def setBatches(opsdb, colmap, args):
# Identify proposal information, etc.
propids, proptags, sqls, metadata = setSQL(opsdb, args.sqlConstraint)
bdict = {}
plotbundles = []
# Intranight (pairs/time)
intranight_all, plots = batches.intraNight(colmap, args.runName, extraSql=args.sqlConstraint,
ditherStacker=args.ditherStacker)
bdict.update(intranight_all)
plotbundles.append(plots)
sql = '(%s) or (%s)' % (sqls['WFD'], sqls['NES'])
md = 'WFD+' + metadata['NES']
intranight_wfdnes, plots = batches.intraNight(colmap, args.runName, extraSql=sql,
extraMetadata=md, ditherStacker=args.ditherStacker)
bdict.update(intranight_wfdnes)
plotbundles.append(plots)
internight_all, plots = batches.interNight(colmap, args.runName, extraSql=args.sqlConstraint,
ditherStacker=args.ditherStacker)
bdict.update(internight_all)
plotbundles.append(plots)
internight_wfd, plots = batches.interNight(colmap, args.runName, extraSql=sqls['WFD'],
extraMetadata=metadata['WFD'],
ditherStacker=args.ditherStacker)
bdict.update(internight_wfd)
plotbundles.append(plots)
return bdict, plots
def setBatches(opsdb, colmap, args):
propids, proptags, sqltags = setSQL(opsdb,
sqlConstraint=args.sqlConstraint)
bdict = {}
#bdict.update(batches.glanceBatch(colmap=colmap, runName=args.runName, sqlConstraint=args.sqlConstraint))
bdict.update(
batches.intraNight(colmap, args.runName, extraSql=args.sqlConstraint))
bdict.update(
batches.interNight(colmap, args.runName, extraSql=args.sqlConstraint))
# All metadata, all proposals.
bdict.update(
batches.allMetadata(colmap,
args.runName,
sqlConstraint=args.sqlConstraint,
metadata='All props'))
# WFD only.
bdict.update(
batches.allMetadata(colmap,
args.runName,
sqlConstraint=sqltags['WFD'],
metadata='WFD'))
# number of observations per proposal and per night.
bdict.update(
batches.nvisitsPerProp(opsdb,
colmap,
args.runName,
sqlConstraint=args.sqlConstraint))
# add nvisits / teff maps.
# All props
bdict.update(
batches.nvisitsM5Maps(colmap,
args.runName,
extraSql=args.sqlConstraint))
# WFD only
bdict.update(
batches.nvisitsM5Maps(colmap,
args.runName,
extraSql=sqltags['WFD'],
extraMetadata='WFD'))
# All props.
bdict.update(
batches.tEffMetrics(colmap, args.runName, extraSql=args.sqlConstraint))
# WFD only.
bdict.update(
batches.tEffMetrics(colmap,
args.runName,
extraSql=sqltags['WFD'],
extraMetadata='WFD'))
bdict.update(batches.slewBasics(colmap, args.runName))
# Per night and whole survey filter changes
bdict.update(batches.filtersPerNightBatch(colmap, args.runName, nights=1))
bdict.update(batches.filtersWholeSurveyBatch(colmap, args.runName))
return bdict
def setBatches(opsdb, colmap, args):
