Metrics and Plots Available in Operations Rehearsal 3

Peter S. Ferguson; Jeffrey L. Carlin

Metrics and Plots Available in Operations Rehearsal 3#

Abstract

This technote explores the metrics/plots generated by the Nightly Validation Pipeline for Operations Rehearsal 3. We describe the methods used to aggregate these plots and metrics, list metrics and plots created by each task, and provide examples of each metricBundle / Plot produced.

Intro#

This document was created as part of the Operations Rehearsal 3, and is not intended to be a definitive source/documentation for plots and metrics. Instead, the hope is that this document can help others learn how to determine what plots/metrics exist and how to access them.

To facilitate this we describe the methods we used to find, access and understand the data products created when running the data reduction pipeline (DRP). This incudes:

Which collections to use for registry queries
Which metrics/plots exist in these collections
The tasks and configuration used to generate these metrics/plots
How to retrieve plots in the notebook aspect of the Rubin Science Platform
How to find plots in the plot navigator

Then we list out the tasks in the Nightly Validation Pipeline that generate metrics and plots. For each task we link the pipeline yaml that configures the task (tagged to w_2024_14) and examples of the metricMeasurementBundles and Plots created by each task. This should allow for a quick exploration of the data products produced as a part of this pipeline.

For each metricBundle we list an example dataId used, the metricBundle’s name, its atools docstring, any additional notes, and an example result. We split these into Visit-level metrics and Tract-level metrics.

Finally, for each plot datasetType we show an example version of the plot, again split into Visit-level plots and Tract-level metrics.

The code used to generate the plots and figures for this technote can be found in the notebook atools_metrics_plots_census-OR3.ipynb

Finding and accessing data products#

In this section we describe the ways we determined which metrics and plots were created by the nightly validation pipeline.

Which collections to use#

For Operations Rehearsal 3 the data reduction campaigns are listed in confluence at https://confluence.lsstcorp.org/display/DM/Campaigns. On that page we can see the following information for the Ops Rehearsal: Daily Validation Pipeline in the collection names cell:

repo = /repo/embargo
Intra-night (step1 + step2a processing): LSSTComCamSim/quickLook/24
    "quickLook" is a misnomer, this is actually nightly validation. Will fix this after the ops rehearsal.
Full (step3+) run: LSSTComCamSim/runs/nightlyvalidation/{day_obs}/d_2024_03_29/DM-43612

This indicates that we should use the following collections to explore the nightly-validation data products:

collections_nv = ['LSSTComCamSim/runs/nightlyvalidation/20240402/d_2024_03_29/DM-43612',
                  'LSSTComCamSim/runs/nightlyvalidation/20240403/d_2024_03_29/DM-43612',
                  'LSSTComCamSim/runs/nightlyvalidation/20240404/d_2024_03_29/DM-43612',
                  'LSSTComCamSim/quickLook/24']

For the intermittent cumulative DRP use the following collections:

collections_icDRP = ['LSSTComCamSim/runs/intermittentcumulativeDRP/20240402_03_04/d_2024_03_29/DM-43865',
                    'LSSTComCamSim/quickLook/24']

What tools/metrics/plots exist#

To see what metrics exist in a given butler collection (note that this assumes the butler has been initialized with the desired repo, the registry extracted via registry = butler.registry, and the collection name assigned to the variable collection), execute the following. This code block finds all datasetTypes with storageClass of MetricMeasurementBundle.

metrics_datasets = []

for datasetType in registry.queryDatasetTypes():
    if registry.queryDatasets(datasetType, collections=collection).any(execute=False, exact=False):
        if datasetType.storageClass_name == 'MetricMeasurementBundle':
            print(datasetType)
            metrics_datasets.append(datasetType)

A similar query with MetricMeasurementBundle replaced by Plot will show you all of the plot datasetTypes that have been defined for the given repo/collection. Some examples are shown below:

DatasetType('matchedVisitCore_metrics', {instrument, skymap, tract}, MetricMeasurementBundle)
DatasetType('matchedVisitCore_g_stellarAstrometricResidualStdDevDec_FocalPlanePlot', {instrument, skymap, tract}, Plot)
DatasetType('preSourceTableCore_skySourceFlux_HistPlot', {band, instrument, physical_filter, visit}, Plot)
DatasetType('objectTableExtended_r_ap12PsfSky_SkyPlot', {skymap, tract}, Plot)

This query compiles a list of datasetType objects, which include the butler dimensions necessary to retrieve the dataset (the items included within the curly braces above; for example, to retrieve matchedVisitCore_metrics from the butler, one would need to specify the desired instrument, skymap, and tract in the dataId passed to the butler). An example of how to retrieve one of these:

did_tract = {'tract':9813, 'skymap':'ops_rehersal_prep_2k_v1', 'instrument':'LSSTComCamSim'}

matchedVisitCoreMetrics = butler.get('matchedVisitCore_metrics', dataId=did_tract, collections=collection)

What was configured and run in the pipeline#

Consider the following pipeline task definition for analyzePreSourceTableCore:

  analyzePreSourceTableCore:
    class: lsst.analysis.tools.tasks.SourceTableVisitAnalysisTask
    config:
      connections.outputName: preSourceTableCore
      atools.skyFluxVisitStatisticMetric: SkyFluxStatisticMetric
      atools.skyFluxVisitStatisticMetric.applyContext: VisitContext
      atools.skySourceSky: SkySourceSkyPlot
      atools.skySourceFlux: SkySourceHistPlot
      python: |
        from lsst.analysis.tools.atools import *
        from lsst.analysis.tools.contexts import *

In a pipeline task definition (e.g., analyzePreSourceTableCore), the output will be called

{connections.outputName}_metrics for metric bundles, and
{connections.outputName}_atool.name_plotType for plots

An example from the above config file:

{connections.outputName}=preSourceTableCore
atool.name=SkySourceSkyPlot
plotType=SkyPlot

will produce the datasetType preSourceTableCore_SkySourceSky_SkyPlot.

