def testRapidRevisitMetric(self):
data = np.zeros(100, dtype=list(zip(['expMJD'], [float])))
# Uniformly distribute time _differences_ between 0 and 100
dtimes = np.arange(100)
data['expMJD'] = dtimes.cumsum()
# Set up "rapid revisit" metric to look for visits between 5 and 25
metric = metrics.RapidRevisitMetric(dTmin=5, dTmax=55, minNvisits=50)
result = metric.run(data)
# This should be uniform.
self.assertTrue(result < 0.1)
self.assertTrue(result >= 0)
# Set up non-uniform distribution of time differences
dtimes = np.zeros(100) + 5
data['expMJD'] = dtimes.cumsum()
result = metric.run(data)
self.assertTrue(result >= 0.5)
dtimes = np.zeros(100) + 15
data['expMJD'] = dtimes.cumsum()
result = metric.run(data)
self.assertTrue(result >= 0.5)
# Let's see how much dmax/result can vary
resmin = 1
resmax = 0
for i in range(10000):
dtimes = np.random.rand(100)
data['expMJD'] = dtimes.cumsum()
metric = metrics.RapidRevisitMetric(dTmin=0.1,
dTmax=0.8,
minNvisits=50)
result = metric.run(data)
resmin = np.min([resmin, result])
resmax = np.max([resmax, result])
print("RapidRevisit .. range", resmin, resmax)
def testRapidRevisitMetric(self):
data = np.zeros(100, dtype=list(zip(['observationStartMJD'], [float])))
dtimes = np.arange(100)/24./60.
data['observationStartMJD'] = dtimes.cumsum()
# Set metric parameters to the actual N1/N2 values for these dtimes.
metric = metrics.RapidRevisitMetric(dTmin=40./60./60./24., dTpairs=20./60./24.,
dTmax=30./60./24., minN1=19, minN2=29)
result = metric.run(data)
self.assertEqual(result, 1)
# Set metric parameters to > N1/N2 values, to see it return 0.
metric = metrics.RapidRevisitMetric(dTmin=40.0/60.0/60.0/24., dTpairs=20./60./24.,
dTmax=30./60./24., minN1=30, minN2=50)
result = metric.run(data)
self.assertEqual(result, 0)
# Test with single value data.
data = np.zeros(1, dtype=list(zip(['observationStartMJD'], [float])))
result = metric.run(data)
self.assertEqual(result, 0)
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 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 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)