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<title>1. Queries &#8212; Astronomical Data in Python</title>
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<a class="reference internal nav-link" href="#outline">
Outline
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<a class="reference internal nav-link" href="#query-language">
Query Language
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Using Jupyter
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Connecting to Gaia
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Databases and Tables
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Writing queries
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Exercise
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Asynchronous queries
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Exercise
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Operators
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Exercise
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Formatting queries
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Exercise
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Summary
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Best practices
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<div class="section" id="queries">
<h1>1. Queries<a class="headerlink" href="#queries" title="Permalink to this headline"></a></h1>
<p>This is the first in a series of lessons about working with astronomical data.</p>
<p>As a running example, we will replicate parts of the analysis in a recent paper, “<a class="reference external" href="https://arxiv.org/abs/1805.00425">Off the beaten path: Gaia reveals GD-1 stars outside of the main stream</a>” by Adrian Price-Whelan and Ana Bonaca.</p>
<div class="section" id="outline">
<h2>Outline<a class="headerlink" href="#outline" title="Permalink to this headline"></a></h2>
<p>This lesson demonstrates the steps for selecting and downloading data from the Gaia Database:</p>
<ol class="simple">
<li><p>First we’ll make a connection to the Gaia server,</p></li>
<li><p>We will explore information about the database and the tables it contains,</p></li>
<li><p>We will write a query and send it to the server, and finally</p></li>
<li><p>We will download the response from the server.</p></li>
</ol>
</div>
<div class="section" id="query-language">
<h2>Query Language<a class="headerlink" href="#query-language" title="Permalink to this headline"></a></h2>
<p>In order to select data from a database, you have to compose a query, which is a program written in a “query language”.
The query language we’ll use is ADQL, which stands for “Astronomical Data Query Language”.</p>
<p>ADQL is a dialect of <a class="reference external" href="https://en.wikipedia.org/wiki/SQL">SQL</a> (Structured Query Language), which is by far the most commonly used query language. Almost everything you will learn about ADQL also works in SQL.</p>
<p><a class="reference external" href="http://www.ivoa.net/documents/ADQL/20180112/PR-ADQL-2.1-20180112.html">The reference manual for ADQL is here</a>.
But you might find it easier to learn from <a class="reference external" href="https://www.gaia.ac.uk/data/gaia-data-release-1/adql-cookbook">this ADQL Cookbook</a>.</p>
</div>
<div class="section" id="using-jupyter">
<h2>Using Jupyter<a class="headerlink" href="#using-jupyter" title="Permalink to this headline"></a></h2>
<p>If you have not worked with Jupyter notebooks before, you might start with <a class="reference external" href="https://jupyter.org/try">the tutorial on from Jupyter.org called “Try Classic Notebook”</a>, or <a class="reference external" href="https://www.dataquest.io/blog/jupyter-notebook-tutorial/">this tutorial from DataQuest</a>.</p>
<p>There are two environments you can use to write and run notebooks:</p>
<ul class="simple">
<li><p>“Jupyter Notebook” is the original, and</p></li>
<li><p>“Jupyter Lab” is a newer environment with more features.</p></li>
</ul>
<p>For these lessons, you can use either one.</p>
<p>If you are too impatient for the tutorials, here are the most important things to know:</p>
<ol class="simple">
<li><p>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 <a class="reference external" href="https://www.markdownguide.org/">Markdown</a>.</p></li>
<li><p>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.</p></li>
<li><p>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.</p></li>
<li><p>You can modify existing cells, but then you have to run them again to see the effect.</p></li>
<li><p>You can add new cells, but again, you have to be careful about the order you run them in.</p></li>
<li><p>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.</p></li>
</ol>
<ul class="simple">
<li><p>If you are using Jupyter notebook, open the <code class="docutils literal notranslate"><span class="pre">Kernel</span></code> menu and select “Restart and Run All”.</p></li>
<li><p>In Jupyter Lab, open the <code class="docutils literal notranslate"><span class="pre">Kernel</span></code> menu and select “Restart Kernel and Run All Cells”</p></li>
<li><p>In Colab, open the <code class="docutils literal notranslate"><span class="pre">Runtime</span></code> menu and select “Restart and run all”</p></li>
</ul>
<p>Before you go on, you might want to explore the other menus and the toolbar to see what else you can do.</p>
</div>
<div class="section" id="connecting-to-gaia">
<h2>Connecting to Gaia<a class="headerlink" href="#connecting-to-gaia" title="Permalink to this headline"></a></h2>
<p>The library we’ll use to get Gaia data is <a class="reference external" href="https://astroquery.readthedocs.io/en/latest/">Astroquery</a>.
Astroquery provides <code class="docutils literal notranslate"><span class="pre">Gaia</span></code>, which is an <a class="reference external" href="https://astroquery.readthedocs.io/en/latest/gaia/gaia.html">object that represents a connection to the Gaia database</a>.</p>
<p>We can connect to the Gaia database like this:</p>
<div class="cell docutils container">
<div class="cell_input docutils container">
<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">astroquery.gaia</span> <span class="kn">import</span> <span class="n">Gaia</span>
</pre></div>
</div>
</div>
<div class="cell_output docutils container">
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Created TAP+ (v1.2.1) - Connection:
Host: gea.esac.esa.int
Use HTTPS: True
Port: 443
SSL Port: 443
Created TAP+ (v1.2.1) - Connection:
Host: geadata.esac.esa.int
Use HTTPS: True
Port: 443
SSL Port: 443
</pre></div>
</div>
</div>
</div>
<p>This import statement creates a <a class="reference external" href="http://www.ivoa.net/documents/TAP/">TAP+</a> connection; TAP stands for “Table Access Protocol”, which is a network protocol for sending queries to the database and getting back the results.</p>
</div>
<div class="section" id="databases-and-tables">
<h2>Databases and Tables<a class="headerlink" href="#databases-and-tables" title="Permalink to this headline"></a></h2>
<p>What is a database, anyway? Most generally, it can be any collection of data, but when we are talking about ADQL or SQL:</p>
<ul class="simple">
<li><p>A database is a collection of one or more named tables.</p></li>
