Add optional 'abs' entry to threshold dict

Author: alejoe91

src/spikeinterface/curation/bombcell_curation.py

@@ -66,7 +66,7 @@ def bombcell_get_default_thresholds() -> dict:
         "mua": {  # failures -> MUA, only applied to units that passed noise thresholds
             "amplitude_median": {"min": 40, "max": None},  # uV
             "snr": {"min": 5, "max": None},
-            "amplitude_cutoff": {"min": None, "max": 0.2},
+            "amplitude_cutoff": {"min": None, "max": 0.2, "abs": True},
             "num_spikes": {"min": 300, "max": None},
             "rp_contamination": {"min": None, "max": 0.1},
             "presence_ratio": {"min": 0.7, "max": None},
@@ -174,10 +174,6 @@ def bombcell_label_units(
         raise ValueError("thresholds must be a dict, a JSON file path, or None")
 
     n_units = len(combined_metrics)
-    absolute_value_metrics = ["amplitude_median"]
-    for metric in absolute_value_metrics:
-        if metric in combined_metrics.columns:
-            combined_metrics[metric] = np.abs(combined_metrics[metric])
 
     noise_thresholds = thresholds_dict.get("noise", {})
     if len(noise_thresholds) > 0:

src/spikeinterface/curation/threshold_metrics_curation.py

@@ -26,7 +26,8 @@ def threshold_metrics_label_units(
     thresholds : dict | str | Path
         A dictionary or JSON file path where keys are metric names and values are threshold values for labeling units.
         Each key should correspond to a quality metric present in the analyzer's quality metrics DataFrame. Values
-        should contain at least "min" and/or "max" keys to specify threshold ranges.
+        should contain at least "min" and/or "max" keys to specify threshold ranges. Optionally, an "abs": True entry
+        can be included to indicate that the metric should be treated as an absolute value when applying thresholds.
     pass_label : str, default: "good"
         The label to assign to units that pass all thresholds.
     fail_label : str, default: "noise"
@@ -89,12 +90,15 @@ def threshold_metrics_label_units(
     for metric_name, threshold in thresholds_dict.items():
         min_value = threshold.get("min", None)
         max_value = threshold.get("max", None)
+        abs_value = threshold.get("abs", False)
 
         # If both disabled, ignore this metric
         if is_threshold_disabled(min_value) and is_threshold_disabled(max_value):
             continue
 
         values = metrics[metric_name].to_numpy()
+        if abs_value:
+            values = np.abs(values)
         is_nan = np.isnan(values)
 
         metric_ok = np.ones(len(values), dtype=bool)

src/spikeinterface/widgets/metrics.py

@@ -335,7 +335,9 @@ class MetricsHistogramsWidget(BaseWidget):
         A SortingAnalyzer object with quality_metrics and/or template_metrics extensions computed.
     thresholds : dict, optional
         Dictionary of metric thresholds. Can be a flat dict with metric names as keys and dicts with 'min' and/or 'max'
-        as values, or a nested dict where top-level keys are different categories.
+        as values, or a nested dict where top-level keys are different categories. Optionally, an "abs": True entry
+        can be included in each metric's dict to indicate that the metric should be treated as an absolute value when
+        applying thresholds. If None, default thresholds from `bombcell_get_default_thresholds` will be used.
     metrics_to_plot : list, default: None
         List of metric names to plot. If None, all metrics with thresholds will be plotted.
     """
@@ -412,15 +414,15 @@ def plot_matplotlib(self, data_plot, **backend_kwargs):
         self.figure, self.axes, self.ax = make_mpl_figure(**backend_kwargs)
 
         colors = plt.cm.tab10(np.linspace(0, 1, 10))
-        absolute_value_metrics = ["amplitude_median"]
 
         axes = self.axes
         for idx, metric_name in enumerate(metrics_to_plot):
             row, col = idx // n_cols, idx % n_cols
             ax = axes[row, col]
 
             values = metrics[metric_name].values
-            if metric_name in absolute_value_metrics:
+            abs_value = thresholds.get(metric_name, {}).get("abs", False)
+            if abs_value:
                 values = np.abs(values)
             values = values[~np.isnan(values) & ~np.isinf(values)]