def scienceRadarBatch(colmap=None,
runName='opsim',
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, TDEsAsciiMetric
if colmap is None:
colmap = ColMapDict('fbs')
if extraSql is None:
extraSql = ''
if extraSql == '':
joiner = ''
else:
joiner = ' and '
bundleList = []
# Get some standard per-filter coloring and sql constraints
filterlist, colors, filterorders, filtersqls, filtermetadata = filterList(
all=False, extraSql=extraSql, extraMetadata=extraMetadata)
standardStats = standardSummary(withCount=False)
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
#########################
fOb = fOBatch(runName=runName,
colmap=colmap,
extraSql=extraSql,
extraMetadata=extraMetadata,
benchmarkArea=benchmarkArea,
benchmarkNvisits=benchmarkNvisits)
astromb = astrometryBatch(runName=runName,
colmap=colmap,
extraSql=extraSql,
extraMetadata=extraMetadata)
rapidb = rapidRevisitBatch(runName=runName,
colmap=colmap,
extraSql=extraSql,
extraMetadata=extraMetadata)
# loop through and modify the display dicts - set SRD as group and their previous 'group' as the subgroup
temp_list = []
for key in fOb:
temp_list.append(fOb[key])
for key in astromb:
temp_list.append(astromb[key])
for key in rapidb:
temp_list.append(rapidb[key])
for metricb in temp_list:
metricb.displayDict['subgroup'] = metricb.displayDict['group'].replace(
'SRD', '').lstrip(' ')
metricb.displayDict['group'] = 'SRD'
bundleList.extend(temp_list)
displayDict = {
'group': 'SRD',
'subgroup': 'Year Coverage',
'order': 0,
'caption': 'Number of years with observations.'
}
slicer = slicers.HealpixSlicer(nside=nside)
metric = metrics.YearCoverageMetric()
for f in filterlist:
plotDict = {'colorMin': 7, 'colorMax': 10, 'color': colors[f]}
summary = [
metrics.AreaSummaryMetric(area=18000,
reduce_func=np.mean,
decreasing=True,
metricName='N Seasons (18k) %s' % f)
]
bundleList.append(
mb.MetricBundle(metric,
slicer,
filtersqls[f],
plotDict=plotDict,
metadata=filtermetadata[f],
displayDict=displayDict,
summaryMetrics=summary))
#########################
# Solar System
#########################
# Generally, we need to run Solar System metrics separately; they're a multi-step process.
#########################
# Cosmology
#########################
displayDict = {
'group': 'Cosmology',
'subgroup': 'Galaxy Counts',
'order': 0,
'caption': None
}
plotDict = {'percentileClip': 95., 'nTicks': 5}
sql = extraSql + joiner + 'filter="i"'
metadata = combineMetadata(extraMetadata, 'i band')
metric = GalaxyCountsMetric_extended(filterBand='i',
redshiftBin='all',
nside=nside)
summary = [
metrics.AreaSummaryMetric(area=18000,
reduce_func=np.sum,
decreasing=True,
metricName='N Galaxies (18k)')
]
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,
metadata=metadata,
displayDict=displayDict,
summaryMetrics=summary,
plotFuncs=subsetPlots)
bundleList.append(bundle)
displayDict['order'] += 1
# let's put Type Ia SN in here
displayDict['subgroup'] = 'SNe Ia'
# XXX-- use the light curves from PLASTICC here
displayDict['caption'] = 'Fraction of normal SNe Ia'
sql = extraSql
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=extraMetadata,
displayDict=displayDict)
bundleList.append(bundle)
displayDict['order'] += 1
displayDict['subgroup'] = 'Camera Rotator'
displayDict[
'caption'] = 'Kuiper statistic (0 is uniform, 1 is delta function) of the '
slicer = slicers.HealpixSlicer(nside=nside)
metric1 = metrics.KuiperMetric('rotSkyPos')
metric2 = metrics.KuiperMetric('rotTelPos')
for f in filterlist:
for m in [metric1, metric2]:
plotDict = {'color': colors[f]}
displayDict['order'] = filterorders[f]
displayDict['caption'] += f"{m.colname} for visits in {f} band."
