def testBlobs(self):
"""
Set up a blob selection survey
"""
nside = 32
survey_length = 2.0 # days
surveys = []
# Set up the DD
dd_surveys = generate_dd_surveys(nside=nside)
surveys.append(dd_surveys)
surveys.append(gen_blob_surveys(nside))
surveys.append(gen_greedy_surveys(nside))
scheduler = Core_scheduler(surveys, nside=nside)
observatory = Model_observatory(nside=nside)
observatory, scheduler, observations = sim_runner(
observatory, scheduler, survey_length=survey_length, filename=None)
# Make sure some blobs executed
assert ('blob, gg, b' in observations['note'])
assert ('blob, gg, a' in observations['note'])
# assert('blob, u' in observations['note'])
# Make sure some greedy executed
assert ('' in observations['note'])
# Check that the a DD was observed
assert ('DD:ELAISS1' in observations['note'])
# Make sure a few different filters were observed
assert (len(np.unique(observations['filter'])) > 3)
# Make sure lots of observations executed
assert (observations.size > 1000)
# Make sure nothing tried to look through the earth
assert (np.min(observations['alt']) > 0)
def testGreedy(self):
"""
Set up a greedy survey and run for a few days. A crude way to touch lots of code.
"""
nside = 32
survey_length = 2.0 # days
surveys = gen_greedy_surveys(nside)
# Depricating Pairs_survey_scripted
#surveys.append(Pairs_survey_scripted(None, ignore_obs='DD'))
# Set up the DD
dd_surveys = generate_dd_surveys(nside=nside)
surveys.extend(dd_surveys)
scheduler = Core_scheduler(surveys, nside=nside)
observatory = Model_observatory(nside=nside)
observatory, scheduler, observations = sim_runner(
observatory, scheduler, survey_length=survey_length, filename=None)
# Check that greedy observed some
assert ('' in observations['note'])
# Check that the a DD was observed
assert ('DD:ELAISS1' in observations['note'])
# Make sure a few different filters were observed
assert (len(np.unique(observations['filter'])) > 3)
# Make sure lots of observations executed
assert (observations.size > 1000)
# Make sure nothing tried to look through the earth
assert (np.min(observations['alt']) > 0)
def gen_greedy_surveys(nside, add_DD=True):
"""
Make a quick set of greedy surveys
"""
target_map = standard_goals(nside=nside)
norm_factor = calc_norm_factor(target_map)
filters = ['u', 'g', 'r', 'i', 'z', 'y']
surveys = []
for filtername in filters:
bfs = []
bfs.append(
bf.M5_diff_basis_function(filtername=filtername, nside=nside))
bfs.append(
bf.Target_map_basis_function(filtername=filtername,
target_map=target_map[filtername],
out_of_bounds_val=np.nan,
nside=nside,
norm_factor=norm_factor))
bfs.append(
bf.Slewtime_basis_function(filtername=filtername, nside=nside))
bfs.append(bf.Strict_filter_basis_function(filtername=filtername))
# Masks, give these 0 weight
bfs.append(
bf.Zenith_shadow_mask_basis_function(nside=nside,
shadow_minutes=60.,
max_alt=76.))
bfs.append(
bf.Moon_avoidance_basis_function(nside=nside, moon_distance=40.))
bfs.append(bf.Clouded_out_basis_function())
bfs.append(bf.Filter_loaded_basis_function(filternames=filtername))
weights = np.array([3.0, 0.3, 3., 3., 0., 0., 0., 0.])
surveys.append(
Greedy_survey(bfs,
weights,
block_size=1,
filtername=filtername,
dither=True,
nside=nside,
ignore_obs='DD'))
surveys.append(Pairs_survey_scripted(None, ignore_obs='DD'))
if add_DD:
dd_surveys = generate_dd_surveys(nside=nside)
surveys.extend(dd_surveys)
return surveys
def run_simple_sim():
nside = 32
survey_length = 2.1 # days
surveys = gen_greedy_surveys(nside)
surveys.append(Pairs_survey_scripted(None, ignore_obs='DD'))
# Set up the DD
dd_surveys = generate_dd_surveys(nside=nside)
surveys.extend(dd_surveys)
scheduler = Core_scheduler(surveys, nside=nside)
observatory = Mock_observatory(nside=nside)
observatory, scheduler, observations = sim_runner(
observatory, scheduler, survey_length=survey_length, filename=None)
def make_scheduler(scale_down=False, max_dither=0.7, nexp=1):
nside = 32
per_night = True # Dither DDF per night
camera_ddf_rot_limit = 75.
extra_info = {}
exec_command = ''
for arg in sys.argv:
exec_command += ' ' + arg
extra_info['exec command'] = exec_command
try:
extra_info['git hash'] = subprocess.check_output(
['git', 'rev-parse', 'HEAD'])
except subprocess.CalledProcessError:
extra_info['git hash'] = 'Not in git repo'
extra_info['file executed'] = os.path.realpath(__file__)
fileroot = 'baseline_nexp%i_' % nexp
file_end = 'v1.6_'
if scale_down:
footprints = nes_light_footprints(nside=nside)
fileroot = fileroot + 'scaleddown_'
else:
footprints = standard_goals(nside=nside)
# Set up the DDF surveys to dither
dither_detailer = detailers.Dither_detailer(per_night=per_night,
max_dither=max_dither)
details = [
detailers.Camera_rot_detailer(min_rot=-camera_ddf_rot_limit,
max_rot=camera_ddf_rot_limit),
dither_detailer
]
ddfs = generate_dd_surveys(nside=nside, nexp=nexp, detailers=details)
greedy = gen_greedy_surveys(nside, nexp=nexp, footprints=footprints)
blobs = generate_blobs(nside, nexp=nexp, footprints=footprints)
surveys = [ddfs, blobs, greedy]
scheduler = Core_scheduler(surveys, nside=nside)
return scheduler
filtername2=filtername2,
ideal_pair_time=pair_time,
nside=nside,
survey_note=survey_name,
ignore_obs='DD',
dither=True))
return surveys
if __name__ == "__main__":
nside = 32
survey_length = 365.25 * 10 # Days
years = int(survey_length / 365.25)
greedy = gen_greedy_surveys(nside)
ddfs = generate_dd_surveys(nside=nside)
blobs = generate_blobs(nside)
surveys = [ddfs, blobs, greedy]
n_visit_limit = None
scheduler = Core_scheduler(surveys, nside=nside)
observatory = Model_observatory(nside=nside)
observatory, scheduler, observations = sim_runner(
observatory,
scheduler,
survey_length=survey_length,
filename='blob_no_slew_%i.db' % years,
n_visit_limit=n_visit_limit)
extra_info['git hash'] = subprocess.check_output(
['git', 'rev-parse', 'HEAD'])
extra_info['file executed'] = os.path.realpath(__file__)
# Just load up all the potential footprints, then use the arg to grab the right one
target_maps = {}
target_maps['big_sky'] = big_sky(nside=nside)
target_maps['gp_heavy'] = gp_smooth(nside=nside)
target_maps['baseline'] = standard_goals(nside=nside)
target_maps['big_sky_nouiy'] = big_sky_nouiy(nside=nside)
target_maps['newA'] = newA(nside=nside)
target_maps['newB'] = newB(nside=nside)
target_maps['bluer_footprint'] = bluer_footprint(nside=nside)
target_maps['stuck_rolling'] = stuck_rolling(nside=nside)
target_maps['big_sky_dust'] = big_sky_dust(nside=nside)
greedy = gen_greedy_surveys(nside,
nexp=nexp,
target_map=target_maps[target_name])
ddfs = generate_dd_surveys(nside=nside, nexp=nexp)
blobs = generate_blobs(nside,
nexp=nexp,
mixed_pairs=True,
target_map=target_maps[target_name])
surveys = [ddfs, blobs, greedy]
run_sched(surveys,
survey_length=survey_length,
fileroot=os.path.join(outDir, target_name),
verbose=verbose,
extra_info=extra_info)
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_ddf(ddf_name, nyears=10, space=2):
previous_ddf = generate_dd_surveys()
survey_names = np.array([survey.survey_name for survey in previous_ddf])
survey_indx = np.where(survey_names == ddf_name)[0].max()
ddf_ra = previous_ddf[survey_indx].ra * u.rad
ddf_dec = previous_ddf[survey_indx].dec * u.rad
site = Site('LSST')
location = EarthLocation(lat=site.latitude,
lon=site.longitude,
height=site.height)
mjd = np.arange(59853.5, 59853.5 + 365.25 * nyears, 20. / 60 / 24.)
