201.10. Visit table#

201_10_Visit_table

201.10. Visit 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: 2026-07-24
Repository: github.com/lsst/tutorial-notebooks
DOI: 10.11578/rubin/dc.20250909.20

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

LSST data products: Visit

Packages: lsst.rsp, lsst.daf.butler

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¶

A Visit refers to an observation of the sky with the camera.

The Visit table contains data about the individual observations, such as the coordinates, date, time, filter, camera rotation, exposure time, and airmass. This table does not contain the image data or data for astrophysical sources.

The contents of the Visit table are associated with the boresight of the entire focal plane. By contrast, the VisitDetector table contains data by individual detectors of the camera, instead of the entire focal plane.

TAP table name: dp2.Visit
Butler table name: visit_table
columns: 15
rows: 28,698

Related tutorials: The tutorial for the VisitDetector table is also in this series. The TAP and Butler data access services are demonstrated in the 100-level "How to" tutorials.

1.1. Import packages¶

Import standard python packages re, 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
from lsst.utils.plotting import (get_multiband_plot_colors,
                                 get_multiband_plot_symbols)

1.2. Define parameters and functions¶

Create an instance of the TAP service.

In [2]:

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

Create an instance of the Rubin data Butler.

In [3]:

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

Define the colors and symbols to represent the LSST filters in plots.

In [4]:

filter_colors = get_multiband_plot_colors()
filter_names = filter_colors.keys()
filter_symbols = get_multiband_plot_symbols()

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 Visit table and run the query job.

In [5]:

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

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

In [6]:

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

Out[6]:

Table length=15

column_name	datatype	description	unit
str64	str64	str512	str64
airmass	double	Airmass of the observed line of sight.
altitude	double	Altitude of focal plane center at the middle of the visit.	deg
azimuth	double	Azimuth of focal plane center at the middle of the visit.	deg
band	char	Name of the band used to take the visit where this source was measured. Abstract filter that is not associated with a particular instrument.
dec	double	Declination of focal plane center	deg
expMidpt	char	Midpoint time for exposure at the fiducial center of the focal plane array. TAI, accurate to 10ms.
expMidptMJD	double	Midpoint time for exposure at the fiducial center of the focal plane array in MJD. TAI, accurate to 10ms.	d
expTime	double	Spatially-averaged duration of visit, accurate to 10ms.	s
obsStart	char	Start time of the visit at the fiducial center of the focal plane array, TAI, accurate to 10ms.
obsStartMJD	double	Start of the exposure in MJD, TAI, accurate to 10ms.	d
physical_filter	char	ID of physical filter, the filter associated with a particular instrument.
ra	double	Right Ascension of focal plane center.	deg
skyRotation	double	Sky rotation angle.	deg
visit	long	Unique identifier.
zenithDistance	double	Zenith distance at the middle of the visit.	deg

The table displayed above has not been truncated, as it is only 15 rows long.

Option to use the regular expressions package re to search for columns for which the description contains the string temp.

In [7]:

# temp = 'time'
# temp = 'filt'
# temp = 'exp'
# for c, desc in enumerate(results['description']):
#     if re.search(temp, desc):
#         print(results['column_name'][c])

Delete the job and the results.

In [8]:

del query, results
job.delete()

3. Data access¶

The Visit table is available via the TAP service and the Butler.

Recommended access method: TAP.

3.1. TAP (Table Access Protocol)¶

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

3.1.2. Demo query¶

Avoid full-table queries. Although the Visit table is relatively small compared to source measurement tables, it is good practice to always include spatial constraints and only retrieve necessary columns.

Search for g- and r-band visits within 3 degrees of the center of the Extended Chandra Deep Field South (ECDFS) field, RA, Dec = $53.13 -28.10$. Return the visit identifier, RA, Dec, and the midpoint time of the exposure in MJD.

In [9]:

query = "SELECT visit, ra, dec, band, expMidptMJD " \
        "FROM dp2.Visit " \
        "WHERE CONTAINS(POINT('ICRS', ra, dec), " \
        "CIRCLE('ICRS', 53.13, -28.10, 3)) = 1 " \
        "AND (band = 'r' OR band = 'g')"
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 [10]:

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

Option to display the table.

In [11]:

# results

As an example, plot the RA and Dec for the g- and r-band visits, and the cumulative distribution of observation MJDs.

In [12]:

fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(7, 3))
for filt in filter_names:
    fx = np.where(results['band'] == filt)[0]
    if len(fx) > 0:
        ax1.plot(results['ra'][fx], results['dec'][fx],
                 filter_symbols[filt], ms=5, mew=0, alpha=0.4,
                 color=filter_colors[filt], label=filt)
ax1.set_xlabel('Right Ascension')
ax1.set_ylabel('Declination')
ax1.legend(loc='best', handletextpad=0)
ax2.hist(results['expMidptMJD'], len(results),
         histtype='step', cumulative=True)
ax2.set_xlabel('MJD')
ax2.set_ylabel('Number of Visits')
plt.tight_layout()
plt.show()

No description has been provided for this image

Figure 1: At left, the RA vs. Dec of the g- and r-band visits (central boresight of the focal plane). At right, the cumulative number of visits as a function of time in MJD.

Clean up.

In [13]:

job.delete()
del query, results

3.2.2. Joinable tables¶

The Visit table can be joined with any table that has a visit or visitId column (they have the same values). This includes the VisitDetector, Source, ForcedSource, DiaSource, and ForcedSourceOnDiaObject tables.

VisitDetector¶

For example, query for visits in the COSMOS field (RA, Dec = 150.1, 2.1 deg) on a specific MJD. Join to the VisitDetector table and return the detector column (the detector number) and the average seeing and zeropoint over all detectors per visit. This particular query can be done on VisitDetector only, without the join -- the join here is just as a demonstration.

In [14]:

query = """SELECT v.visit, v.expMidptMJD, v.band, vd.detector,
        AVG(vd.seeing) AS mean_seeing, AVG(vd.magLim) AS mean_maglim
        FROM dp2.Visit AS v
        JOIN dp2.VisitDetector AS vd ON v.visit = vd.visitId
        WHERE CONTAINS(POINT('ICRS', v.ra, v.dec), CIRCLE('ICRS', 150.1, 2.1, 2)) = 1
        AND (v.expMidptMJD > 60818.7 AND v.expMidptMJD < 60819.7)
        GROUP BY v.visit"""
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

In [15]:

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

Option to display the table.

In [16]:

# results

Plot the mean seeing versus the time of the visit, to show how seeing changed over the night, and versus the magnitude limit, to show how seeing and depth are correlated.

In [17]:

fig, ax = plt.subplots(1, 2, figsize=(8, 3), sharey=True)
for f, filt in enumerate(filter_names):
    fx = np.where(results['band'] == filt)[0]
    ax[0].plot(results['expMidptMJD'][fx]-60819, results['mean_seeing'][fx],
               filter_symbols[filt], mew=0, alpha=0.5, color=filter_colors[filt], label=filt)
    ax[1].plot(results['mean_maglim'][fx], results['mean_seeing'][fx],
               filter_symbols[filt], mew=0, alpha=0.5, color=filter_colors[filt], label=filt)
ax[0].set_xlabel('MJD-60819')
ax[0].set_ylabel('visit mean seeing')
ax[1].set_xlabel('visit mean maglim')
plt.legend(loc='upper right', ncol=2)
fig.subplots_adjust(wspace=0)
plt.show()

Figure 2: At left, the mean seeing over all detectors per visit vs. the MJD of the visit, showing how the seeing changed over the course of the night. At right, the mean seeing vs. the mean magnitude limit over all detectors, to show how seeing and depth are related.

In [18]:

del query, results
job.delete()

Source¶

Joins to the Source, ForcedSource, DiaSource, and ForcedSourceOnDiaObject tables all similarly use the column visit.

As an example, start with a similar query as above, but join to the Source table, which contains $>5\sigma$ detections in the processed visit image, and return the number of sources detected per visit (over all detectors).

In [19]:

query = """SELECT v.visit, v.expMidptMJD, v.band, COUNT(s.sourceId) AS ndetections
        FROM dp2.Visit AS v
        JOIN dp2.Source AS s ON v.visit = s.visit
        WHERE CONTAINS(POINT('ICRS', v.ra, v.dec), CIRCLE('ICRS', 150.1, 2.1, 2)) = 1
        AND (v.expMidptMJD > 60818.7 AND v.expMidptMJD < 60819.7)
        GROUP BY v.visit"""
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'
results = job.fetch_result().to_table()

Job phase is COMPLETED

Plot the number of sources detected per visit as a function of time over the night.

In [20]:

fig = plt.figure(figsize=(4, 3))
for f, filt in enumerate(filter_names):
    fx = np.where(results['band'] == filt)[0]
    plt.plot(results['expMidptMJD'][fx]-60819, results['ndetections'][fx],
             filter_symbols[filt], mew=0, alpha=0.5, color=filter_colors[filt], label=filt)
plt.xlabel('MJD-60819')
plt.ylabel('Number of sources detected')
plt.legend(loc='upper left', ncol=1)
plt.show()

Figure 3: Number of sources detected per visit image vs. time. Compare with Figure 2, and notice that when the seeing for the $u$-band images was poor, fewer sources were detected. That more sources are detected in the $gri$ bands compared to the $u$ band is likely mostly due to the fact that LSST images are deeper in the $gri$-bands, but also due to the improving image quality over the night.

In [21]:

del query, results
job.delete()

3.2. Butler¶

Show that the only dimension for the visit_table is instrument, and that it is required.

In [22]:

butler.get_dataset_type('visit_table')

Out[22]:

DatasetType('visit_table', {instrument}, ArrowAstropy)

In [23]:

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

Out[23]:

{instrument}

Show that there is only one dataset reference for the "LSSTCam" visit_table.

In [24]:

dataset_refs = butler.query_datasets("visit_table", instrument="LSSTCam")
print(len(dataset_refs))
print(dataset_refs[0])

1
visit_table@{instrument: 'LSSTCam'} [sc=ArrowAstropy] (run=LSSTCam/runs/DRP/DP2/v30_0_0/DM-53881/stage2/20260325T031924Z id=019d2301-62c9-7b3b-a3a2-78c7d9adabf0)

Retrieve the entire visit_table.

In [25]:

visit_table = butler.get(dataset_refs[0])
print(len(visit_table))

Option to display the table (automatically truncated).

In [26]:

visit_table

Out[26]:

Table length=28698

visitId	visit	physical_filter	band	ra	dec	decl	skyRotation	azimuth	altitude	zenithDistance	airmass	expTime	expMidpt	expMidptMJD	obsStart	obsStartMJD
int64	int64	str4	str1	float64	float64	float64	float64	float64	float64	float64	float64	float64	datetime64[ns]	float64	datetime64[ns]	float64
2025042400172	2025042400172	r_57	r	185.443060811076	5.98102138504802	5.98102138504802	146.113088289607	1.9614440076983797	53.8990970549479	36.1009029450521	1.23704795139999	29.999915599823	2025-04-25T02:48:48.447066101	60790.11722739659	2025-04-25T02:48:33.447108302	60790.11705378597
2025042400173	2025042400173	r_57	r	187.349245626471	8.23604638508455	8.23604638508455	144.18450391305	4.59267468721633	51.5610973958556	38.4389026041444	1.27595524322485	30.0000872612	2025-04-25T02:49:32.773247240	60790.1177404311	2025-04-25T02:49:17.773203610	60790.117566819485
2025042400174	2025042400174	r_57	r	186.443750064719	7.16885705640284	7.16885705640284	138.496645426916	2.93858842208796	52.6885249328851	37.3114750671149	1.2566323773581	30.0009806156158	2025-04-25T02:50:16.561317680	60790.11824723747	2025-04-25T02:50:01.560827373	60790.11807362069
2025042400175	2025042400175	r_57	r	185.542702196852	6.0999042479778	6.0999042479778	132.754022698538	1.19423996978959	53.7910062641441	36.2089937358559	1.23874981794006	30.0010826587677	2025-04-25T02:51:00.667260028	60790.118757722914	2025-04-25T02:50:45.666718699	60790.118584105534
2025042400176	2025042400176	r_57	r	187.450364693337	8.35450149584232	8.35450149584232	130.813892358106	3.8613155396456	51.471714859197	38.528285140803	1.27753357366488	30.0010211467743	2025-04-25T02:51:45.309465548	60790.11927441511	2025-04-25T02:51:30.308954975	60790.11910079809
2025042400177	2025042400177	r_57	r	185.847533564225	5.8013629485669	5.8013629485669	124.211898222169	1.09097214771777	54.0905751934753	35.9094248065247	1.23405624396335	30.0000793933868	2025-04-25T02:52:29.479243963	60790.1197856394	2025-04-25T02:52:14.479204267	60790.119612027825
2025042400178	2025042400178	r_57	r	187.75336753586	8.05638192333976	8.05638192333976	122.26067429917401	3.78158433488536	51.7731307887482	38.2268692112518	1.27223856646967	30.0000307559967	2025-04-25T02:53:14.019002678	60790.120301145864	2025-04-25T02:52:59.018987301	60790.120127534574
2025042400179	2025042400179	r_57	r	186.848285643417	6.98916841538782	6.98916841538782	116.584389074444	2.09978127522019	52.8876775587752	37.1123224412248	1.25332980626234	30.0011038780212	2025-04-25T02:53:57.435132769	60790.12080364737	2025-04-25T02:53:42.434580830	60790.12063002987
2025042400180	2025042400180	r_57	r	185.947406877035	5.92024017591185	5.92024017591185	110.853352061965	0.324268956119887	53.9765162094151	36.0234837905849	1.23583528164551	30.001017332077	2025-04-25T02:54:41.342426384	60790.12131183364	2025-04-25T02:54:26.341917718	60790.12113821664
2025042400181	2025042400181	r_57	r	187.854178952975	8.17485614320666	8.17485614320666	108.889140076339	3.05461494718365	51.6779416096108	38.3220583903892	1.27390252171799	30.0010986328125	2025-04-25T02:55:25.650037865	60790.121824653215	2025-04-25T02:55:10.649488549	60790.12165103575
2025042400182	2025042400182	r_57	r	185.643272826363	5.82455052931297	5.82455052931297	101.413213591773	359.170162068308	54.0697005466828	35.9302994533172	1.23438151518466	30.0009999275208	2025-04-25T02:56:11.504936034	60790.1223553812	2025-04-25T02:55:56.504436071	60790.1221817643
2025042400183	2025042400183	r_57	r	187.548868127399	8.07957103672195	8.07957103672195	99.4773854200115	1.96819834655187	51.799135130418	38.200864869582	1.27178609798797	30.0000410079956	2025-04-25T02:56:56.175039188	60790.122872396285	2025-04-25T02:56:41.175018685	60790.12269878494
2025042400184	2025042400184	r_57	r	186.643642983593	7.01236130557526	7.01236130557526	93.7939908514344	0.23080595724814604	52.8845273212575	37.1154726787425	1.25338230487889	30.0009963512421	2025-04-25T02:57:40.277875125	60790.12338284578	2025-04-25T02:57:25.277376950	60790.1232092289
2025042400185	2025042400185	r_57	r	185.74296324523	5.94343339020704	5.94343339020704	88.0557569749744	358.405719627179	53.9425446952639	36.0574553047361	1.23636770363808	30.0015485286713	2025-04-25T02:58:24.115260667	60790.12389022292	2025-04-25T02:58:09.114486403	60790.12371660285
2025042400186	2025042400186	r_57	r	187.649548118849	8.19804244067103	8.19804244067103	86.1072978524376	1.23746467867062	51.6916653751537	38.3083346248463	1.2736627900196	30.000072479248	2025-04-25T02:59:08.988900881	60790.12440959376	2025-04-25T02:58:53.988864642	60790.12423598223
2025042400187	2025042400187	r_57	r	185.471185060866	5.40272571999455	5.40272571999455	78.6773665715572	357.269104572305	54.4618234361368	35.5381765638632	1.22833577987909	30.0000083446503	2025-04-25T02:59:54.784825364	60790.12493963918	2025-04-25T02:59:39.784821192	60790.12476602802
2025042400196	2025042400196	r_57	r	187.375759288216	7.65773974047271	7.65773974047271	76.7771396265029	356.009600141957	52.1651008975808	37.8348991024192	1.2654716067816	30.0010366439819	2025-04-25T03:10:59.607578071	60790.13263434697	2025-04-25T03:10:44.607059750	60790.13246072986
2025042400197	2025042400197	r_57	r	186.471352630398	6.59047894909582	6.59047894909582	71.0778573822967	354.098026626793	53.1484082858814	36.8515917141186	1.249056835455	30.0000202655792	2025-04-25T03:11:44.276698427	60790.133151350674	2025-04-25T03:11:29.276688295	60790.13297773944
2025042400198	2025042400198	r_57	r	185.571123624513	5.5215965200526	5.5215965200526	65.3217871312681	352.104923397705	54.0994779180869	35.9005220819131	1.23392031530375	30.0010199546814	2025-04-25T03:12:27.949494085	60790.13365682284	2025-04-25T03:12:12.948984108	60790.13348320583
2025042400199	2025042400199	r_57	r	187.476036005872	7.77622613594882	7.77622613594882	63.40768732720059	355.285768490359	52.0168762045619	37.9831237954381	1.2680154599723	30.0000410079956	2025-04-25T03:13:13.054631973	60790.13417887305	2025-04-25T03:12:58.054611470	60790.1340052617
2025042400200	2025042400200	g_6	g	185.003510815524	5.73722306768995	5.73722306768995	115.74918068646798	349.748539942318	53.6900581532369	36.3099418467631	1.24034927851352	30.0009922981262	2025-04-25T03:15:56.287972419	60790.136068147825	2025-04-25T03:15:41.287476270	60790.13589453097
2025042400201	2025042400201	g_6	g	186.908911727528	7.992241362986901	7.992241362986901	113.868776161353	353.015994748226	51.6751050394469	38.3248949605531	1.27395508160137	30.0010890960693	2025-04-25T03:16:42.111012904	60790.13659850709	2025-04-25T03:16:27.110468356	60790.13642488967
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2026010600076	2026010600076	y_10	y	138.353042498328	-18.5389260682848	-18.5389260682848	31.0068726669446	296.296570383063	67.6315341837999	22.3684658162001	1.08120376172672	30.0009903907776	2026-01-07T08:15:30.831623582	61047.34410684749	2026-01-07T08:15:15.831128387	61047.34393323065
2026010600077	2026010600077	y_10	y	141.103611443935	-16.8121445806364	-16.8121445806364	38.4170930275092	303.957300944051	68.4838707873255	21.5161292126745	1.07475729455947	30.0011456012726	2026-01-07T08:16:15.572810030	61047.3446246853	2026-01-07T08:16:00.572237230	61047.344451067554
2026010600078	2026010600078	y_10	y	138.643347516893	-15.397498367409598	-15.397498367409598	37.3652290462123	302.435475292662	65.6492602969923	24.350739703007704	1.09744745467064	30.0010118484497	2026-01-07T08:16:56.472042406	61047.345098056045	2026-01-07T08:16:41.471536482	61047.34492443908
2026010600079	2026010600079	y_10	y	135.891372596865	-17.0930198908701	-17.0930198908701	30.995638017845998	295.166393428926	64.4580421427495	25.541957857250495	1.10808884250446	30.0011303424835	2026-01-07T08:17:41.531779181	61047.34561958078	2026-01-07T08:17:26.531214010	61047.34544596313
2026010600080	2026010600080	y_10	y	133.05755146353	-18.7249059991847	-18.7249059991847	25.3695142826721	288.592993075444	62.90935736267289	27.090642637327107	1.12297336656401	30.0010375976562	2026-01-07T08:18:25.591673771	61047.34612953327	2026-01-07T08:18:10.591154973	61047.34595591615
2026010600081	2026010600081	y_10	y	129.892776962173	-19.8632039752361	-19.8632039752361	21.214660682387	283.328428361972	60.671091329055805	29.328908670944195	1.14669673664692	30.001128911972	2026-01-07T08:19:06.730246369	61047.346605674145	2026-01-07T08:18:51.729681914	61047.3464320565
2026010600082	2026010600082	y_10	y	130.625235397626	-17.1540539176332	-17.1540539176332	26.2444673789574	288.631034301704	59.84245389708029	30.15754610291971	1.15619533303991	30.001029253006	2026-01-07T08:19:46.412328961	61047.34706495751	2026-01-07T08:19:31.411814335	61047.34689134044
2026010600083	2026010600083	y_10	y	133.460972510709	-15.613403346366102	-15.613403346366102	31.0790349573728	294.074042359234	61.1597243974878	28.8402756025122	1.14128347488908	30.0011100769043	2026-01-07T08:20:25.985890109	61047.34752298484	2026-01-07T08:20:10.985335071	61047.34734936731
2026010600084	2026010600084	y_10	y	136.213892470239	-13.9566184802745	-13.9566184802745	36.6207633575132	300.032716329996	62.157032505725795	27.842967494274205	1.13064664164595	30.0010030269623	2026-01-07T08:21:04.885682714	61047.347973213924	2026-01-07T08:20:49.885181201	61047.34779959701
2026010600085	2026010600085	y_10	y	138.620586800191	-12.1906598051242	-12.1906598051242	42.3297986010708	306.004303955258	62.6076426419637	27.3923573580363	1.12601301590806	30.001140832901	2026-01-07T08:21:44.168852685	61047.348427880235	2026-01-07T08:21:29.168282269	61047.34825426252
2026010600086	2026010600086	y_10	y	136.001652974444	-10.7669089150578	-10.7669089150578	40.8453498332702	304.221110033005	59.647548103660995	30.352451896339005	1.15848327904462	30.0010223388672	2026-01-07T08:22:25.357565164	61047.348904601444	2026-01-07T08:22:10.357053995	61047.34873098441
2026010600087	2026010600087	y_10	y	133.760896235304	-12.5006467580703	-12.5006467580703	36.0131183591272	298.623495630718	59.0037120227157	30.996287977284297	1.16621288338317	30.0011458396912	2026-01-07T08:23:06.567991314	61047.34938157398	2026-01-07T08:22:51.567418395	61047.349207956235
2026010600088	2026010600088	y_10	y	131.004008103533	-14.0680703149896	-14.0680703149896	31.2149722542582	292.985276687941	57.65984915686689	32.34015084313311	1.18316805496568	30.0000126361847	2026-01-07T08:23:47.912575026	61047.349860099246	2026-01-07T08:23:32.912568708	61047.349686488065
2026010600089	2026010600089	y_10	y	128.922131569874	-12.7224484226276	-12.7224484226276	31.3479177750898	292.713070642803	55.0945613187255	34.9054386812745	1.21883782699736	30.0011267662048	2026-01-07T08:24:28.521746833	61047.35033011281	2026-01-07T08:24:13.521183450	61047.35015649517
2026010600090	2026010600090	y_10	y	128.234985835844	-15.060516130309	-15.060516130309	27.7152050211811	288.474793024401	55.6879969989798	34.3120030010202	1.21017945066323	30.0009865760803	2026-01-07T08:25:09.405668435	61047.35080330635	2026-01-07T08:24:54.405175147	61047.35062968953
2026010600091	2026010600091	y_10	y	127.41470931520699	-17.7826049271	-17.7826049271	23.2556677088549	283.416735439661	56.216183668355	33.783816331645	1.20267552647979	30.0011241436005	2026-01-07T08:25:50.732561790	61047.35128162687	2026-01-07T08:25:35.731999719	61047.35110800925
2026010600092	2026010600092	y_10	y	126.590722616156	-20.4325004065026	-20.4325004065026	18.7253776461487	278.375428095406	56.528285584998	33.471714415002	1.19834110308217	30.0000422000885	2026-01-07T08:26:32.322238779	61047.351762988874	2026-01-07T08:26:17.322217679	61047.35158937752
2026010600093	2026010600093	y_10	y	125.740662145996	-23.0704782854122	-23.0704782854122	14.098453826005	273.293105692965	56.638036133845	33.361963866155	1.1968324566164	30.0001401901245	2026-01-07T08:27:14.098462505	61047.35224650998	2026-01-07T08:26:59.098392410	61047.35207289806
2026010600094	2026010600094	y_10	y	123.272973903175	-20.8997951288636	-20.8997951288636	16.9469753126522	275.260962624777	53.5748889354796	36.4251110645204	1.24219724042565	30.0010330677032	2026-01-07T08:27:53.016681885	61047.35269695234	2026-01-07T08:27:38.016165352	61047.35252333525
2026010600095	2026010600095	y_10	y	124.158022602301	-18.2724979712791	-18.2724979712791	21.0235633044719	279.718555179535	53.0904674786764	36.9095325213236	1.25001105172445	30.0011253356934	2026-01-07T08:28:37.002838078	61047.35320605137	2026-01-07T08:28:22.002275411	61047.35303243375
2026010600096	2026010600096	y_10	y	125.064263130519	-15.6133713201472	-15.6133713201472	25.0628841004994	284.150017134643	52.4804903284725	37.5195096715275	1.26012991438198	30.0010321140289	2026-01-07T08:29:16.378595106	61047.3536617893	2026-01-07T08:29:01.378079049	61047.353488172215
2026010600097	2026010600097	y_10	y	126.430329088784	-12.802241260758	-12.802241260758	29.4566056910548	289.056891075302	52.0209479030687	37.9790520969313	1.26795229243855	30.0011446475983	2026-01-07T08:29:55.847315860	61047.354118603194	2026-01-07T08:29:40.846743537	61047.35394498546

Display one row of the table.

In [27]:

tx = np.where(visit_table['visit'] == 2026010600097)[0]
visit_table[tx[0]]

Out[27]:

Row index=28697

visitId	visit	physical_filter	band	ra	dec	decl	skyRotation	azimuth	altitude	zenithDistance	airmass	expTime	expMidpt	expMidptMJD	obsStart	obsStartMJD
int64	int64	str4	str1	float64	float64	float64	float64	float64	float64	float64	float64	float64	datetime64[ns]	float64	datetime64[ns]	float64
2026010600097	2026010600097	y_10	y	126.430329088784	-12.802241260758	-12.802241260758	29.4566056910548	289.056891075302	52.0209479030687	37.9790520969313	1.26795229243855	30.0011446475983	2026-01-07T08:29:55.847315860	61047.354118603194	2026-01-07T08:29:40.846743537	61047.35394498546

Clean up.

In [28]:

del dataset_refs, visit_table, tx

In [ ]: