc3OZPn06Pp+vRbbLkSNHAvY7HFkxQLN4dy9MUnshrKCP6LDaW1hPi7ZXsBYLblaXWYSHJjy0fERm+Yzc5gUyQ2ZmZkzXGdtN1JqAtmYw8QrMWKHNtswi4bzUYYd4+2b1DnmpO5kCVgmAFMfJfbvwz2+fSo0/wDdmreBwQNL7a48RHnQ6GW/fQuO24mjZc889F5/PRygUIhiM7LQ1efJkR0YM1qGkidgExE3YmhlTNp7Xh19WVFS0WOijmX/04YV66MfAGI5qBqeXUC8ctQV4Xq4zg55BxMP8zQSk/nwiGEw8AjMeAaVtRxoL/W6vi6dvZu3p74XZc27WfrIErBIAHQCnDuvFs9+ezJ7DAb72TBE1wXTSrn+JcKc85MvXc3RPpemKSykj+fS1B9CMEVvB7apYO1jFW1uZPqzMNho0WvLy8lpll9SYplm9+m+9hm03Jk6x/MXFxezZs4e5c+e61vLcxGvHOs766522x4TErzp2y1zdlLMSYpqgj+WZtHrurMrCsZXEsQocs3thVpcXc5Pb99c1zDzDqfo5UaKA9NBHHazcUi3H3vWuPOeBj+QXew/J8oUvy/Cf+kn59LkycKQ6GgmjRdRUV1e3iBDSr7Z0E80Tb5y3MeeR17BKszLGiCK7kD59ZIZVu8aIHKe4e63c4sWL5bJly6Jj6mV9hPF/POGUXsJa7Y4nAokMR3WbRM7L+gu3z5x2X7X3xm07du1b/fdy/43vj9XeGWZAhYF2HOhvqjG0bGXlXjl+5gJ55n0fyso9h6Rc/66U9/SQTbOvkKuWfxINc1y4cKH8+9//HmX8ixcvlrNmzZLbtm1rIRSMber/x8qYjA+lWfx+LHVrL6VTJlNt8xw9w3fzomjXun0ZtXJu1lnYhX7GE0bq5fpkh23G8+zYXW8m2GOhxer50ZQALyHP8cBL/UaFSvvtRKcSAB0ExpupT+usnVv9xX458Z735On3fiA37DkiG0tekHJmV9n0n69LGWqUgUBALly4UP7tb3+T77zzTvTaTZs2ybvuuis6M7BqU388FuZkp5F6iZc205SMOWPMGHttbW0rRm6mNTm16Qb6+2Q8bvaCxir89PXGcn28Mw1jXfG255YhW11ndt/jFThmx5ItNN20aab9qxnAcQTjzTQzYVTsPiyn/GmhnDBzgXz+7U9k4yePSTmzq5Sv3SxlU0jW1tbKbdu2ySeffFIuXrw4qs1s2rTJVZvaMSdmbgY3W/85nbfSzqzoMCtrnCloi8+8wIkZGRe66Y/rhZC+T4lYke2GPuP5RKwq9iJI3KZjMBPUXtuOReCkItyOrxftX0prAaCcwCkIqwgWfXTImP5dee3H0+jVJYt7l9fybu6X2Tbi21A+h6a5P6Dss0/ZuXMn1113HRMmTKCyspIhQ4awdOlS0wgRq4VIVs5aq+ggLVzTycHsFH0Dxxy1ZjTq6bCic+7cudHzY8eOpby8nLKyMteONDfRQqFQqFWdPp+vRXpu7ZjesR4PvEQc6ek33hcrJ7FZfW6c48byVs+Bvg6zNu3i8O3a9up8TzV4cVQbQ5zdJLUzhZlUSNXPiTID8IIaf0B++fGlctjt8+U9L34oGxc/GDEHvXyDDNT5W2mddml33WoVTjMAN9e5rd+oRethtzuadt543GiasbL5GzV3bWtDs7bM2klWnhwvMwAzk5qb2V08Ziev5RJpdoyFlvaC3azI7hr9+203DqgZwPGJHp19/OcHZ3D+qD7MXlPPg/5L2Tr6h6StfwvfmzfhS5PRzd2DwWOpkYEWmq1eS3IKZdRrH2Zw0sKctBz98ciza56uWZsZWfXDLBUwtNQwtfr1dBppBWhsbGT16tUtFtlpKCsraxXrbbbuwi780+y/G+hnU1bhrPqyRhqNszEzrTzWuHS35Y0zGq9wQ1+iY+oTNZswG2OzkGhjef1Y6Rc5er5PZlIhVT9qBtAamrRvDDXJ218rk0Nvmy9v/tcqGVj2ZGQmMPsKWbL0o2g5zRegbaGoRctI6c52H09K4ViddMZZjL6e6upqOWvWrBYpsp2SvVnZxfX9M4b/aRqWmbZvlvHUODsxjp3TeTv6