Skip to content
Snippets Groups Projects
Commit 7ff41448 authored by Rayne Liu's avatar Rayne Liu
Browse files

Saved the covariance and correlation matrices as well as the logevidence,... 

Saved the covariance and correlation matrices as well as the logevidence, loosened parameter ranges, suppressed warning
parent aadc12f6
Branches
No related tags found
No related merge requests found
Show whitespace changes
Inline Side-by-side
code/RDGW150914_ptemcee1.py
+ 36
− 12
View file @ 7ff41448
#!/usr/bin/env python
# coding: utf-8
import warnings
warnings.simplefilter("ignore") #Used to suppress the RuntimeWarnings. But if you are changing the code for TESTING, MAKE SURE TO COMMENT IT OUT.
'''
This script calculates the RDGW150914 constraints with ptemcee - specifically, the n=1 case both varying or not varying the fundamental frequencies. It produces the chain plot, corner plot, parameter constraints, and data plotted with the 1-sigma band. Since we are working specifically for the n=1 case, we also add in the corner plot the combined chain of alpha_0 and alpha_1, in order to demonstrate that the two are indistinguishable with each other.
......@@ -39,11 +41,11 @@ tshift=3.6
vary_fund = True
#sampler parameters
npoints=60002
npoints=1002
nwalkers = 42
ntemps=12
ndim = int(4*(nmax+1))
burnin = 5000 #How many points do you burn before doing the corner plot. You need to watch the convergence of the chain plot a bit.
burnin = 500 #How many points do you burn before doing the corner plot. You need to watch the convergence of the chain plot a bit.
#This is trivial but often forgotten: this cannot be more than npoints! Usually 1/5~1/4 npoints is what I observe.
numbins = 42 #corner plot parameter - how many bins you want
datacolor = '#105670' #'#4fa3a7'
......@@ -137,15 +139,15 @@ def log_prior(theta):
y_0 = theta[6]
y_1 = theta[7]
if all([nmax == 1, 0 <= tshift <= 5, vary_fund == True, -0.8 <= a_0 <= 0.8, -1.0 <= a_1 <= 1.0, -1.0 <= b_0 <= 9.0, -1.0 <= b_1 <= 21.0, 0 <= x_0 <= 1.6, 0 <= x_1 <= 1.6, 0 <= y_0 <= 2*np.pi, 0 <= y_1 <= 2*np.pi]):
if all([nmax == 1, 0 <= tshift <= 5, vary_fund == True, -0.8 <= a_0 <= 0.8, -1.0 <= a_1 <= 1.0, -2.0 <= b_0 <= 9.0, -2.0 <= b_1 <= 21.0, 0 <= x_0 <= 1.6, 0 <= x_1 <= 1.6, 0 <= y_0 <= 2*np.pi, 0 <= y_1 <= 2*np.pi]):
return 0.0
elif all([nmax == 1, tshift == 19, vary_fund == True, -10.0 <= a_0 <= 10.0, -10.0 <= a_1 <= 10.0, -1.0 <= b_0 <= 10.0, -1.0 <= b_1 <= 10.0, 0 <= x_0 <= 1.0, 0 <= x_1 <= 1.2, 0 <= y_0 <= 2*np.pi, 0 <= y_1 <= 2*np.pi]):
elif all([nmax == 1, tshift == 19, vary_fund == True, -10.0 <= a_0 <= 10.0, -10.0 <= a_1 <= 10.0, -2.0 <= b_0 <= 10.0, -2.0 <= b_1 <= 10.0, 0 <= x_0 <= 1.0, 0 <= x_1 <= 1.2, 0 <= y_0 <= 2*np.pi, 0 <= y_1 <= 2*np.pi]):
return 0.0
#PAY EXTRA ATTENTION TO THESE TWO CASES, SINCE WE SHIFTED y_0. THE CORNER PLOTS LABELS NEED TO BE TREATED WITH CARE.
elif all([nmax == 1, 0 <= tshift <= 5, vary_fund == False, -1.0 <= a_0 <= 1.0, -1.5 <= a_1 <= 1.5, -1.0 <= b_0 <= 1.0, -1.0 <= b_1 <= 12.0, 0 <= x_0 <= 2.0, 0 <= x_1 <= 2.4, 0 <= y_0-np.pi <= 2*np.pi, 0 <= y_1 <= 2*np.pi,]):
elif all([nmax == 1, 0 <= tshift <= 5, vary_fund == False, -1.0 <= a_0 <= 1.0, -1.5 <= a_1 <= 1.5, -1.0 <= b_0 <= 1.0, -3.0 <= b_1 <= 12.0, 0 <= x_0 <= 2.0, 0 <= x_1 <= 2.4, 0 <= y_0-np.pi <= 2*np.pi, 0 <= y_1 <= 2*np.pi,]):
return 0.0
elif all([nmax == 1, tshift == 19, vary_fund == False, -1.0 <= a_0 <= 1.0, -10.0 <= a_1 <= 10.0, -1.0 <= b_0 <= 1.0, -1.0 <= b_1 <= 10.0, 0 <= x_0 <= 0.6, 0 <= x_1 <= 1.2, 0 <= y_0-np.pi <= 2*np.pi, 0 <= y_1 <= 2*np.pi]):
elif all([nmax == 1, tshift == 19, vary_fund == False, -1.0 <= a_0 <= 1.0, -10.0 <= a_1 <= 10.0, -1.0 <= b_0 <= 1.0, -3.0 <= b_1 <= 10.0, 0 <= x_0 <= 0.6, 0 <= x_1 <= 1.2, 0 <= y_0-np.pi <= 2*np.pi, 0 <= y_1 <= 2*np.pi]):
return 0.0
return -np.inf
......@@ -154,8 +156,10 @@ def log_prior(theta):
# LogLikelihood function. It is just a Gaussian loglikelihood based on computing the residuals^2
def log_likelihood(theta):
modelev = model_dv(theta)
return -np.sum((gw_sxs_bbh_0305rd[:,1] - (modelev.real))**2+(gw_sxs_bbh_0305rd[:,2] - (modelev.imag))**2)
result = -np.sum((gw_sxs_bbh_0305rd[:,1] - (modelev.real))**2+(gw_sxs_bbh_0305rd[:,2] - (modelev.imag))**2)
if np.isnan(result):
return -np.inf
return result
# Logposterior distribution for the residuals case.
# The evidence is just a normalization factor
......@@ -172,9 +176,10 @@ def log_probability(theta):
#Fit with ptemcee
#Set the number of cores of your processors
pool = choose_pool(4)
pool.size = 4
pool = choose_pool(12)
pool.size = 12
vary_param = float(vary_fund)
np.random.seed(42)
pos = np.array([[random.uniform(-0.1,0.1), random.uniform(-0.1,0.1), 4.28313743e-01, random.uniform(2.5, 2.6) + (1-vary_param) * np.pi]])
for i in range (1,nmax+1):
pos_aux = np.array([[random.uniform(-0.1,0.1), random.uniform(-0.1,0.1), random.uniform(0.3,0.4), random.uniform(2.01, 2.02) + (1-vary_param) * np.pi]])
......@@ -255,11 +260,30 @@ for yi in range(naxes):
figcorn.savefig(rootpath+'/git/rdstackingproject/plotsmc/vary'+str(vary_fund)+'nmax='+str(nmax)+'_tshift='+str(tshift)+'_'+str(npoints)+'pt_corner.pdf', format = 'pdf')
#Now export the covariance and correlation matrices, as well as the logevidence
#The covariant matrix
samplesT = samples.T
cov_matr=np.cov(samplesT)
#print('Covariant matrix')
df1 = pd.DataFrame(cov_matr, index = paramlabels, columns=paramlabels)
df1.to_hdf(rootpath+'/git/rdstackingproject/plotsmc/vary'+str(vary_fund)+'nmax='+str(nmax)+'_tshift='+str(tshift)+'_'+str(npoints)+'pt_covariance.h5', key='df')
#The correlation matrix
corr_matr=np.corrcoef(samplesT)
#print('Correlation matrix')
df2 = pd.DataFrame(corr_matr,index = paramlabels, columns=paramlabels)
df2.to_hdf(rootpath+'/git/rdstackingproject/plotsmc/vary'+str(vary_fund)+'nmax='+str(nmax)+'_tshift='+str(tshift)+'_'+str(npoints)+'pt_correlation.h5', key='df')