bundleList.append(
mb.MetricBundle(m,
slicer,
filtersqls[f],
plotDict=plotDict,
displayDict=displayDict,
summaryMetrics=standardStats,
plotFuncs=subsetPlots))
# XXX--need some sort of metric for weak lensing
#########################
# Variables and Transients
#########################
displayDict = {
'group': 'Variables/Transients',
'subgroup': 'Periodic Stars',
'order': 0,
'caption': None
}
for period in [
0.5,
1,
2,
]:
for magnitude in [21., 24.]:
amplitudes = [0.05, 0.1, 1.0]
periods = [period] * len(amplitudes)
starMags = [magnitude] * len(amplitudes)
plotDict = {
'nTicks': 3,
'colorMin': 0,
'colorMax': 3,
'xMin': 0,
'xMax': 3
}
metadata = combineMetadata(
'P_%.1f_Mag_%.0f_Amp_0.05-0.1-1' % (period, magnitude),
extraMetadata)
sql = None
displayDict['caption'] = 'Metric evaluates if a periodic signal of period %.1f days could ' \
'be detected for an r=%i star. A variety of amplitudes of periodicity ' \
'are tested: [1, 0.1, and 0.05] mag amplitudes, which correspond to ' \
'metric values of [1, 2, or 3]. ' % (period, magnitude)
metric = metrics.PeriodicDetectMetric(periods=periods,
starMags=starMags,
amplitudes=amplitudes,
metricName='PeriodDetection')
bundle = mb.MetricBundle(metric,
healslicer,
sql,
metadata=metadata,
displayDict=displayDict,
plotDict=plotDict,
plotFuncs=subsetPlots,
summaryMetrics=standardStats)
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)'
displayDict['order'] = 0
slicer = plasticc_slicer(plcs=plasticc_models_dict['KN'],
seed=43,
badval=0)
metric = Plasticc_metric(metricName='KN')
plotFuncs = [plots.HealpixSkyMap()]
summary_stats = [metrics.MeanMetric(maskVal=0)]
bundle = mb.MetricBundle(metric,
slicer,
extraSql,
runName=runName,
summaryMetrics=summary_stats,
plotFuncs=plotFuncs,
metadata=extraMetadata,
displayDict=displayDict)
bundleList.append(bundle)
# Tidal Disruption Events
displayDict['subgroup'] = 'TDE'
displayDict[
'caption'] = 'Fraction of TDE lightcurves that could be identified, outside of DD fields'
detectSNR = {'u': 5, 'g': 5, 'r': 5, 'i': 5, 'z': 5, 'y': 5}
# light curve parameters
epochStart = -22
peakEpoch = 0
nearPeakT = 10
postPeakT = 14 # two weeks
nPhaseCheck = 1
# condition parameters
nObsTotal = {'u': 0, 'g': 0, 'r': 0, 'i': 0, 'z': 0, 'y': 0}
nObsPrePeak = 1
nObsNearPeak = {'u': 0, 'g': 0, 'r': 0, 'i': 0, 'z': 0, 'y': 0}
nFiltersNearPeak = 3
nObsPostPeak = 0
nFiltersPostPeak = 2
metric = TDEsAsciiMetric(asciifile=None,
detectSNR=detectSNR,
epochStart=epochStart,
peakEpoch=peakEpoch,
nearPeakT=nearPeakT,
postPeakT=postPeakT,
nPhaseCheck=nPhaseCheck,
nObsTotal=nObsTotal,
nObsPrePeak=nObsPrePeak,
nObsNearPeak=nObsNearPeak,
nFiltersNearPeak=nFiltersNearPeak,
nObsPostPeak=nObsPostPeak,
nFiltersPostPeak=nFiltersPostPeak)
slicer = slicers.HealpixSlicer(nside=32)
sql = extraSql + joiner + "note not like '%DD%'"
md = extraMetadata
if md is None:
md = " NonDD"
else:
md += 'NonDD'
bundle = mb.MetricBundle(metric,
slicer,
sql,
runName=runName,
summaryMetrics=standardStats,
plotFuncs=plotFuncs,
metadata=md,
displayDict=displayDict)
bundleList.append(bundle)
# XXX -- would be good to add some microlensing events, for both MW and LMC/SMC.
#########################
# Milky Way
#########################
displayDict = {'group': 'Milky Way', 'subgroup': ''}
displayDict['subgroup'] = 'N stars'
slicer = slicers.HealpixSlicer(nside=nside, useCache=False)
sum_stats = [metrics.SumMetric(metricName='Total N Stars')]
for f in filterlist:
displayDict['order'] = filterorders[f]
displayDict['caption'] = 'Number of stars in %s band with an measurement error due to crowding ' \
'of less than 0.1 mag' % f
# Configure the NstarsMetric - note 'filtername' refers to the filter in which to evaluate crowding
metric = metrics.NstarsMetric(crowding_error=0.1,
filtername='r',
seeingCol=colmap['seeingGeom'],
m5Col=colmap['fiveSigmaDepth'])
plotDict = {'nTicks': 5, 'logScale': True, 'colorMin': 100}
bundle = mb.MetricBundle(metric,
slicer,
filtersqls[f],
runName=runName,
summaryMetrics=sum_stats,
plotFuncs=subsetPlots,
plotDict=plotDict,
displayDict=displayDict)
bundleList.append(bundle)
#########################
# DDF
#########################
if DDF:
# Hide this import to avoid adding a dependency.
from lsst.sims.featureScheduler.surveys import generate_dd_surveys, Deep_drilling_survey
ddf_surveys = generate_dd_surveys()
