101. LSSTCam visits database (2025)#
101. LSSTCam visits database (2025 only)¶
For the Rubin Science Platform at data.lsst.cloud.
Container Size: Large
LSST Science Pipelines version: v29.2.0
Last verified to run: 2026-05-01
Repository: github.com/lsst/tutorial-notebooks
DOI: 10.11578/rubin/dc.20250909.20
Learning objective: How to query and retrieve data from the commissioning visits database file.
LSST data products: The lsstcam_20250930.db file available on the Science Validation survey summary webpage .
Packages: sqlite3, rubin_sim
Credit: Developed by the Rubin Community Science team, using materials from the Rubin Survey Scheduling 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.
The DP2 notebook includes all visits and tracts that will be included in Data Preview 2, and should be used to make plots such as Figure 1, below. The Commissioning notebook presents metadata for LSSTCam visits, some of which will be released as part of the Prompt data products.
1. Introduction¶
This tutorial demonstrates how to query and load data from the SQL-formatted table of commissioning visits that is available on the Science Validation survey summary webpage and also as a shared file in the Rubin Science Platform.
This is a temporary, static database file with non-standard schema and formatting, which only includes LSSTCam visits obtained prior to Sep 30 2025, and which is provided as a convenience.
For more recent visits and a forecast of the Rubin scheduler, see the tutorial notebook for the Rubin Schedule Viewer.
The future Rubin data releases will similar information in their Visit and CcdVisit tables.
Science Validation surveys.
It is recommended to review the Science Validation survey summary webpage for details on the strategy and results of the commissioning surveys.
Science images with LSSTCam began on 04 April 2025, at first acquiring small field survey visits in sequences of $\sim10$ visits per filter with small dithers, similar to the LSSTComCam strategy which resulted in Data Preview 1. These small field survey visits included the images which contributed toward Rubin First Look.
The Science Validation (SV) survey began on 20 June 2025, acquiring visits in a manner consistent with the planned operations survey for the LSST, but within a limited area. The contiguous part of the SV area follows the ecliptic plane from dense regions of the Galactic Bulge through low-dust regions within the planned LSST Wide Fast Deep (WFD). Four of the planned LSST Deep Drilling Fields (DDFs) were included in the SV survey, and a secondary area within the low-dust WFD was included to provide targets when the primary or DDF fields were not available.
Figure 1: All science visits acquired during LSSTCam commissioning. Both the small field surveys and the four SV DDFs appear as non-contiguous yellow regions in this plot. This plot is from the Science Validation survey summary webpage , and instructions for recreating a version of it are in Section 5.
Caveats.
- Image quality (IQ) is variable. The database file includes a total of 21647 commissioning visits. This excludes bad visits, but includes visits with a wide range of data quality due to both cloud extinction and/or delivered IQ or engineering issues. Keep in mind that while these observations were obtained, the Active Optics System (AOS) was being commissioned and it was winter.
- Not all of these visits will be in Data Preview 2 (DP2). Although an initial cut of bad visits have been made, users should expect that additional cuts will be made to the visits that are included and released as part of DP2.
- Measured IQ values may change. Some columns contain NaNs, where the summit quicklook processing did not provide a useful value. Many of these problems will be resolved with later processing. Users should anticipate that some measured IQ values will change.
1.1. Import packages¶
Import sqlite3 to read the SQL-formatted database file, and import the maf module from the rubin_sim package to use the (Metric Analysis Framework) functions. Also import standard python science packages and the lsst.utils.plotting package.
import sqlite3
from rubin_sim import maf
import os
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from tabulate import tabulate
from astropy.time import Time
from lsst.utils.plotting import (get_multiband_plot_colors,
get_multiband_plot_linestyles)
1.2. Define parameters¶
Define the path and name of the database file.
db_filename = '/rubin/cst_repos/tutorial-notebooks-data/data/lsstcam_20250930.db'
Define the default path for output files. Use the shared scratch directory, /deleted-sundays/, and create a subdirectory with your username if one does not already exist.
output_path = os.getenv('SCRATCH_DIR')
if os.path.exists(output_path):
print('Already exists: ', output_path)
else:
os.system('mkdir ' + output_path)
print('Created: ', output_path)
Already exists: /deleted-sundays/melissagraham
Define the LSST filter names and the colors and linestyles to represent the filters.
