estimate_sparsity arg ordering

Author: jakeswann1

src/spikeinterface/core/sortinganalyzer.py

@@ -127,7 +127,7 @@ def create_sorting_analyzer(
             recording.channel_ids, sparsity.channel_ids
         ), "create_sorting_analyzer(): if external sparsity is given unit_ids must correspond"
     elif sparse:
-        sparsity = estimate_sparsity(recording, sorting, **sparsity_kwargs)
+        sparsity = estimate_sparsity(sorting, recording, **sparsity_kwargs)
     else:
         sparsity = None
 

src/spikeinterface/core/sparsity.py

@@ -539,8 +539,8 @@ def compute_sparsity(
 
 
 def estimate_sparsity(
-    recording: BaseRecording,
     sorting: BaseSorting,
+    recording: BaseRecording,
     num_spikes_for_sparsity: int = 100,
     ms_before: float = 1.0,
     ms_after: float = 2.5,
@@ -563,10 +563,10 @@ def estimate_sparsity(
 
     Parameters
     ----------
-    recording: BaseRecording
-        The recording
     sorting: BaseSorting
         The sorting
+    recording: BaseRecording
+        The recording
     num_spikes_for_sparsity: int, default: 100
         How many spikes per units to compute the sparsity
     ms_before: float, default: 1.0

src/spikeinterface/core/tests/test_sparsity.py

@@ -166,8 +166,8 @@ def test_estimate_sparsity():
 
     # small radius should give a very sparse = one channel per unit
     sparsity = estimate_sparsity(
-        recording,
         sorting,
+        recording,
         num_spikes_for_sparsity=50,
         ms_before=1.0,
         ms_after=2.0,
@@ -182,8 +182,8 @@ def test_estimate_sparsity():
 
     # best_channel : the mask should exactly 3 channels per units
     sparsity = estimate_sparsity(
-        recording,
         sorting,
+        recording,
         num_spikes_for_sparsity=50,
         ms_before=1.0,
         ms_after=2.0,

src/spikeinterface/postprocessing/tests/common_extension_tests.py

@@ -79,7 +79,7 @@ class AnalyzerExtensionCommonTestSuite:
     def setUpClass(cls):
         cls.recording, cls.sorting = get_dataset()
         # sparsity is computed once for all cases to save processing time and force a small radius
-        cls.sparsity = estimate_sparsity(cls.recording, cls.sorting, method="radius", radius_um=20)
+        cls.sparsity = estimate_sparsity(cls.sorting, cls.recording, method="radius", radius_um=20)
 
     @property
     def extension_name(self):