def Performance_eccentricity_plotter_clustering(distance_array, ecc_pairs, performance_array,
performance_array_clustering, dist_value, trace_color,
trace_color_clustering, figure_title, file_name):
"""Performance for clustering selected electrodes
Plot performance as a function of eccentricity comparing all vs
clustering-selected electrodes
"""
ecc_values = ecc_pairs[distance_array == dist_value]
performance_array_values = performance_array[distance_array == dist_value]
performance_array_values_clustering = performance_array_clustering[distance_array == dist_value]
y_x, y_x_std = mean_relationship(ecc_values, performance_array_values, np.arange(3, 27, 3))
y_x_clustering, y_x_std_clustering = mean_relationship(ecc_values, performance_array_values_clustering,
np.arange(3, 27, 3))
coeff, p_value = pearsonr(ecc_values, performance_array_values)
coeff, p_value_clustering = pearsonr(ecc_values, performance_array_values_clustering)
fig = plt.figure(figsize=(12, 10))
plt.plot(np.arange(3, 24, 3) + 1.5, y_x, linewidth=3.0, marker='o', color=trace_color)
plt.plot(np.arange(3, 24, 3) + 1.5, y_x_clustering, linewidth=3.0, marker='o', color=trace_color_clustering)
plt.axis('tight')
plt.tight_layout()
plt.ylim(0.3, 1)
plt.ylabel('Performance', fontweight='bold')
plt.xlabel('Eccentricity (deg)', fontweight='bold')
plt.legend(('All p-value={0}'.format(np.round_(p_value, 4)),
'Clustering-selected p-value={0}'.format(np.round_(p_value_clustering, 4))), loc='lower right',
fontsize=28)
plt.title(figure_title, fontweight='bold', loc='center')
plt.show()
fig.savefig(file_name, dpi=200)
def performance_corticaldistance_plotter_clustering(cortical_distances, performance_array, performance_array_clustering,
trace_color, trace_color_clustering, figure_title, file_name):
"""Performance for clustering selected electrodes
Plot performance as a function of cortical distance comparing all vs
clustering-selected electrodes
"""
y_x, y_x_std = mean_relationship(cortical_distances, performance_array, np.arange(0.5, 5.5, 0.5))
y_x_clustering, y_x_std_clustering = mean_relationship(cortical_distances, performance_array_clustering,
np.arange(0.5, 5.5, 0.5))
fig = plt.figure(figsize=(12, 10))
plt.plot(np.arange(0.5, 5, 0.5) + 0.25, y_x, marker='o', linewidth=3.0, color=trace_color)
# plt.fill_between(np.arange(0.5,5,0.5)+0.25,y_x-y_x_std,y_x+y_x_std,color=trace_color)
plt.plot(np.arange(0.5, 5, 0.5) + 0.25, y_x_clustering, marker='o', linewidth=3.0, color=trace_color_clustering)
# plt.fill_between(np.arange(0.5,5,0.5)+0.25,y_x_clustering-y_x_std_clustering,y_x_clustering+y_x_std_clustering,color=trace_color_clustering)
plt.axis('tight')
plt.tight_layout()
plt.ylim(0.3, 1)
plt.ylabel('Performance', fontweight='bold')
plt.xlabel('Cortical distance (mm)', fontweight='bold')
plt.title(figure_title, fontweight='bold', loc='center')
plt.legend(('All', 'Clustering-selected'), loc='lower right', fontsize=32)
plt.show()
fig.savefig(file_name, dpi=200)
def performance_corticaldistance_plotter(
cortical_distances_wide, cortical_distances_medium,
cortical_distances_narrow, performance_array_wide,
performance_array_medium, performance_array_narrow, trace_color_wide,
trace_color_medium, trace_color_narrow, figure_title, file_name):
"""Performance vs cortical distance
This function plots discrimination performance vs cortical distance and
compares resultss for wide, medium, and narrow windows.
"""
fig = plt.figure(figsize=(12, 10))
y_x_wide, y_x_std_wide = mean_relationship(cortical_distances_wide,
performance_array_wide,
np.arange(0.5, 5.5, 0.5))
y_x_medium, y_x_std_medium = mean_relationship(cortical_distances_medium,
performance_array_medium,
np.arange(0.5, 5.5, 0.5))
y_x_narrow, y_x_std_narrow = mean_relationship(cortical_distances_narrow,
performance_array_narrow,
np.arange(0.5, 5.5, 0.5))
plt.plot(np.arange(0.5, 5, 0.5) + 0.25,
y_x_wide,
marker='o',
linewidth=3.0,
color=trace_color_wide)
# plt.fill_between(np.arange(0.5,5,0.5)+0.25,y_x_wide-y_x_std_wide,y_x_wide+y_x_std_wide,color=trace_color_wide)
plt.plot(np.arange(0.5, 5, 0.5) + 0.25,
y_x_medium,
marker='o',
linewidth=3.0,
color=trace_color_medium)
# plt.fill_between(np.arange(0.5,5,0.5)+0.25,y_x_medium-y_x_std_medium,y_x_medium+y_x_std_medium,color=trace_color_medium)
plt.plot(np.arange(0.5, 5, 0.5) + 0.25,
y_x_narrow,
marker='o',
linewidth=3.0,
color=trace_color_narrow)
# plt.fill_between(np.arange(0.5,5,0.5)+0.25,y_x_narrow-y_x_std_narrow,y_x_narrow+y_x_std_narrow,color=trace_color_narrow)
plt.axis('tight')
plt.tight_layout()
plt.ylim(0.3, 1)
plt.ylabel('Performance', fontweight='bold', labelpad=30, fontsize=30)
plt.xlabel('Cortical distance (mm)',
fontweight='bold',
labelpad=30,
fontsize=30)
plt.title(figure_title, fontweight='bold', loc='center')
plt.legend(('Wide', 'Medium', 'Narrow'), loc='lower right', fontsize=32)
plt.show()
fig.savefig(file_name, bbox_inches='tight', dpi=200)
def Performance_eccentricity_onblind_plotter_P3(
distance_array, ecc_pairs, performance_array, performance_array_blind,
dist_value, trace_color, trace_color_blind, figure_title, file_name):
"""Performance-eccentricity
Plots of performance-eccentricity comparing correlation-aware and
correlation-blind decoders
"""
ecc_values = ecc_pairs[distance_array == dist_value]
performance_array_values = performance_array[distance_array == dist_value]
performance_array_values_blind = performance_array_blind[distance_array ==
dist_value]
y_x, y_x_std = mean_relationship(ecc_values, performance_array_values,
np.arange(3, 21, 3))
y_x_blind, y_x_std_blind = mean_relationship(
ecc_values, performance_array_values_blind, np.arange(3, 21, 3))
coeff, p_value = pearsonr(ecc_values, performance_array_values)
coeff, p_value_blind = pearsonr(ecc_values, performance_array_values_blind)
fig = plt.figure(figsize=(12, 10))
plt.plot(np.arange(3, 18, 3) + 1.5,
y_x,
linewidth=3.0,
marker='o',
color=trace_color)
plt.plot(np.arange(3, 18, 3) + 1.5,
y_x_blind,
linewidth=3.0,
marker='o',
color=trace_color_blind)
plt.axis('tight')
plt.tight_layout()
plt.ylim(0.3, 1)
plt.ylabel('Performance', fontweight='bold')
plt.xlabel('Eccentricity (deg)', fontweight='bold')
plt.legend(
('Correlation-aware p-value={0}'.format(np.round_(p_value, 4)),
'Correlation-blind p-value={0}'.format(np.round_(p_value_blind, 4))),
loc='lower right',
fontsize=28)
plt.title(figure_title, fontweight='bold', loc='center')
plt.show()
fig.savefig(file_name, dpi=200)
def performance_corticaldistance_onblind_plotter(
cortical_distances, performance_array, performance_array_blind,
trace_color, trace_color_blind, figure_title, file_name):
"""Performance-cortical distance
Plots of performance-cortical distance comparing correlation-aware and
correlation-blind decoders
"""
fig = plt.figure(figsize=(12, 10))
y_x, y_x_std = mean_relationship(cortical_distances, performance_array,
np.arange(0.5, 5.5, 0.5))
y_x_blind, y_x_std_blind = mean_relationship(cortical_distances,
performance_array_blind,
np.arange(0.5, 5.5, 0.5))
plt.plot(np.arange(0.5, 5, 0.5) + 0.25,
y_x,
marker='o',
linewidth=3.0,
color=trace_color)
plt.plot(np.arange(0.5, 5, 0.5) + 0.25,
y_x_blind,
marker='o',
linewidth=3.0,
color=trace_color_blind)
plt.axis('tight')
plt.tight_layout()
plt.ylim(0.3, 1)
plt.ylabel('Performance', fontweight='bold')
plt.xlabel('Cortical distance (mm)', fontweight='bold')
plt.title(figure_title, fontweight='bold', loc='center')
plt.legend(('Correlation-aware', 'Correlation-blind'),
loc='lower right',
fontsize=32)
plt.show()
fig.savefig(file_name, dpi=200)
def Performance_eccentricity_plotter_importance(distance_array, ecc_pairs, performance_array, performance_array_four,
performance_array_six, performance_array_eight, performance_array_ten,
performance_array_twelve, dist_value, figure_title, file_name):
"""Performance-eccentricity for the best electrodes
Perfromance as a function of eccentricity using the best 4, 6, 8, 10, and
12 electrodes for specific value of probes separation
"""
ecc_values = ecc_pairs[distance_array == dist_value]
performance_array_values = performance_array[distance_array == dist_value]
performance_array_values_four = performance_array_four[distance_array == dist_value]
performance_array_values_six = performance_array_six[distance_array == dist_value]
performance_array_values_eight = performance_array_eight[distance_array == dist_value]
performance_array_values_ten = performance_array_ten[distance_array == dist_value]
performance_array_values_twelve = performance_array_twelve[distance_array == dist_value]
y_x, y_x_std = mean_relationship(ecc_values, performance_array_values, np.arange(3, 27, 3))
y_x_four, y_x_std_four = mean_relationship(ecc_values, performance_array_values_four, np.arange(3, 27, 3))
y_x_six, y_x_std_six = mean_relationship(ecc_values, performance_array_values_six, np.arange(3, 27, 3))
y_x_eight, y_x_std_eight = mean_relationship(ecc_values, performance_array_values_eight, np.arange(3, 27, 3))
y_x_ten, y_x_std_ten = mean_relationship(ecc_values, performance_array_values_ten, np.arange(3, 27, 3))
y_x_twelve, y_x_std_twelve = mean_relationship(ecc_values, performance_array_values_twelve, np.arange(3, 27, 3))
coeff, p_value = pearsonr(ecc_values, performance_array_values)
coeff, p_value_four = pearsonr(ecc_values, performance_array_values_four)
coeff, p_value_six = pearsonr(ecc_values, performance_array_values_six)
coeff, p_value_eight = pearsonr(ecc_values, performance_array_values_eight)
coeff, p_value_ten = pearsonr(ecc_values, performance_array_values_ten)
coeff, p_value_twelve = pearsonr(ecc_values, performance_array_values_twelve)
fig = plt.figure(figsize=(12, 10))
plt.plot(np.arange(3, 24, 3) + 1.5, y_x, linewidth=3.0, marker='o', color=(0.031, 0.031, 0.027))
plt.plot(np.arange(3, 24, 3) + 1.5, y_x_four, linewidth=3.0, marker='o', color=(0.054, 0.925, 0.964))
plt.plot(np.arange(3, 24, 3) + 1.5, y_x_six, linewidth=3.0, marker='o', color=(0.427, 0.964, 0.054))
plt.plot(np.arange(3, 24, 3) + 1.5, y_x_eight, linewidth=3.0, marker='o', color=(0.964, 0.937, 0.054))
plt.plot(np.arange(3, 24, 3) + 1.5, y_x_ten, linewidth=3.0, marker='o', color=(0.964, 0.054, 0.780))
plt.plot(np.arange(3, 24, 3) + 1.5, y_x_twelve, linewidth=3.0, marker='o', color=(0.964, 0.098, 0.054))
plt.axis('tight')
plt.tight_layout()
plt.ylim(0.3, 1)
plt.ylabel('Performance', fontweight='bold')
plt.xlabel('Eccentricity (deg)', fontweight='bold')