<li><p>Each table is a 2-D array with one or more named columns of data.</p></li>
</ul>
<p>We can use <code class="docutils literal notranslate"><span class="pre">Gaia.load_tables</span></code> to get the names of the tables in the Gaia database. With the option <code class="docutils literal notranslate"><span class="pre">only_names=True</span></code>, it loads information about the tables, called “metadata”, not the data itself.</p>
<div class="cell docutils container">
<div class="cell_input docutils container">
<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">tables</span> <span class="o">=</span> <span class="n">Gaia</span><span class="o">.</span><span class="n">load_tables</span><span class="p">(</span><span class="n">only_names</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
</pre></div>
</div>
</div>
<div class="cell_output docutils container">
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>INFO: Retrieving tables... [astroquery.utils.tap.core]
INFO: Parsing tables... [astroquery.utils.tap.core]
INFO: Done. [astroquery.utils.tap.core]
</pre></div>
</div>
</div>
</div>
<p>The following <code class="docutils literal notranslate"><span class="pre">for</span></code> loop prints the names of the tables.</p>
<div class="cell tag_hide-output tag_truncate-output docutils container">
<div class="cell_input docutils container">
<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">for</span> <span class="n">table</span> <span class="ow">in</span> <span class="n">tables</span><span class="p">:</span>
<span class="nb">print</span><span class="p">(</span><span class="n">table</span><span class="o">.</span><span class="n">name</span><span class="p">)</span>
</pre></div>
</div>
</div>
<div class="cell_output docutils container">
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>external.apassdr9
external.gaiadr2_geometric_distance
external.gaiaedr3_distance
external.galex_ais
external.ravedr5_com
external.ravedr5_dr5
external.ravedr5_gra
external.ravedr5_on
external.sdssdr13_photoprimary
external.skymapperdr1_master
external.skymapperdr2_master
external.tmass_xsc
public.hipparcos
public.hipparcos_newreduction
public.hubble_sc
public.igsl_source
public.igsl_source_catalog_ids
public.tycho2
public.dual
tap_config.coord_sys
tap_config.properties
tap_schema.columns
tap_schema.key_columns
tap_schema.keys
tap_schema.schemas
tap_schema.tables
gaiaedr3.gaia_source
gaiaedr3.agn_cross_id
gaiaedr3.commanded_scan_law
gaiaedr3.dr2_neighbourhood
gaiaedr3.frame_rotator_source
gaiaedr3.allwise_best_neighbour
gaiaedr3.allwise_neighbourhood
gaiaedr3.apassdr9_best_neighbour
gaiaedr3.apassdr9_join
gaiaedr3.apassdr9_neighbourhood
gaiaedr3.gsc23_best_neighbour
gaiaedr3.gsc23_join
gaiaedr3.gsc23_neighbourhood
gaiaedr3.hipparcos2_best_neighbour
gaiaedr3.hipparcos2_neighbourhood
gaiaedr3.panstarrs1_best_neighbour
gaiaedr3.panstarrs1_join
gaiaedr3.panstarrs1_neighbourhood
gaiaedr3.ravedr5_best_neighbour
gaiaedr3.ravedr5_join
gaiaedr3.ravedr5_neighbourhood
gaiaedr3.sdssdr13_best_neighbour
gaiaedr3.sdssdr13_join
gaiaedr3.sdssdr13_neighbourhood
gaiaedr3.skymapperdr2_best_neighbour
gaiaedr3.skymapperdr2_join
gaiaedr3.skymapperdr2_neighbourhood
gaiaedr3.tmass_psc_xsc_best_neighbour
gaiaedr3.tmass_psc_xsc_join
gaiaedr3.tmass_psc_xsc_neighbourhood
gaiaedr3.tycho2tdsc_merge_best_neighbour
gaiaedr3.tycho2tdsc_merge_neighbourhood
gaiaedr3.urat1_best_neighbour
gaiaedr3.urat1_neighbourhood
gaiaedr3.gaia_source_simulation
gaiaedr3.gaia_universe_model
gaiaedr3.tycho2tdsc_merge
gaiadr1.aux_qso_icrf2_match
gaiadr1.ext_phot_zero_point
gaiadr1.allwise_best_neighbour
gaiadr1.allwise_neighbourhood
gaiadr1.gsc23_best_neighbour
gaiadr1.gsc23_neighbourhood
gaiadr1.ppmxl_best_neighbour
gaiadr1.ppmxl_neighbourhood
gaiadr1.sdss_dr9_best_neighbour
gaiadr1.sdss_dr9_neighbourhood
gaiadr1.tmass_best_neighbour
gaiadr1.tmass_neighbourhood
gaiadr1.ucac4_best_neighbour
gaiadr1.ucac4_neighbourhood
gaiadr1.urat1_best_neighbour
gaiadr1.urat1_neighbourhood
gaiadr1.cepheid
gaiadr1.phot_variable_time_series_gfov
gaiadr1.phot_variable_time_series_gfov_statistical_parameters
gaiadr1.rrlyrae
gaiadr1.variable_summary
gaiadr1.allwise_original_valid
gaiadr1.gsc23_original_valid
gaiadr1.ppmxl_original_valid
gaiadr1.sdssdr9_original_valid
gaiadr1.tmass_original_valid
gaiadr1.ucac4_original_valid
gaiadr1.urat1_original_valid
gaiadr1.gaia_source
gaiadr1.tgas_source
gaiadr2.aux_allwise_agn_gdr2_cross_id
gaiadr2.aux_iers_gdr2_cross_id
gaiadr2.aux_sso_orbit_residuals
gaiadr2.aux_sso_orbits
gaiadr2.dr1_neighbourhood
gaiadr2.allwise_best_neighbour
gaiadr2.allwise_neighbourhood
gaiadr2.apassdr9_best_neighbour
gaiadr2.apassdr9_neighbourhood
gaiadr2.gsc23_best_neighbour
gaiadr2.gsc23_neighbourhood
gaiadr2.hipparcos2_best_neighbour
gaiadr2.hipparcos2_neighbourhood
gaiadr2.panstarrs1_best_neighbour
gaiadr2.panstarrs1_neighbourhood
gaiadr2.ppmxl_best_neighbour
gaiadr2.ppmxl_neighbourhood
gaiadr2.ravedr5_best_neighbour
gaiadr2.ravedr5_neighbourhood
gaiadr2.sdssdr9_best_neighbour
gaiadr2.sdssdr9_neighbourhood
gaiadr2.tmass_best_neighbour
gaiadr2.tmass_neighbourhood
gaiadr2.tycho2_best_neighbour
gaiadr2.tycho2_neighbourhood
gaiadr2.urat1_best_neighbour
gaiadr2.urat1_neighbourhood
gaiadr2.sso_observation
gaiadr2.sso_source
gaiadr2.vari_cepheid
gaiadr2.vari_classifier_class_definition
gaiadr2.vari_classifier_definition
gaiadr2.vari_classifier_result
gaiadr2.vari_long_period_variable
gaiadr2.vari_rotation_modulation
gaiadr2.vari_rrlyrae
gaiadr2.vari_short_timescale
gaiadr2.vari_time_series_statistics
gaiadr2.panstarrs1_original_valid
gaiadr2.gaia_source
gaiadr2.ruwe
</pre></div>
</div>
</div>
</div>
<p>So that’s a lot of tables. The ones we’ll use are:</p>
<ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">gaiadr2.gaia_source</span></code>, which contains Gaia data from <a class="reference external" href="https://www.cosmos.esa.int/web/gaia/data-release-2">data release 2</a>,</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">gaiadr2.panstarrs1_original_valid</span></code>, which contains the photometry data we’ll use from PanSTARRS, and</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">gaiadr2.panstarrs1_best_neighbour</span></code>, which we’ll use to cross-match each star observed by Gaia with the same star observed by PanSTARRS.</p></li>
</ul>
<p>We can use <code class="docutils literal notranslate"><span class="pre">load_table</span></code> (not <code class="docutils literal notranslate"><span class="pre">load_tables</span></code>) to get the metadata for a single table. The name of this function is misleading, because it only downloads metadata, not the contents of the table.</p>
<div class="cell docutils container">
<div class="cell_input docutils container">
<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">meta</span> <span class="o">=</span> <span class="n">Gaia</span><span class="o">.</span><span class="n">load_table</span><span class="p">(</span><span class="s1">&#39;gaiadr2.gaia_source&#39;</span><span class="p">)</span>
<span class="n">meta</span>
</pre></div>
</div>
</div>
<div class="cell_output docutils container">
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Retrieving table &#39;gaiadr2.gaia_source&#39;
Parsing table &#39;gaiadr2.gaia_source&#39;...
