def get_separate_mcsamples(chain):
"""
Function that returns separate :class:`~getdist.mcsamples.MCSamples`
for each sampler chain.
:param chain: :class:`~getdist.mcsamples.MCSamples` the input chain.
:return: list of :class:`~getdist.mcsamples.MCSamples` with the separate
chains.
"""
# get separate chains:
_chains = chain.getSeparateChains()
# copy the param names and ranges:
_mc_samples = []
for ch in _chains:
temp = MCSamples()
temp.paramNames = chain.getParamNames()
temp.setSamples(ch.samples, weights=ch.weights, loglikes=ch.loglikes)
temp.sampler = chain.sampler
temp.ranges = chain.ranges
temp.updateBaseStatistics()
_mc_samples.append(temp.copy())
#
return _mc_samples
with np.load('sample_EXP_CC+SN+BAO+AGN_4params_deriv.npz') as data:
samples_3 = data['new_samples']
os.chdir(path_datos_global+'/Resultados_cadenas')
with np.load('sample_EXP_CC+SN_4params_deriv.npz') as data:
samples_0 = data['new_samples']
#%%
%matplotlib qt5
ndim = 4
names = ['M_{abs}','\Omega_{m}','b','H_{0}']
labels=names
samples1 = MCSamples(samples=samples_1,names = names, labels = labels,ranges={'b':[0, None]})
samples1 = samples1.copy(label=r'Lowest-order with $0.3\sigma$ smoothing',
settings={'mult_bias_correction_order':0,'smooth_scale_2D':0.3, 'smooth_scale_1D':0.3})
samples2 = MCSamples(samples=samples_2,names = names, labels = labels,ranges={'b':[0, None]})
samples2 = samples2.copy(label=r'Lowest-order with $0.3\sigma$ smoothing',
settings={'mult_bias_correction_order':0,'smooth_scale_2D':0.3, 'smooth_scale_1D':0.3})
samples3 = MCSamples(samples=samples_3,names = names, labels = labels,ranges={'b':[0, None]})
samples3 = samples3.copy(label=r'Lowest-order with $0.3\sigma$ smoothing',
settings={'mult_bias_correction_order':0,'smooth_scale_2D':0.3, 'smooth_scale_1D':0.3})
samples0 = MCSamples(samples=samples_0,names = names, labels = labels,ranges={'b':[0, None]})
samples0 = samples0.copy(label=r'Lowest-order with $0.3\sigma$ smoothing',
settings={'mult_bias_correction_order':0,'smooth_scale_2D':0.3, 'smooth_scale_1D':0.3})
g = plots.get_subplot_plotter()
g.triangle_plot([samples0, samples1, samples2, samples3],
