MVN_COEF = NDIM * np.log(1. / np.sqrt(2. * np.pi)) + np.log(
np.sqrt(np.linalg.det(INVCOV)))
# the logarithm of objective function: log(MVN)
def getLogFunc(point):
'''
Return the logarithm of the MVN PDF.
'''
normedPoint = MEAN - point
return MVN_COEF - 0.5 * (np.dot(normedPoint, np.matmul(
INVCOV, normedPoint)))
import paramonte as pm
pmpd = pm.ParaDRAM() # define a ParaMonte sampler instance
pmpd.mpiEnabled = True # This is essential as it enables the invocation of the MPI-parallelized ParaDRAM routines.
pmpd.spec.overwriteRequested = True # overwrite existing output files if needed
pmpd.spec.randomSeed = 3751 # initialize the random seed to generate reproducible results.
pmpd.spec.outputFileName = "./out/mvn_parallel_singleChain"
pmpd.spec.progressReportPeriod = 20000
pmpd.spec.chainSize = 30000 # the default 100,000 unique points is too large for this simple example.
# call the ParaDRAM sampler
pmpd.runSampler(ndim=4, getLogFunc=getLogFunc)
import paramonte as pm
import os
import numpy as np
from scipy.stats import multivariate_normal
import StarFormationRate
import paramonte as pm
pmpd = pm.ParaDRAM()
pmpd.spec.mpiFinalizeRequested = False
pmpd.spec.sampleSize = 10000
pmpd.spec.domainLowerLimitVec = [0.1]
pmpd.spec.domainUpperLimitVec = [100]
model_names=['H06' # model names being use
,'L08'
,'B10'
,'M14'
,'M17'
,'F18'
]
fileNamesPrefixList = [ "LogRate"+model in model_names] # makes list of LogRateModelName to name directory for multiple models
out_dir='../out'
sfr_functions = [ getattr(StarFormationRate,'get'+PrefixNames) for PrefixNames in fileNamesPrefixList] #calls functions within StarFormationRate given model names
sfr_functions_len = len(sfr_functions) # length of functions used to determine when mpiFinalizeRequested will close
