def get_named_limit(chain_name, perc, burns=0, names=[]):
mcmc_file = fFITS(chain_name)
h = fread_header(chain_name, 'MCMC')
nwalkers = h['NWALKERS']
if names == []:
names = mcmc_file[1].get_colnames()
names = names[1:]
labels = [l.replace('_', ' ') for l in names]
new_labels = [l.replace('_', ' ') for l in names]
data = []
mod = []
perc_list = []
frac_1sigma = 100
for i in range(0, len(names)):
name = names[i]
d = mcmc_file[1][name][burns * nwalkers:]
lp = mcmc_file[1]['lp'][burns * nwalkers:]
mask = np.isfinite(lp)
d = d[mask]
d = d[np.isfinite(d)]
percs = np.percentile(d, [perc])
sigma1 = hpd(d, 0.6827)
perc_list += [percs]
perc_list = np.array(perc_list)
return perc_list
def resume_file(chain_name, ndim, nwalkers):
with fFITS(chain_name, 'r') as fits:
length = fits['MCMC']._info['nrows']
p0 = fits['MCMC']['x'][length - nwalkers:length]
return p0, length
def get_highest_likelihood(chain_name, burns=0, names=[], use_hpd=False):
mcmc_file = fFITS(chain_name)
h = fread_header(chain_name, 'MCMC')
nwalkers = h['NWALKERS']
if names == []:
names = mcmc_file[1].get_colnames()
names = names[1:]
labels = [l.replace('_', ' ') for l in names]
new_labels = [l.replace('_', ' ') for l in names]
data = []
mod = []
chain_list = []
for i in range(0, len(names)):
name = names[i]
d = mcmc_file[1][name][burns * nwalkers:]
lp = mcmc_file[1]['lp'][burns * nwalkers:]
mask = np.isfinite(lp)
d = d[mask]
d = d[np.isfinite(d)]
chain_list += [d]
chain_list = np.array(chain_list)
return chain_list
def load_chain(chain_name, maxlength=1e6):
#keeping this seperate for now in case I want to implement burning or something.
with fFITS(chain_name, 'r') as fits:
length = fits['MCMC']._info['nrows']
if length > maxlength:
data_dict = fits['MCMC'][length - maxlength:length]
else:
data_dict = fits['MCMC'][0:length]
return data_dict
def run_emcee(x, lnprob, args, nwalkers, nruns, fudge, chain_name, burns,
pool=None,
nthreads=1,
namearray=[],
resume=False,
w=False):
ndim = len(x)
p0 = []
if resume == True:
p0, ndone = resume_file(chain_name, ndim, nwalkers)
nruns -= ndone
n = (ndone + burns) / nwalkers
else:
for i in range(0, nwalkers):
shuffle = (10 ** (fudge * (np.random.rand(ndim) - 0.5)))
p0 += [list(shuffle * x)]
initiate_file(chain_name, ndim, blob_list=namearray, w=w)
n = 0
iterations = int(nruns / nwalkers)
if pool != None:
sampler = EnsembleSampler(nwalkers, ndim, lnprob, args=args, pool=pool)
else:
sampler = EnsembleSampler(nwalkers, ndim, lnprob,
args=args,
threads=nthreads)
for result in sampler.sample(p0, iterations=iterations, storechain=False):
n += 1
if (n > burns / nwalkers):
position = result[0]
logl = result[1]
with fFITS(chain_name, 'rw') as fits:
for k in range(position.shape[0]):
output = {
'lp': np.array([logl[k]]),
'x': np.array([position[k]])
}
for i in range(0, len(namearray)):
blob = result[3][k][i]
output[namearray[i]] = np.array([blob])
if np.isfinite(logl[k]):
fits['MCMC'].append(output)
pool.close()
def get_named_vals(chain_name, burns=0, names=[], use_hpd=False):
mcmc_file = fFITS(chain_name)
h = fread_header(chain_name, 'MCMC')
nwalkers = h['NWALKERS']
if names == []:
names = mcmc_file[1].get_colnames()
names = names[1:]
labels = [l.replace('_', ' ') for l in names]
new_labels = [l.replace('_', ' ') for l in names]
data = []
mod = []
perc_list = []
frac_1sigma = 100
for i in range(0, len(names)):
name = names[i]
d = mcmc_file[1][name][burns * nwalkers:]
lp = mcmc_file[1]['lp'][burns * nwalkers:]
mask = np.isfinite(lp)
d = d[mask]
d = d[np.isfinite(d)]
try:
percs = np.percentile(d, [16, 50, 84])
sigma1 = hpd(d, 0.6827)
# sigma1 = hpd(good_sli,0.3)
percs2 = [sigma1[0], mode(d, frac_1sigma=frac_1sigma), sigma1[1]]
if use_hpd == True:
perc_list += [percs2]
else:
perc_list += [percs]
except:
perc_list += [[0, 0, 0]]
perc_list = np.array(perc_list)
return perc_list
def plot_chain(chain_name, burns=0, names=[], alpha=0.01):
mcmc_file = fFITS(chain_name)
h = fread_header(chain_name, 'MCMC')
nwalkers = h['NWALKERS']
if names == []:
names = mcmc_file[1].get_colnames()
for i in range(0, len(names)):
name = names[i]
data = mcmc_file[1][name].read()
# if name == 'xi':
# data = np.log10(data)
all_lp = mcmc_file[1]['lp'].read()
l = np.arange(len(data))
ds = []
for i in range(0, nwalkers):
d = data[i::nwalkers]
lp = all_lp[i::nwalkers]
na = np.arange(0, len(d))
mask = np.isfinite(lp)
ds += [d]
plt.plot(na[mask], d[mask], c='k', alpha=alpha)
ds = np.array(ds)
mean_d = []
upper_d = []
lower_d = []
for i in range(0, len(ds[0])):
sli = ds[:, i]
good_sli = sli[np.isfinite(sli)]
percs = np.percentile(good_sli, [16, 50, 84])
sigma1 = hpd(good_sli, 0.6827)
# sigma1 = hpd(good_sli,0.3)
percs2 = [sigma1[0], mode(good_sli), sigma1[1]]
percs = percs2
mean_d += [percs[1]]
upper_d += [percs[2]]
lower_d += [percs[0]]
fwidth = 20
plt.plot(gaussian_filter1d(mean_d, fwidth), '-', c='r', lw=2)
plt.plot(gaussian_filter1d(lower_d, fwidth), '--', c='r', lw=2)
plt.plot(gaussian_filter1d(upper_d, fwidth), '--', c='r', lw=2)
plt.axhline(gaussian_filter1d(upper_d, fwidth)[-1], ls=':', c='k')
plt.axhline(gaussian_filter1d(mean_d, fwidth)[-1], ls=':', c='k')
plt.axhline(gaussian_filter1d(lower_d, fwidth)[-1], ls=':', c='k')
plt.ylabel('$' + name + '$')
plt.xlabel('steps')
plt.axvline(burns, c='k')
plt.savefig(chain_name.replace('.fits', '_' + name + '.png'),
bbox_inches='tight')
plt.show()
plt.close()
def corner_plot(chain_name,
burns=0,
names=[],
show_contours=True,
use_hpd=False):
mcmc_file = fFITS(chain_name)
h = fread_header(chain_name, 'MCMC')
nwalkers = h['NWALKERS']
if names == []:
names = mcmc_file[1].get_colnames()
names = names[1:]
labels = [l.replace('_', ' ') for l in names]
new_labels = [l.replace('_', ' ') for l in names]
data = []
mod = []
perc_list = []
frac_1sigma = 100
for i in range(0, len(names)):
name = names[i]
d = mcmc_file[1][name][burns * nwalkers:]
lp = mcmc_file[1]['lp'][burns * nwalkers:]
mask = np.isfinite(lp)
d = d[mask]
d = d[np.isfinite(d)]
percs = np.percentile(d, [16, 50, 84])
sigma1 = hpd(d, 0.6827)
# sigma1 = hpd(good_sli,0.3)
percs2 = [sigma1[0], mode(d, frac_1sigma=frac_1sigma), sigma1[1]]
perc_list += [percs2]
d_i = (math.ceil(np.log10(abs(np.median(d)))))
if d_i < -2:
d = d / 10**(d_i - 1)
mod += [d_i - 1]
new_labels[i] += ' (x$10^{' + str(-1 * mod[i]) + '}$)'
else:
mod += [0]
data += [d]
data = np.array(data)
data = data.T
if use_hpd == True:
quantiles = []
else:
quantiles = [0.16, 0.5, 0.84]
corner.corner(data,
quantiles=quantiles,
labels=labels,
show_titles=True,
plot_contours=show_contours,
title_fmt='.2f')
f = plt.gcf()
for i in range(1, len(names)):
index = i * len(names)
f.axes[index].set_ylabel(new_labels[i])
for i in range(0, len(names)):
index = i + len(names) * (len(names) - 1)
f.axes[index].set_xlabel(new_labels[i])
for i in range(0, len(names)):
index = i + i * len(names)
if use_hpd == True:
f.axes[index].axvline(perc_list[i][0] / (10**mod[i]),
ls=':',
color='r')
f.axes[index].axvline(perc_list[i][1] / (10**mod[i]),
ls=':',
color='r')
f.axes[index].axvline(perc_list[i][2] / (10**mod[i]),
ls=':',
color='r')
exist = f.axes[index].title.get_text()
mine = r'${}$ = ${{{:.2f}}}_{{{:.2f}}}^{{+{:.2f}}}$'.format(
names[i], (perc_list[i][1]) / (10**mod[i]),
(perc_list[i][0] - perc_list[i][1]) / (10**mod[i]),
(perc_list[i][2] - perc_list[i][1]) / (10**mod[i]))
f.axes[index].title.set_text(mine)
if mod[i] != 0:
exist = f.axes[index].title.get_text()
f.axes[index].title.set_text(exist + ' x$10^{' + str(mod[i]) +
'}$')
plt.show()
def twochainz(chain_name1, chain_name2, burns=0):
mcmc_file1 = fFITS(chain_name1)
h1 = fread_header(chain_name1, 'MCMC')
nwalkers1 = h1['NWALKERS']
mcmc_file2 = fFITS(chain_name2)
h2 = fread_header(chain_name2, 'MCMC')
nwalkers2 = h2['NWALKERS']
for i in range(0, len(mcmc_file1[1].get_colnames())):
name = mcmc_file1[1].get_colnames()[i]
data1 = mcmc_file1[1][name].read()
all_lp1 = mcmc_file1[1]['lp'].read()
l1 = np.arange(len(data1))
data2 = mcmc_file2[1][name].read()
all_lp2 = mcmc_file2[1]['lp'].read()
l2 = np.arange(len(data2))
d1s = []
d2s = []
for i in range(0, 100):
d = data1[i::nwalkers1]
lp = all_lp1[i::nwalkers1]
na = np.arange(0, len(d))
mask = np.isfinite(lp)
d1s += [d]
plt.plot(na[mask], d[mask], c='k', alpha=0.1)
d = data2[i::nwalkers2]
lp = all_lp2[i::nwalkers2]
na = np.arange(0, len(d))
mask = np.isfinite(lp)
d2s += [d]
plt.plot(na[mask], d[mask], c='r', alpha=0.1)
d1s = np.array(d1s)
d2s = np.array(d2s)
mean_d1 = []
upper_d1 = []
lower_d1 = []
for i in range(0, len(d1s[0])):
sli = d1s[:, i]
percs = np.percentile(sli[np.isfinite(sli)], [16, 50, 84])
mean_d1 += [percs[1]]
upper_d1 += [percs[2]]
lower_d1 += [percs[0]]
mean_d2 = []
upper_d2 = []
lower_d2 = []
for i in range(0, len(d2s[0])):
sli = d2s[:, i]
percs = np.percentile(sli[np.isfinite(sli)], [16, 50, 84])
mean_d2 += [percs[1]]
upper_d2 += [percs[2]]
lower_d2 += [percs[0]]
plt.plot(mean_d1)
plt.plot(mean_d2)
plt.plot(upper_d1)
plt.plot(upper_d2)
plt.plot(lower_d1)
plt.plot(lower_d2)
plt.ylabel('$' + name + '$')
plt.xlabel('steps')
plt.axvline(burns, c='k')
# plt.savefig(chain_name.replace('.fits','_'+name+'.png'),bbox_inches='tight')
plt.show()
plt.close()
