def test_createChainContext_params(self):
keys = ["a", "b"]
params = Params((keys[0], 0), (keys[1], 1))
chain = LikelihoodComputationChain()
chain.params = params
p = np.array([1, 2])
ctx = chain.createChainContext(p)
assert ctx is not None
assert np.all(ctx.getParams().keys == keys)
assert np.all(ctx.getParams()[0] == p[0])
assert np.all(ctx.getParams()[1] == p[1])
def test_createChainContext_params(self):
keys = ["a", "b"]
params = Params((keys[0], 0),
(keys[1], 1))
chain = LikelihoodComputationChain()
chain.params = params
p = np.array([1,2])
ctx = chain.createChainContext(p)
assert ctx is not None
assert np.all(ctx.getParams().keys == keys)
assert np.all(ctx.getParams()[0] == p[0])
assert np.all(ctx.getParams()[1] == p[1])
def __init__(self, data, nbins, noise=0., div=1.0, like_func='c'):
"""
:param data: load your data
:param nbins: number of k-modes in powerspectrum OR
number of triangle contributions in bispectrum (for covariance matrix)
:param noise: system noise, e.g. SKA, MWA noise response (if any), default 0.0,
:param div: likelihood normalization factor, default 1.0,
:param like_func: choose between complex likelihood function (use 'c'), and normal function (use 'n')
"""
chain = LikelihoodComputationChain(min=params[:, 1], max=params[:, 2])
chain.params = params
if like_func == 'n':
chain.addLikelihoodModule(LikeModule(data, nbins, noise, div))
else:
chain.addLikelihoodModule(ComplexLikeModule(data, nbins, noise))
self.chain = chain
from cosmoHammer.util import InMemoryStorageUtil
from cosmoHammer.util import Params
from cosmoHammer.modules import PseudoCmbModule
from cosmoHammer.pso.ParticleSwarmOptimizer import ParticleSwarmOptimizer
#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(params[:, 1], params[:, 2])
chain.params = params
chain.addLikelihoodModule(PseudoCmbModule())
chain.setup()
# find the best fit value and update our params knowledge
print("find best fit point")
pso = ParticleSwarmOptimizer(chain, params[:, 1], params[:, 2])
psoTrace = np.array([pso.gbest.position.copy() for _ in pso.sample()])
params[:, 0] = pso.gbest.position
storageUtil = InMemoryStorageUtil()
sampler = CosmoHammerSampler(params=params,
likelihoodComputationChain=chain,
filePrefix="pseudoCmb_pso",
walkersRatio=50,
from cosmoHammer.util import Params
from cosmoHammer.modules import PseudoCmbModule
from cosmoHammer.pso.ParticleSwarmOptimizer import ParticleSwarmOptimizer
#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(params[:,1], params[:,2])
chain.params = params
chain.addLikelihoodModule(PseudoCmbModule())
chain.setup()
# find the best fit value and update our params knowledge
print("find best fit point")
pso = ParticleSwarmOptimizer(chain, params[:,1], params[:,2])
psoTrace = np.array([pso.gbest.position.copy() for _ in pso.sample()])
params[:, 0] = pso.gbest.position
storageUtil = InMemoryStorageUtil()
sampler = CosmoHammerSampler(
params= params,
likelihoodComputationChain=chain,
filePrefix="pseudoCmb_pso",
