src/batdetect2/plotting/evaluation.py

@@ -100,7 +100,7 @@ def plot_class_examples(
                 preprocessor=preprocessor,
                 duration=duration,
             )
-        except (ValueError, AssertionError):
+        except ValueError:
             continue
 
     for index, match in enumerate(false_positives[:n_examples]):
@@ -112,7 +112,7 @@ def plot_class_examples(
                 preprocessor=preprocessor,
                 duration=duration,
             )
-        except (ValueError, AssertionError):
+        except ValueError:
             continue
 
     for index, match in enumerate(false_negatives[:n_examples]):
@@ -124,11 +124,11 @@ def plot_class_examples(
                 preprocessor=preprocessor,
                 duration=duration,
             )
-        except (ValueError, AssertionError):
+        except ValueError:
             continue
 
     for index, match in enumerate(cross_triggers[:n_examples]):
-        ax = plt.subplot(4, n_examples, 3 * n_examples + index + 1)
+        ax = plt.subplot(4, n_examples, 4 * n_examples + index + 1)
         try:
             plotting.plot_cross_trigger_match(
                 match,
@@ -136,7 +136,7 @@ def plot_class_examples(
                 preprocessor=preprocessor,
                 duration=duration,
             )
-        except (ValueError, AssertionError):
+        except ValueError:
             continue
 
     return fig
@@ -154,7 +154,7 @@ def get_binned_sample(matches: List[MatchEvaluation], n_examples: int = 5):
         ]
     )
 
-    bins = pd.qcut(pred_scores, q=n_examples, labels=False, duplicates="drop")
+    bins = pd.qcut(pred_scores, q=n_examples, labels=False)
     df = pd.DataFrame({"indices": indices, "bins": bins})
     sample = df.groupby("bins").apply(lambda x: x.sample(1))
     return [matches[ind] for ind in sample["indices"]]