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_emcee(self, n_walkers, n_run, n_burn, mean_start, sigma_start):
"""
returns the mcmc analysis of the parameter space
"""
sampler = emcee.EnsembleSampler(n_walkers, self.cosmoParam.numParam, self.chain.likelihood)
p0 = emcee.utils.sample_ball(mean_start, sigma_start, n_walkers)
new_pos, _, _, _ = sampler.run_mcmc(p0, n_burn)
sampler.reset()
store = InMemoryStorageUtil()
for pos, prob, _, _ in sampler.sample(new_pos, iterations=n_run):
store.persistSamplingValues(pos, prob, None)
return store.samples
def setup(self):
self.storageUtil = InMemoryStorageUtil()