# Add on the Euclid fields
# XXX--to update. Should have a spot where all the DDF locations are stored.
ddf_surveys.append(
Deep_drilling_survey([], 58.97, -49.28, survey_name='DD:EDFSa'))
ddf_surveys.append(
Deep_drilling_survey([], 63.6, -47.60, survey_name='DD:EDFSb'))
# 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}
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 f in filterlist:
metric = metrics.Coaddm5Metric(
metricName=survey.survey_name + ', ' + f)
summary = [
metrics.MedianMetric(metricName='Median depth ' +
survey.survey_name + ', ' + f)
]
plotDict = {'color': colors[f]}
sql = filtersqls[f]
displayDict['order'] = filterorders[f]
displayDict['caption'] = 'Coadded m5 depth in %s band.' % (
f)
bundle = mb.MetricBundle(metric,
slicer,
sql,
metadata=filtermetadata[f],
displayDict=displayDict,
summaryMetrics=summary,
plotFuncs=[],
plotDict=plotDict)
bundleList.append(bundle)
displayDict = {'group': 'DDF Transients', 'subgroup': 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 = extraSql
summary_stats = [metrics.MeanMetric(maskVal=0)]
plotFuncs = [plots.HealpixSkyMap()]
bundle = mb.MetricBundle(metric,
slicer,
sql,
runName=runName,
summaryMetrics=summary_stats,
plotFuncs=plotFuncs,
metadata=extraMetadata,
displayDict=displayDict)
bundleList.append(bundle)
displayDict['order'] = 10
# Set the runName for all bundles and return the bundleDict.
for b in bundleList:
b.setRunName(runName)
bundleDict = mb.makeBundlesDictFromList(bundleList)
return bundleDict
def generate_dd_surveys(nside=None, nexp=2, detailers=None, reward_value=100):
"""Utility to return a list of standard deep drilling field surveys.
XXX-Someone double check that I got the coordinates right!
"""
surveys = []
# ELAIS S1
RA = 9.45
dec = -44.
survey_name = 'DD:ELAISS1'
ha_limits = ([0., 1.5], [21.5, 24.])
bfs = dd_bfs(RA, dec, survey_name, ha_limits)
surveys.append(
Deep_drilling_survey(bfs,
RA,
dec,
sequence='rgizy',
nvis=[20, 10, 20, 26, 20],
survey_name=survey_name,
reward_value=reward_value,
nside=nside,
nexp=nexp,
detailers=detailers))
survey_name = 'DD:u,ELAISS1'
bfs = dd_u_bfs(RA, dec, survey_name, ha_limits)
surveys.append(
Deep_drilling_survey(bfs,
RA,
dec,
sequence='u',
nvis=[8],
survey_name=survey_name,
reward_value=reward_value,
nside=nside,
nexp=nexp,
detailers=detailers))
# XMM-LSS
survey_name = 'DD:XMM-LSS'
RA = 35.708333
dec = -4 - 45 / 60.
ha_limits = ([0., 1.5], [21.5, 24.])
bfs = dd_bfs(RA, dec, survey_name, ha_limits)
surveys.append(
Deep_drilling_survey(bfs,
RA,
dec,
sequence='rgizy',
nvis=[20, 10, 20, 26, 20],
survey_name=survey_name,
reward_value=reward_value,
nside=nside,
nexp=nexp,
detailers=detailers))
survey_name = 'DD:u,XMM-LSS'
bfs = dd_u_bfs(RA, dec, survey_name, ha_limits)
surveys.append(
Deep_drilling_survey(bfs,
RA,
dec,
sequence='u',
nvis=[8],
survey_name=survey_name,
reward_value=reward_value,
nside=nside,
nexp=nexp,
detailers=detailers))
# Extended Chandra Deep Field South
RA = 53.125
dec = -28. - 6 / 60.
survey_name = 'DD:ECDFS'
ha_limits = [[0.5, 3.0], [20., 22.5]]
bfs = dd_bfs(RA, dec, survey_name, ha_limits)
surveys.append(
Deep_drilling_survey(bfs,
RA,
dec,
sequence='rgizy',
nvis=[20, 10, 20, 26, 20],
survey_name=survey_name,
reward_value=reward_value,
nside=nside,
nexp=nexp,
detailers=detailers))
survey_name = 'DD:u,ECDFS'
bfs = dd_u_bfs(RA, dec, survey_name, ha_limits)
surveys.append(
Deep_drilling_survey(bfs,
RA,
dec,
sequence='u',
nvis=[8],
survey_name=survey_name,
reward_value=reward_value,
nside=nside,
nexp=nexp,
detailers=detailers))
# COSMOS
RA = 150.1
dec = 2. + 10. / 60. + 55 / 3600.
survey_name = 'DD:COSMOS'
ha_limits = ([0., 2.5], [21.5, 24.])