# Identify proposal information, etc.
propids, proptags, sqls, metadata = setSQL(opsdb, args.sqlConstraint)
bdict = {}
plotbundles = []
# Intranight (pairs/time)
intranight_all, plots = batches.intraNight(
colmap,
args.runName,
extraSql=args.sqlConstraint,
ditherStacker=args.ditherStacker)
bdict.update(intranight_all)
plotbundles.append(plots)
sql = '(%s) or (%s)' % (sqls['WFD'], sqls['NES'])
md = 'WFD+' + metadata['NES']
intranight_wfdnes, plots = batches.intraNight(
colmap,
args.runName,
extraSql=sql,
extraMetadata=md,
ditherStacker=args.ditherStacker)
bdict.update(intranight_wfdnes)
plotbundles.append(plots)
internight_all, plots = batches.interNight(
colmap,
args.runName,
extraSql=args.sqlConstraint,
ditherStacker=args.ditherStacker)
bdict.update(internight_all)
plotbundles.append(plots)
internight_wfd, plots = batches.interNight(
colmap,
args.runName,
extraSql=sqls['WFD'],
extraMetadata=metadata['WFD'],
ditherStacker=args.ditherStacker)
bdict.update(internight_wfd)
plotbundles.append(plots)
return bdict, plots
def setBatches(opsdb, colmap, args):
bdict = {}
bdict.update(batches.intraNight(colmap, args.runName, extraSql=args.sqlConstraint))
bdict.update(batches.interNight(colmap, args.runName, extraSql=args.sqlConstraint))
return bdict
def scienceRadarBatch(colmap=None,
runName='',
extraSql=None,
extraMetadata=None,
nside=64,
benchmarkArea=18000,
benchmarkNvisits=825,
DDF=True):
"""A batch of metrics for looking at survey performance relative to the SRD and the main
science drivers of LSST.
Parameters
----------
"""
# Hide dependencies
from mafContrib.LSSObsStrategy.galaxyCountsMetric_extended import GalaxyCountsMetric_extended
from mafContrib import Plasticc_metric, plasticc_slicer, load_plasticc_lc
if colmap is None:
colmap = ColMapDict('opsimV4')
if extraSql is None:
extraSql = ''
if extraSql == '':
joiner = ''
else:
joiner = ' and '
bundleList = []
healslicer = slicers.HealpixSlicer(nside=nside)
subsetPlots = [plots.HealpixSkyMap(), plots.HealpixHistogram()]
# Load up the plastic light curves
models = ['SNIa-normal', 'KN']
plasticc_models_dict = {}
for model in models:
plasticc_models_dict[model] = list(
load_plasticc_lc(model=model).values())
#########################
# SRD, DM, etc
#########################
sql = extraSql
displayDict = {
'group': 'SRD',
'subgroup': 'fO',
'order': 0,
'caption': None
}
metric = metrics.CountMetric(col=colmap['mjd'], metricName='fO')
plotDict = {
'xlabel': 'Number of Visits',
'Asky': benchmarkArea,
'Nvisit': benchmarkNvisits,
'xMin': 0,
'xMax': 1500
}
summaryMetrics = [
metrics.fOArea(nside=nside,
norm=False,
metricName='fOArea',
Asky=benchmarkArea,
Nvisit=benchmarkNvisits),
metrics.fOArea(nside=nside,
norm=True,
metricName='fOArea/benchmark',
Asky=benchmarkArea,
Nvisit=benchmarkNvisits),
metrics.fONv(nside=nside,
norm=False,
metricName='fONv',
Asky=benchmarkArea,
Nvisit=benchmarkNvisits),
metrics.fONv(nside=nside,
norm=True,
metricName='fONv/benchmark',
Asky=benchmarkArea,
Nvisit=benchmarkNvisits)
]
caption = 'The FO metric evaluates the overall efficiency of observing. '
caption += (
'foNv: out of %.2f sq degrees, the area receives at least X and a median of Y visits '
'(out of %d, if compared to benchmark). ' %
(benchmarkArea, benchmarkNvisits))
caption += ('fOArea: this many sq deg (out of %.2f sq deg if compared '
'to benchmark) receives at least %d visits. ' %
(benchmarkArea, benchmarkNvisits))
displayDict['caption'] = caption
bundle = mb.MetricBundle(metric,
healslicer,
sql,
plotDict=plotDict,
displayDict=displayDict,
summaryMetrics=summaryMetrics,
plotFuncs=[plots.FOPlot()])
bundleList.append(bundle)
displayDict['order'] += 1
displayDict = {
'group': 'SRD',
'subgroup': 'Gaps',
'order': 0,
'caption': None
}
plotDict = {'percentileClip': 95.}
for filtername in 'ugrizy':
sql = extraSql + joiner + 'filter ="%s"' % filtername
metric = metrics.MaxGapMetric()
summaryMetrics = [
metrics.PercentileMetric(
percentile=95,
metricName='95th percentile of Max gap, %s' % filtername)
]
bundle = mb.MetricBundle(metric,
healslicer,
sql,
plotFuncs=subsetPlots,
summaryMetrics=summaryMetrics,
displayDict=displayDict,
plotDict=plotDict)
bundleList.append(bundle)