filter_names = ['u', 'g', 'r', 'i', 'z', 'y']
filter_colors = get_multiband_plot_colors()
filter_linestyles = get_multiband_plot_linestyles()
filter_colors_list = [filter_colors['u'], filter_colors['g'],
filter_colors['r'], filter_colors['i'],
filter_colors['z'], filter_colors['y']]
2. Explore the database¶
The information contained in the database is an aggregation of entries in the "Consolidate Database" (ConsDB), including per-visit summary values from summit quicklook processing (the ConsDB is not yet released to users). The database generally follows the current LSST scheduler output schema, but additional columns were added in post-processing. Rubin data releases have similar information in their Visit and CcdVisit tables.
As this is an aggregate file, descriptions for its columns can be found among those at:
2.1. Key columns¶
The database contains 217 columns in total, but these are the key columns used in this tutorial.
observation_reason: The source of the visit in the Feature Based Scheduler (FBS).target_name: The name of the sky region for the visit.exp_midpt_mjd: The midpoint time of the exposure at the fiducial center of the focal plane (in TAI).fieldRA: The boresight Right Ascension for the visit (degrees).fieldDec: The boresight Declination for the visit (degrees).band: The LSST filter used for the visit, one of $ugrizy$.airmass: The airmass of the visit ($1/\cos(\Theta_z)$, where $\Theta_z$ is the zenith angle).seeingFwhmEff: The full-width half-max of the point spread function (PSF; arcseconds).fiveSigmaDepth: The magnitude of a five-sigma point source detection in the visit (magnitudes).
2.2. Connect with sqlite3¶
Connect to the database file using sqlite3.
db_conn = sqlite3.connect(db_filename)
cursor = db_conn.cursor()
2.2.1. Explore the schema¶
Print the names of all tables in the database. There is only one, the observations table.
cursor.execute("SELECT name FROM sqlite_master WHERE type='table';")
tables = cursor.fetchall()
table_names = [table[0] for table in tables]
print("Tables:", table_names)
del tables, table_names
Tables: ['observations']
Option to print the schema (column names and types) for the observations table. It is a long list, and not printed by default.
# table_name = 'observations'
# query = f"""SELECT sql FROM sqlite_master WHERE type='table'
# AND name='{table_name}';"""
# cursor.execute(query)
# create_table_sql = cursor.fetchone()[0]
# print(f"Schema for {table_name}:\n{create_table_sql}")
# del query, table_name, create_table_sql
2.2.2. Query the database¶
As an example, create a query to return columns fieldRA, fieldDec, and band from the observations table for all $r$-band visits obtained at an airmass less than 1.5 and with an observation_reason of "field_survey_science".
The column observation_reason is discussed in Section 3.1.
query = """SELECT fieldRA, fieldDec, band FROM observations
WHERE observation_reason = 'field_survey_science'
AND band='r' AND airmass < 1.5; """
cursor.execute(query)
results = cursor.fetchall()
print("Number of rows returned: ", len(results))
Number of rows returned: 1521
Option to display the query results.
# results
Clean up, and close the connection with the database.
del query, results
cursor.close()
del cursor
2.3. Read as a pandas dataframe¶
As the file is only 81 M, it is small enough to be loaded in its entirety as a pandas dataframe.
Read the SQL-formatted table as a pandas dataframe, df, and print the number of rows.
df = pd.read_sql_query("SELECT * FROM observations", db_conn)
print(len(df))
21647
Option to display the table (it will automatically truncate).
# df
Option to print the column names.
# list(df.columns)
3. Surveys and targets¶
Every visit has both an observation_reason and a target_name.
The observation_reason is the motivation as to why the visit was obtained.
In other words, it indicates the scheduler mode or the survey.
For this database, for example, observation reasons include the SV WFD configurations, the SV DDF programs, target of opportunity (ToO), and the small field surveys.
The target_name is either a region of the LSST WFD (e.g., bulge, low-dust) or a proper name (for DDFs, or the commissioning small field survey fields).
Since regions can overlap, or a single visit can overlap the boundaries of multiple regions, the target_name is often a comma-separated list.
For example, most DDFs are also in the low-dust extragalactic regions of the LSST WFD.
3.1. Observation reason¶
Print the unique values of the observation_reason column and the number of visits for each.
values, counts = np.unique(df['observation_reason'], return_counts=True)
for value, count in zip(values, counts):
print('%25s %5i' % (value, count))
ddf_ecdfs 160
ddf_edfs_a 87
ddf_edfs_b 92
ddf_elaiss1 539
ddf_xmm_lss 30
field_survey_science 7194
pairs 400
pairs_gr_33.0 1176
pairs_iz_15.0 399
pairs_iz_33.0 2283
pairs_ri_15.0 764
pairs_ri_33.0 1341
pairs_ug_33.0 192
pairs_ur_33.0 46
pairs_yy_15.0 117
pairs_yy_33.0 1509
pairs_zy_15.0 1
pairs_zy_33.0 594
singles_i 19
singles_r 7
singles_z 708
template_area_singles_i 111
template_blob_g_33.0 546
template_blob_i_33.0 553
template_blob_r_33.0 843
template_blob_u_33.0 602
template_blob_y_33.0 260
template_blob_z_33.0 500
too 194
triplet_pairs_gr_33.0 286
triplet_pairs_iz_33.0 94
Print the number of visits done for small field surveys, deep drilling fields, target of opportunity, and template creation.
Nsfs = len(df.query("observation_reason == 'field_survey_science'"))
Nddf = len(df.query("observation_reason.str.contains('ddf')"))
Ntoo = len(df.query("observation_reason.str.contains('too')"))
Ntas = len(df.query("observation_reason == 'template_area_singles_i'"))
print("Number of visits done for small field surveys: ", Nsfs)
print("Number of visits done for SV DDFs: ", Nddf)
print("Number of visits done for target of opportuntity: ", Ntoo)
print("Number of visits done for templates: ", Ntas)
Number of visits done for small field surveys: 7194 Number of visits done for SV DDFs: 908 Number of visits done for target of opportuntity: 194 Number of visits done for templates: 111
Print the number of visits done as part of the primary wide LSST Science Validation (SV WFD -- excluding the SV DDFs and all the other observation reasons above).
Nwfd = len(df) - Nsfs - Nddf - Ntoo - Ntas
print("Number of visits done for the SV WFD: ", Nwfd)
Number of visits done for the SV WFD: 13240
Clean up.
del Nsfs, Nddf, Ntoo, Ntas, Nwfd
3.2. Target names¶
3.2.1. Small field surveys¶
Get the unique values of target_name for each of the small field survey areas, and print the number of visits done for each.
df_sfs = df.query("observation_reason == 'field_survey_science'")
values, counts = np.unique(df_sfs['target_name'], return_counts=True)
for value, count in zip(values, counts):
print('%20s %5i' % (value, count))
del df_sfs, values, counts
Abell_2764 7
COSMOS 664
Carina 124
DEEP_A0 1
DESI_SV3_R1 4
ELAIS_S1 166
M49 1173
New_Horizons 360
Prawn 632
Rubin_SV_212_-7 498
Rubin_SV_216_-17 386
Rubin_SV_225_-40 2052
Rubin_SV_280_-48 148
Rubin_SV_300_-41 38
Rubin_SV_320_-15 273
Trifid-Lagoon 668
Note that in the table of small field survey visits on the Science Validation survey summary webpage , only survey fields with >50 visits are shown.
3.2.2. Wide-fast-deep regions¶
Get the unique values of target_name for visits done as part of the WFD LSST Science Validation survey program.
