def createFakeChains(config,
nclusters,
zcluster,
r_mpc_edges,
beta_s,
galdensity,
shapenoise,
nsamples=1000,
mass=10**15.2):
mtrues = mass * np.ones(nclusters)
r_mpcs, shearprofiles, shearerrs = createClusterSet(
config, mtrues, zcluster, r_mpc_edges, beta_s, galdensity, shapenoise)
fitter = nfwfit.buildFitter(config)
chains = []
for i in range(nclusters):
mcmc_model = None
for j in range(10):
try:
mcmc_model = fitter.model.makeMCMCModel(
r_mpcs[i], shearprofiles[i], shearerrs[i], beta_s,
beta_s**2, zcluster)
break
except pymc.ZeroProbability:
pass
if mcmc_model is None:
raise pymc.ZeroProbability
manager = varcontainer.VarContainer()
options = varcontainer.VarContainer()
manager.options = options
options.singlecore = True
options.adapt_every = 100
options.adapt_after = 100
options.nsamples = nsamples
manager.model = mcmc_model
runner = pma.MyMCMemRunner()
runner.run(manager)
runner.finalize(manager)
chains.append(manager.chain['m200'][200:])
return mtrues, chains
def memsample(parts, samples, adaptevery = 100, adaptafter = 100):
options = varcontainer.VarContainer()
options.singlecore = True
options.nsamples = samples
options.adapt_every = adaptevery
options.adapt_after = adaptafter
manager = varcontainer.VarContainer()
manager.options = options
manager.model = pymc.Model(parts)
runner = pma.MyMCMemRunner()
runner.run(manager)
runner.finalize(manager)
return manager.chain
def __call__(self, profile):
chains = {}
for delta in self.deltas:
mcmc_model = None
for i in range(20):
try:
mcmc_model = self.model.makeMCMCModel(profile, delta = delta)
break
except pymc.ZeroProbability:
pass
if mcmc_model is None:
raise pymc.ZeroProbability
# This sets up Adam Mantz's version of an MCMC sampler for production code calculations.
# This is stored in mymcmc_adapter.py (converts to talk with other MCMC code)
# Imported as pma above.
manager = varcontainer.VarContainer()
options = varcontainer.VarContainer()
manager.options = options
options.singlecore = True
options.adapt_every = 100
options.adapt_after = 100
options.nsamples = self.nsamples
manager.model = mcmc_model
runner = pma.MyMCMemRunner()
runner.run(manager)
runner.finalize(manager)
reducedchain = dict(cdelta = np.hstack(manager.chain['cdelta'][5000::2]).astype(np.float32),
mdelta = np.hstack(manager.chain['mdelta'][5000::2]).astype(np.float32),
likelihood = np.hstack(manager.chain['likelihood'][5000::2]).astype(np.float32))
chains[delta] = reducedchain
return chains
def __call__(self, profile):
chains = {}
for delta in self.deltas:
mcmc_model = None
for i in range(20):
try:
mcmc_model = self.model.makeMCMCModel(profile, delta=delta)
break
except pymc.ZeroProbability:
pass
if mcmc_model is None:
raise pymc.ZeroProbability
manager = varcontainer.VarContainer()
options = varcontainer.VarContainer()
manager.options = options
options.singlecore = True
options.adapt_every = 100
options.adapt_after = 100
options.nsamples = self.nsamples
manager.model = mcmc_model
runner = pma.MyMCMemRunner()
runner.run(manager)
runner.finalize(manager)
reducedchain = dict(
cdelta=np.hstack(manager.chain['cdelta'][5000::2]).astype(
np.float32),
mdelta=np.hstack(manager.chain['mdelta'][5000::2]).astype(
np.float32),
likelihood=np.hstack(
manager.chain['likelihood'][5000::2]).astype(np.float32))
chains[delta] = reducedchain
return chains
def memsample(model, samples, adaptevery=100, adaptafter=100, outputFile=None):
options = varcontainer.VarContainer()
options.singlecore = True
options.nsamples = samples
options.adapt_every = adaptevery
options.adapt_after = adaptafter
if outputFile:
options.outputFile = outputFile
manager = varcontainer.VarContainer()
manager.options = options
manager.model = model
runner = pma.MyMCMemRunner()
runner.run(manager)
if outputFile:
runner.dump(manager)
runner.finalize(manager)
return manager.chain