Change 'min'/'max' to 'greater'/'less' when defining thresholds for threshold_metrics_label_units

doc/how_to/auto_label_units.rst

@@ -79,18 +79,18 @@ curation:
 
 
 1. Quality-metrics based curation
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+----------------------------------
 
 A simple solution is to use a filter based on quality metrics. To do so,
-we can use the ``spikeinterface.curation.qualitymetrics_label_units``
+we can use the ``spikeinterface.curation.threshold_metrics_label_units``
 function and provide a set of thresholds.
 
 .. code:: ipython3
 
     qm_thresholds = {
-        "snr": {"min": 5},
-        "firing_rate": {"min": 0.1, "max": 200},
-        "rp_contamination": {"max": 0.5}
+        "snr": {"greater": 5},
+        "firing_rate": {"greater": 0.1, "less": 200},
+        "rp_contamination": {"less": 0.5}
     }
 
 .. code:: ipython3
@@ -143,7 +143,7 @@ across all units:
 .. image:: auto_label_units_files/auto_label_units_14_0.png
 
 
-1. Bombcell
+2. Bombcell
 -----------
 
 **Bombcell** ([Fabre]_) is another threshold-based method that also uses
@@ -161,24 +161,24 @@ file.
 
 .. parsed-literal::
 
-    {'mua': {'amplitude_cutoff': {'max': 0.2, 'min': None},
-             'amplitude_median': {'max': None, 'min': 40},
-             'drift_ptp': {'max': 100, 'min': None},
-             'num_spikes': {'max': None, 'min': 300},
-             'presence_ratio': {'max': None, 'min': 0.7},
-             'rp_contamination': {'max': 0.1, 'min': None},
-             'snr': {'max': None, 'min': 5}},
-     'noise': {'exp_decay': {'max': 0.1, 'min': 0.01},
-               'num_negative_peaks': {'max': 1, 'min': None},
-               'num_positive_peaks': {'max': 2, 'min': None},
-               'peak_after_to_trough_ratio': {'max': 0.8, 'min': None},
-               'peak_to_trough_duration': {'max': 0.00115, 'min': 0.0001},
-               'waveform_baseline_flatness': {'max': 0.5, 'min': None}},
-     'non-somatic': {'main_peak_to_trough_ratio': {'max': 0.8, 'min': None},
-                     'peak_before_to_peak_after_ratio': {'max': 3, 'min': None},
-                     'peak_before_to_trough_ratio': {'max': 3, 'min': None},
-                     'peak_before_width': {'max': None, 'min': 0.00015},
-                     'trough_width': {'max': None, 'min': 0.0002}}}
+    {'mua': {'amplitude_cutoff': {'greater': None, 'less': 0.2},
+            'amplitude_median': {'abs': True, 'greater': 30, 'less': None},
+            'drift_ptp': {'greater': None, 'less': 100},
+            'num_spikes': {'greater': 300, 'less': None},
+            'presence_ratio': {'greater': 0.7, 'less': None},
+            'rp_contamination': {'greater': None, 'less': 0.1},
+            'snr': {'greater': 5, 'less': None}},
+    'noise': {'exp_decay': {'greater': 0.01, 'less': 0.1},
+            'num_negative_peaks': {'greater': None, 'less': 1},
+            'num_positive_peaks': {'greater': None, 'less': 2},
+            'peak_after_to_trough_ratio': {'greater': None, 'less': 0.8},
+            'peak_to_trough_duration': {'greater': 0.0001, 'less': 0.00115},
+            'waveform_baseline_flatness': {'greater': None, 'less': 0.5}},
+    'non-somatic': {'main_peak_to_trough_ratio': {'greater': None, 'less': 0.8},
+                    'peak_before_to_peak_after_ratio': {'greater': None, 'less': 3},
+                    'peak_before_to_trough_ratio': {'greater': None, 'less': 3},
+                    'peak_before_width': {'greater': 0.00015, 'less': None},
+                    'trough_width': {'greater': 0.0002, 'less': None}}}
 
 
 .. code:: ipython3
@@ -248,8 +248,8 @@ contamination (``rp_contamination``).
 .. image:: auto_label_units_files/auto_label_units_23_1.png
 
 
-UnitRefine
-----------
+3. UnitRefine
+-------------
 
 **UnitRefine** ([Jain]_) also uses quality and template metrics, but in
 a different way. It uses pre-trained classifiers to trained on
@@ -305,12 +305,11 @@ sorting with different strategies. We recommend running **Bombcell** and
 **UnitRefine** as part of your pipeline. These methods will facilitate
 further curation and make downstream analysis cleaner.
 
-To remove units from your ``SortingAnalyzer``, you can simply use the
-``select_units`` function:
-
 Remove units from ``SortingAnalyzer``
 -------------------------------------
 
+To remove units from your ``SortingAnalyzer``, you can use the ``select_units`` function.
+
 After auto-labeling, we can easily remove the “noise” units for
 downstream analysis:
 

doc/modules/curation.rst

@@ -132,8 +132,8 @@ which applies a set of thresholds based on the available metrics (template/quali
     labels = threshold_metrics_label_units(
         sorting_analyzer=sorting_analyzer,
         thresholds={
-            "snr": {"min": 5},
-            "rp_contamination": {"max": 0.2},
+            "snr": {"greater": 5},
+            "rp_contamination": {"less": 0.2},
         },
         pass_label="good",
         fail_label="bad",

examples/how_to/auto_label_units.py

@@ -57,9 +57,9 @@
 
 # %%
 qm_thresholds = {
-    "snr": {"min": 5},
-    "firing_rate": {"min": 0.1, "max": 200},
-    "rp_contamination": {"max": 0.5}
+    "snr": {"greater": 5},
+    "firing_rate": {"greater": 0.1, "less": 200},
+    "rp_contamination": {"less": 0.5}
 }
 
 # %%

src/spikeinterface/curation/bombcell_curation.py

@@ -50,34 +50,34 @@ def bombcell_get_default_thresholds() -> dict:
     """
     bombcell - Returns default thresholds for unit labeling.
 
-    Each metric has 'min' and 'max' values. Use None to disable a threshold (e.g. to ignore a metric completely
-    or to only have a min or a max threshold)
+    Each metric has 'greater' and 'less' values. Use None to disable a threshold (e.g. to ignore a metric completely
+    or to only have a greater or a less threshold)
     """
     # bombcell
     return {
         "noise": {  # failures -> NOISE
-            "num_positive_peaks": {"min": None, "max": 2},
-            "num_negative_peaks": {"min": None, "max": 1},
-            "peak_to_trough_duration": {"min": 0.0001, "max": 0.00115},  # seconds
-            "waveform_baseline_flatness": {"min": None, "max": 0.5},
-            "peak_after_to_trough_ratio": {"min": None, "max": 0.8},
-            "exp_decay": {"min": 0.01, "max": 0.1},
+            "num_positive_peaks": {"greater": None, "less": 2},
+            "num_negative_peaks": {"greater": None, "less": 1},
+            "peak_to_trough_duration": {"greater": 0.0001, "less": 0.00115},  # seconds
+            "waveform_baseline_flatness": {"greater": None, "less": 0.5},
+            "peak_after_to_trough_ratio": {"greater": None, "less": 0.8},
+            "exp_decay": {"greater": 0.01, "less": 0.1},
         },
         "mua": {  # failures -> MUA, only applied to units that passed noise thresholds
-            "amplitude_median": {"min": 30, "max": None, "abs": True},  # uV
-            "snr": {"min": 5, "max": None},
-            "amplitude_cutoff": {"min": None, "max": 0.2},
-            "num_spikes": {"min": 300, "max": None},
-            "rp_contamination": {"min": None, "max": 0.1},
-            "presence_ratio": {"min": 0.7, "max": None},
-            "drift_ptp": {"min": None, "max": 100},  # um
+            "amplitude_median": {"greater": 30, "less": None, "abs": True},  # uV
+            "snr": {"greater": 5, "less": None},
+            "amplitude_cutoff": {"greater": None, "less": 0.2},
+            "num_spikes": {"greater": 300, "less": None},
+            "rp_contamination": {"greater": None, "less": 0.1},
+            "presence_ratio": {"greater": 0.7, "less": None},
+            "drift_ptp": {"greater": None, "less": 100},  # um
         },
         "non-somatic": {
-            "peak_before_to_trough_ratio": {"min": None, "max": 3},
-            "peak_before_width": {"min": 0.00015, "max": None},  # seconds
-            "trough_width": {"min": 0.0002, "max": None},  # seconds
-            "peak_before_to_peak_after_ratio": {"min": None, "max": 3},
-            "main_peak_to_trough_ratio": {"min": None, "max": 0.8},
+            "peak_before_to_trough_ratio": {"greater": None, "less": 3},
+            "peak_before_width": {"greater": 0.00015, "less": None},  # seconds
+            "trough_width": {"greater": 0.0002, "less": None},  # seconds
+            "peak_before_to_peak_after_ratio": {"greater": None, "less": 3},
+            "main_peak_to_trough_ratio": {"greater": None, "less": 0.8},
         },
     }
 
@@ -123,7 +123,7 @@ def bombcell_label_units(
         If provided, metrics are extracted automatically using get_metrics_extension_data().
     thresholds : dict | str | Path | None
         Threshold dict or JSON file, including a three sections ("noise", "mua", "non-somatic") of
-        {"metric": {"min": val, "max": val}}.
+        {"metric": {"greater": val, "less": val}}.
         If None, default Bombcell thresholds are used.
     label_non_somatic : bool, default: True
         If True, detect non-somatic (dendritic, axonal) units.
@@ -336,8 +336,8 @@ def save_bombcell_results(
                     continue
                 value = metrics.loc[unit_id, metric_name]
                 thresh = flat_thresholds[metric_name]
-                thresh_min = thresh.get("min", None)
-                thresh_max = thresh.get("max", None)
+                thresh_min = thresh.get("greater", None)
+                thresh_max = thresh.get("less", None)
 
                 # Determine pass/fail
                 passed = True

src/spikeinterface/curation/tests/test_bombcell_curation.py

@@ -55,8 +55,8 @@ def test_bombcell_label_units_with_threshold_file(sorting_analyzer_with_metrics,
 
     # Define custom thresholds
     custom_thresholds = {
-        "snr": {"min": 5, "max": 100},
-        "isi_violations": {"min": None, "max": 0.2},
+        "snr": {"greater": 5, "less": 100},
+        "isi_violations": {"greater": None, "less": 0.2},
     }
 
     # Save thresholds to a temporary JSON file

src/spikeinterface/curation/tests/test_threshold_metrics_curation.py

@@ -17,8 +17,8 @@ def test_threshold_metrics_label_units_with_dataframe():
         index=[0, 1, 2],
     )
     thresholds = {
-        "snr": {"min": 5.0},
-        "firing_rate": {"min": 0.1, "max": 20.0},
+        "snr": {"greater": 5.0},
+        "firing_rate": {"greater": 0.1, "less": 20.0},
     }
 
     labels = threshold_metrics_label_units(metrics, thresholds)
@@ -39,8 +39,8 @@ def test_threshold_metrics_label_units_with_file(tmp_path):
         index=[0, 1],
     )
     thresholds = {
-        "snr": {"min": 5.0},
-        "firing_rate": {"min": 0.1},
+        "snr": {"greater": 5.0},
+        "firing_rate": {"greater": 0.1},
     }
 
     thresholds_file = tmp_path / "thresholds.json"
@@ -63,8 +63,8 @@ def test_threshold_metrics_label_external_labels():
         index=[0, 1],
     )
     thresholds = {
-        "snr": {"min": 5.0},
-        "firing_rate": {"min": 0.1},
+        "snr": {"greater": 5.0},
+        "firing_rate": {"greater": 0.1},
     }
 
     labels = threshold_metrics_label_units(
@@ -86,7 +86,7 @@ def test_threshold_metrics_label_units_operator_or_with_dataframe():
         },
         index=[0, 1, 2, 3],
     )
-    thresholds = {"m1": {"min": 0.0}, "m2": {"min": 0.0}}
+    thresholds = {"m1": {"greater": 0.0}, "m2": {"greater": 0.0}}
 
     labels_and = threshold_metrics_label_units(
         metrics,
@@ -115,7 +115,7 @@ def test_threshold_metrics_label_units_nan_policy_fail_vs_ignore_and():
         },
         index=[10, 11, 12],
     )
-    thresholds = {"m1": {"min": 0.0}, "m2": {"min": 0.0}}
+    thresholds = {"m1": {"greater": 0.0}, "m2": {"greater": 0.0}}
 
     labels_fail = threshold_metrics_label_units(
         metrics,
@@ -147,7 +147,7 @@ def test_threshold_metrics_label_units_nan_policy_ignore_with_or():
         },
         index=[20, 21],
     )
-    thresholds = {"m1": {"min": 0.0}, "m2": {"min": 0.0}}
+    thresholds = {"m1": {"greater": 0.0}, "m2": {"greater": 0.0}}
 
     labels_ignore_or = threshold_metrics_label_units(
         metrics,
@@ -170,7 +170,7 @@ def test_threshold_metrics_label_units_nan_policy_pass_and_or():
         },
         index=[30, 31, 32, 33],
     )
-    thresholds = {"m1": {"min": 0.0}, "m2": {"min": 0.0}}
+    thresholds = {"m1": {"greater": 0.0}, "m2": {"greater": 0.0}}
 
     labels_and = threshold_metrics_label_units(
         metrics,
@@ -198,7 +198,7 @@ def test_threshold_metrics_label_units_invalid_operator_raises():
     import pandas as pd
 
     metrics = pd.DataFrame({"m1": [1.0]}, index=[0])
-    thresholds = {"m1": {"min": 0.0}}
+    thresholds = {"m1": {"greater": 0.0}}
     with pytest.raises(ValueError, match="operator must be 'and' or 'or'"):
         threshold_metrics_label_units(metrics, thresholds, operator="xor")
 
@@ -207,7 +207,7 @@ def test_threshold_metrics_label_units_invalid_nan_policy_raises():
     import pandas as pd
 
     metrics = pd.DataFrame({"m1": [1.0]}, index=[0])
-    thresholds = {"m1": {"min": 0.0}}
+    thresholds = {"m1": {"greater": 0.0}}
     with pytest.raises(ValueError, match="nan_policy must be"):
         threshold_metrics_label_units(metrics, thresholds, nan_policy="omit")
 
@@ -216,6 +216,15 @@ def test_threshold_metrics_label_units_missing_metric_raises():
     import pandas as pd
 
     metrics = pd.DataFrame({"m1": [1.0]}, index=[0])
-    thresholds = {"does_not_exist": {"min": 0.0}}
+    thresholds = {"does_not_exist": {"greater": 0.0}}
     with pytest.raises(ValueError, match="specified in thresholds are not present"):
         threshold_metrics_label_units(metrics, thresholds)
+
+
+def test_threshold_metrics_label_units_invalid_threshold_keys_raises():
+    import pandas as pd
+
+    metrics = pd.DataFrame({"m1": [1.0]}, index=[0])
+    thresholds = {"m1": {"greater": 0.0, "invalid_key": 1.0}}
+    with pytest.raises(ValueError, match="contains invalid keys"):
+        threshold_metrics_label_units(metrics, thresholds)

src/spikeinterface/curation/threshold_metrics_curation.py

@@ -26,8 +26,9 @@ 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. Optionally, an "abs": True entry
-        can be included to indicate that the metric should be treated as an absolute value when applying thresholds.
+        should contain at least "greater" and/or "less" keys to specify threshold ranges. Thresholds are inclusive, i.e.
+        "greater" is >= and "less" is <=. 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"
@@ -74,6 +75,14 @@ def threshold_metrics_label_units(
             f"Available metrics are: {metrics.columns.tolist()}"
         )
 
+    # Check that threshold dictionaries contain only valid keys
+    valid_keys = {"greater", "less", "abs"}
+    for metric_name, threshold in thresholds_dict.items():
+        if not set(threshold).issubset(valid_keys):
+            raise ValueError(
+                f"Threshold for metric '{metric_name}' contains invalid keys {set(threshold) - valid_keys}."
+            )
+
     if operator not in ("and", "or"):
         raise ValueError("operator must be 'and' or 'or'")
 
@@ -88,8 +97,8 @@ def threshold_metrics_label_units(
     any_threshold_applied = False
 
     for metric_name, threshold in thresholds_dict.items():
-        min_value = threshold.get("min", None)
-        max_value = threshold.get("max", None)
+        min_value = threshold.get("greater", None)
+        max_value = threshold.get("less", None)
         abs_value = threshold.get("abs", False)
 
         # If both disabled, ignore this metric

src/spikeinterface/widgets/bombcell_curation.py

@@ -30,7 +30,7 @@ class BombcellUpsetPlotWidget(BaseWidget):
         "non_soma", "non_soma_good", "non_soma_mua".
     thresholds : dict, optional
         Threshold dictionary with structure "noise", "mua", "non-somatic" as sections. Each section contains
-        metric names keys with "min" and "max" thresholds.
+        metric names keys with "greater" and "less" thresholds.
         If None, uses default thresholds.
     unit_labels_to_plot : list of str, optional
         List of unit labels to include in the plot. If None, defaults to all labels in thresholds.
@@ -197,10 +197,10 @@ def _build_failure_table(self, metrics, thresholds):
                 values = np.abs(values)
 
             failed = np.isnan(values)
-            if not is_threshold_disabled(thresh.get("min", None)):
-                failed |= values < thresh["min"]
-            if not is_threshold_disabled(thresh.get("max", None)):
-                failed |= values > thresh["max"]
+            if not is_threshold_disabled(thresh.get("greater", None)):
+                failed |= values < thresh["greater"]
+            if not is_threshold_disabled(thresh.get("less", None)):
+                failed |= values > thresh["less"]
             failure_data[metric_name] = failed
 
         return pd.DataFrame(failure_data, index=metrics.index)