# Set accepted limit, lim
lims = [1000] # , 5000, 10000]
distances = []
for dist_measure in ['NRMSE']:
distances.extend(['{}_{}'.format(t, dist_measure)
for t in config['targets']])
distances.append(dist_measure)
for lim in lims:
for d in distances:
print("Working on {}".format(d.upper()))
figPath = os.path.join(output_dir, "Figures", str(lim), d)
dir_util.mkpath(figPath)
print("Plotting total histogram")
hist1 = histogram_plot(results, distance=d, frac=100)
hist1.savefig(
os.path.join(figPath, 'full_histogram_{}.png'.format(DATASET)),
bbox_inches='tight')
print("Plotting fraction histogram")
hist2 = histogram_plot(results, distance=d, limit=lim)
hist2.savefig(
os.path.join(
figPath, 'tol_{}_histogram_{}.png'.format(str(lim).replace('.', '_'), DATASET)),
bbox_inches='tight')
print("Considering {} lowest values".format(lim))
# print("Generating scatter plot")
# scatter_dist_plot(results, params, tolerance=tol, n_ticks=4)
print("Generating KDE plot")
g = kde_plot(results, params, limit=lim, n_ticks=4, d=d,
median_file=os.path.join(figPath, "medians.txt"))
distances = []
for dist_measure in ['NRMSE']:
# distances.extend(['{}_{}'.format(t, dist_measure)
# for t in config['targets']])
distances.append(dist_measure)
# for lim in lims:
for tol in tols:
for d in distances:
print("Working on {}".format(d.upper()))
figPath = "/home/buck06191/Dropbox/phd/Bayesian_fitting/{}/{}/{}/{}/{}/{}/"\
"Figures/{}".format(model_name, 'PLOS_paper', 'hypoxia',
'experimental', 'wide_range', 'tolerance', d)
dir_util.mkpath(figPath)
print("Plotting total histogram")
hist1 = histogram_plot(results, distance=d, frac=1)
hist1.savefig(os.path.join(figPath, 'full_histogram_experimental.png'),
bbox_inches='tight')
print("Plotting fraction histogram")
hist2 = histogram_plot(results, distance=d, tolerance=tol)
hist2.savefig(os.path.join(
figPath, 'tol_{}_histogram_experimental.png'.format(
str(tol).replace('.', '_'))),
bbox_inches='tight')
print("Considering {} lowest values".format(tol))
# print("Generating scatter plot")
# scatter_dist_plot(results, params, tolerance=tol, n_ticks=4)
sorted_results = results.sort_values(by=d).head(5000)
print("Generating KDE plot")
g = kde_plot(sorted_results,
params,