In the pipeline for nightly validation, analyzePreSourceTableCore is defined as:

imports:
  - $DRP_PIPE_DIR/pipelines/LSSTComCamSim/DRP-ops-rehearsal-3.yaml
tasks:
  analyzePreSourceTableCore:
    class: lsst.analysis.tools.tasks.SourceTableVisitAnalysisTask
    config:
      connections.data: preSourceTable_visit
      connections.inputName: preSourceTable_visit
      connections.outputName: preSourceTableCore

This slightly reconfigures the version of “analyzePreSourceTableCore” that is imported from DRP-ops-rehearsal-3.yaml, which in turn imports from pipelines/_ingredients. The reconfiguration simply changes the input dataset.

Extracting the exact configuration from butler#

Above we described the general way to see which tasks were run and how they were configured, but given a specific task there is an associated configuration file in the butler. For example, for the analyzePreSourceTableCore task there is a configuration file with the datasetType of analyzePreSourceTableCore_config that can be obtained with butler.get and shows the exact configuration of the task.

Retrieving plots from the butler#

A quick test in a notebook will show that you can not use butler.get to retrieve plots from the butler.

One workaround is to retrieve the plot using:

plot_name = 'preSourceTableCore_skySourceFlux_HistPlot'
ref = butler.registry.findDataset(plot_name, dataId, collections=collections)
uri = butler.getURI(ref)
image_bytes = uri.read()

Then it can be saved:

file_name= uri.basename()
with open(file_name, 'wb') as file:
  file.write(image_bytes)

or displayed in a notebook:

from IPython.display import Image
Image(image_bytes)

Using the plot navigator#

The rubin plot navigator can be accessed at https://usdf-rsp.slac.stanford.edu/plot-navigator/dashboard_gen3 and is described in DMTN-237. This interface allows the user to quickly browse plots that are stored in the butler. To find/select a plot the drop down menus in the top left are used. For example, by selecting the following:

repo: /repo/embargo
collection: LSSTComCamSim/runs/nightlyvalidation/20240402/d_2024_03_29/DM-43612
Tab: Tract
Skymap: ops_rehersal_prep_2k_v1 # note mispelling
Tract: 2494
Plots: matchedVisitCore_g_stellarAstrometricRepeatability1

We can display the plot shown in the screenshot below.

_images/plot_navigator.png — Fig. 1 Example of using the plot navigator to display the matchedVisitCore_g_stellarAstrometricRepeatability1_histPlot.#

Nightly validation pipeline#

https://github.com/lsst/drp_pipe/blob/main/pipelines/LSSTComCamSim/nightly-validation-ops-rehearsal-3.yaml

Visit-level tasks#

This table has 3 columns: Task, datasetType, and StorageClass. The task entry links to the pipeline yaml responsible for the task configuration. The datasetType links to either a description of the metric bundle or an example plot generated as part of the nightly validation run. The StorageClass is either Plot or MetricMeasurementBundle.

Task (link to pipeline yaml)	datasetType	StorageClass
calibrate	calexpSummary_metrics	MetricMeasurementBundle

analyzeMatchedVisitCore	matchedVisitCore_metrics	MetricMeasurementBundle
	matchedvisitcore-{band}-stellarphotometricrepeatability-histplot	Plot
	matchedvisitcore-{band}-stellarastrometricresidualstddevra-focalplaneplot	Plot
	matchedvisitcore-{band}-stellarphotometricresiduals-focalplaneplot	Plot
	matchedvisitcore-{band}-stellarastrometricresidualsdec-focalplaneplot	Plot
	matchedvisitcore-{band}-stellarastrometricresidualsra-focalplaneplot	Plot
	matchedvisitcore-{band}-stellarastrometricresidualstddevdec-focalplaneplot	Plot
	matchedvisitcore-{band}-stellarastrometricrepeatability2-histplot	Plot
	matchedvisitcore-{band}-stellarastrometricrepeatability1-histplot	Plot
	matchedvisitcore-{band}-stellarastrometricrepeatability3-histplot	Plot
	matchedvisitcore-{band}-stellarastrometricselfrepeatabilitydec-histplot	Plot
	matchedvisitcore-{band}-stellarastrometricselfrepeatabilityra-histplot	Plot

analyzePreSourceTableCore	preSourceTableCore_metrics	MetricMeasurementBundle
	presourcetablecore-skysourceflux-histplot	Plot

