def plot_bg_cube_model_comparison(input_dir1, binning_format1, name1,
input_dir2, binning_format2, name2):
"""
Plot background cube model comparison.
Produce a few figures for comparing 2 sets of bg cube models (1
and 2), supposing same binning in both sets of observation
groups (a.k.a. observation bin).
Each figure corresponds to 1 observation group.
Plot strategy in each figure:
* Images:
* rows: similar energy bin
* cols: same bg cube model set
* Spectra:
* rows: similar det bin
* cols: compare both bg cube model sets
The script can be customized by setting a few global variables:
* **group_ids_selection**: groups to compare; if empty: use all
groups
* **NORMALIZE**: normalization to use for the models. If
activate, model 1 is normalized to match model 2. This can be
useful, when comparing reco models w.r.t. true ones. Options:
* *0*: do not normalize
* *1*: normalize w.r.t. cube integral
* *2*: normalize w.r.t images integral; each image (i.e.
energy bin/slice) is normalized independently; this
option can alter the spectral shape of the bg rate, but
is is the way how smoothing method *michi* normalizes the
background cube model, hence it is necessary to compare
to those models that use that particular smoothing
Parameters
----------
input_dir1, input_dir2 : str
Directory where the corresponding set of bg cube models is stored.
binning_format1, binning_format2 : {'default', 'michi'}
String specifying the binning format; accepted values are:
* *default* for the Gammapy format from
`~gammapy.data.ObservationGroups`; an observation groups
ECVS file is expected in the bg cube models dir.
* *michi* for the binning used by Michale Mayer;
this script has methods to convert it to the
*default* format.
ref: [Mayer2015]_ (section 5.2.4)
name1, name2 : str
Name to use for plot labels/legends.
"""
# check binning
accepted_binnings = ['default', 'michi']
if ((binning_format1 not in accepted_binnings)
or (binning_format2 not in accepted_binnings)):
raise ValueError("Invalid binning format: {0} or {1}".format(
binning_format1, binning_format2))
# convert binning, if necessary
if binning_format1 == 'michi' or binning_format2 == 'michi':
convert_obs_groups_binning_def_michi_to_default()
# loop over observation groups: use binning of the 1st set to compare
if binning_format1 == 'michi':
observation_groups = obs_groups_michi
else:
observation_groups = ObservationGroups.read(
input_dir1 + '/bg_observation_groups.ecsv')
groups = observation_groups.list_of_groups
print()
print("list of groups", groups)
for group in groups:
print()
print("group ", group)
# compare only observation groups in group IDs selection
# if empty, use all groups:
if len(group_ids_selection) is not 0:
groups_to_compare = group_ids_selection
else:
groups_to_compare = groups
if group in groups_to_compare:
group_info = observation_groups.info_group(group)
print(group_info)
# get cubes
if binning_format1 == 'michi':
# find corresponding ALT_ID, AZ_ID in lookup table
i_alt, i_az = look_obs_groups_michi(group)
filename1 = input_dir1 + '/hist_alt' + str(i_alt) + \
'_az' + str(i_az) + '.fits.gz'
else:
filename1 = input_dir1 + '/bg_cube_model_group' + str(group) + \
'_table.fits.gz'
if binning_format2 == 'michi':
# find corresponding ALT_ID, AZ_ID in lookup table
i_alt, i_az = look_obs_groups_michi(group)
filename2 = input_dir2 + '/hist_alt' + str(i_alt) + \
'_az' + str(i_az) + '.fits.gz'
else:
filename2 = input_dir2 + '/bg_cube_model_group' + str(group) + \
'_table.fits.gz'
print('filename1', filename1)
print('filename2', filename2)
bg_cube_model1 = FOVCubeBackgroundModel.read(
filename1, format='table').background_cube
bg_cube_model2 = FOVCubeBackgroundModel.read(
filename2, format='table').background_cube
# normalize 1 w.r.t. 2 (i.e. true w.r.t. reco)
if NORMALIZE == 1:
# normalize w.r.t. cube integral
integral1 = bg_cube_model1.integral
integral2 = bg_cube_model2.integral
bg_cube_model1.data *= integral2 / integral1
elif NORMALIZE == 2:
# normalize w.r.t images integral (normalize each image on its own)
integral_images1 = bg_cube_model1.integral_images
integral_images2 = bg_cube_model2.integral_images
for i_energy in np.arange(
len(bg_cube_model1.energy_edges) - 1):
bg_cube_model1.data[i_energy] *= (
integral_images2 / integral_images1)[i_energy]
# compare binning
print("energy edges 1", bg_cube_model1.energy_edges)
print("energy edges 2", bg_cube_model2.energy_edges)
print("detector edges 1 Y", bg_cube_model1.coordy_edges)
print("detector edges 2 Y", bg_cube_model2.coordy_edges)
print("detector edges 1 X", bg_cube_model1.coordx_edges)
print("detector edges 2 X", bg_cube_model2.coordx_edges)
# make sure that both cubes use the same units for the plots
bg_cube_model2.data = bg_cube_model2.data.to(
bg_cube_model1.data.unit)
# plot
fig, axes = plt.subplots(nrows=2, ncols=3)
fig.set_size_inches(30., 15., forward=True)
plt.suptitle(group_info)
# plot images
# rows: similar energy bin
# cols: same file
# bg_cube_model1.plot_image(energy=Quantity(0.5, 'TeV'), ax=axes[0, 0])
bg_cube_model1.plot_image(energy=Quantity(5., 'TeV'),
ax=axes[0, 0])
axes[0, 0].set_title("{0}: {1}".format(name1, axes[0,
0].get_title()))
bg_cube_model1.plot_image(energy=Quantity(50., 'TeV'),
ax=axes[1, 0])
axes[1, 0].set_title("{0}: {1}".format(name1, axes[1,
0].get_title()))
# bg_cube_model2.plot_image(energy=Quantity(0.5, 'TeV'), ax=axes[0, 1])
bg_cube_model2.plot_image(energy=Quantity(5., 'TeV'),
ax=axes[0, 1])
axes[0, 1].set_title("{0}: {1}".format(name2, axes[0,
1].get_title()))
bg_cube_model2.plot_image(energy=Quantity(50., 'TeV'),
ax=axes[1, 1])
axes[1, 1].set_title("{0}: {1}".format(name2, axes[1,
1].get_title()))
# plot spectra
# rows: similar det bin
# cols: compare both files
bg_cube_model1.plot_spectrum(coord=Angle([0., 0.], 'degree'),
ax=axes[0, 2],
style_kwargs=dict(color='blue',
label=name1))
spec_title1 = axes[0, 2].get_title()
bg_cube_model2.plot_spectrum(coord=Angle([0., 0.], 'degree'),
ax=axes[0, 2],
style_kwargs=dict(color='red',
label=name2))
spec_title2 = axes[0, 2].get_title()
if spec_title1 != spec_title2:
s_error = "Expected same det binning, but got "
s_error += "\"{0}\" and \"{1}\"".format(
spec_title1, spec_title2)
raise ValueError(s_error)
else:
axes[0, 2].set_title(spec_title1)
axes[0, 2].legend()
bg_cube_model1.plot_spectrum(coord=Angle([2., 2.], 'degree'),
ax=axes[1, 2],
style_kwargs=dict(color='blue',
label=name1))
spec_title1 = axes[1, 2].get_title()
bg_cube_model2.plot_spectrum(coord=Angle([2., 2.], 'degree'),
ax=axes[1, 2],
style_kwargs=dict(color='red',
label=name2))
spec_title2 = axes[1, 2].get_title()
if spec_title1 != spec_title2:
s_error = "Expected same det binning, but got "
s_error += "\"{0}\" and \"{1}\"".format(
spec_title1, spec_title2)
raise ValueError(s_error)
else:
axes[1, 2].set_title(spec_title1)
axes[1, 2].legend()
plt.draw()
# save
outfile = "bg_cube_model_comparison_group{}.png".format(group)
print('Writing {}'.format(outfile))
fig.savefig(outfile)
plt.show() # don't leave at the end
def plot_bg_cube_model_comparison(input_file1, name1, input_file2, name2):
"""
Plot background cube model comparison.
Produce a figure for comparing 2 bg cube models (1 and 2).
Plot strategy in each figure:
* Images:
* rows: similar energy bin
* cols: same bg cube model set
* Spectra:
* rows: similar det bin
* cols: compare both bg cube model sets
Parameters
----------
input_file1, input_file2 : str
File where the corresponding bg cube model is stored.
name1, name2 : str
Name to use for plot labels/legends.
