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#40 add energy as double axis
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@rmsandu
rmsandu committed Nov 30, 2019
1 parent 19118fc commit 7e7681c
Showing 1 changed file with 42 additions and 52 deletions.
94 changes: 42 additions & 52 deletions interpolation_volumes.py
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Original file line number Diff line number Diff line change
Expand Up @@ -14,39 +14,18 @@
# plt.style.use('ggplot')


def interpolation_fct(df_ablation, df_radiomics, title, fontsize=22, single_interpolation=True, flag_size=False, flag=None):
if single_interpolation is True:
# perform interpolation as a function of energy
df_radiomics.dropna(subset=['Energy [kj]'], inplace=True)
x = np.asarray(df_ablation['Energy_brochure'])
y = np.asarray(df_ablation['Ablation Volume [ml]_brochure'])
f = interp1d(x, y, fill_value="extrapolate")
df_radiomics.dropna(subset=['Ablation Volume [ml]'], inplace=True)
energy = np.asarray(df_radiomics['Energy [kj]'])
print('No of samples for ' + title + ': ', len(energy))
ablation_vol_interpolated_brochure = f(energy)
fig, ax = plt.subplots()
plt.plot(x, y, 'o', energy, f(energy), '*')
plt.legend(['data ' + title, 'linear interpolation'], loc='best')
plt.xlabel('Energy [kJ]')
plt.ylim([0, 120])
plt.xlim([0, 80])
plt.ylabel('Effective Ablation Volume Brochure [ml]')
plt.grid('on')
plt.show()
figpathHist = os.path.join("figures", title + '_interpolation')
gh.save(figpathHist, width=8, height=8, ext=["png"], close=True)
else:
# 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))
points = np.hstack((points_power, points_time))
values = 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 = 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')
def interpolation_fct(df_ablation, df_radiomics, title, fontsize=24, flag_size=False, flag=None, flag_energy_axis=False):

# 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))
points = np.hstack((points_power, points_time))
values = 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 = 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
ablation_vol_measured = np.asarray(df_radiomics['Ablation Volume [ml]']).reshape(len(df_radiomics), 1)
Expand All @@ -57,33 +36,50 @@ def interpolation_fct(df_ablation, df_radiomics, title, fontsize=22, single_inte
size_values = np.asarray(df_radiomics['no_chemo_cycle']).reshape(len(df_radiomics), 1)

fig, ax = plt.subplots()
ax2 = ax.twiny()

if flag_size is True:
size = np.asarray([100 * (n + 1) for n in size_values]).reshape(len(size_values), 1)
size_mask = ~np.isnan(size)
size = size[size_mask]
sc = ax.scatter(ablation_vol_interpolated_brochure, ablation_vol_measured, color='steelblue', marker='o',
alpha=0.7, s=size)
plt.show()
legend_1 = ax.legend(*sc.legend_elements("sizes", num=6, func=lambda x: x / 100 - 1, color='steelblue'),
legend_1 = ax.legend(*sc.legend_elements("sizes", num='auto', func=lambda x: x / 100 - 1, color='steelblue'),
title=flag, 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)
elif flag_energy_axis:
ax.scatter(ablation_vol_interpolated_brochure, ablation_vol_measured, color='steelblue', marker='o', s=100)
ax.set_ylabel('Effective Ablation Volume [ml]', fontsize=fontsize)
ax.set_xlabel('Predicted Ablation Volume Brochure [ml]', fontsize=fontsize)
ax.tick_params(axis='x', labelcolor='steelblue')
ax.set_ylim([0, 100])
ax.set_xlim([0, 100])

energy = df_radiomics['Energy [kj]']
ax2.scatter(energy, ablation_vol_measured, 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:
sc = plt.scatter(ablation_vol_interpolated_brochure, ablation_vol_measured, color='steelblue', marker='o',
s=100)
plt.ylabel('Effective Ablation Volume [ml] for ' + title, fontsize=fontsize)
plt.xlabel('Predicted Ablation Volume Brochure [ml] for ' + title, fontsize=fontsize)
plt.ylim([0, 120])
plt.xlim([0, 120])
# plt.title(title + ' Nr. Samples: ' + str(len(ablation_vol_measured)))
plt.ylabel('Effective Ablation Volume [ml]', fontsize=fontsize)
plt.xlabel('Predicted Ablation Volume Brochure [ml]', fontsize=fontsize)
plt.ylim([0, 120])
plt.xlim([0, 120])
# get the data ready for linear regression
X = ablation_vol_interpolated_brochure.reshape(len(ablation_vol_interpolated_brochure), 1)
Y = ablation_vol_measured.reshape(len(ablation_vol_measured), 1)
mask = ~np.isnan(X) & ~np.isnan(Y)
X = X[mask]
Y = Y[mask]
X = X.reshape(len(X), 1)
Y = Y.reshape(len(Y), 1)
nr_samples = len(X)
regr = linear_model.LinearRegression()
regr.fit(X, Y)
SS_tot = np.sum((Y - np.mean(Y)) ** 2)
Expand All @@ -97,13 +93,10 @@ def interpolation_fct(df_ablation, df_radiomics, title, fontsize=22, single_inte
ax.tick_params(axis='x', labelsize=fontsize, color='k')
plt.tick_params(labelsize=fontsize, color='black')
reg = plt.plot(X, regr.predict(X), color='orange', linewidth=1.5, label='Linear Regression')
plt.plot([], [], ' ', label=label_r)
plt.plot([], [], ' ', label=label_r2)
plt.legend(fontsize=fontsize, loc='upper left')
# ax.xaxis.label.set_color('black')
# ax.yaxis.label.set_color('black')
# ax.xaxis.ticks.set_color('black')
# matplotlib.rc('axes', labelcolor='black')
plt.plot([], [], ' ', label='n = '+ str(nr_samples))
# plt.plot([], [], ' ', label=label_r)
# plt.plot([], [], ' ', label=label_r2)
plt.legend(fontsize=fontsize, loc='best', title=title, title_fontsize=fontsize)
if flag is not None:
figpathHist = os.path.join("figures",
title + '_measured_vs_predicted_volume_power_time_interpolation' + flag)
Expand All @@ -129,9 +122,6 @@ def interpolation_fct(df_ablation, df_radiomics, title, fontsize=22, single_inte
df_radiomics_covidien = df_radiomics[df_radiomics['Device_name'] == 'Covidien (Covidien MWA)']

# flag_options : 1. flag == 'No. chemo cycles' 2. flag == 'Tumour Volume [ml]'
interpolation_fct(df_amica, df_radiomics_amica, 'Amica', single_interpolation=False, flag_size=True,
flag='No. chemo cycles')
interpolation_fct(df_angyodinamics, df_radiomics_angyodinamics, 'Angyodinamics (Solero)',
single_interpolation=False, flag_size=True, flag='No. chemo cycles')
interpolation_fct(df_covidien, df_radiomics_covidien, 'Covidien', single_interpolation=False, flag_size=True,
flag='No. chemo cycles')
interpolation_fct(df_amica, df_radiomics_amica, 'Amica')
interpolation_fct(df_angyodinamics, df_radiomics_angyodinamics, 'Solero')
interpolation_fct(df_covidien, df_radiomics_covidien, 'Covidien')

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