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runLCM.py
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runLCM.py
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"""
Run the MCMC sampling of the luminosity calibration model using the emcee package by Dan Foreman-Mackey.
Here the 1/x prior on the variance hyper-parameter tau=1/sigma^2_M is used.
"""
import numpy as np
from pymc import MCMC, AdaptiveMetropolis, Metropolis
import luminositycalibrationmodels as L
import universemodels as U
from tables import Int32Col, Float64Col, StringCol, IsDescription, openFile
import argparse
from time import time as now
class SurveyParameters(IsDescription):
"""
Class that holds the data model for the simulated parallax survey parameters. Intended for use with the
HDF5 files through the pytables package.
"""
kind = StringCol(itemsize=40)
numberOfStars = Int32Col()
minParallax = Float64Col()
maxParallax = Float64Col()
meanAbsoluteMagnitude = Float64Col()
varianceAbsoluteMagnitude = Float64Col()
parallaxErrorNormalizationMagnitude = Float64Col()
parallaxErrorSlope = Float64Col()
parallaxErrorCalibrationFloor = Float64Col()
magnitudeErrorNormalizationMagnitude = Float64Col()
magnitudeErrorSlope = Float64Col()
magnitudeErrorCalibrationFloor = Float64Col()
apparentMagnitudeLimit = Float64Col()
numberOfStarsInSurvey = Int32Col()
class McmcParameters(IsDescription):
"""
Class that holds the data model for the HDF5 table with the MCMC parameters.
"""
iterations = Int32Col()
burnIn = Int32Col()
thin = Int32Col()
minMeanAbsoluteMagnitude = Float64Col()
maxMeanAbsoluteMagnitude = Float64Col()
priorTau = StringCol(40)
shapeTau = Float64Col()
scaleTau = Float64Col()
def runMCMCmodel(args):
"""
Simulate the survey data and run the MCMC luminosity calibration model.
Parameters
----------
args - Command line arguments
"""
mcmcParams=args['mcmcString']
surveyParams=args['surveyString']
priorParams=args['priorsString']
maxIter=int(mcmcParams[0])
burnIter=int(mcmcParams[1])
thinFactor=int(mcmcParams[2])
if surveyParams[5] == 'Inf':
magLim = np.Inf
else:
magLim = float(surveyParams[5])
S=U.UniformDistributionSingleLuminosity(int(surveyParams[0]), float(surveyParams[1]),
float(surveyParams[2]), float(surveyParams[3]), float(surveyParams[4]),
surveyLimit=magLim)
#S.setRandomNumberSeed(53949896)
S.generateObservations()
lumCalModel=L.UniformSpaceDensityGaussianLF(S,float(surveyParams[1]), float(surveyParams[2]),
float(priorParams[0]), float(priorParams[1]), float(priorParams[2]), float(priorParams[3]))
class SurveyData(IsDescription):
"""
Class that holds the data model for the data from the simulated parallax survey. Intended for use
with the HDF5 files through the pytables package.
"""
trueParallaxes = Float64Col(S.numberOfStarsInSurvey)
absoluteMagnitudes = Float64Col(S.numberOfStarsInSurvey)
apparentMagnitudes = Float64Col(S.numberOfStarsInSurvey)
parallaxErrors = Float64Col(S.numberOfStarsInSurvey)
magnitudeErrors = Float64Col(S.numberOfStarsInSurvey)
observedParallaxes = Float64Col(S.numberOfStarsInSurvey)
observedMagnitudes = Float64Col(S.numberOfStarsInSurvey)
baseName="LumCalSimSurvey-{0}".format(S.numberOfStars)+"-{0}".format(S.minParallax)
baseName=baseName+"-{0}".format(S.maxParallax)+"-{0}".format(S.meanAbsoluteMagnitude)
baseName=baseName+"-{0}".format(S.varianceAbsoluteMagnitude)
h5file = openFile(baseName+".h5", mode = "w", title = "Simulated Survey")
group = h5file.createGroup("/", 'survey', 'Survey parameters, data, and MCMC parameters')
parameterTable = h5file.createTable(group, 'parameters', SurveyParameters, "Survey parameters")
dataTable = h5file.createTable(group, 'data', SurveyData, "Survey data")
mcmcTable = h5file.createTable(group, 'mcmc', McmcParameters, "MCMC parameters")
surveyParams = parameterTable.row
