diff --git a/src/mlstatpy/graph/graph_distance.py b/src/mlstatpy/graph/graph_distance.py
index 6076f8c..01defca 100644
--- a/src/mlstatpy/graph/graph_distance.py
+++ b/src/mlstatpy/graph/graph_distance.py
@@ -232,7 +232,7 @@ def load_from_file(filename, add_loop):
     def _private__init__(self, add_loop, weight_vertex, weight_edge):
         if add_loop:
             for k in self.vertices:
-                if k != self.labelBegin and k != self.labelEnd:
+                if k not in (self.labelBegin, self.labelEnd):
                     self.edges[k, k] = Edge(k, k, "", weight_edge)
         self.connect_root_and_leave(weight_vertex, weight_edge)
         self.compute_predecessor()
diff --git a/src/mlstatpy/image/detection_segment/detection_segment.py b/src/mlstatpy/image/detection_segment/detection_segment.py
index a35ad6c..9e333da 100644
--- a/src/mlstatpy/image/detection_segment/detection_segment.py
+++ b/src/mlstatpy/image/detection_segment/detection_segment.py
@@ -285,7 +285,7 @@ def detect_segments(image, proba_bin=1.0 / 16,
 
             # on ajoute les resultats à la liste
             segment.extend(res)
-            if stop > 0 and len(segment) >= stop:
+            if len(segment) >= stop > 0:
                 break
         else:
             not_aligned += 1
diff --git a/src/mlstatpy/ml/ml_grid_benchmark.py b/src/mlstatpy/ml/ml_grid_benchmark.py
index 8a54e25..73f71b5 100644
--- a/src/mlstatpy/ml/ml_grid_benchmark.py
+++ b/src/mlstatpy/ml/ml_grid_benchmark.py
@@ -192,7 +192,7 @@ def graphs(self, path_to_images):
         @return     list of tuple (image_name, function to create the graph)
         """
         import matplotlib.pyplot as plt
-        import matplotlib.cm as cm
+        import matplotlib.cm as mcm
         df = self.to_df()
 
         def local_graph(vx, vy, ax=None, text=True, figsize=(5, 5)):
@@ -200,7 +200,7 @@ def local_graph(vx, vy, ax=None, text=True, figsize=(5, 5)):
             ymin = df[vy].min()
             ymax = df[vy].max()
             decy = (ymax - ymin) / 50
-            colors = cm.rainbow(numpy.linspace(0, 1, len(btrys)))
+            colors = mcm.rainbow(numpy.linspace(0, 1, len(btrys)))
             if len(btrys) == 0:
                 raise ValueError("The benchmark is empty.")
             if ax is None: