Merge pull request #65 from sct-pipeline/jca/cnr

Add CNR figure for spine-generic data

Author: jcohenadad

gmchallenge/generate_figure_spinegeneric.py

@@ -1,16 +1,20 @@
+#!/usr/bin/env python
+#
+# Generate figures for the spine-generic results
+
 import pandas as pd
 import numpy as np
 import argparse
-import seaborn as sns
 import os
 import matplotlib.pyplot as plt
 import ptitprince as pt
+import seaborn as sns
 from matplotlib.patches import PathPatch
 
 sns.set(style="whitegrid", font_scale=1)
 
 
-def get_parameters():
+def get_parser():
     parser = argparse.ArgumentParser(description='Generate figure for spine generic dataset')
     parser.add_argument("-ir", "--path-input-results",
                         help="Path to results.csv",
@@ -20,19 +24,15 @@ def get_parameters():
                         required=True)
     parser.add_argument("-o", "--path-output",
                         help="Path to save images",
-                        required=True,
-                        )
-    arguments = parser.parse_args()
-    return arguments
+                        required=True)
+    return parser
 
 
 def adjust_box_widths(g, fac):
-    # From https://github.com/mwaskom/seaborn/issues/1076#issuecomment-634541579
-
     """
     Adjust the widths of a seaborn-generated boxplot.
+    Source: From https://github.com/mwaskom/seaborn/issues/1076#issuecomment-634541579
     """
-
     # iterating through Axes instances
     for ax in g.axes:
 
@@ -58,32 +58,50 @@ def adjust_box_widths(g, fac):
 
                 # setting new width of median line
                 for l in ax.lines:
-                    if not l.get_xdata().size == 0:
-                        if np.all(np.equal(l.get_xdata(), [xmin, xmax])):
+                    if not len(l.get_xdata()) == 0:
+                        if np.all(np.equal(l.get_xdata()[0:2], [xmin, xmax])):
                             l.set_xdata([xmin_new, xmax_new])
 
 
 def generate_figure(data_in, column, path_output):
-    # Hue Input for Subgroups
     dx = np.ones(len(data_in[column]))
     dy = column
-    dhue = "Manufacturer"
-    ort = "v"
-    #  dodge blue, limegreen, red
-    colors = [  "#1E90FF", "#32CD32","#FF0000"  ]
-    pal = colors
-    sigma = .2
+    hue = "Manufacturer"
+    pal = ["#1E90FF", "#32CD32", "#FF0000"]
     f, ax = plt.subplots(figsize=(4, 6))
-
-    ax = pt.RainCloud(x=dx, y=dy, hue=dhue, data=data_in, palette=pal, bw=sigma,
-                      width_viol=.5, ax=ax, orient=ort, alpha=.4, dodge=True, width_box=.35,
-                      box_showmeans=True,
-                      box_meanprops={"marker":"^", "markerfacecolor":"black", "markeredgecolor":"black", "markersize":"10"},
-                      box_notch=True)
+    if column == 'CNR_single/t':
+        coeff = 100
+    else:
+        coeff = 1
+    ax = pt.half_violinplot(x=dx, y=dy, data=data_in*coeff, hue=hue, palette=pal, bw=.4, cut=0., linewidth=0.,
+                            scale="area", width=.8, inner=None, orient="v", dodge=False, alpha=.4, offset=0.5)
+    ax = sns.boxplot(x=dx, y=dy, data=data_in*coeff, hue=hue, color="black", palette=pal,
+                     showcaps=True, boxprops={'facecolor': 'none', "zorder": 10}, showmeans=True,
+                     meanprops={"marker": "^", "markerfacecolor": "black", "markeredgecolor": "black",
+                                    "markersize": "8"},
+                     showfliers=True, whiskerprops={'linewidth': 2, "zorder": 10},
+                     saturation=1, orient="v", dodge=True)
+    ax = sns.stripplot(x=dx, y=dy, data=data_in*coeff, hue=hue, palette=pal, edgecolor="white",
+                       size=3, jitter=1, zorder=0, orient="v", dodge=True)
+    plt.xlim([-1, 0.5])
+    handles, labels = ax.get_legend_handles_labels()
+    # The code below doesn't work (the label for CNR is "GEGEGEGEGEGEG...") so i need to hard-code the labels (because
+    # I don't have time to dig further).
+    # _ = plt.legend(handles[0:len(labels) // 3], labels[0:len(labels) // 3],
+    #                bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.,
+    #                title=str(hue))
+    _ = plt.legend(handles[0:3], ['Philips', 'Siemens', 'GE'],
+                   bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.,
+                   title=str(hue))
     f.gca().invert_xaxis()
-    #adjust boxplot width
-    adjust_box_widths(f, 0.4)
-    plt.xlabel(column)
+    adjust_box_widths(f, 0.6)
+    # special hack
+    if column == 'CNR_single/t':
+        plt.xlabel('CNR_single/√t')
+        fname_out = os.path.join(path_output, 'figure_CNR_single_t')
+    else:
+        plt.xlabel(column)
+        fname_out = os.path.join(path_output, 'figure_' + column)
     # remove ylabel
     plt.ylabel('')
     # hide xtick
@@ -93,11 +111,17 @@ def generate_figure(data_in, column, path_output):
         bottom=False,
         top=False,
         labelbottom=False)
-    # plt.legend(title="Line", loc='upper left', handles=handles[::-1])
-    plt.savefig(os.path.join(path_output, 'figure_' + column), bbox_inches='tight', dpi=300)
+    plt.savefig(fname_out, bbox_inches='tight', dpi=300)
+
 
+def main(argv=None):
+    # user params
+    parser = get_parser()
+    args = parser.parse_args(argv)
+    path_input_results = args.path_input_results
+    path_input_participants = args.path_input_participants
+    path_output = args.path_output
 
-def main(path_input_results, path_input_participants, path_output):
     if not os.path.isdir(path_output):
         os.makedirs(path_output)
 
@@ -114,8 +138,8 @@ def main(path_input_results, path_input_participants, path_output):
 
     generate_figure(content_results_csv, 'SNR_single', path_output)
     generate_figure(content_results_csv, 'Contrast', path_output)
+    generate_figure(content_results_csv, 'CNR_single/t', path_output)
 
 
 if __name__ == "__main__":
-    args = get_parameters()
-    main(args.path_input_results, args.path_input_participants, args.path_output)
+    main()