import lsst.sims.maf.metrics as metrics
import lsst.sims.maf.slicers as slicers
import lsst.sims.maf.metricBundles as mb
import lsst.sims.maf.plots as plots
from .colMapDict import ColMapDict
from .common import filterList
__all__ = ['altazHealpix', 'altazLambert']
def basicSetup(metricName, colmap=None, nside=64):
if colmap is None:
colmap = ColMapDict('opsimV4')
slicer = slicers.HealpixSlicer(nside=nside, latCol=colmap['alt'], lonCol=colmap['az'],
latLonDeg=colmap['raDecDeg'], useCache=False)
metric = metrics.CountMetric(colmap['mjd'], metricName=metricName)
return colmap, slicer, metric
[docs]def altazHealpix(colmap=None, runName='opsim', extraSql=None,
extraMetadata=None, metricName='NVisits Alt/Az'):
"""Generate a set of metrics measuring the number visits as a function of alt/az
plotted on a HealpixSkyMap.
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. '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.
metricName : str, opt
Unique name to assign to metric
Returns
-------
metricBundleDict
"""
colmap, slicer, metric = basicSetup(metricName=metricName, colmap=colmap)
# Set up basic all and per filter sql constraints.
filterlist, colors, orders, sqls, metadata = filterList(all=True,
extraSql=extraSql,
extraMetadata=extraMetadata)
bundleList = []
plotDict = {'rot': (90, 90, 90), 'flip': 'geo'}
plotFunc = plots.HealpixSkyMap()
for f in filterlist:
if f is 'all':
subgroup = 'All Observations'
else:
subgroup = 'Per filter'
displayDict = {'group': 'Alt/Az', 'order': orders[f], 'subgroup': subgroup,
'caption':
'Pointing History on the alt-az sky (zenith center) for filter %s' % f}
bundle = mb.MetricBundle(metric, slicer, sqls[f], plotDict=plotDict,
runName=runName, metadata = metadata[f],
plotFuncs=[plotFunc], displayDict=displayDict)
bundleList.append(bundle)
for b in bundleList:
b.setRunName(runName)
return mb.makeBundlesDictFromList(bundleList)
[docs]def altazLambert(colmap=None, runName='opsim', extraSql=None,
extraMetadata=None, metricName='Nvisits as function of Alt/Az'):
"""Generate a set of metrics measuring the number visits as a function of alt/az
plotted on a LambertSkyMap.
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. '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.
metricName : str, opt
Unique name to assign to metric
Returns
-------
metricBundleDict
"""
colmap, slicer, metric = basicSetup(metricName=metricName, colmap=colmap)
# Set up basic all and per filter sql constraints.
filterlist, colors, orders, sqls, metadata = filterList(all=True,
extraSql=extraSql,
extraMetadata=extraMetadata)
bundleList = []
plotFunc = plots.LambertSkyMap()
for f in filterlist:
if f is 'all':
subgroup = 'All Observations'
else:
subgroup = 'Per filter'
displayDict = {'group': 'Alt/Az', 'order': orders[f], 'subgroup': subgroup,
'caption':
'Alt/Az pointing distribution for filter %s' % f}
bundle = mb.MetricBundle(metric, slicer, sqls[f],
runName=runName, metadata = metadata[f],
plotFuncs=[plotFunc], displayDict=displayDict)
bundleList.append(bundle)
for b in bundleList:
b.setRunName(runName)
return mb.makeBundlesDictFromList(bundleList)