def __getOpSimMjd(self, opsim, ra, dec, fil):
colmn = 'observationStartMJD'
opsdb = db.OpsimDatabase(opsim)
# Directory where tmp files are going to be stored TODO eliminate - this
outDir = 'TmpDir'
resultsDb = db.ResultsDb(outDir=outDir)
metric = metrics.PassMetric(cols=[colmn, 'fiveSigmaDepth', 'filter'])
slicer = slicers.UserPointsSlicer(ra=ra, dec=dec)
sqlconstraint = 'filter = \'' + fil + '\''
bundle = mb.MetricBundle(metric, slicer, sqlconstraint, runName='name')
bgroup = mb.MetricBundleGroup({0: bundle},
opsdb,
outDir=outDir,
resultsDb=resultsDb)
bgroup.runAll()
filters = np.unique(bundle.metricValues[0]['filter'])
mv = bundle.metricValues[0]
# Get dates
mjd = mv[colmn]
mjd = np.sort(mjd)
print('Num of visits ' + str(len(mjd)) + ' ' + opsim)
return mjd
def generateTdePopSlicer(t_start=1, t_end=3652, n_events=10000, seed=42, n_files=7):
""" Generate a population of TDE events, and put the info about them into a UserPointSlicer object
Parameters
----------
t_start : float (1)
The night to start tde events on (days)
t_end : float (3652)
The final night of TDE events
n_events : int (10000)
The number of TDE events to generate
seed : float
The seed passed to np.random
n_files : int (7)
The number of different TDE lightcurves to use
"""
ra, dec = uniformSphere(n_events, seed=seed)
peak_times = np.random.uniform(low=t_start, high=t_end, size=n_events)
file_indx = np.floor(np.random.uniform(low=0, high=n_files, size=n_events)).astype(int)
# Set up the slicer to evaluate the catalog we just made
slicer = slicers.UserPointsSlicer(ra, dec, latLonDeg=True, badval=0)
# Add any additional information about each object to the slicer
slicer.slicePoints['peak_time'] = peak_times
slicer.slicePoints['file_indx'] = file_indx
return slicer
def run_maf(dbFile, ra, dec):
"""Retrive min inter_night gap, and observation history with the input of database file name and arrays of RA and DEC.
Note: the observing cadence returned are not ordered by date!!
"""
# establish connection to sqllite database file.
opsimdb = db.OpsimDatabase(dbFile)
# While we're in transition between opsim v3 and v4, this may be helpful: print("{dbFile} is an opsim version {version} database".format(dbFile=dbFile, version=opsimdb.opsimVersion))
if opsimdb.opsimVersion == "V3":
# For v3 databases:
mjdcol = 'expMJD'
degrees = False
cols = ['filter', 'fiveSigmaDepth', mjdcol, 'expDate']
stackerList = []
else:
# For v4 and alternate scheduler databases.
mjdcol = 'observationStartMJD'
degrees = True
cols = ['filter', 'fiveSigmaDepth', mjdcol]
stackerList = [expDateStacker()]
# IntraNightGapsMetric returns the gap (in days) between observations within the same night custom reduceFunc to find min gaps
metric = metrics.cadenceMetrics.IntraNightGapsMetric(reduceFunc=np.amin,
mjdCol=mjdcol)
# PassMetric just pass all values
metric_pass = metrics.simpleMetrics.PassMetric(cols=cols)
# slicer for slicing pointing history
slicer = slicers.UserPointsSlicer(ra,
dec,
lonCol='fieldRA',
latCol='fieldDec',
latLonDeg=degrees)
# sql constrains, 3 for baseline2018a, 1 for rolling m2045
sql = ''
# bundles to combine metric, slicer and sql constrain together
bundle = metricBundles.MetricBundle(metric, slicer, sql)
date_bundle = metricBundles.MetricBundle(metric_pass,
slicer,
sql,
stackerList=stackerList)
# create metric bundle group and returns
bg = metricBundles.MetricBundleGroup(
{
'sep': bundle,
'cadence': date_bundle
},
opsimdb,
outDir=outDir,
resultsDb=resultsDb)
bg.runAll()
opsimdb.close()
return bg
def generateKNPopSlicer(t_start=1,
t_end=3652,
n_events=10000,
seed=42,
n_files=100,
d_min=10,
d_max=300):
""" Generate a population of KNe events, and put the info about them
into a UserPointSlicer object
Parameters
----------
t_start : float (1)
The night to start kilonova events on (days)
t_end : float (3652)
The final night of kilonova events
n_events : int (10000)
The number of kilonova events to generate
seed : float
The seed passed to np.random
n_files : int (7)
The number of different kilonova lightcurves to use
d_min : float or int (10)
Minimum luminosity distance (Mpc)
d_max : float or int (300)
Maximum luminosity distance (Mpc)
"""
def rndm(a, b, g, size=1):
"""Power-law gen for pdf(x)\propto x^{g-1} for a<=x<=b"""
r = np.random.random(size=size)
ag, bg = a**g, b**g
return (ag + (bg - ag) * r)**(1. / g)
ra, dec = uniformSphere(n_events, seed=seed)
peak_times = np.random.uniform(low=t_start, high=t_end, size=n_events)
file_indx = np.floor(np.random.uniform(low=0, high=n_files,
size=n_events)).astype(int)
# Define the distance
distance = rndm(d_min, d_max, 4, size=n_events)
# Set up the slicer to evaluate the catalog we just made
slicer = slicers.UserPointsSlicer(ra, dec, latLonDeg=True, badval=0)
# Add any additional information about each object to the slicer
slicer.slicePoints['peak_time'] = peak_times
slicer.slicePoints['file_indx'] = file_indx
slicer.slicePoints['distance'] = distance
return slicer
def get_cadence(ra, dec, b, snrLimit, nPtsLimit, filters, outDir, opsimdb,
resultsDb):
# The pass metric just passes data straight through.
metric = metrics.PassMetric(cols=['filter', 'fiveSigmaDepth', 'expMJD'])
slicer = slicers.UserPointsSlicer(ra,
dec,
lonCol='ditheredRA',
latCol='ditheredDec')
sql = ''
bundle = metricBundles.MetricBundle(metric, slicer, sql)
bg = metricBundles.MetricBundleGroup({0: bundle},
opsimdb,
outDir=outDir,
resultsDb=resultsDb)
bg.runAll()
bundle.metricValues.data[0]['filter']
print("Plotting...")
colors = {'u': 'cyan', 'g': 'g', 'r': 'y', 'i': 'r', 'z': 'm', 'y': 'k'}
dayZero = bundle.metricValues.data[0]['expMJD'].min()
times = []
depths = []
plt.clf()
for fname in filters:
good = np.where(bundle.metricValues.data[0]['filter'] == fname)
times.append(bundle.metricValues.data[0]['expMJD'][good] - dayZero)
depths.append(bundle.metricValues.data[0]['fiveSigmaDepth'][good])
plt.scatter(bundle.metricValues.data[0]['expMJD'][good] - dayZero,
bundle.metricValues.data[0]['fiveSigmaDepth'][good],
c=colors[fname],
label=fname)
plt.xlabel('Day')
plt.ylabel('5$\sigma$ depth')
plt.legend(scatterpoints=1, loc="upper left", bbox_to_anchor=(1, 1))
plt.savefig("l45b{0}_cadence.pdf".format(int(b)))
return times, depths
from __future__ import print_function
import lsst.sims.maf.slicers as slicers
import lsst.sims.maf.db as db
# Connect to opsim
dbAddress = 'sqlite:///ops1_1140_sqlite.db'
oo = db.OpsimDatabase(dbAddress)
colnames = ['expMJD', 'fieldRA', 'fieldDec']
sqlconstraint = 'filter="r"'
# Get opsim simulation data
simdata = oo.fetchMetricData(colnames, sqlconstraint)
# Init the slicer, set 2 points
slicer = slicers.UserPointsSlicer(ra=[0., .1], dec=[0., -.1])
# Setup slicer (builds kdTree)
slicer.setupSlicer(simdata)
# Slice Point for index zero
ind = slicer._sliceSimData(0)
expMJDs = simdata[ind['idxs']]['expMJD']
print('mjd for the 1st user defined point', expMJDs)
# Find the expMJDs for the 2nd point
ind = slicer._sliceSimData(1)
expMJDs = simdata[ind['idxs']]['expMJD']
print('mjd for the 2nd user defined point', expMJDs)
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
out_file = sys.argv[2]
out_dir = sys.argv[2]
gal_l_min = 0.
gal_l_max = 360.
gal_b_min = -89.
gal_b_max = 89.
diameter = 3.5
step = diameter / np.sqrt(2) # This would be enough on a 2D plane.
step *= 0.85
gal_l_all = np.linspace(gal_l_min, gal_l_max, (gal_l_max-gal_l_min)/step+1)
gal_b_all = np.linspace(gal_b_min, gal_b_max, (gal_b_max-gal_b_min)/step+1)
(gal_l, gal_b) = np.meshgrid(gal_l_all, gal_b_all)
c = SkyCoord(gal_l.flatten(), gal_b.flatten(), unit=u.deg, frame='galactic')
userRA = c.fk5.ra.value
userDec = c.fk5.dec.value
columns = ['observationStartMJD', 'filter', 'fiveSigmaDepth']
metric = metrics.PassMetric(cols=columns)
slicer = slicers.UserPointsSlicer(userRA, userDec)
sqlconstraint = ''
MJDmetric = metricBundles.MetricBundle(metric, slicer, sqlconstraint,
fileRoot=out_file)
bundleDict = {'MJDmetric': MJDmetric}
opsdb = db.OpsimDatabase(database)
group = metricBundles.MetricBundleGroup(bundleDict, opsdb, outDir=out_dir)
group.runAll()
# Set the database and query
runName = 'minion_1018'
opsdb = db.OpsimDatabase(runName + '_sqlite.db')
# Set the output directory
outDir = 'Observations Dictionary'
resultsDb = db.ResultsDb(outDir)
# This creates our database of observations. The pass metric just passes data straight through.
metric = metrics.PassMetric(cols=['expMJD', 'filter', 'fiveSigmaDepth'])
"""use slicer to restrict the ra and decs, use np.random.uniform to get random points,
first coordinate represents ra and second dec. Or, give a list of specific
ra and decs - the second slicer is for the deep drilling fields. One must be commented out."""
#slicer = slicers.UserPointsSlicer(np.random.uniform(0,360,1000), np.random.uniform(-80,0,1000))
slicer = slicers.UserPointsSlicer([349.4, 0.00, 53.0, 34.4, 150.4],
[-63.3, -45.5, -27.4, -5.1, 2.8])
#sql is empty as there are no restrictions currently
sql = ''
bundle = metricBundles.MetricBundle(metric, slicer, sql)
bg = metricBundles.MetricBundleGroup({0: bundle},
opsdb,
outDir=outDir,
resultsDb=resultsDb)
bg.runAll()
def createdict_for_mjd_filter_depth(bundle):
"""This function returns a list of tables of exposure day, filter,
and five sigma depth for each ra and dec chosen"""
number_of_coord = len(bundle.metricValues)
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 getMetrics(self):
colmn = 'observationStartMJD';
opsdb = db.OpsimDatabase(self.opsim)
# Directory where tmp files are going to be stored TODO eliminate - this
outDir = 'TmpDir'
resultsDb = db.ResultsDb(outDir=outDir)
metric=metrics.PassMetric(cols=[colmn,'fiveSigmaDepth', 'filter'])
slicer = slicers.UserPointsSlicer(ra=self.ra,dec=self.dec)
sqlconstraint = 'filter = \'' + self.fil + '\''
bundle = mb.MetricBundle(metric, slicer, sqlconstraint, runName=self.name)
bgroup = mb.MetricBundleGroup({0: bundle}, opsdb, outDir=outDir, resultsDb=resultsDb)
bgroup.runAll();
filters = np.unique(bundle.metricValues[0]['filter'])
mv = bundle.metricValues[0]
# Get dates
self.mjd = mv[colmn]
self.mjd = np.sort(self.mjd)
# Define redshift bins
zbin = np.linspace(0.5,7.5,8)
zbin = np.insert(zbin,0,0)
# Converting MJD to survey days
T=np.int(self.mjd.max()-self.mjd.min()+1)
swop=[]
wedgeop=[]
scop=[]
edgecop=[]
i=0
total = len(zbin)*(self.nlc);
progress = 0;
# We generate a number (nlc) of light curves for each redshift bin
for z in zbin:
for w in range(self.nlc):
# Generating continuous light curve (cadence=1d)
tt, yy = drw_artificial_lc(T, z=z, frame=self.frame)
sn, edgesn = self.sf(tt,yy,z=z)
# Calculating SF for the current continuous light curve
scop.append(sn)
edgecop.append(edgesn)
self.edgesn = edgesn
# Generating OpSim light curve evaluated on the current continuous light curve
top,yop=self.__opsim_lc(tt,yy)
# Calculating SF for the current OpSim light curve
srol,edgesrol=self.sf(top,yop,z=z)
swop.append(srol)
wedgeop.append(edgesrol)
#progressBar(progress, total);
progress = progress + 1;
i=i+1 # counter
swop=np.asarray(swop)
swop=swop.reshape(9,self.nlc,99)
scop=np.asarray(scop)
scop=scop.reshape(9,self.nlc,99)
razrol=[]
for z in range(9):
for r in range(self.nlc):
# Calculating the SF metric
razrol.append((np.nan_to_num(np.sqrt(scop[z,r,:]))-np.nan_to_num(np.sqrt(swop[z,r,:]))))
razrol9=np.asarray(razrol)
razrol9=razrol9.reshape(9,self.nlc,99)
# We take the mean of generated light curves for each redshift bin.
self.raz2=np.nanmean(razrol9[:,:,:],axis=1)
def generateMicrolensingSlicer(min_crossing_time=1,
max_crossing_time=10,
t_start=1,
t_end=3652,
n_events=10000,
seed=42,
nside=128,
filtername='r'):
"""
Generate a UserPointSlicer with a population of microlensing events. To be used with
MicrolensingMetric
Parameters
----------
min_crossing_time : float (1)
The minimum crossing time for the events generated (days)
max_crossing_time : float (10)
The max crossing time for the events generated (days)
t_start : float (1)
The night to start generating peaks (days)
t_end : float (3652)
The night to end generating peaks (days)
n_events : int (10000)
Number of microlensing events to generate
seed : float (42)
Random number seed
nside : int (128)
HEALpix nside, used to pick which stellar density map to load
filtername : str ('r')
The filter to use for the stellar density map
"""
np.random.seed(seed)
crossing_times = np.random.uniform(low=min_crossing_time,
high=max_crossing_time,
size=n_events)
peak_times = np.random.uniform(low=t_start, high=t_end, size=n_events)
impact_paramters = np.random.uniform(low=0, high=1, size=n_events)
mapDir = os.path.join(getPackageDir('sims_maps'), 'TriMaps')
data = np.load(
os.path.join(mapDir,
'TRIstarDensity_%s_nside_%i.npz' % (filtername, nside)))
starDensity = data['starDensity'].copy()
# magnitude bins
bins = data['bins'].copy()
data.close()
star_mag = 22
bin_indx = np.where(bins[1:] >= star_mag)[0].min()
density_used = starDensity[:, bin_indx].ravel()
order = np.argsort(density_used)
# I think the model might have a few outliers at the extreme, let's truncate it a bit
density_used[order[-10:]] = density_used[order[-11]]
# now, let's draw N from that distribution squared
dist = density_used[order]**2
cumm_dist = np.cumsum(dist)
cumm_dist = cumm_dist / np.max(cumm_dist)
uniform_draw = np.random.uniform(size=n_events)
indexes = np.floor(
np.interp(uniform_draw, cumm_dist, np.arange(cumm_dist.size)))
hp_ids = order[indexes.astype(int)]
gal_l, gal_b = hpid2RaDec(nside, hp_ids, nest=True)
ra, dec = equatorialFromGalactic(gal_l, gal_b)
# Set up the slicer to evaluate the catalog we just made
slicer = slicers.UserPointsSlicer(ra, dec, latLonDeg=True, badval=0)
# Add any additional information about each object to the slicer
slicer.slicePoints['peak_time'] = peak_times
slicer.slicePoints['crossing_time'] = crossing_times
slicer.slicePoints['impact_parameter'] = impact_paramters
return slicer
Dls = cosmo.angular_diameter_distance_z1z2(
myinput['system']['zl'], myinput['system']['zs']).value # in Mpc
print(">>>>>>>>>>>>>>>>>> Reading LSST dates and depths <<<<<<<<<<<<<<<<<<<<<")
outDir = outdir + '/output'
dbFile = path_to_dbfile + runName + '.db'
opsimdb = db.opsimDatabase.OpsimDatabase(dbFile)
resultsDb = db.ResultsDb(outDir=outDir)
#ra=ra*15.0
# SNR limit (Don't use points below this limit)
snrLimit = 5.0
# The pass metric just passes data straight through.
metric = metrics.PassMetric(
cols=['filter', 'fiveSigmaDepth', 'observationStartMJD'])
slicer = slicers.UserPointsSlicer(ra, dec, lonCol='fieldRA', latCol='fieldDec')
sql = ''
bundle = metricBundles.MetricBundle(metric, slicer, sql)
bg = metricBundles.MetricBundleGroup({0: bundle},
opsimdb,
outDir=outDir,
resultsDb=resultsDb)
bg.runAll()
bundle.metricValues.data[0]['filter']
filters = myinput['filters']
print('%i Observations total at this point (All SNR levels)' %
bundle.metricValues.data[0].size)
for fname in filters:
good = numpy.where(bundle.metricValues.data[0]['filter'] == fname)
print('%i Observations in %s' % (good[0].size, fname))
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
def spot_inspect(filename,
ra,
dec,
year_max=8.5,
outDir='temp',
season_pad=80):
resultsDb = db.ResultsDb(outDir=outDir)
f2c = {
'u': 'purple',
'g': 'blue',
'r': 'green',
'i': 'cyan',
'z': 'orange',
'y': 'red'
}
name = filename.replace('_v1.7_10yrs.db', '')
conn = db.OpsimDatabase(filename)
bundleList = []
sql = '' #'night > 250 and night < %i' % (365*year_max)
metric = metrics.PassMetric(
['filter', 'observationStartMJD', 'fiveSigmaDepth', 'night'])
slicer = slicers.UserPointsSlicer(ra=ra, dec=dec)
summaryStats = []
plotDict = {}
bundleList.append(
metricBundles.MetricBundle(metric,
slicer,
sql,
plotDict=plotDict,
summaryMetrics=summaryStats,
runName=name))
bd = metricBundles.makeBundlesDictFromList(bundleList)
bg = metricBundles.MetricBundleGroup(bd,
conn,
outDir=outDir,
resultsDb=resultsDb)
bg.runAll()
#bg.plotAll(closefigs=False)
mv = bundleList[0].metricValues[0]
mv.sort(order='observationStartMJD')
all_mjd = np.arange(mv['observationStartMJD'].min() - 1,
mv['observationStartMJD'].max() + 2, 1)
breaks_indx = season_breaks(all_mjd, ra)
breaks1 = all_mjd[breaks_indx] - all_mjd.min() + mv['night'].min()
breaks = [mv['night'].min() - 1]
breaks.extend(breaks1.tolist())
breaks.append(mv['night'].max() + 3)
#breaks = np.array([mv['night'].min()-season_pad] + breaks.tolist() + [mv['night'].max()+season_pad])
fig = plt.figure()
ax1 = fig.add_subplot(1, 1, 1)
di = np.diff(breaks)
mps = breaks[0:-1] + di / 2
counts, med_gaps, unights = gap_stats(mv['night'], bins=breaks)
for fn in f2c:
in_filt = np.where(mv['filter'] == fn)[0]
ax1.plot(mv['night'][in_filt],
mv['fiveSigmaDepth'][in_filt],
'o',
color=f2c[fn],
label=fn,
alpha=0.5)
ax1.set_xlabel('Night')
ax1.set_ylabel(r'5$\sigma$ depth (mags)')
for i in np.arange(mps.size):
plt.annotate('%i\n %.1f \n %i' % (counts[i], med_gaps[i], unights[i]),
[mps[i], 20])
#plt.legend(loc=(1.04,0))
for br in breaks:
ax1.axvline(br)
ax1.set_ylim([19.5, 25.5])
#plt.xlim([1340, 1560])
ax1.set_title(name + '\nra=%.2f, dec=%.2f' % (ra, dec))
return fig, ax1
# OneDSlicer
slicer = slicers.OneDSlicer(sliceColName='night', binsize=10)
metric = metrics.CountMetric(col='expMJD')
bundle = metricBundles.MetricBundle(metric, slicer, sqlWhere)
bundleList.append(bundle)
# OpsimFieldSlicer
slicer = slicers.OpsimFieldSlicer()
metric = metrics.MeanMetric(col='airmass')
bundle = metricBundles.MetricBundle(metric, slicer, sqlWhere)
bundleList.append(bundle)
# UserPointsSlicer
ra = np.arange(0, 101, 1) / 100. * np.pi
dec = np.arange(0, 101, 1) / 100. * (-np.pi)
slicer = slicers.UserPointsSlicer(ra=ra, dec=dec)
metric = metrics.MeanMetric(col='airmass', metricName='meanAirmass_user')
bundle = metricBundles.MetricBundle(metric, slicer, sqlWhere)
bundleList.append(bundle)
# UserPointsSlicer, turn on the camera focal plane geometry
slicer = slicers.UserPointsSlicer(ra=ra, dec=dec, useCamera=True)
metric = metrics.MeanMetric(col='airmass',
metricName='meanAirmass_user_w_camera')
bundle = metricBundles.MetricBundle(metric, slicer, sqlWhere)
bundleList.append(bundle)
# healpixComplexSlicer (healpix slicer + summaryHistogram)
bins = np.arange(0.5, 3.0, 0.1)
slicer = slicers.HealpixSlicer(nside=16)
metric = metrics.TgapsMetric(bins=bins)