Tract-level tasks#

Task (link to pipeline yaml)	datasetType	StorageClass
analyzeDiaSourceTableTract	diaSourceTableTract_metrics	MetricMeasurementBundle
	diasourcetabletract-streakdiasourceplot-diaskyplot	Plot

validateObjectTableCore	objectTableColumnValidate_metrics	MetricMeasurementBundle

analyzeObjectTableCore	objectTableCore_metrics	MetricMeasurementBundle
	objecttablecore-{band}-skyobjectsky-skyplot	Plot
	objecttablecore-xperppsfp-colorcolorfitplot	Plot
	objecttablecore-{band}-shapesizefractionaldiff-scatterplotwithtwohists	Plot
	objecttablecore-xperpcmodel-colorcolorfitplot	Plot
	objecttablecore-{band}-e1diff-scatterplotwithtwohists	Plot
	objecttablecore-wperppsfp-colorcolorfitplot	Plot
	objecttablecore-wperpcmodel-colorcolorfitplot	Plot
	objecttablecore-{band}-skyobjectflux-histplot	Plot
	objecttablecore-{band}-e2diff-scatterplotwithtwohists	Plot
	objecttablecore-{band}-cmodelbulgesizevscmodelbulgemag-scatterplotwithtwohists	Plot
	objecttablecore-{band}-psfcmodelscatter-scatterplotwithtwohists	Plot
	objecttablecore-{band}-cmodeldisksizevscmodeldiskmag-scatterplotwithtwohists	Plot
	objecttablecore-{band}-shapesizedetradiusvspsfmag-scatterplotwithtwohists	Plot
	objecttablecore-{band}-shapesizedetradiusvscmodelmag-scatterplotwithtwohists	Plot

analyzeObjectTableExtended	objectTableExtended_metrics	MetricMeasurementBundle
	objecttableextended-{band}-ap12psfsky-skyplot	Plot
	objecttableextended-{band}-psfcmodelsky-skyplot	Plot

propertyMapTract	propertymaptract-healsparsemap-deepcoadd-dcr-e1-weighted-mean-propertymapplot	Plot
	propertymaptract-healsparsemap-deepcoadd-dcr-dra-weighted-mean-propertymapplot	Plot
	propertymaptract-healsparsemap-deepcoadd-psf-size-weighted-mean-propertymapplot	Plot
	propertymaptract-healsparsemap-deepcoadd-psf-maglim-weighted-mean-propertymapplot	Plot
	propertymaptract-healsparsemap-deepcoadd-dcr-ddec-weighted-mean-propertymapplot	Plot
	propertymaptract-healsparsemap-deepcoadd-sky-noise-weighted-mean-propertymapplot	Plot
	propertymaptract-healsparsemap-deepcoadd-dcr-e2-weighted-mean-propertymapplot	Plot
	propertymaptract-healsparsemap-deepcoadd-exposure-time-sum-propertymapplot	Plot
	propertymaptract-healsparsemap-deepcoadd-psf-e1-weighted-mean-propertymapplot	Plot
	propertymaptract-healsparsemap-deepcoadd-psf-e2-weighted-mean-propertymapplot	Plot
	propertymaptract-healsparsemap-deepcoadd-epoch-mean-propertymapplot	Plot
	propertymaptract-healsparsemap-deepcoadd-epoch-min-propertymapplot	Plot
	propertymaptract-healsparsemap-deepcoadd-epoch-max-propertymapplot	Plot
	propertymaptract-healsparsemap-deepcoadd-sky-background-weighted-mean-propertymapplot	Plot

Visit-level metrics#

Below we list out the metrics associated with each metric bundle at a visit level.

calexpSummary_metrics#

dataId used in examples:

did_visit_det = {'visit':7024040300001, 'skymap':'ops_rehersal_prep_2k_v1', 'instrument':'LSSTComCamSim', 'detector':0}

metricBundle: calexpSummaryMetrics (source)

Docstring: Calibrated exposure summary statistics to load from the butler

Notes: These metrics are computed outside of analysis_tools in the computeExposureSummaryStats pipetask (source),

which has the following docstring: Task to compute exposure summary statistics.

This task computes various quantities suitable for DPDD and other downstream processing at the detector centers, including:

psfSigma
psfArea
psfIxx
psfIyy
psfIxy
ra
dec
zenithDistance
zeroPoint
skyBg
skyNoise
meanVar
raCorners
decCorners
astromOffsetMean
astromOffsetStd

These additional quantities are computed from the stars in the detector:

psfStarDeltaE1Median
psfStarDeltaE2Median
psfStarDeltaE1Scatter
psfStarDeltaE2Scatter
psfStarDeltaSizeMedian
psfStarDeltaSizeScatter
psfStarScaledDeltaSizeScatter

These quantities are computed based on the PSF model and image mask to assess the robustness of the PSF model across a given detector (against, e.g., extrapolation instability):

maxDistToNearestPsf
psfTraceRadiusDelta

These quantities are computed to assess depth:

effTime
effTimePsfSigmaScale
effTimeSkyBgScale
effTimeZeroPointScale

metric name	value	units
psfSigma	1.506499	pix
psfArea	34.678724
psfIxx	2.258628	pix
psfIyy	2.281597	pix
psfIxy	0.049743	pix
ra	124.617999	deg
dec	-15.026213	deg
zenithDistance	17.825242	deg
zeroPoint	31.271871	mag
skyBg	1703.767764	mag
skyNoise	34.471789	mag
meanVar	1175.081472	mag
astromOffsetMean	0.005423	arcsec
astromOffsetStd	0.003085	arcsec
nPsfStar	419.000000
psfStarDeltaE1Median	-0.000103	pix
psfStarDeltaE2Median	-0.000645	pix
psfStarDeltaE1Scatter	0.008924	pix
psfStarDeltaE2Scatter	0.009482	pix
psfStarDeltaSizeMedian	0.000991	pix
psfStarDeltaSizeScatter	0.007213	pix
psfStarScaledDeltaSizeScatter	0.004785	pix
psfTraceRadiusDelta	0.012334	pix
maxDistToNearestPsf	419.527519	deg
effTime	12.083753	s
effTimePsfSigmaScale	1.127983	s
effTimeSkyBgScale	0.344765	s
effTimeZeroPointScale	1.035749	s

matchedVisitCore_metrics#

dataId used in examples:

did_tract = {'tract':9813, 'skymap': 'ops_rehersal_prep_2k_v1', 'band': 'g', 'instrument':'LSSTComCamSim'}

These metrics are for a set of “matched visits,” which is created by taking all objects from the isolated_star_sources table in a given tract, then matching all of the sourceTable catalogs to accumulate all measurements for each source. These collections can be used for repeatability measurements.

