def testStarMap(self):
mapPath = os.environ['SIMS_MAPS_DIR']
if os.path.isfile(
os.path.join(mapPath, 'StarMaps/starDensity_r_nside_64.npz')):
data = makeDataValues()
# check that it works if nside does not match map nside of 64
nsides = [32, 64, 128]
for nside in nsides:
starmap = maps.StellarDensityMap()
slicer1 = slicers.HealpixSlicer(nside=nside)
slicer1.setupSlicer(data)
result1 = starmap.run(slicer1.slicePoints)
assert ('starMapBins' in list(result1.keys()))
assert ('starLumFunc' in list(result1.keys()))
assert (np.max(result1['starLumFunc'] > 0))
fieldData = makeFieldData()
slicer2 = slicers.OpsimFieldSlicer()
slicer2.setupSlicer(data, fieldData)
result2 = starmap.run(slicer2.slicePoints)
assert ('starMapBins' in list(result2.keys()))
assert ('starLumFunc' in list(result2.keys()))
assert (np.max(result2['starLumFunc'] > 0))
else:
warnings.warn('Did not find stellar density map, skipping test.')
def testOut(self):
"""
Check that the metric bundle can generate the expected output
"""
nside = 8
slicer = slicers.HealpixSlicer(nside=nside)
metric = metrics.MeanMetric(col='airmass')
sql = 'filter="r"'
stacker1 = stackers.RandomDitherFieldPerVisitStacker()
stacker2 = stackers.GalacticStacker()
map1 = maps.GalCoordsMap()
map2 = maps.StellarDensityMap()
metricB = metricBundles.MetricBundle(metric,
slicer,
sql,
stackerList=[stacker1, stacker2],
mapsList=[map1, map2])
database = os.path.join(getPackageDir('sims_data'), 'OpSimData',
'astro-lsst-01_2014.db')
opsdb = db.OpsimDatabaseV4(database=database)
resultsDb = db.ResultsDb(outDir=self.outDir)
bgroup = metricBundles.MetricBundleGroup({0: metricB},
opsdb,
outDir=self.outDir,
resultsDb=resultsDb)
bgroup.runAll()
bgroup.plotAll()
bgroup.writeAll()
opsdb.close()
outThumbs = glob.glob(os.path.join(self.outDir, 'thumb*'))
outNpz = glob.glob(os.path.join(self.outDir, '*.npz'))
outPdf = glob.glob(os.path.join(self.outDir, '*.pdf'))
# By default, make 3 plots for healpix
assert (len(outThumbs) == 3)
assert (len(outPdf) == 3)
assert (len(outNpz) == 1)
def testOut(self):
"""
Check that the metric bundle can generate the expected output
"""
nside = 8
slicer = slicers.HealpixSlicer(nside=nside)
metric = metrics.MeanMetric(col='airmass')
sql = 'filter="r"'
stacker1 = stackers.RandomDitherFieldPerVisitStacker()
stacker2 = stackers.GalacticStacker()
map1 = maps.GalCoordsMap()
map2 = maps.StellarDensityMap()
metricB = metricBundles.MetricBundle(metric,
slicer,
sql,
stackerList=[stacker1, stacker2])
filepath = os.path.join(os.getenv('SIMS_MAF_DIR'), 'tests/')
database = os.path.join(filepath, 'opsimblitz1_1133_sqlite.db')
opsdb = db.OpsimDatabase(database=database)
resultsDb = db.ResultsDb(outDir=self.outDir)
bgroup = metricBundles.MetricBundleGroup({0: metricB},
opsdb,
outDir=self.outDir,
resultsDb=resultsDb)
bgroup.runAll()
bgroup.plotAll()
bgroup.writeAll()
outThumbs = glob.glob(os.path.join(self.outDir, 'thumb*'))
outNpz = glob.glob(os.path.join(self.outDir, '*.npz'))
outPdf = glob.glob(os.path.join(self.outDir, '*.pdf'))
# By default, make 3 plots for healpix
assert (len(outThumbs) == 3)
assert (len(outPdf) == 3)
assert (len(outNpz) == 1)
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
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'] = '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