Three Chains
============
Plot three chains together. Name the chains to get a legend.
"""
import numpy as np
from numpy.random import normal, random, multivariate_normal
from chainconsumer import ChainConsumer
if __name__ == "__main__":
np.random.seed(0)
cov = random(size=(3, 3)) + np.identity(3)
data = multivariate_normal(normal(size=3), np.dot(cov, cov.T), size=100000)
cov = random(size=(3, 3)) + np.identity(3)
data2 = multivariate_normal(normal(size=3), np.dot(cov, cov.T), size=100000)
cov = random(size=(3, 3)) + np.identity(3)
data3 = multivariate_normal(normal(size=3), np.dot(cov, cov.T), size=100000)
# If the parameters are the same between chains, you can just pass it the
# first time, and they will become the default parameters.
fig = ChainConsumer()\
.add_chain(data, parameters=["$x$", "$y$", r"$\epsilon$"], name="Test chain")\
.add_chain(data2, name="Chain2")\
.add_chain(data3, name="Chain3") \
.plotter.plot()
fig.set_size_inches(4.5 + fig.get_size_inches()) # Resize fig for doco. You don't need this.
""" ========= One Chain ========= Plot one chain with parameter names. Because we are only plotting one chain, we will get parameter bounds on the marginalised surfaces by default. """ import numpy as np from numpy.random import normal, multivariate_normal from chainconsumer import ChainConsumer np.random.seed(0) cov = 1e2 * normal(size=(3, 3)) data = multivariate_normal(1e3 * normal(size=3), np.dot(cov, cov.T), size=100000) # If you pass in parameter labels and only one chain, you can also get parameter bounds fig = ChainConsumer().add_chain(data, parameters=["$x$", "$y$", r"$\epsilon$"]).plotter.plot() fig.set_size_inches(4.5 + fig.get_size_inches()) # Resize fig for doco. You don't need this.
unpack=True) #======= n_ion,mean free path and no- of halos =====#
Mh = 1.087 * NoH #====================Mh = 1.087*1.e8*NoH
logL = np.loadtxt(path + "Powerspectrum_THANN_prob.out",
usecols=(0),
unpack=True) #=== loglikelyhood =====#
#df = pd.DataFrame({'$\zeta$':n_ion,'$Rmfp$':R_mfp,'$Mhalo_{min}(10^8$ $M_\odot)$':NoH})
data = [n_ion, R_mfp, NoH]
fig = ChainConsumer().add_chain(
data, parameters=["$\zeta$", "$R_{mfp}$", "$Mh_{min}$"]).plotter.plot()
fig.set_size_inches(
3 + fig.get_size_inches()) # Resize fig for doco. You don't need
fig.savefig('plot_nion_{0:.3f}_R_mfp_{1:.3f}_Mhalo_{2:.3f}.png'.format(
n_ion.mean(), R_mfp.mean(), Mh.mean()),
dpi=400)
print('plot_nion_{0:.3f}_R_mfp_{1:.3f}_Mhalo_{2:.3f}.png'.format(
n_ion.mean(), R_mfp.mean(), Mh.mean()))
'''
plt.subplot(312)
plt.ylabel('freq.')
plt.xlabel('$R_{mfp}$')
plt.hist(R_mfp,bins=100,color='orange')
plt.axvline(R_mfp.mean(),color='green',lw=2)
plt.subplot(313)
plt.xlabel('$Mh_{min}$')
plt.ylabel('$freq$')
