reader = emcee.backends.HDFBackend(filename)
# Algunos valores
tau = reader.get_autocorr_time()
burnin = int(2 * np.max(tau))
thin = int(0.5 * np.min(tau))
samples = reader.get_chain(discard=burnin, flat=True, thin=thin)
print(tau)
#%%
%matplotlib qt5
graficar_cadenas(reader,
labels = ['omega_m','beta','gamma','delta'])
#%%
#burnin=1500
#thin=50
graficar_contornos(reader,params_truths=sol,discard=burnin,thin=thin,
labels = ['omega_m','beta','gamma','delta'])
#%%
#Ojo, siempre muestra que convergio, aun cuando no
#plt.figure()
#graficar_taus_vs_n(reader,num_param=0,threshold=1000)
#graficar_taus_vs_n(reader,num_param=1,threshold=1000)
#%% Printeo los valores!
#thin=1
from IPython.display import display, Math
samples = reader.get_chain(discard=burnin, flat=True, thin=thin)
labels = ['omega_m','beta','gamma','delta']
len_chain,nwalkers,ndim=reader.get_chain().shape
print(len_chain)
for i in range(ndim):
mcmc = np.percentile(samples[:, i], [16, 50, 84])
mcmc[1]=sol[i] #Correción de mati: En vez de percentil 50 poner el mu
filename = "sample_HS_BAO_3params_taylor.h5"
reader = emcee.backends.HDFBackend(filename)
# Algunos valores
tau = reader.get_autocorr_time()
burnin = int(2 * np.max(tau))
thin = int(0.5 * np.min(tau))
samples = reader.get_chain(discard=burnin, flat=True, thin=thin)
print(tau)
#%%
%matplotlib qt5
graficar_cadenas(reader,
labels = ['omega_m','b','H0'])
#%%
burnin=50
graficar_contornos(reader,params_truths=sol,discard=burnin,#thin=thin,
labels = ['omega_m','b','H0'])
#%%
#Ojo, siempre muestra que convergio, aun cuando no
plt.figure()
graficar_taus_vs_n(reader,num_param=0,threshold=1000)
graficar_taus_vs_n(reader,num_param=1,threshold=1000)
#%% Printeo los valores!
from IPython.display import display, Math
samples = reader.get_chain(discard=burnin, flat=True, thin=thin)
labels = ['omega_m','b','H0']
len_chain,nwalkers,ndim=reader.get_chain().shape
print(len_chain)
for i in range(ndim):
mcmc = np.percentile(samples[:, i], [16, 50, 84])
mcmc[1]=sol[i] #Correción de mati: En vez de percentil 50 poner el mu
q = np.diff(mcmc)
tau = reader.get_autocorr_time()
burnin = int(2 * np.max(tau))
thin = int(0.5 * np.min(tau))
samples = reader.get_chain(discard=burnin, flat=True, thin=thin)
print(tau)
#%%
%matplotlib qt5
graficar_cadenas(reader,
labels = ['$\Omega_{m}$','b',"H0"],title='SN+CC+H0 HS (Taylor)')
#%%
burnin=300
#burnin = int(2 * np.max(tau))
#thin = int(0.5 * np.min(tau))
graficar_contornos(reader,params_truths=sol,discard=burnin,#thin=thin,
labels= ['$\Omega_{m}$','b',"H0"],
#title='SN+CC+H0',
#title='SN+CC',
#poster=True,color='r',
)
#%% Printeo los valores!
from IPython.display import display, Math
samples = reader.get_chain(discard=burnin, flat=True,thin=thin)
labels = ['\Omega_{m}','b', 'H_{0}']
len_chain,nwalkers,ndim=reader.get_chain().shape
for i in range(ndim):
mcmc = np.percentile(samples[:, i], [16, 50, 84])
mcmc[1]=sol[i] #Correción de mati: En vez de percentil 50 poner el mu
q = np.diff(mcmc)
txt = "\mathrm{{{3}}} = {0:.3f}_{{-{1:.3f}}}^{{{2:.3f}}}"
txt = txt.format(mcmc[1], q[0], q[1], labels[i])
display(Math(txt))
#%%
tau = reader.get_autocorr_time()
burnin = int(2 * np.max(tau))
thin = int(0.5 * np.min(tau))
samples = reader.get_chain(discard=burnin, flat=True, thin=thin)
print(tau)
#%%
%matplotlib qt5
graficar_cadenas(reader,
labels = ['$M_{abs}$','$alpha$','$beta$','$\gamma$'],title='SN HS')
#%%
burnin=100
#burnin = int(2 * np.max(tau))
#thin = int(0.5 * np.min(tau))
graficar_contornos(reader,params_truths=sol,discard=burnin,
#thin=thin,
labels = ['$M_{abs}$','$alpha$','$beta$','$\gamma$'],
#title='SN+CC HS',
poster=False)
#%% Printeo los valores!
from IPython.display import display, Math
samples = reader.get_chain(discard=burnin, flat=True, thin=thin)
labels = ['M_{abs}','$alpha$','$beta$','$\gamma$']
len_chain,nwalkers,ndim=reader.get_chain().shape
for i in range(ndim):
mcmc = np.percentile(samples[:, i], [16, 50, 84])
mcmc[1]=sol[i] #Correción de mati: En vez de percentil 50 poner el mu
q = np.diff(mcmc)
txt = "\mathrm{{{3}}} = {0:.3f}_{{-{1:.3f}}}^{{{2:.3f}}}"
txt = txt.format(mcmc[1], q[0], q[1], labels[i])
tau = reader.get_autocorr_time()
burnin = int(2 * np.max(tau))
thin = int(0.5 * np.min(tau))
samples = reader.get_chain(discard=burnin, flat=True, thin=thin)
print(tau)
#%%
%matplotlib qt5
graficar_cadenas(reader,
labels = ['$M_{abs}$','$\Omega_{m}$','b'],title='SN+CC HS (Taylor)')
#%%
#burnin=300
burnin = int(2 * np.max(tau))
thin = int(0.5 * np.min(tau))
graficar_contornos(reader,params_truths=sol,discard=burnin,
#,thin=thin
labels= ['$M_{abs}$','$\Omega_{m}$','b']
#,title='SN+CC HS (Taylor)'
,poster=True)
#%% Printeo los valores!
from IPython.display import display, Math
samples = reader.get_chain(discard=burnin, flat=True, thin=thin)
labels = ['M_{abs}','\Omega_{m}','b']
len_chain,nwalkers,ndim=reader.get_chain().shape
for i in range(ndim):
mcmc = np.percentile(samples[:, i], [16, 50, 84])
mcmc[1]=sol[i] #Correción de mati: En vez de percentil 50 poner el mu
q = np.diff(mcmc)
txt = "\mathrm{{{3}}} = {0:.3f}_{{-{1:.3f}}}^{{{2:.3f}}}"
txt = txt.format(mcmc[1], q[0], q[1], labels[i])
filename = "sample_HS_BAO_2params_taylor.h5"
reader = emcee.backends.HDFBackend(filename)
# Algunos valores
tau = reader.get_autocorr_time()
burnin = int(2 * np.max(tau))
thin = int(0.5 * np.min(tau))
samples = reader.get_chain(discard=burnin, flat=True, thin=thin)
print(tau)
#%%
%matplotlib qt5
graficar_cadenas(reader,
labels = ['$\Omega_{m}$','b'],title='BAO HS (Taylor)')
#%%
burnin=100
graficar_contornos(reader,params_truths=sol,discard=burnin,#thin=thin,
labels = ['$\Omega_{m}$','b'],title='BAO HS (Taylor)')
#%%
#Ojo, siempre muestra que convergio, aun cuando no
plt.figure()
graficar_taus_vs_n(reader,num_param=0,threshold=1000)
graficar_taus_vs_n(reader,num_param=1,threshold=1000)
#%% Printeo los valores!
from IPython.display import display, Math
samples = reader.get_chain(discard=burnin, flat=True, thin=thin)
labels = ['omega_m','b']
len_chain,nwalkers,ndim=reader.get_chain().shape
print(len_chain)
for i in range(ndim):
mcmc = np.percentile(samples[:, i], [16, 50, 84])
mcmc[1]=sol[i] #Correción de mati: En vez de percentil 50 poner el mu
q = np.diff(mcmc)