bfs = dd_bfs(RA, dec, survey_name, ha_limits)
surveys.append(
Deep_drilling_survey(bfs,
RA,
dec,
sequence='rgizy',
nvis=[20, 10, 20, 26, 20],
survey_name=survey_name,
reward_value=reward_value,
nside=nside,
nexp=nexp,
detailers=detailers))
survey_name = 'DD:u,COSMOS'
bfs = dd_u_bfs(RA, dec, survey_name, ha_limits)
surveys.append(
Deep_drilling_survey(bfs,
RA,
dec,
sequence='u',
nvis=[8],
survey_name=survey_name,
reward_value=reward_value,
nside=nside,
nexp=nexp,
detailers=detailers))
# Euclid Fields
survey_name = 'DD:EDFS1'
RA = 58.97
dec = -49.28
ha_limits = ([0., 1.5], [21.5, 24.])
bfs = dd_bfs(RA, dec, survey_name, ha_limits)
surveys.append(
Deep_drilling_survey(bfs,
RA,
dec,
sequence='rgizy',
nvis=[5, 7, 19, 24, 5],
survey_name=survey_name,
reward_value=reward_value,
nside=nside,
nexp=nexp,
detailers=detailers))
survey_name = 'DD:u,EDFS1'
bfs = dd_u_bfs(RA, dec, survey_name, ha_limits)
surveys.append(
Deep_drilling_survey(bfs,
RA,
dec,
sequence='u',
nvis=[8],
survey_name=survey_name,
reward_value=reward_value,
nside=nside,
nexp=nexp,
detailers=detailers))
survey_name = 'DD:EDFS2'
RA = 63.6
dec = -47.60
ha_limits = ([0., 1.5], [21.5, 24.])
bfs = dd_bfs(RA, dec, survey_name, ha_limits)
surveys.append(
Deep_drilling_survey(bfs,
RA,
dec,
sequence='rgizy',
nvis=[5, 7, 19, 24, 5],
survey_name=survey_name,
reward_value=reward_value,
nside=nside,
nexp=nexp,
detailers=detailers))
survey_name = 'DD:u,EDFS2'
bfs = dd_u_bfs(RA, dec, survey_name, ha_limits)
surveys.append(
Deep_drilling_survey(bfs,
RA,
dec,
sequence='u',
nvis=[8],
survey_name=survey_name,
reward_value=reward_value,
nside=nside,
nexp=nexp,
detailers=detailers))
return surveys
def generate_dd_surveys(nside=None, nexp=2, detailers=None):
"""Utility to return a list of standard deep drilling field surveys.
XXX-Someone double check that I got the coordinates right!
"""
surveys = []
# ELAIS S1
RA = 9.45
dec = -44.
survey_name = 'DD:ELAISS1'
ha_limits = ([0., 1.5], [21.5, 24.])
bfs = dd_bfs(RA, dec, survey_name, ha_limits)
surveys.append(
Deep_drilling_survey(bfs,
RA,
dec,
sequence='grizy',
nvis=[1, 1, 3, 5, 4],
survey_name=survey_name,
reward_value=100,
nside=nside,
nexp=nexp,
detailers=detailers))
survey_name = 'DD:u,ELAISS1'
bfs = dd_u_bfs(RA, dec, survey_name, ha_limits)
surveys.append(
Deep_drilling_survey(bfs,
RA,
dec,
sequence='u',
nvis=[8],
survey_name=survey_name,
reward_value=100,
nside=nside,
nexp=nexp,
detailers=detailers))
# XMM-LSS
survey_name = 'DD:XMM-LSS'
RA = 35.708333
dec = -4 - 45 / 60.
ha_limits = ([0., 1.5], [21.5, 24.])
bfs = dd_bfs(RA, dec, survey_name, ha_limits)
surveys.append(
Deep_drilling_survey(bfs,
RA,
dec,
sequence='grizy',
nvis=[1, 1, 3, 5, 4],
survey_name=survey_name,
reward_value=100,
nside=nside,
nexp=nexp,
detailers=detailers))
survey_name = 'DD:u,XMM-LSS'
bfs = dd_u_bfs(RA, dec, survey_name, ha_limits)
surveys.append(
Deep_drilling_survey(bfs,
RA,
dec,
sequence='u',
nvis=[8],
survey_name=survey_name,
reward_value=100,
nside=nside,
nexp=nexp,
detailers=detailers))
# Extended Chandra Deep Field South
RA = 53.125
dec = -28. - 6 / 60.
survey_name = 'DD:ECDFS'
ha_limits = [[0.5, 3.0], [20., 22.5]]
bfs = dd_bfs(RA, dec, survey_name, ha_limits)
surveys.append(
Deep_drilling_survey(bfs,
RA,
dec,
sequence='grizy',
nvis=[1, 1, 3, 5, 4],
survey_name=survey_name,
reward_value=100,
nside=nside,
nexp=nexp,
detailers=detailers))
survey_name = 'DD:u,ECDFS'
bfs = dd_u_bfs(RA, dec, survey_name, ha_limits)
surveys.append(
Deep_drilling_survey(bfs,
RA,
dec,
sequence='u',
nvis=[8],
survey_name=survey_name,
reward_value=100,
nside=nside,
nexp=nexp,
detailers=detailers))
# COSMOS
RA = 150.1
dec = 2. + 10. / 60. + 55 / 3600.