Note that although these metrics use information from individual visits, their Butler dimension is tract because they aggregate measurements from all visits contributing to a tract.

metricBundle: stellarAstrometricRepeatability1 (source)

Docstring: Calculate the AMx, ADx, AFx metrics and make histograms showing the data used to compute the metrics.

Notes: AMx, ADx, and AFx (where x=[1, 2, 3]) are defined in the Observatory System Specifications (OSS) as requirement OSS-REQ-0388. AMx is a measure of astrometric repeatability on xx arcminute scales, where xx=[5, 20, 200] for x=[1, 2, 3]. ADx defines thresholds for outliers, and AFx captures the percentage of outliers exceeding the ADx outlier limit.

Defined with signal-to-noise (S/N) cuts of 50 < S/N < 50000 and a magnitude range of 17 < mag < 21.5.

stellarAstrometricRepeatability1 measures repeatability on 5 arcminute scales.

metric name	value	units
g_AM1	11.372618	mas
g_AF1	9.814720	%
g_AD1	19.861856	mas
r_AM1	10.864433	mas
r_AF1	8.558647	%
r_AD1	18.849730	mas
i_AM1	10.225130	mas
i_AF1	7.298187	%
i_AD1	17.797588	mas

metricBundle: stellarAstrometricRepeatability2 (source)

Docstring: Calculate the AMx, ADx, AFx metrics and make histograms showing the data used to compute the metrics.

Notes: stellarAstrometricRepeatability2 measures repeatability on 20 arcminute scales.

metric name	value	units
g_AM2	10.659738	mas
g_AF2	8.527521	%
g_AD2	18.899353	mas
r_AM2	10.275115	mas
r_AF2	7.466236	%
r_AD2	17.986514	mas
i_AM2	9.798453	mas
i_AF2	6.640539	%
i_AD2	17.214238	mas

metricBundle: stellarAstrometricRepeatability3 (source)

Docstring: Calculate the AMx, ADx, AFx metrics and make histograms showing the data used to compute the metrics.

Notes: stellarAstrometricRepeatability3 measures repeatability on 200 arcminute scales. There are no measurements for these metrics currently, because we do not have datasets spanning the requisite 200-arcminute scale.

metric name	value	units
g_AM3	nan	mas
g_AF3	nan	%
g_AD3	nan	mas
r_AM3	nan	mas
r_AF3	nan	%
r_AD3	nan	mas
i_AM3	nan	mas
i_AF3	nan	%
i_AD3	nan	mas

metricBundle: stellarAstrometricSelfRepeatabilityDec (source)

Docstring: Calculate the median position RMS of point sources.

Notes: The median of the per-source RMS positional scatter in Declination. Defined with a magnitude selection of 17 < mag < 21.5.

metric name	value	units
g_dmL2AstroErr_Dec	6.699939	mas
r_dmL2AstroErr_Dec	7.066788	mas
i_dmL2AstroErr_Dec	7.651587	mas

metricBundle: stellarAstrometricSelfRepeatabilityRA (source)

Docstring: Calculate the median position RMS of point sources.

Notes: The median of the per-source RMS positional scatter in Right Ascension. Defined with a magnitude selection of 17 < mag < 21.5.

metric name	value	units
g_dmL2AstroErr_RA	8.582950	mas
r_dmL2AstroErr_RA	7.482557	mas
i_dmL2AstroErr_RA	7.975985	mas

metricBundle: stellarPhotometricRepeatability (source)

Docstring: Compute photometric repeatability from multiple measurements of a set of stars. First, a set of per-source quality criteria are applied. Second, the individual source measurements are grouped together by object index and per-group quantities are computed (e.g., a representative S/N for the group based on the median of associated per-source measurements). Third, additional per-group criteria are applied. Fourth, summary statistics are computed for the filtered groups.

Notes: The median and outlier fraction of the per-source RMS magnitude scatter. Defined for stars with S/N > 200. Metrics with “stellarPhotRepeatStdev” in their names correspond to PA1 defined in the OSS as requirement OSS-REQ-0387. Metrics with “stellarPhotRepeatOutlierFraction” correspond to PF1 (the fraction of outliers exceeding the threshold PA2) from the same requirement, with PA2 (the outlier threshold) set to 15 mmag by default. The “_ct” metrics simply capture the number of stars that went into the calculation.

metric name	value	units
g_stellarPhotRepeatStdev	5.502570	mmag
g_stellarPhotRepeatOutlierFraction	5.647383	%
g_ct	726.0	ct
r_stellarPhotRepeatStdev	5.011919	mmag
r_stellarPhotRepeatOutlierFraction	6.167401	%
r_ct	908.0	ct
i_stellarPhotRepeatStdev	5.056592	mmag
i_stellarPhotRepeatOutlierFraction	6.779661	%
i_ct	944.0	ct

metricBundle: stellarPhotometricResiduals (source)

Docstring: Plot mean photometric residuals as a function of the position on the focal plane. First, a set of per-source quality criteria are applied. Second, the individual source measurements are grouped together by object index and the per-group magnitude is computed. The residuals between the individual sources and these magnitudes are then used to construct a plot showing the mean residual as a function of the focal-plane position.