times = Time(mjd, format='mjd', location=location)
airmass_limit = 2.5 # demand airmass lower than this
twilight_limit = -18. # Sun below this altitude in degrees
dist_to_moon_limit = 30. # minimum distance to keep from moon degrees
zenith_limit = 10. # Need to be this far away from zenith to start (20 min = 5 deg)
g_m5_limit = 23.5 # mags
season_gap = 20. # days. Count any gap longer than this as it's own season
season_length_limit = 80 # Days. Demand at least this many days in a season
# How long to keep attempting a DDF
expire_dict = {1: 36. / 24., 2: 0.5}
sun_coords = get_sun(times)
moon_coords = get_moon(times)
sched_downtime_data = ScheduledDowntimeData(Time(mjd[0], format='mjd'))
observatory_up = np.ones(mjd.size, dtype=bool)
for dt in sched_downtime_data():
indx = np.where((mjd >= dt['start'].mjd) & (mjd <= dt['end'].mjd))[0]
observatory_up[indx] = False
lst = times.sidereal_time('mean')
sun_altaz = sun_coords.transform_to(AltAz(location=location))
# generate a night label for each timestep
sun_rise = np.where((sun_altaz.alt[0:-1] < 0) & (sun_altaz.alt[1:] > 0))[0]
night = np.zeros(mjd.size, dtype=int)
night[sun_rise] = 1
night = np.cumsum(night) + 1 # 1-index for night
sun_down = np.where(sun_altaz.alt < twilight_limit * u.deg)[0]
ddf_coord = SkyCoord(ra=ddf_ra, dec=ddf_dec)
ddf_altaz = ddf_coord.transform_to(AltAz(location=location, obstime=times))
ddf_airmass = 1. / np.cos(np.radians(90. - ddf_altaz.az.deg))
zenith = AltAz(alt=90. * u.deg, az=0. * u.deg)
ddf_zenth_dist = zenith.separation(ddf_altaz)
nside = 32
ddf_indx = raDec2Hpid(nside, ddf_coord.ra.deg, ddf_coord.dec.deg)
sm = SkyModelPre()
g_sb = mjd * 0 + np.nan
indices = np.where((sun_altaz.alt < twilight_limit * u.deg)
& (ddf_airmass > airmass_limit))[0]
# In theory, one could reach into the sky brightness model and do a much faster interpolation
# There might be an airmass limit on the sky brightness.
for indx in sun_down:
g_sb[indx] = sm.returnMags(mjd[indx],
indx=[ddf_indx],
filters='g',
badval=np.nan)['g']
dist_to_moon = ddf_coord.separation(moon_coords)
seeing_model = SeeingModel()
ddf_approx_fwhmEff = seeing_model(0.7, ddf_airmass)
# I think this should pluck out the g-filter. Really should be labled
ddf_approx_fwhmEff = ddf_approx_fwhmEff['fwhmEff'][1].ravel()
ddf_m5 = m5_flat_sed('g',
g_sb,
ddf_approx_fwhmEff,
30.,
ddf_airmass,
nexp=1.)
# demand sun down past twilight, ddf is up, and observatory is open, and not too close to the moon
good = np.where((ddf_airmass < airmass_limit)
& (sun_altaz.alt < twilight_limit * u.deg)
& (ddf_airmass > 0) & (observatory_up == True)
& (dist_to_moon > dist_to_moon_limit * u.deg)
& (ddf_zenth_dist > zenith_limit * u.deg)
& (ddf_m5 > g_m5_limit))
potential_nights = np.unique(night[good])
night_gap = potential_nights[1:] - potential_nights[0:-1]
big_gap = np.where(night_gap > season_gap)[0] + 1
season = potential_nights * 0
season[big_gap] = 1
season = np.cumsum(season)
u_seasons = np.unique(season)
season_lengths = []
for se in u_seasons:
in_se = np.where(season == se)
season_lengths.append(
np.max(potential_nights[in_se]) - np.min(potential_nights[in_se]))
season_lengths = np.array(season_lengths)
good_seasons = u_seasons[np.where(season_lengths > season_length_limit)[0]]
gn = np.isin(season, good_seasons)
potential_nights = potential_nights[gn]
season = season[gn]
obs_attempts = []
for sea in np.unique(season):
night_indx = np.where(season == sea)
obs_attempts.append(
place_obs(potential_nights[night_indx], space=space))
obs_attempts = np.concatenate(obs_attempts)
mjd_observe = []
m5_approx = []
for indx in np.where(obs_attempts > 0)[0]:
in_night_indx = np.where(night == potential_nights[indx])[0]
best_depth_indx = np.min(
np.where(
ddf_m5[in_night_indx] == np.nanmax(ddf_m5[in_night_indx]))[0])
mjd_start = mjd[in_night_indx[best_depth_indx]]
m5_approx.append(ddf_m5[in_night_indx[best_depth_indx]])
mjd_end = mjd_start + expire_dict[obs_attempts[indx]]
mjd_observe.append((mjd_start, mjd_end))
result = np.zeros(len(mjd_observe),
dtype=[('mjd_start', '<f8'), ('mjd_end', '<f8'),
('label', '<U10')])
mjd_observe = np.array(mjd_observe)
result['mjd_start'] = mjd_observe[:, 0]
result['mjd_end'] = mjd_observe[:, 1]
result['label'] = ddf_name
return result #, ddf_ra, ddf_dec #, previous_ddf[survey_indx].observations, m5_approx
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
bfs.append(
basis_functions.Filter_loaded_basis_function(filternames=filtername))
weights = np.array([3.0, 0.3, 3., 3., 0., 0., 0., 0.])
surveys.append(
survey.Greedy_survey(bfs,
weights,
block_size=1,
filtername=filtername,
dither=True,
nside=nside,
ignore_obs='DD'))
greedy_surveys.append(surveys[-1])
# Set up the DD surveys
dd_surveys = survey.generate_dd_surveys()
survey_list_o_lists = [dd_surveys, pair_surveys, greedy_surveys]
# Debug to stop at a spot if needed
n_visit_limit = None
# put in as list-of-lists so pairs get evaluated first.
scheduler = fs.Core_scheduler(survey_list_o_lists, nside=nside)
observatory = Speed_observatory(nside=nside, quickTest=True)
observatory, scheduler, observations = sim_runner(
observatory,
scheduler,
survey_length=survey_length,
filename='baseline_test%iyrs.db' % years,
delete_past=True,
n_visit_limit=n_visit_limit)
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
footprints_hp = simple_footprint()
observatory = Model_observatory(nside=nside)
conditions = observatory.return_conditions()
footprints = Footprint(conditions.mjd_start,
sun_RA_start=conditions.sun_RA_start,
nside=nside)
for i, key in enumerate(footprints_hp):
footprints.footprints[i, :] = footprints_hp[key]
# Set up the DDF surveys to dither
dither_detailer = detailers.Dither_detailer(per_night=per_night,
max_dither=max_dither)
details = [detailers.Zero_rot_detailer(), dither_detailer]
ddfs = generate_dd_surveys(nside=nside,
nexp=nexp,
detailers=details,
frac_total=0.0045,
aggressive_frac=0.002)
greedy = gen_greedy_surveys(nside, nexp=nexp, footprints=footprints)
blobs = generate_blobs(nside, nexp=nexp, footprints=footprints)
surveys = [ddfs, blobs, greedy]
run_sched(surveys,
survey_length=survey_length,
verbose=verbose,
fileroot=os.path.join(outDir, fileroot + file_end),
extra_info=extra_info,
nside=nside,
illum_limit=illum_limit)
footprints.footprints[i, :] = footprints_hp[key]
# Set up the DDF surveys to dither
dither_detailer = detailers.Dither_detailer(per_night=per_night,
max_dither=max_dither)
details = [
detailers.Camera_rot_detailer(min_rot=-camera_ddf_rot_limit,
max_rot=camera_ddf_rot_limit),
dither_detailer
]
euclid_detailers = [
detailers.Camera_rot_detailer(min_rot=-camera_ddf_rot_limit,
max_rot=camera_ddf_rot_limit),
detailers.Euclid_dither_detailer()
]
ddfs = generate_dd_surveys(nside=nside,
nexp=nexp,
detailers=details,
euclid_detailers=euclid_detailers)
greedy = gen_greedy_surveys(nside, nexp=nexp, footprints=footprints)
blobs = generate_blobs(nside, nexp=nexp, footprints=footprints)
surveys = [ddfs, blobs, greedy]
run_sched(surveys,
survey_length=survey_length,
verbose=verbose,
fileroot=os.path.join(outDir, fileroot + file_end),
extra_info=extra_info,
nside=nside,
illum_limit=illum_limit)
import numpy as np
import healpy as hp
from lsst.sims.featureScheduler.surveys import generate_dd_surveys
import lsst.sims.skybrightness as sb
from lsst.sims.utils import m5_flat_sed
from lsst.sims.seeingModel import SeeingModel
import sys
if __name__ == "__main__":
dds = generate_dd_surveys()
mjd0 = 59853.5
delta_t = 15./60./24. # to days
survey_length = 10.*365.25
sun_limit = np.radians(-12.) # degrees
nominal_seeing = 0.7 # arcsec
filtername = 'g'
seeing_model = SeeingModel()
seeing_indx = 1 #np.where(seeing_model.filter_list == filtername)[0]
# result
mjds = np.arange(mjd0, mjd0+survey_length, delta_t)
# XXX Temp
#mjds = mjds[0:100]
observatory = Model_observatory(nside=nside)
conditions = observatory.return_conditions()
footprints = Footprint(conditions.mjd_start,
sun_RA_start=conditions.sun_RA_start,
nside=nside)
for i, key in enumerate(footprints_hp):
footprints.footprints[i, :] = footprints_hp[key]
# Set up the DDF surveys to dither
dither_detailer = detailers.Dither_detailer(per_night=per_night,
max_dither=max_dither)
details = [
detailers.Camera_rot_detailer(min_rot=-camera_ddf_rot_limit,
max_rot=camera_ddf_rot_limit),
dither_detailer
]
ddfs = generate_dd_surveys(nside=nside, nexp=nexp, detailers=details)
greedy = gen_greedy_surveys(nside, nexp=nexp, footprints=footprints)
blobs = generate_blobs(nside, nexp=nexp, footprints=footprints)
surveys = [ddfs, blobs, greedy]
run_sched(surveys,
survey_length=survey_length,
verbose=verbose,
fileroot=os.path.join(outDir, fileroot + file_end),
extra_info=extra_info,
nside=nside,
illum_limit=illum_limit,
cloud_limit=cloud_limit)
sun_RA_start=conditions.sun_RA_start,
nside=nside)
for i, key in enumerate(footprints_hp):
footprints.footprints[i, :] = footprints_hp[key]
# Set up the DDF surveys to dither
dither_detailer = detailers.Dither_detailer(per_night=per_night,
max_dither=max_dither)
details = [
detailers.Camera_rot_detailer(min_rot=-camera_ddf_rot_limit,
max_rot=camera_ddf_rot_limit),
dither_detailer
]
ddfs = generate_dd_surveys(nside=nside,
nexp=nexp,
detailers=details,
frac_total=ddf_frac_total,
aggressive_frac=aggressive_frac,
delays=delays)
greedy = gen_greedy_surveys(nside, nexp=nexp, footprints=footprints)
blobs = generate_blobs(nside, nexp=nexp, footprints=footprints)
surveys = [ddfs, blobs, greedy]
run_sched(surveys,
survey_length=survey_length,
verbose=verbose,
fileroot=os.path.join(outDir, fileroot + file_end),
extra_info=extra_info,
nside=nside,
illum_limit=illum_limit)