plt.legend(('All p-value={0}'.format(np.round_(p_value, 4)), '4 p-value={0}'.format(np.round_(p_value_four, 4)),
'6 p-value={0}'.format(np.round_(p_value_six, 4)), '8 p-value={0}'.format(np.round_(p_value_eight, 4)),
'10 p-value={0}'.format(np.round_(p_value_ten, 4)),
'12 p-value={0}'.format(np.round_(p_value_twelve, 4))), loc='lower right', fontsize=26)
plt.title(figure_title, fontweight='bold', loc='center')
plt.show()
fig.savefig(file_name, dpi=200)
def performance_corticaldistance_plotter_importance(cortical_distances, performance_array, performance_array_four,
performance_array_six, performance_array_eight,
performance_array_ten, performance_array_twelve, file_name):
"""Performance-cortical distance for the best electrodes
Perfromance as a function of cortical distance using the best 4, 6, 8, 10,
and 12 electrodes
"""
y_x, y_x_std = mean_relationship(cortical_distances, performance_array, np.arange(0.5, 5.5, 0.5))
y_x_four, y_x_std_four = mean_relationship(cortical_distances, performance_array_four, np.arange(0.5, 5.5, 0.5))
y_x_six, y_x_std_six = mean_relationship(cortical_distances, performance_array_six, np.arange(0.5, 5.5, 0.5))
y_x_eight, y_x_std_eight = mean_relationship(cortical_distances, performance_array_eight, np.arange(0.5, 5.5, 0.5))
y_x_ten, y_x_std_ten = mean_relationship(cortical_distances, performance_array_ten, np.arange(0.5, 5.5, 0.5))
y_x_twelve, y_x_std_twelve = mean_relationship(cortical_distances, performance_array_twelve,
np.arange(0.5, 5.5, 0.5))
fig = plt.figure(figsize=(12, 10))
plt.plot(np.arange(0.5, 5, 0.5) + 0.25, y_x, marker='o', linewidth=3.0, color=(0.031, 0.031, 0.027))
# plt.fill_between(np.arange(0.5,5,0.5)+0.25,y_x-y_x_std,y_x+y_x_std,color=(0.031, 0.031, 0.027))
plt.plot(np.arange(0.5, 5, 0.5) + 0.25, y_x_four, marker='o', linewidth=3.0, color=(0.054, 0.925, 0.964))
# plt.fill_between(np.arange(0.5,5,0.5)+0.25,y_x_four-y_x_std_four,y_x_four+y_x_std_four,color=(0.054, 0.925, 0.964))
plt.plot(np.arange(0.5, 5, 0.5) + 0.25, y_x_six, marker='o', linewidth=3.0, color=(0.427, 0.964, 0.054))
# plt.fill_between(np.arange(0.5,5,0.5)+0.25,y_x_six-y_x_std_six,y_x_six+y_x_std_six,color=(0.427, 0.964, 0.054))
plt.plot(np.arange(0.5, 5, 0.5) + 0.25, y_x_eight, marker='o', linewidth=3.0, color=(0.964, 0.937, 0.054))
# plt.fill_between(np.arange(0.5,5,0.5)+0.25,y_x_eight-y_x_std_eight,y_x_eight+y_x_std_eight,color=(0.964, 0.937, 0.054))
plt.plot(np.arange(0.5, 5, 0.5) + 0.25, y_x_ten, marker='o', linewidth=3.0, color=(0.964, 0.054, 0.780))
# plt.fill_between(np.arange(0.5,5,0.5)+0.25,y_x_ten-y_x_std_ten,y_x_ten+y_x_std_ten,color=(0.964, 0.054, 0.780))
plt.plot(np.arange(0.5, 5, 0.5) + 0.25, y_x_twelve, marker='o', linewidth=3.0, color=(0.964, 0.098, 0.054))
# plt.fill_between(np.arange(0.5,5,0.5)+0.25,y_x_twelve-y_x_std_twelve,y_x_twelve+y_x_std_twelve,color=(0.964, 0.098, 0.054))
plt.axis('tight')
plt.tight_layout()
plt.ylim(0.3, 1)
plt.ylabel('Performance', fontweight='bold')
plt.xlabel('Cortical distance (mm)', fontweight='bold')
plt.legend(('All', '4', '6', '8', '10', '12'), loc='lower right', fontsize=32)
plt.show()
fig.savefig(file_name, dpi=200)
def Bandpass_Performance_eccentricity_plotter(
distance_array, ecc_pairs, performance_array_theta,
performance_array_alpha, performance_array_beta,
performance_array_gamma, performance_array_highgamma, dist_value,
figure_title, file_name):
"""
Plot discrimination performance of LFP versus eccentricity at different
frequency bands
"""
ecc_values = ecc_pairs[distance_array == dist_value]
performance_array_values_theta = performance_array_theta[distance_array ==
dist_value]
performance_array_values_alpha = performance_array_alpha[distance_array ==
dist_value]
performance_array_values_beta = performance_array_beta[distance_array ==
dist_value]
performance_array_values_gamma = performance_array_gamma[distance_array ==
dist_value]
performance_array_values_highgamma = performance_array_highgamma[
distance_array == dist_value]
y_x_theta, y_x_std_theta = mean_relationship(
ecc_values, performance_array_values_theta, np.arange(3, 27, 3))
y_x_alpha, y_x_std_alpha = mean_relationship(
ecc_values, performance_array_values_alpha, np.arange(3, 27, 3))
y_x_beta, y_x_std_beta = mean_relationship(ecc_values,
performance_array_values_beta,
np.arange(3, 27, 3))
y_x_gamma, y_x_std_gamma = mean_relationship(
ecc_values, performance_array_values_gamma, np.arange(3, 27, 3))
y_x_highgamma, y_x_std_highgamma = mean_relationship(
ecc_values, performance_array_values_highgamma, np.arange(3, 27, 3))
coeff, p_value_theta = pearsonr(ecc_values, performance_array_values_theta)
coeff, p_value_alpha = pearsonr(ecc_values, performance_array_values_alpha)
coeff, p_value_beta = pearsonr(ecc_values, performance_array_values_beta)
coeff, p_value_gamma = pearsonr(ecc_values, performance_array_values_gamma)
coeff, p_value_highgamma = pearsonr(ecc_values,
performance_array_values_highgamma)
fig = plt.figure(figsize=(12, 10))
plt.plot(np.arange(3, 24, 3) + 1.5,
y_x_theta,
linewidth=3.0,
marker='o',
color=(0.035, 0.062, 0.682))
plt.plot(np.arange(3, 24, 3) + 1.5,
y_x_alpha,
linewidth=3.0,
marker='o',
color=(0.298, 0.662, 0.941))
plt.plot(np.arange(3, 24, 3) + 1.5,
y_x_beta,
linewidth=3.0,
marker='o',
color=(0.031, 0.568, 0.098))
plt.plot(np.arange(3, 24, 3) + 1.5,
y_x_gamma,
linewidth=3.0,
marker='o',
color=(0.960, 0.050, 0.019))
plt.plot(np.arange(3, 24, 3) + 1.5,
y_x_highgamma,
linewidth=3.0,
marker='o',
color=(0.960, 0.454, 0.019))
plt.axis('tight')
plt.tight_layout()
plt.ylim(0.3, 1)
plt.ylabel('Performance', fontweight='bold')
plt.xlabel('Eccentricity (deg)', fontweight='bold')
plt.legend(
('Theta p-value={0}'.format(np.round_(
p_value_theta, 4)), 'Alpha p-value={0}'.format(
np.round_(p_value_alpha, 4)), 'Beta p-value={0}'.format(
np.round_(p_value_beta, 4)), 'Gamma p-value={0}'.format(
np.round_(p_value_gamma, 4)),
'High-gamma p-value={0}'.format(np.round_(p_value_highgamma, 4))),
loc='upper right',
fontsize=26)
plt.title(figure_title, fontweight='bold', loc='center')
plt.show()
fig.savefig(file_name, dpi=200)
def bandpass_performance_corticaldistance_plotter(
cortical_distances, performance_array_theta, performance_array_alpha,
performance_array_beta, performance_array_gamma,
performance_array_highgamma, figure_title, file_name):
"""
Plot discrimination performance of LFP versus cortical distance at
different frequency bands
"""
fig = plt.figure(figsize=(12, 10))
y_x_theta, y_x_std_theta = mean_relationship(cortical_distances,
performance_array_theta,
np.arange(0.5, 5.5, 0.5))
y_x_alpha, y_x_std_alpha = mean_relationship(cortical_distances,
performance_array_alpha,
np.arange(0.5, 5.5, 0.5))
y_x_beta, y_x_std_beta = mean_relationship(cortical_distances,
performance_array_beta,
np.arange(0.5, 5.5, 0.5))
y_x_gamma, y_x_std_gamma = mean_relationship(cortical_distances,
performance_array_gamma,
np.arange(0.5, 5.5, 0.5))
y_x_highgamma, y_x_std_highgamma = mean_relationship(
cortical_distances, performance_array_highgamma,
np.arange(0.5, 5.5, 0.5))
plt.plot(np.arange(0.5, 5, 0.5) + 0.25,
y_x_theta,
marker='o',
linewidth=3.0,
color=(0.035, 0.062, 0.682))
plt.plot(np.arange(0.5, 5, 0.5) + 0.25,
y_x_alpha,
marker='o',
linewidth=3.0,
color=(0.298, 0.662, 0.941))
plt.plot(np.arange(0.5, 5, 0.5) + 0.25,
y_x_beta,
marker='o',
linewidth=3.0,
color=(0.031, 0.568, 0.098))
plt.plot(np.arange(0.5, 5, 0.5) + 0.25,
y_x_gamma,
marker='o',
linewidth=3.0,
color=(0.960, 0.050, 0.019))
plt.plot(np.arange(0.5, 5, 0.5) + 0.25,
y_x_highgamma,
marker='o',
linewidth=3.0,
color=(0.960, 0.454, 0.019))
plt.axis('tight')
plt.tight_layout()
plt.ylim(0.3, 1)
plt.ylabel('Performance', fontweight='bold')
plt.xlabel('Cortical distance (mm)', fontweight='bold')
plt.title(figure_title, fontweight='bold', loc='center')
plt.legend(('Theta', 'Alpha', 'Beta', 'Gamma', 'High-gamma'),
loc='upper right',
fontsize=28)
plt.show()
fig.savefig(file_name, dpi=200)
def Performance_eccentricity_plotter_P3(
distance_array_wide, distance_array_medium, distance_array_narrow,
ecc_pairs_wide, ecc_pairs_medium, ecc_pairs_narrow,
performance_array_wide, performance_array_medium,
performance_array_narrow, dist_value, trace_color_wide,
trace_color_medium, trace_color_narrow, figure_title, file_name):
"""
This function plots discrimination performance as a function of probes mean
eccentricity for specific probes separation. Then, it calculates linear
correlation p-value between performance and eccentricity.
"""
ecc_values_wide = ecc_pairs_wide[distance_array_wide == dist_value]
performance_array_values_wide = performance_array_wide[distance_array_wide
== dist_value]
ecc_values_medium = ecc_pairs_medium[distance_array_medium == dist_value]
performance_array_values_medium = performance_array_medium[
distance_array_medium == dist_value]
ecc_values_narrow = ecc_pairs_narrow[distance_array_narrow == dist_value]
performance_array_values_narrow = performance_array_narrow[
distance_array_narrow == dist_value]
y_x_wide, y_x_std_wide = mean_relationship(ecc_values_wide,
performance_array_values_wide,
np.arange(3, 21, 3))
y_x_medium, y_x_std_medium = mean_relationship(
ecc_values_medium, performance_array_values_medium,
np.arange(3, 21, 3))
y_x_narrow, y_x_std_narrow = mean_relationship(
ecc_values_narrow, performance_array_values_narrow,
np.arange(3, 21, 3))
coeff, p_value_wide = pearsonr(ecc_values_wide,
performance_array_values_wide)
coeff, p_value_medium = pearsonr(ecc_values_medium,
performance_array_values_medium)
coeff, p_value_narrow = pearsonr(ecc_values_narrow,
performance_array_values_narrow)
fig = plt.figure(figsize=(12, 10))
plt.plot(np.arange(3, 18, 3) + 1.5,
y_x_wide,
linewidth=3.0,
marker='o',
color=trace_color_wide)
plt.plot(np.arange(3, 18, 3) + 1.5,
y_x_medium,
linewidth=3.0,
marker='o',
color=trace_color_medium)
plt.plot(np.arange(3, 18, 3) + 1.5,
y_x_narrow,
linewidth=3.0,
marker='o',
color=trace_color_narrow)
plt.axis('tight')
plt.tight_layout()
plt.ylim(0.3, 1)
plt.ylabel('Performance', fontweight='bold', labelpad=30, fontsize=30)
plt.xlabel('Eccentricity (deg)',
fontweight='bold',
labelpad=30,
fontsize=30)
plt.legend(('Wide p-value={0}'.format(np.round_(p_value_wide, 4)),
'Medium p-value={0}'.format(np.round_(p_value_medium, 4)),
'Narrow p-value={0}'.format(np.round_(p_value_narrow, 4))),
loc='lower right',
fontsize=32)
plt.title(figure_title, fontweight='bold', loc='center')
plt.show()
fig.savefig(file_name, bbox_inches='tight', dpi=200)