Notes: In addition to producing plots of the residuals as a function of focal plane position, the median, standard deviation (“Stdev”), and the median absolute deviation \(\sigma_{MAD}\) (“SigmaMad”) are reported for the tract. Because these are residuals about the median, the median residual should be 0 by construction.

metric name	value	units
g_photResidTractSigmaMad	9.297931	mmag
g_photResidTractStdev	73.996488	mmag
g_photResidTractMedian	0.000000	mmag
r_photResidTractSigmaMad	8.565694	mmag
r_photResidTractStdev	66.920415	mmag
r_photResidTractMedian	0.000000	mmag
i_photResidTractSigmaMad	8.489994	mmag
i_photResidTractStdev	65.658013	mmag
i_photResidTractMedian	0.000000	mmag

preSourceTableCore_metrics#

dataId used in examples:

did_visit_det = {'visit':7024040300002, 'skymap':'ops_rehersal_prep_2k_v1', 'instrument':'LSSTComCamSim', 'detector':0}

metricBundle: skyFluxVisitStatisticMetric source

Docstring: Calculate sky flux statistics. This uses the 9-pixel aperture flux for sky sources/objects, and returns multiple statistics on the measured fluxes. Note that either visitContext (measurement on sourceTable) or coaddContext (measurement on objectTable) must be specified.

metric name	value	units
medianSky	-2.348488	nJy
meanSky	-7.034565	nJy
stdevSky	618.708919	nJy
sigmaMADSky	600.762855	nJy

Note that the default visit (7024040300001) used for this document did not have an entry for preSourceTableCore_metrics, so these values are for visit 7024040300002.

Tract-level metrics#

diaSourceTableTract_metrics#

dataId used in examples:

did_tract = {'tract':9813, 'skymap': 'ops_rehersal_prep_2k_v1', 'band': 'g', 'instrument':'LSSTComCamSim'}

metricBundle: NumDiaSources (source)

Docstring: Count all DiaSources that do not have known bad/quality flags.

metric name	value	units
numDiaSources	96643.0	ct

metricBundle: NumStreakCenterDiaSources (source)

Docstring: Count DiaSources that have the STREAK flag in the center of the source.

metric name	value	units
numStreakCenterDiaSources	115.0	ct

metricBundle: NumStreakDiaSources (source)

Docstring: Count DiaSources that fall in a STREAK flag footprint region.

metric name	value	units
numStreakDiaSources	116.0	ct

objectTableColumnValidate_metrics#

dataId used in examples:

did_tract = {'tract':9813, 'skymap': 'ops_rehersal_prep_2k_v1', 'band': 'g', 'instrument':'LSSTComCamSim'}

metricBundle: validCmodelFluxMetric (source)

Docstring: Calculate the fraction of values in a column that have valid numerical values (i.e., not NaN), and that fall within the specified “reasonable” range for the values.

metric name	value	units
g_validFracColumn	87.698572	%
g_nanFracColumn	10.009657	%
r_validFracColumn	91.172296	%
r_nanFracColumn	6.848321	%
i_validFracColumn	88.884015	%
i_nanFracColumn	8.870868	%

metricBundle: validPsfFluxMetric (source)

Docstring: Calculate the fraction of values in a column that have valid numerical values (i.e., not NaN), and that fall within the specified “reasonable” range for the values.

metric name	value	units
g_validFracColumn	90.162651	%
g_nanFracColumn	6.994101	%
r_validFracColumn	93.751672	%
r_nanFracColumn	3.717816	%
i_validFracColumn	91.044549	%
i_nanFracColumn	5.854848	%

objectTableCore_metrics#

dataId used in examples:

did_tract = {'tract':9813, 'skymap': 'ops_rehersal_prep_2k_v1', 'band': 'g', 'instrument':'LSSTComCamSim'}

metricBundle: blendMetrics (source)

Docstring: Calculate metrics based on the performance of the deblender for blends with multiple children

metric name	value	units
numBlends	54537.000000
meanBlendIterations	16.765004
meanBlendLogL	-8419.284180

metricBundle: e1Diff (source)

Docstring: No docstring

Notes: Statistics of residual of e1 ellipticity measurement of sources relative to the PSF.

metric name	value	units
g_highSNStars_median	-0.002205	pix
g_highSNStars_sigmaMad	0.002918	pix
g_highSNStars_count	295.0	ct
g_lowSNStars_median	-0.001460	pix
g_lowSNStars_sigmaMad	0.004919	pix
g_lowSNStars_count	1512.0	ct
r_highSNStars_median	0.000087	pix
r_highSNStars_sigmaMad	0.001245	pix
r_highSNStars_count	351.0	ct
r_lowSNStars_median	0.000110	pix
r_lowSNStars_sigmaMad	0.002334	pix
r_lowSNStars_count	1827.0	ct
i_highSNStars_median	0.000113	pix
i_highSNStars_sigmaMad	0.001424	pix
i_highSNStars_count	319.0	ct
i_lowSNStars_median	0.000138	pix
i_lowSNStars_sigmaMad	0.002367	pix
i_lowSNStars_count	1835.0	ct

metricBundle: e2Diff (source)

Docstring: No docstring

Notes: Statistics of residual of e2 ellipticity measurement of sources relative to the PSF.

