This is the first in a series of lessons about working with astronomical data.
As a running example, we will replicate parts of the analysis in a recent paper, "Off the beaten path: Gaia reveals GD-1 stars outside of the main stream" by Adrian Price-Whelan and Ana Bonaca.
This lesson demonstrates the steps for selecting and downloading data from the Gaia Database:
First we'll make a connection to the Gaia server,
We will explore information about the database and the tables it contains,
We will write a query and send it to the server, and finally
We will download the response from the server.
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".
ADQL is a dialect of SQL (Structured Query Language), which is by far the most commonly used query language. Almost everything you will learn about ADQL also works in SQL.
The reference manual for ADQL is here. But you might find it easier to learn from this ADQL Cookbook.
If you have not worked with Jupyter notebooks before, you might start with the tutorial on from Jupyter.org called "Try Classic Notebook", or this tutorial from DataQuest.
There are two environments you can use to write and run notebooks:
"Jupyter Notebook" is the original, and
"Jupyter Lab" is a newer environment with more features.
For these lessons, you can use either one.
If you are too impatient for the tutorials, here are the most important things to know:
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.
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.
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.
You can modify existing cells, but then you have to run them again to see the effect.
You can add new cells, but again, you have to be careful about the order you run them in.
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.
If you are using Jupyter notebook, open the Kernel
menu and select "Restart and Run All".
In Jupyter Lab, open the Kernel
menu and select "Restart Kernel and Run All Cells"
In Colab, open the Runtime
menu and select "Restart and run all"
Before you go on, you might want to explore the other menus and the toolbar to see what else you can do.
If you are running this notebook on Colab, you should run the following cell to install the libraries we'll need.
If you are running this notebook on your own computer, you might have to install these libraries yourself.
# If we're running on Colab, install libraries
import sys
IN_COLAB = 'google.colab' in sys.modules
if IN_COLAB:
!pip install astroquery
The library we'll use to get Gaia data is Astroquery.
Astroquery provides Gaia
, which is an object that represents a connection to the Gaia database.
We can connect to the Gaia database like this:
from astroquery.gaia import Gaia
Created TAP+ (v1.2.1) - Connection: Host: gea.esac.esa.int Use HTTPS: True Port: 443 SSL Port: 443 Created TAP+ (v1.2.1) - Connection: Host: geadata.esac.esa.int Use HTTPS: True Port: 443 SSL Port: 443
This import statement creates a TAP+ connection; TAP stands for "Table Access Protocol", which is a network protocol for sending queries to the database and getting back the results.
What is a database, anyway? Most generally, it can be any collection of data, but when we are talking about ADQL or SQL:
A database is a collection of one or more named tables.
Each table is a 2-D array with one or more named columns of data.
We can use Gaia.load_tables
to get the names of the tables in the Gaia database. With the option only_names=True
, it loads information about the tables, called "metadata", not the data itself.
tables = Gaia.load_tables(only_names=True)
INFO: Retrieving tables... [astroquery.utils.tap.core] INFO: Parsing tables... [astroquery.utils.tap.core] INFO: Done. [astroquery.utils.tap.core]
The following for
loop prints the names of the tables.
for table in tables:
print(table.name)
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
So that's a lot of tables. The ones we'll use are:
gaiadr2.gaia_source
, which contains Gaia data from data release 2,
gaiadr2.panstarrs1_original_valid
, which contains the photometry data we'll use from PanSTARRS, and
gaiadr2.panstarrs1_best_neighbour
, which we'll use to cross-match each star observed by Gaia with the same star observed by PanSTARRS.
We can use load_table
(not load_tables
) to get the metadata for a single table. The name of this function is misleading, because it only downloads metadata, not the contents of the table.
meta = Gaia.load_table('gaiadr2.gaia_source')
meta
Retrieving table 'gaiadr2.gaia_source' Parsing table 'gaiadr2.gaia_source'... Done.
<astroquery.utils.tap.model.taptable.TapTableMeta at 0x7f50edd2aeb0>
Jupyter shows that the result is an object of type TapTableMeta
, but it does not display the contents.