displayDict['order'] += 1
#########################
# Solar System
#########################
# XXX -- may want to do Solar system seperatly
# XXX--fraction of NEOs detected (assume some nominal size and albido)
# XXX -- fraction of MBAs detected
# XXX -- fraction of KBOs detected
# XXX--any others? Planet 9s? Comets? Neptune Trojans?
#########################
# Cosmology
#########################
displayDict = {
'group': 'Cosmology',
'subgroup': 'galaxy counts',
'order': 0,
'caption': None
}
plotDict = {'percentileClip': 95.}
sql = extraSql + joiner + 'filter="i"'
metric = GalaxyCountsMetric_extended(filterBand='i',
redshiftBin='all',
nside=nside)
summary = [
metrics.AreaSummaryMetric(area=18000,
reduce_func=np.sum,
decreasing=True,
metricName='N Galaxies (WFD)')
]
summary.append(metrics.SumMetric(metricName='N Galaxies (all)'))
# make sure slicer has cache off
slicer = slicers.HealpixSlicer(nside=nside, useCache=False)
bundle = mb.MetricBundle(metric,
slicer,
sql,
plotDict=plotDict,
displayDict=displayDict,
summaryMetrics=summary,
plotFuncs=subsetPlots)
bundleList.append(bundle)
displayDict['order'] += 1
# let's put Type Ia SN in here
displayDict['subgroup'] = 'SNe Ia'
metadata = ''
# XXX-- use the light curves from PLASTICC here
displayDict['Caption'] = 'Fraction of normal SNe Ia'
sql = ''
slicer = plasticc_slicer(plcs=plasticc_models_dict['SNIa-normal'],
seed=42,
badval=0)
metric = Plasticc_metric(metricName='SNIa')
# Set the maskval so that we count missing objects as zero.
summary_stats = [metrics.MeanMetric(maskVal=0)]
plotFuncs = [plots.HealpixSkyMap()]
bundle = mb.MetricBundle(metric,
slicer,
sql,
runName=runName,
summaryMetrics=summary_stats,
plotFuncs=plotFuncs,
metadata=metadata,
displayDict=displayDict)
bundleList.append(bundle)
displayDict['order'] += 1
# XXX--need some sort of metric for weak lensing and camera rotation.
#########################
# Variables and Transients
#########################
displayDict = {
'group': 'Variables and Transients',
'subgroup': 'Periodic Stars',
'order': 0,
'caption': None
}
periods = [0.1, 0.5, 1., 2., 5., 10., 20.] # days
plotDict = {}
metadata = ''
sql = extraSql
displayDict[
'Caption'] = 'Measure of how well a periodic signal can be measured combining amplitude and phase coverage. 1 is perfect, 0 is no way to fit'
for period in periods:
summary = metrics.PercentileMetric(
percentile=10.,
metricName='10th %%-ile Periodic Quality, Period=%.1f days' %
period)
metric = metrics.PeriodicQualityMetric(
period=period,
starMag=20.,
metricName='Periodic Stars, P=%.1f d' % period)
bundle = mb.MetricBundle(metric,
healslicer,
sql,
metadata=metadata,
displayDict=displayDict,
plotDict=plotDict,
plotFuncs=subsetPlots,
summaryMetrics=summary)
bundleList.append(bundle)
