def testProperMotionMetric(self):
"""
Test the ProperMotion metric.
"""
names = [
'expMJD', 'finSeeing', 'fiveSigmaDepth', 'fieldRA', 'fieldDec',
'filter'
]
types = [float, float, float, float, float, '<U1']
data = np.zeros(700, dtype=list(zip(names, types)))
slicePoint = [0]
stacker = stackers.ParallaxFactorStacker()
normFlags = [False, True]
data['expMJD'] = np.arange(700) + 56762
data['finSeeing'] = 0.7
data['filter'][0:100] = str('r')
data['filter'][100:200] = str('u')
data['filter'][200:] = str('g')
data['fiveSigmaDepth'] = 24.
data = stacker.run(data)
for flag in normFlags:
data['finSeeing'] = 0.7
data['fiveSigmaDepth'] = 24
baseline = metrics.ProperMotionMetric(normalize=flag,
seeingCol='finSeeing').run(
data, slicePoint)
data['finSeeing'] = data['finSeeing'] + .3
worse1 = metrics.ProperMotionMetric(normalize=flag,
seeingCol='finSeeing').run(
data, slicePoint)
worse2 = metrics.ProperMotionMetric(normalize=flag,
rmag=22.,
seeingCol='finSeeing').run(
data, slicePoint)
worse3 = metrics.ProperMotionMetric(normalize=flag,
rmag=22.,
seeingCol='finSeeing').run(
data[0:300], slicePoint)
data['fiveSigmaDepth'] = data['fiveSigmaDepth'] - 1.
worse4 = metrics.ProperMotionMetric(normalize=flag,
rmag=22.,
seeingCol='finSeeing').run(
data[0:300], slicePoint)
# Make sure the RMS increases as seeing increases, the star gets fainter,
# the background gets brighter, or the baseline decreases.
if flag:
# When normalized, mag of star and m5 don't matter (just scheduling).
self.assertAlmostEqual(worse2, worse1)
self.assertAlmostEqual(worse4, worse3)
# But using fewer points should make proper motion worse.
# survey assumed to have same seeing and limiting mags.
assert (worse3 < worse2)
else:
assert (worse1 > baseline)
assert (worse2 > worse1)
assert (worse3 > worse2)
assert (worse4 > worse3)
def testParallaxMetric(self):
"""
Test the parallax metric.
"""
names = [
'expMJD', 'finSeeing', 'fiveSigmaDepth', 'fieldRA', 'fieldDec',
'filter'
]
types = [float, float, float, float, float, '|S1']
data = np.zeros(700, dtype=zip(names, types))
slicePoint = {'sid': 0}
data['expMJD'] = np.arange(700) + 56762
data['finSeeing'] = 0.7
data['filter'][0:100] = 'r'
data['filter'][100:200] = 'u'
data['filter'][200:] = 'g'
data['fiveSigmaDepth'] = 24.
stacker = stackers.ParallaxFactorStacker()
data = stacker.run(data)
normFlags = [False, True]
for flag in normFlags:
data['finSeeing'] = 0.7
data['fiveSigmaDepth'] = 24.
baseline = metrics.ParallaxMetric(normalize=flag,
seeingCol='finSeeing').run(
data, slicePoint)
data['finSeeing'] = data['finSeeing'] + .3
worse1 = metrics.ParallaxMetric(normalize=flag,
seeingCol='finSeeing').run(
data, slicePoint)
worse2 = metrics.ParallaxMetric(normalize=flag,
rmag=22.,
seeingCol='finSeeing').run(
data, slicePoint)
worse3 = metrics.ParallaxMetric(normalize=flag,
rmag=22.,
seeingCol='finSeeing').run(
data[0:300], slicePoint)
data['fiveSigmaDepth'] = data['fiveSigmaDepth'] - 1.
worse4 = metrics.ParallaxMetric(normalize=flag,
rmag=22.,
seeingCol='finSeeing').run(
data[0:300], slicePoint)
# Make sure the RMS increases as seeing increases, the star gets fainter,
# the background gets brighter, or the baseline decreases.
if flag:
pass
# hmm, I need to think how to test the scheduling
#assert(worse1 < baseline)
#assert(worse2 < worse1)
#assert(worse3 < worse2)
#assert(worse4 < worse3)
else:
assert (worse1 > baseline)
assert (worse2 > worse1)
assert (worse3 > worse2)
assert (worse4 > worse3)
def testEQ(self):
"""
Test that stackers can be compared
"""
s1 = stackers.ParallaxFactorStacker()
s2 = stackers.ParallaxFactorStacker()
assert (s1 == s2)
s1 = stackers.RandomDitherFieldPerVisitStacker()
s2 = stackers.RandomDitherFieldPerVisitStacker()
assert (s1 == s2)
# Test if they have numpy array atributes
s1.ack = np.arange(10)
s2.ack = np.arange(10)
assert (s1 == s2)
# Change the array and test
s1.ack += 1
assert (s1 != s2)
s2 = stackers.RandomDitherFieldPerVisitStacker(decCol='blah')
assert (s1 != s2)
def testParallaxFactor(self):
"""
Test the parallax factor.
"""
data = np.zeros(600, dtype=zip(['fieldRA', 'fieldDec', 'expMJD'],
[float, float, float]))
data['fieldRA'] = data['fieldRA'] + .1
data['fieldDec'] = data['fieldDec'] - .1
data['expMJD'] = np.arange(data.size) + 49000.
stacker = stackers.ParallaxFactorStacker()
data = stacker.run(data)
self.assertLess(max(np.abs(data['ra_pi_amp'])), 1.1)
self.assertLess(max(np.abs(data['dec_pi_amp'])), 1.1)
self.assertLess(
np.max(data['ra_pi_amp']**2 + data['dec_pi_amp']**2), 1.1)
self.assertGreater(min(np.abs(data['ra_pi_amp'])), 0.)
self.assertGreater(min(np.abs(data['dec_pi_amp'])), 0.)
def astrometryBatch(colmap=None,
runName='opsim',
extraSql=None,
extraMetadata=None,
nside=64,
ditherStacker=None,
ditherkwargs=None):
"""Metrics for evaluating proper motion and parallax.
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.
ditherStacker: str or lsst.sims.maf.stackers.BaseDitherStacker
Optional dither stacker to use to define ra/dec columns.
ditherkwargs: dict, opt
Optional dictionary of kwargs for the dither stacker.
Returns
-------
metricBundleDict
"""
if colmap is None:
colmap = ColMapDict('opsimV4')
bundleList = []
sql = ''
metadata = 'All visits'
# Add additional sql constraint (such as wfdWhere) and metadata, if provided.
if (extraSql is not None) and (len(extraSql) > 0):
sql = extraSql
if extraMetadata is None:
metadata = extraSql.replace('filter =', '').replace('filter=', '')
metadata = metadata.replace('"', '').replace("'", '')
if extraMetadata is not None:
metadata = extraMetadata
subgroup = metadata
raCol, decCol, degrees, ditherStacker, ditherMeta = radecCols(
ditherStacker, colmap, ditherkwargs)
# Don't want dither info in subgroup (too long), but do want it in bundle name.
metadata = combineMetadata(metadata, ditherMeta)
rmags_para = [22.4, 24.0]
rmags_pm = [20.5, 24.0]
# Set up parallax/dcr stackers.
parallaxStacker = stackers.ParallaxFactorStacker(raCol=raCol,
decCol=decCol,
dateCol=colmap['mjd'],
degrees=degrees)
dcrStacker = stackers.DcrStacker(filterCol=colmap['filter'],
altCol=colmap['alt'],
degrees=degrees,
raCol=raCol,
decCol=decCol,
lstCol=colmap['lst'],
site='LSST',
mjdCol=colmap['mjd'])
# Set up parallax metrics.
slicer = slicers.HealpixSlicer(nside=nside,
lonCol=raCol,
latCol=decCol,
latLonDeg=degrees)
subsetPlots = [plots.HealpixSkyMap(), plots.HealpixHistogram()]
displayDict = {
'group': 'Parallax',
'subgroup': subgroup,
'order': 0,
'caption': None
}
# Expected error on parallax at 10 AU.
plotmaxVals = (2.0, 15.0)
for rmag, plotmax in zip(rmags_para, plotmaxVals):
plotDict = {
'xMin': 0,
'xMax': plotmax,
'colorMin': 0,
'colorMax': plotmax
}
metric = metrics.ParallaxMetric(metricName='Parallax Error @ %.1f' %
(rmag),
rmag=rmag,
seeingCol=colmap['seeingGeom'],
filterCol=colmap['filter'],
m5Col=colmap['fiveSigmaDepth'],
normalize=False)
bundle = mb.MetricBundle(metric,
slicer,
sql,
metadata=metadata,
stackerList=[parallaxStacker, ditherStacker],
displayDict=displayDict,
plotDict=plotDict,
summaryMetrics=standardSummary(),
plotFuncs=subsetPlots)
bundleList.append(bundle)
displayDict['order'] += 1
# Parallax normalized to 'best possible' if all visits separated by 6 months.
# This separates the effect of cadence from depth.
for rmag in rmags_para:
metric = metrics.ParallaxMetric(
metricName='Normalized Parallax @ %.1f' % (rmag),
rmag=rmag,
seeingCol=colmap['seeingGeom'],
filterCol=colmap['filter'],
m5Col=colmap['fiveSigmaDepth'],
normalize=True)
bundle = mb.MetricBundle(metric,
slicer,
sql,
metadata=metadata,
stackerList=[parallaxStacker, ditherStacker],
displayDict=displayDict,
summaryMetrics=standardSummary(),
plotFuncs=subsetPlots)
bundleList.append(bundle)
displayDict['order'] += 1
# Parallax factor coverage.
for rmag in rmags_para:
metric = metrics.ParallaxCoverageMetric(
metricName='Parallax Coverage @ %.1f' % (rmag),
rmag=rmag,
m5Col=colmap['fiveSigmaDepth'],
mjdCol=colmap['mjd'],
filterCol=colmap['filter'],
seeingCol=colmap['seeingGeom'])
bundle = mb.MetricBundle(metric,
slicer,
sql,
metadata=metadata,
stackerList=[parallaxStacker, ditherStacker],
displayDict=displayDict,
summaryMetrics=standardSummary(),
plotFuncs=subsetPlots)
bundleList.append(bundle)
displayDict['order'] += 1
# Parallax problems can be caused by HA and DCR degeneracies. Check their correlation.
for rmag in rmags_para:
metric = metrics.ParallaxDcrDegenMetric(
metricName='Parallax-DCR degeneracy @ %.1f' % (rmag),
rmag=rmag,
seeingCol=colmap['seeingEff'],
filterCol=colmap['filter'],
m5Col=colmap['fiveSigmaDepth'])
caption = 'Correlation between parallax offset magnitude and hour angle for a r=%.1f star.' % (
rmag)
caption += ' (0 is good, near -1 or 1 is bad).'
bundle = mb.MetricBundle(
metric,
slicer,
sql,
metadata=metadata,
stackerList=[dcrStacker, parallaxStacker, ditherStacker],
displayDict=displayDict,
summaryMetrics=standardSummary(),
plotFuncs=subsetPlots)
bundleList.append(bundle)
