Create figure for ghosting comparison

README.md

@@ -31,4 +31,4 @@ sct_run_batch -script process_data.sh -path-data ~/data/ds006347/ -path-output ~
 ```
 ## 3 - Run figure scripts
 >[!Warning]
->TODO
+>python figures.py <PATH_TO_OUTPUT>

figures.py

@@ -0,0 +1,121 @@
+#!/usr/bin/env python3
+
+import argparse
+import matplotlib.pyplot as plt
+import os
+import pandas as pd
+from scipy import stats
+
+
+def create_ghosting_figure(fname_csv, path_output):
+    # Subject-ID/Session-ID/Acquisition  max rec-standard  max rec-navigated  mean rec-standard  mean rec-navigated
+    df = pd.read_csv(fname_csv)
+    df_uppert = df[df['Subject-ID/Session-ID/Acquisition'].str.contains('upperT')]
+    df_lowert = df[df['Subject-ID/Session-ID/Acquisition'].str.contains('lowerT')]
+    df_lse = df[df['Subject-ID/Session-ID/Acquisition'].str.contains('LSE')]
+
+    fig = plt.figure(figsize=(6, 5))
+    x_ticks = [1, 1.5, 2, 2.5]
+    x_offset = 0.05
+    ax = fig.subplots(1,1)
+
+    data_standard = [df_uppert['mean rec-standard'], df_lowert['mean rec-standard'], df_lse['mean rec-standard'], df['mean rec-standard']]
+    data_navigated = [df_uppert['mean rec-navigated'], df_lowert['mean rec-navigated'], df_lse['mean rec-navigated'], df['mean rec-navigated']]
+
+    # Perform independent t-test
+    t_statistics = []
+    p_values = []
+    for i in range(len(data_standard)):
+        t, p = stats.ttest_ind(data_navigated[i], data_standard[i], equal_var=False, alternative='less')
+        t_statistics.append(t)
+        p_values.append(p)
+
+    vp1 = ax.violinplot(data_standard, positions=x_ticks, side='low', showmeans=False, showmedians=False, showextrema=False)
+    vp2 = ax.violinplot(data_navigated, positions=x_ticks, side='high', showmeans=False, showmedians=False, showextrema=False)
+
+    ax.boxplot(data_standard, positions=[x - x_offset for x in x_ticks], widths=0.06, patch_artist=True, boxprops=dict(facecolor='white', color='black', alpha=0.5), medianprops=dict(color='black'), showfliers=False, showcaps=False, whis=0)
+    ax.boxplot(data_navigated, positions=[x + x_offset for x in x_ticks], widths=0.06, patch_artist=True,
+                  boxprops=dict(facecolor='white', color='black', alpha=0.5), medianprops=dict(color='black'),
+                  showfliers=False, showcaps=False, whis=0)
+
+    for i in range(len(x_ticks)):
+        color_standard = '#1a80bb'  # Blue color for standard
+        color_navigated = '#ea801c'  # Orange color for navigated
+        vp1['bodies'][i].set_facecolor(color_standard)
+        vp2['bodies'][i].set_facecolor(color_navigated)
+        if i == len(x_ticks) - 1:
+            vp1['bodies'][i].set_alpha(0.8)
+            vp2['bodies'][i].set_alpha(0.8)
+        else:
+            vp1['bodies'][i].set_alpha(0.5)
+            vp2['bodies'][i].set_alpha(0.5)
+    ax.grid(linestyle='--', alpha=0.5)
+    ax.xaxis.grid(False)
+    ax.set_title('Mean ghost signal intensity for each subject')
+    ax.set_ylabel('Ghost signal intensity')
+    ax.set_xticks([1, 1.5, 2, 2.5])  # Set x-axis ticks for each violin
+    ax.set_xticklabels(['Upper thor.', 'Lower thor.', 'Lumbar', 'All regions'])  # Set x-axis labels
+    ax.set_xlim(min(x_ticks) - 0.35, max(x_ticks) + 0.35)
+    ax.set_ylim(-50, 1200)
+    # Add stars and whiskers
+    for i in range(len(x_ticks)):
+        star_text = p_value_to_stars(p_values[i])
+        if i == 0:
+            y_tick = 900
+        else:
+            y_tick = 1000
+        if star_text:
+            ax.text(x_ticks[i], y_tick, star_text, ha='center', va='bottom', fontsize=10)
+            # Add whiskers
+            ax.errorbar(x_ticks[i], y_tick, xerr=0.075, color='black', linewidth=1, barsabove=True, capsize=5)
+
+    legend_handles = [vp1["bodies"][3], vp2["bodies"][3]]
+    legend_labels = ['Default', 'Navigated']
+    ax.legend(legend_handles, legend_labels, title='Reconstruction', loc='upper left')
+    plt.tight_layout()
+    plt.savefig(os.path.join(path_output, 'fig_ghost_vp.png'))
+    print(f"T-statistic: {t_statistics}")
+    print(f"P-value: {p_values}")
+
+
+def p_value_to_stars(p):
+    # Symbol Meaning
+    # ns P > 0.05
+    # * P ≤ 0.05
+    # ** P ≤ 0.01
+    # *** P ≤ 0.001
+    # **** P ≤ 0.0001
+
+    if p > 0.05:
+        return ''
+    elif p <= 0.0001:
+        return '****'
+    elif p <= 0.001:
+        return '***'
+    elif p <= 0.01:
+        return '**'
+    elif p <= 0.05:
+        return '*'
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description=f"Generate the figures."
+    )
+    # parser.add_argument("path_data", help="Path to the data directory.", default=None)
+    parser.add_argument("path_processed_data", nargs='?', default=None,
+                        help="Path to the processed data directory (output path). Defaults to path_data if not provided.")
+    args = parser.parse_args()
+    if not os.path.isdir(args.path_processed_data):
+        raise OSError(f"Error: path_processed_data does not exists. Provided path: {args.path_processed_data}")
+
+    path_figures = os.path.join(args.path_processed_data, 'figures')
+    if not os.path.exists(path_figures):
+        os.makedirs(path_figures)
+
+    fname_ghosting_metrics = os.path.join(args.path_processed_data, 'results', 'ghosting_metrics.csv')
+    create_ghosting_figure(fname_ghosting_metrics, path_figures)
+
+
+if __name__ == "__main__":
+    main()