displayDict['order'] += 1
# XXX add some PLASTICC metrics for kilovnova and tidal disruption events.
displayDict['subgroup'] = 'KN'
displayDict['caption'] = 'Fraction of Kilonova (from PLASTICC)'
sql = ''
slicer = plasticc_slicer(plcs=plasticc_models_dict['KN'],
seed=43,
badval=0)
metric = Plasticc_metric(metricName='KN')
summary_stats = [metrics.MeanMetric(maskVal=0)]
plotFuncs = [plots.HealpixSkyMap()]
bundle = mb.MetricBundle(metric,
slicer,
sql,
runName=runName,
summaryMetrics=summary_stats,
plotFuncs=plotFuncs,
metadata=metadata,
displayDict=displayDict)
bundleList.append(bundle)
displayDict['order'] += 1
# XXX -- would be good to add some microlensing events, for both MW and LMC/SMC.
#########################
# Milky Way
#########################
# Let's do the proper motion, parallax, and DCR degen of a 20nd mag star
rmag = 20.
displayDict = {
'group': 'Milky Way',
'subgroup': 'Astrometry',
'order': 0,
'caption': None
}
sql = extraSql
metadata = ''
plotDict = {'percentileClip': 95.}
metric = metrics.ParallaxMetric(metricName='Parallax Error r=%.1f' %
(rmag),
rmag=rmag,
seeingCol=colmap['seeingGeom'],
filterCol=colmap['filter'],
m5Col=colmap['fiveSigmaDepth'],
normalize=False)
summary = [
metrics.AreaSummaryMetric(area=18000,
reduce_func=np.median,
decreasing=False,
metricName='Median Parallax Error (WFD)')
]
summary.append(
metrics.PercentileMetric(percentile=95,
metricName='95th Percentile Parallax Error'))
bundle = mb.MetricBundle(metric,
healslicer,
sql,
metadata=metadata,
displayDict=displayDict,
plotDict=plotDict,
plotFuncs=subsetPlots,
summaryMetrics=summary)
bundleList.append(bundle)
displayDict['order'] += 1
metric = metrics.ProperMotionMetric(
metricName='Proper Motion Error r=%.1f' % rmag,
rmag=rmag,
m5Col=colmap['fiveSigmaDepth'],
mjdCol=colmap['mjd'],
filterCol=colmap['filter'],
seeingCol=colmap['seeingGeom'],
normalize=False)
summary = [
metrics.AreaSummaryMetric(
area=18000,
reduce_func=np.median,
decreasing=False,
metricName='Median Proper Motion Error (WFD)')
]
summary.append(
metrics.PercentileMetric(
metricName='95th Percentile Proper Motion Error'))
bundle = mb.MetricBundle(metric,
healslicer,
sql,
metadata=metadata,
displayDict=displayDict,
plotDict=plotDict,
summaryMetrics=summary,
plotFuncs=subsetPlots)
bundleList.append(bundle)
displayDict['order'] += 1
metric = metrics.ParallaxDcrDegenMetric(
metricName='Parallax-DCR degeneracy r=%.1f' % (rmag),
rmag=rmag,
seeingCol=colmap['seeingEff'],
filterCol=colmap['filter'],
m5Col=colmap['fiveSigmaDepth'])
caption = 'Correlation between parallax offset magnitude and hour angle for a r=%.1f star.' % (
rmag)
caption += ' (0 is good, near -1 or 1 is bad).'
# XXX--not sure what kind of summary to do here
summary = [metrics.MeanMetric(metricName='Mean DCR Degeneracy')]
bundle = mb.MetricBundle(metric,
healslicer,
sql,
metadata=metadata,
displayDict=displayDict,
summaryMetrics=summary,
plotFuncs=subsetPlots)
bundleList.append(bundle)
displayDict['order'] += 1
for b in bundleList:
b.setRunName(runName)
#########################
# DDF
#########################
ddf_time_bundleDicts = []
if DDF:
# Hide this import to avoid adding a dependency.
from lsst.sims.featureScheduler.surveys import generate_dd_surveys
ddf_surveys = generate_dd_surveys()
# For doing a high-res sampling of the DDF for co-adds
ddf_radius = 1.8 # Degrees
ddf_nside = 512
ra, dec = hpid2RaDec(ddf_nside, np.arange(hp.nside2npix(ddf_nside)))
displayDict = {
'group': 'DDF depths',
'subgroup': None,
'order': 0,
'caption': None
}
# Run the inter and intra gaps at the center of the DDFs
for survey in ddf_surveys:
slicer = slicers.UserPointsSlicer(ra=np.degrees(survey.ra),
dec=np.degrees(survey.dec),
useCamera=False)
ddf_time_bundleDicts.append(
interNight(colmap=colmap,
slicer=slicer,
runName=runName,
nside=64,
extraSql='note="%s"' % survey.survey_name,
subgroup=survey.survey_name)[0])
ddf_time_bundleDicts.append(
intraNight(colmap=colmap,
slicer=slicer,
runName=runName,
nside=64,
extraSql='note="%s"' % survey.survey_name,
subgroup=survey.survey_name)[0])
for survey in ddf_surveys:
displayDict['subgroup'] = survey.survey_name
# Crop off the u-band only DDF
if survey.survey_name[0:4] != 'DD:u':
dist_to_ddf = angularSeparation(ra, dec, np.degrees(survey.ra),
np.degrees(survey.dec))
goodhp = np.where(dist_to_ddf <= ddf_radius)
slicer = slicers.UserPointsSlicer(ra=ra[goodhp],
dec=dec[goodhp],
useCamera=False)
for filtername in ['u', 'g', 'r', 'i', 'z', 'y']:
metric = metrics.Coaddm5Metric(
metricName=survey.survey_name + ', ' + filtername)
summary = [
metrics.MedianMetric(metricName='median depth ' +
survey.survey_name + ', ' +
filtername)
]
sql = extraSql + joiner + 'filter = "%s"' % filtername
bundle = mb.MetricBundle(metric,
slicer,
sql,
metadata=metadata,
displayDict=displayDict,
summaryMetrics=summary,
plotFuncs=[])
bundleList.append(bundle)
displayDict['order'] += 1
displayDict = {
'group': 'DDF Transients',
'subgroup': None,
'order': 0,
'caption': None
}
for survey in ddf_surveys:
displayDict['subgroup'] = survey.survey_name
if survey.survey_name[0:4] != 'DD:u':
slicer = plasticc_slicer(
plcs=plasticc_models_dict['SNIa-normal'],
seed=42,
ra_cen=survey.ra,
dec_cen=survey.dec,
radius=np.radians(3.),
useCamera=False)
metric = Plasticc_metric(metricName=survey.survey_name +
' SNIa')
sql = ''
summary_stats = [metrics.MeanMetric(maskVal=0)]
plotFuncs = [plots.HealpixSkyMap()]
bundle = mb.MetricBundle(metric,
slicer,
sql,
runName=runName,
summaryMetrics=summary_stats,
plotFuncs=plotFuncs,
metadata=metadata,
displayDict=displayDict)
bundleList.append(bundle)
displayDict['order'] += 1
for b in bundleList:
b.setRunName(runName)
bundleDict = mb.makeBundlesDictFromList(bundleList)
intraDict = intraNight(colmap=colmap,
runName=runName,
nside=nside,
extraSql=extraSql,
extraMetadata=extraMetadata)[0]
interDict = interNight(colmap=colmap,
runName=runName,
nside=nside,
extraSql=extraSql,
extraMetadata=extraMetadata)[0]
bundleDict.update(intraDict)
bundleDict.update(interDict)
for ddf_time in ddf_time_bundleDicts:
bundleDict.update(ddf_time)
return bundleDict
def setBatches(opsdb, colmap, args):
# Set up WFD sql constraint
propids, proptags, sqls, metadata = setSQL(opsdb, sqlConstraint=args.sqlConstraint)
# Set up appropriate metadata - need to combine args.sqlConstraint
# Some of these metrics are reproduced in other scripts - srd and cadence
bdict = {}