To see the metadata, we have to print the object.
print(meta)
TAP Table name: gaiadr2.gaiadr2.gaia_source Description: This table has an entry for every Gaia observed source as listed in the Main Database accumulating catalogue version from which the catalogue release has been generated. It contains the basic source parameters, that is only final data (no epoch data) and no spectra (neither final nor epoch). Num. columns: 96
The following loop prints the names of the columns in the table.
for column in meta.columns:
print(column.name)
solution_id designation source_id random_index ref_epoch ra ra_error dec dec_error parallax parallax_error parallax_over_error pmra pmra_error pmdec pmdec_error ra_dec_corr ra_parallax_corr ra_pmra_corr ra_pmdec_corr dec_parallax_corr dec_pmra_corr dec_pmdec_corr parallax_pmra_corr parallax_pmdec_corr pmra_pmdec_corr astrometric_n_obs_al astrometric_n_obs_ac astrometric_n_good_obs_al astrometric_n_bad_obs_al astrometric_gof_al astrometric_chi2_al astrometric_excess_noise astrometric_excess_noise_sig astrometric_params_solved astrometric_primary_flag astrometric_weight_al astrometric_pseudo_colour astrometric_pseudo_colour_error mean_varpi_factor_al astrometric_matched_observations visibility_periods_used astrometric_sigma5d_max frame_rotator_object_type matched_observations duplicated_source phot_g_n_obs phot_g_mean_flux phot_g_mean_flux_error phot_g_mean_flux_over_error phot_g_mean_mag phot_bp_n_obs phot_bp_mean_flux phot_bp_mean_flux_error phot_bp_mean_flux_over_error phot_bp_mean_mag phot_rp_n_obs phot_rp_mean_flux phot_rp_mean_flux_error phot_rp_mean_flux_over_error phot_rp_mean_mag phot_bp_rp_excess_factor phot_proc_mode bp_rp bp_g g_rp radial_velocity radial_velocity_error rv_nb_transits rv_template_teff rv_template_logg rv_template_fe_h phot_variable_flag l b ecl_lon ecl_lat priam_flags teff_val teff_percentile_lower teff_percentile_upper a_g_val a_g_percentile_lower a_g_percentile_upper e_bp_min_rp_val e_bp_min_rp_percentile_lower e_bp_min_rp_percentile_upper flame_flags radius_val radius_percentile_lower radius_percentile_upper lum_val lum_percentile_lower lum_percentile_upper datalink_url epoch_photometry_url
You can probably 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, read the documentation.
If you want to know what can go wrong when you don't read the documentation, you might like this article.
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?
# Solution
meta2 = Gaia.load_table('gaiadr2.panstarrs1_original_valid')
print(meta2)
for column in meta2.columns:
print(column.name)
Retrieving table 'gaiadr2.panstarrs1_original_valid' Parsing table 'gaiadr2.panstarrs1_original_valid'... Done. TAP Table name: gaiadr2.gaiadr2.panstarrs1_original_valid Description: The Panoramic Survey Telescope and Rapid Response System (Pan-STARRS) is a system for wide-field astronomical imaging developed and operated by the Institute for Astronomy at the University of Hawaii. Pan-STARRS1 (PS1) is the first part of Pan-STARRS to be completed and is the basis for Data Release 1 (DR1). The PS1 survey used a 1.8 meter telescope and its 1.4 Gigapixel camera to image the sky in five broadband filters (g, r, i, z, y). The current table contains a filtered subsample of the 10 723 304 629 entries listed in the original ObjectThin table. We used only ObjectThin and MeanObject tables to extract panstarrs1OriginalValid table, this means that objects detected only in stack images are not included here. The main reason for us to avoid the use of objects detected in stack images is that their astrometry is not as good as the mean objects astrometry: “The stack positions (raStack, decStack) have considerably larger systematic astrometric errors than the mean epoch positions (raMean, decMean).” The astrometry for the MeanObject positions uses Gaia DR1 as a reference catalog, while the stack positions use 2MASS as a reference catalog. In details, we filtered out all objects where: - nDetections = 1 - no good quality data in Pan-STARRS, objInfoFlag 33554432 not set - mean astrometry could not be measured, objInfoFlag 524288 set - stack position used for mean astrometry, objInfoFlag 1048576 set - error on all magnitudes equal to 0 or to -999; - all magnitudes set to -999; - error on RA or DEC greater than 1 arcsec. The number of objects in panstarrs1OriginalValid is 2 264 263 282. The panstarrs1OriginalValid table contains only a subset of the columns available in the combined ObjectThin and MeanObject tables. A description of the original ObjectThin and MeanObjects tables can be found at: https://outerspace.stsci.edu/display/PANSTARRS/PS1+Database+object+and+detection+tables Download: http://mastweb.stsci.edu/ps1casjobs/home.aspx Documentation: https://outerspace.stsci.edu/display/PANSTARRS http://pswww.ifa.hawaii.edu/pswww/ References: The Pan-STARRS1 Surveys, Chambers, K.C., et al. 2016, arXiv:1612.05560 Pan-STARRS Data Processing System, Magnier, E. A., et al. 2016, arXiv:1612.05240 Pan-STARRS Pixel Processing: Detrending, Warping, Stacking, Waters, C. Z., et al. 2016, arXiv:1612.05245 Pan-STARRS Pixel Analysis: Source Detection and Characterization, Magnier, E. A., et al. 2016, arXiv:1612.05244 Pan-STARRS Photometric and Astrometric Calibration, Magnier, E. A., et al. 2016, arXiv:1612.05242 The Pan-STARRS1 Database and Data Products, Flewelling, H. A., et al. 2016, arXiv:1612.05243 Catalogue curator: SSDC - ASI Space Science Data Center https://www.ssdc.asi.it/ Num. columns: 26 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
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.
A query is a string written in a query language like SQL; for the Gaia database, the query language is a dialect of SQL called ADQL.
Here's an example of an ADQL query.
query1 = """SELECT
TOP 10
source_id, ra, dec, parallax
FROM gaiadr2.gaia_source
"""
Python note: We use a triple-quoted string here so we can include line breaks in the query, which makes it easier to read.
The words in uppercase are ADQL keywords:
SELECT
indicates that we are selecting data (as opposed to adding or modifying data).
TOP
indicates that we only want the first 10 rows of the table, which is useful for testing a query before asking for all of the data.
FROM
specifies which table we want data from.
The third line is a list of column names, indicating which columns we want.
In this example, the keywords are capitalized and the column names are lowercase. This is a common style, but it is not required. ADQL and SQL are not case-sensitive.
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.
To run this query, we use the Gaia
object, which represents our connection to the Gaia database, and invoke launch_job
:
job = Gaia.launch_job(query1)
job
<astroquery.utils.tap.model.job.Job at 0x7f50edd2adc0>
The result is an object that represents the job running on a Gaia server.
If you print it, it displays metadata for the forthcoming results.
print(job)
<Table length=10> name dtype unit description n_bad --------- ------- ---- ------------------------------------------------------------------ ----- source_id int64 Unique source identifier (unique within a particular Data Release) 0 ra float64 deg Right ascension 0 dec float64 deg Declination 0 parallax float64 mas Parallax 2 Jobid: None Phase: COMPLETED Owner: None Output file: sync_20210315090602.xml.gz Results: None
Don't worry about Results: None
. That does not actually mean there are no results.
However, Phase: COMPLETED
indicates that the job is complete, so we can get the results like this:
results = job.get_results()
type(results)
astropy.table.table.Table
The type
function indicates that the result is an Astropy Table.
Optional detail: Why is table
repeated three times? The first is the name of the module, the second is the name of the submodule, and the third is the name of the class. Most of the time we only care about the last one. It's like the Linnean name for gorilla, which is Gorilla gorilla gorilla.
An Astropy Table
is similar to a table in an SQL database except:
SQL databases are stored on disk drives, so they are persistent; that is, they "survive" even if you turn off the computer. An Astropy Table
is stored in memory; it disappears when you turn off the computer (or shut down this Jupyter notebook).
SQL databases are designed to process queries. An Astropy Table
can perform some query-like operations, like selecting columns and rows. But these operations use Python syntax, not SQL.
Jupyter knows how to display the contents of a Table
.
results
source_id | ra | dec | parallax |
---|---|---|---|
deg | deg | mas | |
int64 | float64 | float64 | float64 |
5887983246081387776 | 227.978818386372 | -53.64996962450103 | 1.0493172163332998 |
5887971250213117952 | 228.32280834041364 | -53.66270726203726 | 0.29455652682279093 |
5887991866047288704 | 228.1582047014091 | -53.454724911639794 | -0.5789179941669236 |
5887968673232040832 | 228.07420888099884 | -53.8064612895961 | 0.41030970779603076 |
5887979844465854720 | 228.42547805195946 | -53.48882284470035 | -0.23379683441525864 |
5887978607515442688 | 228.23831627636855 | -53.56328249482688 | -0.9252161956789068 |
5887978298278520704 | 228.26015640396173 | -53.607284412896476 | -- |
5887995581231772928 | 228.12871598211902 | -53.373625663608316 | -0.3325818206439385 |
5887982043490374016 | 227.985260087594 | -53.683444499055575 | 0.02878111976456593 |
5887982971205433856 | 227.89884570686218 | -53.67430215342567 | -- |
Each column has a name, units, and a data type.
For example, the units of ra
and dec
are degrees, and their data type is float64
, which is a 64-bit floating-point number, used to store measurements with a fraction part.
This information comes from the Gaia database, and has been stored in the Astropy Table
by Astroquery.
Read the documentation of this table and choose a column that looks interesting to you. Add the column name to the query and run it again. What are the units of the column you selected? What is its data type?
# Solution
# Let's add
#
# radial_velocity : Radial velocity (double, Velocity[km/s] )
#
# Spectroscopic radial velocity in the solar barycentric
# reference frame.
#
# The radial velocity provided is the median value of the
# radial velocity measurements at all epochs.
query = """SELECT
TOP 10
source_id, ra, dec, parallax, radial_velocity
FROM gaiadr2.gaia_source
"""
launch_job
asks the server to run the job "synchronously", which normally means it runs immediately. But synchronous jobs are limited to 2000 rows. For queries that return more rows, you should run "asynchronously", which mean they might take longer to get started.
If you are not sure how many rows a query will return, you can use the SQL command COUNT
to find out how many rows are in the result without actually returning them. We'll see an example in the next lesson.
The results of an asynchronous query are stored in a file on the server, so you can start a query and come back later to get the results. For anonymous users, files are kept for three days.
As an example, let's try a query that's similar to query1
, with these changes:
It selects the first 3000 rows, so it is bigger than we should run synchronously.
It selects two additional columns, pmra
and pmdec
, which are proper motions along the axes of ra
and dec
.
It uses a new keyword, WHERE
.
query2 = """SELECT
TOP 3000
source_id, ra, dec, pmra, pmdec, parallax
FROM gaiadr2.gaia_source
WHERE parallax < 1
"""
A WHERE
clause indicates which rows we want; in this case, the query selects only rows "where" parallax
is less than 1. This has the effect of selecting stars with relatively low parallax, which are farther away.
We'll use this clause to exclude nearby stars that are unlikely to be part of GD-1.
WHERE
is one of the most common clauses in ADQL/SQL, and one of the most useful, because it allows us to download only the rows we need from the database.
We use launch_job_async
to submit an asynchronous query.
job = Gaia.launch_job_async(query2)
job
INFO: Query finished. [astroquery.utils.tap.core]
<astroquery.utils.tap.model.job.Job at 0x7f50edd40f40>
And here are the results.
results = job.get_results()
results
source_id | ra | dec | parallax | radial_velocity |
---|---|---|---|---|
deg | deg | mas | km / s | |
int64 | float64 | float64 | float64 | float64 |
5895270396817359872 | 213.08433715252883 | -56.64104701005694 | 2.041947005434917 | -- |
5895272561481374080 | 213.2606587905109 | -56.55044401535715 | 0.15693467895110133 | -- |
5895247410183786368 | 213.38479712976664 | -56.97008551185148 | -0.19017525742552605 | -- |
5895249226912448000 | 213.41587389088238 | -56.849596577635786 | -- | -- |
5895261875598904576 | 213.5508930114549 | -56.61037780154348 | -0.29471722363529257 | -- |
5895258302187834624 | 213.87631129557286 | -56.678537259039906 | 0.6468437015289753 | -- |
5895247444506644992 | 213.33215109206796 | -56.975347759380995 | 0.390215490234287 | -- |
5895259470417635968 | 213.78815034206346 | -56.64585047451594 | 0.953377710788918 | -- |
5895264899260932352 | 213.21521027193236 | -56.78420864489118 | -- | -- |
5895265925746051584 | 213.17082359534547 | -56.74540885107754 | 0.2986918215101751 | -- |
You might notice that some values of parallax
are negative. As this FAQ explains, "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."
The clauses in a query have to be in the right order. Go back and change the order of the clauses in query2
and run it again.
The modified query should fail, but notice that you don't get much useful debugging information.
For this reason, developing and debugging ADQL queries can be really hard. A few suggestions that might help:
Whenever possible, start with a working query, either an example you find online or a query you have used in the past.
Make small changes and test each change before you continue.
While you are debugging, use TOP
to limit the number of rows in the result. That will make each test run faster, which reduces your development time.
Launching test queries synchronously might make them start faster, too.
# Solution
# In this example, the WHERE clause is in the wrong place
query = """SELECT
TOP 3000
WHERE parallax < 1
source_id, ref_epoch, ra, dec, parallax
FROM gaiadr2.gaia_source
"""
In a WHERE
clause, you can use any of the SQL comparison operators; here are the most common ones:
Symbol | Operation |
---|---|
> |
greater than |
< |
less than |
>= |
greater than or equal |
<= |
less than or equal |
= |
equal |
!= or <> |
not equal |
Most of these are the same as Python, but some are not. In particular, notice that the equality operator is =
, not ==
.
Be careful to keep your Python out of your ADQL!
You can combine comparisons using the logical operators:
Finally, you can use NOT
to invert the result of a comparison.
Read about SQL operators here and then modify the previous query to select rows where bp_rp
is between -0.75
and 2
.
# Solution
# Here's a solution using > and < operators
query = """SELECT
TOP 10
source_id, ref_epoch, ra, dec, parallax
FROM gaiadr2.gaia_source
WHERE parallax < 1
AND bp_rp > -0.75 AND bp_rp < 2
"""
# And here's a solution using the BETWEEN operator
query = """SELECT
TOP 10
source_id, ref_epoch, ra, dec, parallax
FROM gaiadr2.gaia_source
WHERE parallax < 1
AND bp_rp BETWEEN -0.75 AND 2
"""
bp_rp
contains BP-RP color, which is the difference between two other columns, phot_bp_mean_mag
and phot_rp_mean_mag
.
You can read about this variable here.
This 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).
Selecting stars with bp-rp
less than 2 excludes many class M dwarf stars, which are low temperature, low luminosity. A star like that at GD-1's distance would be hard to detect, so if it is detected, it it more likely to be in the foreground.
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.
It is often better to write Python code that assembles a query for you. One useful tool for that is the string format
method.
As an example, we'll divide the previous query into two parts; a list of column names and a "base" for the query that contains everything except the column names.
Here's the list of columns we'll select.
columns = 'source_id, ra, dec, pmra, pmdec, parallax'
And here's the base; it's a string that contains at least one format specifier in curly brackets (braces).
query3_base = """SELECT
TOP 10
{columns}
FROM gaiadr2.gaia_source
WHERE parallax < 1
AND bp_rp BETWEEN -0.75 AND 2
"""
This base query contains one format specifier, {columns}
, which is a placeholder for the list of column names we will provide.
To assemble the query, we invoke format
on the base string and provide a keyword argument that assigns a value to columns
.
query3 = query3_base.format(columns=columns)
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.
The result is a string with line breaks. If you display it, the line breaks appear as \n
.
query3
'SELECT \nTOP 10 \nsource_id, ra, dec, pmra, pmdec\nFROM gaiadr2.gaia_source\nWHERE parallax < 1\n AND bp_rp BETWEEN -0.75 AND 2\n'
But if you print it, the line breaks appear as... line breaks.
print(query3)
SELECT TOP 10 source_id, ra, dec, pmra, pmdec FROM gaiadr2.gaia_source WHERE parallax < 1 AND bp_rp BETWEEN -0.75 AND 2
Notice that the format specifier has been replaced with the value of columns
.
Let's run it and see if it works:
job = Gaia.launch_job(query3)
print(job)
<Table length=10> 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
results = job.get_results()
results
source_id | ra | dec | pmra | pmdec |
---|---|---|---|---|
deg | deg | mas / yr | mas / yr | |
int64 | float64 | float64 | float64 | float64 |
5895272561481374080 | 213.2606587905109 | -56.55044401535715 | 0.3894438898301715 | 1.2299266281737415 |
5895261875598904576 | 213.5508930114549 | -56.61037780154348 | 0.16203641364393007 | -4.672602679543312 |
5895247444506644992 | 213.33215109206796 | -56.975347759380995 | -7.474003156859284 | -3.538080792097856 |
5895259470417635968 | 213.78815034206346 | -56.64585047451594 | -5.287202255231853 | -0.8163762113468646 |
5895265925746051584 | 213.17082359534547 | -56.74540885107754 | -7.880749306158471 | -4.8585444120179595 |
5895260913525974528 | 213.66936020541976 | -56.66655190442016 | -4.7820929042428215 | -1.5566420086447643 |
5895264212062283008 | 213.7755742121852 | -56.51570859067397 | -6.657690998559842 | -1.7616494482071872 |
5895253457497979136 | 213.30929960610283 | -56.78849448744587 | -5.242106718924749 | -0.18655636353898095 |
4143614130253524096 | 269.1749117455479 | -18.53415139972117 | 2.6164274510804826 | 1.3244248889980894 |
4065443904433108992 | 273.26868565443743 | -24.421651815402857 | -1.663096652191022 | -2.6514745376067683 |
Good so far.
This query always selects sources with parallax
less than 1. But suppose you want to take that upper bound as an input.
Modify query3_base
to replace 1
with a format specifier like {max_parallax}
. Now, when you call format
, add a keyword argument that assigns a value to max_parallax
, and confirm that the format specifier gets replaced with the value you provide.
# Solution
query_base = """SELECT
TOP 10
{columns}
FROM gaiadr2.gaia_source
WHERE parallax < {max_parallax} AND
bp_rp BETWEEN -0.75 AND 2
"""
query = query_base.format(columns=columns,
max_parallax=0.5)
print(query)
SELECT TOP 10 source_id, ra, dec, pmra, pmdec FROM gaiadr2.gaia_source WHERE parallax < 0.5 AND bp_rp BETWEEN -0.75 AND 2
This notebook demonstrates the following steps:
Making a connection to the Gaia server,
Exploring information about the database and the tables it contains,
Writing a query and sending it to the server, and finally
Downloading the response from the server as an Astropy Table
.
In the next lesson we will extend these queries to select a particular region of the sky.
If you can't download an entire dataset (or it's not practical) use queries to select the data you need.
Read the metadata and the documentation to make sure you understand the tables, their columns, and what they mean.
Develop queries incrementally: start with something simple, test it, and add a little bit at a time.
Use ADQL features like TOP
and COUNT
to test before you run a query that might return a lot of data.
If you know your query will return fewer than 2000 rows, you can run it synchronously, which might complete faster. If it might return more than 2000 rows, you should run it asynchronously.
ADQL and SQL are not case-sensitive, so you don't have to capitalize the keywords, but you should.
ADQL and SQL don't require you to break a query into multiple lines, but you should.
Jupyter notebooks can be good for developing and testing code, but they have some drawbacks. In particular, if you run the cells out of order, you might find that variables don't have the values you expect.
To mitigate these problems:
Make each section of the notebook self-contained. Try not to use the same variable name in more than one section.
Keep notebooks short. Look for places where you can break your analysis into phases with one notebook per phase.