pep8

scilpy/utils/metrics_tools.py

@@ -299,4 +299,4 @@ def get_bundle_metrics_mean_std_per_point(streamlines, bundle_name,
                 weights=label_weight))
             label_stats['mean'] = float(label_mean)
             label_stats['std'] = float(label_std)
-    return stats
+    return stats

scilpy/viz/plot.py

@@ -3,6 +3,7 @@
 import matplotlib.pyplot as plt
 import numpy as np
 
+
 def plot_metrics_stats(means, stds, title=None, xlabel=None,
                        ylabel=None, figlabel=None, fill_color=None,
                        display_means=False):
@@ -111,9 +112,8 @@ def plot_residuals(data_diff, mask, R_k, q1, q3, iqr, residual_basename):
     # Note that stats will be computed manually and plotted using bxp
     # but could be computed using stats = cbook.boxplot_stats
     # or pyplot.boxplot(x)
-
 
-     # Initializing stats as a List[dict]
+    # Initializing stats as a List[dict]
     stats = [dict.fromkeys(['label', 'mean', 'iqr', 'cilo', 'cihi',
                             'whishi', 'whislo', 'fliers', 'q1',
                             'med', 'q3'], [])
@@ -170,4 +170,4 @@ def plot_residuals(data_diff, mask, R_k, q1, q3, iqr, residual_basename):
         axe[0, 1].set_xlabel('DW image')
         axe[0, 1].set_ylabel('Percentage of outlier voxels')
         axe[0, 1].set_title('Outliers')
-    plt.savefig(residual_basename + '_residuals_stats.png')
+    plt.savefig(residual_basename + '_residuals_stats.png')