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generate_corner_plots.py
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generate_corner_plots.py
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import numpy as np
import matplotlib.pyplot as plt
plt.switch_backend('agg')
import model_list, models, fitting
import matplotlib.lines as mlines
import corner
import copy as cp
from utils import rj2cmb
plt.style.use('seaborn-colorblind')
#plt.rc('text', usetex=True)
#plt.rc('font', family='serif')
print "hello!"
mean_beta = 1.6
mean_temp = 20.
sigma_beta = .2
sigma_temp = 4.
pMBB_broad = model_list.prob1mbb_model
sMBB = model_list.dust_model
cmb = model_list.cmb_model
sync = model_list.sync_model
DUST_I = 50.
DUST_P = 5. / 1.41
amp_I=rj2cmb(353e9, DUST_I)
amp_Q=rj2cmb(353e9, DUST_P)
amp_U=rj2cmb(353e9, DUST_P)
pMBB_narrow = models.ProbSingleMBB(amp_I=rj2cmb(353e9, DUST_I),
amp_Q=rj2cmb(353e9, DUST_P),
amp_U=rj2cmb(353e9, DUST_P),
dust_beta=1.6, dust_T=20.,
sigma_beta=.1 * sigma_beta, sigma_temp=.1 * sigma_temp)
nu_pico = np.asarray([21,25,30, 36.0,43.2,51.8,62.2,74.6,89.6,
107.5,129.0,154.8,185.8,222.9,267.5,321.0,
385.2,462.2,554.7,665.6,798.7]) * 1e9
models_sMBB = [sMBB, cmb, sync]
models_pMBB_broad = [pMBB_broad, cmb, sync]
models_pMBB_narrow = [pMBB_narrow, cmb, sync]
def make_pnames(models_fit):
amp_names = []
param_names = []
for mod in models_fit:
# Parameter names
amp_names += ["%s_%s" % (mod.model, pol) for pol in "IQU"]
param_names += mod.param_names
return amp_names + param_names
pnames_sMBB = make_pnames(models_sMBB)
pnames_pMBB_broad = make_pnames(models_pMBB_broad)
pnames_pMBB_narrow = make_pnames(models_pMBB_narrow)
print pnames_sMBB
fsigma_T=1e3
fsigma_P=1.
beam_mat = np.identity(3*len(nu_pico)) # Beam model
# pvals set the model parameters
params_sMBB = [sMBB.amp_I, sMBB.amp_Q, sMBB.amp_U, cmb.amp_I, cmb.amp_Q, cmb.amp_U,
sync.amp_I, sync.amp_Q, sync.amp_U, sMBB.dust_beta, sMBB.dust_T,
sync.sync_beta]
params_pMBB_broad = [pMBB_broad.amp_I, pMBB_broad.amp_Q, pMBB_broad.amp_U, cmb.amp_I, cmb.amp_Q, cmb.amp_U,
sync.amp_I, sync.amp_Q, sync.amp_U, pMBB_broad.dust_beta, pMBB_broad.dust_T,
pMBB_broad.sigma_beta, pMBB_broad.sigma_temp, sync.sync_beta]
params_pMBB_narrow = [pMBB_narrow.amp_I, pMBB_narrow.amp_Q, pMBB_narrow.amp_U, cmb.amp_I, cmb.amp_Q, cmb.amp_U,
sync.amp_I, sync.amp_Q, sync.amp_U, pMBB_narrow.dust_beta, pMBB_narrow.dust_T,
pMBB_narrow.sigma_beta, pMBB_narrow.sigma_temp, sync.sync_beta]
initial_vals_sMBB = (amp_I, amp_Q, amp_U, cmb.amp_I, cmb.amp_Q, cmb.amp_U,
sync.amp_I, sync.amp_Q, sync.amp_U, mean_beta, mean_temp,
sync.sync_beta)
initial_vals_pMBB_broad = (amp_I, amp_Q, amp_U, cmb.amp_I, cmb.amp_Q, cmb.amp_U,
sync.amp_I, sync.amp_Q, sync.amp_U, mean_beta, mean_temp,
sigma_beta, sigma_temp, sync.sync_beta)
initial_vals_pMBB_narrow = (amp_I, amp_Q, amp_U, cmb.amp_I, cmb.amp_U, cmb.amp_Q,
sync.amp_I, sync.amp_Q, sync.amp_U,mean_beta, mean_temp,
.1 * sigma_beta, .1 * sigma_temp, sync.sync_beta)
parent_model = 'mbb'
D_vec_sMBB, Ninv = fitting.generate_data(nu_pico, fsigma_T, fsigma_P, [sMBB, cmb, sync],
noise_file="data/noise_pico.dat" )
D_vec_pMBB_broad, Ninv = fitting.generate_data(nu_pico, fsigma_T, fsigma_P, [pMBB_broad, cmb, sync],
noise_file="data/noise_pico.dat")
D_vec_pMBB_narrow, Ninv = fitting.generate_data(nu_pico, fsigma_T, fsigma_P, [pMBB_narrow, cmb, sync],
noise_file="data/noise_pico.dat")
data_spec_sMBB = (nu_pico, D_vec_sMBB, Ninv, beam_mat)
data_spec_pMBB_broad = (nu_pico, D_vec_pMBB_broad, Ninv, beam_mat)
data_spec_pMBB_narrow = (nu_pico, D_vec_pMBB_narrow, Ninv, beam_mat)
p_spec_sMBB = (pnames_sMBB, initial_vals_sMBB, parent_model)
p_spec_pMBB_broad = (pnames_pMBB_broad, initial_vals_pMBB_broad, parent_model)
p_spec_pMBB_narrow = (pnames_pMBB_narrow, initial_vals_pMBB_narrow, parent_model)
print "running emcee"
mcmc_sMBB = fitting.joint_mcmc(data_spec_sMBB, models_sMBB, p_spec_sMBB, nwalkers=30,
burn=5000, steps=10000, nthreads=8, sample_file=None)
mcmc_pMBB_broad = fitting.joint_mcmc(data_spec_pMBB_broad, models_sMBB, p_spec_sMBB, nwalkers=30,
burn=5000, steps=10000, nthreads=8, sample_file=None)
mcmc_pMBB_narrow = fitting.joint_mcmc(data_spec_pMBB_narrow, models_sMBB, p_spec_sMBB, nwalkers=30,
burn=5000, steps=10000, nthreads=8, sample_file=None)
ax1 = corner.corner(mcmc_sMBB[1].T, labels=pnames_sMBB,
truths=initial_vals_sMBB, plot_datapoints=False)
ax2 = corner.corner(mcmc_pMBB_broad[1].T, labels=pnames_sMBB,
truths=initial_vals_sMBB, plot_datapoints=False)
ax3 = corner.corner(mcmc_pMBB_narrow[1].T, labels=pnames_sMBB],
truths=initial_vals_sMBB, plot_datapoints=False)
ax1.savefig('sMBB2sMBB.pdf')
ax2.savefig('pMBB_broad2sMBB.pdf')
ax3.savefig('pMBB2_narrowsMBB.pdf')
np.save('mcmc_sMBB', mcmc_sMBB)
np.save('mcmc_pMBB_broad', mcmc_pMBB_broad)
np.save('mcmc_pMBB_narrow', mcmc_pMBB_narrow)