Ignore max spikes per unit

docs/source/pages/how-to-use-driftplots.md

@@ -28,8 +28,7 @@ and not reflect any later changes in Phy (`spike_templates.npy` is used for the
 If passing a `SortingAnalyzer`, it is expected that the required extensions
 have already been computed. See
 [this example](https://github.com/neuroinformatics-unit/driftplots/blob/e8ec328e14cc848feca3e7e90604501bb9e343f1/examples/example_data/create_analyzer.py#L1)
-for the required extensions. Note that the number of spikes displayed will depend
-on the argument set for `max_spikes_per_unit` used when computing `"random_spikes"`.
+for the required extensions.
 
 By default, the number of spikes displayed will be decimated to around `100,000`.
 
@@ -194,5 +193,3 @@ for path_or_analyzer in SORTING_SESSIONS:
 multi = MultiSessionDriftmapWidget(panels)
 
 multi.plot()
-
-```

driftplots/extractors/analyzer_helpers.py

@@ -10,26 +10,19 @@ def get_sorting_analyzer(
     analyzer: si.SortingAnalyzer,
 ) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
     """
-    Get the required data from the SortingAnalyzer. Note that this
-    will not get all detected spikes, but rather the number of spikes
-    specified when creating the analyzer, `max_spikes_per_unit`.
+    Get the required data from the SortingAnalyzer.
     """
-    random_spike_indices = analyzer.get_extension("random_spikes").data[
-        "random_spikes_indices"
-    ]
     spike_vector = analyzer.sorting.to_spike_vector()
     spike_times = (
-        spike_vector["sample_index"][random_spike_indices]
-        / analyzer.sorting.get_sampling_frequency()
+        spike_vector["sample_index"] / analyzer.sorting.get_sampling_frequency()
     )
     spike_amplitudes = np.abs(
         analyzer.get_extension("spike_amplitudes").data["amplitudes"]
     )
-
     spike_depths = analyzer.get_extension("spike_locations").data["spike_locations"][
         "y"
     ]
-    spike_templates = spike_vector["unit_index"][random_spike_indices]
+    spike_templates = spike_vector["unit_index"]
 
     # Get the templates, assume only one method was used. If multiple
     # methods were used, use the first and throw a warning. If people

examples/example_data/create_analyzer.py

@@ -17,9 +17,9 @@
 rec = si_prepro.common_reference(rec, operator="median")
 
 if SAVE_SORTING:
-    #  out_path = base_path / "sorting"
-    # out_path.mkdir()
-    sort = run_sorter("kilosort4", rec, folder=base_path / "sorting")
+    out_path = base_path / "sorting"
+    out_path.mkdir()
+    sort = run_sorter("kilosort4", rec, folder=out_path)
 else:
     sort = si_extractors.read_kilosort(
         base_path / "sorting" / "kilosort4_output" / "sorter_output"
@@ -29,9 +29,6 @@
 analyzer.compute(
     "random_spikes",
     method="uniform",
-    # This determines the number of spikes that
-    # will appear on the SI drift plot
-    max_spikes_per_unit=1_000_000,
 )
 analyzer.compute("waveforms", ms_before=1.0, ms_after=2.0)
 analyzer.compute("templates", operators=["average"])

tests/test_sorting_analyzer.py

@@ -22,15 +22,10 @@ def test_loaded_arrays_match_analyzer_extensions(self):
         analyzer = si.load_sorting_analyzer(ANALYZER_PATH)
         loader = DataLoader(analyzer, verbose=False)
 
-        # Expected values from the analyzer extensions
-        random_spike_indices = analyzer.get_extension("random_spikes").data[
-            "random_spikes_indices"
-        ]
         spike_vector = analyzer.sorting.to_spike_vector()
 
         expected_times = (
-            spike_vector["sample_index"][random_spike_indices]
-            / analyzer.sorting.get_sampling_frequency()
+            spike_vector["sample_index"] / analyzer.sorting.get_sampling_frequency()
         )
         np.testing.assert_array_equal(loader._spike_times, expected_times)
 
@@ -44,7 +39,7 @@ def test_loaded_arrays_match_analyzer_extensions(self):
         ]["y"]
         np.testing.assert_array_equal(loader._spike_depths, expected_depths)
 
-        expected_templates = spike_vector["unit_index"][random_spike_indices]
+        expected_templates = spike_vector["unit_index"]
         np.testing.assert_array_equal(loader._spike_templates, expected_templates)
 
         expected_waveforms = analyzer.get_extension("templates").data["average"]
@@ -65,6 +60,27 @@ def test_loaded_arrays_match_analyzer_extensions(self):
         ):
             assert not arr.flags.writeable
 
+    def test_loaded_spikes_ignore_random_spikes_subset(self):
+        analyzer = si.load_sorting_analyzer(ANALYZER_PATH).copy()
+        spike_vector = analyzer.sorting.to_spike_vector()
+
+        random_spikes = analyzer.get_extension("random_spikes")
+        random_spikes.data["random_spikes_indices"] = random_spikes.data[
+            "random_spikes_indices"
+        ][::2]
+        assert random_spikes.data["random_spikes_indices"].size < spike_vector.size
+
+        loader = DataLoader(analyzer, verbose=False)
+
+        expected_times = (
+            spike_vector["sample_index"] / analyzer.sorting.get_sampling_frequency()
+        )
+        np.testing.assert_array_equal(loader._spike_times, expected_times)
+        np.testing.assert_array_equal(
+            loader._spike_templates, spike_vector["unit_index"]
+        )
+        assert loader._spike_times.size == spike_vector.size
+
     def test_good_units_only_keeps_kslabel_good_units(self):
         analyzer = si.load_sorting_analyzer(ANALYZER_PATH)
         loader = DataLoader(analyzer, verbose=False)