For this database, it is simpler to start by defining the observation_reason values that are not part of the SV WFD (i.e., exclude SV DDFs, ToO, small field surveys, and the template-building observations).
query = "(observation_reason != 'field_survey_science') & "
query += "(observation_reason.str.contains('ddf') == 0) & "
query += "(observation_reason.str.contains('too') == 0) & "
query += "(observation_reason != 'template_area_singles_i')"
df_wfd = df.query(query)
values, counts = np.unique(df_wfd['target_name'], return_counts=True)
for value, count in zip(values, counts):
print('%30s %5i' % (value, count))
bulgy 2600
bulgy, dusty_plane 81
bulgy, lowdust 121
dusty_plane 996
dusty_plane, bulgy 166
dusty_plane, bulgy, lowdust 7
dusty_plane, lowdust 114
lowdust 8412
lowdust, bulgy 36
lowdust, dusty_plane 66
lowdust, nes 94
nes 441
nes, lowdust 106
Separate out the comma-separated lists into one list of unique target names.
temp = []
for value in values:
for name in str(value).split(','):
temp.append(name.strip())
targets = np.unique(temp)
print(targets)
['bulgy' 'dusty_plane' 'lowdust' 'nes']
The unique LSST region target names covered during commissioning are:
bulgy- Galactic bulge regionlowdust- low dust sky regiondusty_plane- dusty regions of the Galactic planenes- North Ecliptic Spur (NES)
Learn more about all of the planned LSST WFD regions on the LSST Baseline Strategy webpage.
Sum up the number of visits that overlap with each unique target name.
for target in targets:
print('%15s %5i' % (target, len(df_wfd.query("target_name.str.contains(@target)"))))
bulgy 3011
dusty_plane 1430
lowdust 8956
nes 641
Clean up.
del query, df_wfd, values, counts, temp, targets
3.2.3. Deep Drilling Fields (DDFs)¶
The LSST will include five Deep Drilling Fields, four of which (all except COSMOS) were observed as part of the LSST Science Validation survey.
Table 1: DDF locations, from the DDF webpage.
Print the unique DDF target names and the number of visits done for each.
df_ddf = df.query("observation_reason.str.contains('ddf')")
values, counts = np.unique(df_ddf['target_name'], return_counts=True)
for value, count in zip(values, counts):
print('%20s %5i' % (value, count))
DDF ECDFS, lowdust 119 DDF EDFS_a, lowdust 55 DDF EDFS_b, lowdust 58 DDF ELAISS1, lowdust 269 DDF XMM_LSS, lowdust 6 lowdust, DDF ECDFS 41 lowdust, DDF EDFS_a 32 lowdust, DDF EDFS_b 34 lowdust, DDF ELAISS1 270 lowdust, DDF XMM_LSS 24
The DDFs overlap the WFD lowdust region, and so the fields have both the DDF name and "lowdust" in their target_name.
Separate out the comma-separated lists into one list of unique target names.
temp = []
for value in values:
for name in str(value).split(','):
temp.append(name.strip())
targets = np.unique(temp)
print(targets)
['DDF ECDFS' 'DDF EDFS_a' 'DDF EDFS_b' 'DDF ELAISS1' 'DDF XMM_LSS' 'lowdust']
Define the list of only the DDF target names.
ddf_names = ['DDF ECDFS', 'DDF EDFS_a', 'DDF EDFS_b', 'DDF ELAISS1', 'DDF XMM_LSS']
Sum up the number of visits for each SV DDF.
for name in ddf_names:
print('%15s %5i' % (name, len(df_ddf.query("target_name.str.contains(@name)"))))
DDF ECDFS 160
DDF EDFS_a 87
DDF EDFS_b 92
DDF ELAISS1 539
DDF XMM_LSS 30
Clean up.
del df_ddf, values, counts, temp, targets, ddf_names
4. Visit metadata¶
Examples of how to visualize and calculate statistics for a few key columns of visit metadata.
4.1. Plot histograms¶
Create a histogram of the Modified Julian Dates of all visits, stacked by filter.
t = Time("2025-04-17T12:00:00", scale='tai')
mjd_to_jd = t.mjd - t.jd
df.loc[:, 'jd'] = np.floor(df.observationStartMJD - mjd_to_jd)
df.loc[:, 'jd'] = df.jd.astype(int)
jds = np.arange(df.jd.min(), df.jd.max()+1, 1)
jdsbins = np.arange(df.jd.min(), df.jd.max()+2, 1)
days = [t.split('T')[0] for t in Time(jds, format='jd', scale='tai').isot]
bar_bottom = np.zeros(len(jds))
plt.figure(figsize=(8, 4))
for b in 'ugrizy':
heights, _ = np.histogram(df.query("band == @b ").jd, bins=jdsbins)
plt.bar(jds, heights, bottom=bar_bottom, width=1, color=filter_colors[b], alpha=0.8, label=b)
bar_bottom += heights
plt.legend()
_ = plt.xticks(jds[::7], labels=days[::7], rotation=90)
plt.grid(alpha=0.2)
plt.ylabel("Number of visits", fontsize='large')
plt.title("LSSTCam Science Visits")
plt.show()
Figure 2: The number of visits per filter over time, in days.