metric name	value	units
g_highSNStars_median	-0.001331	pix
g_highSNStars_sigmaMad	0.002531	pix
g_highSNStars_count	295.0	ct
g_lowSNStars_median	-0.000700	pix
g_lowSNStars_sigmaMad	0.004103	pix
g_lowSNStars_count	1512.0	ct
r_highSNStars_median	-0.000157	pix
r_highSNStars_sigmaMad	0.001335	pix
r_highSNStars_count	351.0	ct
r_lowSNStars_median	-0.000051	pix
r_lowSNStars_sigmaMad	0.002267	pix
r_lowSNStars_count	1827.0	ct
i_highSNStars_median	-0.000034	pix
i_highSNStars_sigmaMad	0.001283	pix
i_highSNStars_count	319.0	ct
i_lowSNStars_median	0.000015	pix
i_lowSNStars_sigmaMad	0.002503	pix
i_lowSNStars_count	1835.0	ct

metricBundle: isolatedDeblenderMetrics (source)

Docstring: Calculate metrics based on the performance of the deblender for parents with only a single child peak.

metric name	value	units
numIsolated	161369.000000
meanIsolatedIterations	16.086714
meanIsolatedLogL	-232.709503

metricBundle: parentDeblenderMetrics (source)

Docstring: Calculate metrics based on the performance of the deblender

metric name	value	units
numParents	215916.000000
numDeblendFailed	8.000000
numIncompleteData	23955.000000
numDetectedPeaks	331274.000000
numDeblendedChildren	331121.000000

metricBundle: psfCModelScatter (source)

Docstring: Creates a scatterPlot showing the difference between PSF and CModel mags

Notes: Metrics give summary statistics of the PSF-cmodel magnitudes.

metric name	value	units
g_psf_cModel_diff_median	-1.953607	mmag
g_psf_cModel_diff_sigmaMad	2.120985	mmag
g_psf_cModel_diff_mean	-1.179624	mmag
r_psf_cModel_diff_median	-0.799037	mmag
r_psf_cModel_diff_sigmaMad	1.698413	mmag
r_psf_cModel_diff_mean	0.120015	mmag
i_psf_cModel_diff_median	-0.566337	mmag
i_psf_cModel_diff_sigmaMad	1.800549	mmag
i_psf_cModel_diff_mean	-0.033813	mmag

metricBundle: shapeSizeFractionalDiff (source)

Docstring: No docstring

Notes: Fractional size residuals (\(S/S_{PSF} - 1\)). Stars with “highSN” have S/N > 2700, and “lowSN” have S/N > 500.

metric name	value	units
g_highSNStars_median	0.003467	pix
g_highSNStars_sigmaMad	0.004422	pix
g_highSNStars_count	295.0	ct
g_lowSNStars_median	0.003165	pix
g_lowSNStars_sigmaMad	0.006352	pix
g_lowSNStars_count	1512.0	ct
r_highSNStars_median	0.000477	pix
r_highSNStars_sigmaMad	0.001387	pix
r_highSNStars_count	351.0	ct
r_lowSNStars_median	0.000496	pix
r_lowSNStars_sigmaMad	0.002080	pix
r_lowSNStars_count	1827.0	ct
i_highSNStars_median	0.000747	pix
i_highSNStars_sigmaMad	0.001530	pix
i_highSNStars_count	319.0	ct
i_lowSNStars_median	0.000921	pix
i_lowSNStars_sigmaMad	0.002236	pix
i_lowSNStars_count	1835.0	ct

metricBundle: skippedDeblenderMetrics (source)

Docstring: Calculate metrics based on blends skipped by the deblender

metric name	value	units
numSkippedBlends	2.000000
numBlendParentTooBig	2.000000
numBlendTooManyPeaks	0.000000
numBlendTooManyMasked	0.000000
numSkippedPeaks	145.000000

metricBundle: skyFluxStatisticMetric (source)

Docstring: Calculate sky flux statistics. This uses the 9-pixel aperture flux for sky sources/objects, and returns multiple statistics on the measured fluxes. Note that either visitContext (measurement on sourceTable) or coaddContext (measurement on objectTable) must be specified.

metric name	value	units
g_medianSky	-1.353270	nJy
g_meanSky	0.828698	nJy
g_stdevSky	148.568045	nJy
g_sigmaMADSky	97.669588	nJy
r_medianSky	-5.492619	nJy
r_meanSky	-1.941782	nJy
r_stdevSky	203.472885	nJy
r_sigmaMADSky	138.705877	nJy
i_medianSky	-9.055690	nJy
i_meanSky	-5.286348	nJy
i_stdevSky	300.578227	nJy
i_sigmaMADSky	211.787172	nJy

metricBundle: wPerpCModel (source)

Docstring: No docstring

Notes: Width of stellar locus in (r-i) vs. (g-r) as defined in Ivezic et al. 2004, measured with cmodel magnitudes.

metric name	value	units
wPerp_cModelFlux_sigmaMAD	10.225373	mmag
wPerp_cModelFlux_median	-0.565891	mmag

metricBundle: wPerpPSFP (source)

Docstring: No docstring

Notes: Width of the blue portion of the stellar locus in (r-i) vs. (g-r) as defined in Ivezic et al. 2004, measured with PSF magnitudes.

metric name	value	units
wPerp_psfFlux_sigmaMAD	10.580971	mmag
wPerp_psfFlux_median	-0.496211	mmag

metricBundle: xPerpCModel (source)

Docstring: No docstring

Notes: Width of the red portion of the stellar locus in (r-i) vs. (g-r) as defined in Ivezic et al. 2004, measured with cmodel magnitudes.

Notes: Width of the blue portion of the stellar locus in (r-i) vs. (g-r) as defined in Ivezic et al. 2004, measured with PSF magnitudes.

metric name	value	units
xPerp_cModelFlux_sigmaMAD	13.572956	mmag
xPerp_cModelFlux_median	-0.158474	mmag

metricBundle: xPerpPSFP (source)

Docstring: No docstring

Notes: Width of the red portion of the stellar locus in (r-i) vs. (g-r) as defined in Ivezic et al. 2004, measured with PSF magnitudes.

metric name	value	units
xPerp_psfFlux_sigmaMAD	14.075556	mmag
xPerp_psfFlux_median	0.286494	mmag

Note the erroneous “P” at the end of “wPerpPSFP” and “xPerpPSFP”. This is due to a typo in the pipelines code.

objectTableExtended_metrics#

dataId used in examples:

did_tract = {'tract':9813, 'skymap': 'ops_rehersal_prep_2k_v1', 'band': 'g', 'instrument':'LSSTComCamSim'}

metricBundle: ap12PsfSky (source)

Docstring: No docstring

Notes: Difference between magnitudes in a 12-pixel aperture and PSF magnitudes. Metrics summarize rolled-up statistics of the plotted quantities.

metric name	value	units
g_ap12_psf_diff_median	-2.497167	mmag
g_ap12_psf_diff_sigmaMad	7.382115	mmag
g_ap12_psf_diff_mean	-3.173155	mmag
r_ap12_psf_diff_median	-1.285786	mmag
r_ap12_psf_diff_sigmaMad	4.182110	mmag
r_ap12_psf_diff_mean	-3.415648	mmag
i_ap12_psf_diff_median	-1.689628	mmag
i_ap12_psf_diff_sigmaMad	4.563705	mmag
i_ap12_psf_diff_mean	-3.558899	mmag

Visit-level plots#

matchedVisitCore#

_images/matchedvisitcore-%7Bband%7D-stellarphotometricresiduals-focalplaneplot.png — Fig. 4 **Plot Name**: matchedVisitCore_g_stellarPhotometricResiduals_FocalPlanePlot#

Associated metrics: matchedVisitCore_metrics

_images/matchedvisitcore-%7Bband%7D-stellarastrometricresidualsdec-focalplaneplot.png — Fig. 5 **Plot Name**: matchedVisitCore_g_stellarAstrometricResidualsDec_FocalPlanePlot#

Associated metrics: matchedVisitCore_metrics

_images/matchedvisitcore-%7Bband%7D-stellarastrometricresidualsra-focalplaneplot.png — Fig. 6 **Plot Name**: matchedVisitCore_g_stellarAstrometricResidualsRA_FocalPlanePlot#

Associated metrics: matchedVisitCore_metrics

_images/matchedvisitcore-%7Bband%7D-stellarastrometricresidualstddevdec-focalplaneplot.png — Fig. 7 **Plot Name**: matchedVisitCore_g_stellarAstrometricResidualStdDevDec_FocalPlanePlot#

Associated metrics: matchedVisitCore_metrics

_images/matchedvisitcore-%7Bband%7D-stellarastrometricrepeatability1-histplot.png — Fig. 8 **Plot Name**: matchedVisitCore_g_stellarAstrometricRepeatability1_HistPlot#

Associated metrics: matchedVisitCore_metrics

_images/matchedvisitcore-%7Bband%7D-stellarastrometricrepeatability2-histplot.png — Fig. 9 **Plot Name**: matchedVisitCore_g_stellarAstrometricRepeatability2_HistPlot#

Associated metrics: matchedVisitCore_metrics

_images/matchedvisitcore-%7Bband%7D-stellarastrometricrepeatability3-histplot.png — Fig. 10 **Plot Name**: matchedVisitCore_g_stellarAstrometricRepeatability3_HistPlot#

Associated metrics: matchedVisitCore_metrics

_images/matchedvisitcore-%7Bband%7D-stellarastrometricselfrepeatabilitydec-histplot.png — Fig. 11 **Plot Name**: matchedVisitCore_g_stellarAstrometricSelfRepeatabilityDec_HistPlot#

Associated metrics: matchedVisitCore_metrics

_images/matchedvisitcore-%7Bband%7D-stellarastrometricselfrepeatabilityra-histplot.png — Fig. 12 **Plot Name**: matchedVisitCore_g_stellarAstrometricSelfRepeatabilityRA_HistPlot#

Associated metrics: matchedVisitCore_metrics

preSourceTableCore#

Tract-level plots#

diaSourceTableTract#

objectTableCore#

_images/objecttablecore-xperppsfp-colorcolorfitplot.png — Fig. 17 **Plot Name**: objectTableCore_xPerpPSFP_ColorColorFitPlot#

Associated metrics: objectTableCore_metrics

_images/objecttablecore-xperpcmodel-colorcolorfitplot.png — Fig. 18 **Plot Name**: objectTableCore_xPerpCModel_ColorColorFitPlot#

Associated metrics: objectTableCore_metrics

_images/objecttablecore-wperppsfp-colorcolorfitplot.png — Fig. 19 **Plot Name**: objectTableCore_wPerpPSFP_ColorColorFitPlot#

Associated metrics: objectTableCore_metrics

_images/objecttablecore-wperpcmodel-colorcolorfitplot.png — Fig. 20 **Plot Name**: objectTableCore_wPerpCModel_ColorColorFitPlot#

