def testRandomDitherPerNight(self):
"""
Test the per-night random dither pattern.
"""
maxDither = 0.5
ndata = 600
# Set seed so the test is stable
np.random.seed(42)
data = np.zeros(ndata,
dtype=list(
zip(['fieldRA', 'fieldDec', 'fieldId', 'night'],
[float, float, int, int])))
data['fieldRA'] = np.random.rand(ndata) * (np.pi) + np.pi / 2.0
data['fieldDec'] = np.random.rand(ndata) * np.pi / 2.0 - np.pi / 4.0
data['fieldId'] = np.floor(np.random.rand(ndata) * ndata)
data['night'] = np.floor(np.random.rand(ndata) * 10).astype('int')
stacker = stackers.RandomDitherPerNightStacker(maxDither=maxDither)
data = stacker.run(data)
diffsra = (np.radians(data['fieldRA']) -
np.radians(data['randomDitherPerNightRa'])) * np.cos(
np.radians(data['fieldDec']))
diffsdec = np.radians(data['fieldDec']) - np.radians(
data['randomDitherPerNightDec'])
self._tDitherRange(diffsra, diffsdec, data['fieldRA'],
data['fieldDec'], maxDither)
# Check that dithers on the same night are the same.
self._tDitherPerNight(diffsra, diffsdec, data['fieldRA'],
data['fieldDec'], data['night'])
def coaddM5Analysis(path,
dbfile,
runName,
slair=False,
WFDandDDFs=False,
noDithOnly=False,
bestDithOnly=False,
someDithOnly=False,
specifiedDith=None,
nside=128,
filterBand='r',
includeDustExtinction=False,
saveunMaskedCoaddData=False,
pixelRadiusForMasking=5,
cutOffYear=None,
plotSkymap=True,
plotCartview=True,
unmaskedColorMin=None,
unmaskedColorMax=None,
maskedColorMin=None,
maskedColorMax=None,
nTicks=5,
plotPowerSpectrum=True,
showPlots=True,
saveFigs=True,
almAnalysis=True,
raRange=[-50, 50],
decRange=[-65, 5],
saveMaskedCoaddData=True):
"""
Analyze the artifacts induced in the coadded 5sigma depth due to imperfect observing strategy.
- Creates an output directory for subdirectories containing the specified things to save.
- Creates, shows, and saves comparison plots.
- Returns the metricBundle object containing the calculated coadded depth, and the output directory name.
Required Parameters
-------------------
* path: str: path to the main directory where output directory is to be saved.
* dbfile: str: path to the OpSim output file, e.g. to a copy of enigma_1189
* runName: str: run name tag to identify the output of specified OpSim output, e.g. 'enigma1189'
Optional Parameters
-------------------
* slair: boolean: set to True if analysis on a SLAIR output.
Default: False
* WFDandDDFs: boolean: set to True if want to consider both WFD survet and DDFs. Otherwise will only work
with WFD. Default: False
* noDithOnly: boolean: set to True if only want to consider the undithered survey. Default: False
* bestDithOnly: boolean: set to True if only want to consider RandomDitherFieldPerVisit.
Default: False
* someDithOnly: boolean: set to True if only want to consider undithered and a few dithered surveys.
Default: False
* specifiedDith: str: specific dither strategy to run.
Default: None
* nside: int: HEALpix resolution parameter. Default: 128
* filterBand: str: any one of 'u', 'g', 'r', 'i', 'z', 'y'. Default: 'r'
* includeDustExtinction: boolean: set to include dust extinction. Default: False
* saveunMaskedCoaddData: boolean: set to True to save data before border masking. Default: False
* pixelRadiusForMasking: int: number of pixels to mask along the shallow border. Default: 5
* cutOffYear: int: year cut to restrict analysis to only a subset of the survey.
Must range from 1 to 9, or None for the full survey analysis (10 yrs).
Default: None
* plotSkymap: boolean: set to True if want to plot skymaps. Default: True
* plotCartview: boolean: set to True if want to plot cartview plots. Default: False
* unmaskedColorMin: float: lower limit on the colorscale for unmasked skymaps. Default: None
* unmaskedColorMax: float: upper limit on the colorscale for unmasked skymaps. Default: None
* maskedColorMin: float: lower limit on the colorscale for border-masked skymaps. Default: None
* maskedColorMax: float: upper limit on the colorscale for border-masked skymaps. Default: None
* nTicks: int: (number of ticks - 1) on the skymap colorbar. Default: 5
* plotPowerSpectrum: boolean: set to True if want to plot powerspectra. Default: True
* showPlots: boolean: set to True if want to show figures. Default: True
* saveFigs: boolean: set to True if want to save figures. Default: True
* almAnalysis: boolean: set to True to perform the alm analysis. Default: True
* raRange: float array: range of right ascention (in degrees) to consider in alm cartview plot;
applicable when almAnalysis=True. Default: [-50,50]
* decRange: float array: range of declination (in degrees) to consider in alm cartview plot;
applicable when almAnalysis=True. Default: [-65,5]
* saveMaskedCoaddData: boolean: set to True to save the coadded depth data after the border
masking. Default: True
"""
# ------------------------------------------------------------------------
# read in the database
if slair:
# slair database
opsdb = db.Database(dbfile, defaultTable='observations')
else:
# OpSim database
opsdb = db.OpsimDatabase(dbfile)
# ------------------------------------------------------------------------
# set up the outDir
zeropt_tag = ''
if cutOffYear is not None: zeropt_tag = '%syearCut' % cutOffYear
else: zeropt_tag = 'fullSurveyPeriod'
if includeDustExtinction: dust_tag = 'withDustExtinction'
else: dust_tag = 'noDustExtinction'
regionType = ''
if WFDandDDFs: regionType = 'WFDandDDFs_'
outDir = 'coaddM5Analysis_%snside%s_%s_%spixelRadiusForMasking_%sBand_%s_%s_directory' % (
regionType, nside, dust_tag, pixelRadiusForMasking, filterBand,
runName, zeropt_tag)
print('# outDir: %s' % outDir)
resultsDb = db.ResultsDb(outDir=outDir)
# ------------------------------------------------------------------------
# set up the sql constraint
if WFDandDDFs:
if cutOffYear is not None:
nightCutOff = (cutOffYear) * 365.25
sqlconstraint = 'night<=%s and filter=="%s"' % (nightCutOff,
filterBand)
else:
sqlconstraint = 'filter=="%s"' % filterBand
else:
# set up the propID and units on the ra, dec
if slair: # no prop ID; only WFD is simulated.
wfdWhere = ''
raDecInDeg = True
else:
propIds, propTags = opsdb.fetchPropInfo()
wfdWhere = '%s and ' % opsdb.createSQLWhere('WFD', propTags)
raDecInDeg = opsdb.raDecInDeg
# set up the year cutoff
if cutOffYear is not None:
nightCutOff = (cutOffYear) * 365.25
sqlconstraint = '%snight<=%s and filter=="%s"' % (
wfdWhere, nightCutOff, filterBand)
else:
sqlconstraint = '%sfilter=="%s"' % (wfdWhere, filterBand)
print('# sqlconstraint: %s' % sqlconstraint)
# ------------------------------------------------------------------------
# setup all the slicers
slicer = {}
stackerList = {}
if specifiedDith is not None: # would like to add all the stackers first and then keep only the one that is specified
bestDithOnly, noDithOnly = False, False
if bestDithOnly:
stackerList['RandomDitherFieldPerVisit'] = [
mafStackers.RandomDitherFieldPerVisitStacker(degrees=raDecInDeg,
randomSeed=1000)
]
slicer['RandomDitherFieldPerVisit'] = slicers.HealpixSlicer(
lonCol='randomDitherFieldPerVisitRa',
latCol='randomDitherFieldPerVisitDec',
latLonDeg=raDecInDeg,
nside=nside,
useCache=False)
else:
if slair:
slicer['NoDither'] = slicers.HealpixSlicer(lonCol='RA',
latCol='dec',
latLonDeg=raDecInDeg,
nside=nside,
useCache=False)
else:
slicer['NoDither'] = slicers.HealpixSlicer(lonCol='fieldRA',
latCol='fieldDec',
latLonDeg=raDecInDeg,
nside=nside,
useCache=False)
if someDithOnly and not noDithOnly:
#stackerList['RepulsiveRandomDitherFieldPerVisit'] = [myStackers.RepulsiveRandomDitherFieldPerVisitStacker(degrees=raDecInDeg,
# randomSeed=1000)]
#slicer['RepulsiveRandomDitherFieldPerVisit'] = slicers.HealpixSlicer(lonCol='repulsiveRandomDitherFieldPerVisitRa',
# latCol='repulsiveRandomDitherFieldPerVisitDec',
# latLonDeg=raDecInDeg, nside=nside,
# useCache=False)
slicer['SequentialHexDitherFieldPerNight'] = slicers.HealpixSlicer(
lonCol='hexDitherFieldPerNightRa',
latCol='hexDitherFieldPerNightDec',
latLonDeg=raDecInDeg,
nside=nside,
useCache=False)
slicer['PentagonDitherPerSeason'] = slicers.HealpixSlicer(
lonCol='pentagonDitherPerSeasonRa',
latCol='pentagonDitherPerSeasonDec',
latLonDeg=raDecInDeg,
nside=nside,
useCache=False)
elif not noDithOnly:
# random dithers on different timescales
stackerList['RandomDitherPerNight'] = [
mafStackers.RandomDitherPerNightStacker(degrees=raDecInDeg,
randomSeed=1000)
]
stackerList['RandomDitherFieldPerNight'] = [
mafStackers.RandomDitherFieldPerNightStacker(
degrees=raDecInDeg, randomSeed=1000)
]
stackerList['RandomDitherFieldPerVisit'] = [
mafStackers.RandomDitherFieldPerVisitStacker(
degrees=raDecInDeg, randomSeed=1000)
]
# rep random dithers on different timescales
#stackerList['RepulsiveRandomDitherPerNight'] = [myStackers.RepulsiveRandomDitherPerNightStacker(degrees=raDecInDeg,
# randomSeed=1000)]
#stackerList['RepulsiveRandomDitherFieldPerNight'] = [myStackers.RepulsiveRandomDitherFieldPerNightStacker(degrees=raDecInDeg,
# randomSeed=1000)]
#stackerList['RepulsiveRandomDitherFieldPerVisit'] = [myStackers.RepulsiveRandomDitherFieldPerVisitStacker(degrees=raDecInDeg,
# randomSeed=1000)]
# set up slicers for different dithers
# random dithers on different timescales
slicer['RandomDitherPerNight'] = slicers.HealpixSlicer(
lonCol='randomDitherPerNightRa',
latCol='randomDitherPerNightDec',
latLonDeg=raDecInDeg,
nside=nside,
useCache=False)
slicer['RandomDitherFieldPerNight'] = slicers.HealpixSlicer(
lonCol='randomDitherFieldPerNightRa',
latCol='randomDitherFieldPerNightDec',
latLonDeg=raDecInDeg,
nside=nside,
useCache=False)
slicer['RandomDitherFieldPerVisit'] = slicers.HealpixSlicer(
lonCol='randomDitherFieldPerVisitRa',
latCol='randomDitherFieldPerVisitDec',
latLonDeg=raDecInDeg,
nside=nside,
useCache=False)
# rep random dithers on different timescales
#slicer['RepulsiveRandomDitherPerNight'] = slicers.HealpixSlicer(lonCol='repulsiveRandomDitherPerNightRa',
# latCol='repulsiveRandomDitherPerNightDec',
# latLonDeg=raDecInDeg, nside=nside, useCache=False)
#slicer['RepulsiveRandomDitherFieldPerNight'] = slicers.HealpixSlicer(lonCol='repulsiveRandomDitherFieldPerNightRa',
# latCol='repulsiveRandomDitherFieldPerNightDec',
# latLonDeg=raDecInDeg, nside=nside,
# useCache=False)
#slicer['RepulsiveRandomDitherFieldPerVisit'] = slicers.HealpixSlicer(lonCol='repulsiveRandomDitherFieldPerVisitRa',
# latCol='repulsiveRandomDitherFieldPerVisitDec',
# latLonDeg=raDecInDeg, nside=nside,
# useCache=False)
# spiral dithers on different timescales
slicer['FermatSpiralDitherPerNight'] = slicers.HealpixSlicer(
lonCol='fermatSpiralDitherPerNightRa',
latCol='fermatSpiralDitherPerNightDec',
latLonDeg=raDecInDeg,
nside=nside,
useCache=False)
slicer['FermatSpiralDitherFieldPerNight'] = slicers.HealpixSlicer(
lonCol='fermatSpiralDitherFieldPerNightRa',
latCol='fermatSpiralDitherFieldPerNightDec',
latLonDeg=raDecInDeg,
nside=nside,
useCache=False)
slicer['FermatSpiralDitherFieldPerVisit'] = slicers.HealpixSlicer(
lonCol='fermatSpiralDitherFieldPerVisitRa',
latCol='fermatSpiralDitherFieldPerVisitDec',
latLonDeg=raDecInDeg,
nside=nside,
useCache=False)
# hex dithers on different timescales
slicer['SequentialHexDitherPerNight'] = slicers.HealpixSlicer(
lonCol='hexDitherPerNightRa',
latCol='hexDitherPerNightDec',
latLonDeg=raDecInDeg,
nside=nside,
useCache=False)
slicer['SequentialHexDitherFieldPerNight'] = slicers.HealpixSlicer(
lonCol='hexDitherFieldPerNightRa',
latCol='hexDitherFieldPerNightDec',
latLonDeg=raDecInDeg,
nside=nside,
useCache=False)
slicer['SequentialHexDitherFieldPerVisit'] = slicers.HealpixSlicer(
lonCol='hexDitherFieldPerVisitRa',
latCol='hexDitherFieldPerVisitDec',
latLonDeg=raDecInDeg,
nside=nside,
useCache=False)
# per season dithers
slicer['PentagonDitherPerSeason'] = slicers.HealpixSlicer(
lonCol='pentagonDitherPerSeasonRa',
latCol='pentagonDitherPerSeasonDec',
latLonDeg=raDecInDeg,
nside=nside,
useCache=False)
slicer['PentagonDiamondDitherPerSeason'] = slicers.HealpixSlicer(
lonCol='pentagonDiamondDitherPerSeasonRa',
latCol='pentagonDiamondDitherPerSeasonDec',
latLonDeg=raDecInDeg,
nside=nside,
useCache=False)
slicer['SpiralDitherPerSeason'] = slicers.HealpixSlicer(
lonCol='spiralDitherPerSeasonRa',
latCol='spiralDitherPerSeasonDec',
latLonDeg=raDecInDeg,
nside=nside,
useCache=False)
if specifiedDith is not None:
stackerList_, slicer_ = {}, {}
if specifiedDith in slicer.keys():
if specifiedDith.__contains__(
'Random'
): # only Random dithers have a stacker object for rand seed specification
stackerList_[specifiedDith] = stackerList[specifiedDith]
slicer_[specifiedDith] = slicer[specifiedDith]
else:
raise ValueError(
'Invalid value for specifiedDith: %s. Allowed values include one of the following:\n%s'
% (specifiedDith, slicer.keys()))
stackerList, slicer = stackerList_, slicer_
# ------------------------------------------------------------------------
if slair:
m5Col = 'fivesigmadepth'
else:
m5Col = 'fiveSigmaDepth'
# set up the metric
if includeDustExtinction:
# include dust extinction when calculating the co-added depth
coaddMetric = metrics.ExgalM5(m5Col=m5Col, lsstFilter=filterBand)
else:
coaddMetric = metrics.Coaddm5Metric(m5col=m5col)
dustMap = maps.DustMap(
interp=False, nside=nside
) # include dustMap; actual in/exclusion of dust is handled by the galaxyCountMetric
# ------------------------------------------------------------------------
# set up the bundle
coaddBundle = {}
for dither in slicer:
if dither in stackerList:
coaddBundle[dither] = metricBundles.MetricBundle(
coaddMetric,
slicer[dither],
sqlconstraint,
stackerList=stackerList[dither],
runName=runName,
metadata=dither,
mapsList=[dustMap])
else:
coaddBundle[dither] = metricBundles.MetricBundle(
coaddMetric,
slicer[dither],
sqlconstraint,
runName=runName,
metadata=dither,
mapsList=[dustMap])
# ------------------------------------------------------------------------
# run the analysis
if includeDustExtinction:
print('\n# Running coaddBundle with dust extinction ...')
else:
print('\n# Running coaddBundle without dust extinction ...')
cGroup = metricBundles.MetricBundleGroup(coaddBundle,
opsdb,
outDir=outDir,
resultsDb=resultsDb,
saveEarly=False)
cGroup.runAll()
# ------------------------------------------------------------------------
# plot and save the data
plotBundleMaps(path,
outDir,
coaddBundle,
dataLabel='$%s$-band Coadded Depth' % filterBand,
filterBand=filterBand,
dataName='%s-band Coadded Depth' % filterBand,
skymap=plotSkymap,
powerSpectrum=plotPowerSpectrum,
cartview=plotCartview,
colorMin=unmaskedColorMin,
colorMax=unmaskedColorMax,
nTicks=nTicks,
showPlots=showPlots,
saveFigs=saveFigs,
outDirNameForSavedFigs='coaddM5Plots_unmaskedBorders')
print('\n# Done saving plots without border masking.\n')
# ------------------------------------------------------------------------
plotHandler = plots.PlotHandler(outDir=outDir,
resultsDb=resultsDb,
thumbnail=False,
savefig=False)
print(
'# Number of pixels in the survey region (before masking the border):')
for dither in coaddBundle:
print(
' %s: %s' %
(dither,
len(np.where(coaddBundle[dither].metricValues.mask == False)[0])))
# ------------------------------------------------------------------------
# save the unmasked data?
if saveunMaskedCoaddData:
outDir_new = 'unmaskedCoaddData'
if not os.path.exists('%s%s/%s' % (path, outDir, outDir_new)):
os.makedirs('%s%s/%s' % (path, outDir, outDir_new))
saveBundleData_npzFormat('%s%s/%s' % (path, outDir, outDir_new),
coaddBundle, 'coaddM5Data_unmasked',
filterBand)
# ------------------------------------------------------------------------
# mask the edges
print('\n# Masking the edges for coadd ...')
coaddBundle = maskingAlgorithmGeneralized(
coaddBundle,
plotHandler,
dataLabel='$%s$-band Coadded Depth' % filterBand,
nside=nside,
pixelRadius=pixelRadiusForMasking,
plotIntermediatePlots=False,
plotFinalPlots=False,
printFinalInfo=True)
if (pixelRadiusForMasking > 0):
# plot and save the masked data
plotBundleMaps(path,
outDir,
coaddBundle,
dataLabel='$%s$-band Coadded Depth' % filterBand,
filterBand=filterBand,
dataName='%s-band Coadded Depth' % filterBand,
skymap=plotSkymap,
powerSpectrum=plotPowerSpectrum,
cartview=plotCartview,
colorMin=maskedColorMin,
colorMax=maskedColorMax,
nTicks=nTicks,
showPlots=showPlots,
saveFigs=saveFigs,
outDirNameForSavedFigs='coaddM5Plots_maskedBorders')
print('\n# Done saving plots with border masking. \n')
# ------------------------------------------------------------------------
# Calculate total power
summarymetric = metrics.TotalPowerMetric()
for dither in coaddBundle:
coaddBundle[dither].setSummaryMetrics(summarymetric)
coaddBundle[dither].computeSummaryStats()
print('# Total power for %s case is %f.' %
(dither, coaddBundle[dither].summaryValues['TotalPower']))
print('')
# ------------------------------------------------------------------------
# run the alm analysis
if almAnalysis:
almPlots(path,
outDir,
copy.deepcopy(coaddBundle),
nside=nside,
filterband=filterBand,
raRange=raRange,
decRange=decRange,
showPlots=showPlots)
# ------------------------------------------------------------------------
# save the masked data?
if saveMaskedCoaddData and (pixelRadiusForMasking > 0):
outDir_new = 'maskedCoaddData'
if not os.path.exists('%s%s/%s' % (path, outDir, outDir_new)):
os.makedirs('%s%s/%s' % (path, outDir, outDir_new))
saveBundleData_npzFormat('%s%s/%s' % (path, outDir, outDir_new),
coaddBundle, 'coaddM5Data_masked', filterBand)
# ------------------------------------------------------------------------
# plot comparison plots
if len(coaddBundle.keys()) > 1: # more than one key
# set up the directory
outDir_comp = 'coaddM5ComparisonPlots'
if not os.path.exists('%s%s/%s' % (path, outDir, outDir_comp)):
os.makedirs('%s%s/%s' % (path, outDir, outDir_comp))
# ------------------------------------------------------------------------
# plot for the power spectra
cl = {}
for dither in plotColor:
if dither in coaddBundle:
cl[dither] = hp.anafast(hp.remove_dipole(
coaddBundle[dither].metricValues.filled(
coaddBundle[dither].slicer.badval)),
lmax=500)
ell = np.arange(np.size(cl[dither]))
plt.plot(ell, (cl[dither] * ell * (ell + 1)) / 2.0 / np.pi,
color=plotColor[dither],
linestyle='-',
label=dither)
plt.xlabel(r'$\ell$')
plt.ylabel(r'$\ell(\ell+1)C_\ell/(2\pi)$')
plt.xlim(0, 500)
fig = plt.gcf()
fig.set_size_inches(12.5, 10.5)
leg = plt.legend(labelspacing=0.001)
for legobj in leg.legendHandles:
legobj.set_linewidth(4.0)
filename = 'powerspectrum_comparison_all.png'
plt.savefig('%s%s/%s/%s' % (path, outDir, outDir_comp, filename),
bbox_inches='tight',
format='png')
plt.show()
# create the histogram
scale = hp.nside2pixarea(nside, degrees=True)
def tickFormatter(y, pos):
return '%d' % (y * scale) # convert pixel count to area
binsize = 0.01
for dither in plotColor:
if dither in coaddBundle:
ind = np.where(
coaddBundle[dither].metricValues.mask == False)[0]
binAll = int(
(max(coaddBundle[dither].metricValues.data[ind]) -
min(coaddBundle[dither].metricValues.data[ind])) /
binsize)
plt.hist(coaddBundle[dither].metricValues.data[ind],
bins=binAll,
label=dither,
histtype='step',
color=plotColor[dither])
ax = plt.gca()
ymin, ymax = ax.get_ylim()
nYticks = 10.
wantedYMax = ymax * scale
wantedYMax = 10. * np.ceil(float(wantedYMax) / 10.)
increment = 5. * np.ceil(float(wantedYMax / nYticks) / 5.)
wantedArray = np.arange(0, wantedYMax, increment)
ax.yaxis.set_ticks(wantedArray / scale)
ax.yaxis.set_major_formatter(FuncFormatter(tickFormatter))
plt.xlabel('$%s$-band Coadded Depth' % filterBand)
plt.ylabel('Area (deg$^2$)')
fig = plt.gcf()
fig.set_size_inches(12.5, 10.5)
leg = plt.legend(labelspacing=0.001, loc=2)
for legobj in leg.legendHandles:
legobj.set_linewidth(2.0)
filename = 'histogram_comparison.png'
plt.savefig('%s%s/%s/%s' % (path, outDir, outDir_comp, filename),
bbox_inches='tight',
format='png')
plt.show()
# ------------------------------------------------------------------------
# plot power spectra for the separte panel
totKeys = len(list(coaddBundle.keys()))
if (totKeys > 1):
plt.clf()
nCols = 2
nRows = int(np.ceil(float(totKeys) / nCols))
fig, ax = plt.subplots(nRows, nCols)
plotRow = 0
plotCol = 0
for dither in list(plotColor.keys()):
if dither in list(coaddBundle.keys()):
ell = np.arange(np.size(cl[dither]))
ax[plotRow, plotCol].plot(ell, (cl[dither] * ell *
(ell + 1)) / 2.0 / np.pi,
color=plotColor[dither],
label=dither)
if (plotRow == nRows - 1):
ax[plotRow, plotCol].set_xlabel(r'$\ell$')
ax[plotRow,
plotCol].set_ylabel(r'$\ell(\ell+1)C_\ell/(2\pi)$')
ax[plotRow,
plotCol].yaxis.set_major_locator(MaxNLocator(3))
if (dither != 'NoDither'):
ax[plotRow, plotCol].set_ylim(0, 0.0035)
ax[plotRow, plotCol].set_xlim(0, 500)
plotRow += 1
if (plotRow > nRows - 1):
plotRow = 0
plotCol += 1
fig.set_size_inches(20, int(nRows * 30 / 7.))
filename = 'powerspectrum_sepPanels.png'
plt.savefig('%s%s/%s/%s' % (path, outDir, outDir_comp, filename),
bbox_inches='tight',
format='png')
plt.show()
return coaddBundle, outDir