Create a histogram of the airmass of each visit, by filter.
fig = plt.figure(figsize=(8, 4))
for f, filt in enumerate(filter_names):
plt.hist(df.query("band == @filt")['airmass'],
bins=40, histtype='step',
ls=filter_linestyles[filt],
color=filter_colors_list[f], label=filt)
plt.legend(loc='best')
plt.xlabel("Airmass")
plt.ylabel("Number of visits")
plt.show()
Figure 3: The number of visits in bins of airmass, by filter.
Create a histogram of the measured seeing of each visit, by filter.
fig = plt.figure(figsize=(8, 4))
for f, filt in enumerate(filter_names):
plt.hist(df.query("band == @filt")['seeingFwhmEff'],
bins=40, histtype='step',
ls=filter_linestyles[filt],
color=filter_colors_list[f], label=filt)
plt.legend(loc='best')
plt.xlabel("Seeing (FWHM of the PSF; arcsec)")
plt.ylabel("Number of visits")
plt.show()
Figure 4: The number of visits in bins of seeing, by filter.
Create a histogram of the $5\sigma$ depth (for point sources) of each visit, by filter.
fig = plt.figure(figsize=(8, 4))
for f, filt in enumerate(filter_names):
plt.hist(df.query("band == @filt")['fiveSigmaDepth'],
bins=40, histtype='step',
ls=filter_linestyles[filt],
color=filter_colors_list[f], label=filt)
plt.legend(loc='best')
plt.xlabel("5-Sigma Depth (mag)")
plt.ylabel("Number of visits")
plt.show()
Figure 5: The number of visits in bins of $5\sigma$ depth for point sources, by filter.
4.2. Summary statistics¶
Recreate parts of the tables on the Science Validation survey summary webpage . This section uses code from the LSSTCam summary notebook in the Sims SV Survey repo.
Display a table of the number of visits by band for each of the small field survey areas with at least 50 visits total.
query = "observation_reason == 'field_survey_science'"
df_sfs = df.query(query).groupby(['target_name', 'band']).agg({'seq_num': 'count'})
df_sfs.rename({'seq_num': 'count'}, axis=1, inplace=True)
df_sfs = df_sfs.reset_index('band').pivot(columns=["band"]).droplevel(0, axis=1)
df_sfs = df_sfs[['u', 'g', 'r', 'i', 'z', 'y']]
df_sfs['all'] = df_sfs.sum(axis=1)
df_sfs = df_sfs.query("all > 50").sort_values('all')
table = tabulate(pd.DataFrame(df_sfs.round(0)), headers='keys')
table = table.replace('nan', ' 0')
print(table)
del query, df_sfs, table
target_name u g r i z y all ---------------- --- --- --- --- --- --- ----- Carina 22 16 23 63 0 0 124 Rubin_SV_280_-48 30 30 29 30 29 0 148 ELAIS_S1 11 30 30 30 35 30 166 Rubin_SV_320_-15 0 29 17 105 52 70 273 New_Horizons 36 49 70 108 74 23 360 Rubin_SV_216_-17 0 64 95 227 0 0 386 Rubin_SV_212_-7 0 139 236 123 0 0 498 Prawn 196 164 149 93 30 0 632 COSMOS 100 82 166 139 111 66 664 Trifid-Lagoon 235 196 122 115 0 0 668 M49 261 280 378 254 0 0 1173 Rubin_SV_225_-40 312 568 441 387 240 104 2052
Display a table of the number of visits, median seeing (FWHM), mean airmass, and timespan (time between first and last visit) for each of the small field survey areas with at least 50 visits total.
