[plotting] black

plotting.py

@@ -73,7 +73,13 @@ def figure_confusion_matrix(
     plt.yticks(tick_marks, class_names)
 
     for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
-        plt.text(j, i, np.around(cm[i, j], decimals=2), horizontalalignment="center", size=7)
+        plt.text(
+            j,
+            i,
+            np.around(cm[i, j], decimals=2),
+            horizontalalignment="center",
+            size=7,
+        )
 
     plt.ylabel("True label" + " (normed)" * (normalize == "true"))
     plt.xlabel("Predicted label" + " (normed)" * (normalize == "pred"))
@@ -109,18 +115,18 @@ def figure_history(history_csv_path):
 
 
 def plot_histories(history_csv_path, path, cut=None, roll=1):
-    pdf = pd.read_csv(history_csv_path)
-    pdf = pdf.set_index("epoch")
-    pdf = pdf.truncate(after=cut)
-    for col in pdf.columns:
+    df = pd.read_csv(history_csv_path)
+    df = df.set_index("epoch")
+    df = df.truncate(after=cut)
+    for col in df.columns:
         if col.startswith("val_"):
             continue
         fig = plt.figure()
-        if "val_" + col in pdf.columns:
+        if "val_" + col in df.columns:
             ind = [col, "val_" + col]
         else:
             ind = col
-        value = pdf[ind]
+        value = df[ind]
         ax = value.rolling(roll, min_periods=1).mean().plot()
         ax.set_xlabel("Epoch")
         ax.set_ylabel(col.capitalize())
@@ -228,7 +234,13 @@ def figure_node_activations(
 
 
 def figure_roc_curve(
-    truth, prediction, indices=[0], class_names=None, sample_weight=None, lw=2, scale="linear"
+    truth,
+    prediction,
+    indices=[0],
+    class_names=None,
+    sample_weight=None,
+    lw=2,
+    scale="linear",
 ):
     fig = plt.figure()
     for index in indices:
@@ -310,7 +322,13 @@ def figure_inputs(
 
 
 def figure_weight_study(
-    class_inps, sample_weights=None, columns=None, label=None, log=False, mode="plain", **kwargs
+    class_inps,
+    sample_weights=None,
+    columns=None,
+    label=None,
+    log=False,
+    mode="plain",
+    **kwargs,
 ):
     multiplot = Multiplot(class_inps[0].shape[1:][::-1])
     rows, cols = multiplot.lenghts()
@@ -352,7 +370,10 @@ def figure_weight_study(
         if mode == "weight":
             mask = sample_weights[0] > 0
             pos_feat, pos_weight = class_inps[0][:, feat][mask], sample_weights[0][mask]
-            neg_feat, neg_weight = class_inps[0][:, feat][~mask], sample_weights[0][~mask]
+            neg_feat, neg_weight = (
+                class_inps[0][:, feat][~mask],
+                sample_weights[0][~mask],
+            )
 
             val_pos, bins = np.histogram(pos_feat, bins=bins, weights=pos_weight)
             val_neg, bins = np.histogram(neg_feat, bins=bins, weights=neg_weight)