def plotBinsSurfacePercentage(data, rootdir, flag_all_ranges=False):
'''plot boxplots per range of mm versus percentage of surface covered - all patients'''
fig, axes = plt.subplots(figsize=(12, 16))
# Horizontal box plot
bplot = plt.boxplot(data,
vert=True, # vertical box aligmnent
patch_artist=True,
showmeans=True) # fill with color
# set the axes ranges and the names
plt.setp(bplot['medians'], color='black',linewidth=1.5)
plt.setp(bplot['means'], marker='D', markeredgecolor='darkred',
markerfacecolor='darkred', label='Mean')
if flag_all_ranges is True:
labels_neg = ['(-'+ str(x-1)+':-'+str(x)+')' for x in range(21,0,-1)]
labels_neg[20] = '(-1:0)'
labels_pos = ['('+ str(x-1)+':'+str(x)+')' for x in range(1,22)]
xticklabels = labels_neg+labels_pos
xtickNames = plt.setp(axes, xticklabels=xticklabels)
plt.setp(xtickNames, rotation=45, fontsize=6)
else:
xticklabels = [r"$(\infty< x < 0$)", r"$(0 \leqslant x \leqslant 5$)", r"$( x > 5)"]
xtickNames = plt.setp(axes, xticklabels=xticklabels)
plt.setp(xtickNames, fontsize=14, color='black')
axes.tick_params(colors='black')
handles, labels = plt.gca().get_legend_handles_labels()
by_label = OrderedDict(zip(labels, handles))
plt.legend(by_label.values(), by_label.keys(), fontsize=14)
plt.xlabel('Range of Distances [mm]', fontsize=14, color='black')
plt.ylabel('Tumor Surface covered by Ablation [%]', fontsize=14, color='black')
plt.title('Boxplots for Percentage of Tumor Surface covered by Ablation. 10 Cases (pooled)', fontsize=14)
# save figure
if flag_all_ranges is True:
figName_hist = 'Boxplots_TheHistogramDistances_AllRanges.png'
else:
figName_hist = 'Boxplots_TheHistogramDistances.png'
figpathHist = os.path.join(rootdir, figName_hist)
gh.save(figpathHist, width=12, height=10)
def interpolation_fct(df_ablation,
df_radiomics,
title,
fontsize=24,
flag=None,
flag_energy_axis=False,
flag_lin_regr=False):
"""
:param df_ablation:
:param df_radiomics:
:param title:
:param fontsize:
:param flag:
:param flag_energy_axis:
:return:
"""
# perform interpolation as a function of power and time (multivariate interpolation)
points_power = np.asarray(df_ablation['Power']).reshape(
(len(df_ablation), 1))
points_time = np.asarray(df_ablation['Time_Duration_Applied']).reshape(
(len(df_ablation), 1))
power_and_time_brochure = np.hstack((points_power, points_time))
ablation_vol_brochure = np.asarray(
df_ablation['Ablation Volume [ml]_brochure']).reshape(
(len(df_ablation), 1))
df_radiomics.dropna(subset=['Power', 'Time_Duration_Applied'],
inplace=True)
grid_x = df_radiomics['Power'].to_numpy()
grid_y = df_radiomics['Time_Duration_Applied'].to_numpy()
grid_x = np.array(pd.to_numeric(grid_x, errors='coerce'))
grid_y = np.array(pd.to_numeric(grid_y, errors='coerce'))
grid_x = grid_x.reshape(len(grid_x), 1)
grid_y = grid_y.reshape(len(grid_y), 1)
power_and_time_effective = np.asarray(np.hstack((grid_x, grid_y)))
ablation_vol_interpolated_brochure = griddata(power_and_time_brochure,
ablation_vol_brochure,
power_and_time_effective,
method='linear')
ablation_vol_interpolated_brochure = ablation_vol_interpolated_brochure.reshape(
len(df_radiomics), )
ablation_vol_measured = np.asarray(
df_radiomics['Ablation Volume [ml]']).reshape(len(df_radiomics), )
# %% PLOT BOXPLOTS
boxplots_PAV_EAV.plot_boxplots_volumes(ablation_vol_interpolated_brochure,
ablation_vol_measured,
flag_subcapsular='all')
# %% PLOT SCATTER PLOTS
fig, ax = plt.subplots()
if flag == 'Tumour Volume [ml]':
size_values = np.asarray(df_radiomics['Tumour Volume [ml]']).reshape(
len(df_radiomics), )
df = pd.DataFrame(data=dict(x=ablation_vol_interpolated_brochure,
y=ablation_vol_measured,
sizes=size_values))
df.dropna(inplace=True)
bins = np.arange(start=0, stop=30, step=6)
grouped = df.groupby(np.digitize(df.sizes, bins))
sizes = [150 * (i + 1.) for i in range(5)]
labels = ['0-5', '5-10', '15-20', '20-25', '25-30']
nr_samples = len(df)
for i, (name, group) in enumerate(grouped):
plt.scatter(group.x,
group.y,
s=sizes[i],
alpha=0.5,
label=labels[name - 1])
legend1 = ax.legend(labelspacing=1,
borderpad=0.75,
title='Tumor Volume [ml]',
handletextpad=1.5,
loc='upper right',
fontsize=fontsize - 2,
title_fontsize=fontsize - 2)
ax.add_artist(legend1)
ax.tick_params(axis='y', labelsize=fontsize)
ax.tick_params(axis='x', labelsize=fontsize)
plt.tick_params(labelsize=fontsize, color='k')
elif flag == 'No. chemo cycles':
size_values = np.asarray(df_radiomics['no_chemo_cycle']).reshape(
len(df_radiomics), )
df = pd.DataFrame()
df['x'] = ablation_vol_interpolated_brochure
df['y'] = ablation_vol_measured
df['sizes'] = size_values
df.dropna(inplace=True)
nr_samples = len(df)
bins = np.arange(start=0, stop=12, step=4)
print(np.digitize(df.sizes, bins, right=True))
grouped = df.groupby(np.digitize(df.sizes, bins, right=True))
sizes = [150 * (i + 1.) for i in range(4)]
labels = ['0', '1-4', '5-8', '9-12']
for i, (name, group) in enumerate(grouped):
plt.scatter(group.x,
group.y,
s=sizes[i],
alpha=0.5,
label=labels[name])
legend1 = ax.legend(labelspacing=1,
borderpad=0.75,
title='Chemotherapy Cycles',
handletextpad=1.5,
loc='upper right',
fontsize=fontsize - 2,
title_fontsize=fontsize - 2)
ax.add_artist(legend1)
elif flag_energy_axis:
energy = df_radiomics['Energy [kj]']
df = pd.DataFrame(data=dict(x=ablation_vol_interpolated_brochure,
energy=energy,
y=ablation_vol_measured))
df.dropna(inplace=True)
nr_samples = len(df)
ax2 = ax.twiny()
ax.scatter(df.x, df.y, color='steelblue', marker='o', s=100, alpha=0.8)
ax.set_ylabel('Effective Ablation Volume [ml]', fontsize=fontsize)
ax.set_xlabel('Predicted Ablation Volume Brochure [ml]',
fontsize=fontsize,
color='steelblue')
ax.tick_params(axis='x', labelcolor='steelblue')
ax.set_ylim([0, 100])
ax.set_xlim([0, 100])
ax2.scatter(df.energy,
df.y,
color='purple',
marker='*',
s=100,
alpha=0.5)
ax2.set_xlabel('Energy [kj]', color='purple', fontsize=fontsize)
ax2.tick_params(axis='x', colors='purple')
ax2.set_ylim([0, 100])
ax2.set_xlim([0, 100])
else:
df = pd.DataFrame(data=dict(x=ablation_vol_interpolated_brochure,
y=ablation_vol_measured))
df.dropna(inplace=True)
sc = plt.scatter(df.x, df.y, color='steelblue', marker='o', s=100)
nr_samples = len(df)
plt.ylabel('Effective Ablation Volume [ml]', fontsize=fontsize)
if flag_energy_axis is False:
plt.xlabel('Predicted Ablation Volume Brochure [ml]',
fontsize=fontsize)
plt.ylim([0, 100])
plt.xlim([0, 100])
if flag_lin_regr is True:
# get the data ready for linear regression
X = np.asarray(df.x).reshape(len(df.x), 1)
Y = np.asarray(df.y).reshape(len(df.y), 1)
regr = linear_model.LinearRegression()
regr.fit(X, Y)
SS_tot = np.sum((Y - np.mean(Y))**2)
residuals = Y - regr.predict(X)
SS_res = np.sum(residuals**2)
r_squared = 1 - (SS_res / SS_tot)
correlation_coef = np.corrcoef(X[:, 0], Y[:, 0])[0, 1]
label_r2 = r'$R^2:{0:.2f}$'.format(r_squared)
label_r = r'$r: {0:.2f}$'.format(correlation_coef)
ax.tick_params(axis='y', labelsize=fontsize)
ax.tick_params(axis='x', labelsize=fontsize)
plt.tick_params(labelsize=fontsize, color='k')
reg = plt.plot(X,
regr.predict(X),
color='orange',
linewidth=2,
label='Linear Regression')
plt.plot([], [], ' ', label=label_r)
plt.plot([], [], ' ', label=label_r2)
plt.legend(fontsize=fontsize, loc='upper right', labelspacing=1)
if flag is not None:
# these are matplotlib.patch.Patch properties
props = dict(boxstyle='round', facecolor='white', edgecolor='gray')
textstr = title + ' (n = ' + str(nr_samples) + ' )'
ax.text(0.05,
0.95,
textstr,
transform=ax.transAxes,
fontsize=20,
verticalalignment='top',
bbox=props)
figpathHist = os.path.join("figures",
title + flag + '_ablation_vol_interpolated')
else:
props = dict(boxstyle='round', facecolor='white', edgecolor='gray')
textstr = title + ' (n = ' + str(nr_samples) + ' )'
ax.text(0.05,
0.95,
textstr,
transform=ax.transAxes,
fontsize=20,
verticalalignment='top',
bbox=props)
plt.legend(fontsize=fontsize, loc='upper right', labelspacing=1)
figpathHist = os.path.join("figures",
title + '_ablation_vol_interpolated')
gh.save(figpathHist,
width=12,
height=12,
ext=["png"],
close=True,
tight=True,
dpi=600)
return ablation_vol_interpolated_brochure
def connected_mev_miv(df_radiomics):
"""
Plots ablation volumes.
:param df_radiomics: DataFrame containing tabular radiomics features
:param ablation_vol_interpolated_brochure: column-like interpolated ablation volume from the brochure
:return: Plot, saved as a PNG image
"""
df = pd.DataFrame()
df['PAV'] = df_radiomics['Predicted_Ablation_Volume']
df['EAV'] = df_radiomics['Ablation Volume [ml]']
# df['MIV'] = df_radiomics['Inner Ellipsoid Volume']
df['MIV'] = df_radiomics['Outer Ellipsoid Volume'] / 3
df['MEV'] = df_radiomics['Outer Ellipsoid Volume']
df = df[df['MEV'] < 150]
# discard
df.dropna(inplace=True)
# plot scatter plots on the same y axis then connect them with a vertical line
fig, ax = plt.subplots()
MIV = np.asarray(df['MIV'])
MEV = np.asarray(df['MEV'])
PAV = np.asarray(df['PAV'])
EAV = np.asarray(df['EAV'])
# x = df['PAV']
x = np.asarray([i for i in range(1, len(MEV) + 1)])
ax.scatter(x,
MIV,
marker='o',
color='green',
label='Maximum Inscribed Ellipsoid')
ax.scatter(x,
EAV,
marker='o',
color='orange',
label='Effective Ablation Volume')
ax.scatter(x,
PAV,
marker='o',
color='blue',
label='Predicted Ablation Volume')
ax.scatter(x,
MEV,
marker='o',
color='red',
label='Minimum Enclosing Ellipsoid')
plt.legend(loc='upper left')
plt.ylabel('Volume (mL)')
for i in np.arange(0, len(x)):
x1, x2 = x[i], x[i]
y1, y2 = MIV[i], MEV[i]
plt.plot([x1, x2], [y1, y2], 'k-')
# plt.ylim([-1, 150])
# labels = np.round(np.asarray(df['PAV']))
# plt.xticks(x, labels, rotation=45, fontsize=24, color='white')
timestr = time.strftime("%H%M%S-%Y%m%d")
folder_path = r"C:\develop\segmentation-eval\figures\MIV_MEV_ellipsoids"
fig_path = os.path.join(folder_path, timestr)
gh.save(fig_path,
width=12,
height=12,
ext=["png"],
close=True,
tight=True,
dpi=600)
def edit_save_plot(ax=None,
p=None,
flag_hue=None,
xlabel='PAV',
ylabel='EAV',
device='',
r_1=None,
r_2=None,
label_1=None,
label_2=None,
ratio_flag=False):
"""
:param ax:
:param p:
:param flag_hue:
:param ylabel:
:param device:
:param r_1:
:param r_2:
:param label_1:
:param label_2:
:return:
"""
fontsize = 20
if flag_hue in ['vessels', 'subcapsular', 'chemotherapy', 'Tumor_Vol']:
ax = p.axes[0, 0]
ax.legend(fontsize=fontsize,
title_fontsize=fontsize,
title=device,
loc='upper left')
leg = ax.get_legend()
L_labels = leg.get_texts()
label_line_1 = r'$R^2:{0:.2f}$'.format(r_1)
label_line_2 = r'$R^2:{0:.2f}$'.format(r_2)
L_labels[0].set_text(label_line_1)
L_labels[1].set_text(label_line_2)
L_labels[2].set_text(label_1)
L_labels[3].set_text(label_2)
else:
ax.legend(fontsize=fontsize,
title_fontsize=fontsize,
title=device,
loc='upper right')
# ax.legend(fontsize=fontsize, loc='upper right')
if ratio_flag is False:
plt.xlim([0, 100])
plt.ylim([0, 100])
plt.xlabel(xlabel, fontsize=fontsize)
plt.ylabel(ylabel, fontsize=fontsize)
timestr = time.strftime("%H%M%S-%Y%m%d")
figpath = os.path.join(
"figures", device + '__EAV_parametrized_PAV_groups_' + str(flag_hue) +
'-' + timestr)
gh.save(figpath,
width=12,
height=12,
ext=["png"],
close=True,
tight=True,
dpi=600)
plt.title(
'Prediction for Random Forest Model on Hold-Out Train/Test Sample: 70/30. '
'Min samples leaf:' + str(min_samples_leaf) + '. No. estimators: ' +
str(n_estimators),
fontsize=10)
ax.set_xlabel('Measured Ablation Volume [ml]', fontsize=10, color='k')
ax.set_ylabel('Predicted Ablation Volume [ml]', fontsize=10, color='k')
plt.legend(fontsize=10)
plt.tick_params(labelsize=10, color='black')
ax.tick_params(colors='black', labelsize=10)
plt.show()
figpathHist = os.path.join(
"figures",
"Random_Forest_Model_Accuracy_Hold_OutTrain_Test_" + 'Min_samples_leaf_' +
str(min_samples_leaf) + '_No_estimators_' + str(n_estimators))
gh.save(figpathHist, ext=['png'], close=True)
# %%
n_estimators = 100
min_samples_leaf = 2
min_sample_split = 2
clf = RandomForestRegressor(n_estimators=n_estimators,
random_state=0,
min_samples_leaf=min_samples_leaf,
min_samples_split=min_samples_leaf,
oob_score=True)
clf.fit(X, y)
# print("Score of the training dataset obtained using an out-of-bag estimate: %0.2f" % clf.oob_score_)
# print("Prediction computed with out-of-bag estimate on the training set: " % clf.oob_prediction_)
importances = list(clf.feature_importances_)
feature_list = X.columns.to_list()
def interpolation_fct(df_ablation,
df_radiomics,
title,
fontsize=24,
flag_tumor=None,
lin_regr=False):
"""
:param df_ablation:
:param df_radiomics:
:param title:
:param fontsize:
:param flag:
:param flag_energy_axis:
:return:
"""
# perform interpolation as a function of power and time (multivariate interpolation)
points_power = np.asarray(df_ablation['Power']).reshape(
(len(df_ablation), 1))
points_time = np.asarray(df_ablation['Time_Duration_Applied']).reshape(
(len(df_ablation), 1))
power_and_time_brochure = np.hstack((points_power, points_time))
ablation_vol_brochure = np.asarray(
df_ablation['Ablation Volume [ml]_brochure']).reshape(
(len(df_ablation), 1))
df_radiomics.dropna(subset=['Power', 'Time_Duration_Applied'],
inplace=True)
grid_x = df_radiomics['Power'].to_numpy()
grid_y = df_radiomics['Time_Duration_Applied'].to_numpy()
grid_x = np.array(pd.to_numeric(grid_x, errors='coerce'))
grid_y = np.array(pd.to_numeric(grid_y, errors='coerce'))
grid_x = grid_x.reshape(len(grid_x), 1)
grid_y = grid_y.reshape(len(grid_y), 1)
power_and_time_effective = np.asarray(np.hstack((grid_x, grid_y)))
ablation_vol_interpolated_brochure = griddata(power_and_time_brochure,
ablation_vol_brochure,
power_and_time_effective,
method='linear')
# interchange ablation_vol_interpolated and ablation_vol_measured for plotting on the "Y-axis"
ablation_vol_interpolated_brochure = ablation_vol_interpolated_brochure.reshape(
len(df_radiomics), )
ablation_vol_measured = np.asarray(
df_radiomics['Ablation Volume [ml]']).reshape(len(df_radiomics), )
# %% PLOT Tumor Volume vs PAV (per colours subcapsular vs. non-subcapsular)
# groupby needed.
fig, ax = plt.subplots()
if flag_tumor == 'Tumour Volume [ml]':
tumor_volume = df_radiomics['Tumour Volume [ml]']
elif flag_tumor == 'Tumour Volume + 10mm margin [ml]':
tumor_volume = df_radiomics['Tumour Volume + 10mm margin [ml]']
subcapsular = df_radiomics['Proximity_to_surface']
df = pd.DataFrame(data=dict(x=tumor_volume,
y=ablation_vol_interpolated_brochure,
subcapsular=subcapsular))
df.dropna(inplace=True)
grouped = df.groupby(subcapsular)
labels = ['Deep Tumors', 'Subcapsular Tumors']
for i, (name, group) in enumerate(grouped):
plt.scatter(group.x, group.y, alpha=0.5, label=labels[i], s=200)
plt.legend(title=title + '(n = ' + str(len(df)) + ' )',
title_fontsize=fontsize,
fontsize=fontsize)
plt.ylabel('Predicted Ablation Volume [ml]', fontsize=fontsize)
plt.xlabel(flag_tumor, fontsize=fontsize)
# plt.xlim([, 200])
ax.tick_params(axis='y', labelsize=fontsize, color='k')
ax.tick_params(axis='x', labelsize=fontsize, color='k')
ax.set_xscale('log')
plt.ylim([-1, 100])
plt.xlim([0.03251566067053182, 684.15])
# xlims = ax.get_xlim()
# plt.axis('square')
plt.tick_params(labelsize=fontsize, color='black')
if lin_regr is True:
X = np.asarray(df.x).reshape(len(df), 1)
Y = np.asarray(df.y).reshape(len(df), 1)
regr = linear_model.LinearRegression()
regr.fit(X, Y)
SS_tot = np.sum((Y - np.mean(Y))**2)
residuals = Y - regr.predict(X)
SS_res = np.sum(residuals**2)
r_squared = 1 - (SS_res / SS_tot)
correlation_coef = np.corrcoef(X[:, 0], Y[:, 0])[0, 1]
label_r2 = r'$R^2:{0:.2f}$'.format(r_squared)
label_r = r'$r: {0:.2f}$'.format(correlation_coef)
ax.tick_params(axis='y', labelsize=fontsize, color='k')
ax.tick_params(axis='x', labelsize=fontsize, color='k')
plt.tick_params(labelsize=fontsize, color='black')
plt.plot([], [], ' ', label=label_r)
plt.plot([], [], ' ', label=label_r2)
reg = plt.plot(X,
regr.predict(X),
color='black',
linewidth=1.5,
label='Linear Regression')
plt.legend(title=title,
title_fontsize=fontsize,
fontsize=fontsize,
loc='best')
plt.show()
figpath = os.path.join("figures", title + '_PAV_vs_' + flag_tumor)
gh.save(figpath,
width=12,
height=12,
ext=["png"],
close=True,
tight=False,
dpi=600)
def plot_boxplots(df):
# %% boxplot chemotherapy
fig, ax = plt.subplots(figsize=(12, 10))
df_chemo = df.copy()
df_chemo['Ablation Volume [ml] / Energy [kJ]'] = df_chemo[
'Ablation Volume [ml]'] / df_chemo['Energy [kj]']
df_chemo.dropna(subset=['Ablation Volume [ml] / Energy [kJ]'],
inplace=True)
df_chemo.dropna(subset=['chemo_before_ablation'], inplace=True)
df_chemo['chemo_before_ablation'].replace('No', False, inplace=True)
df_chemo['chemo_before_ablation'].replace('Yes', True, inplace=True)
df.dropna(subset=['Ablation Volume [ml]'], inplace=True)
df.dropna(subset=['chemo_before_ablation'], inplace=True)
df['chemo_before_ablation'].replace('No', False, inplace=True)
df['chemo_before_ablation'].replace('Yes', True, inplace=True)
# ttest
no_chemo_df = df_chemo[df_chemo['chemo_before_ablation'] == False]
no_chemo = no_chemo_df['Ablation Volume [ml]'].tolist()
chemo_df = df_chemo[df_chemo['chemo_before_ablation'] == True]
chemo = chemo_df['Ablation Volume [ml]'].tolist()
fig, ax = plt.subplots(figsize=(12, 10))
plt.hist(no_chemo)
plt.title('No Chemotherapy')
plt.ylabel('Ablation Volume [ml]')
figpathHist = os.path.join("figures",
"histogram ablation volumes no chemo")
gh.save(figpathHist, ext=['png'], close=True)
fig1, ax = plt.subplots(figsize=(12, 10))
plt.hist(chemo)
plt.title('Chemotherapy')
plt.ylabel('Ablation Volume [ml] ')
figpathHist = os.path.join("figures", "histogram ablation volumes chemo")
gh.save(figpathHist, ext=['png'], close=True)
print('no of tumors with chemo:', str(len(chemo)))
print('no of tumors with no chemo:', str(len(no_chemo)))
#
stat, p_chemo = shapiro(chemo)
# interpret
alpha_chemo = 0.05
if p_chemo > alpha_chemo:
msg = 'Sample Chemo looks Gaussian (fail to reject H0)'
else:
msg = 'Sample Chemo does not look Gaussian (reject H0)'
print(msg)
stat, p_no_chemo = shapiro(no_chemo)
# interpret
alpha_no_chemo = 0.05
if p_no_chemo > alpha_no_chemo:
msg = 'Sample No Chemo looks Gaussian (fail to reject H0)'
else:
msg = 'Sample No Chemo does not look Gaussian (reject H0)'
print(msg)
if p_no_chemo < alpha_no_chemo and p_chemo < alpha_chemo:
t, p = stats.mannwhitneyu(chemo, no_chemo)
print(
'mann withney u test applied for samples coming from a non Gaussian distribution:'
)
print("t = " + str(t))
print("p = " + str(p))
else:
t, p = stats.ttest_ind(chemo, no_chemo)
print('ttest applied for samples coming from a Gaussian distribution:')
print("t = " + str(t))
print("p = " + str(p))
fig, ax = plt.subplots(figsize=(12, 10))
bp_dict = df.boxplot(column=['Ablation Volume [ml]'],
ax=ax,
notch=True,
by='chemo_before_ablation',
patch_artist=True,
return_type='both')
ax.set_xlabel('')
plt.show()
for row_key, (ax, row) in bp_dict.iteritems():
for i, box in enumerate(row['fliers']):
box.set_marker('o')
for i, box in enumerate(row['boxes']):
if i == 0:
box.set_facecolor('Purple')
box.set_edgecolor('DarkMagenta')
else:
box.set_facecolor('LightPink')
box.set_edgecolor('HotPink')
for i, box in enumerate(row['medians']):
box.set_color(color='Black')
box.set_linewidth(2)
for i, box in enumerate(row['whiskers']):
box.set_color(color='Black')
box.set_linewidth(2)
xticklabels = [
'No Chemotherapy before Ablation',
'Chemotherapy Administered before Ablation'
]
xtickNames = plt.setp(ax, xticklabels=xticklabels)
plt.setp(xtickNames, fontsize=10, color='black')
plt.ylim([-2, 120])
plt.ylabel('Ablation Volume [ml]', fontsize=12, color='k')
plt.tick_params(labelsize=10, color='black')
ax.tick_params(colors='black', labelsize=10, color='k')
ax.set_ylim([-2, 120])
plt.xlabel('')
fig.suptitle('')
plt.title('')
# plt.title('Comparison of Ratio (Ablation Volumes [ml] : Energy [kJ]) from MAVERRIC Dataset by Chemotherapy', fontsize=12)
plt.title(
'Comparison of Ablation Volumes [ml] from MAVERRIC Dataset by Chemotherapy',
fontsize=12)
figpathHist = os.path.join(
"figures", "boxplot ablation volumes by chemo before ablation")
gh.save(figpathHist, ext=['png'], close=True)
# %% BOXPLOTS ABLATION VOLUMES
# ttest
df_volumes = df.copy()
df_volumes.dropna(subset=['Ablation Volume [ml]'], inplace=True)
df_volumes.dropna(subset=['Ablation Volume [ml] (manufacturers)'],
inplace=True)
ablation_vol = df_volumes['Ablation Volume [ml]'].tolist()
ablation_vol_brochure = df_volumes[
'Ablation Volume [ml] (manufacturers)'].tolist()
stat, p_brochure = shapiro(ablation_vol_brochure)
# interpret
alpha_brochure = 0.05
if p_brochure > alpha_brochure:
msg = 'Sample Ablation Volume Brochure looks Gaussian (fail to reject H0)'
else:
msg = 'Sample Ablation Volume Brochure does not look Gaussian (reject H0)'
print(msg)
stat, p_voxel = shapiro(ablation_vol)
# interpret
alpha_voxel = 0.05
if p_voxel > alpha_voxel:
msg = 'Sample Ablation Volume looks Gaussian (fail to reject H0)'
else:
msg = 'Sample Ablation Volume does not look Gaussian (reject H0)'
print(msg)
if p_voxel < alpha_voxel and p_brochure < alpha_brochure:
t, p = stats.mannwhitneyu(ablation_vol, ablation_vol_brochure)
print(
'mann withney u test applied for samples coming from a non Gaussian distribution:'
)
print("t = " + str(t))
print("p = " + str(p))
else:
t, p = stats.ttest_ind(ablation_vol, ablation_vol_brochure)
print('ttest applied for samples coming from a Gaussian distribution:')
print("t = " + str(t))
print("p = " + str(p))
fig, ax = plt.subplots(figsize=(12, 10))
bp_dict = df.boxplot(column=[
'Ablation Volume [ml]', 'Ablation Volume [ml] (parametrized_formula)',
'Ablation Volume [ml] (manufacturers)'
],
ax=ax,
notch=True,
patch_artist=True,
return_type='both')
ax.set_xlabel('')
row = bp_dict.lines
# for idx,row in enumerate(lines):
for i, box in enumerate(row['fliers']):
box.set_marker('o')
# box.set_edgecolor('RoyalBlue')
for i, box in enumerate(row['boxes']):
if i == 0:
box.set_facecolor('Blue')
box.set_edgecolor('MediumBlue')
elif i == 1:
box.set_facecolor('BlueViolet')
box.set_edgecolor('BlueViolet')
elif i == 2:
box.set_facecolor('DeepSkyBlue')
box.set_edgecolor('DodgerBlue')
for i, box in enumerate(row['medians']):
box.set_color(color='Black')
box.set_linewidth(2)
for i, box in enumerate(row['whiskers']):
box.set_color(color='Black')
box.set_linewidth(2)
xticklabels = [
'Ablation Volume [ml] (Voxel-Based)',
'Ablation Volume [ml] (Ellipsoid Formula)',
'Ablation Volume [ml] (Manufacturers Brochure)'
]
xtickNames = plt.setp(ax, xticklabels=xticklabels)
plt.setp(xtickNames, fontsize=10, color='black')
plt.ylim([-2, 150])
plt.ylabel('Ablation Volume [ml]', fontsize=14, color='k')
plt.tick_params(labelsize=10, color='black')
ax.tick_params(colors='black', labelsize=10, color='k')
ax.set_ylim([-2, 150])
plt.title('Comparison of Ablation Volumes [ml] from MAVERRIC Dataset',
fontsize=16)
figpathHist = os.path.join("figures", "boxplot volumes")
gh.save(figpathHist, ext=['png'], close=True)
plt.xlabel(
'Percentage of Surface Margin Covered for different ablation margins ranges',
fontsize=20,
color='black')
plt.ylabel('Frequency', fontsize=20, color='black')
plt.title(
'Ablation Surface Margin Coverages [%] Histogram for all MWA device models.'
)
plt.legend(fontsize=20)
plt.xticks(range(0, 101, 10))
figpathHist = os.path.join("figures",
"surface margin frequency percentages overlaid")
plt.tick_params(labelsize=20, color='black')
ax.tick_params(colors='black', labelsize=20)
gh.save(figpathHist, ext=['png'], close=True, width=18, height=16)
# %% percentage distances histograms for angyodinamics
fig, ax = plt.subplots()
df_angyodinamics["safety_margin_distribution_0"].replace(0,
np.nan,
inplace=True)
df_angyodinamics["safety_margin_distribution_5"].replace(0,
np.nan,
inplace=True)
df_angyodinamics["safety_margin_distribution_10"].replace(0,
np.nan,
inplace=True)
idx_margins = df_angyodinamics.columns.get_loc('safety_margin_distribution_0')
df_margins = df_angyodinamics.iloc[:, idx_margins:idx_margins + 3].copy()
df_margins.reset_index(drop=True, inplace=True)
def plot_boxplots_volumes(ablation_vol_brochure,
ablation_vol_measured,
flag_subcapsular=None,
device_name=None):
"""
"""
# drop the nans
effective_ablation_vol = ablation_vol_measured[
~np.isnan(ablation_vol_measured)]
predicted_ablation_vol = ablation_vol_brochure[
~np.isnan(ablation_vol_brochure)]
stat, p_brochure = shapiro(predicted_ablation_vol)
# interpret
alpha_brochure = 0.05
if p_brochure > alpha_brochure:
msg = 'Sample Ablation Volume Brochure looks Gaussian (fail to reject H0)'
else:
msg = 'Sample Ablation Volume Brochure does not look Gaussian (reject H0)'
print(msg)
stat, p_voxel = shapiro(effective_ablation_vol)
# interpret
alpha_voxel = 0.05
if p_voxel > alpha_voxel:
msg = 'Sample Ablation Volume looks Gaussian (fail to reject H0)'
else:
msg = 'Sample Ablation Volume does not look Gaussian (reject H0)'
print(msg)
if p_voxel < alpha_voxel and p_brochure < alpha_brochure:
t, p = stats.mannwhitneyu(effective_ablation_vol,
predicted_ablation_vol)
print(
'mann withney u test applied for samples coming from a non Gaussian distribution:'
)
print("t = " + str(t))
print("p = " + str(p))
else:
t, p = stats.ttest_ind(effective_ablation_vol, predicted_ablation_vol)
print('ttest applied for samples coming from a Gaussian distribution:')
print("t = " + str(t))
print("p = " + str(p))
fig, ax = plt.subplots(figsize=(12, 10))
bplot = plt.boxplot(x=[predicted_ablation_vol, effective_ablation_vol],
notch=False,
patch_artist=True,
widths=0.4)
for element in ['medians', 'fliers', 'whiskers', 'caps']:
plt.setp(bplot[element], color='black', linewidth=2.5)
boxes = bplot['boxes']
plt.setp(boxes[1], color='seagreen')
plt.setp(boxes[0], color='sandybrown')
if flag_subcapsular is False:
xticklabels = ['PAV', 'EAV (Deep Tumors)']
elif flag_subcapsular is True:
xticklabels = ['PAV', 'EAV (Subcapsular Tumors)']
else:
xticklabels = ['PAV', 'EAV']
xtickNames = plt.setp(ax, xticklabels=xticklabels)
plt.setp(xtickNames, fontsize=10, color='black')
plt.ylim([-2, 100])
plt.ylabel('Ablation Volume (mL)', fontsize=24, color='k')
plt.tick_params(labelsize=24, color='black')
ax.tick_params(colors='black', labelsize=24, color='k')
ax.set_ylim([-2, 100])
plt.plot([], [], ' ', label=device_name)
plt.legend(fontsize=24, loc='upper right', labelspacing=1, frameon=False)
# plt.title('Comparison of Ablation Volumes [ml] from MAVERRIC Dataset', fontsize=16)
figpathHist = os.path.join(
"figures", "boxplot volumes EAV vs PAV Solero. Subcapsular - " +
str(flag_subcapsular))
gh.save(figpathHist, ext=['png'], close=True, tight=True)
def plotHistDistances(pat_name, lesion_id, rootdir, distanceMap, num_voxels,
title, ablation_date):
# PLOT THE HISTOGRAM FOR THE MAUERER EUCLIDIAN DISTANCES
lesion_id_str = str(lesion_id)
lesion_id = lesion_id_str.split('.')[0]
figName_hist = 'Pat_' + str(pat_name) + '_Lesion' + str(
lesion_id) + '_AblationDate_' + ablation_date + '_histogram'
min_val = int(np.floor(min(distanceMap)))
max_val = int(np.ceil(max(distanceMap)))
fig, ax = plt.subplots(figsize=(18, 16))
col_height, bins, patches = ax.hist(distanceMap,
ec='darkgrey',
bins=range(min_val - 1, max_val + 1))
voxels_nonablated = []
voxels_insuffablated = []
voxels_ablated = []
for b, p, col_val in zip(bins, patches, col_height):
if b < 0:
voxels_nonablated.append(col_val)
elif 0 <= b <= 5:
voxels_insuffablated.append(col_val)
elif b > 5:
voxels_ablated.append(col_val)
# %%
'''calculate the total percentage of surface for ablated, non-ablated, insufficiently ablated'''
voxels_nonablated = np.asarray(voxels_nonablated)
voxels_insuffablated = np.asarray(voxels_insuffablated)
voxels_ablated = np.asarray(voxels_ablated)
sum_perc_nonablated = ((voxels_nonablated / num_voxels) * 100).sum()
sum_perc_insuffablated = ((voxels_insuffablated / num_voxels) * 100).sum()
sum_perc_ablated = ((voxels_ablated / num_voxels) * 100).sum()
# %%
'''iterate through the bins to change the colors of the patches bases on the range [mm]'''
for b, p, col_val in zip(bins, patches, col_height):
if b < 0:
plt.setp(p,
label='Ablation Surface Margin ' + r'$x < 0$' + 'mm :' +
" %.2f" % sum_perc_nonablated + '%')
elif 0 <= b <= 5:
plt.setp(p,
'facecolor',
'orange',
label='Ablation Surface Margin ' + r'$0 \leq x \leq 5$' +
'mm: ' + "%.2f" % sum_perc_insuffablated + '%')
elif b > 5:
plt.setp(p,
'facecolor',
'darkgreen',
label='Ablation Surface Margin ' + r'$x > 5$' + 'mm: ' +
" %.2f" % sum_perc_ablated + '%')
# %%
'''edit the axes limits and labels'''
# csfont = {'fontname': 'Times New Roman'}
plt.xlabel('Euclidean Distances (mm)', fontsize=30, color='black')
plt.tick_params(labelsize=30, color='black')
ax.tick_params(colors='black', labelsize=30)
plt.grid(True)
# TODO: set equal axis limits
ax.set_xlim([-15, 15])
# edit the y-ticks: change to percentage of surface
yticks, locs = plt.yticks()
percent = (yticks / num_voxels) * 100
percentage_surface_rounded = np.round(percent)
yticks_percent = [str(x) + '%' for x in percentage_surface_rounded]
new_yticks = (percentage_surface_rounded * yticks) / percent
new_yticks[0] = 0
plt.yticks(new_yticks, yticks_percent)
plt.ylabel('Frequency of percentage of tumor surface voxels',
fontsize=30,
color='black')
handles, labels = plt.gca().get_legend_handles_labels()
by_label = OrderedDict(zip(labels, handles))
# font = font_manager.FontProperties(family='Times New Roman',
# style='normal', size=30)
# plt.legend(by_label.values(), by_label.keys(), fontsize=30, loc='best', prop=font)
plt.legend(by_label.values(), by_label.keys(), fontsize=30, loc='best')
# ax.legend(prop=font)
plt.title(title + '. Patient ' + str(pat_name) + '. Lesion ' +
str(lesion_id),
fontsize=30)
figpathHist = os.path.join(rootdir, figName_hist)
gh.save(figpathHist, width=18, height=16, ext=['png'])
# return the percentages
return sum_perc_nonablated, sum_perc_insuffablated, sum_perc_ablated
def interpolation_fct(df_ablation,
df_radiomics,
title=None,
flag_needle_error=False,
flag_overlap='Dice'):
"""
Interpolate the missing ablation volumes using the power and time from the brochure
:param df_ablation:
:param df_radiomics:
:param title:
:return:
"""
fontsize = 20
# perform interpolation as a function of power and time (multivariate interpolation)
# EXTRACT VALUES FROM THE MWA BROCHURE
points_power = np.asarray(df_ablation['Power']).reshape(
(len(df_ablation), 1))
points_time = np.asarray(df_ablation['Time_Duration_Applied']).reshape(
(len(df_ablation), 1))
points = np.hstack((points_power, points_time))
values = np.asarray(df_ablation['Predicted Ablation Volume (ml)']).reshape(
(len(df_ablation), 1))
# EXTRACT VALUES FROM RADIOMICS (Effective measured values)
df_radiomics.dropna(subset=['Power', 'Time_Duration_Applied'],
inplace=True)
grid_x = np.asarray(df_radiomics['Power']).reshape((len(df_radiomics), 1))
grid_y = np.asarray(df_radiomics['Time_Duration_Applied']).reshape(
(len(df_radiomics), 1))
xi = np.hstack((grid_x, grid_y))
ablation_vol_interpolated_brochure = griddata(points,
values,
xi,
method='linear')
# PREDICTED VS MEASURED SCATTER PIE CHART
ablation_vol_measured = np.asarray(
df_radiomics['Ablation Volume [ml]']).reshape(len(df_radiomics), 1)
ratios_0 = df_radiomics.safety_margin_distribution_0.tolist()
ratios_5 = df_radiomics.safety_margin_distribution_5.tolist()
