def test_modules(self):
chain = LikelihoodComputationChain()
assert len(chain.getCoreModules())==0
assert len(chain.getLikelihoodModules())==0
coreModule = DummyModule()
likeModule = DummyModule()
chain.addCoreModule(coreModule)
chain.addLikelihoodModule(likeModule)
assert len(chain.getCoreModules())==1
assert len(chain.getLikelihoodModules())==1
chain.setup()
assert coreModule.init
assert likeModule.init
like, data = chain([0])
assert coreModule.called
assert likeModule.compLike
assert like == DummyModule.like
assert len(data) == 1
assert data["data"] == DummyModule.data
def test_modules(self):
chain = LikelihoodComputationChain()
assert len(chain.getCoreModules()) == 0
assert len(chain.getLikelihoodModules()) == 0
coreModule = DummyModule()
likeModule = DummyModule()
chain.addCoreModule(coreModule)
chain.addLikelihoodModule(likeModule)
assert len(chain.getCoreModules()) == 1
assert len(chain.getLikelihoodModules()) == 1
chain.setup()
assert coreModule.init
assert likeModule.init
like, data = chain([0])
assert coreModule.called
assert likeModule.compLike
assert like == DummyModule.like
assert len(data) == 1
assert data["data"] == DummyModule.data
szs = np.array(hf.get("szs_parms")).astype(int)
mins = params[:, 1]
maxs = params[:, 2]
nparam = len(mins)
#--------------------------------------------------------------------------------
#################################################################################################
####################################### LIKELIHOOD CHAIN ########################
#-------------------- Setup the Chain --------
# if mins, and maxs included, the code performs checkin
# and can calculate a null iteration of Generated Quantities in case of rejection
print 'Setting up chain'
chain = LikelihoodComputationChain()
coremodule = CoreModule(szs)
logPosterior = LogPosteriorModule(data, threads=k)
chain.addCoreModule(coremodule)
chain.addLikelihoodModule(logPosterior)
chain.setup()
#######################################################################################
############################### Particle Swarm Optimizer #############################
pso = MPSO(chain,
low=mins,
high=maxs,
particleCount=partCount,
req=req,
threads=1,
InPos=inpos)
smp = emcee.EnsembleSampler(
partCount,
nparam,
chain,
for tr_j in tracers[ntracers0:]:
if tr_i.split('_')[0] != tr_j.split('_')[0]:
# Generate the appropriate list of tracer combinations to plot
trc_combs.append([tr_i, tr_j])
i += 1
else:
raise NotImplementedError('Only fit_comb = all, auto and cross supported. Aborting.')
logger.info('Fitting tracer combination = {}.'.format(trc_combs))
coremod_config = copy.deepcopy(config)
coremod_config['param_mapping'] = param_mapping
coremod_config['hmparams'] = hmparams
coremod_config['cosmo'] = cosmo
coremod_config['trc_combs'] = trc_combs
chain.addCoreModule(GSKYCore(saccfile_coadd, coremod_config))
chain.addLikelihoodModule(GSKYLike(saccfile_coadd, noise_saccfile_coadd))
chain.setup()
chaindir = os.path.join('chains', config['output_run_dir'])
if not os.path.isdir(get_output_fname(config, chaindir)):
os.makedirs(get_output_fname(config, chaindir))
path2chain = os.path.join('chains', config['output_run_dir'] + '/' + ch_config_params['chainsPrefix'])
if ch_config_params['use_mpi'] == 0:
if ch_config_params['rerun'] == 0:
sampler = CosmoHammerSampler(
params=params,
from cosmoHammer import LikelihoodComputationChain
from cosmoHammer.util import Params
from MCMC.Power_sampler.Power_like import PSlikeModule as slk
from MCMC.Power_sampler.Power_core import PScore
#======================================================================================================================#
params = Params(("n_ion", [250., 10.0, 510.0, 1.0]),
("R_mfp", [38.0, 5.0, 70.0, .5]),
("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)
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!")
#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")