surveyParams['kind']=S.__class__.__name__
surveyParams['numberOfStars']=S.numberOfStars
surveyParams['minParallax']=S.minParallax
surveyParams['maxParallax']=S.maxParallax
surveyParams['meanAbsoluteMagnitude']=S.meanAbsoluteMagnitude
surveyParams['varianceAbsoluteMagnitude']=S.varianceAbsoluteMagnitude
surveyParams['parallaxErrorNormalizationMagnitude']=S.parallaxErrorNormalizationMagnitude
surveyParams['parallaxErrorSlope']=S.parallaxErrorSlope
surveyParams['parallaxErrorCalibrationFloor']=S.parallaxErrorCalibrationFloor
surveyParams['magnitudeErrorNormalizationMagnitude']=S.magnitudeErrorNormalizationMagnitude
surveyParams['magnitudeErrorSlope']=S.magnitudeErrorSlope
surveyParams['magnitudeErrorCalibrationFloor']=S.magnitudeErrorCalibrationFloor
surveyParams['apparentMagnitudeLimit']=S.apparentMagnitudeLimit
surveyParams['numberOfStarsInSurvey']=S.numberOfStarsInSurvey
surveyParams.append()
parameterTable.flush()
surveyData = dataTable.row
surveyData['trueParallaxes']=S.trueParallaxes
surveyData['absoluteMagnitudes']=S.absoluteMagnitudes
surveyData['apparentMagnitudes']=S.apparentMagnitudes
surveyData['parallaxErrors']=S.parallaxErrors
surveyData['magnitudeErrors']=S.magnitudeErrors
surveyData['observedParallaxes']=S.observedParallaxes
surveyData['observedMagnitudes']=S.observedMagnitudes
surveyData.append()
dataTable.flush()
mcmcParameters = mcmcTable.row
mcmcParameters['iterations']=maxIter
mcmcParameters['burnIn']=burnIter
mcmcParameters['thin']=thinFactor
mcmcParameters['minMeanAbsoluteMagnitude']=float(priorParams[0])
mcmcParameters['maxMeanAbsoluteMagnitude']=float(priorParams[1])
mcmcParameters['priorTau']="Inverse-Gamma"
mcmcParameters['shapeTau']=float(priorParams[2])
mcmcParameters['scaleTau']=float(priorParams[3])
mcmcParameters.append()
dataTable.flush()
h5file.close()
# Run MCMC and store in HDF5 database
baseName="LumCalResults-{0}".format(S.numberOfStars)+"-{0}".format(S.minParallax)
baseName=baseName+"-{0}".format(S.maxParallax)+"-{0}".format(S.meanAbsoluteMagnitude)
baseName=baseName+"-{0}".format(S.varianceAbsoluteMagnitude)
M=MCMC(lumCalModel.pyMCModel, db='hdf5', dbname=baseName+".h5", dbmode='w', dbcomplevel=9,
dbcomplib='bzip2')
M.use_step_method(Metropolis, M.priorParallaxes)
M.use_step_method(Metropolis, M.priorAbsoluteMagnitudes)
start=now()
M.sample(iter=maxIter, burn=burnIter, thin=thinFactor)
finish=now()
print "Elapsed time in seconds: %f" % (finish-start)
M.db.close()
def parseCommandLineArguments():
"""
Set up command line parsing.
"""
parser = argparse.ArgumentParser("Run the MCMC sampling of the luminosity calibration model using PyMC.")
parser.add_argument("--mcmc", dest="mcmcString", nargs=3,
help="""White-space-separated list of MCMC parameters:
(1) number of MCMC iterations,
(2) number of initial iterations to discard as burn-in,
(3) thinning factor""")
parser.add_argument("--survey", dest="surveyString", nargs=6,
help="""White-space-separated list of survey parameters:
(1) number of stars,
(2) lower limit parallaxes [mas],
(3) upper limit parallaxes [mas],
(4) mean absolute magnitude,
(5) variance of absolute magnitudes
(6) apparent magnitude limit of survey (Inf allowed)""")
parser.add_argument("--priors", dest="priorsString", nargs=4,
help="""White-space-separated list of prior ranges of luminosity distribution parameters:
(1) lower limit of uniform prior for mean absolute magnitude,
(2) upper limit of uniform prior for mean absolute magnitude,
(3) shape parameter for Inverse-Gamma prior on the inverse variance of the absolute
magnitudes,
(4) scale parameter for Inverse-Gamma prior on the inverse variance of the absolute
magnitudes""")
return vars(parser.parse_args())
if __name__ in ('__main__'):
args = parseCommandLineArguments()
runMCMCmodel(args)