def get_omega_from_summary_path(summary_path):
results = parsing.parse_posterior_summary_file(summary_path)
omega = float(results['PRI.omega']['median'])
hpd = [float(results['PRI.omega']['HPD_95_interval'][0]),
float(results['PRI.omega']['HPD_95_interval'][1])]
if hpd[0] < 0.0:
hpd[0] = 0.0
return omega, (hpd[0], hpd[1])
def main_cli():
sum_file_pattern = re.compile(
r'^d1-m1-s1-(?P<iter_index>\d{1,4})-posterior-summary.txt.gz$')
result_dir = os.path.join(project_util.RESULT_DIR, 'sampling-error',
'pymsbayes-results')
post_dir = os.path.join(result_dir, 'pymsbayes-output',
'd1', 'm1')
post_sum_paths = {}
for f in os.listdir(post_dir):
m = sum_file_pattern.match(f)
if m:
post_sum_paths[int(m.group('iter_index'))] = os.path.join(post_dir,
f)
iter_indices = sorted(post_sum_paths.iterkeys())
nsamples = []
omega_HPD_low = []
omega_HPD_high = []
omega_HPD_low_glm = []
omega_HPD_high_glm = []
for i in iter_indices:
nsamples.append((i + 1) * 100000)
f, close = process_file_arg(post_sum_paths[i], 'r')
post_sum = parse_posterior_summary_file(f)
hpd_int = post_sum['PRI.omega']['HPD_95_interval']
omega_HPD_low.append(float(hpd_int[0]))
omega_HPD_high.append(float(hpd_int[1]))
hpd_int_glm = post_sum['PRI.omega']['HPD_95_interval_glm']
omega_HPD_low_glm.append(float(hpd_int_glm[0]))
omega_HPD_high_glm.append(float(hpd_int_glm[1]))
f.close()
sp_list = []
sd_list = []
sd_list.append(plotting.ScatterData(x = nsamples, y = omega_HPD_low))
sd_list.append(plotting.ScatterData(x = nsamples, y = omega_HPD_high))
sp = plotting.ScatterPlot(
scatter_data_list = sd_list,
y_label = r'$\Omega$ 95% HPD')
sp_list.append(sp)
sd_list = []
sd_list.append(plotting.ScatterData(x = nsamples, y = omega_HPD_low_glm))
sd_list.append(plotting.ScatterData(x = nsamples, y = omega_HPD_high_glm))
sp = plotting.ScatterPlot(
scatter_data_list = sd_list,
y_label = r'$\Omega$ 95% HPD (GLM-adjusted)')
sp_list.append(sp)
pg = plotting.PlotGrid(subplots = sp_list,
num_columns = 1,
title = r'Number of prior samples',
title_top = False,
share_x = True,
share_y = False,
width = 8.0,
height = 10.0,
auto_height = False)
pg.auto_adjust_margins = False
pg.margin_top = 0.99
pg.margin_bottom = 0.01
pg.reset_figure()
pg.savefig(os.path.join(result_dir, 'omega_over_sampling.pdf'))
