201.04. DiaObject table#

201_04_DiaObject_table

201.4. DiaObject table¶

For the Rubin Science Platform at data.lsst.cloud.
Data Release: Data Preview 2
Container Size: Large
LSST Science Pipelines version: r30.0.10
Last verified to run: 2026-07-22
Repository: github.com/lsst/tutorial-notebooks
DOI: 10.11578/rubin/dc.20250909.20

Learning objective: To understand the contents of the DiaObject table and how to access it.

LSST data products: DiaObject

Packages: lsst.rsp, lsst.daf.butler

Credit: Originally developed by the Rubin Community Science team with feedback from Eric Bellm. 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¶

The DiaObject table contains $ugrizy$ light curve summary statistics for static-sky time-varying astronomical objects. These light curve summary statistics (e.g., mean, standard deviation in the flux measurements) are primarily derived from the difference-image flux measurements, and only for visits for which the object was detected at $>5\sigma$ in the difference image (i.e., not from forced photometry; from the DiaSource table, not from ForcedSourceOnDiaObject table). Difference-image detections (called "diaSources" and stored in the DiaSource table) are associated into "diaObjects" using a 1-arcsecond radius.

TAP table name: dp2.DiaObject
butler table name: dia_object
columns: 64
rows: 232,004,216

Related tutorials: The TAP and butler data access services are demonstrated in the 100-level "How to" tutorials. There are a 200-level tutorials on the difference images and the DiaSource and ForcedSourceOnDiaObject tables.

1.1. Import packages¶

Import standard python packages numpy, matplotlib, and astropy.

From the lsst package, import modules for the TAP service and the butler.

In [1]:

import re
import numpy as np
import matplotlib.pyplot as plt
from lsst.rsp import RSPDiscovery
from lsst.daf.butler import Butler

1.2. Define parameters and functions¶

Create an instance of the TAP service, and assert that it exists.

In [2]:

discovery = RSPDiscovery("dp2")
tap_service = discovery.get_tap_client()

Create an instance of the Rubin data butler, and assert that it exists.

In [3]:

butler = Butler("dp2", collections="dp2")

2. Schema (columns)¶

To browse the table schema visit the Rubin schema browser, or use the TAP service via the Portal Aspect or as demonstrated in Section 2.1.

2.1. Retrieve table schema¶

To retrieve the table schema, define a query for the schema columns of the DiaObject table and run the query job.

In [4]:

query = "SELECT column_name, datatype, description, unit " \
        "FROM tap_schema.columns " \
        "WHERE table_name = 'dp2.DiaObject'"
job = tap_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'

Job phase is COMPLETED

Retrieve the query results and display them as an astropy table with the to_table() attribute.

In [5]:

results = job.fetch_result().to_table()
results

Out[5]:

Table length=64

column_name	datatype	description	unit
str64	str64	str512	str64
diaObjectId	long	Unique identifier of this DiaObject.
ra	double	Right Ascension coordinate of the position of the DiaObject.	deg
dec	double	Declination coordinate of the position of the DiaObject.	deg
u_psfFluxMean	double	Weighted mean point-source model magnitude for u filter.	nJy
u_psfFluxMeanErr	double	Standard error of u_psfFluxMean.	nJy
u_psfFluxSigma	double	Standard deviation of the distribution of u_psfFlux.	nJy
u_psfFluxNdata	int	The number of u-band data points.
g_psfFluxMean	double	Weighted mean point-source model magnitude for g filter.	nJy
g_psfFluxMeanErr	double	Standard error of g_psfFluxMean.	nJy
g_psfFluxSigma	double	Standard deviation of the distribution of g_psfFlux.	nJy
g_psfFluxNdata	int	The number of g-band data points.
r_psfFluxMean	double	Weighted mean point-source model magnitude for r filter.	nJy
r_psfFluxMeanErr	double	Standard error of r_psfFluxMean.	nJy
r_psfFluxSigma	double	Standard deviation of the distribution of r_psfFlux.	nJy
r_psfFluxNdata	int	The number of r-band data points.
i_psfFluxMean	double	Weighted mean point-source model magnitude for i filter.	nJy
i_psfFluxMeanErr	double	Standard error of i_psfFluxMean.	nJy
i_psfFluxSigma	double	Standard deviation of the distribution of i_psfFlux.	nJy
i_psfFluxNdata	int	The number of i-band data points.
z_psfFluxMean	double	Weighted mean point-source model magnitude for z filter.	nJy
z_psfFluxMeanErr	double	Standard error of z_psfFluxMean.	nJy
z_psfFluxSigma	double	Standard deviation of the distribution of z_psfFlux.	nJy
...	...	...	...
u_psfFluxErrMean	float	Mean of the u band flux errors.	nJy
g_psfFluxMin	double	Minimum observed g band fluxes.	nJy
g_psfFluxMax	double	Maximum observed g band fluxes.	nJy
g_psfFluxMaxSlope	double	Maximum slope between g band flux obsevations max(delta_flux/delta_time).	nJy/d
g_psfFluxErrMean	float	Mean of the g band flux errors.	nJy
r_psfFluxMin	double	Minimum observed r band fluxes.	nJy
r_psfFluxMax	double	Maximum observed r band fluxes.	nJy
r_psfFluxMaxSlope	double	Maximum slope between r band flux obsevations max(delta_flux/delta_time).	nJy/d
r_psfFluxErrMean	float	Mean of the r band flux errors.	nJy
i_psfFluxMin	double	Minimum observed i band fluxes.	nJy
i_psfFluxMax	double	Maximum observed i band fluxes.	nJy
i_psfFluxMaxSlope	double	Maximum slope between i band flux obsevations max(delta_flux/delta_time).	nJy/d
i_psfFluxErrMean	float	Mean of the i band flux errors.	nJy
z_psfFluxMin	double	Minimum observed z band fluxes.	nJy
z_psfFluxMax	double	Maximum observed z band fluxes.	nJy
z_psfFluxMaxSlope	double	Maximum slope between z band flux obsevations max(delta_flux/delta_time).	nJy/d
z_psfFluxErrMean	float	Mean of the z band flux errors.	nJy
y_psfFluxMin	double	Minimum observed y band fluxes.	nJy
y_psfFluxMax	double	Maximum observed y band fluxes.	nJy
y_psfFluxMaxSlope	double	Maximum slope between y band flux obsevations max(delta_flux/delta_time).	nJy/d
y_psfFluxErrMean	float	Mean of the y band flux errors.	nJy
nDiaSources	long	Total number of DiaSources associated with this DiaObject.

The table displayed above has been truncated.

Option to print every column name as a list.

In [6]:

# for col in results['column_name']:
#    print(col)

Option to use the regular expressions package re to find all column names that hold the psfFluxMean for the six filters.

In [7]:

# for col in results['column_name']:
#     if re.fullmatch('[ugrizy]_psfFluxMean', col):
#         print(col)

Option to search column names that contain the string defined as temp.

In [8]:

# temp = 'Max'
# temp = 'Slope'
# temp = 'psfFluxMean'
# for col in results['column_name']:
#     if re.search(temp, col):
#         print(col)

Delete the job, but not the results.

In [9]:

del query
job.delete()

2.2. Column descriptions¶

There are few columns in the DiaObject table, and all are described below.

2.2.1. Identifier¶

The long integer that uniquely identifies each row of the DiaObject table.

diaObjectId

2.2.2. Coordinates¶

The sky coordinates in decimal degrees for each diaObject.

ra
dec

2.2.3. Number of detections¶

Total number of detections across all filters (i.e., number of associated diaSources.

nDiaSources

2.2.4. Flux statistics¶

A limited number of flux statistics are calculated for DP2, and they are calculated from the $>5\sigma$ detections in the DiaSource table (not from the forced photometry). Fluxes are used for difference-image photometry (not magnitudes) because difference-images fluxes can be negative.

Several types of statistics on the Point Spread Function (PSF) fluxes measured on the difference image detections in the DiaSource table are calculated for diaObjects, as follows.

[f]_psfFluxMean, [f]_psfFluxMeanErr: Weighted mean of PSF flux in band [f], and error on the mean.
[f]_psfFluxSigma: Standard deviation of the distribution of [f]_psfFlux values.
[f]_psfFluxErrMean: Mean of the flux errors.
[f]_psfFluxMax and Min: Maximum and minimum PSF flux.
[f]_psfFluxMaxSlope: Maximum slope between any two sequential [f]-band observations.
[f]_psfFluxNdata: Number of [f]-band data points (from the DiaSource table; number of [f]-band detections).

Average fluxes based on PSF photometry on the visit images (i.e., the "direct" or "science" image) at the position of the detected diaSources associated with a diaObject are also provided ([f]_scienceFluxMean). Use caution with all science-image fluxes for time-varying objects as there may be contamination from static sources (e.g., host galaxies).

2.3. Descriptions and units¶

For a subset of the key columns show the table of their descriptions and units.

In [10]:

col_list = set(['diaObjectId', 'ra', 'dec', 'nDiaSources',
                'r_psfFluxMean', 'r_psfFluxMeanErr', 'r_psfFluxSigma', 'r_psfFluxErrMean',
                'r_psfFluxMax', 'r_psfFluxMin', 'r_psfFluxMaxSlope', 'r_psfFluxNdata'])
tx = [i for i, item in enumerate(results['column_name']) if item in col_list]
results[tx]

Out[10]:

Table length=12

column_name	datatype	description	unit
str64	str64	str512	str64
diaObjectId	long	Unique identifier of this DiaObject.
ra	double	Right Ascension coordinate of the position of the DiaObject.	deg
dec	double	Declination coordinate of the position of the DiaObject.	deg
r_psfFluxMean	double	Weighted mean point-source model magnitude for r filter.	nJy
r_psfFluxMeanErr	double	Standard error of r_psfFluxMean.	nJy
r_psfFluxSigma	double	Standard deviation of the distribution of r_psfFlux.	nJy
r_psfFluxNdata	int	The number of r-band data points.
r_psfFluxMin	double	Minimum observed r band fluxes.	nJy
r_psfFluxMax	double	Maximum observed r band fluxes.	nJy
r_psfFluxMaxSlope	double	Maximum slope between r band flux obsevations max(delta_flux/delta_time).	nJy/d
r_psfFluxErrMean	float	Mean of the r band flux errors.	nJy
nDiaSources	long	Total number of DiaSources associated with this DiaObject.

Clean up.

In [11]:

del col_list, tx, results

3. Data access¶

The DiaObject table is available via the TAP service and the butler.

Recommended access method: TAP.

3.1. Advisory: avoid full-table queries¶

Avoid full-table queries. Always include spatial constraints.

Skipping spatial constraints is not a good habit to form, because future data release DiaObject tables will contain billions of rows.

3.2. TAP (Table Access Protocol)¶

The DiaObject table is stored in Qserv and accessible via the TAP services using ADQL queries.

Include spatial constraints: Qserv stores catalog data sharded by coordinate (RA, Dec), so ADQL query statements that include constraints by coordinate do not requre a whole-catalog search and are typically faster (and can be much faster) than ADQL query statements which only include constraints for other columns. Use either an ADQL cone or polygon search for faster queries (do not use WHERE ... BETWEEN statements to set boundaries on RA and Dec).

3.2.1. Demo query¶

Define a query to return, as an example, $r$-band flux statistics and other key columns for a sample from the DiaObject table.

Impose spatial constraints: search within 0.2 degrees of the center of Extended Chandra Deep Field South (ECDFS), RA, Dec = $53.13, -28.10$. (This is not a special region, just used as an example.) Also impose the constraint that a diaObject must be detected more than 5 times in the $r$-band to be returned.

In [12]:

query = """SELECT diaObjectId, ra, dec, nDiaSources,
           r_psfFluxMean, r_psfFluxMeanErr, r_psfFluxSigma, r_psfFluxErrMean
           r_psfFluxMax, r_psfFluxMin, r_psfFluxMaxSlope, r_psfFluxNdata
           FROM dp2.DiaObject
           WHERE CONTAINS(POINT('ICRS', ra, dec), CIRCLE('ICRS', 53.13, -28.10, 0.2)) = 1
           AND r_psfFluxNdata > 5"""

In [13]:

job = tap_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()

Job phase is COMPLETED

Fetch the results as an astropy table.

In [14]:

assert job.phase == 'COMPLETED'
results = job.fetch_result().to_table()
print(len(results))

Option to display the table.

In [15]:

results

Out[15]:

Table length=143

diaObjectId	ra	dec	nDiaSources	r_psfFluxMean	r_psfFluxMeanErr	r_psfFluxSigma	r_psfFluxMax	r_psfFluxMin	r_psfFluxMaxSlope	r_psfFluxNdata
	deg	deg		nJy	nJy	nJy	nJy	nJy	nJy / d
int64	float64	float64	int64	float64	float64	float64	float32	float64	float64	int32
753904749157285963	53.14832869656018	-28.269922609388026	65	1452.173095703125	90.1224136352539	2293.283203125	399.116	-4651.71630859375	7349594.0546763055	15
755369504803913742	53.317067913368284	-28.065950468747108	50	1347.314697265625	96.15044403076172	895.3720703125	344.23	-392.6014099121094	5672649.624738062	12
755369504803913739	53.33693488938166	-28.177236196683403	82	-1086.5665283203125	226.78231811523438	2771.3349609375	600.068	-5173.630859375	672289.1952654346	6
755369504803913736	53.278304420554484	-28.153992727748907	68	792.35302734375	53.17058563232422	2240.653076171875	220.214	-1986.34228515625	714026.389804091	16
755369504803913829	53.32501128602706	-28.119075044574025	41	81.85987091064453	74.57477569580078	1235.632568359375	250.089	-1603.5677490234375	2738816.39066981	11
755369504803913773	53.29169666040039	-28.147946939339093	47	2660.990478515625	149.6099090576172	1558.83203125	505.097	510.4138488769531	160.2960186757777	10
755369504803913779	53.33076218263827	-28.11934205117699	90	11265.4365234375	228.516845703125	3788.96337890625	1066.3	5264.23193359375	11995031.701225795	19
755369504803913766	53.35594282265226	-28.092876695563337	47	34017.62109375	375.82025146484375	8091.06591796875	1546.95	21943.568359375	41806365.64615777	15
755369504803913788	53.28594620840545	-28.16034732567813	90	3134.499755859375	151.6095733642578	2816.849365234375	490.329	-128.31414794921875	1025336.091491876	9
755369504803913795	53.345871637348694	-28.06365082391602	64	-707.553955078125	134.01622009277344	1970.005859375	339.339	-2759.22314453125	9794073.172698513	6
755369504803913790	53.28928332887084	-28.123854674330964	74	-5756.42529296875	281.1304626464844	4956.833984375	1124.04	-11155.8388671875	26606819.553509325	15
755369642242867310	53.07382012546077	-28.065094906777208	34	811.4575805664062	120.51480865478516	2210.29833984375	397.763	-3830.444091796875	4828912.136808052	9
755369642242867268	53.07078140715689	-28.184983326153162	58	-465.148193359375	85.08258819580078	1350.8214111328125	311.77	-2041.347412109375	7342760.725445082	13
755369573523390753	53.10697024119684	-28.13976912631172	8	-1487.439208984375	93.8515625	133.97791300624658	254.755	-1751.6385498046875	244435.93006496434	7
755369642242867220	53.08663547242815	-28.109397928460112	74	-2153.93603515625	84.38945770263672	772.01953125	352.277	-3027.033203125	1806874.1233449995	17
755369573523390565	53.15537450638214	-28.14646799841838	77	-4579.794921875	96.74684143066406	138.47796771247502	279.127	-4779.17236328125	117088.87022238046	6
755369573523390561	53.184230260966935	-28.174641393612728	24	-485.1006164550781	83.73290252685547	1439.6682610730215	208.378	-1476.043701171875	569978.9185009337	6
755369573523390554	53.27314636928788	-28.175025854605916	28	-1726.572021484375	108.04766082763672	904.509388707277	269.926	-2982.804931640625	71890.08016981928	6
755369573523390551	53.10558381452512	-28.123553494762746	28	1445.5987548828125	109.66188049316406	526.3585184536604	269.138	445.97369384765625	158630.57080357178	6
755369573523390549	53.21015380153802	-28.166538342012714	67	-2233.6611328125	101.32133483886719	1240.1850850029646	388.32	-4264.63037109375	908227.8840665489	14
755369573523390585	53.16445521116566	-28.158445898934758	40	-1505.214111328125	74.87256622314453	925.4451937386344	292.783	-2637.38427734375	9050792.102908118	15
...	...	...	...	...	...	...	...	...	...	...
755370329437634677	52.96665960571907	-27.96385690726116	11	1671.095947265625	74.33536529541016	281.780625757808	224.591	1151.5048828125	1812230.0651059921	9
755370329437634671	53.047748309684984	-27.975415265940793	26	-202.60452270507812	100.34101867675781	1571.735859364193	251.904	-1522.898681640625	2881452.1273804307	6
755370329437634712	53.03908426287628	-27.959472587952106	16	-1162.4127197265625	97.1867446899414	500.3712379624397	293.932	-1945.5924072265625	451172.897629002	9
753904817876762673	53.041801736092566	-28.283187846779615	87	-333.50543212890625	292.84564208984375	8671.607421875	1219.75	-9973.9609375	11277885.47519608	16
755369642242867236	53.01504717320839	-28.161064295052206	64	-1378.846435546875	291.3768005371094	7380.541015625	1158.67	-9190.3759765625	38569339.65934865	15
755369642242867229	52.93201400135129	-28.09200303469767	91	466.9998474121094	219.19773864746094	5881.619140625	954.939	-10358.580078125	16162920.576765658	18
755369642242867241	53.037238852224476	-28.087686425024565	57	542.850341796875	75.90445709228516	1677.7506103515625	312.028	-2131.47265625	8460600.536757926	15
755369642242867239	52.955175463803315	-28.18622025017223	92	-2045.4259033203125	140.6403350830078	3333.327880859375	509.885	-4794.95703125	1439171.1190151158	10
755369642242867215	53.0183572617236	-28.085546962216817	76	13025.775390625	290.8138427734375	7332.29833984375	1277.62	-297.9818420410156	26360312.123108085	18
755369642242867246	52.939860440097355	-28.12765837724968	72	416.6441650390625	111.24632263183594	1516.1920166015625	276.935	-1392.1236572265625	46923.14890119695	6
755369642242867270	52.915465524285345	-28.10265553429223	57	2212.272705078125	158.49856567382812	1784.3719482421875	420.536	-83.61923217773438	3378064.524850892	7
755369642242867470	52.986114257377	-28.158321097494763	80	257.8424377441406	277.3099670410156	5602.517578125	1085.02	-15901.640625	54604113.77074105	14
755369642242867334	53.0322195223229	-28.11756963624152	18	-1332.7474365234375	101.0776138305664	291.2225036621094	250.055	-1584.59814453125	372421.64815243613	6
755369642242867306	52.96706721760997	-28.10591250041826	43	1771.8594970703125	156.97015380859375	902.9248046875	384.763	390.86029052734375	1249678.1663150864	6
755369642242867304	52.915149142426834	-28.15776288677715	31	-1892.4197998046875	109.08297729492188	879.3125610351562	368.647	-3202.69091796875	1707679.7622829515	11
755369642242867303	52.92387316378402	-28.14181947910142	68	-348.50323486328125	143.35914611816406	1486.083740234375	361.295	-1661.521240234375	69572.70894125159	6
755369642242867361	53.03484115001042	-28.097884038734115	23	967.0272216796875	71.70084381103516	801.0784912109375	218.872	-1107.025390625	21046.73298647284	9
755368955048099842	52.95639372755654	-28.193807206510982	79	1592.03759765625	149.0597686767578	2705.080322265625	472.759	-5168.74853515625	5467921.5564886145	10
755368955048099873	52.97256713957272	-28.23271250863613	97	-1564.447998046875	292.197265625	4573.9423828125	848.132	-8610.498046875	4246480.1344348015	8
755368955048099860	53.02383257525794	-28.189513811093505	44	1418.7335205078125	95.9287338256836	317.6007385253906	238.662	1077.6998291015625	22229.822133445992	6
755368955048099889	52.98245983757812	-28.235556215644692	81	-1542.438232421875	181.07656860351562	2586.773193359375	572.749	-4588.62548828125	6652470.451894295	9
755368955048099876	53.03351939853918	-28.243267610701082	91	-10179.51953125	248.3534393310547	7034.67724609375	1203.83	-25118.478515625	38772002.23159717	21

Plot a histogram of the number of detections per diaObject.

In [16]:

plt.figure(figsize=(4, 3))
plt.hist(results['nDiaSources'], bins=20, log=True, color='gray')
plt.xlabel('nDiaSources')
plt.ylabel('Counts of diaObjects')
plt.tight_layout()
plt.show()

No description has been provided for this image

Figure 1: Histogram of the number of detections (nDiaSources) per diaObject.

Plot histograms for a subset of the $r$-band light curve summary statistics.

In [17]:

fig, ax = plt.subplots(3, 2, figsize=(8, 9))
axes = ['r_psfFluxMean', 'r_psfFluxSigma',
        'r_psfFluxMax', 'r_psfFluxMin',
        'r_psfFluxMaxSlope', 'r_psfFluxNdata']
a = 0
for i in range(3):
    for j in range(2):
        ax[i, j].hist(results[axes[a]], bins=30, color='gray')
        ax[i, j].set_xlabel(axes[a])
        ax[i, 0].set_ylabel('Counts of diaObjects')
        a += 1
plt.tight_layout()
plt.show()

Figure 2: Histograms of the $r$-band light curve summary statistics for the retrieved sample, to show the range of values.

Clean up.

In [18]:

job.delete()
del results

3.3. Butler¶

TAP is the recommended way to access the object table, but the butler is a convenient way to retrieve all the objects in a given tract.

Show that the only dimension for the dia_object table is the skymap's tract, and that it is required.

In [19]:

butler.get_dataset_type('dia_object')

Out[19]:

DatasetType('dia_object', {skymap, tract}, ArrowAstropy)

In [20]:

butler.get_dataset_type('dia_object').dimensions.required

Out[20]:

{skymap, tract}

3.3.1. Demo query¶

Include spatial constraints: The butler dia_object table contents are stored and retrieved by individual tract.

Retrieve all dataset_refs for dia_object tables for tracts that overlap the coordinates near the center of the ECDFS field.

In [21]:

query = "tract.region OVERLAPS POINT(53.13, -28.10)"
refs = butler.query_datasets("dia_object", where=query)

Show that one tract overlaps the coordinates.

In [22]:

for ref in refs:
    print(ref.dataId)

{skymap: 'lsst_cells_v2', tract: 5063}

Define the columns to retrieve.

In [23]:

col_list = ['diaObjectId', 'ra', 'dec', 'nDiaSources',
            'r_psfFluxMean', 'r_psfFluxMeanErr',
            'r_psfFluxSigma', 'r_psfFluxErrMean',
            'r_psfFluxMax', 'r_psfFluxMin', 'r_psfFluxMaxSlope', 'r_psfFluxNdata']

Get the data from the butler, and retrieve it as an astropy table.

In [24]:

results = butler.get(refs[0],
                     parameters={'columns': col_list},
                     storageClass='ArrowAstropy')
print(len(results))

Option to display the results.

Notice that because no columns constraints can be applied (e.g., r_psfFluxNdata $>5$, as in the TAP query), many values are NAN because there were insufficient detections to derive a flux statistic.

In [25]:

# results

Constrain the results to diaObjects with more than 5 $r$-band detections.

In [26]:

tx = np.where(results['r_psfFluxNdata'] > 5)[0]
print(len(tx))

To visualize the data, plot the sky coordinates and mean vs. max r-band fluxes for the diaObjects in the tract.

In [27]:

fig, ax = plt.subplots(1, 2, figsize=(7, 3))
ax[0].plot(results['ra'][tx], results['dec'][tx],
           'o', ms=2, mew=0, alpha=0.4, color='grey')
ax[0].set_xlabel('Right Ascension')
ax[0].set_ylabel('Declination')
ax[1].plot(results['r_psfFluxMean'][tx],
           results['r_psfFluxMax'][tx],
           'o', ms=2, mew=0, alpha=0.4, color='grey')
ax[1].set_xlabel('r_psfFluxMean')
ax[1].set_ylabel('r_psfFluxMax')
ax[1].ticklabel_format(axis='both', style='sci', scilimits=(0, 0))
plt.tight_layout()
plt.show()

Figure 3: At left, the RA vs. Dec of retrieved objects demonstrates the extent of the tract for which they were retrieved from the Butler. At right, the maximum vs. the mean r-band difference-image fluxes are plotted just as an example.

In [28]:

del query, refs, col_list, results, tx

In [ ]: