102.3. Table joins with TAP#

102_3_Table_joins_with_TAP

102.3. Table joins with TAP¶

For the Rubin Science Platform at data.lsst.cloud.
Data Release: Data Preview 1
Container Size: large
LSST Science Pipelines version: r29.1.1
Last verified to run: 2025-06-25
Repository: github.com/lsst/tutorial-notebooks

Learning objective: How to join tables in TAP queries on catalogs.

LSST data products: ForcedSourceOnDiaObject, Visit, DiaSource, SSSource tables

Packages: lsst.rsp.get_tap_service

Credit: Originally developed by the Rubin Community Science team. Please consider acknowledging them if this notebook is used for the preparation of journal articles, software releases, or other notebooks.

Get Support: Everyone is encouraged to ask questions or raise issues in the Support Category of the Rubin Community Forum. Rubin staff will respond to all questions posted there.

1. Introduction¶

TAP provides standardized access to catalog data for discovery, search, and retrieval. Full documentation for TAP is provided by the International Virtual Observatory Alliance (IVOA).

ADQL (Astronomy Data Query Language) is similar to SQL (Structured Query Langage). The documentation for ADQL includes more information about syntax and keywords. Not all ADQL functionality is supported yet in the preview-era RSP.

1.1. Table joins¶

Tables can be joined when they have an index or identifier column in common.

There are four main types of table joins in ADQL/SQL:

inner: will return rows that appear in both tables and meet constraints (intersection set)
outer: will return rows from either table that meet constraints (union set)
left: will return rows from the first table that meet constraints, plus matching rows from the second table
right: will return rows from the second table that meet constraints, plus matching rows from the first table

When JOIN is used in an ADQL query, it is an inner join by default.

Generic example: To include a table join in an ADQL query requires the specification of which column to be "joined on". For example, the query below retrieves all rows from "table1" that are within 0.002 degrees of coordinates ra_cen, dec_cen, but only if there is a row in "table2" which has the same value of id (i.e., an inner join, which is default). Columns mag and band from "table2" are also returned for these matching rows.

SELECT t1.id, t1.ra, t1.dec, t2.mag, t2.band
FROM catalog.table1 AS t1
JOIN catalog.table2 AS t2 ON t1.id = t2.id
WHERE CONTAINS(POINT('IRCS', t1.ra, t1.dec), CIRCLE('ICRS', ra_cen, dec_cen, 0.002)) = 1

Related tutorials: The other 100-level tutorials in this series demonstrate the TAP basics. The 200-level tutorials for the individual catalogs list which tables are joinable with them.

1.2. Import packages¶

Import general python packages numpy, matplotlib, the RSP TAP service, and LSST utility functions for plotting.

In [1]:

import numpy as np
import matplotlib.pyplot as plt
from lsst.rsp import get_tap_service
from lsst.utils.plotting import (get_multiband_plot_colors,
                                 get_multiband_plot_symbols)

1.3. Define parameters¶

Instantiate the TAP service.

In [2]:

service = get_tap_service("tap")
assert service is not None

Define filter names, plot markers, and colors for plotting.

In [3]:

filter_names = ['u', 'g', 'r', 'i', 'z', 'y']
filter_colors = get_multiband_plot_colors()
filter_symbols = get_multiband_plot_symbols()

2. Use case: light curves¶

Light curves are an object's brightness over time.

The tables of forced photometry measurements in all individual visit_images and difference_images, which should be used for LSST light curves, are:

ForcedSource: at the locations of all Objects
ForcedSourceOnDiaObject: at the locations of all DiaObjects

They both need to be joined with the Visit table on the shared column of the visit identifier (visit or visitId) to obtain the time (MJD) of the observation, and plot the light curve.

2.1. Example: join Visit with ForcedSourceOnDiaObject¶

For a known variable star that was detected with difference image analysis (DIA) and has a diaObjectId of 614435753623027782, join the ForcedSourceOnDiaObject table with the Visit table.

Retrieve the band (filter) and psfFlux (direct-image forced PSF photometry) for every visit from the ForcedSourceOnDiaObject.

