def tdcBatch(colmap=None, runName='opsim', nside=64, accuracyThreshold=0.04,
extraSql=None, extraMetadata=None):
# The options to add additional sql constraints are removed for now.
if colmap is None:
colmap = ColMapDict('fbs')
# Calculate a subset of DESC WFD-related metrics.
displayDict = {'group': 'Strong Lensing'}
displayDict['subgroup'] = 'Lens Time Delay'
subsetPlots = [plots.HealpixSkyMap(), plots.HealpixHistogram()]
summaryMetrics = [metrics.MeanMetric(), metrics.MedianMetric(), metrics.RmsMetric()]
# Ideally need a way to do better on calculating the summary metrics for the high accuracy area.
slicer = slicers.HealpixSlicer(nside=nside)
tdcMetric = metrics.TdcMetric(metricName='TDC', nightCol=colmap['night'],
expTimeCol=colmap['exptime'], mjdCol=colmap['mjd'])
bundle = mb.MetricBundle(tdcMetric, slicer, constraint=extraSql, metadata=extraMetadata,
displayDict=displayDict, plotFuncs=subsetPlots,
summaryMetrics=summaryMetrics)
bundleList = [bundle]
# Set the runName for all bundles and return the bundleDict.
for b in bundleList:
b.setRunName(runName)
return mb.makeBundlesDictFromList(bundleList)
def plotHist(bundleDicts, metricKey, runNames=None, **kwargs):
'''
Plot histogram of evaluated metrics for each opsim in the bundleDicts on
one figure.
Args:
bundleDicts(dict): A dictionary of bundleDict, keys are run names.
metricKey(tuple): A tuple dictionary key for a specific metric, slicer
and constraint combination.
runNames(list): A list of opsim run names from which the metric values use
to plot histogram are cacluated, default to None, meaning all opsims.
'''
# init handler
ph = plots.PlotHandler(savefig=False)
# init plot
healpixhist = plots.HealpixHistogram()
# option to provide own plotDict for MAF
if kwargs.get('plotDict') is not None:
plotDictTemp = kwargs.get('plotDict')
else:
plotDictTemp = {'figsize': (8, 6), 'fontsize': 15, 'labelsize': 13}
# check plotting key args
if kwargs.get('logScale') is not None:
plotDictTemp['logScale'] = kwargs.get('logScale')
plotDicts = []
bundleList = []
# match keys
metricKeys = key_match(bundleDicts, metricKey)
# loop over all opsims
if runNames is None:
runNames = list(bundleDicts.keys())
# check if provided runName indeed exists
elif not (set(runNames) <= set(bundleDicts.keys())):
raise Exception("Provided runNames don't match the record!")
for runName in runNames:
plotDict = plotDictTemp.copy()
plotDict.update({'label': runName})
plotDicts.append(plotDict)
bundleList.append(bundleDicts[runName][metricKeys[runName]])
# set metrics to plot togehter
ph.setMetricBundles(bundleList)
fn = ph.plot(plotFunc=healpixhist, plotDicts=plotDicts)
# set whether to draw hline
vline = kwargs.get('axvline')
if vline is not None:
plt.figure(fn)
plt.axvline(int(vline), color='k', ls='--')
def plotHist(bundleDicts, metricKey, runNames=None, ddf=False, **kwargs):
'''
Plot histogram of evaluated metrics for each opsim in the bundleDicts on
one figure.
Args:
bundleDicts(dict): A dictionary of bundleDict, keys are run names.
metricKey(tuple): A tuple dictionary key for a specific metric, slicer
and constraint combination.
runNames(list): A list of opsim run names from which the metric values use
to plot histogram are cacluated, default to None, meaning all opsims.
ddf(bool): True if plotting for DDF, default is False.
'''
# init handler
ph = plots.PlotHandler(savefig=False)
# init plot
healpixhist = plots.HealpixHistogram()
plotDictTemp = {'figsize': (8, 6), 'fontsize': 15, 'labelsize': 13}
plotDicts = []
bundleList = []
# check if bins provided
bins = kwargs.get('bins')
if bins is not None:
plotDictTemp['bins'] = int(bins)
# loop over all opsims
if runNames is None:
runNames = list(bundleDicts.keys())
for runName in runNames:
plotDict = plotDictTemp.copy()
plotDict.update({'label': runName})
plotDicts.append(plotDict)
# if plot for ddf, search for key
if ddf:
keys = [*bundleDicts[runName].keys()]
metricKey = [elem for elem in keys if elem[1] == metricKey[1]][0]
bundleList.append(bundleDicts[runName][metricKey])
# set metrics to plot togehter
ph.setMetricBundles(bundleList)
fn = ph.plot(plotFunc=healpixhist, plotDicts=plotDicts)
# set whether to draw hline
vline = kwargs.get('axvline')
if vline is not None:
plt.figure(fn)
plt.axvline(int(vline), color='k', ls='--')
def astrometryBatch(colmap=None, runName='opsim',
extraSql=None, extraMetadata=None,
nside=64):
# Allow user to add dithering.
if colmap is None:
colmap = ColMapDict('opsimV4')
bundleList = []
sql = ''
metadata = 'All visits'
# Add additional sql constraint (such as wfdWhere) and metadata, if provided.
if (extraSql is not None) and (len(extraSql) > 0):
sql = extraSql
if extraMetadata is None:
metadata = extraSql.replace('filter =', '').replace('filter=', '')
metadata = metadata.replace('"', '').replace("'", '')
if extraMetadata is not None:
metadata = extraMetadata
subgroup = metadata
raCol = colmap['ra']
decCol = colmap['dec']
degrees = colmap['raDecDeg']
# Set up stackers.
parallaxStacker = stackers.ParallaxFactorStacker(raCol=raCol, decCol=decCol,
dateCol=colmap['mjd'], degrees=degrees)
dcrStacker = stackers.DcrStacker(filterCol=colmap['filter'], altCol=colmap['alt'], degrees=degrees,
raCol=raCol, decCol=decCol, lstCol=colmap['lst'],
site='LSST', mjdCol=colmap['mjd'])
# Set up parallax metrics.
slicer = slicers.HealpixSlicer(nside=nside, lonCol=raCol, latCol=decCol, latLonDeg=degrees)
subsetPlots = [plots.HealpixSkyMap(), plots.HealpixHistogram()]
displayDict = {'group': 'Parallax', 'subgroup': subgroup,
'order': 0, 'caption': None}
# Expected error on parallax at 10 AU.
for rmag in (20.0, 24.0):
metric = metrics.ParallaxMetric(metricName='Parallax @ %.1f' % (rmag), rmag=rmag,
seeingCol=colmap['seeingGeom'], filterCol=colmap['filter'],
m5Col=colmap['fiveSigmaDepth'], normalize=False)
bundle = mb.MetricBundle(metric, slicer, sql, metadata=metadata,
stackerList=[parallaxStacker],
displayDict=displayDict,
summaryMetrics=standardSummary(),
plotFuncs=subsetPlots)
bundleList.append(bundle)
displayDict['order'] += 1
# Parallax normalized to 'best possible' if all visits separated by 6 months.
# This separates the effect of cadence from depth.
for rmag in (20.0, 24.0):
metric = metrics.ParallaxMetric(metricName='Normalized Parallax @ %.1f' % (rmag), rmag=rmag,
seeingCol=colmap['seeingGeom'], filterCol=colmap['filter'],
m5Col=colmap['fiveSigmaDepth'], normalize=True)
bundle = mb.MetricBundle(metric, slicer, sql, metadata=metadata,
stackerList=[parallaxStacker],
displayDict=displayDict,
summaryMetrics=standardSummary(),
plotFuncs=subsetPlots)
bundleList.append(bundle)
displayDict['order'] += 1
# Parallax factor coverage.
for rmag in (20.0, 24.0):
metric = metrics.ParallaxCoverageMetric(metricName='Parallax Coverage @ %.1f' % (rmag),
rmag=rmag, m5Col=colmap['fiveSigmaDepth'],
mjdCol=colmap['mjd'], filterCol=colmap['filter'],
seeingCol=colmap['seeingGeom'])
bundle = mb.MetricBundle(metric, slicer, sql, metadata=metadata,
stackerList=[parallaxStacker],
displayDict=displayDict, summaryMetrics=standardSummary(),
plotFuncs=subsetPlots)
bundleList.append(bundle)
displayDict['order'] += 1
# Parallax problems can be caused by HA and DCR degeneracies. Check their correlation.
for rmag in (20.0, 24.0):
metric = metrics.ParallaxDcrDegenMetric(metricName='Parallax-DCR degeneracy @ %.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).'
bundle = mb.MetricBundle(metric, slicer, sql, metadata=metadata,
stackerList=[dcrStacker, parallaxStacker],
displayDict=displayDict, summaryMetrics=standardSummary(),
plotFuncs=subsetPlots)
bundleList.append(bundle)
displayDict['order'] += 1
# Proper Motion metrics.
displayDict = {'group': 'Proper Motion', 'subgroup': subgroup, 'order': 0, 'caption': None}
# Proper motion errors.
for rmag in (20.0, 24.0):
metric = metrics.ProperMotionMetric(metricName='Proper Motion %.1f' % rmag,
rmag=rmag, m5Col=colmap['fiveSigmaDepth'],
mjdCol=colmap['mjd'], filterCol=colmap['filter'],
seeingCol=colmap['seeingGeom'], normalize=False)
bundle = mb.MetricBundle(metric, slicer, sql, metadata=metadata,
displayDict=displayDict, summaryMetrics=standardSummary(),
plotFuncs=subsetPlots)
bundleList.append(bundle)
displayDict['order'] += 1
# Normalized proper motion.
for rmag in (20.0, 24.0):
metric = metrics.ProperMotionMetric(metricName='Normalized Proper Motion %.1f' % rmag,
rmag=rmag, m5Col=colmap['fiveSigmaDepth'],
mjdCol=colmap['mjd'], filterCol=colmap['filter'],
seeingCol=colmap['seeingGeom'], normalize=True)
bundle = mb.MetricBundle(metric, slicer, sql, metadata=metadata,
displayDict=displayDict, summaryMetrics=standardSummary(),
plotFuncs=subsetPlots)
bundleList.append(bundle)
displayDict['order'] += 1
# Set the runName for all bundles and return the bundleDict.
for b in bundleList:
b.setRunName(runName)
return mb.makeBundlesDictFromList(bundleList)
def makeBundleList(dbFile, runName=None, nside=64, benchmark='design',
lonCol='fieldRA', latCol='fieldDec', seeingCol='seeingFwhmGeom'):
"""
make a list of metricBundle objects to look at the scientific performance
of an opsim run.
"""
# List to hold everything we're going to make
bundleList = []
# List to hold metrics that shouldn't be saved
noSaveBundleList = []
# Connect to the databse
opsimdb = db.OpsimDatabaseV4(dbFile)
if runName is None:
runName = os.path.basename(dbFile).replace('_sqlite.db', '')
# Fetch the proposal ID values from the database
propids, propTags = opsimdb.fetchPropInfo()
# Fetch the telescope location from config
lat, lon, height = opsimdb.fetchLatLonHeight()
# Add metadata regarding dithering/non-dithered.
commonname = ''.join([a for a in lonCol if a in latCol])
if commonname == 'field':
slicermetadata = ' (non-dithered)'
else:
slicermetadata = ' (%s)' % (commonname)
# Construct a WFD SQL where clause so multiple propIDs can query by WFD:
wfdWhere = opsimdb.createSQLWhere('WFD', propTags)
print('#FYI: WFD "where" clause: %s' % (wfdWhere))
ddWhere = opsimdb.createSQLWhere('DD', propTags)
print('#FYI: DD "where" clause: %s' % (ddWhere))
# Set up benchmark values, scaled to length of opsim run.
runLength = opsimdb.fetchRunLength()
if benchmark == 'requested':
# Fetch design values for seeing/skybrightness/single visit depth.
benchmarkVals = utils.scaleBenchmarks(runLength, benchmark='design')
# Update nvisits with requested visits from config files.
benchmarkVals['nvisits'] = opsimdb.fetchRequestedNvisits(propId=propTags['WFD'])
# Calculate expected coadded depth.
benchmarkVals['coaddedDepth'] = utils.calcCoaddedDepth(benchmarkVals['nvisits'],
benchmarkVals['singleVisitDepth'])
elif (benchmark == 'stretch') or (benchmark == 'design'):
# Calculate benchmarks for stretch or design.
benchmarkVals = utils.scaleBenchmarks(runLength, benchmark=benchmark)
benchmarkVals['coaddedDepth'] = utils.calcCoaddedDepth(benchmarkVals['nvisits'],
benchmarkVals['singleVisitDepth'])
else:
raise ValueError('Could not recognize benchmark value %s, use design, stretch or requested.'
% (benchmark))
# Check that nvisits is not set to zero (for very short run length).
for f in benchmarkVals['nvisits']:
if benchmarkVals['nvisits'][f] == 0:
print('Updating benchmark nvisits value in %s to be nonzero' % (f))
benchmarkVals['nvisits'][f] = 1
# Set values for min/max range of nvisits for All/WFD and DD plots. These are somewhat arbitrary.
nvisitsRange = {}
nvisitsRange['all'] = {'u': [20, 80], 'g': [50, 150], 'r': [100, 250],
'i': [100, 250], 'z': [100, 300], 'y': [100, 300]}
nvisitsRange['DD'] = {'u': [6000, 10000], 'g': [2500, 5000], 'r': [5000, 8000],
'i': [5000, 8000], 'z': [7000, 10000], 'y': [5000, 8000]}
# Scale these ranges for the runLength.
scale = runLength / 10.0
for prop in nvisitsRange:
for f in nvisitsRange[prop]:
for i in [0, 1]:
nvisitsRange[prop][f][i] = int(np.floor(nvisitsRange[prop][f][i] * scale))
# Filter list, and map of colors (for plots) to filters.
filters = ['u', 'g', 'r', 'i', 'z', 'y']
colors = {'u': 'cyan', 'g': 'g', 'r': 'y', 'i': 'r', 'z': 'm', 'y': 'k'}
filtorder = {'u': 1, 'g': 2, 'r': 3, 'i': 4, 'z': 5, 'y': 6}
# Easy way to run through all fi
# Set up a list of common summary stats
commonSummary = [metrics.MeanMetric(), metrics.RobustRmsMetric(), metrics.MedianMetric(),
metrics.PercentileMetric(metricName='25th%ile', percentile=25),
metrics.PercentileMetric(metricName='75th%ile', percentile=75),
metrics.MinMetric(), metrics.MaxMetric()]
allStats = commonSummary
# Set up some 'group' labels
reqgroup = 'A: Required SRD metrics'
depthgroup = 'B: Depth per filter'
uniformitygroup = 'C: Uniformity'
airmassgroup = 'D: Airmass distribution'
seeinggroup = 'E: Seeing distribution'
transgroup = 'F: Transients'
sngroup = 'G: SN Ia'
altAzGroup = 'H: Alt Az'
rangeGroup = 'I: Range of Dates'
intergroup = 'J: Inter-Night'
phaseGroup = 'K: Max Phase Gap'
NEOGroup = 'L: NEO Detection'
# Set up an object to track the metricBundles that we want to combine into merged plots.
mergedHistDict = {}
# Set the histogram merge function.
mergeFunc = plots.HealpixHistogram()
keys = ['NVisits', 'coaddm5', 'NormEffTime', 'Minseeing', 'seeingAboveLimit', 'minAirmass',
'fracAboveAirmass']
for key in keys:
mergedHistDict[key] = plots.PlotBundle(plotFunc=mergeFunc)
##
# Calculate the fO metrics for all proposals and WFD only.
order = 0
for prop in ('All prop', 'WFD only'):
if prop == 'All prop':
metadata = 'All Visits' + slicermetadata
sqlconstraint = ''
if prop == 'WFD only':
metadata = 'WFD only' + slicermetadata
sqlconstraint = '%s' % (wfdWhere)
# Configure the count metric which is what is used for f0 slicer.
m1 = metrics.CountMetric(col='observationStartMJD', metricName='fO')
plotDict = {'xlabel': 'Number of Visits', 'Asky': benchmarkVals['Area'],
'Nvisit': benchmarkVals['nvisitsTotal'], 'xMin': 0, 'xMax': 1500}
summaryMetrics = [metrics.fOArea(nside=nside, norm=False, metricName='fOArea: Nvisits (#)',
Asky=benchmarkVals['Area'], Nvisit=benchmarkVals['nvisitsTotal']),
metrics.fOArea(nside=nside, norm=True, metricName='fOArea: Nvisits/benchmark',
Asky=benchmarkVals['Area'], Nvisit=benchmarkVals['nvisitsTotal']),
metrics.fONv(nside=nside, norm=False, metricName='fONv: Area (sqdeg)',
Asky=benchmarkVals['Area'], Nvisit=benchmarkVals['nvisitsTotal']),
metrics.fONv(nside=nside, norm=True, metricName='fONv: Area/benchmark',
Asky=benchmarkVals['Area'], Nvisit=benchmarkVals['nvisitsTotal'])]
caption = 'The FO metric evaluates the overall efficiency of observing. '
caption += ('fOArea: Nvisits = %.1f sq degrees receive at least this many visits out of %d. '
% (benchmarkVals['Area'], benchmarkVals['nvisitsTotal']))
caption += ('fONv: Area = this many square degrees out of %.1f receive at least %d visits.'
% (benchmarkVals['Area'], benchmarkVals['nvisitsTotal']))
displayDict = {'group': reqgroup, 'subgroup': 'F0', 'displayOrder': order, 'caption': caption}
order += 1
slicer = slicers.HealpixSlicer(nside=nside, lonCol=lonCol, latCol=latCol)
bundle = metricBundles.MetricBundle(m1, slicer, sqlconstraint, plotDict=plotDict,
displayDict=displayDict, summaryMetrics=summaryMetrics,
plotFuncs=[plots.FOPlot()],
runName=runName, metadata=metadata)
bundleList.append(bundle)
###
# Calculate the Rapid Revisit Metrics.
order = 0
metadata = 'All Visits' + slicermetadata
sqlconstraint = ''
dTmin = 40.0 # seconds
dTmax = 30.0*60. # seconds
minNvisit = 100
pixArea = float(hp.nside2pixarea(nside, degrees=True))
scale = pixArea * hp.nside2npix(nside)
cutoff1 = 0.15
extraStats1 = [metrics.FracBelowMetric(cutoff=cutoff1, scale=scale, metricName='Area (sq deg)')]
extraStats1.extend(commonSummary)
slicer = slicers.HealpixSlicer(nside=nside, lonCol=lonCol, latCol=latCol)
m1 = metrics.RapidRevisitMetric(metricName='RapidRevisitUniformity',
dTmin=dTmin / 60.0 / 60.0 / 24.0, dTmax=dTmax / 60.0 / 60.0 / 24.0,
minNvisits=minNvisit)
plotDict = {'xMin': 0, 'xMax': 1}
summaryStats = extraStats1
caption = 'Deviation from uniformity for short revisit timescales, between %s and %s seconds, ' % (
dTmin, dTmax)
caption += 'for pointings with at least %d visits in this time range. ' % (minNvisit)
caption += 'Summary statistic "Area" below indicates the area on the sky which has a '
caption += 'deviation from uniformity of < %.2f.' % (cutoff1)
displayDict = {'group': reqgroup, 'subgroup': 'Rapid Revisit', 'displayOrder': order,
'caption': caption}
bundle = metricBundles.MetricBundle(m1, slicer, sqlconstraint, plotDict=plotDict,
displayDict=displayDict, summaryMetrics=summaryStats,
runName=runName, metadata=metadata)
bundleList.append(bundle)
order += 1
dTmax = dTmax/60.0 # need time in minutes for Nrevisits metric
m2 = metrics.NRevisitsMetric(dT=dTmax)
plotDict = {'xMin': 0.1, 'xMax': 2000, 'logScale': True}
cutoff2 = 800
extraStats2 = [metrics.FracAboveMetric(cutoff=cutoff2, scale=scale, metricName='Area (sq deg)')]
extraStats2.extend(commonSummary)
caption = 'Number of consecutive visits with return times faster than %.1f minutes, ' % (dTmax)
caption += 'in any filter, all proposals. '
caption += 'Summary statistic "Area" below indicates the area on the sky which has more than '
caption += '%d revisits within this time window.' % (cutoff2)
summaryStats = extraStats2
displayDict = {'group': reqgroup, 'subgroup': 'Rapid Revisit', 'displayOrder': order,
'caption': caption}
bundle = metricBundles.MetricBundle(m2, slicer, sqlconstraint, plotDict=plotDict,
displayDict=displayDict, summaryMetrics=summaryStats,
runName=runName, metadata=metadata)
bundleList.append(bundle)
order += 1
m3 = metrics.NRevisitsMetric(dT=dTmax, normed=True)
plotDict = {'xMin': 0, 'xMax': 1, 'cbarFormat': '%.1f'}
cutoff3 = 0.6
extraStats3 = [metrics.FracAboveMetric(cutoff=cutoff3, scale=scale, metricName='Area (sq deg)')]
extraStats3.extend(commonSummary)
summaryStats = extraStats3
caption = 'Fraction of total visits where consecutive visits have return times faster '
caption += 'than %.1f minutes, in any filter, all proposals. ' % (dTmax)
caption += 'Summary statistic "Area" below indicates the area on the sky which has more '
caption += 'than %d revisits within this time window.' % (cutoff3)
displayDict = {'group': reqgroup, 'subgroup': 'Rapid Revisit', 'displayOrder': order,
'caption': caption}
bundle = metricBundles.MetricBundle(m3, slicer, sqlconstraint, plotDict=plotDict,
displayDict=displayDict, summaryMetrics=summaryStats,
runName=runName, metadata=metadata)
bundleList.append(bundle)
order += 1
# And add a histogram of the time between quick revisits.
binMin = 0
binMax = 120.
binsize = 3.
bins_metric = np.arange(binMin / 60.0 / 24.0, (binMax + binsize) / 60. / 24., binsize / 60. / 24.)
bins_plot = bins_metric * 24.0 * 60.0
m1 = metrics.TgapsMetric(bins=bins_metric, metricName='dT visits')
plotDict = {'bins': bins_plot, 'xlabel': 'dT (minutes)'}
caption = ('Histogram of the time between consecutive revisits (<%.1f minutes), over entire sky.'
% (binMax))
displayDict = {'group': reqgroup, 'subgroup': 'Rapid Revisit', 'order': order,
'caption': caption}
slicer = slicers.HealpixSlicer(nside=nside, lonCol=lonCol, latCol=latCol)
plotFunc = plots.SummaryHistogram()
bundle = metricBundles.MetricBundle(m1, slicer, sqlconstraint, plotDict=plotDict,
displayDict=displayDict, runName=runName,
metadata=metadata, plotFuncs=[plotFunc])
bundleList.append(bundle)
order += 1
##
# Trigonometric parallax and proper motion @ r=20 and r=24
slicer = slicers.HealpixSlicer(nside=nside, lonCol=lonCol, latCol=latCol)
sqlconstraint = ''
order = 0
metric = metrics.ParallaxMetric(metricName='Parallax 20', rmag=20, seeingCol=seeingCol)
summaryStats = allStats
plotDict = {'cbarFormat': '%.1f', 'xMin': 0, 'xMax': 3}
displayDict = {'group': reqgroup, 'subgroup': 'Parallax', 'order': order,
'caption': 'Parallax precision at r=20. (without refraction).'}
bundle = metricBundles.MetricBundle(metric, slicer, sqlconstraint, plotDict=plotDict,
displayDict=displayDict, summaryMetrics=summaryStats,
runName=runName, metadata=metadata)
bundleList.append(bundle)
order += 1
metric = metrics.ParallaxMetric(metricName='Parallax 24', rmag=24, seeingCol=seeingCol)
plotDict = {'cbarFormat': '%.1f', 'xMin': 0, 'xMax': 10}
displayDict = {'group': reqgroup, 'subgroup': 'Parallax', 'order': order,
'caption': 'Parallax precision at r=24. (without refraction).'}
bundle = metricBundles.MetricBundle(metric, slicer, sqlconstraint, plotDict=plotDict,
displayDict=displayDict, summaryMetrics=summaryStats,
runName=runName, metadata=metadata)
bundleList.append(bundle)
order += 1
metric = metrics.ParallaxMetric(metricName='Parallax Normed', rmag=24, normalize=True,
seeingCol=seeingCol)
plotDict = {'xMin': 0.5, 'xMax': 1.0}
displayDict = {'group': reqgroup, 'subgroup': 'Parallax', 'order': order,
'caption':
'Normalized parallax (normalized to optimum observation cadence, 1=optimal).'}
bundle = metricBundles.MetricBundle(metric, slicer, sqlconstraint, plotDict=plotDict,
displayDict=displayDict, summaryMetrics=summaryStats,
runName=runName, metadata=metadata)
bundleList.append(bundle)
order += 1
metric = metrics.ParallaxCoverageMetric(metricName='Parallax Coverage 20', rmag=20, seeingCol=seeingCol)
plotDict = {}
caption = "Parallax factor coverage for an r=20 star (0 is bad, 0.5-1 is good). "
caption += "One expects the parallax factor coverage to vary because stars on the ecliptic "
caption += "can be observed when they have no parallax offset while stars at the pole are always "
caption += "offset by the full parallax offset."""
displayDict = {'group': reqgroup, 'subgroup': 'Parallax', 'order': order,
'caption': caption}
bundle = metricBundles.MetricBundle(metric, slicer, sqlconstraint, plotDict=plotDict,
displayDict=displayDict, summaryMetrics=summaryStats,
runName=runName, metadata=metadata)
bundleList.append(bundle)
order += 1
metric = metrics.ParallaxCoverageMetric(metricName='Parallax Coverage 24', rmag=24, seeingCol=seeingCol)
plotDict = {}
caption = "Parallax factor coverage for an r=24 star (0 is bad, 0.5-1 is good). "
caption += "One expects the parallax factor coverage to vary because stars on the ecliptic "
caption += "can be observed when they have no parallax offset while stars at the pole are always "
caption += "offset by the full parallax offset."""
displayDict = {'group': reqgroup, 'subgroup': 'Parallax', 'order': order,
'caption': caption}
bundle = metricBundles.MetricBundle(metric, slicer, sqlconstraint, plotDict=plotDict,
displayDict=displayDict, summaryMetrics=summaryStats,
runName=runName, metadata=metadata)
bundleList.append(bundle)
order += 1
metric = metrics.ParallaxDcrDegenMetric(metricName='Parallax-DCR degeneracy 20', rmag=20,
seeingCol=seeingCol)
plotDict = {}
caption = 'Correlation between parallax offset magnitude and hour angle an r=20 star.'
caption += ' (0 is good, near -1 or 1 is bad).'
displayDict = {'group': reqgroup, 'subgroup': 'Parallax', 'order': order,
'caption': caption}
bundle = metricBundles.MetricBundle(metric, slicer, sqlconstraint, plotDict=plotDict,
displayDict=displayDict, summaryMetrics=summaryStats,
runName=runName, metadata=metadata)
bundleList.append(bundle)
order += 1
metric = metrics.ParallaxDcrDegenMetric(metricName='Parallax-DCR degeneracy 24', rmag=24,
seeingCol=seeingCol)
plotDict = {}
caption = 'Correlation between parallax offset magnitude and hour angle an r=24 star.'
caption += ' (0 is good, near -1 or 1 is bad).'
displayDict = {'group': reqgroup, 'subgroup': 'Parallax', 'order': order,
'caption': caption}
bundle = metricBundles.MetricBundle(metric, slicer, sqlconstraint, plotDict=plotDict,
displayDict=displayDict, summaryMetrics=summaryStats,
runName=runName, metadata=metadata)
bundleList.append(bundle)
order += 1
metric = metrics.ProperMotionMetric(metricName='Proper Motion 20', rmag=20, seeingCol=seeingCol)
summaryStats = allStats
plotDict = {'xMin': 0, 'xMax': 3}
displayDict = {'group': reqgroup, 'subgroup': 'Proper Motion', 'order': order,
'caption': 'Proper Motion precision at r=20.'}
bundle = metricBundles.MetricBundle(metric, slicer, sqlconstraint, plotDict=plotDict,
displayDict=displayDict, summaryMetrics=summaryStats,
runName=runName, metadata=metadata)
bundleList.append(bundle)
order += 1
metric = metrics.ProperMotionMetric(rmag=24, metricName='Proper Motion 24', seeingCol=seeingCol)
summaryStats = allStats
plotDict = {'xMin': 0, 'xMax': 10}
displayDict = {'group': reqgroup, 'subgroup': 'Proper Motion', 'order': order,
'caption': 'Proper Motion precision at r=24.'}
bundle = metricBundles.MetricBundle(metric, slicer, sqlconstraint, plotDict=plotDict,
displayDict=displayDict, summaryMetrics=summaryStats,
runName=runName, metadata=metadata)
bundleList.append(bundle)
order += 1
metric = metrics.ProperMotionMetric(rmag=24, normalize=True, metricName='Proper Motion Normed',
seeingCol=seeingCol)
plotDict = {'xMin': 0.2, 'xMax': 0.7}
caption = 'Normalized proper motion at r=24. '
caption += '(normalized to optimum observation cadence - start/end. 1=optimal).'
displayDict = {'group': reqgroup, 'subgroup': 'Proper Motion', 'order': order,
'caption': caption}
bundle = metricBundles.MetricBundle(metric, slicer, sqlconstraint, plotDict=plotDict,
displayDict=displayDict, summaryMetrics=summaryStats,
runName=runName, metadata=metadata)
bundleList.append(bundle)
order += 1
##
# Calculate the time uniformity in each filter, for each year.
order = 0
slicer = slicers.HealpixSlicer(nside=nside, lonCol=lonCol, latCol=latCol)
plotFuncs = [plots.TwoDMap()]
step = 0.5
bins = np.arange(0, 365.25 * 10 + 40, 40) - step
metric = metrics.AccumulateUniformityMetric(bins=bins)
plotDict = {'xlabel': 'Night (days)', 'xextent': [bins.min(
) + step, bins.max() + step], 'cbarTitle': 'Uniformity'}
for f in filters:
sqlconstraint = 'filter = "%s"' % (f)
caption = 'Deviation from uniformity in %s band. ' % f
caption += 'Northern Healpixels are at the top of the image.'
caption += '(0=perfectly uniform, 1=perfectly nonuniform).'
displayDict = {'group': uniformitygroup, 'subgroup': 'per night',
'order': filtorder[f], 'caption': caption}
metadata = '%s band' % (f) + slicermetadata
bundle = metricBundles.MetricBundle(metric, slicer, sqlconstraint, plotDict=plotDict,
displayDict=displayDict, runName=runName, metadata=metadata,
plotFuncs=plotFuncs)
noSaveBundleList.append(bundle)
##
# Depth metrics.
slicer = slicers.HealpixSlicer(nside=nside, lonCol=lonCol, latCol=latCol)
for f in filters:
propCaption = '%s band, all proposals %s' % (f, slicermetadata)
sqlconstraint = 'filter = "%s"' % (f)
metadata = '%s band' % (f) + slicermetadata
# Number of visits.
metric = metrics.CountMetric(col='observationStartMJD', metricName='NVisits')
plotDict = {'xlabel': 'Number of visits',
'xMin': nvisitsRange['all'][f][0],
'xMax': nvisitsRange['all'][f][1],
'colorMin': nvisitsRange['all'][f][0],
'colorMax': nvisitsRange['all'][f][1],
'binsize': 5,
'logScale': True, 'nTicks': 4, 'colorMin': 1}
summaryStats = allStats
displayDict = {'group': depthgroup, 'subgroup': 'Nvisits', 'order': filtorder[f],
'caption': 'Number of visits in filter %s, %s.' % (f, propCaption)}
histMerge = {'color': colors[f], 'label': '%s' % (f),
'binsize': 5,
'xMin': nvisitsRange['all'][f][0], 'xMax': nvisitsRange['all'][f][1],
'legendloc': 'upper right'}
bundle = metricBundles.MetricBundle(metric, slicer, sqlconstraint, plotDict=plotDict,
displayDict=displayDict, runName=runName, metadata=metadata,
summaryMetrics=summaryStats)
mergedHistDict['NVisits'].addBundle(bundle, plotDict=histMerge)
bundleList.append(bundle)
# Coadded depth.
metric = metrics.Coaddm5Metric()
plotDict = {'zp': benchmarkVals['coaddedDepth'][f], 'xMin': -0.8, 'xMax': 0.8,
'xlabel': 'coadded m5 - %.1f' % benchmarkVals['coaddedDepth'][f]}
summaryStats = allStats
histMerge = {'legendloc': 'upper right', 'color': colors[f], 'label': '%s' % f, 'binsize': .02,
'xlabel': 'coadded m5 - benchmark value'}
caption = ('Coadded depth in filter %s, with %s value subtracted (%.1f), %s. '
% (f, benchmark, benchmarkVals['coaddedDepth'][f], propCaption))
caption += 'More positive numbers indicate fainter limiting magnitudes.'
displayDict = {'group': depthgroup, 'subgroup': 'Coadded Depth',
'order': filtorder[f], 'caption': caption}
bundle = metricBundles.MetricBundle(metric, slicer, sqlconstraint, plotDict=plotDict,
displayDict=displayDict, runName=runName, metadata=metadata,
summaryMetrics=summaryStats)
mergedHistDict['coaddm5'].addBundle(bundle, plotDict=histMerge)
bundleList.append(bundle)
# Effective time.
metric = metrics.TeffMetric(metricName='Normalized Effective Time', normed=True,
fiducialDepth=benchmarkVals['singleVisitDepth'])
plotDict = {'xMin': 0.1, 'xMax': 1.1}
summaryStats = allStats
histMerge = {'legendLoc': 'upper right', 'color': colors[f], 'label': '%s' % f, 'binsize': 0.02}
caption = ('"Time Effective" in filter %s, calculated with fiducial single-visit depth of %s mag. '
% (f, benchmarkVals['singleVisitDepth'][f]))
caption += 'Normalized by the fiducial time effective, if every observation was at '
caption += 'the fiducial depth.'
displayDict = {'group': depthgroup, 'subgroup': 'Time Eff.',
'order': filtorder[f], 'caption': caption}
bundle = metricBundles.MetricBundle(metric, slicer, sqlconstraint, plotDict=plotDict,
displayDict=displayDict, runName=runName, metadata=metadata,
summaryMetrics=summaryStats)
mergedHistDict['NormEffTime'].addBundle(bundle, plotDict=histMerge)
bundleList.append(bundle)
# Put in a z=0.5 Type Ia SN, based on Cambridge 2015 workshop notebook.
# Check for 1) detection in any band, 2) detection on the rise in any band,
# 3) good characterization
peaks = {'uPeak': 25.9, 'gPeak': 23.6, 'rPeak': 22.6, 'iPeak': 22.7, 'zPeak': 22.7, 'yPeak': 22.8}
peakTime = 15.
transDuration = peakTime + 30. # Days
metric = metrics.TransientMetric(riseSlope=-2. / peakTime, declineSlope=1.4 / 30.0,
transDuration=transDuration, peakTime=peakTime,
surveyDuration=runLength,
metricName='SNDetection', **peaks)
caption = 'Fraction of z=0.5 type Ia SN that are detected in any filter'
displayDict = {'group': transgroup, 'subgroup': 'Detected', 'caption': caption}
sqlconstraint = ''
metadata = '' + slicermetadata
plotDict = {}
bundle = metricBundles.MetricBundle(metric, slicer, sqlconstraint, plotDict=plotDict,
displayDict=displayDict, runName=runName, metadata=metadata)
bundleList.append(bundle)
metric = metrics.TransientMetric(riseSlope=-2. / peakTime, declineSlope=1.4 / 30.0,
transDuration=transDuration, peakTime=peakTime,
surveyDuration=runLength,
nPrePeak=1, metricName='SNAlert', **peaks)
caption = 'Fraction of z=0.5 type Ia SN that are detected pre-peak in any filter'
displayDict = {'group': transgroup, 'subgroup': 'Detected on the rise', 'caption': caption}
plotDict = {}
bundle = metricBundles.MetricBundle(metric, slicer, sqlconstraint, plotDict=plotDict,
displayDict=displayDict, runName=runName, metadata=metadata)
bundleList.append(bundle)
metric = metrics.TransientMetric(riseSlope=-2. / peakTime, declineSlope=1.4 / 30.,
transDuration=transDuration, peakTime=peakTime,
surveyDuration=runLength, metricName='SNLots',
nFilters=3, nPrePeak=3, nPerLC=2, **peaks)
caption = 'Fraction of z=0.5 type Ia SN that are observed 6 times, 3 pre-peak, '
caption += '3 post-peak, with observations in 3 filters'
displayDict = {'group': transgroup, 'subgroup': 'Well observed', 'caption': caption}
sqlconstraint = 'filter="r" or filter="g" or filter="i" or filter="z" '
plotDict = {}
bundle = metricBundles.MetricBundle(metric, slicer, sqlconstraint, plotDict=plotDict,
displayDict=displayDict, runName=runName, metadata=metadata)
bundleList.append(bundle)
# Good seeing in r/i band metrics, including in first/second years.
order = 0
for tcolor, tlabel, timespan in zip(['k', 'g', 'r'], ['10 years', '1 year', '2 years'],
['', ' and night<=365', ' and night<=730']):
order += 1
for f in (['r', 'i']):
sqlconstraint = 'filter = "%s" %s' % (f, timespan)
propCaption = '%s band, all proposals %s, over %s.' % (f, slicermetadata, tlabel)
metadata = '%s band, %s' % (f, tlabel) + slicermetadata
seeing_limit = 0.7
airmass_limit = 1.2
metric = metrics.MinMetric(col=seeingCol)
summaryStats = allStats
plotDict = {'xMin': 0.35, 'xMax': 1.5, 'color': tcolor}
displayDict = {'group': seeinggroup, 'subgroup': 'Best Seeing',
'order': filtorder[f] * 100 + order,
'caption': 'Minimum FWHMgeom values in %s.' % (propCaption)}
histMerge = {'label': '%s %s' % (f, tlabel), 'color': tcolor,
'binsize': 0.03, 'xMin': 0.35, 'xMax': 1.5, 'legendloc': 'upper right'}
bundle = metricBundles.MetricBundle(metric, slicer, sqlconstraint, plotDict=plotDict,
displayDict=displayDict, runName=runName, metadata=metadata,
summaryMetrics=summaryStats)
mergedHistDict['Minseeing'].addBundle(bundle, plotDict=histMerge)
bundleList.append(bundle)
metric = metrics.FracAboveMetric(col=seeingCol, cutoff=seeing_limit)
summaryStats = allStats
plotDict = {'xMin': 0, 'xMax': 1.1, 'color': tcolor}
displayDict = {'group': seeinggroup, 'subgroup': 'Good seeing fraction',
'order': filtorder[f] * 100 + order,
'caption': 'Fraction of total images with FWHMgeom worse than %.1f, in %s'
% (seeing_limit, propCaption)}
histMerge = {'color': tcolor, 'label': '%s %s' % (f, tlabel),
'binsize': 0.05, 'legendloc': 'upper right'}
bundle = metricBundles.MetricBundle(metric, slicer, sqlconstraint, plotDict=plotDict,
displayDict=displayDict, runName=runName, metadata=metadata,
summaryMetrics=summaryStats)
mergedHistDict['seeingAboveLimit'].addBundle(bundle, plotDict=histMerge)
bundleList.append(bundle)
metric = metrics.MinMetric(col='airmass')
plotDict = {'xMin': 1, 'xMax': 1.5, 'color': tcolor}
summaryStats = allStats
displayDict = {'group': airmassgroup, 'subgroup': 'Best Airmass',
'order': filtorder[f] * 100 + order, 'caption':
'Minimum airmass in %s.' % (propCaption)}
histMerge = {'color': tcolor, 'label': '%s %s' % (f, tlabel),
'binsize': 0.03, 'legendloc': 'upper right'}
bundle = metricBundles.MetricBundle(metric, slicer, sqlconstraint, plotDict=plotDict,
displayDict=displayDict, runName=runName, metadata=metadata,
summaryMetrics=summaryStats)
mergedHistDict['minAirmass'].addBundle(bundle, plotDict=histMerge)
bundleList.append(bundle)
metric = metrics.FracAboveMetric(col='airmass', cutoff=airmass_limit)
plotDict = {'xMin': 0, 'xMax': 1, 'color': tcolor}
summaryStats = allStats
displayDict = {'group': airmassgroup, 'subgroup': 'Low airmass fraction',
'order': filtorder[f] * 100 + order, 'caption':
'Fraction of total images with airmass higher than %.2f, in %s'
% (airmass_limit, propCaption)}
histMerge = {'color': tcolor, 'label': '%s %s' % (
f, tlabel), 'binsize': 0.05, 'legendloc': 'upper right'}
bundle = metricBundles.MetricBundle(metric, slicer, sqlconstraint, plotDict=plotDict,
displayDict=displayDict, runName=runName, metadata=metadata,
summaryMetrics=summaryStats)
mergedHistDict['fracAboveAirmass'].addBundle(bundle, plotDict=histMerge)
bundleList.append(bundle)
# SNe metrics from UK workshop.
peaks = {'uPeak': 25.9, 'gPeak': 23.6, 'rPeak': 22.6, 'iPeak': 22.7, 'zPeak': 22.7, 'yPeak': 22.8}
peakTime = 15.
transDuration = peakTime + 30. # Days
metric = metrics.TransientMetric(riseSlope=-2. / peakTime, declineSlope=1.4 / 30.0,
transDuration=transDuration, peakTime=peakTime,
surveyDuration=runLength,
metricName='SNDetection', **peaks)
caption = 'Fraction of z=0.5 type Ia SN that are detected at any point in their light curve in any filter'
displayDict = {'group': sngroup, 'subgroup': 'Detected', 'caption': caption}
sqlconstraint = ''
plotDict = {}
bundle = metricBundles.MetricBundle(metric, slicer, sqlconstraint, plotDict=plotDict,
displayDict=displayDict, runName=runName)
bundleList.append(bundle)
metric = metrics.TransientMetric(riseSlope=-2. / peakTime, declineSlope=1.4 / 30.0,
transDuration=transDuration, peakTime=peakTime,
surveyDuration=runLength,
nPrePeak=1, metricName='SNAlert', **peaks)
caption = 'Fraction of z=0.5 type Ia SN that are detected pre-peak in any filter'
displayDict = {'group': sngroup, 'subgroup': 'Detected on the rise', 'caption': caption}
plotDict = {}
bundle = metricBundles.MetricBundle(metric, slicer, sqlconstraint, plotDict=plotDict,
displayDict=displayDict, runName=runName)
bundleList.append(bundle)
metric = metrics.TransientMetric(riseSlope=-2. / peakTime, declineSlope=1.4 / 30.,
transDuration=transDuration, peakTime=peakTime,
surveyDuration=runLength, metricName='SNLots',
nFilters=3, nPrePeak=3, nPerLC=2, **peaks)
caption = 'Fraction of z=0.5 type Ia SN that are observed 6 times, 3 pre-peak, '
caption += '3 post-peak, with observations in 3 filters'
displayDict = {'group': sngroup, 'subgroup': 'Well observed', 'caption': caption}
sqlconstraint = 'filter="r" or filter="g" or filter="i" or filter="z" '
plotDict = {}
bundle = metricBundles.MetricBundle(metric, slicer, sqlconstraint, plotDict=plotDict,
displayDict=displayDict, runName=runName)
bundleList.append(bundle)
propIDOrderDict = {}
orderVal = 100
for propID in propids:
propIDOrderDict[propID] = orderVal
orderVal += 100
# Full range of dates:
metric = metrics.FullRangeMetric(col='observationStartMJD')
plotFuncs = [plots.HealpixSkyMap(), plots.HealpixHistogram()]
caption = 'Time span of survey.'
sqlconstraint = ''
plotDict = {}
displayDict = {'group': rangeGroup, 'caption': caption}
bundle = metricBundles.MetricBundle(metric, slicer, sqlconstraint, plotDict=plotDict,
displayDict=displayDict, runName=runName)
bundleList.append(bundle)
for f in filters:
for propid in propids:
displayDict = {'group': rangeGroup, 'subgroup': propids[propid], 'caption': caption,
'order': filtorder[f]}
md = '%s, %s' % (f, propids[propid])
sql = 'filter="%s" and proposalId=%i' % (f, propid)
bundle = metricBundles.MetricBundle(metric, slicer, sql, plotDict=plotDict,
metadata=md, plotFuncs=plotFuncs,
displayDict=displayDict, runName=runName)
bundleList.append(bundle)
# Alt az plots
slicer = slicers.HealpixSlicer(nside=64, latCol='zenithDistance', lonCol='azimuth', useCache=False)
metric = metrics.CountMetric('observationStartMJD', metricName='Nvisits as function of Alt/Az')
plotDict = {}
plotFuncs = [plots.LambertSkyMap()]
displayDict = {'group': altAzGroup, 'caption': 'Alt Az pointing distribution'}
for f in filters:
for propid in propids:
displayDict = {'group': altAzGroup, 'subgroup': propids[propid],
'caption': 'Alt Az pointing distribution',
'order': filtorder[f]}
md = '%s, %s' % (f, propids[propid])
sql = 'filter="%s" and proposalId=%i' % (f, propid)
bundle = metricBundles.MetricBundle(metric, slicer, sql, plotDict=plotDict,
plotFuncs=plotFuncs, metadata=md,
displayDict=displayDict, runName=runName)
bundleList.append(bundle)
sql = ''
md = 'all observations'
displayDict = {'group': altAzGroup, 'subgroup': 'All Observations',
'caption': 'Alt Az pointing distribution'}
bundle = metricBundles.MetricBundle(metric, slicer, sql, plotDict=plotDict,
plotFuncs=plotFuncs, metadata=md,
displayDict=displayDict, runName=runName)
bundleList.append(bundle)
# Median inter-night gap (each and all filters)
slicer = slicers.HealpixSlicer(nside=nside, lonCol=lonCol, latCol=latCol)
metric = metrics.InterNightGapsMetric(metricName='Median Inter-Night Gap')
sqls = ['filter = "%s"' % f for f in filters]
orders = [filtorder[f] for f in filters]
orders.append(0)
sqls.append('')
for sql, order in zip(sqls, orders):
displayDict = {'group': intergroup, 'subgroup': 'Median Gap', 'caption': 'Median gap between days',
'order': order}
bundle = metricBundles.MetricBundle(metric, slicer, sql, displayDict=displayDict, runName=runName)
bundleList.append(bundle)
# Max inter-night gap in r and all bands
dslicer = slicers.HealpixSlicer(nside=nside, lonCol='ditheredRA', latCol='ditheredDec')
metric = metrics.InterNightGapsMetric(metricName='Max Inter-Night Gap', reduceFunc=np.max)
plotDict = {'percentileClip': 95.}
for sql, order in zip(sqls, orders):
displayDict = {'group': intergroup, 'subgroup': 'Max Gap', 'caption': 'Max gap between nights',
'order': order}
bundle = metricBundles.MetricBundle(metric, dslicer, sql, displayDict=displayDict,
plotDict=plotDict, runName=runName)
bundleList.append(bundle)
# largest phase gap for periods
periods = [0.1, 1.0, 10., 100.]
sqls = {'u': 'filter = "u"', 'r': 'filter="r"',
'g,r,i,z': 'filter="g" or filter="r" or filter="i" or filter="z"',
'all': ''}
for sql in sqls:
for period in periods:
displayDict = {'group': phaseGroup,
'subgroup': 'period=%.2f days, filter=%s' % (period, sql),
'caption': 'Maximum phase gaps'}
metric = metrics.PhaseGapMetric(nPeriods=1, periodMin=period, periodMax=period,
metricName='PhaseGap, %.1f' % period)
bundle = metricBundles.MetricBundle(metric, slicer, sqls[sql],
displayDict=displayDict, runName=runName)
bundleList.append(bundle)
# NEO XY plots
slicer = slicers.UniSlicer()
metric = metrics.PassMetric(metricName='NEODistances')
stacker = stackers.NEODistStacker()
stacker2 = stackers.EclipticStacker()
for f in filters:
plotFunc = plots.NeoDistancePlotter(eclipMax=10., eclipMin=-10.)
caption = 'Observations within 10 degrees of the ecliptic. Distance an H=22 NEO would be detected'
displayDict = {'group': NEOGroup, 'subgroup': 'xy', 'order': filtorder[f],
'caption': caption}
plotDict = {}
sqlconstraint = 'filter = "%s"' % (f)
bundle = metricBundles.MetricBundle(metric, slicer,
sqlconstraint, displayDict=displayDict,
stackerList=[stacker, stacker2],
plotDict=plotDict,
plotFuncs=[plotFunc])
noSaveBundleList.append(bundle)
# Solar elongation
sqls = ['filter = "%s"' % f for f in filters]
orders = [filtorder[f] for f in filters]
sqls.append('')
orders.append(0)
for sql, order in zip(sqls, orders):
plotFuncs = [plots.HealpixSkyMap(), plots.HealpixHistogram()]
displayDict = {'group': NEOGroup, 'subgroup': 'Solar Elongation',
'caption': 'Median solar elongation in degrees', 'order': order}
metric = metrics.MedianMetric('solarElong')
slicer = slicers.HealpixSlicer(nside=nside, lonCol=lonCol, latCol=latCol)
bundle = metricBundles.MetricBundle(metric, slicer, sql, displayDict=displayDict,
plotFuncs=plotFuncs)
bundleList.append(bundle)
plotFuncs = [plots.HealpixSkyMap(), plots.HealpixHistogram()]
displayDict = {'group': NEOGroup, 'subgroup': 'Solar Elongation',
'caption': 'Minimum solar elongation in degrees', 'order': order}
metric = metrics.MinMetric('solarElong')
slicer = slicers.HealpixSlicer(nside=nside, lonCol=lonCol, latCol=latCol)
bundle = metricBundles.MetricBundle(metric, slicer, sql, displayDict=displayDict,
plotFuncs=plotFuncs)
bundleList.append(bundle)
return (metricBundles.makeBundlesDictFromList(bundleList), mergedHistDict,
metricBundles.makeBundlesDictFromList(noSaveBundleList))
def interNight(colmap=None,
runName='opsim',
nside=64,
extraSql=None,
extraMetadata=None):
"""Generate a set of statistics about the spacing between nights with observations.
Parameters
----------
colmap : dict or None, opt
A dictionary with a mapping of column names. Default will use OpsimV4 column names.
runName : str, opt
The name of the simulated survey. Default is "opsim".
nside : int, opt
Nside for the healpix slicer. Default 64.
extraSql : str or None, opt
Additional sql constraint to apply to all metrics.
extraMetadata : str or None, opt
Additional metadata to use for all outputs.
Returns
-------
metricBundleDict
"""
if colmap is None:
colmap = ColMapDict('opsimV4')
bundleList = []
metadata = extraMetadata
if extraSql is None:
extraSql = ''
if extraSql is not None and len(extraSql) > 0:
if metadata is None:
metadata = extraSql
filterlist = ('u', 'g', 'r', 'i', 'z', 'y', 'all')
colors = {
'u': 'cyan',
'g': 'g',
'r': 'y',
'i': 'r',
'z': 'm',
'y': 'b',
'all': 'k'
}
filterorder = {'u': 1, 'g': 2, 'r': 3, 'i': 4, 'z': 5, 'y': 6, 'all': 0}
displayDict = {
'group': 'InterNight',
'subgroup': 'Night gaps',
'caption': None,
'order': 0
}
bins = np.arange(1, 20.5, 1)
metric = metrics.NightgapsMetric(bins=bins,
nightCol=colmap['night'],
metricName='DeltaNight Histogram')
slicer = slicers.HealpixSlicer(nside=nside,
latCol=colmap['dec'],
lonCol=colmap['ra'],
latLonDeg=colmap['raDecDeg'])
plotDict = {'bins': bins, 'xlabel': 'dT (nights)'}
displayDict['caption'] = 'Histogram of the number of nights between consecutive visits to a ' \
'given point on the sky, considering separations between %d and %d.' \
% (bins.min(), bins.max())
plotFunc = plots.SummaryHistogram()
bundle = mb.MetricBundle(metric,
slicer,
extraSql,
plotDict=plotDict,
displayDict=displayDict,
metadata=metadata,
plotFuncs=[plotFunc])
bundleList.append(bundle)
standardStats = standardSummary()
subsetPlots = [plots.HealpixSkyMap(), plots.HealpixHistogram()]
# Median inter-night gap (each and all filters)
metric = metrics.InterNightGapsMetric(metricName='Median Inter-Night Gap',
reduceFunc=np.median)
slicer = slicers.HealpixSlicer(nside=nside,
latCol=colmap['dec'],
lonCol=colmap['ra'],
latLonDeg=colmap['raDecDeg'])
for f in filterlist:
if f is not 'all':
sql = '(%s) and filter = "%s"' % (extraSql, f)
md = '%s band' % f
else:
sql = extraSql
md = 'all bands'
if metadata is not None:
md += metadata
displayDict[
'caption'] = 'Median gap between nights with observations, %s.' % md
displayDict['order'] = filterorder[f]
plotDict = {'color': colors[f]}
bundle = mb.MetricBundle(metric,
slicer,
sql,
metadata=md,
displayDict=displayDict,
plotFuncs=subsetPlots,
plotDict=plotDict,
summaryMetrics=standardStats)
bundleList.append(bundle)
# Maximum inter-night gap (in each and all filters).
metric = metrics.InterNightGapsMetric(metricName='Max Inter-Night Gap',
reduceFunc=np.max)
slicer = slicers.HealpixSlicer(nside=nside,
latCol=colmap['dec'],
lonCol=colmap['ra'],
latLonDeg=colmap['raDecDeg'])
for f in filterlist:
if f is not 'all':
sql = '(%s) and filter = "%s"' % (extraSql, f)
md = '%s band' % f
else:
sql = extraSql
md = 'all bands'
if metadata is not None:
md += metadata
displayDict[
'caption'] = 'Maximum gap between nights with observations, %s.' % md
displayDict['order'] = filterorder[f] - 10
plotDict = {'color': colors[f], 'percentileClip': 95., 'binsize': 5}
bundle = mb.MetricBundle(metric,
slicer,
sql,
metadata=md,
displayDict=displayDict,
plotFuncs=subsetPlots,
plotDict=plotDict,
summaryMetrics=standardStats)
bundleList.append(bundle)
# Set the runName for all bundles and return the bundleDict.
for b in bundleList:
b.setRunName(runName)
return mb.makeBundlesDictFromList(bundleList)
def rapidRevisitBatch(colmap=None,
runName='opsim',
extraSql=None,
extraMetadata=None,
nside=64,
ditherStacker=None,
ditherkwargs=None):
"""Metrics for evaluating proper motion and parallax.
Parameters
----------
colmap : dict or None, opt
A dictionary with a mapping of column names. Default will use OpsimV4 column names.
runName : str, opt
The name of the simulated survey. Default is "opsim".
nside : int, opt
Nside for the healpix slicer. Default 64.
extraSql : str or None, opt
Additional sql constraint to apply to all metrics.
extraMetadata : str or None, opt
Additional metadata to apply to all results.
ditherStacker: str or lsst.sims.maf.stackers.BaseDitherStacker
Optional dither stacker to use to define ra/dec columns.
ditherkwargs: dict, opt
Optional dictionary of kwargs for the dither stacker.
Returns
-------
metricBundleDict
"""
if colmap is None:
colmap = ColMapDict('opsimV4')
bundleList = []
sql = ''
metadata = 'All visits'
# Add additional sql constraint (such as wfdWhere) and metadata, if provided.
if (extraSql is not None) and (len(extraSql) > 0):
sql = extraSql
if extraMetadata is None:
metadata = extraSql.replace('filter =', '').replace('filter=', '')
metadata = metadata.replace('"', '').replace("'", '')
if extraMetadata is not None:
metadata = extraMetadata
subgroup = metadata
raCol, decCol, degrees, ditherStacker, ditherMeta = radecCols(
ditherStacker, colmap, ditherkwargs)
# Don't want dither info in subgroup (too long), but do want it in bundle name.
metadata = combineMetadata(metadata, ditherMeta)
# Set up parallax metrics.
slicer = slicers.HealpixSlicer(nside=nside,
lonCol=raCol,
latCol=decCol,
latLonDeg=degrees)
subsetPlots = [plots.HealpixSkyMap(), plots.HealpixHistogram()]
displayDict = {
'group': 'Rapid Revisits',
'subgroup': subgroup,
'order': 0,
'caption': None
}
# Calculate the uniformity (KS test) of the quick revisits.
dTmin = 40.0 # seconds
dTmax = 30.0 # minutes
minNvisit = 100
pixArea = float(hp.nside2pixarea(nside, degrees=True))
scale = pixArea * hp.nside2npix(nside)
m1 = metrics.RapidRevisitUniformityMetric(
metricName='RapidRevisitUniformity',
mjdCol=colmap['mjd'],
dTmin=dTmin / 60.0 / 60.0 / 24.0,
dTmax=dTmax / 60.0 / 24.0,
minNvisits=minNvisit)
plotDict = {'xMin': 0, 'xMax': 1}
cutoff1 = 0.20
summaryStats = [
metrics.FracBelowMetric(cutoff=cutoff1,
scale=scale,
metricName='Area (sq deg)')
]
summaryStats.extend(standardSummary())
caption = 'Deviation from uniformity for short revisit timescales, between %s seconds and %s minutes, ' \
% (dTmin, dTmax)
caption += 'for pointings with at least %d visits in this time range. ' % (
minNvisit)
caption += 'Summary statistic "Area" indicates the area on the sky which has a '
caption += 'deviation from uniformity of < %.2f.' % (cutoff1)
displayDict['caption'] = caption
bundle = mb.MetricBundle(m1,
slicer,
sql,
plotDict=plotDict,
plotFuncs=subsetPlots,
stackerList=[ditherStacker],
metadata=metadata,
displayDict=displayDict,
summaryMetrics=summaryStats)
bundleList.append(bundle)
displayDict['order'] += 1
# Calculate the actual number of quick revisits.
dTmax = dTmax # time in minutes
m2 = metrics.NRevisitsMetric(dT=dTmax,
mjdCol=colmap['mjd'],
normed=False,
metricName='RapidRevisitN')
plotDict = {'xMin': 600, 'xMax': 1500, 'logScale': False}
cutoff2 = 800
summaryStats = [
metrics.FracAboveMetric(cutoff=cutoff2,
scale=scale,
metricName='Area (sq deg)')
]
summaryStats.extend(standardSummary())
caption = 'Number of consecutive visits with return times faster than %.1f minutes, ' % (
dTmax)
caption += 'in any filter, all proposals. '
caption += 'Summary statistic "Area" indicates the area on the sky which has more than '
caption += '%d revisits within this time window.' % (cutoff2)
displayDict['caption'] = caption
bundle = mb.MetricBundle(m2,
slicer,
sql,
plotDict=plotDict,
plotFuncs=subsetPlots,
stackerList=[ditherStacker],
metadata=metadata,
displayDict=displayDict,
summaryMetrics=summaryStats)
bundleList.append(bundle)
displayDict['order'] += 1
# Calculate whether a healpix gets enough rapid revisits in the right windows.
dTmin = 40.0 / 60.0 # (minutes) 40s minumum for rapid revisit range
dTpairs = 20.0 # minutes (time when pairs should start kicking in)
dTmax = 30.0 # 30 minute maximum for rapid revisit range
nOne = 82 # Number of revisits between 40s-30m required
nTwo = 28 # Number of revisits between 40s - tPairs required.
pixArea = float(hp.nside2pixarea(nside, degrees=True))
scale = pixArea * hp.nside2npix(nside)
m1 = metrics.RapidRevisitMetric(metricName='RapidRevisits',
mjdCol=colmap['mjd'],
dTmin=dTmin / 60.0 / 60.0 / 24.0,
dTpairs=dTpairs / 60.0 / 24.0,
dTmax=dTmax / 60.0 / 24.0,
minN1=nOne,
minN2=nTwo)
plotDict = {
'xMin': 0,
'xMax': 1,
'colorMin': 0,
'colorMax': 1,
'logScale': False
}
cutoff1 = 0.9
summaryStats = [
metrics.FracAboveMetric(cutoff=cutoff1,
scale=scale,
metricName='Area (sq deg)')
]
summaryStats.extend(standardSummary())
caption = 'Area that receives at least %d visits between %.3f and %.1f minutes, ' \
% (nOne, dTmin, dTmax)
caption += 'with at least %d of those visits falling between %.3f and %.1f minutes. ' \
% (nTwo, dTmin, dTpairs)
caption += 'Summary statistic "Area" indicates the area on the sky which meets this requirement.'
displayDict['caption'] = caption
bundle = mb.MetricBundle(m1,
slicer,
sql,
plotDict=plotDict,
plotFuncs=subsetPlots,
stackerList=[ditherStacker],
metadata=metadata,
displayDict=displayDict,
summaryMetrics=summaryStats)
bundleList.append(bundle)
displayDict['order'] += 1
# Set the runName for all bundles and return the bundleDict.
for b in bundleList:
b.setRunName(runName)
return mb.makeBundlesDictFromList(bundleList)
def metadataMaps(value,
colmap=None,
runName='opsim',
valueName=None,
groupName=None,
extraSql=None,
extraMetadata=None,
nside=64):
"""Calculate 25/50/75 percentile values on maps across sky for a single metadata value.
TODO: handle stackers which need configuration (degrees, in particular) more automatically.
Currently have a hack for HA & normairmass.
Parameters
----------
value : str
The column name for the quantity to evaluate. (column name in the database or created by a stacker).
colmap : dict or None, opt
A dictionary with a mapping of column names. Default will use OpsimV4 column names.
runName : str, opt
The name of the simulated survey. Default is "opsim".
valueName : str, opt
The name of the value to be reported in the resultsDb and added to the metric.
This is intended to help standardize metric comparison between sim versions.
value = name as it is in the database (seeingFwhmGeom, etc).
valueName = name to be recorded ('seeingGeom', etc.). Default is None, which will match 'value'.
groupName : str, opt
The group name for this quantity in the displayDict. Default is the same as 'valueName', capitalized.
extraSql : str, opt
Additional constraint to add to any sql constraints (e.g. 'propId=1' or 'fieldID=522').
Default None, for no additional constraints.
extraMetadata : str, opt
Additional metadata to add before any below (i.e. "WFD"). Default is None.
nside : int, opt
Nside value for healpix slicer. Default 64.
If "None" is passed, the healpixslicer-based metrics will be skipped.
Returns
-------
metricBundleDict
"""
if colmap is None:
colmap = ColMapDict('opsimV4')
bundleList = []
if valueName is None:
valueName = value
if groupName is None:
groupName = valueName.capitalize()
subgroup = extraMetadata
else:
groupName = groupName.capitalize()
subgroup = valueName.capitalize()
if subgroup is None:
subgroup = 'All visits'
displayDict = {'group': groupName, 'subgroup': subgroup}
raCol, decCol, degrees, ditherStacker, ditherMeta = radecCols(
None, colmap, None)
extraMetadata = combineMetadata(extraMetadata, ditherMeta)
# Set up basic all and per filter sql constraints.
filterlist, colors, orders, sqls, metadata = filterList(
all=True, extraSql=extraSql, extraMetadata=extraMetadata)
# Hack to make HA work, but really I need to account for any stackers/colmaps.
if value == 'HA':
stackerList = [
stackers.HourAngleStacker(lstCol=colmap['lst'],
raCol=raCol,
degrees=degrees)
]
elif value == 'normairmass':
stackerList = [stackers.NormAirmassStacker(degrees=degrees)]
else:
stackerList = None
# Make maps of 25/median/75 for all and per filter, per RA/Dec, with standard summary stats.
mList = []
mList.append(
metrics.PercentileMetric(value,
percentile=25,
metricName='25thPercentile %s' % (valueName)))
mList.append(
metrics.MedianMetric(value, metricName='Median %s' % (valueName)))
mList.append(
metrics.PercentileMetric(value,
percentile=75,
metricName='75thPercentile %s' % (valueName)))
slicer = slicers.HealpixSlicer(nside=nside,
latCol=decCol,
lonCol=raCol,
latLonDeg=degrees)
subsetPlots = [plots.HealpixSkyMap(), plots.HealpixHistogram()]
for f in filterlist:
for m in mList:
displayDict['caption'] = 'Map of %s' % m.name
if valueName != value:
displayDict['caption'] += ' (%s)' % value
displayDict['caption'] += ' for %s.' % metadata[f]
displayDict['order'] = orders[f]
bundle = mb.MetricBundle(m,
slicer,
sqls[f],
stackerList=stackerList,
metadata=metadata[f],
plotFuncs=subsetPlots,
displayDict=displayDict,
summaryMetrics=standardSummary())
bundleList.append(bundle)
# Set the runName for all bundles and return the bundleDict.
for b in bundleList:
b.setRunName(runName)
plotBundles = []
return mb.makeBundlesDictFromList(bundleList), plotBundles
def descWFDBatch(colmap=None, runName='opsim', nside=64,
bandpass='******', nfilters_needed=6, lim_ebv=0.2,
mag_cuts = {1: 24.75 - 0.1, 3: 25.35 - 0.1, 6: 25.72 - 0.1, 10: 26.0 - 0.1}):
# Hide some dependencies .. we should probably bring these into MAF
from mafContrib.lssmetrics.depthLimitedNumGalMetric import DepthLimitedNumGalMetric
from mafContrib import (Plasticc_metric, plasticc_slicer, load_plasticc_lc)
# The options to add additional sql constraints are removed for now.
if colmap is None:
colmap = ColMapDict('fbs')
# Calculate a subset of DESC WFD-related metrics.
displayDict = {'group': 'Cosmology'}
subgroupCount = 1
standardStats = standardSummary(withCount=False)
subsetPlots = [plots.HealpixSkyMap(), plots.HealpixHistogram()]
if not isinstance(mag_cuts, dict):
if isinstance(mag_cuts, float) or isinstance(mag_cuts, int):
mag_cuts = {10: mag_cuts}
else:
raise TypeError()
yrs = list(mag_cuts.keys())
maxYr = max(yrs)
# Load up the plastic light curves
models = ['SNIa-normal']
plasticc_models_dict = {}
for model in models:
plasticc_models_dict[model] = list(load_plasticc_lc(model=model).values())
# One of the primary concerns for DESC WFD metrics is to add dust extinction and coadded depth limits
# as well as to get some coverage in all 6 bandpasses.
# These cuts figure into many of the general metrics.
displayDict['subgroup'] = f'{subgroupCount}: Static Science'
## Static Science
# Calculate the static science metrics - effective survey area, mean/median coadded depth, stdev of
# coadded depth and the 3x2ptFoM emulator.
dustmap = maps.DustMap(nside=nside, interp=False)
pix_area = hp.nside2pixarea(nside, degrees=True)
summaryMetrics = [metrics.MeanMetric(), metrics.MedianMetric(), metrics.RmsMetric(),
metrics.CountRatioMetric(normVal=1/pix_area, metricName='Effective Area (deg)')]
bundleList = []
displayDict['order'] = 0
for yr_cut in yrs:
ptsrc_lim_mag_i_band = mag_cuts[yr_cut]
sqlconstraint = 'night <= %s' % (yr_cut * 365.25)
sqlconstraint += ' and note not like "DD%"'
metadata = f'{bandpass} band non-DD year {yr_cut}'
ThreebyTwoSummary = metrics.StaticProbesFoMEmulatorMetricSimple(nside=nside, year=yr_cut,
metricName='3x2ptFoM')
print(colmap['fiveSigmaDepth'], colmap['filter'])
m = metrics.ExgalM5_with_cuts(m5Col=colmap['fiveSigmaDepth'], filterCol=colmap['filter'],
lsstFilter=bandpass, nFilters=nfilters_needed,
extinction_cut=lim_ebv, depth_cut=ptsrc_lim_mag_i_band)
s = slicers.HealpixSlicer(nside=nside, useCache=False)
caption = f'Cosmology/Static Science metrics are based on evaluating the region of '
caption += f'the sky that meets the requirements (in year {yr_cut} of coverage in '
caption += f'all {nfilters_needed}, a lower E(B-V) value than {lim_ebv}, and at '
caption += f'least a coadded depth of {ptsrc_lim_mag_i_band} in {bandpass}. '
caption += f'From there the effective survey area, coadded depth, standard deviation of the depth, '
caption += f'and a 3x2pt static science figure of merit emulator are calculated using the '
caption += f'dust-extincted coadded depth map (over that reduced footprint).'
displayDict['caption'] = caption
bundle = mb.MetricBundle(m, s, sqlconstraint, mapsList=[dustmap], metadata=metadata,
summaryMetrics=summaryMetrics + [ThreebyTwoSummary],
displayDict=displayDict)
displayDict['order'] += 1
bundleList.append(bundle)
## LSS Science
# The only metric we have from LSS is the NGals metric - which is similar to the GalaxyCountsExtended
# metric, but evaluated only on the depth/dust cuts footprint.
subgroupCount += 1
displayDict['subgroup'] = f'{subgroupCount}: LSS'
displayDict['order'] = 0
plotDict = {'nTicks': 5}
# Have to include all filters in query, so that we check for all-band coverage.
# Galaxy numbers calculated using 'bandpass' images only though.
sqlconstraint = f'note not like "DD%"'
metadata = f'{bandpass} band galaxies non-DD'
metric = DepthLimitedNumGalMetric(m5Col=colmap['fiveSigmaDepth'], filterCol=colmap['filter'],
nside=nside, filterBand=bandpass, redshiftBin='all',
nfilters_needed=nfilters_needed,
lim_mag_i_ptsrc=mag_cuts[maxYr], lim_ebv=lim_ebv)
summary = [metrics.AreaSummaryMetric(area=18000, reduce_func=np.sum, decreasing=True,
metricName='N Galaxies (18k)')]
summary.append(metrics.SumMetric(metricName='N Galaxies (all)'))
slicer = slicers.HealpixSlicer(nside=nside, useCache=False)
bundle = mb.MetricBundle(metric, slicer, sqlconstraint, plotDict=plotDict,
metadata=metadata, mapsList=[dustmap],
displayDict=displayDict, summaryMetrics=summary,
plotFuncs=subsetPlots)
bundleList.append(bundle)
## WL metrics
# Calculates the number of visits per pointing, after removing parts of the footprint due to dust/depth
subgroupCount += 1
displayDict['subgroup'] = f'{subgroupCount}: WL'
displayDict['order'] = 0
sqlconstraint = f'note not like "DD%" and filter = "{bandpass}"'
metadata = f'{bandpass} band non-DD'
minExpTime = 15
m = metrics.WeakLensingNvisits(m5Col=colmap['fiveSigmaDepth'], expTimeCol=colmap['exptime'],
lsstFilter=bandpass, depthlim=mag_cuts[maxYr],
ebvlim=lim_ebv, min_expTime=minExpTime)
s = slicers.HealpixSlicer(nside=nside, useCache=False)
displayDict['caption'] = f'The number of visits per pointing, over the same reduced footprint as '
displayDict['caption'] += f'described above. A cutoff of {minExpTime} removes very short visits.'
displayDict['order'] = 1
bundle = mb.MetricBundle(m, s, sqlconstraint, mapsList=[dustmap], metadata=metadata,
summaryMetrics=standardStats, displayDict=displayDict)
bundleList.append(bundle)
# This probably will get replaced by @pgris's SN metrics?
subgroupCount += 1
displayDict['subgroup'] = f'{subgroupCount}: SNe Ia'
displayDict['order'] = 0
# XXX-- use the light curves from PLASTICC here
displayDict['caption'] = 'Fraction of normal SNe Ia (using PLaSTICCs)'
sqlconstraint = 'note not like "DD%"'
metadata = 'non-DD'
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, sqlconstraint,
metadata=metadata, summaryMetrics=summary_stats,
plotFuncs=plotFuncs, displayDict=displayDict)
bundleList.append(bundle)
subgroupCount += 1
displayDict['subgroup'] = f'{subgroupCount}: 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')
filterlist, colors, filterorders, filtersqls, filtermetadata = filterList(all=False,
extraSql=None,
extraMetadata=None)
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))
# Set the runName for all bundles and return the bundleDict.
for b in bundleList:
b.setRunName(runName)
return mb.makeBundlesDictFromList(bundleList)
def phaseGap(colmap=None,
runName='opsim',
nside=64,
extraSql=None,
extraMetadata=None,
ditherStacker=None,
ditherkwargs=None):
"""Generate a set of statistics about the pair/triplet/etc. rate within a night.
Parameters
----------
colmap : dict or None, opt
A dictionary with a mapping of column names. Default will use OpsimV4 column names.
runName : str, opt
The name of the simulated survey. Default is "opsim".
nside : int, opt
Nside for the healpix slicer. Default 64.
extraSql : str or None, opt
Additional sql constraint to apply to all metrics.
extraMetadata : str or None, opt
Additional metadata to apply to all results.
ditherStacker: str or lsst.sims.maf.stackers.BaseDitherStacker
Optional dither stacker to use to define ra/dec columns.
ditherkwargs: dict or None, opt
Optional dictionary of kwargs for the dither stacker.
Returns
-------
metricBundleDict
"""
if colmap is None:
colmap = ColMapDict('opsimV4')
metadata = extraMetadata
if extraSql is not None and len(extraSql) > 0:
if metadata is None:
metadata = extraSql
raCol, decCol, degrees, ditherStacker, ditherMeta = radecCols(
ditherStacker, colmap, ditherkwargs)
metadata = combineMetadata(metadata, ditherMeta)
bundleList = []
standardStats = standardSummary()
subsetPlots = [plots.HealpixSkyMap(), plots.HealpixHistogram()]
slicer = slicers.HealpixSlicer(nside=nside,
latCol=decCol,
lonCol=raCol,
latLonDeg=degrees)
# largest phase gap for periods
periods = [0.1, 1.0, 10., 100.]
sqls = [
'filter = "u"', 'filter="r"',
'filter="g" or filter="r" or filter="i" or filter="z"', ''
]
filterNames = ['u', 'r', 'griz', 'all']
metadatas = filterNames
if metadata is not None:
metadatas = [combineMetadata(m, metadata) for m in metadatas]
if extraSql is not None and len(extraSql) > 0:
for sql in sqls:
sqls[sql] = '(%s) and (%s)' % (sqls[sql], extraSql)
for sql, md, f in zip(sqls, metadatas, filterNames):
for period in periods:
displayDict = {
'group':
'PhaseGap',
'subgroup':
'Filter %s: Period %.2f days' % (f, period),
'caption':
'Maximum phase gap, given a period of %.2f days.' % period
}
metric = metrics.PhaseGapMetric(nPeriods=1,
periodMin=period,
periodMax=period,
nVisitsMin=5,
metricName='PhaseGap %.1f day' %
period)
metric.reduceFuncs = {metric.reduceFuncs['reduceLargestGap']}
metric.reduceOrder = {0}
bundle = mb.MetricBundle(metric,
slicer,
constraint=sql,
metadata=md,
displayDict=displayDict,
summaryMetrics=standardStats,
plotFuncs=subsetPlots)
bundleList.append(bundle)
# Set the runName for all bundles and return the bundleDict.
for b in bundleList:
b.setRunName(runName)
plotBundles = None
return mb.makeBundlesDictFromList(bundleList), plotBundles
def seasons(colmap=None,
runName='opsim',
nside=64,
extraSql=None,
extraMetadata=None,
ditherStacker=None,
ditherkwargs=None):
"""Generate a set of statistics about the length and number of seasons.
Parameters
----------
colmap : dict or None, opt
A dictionary with a mapping of column names. Default will use OpsimV4 column names.
runName : str, opt
The name of the simulated survey. Default is "opsim".
nside : int, opt
Nside for the healpix slicer. Default 64.
extraSql : str or None, opt
Additional sql constraint to apply to all metrics.
extraMetadata : str or None, opt
Additional metadata to use for all outputs.
ditherStacker: str or lsst.sims.maf.stackers.BaseDitherStacker
Optional dither stacker to use to define ra/dec columns.
ditherkwargs: dict, opt
Optional dictionary of kwargs for the dither stacker.
Returns
-------
metricBundleDict
"""
if colmap is None:
colmap = ColMapDict('opsimV4')
bundleList = []
# Set up basic all and per filter sql constraints.
raCol, decCol, degrees, ditherStacker, ditherMeta = radecCols(
ditherStacker, colmap, ditherkwargs)
metadata = combineMetadata(extraMetadata, ditherMeta)
filterlist, colors, orders, sqls, metadata = filterList(
all=True, extraSql=extraSql, extraMetadata=metadata)
seasonStacker = stackers.SeasonStacker(mjdCol=colmap['mjd'],
RACol=raCol,
degrees=degrees)
stackerList = [seasonStacker]
if ditherStacker is not None:
stackerList.append(ditherStacker)
slicer = slicers.HealpixSlicer(nside=nside,
latCol=decCol,
lonCol=raCol,
latLonDeg=degrees)
displayDict = {
'group': 'IntraSeason',
'subgroup': 'Season length',
'caption': None,
'order': 0
}
# Histogram of the length of seasons.
"""
bins = np.arange(1, 20.5, 1)
metric = metrics.NightgapsMetric(bins=bins, nightCol=colmap['night'], metricName='DeltaNight Histogram')
plotDict = {'bins': bins, 'xlabel': 'dT (nights)'}
displayDict['caption'] = 'Histogram of the number of nights between consecutive visits to a ' \
'given point on the sky, considering separations between %d and %d' \
% (bins.min(), bins.max())
if metadata['all'] is None or len(metadata['all']) == 0:
displayDict['caption'] += ', all proposals.'
else:
displayDict['caption'] += ', %s.' % metadata['all']
plotFunc = plots.SummaryHistogram()
bundle = mb.MetricBundle(metric, slicer, sqls['all'], plotDict=plotDict,
displayDict=displayDict, metadata=metadata['all'], plotFuncs=[plotFunc])
bundleList.append(bundle)
"""
standardStats = standardSummary()
subsetPlots = [plots.HealpixSkyMap(), plots.HealpixHistogram()]
# Median inter-night gap (each and all filters)
metric = metrics.SeasonLengthMetric(metricName='Median Season Length',
mjdCol=colmap['mjd'],
seasonCol='season',
reduceFunc=np.median)
for f in filterlist:
displayDict['caption'] = 'Median season length, %s.' % metadata[f]
displayDict['order'] = orders[f]
maxS = 250
if f == 'all':
minS = 90
else:
minS = 30
plotDict = {
'color': colors[f],
'colorMin': minS,
'colorMax': maxS,
'xMin': minS,
'xMax': maxS
}
bundle = mb.MetricBundle(metric,
slicer,
sqls[f],
metadata=metadata[f],
stackerList=stackerList,
displayDict=displayDict,
plotFuncs=subsetPlots,
plotDict=plotDict,
summaryMetrics=standardStats)
bundleList.append(bundle)
# Number of seasons?
# Set the runName for all bundles and return the bundleDict.
for b in bundleList:
b.setRunName(runName)
plotBundles = None
return mb.makeBundlesDictFromList(bundleList), plotBundles
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
maps,mjds = make_maps.event_sample(nevents=50)
#sum_maps = sum(maps[i] for i in range(len(maps)))
#plt.clf()
#hp.mollview(maps[0])
#plt.savefig(outdir+'LIGO_test_map.png')
#plt.clf()
#hp.mollview(sum_maps)
#plt.savefig(outdir+'LIGO_MAPS_sum.png')
# evaluate opsim realization of LSST survey
opsdb = db.OpsimDatabase(dirdb+file)
resultsDb = db.ResultsDb(outDir=outdir)
print "Starting to compute AreaProb..."
metric = metrics.AreaProb()
metric.setEvents(maps,mjds)
print "Calling slicer..."
##slicer = slicers.HealpixSlicer(nside=4)
slicer = slicers.UniSlicer()
sql0 = 'filter = "i" and night < 365'
cmap = plt.get_cmap('winter')
plotDict0={'title': 'u-band. error: 0.020, z: 2.1. 1 year. opsim: kraken_1042.', 'logScale': True, 'xlabel': 'Bayes Factor','cmap': cmap,'percentileClip': None, 'colorMin':1.0, 'colorMax':100.0, 'cbarFormat':'%.1g','cbar_edge': True, 'nTicks': 10, 'aspect': 'auto','xextent': None, 'origin': None}
HealpixSkyMap = plots.HealpixSkyMap()
HealpixHistogram = plots.HealpixHistogram()
mb0 = metricBundles.MetricBundle(metric, slicer, sql0, plotDict=plotDict0, plotFuncs=[HealpixSkyMap, HealpixHistogram])
mbD = {0:mb0}
bgroup = metricBundles.MetricBundleGroup(mbD, opsdb, outDir=outdir, resultsDb=resultsDb)
bgroup.runAll()
def metadataBasics(value,
colmap=None,
runName='opsim',
valueName=None,
groupName=None,
extraSql=None,
extraMetadata=None,
nside=64,
filterlist=('u', 'g', 'r', 'i', 'z', 'y')):
"""Calculate basic metrics on visit metadata 'value' (e.g. airmass, normalized airmass, seeing..).
Calculates extended standard metrics (with unislicer) on the quantity (all visits and per filter),
makes histogram of the value (all visits and per filter),
Parameters
----------
value : str
The column name for the quantity to evaluate. (column name in the database or created by a stacker).
colmap : dict or None, opt
A dictionary with a mapping of column names. Default will use OpsimV4 column names.
runName : str, opt
The name of the simulated survey. Default is "opsim".
valueName : str, opt
The name of the value to be reported in the resultsDb and added to the metric.
This is intended to help standardize metric comparison between sim versions.
value = name as it is in the database (seeingFwhmGeom, etc).
valueName = name to be recorded ('seeingGeom', etc.). Default is None, which is set to match value.
groupName : str, opt
The group name for this quantity in the displayDict. Default is the same as 'value', capitalized.
extraSql : str, opt
Additional constraint to add to any sql constraints (e.g. 'propId=1' or 'fieldID=522').
Default None, for no additional constraints.
extraMetadata : str, opt
Additional metadata to add before any below (i.e. "WFD"). Default is None.
nside : int, opt
Nside value for healpix slicer. Default 64.
If "None" is passed, the healpixslicer-based metrics will be skipped.
filterlist : list of str, opt
List of the filternames to use for "per filter" evaluation. Default ('u', 'g', 'r', 'i', 'z', 'y').
If None is passed, the per-filter evaluations will be skipped.
Returns
-------
metricBundleDict
"""
if colmap is None:
colmap = ColMapDict('opsimV4')
bundleList = []
if valueName is None:
valueName = value
if groupName is None:
groupName = valueName.capitalize()
subgroup = extraMetadata
else:
groupName = groupName.capitalize()
subgroup = valueName.capitalize()
displayDict = {'group': groupName, 'subgroup': subgroup}
sqlconstraints = ['']
metadata = ['all bands']
if filterlist is not None:
sqlconstraints += [
'%s = "%s"' % (colmap['filter'], f) for f in filterlist
]
metadata += ['%s band' % f for f in filterlist]
if (extraSql is not None) and (len(extraSql) > 0):
tmp = []
for s in sqlconstraints:
if len(s) == 0:
tmp.append(extraSql)
else:
tmp.append('%s and (%s)' % (s, extraSql))
sqlconstraints = tmp
if extraMetadata is None:
metadata = ['%s %s' % (extraSql, m) for m in metadata]
if extraMetadata is not None:
metadata = ['%s %s' % (extraMetadata, m) for m in metadata]
# Summarize values over all and per filter (min/mean/median/max/percentiles/outliers/rms).
slicer = slicers.UniSlicer()
displayDict['caption'] = None
for sql, meta in zip(sqlconstraints, metadata):
displayDict['order'] = -1
for m in extendedMetrics(value, replace_colname=valueName):
displayDict['order'] += 1
bundle = mb.MetricBundle(m,
slicer,
sql,
metadata=meta,
displayDict=displayDict)
bundleList.append(bundle)
# Histogram values over all and per filter.
for sql, meta in zip(sqlconstraints, metadata):
displayDict['caption'] = 'Histogram of %s' % (value)
if valueName != value:
displayDict['caption'] += ' (%s)' % (valueName)
displayDict['caption'] += ' for %s visits.' % (meta)
displayDict['order'] += 1
m = metrics.CountMetric(value, metricName='%s Histogram' % (valueName))
slicer = slicers.OneDSlicer(sliceColName=value)
bundle = mb.MetricBundle(m,
slicer,
sql,
metadata=meta,
displayDict=displayDict)
bundleList.append(bundle)
# Make maps of min/median/max for all and per filter, per RA/Dec, with standard summary stats.
mList = []
mList.append(metrics.MinMetric(value, metricName='Min %s' % (valueName)))
mList.append(
metrics.MedianMetric(value, metricName='Median %s' % (valueName)))
mList.append(metrics.MaxMetric(value, metricName='Max %s' % (valueName)))
slicer = slicers.HealpixSlicer(nside=nside,
latCol=colmap['dec'],
lonCol=colmap['ra'],
latLonDeg=colmap['raDecDeg'])
subsetPlots = [plots.HealpixSkyMap(), plots.HealpixHistogram()]
displayDict['caption'] = None
displayDict['order'] = -1
for sql, meta in zip(sqlconstraints, metadata):
for m in mList:
displayDict['order'] += 1
bundle = mb.MetricBundle(m,
slicer,
sql,
metadata=meta,
plotFuncs=subsetPlots,
displayDict=displayDict,
summaryMetrics=standardSummary())
bundleList.append(bundle)
# Set the runName for all bundles and return the bundleDict.
for b in bundleList:
b.setRunName(runName)
return mb.makeBundlesDictFromList(bundleList)
def glanceBatch(colmap=None,
runName='opsim',
nside=64,
filternames=('u', 'g', 'r', 'i', 'z', 'y'),
nyears=10,
pairnside=32,
sqlConstraint=None):
"""Generate a handy set of metrics that give a quick overview of how well a survey performed.
This is a meta-set of other batches, to some extent.
Parameters
----------
colmap : dict, opt
A dictionary with a mapping of column names. Default will use OpsimV4 column names.
run_name : str, opt
The name of the simulated survey. Default is "opsim".
nside : int, opt
The nside for the healpix slicers. Default 64.
filternames : list of str, opt
The list of individual filters to use when running metrics.
Default is ('u', 'g', 'r', 'i', 'z', 'y').
There is always an all-visits version of the metrics run as well.
nyears : int (10)
How many years to attempt to make hourglass plots for
pairnside : int (32)
nside to use for the pair fraction metric (it's slow, so nice to use lower resolution)
sqlConstraint : str or None, opt
Additional SQL constraint to apply to all metrics.
Returns
-------
metricBundleDict
"""
if isinstance(colmap, str):
raise ValueError('colmap must be a dictionary, not a string')
if colmap is None:
colmap = ColMapDict('opsimV4')
bundleList = []
if sqlConstraint is None:
sqlC = ''
else:
sqlC = '(%s) and' % sqlConstraint
sql_per_filt = [
'%s %s="%s"' % (sqlC, colmap['filter'], filtername)
for filtername in filternames
]
sql_per_and_all_filters = [sqlConstraint] + sql_per_filt
standardStats = standardSummary()
subsetPlots = [plots.HealpixSkyMap(), plots.HealpixHistogram()]
# Super basic things
displayDict = {'group': 'Basic Stats', 'order': 1}
sql = sqlConstraint
slicer = slicers.UniSlicer()
# Length of Survey
metric = metrics.FullRangeMetric(col=colmap['mjd'],
metricName='Length of Survey (days)')
bundle = metricBundles.MetricBundle(metric,
slicer,
sql,
displayDict=displayDict)
bundleList.append(bundle)
# Total number of filter changes
metric = metrics.NChangesMetric(col=colmap['filter'],
orderBy=colmap['mjd'])
bundle = metricBundles.MetricBundle(metric,
slicer,
sql,
displayDict=displayDict)
bundleList.append(bundle)
# Total open shutter fraction
metric = metrics.OpenShutterFractionMetric(
slewTimeCol=colmap['slewtime'],
expTimeCol=colmap['exptime'],
visitTimeCol=colmap['visittime'])
bundle = metricBundles.MetricBundle(metric,
slicer,
sql,
displayDict=displayDict)
bundleList.append(bundle)
# Total effective exposure time
metric = metrics.TeffMetric(m5Col=colmap['fiveSigmaDepth'],
filterCol=colmap['filter'],
normed=True)
for sql in sql_per_and_all_filters:
bundle = metricBundles.MetricBundle(metric,
slicer,
sql,
displayDict=displayDict)
bundleList.append(bundle)
# Number of observations, all and each filter
metric = metrics.CountMetric(col=colmap['mjd'],
metricName='Number of Exposures')
for sql in sql_per_and_all_filters:
bundle = metricBundles.MetricBundle(metric,
slicer,
sql,
displayDict=displayDict)
bundleList.append(bundle)
# The alt/az plots of all the pointings
slicer = slicers.HealpixSlicer(nside=nside,
latCol='zenithDistance',
lonCol=colmap['az'],
latLonDeg=colmap['raDecDeg'],
useCache=False)
stacker = stackers.ZenithDistStacker(altCol=colmap['alt'],
degrees=colmap['raDecDeg'])
metric = metrics.CountMetric(colmap['mjd'],
metricName='Nvisits as function of Alt/Az')
plotFuncs = [plots.LambertSkyMap()]
for sql in sql_per_and_all_filters:
bundle = metricBundles.MetricBundle(metric,
slicer,
sql,
plotFuncs=plotFuncs,
displayDict=displayDict,
stackerList=[stacker])
bundleList.append(bundle)
# Things to check per night
# Open Shutter per night
displayDict = {'group': 'Pointing Efficency', 'order': 2}
slicer = slicers.OneDSlicer(sliceColName=colmap['night'], binsize=1)
metric = metrics.OpenShutterFractionMetric(
slewTimeCol=colmap['slewtime'],
expTimeCol=colmap['exptime'],
visitTimeCol=colmap['visittime'])
sql = sqlConstraint
bundle = metricBundles.MetricBundle(metric,
slicer,
sql,
summaryMetrics=standardStats,
displayDict=displayDict)
bundleList.append(bundle)
# Number of filter changes per night
slicer = slicers.OneDSlicer(sliceColName=colmap['night'], binsize=1)
metric = metrics.NChangesMetric(col=colmap['filter'],
orderBy=colmap['mjd'],
metricName='Filter Changes')
bundle = metricBundles.MetricBundle(metric,
slicer,
sql,
summaryMetrics=standardStats,
displayDict=displayDict)
bundleList.append(bundle)
# A few basic maps
# Number of observations, coadded depths
displayDict = {'group': 'Basic Maps', 'order': 3}
slicer = slicers.HealpixSlicer(nside=nside,
latCol=colmap['dec'],
lonCol=colmap['ra'],
latLonDeg=colmap['raDecDeg'])
metric = metrics.CountMetric(col=colmap['mjd'])
plotDict = {'percentileClip': 95.}
for sql in sql_per_and_all_filters:
bundle = metricBundles.MetricBundle(metric,
slicer,
sql,
summaryMetrics=standardStats,
displayDict=displayDict,
plotDict=plotDict)
bundleList.append(bundle)
metric = metrics.Coaddm5Metric(m5Col=colmap['fiveSigmaDepth'])
for sql in sql_per_and_all_filters:
bundle = metricBundles.MetricBundle(metric,
slicer,
sql,
summaryMetrics=standardStats,
displayDict=displayDict)
bundleList.append(bundle)
# Checking a few basic science things
# Maybe check astrometry, observation pairs, SN
plotDict = {'percentileClip': 95.}
displayDict = {'group': 'Science', 'subgroup': 'Astrometry', 'order': 4}
stackerList = []
stacker = stackers.ParallaxFactorStacker(raCol=colmap['ra'],
decCol=colmap['dec'],
degrees=colmap['raDecDeg'],
dateCol=colmap['mjd'])
stackerList.append(stacker)
# Maybe parallax and proper motion, fraction of visits in a good pair for SS
displayDict['caption'] = r'Parallax precision of an $r=20$ flat SED star'
metric = metrics.ParallaxMetric(m5Col=colmap['fiveSigmaDepth'],
filterCol=colmap['filter'],
seeingCol=colmap['seeingGeom'])
sql = sqlConstraint
bundle = metricBundles.MetricBundle(metric,
slicer,
sql,
plotFuncs=subsetPlots,
displayDict=displayDict,
stackerList=stackerList,
plotDict=plotDict)
bundleList.append(bundle)
displayDict[
'caption'] = r'Proper motion precision of an $r=20$ flat SED star'
metric = metrics.ProperMotionMetric(m5Col=colmap['fiveSigmaDepth'],
mjdCol=colmap['mjd'],
filterCol=colmap['filter'],
seeingCol=colmap['seeingGeom'])
bundle = metricBundles.MetricBundle(metric,
slicer,
sql,
plotFuncs=subsetPlots,
displayDict=displayDict,
plotDict=plotDict)
bundleList.append(bundle)
# Solar system stuff
displayDict['caption'] = 'Fraction of observations that are in pairs'
displayDict['subgroup'] = 'Solar System'
sql = '%s (filter="g" or filter="r" or filter="i")' % sqlC
pairSlicer = slicers.HealpixSlicer(nside=pairnside,
latCol=colmap['dec'],
lonCol=colmap['ra'],
latLonDeg=colmap['raDecDeg'])
metric = metrics.PairFractionMetric(mjdCol=colmap['mjd'])
bundle = metricBundles.MetricBundle(metric,
pairSlicer,
sql,
plotFuncs=subsetPlots,
displayDict=displayDict)
bundleList.append(bundle)
# stats from the note column
if 'note' in colmap.keys():
displayDict = {'group': 'Basic Stats', 'subgroup': 'Percent stats'}
metric = metrics.StringCountMetric(col=colmap['note'],
percent=True,
metricName='Percents')
sql = ''
slicer = slicers.UniSlicer()
bundle = metricBundles.MetricBundle(metric,
slicer,
sql,
displayDict=displayDict)
bundleList.append(bundle)
displayDict['subgroup'] = 'Count Stats'
metric = metrics.StringCountMetric(col=colmap['note'],
metricName='Counts')
bundle = metricBundles.MetricBundle(metric,
slicer,
sql,
displayDict=displayDict)
bundleList.append(bundle)
for b in bundleList:
b.setRunName(runName)
# Add hourglass plots.
hrDict = hourglassBatch(colmap=colmap,
runName=runName,
nyears=nyears,
extraSql=sqlConstraint)
# Add basic slew stats.
try:
slewDict = slewBasics(colmap=colmap, runName=runName)
except KeyError as e:
warnings.warn(
'Could not add slew stats: missing required key %s from colmap' %
(e))
bd = metricBundles.makeBundlesDictFromList(bundleList)
bd.update(slewDict)
bd.update(hrDict)
return bd
def intraNight(colmap=None, runName='opsim', nside=64, extraSql=None, extraMetadata=None,
slicer=None, display_group='IntraNight', subgroup='Pairs'):
"""Generate a set of statistics about the pair/triplet/etc. rate within a night.
Parameters
----------
colmap : dict or None, opt
A dictionary with a mapping of column names. Default will use OpsimV4 column names.
runName : str, opt
The name of the simulated survey. Default is "opsim".
nside : int, opt
Nside for the healpix slicer. Default 64.
extraSql : str or None, opt
Additional sql constraint to apply to all metrics.
extraMetadata : str or None, opt
Additional metadata to apply to all results.
slicer : slicer object (None)
Optinally use something other than a HealpixSlicer
Returns
-------
metricBundleDict
"""
if colmap is None:
colmap = ColMapDict('opsimV4')
metadata = extraMetadata
if extraSql is not None and len(extraSql) > 0:
if metadata is None:
metadata = extraSql
raCol, decCol, degrees, ditherStacker, ditherMeta = radecCols(None, colmap, None)
metadata = combineMetadata(metadata, ditherMeta)
bundleList = []
standardStats = standardSummary()
subsetPlots = [plots.HealpixSkyMap(), plots.HealpixHistogram()]
if slicer is None:
slicer = slicers.HealpixSlicer(nside=nside, latCol=decCol, lonCol=raCol, latLonDeg=degrees)
# Look for the fraction of visits in gri where there are pairs within dtMin/dtMax.
displayDict = {'group': display_group, 'subgroup': subgroup, 'caption': None, 'order': 0}
if extraSql is not None and len(extraSql) > 0:
sql = '(%s) and (filter="g" or filter="r" or filter="i")' % extraSql
else:
sql = 'filter="g" or filter="r" or filter="i"'
md = 'gri'
if metadata is not None:
md += ' ' + metadata
dtMin = 10.0
dtMax = 60.0
metric = metrics.PairFractionMetric(mjdCol=colmap['mjd'], minGap=dtMin, maxGap=dtMax,
metricName='Fraction of visits in pairs (%.0f-%.0f min)' % (dtMin,
dtMax))
displayDict['caption'] = 'Fraction of %s visits that have a paired visit' \
'between %.1f and %.1f minutes away. ' % (md, dtMin, dtMax)
displayDict['caption'] += 'If all visits were in pairs, this fraction would be 1.'
displayDict['order'] += 1
bundle = mb.MetricBundle(metric, slicer, sql, metadata=md, summaryMetrics=standardStats,
plotFuncs=subsetPlots, displayDict=displayDict)
bundleList.append(bundle)
dtMin = 20.0
dtMax = 90.0
metric = metrics.PairFractionMetric(mjdCol=colmap['mjd'], minGap=dtMin, maxGap=dtMax,
metricName='Fraction of visits in pairs (%.0f-%.0f min)' % (dtMin,
dtMax))
displayDict['caption'] = 'Fraction of %s visits that have a paired visit' \
'between %.1f and %.1f minutes away. ' % (md, dtMin, dtMax)
displayDict['caption'] += 'If all visits were in pairs, this fraction would be 1.'
displayDict['order'] += 1
bundle = mb.MetricBundle(metric, slicer, sql, metadata=md, summaryMetrics=standardStats,
plotFuncs=subsetPlots, displayDict=displayDict)
bundleList.append(bundle)
# Look at the fraction of visits which have another visit within dtMax, gri.
dtMax = 60.0
metric = metrics.NRevisitsMetric(mjdCol=colmap['mjd'], dT=dtMax, normed=True,
metricName='Fraction of visits with a revisit < %.0f min' % dtMax)
displayDict['caption'] = 'Fraction of %s visits that have another visit ' \
'within %.1f min. ' % (md, dtMax)
displayDict['caption'] += 'If all visits were in pairs (only), this fraction would be 0.5.'
displayDict['order'] += 1
bundle = mb.MetricBundle(metric, slicer, sql, metadata=md, summaryMetrics=standardStats,
plotFuncs=subsetPlots, displayDict=displayDict)
bundleList.append(bundle)
# Intranight gap map, all filters. Returns value in hours.
metric = metrics.IntraNightGapsMetric(metricName='Median Intra-Night Gap', mjdCol=colmap['mjd'],
reduceFunc=np.median)
displayDict['caption'] = 'Median gap between consecutive visits within a night, all bands'
if metadata is None or len(metadata) == 0:
displayDict['caption'] += ', all proposals.'
else:
displayDict['caption'] += ', %s.' % metadata
displayDict['order'] += 1
plotDict = {'percentileClip': 95}
bundle = mb.MetricBundle(metric, slicer, extraSql, metadata=metadata, displayDict=displayDict,
plotFuncs=subsetPlots, plotDict=plotDict,
summaryMetrics=standardStats)
bundleList.append(bundle)
# Histogram the number of visits per night.
countbins = np.arange(0, 10, 1)
metric = metrics.NVisitsPerNightMetric(nightCol=colmap['night'], bins=countbins,
metricName="NVisitsPerNight")
plotDict = {'bins': countbins, 'xlabel': 'Number of visits each night'}
displayDict['caption'] = 'Histogram of the number of visits in each night, per point on the sky'
if metadata is None or len(metadata) == 0:
displayDict['caption'] += ', all proposals.'
else:
displayDict['caption'] += ', %s.' % metadata
displayDict['order'] = 0
plotFunc = plots.SummaryHistogram()
bundle = mb.MetricBundle(metric, slicer, extraSql, plotDict=plotDict,
displayDict=displayDict, metadata=metadata, plotFuncs=[plotFunc])
bundleList.append(bundle)
# Histogram of the time between revisits (all filters) within two hours.
binMin = 0
binMax = 120.
binsize = 5.
bins_metric = np.arange(binMin / 60.0 / 24.0, (binMax + binsize) / 60. / 24., binsize / 60. / 24.)
bins_plot = bins_metric * 24.0 * 60.0
metric = metrics.TgapsMetric(bins=bins_metric, timesCol=colmap['mjd'], metricName='DeltaT Histogram')
plotDict = {'bins': bins_plot, 'xlabel': 'dT (minutes)'}
displayDict['caption'] = 'Histogram of the time between consecutive visits to a given point ' \
'on the sky, considering visits between %.1f and %.1f minutes' % (binMin,
binMax)
if metadata is None or len(metadata) == 0:
displayDict['caption'] += ', all proposals.'
else:
displayDict['caption'] += ', %s.' % metadata
displayDict['order'] += 1
plotFunc = plots.SummaryHistogram()
bundle = mb.MetricBundle(metric, slicer, extraSql, plotDict=plotDict,
displayDict=displayDict, metadata=metadata, plotFuncs=[plotFunc])
bundleList.append(bundle)
# Set the runName for all bundles and return the bundleDict.
for b in bundleList:
b.setRunName(runName)
plotBundles = None
return mb.makeBundlesDictFromList(bundleList), plotBundles
def metadataBasicsAngle(value,
colmap=None,
runName='opsim',
valueName=None,
groupName=None,
extraSql=None,
extraMetadata=None,
nside=64,
ditherStacker=None,
ditherkwargs=None):
"""Calculate basic metrics on visit metadata 'value', where value is a wrap-around angle.
Calculates extended standard metrics (with unislicer) on the quantity (all visits and per filter),
makes histogram of the value (all visits and per filter),
Parameters
----------
value : str
The column name for the quantity to evaluate. (column name in the database or created by a stacker).
colmap : dict or None, opt
A dictionary with a mapping of column names. Default will use OpsimV4 column names.
runName : str, opt
The name of the simulated survey. Default is "opsim".
valueName : str, opt
The name of the value to be reported in the resultsDb and added to the metric.
This is intended to help standardize metric comparison between sim versions.
value = name as it is in the database (seeingFwhmGeom, etc).
valueName = name to be recorded ('seeingGeom', etc.). Default is None, which will match 'value'.
groupName : str, opt
The group name for this quantity in the displayDict. Default is the same as 'valueName', capitalized.
extraSql : str, opt
Additional constraint to add to any sql constraints (e.g. 'propId=1' or 'fieldID=522').
Default None, for no additional constraints.
extraMetadata : str, opt
Additional metadata to add before any below (i.e. "WFD"). Default is None.
nside : int, opt
Nside value for healpix slicer. Default 64.
If "None" is passed, the healpixslicer-based metrics will be skipped.
ditherStacker: str or lsst.sims.maf.stackers.BaseDitherStacker
Optional dither stacker to use to define ra/dec columns.
ditherkwargs: dict, opt
Optional dictionary of kwargs for the dither stacker.
Returns
-------
metricBundleDict
"""
if colmap is None:
colmap = ColMapDict('opsimV4')
bundleList = []
if valueName is None:
valueName = value
if groupName is None:
groupName = valueName.capitalize()
subgroup = extraMetadata
else:
groupName = groupName.capitalize()
subgroup = valueName.capitalize()
if subgroup is None:
subgroup = 'All visits'
displayDict = {'group': groupName, 'subgroup': subgroup}
raCol, decCol, degrees, ditherStacker, ditherMeta = radecCols(
ditherStacker, colmap, ditherkwargs)
extraMetadata = combineMetadata(extraMetadata, ditherMeta)
# Set up basic all and per filter sql constraints.
filterlist, colors, orders, sqls, metadata = filterList(
all=True, extraSql=extraSql, extraMetadata=extraMetadata)
stackerList = [ditherStacker]
# Summarize values over all and per filter.
slicer = slicers.UniSlicer()
for f in filterlist:
for m in standardAngleMetrics(value, replace_colname=valueName):
displayDict['caption'] = '%s for %s.' % (m.name, metadata[f])
displayDict['order'] = orders[f]
bundle = mb.MetricBundle(m,
slicer,
sqls[f],
stackerList=stackerList,
metadata=metadata[f],
displayDict=displayDict)
bundleList.append(bundle)
# Histogram values over all and per filter.
for f in filterlist:
displayDict['caption'] = 'Histogram of %s' % (value)
if valueName != value:
displayDict['caption'] += ' (%s)' % (valueName)
displayDict['caption'] += ' for %s.' % (metadata[f])
displayDict['order'] = orders[f]
m = metrics.CountMetric(value, metricName='%s Histogram' % (valueName))
slicer = slicers.OneDSlicer(sliceColName=value)
bundle = mb.MetricBundle(m,
slicer,
sqls[f],
stackerList=stackerList,
metadata=metadata[f],
displayDict=displayDict)
bundleList.append(bundle)
# Make maps of min/median/max for all and per filter, per RA/Dec, with standard summary stats.
mList = []
mList.append(
metrics.MeanAngleMetric(value,
metricName='AngleMean %s' % (valueName)))
mList.append(
metrics.FullRangeAngleMetric(value,
metricName='AngleRange %s' % (valueName)))
mList.append(
metrics.RmsAngleMetric(value, metricName='AngleRms %s' % (valueName)))
slicer = slicers.HealpixSlicer(nside=nside,
latCol=decCol,
lonCol=raCol,
latLonDeg=degrees)
subsetPlots = [plots.HealpixSkyMap(), plots.HealpixHistogram()]
for f in filterlist:
for m in mList:
displayDict['caption'] = 'Map of %s' % m.name
if valueName != value:
displayDict['caption'] += ' (%s)' % value
displayDict['caption'] += ' for %s.' % metadata[f]
displayDict['order'] = orders[f]
bundle = mb.MetricBundle(m,
slicer,
sqls[f],
stackerList=stackerList,
metadata=metadata[f],
plotFuncs=subsetPlots,
displayDict=displayDict,
summaryMetrics=standardSummary())
bundleList.append(bundle)
# Set the runName for all bundles and return the bundleDict.
for b in bundleList:
b.setRunName(runName)
return mb.makeBundlesDictFromList(bundleList)
def interNight(colmap=None, runName='opsim', nside=64, extraSql=None, extraMetadata=None,
slicer=None, display_group='InterNight', subgroup='Night gaps'):
"""Generate a set of statistics about the spacing between nights with observations.
Parameters
----------
colmap : dict or None, opt
A dictionary with a mapping of column names. Default will use OpsimV4 column names.
runName : str, opt
The name of the simulated survey. Default is "opsim".
nside : int, opt
Nside for the healpix slicer. Default 64.
extraSql : str or None, opt
Additional sql constraint to apply to all metrics.
extraMetadata : str or None, opt
Additional metadata to use for all outputs.
slicer : slicer object (None)
Optinally use something other than a HealpixSlicer
Returns
-------
metricBundleDict
"""
if colmap is None:
colmap = ColMapDict('opsimV4')
bundleList = []
# Set up basic all and per filter sql constraints.
raCol, decCol, degrees, ditherStacker, ditherMeta = radecCols(None, colmap, None)
metadata = combineMetadata(extraMetadata, ditherMeta)
filterlist, colors, orders, sqls, metadata = filterList(all=True,
extraSql=extraSql,
extraMetadata=metadata)
if slicer is None:
slicer = slicers.HealpixSlicer(nside=nside, latCol=decCol, lonCol=raCol, latLonDeg=degrees)
displayDict = {'group': display_group, 'subgroup': subgroup, 'caption': None, 'order': 0}
# Histogram of the number of nights between visits.
bins = np.arange(1, 20.5, 1)
metric = metrics.NightgapsMetric(bins=bins, nightCol=colmap['night'], metricName='DeltaNight Histogram')
plotDict = {'bins': bins, 'xlabel': 'dT (nights)'}
displayDict['caption'] = 'Histogram of the number of nights between consecutive visits to a ' \
'given point on the sky, considering separations between %d and %d' \
% (bins.min(), bins.max())
if metadata['all'] is None or len(metadata['all']) == 0:
displayDict['caption'] += ', all proposals.'
else:
displayDict['caption'] += ', %s.' % metadata['all']
plotFunc = plots.SummaryHistogram()
bundle = mb.MetricBundle(metric, slicer, sqls['all'], plotDict=plotDict,
displayDict=displayDict, metadata=metadata['all'], plotFuncs=[plotFunc])
bundleList.append(bundle)
standardStats = standardSummary()
subsetPlots = [plots.HealpixSkyMap(), plots.HealpixHistogram()]
# Median inter-night gap (each and all filters)
metric = metrics.InterNightGapsMetric(metricName='Median Inter-Night Gap', mjdCol=colmap['mjd'],
reduceFunc=np.median)
for f in filterlist:
displayDict['caption'] = 'Median gap between nights with observations, %s.' % metadata[f]
displayDict['order'] = orders[f]
plotDict = {'color': colors[f], 'percentileClip': 95.}
bundle = mb.MetricBundle(metric, slicer, sqls[f], metadata=metadata[f],
displayDict=displayDict,
plotFuncs=subsetPlots, plotDict=plotDict,
summaryMetrics=standardStats)
bundleList.append(bundle)
# Maximum inter-night gap (in each and all filters).
metric = metrics.InterNightGapsMetric(metricName='Max Inter-Night Gap', mjdCol=colmap['mjd'],
reduceFunc=np.max)
for f in filterlist:
displayDict['caption'] = 'Maximum gap between nights with observations, %s.' % metadata[f]
displayDict['order'] = orders[f]
plotDict = {'color': colors[f], 'percentileClip': 95., 'binsize': 5}
bundle = mb.MetricBundle(metric, slicer, sqls[f], metadata=metadata[f], displayDict=displayDict,
plotFuncs=subsetPlots, plotDict=plotDict, summaryMetrics=standardStats)
bundleList.append(bundle)
# Set the runName for all bundles and return the bundleDict.
for b in bundleList:
b.setRunName(runName)
plotBundles = None
return mb.makeBundlesDictFromList(bundleList), plotBundles
def astrometryBatch(colmap=None,
runName='opsim',
extraSql=None,
extraMetadata=None,
nside=64,
ditherStacker=None,
ditherkwargs=None):
"""Metrics for evaluating proper motion and parallax.
Parameters
----------
colmap : dict or None, opt
A dictionary with a mapping of column names. Default will use OpsimV4 column names.
runName : str, opt
The name of the simulated survey. Default is "opsim".
nside : int, opt
Nside for the healpix slicer. Default 64.
extraSql : str or None, opt
Additional sql constraint to apply to all metrics.
extraMetadata : str or None, opt
Additional metadata to apply to all results.
ditherStacker: str or lsst.sims.maf.stackers.BaseDitherStacker
Optional dither stacker to use to define ra/dec columns.
ditherkwargs: dict, opt
Optional dictionary of kwargs for the dither stacker.
Returns
-------
metricBundleDict
"""
if colmap is None:
colmap = ColMapDict('opsimV4')
bundleList = []
sql = ''
metadata = 'All visits'
# Add additional sql constraint (such as wfdWhere) and metadata, if provided.
if (extraSql is not None) and (len(extraSql) > 0):
sql = extraSql
if extraMetadata is None:
metadata = extraSql.replace('filter =', '').replace('filter=', '')
metadata = metadata.replace('"', '').replace("'", '')
if extraMetadata is not None:
metadata = extraMetadata
subgroup = metadata
raCol, decCol, degrees, ditherStacker, ditherMeta = radecCols(
ditherStacker, colmap, ditherkwargs)
# Don't want dither info in subgroup (too long), but do want it in bundle name.
metadata = combineMetadata(metadata, ditherMeta)
rmags_para = [22.4, 24.0]
rmags_pm = [20.5, 24.0]
# Set up parallax/dcr stackers.
parallaxStacker = stackers.ParallaxFactorStacker(raCol=raCol,
decCol=decCol,
dateCol=colmap['mjd'],
degrees=degrees)
dcrStacker = stackers.DcrStacker(filterCol=colmap['filter'],
altCol=colmap['alt'],
degrees=degrees,
raCol=raCol,
decCol=decCol,
lstCol=colmap['lst'],
site='LSST',
mjdCol=colmap['mjd'])
# Set up parallax metrics.
slicer = slicers.HealpixSlicer(nside=nside,
lonCol=raCol,
latCol=decCol,
latLonDeg=degrees)
subsetPlots = [plots.HealpixSkyMap(), plots.HealpixHistogram()]
displayDict = {
'group': 'Parallax',
'subgroup': subgroup,
'order': 0,
'caption': None
}
# Expected error on parallax at 10 AU.
plotmaxVals = (2.0, 15.0)
for rmag, plotmax in zip(rmags_para, plotmaxVals):
plotDict = {
'xMin': 0,
'xMax': plotmax,
'colorMin': 0,
'colorMax': plotmax
}
metric = metrics.ParallaxMetric(metricName='Parallax Error @ %.1f' %
(rmag),
rmag=rmag,
seeingCol=colmap['seeingGeom'],
filterCol=colmap['filter'],
m5Col=colmap['fiveSigmaDepth'],
normalize=False)
bundle = mb.MetricBundle(metric,
slicer,
sql,
metadata=metadata,
stackerList=[parallaxStacker, ditherStacker],
displayDict=displayDict,
plotDict=plotDict,
summaryMetrics=standardSummary(),
plotFuncs=subsetPlots)
bundleList.append(bundle)
displayDict['order'] += 1
# Parallax normalized to 'best possible' if all visits separated by 6 months.
# This separates the effect of cadence from depth.
for rmag in rmags_para:
metric = metrics.ParallaxMetric(
metricName='Normalized Parallax @ %.1f' % (rmag),
rmag=rmag,
seeingCol=colmap['seeingGeom'],
filterCol=colmap['filter'],
m5Col=colmap['fiveSigmaDepth'],
normalize=True)
bundle = mb.MetricBundle(metric,
slicer,
sql,
metadata=metadata,
stackerList=[parallaxStacker, ditherStacker],
displayDict=displayDict,
summaryMetrics=standardSummary(),
plotFuncs=subsetPlots)
bundleList.append(bundle)
displayDict['order'] += 1
# Parallax factor coverage.
for rmag in rmags_para:
metric = metrics.ParallaxCoverageMetric(
metricName='Parallax Coverage @ %.1f' % (rmag),
rmag=rmag,
m5Col=colmap['fiveSigmaDepth'],
mjdCol=colmap['mjd'],
filterCol=colmap['filter'],
seeingCol=colmap['seeingGeom'])
bundle = mb.MetricBundle(metric,
slicer,
sql,
metadata=metadata,
stackerList=[parallaxStacker, ditherStacker],
displayDict=displayDict,
summaryMetrics=standardSummary(),
plotFuncs=subsetPlots)
bundleList.append(bundle)
displayDict['order'] += 1
# Parallax problems can be caused by HA and DCR degeneracies. Check their correlation.
for rmag in rmags_para:
metric = metrics.ParallaxDcrDegenMetric(
metricName='Parallax-DCR degeneracy @ %.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).'
bundle = mb.MetricBundle(
metric,
slicer,
sql,
metadata=metadata,
stackerList=[dcrStacker, parallaxStacker, ditherStacker],
displayDict=displayDict,
summaryMetrics=standardSummary(),
plotFuncs=subsetPlots)
bundleList.append(bundle)
displayDict['order'] += 1
# Proper Motion metrics.
displayDict = {
'group': 'Proper Motion',
'subgroup': subgroup,
'order': 0,
'caption': None
}
# Proper motion errors.
plotmaxVals = (1.0, 5.0)
for rmag, plotmax in zip(rmags_pm, plotmaxVals):
plotDict = {
'xMin': 0,
'xMax': plotmax,
'colorMin': 0,
'colorMax': plotmax
}
metric = metrics.ProperMotionMetric(
metricName='Proper Motion Error @ %.1f' % rmag,
rmag=rmag,
m5Col=colmap['fiveSigmaDepth'],
mjdCol=colmap['mjd'],
filterCol=colmap['filter'],
seeingCol=colmap['seeingGeom'],
normalize=False)
bundle = mb.MetricBundle(metric,
slicer,
sql,
metadata=metadata,
stackerList=[ditherStacker],
displayDict=displayDict,
plotDict=plotDict,
summaryMetrics=standardSummary(),
plotFuncs=subsetPlots)
bundleList.append(bundle)
displayDict['order'] += 1
# Normalized proper motion.
for rmag in rmags_pm:
metric = metrics.ProperMotionMetric(
metricName='Normalized Proper Motion @ %.1f' % rmag,
rmag=rmag,
m5Col=colmap['fiveSigmaDepth'],
mjdCol=colmap['mjd'],
filterCol=colmap['filter'],
seeingCol=colmap['seeingGeom'],
normalize=True)
bundle = mb.MetricBundle(metric,
slicer,
sql,
metadata=metadata,
stackerList=[ditherStacker],
displayDict=displayDict,
summaryMetrics=standardSummary(),
plotFuncs=subsetPlots)
bundleList.append(bundle)
displayDict['order'] += 1
# Set the runName for all bundles and return the bundleDict.
for b in bundleList:
b.setRunName(runName)
return mb.makeBundlesDictFromList(bundleList)
def seasons(colmap=None, runName='opsim', nside=64, extraSql=None, extraMetadata=None):
"""Generate a set of statistics about the length and number of seasons.
Parameters
----------
colmap : dict or None, opt
A dictionary with a mapping of column names. Default will use OpsimV4 column names.
runName : str, opt
The name of the simulated survey. Default is "opsim".
nside : int, opt
Nside for the healpix slicer. Default 64.
extraSql : str or None, opt
Additional sql constraint to apply to all metrics.
extraMetadata : str or None, opt
Additional metadata to use for all outputs.
Returns
-------
metricBundleDict
"""
if colmap is None:
colmap = ColMapDict('opsimV4')
bundleList = []
# Set up basic all and per filter sql constraints.
raCol, decCol, degrees, ditherStacker, ditherMeta = radecCols(None, colmap, None)
metadata = combineMetadata(extraMetadata, ditherMeta)
filterlist, colors, orders, sqls, metadata = filterList(all=True,
extraSql=extraSql,
extraMetadata=metadata)
slicer = slicers.HealpixSlicer(nside=nside, latCol=decCol, lonCol=raCol, latLonDeg=degrees)
displayDict = {'group': 'IntraSeason', 'subgroup': 'Season length', 'caption': None, 'order': 0}
standardStats = standardSummary()
subsetPlots = [plots.HealpixSkyMap(), plots.HealpixHistogram()]
metric = metrics.SeasonLengthMetric(metricName='Median Season Length', mjdCol=colmap['mjd'],
reduceFunc=np.median)
for f in filterlist:
displayDict['caption'] = 'Median season length, %s.' % metadata[f]
displayDict['order'] = orders[f]
maxS = 250
if f == 'all':
minS = 90
else:
minS = 30
plotDict = {'color': colors[f], 'colorMin': minS, 'colorMax': maxS, 'xMin': minS, 'xMax': maxS}
bundle = mb.MetricBundle(metric, slicer, sqls[f], metadata=metadata[f],
displayDict=displayDict,
plotFuncs=subsetPlots, plotDict=plotDict,
summaryMetrics=standardStats)
bundleList.append(bundle)
# Number of seasons
metric = metrics.CampaignLengthMetric(metricName='NSeasons', mjdCol=colmap['mjd'],
expTimeCol=colmap['exptime'], minExpTime=15)
displayDict['caption'] = 'Number of seasons, any filter.'
displayDict['order'] = 0
plotDict = {'color': 'k', 'colorMin': 0, 'colorMax': 11, 'xMin': 0, 'xMax': 11}
bundle = mb.MetricBundle(metric, slicer, sqls['all'], metadata=metadata['all'],
displayDict=displayDict,
plotFuncs=subsetPlots, plotDict=plotDict,
summaryMetrics=standardStats)
bundleList.append(bundle)
# Set the runName for all bundles and return the bundleDict.
for b in bundleList:
b.setRunName(runName)
plotBundles = None
return mb.makeBundlesDictFromList(bundleList), plotBundles
def intraNight(colmap=None,
runName='opsim',
nside=64,
extraSql=None,
extraMetadata=None):
"""Generate a set of statistics about the pair/triplet/etc. rate within a night.
Parameters
----------
colmap : dict or None, opt
A dictionary with a mapping of column names. Default will use OpsimV4 column names.
runName : str, opt
The name of the simulated survey. Default is "opsim".
nside : int, opt
Nside for the healpix slicer. Default 64.
extraSql : str or None, opt
Additional sql constraint to apply to all metrics.
extraMetadata : str or None, opt
Additional metadata to apply to all results.
Returns
-------
metricBundleDict
"""
if colmap is None:
colmap = ColMapDict('opsimV4')
metadata = extraMetadata
if extraSql is None:
extraSql = ''
if extraSql is not None and len(extraSql) > 0:
if metadata is None:
metadata = extraSql
bundleList = []
standardStats = standardSummary()
subsetPlots = [plots.HealpixSkyMap(), plots.HealpixHistogram()]
# Look for the fraction of visits in gri where there are pairs within dtMin/dtMax.
displayDict = {
'group': 'IntraNight',
'subgroup': 'Pairs',
'caption': None,
'order': 0
}
sql = '(%s) and (filter="g" or filter="r" or filter="i")' % extraSql
md = 'gri'
if metadata is not None:
md += metadata
dtMin = 15.0
dtMax = 60.0
metric = metrics.PairFractionMetric(
mjdCol=colmap['mjd'],
minGap=dtMin,
maxGap=dtMax,
metricName='Fraction of visits in pairs (%.0f-%.0f min)' %
(dtMin, dtMax))
slicer = slicers.HealpixSlicer(nside=nside,
latCol=colmap['dec'],
lonCol=colmap['ra'],
latLonDeg=colmap['raDecDeg'])
displayDict['caption'] = 'Fraction of %s visits that have a paired visit' \
'between %.1f and %.1f minutes away. ' % (metadata, dtMin, dtMax)
displayDict[
'caption'] += 'If all visits were in pairs, this fraction would be 1.'
displayDict['order'] += 1
bundle = mb.MetricBundle(metric,
slicer,
sql,
metadata=md,
summaryMetrics=standardStats,
plotFuncs=subsetPlots,
displayDict=displayDict)
bundleList.append(bundle)
# Look at the fraction of visits which have another visit within dtMax, gri.
dtMax = 50.0
metric = metrics.NRevisitsMetric(
mjdCol=colmap['mjd'],
dT=dtMax,
normed=True,
metricName='Fraction of visits with a revisit < %.0f min' % dtMax)
displayDict['caption'] = 'Fraction of %s visits that have another visit ' \
'within %.1f min. ' % (metadata, dtMax)
displayDict[
'caption'] += 'If all visits were in pairs (only), this fraction would be 0.5.'
displayDict['order'] += 1
bundle = mb.MetricBundle(metric,
slicer,
sql,
metadata=md,
summaryMetrics=standardStats,
plotFuncs=subsetPlots,
displayDict=displayDict)
bundleList.append(bundle)
# Histogram of the time between revisits (all filters) within two hours.
binMin = 0
binMax = 120.
binsize = 5.
bins_metric = np.arange(binMin / 60.0 / 24.0,
(binMax + binsize) / 60. / 24.,
binsize / 60. / 24.)
bins_plot = bins_metric * 24.0 * 60.0
metric = metrics.TgapsMetric(bins=bins_metric,
timesCol=colmap['mjd'],
metricName='DeltaT Histogram')
slicer = slicers.HealpixSlicer(nside=nside,
latCol=colmap['dec'],
lonCol=colmap['ra'],
latLonDeg=colmap['raDecDeg'])
plotDict = {'bins': bins_plot, 'xlabel': 'dT (minutes)'}
displayDict['caption'] = 'Histogram of the time between consecutive visits to a given point ' \
'on the sky, considering visits between %.1f and %.1f minutes' % (binMin,
binMax)
displayDict['order'] += 1
plotFunc = plots.SummaryHistogram()
bundle = mb.MetricBundle(metric,
slicer,
extraSql,
plotDict=plotDict,
displayDict=displayDict,
metadata=metadata,
plotFuncs=[plotFunc])
bundleList.append(bundle)
# Set the runName for all bundles and return the bundleDict.
for b in bundleList:
b.setRunName(runName)
return mb.makeBundlesDictFromList(bundleList)
def tEffMetrics(colmap=None,
runName='opsim',
extraSql=None,
extraMetadata=None,
nside=64,
filterlist=('u', 'g', 'r', 'i', 'z', 'y')):
"""Generate a series of Teff metrics. Teff total, per night, and sky maps (all and per filter).
Parameters
----------
colmap : dict, opt
A dictionary with a mapping of column names. Default will use OpsimV4 column names.
runName : str, opt
The name of the simulated survey. Default is "opsim".
extraSql : str, opt
Additional constraint to add to any sql constraints (e.g. 'propId=1' or 'fieldID=522').
Default None, for no additional constraints.
extraMetadata : str, opt
Additional metadata to add before any below (i.e. "WFD"). Default is None.
nside : int, opt
Nside value for healpix slicer. Default 64.
If "None" is passed, the healpixslicer-based metrics will be skipped.
filterlist : list of str, opt
List of the filternames to use for "per filter" evaluation. Default ('u', 'g', 'r', 'i', 'z', 'y').
If None is passed, the per-filter evaluations will be skipped.
Returns
-------
metricBundleDict
"""
if colmap is None:
colmap = ColMapDict('opsimV4')
bundleList = []
subgroup = extraMetadata
if subgroup is None:
subgroup = 'All visits'
# Set up basic all and per filter sql constraints.
sqlconstraints = ['']
metadata = ['all banads']
if filterlist is not None:
sqlconstraints += [
'%s = "%s"' % (colmap['filter'], f) for f in filterlist
]
metadata += ['%s band' % f for f in filterlist]
# Add additional sql constraint (such as wfdWhere) and metadata, if provided.
if (extraSql is not None) and (len(extraSql) > 0):
tmp = []
for s in sqlconstraints:
if len(s) == 0:
tmp.append(extraSql)
else:
tmp.append('%s and (%s)' % (s, extraSql))
sqlconstraints = tmp
if extraMetadata is None:
metadata = ['%s, %s' % (extraSql, m) for m in metadata]
if extraMetadata is not None:
metadata = ['%s %s' % (extraMetadata, m) for m in metadata]
metadataCaption = extraMetadata
if metadataCaption is None:
metadataCaption = 'all visits'
subsetPlots = [plots.HealpixSkyMap(), plots.HealpixHistogram()]
# Total Teff and normalized Teff.
displayDict = {'group': 'T_eff', 'subgroup': subgroup}
displayDict[
'caption'] = 'Total effective time of the survey (see Teff metric).'
displayDict['order'] = 0
metric = metrics.TeffMetric(m5Col=colmap['fiveSigmaDepth'],
filterCol=colmap['filter'],
normed=False,
metricName='Total Teff')
slicer = slicers.UniSlicer()
bundle = mb.MetricBundle(metric,
slicer,
constraint=None,
displayDict=displayDict,
metadata=extraMetadata)
bundleList.append(bundle)
displayDict[
'caption'] = 'Normalized total effective time of the survey (see Teff metric).'
displayDict['order'] = 1
metric = metrics.TeffMetric(m5Col=colmap['fiveSigmaDepth'],
filterCol=colmap['filter'],
normed=True,
metricName='Normalized Teff')
slicer = slicers.UniSlicer()
bundle = mb.MetricBundle(metric,
slicer,
constraint=None,
displayDict=displayDict,
metadata=extraMetadata)
bundleList.append(bundle)
# Generate Teff maps in all and per filters
metric = metrics.TeffMetric(m5Col=colmap['fiveSigmaDepth'],
filterCol=colmap['filter'],
normed=True,
metricName='Normalized Teff')
slicer = slicers.HealpixSlicer(nside=nside,
latCol=colmap['dec'],
lonCol=colmap['ra'],
latLonDeg=colmap['raDecDeg'])
displayDict['order'] = -1
for sql, meta in zip(sqlconstraints, metadata):
displayDict['caption'] = 'Normalized effective time of the survey in %s band(s) ' \
'for %s visits.' % (meta.lstrip('%s ' % extraMetadata), metadataCaption)
displayDict['order'] += 1
bundle = mb.MetricBundle(metric,
slicer,
sql,
metadata=meta,
displayDict=displayDict,
plotFuncs=subsetPlots,
summaryMetrics=standardSummary())
bundleList.append(bundle)
# Set the runName for all bundles and return the bundleDict.
for b in bundleList:
b.setRunName(runName)
return mb.makeBundlesDictFromList(bundleList)
def go(nside=64, rmag=20., SedTemplate='flat', DoRun=False, LFilters = [], \
LNightMax=[], nightMax=1e4, \
CustomPlotLimits=True, \
RunOne=False, MaxRuns=1e3, \
SpatialClip=95.):
# runNames = ['enigma_1189', 'ops2_1093']
# runNames
#runNames = ['ops2_1092', 'kraken_1038', 'kraken_1034', 'ops2_1098']
#runNames = ['kraken_1038', 'kraken_1034', 'ops2_1098']
# 2015-12-23 - put kraken_1038 at the end, it seems to run
# extremely slowly...
runNames = ['enigma_1189', 'ops2_1098', 'kraken_1034', 'kraken_1038']
runNames = ['ops2_1092', 'kraken_1033', 'enigma_1271']
# UPDATE - ops2_1092 ran quite quickly on nside=32... rerun on 64
runNames = ['ops2_1092', 'enigma_1189', 'enigma_1271', 'kraken_1038']
# UPDATE 2015-12-28 -- run with single-filter choices, compare
# enigma to ops2_1092
# WIC 2015-12-28 -- try with single-filter and all then small subset
runNames = ['ops2_1092', 'ops2_1092', 'ops2_1092', 'enigma_1189', 'enigma_1189', 'enigma_1189']
LFilters = ["u", "y", '', "u", "y", '']
LNightMax = [1e4, 1e4, 730, 1e4, 1e4, 730]
# WIC 2015-12-28 - 23:00 - try using a different SED template,
# just go with single filters for now
#
# DO WE NEED THIS??
# WIC 2015-12-28 - 22:00; much to my surprise, that took less than
# half an hour to go all the way through. Try again, this time using slightly more filters.
runNames = ['ops2_1092', 'enigma_1189', 'ops2_1092', 'enigma_1189']
LFilters = ['', '', 'griz', 'griz'] # (griz was not recognized)
# WIC 2015-12-29 - set up for a master-run with all cases, this time with plotting limits
# Break the specifications across lines to make subdivision easier
# Subsets by time first, then by filter, finally the whole shebang
# (Yes the inversion of the first two is deliberate.)
runNames = ['enigma_1189', 'ops2_1092', 'ops2_1092', 'enigma_1189', \
'ops2_1092', 'enigma_1189', 'ops2_1092', 'enigma_1189', \
'ops2_1092', 'enigma_1189']
LFilters = ['', '', '', '', \
'u', 'u', 'y', 'y', \
'', '']
LNightMax = [365, 365, 730, 730, \
1e4, 1e4, 1e4, 1e4, \
1e4, 1e4]
# List of upper limits to parallax and proper motion error. For parallax, 3.0 mas is probably good
LUpperParallax = []
LUpperPropmotion = []
if CustomPlotLimits:
LUpperParallax = [10, 10, 10, 10, \
10, 10, 40, 40, \
3.0, 3.0 ]
# For proper motion, it's a little tricky to say because the
# regular case is so pathological for the field. Try the following:
LUpperPropmotion = [40, 40, 5, 20, \
3.5, 20, 3.5, 20, \
0.5, 5]
print "runAstrom.go INFO - will run the following:"
for iSho in range(len(runNames)):
print "%i: %-12s, %1s, %i" % (iSho, runNames[iSho], LFilters[iSho], LNightMax[iSho])
print "==========================="
# print runNames
# if not DoRun:
# print "Set DoRun=True to actually run this."
# print len(LFilters), len(runNames), len(LFilters) == len(runNames)
# return
#'kraken_1038', 'kraken_1034', 'ops2_1098']
# nside = 64
slicer = slicers.HealpixSlicer(nside=nside)
# Make it so we don't bother with the silly power spectra
plotFuncs = [plots.HealpixSkyMap(), plots.HealpixHistogram()]
# WIC - back up the plotting arguments with a default value
plotFuncsPristine = copy.deepcopy(plotFuncs)
# WIC - the only way this will make sense to me is if I make a
# dictionary of plot arguments. Let's try it...
DPlotArgs = {}
for plotArg in ['parallax', 'propmotion', 'coverage', 'HAdegen']:
DPlotArgs[plotArg] = copy.deepcopy(plotFuncs)
if CustomPlotLimits:
# All spatial maps use percentile clipping
for plotMetric in DPlotArgs.keys():
DPlotArgs[plotMetric][0].defaultPlotDict['percentileClip'] = SpatialClip
# Some limits common to spatial maps and histograms
for iPl in range(0,2):
DPlotArgs['propmotion'][iPl].defaultPlotDict['logScale'] = True
# Standardized range for the histograms for new parallax metrics
DPlotArgs['coverage'][1].defaultPlotDict['xMin'] = 0.
DPlotArgs['coverage'][1].defaultPlotDict['xMax'] = 1.
DPlotArgs['HAdegen'][1].defaultPlotDict['xMin'] = -1.
DPlotArgs['HAdegen'][1].defaultPlotDict['xMax'] = 1.
# Standardize at least the lower bound of the histogram in
# both the proper motion and parallax errors. Upper limit we
# can customize with a loop.
DPlotArgs['propmotion'][1].defaultPlotDict['xMin'] = 1e-2 # should not be zero if log scale!!
DPlotArgs['parallax'][1].defaultPlotDict['xMin'] = 0.
# WIC - try changing the plot dictionary
if not DoRun:
plotFuncs[0].defaultPlotDict['logScale'] = True
print DPlotArgs['propmotion'][0].defaultPlotDict
print DPlotArgs['propmotion'][1].defaultPlotDict
return
# The old runs have the seeing in finSeeing
seeingCol = 'finSeeing'
# Try it out for a 20th mag star with a flat SED (can change mag
# or to OBAFGKM)
# rmag = 20. ## NOW AN ARGUMENT
#SedTemplate='flat'
# Use all the observations. Can change if you want a different
# time span
sqlconstraint = ''
# list of sqlconstraints now used, which gets handled within the loop.
# run some summary stats on everything
summaryMetrics = [metrics.MedianMetric()]
tStart = time.time()
# Running one, or the whole lot?
RunMax = len(runNames)
# allow user to set a different number (say, 2)
if MaxRuns < RunMax and MaxRuns > 0:
RunMax = int(MaxRuns)
# the following keyword overrides
if RunOne:
RunMax = 1
print "Starting runs. RunMax = %i" % (RunMax)
for iRun in range(RunMax):
run = runNames[iRun][:]
# for run in runNames:
# Open the OpSim database
timeStartIteration = time.time()
# Some syntax added to test for existence of the database
dbFil = run+'_sqlite.db'
if not os.access(dbFil, os.R_OK):
print "runAstrom.go FATAL - cannot acces db file %s" % (dbFil)
print "runAstrom.go FATAL - skipping run %s" % (run)
continue
else:
deltaT = time.time()-tStart
print "runAstrom.go INFO - ##################################"
print "runAstrom.go INFO - starting run %s with nside=%i after %.2f minutes" \
% (run, nside, deltaT/60.)
opsdb = db.OpsimDatabase(run+'_sqlite.db')
# Set SQL constraint appropriate for each filter in the
# list. If we supplied a list of filters, use it for
sqlconstraint = ''
ThisFilter = 'ugrizy'
if len(LFilters) == len(runNames):
# Only change the filter if one was actually supplied!
if len(LFilters[iRun]) == 1:
ThisFilter = LFilters[iRun]
sqlconstraint = 'filter = "%s"' % (ThisFilter)
# If nightmax was supplied, use it
ThisNightMax = int(nightMax) # copy not view
if len(LNightMax) == len(runNames):
# Only update nightmax if one was given
try:
ThisNightMax = int(LNightMax[iRun]) # This might be redundant with the fmt statement below.
if len(sqlconstraint) < 1:
sqlconstraint = 'night < %i' % (ThisNightMax)
else:
sqlconstraint = '%s and night < %i' % (sqlconstraint, ThisNightMax)
except:
print "runAstrom.go WARN - run %i problem with NightMax" % (iRun)
dumdum = 1.
# Set where the output should go - include the filter!!
sMag = '%.1f' % (rmag)
sMag = sMag.replace(".","p")
outDir = '%s_nside%i_%s_n%i_r%s' % (run, nside, ThisFilter, ThisNightMax, sMag)
# Ensure we'll be able to find this later on...
if CustomPlotLimits:
outDir = '%s_lims' % (outDir)
# From this point onwards, stuff actually gets run. This is
# the place to output what will actually happen next.
print "runAstrom.go INFO - about to run:"
print "runAstrom.go INFO - sqlconstraint: %s ; run name %s ; nside %i" % (sqlconstraint, run, nside)
print "runAstrom.go INFO - output directory will be %s" % (outDir)
if not DoRun:
continue
resultsDb = db.ResultsDb(outDir=outDir)
bundleList = []
# WIC - to make this at least somewhat uniform, build the plot
# functions including arguments out of our copies above.
plotFuncsPropmotion = copy.deepcopy(DPlotArgs['propmotion'])
plotFuncsParallax = copy.deepcopy(DPlotArgs['parallax'])
plotFuncsCoverage = copy.deepcopy(DPlotArgs['coverage'])
plotFuncsHAdegen = copy.deepcopy(DPlotArgs['HAdegen'])
# if using custom plot limits, will want to include the limits
# for proper motion and parallax too... programming a bit defensively
# here, including an extra check (rather than just the length of the lists
# above).
if CustomPlotLimits:
if len(LUpperParallax) == len(runNames):
plotFuncsParallax[1].defaultPlotDict['xMax'] = float(LUpperParallax[iRun])
if len(LUpperPropmotion) == len(runNames):
plotFuncsPropmotion[1].defaultPlotDict['xMax'] = float(LUpperPropmotion[iRun])
# Configure the metrics
metric = metrics.ParallaxMetric(rmag=rmag, seeingCol=seeingCol, SedTemplate=SedTemplate)
bundle = metricBundles.MetricBundle(metric, slicer, sqlconstraint, runName=run,
# plotFuncs=plotFuncs, \
plotFuncs = plotFuncsParallax, \
summaryMetrics=summaryMetrics)
bundleList.append(bundle)
metric=metrics.ProperMotionMetric(rmag=rmag, seeingCol=seeingCol, SedTemplate=SedTemplate)
bundle = metricBundles.MetricBundle(metric, slicer, sqlconstraint, runName=run,
# plotFuncs=plotFuncs, \
plotFuncs=plotFuncsPropmotion, \
summaryMetrics=summaryMetrics)
bundleList.append(bundle)
metric = metrics.ParallaxCoverageMetric(rmag=rmag, seeingCol=seeingCol, SedTemplate=SedTemplate)
bundle = metricBundles.MetricBundle(metric, slicer, sqlconstraint, runName=run,
# plotFuncs=plotFuncs, \
plotFuncs=plotFuncsCoverage, \
summaryMetrics=summaryMetrics)
bundleList.append(bundle)
metric = metrics.ParallaxHADegenMetric(rmag=rmag, seeingCol=seeingCol, SedTemplate=SedTemplate)
bundle = metricBundles.MetricBundle(metric, slicer, sqlconstraint, runName=run,
# plotFuncs=plotFuncs, \
plotFuncs=plotFuncsHAdegen, \
summaryMetrics=summaryMetrics)
bundleList.append(bundle)
# Run everything and make plots
bundleDict = metricBundles.makeBundlesDictFromList(bundleList)
bgroup = metricBundles.MetricBundleGroup(bundleDict, opsdb, outDir=outDir, resultsDb=resultsDb)
# try:
bgroup.runAll()
print "runAstrom.go INFO - bundles took %.2f minutes" \
% ((time.time() - timeStartIteration) / 60.)
# except KeyboardInterrupt:
# print "runAstrom.go FATAL - keyboard interrupt detected. Halting."
# return
bgroup.plotAll()
print "runAstrom.go INFO - bundles + plotting took %.2f minutes" \
% ((time.time() - timeStartIteration) / 60.)
print "Finished entire set. %i runs took %.2f minutes." % (iRun + 1, (time.time()-tStart)/60.)
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 go(nside=64, rmag=21., SedTemplate='flat', DoRun=False, LFilters = [], \
LNightMax=[], nightMax=1e4, \
CustomPlotLimits=True, \
RunOne=False, MaxRuns=1e3, \
SpatialClip=95., \
seeingCol='FWHMeff', \
sCmap='cubehelix_r', \
checkCorrKind=False, \
wfdPlane=True, \
useGRIZ=False):
# Go to the directory where the sqlite databases are held...
# cd /Users/clarkson/Data/LSST/OpSimRuns/opsim20160411
# WIC 2015-12-29 - set up for a master-run with all cases, this time with plotting limits
# Break the specifications across lines to make subdivision easier
# Subsets by time first, then by filter, finally the whole shebang
# 2016-04-23 - replaced enigma_1189 --> minion_1016
# 2016-04-23 - replaced ops2_1092 --> minion_1020
# (Yes the inversion of the first two is deliberate.)
runNames = ['minion_1016', 'minion_1020', 'minion_1020', 'minion_1016', \
'minion_1020', 'minion_1016', 'minion_1020', 'minion_1016', \
'minion_1020', 'minion_1016']
LFilters = ['', '', '', '', \
'u', 'u', 'y', 'y', \
'', '']
LNightMax = [365, 365, 730, 730, \
1e4, 1e4, 1e4, 1e4, \
1e4, 1e4]
# WIC try again, this time on the new astro_lsst_01_1004 only
if wfdPlane:
LFilters = ['', '', '', 'u', 'y']
LNightMax = [365, 730, 1e4, 1e4, 1e4]
runNames = ['astro_lsst_01_1004' for i in range (len(LFilters)) ]
# WIC 2016-05-01 check correlation
if checkCorrKind:
LFilters = ['', '']
LNightMax = [365, 365]
runNames = ['minion_1016', 'minion_1016']
# Type of correlation used for HA Degen
# checkCorrKind = True
useSpearmanR = [False, True]
if useGRIZ:
runNames=['minion_1016','astro_lsst_01_1004', 'minion_1020']
LFilters = ['griz' for iRun in range(len(runNames)) ]
#LNightMax = [1e4 for iRun in range(len(runNames)) ]
#LNightMax = [730 for iRun in range(len(runNames)) ]
LNightMax = [365 for iRun in range(len(runNames)) ]
# List of upper limits to parallax and proper motion error. For parallax, 3.0 mas is probably good
LUpperParallax = []
LUpperPropmotion = []
if CustomPlotLimits:
LUpperParallax = [10, 10, 10, 10, \
10, 10, 40, 40, \
3.0, 3.0 ]
# For proper motion, it's a little tricky to say because the
# regular case is so pathological for the field. Try the following:
LUpperPropmotion = [40, 40, 5, 20, \
3.5, 20, 3.5, 20, \
0.5, 5]
if len(runNames) < 2:
LUpperPropmotion = [100 for i in range(len(runNames))]
print "runAstrom.go INFO - will run the following:"
for iSho in range(len(runNames)):
sFilThis = ''
# print iSho, len(LFilters)
if iSho <= len(LFilters):
sFilThis = sqlFromFilterString(LFilters[iSho])
print "%i: %-12s, %1s, %i, sqlFilter -- %s" % (iSho, runNames[iSho], LFilters[iSho], LNightMax[iSho], sFilThis)
print "==========================="
print "mag max = %.2f" % (rmag)
print "---------------------------"
# print runNames
# if not DoRun:
# print "Set DoRun=True to actually run this."
# print len(LFilters), len(runNames), len(LFilters) == len(runNames)
# return
#'kraken_1038', 'kraken_1034', 'ops2_1098']
# nside = 64
slicer = slicers.HealpixSlicer(nside=nside)
# Make it so we don't bother with the silly power spectra
plotFuncs = [plots.HealpixSkyMap(), plots.HealpixHistogram()]
# WIC - back up the plotting arguments with a default value
plotFuncsPristine = copy.deepcopy(plotFuncs)
# WIC - the only way this will make sense to me is if I make a
# dictionary of plot arguments. Let's try it...
DPlotArgs = {}
for plotArg in ['parallax', 'propmotion', 'coverage', 'HAdegen']:
DPlotArgs[plotArg] = copy.deepcopy(plotFuncs)
if CustomPlotLimits:
# Use the same color map for all the metrics
for plotMetric in DPlotArgs.keys():
DPlotArgs[plotMetric][0].defaultPlotDict['cmap'] = sCmap
# Apply spatial clipping for all but the HADegen, for which we
# have other limits...
for plotMetric in ['parallax', 'propmotion', 'coverage']:
DPlotArgs[plotMetric][0].defaultPlotDict['percentileClip'] = SpatialClip
# Some limits common to spatial maps and histograms
for iPl in range(0,2):
DPlotArgs['propmotion'][iPl].defaultPlotDict['logScale'] = True
# NOT a loop because we might want to separate out the behavior
# Standardized range for the histograms for new parallax metrics
DPlotArgs['coverage'][1].defaultPlotDict['xMin'] = 0.
DPlotArgs['coverage'][1].defaultPlotDict['xMax'] = 1.
DPlotArgs['HAdegen'][1].defaultPlotDict['xMin'] = -1.
DPlotArgs['HAdegen'][1].defaultPlotDict['xMax'] = 1.
# Standardize the sky map for the HAdegen as well.
DPlotArgs['coverage'][1].defaultPlotDict['xMin'] = 0.
DPlotArgs['coverage'][1].defaultPlotDict['xMax'] = 1.
DPlotArgs['HAdegen'][0].defaultPlotDict['xMin'] = -1.
DPlotArgs['HAdegen'][0].defaultPlotDict['xMax'] = 1.
# Standardize at least the lower bound of the histogram in
# both the proper motion and parallax errors. Upper limit we
# can customize with a loop.
DPlotArgs['propmotion'][1].defaultPlotDict['xMin'] = 1e-2 # should not be zero if log scale!!
DPlotArgs['parallax'][1].defaultPlotDict['xMin'] = 0.
# WIC - try changing the plot dictionary
if not DoRun:
plotFuncs[0].defaultPlotDict['logScale'] = True
print DPlotArgs['propmotion'][0].defaultPlotDict
print DPlotArgs['propmotion'][1].defaultPlotDict
return
# The old runs have the seeing in finSeeing
#seeingCol = 'finSeeing'
### UPDATE THE SEEING COLUMN
#seeingCol = 'FWHMeff' ## Moved up to a command-line argument
# Use all the observations. Can change if you want a different
# time span
# sqlconstraint = ''
# list of sqlconstraints now used, which gets handled within the loop.
# run some summary stats on everything
summaryMetrics = [metrics.MedianMetric()]
tStart = time.time()
# Running one, or the whole lot?
RunMax = len(runNames)
# allow user to set a different number (say, 2)
if MaxRuns < RunMax and MaxRuns > 0:
RunMax = int(MaxRuns)
# the following keyword overrides
if RunOne:
RunMax = 1
print "Starting runs. RunMax = %i" % (RunMax)
for iRun in range(RunMax):
run = runNames[iRun][:]
# for run in runNames:
# Open the OpSim database
timeStartIteration = time.time()
# Some syntax added to test for existence of the database
dbFil = run+'_sqlite.db'
if not os.access(dbFil, os.R_OK):
print "runAstrom.go FATAL - cannot acces db file %s" % (dbFil)
print "runAstrom.go FATAL - skipping run %s" % (run)
continue
else:
deltaT = time.time()-tStart
print "runAstrom.go INFO - ##################################"
print "runAstrom.go INFO - starting run %s with nside=%i after %.2f minutes" \
% (run, nside, deltaT/60.)
opsdb = db.OpsimDatabase(run+'_sqlite.db')
# Set SQL constraint appropriate for each filter in the
# list. If we supplied a list of filters, use it for
sqlconstraint = ''
ThisFilter = 'ugrizy'
if len(LFilters) == len(runNames):
# Only change the filter if one was actually supplied!
if len(LFilters[iRun]) > 0:
ThisFilter = LFilters[iRun]
sqlconstraint = sqlFromFilterString(ThisFilter)
### sqlconstraint = 'filter = "%s"' % (ThisFilter)
# If nightmax was supplied, use it
ThisNightMax = int(nightMax) # copy not view
if len(LNightMax) == len(runNames):
# Only update nightmax if one was given
try:
ThisNightMax = int(LNightMax[iRun]) # This might be redundant with the fmt statement below.
if len(sqlconstraint) < 1:
sqlconstraint = 'night < %i' % (ThisNightMax)
else:
sqlconstraint = '%s and night < %i' % (sqlconstraint, ThisNightMax)
except:
print "runAstrom.go WARN - run %i problem with NightMax" % (iRun)
dumdum = 1.
# Set where the output should go - include the filter!!
sMag = '%.1f' % (rmag)
sMag = sMag.replace(".","p")
outDir = './metricEvals/%s_nside%i_%s_n%i_r%s' % (run, nside, ThisFilter, ThisNightMax, sMag)
# Ensure we'll be able to find this later on...
if CustomPlotLimits:
outDir = '%s_lims' % (outDir)
# if we are testing the kind of correlation used, include that
# in the output here.
if checkCorrKind:
if useSpearmanR[iRun]:
sCorr = 'spearmanR'
else:
sCorr = 'pearsonR'
outDir = '%s_%s' % (outDir, sCorr)
# From this point onwards, stuff actually gets run. This is
# the place to output what will actually happen next.
print "runAstrom.go INFO - about to run:"
print "runAstrom.go INFO - sqlconstraint: %s ; run name %s ; nside %i" % (sqlconstraint, run, nside)
print "runAstrom.go INFO - output directory will be %s" % (outDir)
if not DoRun:
continue
# ensure the output directory actually exists...
if not os.access(outDir, os.R_OK):
print "runAstrom.go INFO - creating output directory %s" % (outDir)
os.makedirs(outDir)
resultsDb = db.ResultsDb(outDir=outDir)
bundleList = []
# WIC - to make this at least somewhat uniform, build the plot
# functions including arguments out of our copies above.
plotFuncsPropmotion = copy.deepcopy(DPlotArgs['propmotion'])
plotFuncsParallax = copy.deepcopy(DPlotArgs['parallax'])
plotFuncsCoverage = copy.deepcopy(DPlotArgs['coverage'])
plotFuncsHAdegen = copy.deepcopy(DPlotArgs['HAdegen'])
# if using custom plot limits, will want to include the limits
# for proper motion and parallax too... programming a bit defensively
# here, including an extra check (rather than just the length of the lists
# above).
if CustomPlotLimits:
if len(LUpperParallax) == len(runNames):
plotFuncsParallax[1].defaultPlotDict['xMax'] = float(LUpperParallax[iRun])
if len(LUpperPropmotion) == len(runNames):
plotFuncsPropmotion[1].defaultPlotDict['xMax'] = float(LUpperPropmotion[iRun])
# Configure the metrics
metric = metrics.ParallaxMetric(rmag=rmag, seeingCol=seeingCol, SedTemplate=SedTemplate)
bundle = metricBundles.MetricBundle(metric, slicer, sqlconstraint, runName=run,
# plotFuncs=plotFuncs, \
plotFuncs = plotFuncsParallax, \
summaryMetrics=summaryMetrics)
bundleList.append(bundle)
metric=metrics.ProperMotionMetric(rmag=rmag, seeingCol=seeingCol, SedTemplate=SedTemplate)
bundle = metricBundles.MetricBundle(metric, slicer, sqlconstraint, runName=run,
# plotFuncs=plotFuncs, \
plotFuncs=plotFuncsPropmotion, \
summaryMetrics=summaryMetrics)
bundleList.append(bundle)
metric = calibrationMetrics.ParallaxCoverageMetric(rmag=rmag, seeingCol=seeingCol, SedTemplate=SedTemplate)
bundle = metricBundles.MetricBundle(metric, slicer, sqlconstraint, runName=run,
# plotFuncs=plotFuncs, \
plotFuncs=plotFuncsCoverage, \
summaryMetrics=summaryMetrics)
bundleList.append(bundle)
# Now for the HA Degen metric. If testing the type of
# correlation, call the metric differently here. Since the
# argument to actually do this is only part of my github fork
# at the moment, we use a different call. Running with default
# arguments (checkCorrKind=False) should then work without
# difficulty.
metric = calibrationMetrics.ParallaxHADegenMetric(rmag=rmag, seeingCol=seeingCol, SedTemplate=SedTemplate)
if checkCorrKind:
metric = calibrationMetrics.ParallaxHADegenMetric(rmag=rmag, seeingCol=seeingCol, SedTemplate=SedTemplate, useSpearmanR=useSpearmanR[iRun])
print "TESTING CORRELATION KIND -- useSpearmanR", useSpearmanR[iRun]
bundle = metricBundles.MetricBundle(metric, slicer, sqlconstraint, runName=run,
# plotFuncs=plotFuncs, \
plotFuncs=plotFuncsHAdegen, \
summaryMetrics=summaryMetrics)
bundleList.append(bundle)
# Run everything and make plots
bundleDict = metricBundles.makeBundlesDictFromList(bundleList)
bgroup = metricBundles.MetricBundleGroup(bundleDict, opsdb, outDir=outDir, resultsDb=resultsDb)
# try:
bgroup.runAll()
print "runAstrom.go INFO - bundles took %.2f minutes" \
% ((time.time() - timeStartIteration) / 60.)
# except KeyboardInterrupt:
# print "runAstrom.go FATAL - keyboard interrupt detected. Halting."
# return
bgroup.plotAll()
print "runAstrom.go INFO - bundles + plotting took %.2f minutes" \
% ((time.time() - timeStartIteration) / 60.)
print "Finished entire set. %i runs took %.2f minutes." % (iRun + 1, (time.time()-tStart)/60.)
def tEffMetrics(colmap=None,
runName='opsim',
extraSql=None,
extraMetadata=None,
nside=64,
ditherStacker=None,
ditherkwargs=None):
"""Generate a series of Teff metrics. Teff total, per night, and sky maps (all and per filter).
Parameters
----------
colmap : dict, opt
A dictionary with a mapping of column names. Default will use OpsimV4 column names.
runName : str, opt
The name of the simulated survey. Default is "opsim".
extraSql : str, opt
Additional constraint to add to any sql constraints (e.g. 'propId=1' or 'fieldID=522').
Default None, for no additional constraints.
extraMetadata : str, opt
Additional metadata to add before any below (i.e. "WFD"). Default is None.
nside : int, opt
Nside value for healpix slicer. Default 64.
If "None" is passed, the healpixslicer-based metrics will be skipped.
ditherStacker: str or lsst.sims.maf.stackers.BaseDitherStacker
Optional dither stacker to use to define ra/dec columns.
ditherkwargs: dict, opt
Optional dictionary of kwargs for the dither stacker.
Returns
-------
metricBundleDict
"""
if colmap is None:
colmap = ColMapDict('opsimV4')
bundleList = []
subgroup = extraMetadata
if subgroup is None:
subgroup = 'All visits'
raCol, decCol, degrees, ditherStacker, ditherMeta = radecCols(
ditherStacker, colmap, ditherkwargs)
# Set up basic all and per filter sql constraints.
filterlist, colors, orders, sqls, metadata = filterList(
all=True, extraSql=extraSql, extraMetadata=extraMetadata)
if metadata['all'] is None:
metadata['all'] = 'All visits'
subsetPlots = [plots.HealpixSkyMap(), plots.HealpixHistogram()]
# Total Teff and normalized Teff.
displayDict = {'group': 'T_eff Summary', 'subgroup': subgroup}
displayDict[
'caption'] = 'Total effective time of the survey (see Teff metric).'
displayDict['order'] = 0
metric = metrics.TeffMetric(m5Col=colmap['fiveSigmaDepth'],
filterCol=colmap['filter'],
normed=False,
metricName='Total Teff')
slicer = slicers.UniSlicer()
bundle = mb.MetricBundle(metric,
slicer,
constraint=sqls['all'],
displayDict=displayDict,
metadata=metadata['all'])
bundleList.append(bundle)
displayDict[
'caption'] = 'Normalized total effective time of the survey (see Teff metric).'
displayDict['order'] = 1
metric = metrics.TeffMetric(m5Col=colmap['fiveSigmaDepth'],
filterCol=colmap['filter'],
normed=True,
metricName='Normalized Teff')
slicer = slicers.UniSlicer()
bundle = mb.MetricBundle(metric,
slicer,
constraint=sqls['all'],
displayDict=displayDict,
metadata=metadata['all'])
bundleList.append(bundle)
# Generate Teff maps in all and per filters
displayDict = {'group': 'T_eff Maps', 'subgroup': subgroup}
if ditherMeta is not None:
for m in metadata:
metadata[m] = combineMetadata(metadata[m], ditherMeta)
metric = metrics.TeffMetric(m5Col=colmap['fiveSigmaDepth'],
filterCol=colmap['filter'],
normed=True,
metricName='Normalized Teff')
slicer = slicers.HealpixSlicer(nside=nside,
latCol=decCol,
lonCol=raCol,
latLonDeg=degrees)
for f in filterlist:
displayDict[
'caption'] = 'Normalized effective time of the survey, for %s' % metadata[
f]
displayDict['order'] = orders[f]
plotDict = {'color': colors[f]}
bundle = mb.MetricBundle(metric,
slicer,
sqls[f],
metadata=metadata[f],
stackerList=ditherStacker,
displayDict=displayDict,
plotFuncs=subsetPlots,
plotDict=plotDict,
summaryMetrics=standardSummary())
bundleList.append(bundle)
# Set the runName for all bundles and return the bundleDict.
for b in bundleList:
b.setRunName(runName)
return mb.makeBundlesDictFromList(bundleList)
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 rapidRevisitBatch(colmap=None, runName='opsim',
extraSql=None, extraMetadata=None, nside=64):
# Allow user to add dithering.
if colmap is None:
colmap = ColMapDict('opsimV4')
bundleList = []
sql = ''
metadata = 'All visits'
# Add additional sql constraint (such as wfdWhere) and metadata, if provided.
if (extraSql is not None) and (len(extraSql) > 0):
sql = extraSql
if extraMetadata is None:
metadata = extraSql.replace('filter =', '').replace('filter=', '')
metadata = metadata.replace('"', '').replace("'", '')
if extraMetadata is not None:
metadata = extraMetadata
subgroup = metadata
raCol = colmap['ra']
decCol = colmap['dec']
degrees = colmap['raDecDeg']
# Set up parallax metrics.
slicer = slicers.HealpixSlicer(nside=nside, lonCol=raCol, latCol=decCol, latLonDeg=degrees)
subsetPlots = [plots.HealpixSkyMap(), plots.HealpixHistogram()]
displayDict = {'group': 'Rapid Revisits', 'subgroup': subgroup,
'order': 0, 'caption': None}
# Calculate the uniformity (KS test) of the quick revisits.
dTmin = 40.0 # seconds
dTmax = 30.0 # minutes
minNvisit = 100
pixArea = float(hp.nside2pixarea(nside, degrees=True))
scale = pixArea * hp.nside2npix(nside)
m1 = metrics.RapidRevisitMetric(metricName='RapidRevisitUniformity', mjdCol=colmap['mjd'],
dTmin=dTmin / 60.0 / 60.0 / 24.0, dTmax=dTmax / 60.0 / 24.0,
minNvisits=minNvisit)
plotDict = {'xMin': 0, 'xMax': 1}
cutoff1 = 0.30
summaryStats = [metrics.FracBelowMetric(cutoff=cutoff1, scale=scale, metricName='Area (sq deg)')]
summaryStats.extend(standardSummary())
caption = 'Deviation from uniformity for short revisit timescales, between %s and %s seconds, ' % (
dTmin, dTmax)
caption += 'for pointings with at least %d visits in this time range. ' % (minNvisit)
caption += 'Summary statistic "Area" indicates the area on the sky which has a '
caption += 'deviation from uniformity of < %.2f.' % (cutoff1)
displayDict['caption'] = caption
bundle = mb.MetricBundle(m1, slicer, sql, plotDict=plotDict, plotFuncs=subsetPlots,
metadata=metadata, displayDict=displayDict, summaryMetrics=summaryStats)
bundleList.append(bundle)
displayDict['order'] += 1
# Calculate the actual number of quick revisits.
dTmax = dTmax # time in minutes
m2 = metrics.NRevisitsMetric(dT=dTmax, mjdCol=colmap['mjd'], normed=False)
plotDict = {'xMin': 0.1, 'xMax': 2000, 'logScale': True}
cutoff2 = 800
summaryStats = [metrics.FracAboveMetric(cutoff=cutoff2, scale=scale, metricName='Area (sq deg)')]
summaryStats.extend(standardSummary())
caption = 'Number of consecutive visits with return times faster than %.1f minutes, ' % (dTmax)
caption += 'in any filter, all proposals. '
caption += 'Summary statistic "Area" indicates the area on the sky which has more than '
caption += '%d revisits within this time window.' % (cutoff2)
displayDict['caption'] = caption
bundle = mb.MetricBundle(m2, slicer, sql, plotDict=plotDict, plotFuncs=subsetPlots,
metadata=metadata, displayDict=displayDict, summaryMetrics=summaryStats)
bundleList.append(bundle)
displayDict['order'] += 1
# Set the runName for all bundles and return the bundleDict.
for b in bundleList:
b.setRunName(runName)
return mb.makeBundlesDictFromList(bundleList)