ratios_10 = df_radiomics.safety_margin_distribution_10.tolist()
# ACTUALLY PLOT STUFF
fig, ax = plt.subplots()
if flag_needle_error is False:
for idx, val in enumerate(ablation_vol_interpolated_brochure):
xs = ablation_vol_interpolated_brochure[idx]
ys = ablation_vol_measured[idx]
ratio_0 = ratios_0[idx] / 100
ratio_5 = ratios_5[idx] / 100
ratio_10 = ratios_10[idx] / 100
if ~(np.isnan(xs)) and ~(np.isnan(ys)):
draw_pie([ratio_0, ratio_5, ratio_10],
xs,
ys,
500,
colors=['red', 'orange', 'green'],
ax=ax)
plt.ylabel('Effective Ablation Volume (mL)', fontsize=fontsize)
plt.xlabel('Predicted Ablation Volume Brochure (mL)',
fontsize=fontsize)
plt.xlim([0, 80])
plt.ylim([0, 80])
else:
y = ablation_vol_measured / ablation_vol_interpolated_brochure # ratio EAV/PAV
fig_title = '_ratio_EAV_PAV'
ylabel_text = 'R(EAV:PAV)'
needle_error = np.asarray(df_radiomics['needle_error']).reshape(
len(df_radiomics), 1)
if flag_overlap == 'Dice':
y = np.asarray(df_radiomics['Dice']).reshape(len(df_radiomics), 1)
fig_title = '_Dice_'
ylabel_text = 'Dice Score'
if flag_overlap == 'Volume Overlap Error':
y = np.asarray(df_radiomics['Volume Overlap Error']).reshape(
len(df_radiomics), 1)
fig_title = '_Volume Overlap Error'
ylabel_text = 'Volume Overlap Error'
if flag_overlap == 'Tumour residual volume [ml]':
y = np.asarray(
df_radiomics['Tumour residual volume [ml]']).reshape(
len(df_radiomics), 1)
# tumor_radius = np.asarray(df_radiomics['major_axis_length_tumor']/2).reshape(len(df_radiomics), 1)
# y_normalized = df_radiomics['Tumour residual volume [ml]'] / df_radiomics['Tumour Volume [ml]']
# x_normalized = needle_error/tumor_radius
# y = np.asarray(y_normalized).reshape(len(df_radiomics), 1)
# x = np.asarray(x_normalized).reshape(len(df_radiomics), 1)
fig_title = '_Tumour residual volume [ml]'
ylabel_text = 'Tumour residual volume (mL)'
for idx, val in enumerate(ablation_vol_interpolated_brochure):
ys = y[idx]
xs = needle_error[idx]
ratio_0 = ratios_0[idx] / 100
ratio_5 = ratios_5[idx] / 100
ratio_10 = ratios_10[idx] / 100
if ~(np.isnan(xs)) and ~(np.isnan(ys)):
draw_pie([ratio_0, ratio_5, ratio_10],
xs,
ys,
500,
colors=['red', 'orange', 'green'],
ax=ax)
# ax.set_xscale('log')
plt.ylabel(ylabel_text, fontsize=fontsize + 2)
plt.xlabel('Lateral Error (mm)', fontsize=fontsize + 2)
plt.xlim([-0.2, 6])
plt.ylim([-0.2, 3])
red_patch = mpatches.Patch(color='red',
label='Ablation Margin ' + r'$x < 0$' + 'mm')
orange_patch = mpatches.Patch(color='orange',
label='Ablation Margin ' +
r'$0 \leq x \leq 5$' + 'mm')
green_patch = mpatches.Patch(color='darkgreen',
label='Ablation Margin ' + r'$x > 5$' + 'mm')
plt.legend(handles=[red_patch, orange_patch, green_patch],
fontsize=fontsize,
loc='best',
title=title,
title_fontsize=21)
props = dict(boxstyle='round', facecolor='white', edgecolor='gray')
# textstr = title
# ax.text(0.05, 0.95, textstr, transform=ax.transAxes, fontsize=20, verticalalignment='top', bbox=props)
ax.tick_params(axis='y', labelsize=fontsize, color='k')
ax.tick_params(axis='x', labelsize=fontsize, color='k')
plt.tick_params(labelsize=fontsize, color='black')
# plt.xlim([8, 46])
# plt.ylim([-0.2, 3.5])
if flag_needle_error is True:
figpath = os.path.join(
"figures", title + "_pie_charts_euclidean_error" + fig_title)
else:
figpath = os.path.join("figures", title + "_pie_charts")
gh.save(figpath, width=14, height=10, close=True, dpi=600, tight=True)
plt.show()
def scatter_plot(df1, **kwargs):
"""
df, x_data, y_data, title, x_label=False, y_label='', lin_reg=''
:param df:
:param x_data:
:param x_data:
:param title:
:param x_label:
:param x_label:
:param lin_reg:
:return:
"""
fig, ax = plt.subplots()
fontsize = 24
if kwargs.get('x_data') is None:
print('No X input data to plot')
return
if kwargs.get('y_data') is None:
print('No Y input data to plot')
return
df_to_plot = df1.copy()
# drop NaNs from both x and y
# df_to_plot.dropna(inplace=True)
df_to_plot.dropna(subset=[kwargs["y_data"], kwargs["x_data"]],
inplace=True)
if kwargs.get('colormap') is not None:
X_scatter = df_to_plot[kwargs['x_data']]
Y_scatter = df_to_plot[kwargs['y_data']]
t = df_to_plot[kwargs['colormap']]
plt.scatter(X_scatter, Y_scatter, c=t, cmap='viridis', s=20)
cbar = plt.colorbar()
cbar.ax.set_title(kwargs['colormap'], fontsize=8)
else:
df_to_plot.plot.scatter(x=kwargs["x_data"],
y=kwargs["y_data"],
s=100,
alpha=0.7,
color='purple',
marker='*')
if kwargs.get('size') is not None:
size = np.asarray(50 * (df_to_plot[kwargs['size']] + 1)).reshape(
len(df_to_plot), 1)
size_mask = ~np.isnan(size)
size = size[size_mask]
# color = 'steelblue'
fig, ax = plt.subplots()
x = df_to_plot[kwargs['x_data']]
y = df_to_plot[kwargs['y_data']]
sc = ax.scatter(x, y, s=size, alpha=0.6, marker='*', color='purple')
legend_1 = ax.legend(*sc.legend_elements("sizes",
num=6,
func=lambda x: x / 50 - 1,
color='steelblue'),
title=kwargs['size'],
labelspacing=1.5,
borderpad=1.5,
handletextpad=3.5,
fontsize=fontsize,
loc='upper right')
legend_1.get_title().set_fontsize(str(fontsize))
ax.add_artist(legend_1)
if kwargs.get('lin_reg') is not None:
X = np.array(df_to_plot[kwargs['x_data']])
Y = np.array(df_to_plot[kwargs['y_data']])
regr = linear_model.LinearRegression()
X = X.reshape(len(X), 1)
Y = Y.reshape(len(Y), 1)
regr.fit(X, Y)
SS_tot = np.sum((Y - np.mean(Y))**2)
residuals = Y - regr.predict(X)
SS_res = np.sum(residuals**2)
r_squared = 1 - (SS_res / SS_tot)
correlation_coef = np.corrcoef(X[:, 0], Y[:, 0])[0, 1]
label_r2 = r'$R^2:{0:.2f}$'.format(r_squared)
label_r = r'$r: {0:.2f}$'.format(correlation_coef)
plt.plot(X,
regr.predict(X),
color='orange',
linewidth=1.5,
label='Linear Regression')
plt.plot([], [], ' ', label='n = ' + str(len(kwargs['x_data'])))
plt.plot([], [], ' ', label=label_r)
plt.plot([], [], ' ', label=label_r2)
if kwargs.get('legend_title'):
plt.legend(fontsize=fontsize,
loc='best',
title=kwargs['legend_title'],
title_fontsize=fontsize)
else:
plt.legend(fontsize=fontsize, loc='upper left')
if kwargs.get('x_lim') is not None and kwargs.get('y_lim') is not None:
plt.xlim([0, kwargs['x_lim']])
plt.ylim([0, kwargs['y_lim']])
title = kwargs['title']
# plt.tick_params(labelsize=fontsize, color='black')
# ax.tick_params(axis='y', labelsize=fontsize, color='k')
# ax.tick_params(axis='x', labelsize=fontsize, color='k')
# ax.xaxis.label.set_color('black')
# ax.yaxis.label.set_color('black')
# matplotlib.rc('axes', labelcolor='black')
figpathHist = os.path.join("figures", title)
if kwargs.get('x_label') is not None and kwargs.get('y_label') is None:
plt.xlabel(kwargs['x_label'], fontsize=fontsize, color='k')
plt.ylabel(kwargs['y_data'], fontsize=fontsize, color='k')
elif kwargs.get('y_label') is not None and kwargs.get('x_label') is None:
plt.ylabel(kwargs['y_label'], fontsize=fontsize, color='k')
plt.xlabel(kwargs['x_data'], fontsize=fontsize, color='k')
elif kwargs.get('x_label') is None and kwargs.get('y_label') is None:
plt.xlabel(kwargs['x_data'], fontsize=fontsize, color='k')
plt.ylabel(kwargs['y_data'], fontsize=fontsize, color='k')
gh.save(figpathHist,
width=12,
height=12,
ext=["png"],
tight=True,
close=True,
dpi=600)
plt.close('all')
def scatter_plot_groups(df1_no_outliers):
groups = df1_no_outliers.groupby('Proximity_to_vessels')
fig, ax = plt.subplots()
lesion_per_device = []
device_name_grp = []
for name, group in groups:
ax.plot(group["Energy [kj]"],
group["Ablation Volume [ml]"],
marker='o',
linestyle='',
ms=14,
label=name)
lesion_per_device.append(len(group))
device_name_grp.append(name)
L = ax.legend()
L_labels = L.get_texts()
for idx, L in enumerate(L_labels):
L.set_text(device_name_grp[idx] + ' N=' + str(lesion_per_device[idx]))
plt.xlabel('Energy [kJ]', fontsize=20, color='black')
plt.ylabel('Ablation Volume [ml]', fontsize=20, color='black')
plt.title("Ablation Volume vs Energy Grouped by Proximity to Vessels",
fontsize=20,
color='black')
plt.tick_params(labelsize=20, color='black')
plt.legend(title_fontsize=20)
ax.tick_params(colors='black', labelsize=20)
figpathHist = os.path.join(
"figures",
"Ablation Volume vs Energy Grouped by Proximity to Vessels.")
gh.save(figpathHist, width=18, height=16, ext=['png'], close=True)
# %% group by device name
groups = df1_no_outliers.groupby('Device_name')
fig, ax = plt.subplots()
lesion_per_device = []
device_name_grp = []
for name, group in groups:
ax.plot(group["Energy [kj]"],
group["Ablation Volume [ml]"],
marker='o',
linestyle='',
ms=14,
label=name)
lesion_per_device.append(len(group))
device_name_grp.append(name)
L = ax.legend()
L_labels = L.get_texts()
for idx, L in enumerate(L_labels):
L.set_text(
str(device_name_grp[idx]) + ' N=' + str(lesion_per_device[idx]))
plt.title('Ablation Volume [ml] vs Energy [kJ] per MWA Device Type.',
fontsize=20)
plt.xlabel('Energy [kJ]', fontsize=20, color='black')
plt.ylabel('Ablation Volume [ml]', fontsize=20, color='black')
plt.tick_params(labelsize=20, color='black')
plt.legend(title_fontsize=20)
ax.tick_params(colors='black', labelsize=20)
figpathHist = os.path.join(
"figures", "Ablation Volume vs Energy per MWA Device Category.")
gh.save(figpathHist, width=18, height=16, ext=['png'], close=True)
# chemotherapy
groups = df1_no_outliers.groupby('chemo_before_ablation')
fig, ax = plt.subplots()
lesion_per_device = []
device_name_grp = []
for name, group in groups:
ax.plot(group["Energy [kj]"],
group["Ablation Volume [ml]"],
marker='o',
linestyle='',
ms=14,
label=name)
lesion_per_device.append(len(group))
device_name_grp.append(name)
L = ax.legend()
L_labels = L.get_texts()
for idx, L in enumerate(L_labels):
L.set_text(
str(device_name_grp[idx]) + ' N=' + str(lesion_per_device[idx]))
plt.title(
'Ablation Volume [ml] vs Energy [kJ] grouped by Chemotherapy Treatment before Ablation.',
fontsize=20)
plt.xlabel('Energy [kJ]', fontsize=20, color='black')
plt.ylabel('Ablation Volume [ml]', fontsize=20, color='black')
plt.tick_params(labelsize=20, color='black')
plt.legend(title_fontsize=20)
ax.tick_params(colors='black', labelsize=20)
figpathHist = os.path.join(
"figures", "Ablation Volume vs Energy grouped by chemotherapy.")
gh.save(figpathHist, width=18, height=16, ext=['png'], close=True)
def plot_boxplots_chemo(df):
"""
boxplot chemotherapy
:param df: dataframe
:return: ttest values
"""
fig, ax = plt.subplots(figsize=(12, 10))
df_chemo = df.copy()
df_chemo['Ablation Volume [ml] / Energy [kJ]'] = df_chemo[
'Ablation Volume [ml]'] / df_chemo['Energy [kj]']
df_chemo.dropna(subset=['Ablation Volume [ml] / Energy [kJ]'],
inplace=True)
df_chemo.dropna(subset=['chemo_before_ablation'], inplace=True)
df_chemo['chemo_before_ablation'].replace('No', False, inplace=True)
df_chemo['chemo_before_ablation'].replace('Yes', True, inplace=True)
df.dropna(subset=['Ablation Volume [ml]'], inplace=True)
df.dropna(subset=['chemo_before_ablation'], inplace=True)
df['chemo_before_ablation'].replace('No', False, inplace=True)
df['chemo_before_ablation'].replace('Yes', True, inplace=True)
# ttest
no_chemo_df = df_chemo[df_chemo['chemo_before_ablation'] == False]
no_chemo = no_chemo_df['Ablation Volume [ml]'].tolist()
chemo_df = df_chemo[df_chemo['chemo_before_ablation'] == True]
chemo = chemo_df['Ablation Volume [ml]'].tolist()
fig, ax = plt.subplots(figsize=(12, 10))
plt.hist(no_chemo)
plt.title('No Chemotherapy')
plt.ylabel('Ablation Volume [ml]')
figpathHist = os.path.join("figures",
"histogram ablation volumes no chemo")
gh.save(figpathHist, ext=['png'], close=True)
fig1, ax = plt.subplots(figsize=(12, 10))
plt.hist(chemo)
plt.title('Chemotherapy')
plt.ylabel('Ablation Volume [ml] ')
figpathHist = os.path.join("figures", "histogram ablation volumes chemo")
gh.save(figpathHist, ext=['png'], close=True)
print('no of tumors with chemo:', str(len(chemo)))
print('no of tumors with no chemo:', str(len(no_chemo)))
#
stat, p_chemo = shapiro(chemo)
# interpret
alpha_chemo = 0.05
if p_chemo > alpha_chemo:
msg = 'Sample Chemo looks Gaussian (fail to reject H0)'
else:
msg = 'Sample Chemo does not look Gaussian (reject H0)'
print(msg)
stat, p_no_chemo = shapiro(no_chemo)
# interpret
alpha_no_chemo = 0.05
if p_no_chemo > alpha_no_chemo:
msg = 'Sample No Chemo looks Gaussian (fail to reject H0)'
else:
msg = 'Sample No Chemo does not look Gaussian (reject H0)'
print(msg)
if p_no_chemo < alpha_no_chemo and p_chemo < alpha_chemo:
t, p = stats.mannwhitneyu(chemo, no_chemo)
print(
'mann withney u test applied for samples coming from a non Gaussian distribution:'
)
print("t = " + str(t))
print("p = " + str(p))
else:
t, p = stats.ttest_ind(chemo, no_chemo)
print('ttest applied for samples coming from a Gaussian distribution:')
print("t = " + str(t))
print("p = " + str(p))
fig, ax = plt.subplots(figsize=(12, 10))
bp_dict = df.boxplot(column=['Ablation Volume [ml]'],
ax=ax,
notch=True,
by='chemo_before_ablation',
patch_artist=True,
return_type='both')
ax.set_xlabel('')
plt.show()
for row_key, (ax, row) in bp_dict.iteritems():
for i, box in enumerate(row['fliers']):
box.set_marker('o')
for i, box in enumerate(row['boxes']):
if i == 0:
box.set_facecolor('Purple')
box.set_edgecolor('DarkMagenta')
else:
box.set_facecolor('LightPink')
box.set_edgecolor('HotPink')
for i, box in enumerate(row['medians']):
box.set_color(color='Black')
box.set_linewidth(2)
for i, box in enumerate(row['whiskers']):
box.set_color(color='Black')
box.set_linewidth(2)
xticklabels = [
'No Chemotherapy before Ablation',
'Chemotherapy Administered before Ablation'
]
xtickNames = plt.setp(ax, xticklabels=xticklabels)
plt.setp(xtickNames, fontsize=10, color='black')
plt.ylim([-2, 120])
plt.ylabel('Ablation Volume [ml]', fontsize=12, color='k')
plt.tick_params(labelsize=10, color='black')
ax.tick_params(colors='black', labelsize=10, color='k')
ax.set_ylim([-2, 120])
plt.xlabel('')
fig.suptitle('')
plt.title('')
# plt.title('Comparison of Ratio (Ablation Volumes [ml] : Energy [kJ]) from MAVERRIC Dataset by Chemotherapy', fontsize=12)
plt.title(
'Comparison of Ablation Volumes [ml] from MAVERRIC Dataset by Chemotherapy',
fontsize=12)
figpathHist = os.path.join(
"figures", "boxplot ablation volumes by chemo before ablation")
gh.save(figpathHist, ext=['png'], close=True)
def plot_subplots(df_radiomics):
"""
Plot a 3-subplot of pav vs eav, subcapsular and chemo
:param df_radiomics:
:return: plot fo png file
"""
# Set up the matplotlib figure
f, axes = plt.subplots(1, 3, figsize=(20, 20))
fontsize = 10
fontsize_legend = 9
df = pd.DataFrame()
df['PAV'] = df_radiomics['Predicted_Ablation_Volume']
df['EAV'] = df_radiomics['Ablation Volume [ml]']
df['Energy (kJ)'] = df_radiomics['Energy [kj]']
df['MWA Systems'] = df_radiomics['Device_name']
df['Proximity_to_surface'] = df_radiomics['Proximity_to_surface']
df['Chemotherapy'] = df_radiomics['chemo_before_ablation']
df['Chemo_yes'] = df['EAV']
df['Chemo_no'] = df['EAV']
df['Subcapsular'] = df['EAV']
df['Non-Subcapsular'] = df['EAV']
df.loc[
df.Proximity_to_surface == False,
'Subcapsular'] = np.nan # only keep those with value true, ie subcapsular
df.loc[df.Proximity_to_surface == True, 'Non-Subcapsular'] = np.nan
df.loc[df.Chemotherapy == 'No', 'Chemo_yes'] = np.nan
df.loc[df.Chemotherapy == 'Yes', 'Chemo_no'] = np.nan # chemo no
print('Nr Samples used:', str(len(df)))
# 1st plot PAV vs EAV with lin regr
slope, intercept, r_square, p_value, std_err = stats.linregress(
df['EAV'], df['PAV'])
print('slope value PAV vs. EAV:', slope)
print('p-value PAV vs EAV:', p_value)
sns.regplot(x="PAV",
y="EAV",
data=df,
scatter_kws={
"s": 11,
"alpha": 0.6
},
color=sns.xkcd_rgb["violet"],
line_kws={'label': r'$r:{0:.2f}$'.format(r_square)},
ax=axes[0])
axes[0].legend(fontsize=fontsize_legend, loc='upper left')
axes[0].set_ylabel('EAV (mL)', fontsize=fontsize)
axes[0].set_xlabel('PAV (mL)', fontsize=fontsize)
# Subcapsular 2nd plot
subcapsular_false = df[df['Proximity_to_surface'] == False]
subcapsular_true = df[df['Proximity_to_surface'] == True]
slope, intercept, r_1, p_value, std_err = stats.linregress(
subcapsular_false['PAV'], subcapsular_false['EAV'])
slope, intercept, r_2, p_value, std_err = stats.linregress(
subcapsular_true['PAV'], subcapsular_true['EAV'])
# Wilcoxon paired signed rank test
w, p = stats.wilcoxon(subcapsular_true['PAV'], subcapsular_true['EAV'])
print('p-val wilcoxon subcapsular true:', p)
w, p = stats.wilcoxon(subcapsular_false['PAV'], subcapsular_false['EAV'])
print('p-val wilcoxon subcapsular false:', p)
sns.regplot(y="Non-Subcapsular",
x="PAV",
data=df,
scatter_kws={
"s": 11,
"alpha": 0.6
},
line_kws={'label': r'No: $r = {0:.2f}$'.format(r_1)},
ax=axes[1])
sns.regplot(y="Subcapsular",
x="PAV",
data=df,
scatter_kws={
"s": 11,
"alpha": 0.6
},
color=sns.xkcd_rgb["orange"],
line_kws={'label': r'Yes: $r = {0:.2f}$'.format(r_2)},
ax=axes[1])
axes[1].legend(fontsize=fontsize_legend,
loc='best',
title='Subcapsular',
title_fontsize=fontsize_legend)
axes[1].set_yticklabels([])
axes[1].set_ylabel('')
axes[1].set_xlabel('PAV (mL)', fontsize=fontsize)
axes[1].set_title(
'Predicted (PAV) vs Effective Ablation Volume (EAV) for 3 MWA Devices',
fontsize=fontsize,
pad=20)
# Chemo 3rd plot
chemo_false = df[df['Chemotherapy'] == 'No']
chemo_true = df[df['Chemotherapy'] == 'Yes']
x1 = chemo_false['PAV']
y1 = chemo_false['EAV']
x2 = chemo_true['PAV']
y2 = chemo_true['EAV']
slope, intercept, r_1, p_value, std_err = stats.linregress(x1, y1)
slope, intercept, r_2, p_value, std_err = stats.linregress(x2, y2)
w, p = stats.wilcoxon(chemo_true['PAV'], chemo_true['EAV'])
print('p-val chemotherapy yes:', p)
w, p = stats.wilcoxon(chemo_false['PAV'], chemo_false['EAV'])
print('p-val chemotherapy no:', p)
sns.regplot(y='Chemo_yes',
x="PAV",
data=df,
scatter_kws={
"s": 11,
"alpha": 0.6
},
color=sns.xkcd_rgb["teal green"],
ax=axes[2],
line_kws={'label': r'Yes: $r = {0:.2f}$'.format(r_2)})
sns.regplot(y='Chemo_no',
x="PAV",
data=df,
scatter_kws={
"s": 11,
"alpha": 0.6
},
color=sns.xkcd_rgb["slate grey"],
ax=axes[2],
line_kws={'label': r'No: $r = {0:.2f}$'.format(r_1)})
axes[2].legend(fontsize=fontsize_legend,
loc='best',
title='Chemotherapy',
title_fontsize=fontsize_legend)
axes[2].set_yticklabels([])
axes[2].set_ylabel('')
axes[2].set_xlabel('PAV (mL)', fontsize=fontsize)
# add major title to subplot
# f.suptitle('Predicted (PAV) vs Effective Ablation Volume (EAV) for 3 MWA Devices', fontsize=10)
# set the axes limits and new ticks
axes[2].set_xlim([0, 81])
axes[1].set_xlim([0, 81])
axes[0].set_xlim([0, 81])
axes[2].set_ylim([0, 81])
axes[1].set_ylim([0, 81])
axes[0].set_ylim([0, 81])
axes[0].xaxis.set_ticks(np.arange(0, 81, 20))
axes[1].xaxis.set_ticks(np.arange(0, 81, 20))
axes[2].xaxis.set_ticks(np.arange(0, 81, 20))
plt.subplots_adjust(wspace=0.1)
# set the fontsize of the ticks of the subplots
for ax in axes:
ax.set(adjustable='box', aspect='equal')
ax.tick_params(axis='both', which='major', labelsize=fontsize)
# save the figure
timestr = time.strftime("%H%M%S-%Y%m%d")
figpath = os.path.join("figures", 'All_3MWA_SUbcapsular_Chemo_' + timestr)
gh.save(figpath,
ext=["png"],
width=12,
height=12,
close=True,
tight=True,
dpi=600)
plt.setp(xtickNames, fontsize=20, color='black')
plt.ylabel('Ablation Margin Distances [mm]', fontsize=18, color='black')
ax.tick_params(colors='black')
plt.xticks(fontsize=18)
ax.tick_params(axis='both', labelsize=20)
# [ax_tmp.set_xlabel('aaaa') for ax_tmp in np.asarray(bp).reshape(-1)]
# fig = np.asarray(bp).reshape(-1)[0].get_figure()
plt.title(
'Ablation Margin by Local Tumor Progression (LTP). Number of samples: ' +
str(len(df_final)) + '.',
fontsize=20)
fig.suptitle('')
# ax.set_ylim([-10, 17])
figpathHist = os.path.join(
"figures", "boxplot LTP ablation margin_not_subcapsular_outliers")
gh.save(figpathHist, ext=['png'], close=True)
# %%
fig, ax = plt.subplots(figsize=(10, 8))
bp_dict = df_final.boxplot(column='Ablation Volume [ml]',
by='LTP',
ax=ax,
return_type='both',
patch_artist=True)
for row_key, (ax, row) in bp_dict.iteritems():
ax.set_xlabel('')
for i, box in enumerate(row['boxes']):
box.set_facecolor('CornflowerBlue')
box.set_edgecolor('RoyalBlue')
for i, box in enumerate(row['medians']):
box.set_color(color='Black')
def call_plot_pies(df_radiomics,
title=None,
flag_plot_type=None,
flag_overlap=None):
"""
PREDICTED VS MEASURED SCATTER PIE CHART with distances represented
:param flag_overlap:
:param flag_plot_type:
:param df_radiomics:
:param title:
:return:
"""
fontsize = 18
ablation_vol_interpolated_brochure = np.asanyarray(
df_radiomics['Predicted_Ablation_Volume']).reshape(
len(df_radiomics), 1)
ablation_vol_measured = np.asarray(
df_radiomics['Ablation Volume [ml]']).reshape(len(df_radiomics), 1)
df_radiomics['MEV-MIV'] = df_radiomics[
'Outer Ellipsoid Volume'] - df_radiomics['Inner Ellipsoid Volume']
df_radiomics['R(EAV:PAV)'] = df_radiomics[
'Ablation Volume [ml]'] / df_radiomics['Predicted_Ablation_Volume']
ratios_0 = df_radiomics.safety_margin_distribution_0.tolist()
ratios_5 = df_radiomics.safety_margin_distribution_5.tolist()
ratios_10 = df_radiomics.safety_margin_distribution_10.tolist()
# %% ACTUALLY PLOT STUFF
fig, ax = plt.subplots()
if flag_plot_type == 'PAV_EAV':
for idx, val in enumerate(ablation_vol_interpolated_brochure):
xs = ablation_vol_interpolated_brochure[idx]
ys = ablation_vol_measured[idx]
ratio_0 = ratios_0[idx] / 100
ratio_5 = ratios_5[idx] / 100
ratio_10 = ratios_10[idx] / 100
if ~(np.isnan(xs)) and ~(np.isnan(ys)):
draw_pie([ratio_0, ratio_5, ratio_10],
xs,
ys,
500,
colors=['red', 'orange', 'green'],
ax=ax)
plt.ylabel('Effective Ablation Volume (mL)', fontsize=fontsize)
plt.xlabel('Predicted Ablation Volume (mL)', fontsize=fontsize)
plt.xlim([0, 80])
plt.ylim([0, 80])
elif flag_plot_type == 'MEV_MIV':
# drop the rows where MIV > MEV
# since the minimum inscribed ellipsoid (MIV) should always be smaller than the maximum enclosing ellipsoid (MEV)
df_radiomics = df_radiomics[
df_radiomics['Outer Ellipsoid Volume'] < 150]
print('Nr Samples used for Outer Ellipsoid Volume < 150 ml:',
len(df_radiomics))
df_radiomics = df_radiomics[df_radiomics['MEV-MIV'] >= 0]
print('Nr Samples used for MEV-MIV >=0 :', len(df_radiomics))
r_eav_pav = np.asarray(df_radiomics['R(EAV:PAV)']).reshape(
len(df_radiomics), 1)
mev_miv = np.asarray(df_radiomics['MEV-MIV']).reshape(
len(df_radiomics), 1)
for idx, val in enumerate(mev_miv):
xs = mev_miv[idx]
ys = r_eav_pav[idx]
ratio_0 = ratios_0[idx] / 100
ratio_5 = ratios_5[idx] / 100
ratio_10 = ratios_10[idx] / 100
if ~(np.isnan(xs)) and ~(np.isnan(ys)):
draw_pie([ratio_0, ratio_5, ratio_10],
xs,
ys,
500,
colors=['red', 'orange', 'green'],
ax=ax)
plt.xlabel('Ablation Volume Irregularity (MEV-MIV) (mL)',
fontsize=fontsize)
plt.ylabel('R(EAV:PAV)', fontsize=fontsize)
# plt.xlim([0, 80])
# plt.ylim([0, 80])
else: # other x and y labels
y = ablation_vol_measured / ablation_vol_interpolated_brochure # ratio EAV/PAV
fig_title = '_ratio_EAV_PAV'
ylabel_text = 'R(EAV:PAV)'
needle_error = np.asarray(df_radiomics['needle_error']).reshape(
len(df_radiomics), 1)
if flag_overlap == 'Dice':
y = np.asarray(df_radiomics['Dice']).reshape(len(df_radiomics), 1)
fig_title = '_Dice_'
ylabel_text = 'Dice Score'
if flag_overlap == 'Volume Overlap Error':
y = np.asarray(df_radiomics['Volume Overlap Error']).reshape(
len(df_radiomics), 1)
fig_title = '_Volume Overlap Error'
ylabel_text = 'Volume Overlap Error'
if flag_overlap == 'Tumour residual volume [ml]':
y = np.asarray(
df_radiomics['Tumour residual volume [ml]']).reshape(
len(df_radiomics), 1)
# tumor_radius = np.asarray(df_radiomics['major_axis_length_tumor']/2).reshape(len(df_radiomics), 1)
# y_normalized = df_radiomics['Tumour residual volume [ml]'] / df_radiomics['Tumour Volume [ml]']
# x_normalized = needle_error/tumor_radius
# y = np.asarray(y_normalized).reshape(len(df_radiomics), 1)
# x = np.asarray(x_normalized).reshape(len(df_radiomics), 1)
fig_title = '_Tumour residual volume [ml]'
ylabel_text = 'Tumour residual volume (mL)'
for idx, val in enumerate(ablation_vol_interpolated_brochure):
ys = y[idx]
xs = needle_error[idx]
ratio_0 = ratios_0[idx] / 100
ratio_5 = ratios_5[idx] / 100
ratio_10 = ratios_10[idx] / 100
if ~(np.isnan(xs)) and ~(np.isnan(ys)):
draw_pie([ratio_0, ratio_5, ratio_10],
xs,
ys,
500,
colors=['red', 'orange', 'green'],
ax=ax)
# ax.set_xscale('log')
plt.ylabel(ylabel_text, fontsize=fontsize + 2)
plt.xlabel('Lateral Error (mm)', fontsize=fontsize + 2)
plt.xlim([-0.2, 6])
plt.ylim([-0.2, 3])
# %% EDIT THE PLOTS with colors
red_patch = mpatches.Patch(color='red',
label='Ablation Margin ' + r'$x < 0$' + 'mm')
orange_patch = mpatches.Patch(color='orange',
label='Ablation Margin ' +
r'$0 \leq x < 5$' + 'mm')
green_patch = mpatches.Patch(color='darkgreen',
label='Ablation Margin ' + r'$x \geq 5$' +
'mm')
plt.legend(handles=[red_patch, orange_patch, green_patch],
fontsize=fontsize,
loc='best',
title=title,
title_fontsize=21)
props = dict(boxstyle='round', facecolor='white', edgecolor='gray')
# textstr = title
# ax.text(0.05, 0.95, textstr, transform=ax.transAxes, fontsize=20, verticalalignment='top', bbox=props)
ax.tick_params(axis='y', labelsize=fontsize, color='k')
ax.tick_params(axis='x', labelsize=fontsize, color='k')
plt.tick_params(labelsize=fontsize, color='black')
timestr = time.strftime("%H%M%S-%Y%m%d")
figpath = os.path.join("figures", 'pie_charts_' + flag_plot_type + timestr)
gh.save(figpath,
ext=["png"],
width=12,
height=12,
close=True,
tight=True,
dpi=300)
def scatter_plot(df1, **kwargs):
"""
df, x_data, y_data, title, x_label=False, y_label='', lin_reg=''
:param df:
:param x_data:
:param x_data:
:param title:
:param x_label:
:param x_label:
:param lin_reg:
:return:
"""
fig, ax = plt.subplots()
if kwargs.get('x_data') is None:
print('No X input data to plot')
return
if kwargs.get('y_data') is None:
print('No Y input data to plot')
return
df_to_plot = df1.copy()
# drop nans
df_to_plot.dropna(subset=[kwargs["x_data"]], inplace=True)
df_to_plot.dropna(subset=[kwargs["y_data"]], inplace=True)
if kwargs.get('colormap') is not None:
X_scatter = df_to_plot[kwargs['x_data']]
Y_scatter = df_to_plot[kwargs['y_data']]
t = df_to_plot[kwargs['colormap']]
plt.scatter(X_scatter, Y_scatter, c=t, cmap='viridis', s=20)
cbar = plt.colorbar()
cbar.ax.set_title(kwargs['colormap'], fontsize=8)
else:
df_to_plot.plot.scatter(x=kwargs["x_data"], y=kwargs["y_data"], s=14)
if kwargs.get('size') is not None:
size = df_to_plot[kwargs['size']] + 10
df_to_plot.plot.scatter(x=kwargs["x_data"], y=kwargs["y_data"], s=size)
# cbar = plt.colorbar()
# cbar.ax.set_title(kwargs['colormap'], fontsize=8)
if kwargs.get('x_label') is not None and kwargs.get('y_label') is None:
plt.xlabel(kwargs['x_label'], fontsize=8, color='k')
plt.ylabel(kwargs['y_data'], fontsize=8, color='k')
elif kwargs.get('y_label') is not None and kwargs.get('x_label') is None:
plt.ylabel(kwargs['y_label'], fontsize=8, color='k')
plt.xlabel(kwargs['x_data'], fontsize=8, color='k')
elif kwargs.get('x_label') is None and kwargs.get('y_label') is None:
plt.xlabel(kwargs['x_data'], fontsize=8, color='k')
plt.ylabel(kwargs['y_data'], fontsize=8, color='k')
if kwargs.get('lin_reg') is not None:
X = np.array(df_to_plot[kwargs['x_data']])
Y = np.array(df_to_plot[kwargs['y_data']])
regr = linear_model.LinearRegression()
X = X.reshape(len(X), 1)
Y = Y.reshape(len(Y), 1)
regr.fit(X, Y)
SS_tot = np.sum((Y - np.mean(Y))**2)
residuals = Y - regr.predict(X)
SS_res = np.sum(residuals**2)
r_squared = 1 - (SS_res / SS_tot)
correlation_coef = np.corrcoef(X[:, 0], Y[:, 0])[0, 1]
label = r'$R^2: $ {0:.2f}; r: {1:.2f}'.format(r_squared,
correlation_coef)
plt.plot(X,
regr.predict(X),
color='orange',
linewidth=1.5,
label=label)
nr_samples = ' Nr. samples: ' + str(len(df_to_plot))
plt.title(kwargs['title'] + nr_samples, fontsize=8)
plt.legend(fontsize=10)
plt.tick_params(labelsize=8, color='black')
ax.tick_params(axis='y', labelsize=8, color='k')
ax.tick_params(axis='x', labelsize=8, color='k')
ax.xaxis.label.set_color('black')
ax.yaxis.label.set_color('black')
matplotlib.rc('axes', labelcolor='black')
figpathHist = os.path.join("figures", kwargs['title'])
gh.save(figpathHist, ext=["png"], close=True)
plt.close('all')
