Merge pull request #3476 from samuelgarcia/localization_and_sparsity

Unit localization

src/spikeinterface/postprocessing/localization_tools.py

@@ -77,7 +77,11 @@ def compute_monopolar_triangulation(
 
     contact_locations = sorting_analyzer_or_templates.get_channel_locations()
 
-    sparsity = compute_sparsity(sorting_analyzer_or_templates, method="radius", radius_um=radius_um)
+    if sorting_analyzer_or_templates.sparsity is None:
+        sparsity = compute_sparsity(sorting_analyzer_or_templates, method="radius", radius_um=radius_um)
+    else:
+        sparsity = sorting_analyzer_or_templates.sparsity
+
     templates = get_dense_templates_array(
         sorting_analyzer_or_templates, return_scaled=get_return_scaled(sorting_analyzer_or_templates)
     )
@@ -157,9 +161,13 @@ def compute_center_of_mass(
 
     assert feature in ["ptp", "mean", "energy", "peak_voltage"], f"{feature} is not a valid feature"
 
-    sparsity = compute_sparsity(
-        sorting_analyzer_or_templates, peak_sign=peak_sign, method="radius", radius_um=radius_um
-    )
+    if sorting_analyzer_or_templates.sparsity is None:
+        sparsity = compute_sparsity(
+            sorting_analyzer_or_templates, peak_sign=peak_sign, method="radius", radius_um=radius_um
+        )
+    else:
+        sparsity = sorting_analyzer_or_templates.sparsity
+
     templates = get_dense_templates_array(
         sorting_analyzer_or_templates, return_scaled=get_return_scaled(sorting_analyzer_or_templates)
     )
@@ -650,8 +658,55 @@ def get_convolution_weights(
     enforce_decrease_shells = numba.jit(enforce_decrease_shells_data, nopython=True)
 
 
+def compute_location_max_channel(
+    templates_or_sorting_analyzer: SortingAnalyzer | Templates,
+    unit_ids=None,
+    peak_sign: "neg" | "pos" | "both" = "neg",
+    mode: "extremum" | "at_index" | "peak_to_peak" = "extremum",
+) -> np.ndarray:
+    """
+    Localize a unit using max channel.
+
+    This uses internally `get_template_extremum_channel()`
+
+
+    Parameters
+    ----------
+    templates_or_sorting_analyzer : SortingAnalyzer | Templates
+        A SortingAnalyzer or Templates object
+    unit_ids: list[str] | list[int] | None
+        A list of unit_id to restrict the computation
+    peak_sign :  "neg" | "pos" | "both"
+        Sign of the template to find extremum channels
+    mode : "extremum" | "at_index" | "peak_to_peak", default: "at_index"
+        Where the amplitude is computed
+        * "extremum" : take the peak value (max or min depending on `peak_sign`)
+        * "at_index" : take value at `nbefore` index
+        * "peak_to_peak" : take the peak-to-peak amplitude
+
+    Returns
+    -------
+    unit_locations: np.ndarray
+        2d
+    """
+    extremum_channels_index = get_template_extremum_channel(
+        templates_or_sorting_analyzer, peak_sign=peak_sign, mode=mode, outputs="index"
+    )
+    contact_locations = templates_or_sorting_analyzer.get_channel_locations()
+    if unit_ids is None:
+        unit_ids = templates_or_sorting_analyzer.unit_ids
+    else:
+        unit_ids = np.asarray(unit_ids)
+    unit_locations = np.zeros((unit_ids.size, 2), dtype="float32")
+    for i, unit_id in enumerate(unit_ids):
+        unit_locations[i, :] = contact_locations[extremum_channels_index[unit_id]]
+
+    return unit_locations
+
+
 _unit_location_methods = {
     "center_of_mass": compute_center_of_mass,
     "grid_convolution": compute_grid_convolution,
     "monopolar_triangulation": compute_monopolar_triangulation,
+    "max_channel": compute_location_max_channel,
 }

src/spikeinterface/postprocessing/tests/test_unit_locations.py

@@ -13,6 +13,7 @@ class TestUnitLocationsExtension(AnalyzerExtensionCommonTestSuite):
             dict(method="grid_convolution", radius_um=150, weight_method={"mode": "gaussian_2d"}),
             dict(method="monopolar_triangulation", radius_um=150),
             dict(method="monopolar_triangulation", radius_um=150, optimizer="minimize_with_log_penality"),
+            dict(method="max_channel"),
         ],
     )
     def test_extension(self, params):