Done.
</pre></div>
</div>
<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;astroquery.utils.tap.model.taptable.TapTableMeta at 0x7f50edd2aeb0&gt;
</pre></div>
</div>
</div>
</div>
<p>Jupyter shows that the result is an object of type <code class="docutils literal notranslate"><span class="pre">TapTableMeta</span></code>, but it does not display the contents.</p>
<p>To see the metadata, we have to print the object.</p>
<div class="cell docutils container">
<div class="cell_input docutils container">
<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">meta</span><span class="p">)</span>
</pre></div>
</div>
</div>
<div class="cell_output docutils container">
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>TAP Table name: gaiadr2.gaiadr2.gaia_source
Description: This table has an entry for every Gaia observed source as listed in the
Main Database accumulating catalogue version from which the catalogue
release has been generated. It contains the basic source parameters,
that is only final data (no epoch data) and no spectra (neither final
nor epoch).
Num. columns: 96
</pre></div>
</div>
</div>
</div>
</div>
<div class="section" id="columns">
<h2>Columns<a class="headerlink" href="#columns" title="Permalink to this headline"></a></h2>
<p>The following loop prints the names of the columns in the table.</p>
<div class="cell tag_hide-output tag_truncate-output docutils container">
<div class="cell_input docutils container">
<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">for</span> <span class="n">column</span> <span class="ow">in</span> <span class="n">meta</span><span class="o">.</span><span class="n">columns</span><span class="p">:</span>
<span class="nb">print</span><span class="p">(</span><span class="n">column</span><span class="o">.</span><span class="n">name</span><span class="p">)</span>
</pre></div>
</div>
</div>
<div class="cell_output docutils container">
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>solution_id
designation
source_id
random_index
ref_epoch
ra
ra_error
dec
dec_error
parallax
parallax_error
parallax_over_error
pmra
pmra_error
pmdec
pmdec_error
ra_dec_corr
ra_parallax_corr
ra_pmra_corr
ra_pmdec_corr
dec_parallax_corr
dec_pmra_corr
dec_pmdec_corr
parallax_pmra_corr
parallax_pmdec_corr
pmra_pmdec_corr
astrometric_n_obs_al
astrometric_n_obs_ac
astrometric_n_good_obs_al
astrometric_n_bad_obs_al
astrometric_gof_al
astrometric_chi2_al
astrometric_excess_noise
astrometric_excess_noise_sig
astrometric_params_solved
astrometric_primary_flag
astrometric_weight_al
astrometric_pseudo_colour
astrometric_pseudo_colour_error
mean_varpi_factor_al
astrometric_matched_observations
visibility_periods_used
astrometric_sigma5d_max
frame_rotator_object_type
matched_observations
duplicated_source
phot_g_n_obs
phot_g_mean_flux
phot_g_mean_flux_error
phot_g_mean_flux_over_error
phot_g_mean_mag
phot_bp_n_obs
phot_bp_mean_flux
phot_bp_mean_flux_error
phot_bp_mean_flux_over_error
phot_bp_mean_mag
phot_rp_n_obs
phot_rp_mean_flux
phot_rp_mean_flux_error
phot_rp_mean_flux_over_error
phot_rp_mean_mag
phot_bp_rp_excess_factor
phot_proc_mode
bp_rp
bp_g
g_rp
radial_velocity
radial_velocity_error
rv_nb_transits
rv_template_teff
rv_template_logg
rv_template_fe_h
phot_variable_flag
l
b
ecl_lon
ecl_lat
priam_flags
teff_val
teff_percentile_lower
teff_percentile_upper
a_g_val
a_g_percentile_lower
a_g_percentile_upper
e_bp_min_rp_val
e_bp_min_rp_percentile_lower
e_bp_min_rp_percentile_upper
flame_flags
radius_val
radius_percentile_lower
radius_percentile_upper
lum_val
lum_percentile_lower
lum_percentile_upper
datalink_url
epoch_photometry_url
</pre></div>
</div>
</div>
</div>
<p>You can probably infer what many of these columns are by looking at the names, but you should resist the temptation to guess.
To find out what the columns mean, <a class="reference external" href="https://gea.esac.esa.int/archive/documentation/GDR2/Gaia_archive/chap_datamodel/sec_dm_main_tables/ssec_dm_gaia_source.html">read the documentation</a>.</p>
<p>If you want to know what can go wrong when you don’t read the documentation, <a class="reference external" href="https://www.vox.com/future-perfect/2019/6/4/18650969/married-women-miserable-fake-paul-dolan-happiness">you might like this article</a>.</p>
<div class="section" id="exercise">
<h3>Exercise<a class="headerlink" href="#exercise" title="Permalink to this headline"></a></h3>
<p>One of the other tables we’ll use is <code class="docutils literal notranslate"><span class="pre">gaiadr2.panstarrs1_original_valid</span></code>. Use <code class="docutils literal notranslate"><span class="pre">load_table</span></code> to get the metadata for this table. How many columns are there and what are their names?</p>
<div class="cell tag_hide-cell docutils container">
<div class="cell_input docutils container">
<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Solution</span>
<span class="n">meta2</span> <span class="o">=</span> <span class="n">Gaia</span><span class="o">.</span><span class="n">load_table</span><span class="p">(</span><span class="s1">&#39;gaiadr2.panstarrs1_original_valid&#39;</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">meta2</span><span class="p">)</span>
<span class="k">for</span> <span class="n">column</span> <span class="ow">in</span> <span class="n">meta2</span><span class="o">.</span><span class="n">columns</span><span class="p">:</span>
<span class="nb">print</span><span class="p">(</span><span class="n">column</span><span class="o">.</span><span class="n">name</span><span class="p">)</span>
</pre></div>
</div>
</div>
<div class="cell_output docutils container">
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Retrieving table &#39;gaiadr2.panstarrs1_original_valid&#39;
Parsing table &#39;gaiadr2.panstarrs1_original_valid&#39;...
Done.
TAP Table name: gaiadr2.gaiadr2.panstarrs1_original_valid
Description: The Panoramic Survey Telescope and Rapid Response System (Pan-STARRS) is
a system for wide-field astronomical imaging developed and operated by
the Institute for Astronomy at the University of Hawaii. Pan-STARRS1
(PS1) is the first part of Pan-STARRS to be completed and is the basis
for Data Release 1 (DR1). The PS1 survey used a 1.8 meter telescope and
its 1.4 Gigapixel camera to image the sky in five broadband filters (g,
r, i, z, y).
The current table contains a filtered subsample of the 10 723 304 629
entries listed in the original ObjectThin table.
We used only ObjectThin and MeanObject tables to extract
panstarrs1OriginalValid table, this means that objects detected only in
stack images are not included here. The main reason for us to avoid the
use of objects detected in stack images is that their astrometry is not
as good as the mean objects astrometry: “The stack positions (raStack,
decStack) have considerably larger systematic astrometric errors than
the mean epoch positions (raMean, decMean).” The astrometry for the
MeanObject positions uses Gaia DR1 as a reference catalog, while the
stack positions use 2MASS as a reference catalog.