survey_name = 'DD:COSMOS'
ha_limits = ([0., 2.5], [21.5, 24.])
bfs = dd_bfs(RA, dec, survey_name, ha_limits)
surveys.append(
Deep_drilling_survey(bfs,
RA,
dec,
sequence='grizy',
nvis=[1, 1, 3, 5, 4],
survey_name=survey_name,
reward_value=100,
nside=nside,
nexp=nexp,
detailers=detailers))
survey_name = 'DD:u,COSMOS'
bfs = dd_u_bfs(RA, dec, survey_name, ha_limits)
surveys.append(
Deep_drilling_survey(bfs,
RA,
dec,
sequence='u',
nvis=[8],
survey_name=survey_name,
reward_value=100,
nside=nside,
nexp=nexp,
detailers=detailers))
# Extra DD Field, just to get to 5. Still not closed on this one
survey_name = 'DD:290'
RA = 349.386443
dec = -63.321004
ha_limits = ([0., 1.5], [21.5, 24.])
bfs = dd_bfs(RA, dec, survey_name, ha_limits)
surveys.append(
Deep_drilling_survey(bfs,
RA,
dec,
sequence='grizy',
nvis=[1, 1, 3, 5, 4],
survey_name=survey_name,
reward_value=100,
nside=nside,
nexp=nexp,
detailers=detailers))
survey_name = 'DD:u,290'
bfs = dd_u_bfs(RA, dec, survey_name, ha_limits)
surveys.append(
Deep_drilling_survey(bfs,
RA,
dec,
sequence='u',
nvis=[8],
survey_name=survey_name,
reward_value=100,
nside=nside,
nexp=nexp,
detailers=detailers))
return surveys
def generate_dd_surveys(nside=None, nexp=2, detailers=None):
"""Utility to return a list of standard deep drilling field surveys.
XXX-Someone double check that I got the coordinates right!
"""
surveys = []
# ELAIS S1
RA = 9.45
dec = -44.
survey_name = 'DD:ELAISS1'
ha_limits = ([0., 1.5], [21.5, 24.])
bfs = dd_bfs(RA, dec, survey_name, ha_limits)
surveys.append(
Deep_drilling_survey(bfs,
RA,
dec,
sequence='grizy',
nvis=[1, 1, 3, 5, 4],
survey_name=survey_name,
reward_value=100,
nside=nside,
nexp=nexp,
detailers=detailers))
survey_name = 'DD:u,ELAISS1'
bfs = dd_u_bfs(RA, dec, survey_name, ha_limits)
surveys.append(
Deep_drilling_survey(bfs,
RA,
dec,
sequence='u',
nvis=[8],
survey_name=survey_name,
reward_value=100,
nside=nside,
nexp=nexp,
detailers=detailers))
# XMM-LSS
survey_name = 'DD:XMM-LSS'
RA = 35.708333
dec = -4 - 45 / 60.
ha_limits = ([0., 1.5], [21.5, 24.])
bfs = dd_bfs(RA, dec, survey_name, ha_limits)
surveys.append(
Deep_drilling_survey(bfs,
RA,
dec,
sequence='grizy',
nvis=[1, 1, 3, 5, 4],
survey_name=survey_name,
reward_value=100,
nside=nside,
nexp=nexp,
detailers=detailers))
survey_name = 'DD:u,XMM-LSS'
bfs = dd_u_bfs(RA, dec, survey_name, ha_limits)
surveys.append(
Deep_drilling_survey(bfs,
RA,
dec,
sequence='u',
nvis=[8],
survey_name=survey_name,
reward_value=100,
nside=nside,
nexp=nexp,
detailers=detailers))
# Extended Chandra Deep Field South
RA = 53.125
dec = -28. - 6 / 60.
survey_name = 'DD:ECDFS'
ha_limits = [[0.5, 3.0], [20., 22.5]]
bfs = dd_bfs(RA, dec, survey_name, ha_limits)
surveys.append(
Deep_drilling_survey(bfs,
RA,
dec,
sequence='grizy',
nvis=[1, 1, 3, 5, 4],
survey_name=survey_name,
reward_value=100,
nside=nside,
nexp=nexp,
detailers=detailers))
survey_name = 'DD:u,ECDFS'
bfs = dd_u_bfs(RA, dec, survey_name, ha_limits)
surveys.append(
Deep_drilling_survey(bfs,
RA,
dec,
sequence='u',
nvis=[8],
survey_name=survey_name,
reward_value=100,
nside=nside,
nexp=nexp,
detailers=detailers))
# COSMOS
RA = 150.1
dec = 2. + 10. / 60. + 55 / 3600.
survey_name = 'DD:COSMOS'
ha_limits = ([0., 2.5], [21.5, 24.])
bfs = dd_bfs(RA, dec, survey_name, ha_limits)
surveys.append(
Deep_drilling_survey(bfs,
RA,
dec,
sequence='grizy',
nvis=[1, 1, 3, 5, 4],
survey_name=survey_name,
reward_value=100,
nside=nside,
nexp=nexp,
detailers=detailers))
survey_name = 'DD:u,COSMOS'
bfs = dd_u_bfs(RA, dec, survey_name, ha_limits)
surveys.append(
Deep_drilling_survey(bfs,
RA,
dec,
sequence='u',
nvis=[8],
survey_name=survey_name,
reward_value=100,
nside=nside,
nexp=nexp,
detailers=detailers))
# Euclid Fields
# I can use the sequence kwarg to do two positions per sequence
filters = 'grizy'
nviss = [1, 1, 3, 5, 4]
survey_name = 'DD:EDFS'
# Note the sequences need to be in radians since they are using observation objects directly
RAs = np.radians([58.97, 63.6])
decs = np.radians([-49.28, -47.60])
sequence = []
exptime = 30
for filtername, nvis in zip(filters, nviss):
for ra, dec in zip(RAs, decs):
for num in range(nvis):
obs = empty_observation()
obs['filter'] = filtername
obs['exptime'] = exptime
obs['RA'] = ra
obs['dec'] = dec
obs['nexp'] = nexp
obs['note'] = survey_name
sequence.append(obs)
ha_limits = ([0., 1.5], [22.5, 24.])
# And back to degrees for the basis function
bfs = dd_bfs(np.degrees(RAs[0]), np.degrees(decs[0]), survey_name,
ha_limits)
surveys.append(
Deep_drilling_survey(bfs,
RA,
dec,
sequence=sequence,
survey_name=survey_name,
reward_value=100,
nside=nside,
nexp=nexp,
detailers=detailers))
filters = 'u'
nviss = [4]
survey_name = 'DD:u, EDFS'
sequence = []
exptime = 30
for filtername, nvis in zip(filters, nviss):
for ra, dec in zip(RAs, decs):
for num in range(nvis):
obs = empty_observation()
obs['filter'] = filtername
obs['exptime'] = exptime
obs['RA'] = ra
obs['dec'] = dec
obs['nexp'] = nexp
obs['note'] = survey_name
sequence.append(obs)
bfs = dd_u_bfs(np.degrees(RAs[0]), np.degrees(decs[0]), survey_name,
ha_limits)
surveys.append(
Deep_drilling_survey(bfs,
RA,
dec,
sequence=sequence,
survey_name=survey_name,
reward_value=100,
nside=nside,
nexp=nexp,
detailers=detailers))
return surveys
def scienceRadarBatch(colmap=None, runName='opsim', 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,
TdePopMetric, generateTdePopSlicer,
generateMicrolensingSlicer, MicrolensingMetric)
if colmap is None:
colmap = ColMapDict('fbs')
if extraSql is None:
extraSql = ''
if extraSql == '':
joiner = ''
else:
joiner = ' and '
bundleList = []
# Get some standard per-filter coloring and sql constraints
filterlist, colors, filterorders, filtersqls, filtermetadata = filterList(all=False,
extraSql=extraSql,
extraMetadata=extraMetadata)
standardStats = standardSummary(withCount=False)
healslicer = slicers.HealpixSlicer(nside=nside)
subsetPlots = [plots.HealpixSkyMap(), plots.HealpixHistogram()]
# Load up the plastic light curves - SNIa-normal are loaded in descWFDBatch
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
#########################
fOb = fOBatch(runName=runName, colmap=colmap, extraSql=extraSql, extraMetadata=extraMetadata,
benchmarkArea=benchmarkArea, benchmarkNvisits=benchmarkNvisits)
astromb = astrometryBatch(runName=runName, colmap=colmap, extraSql=extraSql, extraMetadata=extraMetadata)
rapidb = rapidRevisitBatch(runName=runName, colmap=colmap, extraSql=extraSql, extraMetadata=extraMetadata)
# loop through and modify the display dicts - set SRD as group and their previous 'group' as the subgroup
temp_list = []
for key in fOb:
temp_list.append(fOb[key])
for key in astromb:
temp_list.append(astromb[key])
for key in rapidb:
temp_list.append(rapidb[key])
for metricb in temp_list:
metricb.displayDict['subgroup'] = metricb.displayDict['group'].replace('SRD', '').lstrip(' ')
metricb.displayDict['group'] = 'SRD'
bundleList.extend(temp_list)
displayDict = {'group': 'SRD', 'subgroup': 'Year Coverage', 'order': 0,
'caption': 'Number of years with observations.'}
slicer = slicers.HealpixSlicer(nside=nside)
metric = metrics.YearCoverageMetric()
for f in filterlist:
plotDict = {'colorMin': 7, 'colorMax': 10, 'color': colors[f]}
summary = [metrics.AreaSummaryMetric(area=18000, reduce_func=np.mean, decreasing=True,
metricName='N Seasons (18k) %s' % f)]
bundleList.append(mb.MetricBundle(metric, slicer, filtersqls[f],
plotDict=plotDict, metadata=filtermetadata[f],
displayDict=displayDict, summaryMetrics=summary))
#########################
# Solar System
#########################
# Generally, we need to run Solar System metrics separately; they're a multi-step process.
#########################
# Galaxies
#########################
displayDict = {'group': 'Galaxies', 'subgroup': 'Galaxy Counts', 'order': 0, 'caption': None}
plotDict = {'percentileClip': 95., 'nTicks': 5}
sql = extraSql + joiner + 'filter="i"'
metadata = combineMetadata(extraMetadata, 'i band')
metric = GalaxyCountsMetric_extended(filterBand='i', redshiftBin='all', nside=nside)
summary = [metrics.AreaSummaryMetric(area=18000, reduce_func=np.sum, decreasing=True,
metricName='N Galaxies (18k)')]
summary.append(metrics.SumMetric(metricName='N Galaxies (all)'))
# make sure slicer has cache off
slicer = slicers.HealpixSlicer(nside=nside, useCache=False)
displayDict['caption'] = 'Number of galaxies across the sky, in i band. Generally, full survey footprint.'
bundle = mb.MetricBundle(metric, slicer, sql, plotDict=plotDict,
metadata=metadata,
displayDict=displayDict, summaryMetrics=summary,
plotFuncs=subsetPlots)
bundleList.append(bundle)
displayDict['order'] += 1
#########################
# Cosmology
#########################
# note the desc batch does not currently take the extraSql or extraMetadata arguments.
descBundleDict = descWFDBatch(colmap=colmap, runName=runName, nside=nside)
for d in descBundleDict:
bundleList.append(descBundleDict[d])
#########################
# Variables and Transients
#########################
displayDict = {'group': 'Variables/Transients',
'subgroup': 'Periodic Stars',
'order': 0, 'caption': None}
for period in [0.5, 1, 2,]:
for magnitude in [21., 24.]:
amplitudes = [0.05, 0.1, 1.0]
periods = [period] * len(amplitudes)
starMags = [magnitude] * len(amplitudes)
plotDict = {'nTicks': 3, 'colorMin': 0, 'colorMax': 3, 'xMin': 0, 'xMax': 3}
metadata = combineMetadata('P_%.1f_Mag_%.0f_Amp_0.05-0.1-1' % (period, magnitude),
extraMetadata)
sql = None
displayDict['caption'] = 'Metric evaluates if a periodic signal of period %.1f days could ' \
'be detected for an r=%i star. A variety of amplitudes of periodicity ' \
'are tested: [1, 0.1, and 0.05] mag amplitudes, which correspond to ' \
'metric values of [1, 2, or 3]. ' % (period, magnitude)
metric = metrics.PeriodicDetectMetric(periods=periods, starMags=starMags,
amplitudes=amplitudes,
metricName='PeriodDetection')
bundle = mb.MetricBundle(metric, healslicer, sql, metadata=metadata,
displayDict=displayDict, plotDict=plotDict,
plotFuncs=subsetPlots, summaryMetrics=standardStats)
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)'
displayDict['order'] = 0
slicer = plasticc_slicer(plcs=plasticc_models_dict['KN'], seed=43, badval=0)
metric = Plasticc_metric(metricName='KN')
plotFuncs = [plots.HealpixSkyMap()]
summary_stats = [metrics.MeanMetric(maskVal=0)]
bundle = mb.MetricBundle(metric, slicer, extraSql, runName=runName, summaryMetrics=summary_stats,
plotFuncs=plotFuncs, metadata=extraMetadata,
displayDict=displayDict)
bundleList.append(bundle)
# Tidal Disruption Events
displayDict['subgroup'] = 'TDE'
displayDict['caption'] = 'TDE lightcurves that could be identified'
metric = TdePopMetric()
slicer = generateTdePopSlicer()
sql = ''
plotDict = {'reduceFunc': np.sum, 'nside': 128}
plotFuncs = [plots.HealpixSkyMap()]
bundle = mb.MetricBundle(metric, slicer, sql, runName=runName,
plotDict=plotDict, plotFuncs=plotFuncs,
summaryMetrics=[metrics.MeanMetric(maskVal=0)],
displayDict=displayDict)
bundleList.append(bundle)
# Microlensing events
displayDict['subgroup'] = 'Microlensing'
displayDict['caption'] = 'Fast microlensing events'
plotDict = {'nside': 128}
sql = ''
slicer = generateMicrolensingSlicer(min_crossing_time=1, max_crossing_time=10)
metric = MicrolensingMetric(metricName='Fast Microlensing')
bundle = mb.MetricBundle(metric, slicer, sql, runName=runName,
summaryMetrics=[metrics.MeanMetric(maskVal=0)],
plotFuncs=[plots.HealpixSkyMap()], metadata=extraMetadata,
displayDict=displayDict, plotDict=plotDict)
bundleList.append(bundle)
displayDict['caption'] = 'Slow microlensing events'
slicer = generateMicrolensingSlicer(min_crossing_time=100, max_crossing_time=1500)
metric = MicrolensingMetric(metricName='Slow Microlensing')
bundle = mb.MetricBundle(metric, slicer, sql, runName=runName,
summaryMetrics=[metrics.MeanMetric(maskVal=0)],
plotFuncs=[plots.HealpixSkyMap()], metadata=extraMetadata,
displayDict=displayDict, plotDict=plotDict)
bundleList.append(bundle)
#########################
# Milky Way
#########################
displayDict = {'group': 'Milky Way', 'subgroup': ''}
displayDict['subgroup'] = 'N stars'
slicer = slicers.HealpixSlicer(nside=nside, useCache=False)
sum_stats = [metrics.SumMetric(metricName='Total N Stars, crowding')]
for f in filterlist:
stellar_map = maps.StellarDensityMap(filtername=f)
displayDict['order'] = filterorders[f]
displayDict['caption'] = 'Number of stars in %s band with an measurement error due to crowding ' \
'of less than 0.2 mag' % f
# Configure the NstarsMetric - note 'filtername' refers to the filter in which to evaluate crowding
metric = metrics.NstarsMetric(crowding_error=0.2, filtername=f, ignore_crowding=False,
seeingCol=colmap['seeingGeom'], m5Col=colmap['fiveSigmaDepth'],
maps=[])
plotDict = {'nTicks': 5, 'logScale': True, 'colorMin': 100}
bundle = mb.MetricBundle(metric, slicer, filtersqls[f], runName=runName,
summaryMetrics=sum_stats,
plotFuncs=subsetPlots, plotDict=plotDict,
displayDict=displayDict, mapsList=[stellar_map])
bundleList.append(bundle)
slicer = slicers.HealpixSlicer(nside=nside, useCache=False)
sum_stats = [metrics.SumMetric(metricName='Total N Stars, no crowding')]
for f in filterlist:
stellar_map = maps.StellarDensityMap(filtername=f)
displayDict['order'] = filterorders[f]
displayDict['caption'] = 'Number of stars in %s band with an measurement error ' \
'of less than 0.2 mag, not considering crowding' % f
# Configure the NstarsMetric - note 'filtername' refers to the filter in which to evaluate crowding
metric = metrics.NstarsMetric(crowding_error=0.2, filtername=f, ignore_crowding=True,
seeingCol=colmap['seeingGeom'], m5Col=colmap['fiveSigmaDepth'],
metricName='Nstars_no_crowding', maps=[])
plotDict = {'nTicks': 5, 'logScale': True, 'colorMin': 100}
bundle = mb.MetricBundle(metric, slicer, filtersqls[f], runName=runName,
summaryMetrics=sum_stats,
plotFuncs=subsetPlots, plotDict=plotDict,
displayDict=displayDict, mapsList=[stellar_map])
bundleList.append(bundle)
#########################
# DDF
#########################
if DDF:
# Hide this import to avoid adding a dependency.
from lsst.sims.featureScheduler.surveys import generate_dd_surveys, Deep_drilling_survey
ddf_surveys = generate_dd_surveys()
# Add on the Euclid fields
# XXX--to update. Should have a spot where all the DDF locations are stored.
ddf_surveys.append(Deep_drilling_survey([], 58.97, -49.28, survey_name='DD:EDFSa'))
ddf_surveys.append(Deep_drilling_survey([], 63.6, -47.60, survey_name='DD:EDFSb'))
# 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}
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 f in filterlist:
metric = metrics.Coaddm5Metric(metricName=survey.survey_name + ', ' + f)
summary = [metrics.MedianMetric(metricName='Median depth ' + survey.survey_name+', ' + f)]
plotDict = {'color': colors[f]}
sql = filtersqls[f]
displayDict['order'] = filterorders[f]
displayDict['caption'] = 'Coadded m5 depth in %s band.' % (f)
bundle = mb.MetricBundle(metric, slicer, sql, metadata=filtermetadata[f],
displayDict=displayDict, summaryMetrics=summary,
plotFuncs=[], plotDict=plotDict)
bundleList.append(bundle)
displayDict = {'group': 'DDF Transients', 'subgroup': 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 = extraSql
summary_stats = [metrics.MeanMetric(maskVal=0)]
plotFuncs = [plots.HealpixSkyMap()]
bundle = mb.MetricBundle(metric, slicer, sql, runName=runName,
summaryMetrics=summary_stats,
plotFuncs=plotFuncs, metadata=extraMetadata,
displayDict=displayDict)
bundleList.append(bundle)
displayDict['order'] = 10
# Set the runName for all bundles and return the bundleDict.
for b in bundleList:
b.setRunName(runName)
bundleDict = mb.makeBundlesDictFromList(bundleList)
return bundleDict