jceN+w570dhj9fO0FVJZU9fPIN2GlTrV52YDJjehn7H6ANqdqXv5KAHQEsYHIhwOy1lLNstht8+Xlz26RB5c/ryUv8+TTY9PlTXbKqLMQmP+2rcXB2UiTAOxwPhC6Ke8VVVVtozUytRjVlZzFM+ePTu6W5k+EstsjIxmJa1eMyGib9cqosms73aMQn8PY2Hcdu0uWrQoJTJcejF1tVX7+j2nre6F1/fBrZDWEOu9AFZLZQI6vmCcNgoh+MHZw3nuO1PYVl3HhR8NZPOFsxEHt8I/L+Rw5SqEEPh8PqSU0X10NQelcdWi3qxhZVKIZUVmrH3VvvVT3rFjx7J9+/ZWU2ljm42NjaZ16un3+/2Ul5fj8/m45JJL2LJlSwuTVW5ubrRto8lHW3Gtn5brdyLTMkVq9ZklyrMbR7ud2vTOZm0HMTf3xOyeGs+vX7+e1atXJ8WB6saZHcv5WNo0QhtnLZGg1b3w+j64WfGrN5E5BVjYwDTtsxIAHRxmNsPzRvfhlZumkJ2ZxmVvZ/LRGc+R40un17xvMKnHUXw+H2PGjGkVNaN/gP1+fzSKxmppvta+dr0djLZ1/XGv0EdF6Zmyvq7i4uIo7eXl5a1s/cb2g8HIVpONjY0Eg0E2b97M6NGjmTZtWosX3irVgvZyahEZGl0arcb0BW7TGfj9fpxSoJulvjD20UvEj4bc3Fy++c1vMnXq1DbPS2NHjxufUaypvL0IDivGbywTr7C0osutb8kLlAA4DmB8AYLBIIe3r+e/N51GweDufH9hE0+OnIXM6UP2q19j8yt38uorr7Bnz54oYy8uLqaoqAggGjqp7alqldPEzPFqBmNop9HpZ5W4zKnPdk7KUCjE6tWrASgsLGTatGmtaNILOYApU6YwdepUgsEgoVCI9evXm/bDrD39LMHv97fS+LUyWj36/1bQCyUv46K/PhiM7L1s3FvYLdM07i/RVvASGACtczzFSmN75USyg9NYWOXMiqVNJQCOExgfFikleZ19PPetU7h0RCce+bSRG7iXfd1PZczmWVwefo+N69YyduxYAAoKCsjMzKSmpoY5c+ZQVFTUok69xhoMRjZg0SJirCJK9LQZ1zHoTSGxTGv1wsMscmTChAlRrd8Y/aNn5DNmzMDn80W17GAwyLx585gwYUIL04pWj6bhW5kAtD0YSkpKomNrRreVpm6sb8qUKUyePNkVk9Kbg4LBYIv7M3r0aNOIH6u22wNuGbHx3sdq+okl4sisHj1inY0Y4Xa2o58VOIyFMD1q5hhI5AcYDCwC1gGfA7c0H88DFgKbmr97ONWlnMDuYXSCvl2+W46/e4EsmPmO3PjSL6Wc2VUeemiKrNn2eTSPjX6LRjdONzdOVTtnWbyJtswcnto2lIsXL24RGaSdM6NLc44bI36s+mcWjaSH/nqnRG1OkSV2Y2rljNTOac5hN05sq+OJgtu6vJaLh0an++iGhlRZ72FXJhAISKBCtkcUENAfOLX5dxciG8GPBR4Abm8+fjtwv1NdSgDEh01VB+Wlj3wsh942X774z0cimUTvP0lWr3wlWsa42MlteKD235hgzuoFMSZ2MyKWSApjhJCXiCUzgWCWT8jITGOJTtHTu2jRohZRPGbXGIWY1r4ZjXZ90l9vbMtrWKoXuK0r2QzVeB+dBILdvYyVBi+02pV3qgv4VLaHAGjVIPwPuBDYAPSXx4TEBqdrlQDwBjMGeMRfJ+9+o1wOvW2+/N4DL0r/w5OlnNlVBubfJgN1/ihT0WYE+rA3jTEak63pNXqNqdtpaEZN2+x8rHHsbjNyuqlX/9GPhVvN0apves188eLFnhLzGWcgsWjCVkw+UTOAeBhlMssZ76+WIdftTMwLHWZte8kL5dQPJ+XGagaQVB+AEGIYcAqwEugrpawCaP7uY3HNzUKIEiFEyf79+5NGa0eH1WrOLp078fsvT+CFG09jTaAPU/b9lrI+X8ZX/CQZL17FlJF9GT16NOXl5axYsYJQKBTdhlFzjkLLFbR6B6hmUzeupDWjS18uFgelvqxWt50/wY3T1VhWc46vXr06utOaFgZqVp+dY1zrp5SSYDDI