plotbundles = []
for tag in ['All', 'WFD']:
fO = batches.fOBatch(colmap=colmap, runName=args.runName,
extraSql=sqls[tag], extraMetadata=metadata[tag],
ditherStacker=args.ditherStacker)
bdict.update(fO)
astrometry = batches.astrometryBatch(colmap=colmap, runName=args.runName,
extraSql=sqls[tag], extraMetadata=metadata[tag],
ditherStacker=args.ditherStacker)
bdict.update(astrometry)
rapidrevisit = batches.rapidRevisitBatch(colmap=colmap, runName=args.runName,
extraSql=sqls[tag], extraMetadata=metadata[tag],
ditherStacker=args.ditherStacker)
bdict.update(rapidrevisit)
# Intranight (pairs/time)
intranight_all, plots = batches.intraNight(colmap, args.runName, extraSql=args.sqlConstraint,
ditherStacker=args.ditherStacker)
bdict.update(intranight_all)
plotbundles.append(plots)
sql = '(%s) or (%s)' % (sqls['WFD'], sqls['NES'])
md = 'WFD+' + metadata['NES']
intranight_wfdnes, plots = batches.intraNight(colmap, args.runName, extraSql=sql,
extraMetadata=md, ditherStacker=args.ditherStacker)
bdict.update(intranight_wfdnes)
plotbundles.append(plots)
# Internight (nights between visits)
for tag in ['All', 'WFD']:
internight, plots = batches.interNight(colmap, args.runName, extraSql=sqls[tag],
extraMetadata=metadata[tag],
ditherStacker=args.ditherStacker)
bdict.update(internight)
plotbundles.append(plots)
# Intraseason (length of season)
for tag in ['All', 'WFD']:
season, plots = batches.seasons(colmap=colmap, runName=args.runName,
extraSql=sqls[tag], extraMetadata=metadata[tag],
ditherStacker=args.ditherStacker)
bdict.update(season)
plotbundles.append(plots)
# Run all metadata metrics, All and just WFD.
for tag in ['All', 'WFD']:
bdict.update(batches.allMetadata(colmap, args.runName, extraSql=sqls[tag],
extraMetadata=metadata[tag],
ditherStacker=args.ditherStacker))
# Nvisits + m5 maps + Teff maps, All and just WFD.
for tag in ['All', 'WFD']:
bdict.update(batches.nvisitsM5Maps(colmap, args.runName, runLength=args.nyears,
extraSql=sqls[tag], extraMetadata=metadata[tag],
ditherStacker=args.ditherStacker))
bdict.update(batches.tEffMetrics(colmap, args.runName, extraSql=sqls[tag],
extraMetadata=metadata[tag],
ditherStacker=args.ditherStacker))
# Nvisits per proposal and per night.
bdict.update(batches.nvisitsPerProp(opsdb, colmap, args.runName,
extraSql=args.sqlConstraint))
# NVisits alt/az LambertSkyMap (all filters, per filter)
bdict.update(batches.altazLambert(colmap, args.runName, extraSql=args.sqlConstraint))
# Slew metrics.
bdict.update(batches.slewBasics(colmap, args.runName, sqlConstraint=args.sqlConstraint))
# Open shutter metrics.
bdict.update(batches.openshutterFractions(colmap, args.runName, extraSql=args.sqlConstraint))
# Per night and whole survey filter changes.
bdict.update(batches.filtersPerNight(colmap, args.runName, nights=1, extraSql=args.sqlConstraint))
bdict.update(batches.filtersWholeSurvey(colmap, args.runName, extraSql=args.sqlConstraint))
# Hourglass plots.
bdict.update(batches.hourglassPlots(colmap, args.runName,
nyears=args.nyears, extraSql=args.sqlConstraint))
return bdict
def setBatches(opsdb, colmap, args):
bdict = {}
bdict.update(batches.intraNight(colmap, args.runName))
return bdict
def setBatches(opsdb, colmap, args):
