def plot_model_res(ax, t_epoch, t_model, rv_model, sim_rv, n_rvset, labels,
colors, markers):
t_rv = sim_rv[:, 0] - t_epoch
rv_obs = sim_rv[:, 1] - sim_rv[:, 5]
rv_sim = sim_rv[:, 3]
res_rv = rv_obs - rv_sim
xlim1, xlim2 = anc.compute_limits(t_rv, 0.05)
miny = np.min(res_rv - sim_rv[:, 2])
maxy = np.max(res_rv + sim_rv[:, 2])
ylim1, ylim2 = anc.compute_limits(np.array([miny, maxy]), 0.05)
ax.axhline(0., color='gray', ls='-')
for iset in range(0, n_rvset):
sel = sim_rv[:, 7].astype(int) == iset + 1
ax.errorbar(t_rv[sel],
res_rv[sel],
yerr=sim_rv[sel, 2],
fmt=markers[iset],
mfc=colors[iset],
mec='black',
ecolor=colors[iset],
capsize=0,
label=labels[iset])
ax.set_xlabel('BJD-%.3f' % (t_epoch))
ax.set_xlim([xlim1, xlim2])
ax.set_ylim([ylim1, ylim2])
ax.set_ylabel('res [m/s]')
plt.draw()
return ax
def plot_model_data(ax, t_epoch, t_model, rv_model, sim_rv, n_rvset, labels, colors, markers):
t_rv = sim_rv[:,0] - t_epoch
rv_obs = sim_rv[:,1] - sim_rv[:,5]
rv_sim = sim_rv[:,3]
xlim1, xlim2 = anc.compute_limits(t_rv, 0.05)
miny = min(np.min(rv_obs-sim_rv[:,2]), np.min(rv_sim), np.min(rv_model))
maxy = max(np.max(rv_obs+sim_rv[:,2]), np.min(rv_sim), np.min(rv_model))
ylim1, ylim2 = anc.compute_limits(np.array([miny, maxy]), 0.05)
ax.axhline(0., color='gray', ls='-')
ax.plot(t_model-t_epoch, rv_model, marker='None', ls=':', lw=0.5, color='darkgray', label='model $N$-body')
for iset in range(0, n_rvset):
sel = sim_rv[:,7].astype(int) == iset+1
ax.errorbar(t_rv[sel], rv_obs[sel], yerr=sim_rv[sel,2], fmt=markers[iset], mfc=colors[iset], mec='black', ecolor=colors[iset], capsize=0, label=labels[iset])
ax.plot(t_rv[sel], rv_sim[sel], marker=markers[iset], mfc='None', mec=colors[iset], mew=1., ms=8, ls='', label='%s (TRADES)' %(labels[iset]))
ax.legend(bbox_to_anchor=(0., 1.02, 1., .102), loc=3,\
ncol=2*(n_rvset)+1, mode="expand", \
borderaxespad=0., numpoints=1,\
fontsize=9)
ax.set_xlim([xlim1, xlim2])
ax.set_xticklabels([])
ax.set_ylim([ylim1, ylim2])
ax.set_ylabel('RV [m/s]')
plt.draw()
return ax
def plot_model_data(ax, t_epoch, t_model, rv_model, sim_rv, n_rvset, labels,
colors, markers):
t_rv = sim_rv[:, 0] - t_epoch
rv_obs = sim_rv[:, 1] - sim_rv[:, 5]
rv_sim = sim_rv[:, 3]
xlim1, xlim2 = anc.compute_limits(t_rv, 0.05)
miny = min(np.min(rv_obs - sim_rv[:, 2]), np.min(rv_sim), np.min(rv_model))
maxy = max(np.max(rv_obs + sim_rv[:, 2]), np.min(rv_sim), np.min(rv_model))
ylim1, ylim2 = anc.compute_limits(np.array([miny, maxy]), 0.05)
ax.axhline(0., color='gray', ls='-')
ax.plot(t_model - t_epoch,
rv_model,
marker='None',
ls=':',
lw=0.5,
color='darkgray',
label='model $N$-body')
for iset in range(0, n_rvset):
sel = sim_rv[:, 7].astype(int) == iset + 1
ax.errorbar(t_rv[sel],
rv_obs[sel],
yerr=sim_rv[sel, 2],
fmt=markers[iset],
mfc=colors[iset],
mec='black',
ecolor=colors[iset],
capsize=0,
label=labels[iset])
ax.plot(t_rv[sel],
rv_sim[sel],
marker=markers[iset],
mfc='None',
mec=colors[iset],
mew=1.,
ms=8,
ls='',
label='%s (TRADES)' % (labels[iset]))
ax.legend(bbox_to_anchor=(0., 1.02, 1., .102), loc=3,\
ncol=2*(n_rvset)+1, mode="expand", \
borderaxespad=0., numpoints=1,\
fontsize=9)
ax.set_xlim([xlim1, xlim2])
ax.set_xticklabels([])
ax.set_ylim([ylim1, ylim2])
ax.set_ylabel('RV [m/s]')
plt.draw()
return ax
def plot_phase_res(ax, phi_rv, rv_opl, rv_spl, res_rv, sim_rv, rv_kep, n_rvset, labels, colors, markers, pl_id):
res_okep = rv_opl - rv_kep
res_skep = rv_spl - rv_kep
#xlim1, xlim2 = anc.compute_limits(phi_rv, 0.05)
xlim1, xlim2 = 0., 1.
#miny = np.min(res_rv-sim_rv[:,2])
#maxy = np.max(res_rv+sim_rv[:,2])
miny = min(np.min(res_okep-sim_rv[:,2]), np.min(res_skep-sim_rv[:,2]))
maxy = max(np.max(res_okep+sim_rv[:,2]), np.max(res_skep+sim_rv[:,2]))
ylim1, ylim2 = anc.compute_limits(np.array([miny, maxy]), 0.05)
ax.axhline(0., color='gray', ls='-')
for iset in range(0, n_rvset):
sel = sim_rv[:,7].astype(int) == iset+1
ax.errorbar(phi_rv[sel], res_okep[sel], yerr=sim_rv[sel,2], fmt=markers[iset], mfc=colors[iset], mec='black', ecolor=colors[iset], capsize=0, label=labels[iset])
ax.errorbar(phi_rv[sel], res_skep[sel], yerr=sim_rv[sel,2], fmt=markers[iset], ms= 8, mfc='None', mec=colors[iset], mew=1., ecolor=colors[iset], capsize=0, label=labels[iset])
#ax.errorbar(phi_rv[sel], res_rv[sel], yerr=sim_rv[sel,2], fmt=markers[iset], mfc=colors[iset], mec='black', ecolor=colors[iset], capsize=0, label=labels[iset])
ax.set_xlabel('$\phi$ [phase planet %s]' %(pl_id))
ax.set_xlim([xlim1, xlim2])
ax.set_ylim([ylim1, ylim2])
ax.set_ylabel('res [m/s]')
plt.draw()
return ax
def plot_model_res(ax, t_epoch, t_model, rv_model, sim_rv, n_rvset, labels, colors, markers):
t_rv = sim_rv[:,0] - t_epoch
rv_obs = sim_rv[:,1] - sim_rv[:,5]
rv_sim = sim_rv[:,3]
res_rv = rv_obs - rv_sim
xlim1, xlim2 = anc.compute_limits(t_rv, 0.05)
miny = np.min(res_rv-sim_rv[:,2])
maxy = np.max(res_rv+sim_rv[:,2])
ylim1, ylim2 = anc.compute_limits(np.array([miny, maxy]), 0.05)
ax.axhline(0., color='gray', ls='-')
for iset in range(0, n_rvset):
sel = sim_rv[:,7].astype(int) == iset+1
ax.errorbar(t_rv[sel], res_rv[sel], yerr=sim_rv[sel,2], fmt=markers[iset], mfc=colors[iset], mec='black', ecolor=colors[iset], capsize=0, label=labels[iset])
ax.set_xlabel('BJD-%.3f' %(t_epoch))
ax.set_xlim([xlim1, xlim2])
ax.set_ylim([ylim1, ylim2])
ax.set_ylabel('res [m/s]')
plt.draw()
return ax
def plot_phase_res(ax, phi_rv, rv_opl, rv_spl, res_rv, sim_rv, rv_kep, n_rvset,
labels, colors, markers, pl_id):
res_okep = rv_opl - rv_kep
res_skep = rv_spl - rv_kep
#xlim1, xlim2 = anc.compute_limits(phi_rv, 0.05)
xlim1, xlim2 = 0., 1.
#miny = np.min(res_rv-sim_rv[:,2])
#maxy = np.max(res_rv+sim_rv[:,2])
miny = min(np.min(res_okep - sim_rv[:, 2]),
np.min(res_skep - sim_rv[:, 2]))
maxy = max(np.max(res_okep + sim_rv[:, 2]),
np.max(res_skep + sim_rv[:, 2]))
ylim1, ylim2 = anc.compute_limits(np.array([miny, maxy]), 0.05)
ax.axhline(0., color='gray', ls='-')
for iset in range(0, n_rvset):
sel = sim_rv[:, 7].astype(int) == iset + 1
ax.errorbar(phi_rv[sel],
res_okep[sel],
yerr=sim_rv[sel, 2],
fmt=markers[iset],
mfc=colors[iset],
mec='black',
ecolor=colors[iset],
capsize=0,
label=labels[iset])
ax.errorbar(phi_rv[sel],
res_skep[sel],
yerr=sim_rv[sel, 2],
fmt=markers[iset],
ms=8,
mfc='None',
mec=colors[iset],
mew=1.,
ecolor=colors[iset],
capsize=0,
label=labels[iset])
#ax.errorbar(phi_rv[sel], res_rv[sel], yerr=sim_rv[sel,2], fmt=markers[iset], mfc=colors[iset], mec='black', ecolor=colors[iset], capsize=0, label=labels[iset])
ax.set_xlabel('$\phi$ [phase planet %s]' % (pl_id))
ax.set_xlim([xlim1, xlim2])
ax.set_ylim([ylim1, ylim2])
ax.set_ylabel('res [m/s]')
plt.draw()
return ax
def plot_phase_rv(ax, phi_model, rv_model, phi_rv, rv_opl, rv_spl, sim_rv,
n_rvset, labels, colors, markers):
#xlim1, xlim2 = anc.compute_limits(phi_rv, 0.05)
xlim1, xlim2 = 0., 1.
miny = np.min(rv_opl - sim_rv[:, 2])
maxy = np.max(rv_opl + sim_rv[:, 2])
ylim1, ylim2 = anc.compute_limits(np.array([miny, maxy]), 0.05)
ax.axhline(0., color='gray', ls='-')
ax.plot(phi_model,
rv_model,
marker='None',
ls='--',
lw=0.5,
color='darkgray',
label='model Keplerian')
for iset in range(0, n_rvset):
sel = sim_rv[:, 7].astype(int) == iset + 1
ax.errorbar(phi_rv[sel],
rv_opl[sel],
yerr=sim_rv[sel, 2],
fmt=markers[iset],
mfc=colors[iset],
mec='black',
ecolor=colors[iset],
capsize=0)
ax.plot(phi_rv[sel],
rv_spl[sel],
marker=markers[iset],
mfc='None',
mec=colors[iset],
mew=1.,
ms=8,
ls='') # skip simulated
ax.legend(loc=0, fontsize=9, numpoints=1)
ax.set_xlim([xlim1, xlim2])
ax.set_xticklabels([])
ax.set_ylim([ylim1, ylim2])
ax.set_ylabel('RV [m/s]')
plt.draw()
return ax
def plot_phase_rv(ax, phi_model, rv_model, phi_rv, rv_opl, rv_spl, sim_rv, n_rvset, labels, colors, markers):
#xlim1, xlim2 = anc.compute_limits(phi_rv, 0.05)
xlim1, xlim2 = 0., 1.