6tWrkVLahtZq1xQXR3Z+0+cS0nwRsTgezXw3bn0xZitU9eesIljcwG34aqI2n9H8ANqzm5eXZ/rsmT2T8Thgvfi/nMJ6XTrQ601PmkmFtvgAucBq4Nrm/4cM5w861aFmAN7gpDV8sKRIfn/2Cjn0tvnyob/dJ4N/6Ceb/jxIbvzfX2V1dbVctGiRrKqqkrNmzYr6BYzmCn192sxB++/WNBFrimQzTTbR9ly9OUmfT0mbCZmZZ8y0cr3JRptd6cfLrl9m6a2trrH7rf03rgA3a9dO43UygcVrwnE780tEXYmg1euM1en+xFq3HWjPXEBCiEzgNeAlKeXrzYf3CiH6N5/vD+xLBi0nEpy0hrNPP5VZ3zmNB2ZM5J81BVzReB976MPIT/9At0W3k9FUh8/nY+TIkdFwQP3CMSO0mHtNo7GL69ZDHz+vr8sOxpmEMcrIC4x16I9rC840OmtqalizZg319fWUlJRQUlISXW9g7IdxAY8+Q2hFRQUTJ05ska9I0yT1IZuaxq+P7jGDWWitmQbs8/mYOHGiaV3G2YSdJmy3jiGWeHz9bzfafaJmFImI1nE7Y9VmckVFRabhq8YZhVnodMJhJhUS+SESfvQC8Ijh+IO0dAI/4FSXmgEkDkaNYlPVQXnpgwtk/m1vyrkP/kiG7+kh6+4dLtfMe7yFlmrmJNZrjk72fzPEmk0z1hmDVVtmdBu1+GXLlsn333+/RW4gN7MPq5mFdk6vWWt+AaON2O24aHZss3uht/db1RXPpjVeZ2FmfYpX27WqIxH1uoGdXV/vSzJDosdBA+04AzgT+BZwvhCitPlzGXAfcKEQYhMRp/B9SaBFgWOaiF7Lyu/Xndd/Op3fXTmeu49czfWhezjSmMb41XeQu/ReaKxnz549PP/88xQVFVFTUxOtR6+xlpeXs3z58hZ2S6ON1Phbs4PaLewygxetyM3qSqfY8tzcXCZOnEjnzp2jbZvt1WDWtvF/WVlZdPagTx/h8/nIyMgwtRFbzVKMdVdWVpKfnx+dvZjFzdtp8LFsug5EFxRq/XIDq0V1ZnBrZ7fyv8S7QMupTaDV4kIjcnNzo+kkzOBmHBIJIaX5EvlURGFhoXRaFq/gDM3MYFzopOGLfYe55cWVbNzn5+Eer3NZ/VuE8/KZ23Qhu0U/rr32Wj766KOowxeOPaxaPp3CwsJofSUlJUgpGTNmDLm5uZSWlkbTJGj0QIQBmzEeKyeYUci4mYLbldHO6781mvR91OgtLi62fZm1ssXFxYRCoRYpFfRM3dh//XGNWVvVbTZmxn5YHXOC17LafYWIuUzvYDajz0ubZn21u/9+v7/VuHnpjxOMbetpM2s7lnoTWV4IsVpKWdjqhNm0IFU/ygSUODhNLxsaGuR/irbI8Xe9Lb9151/k4T+NkOGZ3WXdm7dKGfDbZrI0mjQCgUCLEFF9FlInmowmAjMHqdGME8/U2YwmM+eu3iHuBL0T14sjV5990i29sR43KxdPZkv9WOn7YFevU5vG+u3SJCfCcWpHR1uZrbzQbWa+tAIWJiA1A1Cwxd5Dfh75aAtvr1rPPTn/5drw+4S7DmZd/g8Zev73ohpPZWVli41ntOOaJgjHtN6ysrJWGrEZ7DRys/rh2GzDSTM3tqH91tev18z1zl2zWYGbdvTX2mmr+v56aUN/vdXMQN8XuzBWr7MFO1qMs81YZgBWZa1ojZd2qzH0Un8sNLi9xviMmM0e9fVYzQCUAFBwRDAY5NWPinlxXYgu+0p4uNM/GRzexZ6+09kw7Dus27aPL3/5y2zfvj1qtzZmLSwuLqagoICKigry8/Ntk4zZMUsjw4ZjOXg04aMdc9MvM7OCBv1LZfxvZmKxa7empoa8vLxWETmazVgzp2k+Ea8CRqPByjyib6+8vJzGxsaoANYzE02AFhQUtDDRGct5oS2RphdjvXZM2th2W5lX7GjTmzoT1Y6VUIVjyoreBAfWAkAlg1NwhM/n48tnFTBzajYXX3o1Vzbdz9+brqX3vqWcXfYLzu2xh7wePVqEB0LL5GvaPgRaHnwr2IUhmmnr0Nrx5kWDMoafanRDy/A7vbNSPxsJBoPRlM1m20VChPk/8sgj7NmzB6BFWf3iuZKSEsrKyqJ2dC9OS6OTU6O7pqYmGnqoCcmJEyeSkZFhGipaWFgYFdTavdM7kGtqamz7GitiqUvvwDerS983t05g4z4PscLMiW8Gt3QZz2uKu/5+a+OhfwcdYWYXStWP8gG0LzQ7497D9fJn//lUXnD7U3LNPYWRrSefu1zKvetalDeGTRrrsYI+KZ3+GrtwTX0yOzfho1aLmcwW6tjZrI2+CCtUVVW1SKthFiqo9SHWJGVmoabazmYLFy5sYYvXfDLV1dWmG4qb2bf1oalufS1u7PpebP9O9bvphxX06SASBTf+G7fPq5WfzcxvZTyH2hJSIdFYtmm/vOShj+Rv77xFHpnZX4bvyZOhd+6QsuFItExVVZV88sknHRmkEWbJ4qweeiMjdgMrIRJLvhc3bQYCgWh8vxmt2vmFCxfKWbNmucppZPXi65mz1p62FagemjPeuM2hW6blZfWr1XGz9QrGe+sUN+/1ftnBLBdQIhy8RsTi8HX6baxXf85KACgT0AmOeKbx0/J78dYt5zLqylu5UjzGK6GzSV/5OE2PFcKauQQDATZs2MCIESMAdyYN7XxeXl50Qxo9jHHj+th2fZ4cJxjDVzXbu5lJyMlR7TafC0Qc4ICpeSAjI4MzzjiDK664olWIrL4e/foBvblDM/UsWbIkuiOaZhooKChg/fr1LerLy8trtc2hlUnCaFoyi9+3gl3Mu357T61vevOSZj40g9Znt+25gTEXkFsTjVd4GT+tvBk9Zs+qp1XxZlIhVT9qBpBYJCpcTkopDx0NyrvfKJfX3PGIXDtzkpQzu8rGZy+Rn773H9P8QEYzh5MpwErz1za3j2flqtnvWOuwOm9mJjLT3jQzhF5zN46RlcauzzFkpt07hZTa0ZXoldlWMw7jcbtZnXb/20JD1yPWjdjjGa9Yz1kBZQJSMEOiX56KnTXyO88ul3fceas8PHOAbLonT4bf+Y2UR6ujL3RVVVWL1Ltu0ilYTfWNzNBtn9zYnhMJL23ohaN+/YAxb71WRvOXmOW1dzMubhhVrHZqq7JWaxy8mnNi7ZNbxPqMOF1ndz6eVBxWbSkBoJBw2D2g763ZKS/9wxz50m+/LEMzu8vGPw+RsuhJWXuoRq5cuVJu27atRR1OzN/O2WX872bzjHizh5ohVk1RDyM9dj4QveavCVIj83CiyYlxe2GAXq6zEuix2sX1/+Nh2l6OO5WJRXg5zXpirQ9YLU14qvIBKMQEJ9vo9JN7c8d5g5CXP8y3Mx6kqG4QLLgN33PnMyZtKx99+GGrnPpalkRjnWb2UmP7+rBQN3HhZnbSeOy8TjlgzOgwO2bskzEXkD700SyvT0VFBUOGDGmxxsCJJjubt36s9DTradSgH0s3Nm47+7UTrO6/8f7GEkZr9vx5ocXuOqvx0h8z82W58TE55LoyX/BlJhVS9aNmAKkFJy1b+9QFQvIfH26UP7n7Xll51ygpZ3aVR5+5RMqqNdHytbW1ctasWbKqqsoyjM/43y7/vhVNsaYhcAMvEUhuc+nb0WPWnj7qSl/GS5/NZgPGmYfdngBOmrTedBcP3JiPvEaG6U1qdnU70WJVJtaZiRtfh9E8qAcqCkgh0bDTsjVtpLS0lHr/YW46ayhXnns2Dw98hN+HvkNwZznhp84m+Mb/cXTvFnw+H9dee210NbGxHuN/o6bnll67HPbx5l03i0Ay09yc6LD7rx8L/d4L2rH169fT0NDQQoO30w7Nop709WmLwIAWsyb9giO7mZnVcavIHjewi4Ax659ZlJbVffH5fOTn5zNv3jxqamps+2TWtlOZWJ+xCA+3RzAY9DQLBZUKQqGNoK06rampYd68eS1SHSwuXsPibfWMqXyGb2e8TxPp7B5yDYO+eh9kRZiottQdWr7YmtnImPPHTVqARMNpCb+RJivGZXXcrIw+HYUxG6kmGFesWEGnTp2iK3qt0kpo9Bkzs+rbCAYjqauFEJbZY92Oi3Elt9Uqb6djXu+zVo/+W0tNYhU2XFNTQ2VlpWVGU6t22uoZdPOMgHkWVFDZQBWSCONU12zDl0AgID/9Yr+89fH/yrd+d6GUM7vK+j+fJINLH5eBOvNNM/TTc+NqVLPprxdzSqKdnMZrAoGAXLRoUYupvEa/FhFjF+5oFQGkN/HoTTRuVxObhdDGkmHVrRnOrn/GvlmZPezachNIEAhEFt3FmmnVDokIKkiEidJYDmUCUkgGzEwzZhpJaWkp4wZ05b4fXMWuyb/lltyH+KyuL5kL7yD0jzM4rfMu0M1ONc0/Pz+fV199tcViKG0jkvLy8uj0V9PG3Ezj3Uzx7ZyjdjA6RvXbMWpOa2P+HbM6NI28rKyshRlo7ty51NTUtKJH27jezFmr/Q8Gg9EsrsYFcdDS5ONmbOw207G7VjPV6PsGrfPd6MfD7J453Ud9f6ZOnUpBQYEnJ7WbfrkxBdnByZnrhiZthuOKBjOpkKofNQNIbXgNGdTPEJqawvKN1TvkL+99QFbcNU7KmV3lvvsny7rPF0QdxJpmunjx4hZrCLT6jE5JYzqJeLU9s5BML3DjtHSicfHixS3K6DV4oxa/aNGiVo51/azDqj3jMf3iNKtyVrM9q7r1DlcjTcbxcMrFZKzXrIxZqGusufet+hRr3U71eaXJbCxR6wAUkgEvzN+4gCkQCMhgqEm+sHSj/N3M2+XOu0+ScmZXWfPU5bJ4fmtGbpa0y8gIvOYgsmLSejNFIBCJYvHCRN2058Q4tHarq6tbMHMzJqcdM+4PrP02mj+M1xmhtanBqg679RX69s0S+9n12+q4UzSSdtzuWXEDq+fIjgm7qdMJbkxvTs9fINCOC8GAwcAiYB3wOXBL8/GvNv8PA4Vu6lICoOND/8Los34aX6Qj/jo5e/E6+fA9/ycP3t1fNs3sJr/4xzUysK9SSmmtlRrb0JimlYZod51RkzIyLrvEbm4Wo1lppnaorq6Ws2bNkgsXLmxhx3YSBsb6zRi3lkbCjLEb00osW7as1WzErl9u2rcTAlZwmt1pzD+eLJ92WUJjoVlfp1PSPy+LGq1os1sIlgwB0B84tfl3F2AjMBYYA4wCFisBcOLA+MI4pYHYW31I3jXrTTnrrhtkw909ZfCenvLgG7+WgYO7bV8ePWNYuHChbcpj/bd+Na1RyzVqrNo5q7TS+m/jGMSzElnvANe/5G5SahhpMhsDM6ZulsfIbRtW58zqc8r9o4dVf82Ei3F9iRONRlhlCfWyDsXYl7aaARiFsN0MoM2dwFLKKinlp82/a5tnAgOllOuklBvaun2F1IHe8alBH0tu5uDaWrmB31z/Ja7+5dPMnvwa88Jn0eWzZ2h6dBKNix+A+kOtVm9qW0XqHZ1a+LA+vFHvuNQcyatXr45uypKbm9sqzFTfjhbLrjkx9dCHG9o59GJZiaw51Y2ZOc3G0i4e37h6VqvLGHarbVRj7IMXB6qxfaPDWKvPLm7fbJW0fvMcsw1utLHp16+f6Ypnu5h5PQ3GLKH6uu3WWtiNgZtQUTunstn7FAwGWzzrTvcpqTZ8YBiwHeiqO7YYNQM4YeBV4zVqOPuONMh/vDJfzr/rIilndpV1fxgg18/+qVy+eKGlFmm0mZuZcfRtWdFo1AKN5hAr04mbPmt0uTFTmGn8duXMNESz32bXL1u2rNUY2ZV3A6dZh1U9dk5ot7Mf/XV24+cmIZvduDrBa1m3IbH6maketLcTGMgFVgPXGo7bCgDgZqAEKBkyZIjrQVPoeLAyTZi9iLsO1slHX5wrF941XcqZXaX/j0Pk0UWPSBmsM52mmzEIN3ZYs3rMaLRiPG6PSSk92aqNqR68tmUGM4ZmNIHFk0rD6rqlS5ea+hPs6rcyezjBTfoQJ1OUGT1tiUCgdfSX8byTnwv4VLaXAAAygfeAX5icUzMAhVaauRvbam1trfzfh8vlX5/9t1xy15lSzuwqj/xpuPR/8qSUje7s8V6yLhoZfiw2ZWM/jde42dvASqjZ2cPtmIeUzvZ0q283bRj7bYRZRJFZfVaCyG6mY6UMONHrxT5vN7bxzBL01zjtiuY002y3GQAggBeARyzOKwGgIKV0b5rQzutf5k17j8hHn31OrrpripQzu8pD946StUXPSRlqTDiNVozS7cpg7WOXE9+LGcN4nZekbfrzZo5ys75ptJuln7brh5fZgRkddqYos/9WEU1W9du1bUe32XiZte/EyJ3aiee8lQBo81xAQoizgE+ANURCPgHuBLKAvwO9gUNAqZTyYru6VC4gBQ1muVGCwSDzPy5m1xdrOGfn0xSkbaEmezC+C35H7qnXQVrrmAdjnhizc3a/rcpb0ew2N5A+z49VfhezXD5WtFq1pdXt1D9jPcuXL0cIQUZGBmPGjCE3N7fVvSguLnadQ8iKZu2Y3+9nzpw5jBw5kqlTp7bqixXNZn029t2MDqsxd6rHmEtJg9YfuxxX+joSnUtI5QJSOG5gpYVr56SUcmPVYfnU04/Kddqq4vtOkf7PXpeBhoZW9ViFcVqZpOKl3U0ZbUrvtN7BSQPX/07kaljtU1tba5lXx81Mxliv3WxFH/7qxgRkHANjXVbjYRfWa0e7WVkzWo3HjNuAtsWzRns7gRPxUQJAQYNbxryh6pD851MPRvch2P6nAnmwZK6U4XC0Hv23sQ2z34mGU9tWC5DcrLp16oPXfplF4mjmIDu4NY2ZOV/dMFk3Y6CV01ZxWyUpNPbTzVhb1eN0jVV7buBkUtP3XQkAheMSbl+W9btq5POP/0l+cddIKWd2lXsemCJrS9+MCoL2oEkrG0sop5t2nOzkbupxM2MwMjy319m1ZyxrFQLqZvyMyoJZWgenGZ9bhUO/mtopNDUebd9MuJmV0aAEgIKClHLN1r3y+Sf+LLc0C4K9DxRKfwIFQSwbejsx6kSZA5zy5Rjh1hRiNJ14uc4NrW7+m30by9rNMozXWYXiWt0bY916zd5uBhEr8zd+OwlB2isVRCI/SgAoJArrdlUnXBDYMRevdbSFyclKW3Ri6rHU7WZ2YrdIzem42QzBrfBx2srSbZ4eM8HnRvjEy/TdLO7Tl1+2bJkE1kkTnqp2BFM4obFuVw0r/vc05+55nuFpe9jXeSSdL/odnSdcZRo15IRERHC0RRSIvm63u1Ylig6rSCstHYVZBI1VlJXZeWM0lB3dZlFPZnXbRQHpy2r0a+lH9LuvWUWqOUUvOfXb7hot0kjKlrvlZWVlfSqlnGwsrwSAggJQsbOGlfN0giBnJJ0v+i2dJ14dkyBIZbSlgDFrS2POGoPUh3bOnTuXGTNmtAqBdAq1NYaNgrvtEo3tGet2U4++rFlYr1V/na636qtVv51o0+Dz+SzDQJUAUFDQ4fOd1aycN4vpe2Y3C4J8Ol1wJ10mXXPcCQJwLwy8Cg0z5qXt6mYV/66/Vh9Lb7cvMGC6FsIKdjMAra5Y9hs2O2YnxIxrBfRCUaPPSIvX2Zt+HYbVDOD4e6IVFOLAuEE9ufEntxP8URH/6ncnfv9Rusy7kb0PTuFwyasQDjtX0kHgNoOll0yXZuU1ZlhWVkZJSUmLesyYtn4LTGMGUP15qwyixm899MxVO69lJgU8MX+NFq/njNs7arRoMxRNUJplmHVLn8/no6CgILroDMgxK6dmAAoKNqjcc5gV855h2s5/Mjytin3ZJ5F53q/pMeV6SEtvb/LiRjJmAPpj4M7EYqzHatWzXTmjCcbKpGKs12lVuBs6Y51FaMwf8LSS2gi9T6LZBKR8AAoKsWLrviMsm/csU7b/k5PTdnLAN4i0c39N3hk3QHpme5OXUkikj0HPWLU9AMycwGb/3VxrvN7J9AL2Jimjqcut4DCry3jei5+gpKSEiRMnRoWalQ9AmYAUFFxgWJ+u3PCDX9DplpW8NPRP7A+kk7fwFqrvm0D1x89AyJ155HiHV3ORm7o0E42ZCcSsPT3D1JihtsmP3llrLK/9ttr4BTDtm/4av98fNXUZN73R02xHu779srIyioqKTDcW0vqmCTj9Rj9SSqPAEGZjrGYACgoxYPfBOha99QITNj/DRLGZgxl9CE27hd5n/wAys9ubvHZFW80A7EIfnbRsOCYQvDiNvdKh1W01A/AS0qmnXaPfeE6fNNAYhaT5OwCysrLWSSlbOhVQAkBBIS7sO1zPB/P/w+iNT3Gq2MihjF4ET/8pfc79IfhM/W4K7QyjfdwJTiYgY1njLMNNKKfX9QFmZi4r/8akSZNUFJCCQlugT7dOfOOG7zPkV5/w8ujH2djYhz7L7uHI/WPZ8+79ELDec1ahfaDfO9kNjCYgu32E4Zhmrjfd6M1BVj4EO9OQ/re+LqP5R6NXT3fzf1NNX80AFBQSiINHg7z37usMWfM400Q5tWld8Z/yQ/pf+P8gu2t7k6cQJ6xmD8Z1BEY4mZ3sZgBW6x6A6MI2O0cxWDuBlQBQUGgDHK5vZMG7b9G//DHO4TOOilxqxt/IoEt/gcjp0d7kKcQBI7M2SwPhdE08bRrDRo0Lx4w0BINBsrKyVBSQgkKy0K1TJl+79lpOuX0hcwtfZBVjGbzmMeofHMPmObcR9h9obxIVYoSRuVZUVLRw/DpdE2+b+t/ajMJu8VzzIjcVBaSg0F5oaGzig8UfkbPiYaaHigiILHYM/xonXflrMnsMbm/yFOJAIqOeEgEzc5LVDEAJAAWFJCLUFGbJsk+QnzzMucElIATbBlzGoMtvI2vghPYmT+E4RbstBBNCDBZCLBJCrBNCfC6EuKX5+INCiPVCiHIhxBtCiO5tTYuCQnsjIz2N8885l/PueJMVV3zAgk6X03/X+2TNOottj12Gf/1H0IGUMoWOjTafAQgh+gP9pZSfCiG6AKuBLwODgI+klCEhxP0AUsrb7OpSMwCF4w1SSlav28zW9/7OuYdep7c4wq6c0WSdcyu9psyA9Iz2JlHhOEC7zQCklFVSyk+bf9cC64CBUsr3pZSh5mIriAgEBYUTCkIICsfmM+PWR9l3Ywmv9vslwaOH6LXgh+y/bwLbFzwKwbr2JlPhOEVSfQBCiGHAEmC8lPKI7vhbwCtSyhdNrrkZuLn573hgbRJITSR6AR0p5KOj0QuK5mSgo9ELHY/mtqR3qJSyt/Fg0gSAECIX+Bi4V0r5uu74b4FC4FrpQIwQosRsGpPK6Gg0dzR6QdGcDHQ0eqHj0dwe9CbFwCiEyAReA14yMP/vAFcAX3Ji/goKCgoKiUWbCwAhhAD+SWRX+od1xy8BbgPOlVIqI6eCgoJCkpGMGcCZwLeANUKI0uZjdwKPAVnAwoiMYIWU8kcOdT3TVkS2IToazR2NXlA0JwMdjV7oeDQnnd4OtRBMQUFBQSFxULmAFBQUFE5QKAGgoKCgcIIiJQWAEOISIcQGIUSlEOJ2k/NCCPFY8/lyIcSp7UGnjh4nem9oprNcCLFcCFHQHnQaaLKlWVduihCiSQgxI5n0mdDhSK8QYroQorQ55cjHyabRhB6n56KbEOItIURZM83faw86dfQ8J4TYJ4QwXWuTau9dM01ONKfiu2dLs65c2797UsqU+gDpwGZgOOADyoCxhjKXAe8SSXF6BrAyxemdBvRo/n1pe9LrlmZduY+Ad4AZqUwv0B2oAIY0/++T6mNMJBji/ubfvYEawNeONJ8DnAqstTifMu+dB5pT6t1zQ7Pu+Wnzdy8VZwCnAZVSyi1SyiAwB7jaUOZq4AUZwQqge3POofaAI71SyuVSyoPNf1Mh7YWbMQb4GZH1G/uSSZwJ3ND7DeB1KeV2ACllR6BZAl2aQ6VziQiAEO0EKeWSZhqskErvHeBMcwq+e27GGZL07qWiABgI7ND939l8zGuZZMErLd8nokW1JxxpFkIMBK4BnkoiXVZwM8YnAz2EEIuFEKuFEN9OGnXmcEPzP4AxwG5gDXCLlDKcHPJiQiq9d7EgFd49RyTz3UvFVINmO9cYY1XdlEkWXNMihDiPyEN4VptS5Aw3ND8C3CalbGpep9GecENvBjAZ+BLQCSgSQqyQUm5sa+Is4Ibmi4FS4HxgBJE1MZ9IXZ6sFEMqvXeekELvnhs8QpLevVQUADsB/RZJg4hoSF7LJAuuaBFCTASeBS6VUlYniTYruKG5EJjT/AD2Ai4TQoSklG8mhcKWcPtMHJBSHgWOCiGWAAVAewkANzR/D7hPRoy+lUKIL4DRwKrkkOgZqfTeuUaKvXtukLx3r70dIibOjwxgC3ASx5xn4wxlLqelM2pVitM7BKgEprX3+Lql2VD+edrXCexmjMcAHzaXzSGSNXZ8itP8JHBP8+++wC6gVzs