# Set up WFD sql constraint, if possible.
propids, proptags, sqls, metadata = setSQL(
opsdb, sqlConstraint=args.sqlConstraint)
if 'WFD' in sqls:
tags = ['All', 'WFD']
else:
tags = ['All']
# Set up appropriate metadata - need to combine args.sqlConstraint
# Some of these metrics are reproduced in other scripts - srd and cadence
bdict = {}
plotbundles = []
for tag in tags:
fO = batches.fOBatch(colmap=colmap,
runName=args.runName,
extraSql=sqls[tag],
extraMetadata=metadata[tag],
ditherStacker=args.ditherStacker)
bdict.update(fO)
astrometry = batches.astrometryBatch(colmap=colmap,
runName=args.runName,
extraSql=sqls[tag],
extraMetadata=metadata[tag],
ditherStacker=args.ditherStacker)
bdict.update(astrometry)
rapidrevisit = batches.rapidRevisitBatch(
colmap=colmap,
runName=args.runName,
extraSql=sqls[tag],
extraMetadata=metadata[tag],
ditherStacker=args.ditherStacker)
bdict.update(rapidrevisit)
# Intranight (pairs/time)
intranight_all, plots = batches.intraNight(
colmap,
args.runName,
extraSql=args.sqlConstraint,
ditherStacker=args.ditherStacker)
bdict.update(intranight_all)
plotbundles.append(plots)
if ('WFD' in sqls) and ('NES' in sqls):
sql = '(%s) or (%s)' % (sqls['WFD'], sqls['NES'])
md = 'WFD+' + metadata['NES']
intranight_wfdnes, plots = batches.intraNight(
colmap,
args.runName,
extraSql=sql,
extraMetadata=md,
ditherStacker=args.ditherStacker)
bdict.update(intranight_wfdnes)
plotbundles.append(plots)
# Internight (nights between visits)
for tag in tags:
internight, plots = batches.interNight(
colmap,
args.runName,
extraSql=sqls[tag],
extraMetadata=metadata[tag],
ditherStacker=args.ditherStacker)
bdict.update(internight)
plotbundles.append(plots)
# Intraseason (length of season)
for tag in tags:
season, plots = batches.seasons(colmap=colmap,
runName=args.runName,
extraSql=sqls[tag],
extraMetadata=metadata[tag],
ditherStacker=args.ditherStacker)
bdict.update(season)
plotbundles.append(plots)
# Run all metadata metrics, All and just WFD.
for tag in tags:
bdict.update(
batches.allMetadata(colmap,
args.runName,
extraSql=sqls[tag],
extraMetadata=metadata[tag],
ditherStacker=args.ditherStacker))
# Nvisits + m5 maps + Teff maps, All and just WFD.
for tag in tags:
bdict.update(
batches.nvisitsM5Maps(colmap,
args.runName,
runLength=args.nyears,
extraSql=sqls[tag],
extraMetadata=metadata[tag],
ditherStacker=args.ditherStacker))
bdict.update(
batches.tEffMetrics(colmap,
args.runName,
extraSql=sqls[tag],
extraMetadata=metadata[tag],
ditherStacker=args.ditherStacker))
# Nvisits per proposal and per night.
bdict.update(
batches.nvisitsPerProp(opsdb,
colmap,
args.runName,
extraSql=args.sqlConstraint))
# NVisits alt/az LambertSkyMap (all filters, per filter)
bdict.update(
batches.altazLambert(colmap, args.runName,
extraSql=args.sqlConstraint))
# Slew metrics.
bdict.update(
batches.slewBasics(colmap,
args.runName,
sqlConstraint=args.sqlConstraint))
# Open shutter metrics.
bdict.update(
batches.openshutterFractions(colmap,
args.runName,
extraSql=args.sqlConstraint))
# Per night and whole survey filter changes.
bdict.update(
batches.filtersPerNight(colmap,
args.runName,
nights=1,
extraSql=args.sqlConstraint))
bdict.update(
batches.filtersWholeSurvey(colmap,
args.runName,
extraSql=args.sqlConstraint))
# Hourglass plots.
bdict.update(
batches.hourglassPlots(colmap,
args.runName,
nyears=args.nyears,
extraSql=args.sqlConstraint))
return bdict