query = "observation_reason == 'field_survey_science'"
df_sfs = df.query(query).groupby(['target_name']).agg({'seq_num': 'count',
'seeingFwhmEff': 'median',
'airmass': 'mean',
'exp_midpt_mjd': np.ptp})
df_sfs.rename({'seq_num': 'nvisits',
'seeingFwhmEff': 'median fwhm (arcsec)',
'airmass': 'mean airmass',
'exp_midpt_mjd': 'timespan (days)'}, axis=1, inplace=True)
df_sfs = df_sfs.query("nvisits > 50").sort_values('nvisits')
df_sfs['timespan (days)'] = df_sfs['timespan (days)'].astype(int) + 1
df_sfs.round(2)
table = tabulate(pd.DataFrame(df_sfs.round(2)), headers='keys')
table = table.replace('nan', ' 0')
print(table)
del query, df_sfs, table
target_name nvisits median fwhm (arcsec) mean airmass timespan (days) ---------------- --------- ---------------------- -------------- ----------------- Carina 124 0 1.22 4 Rubin_SV_280_-48 148 1.52 1.62 1 ELAIS_S1 166 1.38 1.03 17 Rubin_SV_320_-15 273 1.22 1.08 6 New_Horizons 360 1.05 1.1 62 Rubin_SV_216_-17 386 1.29 1.06 8 Rubin_SV_212_-7 498 1.21 1.26 7 Prawn 632 1.37 1.2 79 COSMOS 664 1.22 1.41 15 Trifid-Lagoon 668 1.14 1.15 10 M49 1173 1.29 1.33 13 Rubin_SV_225_-40 2052 1.36 1.18 97
Recreate the two tables above, but for the SV DDF, and include all fields (do not restrict to fields with >50 visits).
query = "observation_reason.str.contains('ddf')"
df_ddf = df.query(query).groupby(['observation_reason', 'band']).agg({'seq_num': 'count'})
df_ddf.rename({'seq_num': 'count'}, axis=1, inplace=True)
df_ddf = df_ddf.reset_index('band').pivot(columns=["band"]).droplevel(0, axis=1)
df_ddf = df_ddf[['u', 'g', 'r', 'i', 'z', 'y']]
df_ddf['all'] = df_ddf.sum(axis=1)
df_ddf = df_ddf.query("all > 0").sort_values('all')
table = tabulate(pd.DataFrame(df_ddf.round(0)), headers='keys')
table = table.replace('nan', ' 0')
print(table)
del query, df_ddf, table
observation_reason u g r i z y all -------------------- --- --- --- --- --- --- ----- ddf_xmm_lss 30 0 0 0 0 0 30 ddf_edfs_a 0 20 21 28 18 0 87 ddf_edfs_b 0 24 21 29 18 0 92 ddf_ecdfs 0 36 39 41 44 0 160 ddf_elaiss1 39 101 103 168 112 16 539
query = "observation_reason.str.contains('ddf')"
df_ddf = df.query(query).groupby(['observation_reason']).agg({'seq_num': 'count',
'seeingFwhmEff': 'median',
'airmass': 'mean',
'exp_midpt_mjd': np.ptp})
df_ddf.rename({'seq_num': 'nvisits',
'seeingFwhmEff': 'median fwhm (arcsec)',
'airmass': 'mean airmass',
'exp_midpt_mjd': 'timespan (days)'}, axis=1, inplace=True)
df_ddf = df_ddf.sort_values('nvisits')
df_ddf['timespan (days)'] = df_ddf['timespan (days)'].astype(int) + 1
df_ddf.round(2)
table = tabulate(pd.DataFrame(df_ddf.round(2)), headers='keys')
table = table.replace('nan', ' 0')
print(table)
del query, df_ddf, table
observation_reason nvisits median fwhm (arcsec) mean airmass timespan (days) -------------------- --------- ---------------------- -------------- ----------------- ddf_xmm_lss 30 1.47 1.79 10 ddf_edfs_a 87 1.32 1.79 55 ddf_edfs_b 92 1.37 1.88 55 ddf_ecdfs 160 1.51 1.92 57 ddf_elaiss1 539 1.4 1.34 84
5. MAF sky map¶
Recreate the sky map diagram in Figure 1.
The commissioning visits database file has been generated using the same format and schema as the Operations Simulations (opsim) databases, and so can be read by the MAF (Metric Analysis Frameworks) package from the rubin_sim package.
