data_list.append(score_array)
label_list.append(json_dict["label"])
# PLOT STATISTICS #########################################################
fig, ax1 = plt.subplots(nrows=1, ncols=1, figsize=(16, 9))
common.plot_hist1d(ax1,
data_list,
label_list=label_list,
logx=logx,
logy=logy,
xmax=max_abscissa,
overlaid=overlaid,
xlabel="Score",
title=title,
num_bins=30,
#num_bins=[0., 0.0011, 0.0022, 0.0033],
tight=tight,
plot_ratio=plot_ratio,
degx=degx)
# Save file and plot ########
if not notebook:
plt.savefig(output_file_path, bbox_inches='tight')
if not quiet:
plt.show()
data_list = parse_fits_files(fits_file_name_list,
progress_bar=not notebook)
# PLOT STATISTICS #########################################################
if not notebook:
print("Plotting...")
fig, ax1 = plt.subplots(nrows=1, ncols=1, figsize=(16, 9))
common.plot_hist1d(axis=ax1,
data_list=[np.array(data_list).flatten()],
label_list=[],
logy=logy,
xlabel="Photoelectrons",
xylabel_fontsize=16,
title=title,
linear_xlabel_style=None,
linear_ylabel_style=None,
num_bins=None,
info_box_rms=False,
info_box_std=True)
# Save file and plot ########
if not notebook:
plt.tight_layout()
plt.savefig(output_file_path, bbox_inches='tight')
if not quiet:
plt.show()
else:
errors_str = None
if errors_str is not None:
title = "{} ({}) histogram for {}".format(key, errors_str, label)
else:
title = "{} histogram for {}".format(key, label)
# PLOT STATISTICS #########################################################
fig, ax1 = plt.subplots(nrows=1, ncols=1, figsize=(16, 9))
common.plot_hist1d(ax1, [data_array],
num_bins=50,
logx=logx,
logy=logy,
xlabel=key,
title=title,
tight=tight,
info_box_rms=False,
info_box_std=True,
plot_ratio=False)
# SAVE FILE AND PLOT ######################################################
if not notebook:
plt.savefig(output_file_path, bbox_inches='tight')
if not quiet:
plt.show()
if errors_str is not None:
title = "{} ({}) histogram for {}".format(key, errors_str, label)
else:
title = "{} histogram for {}".format(key, label)
# PLOT STATISTICS #########################################################
fig, ax1 = plt.subplots(nrows=1, ncols=1, figsize=(16, 9))
common.plot_hist1d(ax1,
[data_array],
num_bins=50,
logx=logx,
logy=logy,
xlabel=key,
title=title,
tight=tight,
info_box_rms=False,
info_box_std=True,
plot_ratio=False)
# SAVE FILE AND PLOT ######################################################
if not notebook:
plt.savefig(output_file_path, bbox_inches='tight')
if not quiet:
plt.show()
fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(nrows=2, ncols=2, figsize=(16, 9))
num_bins = 30
legend_fontsize = 14
show_info_box = True
info_box_num_samples = True
info_box_mean = False
info_box_rms = False
info_box_std = False
common.plot_hist1d(axis=ax1,
data_list=data_list1,
label_list=label_list,
logx=logx,
logy=logy,
num_bins=num_bins,
legend_fontsize=legend_fontsize,
show_info_box=show_info_box,
info_box_num_samples=info_box_num_samples,
info_box_mean=info_box_mean,
info_box_rms=info_box_rms,
info_box_std=info_box_std)
common.plot_hist1d(axis=ax2,
data_list=data_list2,
label_list=label_list,
logx=logx,
logy=logy,
num_bins=num_bins,
legend_fontsize=legend_fontsize,
show_info_box=show_info_box,
info_box_num_samples=info_box_num_samples,
# Parse FITS files
data_list = parse_fits_files(fits_file_name_list, progress_bar=not notebook)
# PLOT STATISTICS #########################################################
if not notebook:
print("Plotting...")
fig, ax1 = plt.subplots(nrows=1, ncols=1, figsize=(16, 9))
common.plot_hist1d(axis=ax1,
data_list=[np.array(data_list).flatten()],
label_list=[],
logy=logy,
xlabel="Photoelectrons",
xylabel_fontsize=16,
title=title,
linear_xlabel_style=None,
linear_ylabel_style=None,
num_bins=None,
info_box_rms=False,
info_box_std=True)
# Save file and plot ########
if not notebook:
plt.tight_layout()
plt.savefig(output_file_path, bbox_inches='tight')
if not quiet:
plt.show()
# PLOT STATISTICS #########################################################
print("Plotting...")
fig, ax1 = plt.subplots(nrows=1, ncols=1, figsize=(16, 9))
common.plot_hist1d(axis=ax1,
data_list=[
np.array(data_list).flatten(),
np.random.poisson(lam=2, size=40 * 40),
np.random.poisson(lam=3, size=40 * 40)
],
label_list=[
"Noise", r"Poisson dist. ($\lambda$=2)",
r"Poisson dist. ($\lambda$=3)"
],
logy=logy,
xlabel="Photoelectrons",
xylabel_fontsize=16,
title=title,
linear_xlabel_style=None,
linear_ylabel_style=None,
num_bins=None,
info_box_num_samples=False,
info_box_rms=False,
info_box_std=True)
# Save file and plot ########
plt.tight_layout()
plt.savefig(output_file_path, bbox_inches='tight')