In details, we filtered out all objects where:
- nDetections = 1
- no good quality data in Pan-STARRS, objInfoFlag 33554432 not set
- mean astrometry could not be measured, objInfoFlag 524288 set
- stack position used for mean astrometry, objInfoFlag 1048576 set
- error on all magnitudes equal to 0 or to -999;
- all magnitudes set to -999;
- error on RA or DEC greater than 1 arcsec.
The number of objects in panstarrs1OriginalValid is 2 264 263 282.
The panstarrs1OriginalValid table contains only a subset of the columns
available in the combined ObjectThin and MeanObject tables. A
description of the original ObjectThin and MeanObjects tables can be
found at:
https://outerspace.stsci.edu/display/PANSTARRS/PS1+Database+object+and+detection+tables
Download:
http://mastweb.stsci.edu/ps1casjobs/home.aspx
Documentation:
https://outerspace.stsci.edu/display/PANSTARRS
http://pswww.ifa.hawaii.edu/pswww/
References:
The Pan-STARRS1 Surveys, Chambers, K.C., et al. 2016, arXiv:1612.05560
Pan-STARRS Data Processing System, Magnier, E. A., et al. 2016,
arXiv:1612.05240
Pan-STARRS Pixel Processing: Detrending, Warping, Stacking, Waters, C.
Z., et al. 2016, arXiv:1612.05245
Pan-STARRS Pixel Analysis: Source Detection and Characterization,
Magnier, E. A., et al. 2016, arXiv:1612.05244
Pan-STARRS Photometric and Astrometric Calibration, Magnier, E. A., et
al. 2016, arXiv:1612.05242
The Pan-STARRS1 Database and Data Products, Flewelling, H. A., et al.
2016, arXiv:1612.05243
Catalogue curator:
SSDC - ASI Space Science Data Center
https://www.ssdc.asi.it/
Num. columns: 26
obj_name
obj_id
ra
dec
ra_error
dec_error
epoch_mean
g_mean_psf_mag
g_mean_psf_mag_error
g_flags
r_mean_psf_mag
r_mean_psf_mag_error
r_flags
i_mean_psf_mag
i_mean_psf_mag_error
i_flags
z_mean_psf_mag
z_mean_psf_mag_error
z_flags
y_mean_psf_mag
y_mean_psf_mag_error
y_flags
n_detections
zone_id
obj_info_flag
quality_flag
</pre></div>
</div>
</div>
</div>
</div>
</div>
<div class="section" id="writing-queries">
<h2>Writing queries<a class="headerlink" href="#writing-queries" title="Permalink to this headline"></a></h2>
<p>By now you might be wondering how we download these tables. With tables this big, you generally don’t. Instead, you use queries to select only the data you want.</p>
<p>A query is a string written in a query language like SQL; for the Gaia database, the query language is a dialect of SQL called ADQL.</p>
<p>Here’s an example of an ADQL query.</p>
<div class="cell docutils container">
<div class="cell_input docutils container">
<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">query1</span> <span class="o">=</span> <span class="s2">&quot;&quot;&quot;SELECT </span>
<span class="s2">TOP 10</span>
<span class="s2">source_id, ra, dec, parallax </span>
<span class="s2">FROM gaiadr2.gaia_source</span>
<span class="s2">&quot;&quot;&quot;</span>
</pre></div>
</div>
</div>
</div>
<p><strong>Python note:</strong> We use a <a class="reference external" href="https://docs.python.org/3/tutorial/introduction.html#strings">triple-quoted string</a> here so we can include line breaks in the query, which makes it easier to read.</p>
<p>The words in uppercase are ADQL keywords:</p>
<ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">SELECT</span></code> indicates that we are selecting data (as opposed to adding or modifying data).</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">TOP</span></code> indicates that we only want the first 10 rows of the table, which is useful for testing a query before asking for all of the data.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">FROM</span></code> specifies which table we want data from.</p></li>
</ul>
<p>The third line is a list of column names, indicating which columns we want.</p>
<p>In this example, the keywords are capitalized and the column names are lowercase. This is a common style, but it is not required. ADQL and SQL are not case-sensitive.</p>
<p>Also, the query is broken into multiple lines to make it more readable. This is a common style, but not required. Line breaks don’t affect the behavior of the query.</p>
<p>To run this query, we use the <code class="docutils literal notranslate"><span class="pre">Gaia</span></code> object, which represents our connection to the Gaia database, and invoke <code class="docutils literal notranslate"><span class="pre">launch_job</span></code>:</p>
<div class="cell docutils container">
<div class="cell_input docutils container">
<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">job</span> <span class="o">=</span> <span class="n">Gaia</span><span class="o">.</span><span class="n">launch_job</span><span class="p">(</span><span class="n">query1</span><span class="p">)</span>
<span class="n">job</span>
</pre></div>
</div>
</div>
<div class="cell_output docutils container">
<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;astroquery.utils.tap.model.job.Job at 0x7f50edd2adc0&gt;
</pre></div>
</div>
</div>
</div>
<p>The result is an object that represents the job running on a Gaia server.</p>
<p>If you print it, it displays metadata for the forthcoming results.</p>
<div class="cell docutils container">
<div class="cell_input docutils container">
<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">job</span><span class="p">)</span>
</pre></div>
</div>
</div>
<div class="cell_output docutils container">
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;Table length=10&gt;
name dtype unit description n_bad
--------- ------- ---- ------------------------------------------------------------------ -----
source_id int64 Unique source identifier (unique within a particular Data Release) 0
ra float64 deg Right ascension 0
dec float64 deg Declination 0
parallax float64 mas Parallax 2
Jobid: None
Phase: COMPLETED
Owner: None
Output file: sync_20210315090602.xml.gz
Results: None
</pre></div>
</div>
</div>
</div>
<p>Don’t worry about <code class="docutils literal notranslate"><span class="pre">Results:</span> <span class="pre">None</span></code>. That does not actually mean there are no results.</p>
<p>However, <code class="docutils literal notranslate"><span class="pre">Phase:</span> <span class="pre">COMPLETED</span></code> indicates that the job is complete, so we can get the results like this:</p>
<div class="cell docutils container">
<div class="cell_input docutils container">
<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">results</span> <span class="o">=</span> <span class="n">job</span><span class="o">.</span><span class="n">get_results</span><span class="p">()</span>
<span class="nb">type</span><span class="p">(</span><span class="n">results</span><span class="p">)</span>
</pre></div>
</div>
</div>
<div class="cell_output docutils container">
<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>astropy.table.table.Table
</pre></div>
</div>
</div>
</div>
<p>The <code class="docutils literal notranslate"><span class="pre">type</span></code> function indicates that the result is an <a class="reference external" href="https://docs.astropy.org/en/stable/table/">Astropy Table</a>.</p>
<p><strong>Optional detail:</strong> Why is <code class="docutils literal notranslate"><span class="pre">table</span></code> repeated three times? The first is the name of the module, the second is the name of the submodule, and the third is the name of the class. Most of the time we only care about the last one. It’s like the Linnean name for gorilla, which is <em>Gorilla gorilla gorilla</em>.</p>
<p>An Astropy <code class="docutils literal notranslate"><span class="pre">Table</span></code> is similar to a table in an SQL database except:</p>