"""
# get cubes
filename1 = input_file1
filename2 = input_file2
bg_cube_model1 = FOVCubeBackgroundModel.read(
filename1, format='table').background_cube
bg_cube_model2 = FOVCubeBackgroundModel.read(
filename2, format='table').background_cube
# normalize 1 w.r.t. 2 (i.e. true w.r.t. reco)
# normalize w.r.t. cube integral
integral1 = bg_cube_model1.integral
integral2 = bg_cube_model2.integral
bg_cube_model1.data *= integral2 / integral1
# make sure that both cubes use the same units for the plots
bg_cube_model2.data = bg_cube_model2.data.to(bg_cube_model1.data.unit)
# plot
fig, axes = plt.subplots(nrows=2, ncols=3)
fig.set_size_inches(30., 15., forward=True)
group_info = 'group 27: ALT = [72.0, 90.0) deg, AZ = [90.0, 270.0) deg'
plt.suptitle(group_info)
# plot images
# rows: similar energy bin
# cols: same file
bg_cube_model1.plot_image(energy=Quantity(1., 'TeV'), ax=axes[0, 0])
axes[0, 0].set_title("{0}: {1}".format(name1, axes[0, 0].get_title()))
bg_cube_model1.plot_image(energy=Quantity(10., 'TeV'), ax=axes[1, 0])
axes[1, 0].set_title("{0}: {1}".format(name1, axes[1, 0].get_title()))
bg_cube_model2.plot_image(energy=Quantity(1., 'TeV'), ax=axes[0, 1])
axes[0, 1].set_title("{0}: {1}".format(name2, axes[0, 1].get_title()))
bg_cube_model2.plot_image(energy=Quantity(10., 'TeV'), ax=axes[1, 1])
axes[1, 1].set_title("{0}: {1}".format(name2, axes[1, 1].get_title()))
# plot spectra
# rows: similar det bin
# cols: compare both files
bg_cube_model1.plot_spectrum(coord=Angle([0., 0.], 'degree'),
ax=axes[0, 2],
style_kwargs=dict(color='blue', label=name1))
spec_title1 = axes[0, 2].get_title()
bg_cube_model2.plot_spectrum(coord=Angle([0., 0.], 'degree'),
ax=axes[0, 2],
style_kwargs=dict(color='red', label=name2))
spec_title2 = axes[0, 2].get_title()
if spec_title1 != spec_title2:
s_error = "Expected same det binning, but got "
s_error += "\"{0}\" and \"{1}\"".format(spec_title1, spec_title2)
raise ValueError(s_error)
else:
axes[0, 2].set_title(spec_title1)
# plot normalized models on top
plot_data_x, normed_pl = get_normed_pl(plot_data=axes[0, 2].get_lines()[0],
E_0=E_REF,
norm=NORM,
index=INDEX)
axes[0, 2].plot(plot_data_x,
normed_pl,
color='blue',
linestyle='dotted',
linewidth=2,
label='model index = {}'.format(INDEX))
plot_data_x, normed_pl = get_normed_pl(plot_data=axes[0, 2].get_lines()[0],
E_0=E_REF,
norm=NORM,
index=INDEX + 1)
axes[0, 2].plot(plot_data_x,
normed_pl,
color='blue',
linestyle='dashed',
linewidth=2,
label='model index = {}'.format(INDEX + 1))
axes[0, 2].legend()
bg_cube_model1.plot_spectrum(coord=Angle([2., 2.], 'degree'),
ax=axes[1, 2],
style_kwargs=dict(color='blue', label=name1))
spec_title1 = axes[1, 2].get_title()
bg_cube_model2.plot_spectrum(coord=Angle([2., 2.], 'degree'),
ax=axes[1, 2],
style_kwargs=dict(color='red', label=name2))
spec_title2 = axes[1, 2].get_title()
if spec_title1 != spec_title2:
s_error = "Expected same det binning, but got "
s_error += "\"{0}\" and \"{1}\"".format(spec_title1, spec_title2)
raise ValueError(s_error)
else:
axes[1, 2].set_title(spec_title1)
# plot normalized models on top
plot_data_x, normed_pl = get_normed_pl(plot_data=axes[1, 2].get_lines()[0],
E_0=E_REF,
norm=NORM,
index=INDEX)
axes[1, 2].plot(plot_data_x,
normed_pl,
color='blue',
linestyle='dotted',
linewidth=2,
label='model index = {}'.format(INDEX))
plot_data_x, normed_pl = get_normed_pl(plot_data=axes[1, 2].get_lines()[0],
E_0=E_REF,
norm=NORM,
index=INDEX + 1)
axes[1, 2].plot(plot_data_x,
normed_pl,
color='blue',
linestyle='dashed',
linewidth=2,
label='model index = {}'.format(INDEX + 1))
axes[1, 2].legend()
plt.show()
def plot_bg_cube_model_comparison(input_dir1, binning_format1, name1,
input_dir2, binning_format2, name2):
"""
Plot background cube model comparison.
Produce a few figures for comparing 2 sets of bg cube models (1
and 2), supposing same binning in both sets of observation
groups (a.k.a. observation bin).
Each figure corresponds to 1 observation group.
Plot strategy in each figure:
* Images:
* rows: similar energy bin
* cols: same bg cube model set
* Spectra:
* rows: similar det bin
* cols: compare both bg cube model sets
The script can be customized by setting a few global variables:
* **group_ids_selection**: groups to compare; if empty: use all
groups
* **NORMALIZE**: normalization to use for the models. If
activate, model 1 is normalized to match model 2. This can be
useful, when comparing reco models w.r.t. true ones. Options:
* *0*: do not normalize
* *1*: normalize w.r.t. cube integral
* *2*: normalize w.r.t images integral; each image (i.e.
energy bin/slice) is normalized independently; this
option can alter the spectral shape of the bg rate, but
is is the way how smoothing method *michi* normalizes the
background cube model, hence it is necessary to compare
to those models that use that particular smoothing
Parameters
----------
input_dir1, input_dir2 : str
Directory where the corresponding set of bg cube models is stored.
binning_format1, binning_format2 : {'default', 'michi'}
String specifying the binning format; accepted values are:
* *default* for the Gammapy format from
`~gammapy.data.ObservationGroups`; an observation groups
ECVS file is expected in the bg cube models dir.
* *michi* for the binning used by Michale Mayer;
this script has methods to convert it to the
*default* format.
ref: [Mayer2015]_ (section 5.2.4)
name1, name2 : str
Name to use for plot labels/legends.
"""
# check binning
accepted_binnings = ['default', 'michi']
if ((binning_format1 not in accepted_binnings) or
(binning_format2 not in accepted_binnings)):
raise ValueError("Invalid binning format: {0} or {1}".format(binning_format1,
binning_format2))
# convert binning, if necessary
if binning_format1 == 'michi' or binning_format2 == 'michi':
convert_obs_groups_binning_def_michi_to_default()
# loop over observation groups: use binning of the 1st set to compare
if binning_format1 == 'michi':
observation_groups = obs_groups_michi
else:
observation_groups = ObservationGroups.read(input_dir1 + '/bg_observation_groups.ecsv')
groups = observation_groups.list_of_groups
print()
print("list of groups", groups)
for group in groups:
print()
print("group ", group)
# compare only observation groups in group IDs selection
# if empty, use all groups:
if len(group_ids_selection) is not 0:
groups_to_compare = group_ids_selection
else:
groups_to_compare = groups
if group in groups_to_compare:
group_info = observation_groups.info_group(group)
print(group_info)
# get cubes
if binning_format1 == 'michi':
# find corresponding ALT_ID, AZ_ID in lookup table
i_alt, i_az = look_obs_groups_michi(group)
filename1 = input_dir1 + '/hist_alt' + str(i_alt) + \
'_az' + str(i_az) + '.fits.gz'
else:
filename1 = input_dir1 + '/bg_cube_model_group' + str(group) + \
'_table.fits.gz'
if binning_format2 == 'michi':
# find corresponding ALT_ID, AZ_ID in lookup table
i_alt, i_az = look_obs_groups_michi(group)
filename2 = input_dir2 + '/hist_alt' + str(i_alt) + \
'_az' + str(i_az) + '.fits.gz'
else:
filename2 = input_dir2 + '/bg_cube_model_group' + str(group) + \
'_table.fits.gz'
print('filename1', filename1)
print('filename2', filename2)
bg_cube_model1 = FOVCubeBackgroundModel.read(filename1,
format='table').background_cube
bg_cube_model2 = FOVCubeBackgroundModel.read(filename2,
format='table').background_cube
# normalize 1 w.r.t. 2 (i.e. true w.r.t. reco)
if NORMALIZE == 1:
# normalize w.r.t. cube integral
integral1 = bg_cube_model1.integral
integral2 = bg_cube_model2.integral
bg_cube_model1.data *= integral2 / integral1
elif NORMALIZE == 2:
# normalize w.r.t images integral (normalize each image on its own)
integral_images1 = bg_cube_model1.integral_images
integral_images2 = bg_cube_model2.integral_images
for i_energy in np.arange(len(bg_cube_model1.energy_edges) - 1):
bg_cube_model1.data[i_energy] *= (integral_images2 / integral_images1)[i_energy]
# compare binning
print("energy edges 1", bg_cube_model1.energy_edges)
print("energy edges 2", bg_cube_model2.energy_edges)
print("detector edges 1 Y", bg_cube_model1.coordy_edges)
print("detector edges 2 Y", bg_cube_model2.coordy_edges)
print("detector edges 1 X", bg_cube_model1.coordx_edges)
print("detector edges 2 X", bg_cube_model2.coordx_edges)
# make sure that both cubes use the same units for the plots
bg_cube_model2.data = bg_cube_model2.data.to(bg_cube_model1.data.unit)
# plot
fig, axes = plt.subplots(nrows=2, ncols=3)
fig.set_size_inches(30., 15., forward=True)
plt.suptitle(group_info)
# plot images
# rows: similar energy bin
# cols: same file
# bg_cube_model1.plot_image(energy=Quantity(0.5, 'TeV'), ax=axes[0, 0])
bg_cube_model1.plot_image(energy=Quantity(5., 'TeV'), ax=axes[0, 0])
axes[0, 0].set_title("{0}: {1}".format(name1, axes[0, 0].get_title()))
bg_cube_model1.plot_image(energy=Quantity(50., 'TeV'), ax=axes[1, 0])
axes[1, 0].set_title("{0}: {1}".format(name1, axes[1, 0].get_title()))
# bg_cube_model2.plot_image(energy=Quantity(0.5, 'TeV'), ax=axes[0, 1])
bg_cube_model2.plot_image(energy=Quantity(5., 'TeV'), ax=axes[0, 1])
axes[0, 1].set_title("{0}: {1}".format(name2, axes[0, 1].get_title()))
bg_cube_model2.plot_image(energy=Quantity(50., 'TeV'), ax=axes[1, 1])
axes[1, 1].set_title("{0}: {1}".format(name2, axes[1, 1].get_title()))
# plot spectra
# rows: similar det bin
# cols: compare both files
bg_cube_model1.plot_spectrum(coord=Angle([0., 0.], 'degree'),
ax=axes[0, 2],
style_kwargs=dict(color='blue',
label=name1))
spec_title1 = axes[0, 2].get_title()
bg_cube_model2.plot_spectrum(coord=Angle([0., 0.], 'degree'),
ax=axes[0, 2],
style_kwargs=dict(color='red',
label=name2))
spec_title2 = axes[0, 2].get_title()
if spec_title1 != spec_title2:
s_error = "Expected same det binning, but got "
s_error += "\"{0}\" and \"{1}\"".format(spec_title1, spec_title2)
raise ValueError(s_error)
else:
axes[0, 2].set_title(spec_title1)
axes[0, 2].legend()
bg_cube_model1.plot_spectrum(coord=Angle([2., 2.], 'degree'),
ax=axes[1, 2],
style_kwargs=dict(color='blue',
label=name1))
spec_title1 = axes[1, 2].get_title()
bg_cube_model2.plot_spectrum(coord=Angle([2., 2.], 'degree'),
ax=axes[1, 2],
style_kwargs=dict(color='red',
label=name2))
spec_title2 = axes[1, 2].get_title()
if spec_title1 != spec_title2:
s_error = "Expected same det binning, but got "
s_error += "\"{0}\" and \"{1}\"".format(spec_title1, spec_title2)
raise ValueError(s_error)
else:
axes[1, 2].set_title(spec_title1)
axes[1, 2].legend()
plt.draw()
# save
outfile = "bg_cube_model_comparison_group{}.png".format(group)
print('Writing {}'.format(outfile))
fig.savefig(outfile)
plt.show() # don't leave at the end
def plot_bg_cube_model_comparison(input_file1, name1,
input_file2, name2):
"""
Plot background cube model comparison.
Produce a figure for comparing 2 bg cube models (1 and 2).
Plot strategy in each figure:
* Images:
* rows: similar energy bin
* cols: same bg cube model set
* Spectra:
* rows: similar det bin
* cols: compare both bg cube model sets
Parameters
----------
input_file1, input_file2 : str
File where the corresponding bg cube model is stored.
name1, name2 : str
Name to use for plot labels/legends.
"""
# get cubes
filename1 = input_file1
filename2 = input_file2
bg_cube_model1 = FOVCubeBackgroundModel.read(filename1,
format='table').background_cube
bg_cube_model2 = FOVCubeBackgroundModel.read(filename2,
format='table').background_cube
# normalize 1 w.r.t. 2 (i.e. true w.r.t. reco)
# normalize w.r.t. cube integral
integral1 = bg_cube_model1.integral
integral2 = bg_cube_model2.integral
bg_cube_model1.data *= integral2 / integral1
# make sure that both cubes use the same units for the plots
bg_cube_model2.data = bg_cube_model2.data.to(bg_cube_model1.data.unit)
# plot
fig, axes = plt.subplots(nrows=2, ncols=3)
fig.set_size_inches(30., 15., forward=True)
group_info = 'group 27: ALT = [72.0, 90.0) deg, AZ = [90.0, 270.0) deg'
plt.suptitle(group_info)
# plot images
# rows: similar energy bin
# cols: same file
bg_cube_model1.plot_image(energy=Quantity(1., 'TeV'), ax=axes[0, 0])
axes[0, 0].set_title("{0}: {1}".format(name1, axes[0, 0].get_title()))
bg_cube_model1.plot_image(energy=Quantity(10., 'TeV'), ax=axes[1, 0])
axes[1, 0].set_title("{0}: {1}".format(name1, axes[1, 0].get_title()))
bg_cube_model2.plot_image(energy=Quantity(1., 'TeV'), ax=axes[0, 1])
axes[0, 1].set_title("{0}: {1}".format(name2, axes[0, 1].get_title()))
bg_cube_model2.plot_image(energy=Quantity(10., 'TeV'), ax=axes[1, 1])
axes[1, 1].set_title("{0}: {1}".format(name2, axes[1, 1].get_title()))
# plot spectra
# rows: similar det bin
# cols: compare both files
bg_cube_model1.plot_spectrum(coord=Angle([0., 0.], 'degree'),
ax=axes[0, 2],
style_kwargs=dict(color='blue',
label=name1))
spec_title1 = axes[0, 2].get_title()
bg_cube_model2.plot_spectrum(coord=Angle([0., 0.], 'degree'),
ax=axes[0, 2],
style_kwargs=dict(color='red',
label=name2))
spec_title2 = axes[0, 2].get_title()
if spec_title1 != spec_title2:
s_error = "Expected same det binning, but got "
s_error += "\"{0}\" and \"{1}\"".format(spec_title1, spec_title2)
raise ValueError(s_error)
else:
axes[0, 2].set_title(spec_title1)
# plot normalized models on top
plot_data_x, normed_pl = get_normed_pl(plot_data=axes[0, 2].get_lines()[0],
E_0=E_REF, norm=NORM, index=INDEX)
axes[0, 2].plot(plot_data_x, normed_pl, color='blue',
linestyle='dotted', linewidth=2,
label='model index = {}'.format(INDEX))
plot_data_x, normed_pl = get_normed_pl(plot_data=axes[0, 2].get_lines()[0],
E_0=E_REF, norm=NORM, index=INDEX + 1)
axes[0, 2].plot(plot_data_x, normed_pl, color='blue',
linestyle='dashed', linewidth=2,
label='model index = {}'.format(INDEX + 1))
axes[0, 2].legend()
bg_cube_model1.plot_spectrum(coord=Angle([2., 2.], 'degree'),
ax=axes[1, 2],
style_kwargs=dict(color='blue',
label=name1))
spec_title1 = axes[1, 2].get_title()
bg_cube_model2.plot_spectrum(coord=Angle([2., 2.], 'degree'),
ax=axes[1, 2],
style_kwargs=dict(color='red',
label=name2))
spec_title2 = axes[1, 2].get_title()
if spec_title1 != spec_title2:
s_error = "Expected same det binning, but got "
s_error += "\"{0}\" and \"{1}\"".format(spec_title1, spec_title2)
raise ValueError(s_error)
else:
axes[1, 2].set_title(spec_title1)
# plot normalized models on top
plot_data_x, normed_pl = get_normed_pl(plot_data=axes[1, 2].get_lines()[0],
E_0=E_REF, norm=NORM, index=INDEX)
axes[1, 2].plot(plot_data_x, normed_pl, color='blue',
linestyle='dotted', linewidth=2,
label='model index = {}'.format(INDEX))
plot_data_x, normed_pl = get_normed_pl(plot_data=axes[1, 2].get_lines()[0],
E_0=E_REF, norm=NORM, index=INDEX + 1)
axes[1, 2].plot(plot_data_x, normed_pl, color='blue',
linestyle='dashed', linewidth=2,
label='model index = {}'.format(INDEX + 1))
axes[1, 2].legend()
plt.show()