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runNEOs.py
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import os, argparse
import numpy as np
import matplotlib.pyplot as plt
import moObs as moObs
import lsst.sims.maf.plots as plots
from moSlicer import MoSlicer
import moMetrics as moMetrics
import moSummaryMetrics as moSummaryMetrics
import moPlots as moPlots
import moMetricBundle as mmb
def makeObs(orbitfile, obsfile, opsimdb, rFov, useCamera):
"""
Generates and writes observations of objects in orbitfile into obsfile, using observations from opsimdb.
This is assuming that the population is NEO-ish.
"""
# rFov = fov in radians
# useCamera = True/False (use camera footprint)
moObs.runMoObs(orbitfile, obsfile, opsimdb, rFov=rFov, useCamera=useCamera, tstep=2./24.)
def calcCompleteness(orbitfile, obsfile, outDir, runName, metadata=None):
"""
Calculate a basic set of metrics on the NEOs, including completeness.
Saves the plots to disk.
"""
# Read data back from disk.
mos = MoSlicer(orbitfile, Hrange=np.arange(15, 27, 0.25))
mos.readObs(obsfile)
# Nobs
metric = moMetrics.NObsMetric()
slicer = mos
pandasConstraint = None
plotDict = {'nxbins':200, 'nybins':200}
nobs = mmb.MoMetricBundle(metric, slicer, pandasConstraint,
runName=runName, metadata=metadata, plotDict=plotDict)
# Calculate completeness. First we must calculate "DiscoveryChances".
# Set up an example metric bundle.
discovery = {}
nObsList = [2, 3, 4]
nyears = [3, 5, 10, 12, 15]
for yr in nyears:
discovery[yr] = {}
for nObs in nObsList:
md = metadata + ' %d visits/night after %d years' %(nObs, yr)
#metric = moMetrics.DiscoveryMetric(tMin=0, tMax=90./60/24., nObsPerNight=nObs, nNightsPerWindow=3, tWindow=30)
metric = moMetrics.DiscoveryChancesMetric(tNight=90./60./24., nObsPerNight=nObs, nNightsPerWindow=3, tWindow=15)
slicer = mos
pandasConstraint = 'night <= %d' %(yr*365)
discovery[yr][nObs] = mmb.MoMetricBundle(metric, slicer, pandasConstraint,
runName=runName, metadata=md,
plotDict=plotDict, plotFuncs=[moPlots.MetricVsH()])
#metric = moMetrics.DiscoveryMetric(tMin=0, tMax=90./60/24., nObsPerNight=2, nNightsPerWindow=4, tWindow=30)
metric = moMetrics.DiscoveryChancesMetric(tNight=90./60./24., nObsPerNight=2, nNightsPerWindow=4, tWindow=30)
discovery[yr]['4nights'] = mmb.MoMetricBundle(metric=metric,
slicer=mos, constraint=pandasConstraint, runName=runName,
metadata = metadata+'4 nights/track after %d years' %(yr),
plotDict=plotDict, plotFuncs=[moPlots.MetricVsH()])
bdict = {}
for yr in nyears:
for nObs in nObsList:
bdict['discovery_%s_%d' %(nObs, yr)] = discovery[yr][nObs]
bdict['4nights_%d' %(yr)] = discovery[yr]['4nights']
bdict['nobs'] = nobs
bg = mmb.MoMetricBundleGroup(bdict, outDir=outDir)
bg.runAll()
bg.plotAll()
Hmark = 22
completeness = {}
completenessInt = {}
hVals = mos.Hrange
for yr in nyears:
completeness[yr] = {}
completenessInt[yr] = {}
for nObs in nObsList:
#discChances = discovery[nObs].childBundles['N_Chances']
discChances = discovery[yr][nObs]
discChances.setSummaryMetrics([moSummaryMetrics.CompletenessMetric(), moSummaryMetrics.CumulativeCompletenessMetric()])
discChances.computeSummaryStats()
completeness[yr][nObs] = discChances.summaryValues['Completeness'][0]
completenessInt[yr][nObs] = discChances.summaryValues['CumulativeCompleteness'][0]
for nObs in ['4nights']:
#discChances = discovery[nObs].childBundles['N_Chances']
discChances = discovery[yr][nObs]
discChances.setSummaryMetrics([moSummaryMetrics.CompletenessMetric(), moSummaryMetrics.CumulativeCompletenessMetric()])
discChances.computeSummaryStats()
completeness[yr][nObs] = discChances.summaryValues['Completeness'][0]
completenessInt[yr][nObs] = discChances.summaryValues['CumulativeCompleteness'][0]
# Make a figure of completeness with the different discovery patterns, for one year.
yr = 12
Hidx = np.where(hVals == Hmark)[0]
plt.figure()
plt.title('Completeness %s' %(runName))
for nObs in nObsList:
cval = completeness[yr][nObs][Hidx]
plt.plot(hVals, completeness[yr][nObs], label='%d obs/tracklet, %.0f%s H @ %.1f' %(nObs, cval*100, '%', Hmark))
for nObs in ['4nights']:
cval = completeness[yr][nObs][Hidx]
plt.plot(hVals, completeness[yr][nObs], label='4 pairs/track, %.0f%s H @ %.1f' %(cval*100, '%', Hmark))
plt.axvline(Hmark, color='r', linestyle=':')
plt.xlabel('H')
plt.ylabel('Completeness @ H')
plt.legend(loc='upper right', fancybox=True, numpoints=1, fontsize='smaller')
plt.savefig(os.path.join(outDir, 'completeness.pdf'), format='pdf')
plt.figure()
plt.title('Cumulative completeness %s' %(runName))
for nObs in nObsList:
cval = completenessInt[yr][nObs][Hidx]
plt.plot(hVals, completenessInt[yr][nObs], label='%d obs/tracklet, %.0f%s H <= %.1f' %(nObs, cval*100, '%', Hmark))
for nObs in ['4nights']:
cval = completenessInt[yr][nObs][Hidx]
plt.plot(hVals, completenessInt[yr][nObs], label='4 pairs/track, %.0f%s H <= %.1f' %(cval*100, '%', Hmark))
plt.axvline(Hmark, color='r', linestyle=':')
plt.xlabel('H')
plt.ylabel('Completeness <= H')
plt.legend(loc='upper right', fancybox=True, numpoints=1, fontsize='smaller')
plt.savefig(os.path.join(outDir, 'completenessInt.pdf'), format='pdf')
# And make a figure for one set of discovery criterion, over time.
nObs = 2
plt.figure()
plt.title('Completeness over time %s' %(runName))
for yr in nyears:
cval = completeness[yr][nObs][Hidx]
plt.plot(hVals, completeness[yr][nObs], label='Year %d: %.0f @ H=%.1f' %(yr, cval*100, Hmark))
plt.axvline(Hmark, color='r', linestyle=':')
plt.xlabel('H')
plt.ylabel('Completeness @ H')
plt.legend(loc='upper right', fancybox=True, numpoints=1, fontsize='smaller')
plt.savefig(os.path.join(outDir, 'completenessTime.pdf'), format='pdf')
nObs = 2
plt.figure()
plt.title('Completeness over time %s' %(runName))
for yr in nyears:
cval = completenessInt[yr][nObs][Hidx]
plt.plot(hVals, completenessInt[yr][nObs], label='Year %d: %.0f <= H=%.1f' %(yr, cval*100, Hmark))
plt.axvline(Hmark, color='r', linestyle=':')
plt.xlabel('H')
plt.ylabel('Completeness <= H')
plt.legend(loc='upper right', fancybox=True, numpoints=1, fontsize='smaller')
plt.savefig(os.path.join(outDir, 'completenessIntTime.pdf'), format='pdf')
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Python script to run MAF NEO completeness analysis')
parser.add_argument('--orbitFile', type=str, default='pha20141031.des', help="full file path to the orbit file")
parser.add_argument('--obsFile', type=str, default=None, help='full file path to the observation file')
parser.add_argument("--outDir",type=str, default=None, help='Output directory for MAF outputs. Default opsimdb_orbitroot')
parser.add_argument('--plotOnly', dest='plotOnly', action='store_true',
default=False, help="Reload the metric values from disk and re-plot them.")
parser.add_argument('dbFile', type=str, help="full file path to the opsim sqlite file")
parser.set_defaults()
args, extras = parser.parse_known_args()
orbitfile = args.orbitFile
orbitroot = os.path.split(orbitfile)[-1].replace('.txt', '').replace('.des', '').replace('.dat', '')
if args.obsFile is None:
obsfile = orbitroot + '_allObs.txt'
else:
obsfile = args.obsFile
opsimdb = args.dbFile
runName = os.path.split(opsimdb)[-1].replace('_sqlite.db', '')
metadata = '%s' %(orbitroot)
rFov = np.radians(1.75)
useCamera = True
outDir = args.outDir
if outDir is None:
outDir = runName + '_' + orbitroot
if not os.path.isdir(outDir):
os.makedirs(outDir)
if not args.plotOnly:
makeObs(orbitfile, os.path.join(outDir, obsfile), opsimdb, rFov, useCamera)
print 'Made observations'
calcCompleteness(orbitfile, os.path.join(outDir, obsfile), outDir, runName, metadata)
print "All Done!"