<ul class="simple">
<li><p>SQL databases are stored on disk drives, so they are persistent; that is, they “survive” even if you turn off the computer. An Astropy <code class="docutils literal notranslate"><span class="pre">Table</span></code> is stored in memory; it disappears when you turn off the computer (or shut down this Jupyter notebook).</p></li>
<li><p>SQL databases are designed to process queries. An Astropy <code class="docutils literal notranslate"><span class="pre">Table</span></code> can perform some query-like operations, like selecting columns and rows. But these operations use Python syntax, not SQL.</p></li>
</ul>
<p>Jupyter knows how to display the contents of a <code class="docutils literal notranslate"><span class="pre">Table</span></code>.</p>
<div class="cell docutils container">
<div class="cell_input docutils container">
<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">results</span>
</pre></div>
</div>
</div>
<div class="cell_output docutils container">
<div class="output text_html"><i>Table length=10</i>
<table id="table139985564118224" class="table-striped table-bordered table-condensed">
<thead><tr><th>source_id</th><th>ra</th><th>dec</th><th>parallax</th></tr></thead>
<thead><tr><th></th><th>deg</th><th>deg</th><th>mas</th></tr></thead>
<thead><tr><th>int64</th><th>float64</th><th>float64</th><th>float64</th></tr></thead>
<tr><td>5887983246081387776</td><td>227.978818386372</td><td>-53.64996962450103</td><td>1.0493172163332998</td></tr>
<tr><td>5887971250213117952</td><td>228.32280834041364</td><td>-53.66270726203726</td><td>0.29455652682279093</td></tr>
<tr><td>5887991866047288704</td><td>228.1582047014091</td><td>-53.454724911639794</td><td>-0.5789179941669236</td></tr>
<tr><td>5887968673232040832</td><td>228.07420888099884</td><td>-53.8064612895961</td><td>0.41030970779603076</td></tr>
<tr><td>5887979844465854720</td><td>228.42547805195946</td><td>-53.48882284470035</td><td>-0.23379683441525864</td></tr>
<tr><td>5887978607515442688</td><td>228.23831627636855</td><td>-53.56328249482688</td><td>-0.9252161956789068</td></tr>
<tr><td>5887978298278520704</td><td>228.26015640396173</td><td>-53.607284412896476</td><td>--</td></tr>
<tr><td>5887995581231772928</td><td>228.12871598211902</td><td>-53.373625663608316</td><td>-0.3325818206439385</td></tr>
<tr><td>5887982043490374016</td><td>227.985260087594</td><td>-53.683444499055575</td><td>0.02878111976456593</td></tr>
<tr><td>5887982971205433856</td><td>227.89884570686218</td><td>-53.67430215342567</td><td>--</td></tr>
</table></div></div>
</div>
<p>Each column has a name, units, and a data type.</p>
<p>For example, the units of <code class="docutils literal notranslate"><span class="pre">ra</span></code> and <code class="docutils literal notranslate"><span class="pre">dec</span></code> are degrees, and their data type is <code class="docutils literal notranslate"><span class="pre">float64</span></code>, which is a 64-bit <a class="reference external" href="https://en.wikipedia.org/wiki/Floating-point_arithmetic">floating-point number</a>, used to store measurements with a fraction part.</p>
<p>This information comes from the Gaia database, and has been stored in the Astropy <code class="docutils literal notranslate"><span class="pre">Table</span></code> by Astroquery.</p>
<div class="section" id="id1">
<h3>Exercise<a class="headerlink" href="#id1" title="Permalink to this headline"></a></h3>
<p>Read <a class="reference external" href="https://gea.esac.esa.int/archive/documentation/GDR2/Gaia_archive/chap_datamodel/sec_dm_main_tables/ssec_dm_gaia_source.html">the documentation</a> of this table and choose a column that looks interesting to you. Add the column name to the query and run it again. What are the units of the column you selected? What is its data type?</p>
<div class="cell tag_hide-cell docutils container">
<div class="cell_input docutils container">
<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Solution</span>
<span class="c1"># Let&#39;s add</span>
<span class="c1">#</span>
<span class="c1"># radial_velocity : Radial velocity (double, Velocity[km/s] )</span>
<span class="c1">#</span>
<span class="c1"># Spectroscopic radial velocity in the solar barycentric </span>
<span class="c1"># reference frame.</span>
<span class="c1">#</span>
<span class="c1"># The radial velocity provided is the median value of the </span>
<span class="c1"># radial velocity measurements at all epochs.</span>
<span class="n">query</span> <span class="o">=</span> <span class="s2">&quot;&quot;&quot;SELECT </span>
<span class="s2">TOP 10</span>
<span class="s2">source_id, ra, dec, parallax, radial_velocity</span>
<span class="s2">FROM gaiadr2.gaia_source</span>
<span class="s2">&quot;&quot;&quot;</span>
</pre></div>
</div>
</div>
</div>
</div>
</div>
<div class="section" id="asynchronous-queries">
<h2>Asynchronous queries<a class="headerlink" href="#asynchronous-queries" title="Permalink to this headline"></a></h2>
<p><code class="docutils literal notranslate"><span class="pre">launch_job</span></code> asks the server to run the job “synchronously”, which normally means it runs immediately. But synchronous jobs are limited to 2000 rows. For queries that return more rows, you should run “asynchronously”, which mean they might take longer to get started.</p>
<p>If you are not sure how many rows a query will return, you can use the SQL command <code class="docutils literal notranslate"><span class="pre">COUNT</span></code> to find out how many rows are in the result without actually returning them. We’ll see an example in the next lesson.</p>
<p>The results of an asynchronous query are stored in a file on the server, so you can start a query and come back later to get the results.
For anonymous users, files are kept for three days.</p>
<p>As an example, let’s try a query that’s similar to <code class="docutils literal notranslate"><span class="pre">query1</span></code>, with these changes:</p>
<ul class="simple">
<li><p>It selects the first 3000 rows, so it is bigger than we should run synchronously.</p></li>
<li><p>It selects two additional columns, <code class="docutils literal notranslate"><span class="pre">pmra</span></code> and <code class="docutils literal notranslate"><span class="pre">pmdec</span></code>, which are proper motions along the axes of <code class="docutils literal notranslate"><span class="pre">ra</span></code> and <code class="docutils literal notranslate"><span class="pre">dec</span></code>.</p></li>
<li><p>It uses a new keyword, <code class="docutils literal notranslate"><span class="pre">WHERE</span></code>.</p></li>
</ul>
<div class="cell docutils container">
<div class="cell_input docutils container">
<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">query2</span> <span class="o">=</span> <span class="s2">&quot;&quot;&quot;SELECT </span>
<span class="s2">TOP 3000</span>
<span class="s2">source_id, ra, dec, pmra, pmdec, parallax</span>
<span class="s2">FROM gaiadr2.gaia_source</span>
<span class="s2">WHERE parallax &lt; 1</span>
<span class="s2">&quot;&quot;&quot;</span>
</pre></div>
</div>
</div>
</div>
<p>A <code class="docutils literal notranslate"><span class="pre">WHERE</span></code> clause indicates which rows we want; in this case, the query selects only rows “where” <code class="docutils literal notranslate"><span class="pre">parallax</span></code> is less than 1. This has the effect of selecting stars with relatively low parallax, which are farther away.