#The logevidence (with error) and save it
logevidence = sampler.log_evidence_estimate(fburnin=0.2)
df3 = pd.DataFrame(logevidence,index = [r'logevidence', r'error'], columns=[r'vary='+str(vary_fund)+', nmax='+str(nmax)+', tshift='+str(tshift)+', '+str(npoints)+'pts'])
df3.to_hdf(rootpath+'/git/rdstackingproject/plotsmc/vary'+str(vary_fund)+'nmax='+str(nmax)+'_tshift='+str(tshift)+'_'+str(npoints)+'pt_logevidence.h5', key='df')
#Now plot alpha_0 on top of alpha_1 - only on top, not stacked, and only valid for vary_fund == True
if vary_fund == True:
samplea0 = samples.T[0]
samplea1 = samples.T[1]
samplea0 = samplesT[0]
samplea1 = samplesT[1]
fighist1 = plt.figure(figsize = (8, 6))
n0, bins0, patches0 = plt.hist(samplea0, bins = numbins * 6, alpha = 0.5, label = r'$\alpha_0$')
n1, bins1, patches1 = plt.hist(samplea1, bins = numbins * 10, alpha = 0.5, label = r'$\alpha_1$')
......
code/RDGW150914_ptemcee2.py
+ 30
− 7
View file @ 7ff41448
#!/usr/bin/env python
# coding: utf-8
import warnings
warnings.simplefilter("ignore") #Used to suppress the RuntimeWarnings. But if you are changing the code for TESTING, MAKE SURE TO COMMENT IT OUT.
'''
This script calculates the RDGW150914 constraints with ptemcee - specifically, the n=1 case both varying or not varying the fundamental frequencies. It produces the chain plot, corner plot, parameter constraints, and data plotted with the 1-sigma band. Since we are working specifically for the n=1 case, we also add in the corner plot the combined chain of alpha_0 and alpha_1, in order to demonstrate that the two are indistinguishable with each other.
......@@ -40,7 +42,7 @@ vary_fund = True
#sampler parameters
npoints=60001
nwalkers = 42
nwalkers = 840
ntemps=12
ndim = int(4*(nmax+1))
burnin = 5000 #How many points do you burn before doing the corner plot. You need to watch the convergence of the chain plot a bit.
......@@ -137,15 +139,15 @@ def log_prior(theta):
y_0 = theta[6]
y_1 = theta[7]
if all([nmax == 1, 0 <= tshift <= 5, vary_fund == True, -0.8 <= a_0 <= 0.8, -1.0 <= a_1 <= 1.0, -1.0 <= b_0 <= 9.0, -1.0 <= b_1 <= 21.0, 0 <= x_0 <= 1.6, 0 <= x_1 <= 1.6, 0 <= y_0 <= 2*np.pi, 0 <= y_1 <= 2*np.pi]):
if all([nmax == 1, 0 <= tshift <= 5, vary_fund == True, -0.8 <= a_0 <= 0.8, -1.0 <= a_1 <= 1.0, -2.0 <= b_0 <= 9.0, -2.0 <= b_1 <= 21.0, 0 <= x_0 <= 1.6, 0 <= x_1 <= 1.6, 0 <= y_0 <= 2*np.pi, 0 <= y_1 <= 2*np.pi]):
return 0.0
elif all([nmax == 1, tshift == 19, vary_fund == True, -10.0 <= a_0 <= 10.0, -10.0 <= a_1 <= 10.0, -1.0 <= b_0 <= 10.0, -1.0 <= b_1 <= 10.0, 0 <= x_0 <= 1.0, 0 <= x_1 <= 1.2, 0 <= y_0 <= 2*np.pi, 0 <= y_1 <= 2*np.pi]):
elif all([nmax == 1, tshift == 19, vary_fund == True, -10.0 <= a_0 <= 10.0, -10.0 <= a_1 <= 10.0, -2.0 <= b_0 <= 10.0, -2.0 <= b_1 <= 10.0, 0 <= x_0 <= 1.0, 0 <= x_1 <= 1.2, 0 <= y_0 <= 2*np.pi, 0 <= y_1 <= 2*np.pi]):
return 0.0
#PAY EXTRA ATTENTION TO THESE TWO CASES, SINCE WE SHIFTED y_0. THE CORNER PLOTS LABELS NEED TO BE TREATED WITH CARE.
