"""Sets of metrics to look at time between visits/pairs, etc.
"""
import numpy as np
import lsst.sims.maf.metrics as metrics
import lsst.sims.maf.slicers as slicers
import lsst.sims.maf.plots as plots
import lsst.sims.maf.metricBundles as mb
from .colMapDict import ColMapDict
from .common import standardSummary, filterList, combineMetadata, radecCols
__all__ = ['intraNight', 'interNight', 'seasons']
[docs]def intraNight(colmap=None, runName='opsim', nside=64, extraSql=None, extraMetadata=None,
slicer=None, display_group='IntraNight', subgroup='Pairs'):
"""Generate a set of statistics about the pair/triplet/etc. rate within a night.
Parameters
----------
colmap : dict or None, opt
A dictionary with a mapping of column names. Default will use OpsimV4 column names.
runName : str, opt
The name of the simulated survey. Default is "opsim".
nside : int, opt
Nside for the healpix slicer. Default 64.
extraSql : str or None, opt
Additional sql constraint to apply to all metrics.
extraMetadata : str or None, opt
Additional metadata to apply to all results.
slicer : slicer object (None)
Optinally use something other than a HealpixSlicer
Returns
-------
metricBundleDict
"""
if colmap is None:
colmap = ColMapDict('opsimV4')
metadata = extraMetadata
if extraSql is not None and len(extraSql) > 0:
if metadata is None:
metadata = extraSql
raCol, decCol, degrees, ditherStacker, ditherMeta = radecCols(None, colmap, None)
metadata = combineMetadata(metadata, ditherMeta)
bundleList = []
standardStats = standardSummary()
subsetPlots = [plots.HealpixSkyMap(), plots.HealpixHistogram()]
if slicer is None:
slicer = slicers.HealpixSlicer(nside=nside, latCol=decCol, lonCol=raCol, latLonDeg=degrees)
# Look for the fraction of visits in gri where there are pairs within dtMin/dtMax.
displayDict = {'group': display_group, 'subgroup': subgroup, 'caption': None, 'order': 0}
if extraSql is not None and len(extraSql) > 0:
sql = '(%s) and (filter="g" or filter="r" or filter="i")' % extraSql
else:
sql = 'filter="g" or filter="r" or filter="i"'
md = 'gri'
if metadata is not None:
md += ' ' + metadata
dtMin = 10.0
dtMax = 60.0
metric = metrics.PairFractionMetric(mjdCol=colmap['mjd'], minGap=dtMin, maxGap=dtMax,
metricName='Fraction of visits in pairs (%.0f-%.0f min)' % (dtMin,
dtMax))
displayDict['caption'] = 'Fraction of %s visits that have a paired visit' \
'between %.1f and %.1f minutes away. ' % (md, dtMin, dtMax)
displayDict['caption'] += 'If all visits were in pairs, this fraction would be 1.'
displayDict['order'] += 1
bundle = mb.MetricBundle(metric, slicer, sql, metadata=md, summaryMetrics=standardStats,
plotFuncs=subsetPlots, displayDict=displayDict)
bundleList.append(bundle)
dtMin = 20.0
dtMax = 90.0
metric = metrics.PairFractionMetric(mjdCol=colmap['mjd'], minGap=dtMin, maxGap=dtMax,
metricName='Fraction of visits in pairs (%.0f-%.0f min)' % (dtMin,
dtMax))
displayDict['caption'] = 'Fraction of %s visits that have a paired visit' \
'between %.1f and %.1f minutes away. ' % (md, dtMin, dtMax)
displayDict['caption'] += 'If all visits were in pairs, this fraction would be 1.'
displayDict['order'] += 1
bundle = mb.MetricBundle(metric, slicer, sql, metadata=md, summaryMetrics=standardStats,
plotFuncs=subsetPlots, displayDict=displayDict)
bundleList.append(bundle)
# Look at the fraction of visits which have another visit within dtMax, gri.
dtMax = 60.0
metric = metrics.NRevisitsMetric(mjdCol=colmap['mjd'], dT=dtMax, normed=True,
metricName='Fraction of visits with a revisit < %.0f min' % dtMax)
displayDict['caption'] = 'Fraction of %s visits that have another visit ' \
'within %.1f min. ' % (md, dtMax)
displayDict['caption'] += 'If all visits were in pairs (only), this fraction would be 0.5.'
displayDict['order'] += 1
bundle = mb.MetricBundle(metric, slicer, sql, metadata=md, summaryMetrics=standardStats,
plotFuncs=subsetPlots, displayDict=displayDict)
bundleList.append(bundle)
# Intranight gap map, all filters. Returns value in hours.
metric = metrics.IntraNightGapsMetric(metricName='Median Intra-Night Gap', mjdCol=colmap['mjd'],
reduceFunc=np.median)
displayDict['caption'] = 'Median gap between consecutive visits within a night, all bands'
if metadata is None or len(metadata) == 0:
displayDict['caption'] += ', all proposals.'
else:
displayDict['caption'] += ', %s.' % metadata
displayDict['order'] += 1
plotDict = {'percentileClip': 95}
bundle = mb.MetricBundle(metric, slicer, extraSql, metadata=metadata, displayDict=displayDict,
plotFuncs=subsetPlots, plotDict=plotDict,
summaryMetrics=standardStats)
bundleList.append(bundle)
# Histogram the number of visits per night.
countbins = np.arange(0, 10, 1)
metric = metrics.NVisitsPerNightMetric(nightCol=colmap['night'], bins=countbins,
metricName="NVisitsPerNight")
plotDict = {'bins': countbins, 'xlabel': 'Number of visits each night'}
displayDict['caption'] = 'Histogram of the number of visits in each night, per point on the sky'
if metadata is None or len(metadata) == 0:
displayDict['caption'] += ', all proposals.'
else:
displayDict['caption'] += ', %s.' % metadata
displayDict['order'] = 0
plotFunc = plots.SummaryHistogram()
bundle = mb.MetricBundle(metric, slicer, extraSql, plotDict=plotDict,
displayDict=displayDict, metadata=metadata, plotFuncs=[plotFunc])
bundleList.append(bundle)
# Histogram of the time between revisits (all filters) within two hours.
binMin = 0
binMax = 120.
binsize = 5.
bins_metric = np.arange(binMin / 60.0 / 24.0, (binMax + binsize) / 60. / 24., binsize / 60. / 24.)
bins_plot = bins_metric * 24.0 * 60.0
metric = metrics.TgapsMetric(bins=bins_metric, timesCol=colmap['mjd'], metricName='DeltaT Histogram')
plotDict = {'bins': bins_plot, 'xlabel': 'dT (minutes)'}
displayDict['caption'] = 'Histogram of the time between consecutive visits to a given point ' \
'on the sky, considering visits between %.1f and %.1f minutes' % (binMin,
binMax)
if metadata is None or len(metadata) == 0:
displayDict['caption'] += ', all proposals.'
else:
displayDict['caption'] += ', %s.' % metadata
displayDict['order'] += 1
plotFunc = plots.SummaryHistogram()
bundle = mb.MetricBundle(metric, slicer, extraSql, plotDict=plotDict,
displayDict=displayDict, metadata=metadata, plotFuncs=[plotFunc])
bundleList.append(bundle)
# Set the runName for all bundles and return the bundleDict.
for b in bundleList:
b.setRunName(runName)
plotBundles = None
return mb.makeBundlesDictFromList(bundleList), plotBundles
[docs]def interNight(colmap=None, runName='opsim', nside=64, extraSql=None, extraMetadata=None,
slicer=None, display_group='InterNight', subgroup='Night gaps'):
"""Generate a set of statistics about the spacing between nights with observations.
Parameters
----------
colmap : dict or None, opt
A dictionary with a mapping of column names. Default will use OpsimV4 column names.
runName : str, opt
The name of the simulated survey. Default is "opsim".
nside : int, opt
Nside for the healpix slicer. Default 64.
extraSql : str or None, opt
Additional sql constraint to apply to all metrics.
extraMetadata : str or None, opt
Additional metadata to use for all outputs.
slicer : slicer object (None)
Optinally use something other than a HealpixSlicer
Returns
-------
metricBundleDict
"""
if colmap is None:
colmap = ColMapDict('opsimV4')
bundleList = []
# Set up basic all and per filter sql constraints.
raCol, decCol, degrees, ditherStacker, ditherMeta = radecCols(None, colmap, None)
metadata = combineMetadata(extraMetadata, ditherMeta)
filterlist, colors, orders, sqls, metadata = filterList(all=True,
extraSql=extraSql,
extraMetadata=metadata)
if slicer is None:
slicer = slicers.HealpixSlicer(nside=nside, latCol=decCol, lonCol=raCol, latLonDeg=degrees)
displayDict = {'group': display_group, 'subgroup': subgroup, 'caption': None, 'order': 0}
# Histogram of the number of nights between visits.
bins = np.arange(1, 20.5, 1)
metric = metrics.NightgapsMetric(bins=bins, nightCol=colmap['night'], metricName='DeltaNight Histogram')
plotDict = {'bins': bins, 'xlabel': 'dT (nights)'}
displayDict['caption'] = 'Histogram of the number of nights between consecutive visits to a ' \
'given point on the sky, considering separations between %d and %d' \
% (bins.min(), bins.max())
if metadata['all'] is None or len(metadata['all']) == 0:
displayDict['caption'] += ', all proposals.'
else:
displayDict['caption'] += ', %s.' % metadata['all']
plotFunc = plots.SummaryHistogram()
bundle = mb.MetricBundle(metric, slicer, sqls['all'], plotDict=plotDict,
displayDict=displayDict, metadata=metadata['all'], plotFuncs=[plotFunc])
bundleList.append(bundle)
standardStats = standardSummary()
subsetPlots = [plots.HealpixSkyMap(), plots.HealpixHistogram()]
# Median inter-night gap (each and all filters)
metric = metrics.InterNightGapsMetric(metricName='Median Inter-Night Gap', mjdCol=colmap['mjd'],
reduceFunc=np.median)
for f in filterlist:
displayDict['caption'] = 'Median gap between nights with observations, %s.' % metadata[f]
displayDict['order'] = orders[f]
plotDict = {'color': colors[f], 'percentileClip': 95.}
bundle = mb.MetricBundle(metric, slicer, sqls[f], metadata=metadata[f],
displayDict=displayDict,
plotFuncs=subsetPlots, plotDict=plotDict,
summaryMetrics=standardStats)
bundleList.append(bundle)
# Maximum inter-night gap (in each and all filters).
metric = metrics.InterNightGapsMetric(metricName='Max Inter-Night Gap', mjdCol=colmap['mjd'],
reduceFunc=np.max)
for f in filterlist:
displayDict['caption'] = 'Maximum gap between nights with observations, %s.' % metadata[f]
displayDict['order'] = orders[f]
plotDict = {'color': colors[f], 'percentileClip': 95., 'binsize': 5}
bundle = mb.MetricBundle(metric, slicer, sqls[f], metadata=metadata[f], displayDict=displayDict,
plotFuncs=subsetPlots, plotDict=plotDict, summaryMetrics=standardStats)
bundleList.append(bundle)
# Set the runName for all bundles and return the bundleDict.
for b in bundleList:
b.setRunName(runName)
plotBundles = None
return mb.makeBundlesDictFromList(bundleList), plotBundles
[docs]def seasons(colmap=None, runName='opsim', nside=64, extraSql=None, extraMetadata=None):
"""Generate a set of statistics about the length and number of seasons.
Parameters
----------
colmap : dict or None, opt
A dictionary with a mapping of column names. Default will use OpsimV4 column names.
runName : str, opt
The name of the simulated survey. Default is "opsim".
nside : int, opt
Nside for the healpix slicer. Default 64.
extraSql : str or None, opt
Additional sql constraint to apply to all metrics.
extraMetadata : str or None, opt
Additional metadata to use for all outputs.
Returns
-------
metricBundleDict
"""
if colmap is None:
colmap = ColMapDict('opsimV4')
bundleList = []
# Set up basic all and per filter sql constraints.
raCol, decCol, degrees, ditherStacker, ditherMeta = radecCols(None, colmap, None)
metadata = combineMetadata(extraMetadata, ditherMeta)
filterlist, colors, orders, sqls, metadata = filterList(all=True,
extraSql=extraSql,
extraMetadata=metadata)
slicer = slicers.HealpixSlicer(nside=nside, latCol=decCol, lonCol=raCol, latLonDeg=degrees)
displayDict = {'group': 'IntraSeason', 'subgroup': 'Season length', 'caption': None, 'order': 0}
standardStats = standardSummary()
subsetPlots = [plots.HealpixSkyMap(), plots.HealpixHistogram()]
metric = metrics.SeasonLengthMetric(metricName='Median Season Length', mjdCol=colmap['mjd'],
reduceFunc=np.median)
for f in filterlist:
displayDict['caption'] = 'Median season length, %s.' % metadata[f]
displayDict['order'] = orders[f]
maxS = 250
if f == 'all':
minS = 90
else:
minS = 30
plotDict = {'color': colors[f], 'colorMin': minS, 'colorMax': maxS, 'xMin': minS, 'xMax': maxS}
bundle = mb.MetricBundle(metric, slicer, sqls[f], metadata=metadata[f],
displayDict=displayDict,
plotFuncs=subsetPlots, plotDict=plotDict,
summaryMetrics=standardStats)
bundleList.append(bundle)
# Number of seasons
metric = metrics.CampaignLengthMetric(metricName='NSeasons', mjdCol=colmap['mjd'],
expTimeCol=colmap['exptime'], minExpTime=15)
displayDict['caption'] = 'Number of seasons, any filter.'
displayDict['order'] = 0
plotDict = {'color': 'k', 'colorMin': 0, 'colorMax': 11, 'xMin': 0, 'xMax': 11}
bundle = mb.MetricBundle(metric, slicer, sqls['all'], metadata=metadata['all'],
displayDict=displayDict,
plotFuncs=subsetPlots, plotDict=plotDict,
summaryMetrics=standardStats)
bundleList.append(bundle)
# Set the runName for all bundles and return the bundleDict.
for b in bundleList:
b.setRunName(runName)
plotBundles = None
return mb.makeBundlesDictFromList(bundleList), plotBundles