def get_histograms(config_path,
info_path,
num_samples = 10000,
num_div_values = None,
div_model_values = None,
ordered_div_model_values = None,
iteration_index = 99,
y_limits = [0.45, 0.45, 0.05, 0.05],
xtick_label_size = 8.0):
cfg = MsBayesConfig(config_path)
dmc = DMCSimulationResults(info_path)
npairs = dmc.num_taxon_pairs
psi_path = (dmc.get_result_path_prefix(1, 1, 1) +
'{0}-psi-results.txt'.format(iteration_index))
sum_path = (dmc.get_result_path_prefix(1, 1, 1) +
'{0}-posterior-summary.txt'.format(iteration_index))
psis = get_values_psi_path(psi_path)
omega, omega_hpd = get_omega_from_summary_path(sum_path)
(num_div_prior_psis, div_model_prior_psis, ordered_div_model_prior_psis) = (
num_div_values, div_model_values, ordered_div_model_values)
if ((not num_div_values) or (not div_model_values) or
(not ordered_div_model_values)):
if cfg.div_model_prior == 'dpp':
num_div_prior_psis, div_model_prior_psis, ordered_div_model_prior_psis = get_dpp_prior_values(
config_path = config_path,
num_samples = num_samples)
elif cfg.div_model_prior == 'uniform':
num_div_prior_psis, div_model_prior_psis, ordered_div_model_prior_psis = get_uniform_prior_values(
npairs = npairs,
num_samples = num_samples)
elif cfg.div_model_prior == 'psi':
num_div_prior_psis, div_model_prior_psis, ordered_div_model_prior_psis = get_psi_uniform_prior_values(
npairs = npairs,
num_samples = num_samples)
# Extra bin for zero values
bins = range(0, npairs + 2)
hds = []
for p in [psis, num_div_prior_psis, div_model_prior_psis, ordered_div_model_prior_psis]:
hds.append(HistData(x = p,
normed = True,
bins = bins,
histtype = 'bar',
align = 'mid',
orientation = 'vertical',
zorder = 0))
tick_labels = []
for x in bins[0:-1]:
if x % 2:
tick_labels.append(str(x))
else:
tick_labels.append('')
xticks_obj = Ticks(ticks = bins,
labels = tick_labels,
horizontalalignment = 'left',
size = xtick_label_size)
hists = []
for i, hd in enumerate(hds):
right_text = ''
if i == 0:
right_text = r'$D_T = {0:.2f} ({1:.2f}-{2:.2f})$'.format(omega,
omega_hpd[0],
omega_hpd[1])
hist = ScatterPlot(hist_data_list = [hd],
right_text = right_text,
xticks_obj = xticks_obj)
# cut off extra zero-valued bin
hist.set_xlim(left = bins[1], right = bins[-1])
top = y_limits[i]
hist.set_ylim(bottom = 0.0, top = top)
hist.right_text_size = 10.0
hist.plot_label_size = 12.0
yticks = [i for i in hist.ax.get_yticks()]
ytick_labels = [i for i in yticks]
if len(ytick_labels) > 5:
for i in range(1, len(ytick_labels), 2):
ytick_labels[i] = ''
yticks_obj = Ticks(ticks = yticks,
labels = ytick_labels,
size = 10.0)
hist.yticks_obj = yticks_obj
hists.append(hist)
return hists
