def test_copy(self):
params = Params(("attr1", 1), ("attr2", 2))
params2 = params.copy()
assert np.all(params.keys == params2.keys)
assert np.all(params.values == params2.values)
def test_get(self):
params = Params(("attr1", 1), ("attr2", 2))
assert params.get("attr1") == 1
assert params.get("attr2") == 2
with pytest.raises(KeyError):
params.get("inexistent")
def test_copy(self):
params = Params(("attr1", 1),
("attr2", 2))
params2 = params.copy()
assert np.all(params.keys == params2.keys)
assert np.all(params.values == params2.values)
def test_get(self):
params = Params(("attr1", 1),
("attr2", 2))
assert params.get("attr1") == 1
assert params.get("attr2") == 2
with pytest.raises(KeyError):
params.get("inexistent")
def test_names_immutability(self):
keys = ["attr1", "attr2"]
params = Params((keys[0], 1), (keys[1], 2))
params.keys.append("irrelevant")
assert np.all(params.keys == keys)
def test_attr_looup(self):
params = Params(("attr1", 1), ("attr2", 2))
assert params.attr1 == 1
assert params.attr2 == 2
with pytest.raises(AttributeError):
params.inexistent
def createChainContext(self, p):
"""
Returns a new instance of a chain context
"""
try:
p = Params(*zip(self.params.keys, p))
except Exception:
# no params or params has no keys
pass
return ChainContext(self, p)
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_str(self):
params = Params(("attr1", 1))
s = str(params)
assert "=" in s
#!/usr/bin/env python
"""
Runs CosmoHammer with a rosenbrock density module
"""
from __future__ import print_function, division, absolute_import, unicode_literals
from cosmoHammer import CosmoHammerSampler
from cosmoHammer import LikelihoodComputationChain
from cosmoHammer.modules import RosenbrockModule
from cosmoHammer.util import Params
#parameter start center, min, max, start width
params = Params(("x", [1, -10, 10, 0.1]), ("y", [1, -10, 10, 0.1]))
chain = LikelihoodComputationChain()
rosenbrock = RosenbrockModule()
chain.addLikelihoodModule(rosenbrock)
chain.setup()
sampler = CosmoHammerSampler(params=params,
likelihoodComputationChain=chain,
filePrefix="rosenbrock",
walkersRatio=50,
burninIterations=100,
sampleIterations=100)
sampler.startSampling()
try:
def test_duplicated_key(self):
with pytest.raises(KeyError):
_ = Params(("attr1", 1), ("attr1", 2))
def test_value_assignment(self):
values = np.array([[1, 2, 3, 4], [5, 6, 7, 8]])
params = Params(("attr1", values[0]), ("attr2", values[1]))
params[:, 0] = 0
assert np.all(params[:, 0] == 0)
import os
import time
from cosmoHammer.util import Params
from cosmoHammer import MpiCosmoHammerSampler
from cosmoHammer import CosmoHammerSampler
from cosmoHammer import LikelihoodComputationChain
# from cosmoHammer.pso.MpiParticleSwarmOptimizer import MpiParticleSwarmOptimizer
# The parameter space is defined
# paramters = [peak, min., max., jump] ===> A rough idea about the prior
path = os.path.abspath(os.path.join(__file__, os.pardir))
path = path + '/existing_models/'
params = Params(("NoH", [275, 10, 550, 3]),
("n_ion", [90.00, 10.00, 180.00, 1]),
("R_mfp", [30.00, 5.00, 60.00, 0.5]))
class RunMCMC:
""" sampler & MPI sampler class """
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')
"""
def test_names_access(self):
keys = ["attr1", "attr2"]
params = Params((keys[0], 1), (keys[1], 2))
assert np.all(params.keys == keys)
def test_slicing(self):
values = np.array([[1, 2, 3, 4], [5, 6, 7, 8]])
params = Params(("attr1", values[0]), ("attr2", values[1]))
assert np.all(values[0] == values[0])
assert np.all(values[:, 1] == values[:, 1])
def test_params_access_2d(self):
values = np.array([[1, 2, 3, 4], [5, 6, 7, 8]])
params = Params(("attr1", values[0]), ("attr2", values[1]))
assert np.all(params.values == values)
def test_params_access_1d(self):
values = [1, 2]
params = Params(("attr1", values[0]), ("attr2", values[1]))
assert np.all(params.values == values)
from __future__ import print_function, division, absolute_import, unicode_literals
import numpy as np
from cosmoHammer import LikelihoodComputationChain
from cosmoHammer import CosmoHammerSampler
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
from cosmoHammer import MpiCosmoHammerSampler
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,