Join to the Visit table on the visit identifier to retrieve each visit's Modified Julian Date, so that flux vs. time can be plotted (i.e., the light curve).

In [4]:

query = """SELECT fsodo.band, fsodo.psfFlux, vis.expMidptMJD
        FROM dp1.ForcedSourceOnDiaObject as fsodo
        JOIN dp1.Visit as vis
        ON vis.visit = fsodo.visit
        WHERE fsodo.diaObjectId = 614435753623027782"""
print(query)

SELECT fsodo.band, fsodo.psfFlux, vis.expMidptMJD
        FROM dp1.ForcedSourceOnDiaObject as fsodo
        JOIN dp1.Visit as vis
        ON vis.visit = fsodo.visit
        WHERE fsodo.diaObjectId = 614435753623027782

In [5]:

job = service.submit_job(query)
job.run()
job.wait(phases=['COMPLETED', 'ERROR'])
print('Job phase is', job.phase)
if job.phase == 'ERROR':
    job.raise_if_error()
assert job.phase == 'COMPLETED'
results = job.fetch_result().to_table()
print(len(results))

Job phase is COMPLETED
225

In [6]:

results.colnames

Out[6]:

['band', 'psfFlux', 'expMidptMJD']

Plot the light curve.

In [7]:

fig = plt.figure(figsize=(4, 3))
for filt in filter_names:
    fx = np.where(results['band'] == filt)[0]
    plt.plot(results['expMidptMJD'][fx],
             results['psfFlux'][fx], filter_symbols[filt],
             color=filter_colors[filt], label=filt)
plt.xlabel('MJD')
plt.ylabel('PSF direct image flux (nJy)')
plt.legend(ncols=2)
plt.title('DiaObject 614435753623027782')
plt.show()

No description has been provided for this image

Figure 1: A light curve created via table join.

3. Use case: moving objects¶

All of the Solar System catalogs are joinable to each other on identifiers diaSourceId (individual detections in the difference images), ssObjectId (associations of detections into single moving objects).

3.1. Example: join SSSource and DiaSource¶

Use the ssObjectId of a known moving object, 21163620217073748, and retrieve the heliocentric X and Y coordinates at the times of the individual observations from the SSSource table. Join to the DiaSource table to retrieve the MJD times of the individual observations. Sort the results by MJD.

In [8]:

query = """SELECT sss.heliocentricX, sss.heliocentricY,
        dias.midpointMjdTai
        FROM dp1.SSSource as sss
        JOIN dp1.DiaSource as dias
        ON dias.diaSourceId = sss.diaSourceId
        WHERE sss.ssObjectId = 21163620217073748
        ORDER BY dias.midpointMjdTai ASC"""
print(query)

SELECT sss.heliocentricX, sss.heliocentricY,
        dias.midpointMjdTai
        FROM dp1.SSSource as sss
        JOIN dp1.DiaSource as dias
        ON dias.diaSourceId = sss.diaSourceId
        WHERE sss.ssObjectId = 21163620217073748
        ORDER BY dias.midpointMjdTai ASC

In [9]:

job = service.submit_job(query)
job.run()
job.wait(phases=['COMPLETED', 'ERROR'])
print('Job phase is', job.phase)
if job.phase == 'ERROR':
    job.raise_if_error()
assert job.phase == 'COMPLETED'
results = job.fetch_result().to_table()
print(len(results))

Job phase is COMPLETED
133

In [10]:

# results

Plot the heliocentric X and Y coordinates for the individual observations, and color the points by days since first detection to visualize the orbit.

In [11]:

day1 = np.floor(results['midpointMjdTai'][0])
fig = plt.figure(figsize=(4, 3))
plt.scatter(results['heliocentricX'],
            results['heliocentricY'],
            c=results['midpointMjdTai']-day1,
            cmap='viridis', alpha=0.5, edgecolors='none')
plt.xlabel('Heliocentric X')
plt.ylabel('Heliocentric Y')
plt.colorbar(label='MJD - '+str(day1))
plt.show()

Figure 2: A moving object's trajectory created via table join.

In [ ]: