def __getOpSimMjd(self, opsim, ra, dec, fil):
colmn = 'observationStartMJD'
opsdb = db.OpsimDatabase(opsim)
# Directory where tmp files are going to be stored TODO eliminate - this
outDir = 'TmpDir'
resultsDb = db.ResultsDb(outDir=outDir)
metric = metrics.PassMetric(cols=[colmn, 'fiveSigmaDepth', 'filter'])
slicer = slicers.UserPointsSlicer(ra=ra, dec=dec)
sqlconstraint = 'filter = \'' + fil + '\''
bundle = mb.MetricBundle(metric, slicer, sqlconstraint, runName='name')
bgroup = mb.MetricBundleGroup({0: bundle},
opsdb,
outDir=outDir,
resultsDb=resultsDb)
bgroup.runAll()
filters = np.unique(bundle.metricValues[0]['filter'])
mv = bundle.metricValues[0]
# Get dates
mjd = mv[colmn]
mjd = np.sort(mjd)
print('Num of visits ' + str(len(mjd)) + ' ' + opsim)
return mjd
def get_cadence(ra, dec, b, snrLimit, nPtsLimit, filters, outDir, opsimdb,
resultsDb):
# The pass metric just passes data straight through.
metric = metrics.PassMetric(cols=['filter', 'fiveSigmaDepth', 'expMJD'])
slicer = slicers.UserPointsSlicer(ra,
dec,
lonCol='ditheredRA',
latCol='ditheredDec')
sql = ''
bundle = metricBundles.MetricBundle(metric, slicer, sql)
bg = metricBundles.MetricBundleGroup({0: bundle},
opsimdb,
outDir=outDir,
resultsDb=resultsDb)
bg.runAll()
bundle.metricValues.data[0]['filter']
print("Plotting...")
colors = {'u': 'cyan', 'g': 'g', 'r': 'y', 'i': 'r', 'z': 'm', 'y': 'k'}
dayZero = bundle.metricValues.data[0]['expMJD'].min()
times = []
depths = []
plt.clf()
for fname in filters:
good = np.where(bundle.metricValues.data[0]['filter'] == fname)
times.append(bundle.metricValues.data[0]['expMJD'][good] - dayZero)
depths.append(bundle.metricValues.data[0]['fiveSigmaDepth'][good])
plt.scatter(bundle.metricValues.data[0]['expMJD'][good] - dayZero,
bundle.metricValues.data[0]['fiveSigmaDepth'][good],
c=colors[fname],
label=fname)
plt.xlabel('Day')
plt.ylabel('5$\sigma$ depth')
plt.legend(scatterpoints=1, loc="upper left", bbox_to_anchor=(1, 1))
plt.savefig("l45b{0}_cadence.pdf".format(int(b)))
return times, depths
def makeBundleList(dbFile, runName=None, nside=64, benchmark='design',
lonCol='fieldRA', latCol='fieldDec', seeingCol='seeingFwhmGeom'):
"""
make a list of metricBundle objects to look at the scientific performance
of an opsim run.
"""
# List to hold everything we're going to make
bundleList = []
# List to hold metrics that shouldn't be saved
noSaveBundleList = []
# Connect to the databse
opsimdb = db.OpsimDatabaseV4(dbFile)
if runName is None:
runName = os.path.basename(dbFile).replace('_sqlite.db', '')
# Fetch the proposal ID values from the database
propids, propTags = opsimdb.fetchPropInfo()
# Fetch the telescope location from config
lat, lon, height = opsimdb.fetchLatLonHeight()
# Add metadata regarding dithering/non-dithered.
commonname = ''.join([a for a in lonCol if a in latCol])
if commonname == 'field':
slicermetadata = ' (non-dithered)'
else:
slicermetadata = ' (%s)' % (commonname)
# Construct a WFD SQL where clause so multiple propIDs can query by WFD:
wfdWhere = opsimdb.createSQLWhere('WFD', propTags)
print('#FYI: WFD "where" clause: %s' % (wfdWhere))
ddWhere = opsimdb.createSQLWhere('DD', propTags)
print('#FYI: DD "where" clause: %s' % (ddWhere))
# Set up benchmark values, scaled to length of opsim run.
runLength = opsimdb.fetchRunLength()
if benchmark == 'requested':
# Fetch design values for seeing/skybrightness/single visit depth.
benchmarkVals = utils.scaleBenchmarks(runLength, benchmark='design')
# Update nvisits with requested visits from config files.
benchmarkVals['nvisits'] = opsimdb.fetchRequestedNvisits(propId=propTags['WFD'])
# Calculate expected coadded depth.
benchmarkVals['coaddedDepth'] = utils.calcCoaddedDepth(benchmarkVals['nvisits'],
benchmarkVals['singleVisitDepth'])
elif (benchmark == 'stretch') or (benchmark == 'design'):
# Calculate benchmarks for stretch or design.
benchmarkVals = utils.scaleBenchmarks(runLength, benchmark=benchmark)
benchmarkVals['coaddedDepth'] = utils.calcCoaddedDepth(benchmarkVals['nvisits'],
benchmarkVals['singleVisitDepth'])
else:
raise ValueError('Could not recognize benchmark value %s, use design, stretch or requested.'
% (benchmark))
# Check that nvisits is not set to zero (for very short run length).
for f in benchmarkVals['nvisits']:
if benchmarkVals['nvisits'][f] == 0:
print('Updating benchmark nvisits value in %s to be nonzero' % (f))
benchmarkVals['nvisits'][f] = 1
# Set values for min/max range of nvisits for All/WFD and DD plots. These are somewhat arbitrary.
nvisitsRange = {}
nvisitsRange['all'] = {'u': [20, 80], 'g': [50, 150], 'r': [100, 250],
'i': [100, 250], 'z': [100, 300], 'y': [100, 300]}
nvisitsRange['DD'] = {'u': [6000, 10000], 'g': [2500, 5000], 'r': [5000, 8000],
'i': [5000, 8000], 'z': [7000, 10000], 'y': [5000, 8000]}
# Scale these ranges for the runLength.
scale = runLength / 10.0
for prop in nvisitsRange:
for f in nvisitsRange[prop]:
for i in [0, 1]:
nvisitsRange[prop][f][i] = int(np.floor(nvisitsRange[prop][f][i] * scale))
# Filter list, and map of colors (for plots) to filters.
filters = ['u', 'g', 'r', 'i', 'z', 'y']
colors = {'u': 'cyan', 'g': 'g', 'r': 'y', 'i': 'r', 'z': 'm', 'y': 'k'}
filtorder = {'u': 1, 'g': 2, 'r': 3, 'i': 4, 'z': 5, 'y': 6}
# Easy way to run through all fi
# Set up a list of common summary stats
commonSummary = [metrics.MeanMetric(), metrics.RobustRmsMetric(), metrics.MedianMetric(),
metrics.PercentileMetric(metricName='25th%ile', percentile=25),
metrics.PercentileMetric(metricName='75th%ile', percentile=75),
metrics.MinMetric(), metrics.MaxMetric()]
allStats = commonSummary
# Set up some 'group' labels
reqgroup = 'A: Required SRD metrics'
depthgroup = 'B: Depth per filter'
uniformitygroup = 'C: Uniformity'
airmassgroup = 'D: Airmass distribution'
seeinggroup = 'E: Seeing distribution'
transgroup = 'F: Transients'
sngroup = 'G: SN Ia'
altAzGroup = 'H: Alt Az'
rangeGroup = 'I: Range of Dates'
intergroup = 'J: Inter-Night'
phaseGroup = 'K: Max Phase Gap'
NEOGroup = 'L: NEO Detection'
# Set up an object to track the metricBundles that we want to combine into merged plots.
mergedHistDict = {}
# Set the histogram merge function.
mergeFunc = plots.HealpixHistogram()
keys = ['NVisits', 'coaddm5', 'NormEffTime', 'Minseeing', 'seeingAboveLimit', 'minAirmass',
'fracAboveAirmass']
for key in keys:
mergedHistDict[key] = plots.PlotBundle(plotFunc=mergeFunc)
##
# Calculate the fO metrics for all proposals and WFD only.
order = 0
for prop in ('All prop', 'WFD only'):
if prop == 'All prop':
metadata = 'All Visits' + slicermetadata
sqlconstraint = ''
if prop == 'WFD only':
metadata = 'WFD only' + slicermetadata
sqlconstraint = '%s' % (wfdWhere)
# Configure the count metric which is what is used for f0 slicer.
m1 = metrics.CountMetric(col='observationStartMJD', metricName='fO')
plotDict = {'xlabel': 'Number of Visits', 'Asky': benchmarkVals['Area'],
'Nvisit': benchmarkVals['nvisitsTotal'], 'xMin': 0, 'xMax': 1500}
summaryMetrics = [metrics.fOArea(nside=nside, norm=False, metricName='fOArea: Nvisits (#)',
Asky=benchmarkVals['Area'], Nvisit=benchmarkVals['nvisitsTotal']),
metrics.fOArea(nside=nside, norm=True, metricName='fOArea: Nvisits/benchmark',
Asky=benchmarkVals['Area'], Nvisit=benchmarkVals['nvisitsTotal']),
metrics.fONv(nside=nside, norm=False, metricName='fONv: Area (sqdeg)',
Asky=benchmarkVals['Area'], Nvisit=benchmarkVals['nvisitsTotal']),
metrics.fONv(nside=nside, norm=True, metricName='fONv: Area/benchmark',
Asky=benchmarkVals['Area'], Nvisit=benchmarkVals['nvisitsTotal'])]
caption = 'The FO metric evaluates the overall efficiency of observing. '
caption += ('fOArea: Nvisits = %.1f sq degrees receive at least this many visits out of %d. '
% (benchmarkVals['Area'], benchmarkVals['nvisitsTotal']))
caption += ('fONv: Area = this many square degrees out of %.1f receive at least %d visits.'
% (benchmarkVals['Area'], benchmarkVals['nvisitsTotal']))
displayDict = {'group': reqgroup, 'subgroup': 'F0', 'displayOrder': order, 'caption': caption}
order += 1
slicer = slicers.HealpixSlicer(nside=nside, lonCol=lonCol, latCol=latCol)
bundle = metricBundles.MetricBundle(m1, slicer, sqlconstraint, plotDict=plotDict,
displayDict=displayDict, summaryMetrics=summaryMetrics,
plotFuncs=[plots.FOPlot()],
runName=runName, metadata=metadata)
bundleList.append(bundle)
###
# Calculate the Rapid Revisit Metrics.
order = 0
metadata = 'All Visits' + slicermetadata
sqlconstraint = ''
dTmin = 40.0 # seconds
dTmax = 30.0*60. # seconds
minNvisit = 100
pixArea = float(hp.nside2pixarea(nside, degrees=True))
scale = pixArea * hp.nside2npix(nside)
cutoff1 = 0.15
extraStats1 = [metrics.FracBelowMetric(cutoff=cutoff1, scale=scale, metricName='Area (sq deg)')]
extraStats1.extend(commonSummary)
slicer = slicers.HealpixSlicer(nside=nside, lonCol=lonCol, latCol=latCol)
m1 = metrics.RapidRevisitMetric(metricName='RapidRevisitUniformity',
dTmin=dTmin / 60.0 / 60.0 / 24.0, dTmax=dTmax / 60.0 / 60.0 / 24.0,
minNvisits=minNvisit)
plotDict = {'xMin': 0, 'xMax': 1}
summaryStats = extraStats1
caption = 'Deviation from uniformity for short revisit timescales, between %s and %s seconds, ' % (
dTmin, dTmax)
caption += 'for pointings with at least %d visits in this time range. ' % (minNvisit)
caption += 'Summary statistic "Area" below indicates the area on the sky which has a '
caption += 'deviation from uniformity of < %.2f.' % (cutoff1)
displayDict = {'group': reqgroup, 'subgroup': 'Rapid Revisit', 'displayOrder': order,
'caption': caption}
bundle = metricBundles.MetricBundle(m1, slicer, sqlconstraint, plotDict=plotDict,
displayDict=displayDict, summaryMetrics=summaryStats,
runName=runName, metadata=metadata)
bundleList.append(bundle)
order += 1
dTmax = dTmax/60.0 # need time in minutes for Nrevisits metric
m2 = metrics.NRevisitsMetric(dT=dTmax)
plotDict = {'xMin': 0.1, 'xMax': 2000, 'logScale': True}
cutoff2 = 800
extraStats2 = [metrics.FracAboveMetric(cutoff=cutoff2, scale=scale, metricName='Area (sq deg)')]
extraStats2.extend(commonSummary)
caption = 'Number of consecutive visits with return times faster than %.1f minutes, ' % (dTmax)
caption += 'in any filter, all proposals. '
caption += 'Summary statistic "Area" below indicates the area on the sky which has more than '
caption += '%d revisits within this time window.' % (cutoff2)
summaryStats = extraStats2
displayDict = {'group': reqgroup, 'subgroup': 'Rapid Revisit', 'displayOrder': order,
'caption': caption}
bundle = metricBundles.MetricBundle(m2, slicer, sqlconstraint, plotDict=plotDict,
displayDict=displayDict, summaryMetrics=summaryStats,
runName=runName, metadata=metadata)
bundleList.append(bundle)
order += 1
m3 = metrics.NRevisitsMetric(dT=dTmax, normed=True)
plotDict = {'xMin': 0, 'xMax': 1, 'cbarFormat': '%.1f'}
cutoff3 = 0.6
extraStats3 = [metrics.FracAboveMetric(cutoff=cutoff3, scale=scale, metricName='Area (sq deg)')]
extraStats3.extend(commonSummary)
summaryStats = extraStats3
caption = 'Fraction of total visits where consecutive visits have return times faster '
caption += 'than %.1f minutes, in any filter, all proposals. ' % (dTmax)
caption += 'Summary statistic "Area" below indicates the area on the sky which has more '
caption += 'than %d revisits within this time window.' % (cutoff3)
displayDict = {'group': reqgroup, 'subgroup': 'Rapid Revisit', 'displayOrder': order,
'caption': caption}
bundle = metricBundles.MetricBundle(m3, slicer, sqlconstraint, plotDict=plotDict,
displayDict=displayDict, summaryMetrics=summaryStats,
runName=runName, metadata=metadata)
bundleList.append(bundle)
order += 1
# And add a histogram of the time between quick revisits.
binMin = 0
binMax = 120.
binsize = 3.
bins_metric = np.arange(binMin / 60.0 / 24.0, (binMax + binsize) / 60. / 24., binsize / 60. / 24.)
bins_plot = bins_metric * 24.0 * 60.0
m1 = metrics.TgapsMetric(bins=bins_metric, metricName='dT visits')
plotDict = {'bins': bins_plot, 'xlabel': 'dT (minutes)'}
caption = ('Histogram of the time between consecutive revisits (<%.1f minutes), over entire sky.'
% (binMax))
displayDict = {'group': reqgroup, 'subgroup': 'Rapid Revisit', 'order': order,
'caption': caption}
slicer = slicers.HealpixSlicer(nside=nside, lonCol=lonCol, latCol=latCol)
plotFunc = plots.SummaryHistogram()
bundle = metricBundles.MetricBundle(m1, slicer, sqlconstraint, plotDict=plotDict,
displayDict=displayDict, runName=runName,
metadata=metadata, plotFuncs=[plotFunc])
bundleList.append(bundle)
order += 1
##
# Trigonometric parallax and proper motion @ r=20 and r=24
slicer = slicers.HealpixSlicer(nside=nside, lonCol=lonCol, latCol=latCol)
sqlconstraint = ''
order = 0
metric = metrics.ParallaxMetric(metricName='Parallax 20', rmag=20, seeingCol=seeingCol)
summaryStats = allStats
plotDict = {'cbarFormat': '%.1f', 'xMin': 0, 'xMax': 3}
displayDict = {'group': reqgroup, 'subgroup': 'Parallax', 'order': order,
'caption': 'Parallax precision at r=20. (without refraction).'}
bundle = metricBundles.MetricBundle(metric, slicer, sqlconstraint, plotDict=plotDict,
displayDict=displayDict, summaryMetrics=summaryStats,
runName=runName, metadata=metadata)
bundleList.append(bundle)
order += 1
metric = metrics.ParallaxMetric(metricName='Parallax 24', rmag=24, seeingCol=seeingCol)
plotDict = {'cbarFormat': '%.1f', 'xMin': 0, 'xMax': 10}
displayDict = {'group': reqgroup, 'subgroup': 'Parallax', 'order': order,
'caption': 'Parallax precision at r=24. (without refraction).'}
bundle = metricBundles.MetricBundle(metric, slicer, sqlconstraint, plotDict=plotDict,
displayDict=displayDict, summaryMetrics=summaryStats,
runName=runName, metadata=metadata)
bundleList.append(bundle)
order += 1
metric = metrics.ParallaxMetric(metricName='Parallax Normed', rmag=24, normalize=True,
seeingCol=seeingCol)
plotDict = {'xMin': 0.5, 'xMax': 1.0}
displayDict = {'group': reqgroup, 'subgroup': 'Parallax', 'order': order,
'caption':
'Normalized parallax (normalized to optimum observation cadence, 1=optimal).'}
bundle = metricBundles.MetricBundle(metric, slicer, sqlconstraint, plotDict=plotDict,
displayDict=displayDict, summaryMetrics=summaryStats,
runName=runName, metadata=metadata)
bundleList.append(bundle)
order += 1
metric = metrics.ParallaxCoverageMetric(metricName='Parallax Coverage 20', rmag=20, seeingCol=seeingCol)
plotDict = {}
caption = "Parallax factor coverage for an r=20 star (0 is bad, 0.5-1 is good). "
caption += "One expects the parallax factor coverage to vary because stars on the ecliptic "
caption += "can be observed when they have no parallax offset while stars at the pole are always "
caption += "offset by the full parallax offset."""
displayDict = {'group': reqgroup, 'subgroup': 'Parallax', 'order': order,
'caption': caption}
bundle = metricBundles.MetricBundle(metric, slicer, sqlconstraint, plotDict=plotDict,
displayDict=displayDict, summaryMetrics=summaryStats,
runName=runName, metadata=metadata)
bundleList.append(bundle)
order += 1
metric = metrics.ParallaxCoverageMetric(metricName='Parallax Coverage 24', rmag=24, seeingCol=seeingCol)
plotDict = {}
caption = "Parallax factor coverage for an r=24 star (0 is bad, 0.5-1 is good). "
caption += "One expects the parallax factor coverage to vary because stars on the ecliptic "
caption += "can be observed when they have no parallax offset while stars at the pole are always "
caption += "offset by the full parallax offset."""
displayDict = {'group': reqgroup, 'subgroup': 'Parallax', 'order': order,
'caption': caption}
bundle = metricBundles.MetricBundle(metric, slicer, sqlconstraint, plotDict=plotDict,
displayDict=displayDict, summaryMetrics=summaryStats,
runName=runName, metadata=metadata)
bundleList.append(bundle)
order += 1
metric = metrics.ParallaxDcrDegenMetric(metricName='Parallax-DCR degeneracy 20', rmag=20,
seeingCol=seeingCol)
plotDict = {}
caption = 'Correlation between parallax offset magnitude and hour angle an r=20 star.'
caption += ' (0 is good, near -1 or 1 is bad).'
displayDict = {'group': reqgroup, 'subgroup': 'Parallax', 'order': order,
'caption': caption}
bundle = metricBundles.MetricBundle(metric, slicer, sqlconstraint, plotDict=plotDict,
displayDict=displayDict, summaryMetrics=summaryStats,
runName=runName, metadata=metadata)
bundleList.append(bundle)
order += 1
metric = metrics.ParallaxDcrDegenMetric(metricName='Parallax-DCR degeneracy 24', rmag=24,
seeingCol=seeingCol)
plotDict = {}
caption = 'Correlation between parallax offset magnitude and hour angle an r=24 star.'
caption += ' (0 is good, near -1 or 1 is bad).'
displayDict = {'group': reqgroup, 'subgroup': 'Parallax', 'order': order,
'caption': caption}
bundle = metricBundles.MetricBundle(metric, slicer, sqlconstraint, plotDict=plotDict,
displayDict=displayDict, summaryMetrics=summaryStats,
runName=runName, metadata=metadata)
bundleList.append(bundle)
order += 1
metric = metrics.ProperMotionMetric(metricName='Proper Motion 20', rmag=20, seeingCol=seeingCol)
summaryStats = allStats
plotDict = {'xMin': 0, 'xMax': 3}
displayDict = {'group': reqgroup, 'subgroup': 'Proper Motion', 'order': order,
'caption': 'Proper Motion precision at r=20.'}
bundle = metricBundles.MetricBundle(metric, slicer, sqlconstraint, plotDict=plotDict,
displayDict=displayDict, summaryMetrics=summaryStats,
runName=runName, metadata=metadata)
bundleList.append(bundle)
order += 1
metric = metrics.ProperMotionMetric(rmag=24, metricName='Proper Motion 24', seeingCol=seeingCol)
summaryStats = allStats
plotDict = {'xMin': 0, 'xMax': 10}
displayDict = {'group': reqgroup, 'subgroup': 'Proper Motion', 'order': order,
'caption': 'Proper Motion precision at r=24.'}
bundle = metricBundles.MetricBundle(metric, slicer, sqlconstraint, plotDict=plotDict,
displayDict=displayDict, summaryMetrics=summaryStats,
runName=runName, metadata=metadata)
bundleList.append(bundle)
order += 1
metric = metrics.ProperMotionMetric(rmag=24, normalize=True, metricName='Proper Motion Normed',
seeingCol=seeingCol)
plotDict = {'xMin': 0.2, 'xMax': 0.7}
caption = 'Normalized proper motion at r=24. '
caption += '(normalized to optimum observation cadence - start/end. 1=optimal).'
displayDict = {'group': reqgroup, 'subgroup': 'Proper Motion', 'order': order,
'caption': caption}
bundle = metricBundles.MetricBundle(metric, slicer, sqlconstraint, plotDict=plotDict,
displayDict=displayDict, summaryMetrics=summaryStats,
runName=runName, metadata=metadata)
bundleList.append(bundle)
order += 1
##
# Calculate the time uniformity in each filter, for each year.
order = 0
slicer = slicers.HealpixSlicer(nside=nside, lonCol=lonCol, latCol=latCol)
plotFuncs = [plots.TwoDMap()]
step = 0.5
bins = np.arange(0, 365.25 * 10 + 40, 40) - step
metric = metrics.AccumulateUniformityMetric(bins=bins)
plotDict = {'xlabel': 'Night (days)', 'xextent': [bins.min(
) + step, bins.max() + step], 'cbarTitle': 'Uniformity'}
for f in filters:
sqlconstraint = 'filter = "%s"' % (f)
caption = 'Deviation from uniformity in %s band. ' % f
caption += 'Northern Healpixels are at the top of the image.'
caption += '(0=perfectly uniform, 1=perfectly nonuniform).'
displayDict = {'group': uniformitygroup, 'subgroup': 'per night',
'order': filtorder[f], 'caption': caption}
metadata = '%s band' % (f) + slicermetadata
bundle = metricBundles.MetricBundle(metric, slicer, sqlconstraint, plotDict=plotDict,
displayDict=displayDict, runName=runName, metadata=metadata,
plotFuncs=plotFuncs)
noSaveBundleList.append(bundle)
##
# Depth metrics.
slicer = slicers.HealpixSlicer(nside=nside, lonCol=lonCol, latCol=latCol)
for f in filters:
propCaption = '%s band, all proposals %s' % (f, slicermetadata)
sqlconstraint = 'filter = "%s"' % (f)
metadata = '%s band' % (f) + slicermetadata
# Number of visits.
metric = metrics.CountMetric(col='observationStartMJD', metricName='NVisits')
plotDict = {'xlabel': 'Number of visits',
'xMin': nvisitsRange['all'][f][0],
'xMax': nvisitsRange['all'][f][1],
'colorMin': nvisitsRange['all'][f][0],
'colorMax': nvisitsRange['all'][f][1],
'binsize': 5,
'logScale': True, 'nTicks': 4, 'colorMin': 1}
summaryStats = allStats
displayDict = {'group': depthgroup, 'subgroup': 'Nvisits', 'order': filtorder[f],
'caption': 'Number of visits in filter %s, %s.' % (f, propCaption)}
histMerge = {'color': colors[f], 'label': '%s' % (f),
'binsize': 5,
'xMin': nvisitsRange['all'][f][0], 'xMax': nvisitsRange['all'][f][1],
'legendloc': 'upper right'}
bundle = metricBundles.MetricBundle(metric, slicer, sqlconstraint, plotDict=plotDict,
displayDict=displayDict, runName=runName, metadata=metadata,
summaryMetrics=summaryStats)
mergedHistDict['NVisits'].addBundle(bundle, plotDict=histMerge)
bundleList.append(bundle)
# Coadded depth.
metric = metrics.Coaddm5Metric()
plotDict = {'zp': benchmarkVals['coaddedDepth'][f], 'xMin': -0.8, 'xMax': 0.8,
'xlabel': 'coadded m5 - %.1f' % benchmarkVals['coaddedDepth'][f]}
summaryStats = allStats
histMerge = {'legendloc': 'upper right', 'color': colors[f], 'label': '%s' % f, 'binsize': .02,
'xlabel': 'coadded m5 - benchmark value'}
caption = ('Coadded depth in filter %s, with %s value subtracted (%.1f), %s. '
% (f, benchmark, benchmarkVals['coaddedDepth'][f], propCaption))
caption += 'More positive numbers indicate fainter limiting magnitudes.'
displayDict = {'group': depthgroup, 'subgroup': 'Coadded Depth',
'order': filtorder[f], 'caption': caption}
bundle = metricBundles.MetricBundle(metric, slicer, sqlconstraint, plotDict=plotDict,
displayDict=displayDict, runName=runName, metadata=metadata,
summaryMetrics=summaryStats)
mergedHistDict['coaddm5'].addBundle(bundle, plotDict=histMerge)
bundleList.append(bundle)
# Effective time.
metric = metrics.TeffMetric(metricName='Normalized Effective Time', normed=True,
fiducialDepth=benchmarkVals['singleVisitDepth'])
plotDict = {'xMin': 0.1, 'xMax': 1.1}
summaryStats = allStats
histMerge = {'legendLoc': 'upper right', 'color': colors[f], 'label': '%s' % f, 'binsize': 0.02}
caption = ('"Time Effective" in filter %s, calculated with fiducial single-visit depth of %s mag. '
% (f, benchmarkVals['singleVisitDepth'][f]))
caption += 'Normalized by the fiducial time effective, if every observation was at '
caption += 'the fiducial depth.'
displayDict = {'group': depthgroup, 'subgroup': 'Time Eff.',
'order': filtorder[f], 'caption': caption}
bundle = metricBundles.MetricBundle(metric, slicer, sqlconstraint, plotDict=plotDict,
displayDict=displayDict, runName=runName, metadata=metadata,
summaryMetrics=summaryStats)
mergedHistDict['NormEffTime'].addBundle(bundle, plotDict=histMerge)
bundleList.append(bundle)
# Put in a z=0.5 Type Ia SN, based on Cambridge 2015 workshop notebook.
# Check for 1) detection in any band, 2) detection on the rise in any band,
# 3) good characterization
peaks = {'uPeak': 25.9, 'gPeak': 23.6, 'rPeak': 22.6, 'iPeak': 22.7, 'zPeak': 22.7, 'yPeak': 22.8}
peakTime = 15.
transDuration = peakTime + 30. # Days
metric = metrics.TransientMetric(riseSlope=-2. / peakTime, declineSlope=1.4 / 30.0,
transDuration=transDuration, peakTime=peakTime,
surveyDuration=runLength,
metricName='SNDetection', **peaks)
caption = 'Fraction of z=0.5 type Ia SN that are detected in any filter'
displayDict = {'group': transgroup, 'subgroup': 'Detected', 'caption': caption}
sqlconstraint = ''
metadata = '' + slicermetadata
plotDict = {}
bundle = metricBundles.MetricBundle(metric, slicer, sqlconstraint, plotDict=plotDict,
displayDict=displayDict, runName=runName, metadata=metadata)
bundleList.append(bundle)
metric = metrics.TransientMetric(riseSlope=-2. / peakTime, declineSlope=1.4 / 30.0,
transDuration=transDuration, peakTime=peakTime,
surveyDuration=runLength,
nPrePeak=1, metricName='SNAlert', **peaks)
caption = 'Fraction of z=0.5 type Ia SN that are detected pre-peak in any filter'
displayDict = {'group': transgroup, 'subgroup': 'Detected on the rise', 'caption': caption}
plotDict = {}
bundle = metricBundles.MetricBundle(metric, slicer, sqlconstraint, plotDict=plotDict,
displayDict=displayDict, runName=runName, metadata=metadata)
bundleList.append(bundle)
metric = metrics.TransientMetric(riseSlope=-2. / peakTime, declineSlope=1.4 / 30.,
transDuration=transDuration, peakTime=peakTime,
surveyDuration=runLength, metricName='SNLots',
nFilters=3, nPrePeak=3, nPerLC=2, **peaks)
caption = 'Fraction of z=0.5 type Ia SN that are observed 6 times, 3 pre-peak, '
caption += '3 post-peak, with observations in 3 filters'
displayDict = {'group': transgroup, 'subgroup': 'Well observed', 'caption': caption}
sqlconstraint = 'filter="r" or filter="g" or filter="i" or filter="z" '
plotDict = {}
bundle = metricBundles.MetricBundle(metric, slicer, sqlconstraint, plotDict=plotDict,
displayDict=displayDict, runName=runName, metadata=metadata)
bundleList.append(bundle)
# Good seeing in r/i band metrics, including in first/second years.
order = 0
for tcolor, tlabel, timespan in zip(['k', 'g', 'r'], ['10 years', '1 year', '2 years'],
['', ' and night<=365', ' and night<=730']):
order += 1
for f in (['r', 'i']):
sqlconstraint = 'filter = "%s" %s' % (f, timespan)
propCaption = '%s band, all proposals %s, over %s.' % (f, slicermetadata, tlabel)
metadata = '%s band, %s' % (f, tlabel) + slicermetadata
seeing_limit = 0.7
airmass_limit = 1.2
metric = metrics.MinMetric(col=seeingCol)
summaryStats = allStats
plotDict = {'xMin': 0.35, 'xMax': 1.5, 'color': tcolor}
displayDict = {'group': seeinggroup, 'subgroup': 'Best Seeing',
'order': filtorder[f] * 100 + order,
'caption': 'Minimum FWHMgeom values in %s.' % (propCaption)}
histMerge = {'label': '%s %s' % (f, tlabel), 'color': tcolor,
'binsize': 0.03, 'xMin': 0.35, 'xMax': 1.5, 'legendloc': 'upper right'}
bundle = metricBundles.MetricBundle(metric, slicer, sqlconstraint, plotDict=plotDict,
displayDict=displayDict, runName=runName, metadata=metadata,
summaryMetrics=summaryStats)
mergedHistDict['Minseeing'].addBundle(bundle, plotDict=histMerge)
bundleList.append(bundle)
metric = metrics.FracAboveMetric(col=seeingCol, cutoff=seeing_limit)
summaryStats = allStats
plotDict = {'xMin': 0, 'xMax': 1.1, 'color': tcolor}
displayDict = {'group': seeinggroup, 'subgroup': 'Good seeing fraction',
'order': filtorder[f] * 100 + order,
'caption': 'Fraction of total images with FWHMgeom worse than %.1f, in %s'
% (seeing_limit, propCaption)}
histMerge = {'color': tcolor, 'label': '%s %s' % (f, tlabel),
'binsize': 0.05, 'legendloc': 'upper right'}
bundle = metricBundles.MetricBundle(metric, slicer, sqlconstraint, plotDict=plotDict,
displayDict=displayDict, runName=runName, metadata=metadata,
summaryMetrics=summaryStats)
mergedHistDict['seeingAboveLimit'].addBundle(bundle, plotDict=histMerge)
bundleList.append(bundle)
metric = metrics.MinMetric(col='airmass')
plotDict = {'xMin': 1, 'xMax': 1.5, 'color': tcolor}
summaryStats = allStats
displayDict = {'group': airmassgroup, 'subgroup': 'Best Airmass',
'order': filtorder[f] * 100 + order, 'caption':
'Minimum airmass in %s.' % (propCaption)}
histMerge = {'color': tcolor, 'label': '%s %s' % (f, tlabel),
'binsize': 0.03, 'legendloc': 'upper right'}
bundle = metricBundles.MetricBundle(metric, slicer, sqlconstraint, plotDict=plotDict,
displayDict=displayDict, runName=runName, metadata=metadata,
summaryMetrics=summaryStats)
mergedHistDict['minAirmass'].addBundle(bundle, plotDict=histMerge)
bundleList.append(bundle)
metric = metrics.FracAboveMetric(col='airmass', cutoff=airmass_limit)
plotDict = {'xMin': 0, 'xMax': 1, 'color': tcolor}
summaryStats = allStats
displayDict = {'group': airmassgroup, 'subgroup': 'Low airmass fraction',
'order': filtorder[f] * 100 + order, 'caption':
'Fraction of total images with airmass higher than %.2f, in %s'
% (airmass_limit, propCaption)}
histMerge = {'color': tcolor, 'label': '%s %s' % (
f, tlabel), 'binsize': 0.05, 'legendloc': 'upper right'}
bundle = metricBundles.MetricBundle(metric, slicer, sqlconstraint, plotDict=plotDict,
displayDict=displayDict, runName=runName, metadata=metadata,
summaryMetrics=summaryStats)
mergedHistDict['fracAboveAirmass'].addBundle(bundle, plotDict=histMerge)
bundleList.append(bundle)
# SNe metrics from UK workshop.
peaks = {'uPeak': 25.9, 'gPeak': 23.6, 'rPeak': 22.6, 'iPeak': 22.7, 'zPeak': 22.7, 'yPeak': 22.8}
peakTime = 15.
transDuration = peakTime + 30. # Days
metric = metrics.TransientMetric(riseSlope=-2. / peakTime, declineSlope=1.4 / 30.0,
transDuration=transDuration, peakTime=peakTime,
surveyDuration=runLength,
metricName='SNDetection', **peaks)
caption = 'Fraction of z=0.5 type Ia SN that are detected at any point in their light curve in any filter'
displayDict = {'group': sngroup, 'subgroup': 'Detected', 'caption': caption}
sqlconstraint = ''
plotDict = {}
bundle = metricBundles.MetricBundle(metric, slicer, sqlconstraint, plotDict=plotDict,
displayDict=displayDict, runName=runName)
bundleList.append(bundle)
metric = metrics.TransientMetric(riseSlope=-2. / peakTime, declineSlope=1.4 / 30.0,
transDuration=transDuration, peakTime=peakTime,
surveyDuration=runLength,
nPrePeak=1, metricName='SNAlert', **peaks)
caption = 'Fraction of z=0.5 type Ia SN that are detected pre-peak in any filter'
displayDict = {'group': sngroup, 'subgroup': 'Detected on the rise', 'caption': caption}
plotDict = {}
bundle = metricBundles.MetricBundle(metric, slicer, sqlconstraint, plotDict=plotDict,
displayDict=displayDict, runName=runName)
bundleList.append(bundle)
metric = metrics.TransientMetric(riseSlope=-2. / peakTime, declineSlope=1.4 / 30.,
transDuration=transDuration, peakTime=peakTime,
surveyDuration=runLength, metricName='SNLots',
nFilters=3, nPrePeak=3, nPerLC=2, **peaks)
caption = 'Fraction of z=0.5 type Ia SN that are observed 6 times, 3 pre-peak, '
caption += '3 post-peak, with observations in 3 filters'
displayDict = {'group': sngroup, 'subgroup': 'Well observed', 'caption': caption}
sqlconstraint = 'filter="r" or filter="g" or filter="i" or filter="z" '
plotDict = {}
bundle = metricBundles.MetricBundle(metric, slicer, sqlconstraint, plotDict=plotDict,
displayDict=displayDict, runName=runName)
bundleList.append(bundle)
propIDOrderDict = {}
orderVal = 100
for propID in propids:
propIDOrderDict[propID] = orderVal
orderVal += 100
# Full range of dates:
metric = metrics.FullRangeMetric(col='observationStartMJD')
plotFuncs = [plots.HealpixSkyMap(), plots.HealpixHistogram()]
caption = 'Time span of survey.'
sqlconstraint = ''
plotDict = {}
displayDict = {'group': rangeGroup, 'caption': caption}
bundle = metricBundles.MetricBundle(metric, slicer, sqlconstraint, plotDict=plotDict,
displayDict=displayDict, runName=runName)
bundleList.append(bundle)
for f in filters:
for propid in propids:
displayDict = {'group': rangeGroup, 'subgroup': propids[propid], 'caption': caption,
'order': filtorder[f]}
md = '%s, %s' % (f, propids[propid])
sql = 'filter="%s" and proposalId=%i' % (f, propid)
bundle = metricBundles.MetricBundle(metric, slicer, sql, plotDict=plotDict,
metadata=md, plotFuncs=plotFuncs,
displayDict=displayDict, runName=runName)
bundleList.append(bundle)
# Alt az plots
slicer = slicers.HealpixSlicer(nside=64, latCol='zenithDistance', lonCol='azimuth', useCache=False)
metric = metrics.CountMetric('observationStartMJD', metricName='Nvisits as function of Alt/Az')
plotDict = {}
plotFuncs = [plots.LambertSkyMap()]
displayDict = {'group': altAzGroup, 'caption': 'Alt Az pointing distribution'}
for f in filters:
for propid in propids:
displayDict = {'group': altAzGroup, 'subgroup': propids[propid],
'caption': 'Alt Az pointing distribution',
'order': filtorder[f]}
md = '%s, %s' % (f, propids[propid])
sql = 'filter="%s" and proposalId=%i' % (f, propid)
bundle = metricBundles.MetricBundle(metric, slicer, sql, plotDict=plotDict,
plotFuncs=plotFuncs, metadata=md,
displayDict=displayDict, runName=runName)
bundleList.append(bundle)
sql = ''
md = 'all observations'
displayDict = {'group': altAzGroup, 'subgroup': 'All Observations',
'caption': 'Alt Az pointing distribution'}
bundle = metricBundles.MetricBundle(metric, slicer, sql, plotDict=plotDict,
plotFuncs=plotFuncs, metadata=md,
displayDict=displayDict, runName=runName)
bundleList.append(bundle)
# Median inter-night gap (each and all filters)
slicer = slicers.HealpixSlicer(nside=nside, lonCol=lonCol, latCol=latCol)
metric = metrics.InterNightGapsMetric(metricName='Median Inter-Night Gap')
sqls = ['filter = "%s"' % f for f in filters]
orders = [filtorder[f] for f in filters]
orders.append(0)
sqls.append('')
for sql, order in zip(sqls, orders):
displayDict = {'group': intergroup, 'subgroup': 'Median Gap', 'caption': 'Median gap between days',
'order': order}
bundle = metricBundles.MetricBundle(metric, slicer, sql, displayDict=displayDict, runName=runName)
bundleList.append(bundle)
# Max inter-night gap in r and all bands
dslicer = slicers.HealpixSlicer(nside=nside, lonCol='ditheredRA', latCol='ditheredDec')
metric = metrics.InterNightGapsMetric(metricName='Max Inter-Night Gap', reduceFunc=np.max)
plotDict = {'percentileClip': 95.}
for sql, order in zip(sqls, orders):
displayDict = {'group': intergroup, 'subgroup': 'Max Gap', 'caption': 'Max gap between nights',
'order': order}
bundle = metricBundles.MetricBundle(metric, dslicer, sql, displayDict=displayDict,
plotDict=plotDict, runName=runName)
bundleList.append(bundle)
# largest phase gap for periods
periods = [0.1, 1.0, 10., 100.]
sqls = {'u': 'filter = "u"', 'r': 'filter="r"',
'g,r,i,z': 'filter="g" or filter="r" or filter="i" or filter="z"',
'all': ''}
for sql in sqls:
for period in periods:
displayDict = {'group': phaseGroup,
'subgroup': 'period=%.2f days, filter=%s' % (period, sql),
'caption': 'Maximum phase gaps'}
metric = metrics.PhaseGapMetric(nPeriods=1, periodMin=period, periodMax=period,
metricName='PhaseGap, %.1f' % period)
bundle = metricBundles.MetricBundle(metric, slicer, sqls[sql],
displayDict=displayDict, runName=runName)
bundleList.append(bundle)
# NEO XY plots
slicer = slicers.UniSlicer()
metric = metrics.PassMetric(metricName='NEODistances')
stacker = stackers.NEODistStacker()
stacker2 = stackers.EclipticStacker()
for f in filters:
plotFunc = plots.NeoDistancePlotter(eclipMax=10., eclipMin=-10.)
caption = 'Observations within 10 degrees of the ecliptic. Distance an H=22 NEO would be detected'
displayDict = {'group': NEOGroup, 'subgroup': 'xy', 'order': filtorder[f],
'caption': caption}
plotDict = {}
sqlconstraint = 'filter = "%s"' % (f)
bundle = metricBundles.MetricBundle(metric, slicer,
sqlconstraint, displayDict=displayDict,
stackerList=[stacker, stacker2],
plotDict=plotDict,
plotFuncs=[plotFunc])
noSaveBundleList.append(bundle)
# Solar elongation
sqls = ['filter = "%s"' % f for f in filters]
orders = [filtorder[f] for f in filters]
sqls.append('')
orders.append(0)
for sql, order in zip(sqls, orders):
plotFuncs = [plots.HealpixSkyMap(), plots.HealpixHistogram()]
displayDict = {'group': NEOGroup, 'subgroup': 'Solar Elongation',
'caption': 'Median solar elongation in degrees', 'order': order}
metric = metrics.MedianMetric('solarElong')
slicer = slicers.HealpixSlicer(nside=nside, lonCol=lonCol, latCol=latCol)
bundle = metricBundles.MetricBundle(metric, slicer, sql, displayDict=displayDict,
plotFuncs=plotFuncs)
bundleList.append(bundle)
plotFuncs = [plots.HealpixSkyMap(), plots.HealpixHistogram()]
displayDict = {'group': NEOGroup, 'subgroup': 'Solar Elongation',
'caption': 'Minimum solar elongation in degrees', 'order': order}
metric = metrics.MinMetric('solarElong')
slicer = slicers.HealpixSlicer(nside=nside, lonCol=lonCol, latCol=latCol)
bundle = metricBundles.MetricBundle(metric, slicer, sql, displayDict=displayDict,
plotFuncs=plotFuncs)
bundleList.append(bundle)
return (metricBundles.makeBundlesDictFromList(bundleList), mergedHistDict,
metricBundles.makeBundlesDictFromList(noSaveBundleList))
out_file = sys.argv[2]
out_dir = sys.argv[2]
gal_l_min = 0.
gal_l_max = 360.
gal_b_min = -89.
gal_b_max = 89.
diameter = 3.5
step = diameter / np.sqrt(2) # This would be enough on a 2D plane.
step *= 0.85
gal_l_all = np.linspace(gal_l_min, gal_l_max, (gal_l_max-gal_l_min)/step+1)
gal_b_all = np.linspace(gal_b_min, gal_b_max, (gal_b_max-gal_b_min)/step+1)
(gal_l, gal_b) = np.meshgrid(gal_l_all, gal_b_all)
c = SkyCoord(gal_l.flatten(), gal_b.flatten(), unit=u.deg, frame='galactic')
userRA = c.fk5.ra.value
userDec = c.fk5.dec.value
columns = ['observationStartMJD', 'filter', 'fiveSigmaDepth']
metric = metrics.PassMetric(cols=columns)
slicer = slicers.UserPointsSlicer(userRA, userDec)
sqlconstraint = ''
MJDmetric = metricBundles.MetricBundle(metric, slicer, sqlconstraint,
fileRoot=out_file)
bundleDict = {'MJDmetric': MJDmetric}
opsdb = db.OpsimDatabase(database)
group = metricBundles.MetricBundleGroup(bundleDict, opsdb, outDir=out_dir)
group.runAll()
filterNames = ['u', 'g', 'r', 'i', 'z']
colors = {'u': 'purple', 'g': 'g', 'r': 'r', 'i': 'blue', 'z': 'm'}
#create list of peak days and magnitudes to iterate over
day_of_peak = np.arange(59580, 63232, 30)
mag_of_peak = np.arange(17, 25, 1)
# Set the database and query
runName = 'minion_1018'
opsdb = db.OpsimDatabase(runName + '_sqlite.db')
# Set the output directory
outDir = 'Observations Dictionary'
resultsDb = db.ResultsDb(outDir)
# This creates our database of observations. The pass metric just passes data straight through.
metric = metrics.PassMetric(cols=['expMJD', 'filter', 'fiveSigmaDepth'])
"""use slicer to restrict the ra and decs, use np.random.uniform to get random points,
first coordinate represents ra and second dec. Or, give a list of specific
ra and decs - the second slicer is for the deep drilling fields. One must be commented out."""
#slicer = slicers.UserPointsSlicer(np.random.uniform(0,360,1000), np.random.uniform(-80,0,1000))
slicer = slicers.UserPointsSlicer([349.4, 0.00, 53.0, 34.4, 150.4],
[-63.3, -45.5, -27.4, -5.1, 2.8])
#sql is empty as there are no restrictions currently
sql = ''
bundle = metricBundles.MetricBundle(metric, slicer, sql)
bg = metricBundles.MetricBundleGroup({0: bundle},
opsdb,
outDir=outDir,
resultsDb=resultsDb)
bg.runAll()
def getMetrics(self):
colmn = 'observationStartMJD';
opsdb = db.OpsimDatabase(self.opsim)
# Directory where tmp files are going to be stored TODO eliminate - this
outDir = 'TmpDir'
resultsDb = db.ResultsDb(outDir=outDir)
metric=metrics.PassMetric(cols=[colmn,'fiveSigmaDepth', 'filter'])
slicer = slicers.UserPointsSlicer(ra=self.ra,dec=self.dec)
sqlconstraint = 'filter = \'' + self.fil + '\''
bundle = mb.MetricBundle(metric, slicer, sqlconstraint, runName=self.name)
bgroup = mb.MetricBundleGroup({0: bundle}, opsdb, outDir=outDir, resultsDb=resultsDb)
bgroup.runAll();
filters = np.unique(bundle.metricValues[0]['filter'])
mv = bundle.metricValues[0]
# Get dates
self.mjd = mv[colmn]
self.mjd = np.sort(self.mjd)
# Define redshift bins
zbin = np.linspace(0.5,7.5,8)
zbin = np.insert(zbin,0,0)
# Converting MJD to survey days
T=np.int(self.mjd.max()-self.mjd.min()+1)
swop=[]
wedgeop=[]
scop=[]
edgecop=[]
i=0
total = len(zbin)*(self.nlc);
progress = 0;
# We generate a number (nlc) of light curves for each redshift bin
for z in zbin:
for w in range(self.nlc):
# Generating continuous light curve (cadence=1d)
tt, yy = drw_artificial_lc(T, z=z, frame=self.frame)
sn, edgesn = self.sf(tt,yy,z=z)
# Calculating SF for the current continuous light curve
scop.append(sn)
edgecop.append(edgesn)
self.edgesn = edgesn
# Generating OpSim light curve evaluated on the current continuous light curve
top,yop=self.__opsim_lc(tt,yy)
# Calculating SF for the current OpSim light curve
srol,edgesrol=self.sf(top,yop,z=z)
swop.append(srol)
wedgeop.append(edgesrol)
#progressBar(progress, total);
progress = progress + 1;
i=i+1 # counter
swop=np.asarray(swop)
swop=swop.reshape(9,self.nlc,99)
scop=np.asarray(scop)
scop=scop.reshape(9,self.nlc,99)
razrol=[]
for z in range(9):
for r in range(self.nlc):
# Calculating the SF metric
razrol.append((np.nan_to_num(np.sqrt(scop[z,r,:]))-np.nan_to_num(np.sqrt(swop[z,r,:]))))
razrol9=np.asarray(razrol)
razrol9=razrol9.reshape(9,self.nlc,99)
# We take the mean of generated light curves for each redshift bin.
self.raz2=np.nanmean(razrol9[:,:,:],axis=1)
Ds = cosmo.angular_diameter_distance(myinput['system']['zs']).value # in Mpc
Dl = cosmo.angular_diameter_distance(myinput['system']['zl']).value # in Mpc
Dls = cosmo.angular_diameter_distance_z1z2(
myinput['system']['zl'], myinput['system']['zs']).value # in Mpc
print(">>>>>>>>>>>>>>>>>> Reading LSST dates and depths <<<<<<<<<<<<<<<<<<<<<")
outDir = outdir + '/output'
dbFile = path_to_dbfile + runName + '.db'
opsimdb = db.opsimDatabase.OpsimDatabase(dbFile)
resultsDb = db.ResultsDb(outDir=outDir)
#ra=ra*15.0
# SNR limit (Don't use points below this limit)
snrLimit = 5.0
# The pass metric just passes data straight through.
metric = metrics.PassMetric(
cols=['filter', 'fiveSigmaDepth', 'observationStartMJD'])
slicer = slicers.UserPointsSlicer(ra, dec, lonCol='fieldRA', latCol='fieldDec')
sql = ''
bundle = metricBundles.MetricBundle(metric, slicer, sql)
bg = metricBundles.MetricBundleGroup({0: bundle},
opsimdb,
outDir=outDir,
resultsDb=resultsDb)
bg.runAll()
bundle.metricValues.data[0]['filter']
filters = myinput['filters']
print('%i Observations total at this point (All SNR levels)' %
bundle.metricValues.data[0].size)
for fname in filters:
good = numpy.where(bundle.metricValues.data[0]['filter'] == fname)
def save_csv_dithers(dbs_path,
outDir,
db_files_only=None,
rot_rand_seed=42,
trans_rand_seed=42,
print_progress=True,
show_diagnostic_plots=False,
save_plots=False):
"""
The goal here is to calculate the translational and rotational dithers for
various cadences and save the output as a csv file. These dithers are largely
the same as in DC1/DC2:
- Translational dithers:
- WFD: large random offsets (as large as 1.75 deg) applied after every visit.
- DD: small random offsets (as large as 7 arcmin) applied after every visit.
- Else: no dithers, so `fieldRA`, `fieldDec` are returned.
- Rotational dithers:
- All surveys (WFD, DD, else): random between -90, 90 degrees applied after
every filter change. (Break from DC2: Some visits
dont get dithered since they are forced outside
the rotator range.
See RotStacker info for details.)
Supports OpSim V3/V4 outputs.
Required Inputs
---------------
* dbs_path: str: path to the directory that contains the .db files; could have non-.db files.
* outDir: str: path to the directory where the output should be saved.
Optional Inputs
---------------
* db_files_only: list of str: list of names of the db files to run.
Default: None. Runs over all the files in db_path.
* rot_rand_seed: int: seed for random number generator for rotational dithers.
Default: 42
* trans_rand_seed: int: seed for random number generator for translational dithers.
Default: 42
* print_progress: bool: set to False to not print progress.
Default: True
* show_diagnostic_plots: bool: set to True to show histogram of added dithers.
Default: False
* save_plots: bool: set to True to save the histogram for descDithers in outDir.
Default: False
Saved file format
-----------------
.csv file with four columns:
obsIDcol, 'descDitheredRA', 'descDitheredDec', 'descDitheredRotTelPos'
where
obsIDcol = 'observationId' for V4 outputs and 'obsHistID' for V3 outputs.
Saved filename = descDithers_<database name>.csv
"""
startTime_0 = time.time()
readme = '##############################\n%s' % (datetime.date.isoformat(
datetime.date.today()))
readme += '\nRunning with lsst.sims.maf.__version__: %s' % lsst.sims.maf.__version__
readme += '\n\nsave_csv_dithers run:\ndbs_path= %s\n' % dbs_path
readme += 'outDir: %s' % outDir
readme += 'db_files_only: %s' % db_files_only
readme += 'rot_rand_seed=%s\ntrans_rand_seed=%s' % (rot_rand_seed,
trans_rand_seed)
readme += 'print_progress=%s\show_diagnostic_plots=%s\n' % (
print_progress, show_diagnostic_plots)
dbfiles = [f for f in os.listdir(dbs_path)
if f.endswith('db')] # select db files
if print_progress: print('Found files: %s\n' % dbfiles)
if db_files_only is not None:
dbfiles = [f for f in dbfiles if f in db_files_only] # select db files
readme += '\nReading for files: %s\n\n' % dbfiles
if print_progress and db_files_only is not None:
print('Running over: %s\n' % dbfiles)
for i, dbfile in enumerate(dbfiles): # loop over all the db files
startTime = time.time()
if (i != 0): readme = ''
readme += '%s' % dbfile
if print_progress: print('Starting: %s\n' % dbfile)
opsdb = db.OpsimDatabase('%s/%s' %
(dbs_path, dbfile)) # connect to the database
# specify the column names to get from the db file
colnames = [
'proposalId', 'observationId', 'fieldRA', 'fieldDec', 'rotTelPos'
]
propIDcol, obsIDcol = 'proposalId', 'observationId'
if (opsdb.opsimVersion == 'V3'):
# V3 outputs have somewhat different column names
colnames = [
'propID', 'obsHistID', 'fieldRA', 'fieldDec', 'rotTelPos'
]
propIDcol, obsIDcol = 'propID', 'obsHistID'
# get the data
simdata = opsdb.fetchMetricData(colnames=colnames, sqlconstraint=None)
# set up to run the stackers that add columns for translational and rotational dithers.
metric = metrics.PassMetric(
) # want to access the database; no analysis needed
slicer = slicers.OneDSlicer(
sliceColName='night', binsize=1,
verbose=print_progress) # essentially accessing all nights
sqlconstraint = None
resultsDb = db.ResultsDb(outDir=outDir)
################################################################################################
# set up metric bundle to run stackers for large translational dithers + rotational dithers
if print_progress:
print('Setting up for WFD translational dithers + rot dithers.')
bgroup = {}
stackerList = [
stackers.RandomDitherFieldPerVisitStacker(
degrees=opsdb.raDecInDeg, randomSeed=trans_rand_seed),
stackers.RandomRotDitherPerFilterChangeStacker(
degrees=opsdb.raDecInDeg, randomSeed=rot_rand_seed)
]
bundle = metricBundles.MetricBundle(metric,
slicer,
sqlconstraint=sqlconstraint,
stackerList=stackerList)
bgroup['WFD'] = metricBundles.MetricBundleGroup({0: bundle},
opsdb,
outDir=outDir,
resultsDb=resultsDb,
saveEarly=False,
verbose=print_progress)
# run the bundle
bgroup['WFD'].runAll()
# set up the bundle for small translational dithers
if print_progress: print('\nSetting up for DD translational dithers.')
chipSize = 1.75 * 2 / 15
chipMaxDither = chipSize / 2.
stackerList = [
stackers.RandomDitherFieldPerVisitStacker(
maxDither=chipMaxDither,
degrees=opsdb.raDecInDeg,
randomSeed=trans_rand_seed)
]
bundle = metricBundles.MetricBundle(metric,
slicer,
sqlconstraint=sqlconstraint,
stackerList=stackerList)
bgroup['DD'] = metricBundles.MetricBundleGroup({0: bundle},
opsdb,
outDir=outDir,
resultsDb=resultsDb,
saveEarly=False,
verbose=print_progress)
# run the bundle
bgroup['DD'].runAll()
################################################################################################
# access the relevant columns
dithered_RA, dithered_Dec = {}, {}
for key in bgroup:
dithered_RA[key] = bgroup[key].simData[
'randomDitherFieldPerVisitRa']
dithered_Dec[key] = bgroup[key].simData[
'randomDitherFieldPerVisitDec']
dithered_rotTelPos = bgroup['WFD'].simData[
'randomDitherPerFilterChangeRotTelPos']
################################################################################################
# diagnostic plots
if show_diagnostic_plots:
# histograms of dithers
fig, axes = plt.subplots(nrows=1, ncols=3)
for key in bgroup:
# ra
axes[0].hist(dithered_RA[key] - simdata['fieldRA'],
label='%s dithers: delRA' % key,
histtype='step',
lw=2,
bins=30)
# dec
axes[1].hist(dithered_Dec[key] - simdata['fieldDec'],
label='%s dithers: delDec' % key,
histtype='step',
lw=2)
# tel pos
axes[2].hist(dithered_rotTelPos - simdata['rotTelPos'],
label='rot dithers: rotTelPos',
histtype='step',
lw=2)
for ax in axes:
ax.ticklabel_format(style='sci', axis='y', scilimits=(0, 0))
ax.set_ylabel('Counts')
axes[0].legend()
axes[1].legend()
if opsdb.raDecInDeg: unitlabel = 'degrees'
else: unitlabel = 'radians'
axes[0].set_xlabel('delRA (%s)' % unitlabel)
axes[1].set_xlabel('delDec (%s)' % unitlabel)
axes[2].set_xlabel('delRotTelPos (%s)' % unitlabel)
plt.title(dbfile)
fig.set_size_inches(20, 5)
################################################################################################
# initiate the final arrays as undithered fieldRA, fieldDec as nonWFD, nonDDF should remain unchanged
descDitheredRA = simdata['fieldRA'].copy()
descDitheredDec = simdata['fieldDec'].copy()
descDitheredRot = simdata['rotTelPos'].copy()
# need to find the indices for WFD vs. DD observations since we are adding different
# translational dithers for WFD/DDF visits + none for other surveys
propIds, propTags = opsdb.fetchPropInfo()
# ok work with WFD visits now
ind_WFD = np.where(simdata[propIDcol] == propTags['WFD'])[0]
if print_progress:
tot = len(simdata)
print('Total visits: ', tot)
print('propTags: ', propTags)
print('%s WFD visits out of total %s' % (len(ind_WFD), tot))
descDitheredRA[ind_WFD] = dithered_RA['WFD'][ind_WFD]
descDitheredDec[ind_WFD] = dithered_Dec['WFD'][ind_WFD]
# work with DD visits now
ind_DD = np.where(simdata[propIDcol] == propTags['DD'])[0]
if print_progress:
print('%s DD visits out of total %s' % (len(ind_DD), tot))
descDitheredRA[ind_DD] = dithered_RA['DD'][ind_DD]
descDitheredDec[ind_DD] = dithered_Dec['DD'][ind_DD]
# add rotational dithers to everything
descDitheredRot = dithered_rotTelPos
###############################################################
# diagnostic plots
if show_diagnostic_plots or save_plots:
# histograms of desc dithered positions
fig, axes = plt.subplots(nrows=1, ncols=3)
_, bins, _ = axes[0].hist(descDitheredRA,
label='descDitheredRA',
histtype='step',
lw=2)
axes[0].hist(simdata['fieldRA'],
label='fieldRA',
histtype='step',
lw=2,
bins=bins)
_, bins, _ = axes[1].hist(descDitheredDec,
label='descDitheredDec',
histtype='step',
lw=2)
axes[1].hist(simdata['fieldDec'],
label='fieldDec',
histtype='step',
lw=2,
bins=bins)
_, bins, _ = axes[2].hist(descDitheredRot,
label='descDitheredRot',
histtype='step',
lw=2)
axes[2].hist(simdata['rotTelPos'],
label='rotTelPos',
histtype='step',
lw=2,
bins=bins)
if opsdb.raDecInDeg: xlabel = 'degrees'
else: xlabel = 'radians'
for ax in axes:
ax.legend()
ax.set_xlabel(xlabel)
ax.set_ylabel('Counts')
plt.suptitle(dbfile)
fig.set_size_inches(20, 5)
if save_plots:
filename = 'hist_descDithers_%s.png' % (dbfile.split('.db')[0])
plt.savefig('%s/%s' % (outDir, filename),
format='png',
bbox_inches='tight')
readme += '\nSaved hist for descDithers in %s.' % filename
if print_progress:
print('\nSaved hist plot in %s' % filename)
if show_diagnostic_plots:
plt.show()
else:
plt.close('all')
###############################################################
# save the columns as a csv file.
d = {
obsIDcol: simdata[obsIDcol],
'descDitheredRA': descDitheredRA,
'descDitheredDec': descDitheredDec,
'descDitheredRotTelPos': descDitheredRot
}
filename = 'descDithers_%s.csv' % (dbfile.split('.db')[0])
pd.DataFrame(d).to_csv('%s/%s' % (outDir, filename), index=False)
readme += '\nSaved the dithers in %s' % filename
readme += '\nTime taken: %.2f (min)\n\n' % (
(time.time() - startTime) / 60.)
if print_progress:
print('\nSaved the dithers in %s' % filename)
print('Time taken: %.2f (min)\n\n' %
((time.time() - startTime) / 60.))
readme_file = open('%s/readme.txt' % (outDir), 'a')
readme_file.write(readme)
readme_file.close()
# mark the end in the readme.
readme_file = open('%s/readme.txt' % (outDir), 'a')
readme_file.write('All done. Total time taken: %.2f (min)\n\n' %
((time.time() - startTime_0) / 60.))
readme_file.close()
def spot_inspect(filename,
ra,
dec,
year_max=8.5,
outDir='temp',
season_pad=80):
resultsDb = db.ResultsDb(outDir=outDir)
f2c = {
'u': 'purple',
'g': 'blue',
'r': 'green',
'i': 'cyan',
'z': 'orange',
'y': 'red'
}
name = filename.replace('_v1.7_10yrs.db', '')
conn = db.OpsimDatabase(filename)
bundleList = []
sql = '' #'night > 250 and night < %i' % (365*year_max)
metric = metrics.PassMetric(
['filter', 'observationStartMJD', 'fiveSigmaDepth', 'night'])
slicer = slicers.UserPointsSlicer(ra=ra, dec=dec)
summaryStats = []
plotDict = {}
bundleList.append(
metricBundles.MetricBundle(metric,
slicer,
sql,
plotDict=plotDict,
summaryMetrics=summaryStats,
runName=name))
bd = metricBundles.makeBundlesDictFromList(bundleList)
bg = metricBundles.MetricBundleGroup(bd,
conn,
outDir=outDir,
resultsDb=resultsDb)
bg.runAll()
#bg.plotAll(closefigs=False)
mv = bundleList[0].metricValues[0]
mv.sort(order='observationStartMJD')
all_mjd = np.arange(mv['observationStartMJD'].min() - 1,
mv['observationStartMJD'].max() + 2, 1)
breaks_indx = season_breaks(all_mjd, ra)
breaks1 = all_mjd[breaks_indx] - all_mjd.min() + mv['night'].min()
breaks = [mv['night'].min() - 1]
breaks.extend(breaks1.tolist())
breaks.append(mv['night'].max() + 3)
#breaks = np.array([mv['night'].min()-season_pad] + breaks.tolist() + [mv['night'].max()+season_pad])
fig = plt.figure()
ax1 = fig.add_subplot(1, 1, 1)
di = np.diff(breaks)
mps = breaks[0:-1] + di / 2
counts, med_gaps, unights = gap_stats(mv['night'], bins=breaks)
for fn in f2c:
in_filt = np.where(mv['filter'] == fn)[0]
ax1.plot(mv['night'][in_filt],
mv['fiveSigmaDepth'][in_filt],
'o',
color=f2c[fn],
label=fn,
alpha=0.5)
ax1.set_xlabel('Night')
ax1.set_ylabel(r'5$\sigma$ depth (mags)')
for i in np.arange(mps.size):
plt.annotate('%i\n %.1f \n %i' % (counts[i], med_gaps[i], unights[i]),
[mps[i], 20])
#plt.legend(loc=(1.04,0))
for br in breaks:
ax1.axvline(br)
ax1.set_ylim([19.5, 25.5])
#plt.xlim([1340, 1560])
ax1.set_title(name + '\nra=%.2f, dec=%.2f' % (ra, dec))
return fig, ax1