def mpi_sampler(self, walker_ratio, burnin, samples, num, threads=-1):
"""
:param walker_ratio: the ratio of walkers and the count of sampled parameters
:param burnin: burin iterations
:param samples: no. of sample iterations
:param num: number to put in output files e.g: string(name+num)=Pk_1,Bk_1
:param threads: no. of cpu threads
"""
# self.chain.setup()
# print("find best fit point")
# pso = MpiParticleSwarmOptimizer(self.chain, params[:, 1], params[:, 2])
# psoTrace = np.array([pso.gbest.position.copy() for _ in pso.sample()])
# params[:, 0] = pso.gbest.position
mpi_sampler = MpiCosmoHammerSampler(
params=params,
likelihoodComputationChain=self.chain,
filePrefix='%s' % self.name + '%d' % num,
walkersRatio=walker_ratio,
burninIterations=burnin,
sampleIterations=samples,
threadCount=threads)
print("started sampling:")
start = time.time()
mpi_sampler.startSampling()
end = time.time()
tics = end - start
print("The time taken %.2f sec. done!" % tics)
print('Done!')
def mcmc_CH(self, walkerRatio, n_run, n_burn, mean_start, sigma_start, lowerLimit, upperLimit, threadCount=1, init_pos=None, mpi_monch=False):
"""
runs mcmc on the parameter space given parameter bounds with CosmoHammerSampler
returns the chain
"""
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, store.prob
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
# if mins, and maxs included, the code performs checkin
# and can calculate a null iteration of Generated Quantities in case of rejection
chain = LikelihoodComputationChain()
coremodule = CoreModule(szs)
logPosterior = LogPosteriorModule(data, threads=k)
# logPosterior = LogPosteriorModule(data,hypr)
chain.addCoreModule(coremodule)
chain.addLikelihoodModule(logPosterior)
chain.setup()
#---------- Initial Position Generator --------
pos_gen = UniformIntervalPositionGenerator()
############################## SAMPLER ########################################
#-------- Writes Derived parameters into a file------
storageUtil = DerivedParamterFileUtil(samp)
#------------- Setup the sampler -----------
sampler = CHSampler(params=params,
likelihoodComputationChain=chain,
filePrefix=samp,
walkersRatio=wr,
burninIterations=Nbi,
sampleIterations=Nit,
storageUtil=storageUtil,
initPositions=positions,
threadCount=1,
initPositionGenerator=pos_gen)
#----------------------- Start sampling-------------------------
sampler._sampler.a = a
sampler.startSampling()
print "Done!"
sys.exit(0)
path2chain = args.path2rerunchain
sampler = CosmoHammerSampler(
params=params,
likelihoodComputationChain=chain,
filePrefix=get_output_fname(config, path2chain),
walkersRatio=ch_config_params['walkersRatio'],
burninIterations=ch_config_params['burninIterations'],
sampleIterations=ch_config_params['sampleIterations'],
initPositionGenerator=InitializeFromChain(ch_config_params['path2rerunchain'], fraction=0.8))
else:
if ch_config_params['rerun'] == 0:
sampler = MpiCosmoHammerSampler(
params=params,
likelihoodComputationChain=chain,
filePrefix=get_output_fname(config, path2chain),
walkersRatio=ch_config_params['walkersRatio'],
burninIterations=ch_config_params['burninIterations'],
sampleIterations=ch_config_params['sampleIterations'])
else:
assert ch_config_params[
'path2rerunchain'] is not None, 'rerun is {}, but path to rerun chains not set. Aborting.'.format(
ch_config_params['rerun'])
sampler = MpiCosmoHammerSampler(
params=params,
likelihoodComputationChain=chain,
filePrefix=get_output_fname(config, path2chain),
walkersRatio=ch_config_params['walkersRatio'],
burninIterations=ch_config_params['burninIterations'],
sampleIterations=ch_config_params['sampleIterations'],
initPositionGenerator=InitializeFromChain(ch_config_params['path2rerunchain'], fraction=0.8))
("NoH", [750.0, 10.0, 1510.0, 1.0]))
#==========The parameter space is defined================================#
#======================================================================================================================#
chain = LikelihoodComputationChain(min=params[:, 1], max=params[:, 2])
chain.addCoreModule(
PScore()) #=========setting up the modules===================#
chain.addLikelihoodModule(slk())
chain.setup()
#======================================================================================================================#
sampler = MpiCosmoHammerSampler(
params=params,
likelihoodComputationChain=
chain, #=============mpi sampler===============================#
filePrefix="Powerspectrum_THANN_",
walkersRatio=10,
burninIterations=250,
sampleIterations=250)
#======================================================================================================================#
print("start sampling: Here we go with the power of zeus.")
sampler.startSampling()
print("done!")
#======================================================================================================================#
from wmap9Wrapper import WmapExtLikelihoodModule as wmap9
from cambWrapper import CambCoreModule
#parameter start center, min, max, start width
params = Params(
("hubble", [70, 65, 80, 3]), ("ombh2", [0.0226, 0.01, 0.03, 0.001]),
("omch2", [0.122, 0.09, 0.2, 0.01]),
("scalar_amp", [2.1e-9, 1.8e-9, 2.35e-9, 1e-10]),
("scalar_spectral_index", [0.96, 0.8, 1.2, 0.02]),
("re_optical_depth", [0.09, 0.01, 0.1, 0.03]), ("sz_amp", [1, 0, 2, 0.4]))
chain = LikelihoodComputationChain(min=params[:, 1], max=params[:, 2])
camb = CambCoreModule.CambCoreModule()
chain.addCoreModule(camb)
chain.addLikelihoodModule(wmap9.WmapExtLikelihoodModule())
chain.setup()
sampler = MpiCosmoHammerSampler(params=params,
likelihoodComputationChain=chain,
filePrefix="cosmoHammerWmap9_",
walkersRatio=20,
burninIterations=0,
sampleIterations=50)
print("start sampling")
sampler.startSampling()
print("done!")
("ombh2", [0.0226, 0.01, 0.03, 0.001]),
("omch2", [0.122, 0.09, 0.2, 0.01]),
("scalar_amp", [2.1e-9, 1.8e-9, 2.35e-9, 1e-10]),
("scalar_spectral_index", [0.96, 0.8, 1.2, 0.02]),
("re_optical_depth", [0.09, 0.01, 0.1, 0.03]),
("sz_amp", [1,0,2,0.4]))
chain = LikelihoodComputationChain(
min=params[:,1],
max=params[:,2])
camb = CambCoreModule.CambCoreModule()
chain.addCoreModule(camb)
chain.addLikelihoodModule(wmap9.WmapExtLikelihoodModule())
chain.setup()
sampler = MpiCosmoHammerSampler(
params= params,
likelihoodComputationChain=chain,
filePrefix="cosmoHammerWmap9_",
walkersRatio=20,
burninIterations=0,
sampleIterations=50)
print("start sampling")
sampler.startSampling()
print("done!")