def nvisitsPerProp(opsdb, colmap=None, runName='opsim', binNights=1):
"""Set up a group of all and per-proposal nvisits metrics.
Parameters
----------
opsdb : lsst.sims.maf.db.Database or lsst.sims.maf.db.OpsimDatabase* object
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".
binNights : int, opt
Number of nights to count in each bin. Default = 1, count number of visits in each night.
Returns
-------
metricBundle
"""
if colmap is None:
colmap = getColMap(opsdb)
propids, proptags = opsdb.fetchPropInfo()
# Calculate the total number of visits per proposal, and their fraction compared to total.
totvisits = opsdb.fetchNVisits()
metric = metrics.CountMetric(colmap['mjd'], metricName='Nvisits')
slicer = slicers.UniSlicer()
summaryMetrics = [
metrics.IdentityMetric(metricName='Count'),
metrics.NormalizeMetric(normVal=totvisits,
metricName='Fraction of total')
]
bundleList = []
displayDict = {
'group': 'Visit Summary',
'subgroup': 'Proposal distribution',
'order': -1
}
bdict = {}
bdict.update(
nvisitsPerNight(colmap=colmap,
runName=runName,
binNights=binNights,
sql=None,
metadata='All visits'))
# Look for any multi-proposal groups that we should include.
for tag in proptags:
if len(proptags[tag]) > 1:
pids = proptags[tag]
sql = '('
for pid in pids[:-1]:
sql += 'proposalId=%d or ' % pid
sql += ' proposalId=%d)' % pids[-1]
metadata = '%s' % (tag)
bdict.update(
nvisitsPerNight(colmap=colmap,
runName=runName,
binNights=binNights,
sql=sql,
metadata=metadata))
displayDict['order'] += 1
displayDict[
'caption'] = 'Number of visits and fraction of total visits, for %s.' % metadata
bundle = mb.MetricBundle(metric,
slicer,
sql=sql,
metadata=metadata,
summaryMetrics=summaryMetrics,
displayDict=displayDict)
bundleList.append(bundle)
# And then just run each proposal separately.
for propid in propids:
sql = 'proposalId=%d' % (propid)
metadata = '%s' % (propids[propid])
bdict.update(
nvisitsPerNight(colmap=colmap,
runName=runName,
binNights=binNights,
sql=sql,
metadata=metadata))
displayDict['order'] += 1
displayDict[
'caption'] = 'Number of visits and fraction of total visits, for %s.' % metadata
bundle = mb.MetricBundle(metric,
slicer,
constraint=sql,
metadata=metadata,
summaryMetrics=summaryMetrics,
displayDict=displayDict)
bundleList.append(bundle)
for b in bundleList:
b.setRunName(runName)
bdict.update(mb.makeBundlesDictFromList(bundleList))
return bdict
def nvisitsPerProp(opsdb,
colmap=None,
runName='opsim',
binNights=1,
extraSql=None):
"""Set up a group of all and per-proposal nvisits metrics.
Parameters
----------
opsdb : lsst.sims.maf.db.Database or lsst.sims.maf.db.OpsimDatabase* object
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".
binNights : int, opt
Number of nights to count in each bin. Default = 1, count number of visits in each night.
sqlConstraint : str or None, opt
SQL constraint to add to all metrics.
Returns
-------
metricBundle
"""
if colmap is None:
colmap = getColMap(opsdb)
propids, proptags = opsdb.fetchPropInfo()
bdict = {}
bundleList = []
totvisits = opsdb.fetchNVisits()
metadata = 'All props'
if extraSql is not None and len(extraSql) > 0:
metadata += ' %s' % extraSql
# Nvisits per night, all proposals.
bdict.update(
nvisitsPerNight(colmap=colmap,
runName=runName,
binNights=binNights,
extraSql=extraSql,
extraMetadata=metadata,
subgroup='All proposals'))
# Nvisits total, all proposals.
metric = metrics.CountMetric(colmap['mjd'], metricName='Nvisits')
slicer = slicers.UniSlicer()
summaryMetrics = [
metrics.IdentityMetric(metricName='Count'),
metrics.NormalizeMetric(normVal=totvisits,
metricName='Fraction of total')
]
displayDict = {
'group': 'Nvisit Summary',
'subgroup': 'Proposal distribution',
'order': -1
}
displayDict['caption'] = 'Total number of visits for all proposals.'
if extraSql is not None and len(extraSql) > 0:
displayDict['caption'] += ' (with constraint %s.)' % extraSql
bundle = mb.MetricBundle(metric,
slicer,
extraSql,
metadata=metadata,
displayDict=displayDict,
summaryMetrics=summaryMetrics)
bundleList.append(bundle)
# Look for any multi-proposal groups that we should include.
for tag in proptags:
if len(proptags[tag]) > 1 or tag in ('WFD', 'DD'):
pids = proptags[tag]
sql = '('
for pid in pids[:-1]:
sql += '%s=%d or ' % (colmap['proposalId'], pid)
sql += ' %s=%d)' % (colmap['proposalId'], pids[-1])
metadata = '%s' % tag
if extraSql is not None:
sql = '(%s) and (%s)' % (sql, extraSql)
metadata += ' %s' % (extraSql)
bdict.update(
nvisitsPerNight(colmap=colmap,
runName=runName,
binNights=binNights,
extraSql=sql,
extraMetadata=metadata,
subgroup=tag))
displayDict['order'] += 1
displayDict[
'caption'] = 'Number of visits and fraction of total visits, for %s.' % metadata
bundle = mb.MetricBundle(metric,
slicer,
sql,
metadata=metadata,
summaryMetrics=summaryMetrics,
displayDict=displayDict)
bundleList.append(bundle)
# And each proposal separately.
for propid in propids:
sql = '%s=%d' % (colmap['proposalId'], propid)
metadata = '%s' % (propids[propid])
if extraSql is not None:
sql += ' and (%s)' % (extraSql)
metadata += ' %s' % extraSql
bdict.update(
nvisitsPerNight(colmap=colmap,
runName=runName,
binNights=binNights,
extraSql=sql,
extraMetadata=metadata,
subgroup='Per proposal'))
displayDict['order'] += 1
displayDict[
'caption'] = 'Number of visits and fraction of total visits, for %s.' % metadata
bundle = mb.MetricBundle(metric,
slicer,
constraint=sql,
metadata=metadata,
summaryMetrics=summaryMetrics,
displayDict=displayDict)
bundleList.append(bundle)
for b in bundleList:
b.setRunName(runName)
bdict.update(mb.makeBundlesDictFromList(bundleList))
return bdict
def testNormalizeMetric(self):
"""Test normalize metric."""
data = np.ones(10, dtype=zip(['testcol'], ['float']))
metric = metrics.NormalizeMetric(col='testcol', normVal=5.5)
result = metric.run(data)
np.testing.assert_equal(result, np.ones(10, float) / 5.5)