def mcmc_CH(self, walkerRatio, n_run, n_burn, mean_start, sigma_start, threadCount=1, init_pos=None, mpi_monch=False):
"""
runs mcmc on the parameter space given parameter bounds with CosmoHammerSampler
returns the chain
"""
lowerLimit, upperLimit = self.cosmoParam.param_bounds
params = np.array([mean_start, lowerLimit, upperLimit, sigma_start]).T
chain = LikelihoodComputationChain(
min=lowerLimit,
max=upperLimit)
temp_dir = tempfile.mkdtemp("Hammer")
file_prefix = os.path.join(temp_dir, "logs")
# chain.addCoreModule(CambCoreModule())
chain.addLikelihoodModule(self.chain)
chain.setup()
store = InMemoryStorageUtil()
if mpi_monch is True:
sampler = MpiCosmoHammerSampler(
params=params,
likelihoodComputationChain=chain,
filePrefix=file_prefix,
walkersRatio=walkerRatio,
burninIterations=n_burn,
sampleIterations=n_run,
threadCount=1,
initPositionGenerator=init_pos,
storageUtil=store)
else:
sampler = CosmoHammerSampler(
params=params,
likelihoodComputationChain=chain,
filePrefix=file_prefix,
walkersRatio=walkerRatio,
burninIterations=n_burn,
sampleIterations=n_run,
threadCount=threadCount,
initPositionGenerator=init_pos,
storageUtil=store)
time_start = time.time()
if sampler.isMaster():
print('Computing the MCMC...')
print('Number of walkers = ', len(mean_start)*walkerRatio)
print('Burn-in itterations: ', n_burn)
print('Sampling itterations:', n_run)
sampler.startSampling()
if sampler.isMaster():
time_end = time.time()
print(time_end - time_start, 'time taken for MCMC sampling')
# if sampler._sampler.pool is not None:
# sampler._sampler.pool.close()
try:
shutil.rmtree(temp_dir)
except Exception as ex:
print(ex)
pass
return store.samples
def mcmc_CH(self,
walkerRatio,
n_run,
n_burn,
mean_start,
sigma_start,
threadCount=1,
init_pos=None,
mpi=False):
"""
runs mcmc on the parameter space given parameter bounds with CosmoHammerSampler
returns the chain
"""
lowerLimit, upperLimit = self.lower_limit, self.upper_limit
mean_start = np.maximum(lowerLimit, mean_start)
mean_start = np.minimum(upperLimit, mean_start)
low_start = mean_start - sigma_start
high_start = mean_start + sigma_start
low_start = np.maximum(lowerLimit, low_start)
high_start = np.minimum(upperLimit, high_start)
sigma_start = (high_start - low_start) / 2
mean_start = (high_start + low_start) / 2
params = np.array([mean_start, lowerLimit, upperLimit, sigma_start]).T
chain = LikelihoodComputationChain(min=lowerLimit, max=upperLimit)
temp_dir = tempfile.mkdtemp("Hammer")
file_prefix = os.path.join(temp_dir, "logs")
#file_prefix = "./lenstronomy_debug"
# chain.addCoreModule(CambCoreModule())
chain.addLikelihoodModule(self.chain)
chain.setup()
store = InMemoryStorageUtil()
#store = None
if mpi is True:
sampler = MpiCosmoHammerSampler(params=params,
likelihoodComputationChain=chain,
filePrefix=file_prefix,
walkersRatio=walkerRatio,
burninIterations=n_burn,
sampleIterations=n_run,
threadCount=1,
initPositionGenerator=init_pos,
storageUtil=store)
else:
sampler = CosmoHammerSampler(params=params,
likelihoodComputationChain=chain,
filePrefix=file_prefix,
walkersRatio=walkerRatio,
burninIterations=n_burn,
sampleIterations=n_run,
threadCount=threadCount,
initPositionGenerator=init_pos,
storageUtil=store)
time_start = time.time()
if sampler.isMaster():
print('Computing the MCMC...')
print('Number of walkers = ', len(mean_start) * walkerRatio)
print('Burn-in iterations: ', n_burn)
print('Sampling iterations:', n_run)
sampler.startSampling()
if sampler.isMaster():
time_end = time.time()
print(time_end - time_start, 'time taken for MCMC sampling')
# if sampler._sampler.pool is not None:
# sampler._sampler.pool.close()
try:
shutil.rmtree(temp_dir)
except Exception as ex:
print(ex, 'shutil.rmtree did not work')
pass
#samples = np.loadtxt(file_prefix+".out")
#prob = np.loadtxt(file_prefix+"prob.out")
return store.samples, store.prob