Code lintting

tsfel/feature_extraction/calc_features.py

@@ -262,8 +262,6 @@ def time_series_features_extractor(
     DataFrame
         Extracted features
     """
-    # if verbose == 1:
-        # print("*** Feature extraction started ***")
 
     window_size = kwargs.get("window_size", None)
     overlap = kwargs.get("overlap", 0)
@@ -385,9 +383,6 @@ def time_series_features_extractor(
             single_window=True,
         )
 
-    # if verbose == 1:
-        # print("\n" + "*** Feature extraction finished ***")
-
     # Assuring the same feature extraction order
     features_final = features_final.reindex(sorted(features_final.columns), axis=1)
     return features_final.reset_index(drop=True)

tsfel/feature_extraction/features.py

@@ -496,16 +496,20 @@ def hist(signal, nbins=10, r=1):
 
     if not future_warn_flag:
         warnings.warn(
-            ("The histogram feature was deprecated in version 0.1.8 and will be replaced by the mode of histogram in 0.1.9."
-            " From then on, only a single feature value will be returned."), 
-            FutureWarning
+            (
+                "The histogram feature was deprecated in version 0.1.8 and will be replaced by the mode of histogram in 0.1.9."
+                " From then on, only a single feature value will be returned."
+            ),
+            FutureWarning,
         )
         future_warn_flag = True
-    
+
     # TODO: r value must be revised!
     histsig, bin_edges = np.histogram(signal, bins=nbins, range=[-r, r])
 
-    names = [str(np.around(bin_edges[i], 2)) + ':' + str(np.around(bin_edges[i + 1], 2)) for i in range(len(bin_edges) - 1)]
+    names = [
+        str(np.around(bin_edges[i], 2)) + ":" + str(np.around(bin_edges[i + 1], 2)) for i in range(len(bin_edges) - 1)
+    ]
 
     return {"names": names, "values": histsig}
 
@@ -1563,7 +1567,7 @@ def spectrogram_mean_coeff(signal, fs, bins=32):
     frequencies, _, Sxx = scipy.signal.spectrogram(signal, fs, nperseg=bins * 2 - 2)
     Sxx_mean = Sxx.mean(1)
     f_keys = np.round(frequencies, 2).astype(str)
-    
+
     return {"names": [f + "Hz" for f in f_keys], "values": Sxx_mean}
 
 

tsfel/feature_extraction/features_settings.py

@@ -42,7 +42,7 @@ def get_features_by_domain(domain=None, json_path=None):
     Dict
         Dictionary with the features settings
     """
-        
+
     valid_domains = ["statistical", "temporal", "spectral", "fractal", "all"]
 
     if json_path is None:
@@ -77,7 +77,7 @@ def get_features_by_domain(domain=None, json_path=None):
                 for k in dict_features[d]:
                     dict_features[d][k]["use"] = "yes"
             d_feat.update({d: dict_features[d]})
-        return d_feat           
+        return d_feat
 
 
 def get_features_by_tag(tag=None, json_path=None):