This section follows the Jupyter Notebook tutorial for visualizing the survey footprint in the rubin_sim_notebook repository.
Set the path to the folder containing rubin_sim data in the RSP at data.lsst.cloud.
os.environ['RUBIN_SIM_DATA_DIR'] = '/rubin/rubin_sim_data'
Define the "opsim" filename and the "run name" -- in this case, the name of the commissioning visits database.
opsim_fname = db_filename
temp = db_filename.split('/')[-1]
run_name = temp.split('.')[0]
print(run_name)
lsstcam_20250930
Define the metric to be plotted, in this case, Nvisits the number of visits. Define the size of the healpix to use for the map. Do not define any constraints, and include all visits.
metric = maf.metrics.CountMetric(col='observationStartMJD',
metric_name='Nvisits')
nside = 64
slicer = maf.slicers.HealpixSlicer(nside=nside)
constraint = None
Healpix slicer using NSIDE=64, approximate resolution 54.967783 arcminutes
Bundle together the components of the metric.
bundle = maf.MetricBundle(metric, slicer, constraint, run_name=run_name)
Define the bundle group to plot.
group = maf.MetricBundleGroup({'nvisits': bundle},
opsim_fname, out_dir=output_path)
Calculate the metric. The following step will generate the file lsstcam_20250930_Nvisits_HEAL.npz in the output_path defined in Section 1.2.
group.run_all()
Show the plots for this metric.
plot_dict = {'color_min': 10, 'color_max': 100,
'x_min': -2, 'x_max': 100, 'bins': 50, 'extend': 'both'}
bundle.set_plot_dict(plot_dict)
_ = bundle.plot()
Figure 6: Top, the sky map of the number of visits obtained during commissioning. Bottom, the sky area binned by number of visits, showing that most of the commissioning area was shallow.
Read more about they sky coverage for the SV wide-area survey on the Science Validation survey summary webpage .
5.1. Sky map for DDFs only¶
Create a sky map for only the DDFs, which will be where alert production will start in early 2026.
constraint = "observation_reason like \'%ddf%\'"
bundle = maf.MetricBundle(metric, slicer, constraint, run_name=run_name)
group = maf.MetricBundleGroup({'nvisits': bundle},
opsim_fname, out_dir=output_path)
group.run_all()
ph = maf.PlotHandler(savefig=False, fig_format='png', thumbnail=False, dpi=270)
ph.set_metric_bundles([bundle])
ph.plot(plot_func=maf.plots.HealpixSkyMap(),
plot_dicts={'color_min': 20, 'color_max': 100, 'figsize': (6, 4),
'labelsize': 'x-large', 'fontsize': 'x-large', 'extend': 'max',
'title': 'Deep Drilling Field LSSTCam Visits as of 2025-09-30'})
plt.figtext(0.40, 0.62, 'XMM LSS', fontsize='large', fontweight='bold', color='white')
plt.figtext(0.36, 0.50, 'ECDFS', fontsize='large', fontweight='bold', color='white')
plt.figtext(0.36, 0.33, 'EDFS', fontsize='large', fontweight='bold', color='white')
plt.figtext(0.47, 0.43, 'ELAIS-S1', fontsize='large', fontweight='bold', color='white')
plt.show()
Figure 7: The locations of the four Deep Drilling Fields (DDFs), with their names labeled. See Figure 1 for the field central coordinates.
6. Exercises for the learner¶
Of the 217 columns in the commissioning visits database, only nine were mentioned as key columns in Section 2.1. This did not include the column cloud_extinction.
Review the description of the cloud_extinction on the Science Validation survey summary webpage :
The visit database "also includes an estimate of the mean cloud extinction in the images. These are estimates based on the measured zeropoints for the images, compared to the expected zeropoint for an image in that bandpass at that airmass. A potential issue here is that visits with very heavy cloud extinction (or other problem with the quicklook image processing occuring immediately after image acquisition) may not succeed in measuring a zeropoint for the image at all, and thus no estimate for the cloud extinction will be possible either."
As in Section 4.1, create a histogram of the cloud_extinction values. Use the function defined in Section 4.2 to calculate the mean cloud extinction in magnitudes for a subset of the visits.