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'characterizationBatch']
""" Set up the slicer and read orbitFile and obsFile from disk.
Parameters ---------- orbitFile : str The file containing the orbit information. Hrange : numpy.ndarray or None The Hrange parameter to pass to slicer.readOrbits obsFile : str, optional The file containing the observations of each object, optional. If not provided (default, None), then the slicer will not be able to 'slice', but can still plot.
Returns ------- ~lsst.sims.maf.slicer.MoObjSlicer """ # Read the orbit file and set the H values for the slicer. slicer = slicers.MoObjSlicer(Hrange=Hrange) slicer.setupSlicer(orbitFile=orbitFile, obsFile=obsFile) return slicer
albedo=None, Hmark=None, npReduce=np.mean, times=None, constraint=None): if colmap is None: colmap = ColMapDict('opsimV4') bundleList = [] plotBundles = []
basicPlotDict = {'albedo': albedo, 'Hmark': Hmark, 'npReduce': npReduce, 'nxbins': 200, 'nybins': 200} plotFuncs = [plots.MetricVsH()] displayDict ={'group': 'Discovery'}
if detectionLosses not in ('detection', 'trailing'): raise ValueError('Please choose detection or trailing as options for detectionLosses.') if detectionLosses == 'trailing': magStacker = stackers.MoMagStacker(lossCol='dmagTrail') detectionLosses = ' trailing loss' else: magStacker = stackers.MoMagStacker(lossCol='dmagDetect') detectionLosses = ' detection loss'
if times is None: try: timestep = 30 times = np.arange(slicer.obs[colmap['mjd']].min(), slicer.obs[colmap['mjd']].max() + timestep/2, timestep) except AttributeError: raise warnings.warn('Cannot set times for completeness summary metrics. Will set up bundles, ' 'but without summary metrics.')
if Hmark is None: Hval = slicer.Hrange.mean() else: Hval = Hmark
# Set up the summary metrics. if times is not None: summaryTimeMetrics = summaryCompletenessAtTime(times, Hval=Hval, Hindex=0.33) else: summaryTimeMetrics = None summaryHMetrics = summaryCompletenessOverH(requiredChances=1, Hindex=0.33)
# Set up a dictionary to pass to each metric for the column names. colkwargs = {'mjdCol': colmap['mjd'], 'seeingCol': colmap['seeingGeom'], 'expTimeCol': colmap['exptime'], 'm5Col': colmap['fiveSigmaDepth'], 'nightCol': colmap['night'], 'filterCol': colmap['filter']}
def _setup_child_metrics(parentMetric): childMetrics = {} childMetrics['Time'] = metrics.Discovery_TimeMetric(parentMetric, **colkwargs) childMetrics['N_Chances'] = metrics.Discovery_N_ChancesMetric(parentMetric, **colkwargs) # Could expand to add N_chances per year, but not really necessary. return childMetrics
def _configure_child_bundles(parentBundle): dispDict = {'group': 'Discovery', 'subgroup': 'Time', 'caption': 'Time of discovery of objects', 'order': 0} parentBundle.childBundles['Time'].setDisplayDict(dispDict) parentBundle.childBundles['Time'].setSummaryMetrics(summaryTimeMetrics) dispDict = {'group': 'Discovery', 'subgroup': 'NChances', 'caption': 'Number of chances for discovery of objects', 'order': 0} parentBundle.childBundles['N_Chances'].setDisplayDict(dispDict) parentBundle.childBundles['N_Chances'].setSummaryMetrics(summaryHMetrics) return
# 3 pairs in 15 md = metadata + ' 3 pairs in 15 nights SNR=5' + detectionLosses # Set up plot dict. plotDict = {'title': '%s: %s' % (runName, md)} plotDict.update(basicPlotDict) metric = metrics.DiscoveryMetric(nObsPerNight=2, tMin=0, tMax=90./60./24., nNightsPerWindow=3, tWindow=15, **colkwargs) childMetrics = _setup_child_metrics(metric) bundle = mb.MoMetricBundle(metric, slicer, constraint, stackerList=[magStacker], runName=runName, metadata=md, childMetrics=childMetrics, plotDict=plotDict, plotFuncs=plotFuncs, displayDict=displayDict) _configure_child_bundles(bundle) bundleList.append(bundle)
# 3 pairs in 30 md = metadata + ' 3 pairs in 30 nights SNR=5' + detectionLosses plotDict = {'title': '%s: %s' % (runName, md)} plotDict.update(basicPlotDict) metric = metrics.DiscoveryMetric(nObsPerNight=2, tMin=0, tMax=90. / 60. / 24., nNightsPerWindow=3, tWindow=30, snrLimit=5, **colkwargs) childMetrics = _setup_child_metrics(metric) bundle = mb.MoMetricBundle(metric, slicer, constraint, stackerList=[magStacker], runName=runName, metadata=md, childMetrics=childMetrics, plotDict=plotDict, plotFuncs=plotFuncs, displayDict=displayDict) _configure_child_bundles(bundle) bundleList.append(bundle)
# Set the runName for all bundles and return the bundleDict. for b in bundleList: b.setRunName(runName) return mb.makeBundlesDictFromList(bundleList), plotBundles
albedo=None, Hmark=None, npReduce=np.mean, times=None, constraint=None): if colmap is None: colmap = ColMapDict('opsimV4') bundleList = [] plotBundles = []
basicPlotDict = {'albedo': albedo, 'Hmark': Hmark, 'npReduce': npReduce, 'nxbins': 200, 'nybins': 200} plotFuncs = [plots.MetricVsH()] displayDict ={'group': 'Discovery'}
if detectionLosses not in ('detection', 'trailing'): raise ValueError('Please choose detection or trailing as options for detectionLosses.') if detectionLosses == 'trailing': # These are the SNR-losses only. magStacker = stackers.MoMagStacker(lossCol='dmagTrail') detectionLosses = ' trailing loss' else: # This is SNR losses, plus additional loss due to detecting with stellar PSF. magStacker = stackers.MoMagStacker(lossCol='dmagDetect') detectionLosses = ' detection loss'
if times is None: try: timestep = 30 times = np.arange(slicer.obs[colmap['mjd']].min(), slicer.obs[colmap['mjd']].max() + timestep/2, timestep) except AttributeError: raise warnings.warn('Cannot set times for completeness summary metrics. Will set up bundles, ' 'but without summary metrics.')
if Hmark is None: Hval = slicer.Hrange.mean() else: Hval = Hmark
# Set up the summary metrics. if times is not None: summaryTimeMetrics = summaryCompletenessAtTime(times, Hval=Hval, Hindex=0.33) else: summaryTimeMetrics = None summaryHMetrics = summaryCompletenessOverH(requiredChances=1, Hindex=0.33)
# Set up a dictionary to pass to each metric for the column names. colkwargs = {'mjdCol': colmap['mjd'], 'seeingCol': colmap['seeingGeom'], 'expTimeCol': colmap['exptime'], 'm5Col': colmap['fiveSigmaDepth'], 'nightCol': colmap['night'], 'filterCol': colmap['filter']}
def _setup_child_metrics(parentMetric): childMetrics = {} childMetrics['Time'] = metrics.Discovery_TimeMetric(parentMetric, **colkwargs) childMetrics['N_Chances'] = metrics.Discovery_N_ChancesMetric(parentMetric, **colkwargs) # Could expand to add N_chances per year, but not really necessary. return childMetrics
def _configure_child_bundles(parentBundle): dispDict = {'group': 'Discovery', 'subgroup': 'Time', 'caption': 'Time of discovery of objects', 'order': 0} parentBundle.childBundles['Time'].setDisplayDict(dispDict) parentBundle.childBundles['Time'].setSummaryMetrics(summaryTimeMetrics) dispDict = {'group': 'Discovery', 'subgroup': 'NChances', 'caption': 'Number of chances for discovery of objects', 'order': 0} parentBundle.childBundles['N_Chances'].setDisplayDict(dispDict) parentBundle.childBundles['N_Chances'].setSummaryMetrics(summaryHMetrics) return
# First standard SNR / probabilistic visibility (SNR~5) # 3 pairs in 15 md = metadata + ' 3 pairs in 15 nights' + detectionLosses # Set up plot dict. plotDict = {'title': '%s: %s' % (runName, md)} plotDict.update(basicPlotDict) metric = metrics.DiscoveryMetric(nObsPerNight=2, tMin=0, tMax=90./60./24., nNightsPerWindow=3, tWindow=15, **colkwargs) childMetrics = _setup_child_metrics(metric) bundle = mb.MoMetricBundle(metric, slicer, constraint, stackerList=[magStacker], runName=runName, metadata=md, childMetrics=childMetrics, plotDict=plotDict, plotFuncs=plotFuncs, displayDict=displayDict) _configure_child_bundles(bundle) bundleList.append(bundle)
# 3 pairs in 12 md = metadata + ' 3 pairs in 12 nights' + detectionLosses plotDict = {'title': '%s: %s' % (runName, md)} plotDict.update(basicPlotDict) metric = metrics.DiscoveryMetric(nObsPerNight=2, tMin=0, tMax=90. / 60. / 24., nNightsPerWindow=3, tWindow=12, **colkwargs) childMetrics = _setup_child_metrics(metric) bundle = mb.MoMetricBundle(metric, slicer, constraint, stackerList=[magStacker], runName=runName, metadata=md, childMetrics=childMetrics, plotDict=plotDict, plotFuncs=plotFuncs, displayDict=displayDict) _configure_child_bundles(bundle) bundleList.append(bundle)
# 3 pairs in 20 md = metadata + ' 3 pairs in 20 nights' + detectionLosses plotDict = {'title': '%s: %s' % (runName, md)} plotDict.update(basicPlotDict) metric = metrics.DiscoveryMetric(nObsPerNight=2, tMin=0, tMax=90. / 60. / 24., nNightsPerWindow=3, tWindow=20, **colkwargs) childMetrics = _setup_child_metrics(metric) bundle = mb.MoMetricBundle(metric, slicer, constraint, stackerList=[magStacker], runName=runName, metadata=md, childMetrics=childMetrics, plotDict=plotDict, plotFuncs=plotFuncs, displayDict=displayDict) _configure_child_bundles(bundle) bundleList.append(bundle)
# 3 pairs in 25 md = metadata + ' 3 pairs in 25 nights' + detectionLosses plotDict = {'title': '%s: %s' % (runName, md)} plotDict.update(basicPlotDict) metric = metrics.DiscoveryMetric(nObsPerNight=2, tMin=0, tMax=90. / 60. / 24., nNightsPerWindow=3, tWindow=25, **colkwargs) childMetrics = _setup_child_metrics(metric) bundle = mb.MoMetricBundle(metric, slicer, constraint, stackerList=[magStacker], runName=runName, metadata=md, childMetrics=childMetrics, plotDict=plotDict, plotFuncs=plotFuncs, displayDict=displayDict) _configure_child_bundles(bundle) bundleList.append(bundle)
# 3 pairs in 30 md = metadata + ' 3 pairs in 30 nights' + detectionLosses plotDict = {'title': '%s: %s' % (runName, md)} plotDict.update(basicPlotDict) metric = metrics.DiscoveryMetric(nObsPerNight=2, tMin=0, tMax=90. / 60. / 24., nNightsPerWindow=3, tWindow=30, **colkwargs) childMetrics = _setup_child_metrics(metric) bundle = mb.MoMetricBundle(metric, slicer, constraint, stackerList=[magStacker], runName=runName, metadata=md, childMetrics=childMetrics, plotDict=plotDict, plotFuncs=plotFuncs, displayDict=displayDict) _configure_child_bundles(bundle) bundleList.append(bundle)
# 4 pairs in 20 md = metadata + ' 4 pairs in 20 nights' + detectionLosses plotDict = {'title': '%s: %s' % (runName, md)} plotDict.update(basicPlotDict) metric = metrics.DiscoveryMetric(nObsPerNight=2, tMin=0, tMax=90. / 60. / 24., nNightsPerWindow=4, tWindow=20, **colkwargs) childMetrics = _setup_child_metrics(metric) bundle = mb.MoMetricBundle(metric, slicer, constraint, stackerList=[magStacker], runName=runName, metadata=md, childMetrics=childMetrics, plotDict=plotDict, plotFuncs=plotFuncs, displayDict=displayDict) _configure_child_bundles(bundle) bundleList.append(bundle)
# 3 triplets in 30 md = metadata + ' 3 triplets in 30 nights' + detectionLosses plotDict = {'title': '%s: %s' % (runName, md)} plotDict.update(basicPlotDict) metric = metrics.DiscoveryMetric(nObsPerNight=3, tMin=0, tMax=120. / 60. / 24., nNightsPerWindow=3, tWindow=30, **colkwargs) childMetrics = _setup_child_metrics(metric) bundle = mb.MoMetricBundle(metric, slicer, constraint, stackerList=[magStacker], runName=runName, metadata=md, childMetrics=childMetrics, plotDict=plotDict, plotFuncs=plotFuncs, displayDict=displayDict) _configure_child_bundles(bundle) bundleList.append(bundle)
# 3 quads in 30 md = metadata + ' 3 quads in 30 nights' + detectionLosses plotDict = {'title': '%s: %s' % (runName, md)} plotDict.update(basicPlotDict) metric = metrics.DiscoveryMetric(nObsPerNight=4, tMin=0, tMax=150. / 60. / 24., nNightsPerWindow=3, tWindow=30, **colkwargs) childMetrics = _setup_child_metrics(metric) bundle = mb.MoMetricBundle(metric, slicer, constraint, stackerList=[magStacker], runName=runName, metadata=md, childMetrics=childMetrics, plotDict=plotDict, plotFuncs=plotFuncs, displayDict=displayDict) _configure_child_bundles(bundle) bundleList.append(bundle)
# Play with SNR. SNR=4. Normal detection losses. # 3 pairs in 15 md = metadata + ' 3 pairs in 15 nights SNR=4' + detectionLosses plotDict = {'title': '%s: %s' % (runName, md)} plotDict.update(basicPlotDict) metric = metrics.DiscoveryMetric(nObsPerNight=2, tMin=0, tMax=90. / 60. / 24., nNightsPerWindow=3, tWindow=15, snrLimit=4, **colkwargs) childMetrics = _setup_child_metrics(metric) bundle = mb.MoMetricBundle(metric, slicer, constraint, stackerList=[magStacker], runName=runName, metadata=md, childMetrics=childMetrics, plotDict=plotDict, plotFuncs=plotFuncs, displayDict=displayDict) _configure_child_bundles(bundle) bundleList.append(bundle)
# 3 pairs in 30, SNR=4 md = metadata + ' 3 pairs in 30 nights SNR=4' + detectionLosses plotDict = {'title': '%s: %s' % (runName, md)} plotDict.update(basicPlotDict) metric = metrics.DiscoveryMetric(nObsPerNight=2, tMin=0, tMax=90. / 60. / 24., nNightsPerWindow=3, tWindow=30, snrLimit=4, **colkwargs) childMetrics = _setup_child_metrics(metric) bundle = mb.MoMetricBundle(metric, slicer, constraint, stackerList=[magStacker], runName=runName, metadata=md, childMetrics=childMetrics, plotDict=plotDict, plotFuncs=plotFuncs, displayDict=displayDict) _configure_child_bundles(bundle) bundleList.append(bundle)
# Play with SNR. SNR=3 # 3 pairs in 15, SNR=3 md = metadata + ' 3 pairs in 15 nights SNR=3' + detectionLosses plotDict = {'title': '%s: %s' % (runName, md)} plotDict.update(basicPlotDict) metric = metrics.DiscoveryMetric(nObsPerNight=2, tMin=0, tMax=90. / 60. / 24., nNightsPerWindow=3, tWindow=15, snrLimit=3, **colkwargs) childMetrics = _setup_child_metrics(metric) bundle = mb.MoMetricBundle(metric, slicer, constraint, stackerList=[magStacker], runName=runName, metadata=md, childMetrics=childMetrics, plotDict=plotDict, plotFuncs=plotFuncs, displayDict=displayDict) _configure_child_bundles(bundle) bundleList.append(bundle)
# SNR = 0 # 3 pairs in 15, SNR=0 md = metadata + ' 3 pairs in 15 nights SNR=0' + detectionLosses plotDict = {'title': '%s: %s' % (runName, md)} plotDict.update(basicPlotDict) metric = metrics.DiscoveryMetric(nObsPerNight=2, tMin=0, tMax=90. / 60. / 24., nNightsPerWindow=3, tWindow=15, snrLimit=0, **colkwargs) childMetrics = _setup_child_metrics(metric) bundle = mb.MoMetricBundle(metric, slicer, constraint, stackerList=[magStacker], runName=runName, metadata=md, childMetrics=childMetrics, plotDict=plotDict, plotFuncs=plotFuncs, displayDict=displayDict) _configure_child_bundles(bundle) bundleList.append(bundle)
# Play with weird strategies. # Single detection. md = metadata + ' Single detection' + detectionLosses plotDict = {'title': '%s: %s' % (runName, md)} plotDict.update(basicPlotDict) metric = metrics.DiscoveryMetric(nObsPerNight=1, tMin=0, tMax=90. / 60. / 24., nNightsPerWindow=1, tWindow=5, **colkwargs) childMetrics = _setup_child_metrics(metric) bundle = mb.MoMetricBundle(metric, slicer, constraint, stackerList=[magStacker], runName=runName, metadata=md, childMetrics=childMetrics, plotDict=plotDict, plotFuncs=plotFuncs, displayDict=displayDict) _configure_child_bundles(bundle) bundleList.append(bundle)
# Single pair of detections. md = metadata + ' Single pair' + detectionLosses plotDict = {'title': '%s: %s' % (runName, md)} plotDict.update(basicPlotDict) metric = metrics.DiscoveryMetric(nObsPerNight=2, tMin=0, tMax=90. / 60. / 24., nNightsPerWindow=1, tWindow=5, **colkwargs) childMetrics = _setup_child_metrics(metric) bundle = mb.MoMetricBundle(metric, slicer, constraint, stackerList=[magStacker], runName=runName, metadata=md, childMetrics=childMetrics, plotDict=plotDict, plotFuncs=plotFuncs, displayDict=displayDict) _configure_child_bundles(bundle) bundleList.append(bundle)
# High velocity discovery. displayDict['subgroup'] = 'High Velocity'
# High velocity. md = metadata + ' High velocity pair' + detectionLosses plotDict = {'title': '%s: %s' % (runName, md)} plotDict.update(basicPlotDict) metric = metrics.HighVelocityNightsMetric(psfFactor=2., nObsPerNight=2, **colkwargs) bundle = mb.MoMetricBundle(metric, slicer, constraint, stackerList=[magStacker], runName=runName, metadata=md, plotDict=plotDict, plotFuncs=plotFuncs, summaryMetrics=summaryTimeMetrics, displayDict=displayDict) bundleList.append(bundle)
# "magic" detection - 6 in 60 days (at SNR=5). md = metadata + ' 6 detections in 60 nights' + detectionLosses plotDict = {'title': '%s: %s' % (runName, md)} plotDict.update(basicPlotDict) metric = metrics.MagicDiscoveryMetric(nObs=6, tWindow=60, **colkwargs) bundle = mb.MoMetricBundle(metric, slicer, constraint, stackerList=[magStacker], runName=runName, metadata=md, plotDict=plotDict, plotFuncs=plotFuncs, summaryMetrics=summaryHMetrics, displayDict=displayDict) bundleList.append(bundle)
# Set the runName for all bundles and return the bundleDict. for b in bundleList: b.setRunName(runName) return mb.makeBundlesDictFromList(bundleList), plotBundles
""" Generate completeness bundles from all N_Chances and Time child metrics of the (discovery) bundles in bdict, and write completeness at Hmark to resultsDb, save bundle to disk.
Parameters ---------- bdict : dict of metricBundles Dict containing ~lsst.sims.maf.MoMetricBundles, including bundles we're expecting to contain completeness. Hmark : float Hmark value to add to completeness plotting dict. outDir : str Output directory to save completeness bundles to disk. resultsDb : ~lsst.sims.maf.db.ResultsDb Results database to save information about completeness bundle.
Returns ------- dict of metricBundles Now the resulting metricBundles also includes new nested dicts with keys "DifferentialCompleteness" and "CumulativeCompleteness", which contain bundles of completeness metrics at each year. """ # Add completeness bundles and write completeness at Hmark to resultsDb. completeness = {} group = 'Discovery' subgroup = 'Completeness @ H=%.1f' % (Hmark)
def _compbundles(b, bundle, Hmark, resultsDb): comp = {} newkey = b + ' differential completeness' comp[newkey] = mb.makeCompletenessBundle(bundle, summaryName='DifferentialCompleteness', Hmark=Hmark, resultsDb=resultsDb) newkey = b + ' cumulative completeness' comp[newkey] = mb.makeCompletenessBundle(bundle, summaryName='CumulativeCompleteness', Hmark=Hmark, resultsDb=resultsDb) return comp
# Generate the completeness bundles for the various discovery metrics. for b, bundle in bdict.items(): if isinstance(bundle.metric, metrics.DiscoveryMetric): childkeys = ['Time', 'N_Chances'] for k in bundle.childBundles: if k in childkeys: childbundle = bundle.childBundles[k] completeness.update(_compbundles(b, childbundle, Hmark, resultsDb)) if isinstance(bundle.metric, metrics.HighVelocityNightsMetric): completeness.update(_compbundles(b, bundle, Hmark, resultsDb)) if isinstance(bundle.metric, metrics.MagicDiscoveryMetric): completeness.update(_compbundles(b, bundle, Hmark, resultsDb))
# Write the completeness bundles to disk, so we can re-read them later. # (also set the display dict properties, for the resultsDb output). for b, bundle in completeness.items(): bundle.setDisplayDict({'group': group, 'subgroup': subgroup}) bundle.write(outDir=outDir, resultsDb=resultsDb)
return completeness
albedo=None, Hmark=None, npReduce=np.mean, constraint=None, windows=None, bins=None):
if colmap is None: colmap = ColMapDict('opsimV4') bundleList = [] plotBundles = []
# Set up a dictionary to pass to each metric for the column names. colkwargs = {'mjdCol': colmap['mjd'], 'seeingCol': colmap['seeingGeom'], 'expTimeCol': colmap['exptime'], 'm5Col': colmap['fiveSigmaDepth'], 'nightCol': colmap['night'], 'filterCol': colmap['filter']}
basicPlotDict = {'albedo': albedo, 'Hmark': Hmark, 'npReduce': npReduce, 'nxbins': 200, 'nybins': 200} plotFuncs = [plots.MetricVsH()] displayDict ={'group': 'Characterization'}
if windows is None: windows = np.arange(1, 200, 15.) if bins is None: bins = np.arange(5, 95, 10.)
# Number of observations. md = metadata plotDict = {'ylabel': 'Number of observations (#)', 'title': '%s: Number of observations %s' % (runName, md)} plotDict.update(basicPlotDict) metric = metrics.NObsMetric(**colkwargs) bundle = mb.MoMetricBundle(metric, slicer, constraint, runName=runName, metadata=md, plotDict=plotDict, plotFuncs=plotFuncs, summaryMetrics=None, displayDict=displayDict) bundleList.append(bundle)
# Observational arc. md = metadata plotDict = {'ylabel': 'Observational Arc (days)', 'title': '%s: Observational Arc Length %s' % (runName, md)} plotDict.update(basicPlotDict) metric = metrics.ObsArcMetric(**colkwargs) bundle = mb.MoMetricBundle(metric, slicer, constraint, runName=runName, metadata=md, plotDict=plotDict, plotFuncs=plotFuncs, summaryMetrics=None, displayDict=displayDict) bundleList.append(bundle)
# Activity detection. for w in windows: md = metadata + ' activity lasting %.0f days' % w plotDict = {'title': '%s: Chances of detecting %s' % (runName, md), 'ylabel': 'Probability of detection per %.0f day window' % w} metricName = 'Chances of detecting activity lasting %.0f days' % w metric = metrics.ActivityOverTimeMetric(w, metricName=metricName, **colkwargs) bundle = mb.MoMetricBundle(metric, slicer, constraint, runName=runName, metadata=metadata, plotDict=plotDict, plotFuncs=plotFuncs, displayDict=displayDict) bundleList.append(bundle)
for b in bins: md = metadata + ' activity lasting %.2f of period' % (b/360.) plotDict = {'title': '%s: Chances of detecting %s' % (runName, md), 'ylabel': 'Probability of detection per %.2f deg window' % b} metricName = 'Chances of detecting activity lasting %.2f of the period' % (b/360.) metric = metrics.ActivityOverPeriodMetric(b, metricName=metricName, **colkwargs) bundle = mb.MoMetricBundle(metric, slicer, constraint, runName=runName, metadata=metadata, plotDict=plotDict, plotFuncs=plotFuncs, displayDict=displayDict) bundleList.append(bundle)
# Lightcurve inversion. md = metadata plotDict = {'yMin': 0, 'yMax': 1, 'ylabel': 'Fraction of objects', 'title': '%s: Fraction with potential lightcurve inversion %s' % (runName, md)} plotDict.update(basicPlotDict) metric = metrics.LightcurveInversionMetric(snrLimit=20, nObs=100, nDays=5*365, **colkwargs) bundle = mb.MoMetricBundle(metric, slicer, constraint, runName=runName, metadata=md, plotDict=plotDict, plotFuncs=plotFuncs, summaryMetrics=None, displayDict=displayDict) bundleList.append(bundle)
# Color determination. snrLimit = 10 nHours = 2.0 nPairs = 1 md = metadata + ' u-g color' plotDict = {'label': md, 'title': '%s: Fraction with potential u-g color measurement %s' % (runName, md)} plotDict.update(basicPlotDict) metric = metrics.ColorDeterminationMetric(nPairs=nPairs, snrLimit=snrLimit, nHours=nHours, bOne='u', bTwo='g', **colkwargs) bundle = mb.MoMetricBundle(metric, slicer, constraint, runName=runName, metadata=md, plotDict=plotDict, plotFuncs=plotFuncs, summaryMetrics=None, displayDict=displayDict) bundleList.append(bundle)
md = metadata + ' g-r color' plotDict = {'label': md, 'title': '%s: Fraction with potential g-r color measurement %s' % (runName, md)} plotDict.update(basicPlotDict) metric = metrics.ColorDeterminationMetric(nPairs=nPairs, snrLimit=snrLimit, nHours=nHours, bOne='g', bTwo='r', **colkwargs) bundle = mb.MoMetricBundle(metric, slicer, constraint, runName=runName, metadata=md, plotDict=plotDict, plotFuncs=plotFuncs, summaryMetrics=None, displayDict=displayDict) bundleList.append(bundle)
md = metadata + ' r-i color' plotDict = {'label': md, 'title': '%s: Fraction with potential r-i color measurement %s' % (runName, md)} plotDict.update(basicPlotDict) metric = metrics.ColorDeterminationMetric(nPairs=nPairs, snrLimit=snrLimit, nHours=nHours, bOne='r', bTwo='i', **colkwargs) bundle = mb.MoMetricBundle(metric, slicer, constraint, runName=runName, metadata=md, plotDict=plotDict, plotFuncs=plotFuncs, summaryMetrics=None, displayDict=displayDict) bundleList.append(bundle)
md = metadata + ' i-z color' plotDict = {'label': md, 'title': '%s: Fraction with potential i-z color measurement %s' % (runName, md)} plotDict.update(basicPlotDict) metric = metrics.ColorDeterminationMetric(nPairs=nPairs, snrLimit=snrLimit, nHours=nHours, bOne='i', bTwo='z', **colkwargs) bundle = mb.MoMetricBundle(metric, slicer, constraint, runName=runName, metadata=md, plotDict=plotDict, plotFuncs=plotFuncs, summaryMetrics=None, displayDict=displayDict) bundleList.append(bundle)
md = metadata + ' z-y color' plotDict = {'label': md, 'title': '%s: Fraction with potential z-y color measurement %s' % (runName, md)} plotDict.update(basicPlotDict) metric = metrics.ColorDeterminationMetric(nPairs=nPairs, snrLimit=snrLimit, nHours=nHours, bOne='z', bTwo='y', **colkwargs) bundle = mb.MoMetricBundle(metric, slicer, constraint, runName=runName, metadata=md, plotDict=plotDict, plotFuncs=plotFuncs, summaryMetrics=None, displayDict=displayDict) bundleList.append(bundle)
# Set the runName for all bundles and return the bundleDict. for b in bundleList: b.setRunName(runName) return mb.makeBundlesDictFromList(bundleList), plotBundles |