miny = np.min(rv_opl-sim_rv[:,2])
maxy = np.max(rv_opl+sim_rv[:,2])
ylim1, ylim2 = anc.compute_limits(np.array([miny, maxy]), 0.05)
ax.axhline(0., color='gray', ls='-')
ax.plot(phi_model, rv_model, marker='None', ls='--', lw=0.5, color='darkgray', label='model Keplerian')
for iset in range(0, n_rvset):
sel = sim_rv[:,7].astype(int) == iset+1
ax.errorbar(phi_rv[sel], rv_opl[sel], yerr=sim_rv[sel,2], fmt=markers[iset], mfc=colors[iset], mec='black', ecolor=colors[iset], capsize=0)
ax.plot(phi_rv[sel], rv_spl[sel], marker=markers[iset], mfc='None', mec=colors[iset], mew=1., ms=8, ls='') # skip simulated
ax.legend(loc=0, fontsize=9, numpoints=1)
ax.set_xlim([xlim1, xlim2])
ax.set_xticklabels([])
ax.set_ylim([ylim1, ylim2])
ax.set_ylabel('RV [m/s]')
plt.draw()
return ax
def main():
cli = anc.get_args()
# read derived posterior file
derived_file = os.path.join(cli.full_path, 'derived_posterior.hdf5')
h5f = h5py.File(derived_file, 'r')
derived_names = np.array(h5f['derived_names'], dtype='S10')
derived_posterior_in = np.array(h5f['derived_posterior'], dtype=np.float64)
h5f.close()
n_der = derived_names.shape[0]
n_flatchain = derived_posterior_in.shape[0]
derived_posterior = anc.derived_posterior_check(derived_names,
derived_posterior_in)
label_separation = -0.90 # if uses this, comment ax.xyaxis.labelpad = label_pad
label_pad = 12 # it uses this, comment ax.xyaxis.set_label_coords()...
label_size = 8
ticklabel_size = 4
if (n_der > 2):
#label_separation = -0.1 - ( 0.075 * (n_der-2) )
label_separation = -0.15 - (0.125 * (n_der - 2))
#else:
#label_separation = -0.15
#label_size = label_size - 1 * int(n_der / 10.)
#label_size = label_size - 1 * int(n_der / 5.)
label_size = label_size - 1 * int(n_der / 2.5)
labels_list = anc.derived_labels(derived_names, cli.m_type)
k = anc.get_bins(derived_posterior, rule='doane')
if (cli.overplot is not None):
## OPEN summary_parameters.hdf5 FILE
s_h5f = h5py.File(
os.path.join(cli.full_path, 'summary_parameters.hdf5'), 'r')
# take only the selected sample
s_overplot = '%04d' % (cli.overplot)
#overp_der = s_h5f['parameters/%s/derived/parameters' %(s_overplot)][...]
read_der = s_h5f['parameters/%s/derived/parameters' %
(s_overplot)][...]
s_h5f.close()
overp_der = anc.derived_parameters_check(derived_names, read_der,
derived_posterior)
#fig = plt.figure(figsize=(12,12))
fig = plt.figure(figsize=(6, 6))
fig.subplots_adjust(hspace=0.05, wspace=0.05)
for ix in range(0, n_der):
x_data = derived_posterior[:, ix]
x_min, x_max = anc.compute_limits(x_data, 0.05)
if (x_min == x_max):
x_min = x_min - 1.
x_max = x_max + 1.
for iy in range(0, n_der):
y_data = derived_posterior[:, iy]
y_min, y_max = anc.compute_limits(y_data, 0.05)
if (y_min == y_max):
y_min = y_min - 1.
y_max = y_max + 1.
if (iy > ix): # correlation plot
anc.print_both('correlation %s vs %s' %
(derived_names[ix], derived_names[iy]))
ax = plt.subplot2grid((n_der + 1, n_der), (iy, ix))
hist2d_counts, xedges, yedges, image2d = ax.hist2d(\
x_data, y_data, bins=k,
range=[[x_data.min(), x_data.max()],[y_data.min(), y_data.max()]],
cmap=cm.gray_r,
#normed=True
normed=False
)
#new_k = int(k/3)
new_k = k
hist2d_counts_2, xedges_2, yedges_2 = np.histogram2d(\
x_data, y_data, bins=new_k,
range=[[x_data.min(), x_data.max()],[y_data.min(), y_data.max()]],
#normed=True
density=False
)
x_bins = [
0.5 * (xedges_2[i] + xedges_2[i + 1])
for i in range(0, new_k)
]
y_bins = [
0.5 * (yedges_2[i] + yedges_2[i + 1])
for i in range(0, new_k)
]
nl = 5
levels = [1. - np.exp(-0.5 * ii) for ii in range(0, nl)
] # 2D sigmas: 0sigma, 1sigma, 2sigma, 3sigma, ..
ax.contour(
x_bins,
y_bins,
hist2d_counts_2.T,
nl,
cmap=cm.viridis,
linestyles='solid',
linewidths=0.5,
#normed=True
)
if (cli.overplot is not None):
# plot selected overplot sample
# check angle and plot %360 and %-360...
if ('w' in derived_names[ix] or 'lN' in derived_names[ix]
or 'mA' in derived_names[ix]):
ax.axvline(overp_der[ix] % 360.,
color='C0',
ls='--',
lw=1.1,
alpha=0.7)
ax.axvline(overp_der[ix] % -360.,
color='C0',
ls='--',
lw=1.1,
alpha=0.7)
else:
ax.axvline(overp_der[ix],
color='C0',
ls='--',
lw=1.1,
alpha=0.7)
if ('w' in derived_names[iy] or 'lN' in derived_names[iy]
or 'mA' in derived_names[iy]):
ax.axhline(overp_der[iy] % 360.,
color='C0',
ls='--',
lw=1.1,
alpha=0.7)
ax.axhline(overp_der[iy] % -360.,
color='C0',
ls='--',
lw=1.1,
alpha=0.7)
else:
ax.axhline(overp_der[iy],
color='C0',
ls='--',
lw=1.1,
alpha=0.7)
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
if (iy == n_der - 1):
set_xaxis(ax, label_size, label_separation, label_pad,
ticklabel_size, labels_list[ix],
[xedges[0], xedges[-1], 4])
if (ix == 0):
set_yaxis(ax, label_size, label_separation, label_pad,
ticklabel_size, labels_list[iy],
[yedges[0], yedges[-1], 5])
ax.set_ylim([y_min, y_max])
ax.set_xlim([x_min, x_max])
plt.draw()
elif (iy == ix): # distribution plot
anc.print_both('%s histogram' % (derived_names[ix]))
ax = plt.subplot2grid((n_der + 1, n_der), (ix, ix))
if (ix == n_der - 1):
hist_orientation = 'horizontal'
else:
hist_orientation = 'vertical'
idx = np.argsort(x_data)
if (not cli.cumulative):
# HISTOGRAM
hist_counts, edges, patces = ax.hist(
x_data,
bins=k,
range=[x_data.min(), x_data.max()],
histtype='stepfilled',
color='darkgrey',
#edgecolor='lightgray',
edgecolor='None',
align='mid',
orientation=hist_orientation,
#normed=True,
density=True,
stacked=True)
else:
# CUMULATIVE HISTOGRAM
hist_counts, edges, patces = ax.hist(
x_data,
bins=k,
range=[x_data.min(), x_data.max()],
histtype='stepfilled',
color='darkgrey',
#edgecolor='lightgray',
edgecolor='None',
align='mid',
orientation=hist_orientation,
density=True,
stacked=True,
cumulative=True)
#print parameter_names_emcee[ix], overp_der[ix]
if (ix == n_der - 1):
if (cli.overplot is not None):
# check angle and plot %360 and %-360...
if ('w' in derived_names[ix]
or 'lN' in derived_names[ix]
or 'mA' in derived_names[ix]):
ax.axhline(overp_der[ix] % 360.,
color='C0',
ls='--',
lw=1.1,
alpha=0.7)
ax.axhline(overp_der[ix] % -360.,
color='C0',
ls='--',
lw=1.1,
alpha=0.7)
else:
# plot selected overplot sample
ax.axhline(overp_der[ix],
color='C0',
ls='--',
lw=1.1,
alpha=0.7)
ax.set_ylim([y_min, y_max])
else:
if (cli.overplot is not None):
if ('w' in derived_names[ix]
or 'lN' in derived_names[ix]
or 'mA' in derived_names[ix]):
ax.axvline(overp_der[ix] % 360.,
color='C0',
ls='--',
lw=1.1,
alpha=0.7)
ax.axvline(overp_der[ix] % -360.,
color='C0',
ls='--',
lw=1.1,
alpha=0.7)
else:
# plot selected overplot sample
ax.axvline(overp_der[ix],
color='C0',
ls='--',
lw=1.1,
alpha=0.7)
ax.set_xlim([x_min, x_max])
if (cli.overplot is not None):
print derived_names[ix], ' overplot val = ', overp_der[
ix], ' min = ', x_data.min(), ' max = ', x_data.max()
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
ax.set_title(labels_list[ix], fontsize=label_size)
plt.draw()
plot_folder = os.path.join(cli.full_path, 'plots')
if (not os.path.isdir(plot_folder)):
os.makedirs(plot_folder)
correlation_file = os.path.join(plot_folder, 'derived_triangle.png')
fig.savefig(correlation_file, bbox_inches='tight', dpi=300)
anc.print_both('png done')
correlation_file = os.path.join(plot_folder, 'derived_triangle.pdf')
fig.savefig(correlation_file, bbox_inches='tight', dpi=96)
anc.print_both('pdf done')
plt.close(fig)
return
def main():
# ---
# initialize logger
logger = logging.getLogger("Main_log")
logger.setLevel(logging.DEBUG)
formatter = logging.Formatter("%(asctime)s - %(message)s")
# global variables
label_separation=-0.90 # if uses this, comment ax.xyaxis.labelpad = label_pad
label_pad = 12 # it uses this, comment ax.xyaxis.set_label_coords()...
label_size = 8
ticklabel_size = 4
def set_xaxis(ax, label_size, label_separation, label_pad, ticklabel_size, kel_label, ticks_formatter, tick_fmt='%.4f'):
ax.get_xaxis().set_visible(True)
ax.xaxis.set_tick_params(labelsize=ticklabel_size)
ax.xaxis.set_label_coords(0.5, label_separation)
#ax.ticklabel_format(style='plain', axis='both', useOffset=False)
plt.setp(ax.xaxis.get_majorticklabels(), rotation=70 )
#ax.xaxis.labelpad = label_pad
ax.set_xlabel(kel_label, fontsize=label_size)
tick_step = (ticks_formatter[1] - ticks_formatter[0]) / ticks_formatter[2]
ax.xaxis.set_ticks(np.arange(ticks_formatter[0], ticks_formatter[1], tick_step))
tick_formatter = FormatStrFormatter(tick_fmt)
ax.xaxis.set_major_formatter(tick_formatter)
return
def set_yaxis(ax, label_size, label_separation, label_pad, ticklabel_size, kel_label, ticks_formatter, tick_fmt='%.4f'):
ax.get_yaxis().set_visible(True)
ax.yaxis.set_tick_params(labelsize=ticklabel_size)
ax.yaxis.set_label_coords(label_separation,0.5)
#ax.ticklabel_format(style='plain', axis='both', useOffset=False)
#ax.yaxis.labelpad = label_pad
ax.set_ylabel(kel_label, fontsize=label_size)
tick_step = (ticks_formatter[1] - ticks_formatter[0]) / ticks_formatter[2]
ax.yaxis.set_ticks(np.arange(ticks_formatter[0], ticks_formatter[1], tick_step))
tick_formatter = FormatStrFormatter(tick_fmt)
ax.yaxis.set_major_formatter(tick_formatter)
return
print
print ' ================== '
print ' CORRELATION PLOTS'
print ' ================== '
print
# read cli arguments
cli = anc.get_args()
#plot_folder = prepare_plot_folder(working_path)
emcee_plots = anc.prepare_emcee_plot_folder(cli.full_path)
log_file = os.path.join(emcee_plots, 'emcee_triangle_log.txt')
flog = logging.FileHandler(log_file, 'w')
flog.setLevel(logging.DEBUG)
flog.setFormatter(formatter)
logger.addHandler(flog)
# log screen
slog = logging.StreamHandler()
slog.setLevel(logging.DEBUG)
slog.setFormatter(formatter)
logger.addHandler(slog)
# computes mass conversion factor
#m_factor = anc.mass_conversion_factor(cli.m_type)
m_factor, m_unit = anc.mass_type_factor(1., cli.m_type, False)
# set emcee and trades folder
emcee_folder = cli.full_path
trades_folder = os.path.join(os.path.dirname(cli.full_path), '')
# and best folder
emcee_file, emcee_best, folder_best = anc.get_emcee_file_and_best(emcee_folder, cli.temp_status)
# get data from the hdf5 file
parameter_names_emcee, parameter_boundaries, chains, acceptance_fraction, autocor_time, lnprobability, ln_err_const, completed_steps = anc.get_data(emcee_file, cli.temp_status)
# print Memory occupation of ...
anc.print_memory_usage(chains)
nfit, nwalkers, nruns, nburnin, nruns_sel = anc.get_emcee_parameters(chains, cli.temp_status, cli.nburnin, completed_steps)
logger.info('nfit(%d), nwalkers(%d), nruns(%d), nburnin(%d), nruns_sel(%d)' %(nfit, nwalkers, nruns, nburnin, nruns_sel))
# test label_separation
#if (nfit <= 3): label_separation = -0.1
if(nfit > 2):
#label_separation = -0.1 - ( 0.075 * (nfit-2) ) # good for figsize=(12,12)
label_separation = -0.15 - ( 0.125 * (nfit-2) ) # testing
#else:
#label_separation = -0.15
#label_size = label_size - 1 * int(nfit / 5.)
label_size = label_size - 1 * int(nfit / 2.5)
# set label and legend names
kel_plot_labels = anc.keplerian_legend(parameter_names_emcee, cli.m_type)
chains_T_full, parameter_boundaries = anc.select_transpose_convert_chains(nfit, nwalkers, nburnin, nruns, nruns_sel, m_factor, parameter_names_emcee, parameter_boundaries, chains)
chains_T, flatchain_posterior_0, lnprob_burnin, thin_steps = anc.thin_the_chains(cli.use_thin, nburnin, nruns, nruns_sel, autocor_time, chains_T_full, lnprobability, burnin_done=False)
flatchain_posterior_0 = anc.fix_lambda(flatchain_posterior_0,
parameter_names_emcee
)
if(cli.boot_id > 0):
flatchain_posterior_msun = anc.posterior_back_to_msun(m_factor,parameter_names_emcee,flatchain_posterior_0)
boot_file = anc.save_bootstrap_like(emcee_folder, cli.boot_id, parameter_names_emcee, flatchain_posterior_msun)
logger.info('saved bootstrap like file: %s' %(boot_file))
del flatchain_posterior_msun
k = anc.get_auto_bins(flatchain_posterior_0)
if(cli.overplot is not None):
if(cli.adhoc is not None):
overp_names, read_par = anc.read_fitted_file(cli.adhoc)
cli.overplot = 777
else:
## OPEN summary_parameters.hdf5 FILE
s_h5f = h5py.File(os.path.join(cli.full_path, 'summary_parameters.hdf5'), 'r')
# take only the selected sample
s_overplot = '%04d' %(cli.overplot)
read_par = s_h5f['parameters/%s/fitted/parameters' %(s_overplot)][...]
s_h5f.close()
# fitted parameters has always Mp/Ms in Msun/Mstar, so it is needed to rescale it properly
overp_par = read_par.copy()
for ii in range(0, nfit):
if('Ms' in parameter_names_emcee[ii]):
#if('Ms' in overp_names[ii]):
overp_par[ii] = overp_par[ii]*m_factor
#fig, ax = plt.subplots(nrows = nfit-1, ncols=nfit, figsize=(12,12))
#fig = plt.figure(figsize=(12,12))
fig = plt.figure(figsize=(6,6))
fig.subplots_adjust(hspace=0.05, wspace=0.05)
for ix in range(0, nfit, 1):
x_data = flatchain_posterior_0[:, ix]
##x_med = median_parameters[ix]
x_min, x_max = anc.compute_limits(x_data, 0.05)
if(x_min == x_max):
x_min = parameter_boundaries[ix,0]
x_max = parameter_boundaries[ix,1]
#x_max_mean = mode_parameters[ix]
for iy in range(nfit-1, -1, -1):
y_data = flatchain_posterior_0[:, iy]
y_min, y_max = anc.compute_limits(y_data, 0.05)
if(y_min == y_max):
y_min = parameter_boundaries[iy,0]
y_max = parameter_boundaries[iy,1]
#y_max_mean = mode_parameters[iy]
if(iy > ix): # correlation plot
logger.info('%s vs %s' %(parameter_names_emcee[ix], parameter_names_emcee[iy]))
ax = plt.subplot2grid((nfit+1, nfit), (iy,ix))
#hist2d_counts, xedges, yedges, image2d = ax.hist2d(x_data, y_data, bins=k, range=[[x_data.min(), x_data.max()],[y_data.min(), y_data.max()]], cmap=cm.gray_r, normed=True)
hist2d_counts, xedges, yedges, image2d = ax.hist2d(\
x_data, y_data, bins=k,
range=[[x_data.min(), x_data.max()],[y_data.min(), y_data.max()]],
cmap=cm.gray_r,
normed=False
#density=False
)
#new_k = int(k/3)
new_k = k
hist2d_counts_2, xedges_2, yedges_2 = np.histogram2d(\
x_data, y_data, bins=new_k,
range=[[x_data.min(), x_data.max()],[y_data.min(), y_data.max()]],
#normed=True
density=False
)
x_bins = [0.5*(xedges_2[i]+xedges_2[i+1]) for i in range(0, new_k)]
y_bins = [0.5*(yedges_2[i]+yedges_2[i+1]) for i in range(0, new_k)]
#ax.contour(x_bins, y_bins, hist2d_counts_2.T, 3, cmap=cm.gray, linestyle='solid', linewidths=(0.7, 0.7, 0.7))
nl = 5
levels = [1.-np.exp(-0.5*ii) for ii in range(0,nl)] # 2D sigmas: 0sigma, 1sigma, 2sigma, 3sigma, ..
#ax.contour(x_bins, y_bins, hist2d_counts_2.T, levels, cmap=cm.gray, linestyle='solid', linewidths=(0.7, 0.7, 0.7))
#ax.contour(x_bins, y_bins, hist2d_counts_2.T, levels, cmap=cm.viridis, linestyle='solid', linewidths=1.)
#ax.contour(x_bins, y_bins, hist2d_counts_2.T, cmap=cm.viridis, linestyle='solid', linewidths=0.7)
#ax.contour(x_bins, y_bins, hist2d_counts_2.T, levels, cmap=cm.viridis, linestyle='solid', linewidths=0.7, normed=True)
ax.contour(x_bins, y_bins, hist2d_counts_2.T,
nl, cmap=cm.viridis,
linestyles='solid', linewidths=0.5,
#normed=True
)
if(cli.overplot is not None):
# plot selected overplot sample
ax.axvline(overp_par[ix], color='C0', ls='--', lw=1.1, alpha=0.5)
ax.axhline(overp_par[iy], color='C0', ls='--', lw=1.1, alpha=0.5)
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
if(iy == nfit-1):
set_xaxis(ax, label_size, label_separation, label_pad, ticklabel_size, kel_plot_labels[ix], [xedges[0], xedges[-1], 4])
if(ix == 0):
set_yaxis(ax, label_size, label_separation, label_pad, ticklabel_size, kel_plot_labels[iy], [yedges[0], yedges[-1], 5])
ax.set_ylim([y_min, y_max])
ax.set_xlim([x_min, x_max])
plt.draw()
elif(iy == ix): # distribution plot
logger.info('%s histogram' %(parameter_names_emcee[ix]))
ax = plt.subplot2grid((nfit+1, nfit), (ix,ix))
if (ix == nfit-1):
hist_orientation='horizontal'
else:
hist_orientation='vertical'
idx = np.argsort(x_data)
if(not cli.cumulative):
# HISTOGRAM
hist_counts, edges, patces = ax.hist(x_data, bins=k,
range=[x_data.min(), x_data.max()], histtype='stepfilled',
color='darkgrey',
#edgecolor='lightgray',
edgecolor='None',
align='mid',
orientation=hist_orientation,
#normed=True,
density=True,
stacked=True
)
else:
# CUMULATIVE HISTOGRAM
hist_counts, edges, patces = ax.hist(x_data, bins=k,
range=[x_data.min(), x_data.max()],
histtype='stepfilled',
color='darkgrey',
#edgecolor='lightgray',
edgecolor='None',
align='mid',
orientation=hist_orientation,
density=True,
stacked=True,
cumulative=True
)
if (ix == nfit-1):
ax.set_ylim([y_min, y_max])
if(cli.overplot is not None):
# plot selected overplot sample
ax.axhline(overp_par[ix], color='C0', ls='--', lw=1.1, alpha=0.5)
else:
ax.set_xlim([x_min, x_max])
if(cli.overplot is not None):
# plot selected overplot sample
ax.axvline(overp_par[ix], color='C0', ls='--', lw=1.1, alpha=0.5)
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
ax.set_title(kel_plot_labels[ix], fontsize=label_size)
plt.draw()
logger.info('saving plot')
emcee_fig_file = os.path.join(emcee_plots, 'emcee_triangle.png')
fig.savefig(emcee_fig_file, bbox_inches='tight', dpi=300)
logger.info('png done')
emcee_fig_file = os.path.join(emcee_plots, 'emcee_triangle.pdf')
fig.savefig(emcee_fig_file, bbox_inches='tight', dpi=96)
logger.info('pdf done')
plt.close(fig)
logger.info('')
return
def main():
print
print ' ======================== '
print ' TRADES+EMCEE CHAIN PLOTS'
print ' ======================== '
print
# read cli arguments
cli = anc.get_args()
# computes mass conversion factor
#m_factor, m_unit = anc.mass_conversion_factor_and_unit(cli.m_type)
m_factor, m_unit = anc.mass_type_factor(1., cli.m_type, False)
# set emcee and trades folder
emcee_folder = cli.full_path
trades_folder = os.path.join(os.path.dirname(cli.full_path), '')
# and best folder
emcee_file, emcee_best, folder_best = anc.get_emcee_file_and_best(emcee_folder, cli.temp_status)
parameter_names_emcee, parameter_boundaries, chains, acceptance_fraction, autocor_time, lnprobability, ln_err_const, completed_steps = anc.get_data(emcee_file, cli.temp_status)
# set label and legend names
kel_labels = anc.keplerian_legend(parameter_names_emcee, cli.m_type)
nfit, nwalkers, nruns, nburnin, nruns_sel = anc.get_emcee_parameters(chains, cli.temp_status, cli.nburnin, completed_steps)
anc.print_memory_usage(chains)
chains_T_full, parameter_boundaries = anc.select_transpose_convert_chains(nfit, nwalkers, nburnin, nruns, nruns_sel, m_factor, parameter_names_emcee, parameter_boundaries, chains)
if(cli.use_thin or cli.use_thin > 0):
chains_T, flatchain_posterior_0, lnprob_burnin, thin_steps, chains_T_full_thinned = anc.thin_the_chains(cli.use_thin, nburnin, nruns, nruns_sel, autocor_time, chains_T_full, lnprobability, burnin_done=False, full_chains_thinned=True)
nburnin_plt = np.rint(nburnin / thin_steps).astype(int)
nend = np.rint(nruns / thin_steps).astype(int)
else:
chains_T, flatchain_posterior_0, lnprob_burnin, thin_steps = anc.thin_the_chains(cli.use_thin, nburnin, nruns, nruns_sel, autocor_time, chains_T_full, lnprobability, burnin_done=False, full_chains_thinned=False)
nburnin_plt = nburnin
nend = nruns
#name_par, name_excluded = anc.get_sample_list(cli.sample_str, parameter_names_emcee)
#sample_parameters, idx_sample = anc.pick_sample_parameters(flatchain_posterior_0, parameter_names_emcee, name_par = name_par, name_excluded = name_excluded)
#flatchain_posterior_1 = flatchain_posterior_0
# fix lambda?
#flatchain_posterior_0 = anc.fix_lambda(flatchain_posterior_0,
#parameter_names_emcee
#)
if(cli.boot_id > 0):
flatchain_posterior_msun = anc.posterior_back_to_msun(m_factor,parameter_names_emcee,flatchain_posterior_0)
boot_file = anc.save_bootstrap_like(emcee_folder, cli.boot_id, parameter_names_emcee, flatchain_posterior_msun)
logger.info('saved bootstrap like file: %s' %(boot_file))
del flatchain_posterior_msun
k = anc.get_auto_bins(flatchain_posterior_0)
try:
overplot = int(cli.overplot)
except:
overplot = None
## OPEN summary_parameters.hdf5 FILE
s_h5f = h5py.File(os.path.join(cli.full_path, 'summary_parameters.hdf5'), 'r')
if(overplot is not None):
# sample_parameters
ci_fitted = s_h5f['confidence_intervals/fitted/ci'][...]
sample_parameters = s_h5f['parameters/0666/fitted/parameters'][...]
sample_lgllhd = s_h5f['parameters/0666'].attrs['lgllhd']
try:
sample2_parameters = s_h5f['parameters/0667/fitted/parameters'][...]
sample2_lgllhd = s_h5f['parameters/0667'].attrs['lgllhd']
except:
sample2_parameters = None
sample2_lgllhd = None
try:
sample3_parameters = s_h5f['parameters/0668/fitted/parameters'][...]
sample3_lgllhd = s_h5f['parameters/0668'].attrs['lgllhd']
except:
sample3_parameters = None
sample3_lgllhd = None
median_parameters = s_h5f['parameters/1051/fitted/parameters'][...]
median_lgllhd = s_h5f['parameters/1051'].attrs['lgllhd']
max_lnprob_parameters = s_h5f['parameters/2050/fitted/parameters'][...]
max_lgllhd = s_h5f['parameters/2050'].attrs['lgllhd']
try:
mode_parameters = s_h5f['parameters/3051/fitted/parameters'][...]
mode_lgllhd = s_h5f['parameters/3051'].attrs['lgllhd']
except:
mode_parameters = None
mode_lgllhd = None
overp_par = s_h5f['parameters/%04d/fitted/parameters' %(overplot)][...]
overp_lgllhd = s_h5f['parameters/%04d' %(overplot)].attrs['lgllhd']
#nfit = s_h5f['confidence_intervals/fitted'].attrs['nfit']
ndata = s_h5f['confidence_intervals/fitted'].attrs['ndata']
dof = s_h5f['confidence_intervals/fitted'].attrs['dof']
s_h5f.close()
emcee_plots = os.path.join(cli.full_path,'plots')
if (not os.path.isdir(emcee_plots)):
os.makedirs(emcee_plots)
for i in range(0, nfit):
if('Ms' in parameter_names_emcee[i]):
conv_plot = m_factor
else:
conv_plot = 1.
emcee_fig_file = os.path.join(emcee_plots, 'chain_%03d_%s.png' %(i+1, parameter_names_emcee[i].strip()))
print ' %s' %(emcee_fig_file),
#fig, (axChain, axHist) = plt.subplots(nrows=1, ncols=2, figsize=(12,12))
fig, (axChain, axHist) = plt.subplots(nrows=1, ncols=2, figsize=(6,6))
(counts, bins_val, patches) = axHist.hist(flatchain_posterior_0[:,i], bins=k,
range=(flatchain_posterior_0[:,i].min(), flatchain_posterior_0[:,i].max()),
orientation='horizontal',
density=True, stacked=True,
histtype='stepfilled',
color='darkgrey', edgecolor='lightgray',
align='mid'
)
xpdf = scipy_norm.pdf(flatchain_posterior_0[:,i], loc = flatchain_posterior_0[:,i].mean(), scale = flatchain_posterior_0[:,i].std())
idx = np.argsort(flatchain_posterior_0[:,i])
axHist.plot(xpdf[idx], flatchain_posterior_0[idx,i], color='black', marker='None', ls='-.', lw=1.5, label='pdf')
# chains after burn-in
#axChain.plot(chains_T[:,:,i], '-', alpha=0.3)
# chains with the burn-in
if(cli.use_thin):
axChain.plot(chains_T_full_thinned[:,:,i], '-', alpha=0.3)
else:
axChain.plot(chains_T_full[:,:,i], '-', alpha=0.3)
axChain.axvspan(0, nburnin_plt, color='gray', alpha=0.45)
axChain.axvline(nburnin_plt, color='gray', ls='-', lw=1.5)
if(overplot is not None):
if(mode_parameters is not None):
# plot of mode (mean of higher peak/bin)
axChain.axhline(mode_parameters[i]*conv_plot, color='red', ls='-', lw=2.1, alpha=1, label='mode')
# plot of median
axChain.axhline(median_parameters[i]*conv_plot, marker='None', c='blue',ls='-', lw=2.1, alpha=1.0, label='median fit')
# plot of max_lnprob
axChain.axhline(max_lnprob_parameters[i]*conv_plot, marker='None', c='black',ls='-', lw=1.1, alpha=1.0, label='max lnprob')
if(sample_parameters is not None):
# plot of sample_parameters
axChain.axhline(sample_parameters[i]*conv_plot, marker='None', c='orange',ls='--', lw=2.3, alpha=0.77, label='picked: %12.7f' %(sample_parameters[i]))
if(sample2_parameters is not None):
# plot of sample2_parameters
axChain.axhline(sample2_parameters[i]*conv_plot, marker='None', c='cyan',ls=':', lw=2.7, alpha=0.77, label='close lgllhd: %12.7f' %(sample2_parameters[i]))
if(sample3_parameters is not None):
# plot of sample3_parameters
axChain.axhline(sample3_parameters[i]*conv_plot, marker='None', c='yellow',ls='-', lw=3.1, alpha=0.66, label='close lgllhd: %12.7f' %(sample3_parameters[i]))
if(overplot not in [1050, 1051, 2050, 3050, 3051]):
axChain.axhline(overp_par[i]*conv_plot, marker='None', c='black',ls='--', lw=2.5, alpha=0.6, label='overplot %d' %(overplot))
# plot ci
axChain.axhline(ci_fitted[i,0]*conv_plot, marker='None', c='forestgreen',ls='-', lw=2.1, alpha=1.0, label='CI 15.865th (%.5f)' %(ci_fitted[i,0]*conv_plot))
axChain.axhline(ci_fitted[i,1]*conv_plot, marker='None', c='forestgreen',ls='-', lw=2.1, alpha=1.0, label='CI 84.135th (%.5f)' %(ci_fitted[i,1]*conv_plot))
axChain.ticklabel_format(useOffset=False)
xlabel = '$N_\mathrm{steps}$'
if(cli.use_thin):
xlabel = '$N_\mathrm{steps} \\times %d$' %(thin_steps)
axChain.set_xlabel(xlabel)
axChain.set_xlim([0, nend])
axChain.set_ylabel(kel_labels[i])
y_min = flatchain_posterior_0[:,i].min()
y_max = flatchain_posterior_0[:,i].max()
axChain.set_ylim([y_min, y_max])
axChain.set_title('Full chain %s:=[%.3f , %.3f]' %(kel_labels[i], parameter_boundaries[i,0], parameter_boundaries[i,1]))
plt.draw()
axHist.ticklabel_format(useOffset=False)
axHist.tick_params(direction='inout', labelleft=False)
axHist.set_ylim([y_min, y_max])
if(overplot is not None):
if(mode_parameters is not None):
# plot mode
axHist.axhline(mode_parameters[i]*conv_plot, color='red', ls='-', lw=2.1, alpha=1, label='mode')
# plot median
axHist.axhline(median_parameters[i]*conv_plot, marker='None', c='blue',ls='-', lw=2.1, alpha=1.0, label='median fit')
# plot of max_lnprob
axHist.axhline(max_lnprob_parameters[i]*conv_plot, marker='None', c='black',ls='-', lw=1.1, alpha=1.0, label='max lnprob')
if(sample_parameters is not None):
# plot of sample_parameters
axHist.axhline(sample_parameters[i]*conv_plot, marker='None', c='orange',ls='--', lw=2.3, alpha=0.77, label='picked: %12.7f' %(sample_parameters[i]*conv_plot))
if(sample2_parameters is not None):
# plot of sample2_parameters
axHist.axhline(sample2_parameters[i]*conv_plot, marker='None', c='cyan',ls=':', lw=2.7, alpha=0.77, label='close lgllhd: %12.7f' %(sample2_parameters[i]))
if(sample3_parameters is not None):
# plot of sample3_parameters
axHist.axhline(sample3_parameters[i]*conv_plot, marker='None', c='yellow',ls='-', lw=3.1, alpha=0.66, label='close lgllhd: %12.7f' %(sample3_parameters[i]))
if(overplot not in [1050, 1051, 2050, 3050, 3051]):
axHist.axhline(overp_par[i]*conv_plot, marker='None', c='black',ls='--', lw=2.5, alpha=0.8, label='overplot %d' %(overplot))
# plot ci
axHist.axhline(ci_fitted[i,0]*conv_plot, marker='None', c='forestgreen',ls='-', lw=2.1, alpha=1.0, label='CI 15.865th (%.5f)' %(ci_fitted[i,0]*conv_plot))
axHist.axhline(ci_fitted[i,1]*conv_plot, marker='None', c='forestgreen',ls='-', lw=2.1, alpha=1.0, label='CI 84.135th (%.5f)' %(ci_fitted[i,1]*conv_plot))
axHist.set_title('Distribution of posterior chain')
axHist.legend(loc='center left', fontsize=9, bbox_to_anchor=(1, 0.5))
plt.draw()
fig.savefig(emcee_fig_file, bbox_inches='tight', dpi=150)
print ' saved'
print
#fig = plt.figure(figsize=(12,12))
fig = plt.figure(figsize=(6,6))
# lnprob
xlabel = '$N_\mathrm{steps}$'
if(cli.use_thin):
xlabel = '$N_\mathrm{steps} \\times %d$' %(thin_steps)
ax = plt.subplot2grid((2,1), (0,0))
ax.plot(lnprob_burnin.T, '-', alpha=0.3)
if(overplot is not None):
posterior_msun = anc.posterior_back_to_msun(m_factor,parameter_names_emcee,flatchain_posterior_0)
post_sel, lnprob_sel = anc.select_within_all_ci(posterior_msun, ci_fitted[:,0:2], lnprob_burnin.T.reshape(-1))
#lnprob_sel = lnprob_burnin.T.reshape((-1))
lgllhd_med = np.percentile(lnprob_burnin.T.reshape(-1), 50., interpolation='midpoint')
abs_dlg = np.abs(lnprob_sel - lgllhd_med)
lgllhd_mad = np.percentile(abs_dlg, 50., interpolation='midpoint')
#lnp_min = np.min(lnprob_sel)
#lnp_max = np.max(lnprob_sel)
lnp_min = lgllhd_med - lgllhd_mad
lnp_max = lgllhd_med + lgllhd_mad
print ' lgllhd_med & mad = ',lgllhd_med, lgllhd_mad
print ' lnp_min = ',lnp_min, ' lnp_max = ',lnp_max
print ' lnl_668 = ',sample3_lgllhd
ax.axhline(lgllhd_med, color='black', ls='-', lw=1.6, alpha=0.77)
#if(sample2_lgllhd is not None):
#ax.axhline(sample2_lgllhd, marker='None', c='cyan',ls=':', lw=2.7, alpha=0.9)
if(sample3_lgllhd is not None):
ax.axhline(sample3_lgllhd, marker='None', c='yellow',ls='-', lw=3.1, alpha=0.9)
ax.axhspan(lnp_min, lnp_max, color='gray', alpha=0.77)
ax.axhline(lnp_min, color='black', ls='--', lw=1.6, alpha=0.77)
ax.axhline(lnp_max, color='black', ls='--', lw=1.6, alpha=0.77)
min_lnp = np.min(lnprob_burnin.T, axis=0).min()
max_lnp = np.max(lnprob_burnin.T, axis=0).max()
y_min, y_max = anc.compute_limits(np.asarray([min_lnp, max_lnp]), 0.05)
ax.set_ylim((y_min, y_max))
ax.set_ylabel('lnprob')
#ax.get_xaxis().set_visible(False)
ax.set_xlabel(xlabel)
# chi2r
chi2r = -2.*(lnprob_burnin.T-ln_err_const)/np.float64(dof)
ax = plt.subplot2grid((2,1), (1,0))
ax.axhline(1.0, color='gray', ls='-')
ax.plot(chi2r, '-', alpha=0.3)
if(overplot is not None):
c2r_med = -(2.*(lgllhd_med - ln_err_const))/np.float64(dof)
c2r_smax = -(2.*(lnp_min - ln_err_const))/np.float64(dof)
c2r_smin = -(2.*(lnp_max - ln_err_const))/np.float64(dof)
print ' c2r_med = ',c2r_med
print ' c2r_smin = ',c2r_smin, ' c2r_smax = ', c2r_smax
ax.axhline(c2r_med, color='black', ls='-', lw=1.6, alpha=0.77)
ax.axhspan(c2r_smin, c2r_smax, color='gray', alpha=0.77)
ax.axhline(c2r_smin, color='black', ls='--', lw=1.6, alpha=0.77)
ax.axhline(c2r_smax, color='black', ls='--', lw=1.6, alpha=0.77)
#if(sample2_lgllhd is not None):
#c2r_sample2 = -2.*(sample2_lgllhd - ln_err_const)/np.float64(dof)
#ax.axhline(c2r_sample2, marker='None', c='cyan',ls=':', lw=2.7, alpha=0.9)
if(sample3_lgllhd is not None):
c2r_sample3 = -2.*(sample3_lgllhd - ln_err_const)/np.float64(dof)
ax.axhline(c2r_sample3, marker='None', c='yellow',ls='-', lw=3.1, alpha=0.9)
c2r_min = -2.*(y_max - ln_err_const)/np.float64(dof)
c2r_max = -2.*(y_min - ln_err_const)/np.float64(dof)
ax.set_ylim((c2r_min, c2r_max))
ax.set_ylabel('$\chi^{2}/\mathrm{dof}$')
#ax.get_xaxis().set_visible(True)
ax.set_xlabel(xlabel)
fig.savefig(os.path.join(emcee_plots, 'emcee_lnprobability.png'), bbox_inches='tight', dpi=150)
print ' %s saved' %(os.path.join(emcee_plots, 'emcee_lnprobability.png'))
return
def main():
cli = anc.get_args()
# read derived posterior file
derived_file = os.path.join(cli.full_path, 'derived_posterior.hdf5')
h5f = h5py.File(derived_file, 'r')
derived_names = np.array(h5f['derived_names'], dtype='S10')
derived_posterior_in = np.array(h5f['derived_posterior'], dtype=np.float64)
h5f.close()
n_der = derived_names.shape[0]
n_flatchain = derived_posterior_in.shape[0]
derived_posterior = anc.derived_posterior_check(derived_names, derived_posterior_in)
label_separation=-0.90 # if uses this, comment ax.xyaxis.labelpad = label_pad
label_pad = 12 # it uses this, comment ax.xyaxis.set_label_coords()...
label_size = 8
ticklabel_size = 4
if(n_der > 2):
#label_separation = -0.1 - ( 0.075 * (n_der-2) )
label_separation = -0.15 - ( 0.125 * (n_der-2) )
#else:
#label_separation = -0.15
#label_size = label_size - 1 * int(n_der / 10.)
#label_size = label_size - 1 * int(n_der / 5.)
label_size = label_size - 1 * int(n_der / 2.5)
labels_list = anc.derived_labels(derived_names, cli.m_type)
k = anc.get_bins(derived_posterior, rule='doane')
if(cli.overplot is not None):
## OPEN summary_parameters.hdf5 FILE
s_h5f = h5py.File(os.path.join(cli.full_path, 'summary_parameters.hdf5'), 'r')
# take only the selected sample
s_overplot = '%04d' %(cli.overplot)
#overp_der = s_h5f['parameters/%s/derived/parameters' %(s_overplot)][...]
read_der = s_h5f['parameters/%s/derived/parameters' %(s_overplot)][...]
s_h5f.close()
overp_der = anc.derived_parameters_check(derived_names, read_der, derived_posterior)
#fig = plt.figure(figsize=(12,12))
fig = plt.figure(figsize=(6,6))
fig.subplots_adjust(hspace=0.05, wspace=0.05)
for ix in range(0, n_der):
x_data = derived_posterior[:,ix]
x_min, x_max = anc.compute_limits(x_data, 0.05)
if(x_min == x_max):
x_min = x_min - 1.
x_max = x_max + 1.
for iy in range(0, n_der):
y_data = derived_posterior[:,iy]
y_min, y_max = anc.compute_limits(y_data, 0.05)
if(y_min == y_max):
y_min = y_min - 1.
y_max = y_max + 1.
if(iy > ix): # correlation plot
anc.print_both('correlation %s vs %s' %(derived_names[ix], derived_names[iy]) )
ax = plt.subplot2grid((n_der+1, n_der), (iy,ix))
hist2d_counts, xedges, yedges, image2d = ax.hist2d(\
x_data, y_data, bins=k,
range=[[x_data.min(), x_data.max()],[y_data.min(), y_data.max()]],
cmap=cm.gray_r,
#normed=True
normed=False
)
#new_k = int(k/3)
new_k = k
hist2d_counts_2, xedges_2, yedges_2 = np.histogram2d(\
x_data, y_data, bins=new_k,
range=[[x_data.min(), x_data.max()],[y_data.min(), y_data.max()]],
#normed=True
density=False
)
x_bins = [0.5*(xedges_2[i]+xedges_2[i+1]) for i in range(0, new_k)]
y_bins = [0.5*(yedges_2[i]+yedges_2[i+1]) for i in range(0, new_k)]
nl = 5
levels = [1.-np.exp(-0.5*ii) for ii in range(0,nl)] # 2D sigmas: 0sigma, 1sigma, 2sigma, 3sigma, ..
ax.contour(x_bins, y_bins, hist2d_counts_2.T,
nl, cmap=cm.viridis,
linestyles='solid', linewidths=0.5,
#normed=True
)
if(cli.overplot is not None):
# plot selected overplot sample
# check angle and plot %360 and %-360...
if('w' in derived_names[ix] or
'lN' in derived_names[ix] or
'mA' in derived_names[ix]):
ax.axvline(overp_der[ix]%360., color='C0', ls='--', lw=1.1, alpha=0.7)
ax.axvline(overp_der[ix]%-360., color='C0', ls='--', lw=1.1, alpha=0.7)
else:
ax.axvline(overp_der[ix], color='C0', ls='--', lw=1.1, alpha=0.7)
if('w' in derived_names[iy] or
'lN' in derived_names[iy] or
'mA' in derived_names[iy]):
ax.axhline(overp_der[iy]%360., color='C0', ls='--', lw=1.1, alpha=0.7)
ax.axhline(overp_der[iy]%-360., color='C0', ls='--', lw=1.1, alpha=0.7)
else:
ax.axhline(overp_der[iy], color='C0', ls='--', lw=1.1, alpha=0.7)
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
if(iy == n_der-1):
set_xaxis(ax, label_size, label_separation, label_pad, ticklabel_size, labels_list[ix], [xedges[0], xedges[-1], 4])
if(ix == 0):
set_yaxis(ax, label_size, label_separation, label_pad, ticklabel_size, labels_list[iy], [yedges[0], yedges[-1], 5])
ax.set_ylim([y_min, y_max])
ax.set_xlim([x_min, x_max])
plt.draw()
elif(iy == ix): # distribution plot
anc.print_both('%s histogram' %(derived_names[ix]))
ax = plt.subplot2grid((n_der+1, n_der), (ix,ix))
if (ix == n_der-1):
hist_orientation='horizontal'
else:
hist_orientation='vertical'
idx = np.argsort(x_data)
if(not cli.cumulative):
# HISTOGRAM
hist_counts, edges, patces = ax.hist(x_data, bins=k,
range=[x_data.min(), x_data.max()],
histtype='stepfilled',
color='darkgrey',
#edgecolor='lightgray',
edgecolor='None',
align='mid',
orientation=hist_orientation,
#normed=True,
density=True,
stacked=True
)
else:
# CUMULATIVE HISTOGRAM
hist_counts, edges, patces = ax.hist(x_data, bins=k,
range=[x_data.min(), x_data.max()],
histtype='stepfilled',
color='darkgrey',
#edgecolor='lightgray',
edgecolor='None',
align='mid',
orientation=hist_orientation,
density=True,
stacked=True,
cumulative=True
)
#print parameter_names_emcee[ix], overp_der[ix]
if (ix == n_der-1):
if(cli.overplot is not None):
# check angle and plot %360 and %-360...
if('w' in derived_names[ix] or
'lN' in derived_names[ix] or
'mA' in derived_names[ix]):
ax.axhline(overp_der[ix]%360., color='C0', ls='--', lw=1.1, alpha=0.7)
ax.axhline(overp_der[ix]%-360., color='C0', ls='--', lw=1.1, alpha=0.7)
else:
# plot selected overplot sample
ax.axhline(overp_der[ix], color='C0', ls='--', lw=1.1, alpha=0.7)
ax.set_ylim([y_min, y_max])
else:
if(cli.overplot is not None):
if('w' in derived_names[ix] or
'lN' in derived_names[ix] or
'mA' in derived_names[ix]):
ax.axvline(overp_der[ix]%360., color='C0', ls='--', lw=1.1, alpha=0.7)
ax.axvline(overp_der[ix]%-360., color='C0', ls='--', lw=1.1, alpha=0.7)
else:
# plot selected overplot sample
ax.axvline(overp_der[ix], color='C0', ls='--', lw=1.1, alpha=0.7)
ax.set_xlim([x_min, x_max])
if(cli.overplot is not None):
print derived_names[ix], ' overplot val = ', overp_der[ix], ' min = ', x_data.min(), ' max = ', x_data.max()
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
ax.set_title(labels_list[ix], fontsize=label_size)
plt.draw()
plot_folder = os.path.join(cli.full_path, 'plots')
if (not os.path.isdir(plot_folder)):
os.makedirs(plot_folder)
correlation_file = os.path.join(plot_folder, 'derived_triangle.png')
fig.savefig(correlation_file, bbox_inches='tight', dpi=300)
anc.print_both('png done')
correlation_file = os.path.join(plot_folder, 'derived_triangle.pdf')
fig.savefig(correlation_file, bbox_inches='tight', dpi=96)
anc.print_both('pdf done')
plt.close(fig)
return
def main():
# ---
# initialize logger
logger = logging.getLogger("Main_log")
logger.setLevel(logging.DEBUG)
formatter = logging.Formatter("%(asctime)s - %(message)s")
# global variables
label_separation = -0.90 # if uses this, comment ax.xyaxis.labelpad = label_pad
label_pad = 12 # it uses this, comment ax.xyaxis.set_label_coords()...
label_size = 8
ticklabel_size = 4
def set_xaxis(ax,
label_size,
label_separation,
label_pad,
ticklabel_size,
kel_label,
ticks_formatter,
tick_fmt='%.4f'):
ax.get_xaxis().set_visible(True)
ax.xaxis.set_tick_params(labelsize=ticklabel_size)
ax.xaxis.set_label_coords(0.5, label_separation)
#ax.ticklabel_format(style='plain', axis='both', useOffset=False)
plt.setp(ax.xaxis.get_majorticklabels(), rotation=70)
#ax.xaxis.labelpad = label_pad
ax.set_xlabel(kel_label, fontsize=label_size)
tick_step = (ticks_formatter[1] -
ticks_formatter[0]) / ticks_formatter[2]
ax.xaxis.set_ticks(
np.arange(ticks_formatter[0], ticks_formatter[1], tick_step))
tick_formatter = FormatStrFormatter(tick_fmt)
ax.xaxis.set_major_formatter(tick_formatter)
return
def set_yaxis(ax,
label_size,
label_separation,
label_pad,
ticklabel_size,
kel_label,
ticks_formatter,
tick_fmt='%.4f'):
ax.get_yaxis().set_visible(True)
ax.yaxis.set_tick_params(labelsize=ticklabel_size)
ax.yaxis.set_label_coords(label_separation, 0.5)
#ax.ticklabel_format(style='plain', axis='both', useOffset=False)
#ax.yaxis.labelpad = label_pad
ax.set_ylabel(kel_label, fontsize=label_size)
tick_step = (ticks_formatter[1] -
ticks_formatter[0]) / ticks_formatter[2]
ax.yaxis.set_ticks(
np.arange(ticks_formatter[0], ticks_formatter[1], tick_step))
tick_formatter = FormatStrFormatter(tick_fmt)
ax.yaxis.set_major_formatter(tick_formatter)
return
print
print ' ================== '
print ' CORRELATION PLOTS'
print ' ================== '
print
# read cli arguments
cli = anc.get_args()
#plot_folder = prepare_plot_folder(working_path)
emcee_plots = anc.prepare_emcee_plot_folder(cli.full_path)
log_file = os.path.join(emcee_plots, 'emcee_triangle_log.txt')
flog = logging.FileHandler(log_file, 'w')
flog.setLevel(logging.DEBUG)
flog.setFormatter(formatter)
logger.addHandler(flog)
# log screen
slog = logging.StreamHandler()
slog.setLevel(logging.DEBUG)
slog.setFormatter(formatter)
logger.addHandler(slog)
# computes mass conversion factor
#m_factor = anc.mass_conversion_factor(cli.m_type)
m_factor, m_unit = anc.mass_type_factor(1., cli.m_type, False)
# set emcee and trades folder
emcee_folder = cli.full_path
trades_folder = os.path.join(os.path.dirname(cli.full_path), '')
# and best folder
emcee_file, emcee_best, folder_best = anc.get_emcee_file_and_best(
emcee_folder, cli.temp_status)
# get data from the hdf5 file
parameter_names_emcee, parameter_boundaries, chains, acceptance_fraction, autocor_time, lnprobability, ln_err_const, completed_steps = anc.get_data(
emcee_file, cli.temp_status)
# print Memory occupation of ...
anc.print_memory_usage(chains)
nfit, nwalkers, nruns, nburnin, nruns_sel = anc.get_emcee_parameters(
chains, cli.temp_status, cli.nburnin, completed_steps)
logger.info(
'nfit(%d), nwalkers(%d), nruns(%d), nburnin(%d), nruns_sel(%d)' %
(nfit, nwalkers, nruns, nburnin, nruns_sel))
# test label_separation
#if (nfit <= 3): label_separation = -0.1
if (nfit > 2):
#label_separation = -0.1 - ( 0.075 * (nfit-2) ) # good for figsize=(12,12)
label_separation = -0.15 - (0.125 * (nfit - 2)) # testing
#else:
#label_separation = -0.15
#label_size = label_size - 1 * int(nfit / 5.)
label_size = label_size - 1 * int(nfit / 2.5)
# set label and legend names
kel_plot_labels = anc.keplerian_legend(parameter_names_emcee, cli.m_type)
chains_T_full, parameter_boundaries = anc.select_transpose_convert_chains(
nfit, nwalkers, nburnin, nruns, nruns_sel, m_factor,
parameter_names_emcee, parameter_boundaries, chains)
chains_T, flatchain_posterior_0, lnprob_burnin, thin_steps = anc.thin_the_chains(
cli.use_thin,
nburnin,
nruns,
nruns_sel,
autocor_time,
chains_T_full,
lnprobability,
burnin_done=False)
flatchain_posterior_0 = anc.fix_lambda(flatchain_posterior_0,
parameter_names_emcee)
if (cli.boot_id > 0):
flatchain_posterior_msun = anc.posterior_back_to_msun(
m_factor, parameter_names_emcee, flatchain_posterior_0)
boot_file = anc.save_bootstrap_like(emcee_folder, cli.boot_id,
parameter_names_emcee,
flatchain_posterior_msun)
logger.info('saved bootstrap like file: %s' % (boot_file))
del flatchain_posterior_msun
k = anc.get_auto_bins(flatchain_posterior_0)
if (cli.overplot is not None):
if (cli.adhoc is not None):
overp_names, read_par = anc.read_fitted_file(cli.adhoc)
cli.overplot = 777
else:
## OPEN summary_parameters.hdf5 FILE
s_h5f = h5py.File(
os.path.join(cli.full_path, 'summary_parameters.hdf5'), 'r')
# take only the selected sample
s_overplot = '%04d' % (cli.overplot)
read_par = s_h5f['parameters/%s/fitted/parameters' %
(s_overplot)][...]
s_h5f.close()
# fitted parameters has always Mp/Ms in Msun/Mstar, so it is needed to rescale it properly
overp_par = read_par.copy()
for ii in range(0, nfit):
if ('Ms' in parameter_names_emcee[ii]):
#if('Ms' in overp_names[ii]):
overp_par[ii] = overp_par[ii] * m_factor
#fig, ax = plt.subplots(nrows = nfit-1, ncols=nfit, figsize=(12,12))
#fig = plt.figure(figsize=(12,12))
fig = plt.figure(figsize=(6, 6))
fig.subplots_adjust(hspace=0.05, wspace=0.05)
for ix in range(0, nfit, 1):
x_data = flatchain_posterior_0[:, ix]
##x_med = median_parameters[ix]
x_min, x_max = anc.compute_limits(x_data, 0.05)
if (x_min == x_max):
x_min = parameter_boundaries[ix, 0]
x_max = parameter_boundaries[ix, 1]
#x_max_mean = mode_parameters[ix]
for iy in range(nfit - 1, -1, -1):
y_data = flatchain_posterior_0[:, iy]
y_min, y_max = anc.compute_limits(y_data, 0.05)
if (y_min == y_max):
y_min = parameter_boundaries[iy, 0]
y_max = parameter_boundaries[iy, 1]
#y_max_mean = mode_parameters[iy]
if (iy > ix): # correlation plot
logger.info(
'%s vs %s' %
(parameter_names_emcee[ix], parameter_names_emcee[iy]))
ax = plt.subplot2grid((nfit + 1, nfit), (iy, ix))
#hist2d_counts, xedges, yedges, image2d = ax.hist2d(x_data, y_data, bins=k, range=[[x_data.min(), x_data.max()],[y_data.min(), y_data.max()]], cmap=cm.gray_r, normed=True)
hist2d_counts, xedges, yedges, image2d = ax.hist2d(\
x_data, y_data, bins=k,
range=[[x_data.min(), x_data.max()],[y_data.min(), y_data.max()]],
cmap=cm.gray_r,
normed=False
#density=False
)
#new_k = int(k/3)
new_k = k
hist2d_counts_2, xedges_2, yedges_2 = np.histogram2d(\
x_data, y_data, bins=new_k,
range=[[x_data.min(), x_data.max()],[y_data.min(), y_data.max()]],
#normed=True
density=False
)
x_bins = [
0.5 * (xedges_2[i] + xedges_2[i + 1])
for i in range(0, new_k)
]
y_bins = [
0.5 * (yedges_2[i] + yedges_2[i + 1])
for i in range(0, new_k)
]
#ax.contour(x_bins, y_bins, hist2d_counts_2.T, 3, cmap=cm.gray, linestyle='solid', linewidths=(0.7, 0.7, 0.7))
nl = 5
levels = [1. - np.exp(-0.5 * ii) for ii in range(0, nl)
] # 2D sigmas: 0sigma, 1sigma, 2sigma, 3sigma, ..
#ax.contour(x_bins, y_bins, hist2d_counts_2.T, levels, cmap=cm.gray, linestyle='solid', linewidths=(0.7, 0.7, 0.7))
#ax.contour(x_bins, y_bins, hist2d_counts_2.T, levels, cmap=cm.viridis, linestyle='solid', linewidths=1.)
#ax.contour(x_bins, y_bins, hist2d_counts_2.T, cmap=cm.viridis, linestyle='solid', linewidths=0.7)
#ax.contour(x_bins, y_bins, hist2d_counts_2.T, levels, cmap=cm.viridis, linestyle='solid', linewidths=0.7, normed=True)
ax.contour(
x_bins,
y_bins,
hist2d_counts_2.T,
nl,
cmap=cm.viridis,
linestyles='solid',
linewidths=0.5,
#normed=True
)
if (cli.overplot is not None):
# plot selected overplot sample
ax.axvline(overp_par[ix],
color='C0',
ls='--',
lw=1.1,
alpha=0.5)
ax.axhline(overp_par[iy],
color='C0',
ls='--',
lw=1.1,
alpha=0.5)
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
if (iy == nfit - 1):
set_xaxis(ax, label_size, label_separation, label_pad,
ticklabel_size, kel_plot_labels[ix],
[xedges[0], xedges[-1], 4])
if (ix == 0):
set_yaxis(ax, label_size, label_separation, label_pad,
ticklabel_size, kel_plot_labels[iy],
[yedges[0], yedges[-1], 5])
ax.set_ylim([y_min, y_max])
ax.set_xlim([x_min, x_max])
plt.draw()
elif (iy == ix): # distribution plot
logger.info('%s histogram' % (parameter_names_emcee[ix]))
ax = plt.subplot2grid((nfit + 1, nfit), (ix, ix))
if (ix == nfit - 1):
hist_orientation = 'horizontal'
else:
hist_orientation = 'vertical'
idx = np.argsort(x_data)
if (not cli.cumulative):
# HISTOGRAM
hist_counts, edges, patces = ax.hist(
x_data,
bins=k,
range=[x_data.min(), x_data.max()],
histtype='stepfilled',
color='darkgrey',
#edgecolor='lightgray',
edgecolor='None',
align='mid',
orientation=hist_orientation,
#normed=True,
density=True,
stacked=True)
else:
# CUMULATIVE HISTOGRAM
hist_counts, edges, patces = ax.hist(
x_data,
bins=k,
range=[x_data.min(), x_data.max()],
histtype='stepfilled',
color='darkgrey',
#edgecolor='lightgray',
edgecolor='None',
align='mid',
orientation=hist_orientation,
density=True,
stacked=True,
cumulative=True)
if (ix == nfit - 1):
ax.set_ylim([y_min, y_max])
if (cli.overplot is not None):
# plot selected overplot sample
ax.axhline(overp_par[ix],
color='C0',
ls='--',
lw=1.1,
alpha=0.5)
else:
ax.set_xlim([x_min, x_max])
if (cli.overplot is not None):
# plot selected overplot sample
ax.axvline(overp_par[ix],
color='C0',
ls='--',
lw=1.1,
alpha=0.5)
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
ax.set_title(kel_plot_labels[ix], fontsize=label_size)
plt.draw()
logger.info('saving plot')
emcee_fig_file = os.path.join(emcee_plots, 'emcee_triangle.png')
fig.savefig(emcee_fig_file, bbox_inches='tight', dpi=300)
logger.info('png done')
emcee_fig_file = os.path.join(emcee_plots, 'emcee_triangle.pdf')
fig.savefig(emcee_fig_file, bbox_inches='tight', dpi=96)
logger.info('pdf done')
plt.close(fig)
logger.info('')
return
def main():
print
print ' ======================== '
print ' TRADES+EMCEE CHAIN PLOTS'
print ' ======================== '
print
# read cli arguments
cli = anc.get_args()
# computes mass conversion factor
#m_factor, m_unit = anc.mass_conversion_factor_and_unit(cli.m_type)
m_factor, m_unit = anc.mass_type_factor(1., cli.m_type, False)
# set emcee and trades folder
emcee_folder = cli.full_path
trades_folder = os.path.join(os.path.dirname(cli.full_path), '')
# and best folder
emcee_file, emcee_best, folder_best = anc.get_emcee_file_and_best(
emcee_folder, cli.temp_status)
parameter_names_emcee, parameter_boundaries, chains, acceptance_fraction, autocor_time, lnprobability, ln_err_const, completed_steps = anc.get_data(
emcee_file, cli.temp_status)
# set label and legend names
kel_labels = anc.keplerian_legend(parameter_names_emcee, cli.m_type)
nfit, nwalkers, nruns, nburnin, nruns_sel = anc.get_emcee_parameters(
chains, cli.temp_status, cli.nburnin, completed_steps)
anc.print_memory_usage(chains)
chains_T_full, parameter_boundaries = anc.select_transpose_convert_chains(
nfit, nwalkers, nburnin, nruns, nruns_sel, m_factor,
parameter_names_emcee, parameter_boundaries, chains)
if (cli.use_thin or cli.use_thin > 0):
chains_T, flatchain_posterior_0, lnprob_burnin, thin_steps, chains_T_full_thinned = anc.thin_the_chains(
cli.use_thin,
nburnin,
nruns,
nruns_sel,
autocor_time,
chains_T_full,
lnprobability,
burnin_done=False,
full_chains_thinned=True)
nburnin_plt = np.rint(nburnin / thin_steps).astype(int)
nend = np.rint(nruns / thin_steps).astype(int)
else:
chains_T, flatchain_posterior_0, lnprob_burnin, thin_steps = anc.thin_the_chains(
cli.use_thin,
nburnin,
nruns,
nruns_sel,
autocor_time,
chains_T_full,
lnprobability,
burnin_done=False,
full_chains_thinned=False)
nburnin_plt = nburnin
nend = nruns
#name_par, name_excluded = anc.get_sample_list(cli.sample_str, parameter_names_emcee)
#sample_parameters, idx_sample = anc.pick_sample_parameters(flatchain_posterior_0, parameter_names_emcee, name_par = name_par, name_excluded = name_excluded)
#flatchain_posterior_1 = flatchain_posterior_0
# fix lambda?
#flatchain_posterior_0 = anc.fix_lambda(flatchain_posterior_0,
#parameter_names_emcee
#)
if (cli.boot_id > 0):
flatchain_posterior_msun = anc.posterior_back_to_msun(
m_factor, parameter_names_emcee, flatchain_posterior_0)
boot_file = anc.save_bootstrap_like(emcee_folder, cli.boot_id,
parameter_names_emcee,
flatchain_posterior_msun)
logger.info('saved bootstrap like file: %s' % (boot_file))
del flatchain_posterior_msun
k = anc.get_auto_bins(flatchain_posterior_0)
try:
overplot = int(cli.overplot)
except:
overplot = None
## OPEN summary_parameters.hdf5 FILE
s_h5f = h5py.File(os.path.join(cli.full_path, 'summary_parameters.hdf5'),
'r')
if (overplot is not None):
# sample_parameters
ci_fitted = s_h5f['confidence_intervals/fitted/ci'][...]
sample_parameters = s_h5f['parameters/0666/fitted/parameters'][...]
sample_lgllhd = s_h5f['parameters/0666'].attrs['lgllhd']
try:
sample2_parameters = s_h5f['parameters/0667/fitted/parameters'][
...]
sample2_lgllhd = s_h5f['parameters/0667'].attrs['lgllhd']
except:
sample2_parameters = None
sample2_lgllhd = None
try:
sample3_parameters = s_h5f['parameters/0668/fitted/parameters'][
...]
sample3_lgllhd = s_h5f['parameters/0668'].attrs['lgllhd']
except:
sample3_parameters = None
sample3_lgllhd = None
median_parameters = s_h5f['parameters/1051/fitted/parameters'][...]
median_lgllhd = s_h5f['parameters/1051'].attrs['lgllhd']
max_lnprob_parameters = s_h5f['parameters/2050/fitted/parameters'][...]
max_lgllhd = s_h5f['parameters/2050'].attrs['lgllhd']
try:
mode_parameters = s_h5f['parameters/3051/fitted/parameters'][...]
mode_lgllhd = s_h5f['parameters/3051'].attrs['lgllhd']
except:
mode_parameters = None
mode_lgllhd = None
overp_par = s_h5f['parameters/%04d/fitted/parameters' %
(overplot)][...]
overp_lgllhd = s_h5f['parameters/%04d' % (overplot)].attrs['lgllhd']
#nfit = s_h5f['confidence_intervals/fitted'].attrs['nfit']
ndata = s_h5f['confidence_intervals/fitted'].attrs['ndata']
dof = s_h5f['confidence_intervals/fitted'].attrs['dof']
s_h5f.close()
emcee_plots = os.path.join(cli.full_path, 'plots')
if (not os.path.isdir(emcee_plots)):
os.makedirs(emcee_plots)
for i in range(0, nfit):
if ('Ms' in parameter_names_emcee[i]):
conv_plot = m_factor
else:
conv_plot = 1.
emcee_fig_file = os.path.join(
emcee_plots,
'chain_%03d_%s.png' % (i + 1, parameter_names_emcee[i].strip()))
print ' %s' % (emcee_fig_file),
#fig, (axChain, axHist) = plt.subplots(nrows=1, ncols=2, figsize=(12,12))
fig, (axChain, axHist) = plt.subplots(nrows=1, ncols=2, figsize=(6, 6))
(counts, bins_val,
patches) = axHist.hist(flatchain_posterior_0[:, i],
bins=k,
range=(flatchain_posterior_0[:, i].min(),
flatchain_posterior_0[:, i].max()),
orientation='horizontal',
density=True,
stacked=True,
histtype='stepfilled',
color='darkgrey',
edgecolor='lightgray',
align='mid')
xpdf = scipy_norm.pdf(flatchain_posterior_0[:, i],
loc=flatchain_posterior_0[:, i].mean(),
scale=flatchain_posterior_0[:, i].std())
idx = np.argsort(flatchain_posterior_0[:, i])
axHist.plot(xpdf[idx],
flatchain_posterior_0[idx, i],
color='black',
marker='None',
ls='-.',
lw=1.5,
label='pdf')
# chains after burn-in
#axChain.plot(chains_T[:,:,i], '-', alpha=0.3)
# chains with the burn-in
if (cli.use_thin):
axChain.plot(chains_T_full_thinned[:, :, i], '-', alpha=0.3)
else:
axChain.plot(chains_T_full[:, :, i], '-', alpha=0.3)
axChain.axvspan(0, nburnin_plt, color='gray', alpha=0.45)
axChain.axvline(nburnin_plt, color='gray', ls='-', lw=1.5)
if (overplot is not None):
if (mode_parameters is not None):
# plot of mode (mean of higher peak/bin)
axChain.axhline(mode_parameters[i] * conv_plot,
color='red',
ls='-',
lw=2.1,
alpha=1,
label='mode')
# plot of median
axChain.axhline(median_parameters[i] * conv_plot,
marker='None',
c='blue',
ls='-',
lw=2.1,
alpha=1.0,
label='median fit')
# plot of max_lnprob
axChain.axhline(max_lnprob_parameters[i] * conv_plot,
marker='None',
c='black',
ls='-',
lw=1.1,
alpha=1.0,
label='max lnprob')
if (sample_parameters is not None):
# plot of sample_parameters
axChain.axhline(sample_parameters[i] * conv_plot,
marker='None',
c='orange',
ls='--',
lw=2.3,
alpha=0.77,
label='picked: %12.7f' %
(sample_parameters[i]))
if (sample2_parameters is not None):
# plot of sample2_parameters
axChain.axhline(sample2_parameters[i] * conv_plot,
marker='None',
c='cyan',
ls=':',
lw=2.7,
alpha=0.77,
label='close lgllhd: %12.7f' %
(sample2_parameters[i]))
if (sample3_parameters is not None):
# plot of sample3_parameters
axChain.axhline(sample3_parameters[i] * conv_plot,
marker='None',
c='yellow',
ls='-',
lw=3.1,
alpha=0.66,
label='close lgllhd: %12.7f' %
(sample3_parameters[i]))
if (overplot not in [1050, 1051, 2050, 3050, 3051]):
axChain.axhline(overp_par[i] * conv_plot,
marker='None',
c='black',
ls='--',
lw=2.5,
alpha=0.6,
label='overplot %d' % (overplot))
# plot ci
axChain.axhline(ci_fitted[i, 0] * conv_plot,
marker='None',
c='forestgreen',
ls='-',
lw=2.1,
alpha=1.0,
label='CI 15.865th (%.5f)' %
(ci_fitted[i, 0] * conv_plot))
axChain.axhline(ci_fitted[i, 1] * conv_plot,
marker='None',
c='forestgreen',
ls='-',
lw=2.1,
alpha=1.0,
label='CI 84.135th (%.5f)' %
(ci_fitted[i, 1] * conv_plot))
axChain.ticklabel_format(useOffset=False)
xlabel = '$N_\mathrm{steps}$'
if (cli.use_thin):
xlabel = '$N_\mathrm{steps} \\times %d$' % (thin_steps)
axChain.set_xlabel(xlabel)
axChain.set_xlim([0, nend])
axChain.set_ylabel(kel_labels[i])
y_min = flatchain_posterior_0[:, i].min()
y_max = flatchain_posterior_0[:, i].max()
axChain.set_ylim([y_min, y_max])
axChain.set_title('Full chain %s:=[%.3f , %.3f]' %
(kel_labels[i], parameter_boundaries[i, 0],
parameter_boundaries[i, 1]))
plt.draw()
axHist.ticklabel_format(useOffset=False)
axHist.tick_params(direction='inout', labelleft=False)
axHist.set_ylim([y_min, y_max])
if (overplot is not None):
if (mode_parameters is not None):
# plot mode
axHist.axhline(mode_parameters[i] * conv_plot,
color='red',
ls='-',
lw=2.1,
alpha=1,
label='mode')
# plot median
axHist.axhline(median_parameters[i] * conv_plot,
marker='None',
c='blue',
ls='-',
lw=2.1,
alpha=1.0,
label='median fit')
# plot of max_lnprob
axHist.axhline(max_lnprob_parameters[i] * conv_plot,
marker='None',
c='black',
ls='-',
lw=1.1,
alpha=1.0,
label='max lnprob')
if (sample_parameters is not None):
# plot of sample_parameters
axHist.axhline(sample_parameters[i] * conv_plot,
marker='None',
c='orange',
ls='--',
lw=2.3,
alpha=0.77,
label='picked: %12.7f' %
(sample_parameters[i] * conv_plot))
if (sample2_parameters is not None):
# plot of sample2_parameters
axHist.axhline(sample2_parameters[i] * conv_plot,
marker='None',
c='cyan',
ls=':',
lw=2.7,
alpha=0.77,
label='close lgllhd: %12.7f' %
(sample2_parameters[i]))
if (sample3_parameters is not None):
# plot of sample3_parameters
axHist.axhline(sample3_parameters[i] * conv_plot,
marker='None',
c='yellow',
ls='-',
lw=3.1,
alpha=0.66,
label='close lgllhd: %12.7f' %
(sample3_parameters[i]))
if (overplot not in [1050, 1051, 2050, 3050, 3051]):
axHist.axhline(overp_par[i] * conv_plot,
marker='None',
c='black',
ls='--',
lw=2.5,
alpha=0.8,
label='overplot %d' % (overplot))
# plot ci
axHist.axhline(ci_fitted[i, 0] * conv_plot,
marker='None',
c='forestgreen',
ls='-',
lw=2.1,
alpha=1.0,
label='CI 15.865th (%.5f)' %
(ci_fitted[i, 0] * conv_plot))
axHist.axhline(ci_fitted[i, 1] * conv_plot,
marker='None',
c='forestgreen',
ls='-',
lw=2.1,
alpha=1.0,
label='CI 84.135th (%.5f)' %
(ci_fitted[i, 1] * conv_plot))
axHist.set_title('Distribution of posterior chain')
axHist.legend(loc='center left', fontsize=9, bbox_to_anchor=(1, 0.5))
plt.draw()
fig.savefig(emcee_fig_file, bbox_inches='tight', dpi=150)
print ' saved'
print
#fig = plt.figure(figsize=(12,12))
fig = plt.figure(figsize=(6, 6))
# lnprob
xlabel = '$N_\mathrm{steps}$'
if (cli.use_thin):
xlabel = '$N_\mathrm{steps} \\times %d$' % (thin_steps)
ax = plt.subplot2grid((2, 1), (0, 0))
ax.plot(lnprob_burnin.T, '-', alpha=0.3)
if (overplot is not None):
posterior_msun = anc.posterior_back_to_msun(m_factor,
parameter_names_emcee,
flatchain_posterior_0)
post_sel, lnprob_sel = anc.select_within_all_ci(
posterior_msun, ci_fitted[:, 0:2], lnprob_burnin.T.reshape(-1))
#lnprob_sel = lnprob_burnin.T.reshape((-1))
lgllhd_med = np.percentile(lnprob_burnin.T.reshape(-1),
50.,
interpolation='midpoint')
abs_dlg = np.abs(lnprob_sel - lgllhd_med)
lgllhd_mad = np.percentile(abs_dlg, 50., interpolation='midpoint')
#lnp_min = np.min(lnprob_sel)
#lnp_max = np.max(lnprob_sel)
lnp_min = lgllhd_med - lgllhd_mad
lnp_max = lgllhd_med + lgllhd_mad
print ' lgllhd_med & mad = ', lgllhd_med, lgllhd_mad
print ' lnp_min = ', lnp_min, ' lnp_max = ', lnp_max
print ' lnl_668 = ', sample3_lgllhd
ax.axhline(lgllhd_med, color='black', ls='-', lw=1.6, alpha=0.77)
#if(sample2_lgllhd is not None):
#ax.axhline(sample2_lgllhd, marker='None', c='cyan',ls=':', lw=2.7, alpha=0.9)
if (sample3_lgllhd is not None):
ax.axhline(sample3_lgllhd,
marker='None',
c='yellow',
ls='-',
lw=3.1,
alpha=0.9)
ax.axhspan(lnp_min, lnp_max, color='gray', alpha=0.77)
ax.axhline(lnp_min, color='black', ls='--', lw=1.6, alpha=0.77)
ax.axhline(lnp_max, color='black', ls='--', lw=1.6, alpha=0.77)
min_lnp = np.min(lnprob_burnin.T, axis=0).min()
max_lnp = np.max(lnprob_burnin.T, axis=0).max()
y_min, y_max = anc.compute_limits(np.asarray([min_lnp, max_lnp]), 0.05)
ax.set_ylim((y_min, y_max))
ax.set_ylabel('lnprob')
#ax.get_xaxis().set_visible(False)
ax.set_xlabel(xlabel)
# chi2r
chi2r = -2. * (lnprob_burnin.T - ln_err_const) / np.float64(dof)
ax = plt.subplot2grid((2, 1), (1, 0))
ax.axhline(1.0, color='gray', ls='-')
ax.plot(chi2r, '-', alpha=0.3)
if (overplot is not None):
c2r_med = -(2. * (lgllhd_med - ln_err_const)) / np.float64(dof)
c2r_smax = -(2. * (lnp_min - ln_err_const)) / np.float64(dof)
c2r_smin = -(2. * (lnp_max - ln_err_const)) / np.float64(dof)
print ' c2r_med = ', c2r_med
print ' c2r_smin = ', c2r_smin, ' c2r_smax = ', c2r_smax
ax.axhline(c2r_med, color='black', ls='-', lw=1.6, alpha=0.77)
ax.axhspan(c2r_smin, c2r_smax, color='gray', alpha=0.77)
ax.axhline(c2r_smin, color='black', ls='--', lw=1.6, alpha=0.77)
ax.axhline(c2r_smax, color='black', ls='--', lw=1.6, alpha=0.77)
#if(sample2_lgllhd is not None):
#c2r_sample2 = -2.*(sample2_lgllhd - ln_err_const)/np.float64(dof)
#ax.axhline(c2r_sample2, marker='None', c='cyan',ls=':', lw=2.7, alpha=0.9)
if (sample3_lgllhd is not None):
c2r_sample3 = -2. * (sample3_lgllhd -
ln_err_const) / np.float64(dof)
ax.axhline(c2r_sample3,
marker='None',
c='yellow',
ls='-',
lw=3.1,
alpha=0.9)
c2r_min = -2. * (y_max - ln_err_const) / np.float64(dof)
c2r_max = -2. * (y_min - ln_err_const) / np.float64(dof)
ax.set_ylim((c2r_min, c2r_max))
ax.set_ylabel('$\chi^{2}/\mathrm{dof}$')
#ax.get_xaxis().set_visible(True)
ax.set_xlabel(xlabel)
fig.savefig(os.path.join(emcee_plots, 'emcee_lnprobability.png'),
bbox_inches='tight',
dpi=150)
print ' %s saved' % (os.path.join(emcee_plots, 'emcee_lnprobability.png'))
return