We’ll use this clause to exclude nearby stars that are unlikely to be part of GD-1.</p>
<p><code class="docutils literal notranslate"><span class="pre">WHERE</span></code> is one of the most common clauses in ADQL/SQL, and one of the most useful, because it allows us to download only the rows we need from the database.</p>
<p>We use <code class="docutils literal notranslate"><span class="pre">launch_job_async</span></code> to submit an asynchronous query.</p>
<div class="cell docutils container">
<div class="cell_input docutils container">
<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">job</span> <span class="o">=</span> <span class="n">Gaia</span><span class="o">.</span><span class="n">launch_job_async</span><span class="p">(</span><span class="n">query2</span><span class="p">)</span>
<span class="n">job</span>
</pre></div>
</div>
</div>
<div class="cell_output docutils container">
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>INFO: Query finished. [astroquery.utils.tap.core]
</pre></div>
</div>
<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;astroquery.utils.tap.model.job.Job at 0x7f50edd40f40&gt;
</pre></div>
</div>
</div>
</div>
<p>And here are the results.</p>
<div class="cell docutils container">
<div class="cell_input docutils container">
<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">results</span> <span class="o">=</span> <span class="n">job</span><span class="o">.</span><span class="n">get_results</span><span class="p">()</span>
<span class="n">results</span>
</pre></div>
</div>
</div>
<div class="cell_output docutils container">
<div class="output text_html"><i>Table length=10</i>
<table id="table139986226873968" class="table-striped table-bordered table-condensed">
<thead><tr><th>source_id</th><th>ra</th><th>dec</th><th>parallax</th><th>radial_velocity</th></tr></thead>
<thead><tr><th></th><th>deg</th><th>deg</th><th>mas</th><th>km / s</th></tr></thead>
<thead><tr><th>int64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th></tr></thead>
<tr><td>5895270396817359872</td><td>213.08433715252883</td><td>-56.64104701005694</td><td>2.041947005434917</td><td>--</td></tr>
<tr><td>5895272561481374080</td><td>213.2606587905109</td><td>-56.55044401535715</td><td>0.15693467895110133</td><td>--</td></tr>
<tr><td>5895247410183786368</td><td>213.38479712976664</td><td>-56.97008551185148</td><td>-0.19017525742552605</td><td>--</td></tr>
<tr><td>5895249226912448000</td><td>213.41587389088238</td><td>-56.849596577635786</td><td>--</td><td>--</td></tr>
<tr><td>5895261875598904576</td><td>213.5508930114549</td><td>-56.61037780154348</td><td>-0.29471722363529257</td><td>--</td></tr>
<tr><td>5895258302187834624</td><td>213.87631129557286</td><td>-56.678537259039906</td><td>0.6468437015289753</td><td>--</td></tr>
<tr><td>5895247444506644992</td><td>213.33215109206796</td><td>-56.975347759380995</td><td>0.390215490234287</td><td>--</td></tr>
<tr><td>5895259470417635968</td><td>213.78815034206346</td><td>-56.64585047451594</td><td>0.953377710788918</td><td>--</td></tr>
<tr><td>5895264899260932352</td><td>213.21521027193236</td><td>-56.78420864489118</td><td>--</td><td>--</td></tr>
<tr><td>5895265925746051584</td><td>213.17082359534547</td><td>-56.74540885107754</td><td>0.2986918215101751</td><td>--</td></tr>
</table></div></div>
</div>
<p>You might notice that some values of <code class="docutils literal notranslate"><span class="pre">parallax</span></code> are negative. As <a class="reference external" href="https://www.cosmos.esa.int/web/gaia/archive-tips#negative%20parallax">this FAQ explains</a>, “Negative parallaxes are caused by errors in the observations.” They have “no physical meaning,” but they can be a “useful diagnostic on the quality of the astrometric solution.”</p>
<div class="section" id="id2">
<h3>Exercise<a class="headerlink" href="#id2" title="Permalink to this headline"></a></h3>
<p>The clauses in a query have to be in the right order. Go back and change the order of the clauses in <code class="docutils literal notranslate"><span class="pre">query2</span></code> and run it again.
The modified query should fail, but notice that you don’t get much useful debugging information.</p>
<p>For this reason, developing and debugging ADQL queries can be really hard. A few suggestions that might help:</p>
<ul class="simple">
<li><p>Whenever possible, start with a working query, either an example you find online or a query you have used in the past.</p></li>
<li><p>Make small changes and test each change before you continue.</p></li>
<li><p>While you are debugging, use <code class="docutils literal notranslate"><span class="pre">TOP</span></code> to limit the number of rows in the result. That will make each test run faster, which reduces your development time.</p></li>
<li><p>Launching test queries synchronously might make them start faster, too.</p></li>
</ul>
<div class="cell tag_hide-cell docutils container">
<div class="cell_input docutils container">
<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Solution</span>
<span class="c1"># In this example, the WHERE clause is in the wrong place</span>
<span class="n">query</span> <span class="o">=</span> <span class="s2">&quot;&quot;&quot;SELECT </span>
<span class="s2">TOP 3000</span>
<span class="s2">WHERE parallax &lt; 1</span>
<span class="s2">source_id, ref_epoch, ra, dec, parallax</span>
<span class="s2">FROM gaiadr2.gaia_source</span>
<span class="s2">&quot;&quot;&quot;</span>
</pre></div>
</div>
</div>
</div>
</div>
</div>
<div class="section" id="operators">
<h2>Operators<a class="headerlink" href="#operators" title="Permalink to this headline"></a></h2>
<p>In a <code class="docutils literal notranslate"><span class="pre">WHERE</span></code> clause, you can use any of the <a class="reference external" href="https://www.w3schools.com/sql/sql_operators.asp">SQL comparison operators</a>; here are the most common ones:</p>
<table class="colwidths-auto table">
<thead>
<tr class="row-odd"><th class="head"><p>Symbol</p></th>
<th class="text-align:left head"><p>Operation</p></th>
</tr>
</thead>
<tbody>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">&gt;</span></code></p></td>
<td class="text-align:left"><p>greater than</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">&lt;</span></code></p></td>
<td class="text-align:left"><p>less than</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">&gt;=</span></code></p></td>
<td class="text-align:left"><p>greater than or equal</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">&lt;=</span></code></p></td>
<td class="text-align:left"><p>less than or equal</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">=</span></code></p></td>
<td class="text-align:left"><p>equal</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">!=</span></code> or <code class="docutils literal notranslate"><span class="pre">&lt;&gt;</span></code></p></td>
<td class="text-align:left"><p>not equal</p></td>
</tr>
</tbody>
</table>
<p>Most of these are the same as Python, but some are not. In particular, notice that the equality operator is <code class="docutils literal notranslate"><span class="pre">=</span></code>, not <code class="docutils literal notranslate"><span class="pre">==</span></code>.