Associated metrics: objectTableCore_metrics

_images/objecttablecore-%7Bband%7D-shapesizefractionaldiff-scatterplotwithtwohists.png — Fig. 21 **Plot Name**: objectTableCore_g_shapeSizeFractionalDiff_ScatterPlotWithTwoHists#

Associated metrics: objectTableCore_metrics

_images/objecttablecore-%7Bband%7D-shapesizedetradiusvspsfmag-scatterplotwithtwohists.png — Fig. 22 **Plot Name**: objectTableCore_g_shapeSizeDetRadiusVsPsfMag_ScatterPlotWithTwoHists#

Associated metrics: objectTableCore_metrics

_images/objecttablecore-%7Bband%7D-shapesizedetradiusvscmodelmag-scatterplotwithtwohists.png — Fig. 23 **Plot Name**: objectTableCore_g_shapeSizeDetRadiusVsCmodelMag_ScatterPlotWithTwoHists#

Associated metrics: objectTableCore_metrics

_images/objecttablecore-%7Bband%7D-e1diff-scatterplotwithtwohists.png — Fig. 24 **Plot Name**: objectTableCore_g_e1Diff_ScatterPlotWithTwoHists#

Associated metrics: objectTableCore_metrics

_images/objecttablecore-%7Bband%7D-e2diff-scatterplotwithtwohists.png — Fig. 25 **Plot Name**: objectTableCore_g_e2Diff_ScatterPlotWithTwoHists#

Associated metrics: objectTableCore_metrics

_images/objecttablecore-%7Bband%7D-cmodelbulgesizevscmodelbulgemag-scatterplotwithtwohists.png — Fig. 26 **Plot Name**: objectTableCore_g_cModelBulgeSizeVsCmodelBulgeMag_ScatterPlotWithTwoHists#

Associated metrics: objectTableCore_metrics

_images/objecttablecore-%7Bband%7D-cmodeldisksizevscmodeldiskmag-scatterplotwithtwohists.png — Fig. 27 **Plot Name**: objectTableCore_g_cModelDiskSizeVsCmodelDiskMag_ScatterPlotWithTwoHists#

Associated metrics: objectTableCore_metrics

_images/objecttablecore-%7Bband%7D-psfcmodelscatter-scatterplotwithtwohists.png — Fig. 28 **Plot Name**: objectTableCore_g_psfCModelScatter_ScatterPlotWithTwoHists#

Associated metrics: objectTableCore_metrics

objectTableExtended#

PropertyMapTract#

_images/propertymaptract-healsparsemap-deepcoadd-psf-size-weighted-mean-propertymapplot.png — Fig. 33 **Plot Name**: propertyMapTract_HealSparseMap_deepCoadd_psf_size_weighted_mean_PropertyMapPlot#

_images/propertymaptract-healsparsemap-deepcoadd-psf-maglim-weighted-mean-propertymapplot.png — Fig. 34 **Plot Name**: propertyMapTract_HealSparseMap_deepCoadd_psf_maglim_weighted_mean_PropertyMapPlot#

_images/propertymaptract-healsparsemap-deepcoadd-sky-noise-weighted-mean-propertymapplot.png — Fig. 35 **Plot Name**: propertyMapTract_HealSparseMap_deepCoadd_sky_noise_weighted_mean_PropertyMapPlot#

_images/propertymaptract-healsparsemap-deepcoadd-psf-e1-weighted-mean-propertymapplot.png — Fig. 36 **Plot Name**: propertyMapTract_HealSparseMap_deepCoadd_psf_e1_weighted_mean_PropertyMapPlot#

_images/propertymaptract-healsparsemap-deepcoadd-psf-e2-weighted-mean-propertymapplot.png — Fig. 37 **Plot Name**: propertyMapTract_HealSparseMap_deepCoadd_psf_e2_weighted_mean_PropertyMapPlot#

_images/propertymaptract-healsparsemap-deepcoadd-dcr-e1-weighted-mean-propertymapplot.png — Fig. 38 **Plot Name**: propertyMapTract_HealSparseMap_deepCoadd_dcr_e1_weighted_mean_PropertyMapPlot#

_images/propertymaptract-healsparsemap-deepcoadd-dcr-e2-weighted-mean-propertymapplot.png — Fig. 39 **Plot Name**: propertyMapTract_HealSparseMap_deepCoadd_dcr_e2_weighted_mean_PropertyMapPlot#

_images/propertymaptract-healsparsemap-deepcoadd-exposure-time-sum-propertymapplot.png — Fig. 40 **Plot Name**: propertyMapTract_HealSparseMap_deepCoadd_exposure_time_sum_PropertyMapPlot#

_images/propertymaptract-healsparsemap-deepcoadd-epoch-mean-propertymapplot.png — Fig. 41 **Plot Name**: propertyMapTract_HealSparseMap_deepCoadd_epoch_mean_PropertyMapPlot#

_images/propertymaptract-healsparsemap-deepcoadd-epoch-min-propertymapplot.png — Fig. 42 **Plot Name**: propertyMapTract_HealSparseMap_deepCoadd_epoch_min_PropertyMapPlot#

_images/propertymaptract-healsparsemap-deepcoadd-epoch-max-propertymapplot.png — Fig. 43 **Plot Name**: propertyMapTract_HealSparseMap_deepCoadd_epoch_max_PropertyMapPlot#

_images/propertymaptract-healsparsemap-deepcoadd-sky-background-weighted-mean-propertymapplot.png — Fig. 44 **Plot Name**: propertyMapTract_HealSparseMap_deepCoadd_sky_background_weighted_mean_PropertyMapPlot#