displayDict['order'] += 1
# Proper Motion metrics.
displayDict = {
'group': 'Proper Motion',
'subgroup': subgroup,
'order': 0,
'caption': None
}
# Proper motion errors.
plotmaxVals = (1.0, 5.0)
for rmag, plotmax in zip(rmags_pm, plotmaxVals):
plotDict = {
'xMin': 0,
'xMax': plotmax,
'colorMin': 0,
'colorMax': plotmax
}
metric = metrics.ProperMotionMetric(
metricName='Proper Motion Error @ %.1f' % rmag,
rmag=rmag,
m5Col=colmap['fiveSigmaDepth'],
mjdCol=colmap['mjd'],
filterCol=colmap['filter'],
seeingCol=colmap['seeingGeom'],
normalize=False)
bundle = mb.MetricBundle(metric,
slicer,
sql,
metadata=metadata,
stackerList=[ditherStacker],
displayDict=displayDict,
plotDict=plotDict,
summaryMetrics=standardSummary(),
plotFuncs=subsetPlots)
bundleList.append(bundle)
displayDict['order'] += 1
# Normalized proper motion.
for rmag in rmags_pm:
metric = metrics.ProperMotionMetric(
metricName='Normalized Proper Motion @ %.1f' % rmag,
rmag=rmag,
m5Col=colmap['fiveSigmaDepth'],
mjdCol=colmap['mjd'],
filterCol=colmap['filter'],
seeingCol=colmap['seeingGeom'],
normalize=True)
bundle = mb.MetricBundle(metric,
slicer,
sql,
metadata=metadata,
stackerList=[ditherStacker],
displayDict=displayDict,
summaryMetrics=standardSummary(),
plotFuncs=subsetPlots)
bundleList.append(bundle)
displayDict['order'] += 1
# Set the runName for all bundles and return the bundleDict.
for b in bundleList:
b.setRunName(runName)
return mb.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
def testBaseDitherStacker(self):
# Test that the base dither stacker matches the type of a stacker.
s = stackers.HexDitherFieldPerNightStacker()
self.assertTrue(isinstance(s, stackers.BaseDitherStacker))
s = stackers.ParallaxFactorStacker()
self.assertFalse(isinstance(s, stackers.BaseDitherStacker))
def astrometryBatch(colmap=None, runName='opsim',
extraSql=None, extraMetadata=None,
nside=64):
# Allow user to add dithering.
if colmap is None:
colmap = ColMapDict('opsimV4')
bundleList = []
sql = ''
metadata = 'All visits'
# Add additional sql constraint (such as wfdWhere) and metadata, if provided.
if (extraSql is not None) and (len(extraSql) > 0):
sql = extraSql
if extraMetadata is None:
metadata = extraSql.replace('filter =', '').replace('filter=', '')
metadata = metadata.replace('"', '').replace("'", '')
if extraMetadata is not None:
metadata = extraMetadata
subgroup = metadata
raCol = colmap['ra']
decCol = colmap['dec']
degrees = colmap['raDecDeg']
# Set up stackers.
parallaxStacker = stackers.ParallaxFactorStacker(raCol=raCol, decCol=decCol,
dateCol=colmap['mjd'], degrees=degrees)
dcrStacker = stackers.DcrStacker(filterCol=colmap['filter'], altCol=colmap['alt'], degrees=degrees,
raCol=raCol, decCol=decCol, lstCol=colmap['lst'],
site='LSST', mjdCol=colmap['mjd'])
# Set up parallax metrics.
slicer = slicers.HealpixSlicer(nside=nside, lonCol=raCol, latCol=decCol, latLonDeg=degrees)
subsetPlots = [plots.HealpixSkyMap(), plots.HealpixHistogram()]
displayDict = {'group': 'Parallax', 'subgroup': subgroup,
'order': 0, 'caption': None}
# Expected error on parallax at 10 AU.
for rmag in (20.0, 24.0):
metric = metrics.ParallaxMetric(metricName='Parallax @ %.1f' % (rmag), rmag=rmag,
seeingCol=colmap['seeingGeom'], filterCol=colmap['filter'],
m5Col=colmap['fiveSigmaDepth'], normalize=False)
bundle = mb.MetricBundle(metric, slicer, sql, metadata=metadata,
stackerList=[parallaxStacker],
displayDict=displayDict,
summaryMetrics=standardSummary(),
plotFuncs=subsetPlots)
bundleList.append(bundle)
displayDict['order'] += 1
# Parallax normalized to 'best possible' if all visits separated by 6 months.
# This separates the effect of cadence from depth.
for rmag in (20.0, 24.0):
metric = metrics.ParallaxMetric(metricName='Normalized Parallax @ %.1f' % (rmag), rmag=rmag,
seeingCol=colmap['seeingGeom'], filterCol=colmap['filter'],
m5Col=colmap['fiveSigmaDepth'], normalize=True)
bundle = mb.MetricBundle(metric, slicer, sql, metadata=metadata,
stackerList=[parallaxStacker],
displayDict=displayDict,
summaryMetrics=standardSummary(),
plotFuncs=subsetPlots)
bundleList.append(bundle)
displayDict['order'] += 1
# Parallax factor coverage.
for rmag in (20.0, 24.0):
metric = metrics.ParallaxCoverageMetric(metricName='Parallax Coverage @ %.1f' % (rmag),
rmag=rmag, m5Col=colmap['fiveSigmaDepth'],
mjdCol=colmap['mjd'], filterCol=colmap['filter'],
seeingCol=colmap['seeingGeom'])
bundle = mb.MetricBundle(metric, slicer, sql, metadata=metadata,
stackerList=[parallaxStacker],
displayDict=displayDict, summaryMetrics=standardSummary(),
plotFuncs=subsetPlots)
bundleList.append(bundle)
displayDict['order'] += 1
# Parallax problems can be caused by HA and DCR degeneracies. Check their correlation.
for rmag in (20.0, 24.0):
metric = metrics.ParallaxDcrDegenMetric(metricName='Parallax-DCR degeneracy @ %.1f' % (rmag),
rmag=rmag, seeingCol=colmap['seeingEff'],
filterCol=colmap['filter'], m5Col=colmap['fiveSigmaDepth'])
caption = 'Correlation between parallax offset magnitude and hour angle for a r=%.1f star.' % (rmag)
caption += ' (0 is good, near -1 or 1 is bad).'
bundle = mb.MetricBundle(metric, slicer, sql, metadata=metadata,
stackerList=[dcrStacker, parallaxStacker],
displayDict=displayDict, summaryMetrics=standardSummary(),
plotFuncs=subsetPlots)
bundleList.append(bundle)
displayDict['order'] += 1
# Proper Motion metrics.
displayDict = {'group': 'Proper Motion', 'subgroup': subgroup, 'order': 0, 'caption': None}
# Proper motion errors.
for rmag in (20.0, 24.0):
metric = metrics.ProperMotionMetric(metricName='Proper Motion %.1f' % rmag,
rmag=rmag, m5Col=colmap['fiveSigmaDepth'],
mjdCol=colmap['mjd'], filterCol=colmap['filter'],
seeingCol=colmap['seeingGeom'], normalize=False)
bundle = mb.MetricBundle(metric, slicer, sql, metadata=metadata,
displayDict=displayDict, summaryMetrics=standardSummary(),
plotFuncs=subsetPlots)
bundleList.append(bundle)
displayDict['order'] += 1
# Normalized proper motion.
for rmag in (20.0, 24.0):
metric = metrics.ProperMotionMetric(metricName='Normalized Proper Motion %.1f' % rmag,
rmag=rmag, m5Col=colmap['fiveSigmaDepth'],
mjdCol=colmap['mjd'], filterCol=colmap['filter'],
seeingCol=colmap['seeingGeom'], normalize=True)
bundle = mb.MetricBundle(metric, slicer, sql, metadata=metadata,
displayDict=displayDict, summaryMetrics=standardSummary(),
plotFuncs=subsetPlots)
bundleList.append(bundle)
displayDict['order'] += 1
# Set the runName for all bundles and return the bundleDict.
for b in bundleList:
b.setRunName(runName)
return mb.makeBundlesDictFromList(bundleList)