Be careful to keep your Python out of your ADQL!</p>
<p>You can combine comparisons using the logical operators:</p>
<ul class="simple">
<li><p>AND: true if both comparisons are true</p></li>
<li><p>OR: true if either or both comparisons are true</p></li>
</ul>
<p>Finally, you can use <code class="docutils literal notranslate"><span class="pre">NOT</span></code> to invert the result of a comparison.</p>
<div class="section" id="id3">
<h3>Exercise<a class="headerlink" href="#id3" title="Permalink to this headline"></a></h3>
<p><a class="reference external" href="https://www.w3schools.com/sql/sql_operators.asp">Read about SQL operators here</a> and then modify the previous query to select rows where <code class="docutils literal notranslate"><span class="pre">bp_rp</span></code> is between <code class="docutils literal notranslate"><span class="pre">-0.75</span></code> and <code class="docutils literal notranslate"><span class="pre">2</span></code>.</p>
<div class="cell tag_hide-cell docutils container">
<div class="cell_input docutils container">
<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Solution</span>
<span class="c1"># Here&#39;s a solution using &gt; and &lt; operators</span>
<span class="n">query</span> <span class="o">=</span> <span class="s2">&quot;&quot;&quot;SELECT </span>
<span class="s2">TOP 10</span>
<span class="s2">source_id, ref_epoch, ra, dec, parallax</span>
<span class="s2">FROM gaiadr2.gaia_source</span>
<span class="s2">WHERE parallax &lt; 1 </span>
<span class="s2"> AND bp_rp &gt; -0.75 AND bp_rp &lt; 2</span>
<span class="s2">&quot;&quot;&quot;</span>
<span class="c1"># And here&#39;s a solution using the BETWEEN operator</span>
<span class="n">query</span> <span class="o">=</span> <span class="s2">&quot;&quot;&quot;SELECT </span>
<span class="s2">TOP 10</span>
<span class="s2">source_id, ref_epoch, ra, dec, parallax</span>
<span class="s2">FROM gaiadr2.gaia_source</span>
<span class="s2">WHERE parallax &lt; 1 </span>
<span class="s2"> AND bp_rp BETWEEN -0.75 AND 2</span>
<span class="s2">&quot;&quot;&quot;</span>
</pre></div>
</div>
</div>
</div>
<p><code class="docutils literal notranslate"><span class="pre">bp_rp</span></code> contains BP-RP color, which is the difference between two other columns, <code class="docutils literal notranslate"><span class="pre">phot_bp_mean_mag</span></code> and <code class="docutils literal notranslate"><span class="pre">phot_rp_mean_mag</span></code>.
You can <a class="reference external" href="https://gea.esac.esa.int/archive/documentation/GDR2/Gaia_archive/chap_datamodel/sec_dm_main_tables/ssec_dm_gaia_source.html">read about this variable here</a>.</p>
<p>This <a class="reference external" href="https://sci.esa.int/web/gaia/-/60198-gaia-hertzsprung-russell-diagram">Hertzsprung-Russell diagram</a> shows the BP-RP color and luminosity of stars in the Gaia catalog (Copyright: ESA/Gaia/DPAC, CC BY-SA 3.0 IGO).</p>
<a class="reference internal image-reference" href="https://github.com/AllenDowney/AstronomicalData/raw/main/images/1567214809100-ESA_Gaia_DR2_HRD_Gaia_625.jpg"><img alt="https://github.com/AllenDowney/AstronomicalData/raw/main/images/1567214809100-ESA_Gaia_DR2_HRD_Gaia_625.jpg" src="https://github.com/AllenDowney/AstronomicalData/raw/main/images/1567214809100-ESA_Gaia_DR2_HRD_Gaia_625.jpg" style="width: 300px;" /></a>
<p>Selecting stars with <code class="docutils literal notranslate"><span class="pre">bp-rp</span></code> less than 2 excludes many <a class="reference external" href="https://xkcd.com/2360/">class M dwarf stars</a>, which are low temperature, low luminosity. A star like that at GD-1’s distance would be hard to detect, so if it is detected, it it more likely to be in the foreground.</p>
</div>
</div>
<div class="section" id="formatting-queries">
<h2>Formatting queries<a class="headerlink" href="#formatting-queries" title="Permalink to this headline"></a></h2>
<p>The queries we have written so far are string “literals”, meaning that the entire string is part of the program.
But writing queries yourself can be slow, repetitive, and error-prone.</p>
<p>It is often better to write Python code that assembles a query for you. One useful tool for that is the <a class="reference external" href="https://www.w3schools.com/python/ref_string_format.asp">string <code class="docutils literal notranslate"><span class="pre">format</span></code> method</a>.</p>
<p>As an example, we’ll divide the previous query into two parts; a list of column names and a “base” for the query that contains everything except the column names.</p>
<p>Here’s the list of columns we’ll select.</p>
<div class="cell docutils container">
<div class="cell_input docutils container">
<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">columns</span> <span class="o">=</span> <span class="s1">&#39;source_id, ra, dec, pmra, pmdec, parallax&#39;</span>
</pre></div>
</div>
</div>
</div>
<p>And here’s the base; it’s a string that contains at least one format specifier in curly brackets (braces).</p>
<div class="cell docutils container">
<div class="cell_input docutils container">
<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">query3_base</span> <span class="o">=</span> <span class="s2">&quot;&quot;&quot;SELECT </span>
<span class="s2">TOP 10 </span>
<span class="si">{columns}</span><span class="s2"></span>
<span class="s2">FROM gaiadr2.gaia_source</span>
<span class="s2">WHERE parallax &lt; 1</span>
<span class="s2"> AND bp_rp BETWEEN -0.75 AND 2</span>
<span class="s2">&quot;&quot;&quot;</span>
</pre></div>
</div>
</div>
</div>
<p>This base query contains one format specifier, <code class="docutils literal notranslate"><span class="pre">{columns}</span></code>, which is a placeholder for the list of column names we will provide.</p>
<p>To assemble the query, we invoke <code class="docutils literal notranslate"><span class="pre">format</span></code> on the base string and provide a keyword argument that assigns a value to <code class="docutils literal notranslate"><span class="pre">columns</span></code>.</p>
<div class="cell docutils container">
<div class="cell_input docutils container">
<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">query3</span> <span class="o">=</span> <span class="n">query3_base</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="n">columns</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<p>In this example, the variable that contains the column names and the variable in the format specifier have the same name.
