201.11. CcdVisit table

201.11. CcdVisit table¶

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: 2025-06-21
Repository: github.com/lsst/tutorial-notebooks

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

LSST data products: CcdVisit

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 CcdVisit refers to an observation of the sky with an individual detector of the camera.

The CcdVisit table contains data about the individual observations for every detector of a Visit, such as the coordinates, date, time, filter, camera rotation, and exposure time. It also contains image characterization data for the associated visit_image (the processed, calibrated pixel data for one detector), such as the point-spread function (PSF) size, seeing, sky background, and zeropoint.

The contents of the CcdVisit table are associated with the individual detectors of the camera (9 for LSSTComCam). By contrast, the Visit contains data associated with the boresight of the entire focal plane.

• TAP table name: dp1.CcdVisit
• Butler table name: visit_detector_table
• columns: 51
• rows: 16071

Related tutorials: The tutorial for the Visit 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 get_tap_service
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, and assert that it exists.

In [2]:

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

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

In [3]:

butler = Butler('dp1', collections="LSSTComCam/DP1")
assert butler is not None

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

In [4]:

filter_names = ['u', 'g', 'r', 'i', 'z', 'y']
filter_colors = get_multiband_plot_colors()
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 CcdVisit table and run the query job.

In [5]:

query = "SELECT column_name, datatype, description, unit " \
        "FROM tap_schema.columns " \
        "WHERE table_name = 'dp1.CcdVisit'"
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()

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()
assert len(results) == 51
results

Out[6]:

Table length=51

column_name	datatype	description	unit
str64	str64	str512	str64
astromOffsetMean	double	Mean offset of astrometric calibration matches (arcsec)	arcsec
astromOffsetStd	double	Standard deviation of offsets of astrometric calibration matches (arcsec)	arcsec
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.
ccdVisitId	long	Primary key (unique identifier).
darkTime	double	Average dark current accumulation time, accurate to 10ms.	s
dec	double	Declination of Ccd center.	deg
decl	double	Deprecated duplicate of dec.	deg
detector	long	Detector ID. A detector associated with a particular instrument (not an observation of that detector).
effTime	double	Effective time metric	s
effTimePsfSigmaScale	double	Effective time metric -- PSF size component
effTimeSkyBgScale	double	Effective time metric -- Sky background component
effTimeZeroPointScale	double	Effective time metric -- Throughput component
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
llcdec	double	Declination of lower left corner.	deg
llcra	double	Right Ascension of lower left corner.	deg
lrcdec	double	Declination of lower right corner.	deg
lrcra	double	Right Ascension of lower right corner.	deg
magLim	double	5-sigma limiting magnitude	mag
maxDistToNearestPsf	double	Maximum distance of an unmasked pixel to its nearest model psf star (pixel)	pixel
nPsfStar	int	Number of stars used for PSF model
...	...	...	...
psfStarDeltaE1Median	double	Median E1 residual (starE1 - psfE1) for psf stars
psfStarDeltaE1Scatter	double	Scatter (via MAD) of E1 residual (starE1 - psfE1) for psf stars
psfStarDeltaE2Median	double	Median E2 residual (starE2 - psfE2) for psf stars
psfStarDeltaE2Scatter	double	Scatter (via MAD) of E2 residual (starE2 - psfE2) for psf stars
psfStarDeltaSizeMedian	double	Median size residual (starSize - psfSize) for psf stars (pixel)	pixel
psfStarDeltaSizeScatter	double	Scatter (via MAD) of size residual (starSize - psfSize) for stars (pixel)	pixel
psfStarScaledDeltaSizeScatter	double	Scatter (via MAD) of size residual scaled by median size squared
psfTraceRadiusDelta	double	Delta (max - min) of model psf trace radius values evaluated on a grid of unmasked pixels (pixel)	pixel
ra	double	Right Ascension of Ccd center.	deg
seeing	double	Mean measured FWHM of the PSF.	arcsec
skyBg	float	Average sky background.	adu
skyNoise	float	RMS noise of the sky background.	adu
skyRotation	double	Sky rotation angle.	deg
ulcdec	double	Declination of upper left corner.	deg
ulcra	double	Right Ascension of upper left corner.	deg
urcdec	double	Declination of upper right corner.	deg
urcra	double	Right Ascension of upper right corner.	deg
visitId	long	Reference to the corresponding entry in the Visit table.
xSize	long	Number of columns in the image.	pixel
ySize	long	Number of rows in the image.	pixel
zenithDistance	float	Zenith distance at observation mid-point.	deg
zeroPoint	float	Zero-point for the Ccd, estimated at Ccd center.	mag

The table displayed above has been truncated.

Option to print every column name as a list.

In [7]:

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

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

In [8]:

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

Delete the job, but not the results.

In [9]:

del query
job.delete()

2.2. Key columns¶

Descriptions for a few of the columns that are the most commonly used.

2.2.1. Visit Id and detector¶

Integers that uniquely identify the visit and detector (CCD) of the visit_image.

• visitId
• detector

There is also a ccdVisitId column but it is not used in any other table, and is not used to retrieve images. The visitId and detector columns are important for joining tables and retrieving images from the butler.

2.2.2. Coordinates¶

The sky coordinates in decimal degrees of the center of the detector (CCD).

• ra
• dec

The RA, Dec of the four corners of the corresponding visit_image are also available. The column naming conventions are:

• "lower left corner" (llcra, llcdec)
• "lower right corner" (lrcra, lrcdec)
• "upper left corner" (ulcra, ulcdec)
• "upper right corner" (urcra, urcdec)

2.2.3. Filter¶

The name of the filter that was used for the observation.

• band

There is also the column physical_filter which is unique to the physical filter (i.e., the coated glass plate). As of the DP1 release, there was only one glass filter per band ($ugrizy$).

2.2.4. Date and time¶

The date and time of the observation, as a Modified Julian Date (MJD).

• expMidptMJD

Related columns include the start of the observation (obsStartMJD) and the exposure time (expTime).

2.2.5. Image characterization¶

Derived properties of the corresponding visit_image.

• magLim, the 5-sigma limiting magnitude (depth)
• psfSigma, radius of the PSF at the CCD center
• seeing, mean measured FWHM of the PSF
• skyBg, average sky background (counts)
• zeroPoint, magnitude of 1 count/second

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(['visitId', 'detector', 'ra', 'dec', 'band',
                'expMidptMJD', 'magLim', 'psfSigma', 'seeing',
                'skyBg', 'zeroPoint'])
tx = [i for i, item in enumerate(results['column_name']) if item in col_list]
results[tx]

Out[10]:

Table length=11

column_name	datatype	description	unit
str64	str64	str512	str64
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 Ccd center.	deg
detector	long	Detector ID. A detector associated with a particular instrument (not an observation of that detector).
expMidptMJD	double	Midpoint time for exposure at the fiducial center of the focal plane array in MJD. TAI, accurate to 10ms.	d
magLim	double	5-sigma limiting magnitude	mag
psfSigma	float	PSF model second-moments determinant radius (center of chip)	pixel
ra	double	Right Ascension of Ccd center.	deg
seeing	double	Mean measured FWHM of the PSF.	arcsec
skyBg	float	Average sky background.	adu
visitId	long	Reference to the corresponding entry in the Visit table.
zeroPoint	float	Zero-point for the Ccd, estimated at Ccd center.	mag

Clean up.

In [11]:

del col_list, tx, results

3. Data access¶

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

Recommended access method: TAP.

3.1. TAP (Table Access Protocol)¶

The CcdVisit 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 DP1 CcdVisit table is relatively small, it is good practice to always include spatial constraints and only retrieve necessary columns.

