Basic queries
Overview
Teaching: 65 min
Exercises: 25 minQuestions
How can we select and download the data we want from the Gaia server?
Objectives
Compose a basic query in ADQL/SQL.
Use queries to explore a database and its tables.
Use queries to download data.
Develop, test, and debug a query incrementally.
1. Queries
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.
Outline
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.
Query Language
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.
Using Jupyter
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.
Connecting to Gaia
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.
Databases and Tables
What is a database, anyway? Most generally, it can be any collection of data, but when we are talking about ADQL or SQL:
-
A database is a collection of one or more named tables.
-
Each table is a 2-D array with one or more named columns of data.
We can use Gaia.load_tables
to get the names of the tables in the
Gaia database. With the option only_names=True
, it loads
information about the tables, called “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
[Output truncated]
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
Columns
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
[Output truncated]
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.
Exercise (2 minutes)
One of the other tables we’ll use is
gaiadr2.panstarrs1_original_valid
. Useload_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)
Writing queries
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
[Output truncated]
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.
Repetition
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
<Table length=10>
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
[Output truncated]
Each column has a name, units, and a data type.
For example, the units of ra
and dec
are degrees, and their data
type is float64
, which is a 64-bit floating-point
number, used
to store measurements with a fraction part.
This information comes from the Gaia database, and has been stored in
the Astropy Table
by Astroquery.
Exercise (3 minutes)
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 """
Asynchronous queries
launch_job
asks the server to run the job “synchronously”, which
normally means it runs immediately. But synchronous jobs are limited
to 2000 rows. For queries that return more rows, you should run
“asynchronously”, which mean they might take longer to get started.
If you are not sure how many rows a query will return, you can use the
SQL command COUNT
to find out how many rows are in the result
without actually returning them. We’ll see an example 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
andpmdec
, which are proper motions along the axes ofra
anddec
. -
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
<Table length=10>
source_id ra ... parallax radial_velocity
deg ... mas km / s
int64 float64 ... float64 float64
------------------- ------------------ ... -------------------- ---------------
5895270396817359872 213.08433715252883 ... 2.041947005434917 --
5895272561481374080 213.2606587905109 ... 0.15693467895110133 --
5895247410183786368 213.38479712976664 ... -0.19017525742552605 --
5895249226912448000 213.41587389088238 ... -- --
5895261875598904576 213.5508930114549 ... -0.29471722363529257 --
5895258302187834624 213.87631129557286 ... 0.6468437015289753 --
[Output truncated]
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.”
Exercise (5 minutes)
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 """
Operators
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:
- AND: true if both comparisons are true
- OR: true if either or both comparisons are true
Finally, you can use NOT
to invert the result of a comparison.
Exercise (5 minutes)
Read about SQL operators here and then modify the previous query to select rows where
bp_rp
is between-0.75
and2
.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.
Formatting queries
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 truncated]
results = job.get_results()
results
<Table length=10>
source_id ra ... pmdec
deg ... mas / yr
int64 float64 ... float64
------------------- ------------------ ... --------------------
5895272561481374080 213.2606587905109 ... 1.2299266281737415
5895261875598904576 213.5508930114549 ... -4.672602679543312
5895247444506644992 213.33215109206796 ... -3.538080792097856
5895259470417635968 213.78815034206346 ... -0.8163762113468646
5895265925746051584 213.17082359534547 ... -4.8585444120179595
5895260913525974528 213.66936020541976 ... -1.5566420086447643
[Output truncated]
Good so far.
Exercise (10 minutes)
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 replace1
with a format specifier like{max_parallax}
. Now, when you callformat
, add a keyword argument that assigns a value tomax_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)
Summary
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.
Key Points
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
andCOUNT
to test before you run a query that might return a lot of data.If you know your query will return fewer than 3000 rows, you can run it synchronously, which might complete faster (but it doesn’t seem to make much difference). If it might return more than 3000 rows, you should run it asynchronously.
ADQL and SQL are not case-sensitive, so you don’t have to capitalize the keywords, but you should.
ADQL and SQL don’t require you to break a query into multiple lines, but you should.
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.