def setupMetrics(colmap, wholesurvey=False):
metricList = []
captionList = []
# Number of filter changes (per slice - either whole survey or X nights)
if wholesurvey:
metricList.append(metrics.NChangesMetric(col=colmap['filter'], orderBy=colmap['mjd'],
metricName='Total Filter Changes'))
else:
metricList.append(metrics.NChangesMetric(col=colmap['filter'], orderBy=colmap['mjd'],
metricName='Filter Changes'))
captionList.append('Total filter changes ')
# Minimum time between filter changes
metricList.append(metrics.MinTimeBetweenStatesMetric(changeCol=colmap['filter'], timeCol=colmap['mjd']))
captionList.append('Minimum time between filter changes ')
# Number of filter changes faster than 10 minutes
metricList.append(metrics.NStateChangesFasterThanMetric(changeCol=colmap['filter'], timeCol=colmap['mjd'],
cutoff=10))
captionList.append('Number of filter changes faster than 10 minutes ')
# Number of filter changes faster than 20 minutes
metricList.append(metrics.NStateChangesFasterThanMetric(changeCol=colmap['filter'], timeCol=colmap['mjd'],
cutoff=20))
captionList.append('Number of filter changes faster than 20 minutes ')
# Maximum number of filter changes faster than 10 minutes within slice
metricList.append(metrics.MaxStateChangesWithinMetric(changeCol=colmap['filter'], timeCol=colmap['mjd'],
timespan=10))
captionList.append('Max number of filter changes within a window of 10 minutes ')
# Maximum number of filter changes faster than 20 minutes within slice
metricList.append(metrics.MaxStateChangesWithinMetric(changeCol=colmap['filter'], timeCol=colmap['mjd'],
timespan=20))
captionList.append('Max number of filter changes within a window of 20 minutes ')
return metricList, captionList
def testNChangesMetric(self):
"""
Test the NChanges metric.
"""
filters = np.array(['u', 'u', 'g', 'g', 'r'])
visitTimes = np.arange(0, filters.size, 1)
data = np.core.records.fromarrays(
[visitTimes, filters], names=['observationStartMJD', 'filter'])
metric = metrics.NChangesMetric()
result = metric.run(data)
self.assertEqual(result, 2)
filters = np.array(['u', 'g', 'u', 'g', 'r'])
data = np.core.records.fromarrays(
[visitTimes, filters], names=['observationStartMJD', 'filter'])
metric = metrics.NChangesMetric()
result = metric.run(data)
self.assertEqual(result, 4)
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 glanceBatch(colmap=None,
runName='opsim',
nside=64,
filternames=('u', 'g', 'r', 'i', 'z', 'y'),
nyears=10,
pairnside=32,
sqlConstraint=None):
"""Generate a handy set of metrics that give a quick overview of how well a survey performed.
This is a meta-set of other batches, to some extent.
Parameters
----------
colmap : dict, opt
A dictionary with a mapping of column names. Default will use OpsimV4 column names.
run_name : str, opt
The name of the simulated survey. Default is "opsim".
nside : int, opt
The nside for the healpix slicers. Default 64.
filternames : list of str, opt
The list of individual filters to use when running metrics.
Default is ('u', 'g', 'r', 'i', 'z', 'y').
There is always an all-visits version of the metrics run as well.
nyears : int (10)
How many years to attempt to make hourglass plots for
pairnside : int (32)
nside to use for the pair fraction metric (it's slow, so nice to use lower resolution)
sqlConstraint : str or None, opt
Additional SQL constraint to apply to all metrics.
Returns
-------
metricBundleDict
"""
if isinstance(colmap, str):
raise ValueError('colmap must be a dictionary, not a string')
if colmap is None:
colmap = ColMapDict('opsimV4')
bundleList = []
if sqlConstraint is None:
sqlC = ''
else:
sqlC = '(%s) and' % sqlConstraint
sql_per_filt = [
'%s %s="%s"' % (sqlC, colmap['filter'], filtername)
for filtername in filternames
]
sql_per_and_all_filters = [sqlConstraint] + sql_per_filt
standardStats = standardSummary()
subsetPlots = [plots.HealpixSkyMap(), plots.HealpixHistogram()]
# Super basic things
displayDict = {'group': 'Basic Stats', 'order': 1}
sql = sqlConstraint
slicer = slicers.UniSlicer()
# Length of Survey
metric = metrics.FullRangeMetric(col=colmap['mjd'],
metricName='Length of Survey (days)')
bundle = metricBundles.MetricBundle(metric,
slicer,
sql,
displayDict=displayDict)
bundleList.append(bundle)
# Total number of filter changes
metric = metrics.NChangesMetric(col=colmap['filter'],
orderBy=colmap['mjd'])
bundle = metricBundles.MetricBundle(metric,
slicer,
sql,
displayDict=displayDict)
bundleList.append(bundle)
# Total open shutter fraction
metric = metrics.OpenShutterFractionMetric(
slewTimeCol=colmap['slewtime'],
expTimeCol=colmap['exptime'],
visitTimeCol=colmap['visittime'])
bundle = metricBundles.MetricBundle(metric,
slicer,
sql,
displayDict=displayDict)
bundleList.append(bundle)
# Total effective exposure time
metric = metrics.TeffMetric(m5Col=colmap['fiveSigmaDepth'],
filterCol=colmap['filter'],
normed=True)
for sql in sql_per_and_all_filters:
bundle = metricBundles.MetricBundle(metric,
slicer,
sql,
displayDict=displayDict)
bundleList.append(bundle)
# Number of observations, all and each filter
metric = metrics.CountMetric(col=colmap['mjd'],
metricName='Number of Exposures')
for sql in sql_per_and_all_filters:
bundle = metricBundles.MetricBundle(metric,
slicer,
sql,
displayDict=displayDict)
bundleList.append(bundle)
# The alt/az plots of all the pointings
slicer = slicers.HealpixSlicer(nside=nside,
latCol='zenithDistance',
lonCol=colmap['az'],
latLonDeg=colmap['raDecDeg'],
useCache=False)
stacker = stackers.ZenithDistStacker(altCol=colmap['alt'],
degrees=colmap['raDecDeg'])
metric = metrics.CountMetric(colmap['mjd'],
metricName='Nvisits as function of Alt/Az')
plotFuncs = [plots.LambertSkyMap()]
for sql in sql_per_and_all_filters:
bundle = metricBundles.MetricBundle(metric,
slicer,
sql,
plotFuncs=plotFuncs,
displayDict=displayDict,
stackerList=[stacker])
bundleList.append(bundle)
# Things to check per night
# Open Shutter per night
displayDict = {'group': 'Pointing Efficency', 'order': 2}
slicer = slicers.OneDSlicer(sliceColName=colmap['night'], binsize=1)
metric = metrics.OpenShutterFractionMetric(
slewTimeCol=colmap['slewtime'],
expTimeCol=colmap['exptime'],
visitTimeCol=colmap['visittime'])
sql = sqlConstraint
bundle = metricBundles.MetricBundle(metric,
slicer,
sql,
summaryMetrics=standardStats,
displayDict=displayDict)
bundleList.append(bundle)
# Number of filter changes per night
slicer = slicers.OneDSlicer(sliceColName=colmap['night'], binsize=1)
metric = metrics.NChangesMetric(col=colmap['filter'],
orderBy=colmap['mjd'],
metricName='Filter Changes')
bundle = metricBundles.MetricBundle(metric,
slicer,
sql,
summaryMetrics=standardStats,
displayDict=displayDict)
bundleList.append(bundle)
# A few basic maps
# Number of observations, coadded depths
displayDict = {'group': 'Basic Maps', 'order': 3}
slicer = slicers.HealpixSlicer(nside=nside,
latCol=colmap['dec'],
lonCol=colmap['ra'],
latLonDeg=colmap['raDecDeg'])
metric = metrics.CountMetric(col=colmap['mjd'])
plotDict = {'percentileClip': 95.}
for sql in sql_per_and_all_filters:
bundle = metricBundles.MetricBundle(metric,
slicer,
sql,
summaryMetrics=standardStats,
displayDict=displayDict,
plotDict=plotDict)
bundleList.append(bundle)
metric = metrics.Coaddm5Metric(m5Col=colmap['fiveSigmaDepth'])
for sql in sql_per_and_all_filters:
bundle = metricBundles.MetricBundle(metric,
slicer,
sql,
summaryMetrics=standardStats,
displayDict=displayDict)
bundleList.append(bundle)
# Checking a few basic science things
# Maybe check astrometry, observation pairs, SN
plotDict = {'percentileClip': 95.}
displayDict = {'group': 'Science', 'subgroup': 'Astrometry', 'order': 4}
stackerList = []
stacker = stackers.ParallaxFactorStacker(raCol=colmap['ra'],
decCol=colmap['dec'],
degrees=colmap['raDecDeg'],
dateCol=colmap['mjd'])
stackerList.append(stacker)
# Maybe parallax and proper motion, fraction of visits in a good pair for SS
displayDict['caption'] = r'Parallax precision of an $r=20$ flat SED star'
metric = metrics.ParallaxMetric(m5Col=colmap['fiveSigmaDepth'],
filterCol=colmap['filter'],
seeingCol=colmap['seeingGeom'])
sql = sqlConstraint
bundle = metricBundles.MetricBundle(metric,
slicer,
sql,
plotFuncs=subsetPlots,
displayDict=displayDict,
stackerList=stackerList,
plotDict=plotDict)
bundleList.append(bundle)
displayDict[
'caption'] = r'Proper motion precision of an $r=20$ flat SED star'
metric = metrics.ProperMotionMetric(m5Col=colmap['fiveSigmaDepth'],
mjdCol=colmap['mjd'],
filterCol=colmap['filter'],
seeingCol=colmap['seeingGeom'])
bundle = metricBundles.MetricBundle(metric,
slicer,
sql,
plotFuncs=subsetPlots,
displayDict=displayDict,
plotDict=plotDict)
bundleList.append(bundle)
# Solar system stuff
displayDict['caption'] = 'Fraction of observations that are in pairs'
displayDict['subgroup'] = 'Solar System'
sql = '%s (filter="g" or filter="r" or filter="i")' % sqlC
pairSlicer = slicers.HealpixSlicer(nside=pairnside,
latCol=colmap['dec'],
lonCol=colmap['ra'],
latLonDeg=colmap['raDecDeg'])
metric = metrics.PairFractionMetric(mjdCol=colmap['mjd'])
bundle = metricBundles.MetricBundle(metric,
pairSlicer,
sql,
plotFuncs=subsetPlots,
displayDict=displayDict)
bundleList.append(bundle)
# stats from the note column
if 'note' in colmap.keys():
displayDict = {'group': 'Basic Stats', 'subgroup': 'Percent stats'}
metric = metrics.StringCountMetric(col=colmap['note'],
percent=True,
metricName='Percents')
sql = ''
slicer = slicers.UniSlicer()
bundle = metricBundles.MetricBundle(metric,
slicer,
sql,
displayDict=displayDict)
bundleList.append(bundle)
displayDict['subgroup'] = 'Count Stats'
metric = metrics.StringCountMetric(col=colmap['note'],
metricName='Counts')
bundle = metricBundles.MetricBundle(metric,
slicer,
sql,
displayDict=displayDict)
bundleList.append(bundle)
for b in bundleList:
b.setRunName(runName)
# Add hourglass plots.
hrDict = hourglassBatch(colmap=colmap,
runName=runName,
nyears=nyears,
extraSql=sqlConstraint)
# Add basic slew stats.
try:
slewDict = slewBasics(colmap=colmap, runName=runName)
except KeyError as e:
warnings.warn(
'Could not add slew stats: missing required key %s from colmap' %
(e))
bd = metricBundles.makeBundlesDictFromList(bundleList)
bd.update(slewDict)
bd.update(hrDict)
return bd