Search for g- and r-band CCD 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 identifiers, RA, Dec, band, the midpoint time of the exposure in MJD, and the seeing, sky background, and magnitude limit for the corresponding visit_image.

In [12]:

query = "SELECT ccdVisitId, visitId, detector, " \
        "ra, dec, band, expMidptMJD, " \
        "seeing, skyBg, magLim " \
        "FROM dp1.CcdVisit " \
        "WHERE CONTAINS(POINT('ICRS', ra, dec), " \
        "CIRCLE('ICRS', 53.13, -28.10, 3)) = 1 " \
        "AND (band = 'r' OR band = 'g') " \
        "ORDER BY expMidptMJD ASC "
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()

Job phase is COMPLETED

Fetch the results as an astropy table.

In [13]:

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

4203

Option to display the table.

In [14]:

# results

As an example, plot the magnitude limit as a function of the seeing, and the sky background as a function of time.

In [15]:

fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(7, 3))
ax2.axvline(60629, ls='dashed', color='grey')
ax2.axvline(60659, ls='dashed', color='grey')
ax2.text(60625.0, 12000, 'full')
ax2.text(60622.8, 11000, 'moon')
for filt in filter_names:
    fx = np.where(results['band'] == filt)[0]
    if len(fx) > 0:
        ax1.plot(results['seeing'][fx], results['magLim'][fx],
                 filter_symbols[filt], ms=5, mew=0, alpha=0.4,
                 color=filter_colors[filt], label=filt)
        ax2.plot(results['expMidptMJD'][fx], results['skyBg'][fx],
                 filter_symbols[filt], ms=5, mew=0, alpha=0.4,
                 color=filter_colors[filt], label=filt)
ax1.set_xlabel('seeing')
ax1.set_ylabel('magnitude limit')
ax2.set_xlabel('MJD')
ax2.set_ylabel('sky background')
ax2.legend(loc='upper center', handletextpad=0)
plt.tight_layout()
plt.show()

Figure 1: At left, the magnitude limit of the image declines as the seeing increases. At right, the sky background as a function of time over the LSSTComCam campaign.

Clean up.

In [16]:

job.delete()
del query, results

3.2.2. Joinable tables¶

The CcdVisit table can be joined with Visit on column visit.

Like the Visit table, the CcdVisit table can be joined with the Source, ForcedSource, DiaSource, and ForcedSourceOnDiaObject tables on column visit.

No other TAP table has the ccdVisitId column.

Visit¶

For example, start with the same query as in Section 3.1.2. Add the selection of column airmass from the Visit table, but otherwise keep the query constraints the same.

In [17]:

query = "SELECT cv.ccdVisitId, cv.visitId, cv.detector, " \
        "cv.ra, cv.dec, cv.band, cv.expMidptMJD, " \
        "cv.seeing, cv.skyBg, cv.magLim, v.airmass " \
        "FROM dp1.CcdVisit AS cv " \
        "JOIN dp1.Visit AS v ON cv.visitId = v.visit " \
        "WHERE CONTAINS(POINT('ICRS', cv.ra, cv.dec), " \
        "CIRCLE('ICRS', 53.13, -28.10, 3)) = 1 " \
        "AND (cv.band = 'r' OR cv.band = 'g') " \
        "ORDER BY cv.expMidptMJD ASC "
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()

Job phase is COMPLETED

In [18]:

assert job.phase == 'COMPLETED'
results = job.fetch_result().to_table()
assert len(results) == 4203
print(len(results))

4203

For example, plot the airmass from the Visit table versus the seeing and MJD of the observation from the CcdVisit table and

In [19]:

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['airmass'][fx], results['seeing'][fx],
                 filter_symbols[filt], ms=5, mew=0, alpha=0.4,
                 color=filter_colors[filt], label=filt)
        ax2.plot(results['expMidptMJD'][fx], results['airmass'][fx],
                 filter_symbols[filt], ms=5, mew=0, alpha=0.4,
                 color=filter_colors[filt], label=filt)
ax1.set_xlabel('airmass')
ax1.set_ylabel('seeing')
ax2.set_xlabel('MJD')
ax2.set_ylabel('airmass')
ax2.legend(loc='best', handletextpad=0)
plt.tight_layout()
plt.show()

Figure 2: At left, the seeing increases with the airmass of the observation, as expected. At right, the airmass of the observation over the LSSTComCam campaign.

3.2. Butler¶

The visit_detector_table is only available in full from the Butler (no subsetting), but for DP1 it will fit in a large container.

Show that the only dimension for the visit_detector_table is instrument.

In [20]:

butler.get_dataset_type('visit_detector_table')

Out[20]:

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

In [21]:

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

Out[21]:

{instrument}

Show that there is only one dataset references for the "LSSTComCam" visit_detector_table.

In [22]:

dataset_refs = butler.query_datasets("visit_detector_table", instrument="LSSTComCam")
print(len(dataset_refs))
print(dataset_refs[0])

1
visit_detector_table@{instrument: 'LSSTComCam'} [sc=ArrowAstropy] (run=LSSTComCam/runs/DRP/DP1/v29_0_0/DM-50260/20250416T185152Z id=420c9a41-907e-4e16-8a66-e318422c49be)

Retrieve the entire visit_detector_table.

In [23]:

visit_detector_table = butler.get(dataset_refs[0])

Confirm that the visit_detector_table has 16071 rows.

In [24]:

assert len(visit_detector_table) == 16071
print(len(visit_detector_table))

16071

Option to display the table (automatically truncated).

In [25]:

# visit_detector_table

Display the rows of the table for one visit.

In [26]:

tx = np.where(visit_detector_table['visitId'] == 2024110800245)[0]
visit_detector_table[tx]

Out[26]:

Table length=9

detectorId	visitId	physical_filter	band	ra	dec	pixelScale	zenithDistance	expTime	zeroPoint	psfSigma	skyBg	skyNoise	astromOffsetMean	astromOffsetStd	nPsfStar	psfStarDeltaE1Median	psfStarDeltaE2Median	psfStarDeltaE1Scatter	psfStarDeltaE2Scatter	psfStarDeltaSizeMedian	psfStarDeltaSizeScatter	psfStarScaledDeltaSizeScatter	psfTraceRadiusDelta	psfApFluxDelta	psfApCorrSigmaScaledDelta	maxDistToNearestPsf	effTime	effTimePsfSigmaScale	effTimeSkyBgScale	effTimeZeroPointScale	magLim	decl	ccdVisitId	detector	seeing	skyRotation	expMidpt	expMidptMJD	obsStart	obsStartMJD	darkTime	xSize	ySize	llcra	llcdec	ulcra	ulcdec	urcra	urcdec	lrcra	lrcdec
				deg	deg	arcsec / pix	deg	s	mag	pix	adu	adu	arcsec	arcsec						pix	pix		pix			pix	s				mag	deg										pix	pix	deg	deg	deg	deg	deg	deg	deg	deg
int64	int64	str32	str32	float64	float64	float32	float32	float32	float32	float32	float32	float32	float32	float32	int32	float32	float32	float32	float32	float32	float32	float32	float32	float32	float32	float32	float32	float32	float32	float32	float32	float64	int64	int64	float64	float64	datetime64[ns]	float64	datetime64[ns]	float64	float64	int32	int32	float64	float64	float64	float64	float64	float64	float64	float64
0	2024110800245	i_06	i	53.00453551947407	-28.190330812465483	0.20034255	15.811996	30.0	31.838995	1.8325734	1425.881	40.684032	0.013188731	0.0075904555	77	-0.0017561167	-0.00053596497	0.014050585	0.012365655	-0.0008765209	0.01351533	0.0073382757	0.27481803	0.12636535	0.021924777	794.26764	21.713379	0.6789396	1.1138377	0.95709074	24.19634	-28.190330812465483	1145019888896	0	0.8645543744763075	102.799378733233	2024-11-09T06:12:16.184491000	60623.258520653835	2024-11-09T06:12:01.184491000	60623.25834704273	30.455	4071	3999	52.852308023843385	-28.27574847408543	52.910473334596425	-28.054937141362956	53.156342170329744	-28.104861296054786	53.098772925780914	-28.32570039342301
1	2024110800245	i_06	i	53.064355266935024	-27.961231391766976	0.2002798	15.809622	30.0	31.83873	1.7468716	1422.5039	37.339367	0.008955252	0.0046226964	88	-0.0002264753	0.0003960058	0.012073228	0.009223385	-0.00011816817	0.009787155	0.0055632405	0.12629928	0.12094698	0.01348997	628.4158	23.941256	0.7471915	1.116482	0.95662564	24.239191	-27.961231391766976	1145019888897	1	0.8238645989113716	102.799378733233	2024-11-09T06:12:16.184491000	60623.258520653835	2024-11-09T06:12:01.184491000	60623.25834704273	30.455	4071	3999	52.91252384993021	-28.046435296749927	52.97055212375497	-27.82575210296401	53.21584226871104	-27.875887370344532	53.15831205371236	-28.09659857669357
2	2024110800245	i_06	i	53.12390882343404	-27.731683579156535	0.20034228	15.810765	30.0	31.836647	1.78905	1418.3729	39.185055	0.008094447	0.003990346	99	0.0013976768	-0.0001938045	0.011579151	0.013788158	-0.003783381	0.019315107	0.010649642	0.1963391	0.05447564	0.014677442	682.1465	22.80446	0.71237546	1.1197337	0.9529604	24.208672	-27.731683579156535	1145019888898	2	0.8440201785550079	102.799378733233	2024-11-09T06:12:16.184491000	60623.258520653835	2024-11-09T06:12:01.184491000	60623.25834704273	30.455	4071	3999	52.972624972756044	-27.81711398954253	53.02996447830659	-27.59619947077631	53.27491006747064	-27.646045239388027	53.21796965595435	-27.86696622817951
3	2024110800245	i_06	i	53.26469522335325	-28.242507508599267	0.20027414	15.576916	30.0	31.837961	1.82284	1425.8518	39.428383	0.008271602	0.004680098	101	7.957034e-05	-0.00013038516	0.009846868	0.009884624	-0.0019776148	0.015945798	0.008746329	0.19495857	0.03981032	0.0026005514	654.0298	21.90459	0.6862097	1.1138605	0.9552703	24.180592	-28.242507508599267	1145019888899	3	0.8596687858227765	102.799378733233	2024-11-09T06:12:16.184491000	60623.258520653835	2024-11-09T06:12:01.184491000	60623.25834704273	30.455	4071	3999	53.11283716752131	-28.32820796624842	53.17024943547527	-28.107341263035114	53.41625005158294	-28.156717272507557	53.35939301000984	-28.377704866033213
4	2024110800245	i_06	i	53.32375176154874	-28.013505219153245	0.200218	15.574672	30.0	31.83915	1.7138672	1424.3231	39.147896	0.0077907415	0.0045658858	97	-9.317696e-05	0.00066259503	0.010088383	0.009745861	0.0014382002	0.012073453	0.0069978684	0.074722014	0.020994948	0.015410803	767.8301	24.859604	0.7762463	1.1150559	0.9573632	24.24863	-28.013505219153245	1145019888900	4	0.8080495969829284	102.799378733233	2024-11-09T06:12:16.184491000	60623.258520653835	2024-11-09T06:12:01.184491000	60623.25834704273	30.455	4071	3999	53.172275203022274	-28.09912010912059	53.22950309634053	-27.878345097197474	53.47498276541747	-27.927728783593288	53.41823206662435	-28.148601948634692
5	2024110800245	i_06	i	53.38273956426954	-27.784217664042103	0.20027883	15.575785	30.0	31.838747	1.7853589	1414.5787	40.260956	0.0063703256	0.0036208716	84	-0.0011746939	-0.00051392615	0.0120537095	0.008244637	-0.00039105458	0.0117064845	0.006516607	0.17354898	0.08796143	0.008903709	796.4788	23.049265	0.71532404	1.122737	0.95665413	24.217522	-27.784217664042103	1145019888901	5	0.8420120736117171	102.799378733233	2024-11-09T06:12:16.184491000	60623.258520653835	2024-11-09T06:12:01.184491000	60623.25834704273	30.455	4071	3999	53.231612393760045	-27.8698855517982	53.28863448276135	-27.648983606061194	53.533682994869196	-27.698319087365316	53.47705570153107	-27.91930767141365
6	2024110800245	i_06	i	53.5246792145985	-28.294803370278277	0.20035267	15.342082	30.0	31.8388	1.8442194	1428.1514	39.904015	0.011732553	0.007160589	104	-0.00047815964	-0.0011714646	0.011638308	0.010554459	-0.0015049732	0.015615952	0.008536858	0.22199431	0.05017352	0.014885595	743.4191	21.398252	0.67039186	1.112067	0.9567476	24.194454	-28.294803370278277	1145019888902	6	0.8700925586378995	102.799378733233	2024-11-09T06:12:16.184491000	60623.258520653835	2024-11-09T06:12:01.184491000	60623.25834704273	30.455	4071	3999	53.372862299149226	-28.380596416599765	53.42999505334116	-28.159654348965617	53.67627838083212	-28.20890081763317	53.61969271256924	-28.430046476975857
7	2024110800245	i_06	i	53.583628284352976	-28.065367862820302	0.20027706	15.339507	30.0	31.840176	1.7520168	1415.0815	39.239273	0.0071751294	0.0044429163	101	-0.0018707961	-0.0017534588	0.010023365	0.012411433	-0.00050531386	0.015835546	0.0089816665	0.15744267	0.073010385	0.01119243	700.81665	23.989433	0.7428094	1.1223382	0.95917445	24.22043	-28.065367862820302	1145019888903	7	0.8262798830285275	102.799378733233	2024-11-09T06:12:16.184491000	60623.258520653835	2024-11-09T06:12:01.184491000	60623.25834704273	30.455	4071	3999	53.432247634750404	-28.151230971057984	53.4890276591747	-27.93036234996983	53.73485804226679	-27.979322054849824	53.678536827791625	-28.20035891531077
8	2024110800245	i_06	i	53.64216871041843	-27.83604930238401	0.20035034	15.340555	30.0	31.838373	1.8212446	1417.9701	40.677723	0.008442235	0.0040146704	70	0.00043812394	-0.0025645457	0.00977064	0.0133948205	-0.00044736837	0.011746131	0.0064166808	0.25612888	0.049838874	0.017017128	948.7077	22.081709	0.6874124	1.1200519	0.95599574	24.217436	-27.83604930238401	1145019888904	8	0.8592431495439948	102.799378733233	2024-11-09T06:12:16.184491000	60623.258520653835	2024-11-09T06:12:01.184491000	60623.25834704273	30.455	4071	3999	53.49117723334687	-27.922133426543837	53.54758409936598	-27.701094439110708	53.79308825386978	-27.749709860478767	53.737039100130495	-27.97090847345148

3.2.1. Visit images¶

The CcdVisit table's rows contain metadata for the corresponding visit_image.

Option to retrieve the visit_image for the first row of the table displayed above.

In [27]:

# visit = visit_detector_table[tx[0]]['visitId']
# detector = visit_detector_table[tx[0]]['detectorId']

# visit_image = butler.get('visit_image',
#                          visit = visit,
#                          detector = detector)

In [28]:

# del visit_image

Clean up.

In [29]:

del dataset_refs, visit_detector_table, tx

In [ ]: