def standardAngleMetrics(colname, replace_colname=None):
"""A set of standard simple metrics for some quantity which is a wrap-around angle.
Parameters
----------
colname : str
The column name to apply the metrics to.
replace_colname: str or None, opt
Value to replace colname with in the metricName.
i.e. if replace_colname='' then metric name is Mean, instead of Mean Airmass, or
if replace_colname='seeingGeom', then metric name is Mean seeingGeom instead of Mean seeingFwhmGeom.
Default is None, which does not alter the metric name.
Returns
-------
List of configured metrics.
"""
standardAngleMetrics = [
metrics.MeanAngleMetric(colname),
metrics.RmsAngleMetric(colname),
metrics.FullRangeAngleMetric(colname),
metrics.MinMetric(colname),
metrics.MaxMetric(colname)
]
if replace_colname is not None:
for m in standardAngleMetrics:
if len(replace_colname) > 0:
m.name = m.name.replace('%s' % colname, '%s' % replace_colname)
else:
m.name = m.name.rstrip(' %s' % colname)
return standardAngleMetrics
def standardMetrics(colname, replace_colname=None):
"""A set of standard simple metrics for some quanitity. Typically would be applied with unislicer.
Parameters
----------
colname : str
The column name to apply the metrics to.
replace_colname: str or None, opt
Value to replace colname with in the metricName.
i.e. if replace_colname='' then metric name is Mean, instead of Mean Airmass, or
if replace_colname='seeingGeom', then metric name is Mean seeingGeom instead of Mean seeingFwhmGeom.
Default is None, which does not alter the metric name.
Returns
-------
List of configured metrics.
"""
standardMetrics = [
metrics.MeanMetric(colname),
metrics.MedianMetric(colname),
metrics.MinMetric(colname),
metrics.MaxMetric(colname)
]
if replace_colname is not None:
for m in standardMetrics:
if len(replace_colname) > 0:
m.name = m.name.replace('%s' % colname, '%s' % replace_colname)
else:
m.name = m.name.rstrip(' %s' % colname)
return standardMetrics
def meanRADec(colmap=None, runName='opsim', extraSql=None, extraMetadata=None):
"""Plot the range of RA/Dec as a function of night.
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. 'night<365')
Default None, for no additional constraints.
extraMetadata : str, opt
Additional metadata to add before any below (i.e. "WFD"). Default is None.
"""
if colmap is None:
colmap = ColMapDict('opsimV4')
bundleList = []
plotBundles = []
group = 'RA Dec coverage'
subgroup = 'All visits'
if extraMetadata is not None:
subgroup = extraMetadata
displayDict = {'group': group, 'subgroup': subgroup, 'order': 0}
ra_metrics = [metrics.MeanAngleMetric(colmap['ra']), metrics.FullRangeAngleMetric(colmap['ra'])]
dec_metrics = [metrics.MeanMetric(colmap['dec']), metrics.MinMetric(colmap['dec']),
metrics.MaxMetric(colmap['dec'])]
for m in ra_metrics:
slicer = slicers.OneDSlicer(sliceColName=colmap['night'], binsize=1)
if not colmap['raDecDeg']:
plotDict = {'yMin': np.radians(-5), 'yMax': np.radians(365)}
else:
plotDict = {'yMin': -5, 'yMax': 365}
bundle = mb.MetricBundle(m, slicer, extraSql, metadata=extraMetadata,
displayDict=displayDict, plotDict=plotDict)
bundleList.append(bundle)
for m in dec_metrics:
slicer = slicers.OneDSlicer(sliceColName=colmap['night'], binsize=1)
bundle = mb.MetricBundle(m, slicer, extraSql, metadata=extraMetadata,
displayDict=displayDict)
bundleList.append(bundle)
# Set the runName for all bundles and return the bundleDict.
for b in bundleList:
b.setRunName(runName)
return mb.makeBundlesDictFromList(bundleList), plotBundles
def standardSummary():
"""A set of standard summary metrics, to calculate Mean, RMS, Median, #, Max/Min, and # 3-sigma outliers.
"""
standardSummary = [
metrics.MeanMetric(),
metrics.RmsMetric(),
metrics.MedianMetric(),
metrics.CountMetric(),
metrics.MaxMetric(),
metrics.MinMetric(),
metrics.NoutliersNsigmaMetric(metricName='N(+3Sigma)', nSigma=3),
metrics.NoutliersNsigmaMetric(metricName='N(-3Sigma)', nSigma=-3.)
]
return standardSummary
def makeBundleList(dbFile,
night=1,
nside=64,
latCol='ditheredDec',
lonCol='ditheredRA'):
"""
Make a bundleList of things to run
"""
# Construct sql queries for each filter and all filters
filters = ['u', 'g', 'r', 'i', 'z', 'y']
sqls = ['night=%i and filter="%s"' % (night, f) for f in filters]
sqls.append('night=%i' % night)
bundleList = []
plotFuncs_lam = [plots.LambertSkyMap()]
reg_slicer = slicers.HealpixSlicer(nside=nside,
lonCol=lonCol,
latCol=latCol,
latLonDeg=False)
altaz_slicer = slicers.HealpixSlicer(nside=nside,
latCol='altitude',
latLonDeg=False,
lonCol='azimuth',
useCache=False)
unislicer = slicers.UniSlicer()
for sql in sqls:
# Number of exposures
metric = metrics.CountMetric('expMJD', metricName='N visits')
bundle = metricBundles.MetricBundle(metric, reg_slicer, sql)
bundleList.append(bundle)
metric = metrics.CountMetric('expMJD', metricName='N visits alt az')
bundle = metricBundles.MetricBundle(metric,
altaz_slicer,
sql,
plotFuncs=plotFuncs_lam)
bundleList.append(bundle)
metric = metrics.MeanMetric('expMJD', metricName='Mean Visit Time')
bundle = metricBundles.MetricBundle(metric, reg_slicer, sql)
bundleList.append(bundle)
metric = metrics.MeanMetric('expMJD',
metricName='Mean Visit Time alt az')
bundle = metricBundles.MetricBundle(metric,
altaz_slicer,
sql,
plotFuncs=plotFuncs_lam)
bundleList.append(bundle)
metric = metrics.CountMetric('expMJD', metricName='N_visits')
bundle = metricBundles.MetricBundle(metric, unislicer, sql)
bundleList.append(bundle)
# Need pairs in window to get a map of how well it gathered SS pairs.
# Moon phase.
metric = metrics.NChangesMetric(col='filter', metricName='Filter Changes')
bundle = metricBundles.MetricBundle(metric, unislicer, 'night=%i' % night)
bundleList.append(bundle)
metric = metrics.OpenShutterFractionMetric()
bundle = metricBundles.MetricBundle(metric, unislicer, 'night=%i' % night)
bundleList.append(bundle)
metric = metrics.MeanMetric('slewTime')
bundle = metricBundles.MetricBundle(metric, unislicer, 'night=%i' % night)
bundleList.append(bundle)
metric = metrics.MinMetric('slewTime')
bundle = metricBundles.MetricBundle(metric, unislicer, 'night=%i' % night)
bundleList.append(bundle)
metric = metrics.MaxMetric('slewTime')
bundle = metricBundles.MetricBundle(metric, unislicer, 'night=%i' % night)
bundleList.append(bundle)
# Make plots of the solar system pairs that were taken in the night
metric = metrics.PairMetric()
sql = 'night=%i and (filter ="r" or filter="g" or filter="i")' % night
bundle = metricBundles.MetricBundle(metric, reg_slicer, sql)
bundleList.append(bundle)
metric = metrics.PairMetric(metricName='z Pairs')
sql = 'night=%i and filter="z"' % night
bundle = metricBundles.MetricBundle(metric, reg_slicer, sql)
bundleList.append(bundle)
# Plot up each visit
metric = metrics.NightPointingMetric()
slicer = slicers.UniSlicer()
sql = sql = 'night=%i' % night
plotFuncs = [plots.NightPointingPlotter()]
bundle = metricBundles.MetricBundle(metric,
slicer,
sql,
plotFuncs=plotFuncs)
bundleList.append(bundle)
return metricBundles.makeBundlesDictFromList(bundleList)
def makeBundleList(dbFile,
night=1,
nside=64,
latCol='fieldDec',
lonCol='fieldRA',
notes=True,
colmap=None):
"""
Make a bundleList of things to run
"""
if colmap is None:
colmap = ColMapDict('opsimV4')
mjdCol = 'observationStartMJD'
altCol = 'altitude'
azCol = 'azimuth'
# Construct sql queries for each filter and all filters
filters = ['u', 'g', 'r', 'i', 'z', 'y']
sqls = ['night=%i and filter="%s"' % (night, f) for f in filters]
sqls.append('night=%i' % night)
bundleList = []
plotFuncs_lam = [plots.LambertSkyMap()]
# Hourglass
hourslicer = slicers.HourglassSlicer()
displayDict = {'group': 'Hourglass'}
md = ''
sql = 'night=%i' % night
metric = metrics.HourglassMetric(nightCol=colmap['night'],
mjdCol=colmap['mjd'],
metricName='Hourglass')
bundle = metricBundles.MetricBundle(metric,
hourslicer,
constraint=sql,
metadata=md,
displayDict=displayDict)
bundleList.append(bundle)
reg_slicer = slicers.HealpixSlicer(nside=nside,
lonCol=lonCol,
latCol=latCol,
latLonDeg=True)
altaz_slicer = slicers.HealpixSlicer(nside=nside,
latCol=altCol,
latLonDeg=True,
lonCol=azCol,
useCache=False)
unislicer = slicers.UniSlicer()
for sql in sqls:
# Number of exposures
metric = metrics.CountMetric(mjdCol, metricName='N visits')
bundle = metricBundles.MetricBundle(metric, reg_slicer, sql)
bundleList.append(bundle)
metric = metrics.CountMetric(mjdCol, metricName='N visits alt az')
bundle = metricBundles.MetricBundle(metric,
altaz_slicer,
sql,
plotFuncs=plotFuncs_lam)
bundleList.append(bundle)
metric = metrics.MeanMetric(mjdCol, metricName='Mean Visit Time')
bundle = metricBundles.MetricBundle(metric, reg_slicer, sql)
bundleList.append(bundle)
metric = metrics.MeanMetric(mjdCol,
metricName='Mean Visit Time alt az')
bundle = metricBundles.MetricBundle(metric,
altaz_slicer,
sql,
plotFuncs=plotFuncs_lam)
bundleList.append(bundle)
metric = metrics.CountMetric(mjdCol, metricName='N_visits')
bundle = metricBundles.MetricBundle(metric, unislicer, sql)
bundleList.append(bundle)
# Need pairs in window to get a map of how well it gathered SS pairs.
# Moon phase.
metric = metrics.NChangesMetric(col='filter', metricName='Filter Changes')
bundle = metricBundles.MetricBundle(metric, unislicer, 'night=%i' % night)
bundleList.append(bundle)
metric = metrics.BruteOSFMetric()
bundle = metricBundles.MetricBundle(metric, unislicer, 'night=%i' % night)
bundleList.append(bundle)
metric = metrics.MeanMetric('slewTime')
bundle = metricBundles.MetricBundle(metric, unislicer, 'night=%i' % night)
bundleList.append(bundle)
metric = metrics.MinMetric('slewTime')
bundle = metricBundles.MetricBundle(metric, unislicer, 'night=%i' % night)
bundleList.append(bundle)
metric = metrics.MaxMetric('slewTime')
bundle = metricBundles.MetricBundle(metric, unislicer, 'night=%i' % night)
bundleList.append(bundle)
# Make plots of the solar system pairs that were taken in the night
metric = metrics.PairMetric(mjdCol=mjdCol)
sql = 'night=%i and (filter ="r" or filter="g" or filter="i")' % night
bundle = metricBundles.MetricBundle(metric, reg_slicer, sql)
bundleList.append(bundle)
metric = metrics.PairMetric(mjdCol=mjdCol, metricName='z Pairs')
sql = 'night=%i and filter="z"' % night
bundle = metricBundles.MetricBundle(metric, reg_slicer, sql)
bundleList.append(bundle)
# Plot up each visit
metric = metrics.NightPointingMetric(mjdCol=mjdCol)
slicer = slicers.UniSlicer()
sql = 'night=%i' % night
plotFuncs = [plots.NightPointingPlotter()]
bundle = metricBundles.MetricBundle(metric,
slicer,
sql,
plotFuncs=plotFuncs)
bundleList.append(bundle)
# stats from the note column
if notes:
displayDict = {'group': 'Basic Stats', 'subgroup': 'Percent stats'}
metric = metrics.StringCountMetric(col='note',
percent=True,
metricName='Percents')
bundle = metricBundles.MetricBundle(metric,
unislicer,
sql,
displayDict=displayDict)
bundleList.append(bundle)
displayDict['subgroup'] = 'Count Stats'
metric = metrics.StringCountMetric(col='note', metricName='Counts')
bundle = metricBundles.MetricBundle(metric,
unislicer,
sql,
displayDict=displayDict)
bundleList.append(bundle)
return metricBundles.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))
simdata = randomdither.run(simdata)
# Add columns showing the actual dither values
# Note that because RA is wrapped around 360, there will be large values of 'radith' near this point
basestacker = utils.BaseStacker()
basestacker.colsAdded = ['radith', 'decdith']
simdata = basestacker._addStackers(simdata)
simdata['radith'] = simdata['randomRADither'] - simdata['fieldRA']
simdata['decdith'] = simdata['randomDecDither'] - simdata['fieldDec']
metriclist = []
metriclist.append(metrics.MeanMetric('radith'))
metriclist.append(metrics.MeanMetric('decdith'))
metriclist.append(metrics.RmsMetric('radith'))
metriclist.append(metrics.RmsMetric('decdith'))
metriclist.append(metrics.FullRangeMetric('radith'))
metriclist.append(metrics.FullRangeMetric('decdith'))
metriclist.append(metrics.MaxMetric('decdith'))
metriclist.append(metrics.MinMetric('decdith'))
slicer = slicers.OpsimFieldSlicer()
slicer.setupSlicer(simdata, fielddata)
gm = sliceMetrics.BaseSliceMetric()
gm.setSlicer(slicer)
gm.setMetrics(metriclist)
gm.runSlices(simdata, 'Dither Test')
gm.plotAll(savefig=False)
plt.show()
def metadataBasics(value,
colmap=None,
runName='opsim',
valueName=None,
groupName=None,
extraSql=None,
extraMetadata=None,
nside=64):
"""Calculate basic metrics on visit metadata 'value' (e.g. airmass, normalized airmass, seeing..).
Calculates this around the sky (HealpixSlicer), makes histograms of all visits (OneDSlicer),
and calculates statistics on all visits (UniSlicer) for the quantity in all visits and per filter.
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('fbs')
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
# Summarize values over all and per filter (min/mean/median/max/percentiles/outliers/rms).
slicer = slicers.UniSlicer()
for f in filterlist:
for m in extendedMetrics(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.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=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 slewActivities(colmap=None,
runName='opsim',
totalSlewN=1,
sqlConstraint=None):
"""Generate a set of slew statistics focused on finding the contributions to the overall slew time.
These slew statistics must be run on the SlewActivities table in opsimv4 and opsimv3.
Note that the type of activities listed are different between v3 and v4.
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".
totalSlewN : int, opt
The total number of slews in the simulated survey.
Used to calculate % of slew activities for each component.
Default is 1.
sqlConstraint : str or None, opt
SQL constraint to apply to metrics. Note this runs on Slew*State table, so constraints
should generally be based on slew_slewCount.
Returns
-------
metricBundleDict
"""
if totalSlewN == 1:
warnings.warn(
'TotalSlewN should be set (using 1). Percents from activities may be incorrect.'
)
if colmap is None:
colmap = ColMapDict('opsimV4')
bundleList = []
# All of these metrics run with a unislicer, on all the slew data.
slicer = slicers.UniSlicer()
print(colmap.keys())
if 'slewactivities' not in colmap:
raise ValueError(
"List of slewactivities not in colmap! Will not create slewActivities bundles."
)
slewTypes = colmap['slewactivities']
displayDict = {
'group': 'Slew',
'subgroup': 'Slew Activities',
'order': -1,
'caption': None
}
for slewType in slewTypes:
metadata = slewType
tableValue = colmap[slewType]
# Metrics for all activities of this type.
sql = 'activityDelay>0 and activity="%s"' % tableValue
if sqlConstraint is not None:
sqlconstraint = '(%s) and (%s)' % (sql, sqlConstraint)
metric = metrics.CountRatioMetric(col='activityDelay',
normVal=totalSlewN / 100.0,
metricName='ActivePerc')
displayDict[
'caption'] = 'Percent of total slews which include %s movement.' % slewType
displayDict['order'] += 1
bundle = mb.MetricBundle(metric,
slicer,
sqlconstraint,
displayDict=displayDict,
metadata=metadata)
bundleList.append(bundle)
metric = metrics.MeanMetric(col='activityDelay',
metricName='ActiveAve')
displayDict[
'caption'] = 'Mean amount of time (in seconds) for %s movements.' % (
slewType)
displayDict['order'] += 1
bundle = mb.MetricBundle(metric,
slicer,
sqlconstraint,
displayDict=displayDict,
metadata=metadata)
bundleList.append(bundle)
metric = metrics.MaxMetric(col='activityDelay', metricName='Max')
displayDict[
'caption'] = 'Max amount of time (in seconds) for %s movement.' % (
slewType)
displayDict['order'] += 1
bundle = mb.MetricBundle(metric,
slicer,
sqlconstraint,
displayDict=displayDict,
metadata=metadata)
bundleList.append(bundle)
# Metrics for activities of this type which are in the critical path.
sql = 'activityDelay>0 and inCriticalPath="True" and activity="%s"' % tableValue
if sqlConstraint is not None:
sqlconstraint = '(%s) and (%s)' % (sql, sqlConstraint)
metric = metrics.CountRatioMetric(col='activityDelay',
normVal=totalSlewN / 100.0,
metricName='ActivePerc in crit')
displayDict['caption'] = 'Percent of total slew which include %s movement, ' \
'and are in critical path.' % (slewType)
displayDict['order'] += 1
bundle = mb.MetricBundle(metric,
slicer,
sqlconstraint,
displayDict=displayDict,
metadata=metadata)
bundleList.append(bundle)
metric = metrics.MeanMetric(col='activityDelay',
metricName='ActiveAve in crit')
displayDict['caption'] = 'Mean time (in seconds) for %s movements, ' \
'when in critical path.' % (slewType)
displayDict['order'] += 1
bundle = mb.MetricBundle(metric,
slicer,
sqlconstraint,
displayDict=displayDict,
metadata=metadata)
bundleList.append(bundle)
for b in bundleList:
b.setRunName(runName)
return mb.makeBundlesDictFromList(bundleList)
def testMaxMetric(self):
"""Test max metric."""
testmetric = metrics.MaxMetric('testdata')
self.assertEqual(testmetric.run(self.dv), self.dv['testdata'].max())
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)