/G8OwdqimzHvngeaUevfc0Gwo16bvXsrNAGRkg5ifAu8R8YQ/J6X8XAjxo+bzTxHxjF9G5MbWEdGkUpneu4GewBPNUj0k2zFLoUuaUwZu6JVSrhNCLADKgTDwrJSy3VKHuxzjPwLPCyHWEGGqt0kp2y19sRDiZWA60EsIsROYCWRC6r13GlzQnFLvHriiOXm0NEsZBQUFBYUTDKkYBaSgoKCgkAQoAaCgoKBwgkIJAAUFBYUTFEoAKCgoKJygUAJAQUFB4QSFEgAKCgoKJyiUAFBQUFA4QaEEgIJCM4QQnYQQHwsh0hNc7zQhxO+FED4hxBIhRMotwFQ4MaEWgikoNEMI8X9AhpTy0TZsYyaRNNEvtVUbCgpuoWYACgrHcAPwPwAhxJhmbb1cCPFrIURlrJUKIf4rhNCyUL7Z3I6CQrtDCQAFBUAI4QOGSym3NptoXiKSn38ikV294skrNJ5Ivn+a65kSF7EKCgmCskUqKETQCzjU/PtaoExK+Vnz/wpMdmYSQnwA9DOp67dSSm0mkQ1kSikPA8hIjvegEKILkaR1TwBBYLEyCykkG0oAKChEUA9kN/+eSGSjFg3jgQXGC6SUF7iodxwRAaJHFtAAXA/MlVK+JYR4hcisQ0EhaVAmIAUFQEb2jU1v1tiriWwxiRBiEvBNIvn8Y8EEIimqaa6vJ7BfStlIZEMVbYvFphjrV1CIGUoAKCgcw/tEtgz8N1AohCgGbgS2Sim3xFhnCwEAnEckrz5EdtjSNilX76JC0qHCQBUUmiGEOAX4BfBjKaW/+divgW5Syt8lqI3XgTuklBuEEJ2JbAzfACxVPgCFZEMJAAUFHYQQNxLZ9/Y6oBFYBvxCShlIQN0+4Hop5Qvx1qWgkAgoAaCgoKBwgkLZHRUUFBROUCgBoKCgoHCCQgkABQUFhRMUSgAoKCgonKBQAkBBQUHhBIUSAAoKCgonKJQAUFBQUDhB8f8BjP/CRpV6NXQAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "plot_cmd(photo_table)\n", + "plt.plot(gi1, g1)\n", + "plt.plot(gi2, g2)" + ] + }, + { + "cell_type": "code", + "execution_count": 173, + "metadata": {}, + "outputs": [], + "source": [ + "left_gi = gi - 0.5*(g/28)**5\n", + "right_gi = gi + 0.55*(g/28)**5" + ] + }, + { + "cell_type": "code", + "execution_count": 172, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 172, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "plot_cmd(photo_table)\n", + "plt.plot(gi1, g1)\n", + "plt.plot(gi2, g2)" + ] + }, + { + "cell_type": "code", + "execution_count": 174, + "metadata": {}, + "outputs": [], + "source": [ + "# TODO\n", + "# ind = (poly[:,1]<21.) & (poly[:,1]>17.8)" + ] + }, { "cell_type": "markdown", "metadata": {}, diff --git a/07_plot.ipynb b/07_plot.ipynb index b6cf1b3..15e961c 100644 --- a/07_plot.ipynb +++ b/07_plot.ipynb @@ -69,7 +69,7 @@ "IN_COLAB = 'google.colab' in sys.modules\n", "\n", "if IN_COLAB:\n", - " !pip install astroquery astro-gala pyia python-wget" + " !pip install astroquery astro-gala python-wget" ] }, {