That’s not required, but it is a common style.</p>
<p>The result is a string with line breaks. If you display it, the line breaks appear as <code class="docutils literal notranslate"><span class="pre">\n</span></code>.</p>
<div class="cell docutils container">
<div class="cell_input docutils container">
<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">query3</span>
</pre></div>
</div>
</div>
<div class="cell_output docutils container">
<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&#39;SELECT \nTOP 10 \nsource_id, ra, dec, pmra, pmdec\nFROM gaiadr2.gaia_source\nWHERE parallax &lt; 1\n AND bp_rp BETWEEN -0.75 AND 2\n&#39;
</pre></div>
</div>
</div>
</div>
<p>But if you print it, the line breaks appear as… line breaks.</p>
<div class="cell docutils container">
<div class="cell_input docutils container">
<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">query3</span><span class="p">)</span>
</pre></div>
</div>
</div>
<div class="cell_output docutils container">
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>SELECT
TOP 10
source_id, ra, dec, pmra, pmdec
FROM gaiadr2.gaia_source
WHERE parallax &lt; 1
AND bp_rp BETWEEN -0.75 AND 2
</pre></div>
</div>
</div>
</div>
<p>Notice that the format specifier has been replaced with the value of <code class="docutils literal notranslate"><span class="pre">columns</span></code>.</p>
<p>Let’s run it and see if it works:</p>
<div class="cell docutils container">
<div class="cell_input docutils container">
<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">job</span> <span class="o">=</span> <span class="n">Gaia</span><span class="o">.</span><span class="n">launch_job</span><span class="p">(</span><span class="n">query3</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">job</span><span class="p">)</span>
</pre></div>
</div>
</div>
<div class="cell_output docutils container">
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;Table length=10&gt;
name dtype unit description
--------- ------- -------- ------------------------------------------------------------------
source_id int64 Unique source identifier (unique within a particular Data Release)
ra float64 deg Right ascension
dec float64 deg Declination
pmra float64 mas / yr Proper motion in right ascension direction
pmdec float64 mas / yr Proper motion in declination direction
Jobid: None
Phase: COMPLETED
Owner: None
Output file: sync_20210315091929.xml.gz
Results: None
</pre></div>
</div>
</div>
</div>
<div class="cell docutils container">
<div class="cell_input docutils container">
<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">results</span> <span class="o">=</span> <span class="n">job</span><span class="o">.</span><span class="n">get_results</span><span class="p">()</span>
<span class="n">results</span>
</pre></div>
</div>
</div>
<div class="cell_output docutils container">
<div class="output text_html"><i>Table length=10</i>
<table id="table139985564118512" class="table-striped table-bordered table-condensed">
<thead><tr><th>source_id</th><th>ra</th><th>dec</th><th>pmra</th><th>pmdec</th></tr></thead>
<thead><tr><th></th><th>deg</th><th>deg</th><th>mas / yr</th><th>mas / yr</th></tr></thead>
<thead><tr><th>int64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th></tr></thead>
<tr><td>5895272561481374080</td><td>213.2606587905109</td><td>-56.55044401535715</td><td>0.3894438898301715</td><td>1.2299266281737415</td></tr>
<tr><td>5895261875598904576</td><td>213.5508930114549</td><td>-56.61037780154348</td><td>0.16203641364393007</td><td>-4.672602679543312</td></tr>
<tr><td>5895247444506644992</td><td>213.33215109206796</td><td>-56.975347759380995</td><td>-7.474003156859284</td><td>-3.538080792097856</td></tr>
<tr><td>5895259470417635968</td><td>213.78815034206346</td><td>-56.64585047451594</td><td>-5.287202255231853</td><td>-0.8163762113468646</td></tr>
<tr><td>5895265925746051584</td><td>213.17082359534547</td><td>-56.74540885107754</td><td>-7.880749306158471</td><td>-4.8585444120179595</td></tr>
<tr><td>5895260913525974528</td><td>213.66936020541976</td><td>-56.66655190442016</td><td>-4.7820929042428215</td><td>-1.5566420086447643</td></tr>
<tr><td>5895264212062283008</td><td>213.7755742121852</td><td>-56.51570859067397</td><td>-6.657690998559842</td><td>-1.7616494482071872</td></tr>
<tr><td>5895253457497979136</td><td>213.30929960610283</td><td>-56.78849448744587</td><td>-5.242106718924749</td><td>-0.18655636353898095</td></tr>
<tr><td>4143614130253524096</td><td>269.1749117455479</td><td>-18.53415139972117</td><td>2.6164274510804826</td><td>1.3244248889980894</td></tr>
<tr><td>4065443904433108992</td><td>273.26868565443743</td><td>-24.421651815402857</td><td>-1.663096652191022</td><td>-2.6514745376067683</td></tr>
</table></div></div>
</div>
<p>Good so far.</p>
<div class="section" id="id4">
<h3>Exercise<a class="headerlink" href="#id4" title="Permalink to this headline"></a></h3>
<p>This query always selects sources with <code class="docutils literal notranslate"><span class="pre">parallax</span></code> less than 1. But suppose you want to take that upper bound as an input.</p>
<p>Modify <code class="docutils literal notranslate"><span class="pre">query3_base</span></code> to replace <code class="docutils literal notranslate"><span class="pre">1</span></code> with a format specifier like <code class="docutils literal notranslate"><span class="pre">{max_parallax}</span></code>. Now, when you call <code class="docutils literal notranslate"><span class="pre">format</span></code>, add a keyword argument that assigns a value to <code class="docutils literal notranslate"><span class="pre">max_parallax</span></code>, and confirm that the format specifier gets replaced with the value you provide.</p>
<div class="cell tag_hide-cell docutils container">
<div class="cell_input docutils container">
<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Solution</span>
<span class="n">query_base</span> <span class="o">=</span> <span class="s2">&quot;&quot;&quot;SELECT </span>
<span class="s2">TOP 10</span>
<span class="si">{columns}</span><span class="s2"></span>
<span class="s2">FROM gaiadr2.gaia_source</span>
<span class="s2">WHERE parallax &lt; </span><span class="si">{max_parallax}</span><span class="s2"> AND </span>
<span class="s2">bp_rp BETWEEN -0.75 AND 2</span>
<span class="s2">&quot;&quot;&quot;</span>
<span class="n">query</span> <span class="o">=</span> <span class="n">query_base</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="n">columns</span><span class="p">,</span>
<span class="n">max_parallax</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">query</span><span class="p">)</span>
</pre></div>
</div>
</div>
<div class="cell_output docutils container">
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>SELECT
TOP 10
source_id, ra, dec, pmra, pmdec
FROM gaiadr2.gaia_source
WHERE parallax &lt; 0.5 AND
bp_rp BETWEEN -0.75 AND 2
</pre></div>
</div>
</div>
</div>
</div>
</div>
<div class="section" id="summary">
<h2>Summary<a class="headerlink" href="#summary" title="Permalink to this headline"></a></h2>
<p>This notebook demonstrates the following steps:</p>
<ol class="simple">
<li><p>Making a connection to the Gaia server,</p></li>
<li><p>Exploring information about the database and the tables it contains,</p></li>
<li><p>Writing a query and sending it to the server, and finally</p></li>
<li><p>Downloading the response from the server as an Astropy <code class="docutils literal notranslate"><span class="pre">Table</span></code>.</p></li>
</ol>
<p>In the next lesson we will extend these queries to select a particular region of the sky.</p>
</div>
<div class="section" id="best-practices">
<h2>Best practices<a class="headerlink" href="#best-practices" title="Permalink to this headline"></a></h2>
<ul class="simple">
<li><p>If you can’t download an entire dataset (or it’s not practical) use queries to select the data you need.</p></li>
<li><p>Read the metadata and the documentation to make sure you understand the tables, their columns, and what they mean.</p></li>
<li><p>Develop queries incrementally: start with something simple, test it, and add a little bit at a time.</p></li>
<li><p>Use ADQL features like <code class="docutils literal notranslate"><span class="pre">TOP</span></code> and <code class="docutils literal notranslate"><span class="pre">COUNT</span></code> to test before you run a query that might return a lot of data.</p></li>
<li><p>If you know your query will return fewer than 2000 rows, you can run it synchronously, which might complete faster. If it might return more than 2000 rows, you should run it asynchronously.</p></li>
<li><p>ADQL and SQL are not case-sensitive, so you don’t have to capitalize the keywords, but you should.</p></li>
<li><p>ADQL and SQL don’t require you to break a query into multiple lines, but you should.</p></li>
</ul>
<p>Jupyter notebooks can be good for developing and testing code, but they have some drawbacks. In particular, if you run the cells out of order, you might find that variables don’t have the values you expect.</p>
<p>To mitigate these problems:</p>
<ul class="simple">
<li><p>Make each section of the notebook self-contained. Try not to use the same variable name in more than one section.</p></li>
<li><p>Keep notebooks short. Look for places where you can break your analysis into phases with one notebook per phase.</p></li>
</ul>
</div>
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