def boundplot(self, **kwargs):
dyplot.boundplot(results=self.samples.results,
labels=self.model.parameter_labels_latex,
**kwargs)
self.output.to_figure(structure=None, auto_filename="boundplot")
def boundplot(self, **kwargs):
if conf.instance["general"]["test"]["test_mode"]:
return None
dyplot.boundplot(results=self.samples.results,
labels=self.model.parameter_labels_latex,
**kwargs)
self.output.to_figure(structure=None, auto_filename="boundplot")
self.mat_plot_1d.figure.close()
def test_gaussian():
logz_tol = 1
sampler = dynesty.NestedSampler(loglikelihood_gau,
prior_transform_gau,
ndim_gau,
nlive=nlive)
sampler.run_nested(print_progress=printing)
# add samples
# check continuation behavior
sampler.run_nested(dlogz=0.1, print_progress=printing)
# get errors
nerr = 2
for i in range(nerr):
sampler.reset()
sampler.run_nested(print_progress=False)
results = sampler.results
pos = results.samples
wts = np.exp(results.logwt - results.logz[-1])
mean, cov = dyfunc.mean_and_cov(pos, wts)
logz = results.logz[-1]
assert (np.abs(logz - logz_truth_gau) < logz_tol)
# check summary
res = sampler.results
res.summary()
# check plots
dyplot.runplot(sampler.results)
plt.close()
dyplot.traceplot(sampler.results)
plt.close()
dyplot.cornerpoints(sampler.results)
plt.close()
dyplot.cornerplot(sampler.results)
plt.close()
dyplot.boundplot(sampler.results,
dims=(0, 1),
it=3000,
prior_transform=prior_transform_gau,
show_live=True,
span=[(-10, 10), (-10, 10)])
plt.close()
dyplot.cornerbound(sampler.results,
it=3500,
prior_transform=prior_transform_gau,
show_live=True,
span=[(-10, 10), (-10, 10)])
plt.close()
def test_gaussianx(bound):
ndim = 3
rstate = get_rstate()
g = Gaussian(ndim=ndim)
sampler = dynesty.NestedSampler(g.loglikelihood,
g.prior_transform,
g.ndim,
nlive=nlive,
rstate=rstate,
bound=bound)
sampler.run_nested(print_progress=printing)
results = sampler.results
dyplot.boundplot(results,
dims=(0, 1)[:min(ndim, 2)],
it=1000,
prior_transform=g.prior_transform,
show_live=False)
dyplot.cornerbound(results,
it=3000,
prior_transform=g.prior_transform,
show_live=False)
def test_gaussian(dynamic, periodic, ndim, bound):
rstate = get_rstate()
g = Gaussian(ndim=ndim)
if periodic:
periodic = [0]
else:
periodic = None
if dynamic:
sampler = dynesty.DynamicNestedSampler(g.loglikelihood,
g.prior_transform,
g.ndim,
nlive=nlive,
rstate=rstate,
periodic=periodic,
bound=bound)
else:
sampler = dynesty.NestedSampler(g.loglikelihood,
g.prior_transform,
g.ndim,
nlive=nlive,
rstate=rstate,
periodic=periodic,
bound=bound)
sampler.run_nested(print_progress=printing)
results = sampler.results
# check plots
dyplot.runplot(results)
dyplot.runplot(results, span=[(0., 10.), 0.001, 0.2, (5., 6.)])
dyplot.runplot(results, logplot=True)
dyplot.runplot(results,
fig=(plt.gcf(), plt.gcf().axes),
max_x_ticks=0,
max_y_ticks=0)
plt.close()
dyplot.traceplot(results)
dyplot.traceplot(results, smooth=[10] * ndim)
dyplot.traceplot(results, connect=True)
dyplot.traceplot(results,
fig=(plt.gcf(), plt.gcf().axes),
show_titles=True,
truths=np.zeros(ndim),
verbose=True,
max_n_ticks=0)
plt.close()
truths = np.zeros(ndim)
truths[0] = -.1
span = [[-10, 10]] * ndim
if ndim > 1:
truths[1] = .1
span[1] = .9
dyplot.cornerplot(results, show_titles=True, truths=truths)
dyplot.cornerplot(results,
smooth=10,
verbose=True,
hist2d_kwargs=dict(plot_datapoints=True),
max_n_ticks=0)
plt.close()
if ndim != 1:
# cornerbound
dyplot.cornerbound(results,
it=500,
prior_transform=g.prior_transform,
show_live=True,
span=span)
dyplot.cornerbound(results,
it=500,
show_live=True,
span=span,
fig=(plt.gcf(), plt.gcf().axes))
plt.close()
# boundplot
dyplot.boundplot(results,
dims=(0, 1)[:min(ndim, 2)],
it=1000,
prior_transform=g.prior_transform,
show_live=True,
span=span)
dyplot.boundplot(results, dims=(0, 1)[:min(ndim, 2)], it=1000)
plt.close()
# cornerpoints
dyplot.cornerpoints(results)
plt.close()
dyplot.cornerpoints(results, span=span, truths=truths, max_n_ticks=0)
plt.close()
sys.stderr.write('Summary/Run Plot\n')
dyplot.runplot(sampler.results)
plt.close()
sys.stderr.write('Trace Plot\n')
dyplot.traceplot(sampler.results)
plt.close()
sys.stderr.write('Sub-Corner Plot (Points)\n')
dyplot.cornerpoints(sampler.results)
plt.close()
sys.stderr.write('Corner Plot (Contours)\n')
dyplot.cornerplot(sampler.results)
plt.close()
sys.stderr.write('2-D Bound Plot\n')
dyplot.boundplot(sampler.results,
dims=(0, 1),
it=3000,
prior_transform=prior_transform,
show_live=True,
span=[(-10, 10), (-10, 10)])
plt.close()
sys.stderr.write('Sub-Corner Plot (Bounds)\n')
dyplot.cornerbound(sampler.results,
it=3500,
prior_transform=prior_transform,
show_live=True,
span=[(-10, 10), (-10, 10)])
plt.close()
# check various bounding methods
for bound in ['none', 'single', 'multi', 'balls', 'cubes']:
sys.stderr.write('\n' + bound + '\n')
sampler = dynesty.NestedSampler(loglikelihood,
def MCMC_diagnostic(path, ndim, p, loglike, ptform, galname, nlive, **pdict):
pdict = pdict['pdict']
start = time.time()
pdict['start'] = start
if ndim == 10:
nparams = '_10P'
sampler = NestedSampler(loglike,
ptform,
ndim=ndim,
nlive=nlive,
sample='unif',
bound='multi',
logl_kwargs=pdict,
update_interval=.8,
dlogz=0.5,
first_update={
'min_ncall': nlive,
'min_eff': 50.
},
pool=p)
sampler.run_nested(maxiter=15000, maxcall=50000)
res1 = sampler.results
# Save nested data
# obtain KL divergence
klds = []
for i in range(500):
kld = dyfunc.kld_error(res1, error='simulate')
klds.append(kld[-1])
print(np.mean(klds))
res1['KLval'] = np.mean(klds)
with open(path + '/result_nested_P' + '{}'.format(nlive) + '.json',
'w') as ff:
ff.write(json.dumps(res1, cls=NumpyEncoder))
lnz_truth = 10 * -np.log(2 * 30.)
fig, axes = dyplot.runplot(res1, lnz_truth=lnz_truth)
plt.savefig(path + '/runplot_' + galname + nparams + '.png')
plt.close()
fig, axes = dyplot.traceplot(
res1,
truths=np.array([
pdict['vrot'][0], pdict['vrot'][1], pdict['vrot'][2],
pdict['vrot'][3], pdict['vdisp'][0], pdict['vdisp'][1],
pdict['vdisp'][2], pdict['vdisp'][3], pdict['inc'],
pdict['phi']
]),
truth_color='black',
show_titles=True,
trace_cmap='viridis',
connect=True,
smooth=0.02,
connect_highlight=range(8),
labels=[
r'$v_{rot,225}$', r'$v_{rot,450}$', r'$v_{rot,675}$',
r'$v_{rot,900}$', r'$\sigma_{225}$', r'$\sigma_{450}$',
r'$\sigma_{675}$', r'$\sigma_{900}$', r'$i$', r'$\phi$'
])
plt.savefig(path + '/traceplot_' + galname + nparams + '.png')
plt.close()
# initialize figure
fig, axes = plt.subplots(2, 3, figsize=(15, 10))
# plot 6 snapshots over the course of the run
for i, a in enumerate(axes.flatten()):
it = int((i + 1) * res1.niter / 8.)
# overplot the result onto each subplot
temp = dyplot.boundplot(res1,
dims=(0, 1),
it=it,
prior_transform=ptform,
max_n_ticks=3,
show_live=True,
span=[(70, 150), (70, 150)],
fig=(fig, a))
a.set_title('Iteration {0}'.format(it), fontsize=26)
fig.tight_layout()
plt.savefig(path + '/boundplot_' + galname + nparams + '.png')
plt.close()
fg, ax = dyplot.cornerplot(
res1,
color='blue',
truths=np.array([
pdict['vrot'][0], pdict['vrot'][1], pdict['vrot'][2],
pdict['vrot'][3], pdict['vdisp'][0], pdict['vdisp'][1],
pdict['vdisp'][2], pdict['vdisp'][3], pdict['inc'],
pdict['phi']
]), # 91.8,98.3,8.88,6.5,60,60
truth_color='black',
show_titles=True,
smooth=0.02,
max_n_ticks=5,
quantiles=None,
labels=[
r'$v_{rot,225}$', r'$v_{rot,450}$', r'$v_{rot,675}$',
r'$v_{rot,900}$', r'$\sigma_{225}$', r'$\sigma_{450}$',
r'$\sigma_{675}$', r'$\sigma_{900}$', r'$i$', r'$\phi$'
])
plt.savefig(path + '/cornerplot_' + galname + nparams + '.png')
plt.close()
with open(path + '/' + galname + '.txt', 'w+') as f:
f.write('Running took: {} hours'.format(
(time.time() - start) / 3600))
# Save the model data
samples, weights = res1.samples, np.exp(res1.logwt - res1.logz[-1])
mean, cov = dyfunc.mean_and_cov(samples, weights)
MaP = res1['samples'][res1['logl'].tolist().index(
max(res1['logl'].tolist()))]
quantiles = [
dyfunc.quantile(samps, [0.025, 0.5, 0.975], weights=weights)
for samps in samples.T
]
print(quantiles)
# vrotsigma
sigmavrot1_l = [i for i in samples[:, 0] if (i - MaP[0]) < 0]
sigmavrot1_r = [i for i in samples[:, 0] if (i - MaP[0]) > 0]
sigmavrot2_l = [i for i in samples[:, 1] if (i - MaP[1]) < 0]
sigmavrot2_r = [i for i in samples[:, 1] if (i - MaP[1]) > 0]
sigmavrot3_l = [i for i in samples[:, 2] if (i - MaP[2]) < 0]
sigmavrot3_r = [i for i in samples[:, 2] if (i - MaP[2]) > 0]
sigmavrot4_l = [i for i in samples[:, 3] if (i - MaP[3]) < 0]
sigmavrot4_r = [i for i in samples[:, 3] if (i - MaP[3]) > 0]
if len(sigmavrot1_l) == 0: sigmavrot1_l.append(0)
if len(sigmavrot1_r) == 0: sigmavrot1_r.append(0)
if len(sigmavrot2_l) == 0: sigmavrot2_l.append(0)
if len(sigmavrot2_r) == 0: sigmavrot2_r.append(0)
if len(sigmavrot3_l) == 0: sigmavrot3_l.append(0)
if len(sigmavrot3_r) == 0: sigmavrot3_r.append(0)
if len(sigmavrot4_l) == 0: sigmavrot4_l.append(0)
if len(sigmavrot4_r) == 0: sigmavrot4_r.append(0)
# vdispsigma
sigmavdisp1_l = [i for i in samples[:, 4] if (i - MaP[4]) < 0]
sigmavdisp1_r = [i for i in samples[:, 4] if (i - MaP[4]) > 0]
sigmavdisp2_l = [i for i in samples[:, 5] if (i - MaP[5]) < 0]
sigmavdisp2_r = [i for i in samples[:, 5] if (i - MaP[5]) > 0]
sigmavdisp3_l = [i for i in samples[:, 6] if (i - MaP[6]) < 0]
sigmavdisp3_r = [i for i in samples[:, 6] if (i - MaP[6]) > 0]
sigmavdisp4_l = [i for i in samples[:, 7] if (i - MaP[7]) < 0]
sigmavdisp4_r = [i for i in samples[:, 7] if (i - MaP[7]) > 0]
if len(sigmavdisp1_l) == 0: sigmavdisp1_l.append(0)
if len(sigmavdisp1_r) == 0: sigmavdisp1_r.append(0)
if len(sigmavdisp2_l) == 0: sigmavdisp2_l.append(0)
if len(sigmavdisp2_r) == 0: sigmavdisp2_r.append(0)
if len(sigmavdisp3_l) == 0: sigmavdisp3_l.append(0)
if len(sigmavdisp3_r) == 0: sigmavdisp3_r.append(0)
if len(sigmavdisp4_l) == 0: sigmavdisp4_l.append(0)
if len(sigmavdisp4_r) == 0: sigmavdisp4_r.append(0)
pdict['sigmavrot'] = [(np.std(sigmavrot1_l), np.std(sigmavrot1_r)),
(np.std(sigmavrot2_l), np.std(sigmavrot2_r)),
(np.std(sigmavrot3_l), np.std(sigmavrot3_r)),
(np.std(sigmavrot4_l), np.std(sigmavrot4_r))]
pdict['sigmavdisp'] = [(np.std(sigmavdisp1_l), np.std(sigmavdisp1_r)),
(np.std(sigmavdisp2_l), np.std(sigmavdisp2_r)),
(np.std(sigmavdisp3_l), np.std(sigmavdisp3_r)),
(np.std(sigmavdisp4_l), np.std(sigmavdisp4_r))]
if len(MaP) == 8:
pdict['vrot'] = MaP[0:4]
pdict['vdisp'] = MaP[4:8]
if len(MaP) == 9:
pdict['vrot'] = MaP[0:4]
pdict['vdisp'] = MaP[4:8]
pdict['inc'] = MaP[8]
# inc
sigmainc_l = [i for i in samples[:, 8] if (i - MaP[8]) < 0]
sigmainc_r = [i for i in samples[:, 8] if (i - MaP[8]) > 0]
if len(sigmainc_l) == 0: sigmainc_l.append(0)
if len(sigmainc_r) == 0: sigmainc_r.append(0)
pdict['sigmainc'] = [(np.std(sigmainc_l), np.std(sigmainc_r))]
if len(MaP) == 10:
pdict['vrot'] = MaP[0:4]
pdict['vdisp'] = MaP[4:8]
pdict['inc'] = MaP[8]
pdict['phi'] = MaP[9]
# inc
sigmainc_l = [i for i in samples[:, 8] if (i - MaP[8]) < 0]
sigmainc_r = [i for i in samples[:, 8] if (i - MaP[8]) > 0]
if len(sigmainc_l) == 0: sigmainc_l.append(0)
if len(sigmainc_r) == 0: sigmainc_r.append(0)
pdict['sigmainc'] = [(np.std(sigmainc_l), np.std(sigmainc_r))]
# phi
sigmaphi_l = [i for i in samples[:, 9] if (i - MaP[9]) < 0]
sigmaphi_r = [i for i in samples[:, 9] if (i - MaP[9]) > 0]
if len(sigmaphi_l) == 0: sigmaphi_l.append(0)
if len(sigmaphi_r) == 0: sigmaphi_r.append(0)
pdict['sigmaphi'] = [(np.std(sigmaphi_l), np.std(sigmaphi_r))]
# We don't need data entry
pdict['Data'] = None
with open(path + '/params_model.json', 'w') as f:
f.write(json.dumps(pdict, cls=NumpyEncoder))
def runMCMC(path, ndim, p, loglike, ptform, galname, **pdict):
pdict = pdict['pdict']
start = time.time()
pdict['start'] = start
if ndim == 8:
nparams = '_8P'
sampler = NestedSampler(loglike,
ptform,
ndim=ndim,
nlive=250,
sample='unif',
bound='multi',
logl_kwargs=pdict,
update_interval=0.8,
dlogz=0.5,
first_update={
'min_ncall': 300,
'min_eff': 50.
},
pool=p)
sampler.run_nested(maxiter=15000, maxcall=50000)
res1 = sampler.results
with open(path + '/result_nested_P' + '{}'.format(ndim) + '.json',
'w') as ff:
ff.write(json.dumps(res1, cls=NumpyEncoder))
lnz_truth = 10 * -np.log(2 * 30.)
fig, axes = dyplot.runplot(res1, lnz_truth=lnz_truth)
plt.savefig(path + '/runplot_' + galname + nparams + '.png')
plt.close()
fig, axes = dyplot.traceplot(res1,
truths=np.array([
pdict['vrot'][0], pdict['vrot'][1],
pdict['vrot'][2], pdict['vrot'][3],
pdict['vdisp'][0], pdict['vdisp'][1],
pdict['vdisp'][2], pdict['vdisp'][3]
]),
truth_color='black',
show_titles=True,
trace_cmap='viridis',
connect=True,
smooth=0.02,
connect_highlight=range(8),
labels=[
r'$v_{rot,225}$', r'$v_{rot,450}$',
r'$v_{rot,675}$', r'$v_{rot,900}$',
r'$\sigma_{225}$', r'$\sigma_{450}$',
r'$\sigma_{675}$', r'$\sigma_{900}$'
])
plt.savefig(path + '/traceplot_' + galname + nparams + '.png')
plt.close()
# plot 6 snapshots over the course of the run
for i, a in enumerate(axes.flatten()):
it = int((i + 1) * res1.niter / 8.)
# overplot the result onto each subplot
temp = dyplot.boundplot(res1,
dims=(0, 1),
it=it,
prior_transform=ptform,
max_n_ticks=5,
show_live=True,
span=[(70, 150), (70, 150)],
fig=(fig, a))
a.set_title('Iteration {0}'.format(it), fontsize=26)
fig.tight_layout()
plt.savefig(path + '/boundplot_' + galname + nparams + '.png')
plt.close()
matplotlib.rcParams.update({'font.size': 16})
fig, axes = dyplot.cornerplot(
res1,
color='blue',
truths=np.array([
pdict['vrot'][0], pdict['vrot'][1], pdict['vrot'][2],
pdict['vrot'][3], pdict['vdisp'][0], pdict['vdisp'][1],
pdict['vdisp'][2], pdict['vdisp'][3], pdict['inc'],
pdict['phi']
]),
truth_color='black',
show_titles=True,
smooth=0.02,
max_n_ticks=5,
quantiles=[0.16, 0.5, 0.84],
labels=[
r'$V_{225}[km/s]$', r'$V_{450}[km/s]$', r'$V_{675}[km/s]$',
r'$V_{900}[km/s]$', r'$\sigma_{gas,225}[km/s]$',
r'$\sigma_{gas,450}[km/s]$', r'$\sigma_{gas,675}[km/s]$',
r'$\sigma_{gas,900}[km/s]$', r'$i[deg]$', r'$\phi[deg]$'
])
# Save the model data
samples, weights = res1.samples, np.exp(res1.logwt - res1.logz[-1])
mean, cov = dyfunc.mean_and_cov(samples, weights)
MaP = res1['samples'][res1['logl'].tolist().index(
max(res1['logl'].tolist()))]
quantiles = [
dyfunc.quantile(samps, [0.16, 0.5, 0.84], weights=weights)
for samps in samples.T
]
labels = [
r'$V_{225}$', r'$V_{450}$', r'$V_{675}$', r'$V_{900}$',
r'$\sigma_{gas,225}$', r'$\sigma_{gas,450}$',
r'$\sigma_{gas,675}$', r'$\sigma_{gas,900}$', r'$i$', r'$\phi$'
]
units = [
' [km/s]', ' [km/s]', ' [km/s]', ' [km/s]', ' [km/s]', ' [km/s]',
' [km/s]', ' [km/s]', ' [deg]', ' [deg]'
]
for i in range(ndim):
ax = axes[i, i]
q5 = np.round(quantiles[i][1], 2)
q14 = np.round(quantiles[i][0], 2)
q84 = np.round(quantiles[i][2], 2)
ax.set_title(r"$%.2f_{%.2f}^{+%.2f}$" %
(q5, -1 * abs(q5 - q14), abs(q5 - q84)) + units[i])
# Loop over the histograms
for yi in range(ndim):
axes[yi, 0].set_ylabel(labels[yi] + units[yi],
labelpad=30,
fontsize=20)
axes[-1, yi].set_xlabel(labels[yi] + units[yi],
labelpad=30,
fontsize=20)
axes[yi, 0].tick_params(axis='y', which='major', labelsize=14)
axes[-1, yi].tick_params(axis='x', which='major', labelsize=14)
fig.tight_layout()
plt.savefig(path + '/cornerplot_' + galname + nparams + '.pdf')
plt.close()
with open(path + '/' + galname + '.txt', 'w+') as f:
f.write('Running took: {} hours'.format(
(time.time() - start) / 3600))
elif ndim == 9:
nparams = '_9P'
sampler = NestedSampler(loglike,
ptform,
ndim=ndim,
nlive=250,
sample='unif',
bound='multi',
logl_kwargs=pdict,
update_interval=0.8,
dlogz=0.5,
first_update={
'min_ncall': 300,
'min_eff': 50.
},
pool=p)
sampler.run_nested(maxiter=15000, maxcall=50000)
res1 = sampler.results
with open(path + '/result_nested_P' + '{}'.format(ndim) + '.json',
'w') as ff:
ff.write(json.dumps(res1, cls=NumpyEncoder))
lnz_truth = 10 * -np.log(2 * 30.)
fig, axes = dyplot.runplot(res1, lnz_truth=lnz_truth)
plt.savefig(path + '/runplot_' + galname + nparams + '.png')
plt.close()
fig, axes = dyplot.traceplot(
res1,
truths=np.array([
pdict['vrot'][0], pdict['vrot'][1], pdict['vrot'][2],
pdict['vrot'][3], pdict['vdisp'][0], pdict['vdisp'][1],
pdict['vdisp'][2], pdict['vdisp'][3], pdict['inc']
]),
truth_color='black',
show_titles=True,
trace_cmap='viridis',
connect=True,
smooth=0.02,
connect_highlight=range(8),
labels=[
r'$v_{rot,225}$', r'$v_{rot,450}$', r'$v_{rot,675}$',
r'$v_{rot,900}$', r'$\sigma_{225}$', r'$\sigma_{450}$',
r'$\sigma_{675}$', r'$\sigma_{900}$', r'$i$'
])
plt.savefig(path + '/traceplot_' + galname + nparams + '.png')
plt.close()
# initialize figure
fig, axes = plt.subplots(2, 3, figsize=(15, 10))
# plot 6 snapshots over the course of the run
for i, a in enumerate(axes.flatten()):
it = int((i + 1) * res1.niter / 8.)
# overplot the result onto each subplot
temp = dyplot.boundplot(res1,
dims=(0, 1),
it=it,
prior_transform=ptform,
max_n_ticks=3,
show_live=True,
span=[(70, 150), (70, 150)],
fig=(fig, a))
a.set_title('Iteration {0}'.format(it), fontsize=26)
fig.tight_layout()
plt.savefig(path + '/boundplot_' + galname + nparams + '.png')
plt.close()
matplotlib.rcParams.update({'font.size': 16})
fig, axes = dyplot.cornerplot(
res1,
color='blue',
truths=np.array([
pdict['vrot'][0], pdict['vrot'][1], pdict['vrot'][2],
pdict['vrot'][3], pdict['vdisp'][0], pdict['vdisp'][1],
pdict['vdisp'][2], pdict['vdisp'][3], pdict['inc']
]),
truth_color='black',
show_titles=True,
smooth=0.02,
max_n_ticks=5,
quantiles=[0.16, 0.5, 0.84],
labels=[
r'$V_{225}[km/s]$', r'$V_{450}[km/s]$', r'$V_{675}[km/s]$',
r'$V_{900}[km/s]$', r'$\sigma_{gas,225}[km/s]$',
r'$\sigma_{gas,450}[km/s]$', r'$\sigma_{gas,675}[km/s]$',
r'$\sigma_{gas,900}[km/s]$', r'$i[deg]$'
])
# Save the model data
samples, weights = res1.samples, np.exp(res1.logwt - res1.logz[-1])
mean, cov = dyfunc.mean_and_cov(samples, weights)
MaP = res1['samples'][res1['logl'].tolist().index(
max(res1['logl'].tolist()))]
quantiles = [
dyfunc.quantile(samps, [0.16, 0.5, 0.84], weights=weights)
for samps in samples.T
]
labels = [
r'$V_{225}$', r'$V_{450}$', r'$V_{675}$', r'$V_{900}$',
r'$\sigma_{gas,225}$', r'$\sigma_{gas,450}$',
r'$\sigma_{gas,675}$', r'$\sigma_{gas,900}$', r'$i$', r'$\phi$'
]
units = [
' [km/s]', ' [km/s]', ' [km/s]', ' [km/s]', ' [km/s]', ' [km/s]',
' [km/s]', ' [km/s]', ' [deg]', ' [deg]'
]
for i in range(ndim):
ax = axes[i, i]
q5 = np.round(quantiles[i][1], 2)
q14 = np.round(quantiles[i][0], 2)
q84 = np.round(quantiles[i][2], 2)
ax.set_title(r"$%.2f_{%.2f}^{+%.2f}$" %
(q5, -1 * abs(q5 - q14), abs(q5 - q84)) + units[i])
# Loop over the histograms
for yi in range(ndim):
axes[yi, 0].set_ylabel(labels[yi] + units[yi],
labelpad=30,
fontsize=20)
axes[-1, yi].set_xlabel(labels[yi] + units[yi],
labelpad=30,
fontsize=20)
axes[yi, 0].tick_params(axis='y', which='major', labelsize=14)
axes[-1, yi].tick_params(axis='x', which='major', labelsize=14)
fig.tight_layout()
plt.savefig(path + '/cornerplot_' + galname + nparams + '.pdf')
plt.close()
with open(path + '/' + galname + '.txt', 'w+') as f:
f.write('Running took: {} hours'.format(
(time.time() - start) / 3600))
elif ndim == 10:
nparams = '_10P'
sampler = NestedSampler(loglike,
ptform,
ndim=ndim,
nlive=250,
sample='unif',
bound='multi',
logl_kwargs=pdict,
update_interval=.8,
dlogz=0.5,
first_update={
'min_ncall': 300,
'min_eff': 50.
},
pool=p)
sampler.run_nested(maxiter=15000, maxcall=50000)
res1 = sampler.results
with open(path + '/result_nested_P' + '{}'.format(ndim) + '.json',
'w') as ff:
ff.write(json.dumps(res1, cls=NumpyEncoder))
lnz_truth = 10 * -np.log(2 * 30.)
fig, axes = dyplot.runplot(res1, lnz_truth=lnz_truth)
plt.savefig(path + '/runplot_' + galname + nparams + '.png')
plt.close()
fig, axes = dyplot.traceplot(
res1,
truths=np.array([
pdict['vrot'][0], pdict['vrot'][1], pdict['vrot'][2],
pdict['vrot'][3], pdict['vdisp'][0], pdict['vdisp'][1],
pdict['vdisp'][2], pdict['vdisp'][3], pdict['inc'],
pdict['phi']
]),
truth_color='black',
show_titles=True,
trace_cmap='viridis',
connect=True,
smooth=0.02,
connect_highlight=range(8),
labels=[
r'$v_{rot,225}$', r'$v_{rot,450}$', r'$v_{rot,675}$',
r'$v_{rot,900}$', r'$\sigma_{225}$', r'$\sigma_{450}$',
r'$\sigma_{675}$', r'$\sigma_{900}$', r'$i$', r'$\phi$'
])
plt.savefig(path + '/traceplot_' + galname + nparams + '.png')
plt.close()
# initialize figure
fig, axes = plt.subplots(2, 3, figsize=(15, 10))
# plot 6 snapshots over the course of the run
for i, a in enumerate(axes.flatten()):
it = int((i + 1) * res1.niter / 8.)
# overplot the result onto each subplot
temp = dyplot.boundplot(res1,
dims=(0, 1),
it=it,
prior_transform=ptform,
max_n_ticks=3,
show_live=True,
span=[(70, 150), (70, 150)],
fig=(fig, a))
a.set_title('Iteration {0}'.format(it), fontsize=26)
fig.tight_layout()
plt.savefig(path + '/boundplot_' + galname + nparams + '.png')
plt.close()
matplotlib.rcParams.update({'font.size': 16})
fig, axes = dyplot.cornerplot(
res1,
color='blue',
truths=np.array([
pdict['vrot'][0], pdict['vrot'][1], pdict['vrot'][2],
pdict['vrot'][3], pdict['vdisp'][0], pdict['vdisp'][1],
pdict['vdisp'][2], pdict['vdisp'][3], pdict['inc'],
pdict['phi']
]),
truth_color='black',
show_titles=True,
smooth=0.02,
max_n_ticks=5,
quantiles=[0.16, 0.5, 0.84],
labels=[
r'$V_{225}[km/s]$', r'$V_{450}[km/s]$', r'$V_{675}[km/s]$',
r'$V_{900}[km/s]$', r'$\sigma_{gas,225}[km/s]$',
r'$\sigma_{gas,450}[km/s]$', r'$\sigma_{gas,675}[km/s]$',
r'$\sigma_{gas,900}[km/s]$', r'$i[deg]$', r'$\phi[deg]$'
])
# Save the model data
samples, weights = res1.samples, np.exp(res1.logwt - res1.logz[-1])
mean, cov = dyfunc.mean_and_cov(samples, weights)
MaP = res1['samples'][res1['logl'].tolist().index(
max(res1['logl'].tolist()))]
quantiles = [
dyfunc.quantile(samps, [0.16, 0.5, 0.84], weights=weights)
for samps in samples.T
]
labels = [
r'$V_{225}$', r'$V_{450}$', r'$V_{675}$', r'$V_{900}$',
r'$\sigma_{gas,225}$', r'$\sigma_{gas,450}$',
r'$\sigma_{gas,675}$', r'$\sigma_{gas,900}$', r'$i$', r'$\phi$'
]
units = [
' [km/s]', ' [km/s]', ' [km/s]', ' [km/s]', ' [km/s]', ' [km/s]',
' [km/s]', ' [km/s]', ' [deg]', ' [deg]'
]
for i in range(ndim):
ax = axes[i, i]
q5 = np.round(quantiles[i][1], 2)
q14 = np.round(quantiles[i][0], 2)
q84 = np.round(quantiles[i][2], 2)
ax.set_title(r"$%.2f_{%.2f}^{+%.2f}$" %
(q5, -1 * abs(q5 - q14), abs(q5 - q84)) + units[i])
# Loop over the histograms
for yi in range(ndim):
axes[yi, 0].set_ylabel(labels[yi] + units[yi],
labelpad=30,
fontsize=20)
axes[-1, yi].set_xlabel(labels[yi] + units[yi],
labelpad=30,
fontsize=20)
axes[yi, 0].tick_params(axis='y', which='major', labelsize=14)
axes[-1, yi].tick_params(axis='x', which='major', labelsize=14)
fig.tight_layout()
plt.savefig(path + '/cornerplot_' + galname + nparams + '.pdf')
plt.close()
with open(path + '/' + galname + '.txt', 'w+') as f:
f.write('Running took: {} hours'.format(
(time.time() - start) / 3600))
# Save the model data
samples, weights = res1.samples, np.exp(res1.logwt - res1.logz[-1])
mean, cov = dyfunc.mean_and_cov(samples, weights)
MaP = res1['samples'][res1['logl'].tolist().index(
max(res1['logl'].tolist()))]
quantiles = [
dyfunc.quantile(samps, [0.16, 0.5, 0.84], weights=weights)
for samps in samples.T
]
pdict['sigmavrot'] = [(quantiles[0][0], quantiles[0][2]),
(quantiles[1][0], quantiles[1][2]),
(quantiles[2][0], quantiles[2][2]),
(quantiles[3][0], quantiles[3][2])]
pdict['sigmavdisp'] = [(quantiles[4][0], quantiles[4][2]),
(quantiles[5][0], quantiles[5][2]),
(quantiles[6][0], quantiles[6][2]),
(quantiles[7][0], quantiles[7][2])]
pdict['vrot'] = [
quantiles[0][1], quantiles[1][1], quantiles[2][1], quantiles[3][1]
]
pdict['vdisp'] = [
quantiles[4][1], quantiles[5][1], quantiles[6][1], quantiles[7][1]
]
if len(quantiles) == 9:
pdict['inc'] = quantiles[8][1]
pdict['sigmainc'] = [(quantiles[8][0], quantiles[8][2])]
if len(quantiles) == 10:
pdict['inc'] = quantiles[8][1]
pdict['sigmainc'] = [(quantiles[8][0], quantiles[8][2])]
pdict['phi'] = quantiles[9][1]
pdict['sigmaphi'] = [(quantiles[9][0], quantiles[9][2])]
# We don't need data entry, waste of space
pdict['Data'] = None
with open(path + '/params_model.json', 'w') as f:
f.write(json.dumps(pdict, cls=NumpyEncoder))