elif all([nmax == 1, 0 <= tshift <= 5, vary_fund == False, -1.0 <= a_0 <= 1.0, -1.5 <= a_1 <= 1.5, -1.0 <= b_0 <= 1.0, -1.0 <= b_1 <= 12.0, 0 <= x_0 <= 2.0, 0 <= x_1 <= 2.4, 0 <= y_0-np.pi <= 2*np.pi, 0 <= y_1 <= 2*np.pi,]):
elif all([nmax == 1, 0 <= tshift <= 5, vary_fund == False, -1.0 <= a_0 <= 1.0, -1.5 <= a_1 <= 1.5, -1.0 <= b_0 <= 1.0, -3.0 <= b_1 <= 12.0, 0 <= x_0 <= 2.0, 0 <= x_1 <= 2.4, 0 <= y_0-np.pi <= 2*np.pi, 0 <= y_1 <= 2*np.pi,]):
return 0.0
elif all([nmax == 1, tshift == 19, vary_fund == False, -1.0 <= a_0 <= 1.0, -10.0 <= a_1 <= 10.0, -1.0 <= b_0 <= 1.0, -1.0 <= b_1 <= 10.0, 0 <= x_0 <= 0.6, 0 <= x_1 <= 1.2, 0 <= y_0-np.pi <= 2*np.pi, 0 <= y_1 <= 2*np.pi]):
elif all([nmax == 1, tshift == 19, vary_fund == False, -1.0 <= a_0 <= 1.0, -10.0 <= a_1 <= 10.0, -1.0 <= b_0 <= 1.0, -3.0 <= b_1 <= 10.0, 0 <= x_0 <= 0.6, 0 <= x_1 <= 1.2, 0 <= y_0-np.pi <= 2*np.pi, 0 <= y_1 <= 2*np.pi]):
return 0.0
return -np.inf
......@@ -172,9 +174,10 @@ def log_probability(theta):
#Fit with ptemcee
#Set the number of cores of your processors
pool = choose_pool(4)
pool.size = 4
pool = choose_pool(12)
pool.size = 12
vary_param = float(vary_fund)
np.random.seed(42)
pos = np.array([[random.uniform(-0.1,0.1), random.uniform(-0.1,0.1), 4.28313743e-01, random.uniform(2.5, 2.6) + (1-vary_param) * np.pi]])
for i in range (1,nmax+1):
pos_aux = np.array([[random.uniform(-0.1,0.1), random.uniform(-0.1,0.1), random.uniform(0.3,0.4), random.uniform(2.01, 2.02) + (1-vary_param) * np.pi]])
......@@ -256,6 +259,26 @@ for yi in range(naxes):
figcorn.savefig(rootpath+'/git/rdstackingproject/plotsmc/vary'+str(vary_fund)+'nmax='+str(nmax)+'_tshift='+str(tshift)+'_'+str(npoints)+'pt_corner.pdf', format = 'pdf')
#Now export the covariance and correlation matrices, as well as the logevidence
#The covariant matrix
samplesT = samples.T
cov_matr=np.cov(samplesT)
#print('Covariant matrix')
df1 = pd.DataFrame(cov_matr, index = paramlabels, columns=paramlabels)
df1.to_hdf(rootpath+'/git/rdstackingproject/plotsmc/vary'+str(vary_fund)+'nmax='+str(nmax)+'_tshift='+str(tshift)+'_'+str(npoints)+'pt_covariance.h5', key='df')
#The correlation matrix
corr_matr=np.corrcoef(samplesT)
#print('Correlation matrix')
df2 = pd.DataFrame(corr_matr,index = paramlabels, columns=paramlabels)
df2.to_hdf(rootpath+'/git/rdstackingproject/plotsmc/vary'+str(vary_fund)+'nmax='+str(nmax)+'_tshift='+str(tshift)+'_'+str(npoints)+'pt_correlation.h5', key='df')
#The logevidence (with error) and save it
logevidence = sampler.log_evidence_estimate(fburnin=0.2)
df3 = pd.DataFrame(logevidence,index = [r'logevidence', r'error'], columns=[r'vary='+str(vary_fund)+', nmax='+str(nmax)+', tshift='+str(tshift)+', '+str(npoints)+'pts'])
df3.to_hdf(rootpath+'/git/rdstackingproject/plotsmc/vary'+str(vary_fund)+'nmax='+str(nmax)+'_tshift='+str(tshift)+'_'+str(npoints)+'pt_logevidence.h5', key='df')
#Now plot alpha_0 on top of alpha_1 - only on top, not stacked, and only valid for vary_fund == True
if vary_fund == True:
......
code/RDGW150914_ptemcee3.py
+ 30
− 7
View file @ 7ff41448
#!/usr/bin/env python
# coding: utf-8
import warnings
warnings.simplefilter("ignore") #Used to suppress the RuntimeWarnings. But if you are changing the code for TESTING, MAKE SURE TO COMMENT IT OUT.
'''
This script calculates the RDGW150914 constraints with ptemcee - specifically, the n=1 case both varying or not varying the fundamental frequencies. It produces the chain plot, corner plot, parameter constraints, and data plotted with the 1-sigma band. Since we are working specifically for the n=1 case, we also add in the corner plot the combined chain of alpha_0 and alpha_1, in order to demonstrate that the two are indistinguishable with each other.
......@@ -40,7 +43,7 @@ vary_fund = False
#sampler parameters
npoints=60000
nwalkers = 42
nwalkers = 840
ntemps=12
ndim = int(4*(nmax+1))
burnin = 5000 #How many points do you burn before doing the corner plot. You need to watch the convergence of the chain plot a bit.
......@@ -137,15 +140,15 @@ def log_prior(theta):
y_0 = theta[6]
y_1 = theta[7]
if all([nmax == 1, 0 <= tshift <= 5, vary_fund == True, -0.8 <= a_0 <= 0.8, -1.0 <= a_1 <= 1.0, -1.0 <= b_0 <= 9.0, -1.0 <= b_1 <= 21.0, 0 <= x_0 <= 1.6, 0 <= x_1 <= 1.6, 0 <= y_0 <= 2*np.pi, 0 <= y_1 <= 2*np.pi]):
if all([nmax == 1, 0 <= tshift <= 5, vary_fund == True, -0.8 <= a_0 <= 0.8, -1.0 <= a_1 <= 1.0, -2.0 <= b_0 <= 9.0, -2.0 <= b_1 <= 21.0, 0 <= x_0 <= 1.6, 0 <= x_1 <= 1.6, 0 <= y_0 <= 2*np.pi, 0 <= y_1 <= 2*np.pi]):
return 0.0
elif all([nmax == 1, tshift == 19, vary_fund == True, -10.0 <= a_0 <= 10.0, -10.0 <= a_1 <= 10.0, -1.0 <= b_0 <= 10.0, -1.0 <= b_1 <= 10.0, 0 <= x_0 <= 1.0, 0 <= x_1 <= 1.2, 0 <= y_0 <= 2*np.pi, 0 <= y_1 <= 2*np.pi]):
elif all([nmax == 1, tshift == 19, vary_fund == True, -10.0 <= a_0 <= 10.0, -10.0 <= a_1 <= 10.0, -2.0 <= b_0 <= 10.0, -2.0 <= b_1 <= 10.0, 0 <= x_0 <= 1.0, 0 <= x_1 <= 1.2, 0 <= y_0 <= 2*np.pi, 0 <= y_1 <= 2*np.pi]):
return 0.0
#PAY EXTRA ATTENTION TO THESE TWO CASES, SINCE WE SHIFTED y_0. THE CORNER PLOTS LABELS NEED TO BE TREATED WITH CARE.
elif all([nmax == 1, 0 <= tshift <= 5, vary_fund == False, -1.0 <= a_0 <= 1.0, -1.5 <= a_1 <= 1.5, -1.0 <= b_0 <= 1.0, -1.0 <= b_1 <= 12.0, 0 <= x_0 <= 2.0, 0 <= x_1 <= 2.4, 0 <= y_0-np.pi <= 2*np.pi, 0 <= y_1 <= 2*np.pi,]):
elif all([nmax == 1, 0 <= tshift <= 5, vary_fund == False, -1.0 <= a_0 <= 1.0, -1.5 <= a_1 <= 1.5, -1.0 <= b_0 <= 1.0, -3.0 <= b_1 <= 12.0, 0 <= x_0 <= 2.0, 0 <= x_1 <= 2.4, 0 <= y_0-np.pi <= 2*np.pi, 0 <= y_1 <= 2*np.pi,]):
return 0.0
elif all([nmax == 1, tshift == 19, vary_fund == False, -1.0 <= a_0 <= 1.0, -10.0 <= a_1 <= 10.0, -1.0 <= b_0 <= 1.0, -1.0 <= b_1 <= 10.0, 0 <= x_0 <= 0.6, 0 <= x_1 <= 1.2, 0 <= y_0-np.pi <= 2*np.pi, 0 <= y_1 <= 2*np.pi]):
elif all([nmax == 1, tshift == 19, vary_fund == False, -1.0 <= a_0 <= 1.0, -10.0 <= a_1 <= 10.0, -1.0 <= b_0 <= 1.0, -3.0 <= b_1 <= 10.0, 0 <= x_0 <= 0.6, 0 <= x_1 <= 1.2, 0 <= y_0-np.pi <= 2*np.pi, 0 <= y_1 <= 2*np.pi]):
return 0.0
return -np.inf
......@@ -172,9 +175,10 @@ def log_probability(theta):
#Fit with ptemcee
#Set the number of cores of your processors
pool = choose_pool(6)
pool.size = 6
pool = choose_pool(12)
pool.size = 12
vary_param = float(vary_fund)
np.random.seed(42)
pos = np.array([[random.uniform(-0.1,0.1), random.uniform(-0.1,0.1), 4.28313743e-01, random.uniform(2.5, 2.6) + (1-vary_param) * np.pi]])
for i in range (1,nmax+1):
pos_aux = np.array([[random.uniform(-0.1,0.1), random.uniform(-0.1,0.1), random.uniform(0.3,0.4), random.uniform(2.01, 2.02) + (1-vary_param) * np.pi]])
......@@ -263,6 +267,25 @@ for yi in range(naxes):
figcorn.savefig(rootpath+'/git/rdstackingproject/plotsmc/vary'+str(vary_fund)+'nmax='+str(nmax)+'_tshift='+str(tshift)+'_'+str(npoints)+'pt_corner.pdf', format = 'pdf')
#Now export the covariance and correlation matrices, as well as the logevidence
#The covariant matrix
samplesT = samples.T
cov_matr=np.cov(samplesT)
#print('Covariant matrix')
df1 = pd.DataFrame(cov_matr, index = paramlabels, columns=paramlabels)
df1.to_hdf(rootpath+'/git/rdstackingproject/plotsmc/vary'+str(vary_fund)+'nmax='+str(nmax)+'_tshift='+str(tshift)+'_'+str(npoints)+'pt_covariance.h5', key='df')
#The correlation matrix
corr_matr=np.corrcoef(samplesT)
#print('Correlation matrix')
df2 = pd.DataFrame(corr_matr,index = paramlabels, columns=paramlabels)
df2.to_hdf(rootpath+'/git/rdstackingproject/plotsmc/vary'+str(vary_fund)+'nmax='+str(nmax)+'_tshift='+str(tshift)+'_'+str(npoints)+'pt_correlation.h5', key='df')
#The logevidence (with error) and save it
logevidence = sampler.log_evidence_estimate(fburnin=0.2)
df3 = pd.DataFrame(logevidence,index = [r'logevidence', r'error'], columns=[r'vary='+str(vary_fund)+', nmax='+str(nmax)+', tshift='+str(tshift)+', '+str(npoints)+'pts'])
df3.to_hdf(rootpath+'/git/rdstackingproject/plotsmc/vary'+str(vary_fund)+'nmax='+str(nmax)+'_tshift='+str(tshift)+'_'+str(npoints)+'pt_logevidence.h5', key='df')
#Now plot alpha_0 on top of alpha_1 - only on top, not stacked, and only valid for vary_fund == True
if vary_fund == True:
......
code/RDGW150914_ptemcee4.py
+ 30
− 7
View file @ 7ff41448
#!/usr/bin/env python
# coding: utf-8
import warnings
warnings.simplefilter("ignore") #Used to suppress the RuntimeWarnings. But if you are changing the code for TESTING, MAKE SURE TO COMMENT IT OUT.
'''
This script calculates the RDGW150914 constraints with ptemcee - specifically, the n=1 case both varying or not varying the fundamental frequencies. It produces the chain plot, corner plot, parameter constraints, and data plotted with the 1-sigma band. Since we are working specifically for the n=1 case, we also add in the corner plot the combined chain of alpha_0 and alpha_1, in order to demonstrate that the two are indistinguishable with each other.
......@@ -40,7 +43,7 @@ vary_fund = False
#sampler parameters
npoints=1002
nwalkers = 42
nwalkers = 840
ntemps=12
ndim = int(4*(nmax+1))
burnin = 20 #How many points do you burn before doing the corner plot. You need to watch the convergence of the chain plot a bit.
......@@ -137,15 +140,15 @@ def log_prior(theta):
y_0 = theta[6]
y_1 = theta[7]
if all([nmax == 1, 0 <= tshift <= 5, vary_fund == True, -0.8 <= a_0 <= 0.8, -1.0 <= a_1 <= 1.0, -1.0 <= b_0 <= 9.0, -1.0 <= b_1 <= 21.0, 0 <= x_0 <= 1.6, 0 <= x_1 <= 1.6, 0 <= y_0 <= 2*np.pi, 0 <= y_1 <= 2*np.pi]):
if all([nmax == 1, 0 <= tshift <= 5, vary_fund == True, -0.8 <= a_0 <= 0.8, -1.0 <= a_1 <= 1.0, -2.0 <= b_0 <= 9.0, -2.0 <= b_1 <= 21.0, 0 <= x_0 <= 1.6, 0 <= x_1 <= 1.6, 0 <= y_0 <= 2*np.pi, 0 <= y_1 <= 2*np.pi]):
return 0.0
elif all([nmax == 1, tshift == 19, vary_fund == True, -10.0 <= a_0 <= 10.0, -10.0 <= a_1 <= 10.0, -1.0 <= b_0 <= 10.0, -1.0 <= b_1 <= 10.0, 0 <= x_0 <= 1.0, 0 <= x_1 <= 1.2, 0 <= y_0 <= 2*np.pi, 0 <= y_1 <= 2*np.pi]):
elif all([nmax == 1, tshift == 19, vary_fund == True, -10.0 <= a_0 <= 10.0, -10.0 <= a_1 <= 10.0, -2.0 <= b_0 <= 10.0, -2.0 <= b_1 <= 10.0, 0 <= x_0 <= 1.0, 0 <= x_1 <= 1.2, 0 <= y_0 <= 2*np.pi, 0 <= y_1 <= 2*np.pi]):
return 0.0
#PAY EXTRA ATTENTION TO THESE TWO CASES, SINCE WE SHIFTED y_0. THE CORNER PLOTS LABELS NEED TO BE TREATED WITH CARE.
elif all([nmax == 1, 0 <= tshift <= 5, vary_fund == False, -1.0 <= a_0 <= 1.0, -1.5 <= a_1 <= 1.5, -1.0 <= b_0 <= 1.0, -1.0 <= b_1 <= 12.0, 0 <= x_0 <= 2.0, 0 <= x_1 <= 2.4, 0 <= y_0-np.pi <= 2*np.pi, 0 <= y_1 <= 2*np.pi,]):
elif all([nmax == 1, 0 <= tshift <= 5, vary_fund == False, -1.0 <= a_0 <= 1.0, -1.5 <= a_1 <= 1.5, -1.0 <= b_0 <= 1.0, -3.0 <= b_1 <= 12.0, 0 <= x_0 <= 2.0, 0 <= x_1 <= 2.4, 0 <= y_0-np.pi <= 2*np.pi, 0 <= y_1 <= 2*np.pi,]):
return 0.0
elif all([nmax == 1, tshift == 19, vary_fund == False, -1.0 <= a_0 <= 1.0, -10.0 <= a_1 <= 10.0, -1.0 <= b_0 <= 1.0, -1.0 <= b_1 <= 10.0, 0 <= x_0 <= 0.6, 0 <= x_1 <= 1.2, 0 <= y_0-np.pi <= 2*np.pi, 0 <= y_1 <= 2*np.pi]):
elif all([nmax == 1, tshift == 19, vary_fund == False, -1.0 <= a_0 <= 1.0, -10.0 <= a_1 <= 10.0, -1.0 <= b_0 <= 1.0, -3.0 <= b_1 <= 10.0, 0 <= x_0 <= 0.6, 0 <= x_1 <= 1.2, 0 <= y_0-np.pi <= 2*np.pi, 0 <= y_1 <= 2*np.pi]):
return 0.0
return -np.inf
......@@ -172,9 +175,10 @@ def log_probability(theta):
#Fit with ptemcee
#Set the number of cores of your processors
pool = choose_pool(6)
pool.size = 6
pool = choose_pool(12)
pool.size = 12
vary_param = float(vary_fund)
np.random.seed(42)
pos = np.array([[random.uniform(-0.1,0.1), random.uniform(-0.1,0.1), 4.28313743e-01, random.uniform(2.5, 2.6) + (1-vary_param) * np.pi]])
for i in range (1,nmax+1):
pos_aux = np.array([[random.uniform(-0.1,0.1), random.uniform(-0.1,0.1), random.uniform(0.3,0.4), random.uniform(2.01, 2.02) + (1-vary_param) * np.pi]])
......@@ -262,6 +266,25 @@ for yi in range(naxes):
figcorn.savefig(rootpath+'/git/rdstackingproject/plotsmc/vary'+str(vary_fund)+'nmax='+str(nmax)+'_tshift='+str(tshift)+'_'+str(npoints)+'pt_corner.pdf', format = 'pdf')
#Now export the covariance and correlation matrices, as well as the logevidence
#The covariant matrix
samplesT = samples.T
cov_matr=np.cov(samplesT)
#print('Covariant matrix')
df1 = pd.DataFrame(cov_matr, index = paramlabels, columns=paramlabels)
df1.to_hdf(rootpath+'/git/rdstackingproject/plotsmc/vary'+str(vary_fund)+'nmax='+str(nmax)+'_tshift='+str(tshift)+'_'+str(npoints)+'pt_covariance.h5', key='df')
#The correlation matrix
corr_matr=np.corrcoef(samplesT)
#print('Correlation matrix')
df2 = pd.DataFrame(corr_matr,index = paramlabels, columns=paramlabels)
df2.to_hdf(rootpath+'/git/rdstackingproject/plotsmc/vary'+str(vary_fund)+'nmax='+str(nmax)+'_tshift='+str(tshift)+'_'+str(npoints)+'pt_correlation.h5', key='df')
#The logevidence (with error) and save it
logevidence = sampler.log_evidence_estimate(fburnin=0.2)
df3 = pd.DataFrame(logevidence,index = [r'logevidence', r'error'], columns=[r'vary='+str(vary_fund)+', nmax='+str(nmax)+', tshift='+str(tshift)+', '+str(npoints)+'pts'])
df3.to_hdf(rootpath+'/git/rdstackingproject/plotsmc/vary'+str(vary_fund)+'nmax='+str(nmax)+'_tshift='+str(tshift)+'_'+str(npoints)+'pt_logevidence.h5', key='df')
#Now plot alpha_0 on top of alpha_1 - only on top, not stacked, and only valid for vary_fund == True
if vary_fund == True:
......
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Please register or to comment

AEI Privacy Policy Imprint