Fix firing range when bin size is to small

src/spikeinterface/qualitymetrics/misc_metrics.py

@@ -602,6 +602,15 @@ def compute_firing_ranges(waveform_extractor, bin_size_s=5, percentiles=(5, 95),
     if unit_ids is None:
         unit_ids = sorting.unit_ids
 
+    if all(
+        [
+            waveform_extractor.get_num_samples(segment_index) < bin_size_samples
+            for segment_index in range(waveform_extractor.get_num_segments())
+        ]
+    ):
+        warnings.warn(f"Bin size of {bin_size_s}s is larger than each segment duration. Firing ranges are set to NaN.")
+        return {unit_id: np.nan for unit_id in unit_ids}
+
     # for each segment, we compute the firing rate histogram and we concatenate them
     firing_rate_histograms = {unit_id: np.array([], dtype=float) for unit_id in sorting.unit_ids}
     for segment_index in range(waveform_extractor.get_num_segments()):

src/spikeinterface/qualitymetrics/tests/test_metrics_functions.py

@@ -220,6 +220,10 @@ def test_calculate_firing_range(waveform_extractor_simple):
     firing_ranges = compute_firing_ranges(we)
     print(firing_ranges)
 
+    with pytest.warns(UserWarning) as w:
+        firing_ranges_nan = compute_firing_ranges(we, bin_size_s=we.get_total_duration() + 1)
+        assert np.all([np.isnan(f) for f in firing_ranges_nan.values()])
+
 
 def test_calculate_amplitude_cutoff(waveform_extractor_simple):
     we = waveform_extractor_simple
@@ -378,7 +382,7 @@ def test_calculate_drift_metrics(waveform_extractor_simple):
 if __name__ == "__main__":
     sim_data = _simulated_data()
     we = _waveform_extractor_simple()
-    we_violations = _waveform_extractor_violations(sim_data)
+    # we_violations = _waveform_extractor_violations(sim_data)
     # test_calculate_amplitude_cutoff(we)
     # test_calculate_presence_ratio(we)
     # test_calculate_amplitude_median(we)
@@ -387,4 +391,4 @@ def test_calculate_drift_metrics(waveform_extractor_simple):
     # test_calculate_drift_metrics(we)
     # test_synchrony_metrics(we)
     test_calculate_firing_range(we)
-    test_calculate_amplitude_cv_metrics(we)
+    # test_calculate_amplitude_cv_metrics(we)

src/spikeinterface/sortingcomponents/benchmark/benchmark_motion_estimation.py

@@ -584,13 +584,13 @@ def plot_motions_several_benchmarks(benchmarks):
     _simpleaxis(ax)
 
 
-def plot_speed_several_benchmarks(benchmarks, detailed=True,  ax=None, colors=None):
+def plot_speed_several_benchmarks(benchmarks, detailed=True, ax=None, colors=None):
     if ax is None:
         fig, ax = plt.subplots(figsize=(5, 5))
 
     for count, benchmark in enumerate(benchmarks):
         color = colors[count] if colors is not None else None
-        
+
         if detailed:
             bottom = 0
             i = 0
@@ -606,8 +606,6 @@ def plot_speed_several_benchmarks(benchmarks, detailed=True,  ax=None, colors=No
         else:
             total_run_time = np.sum([value for key, value in benchmark.run_times.items()])
             ax.bar([count], [total_run_time], color=color, edgecolor="black")
-
-
 
     # ax.legend()
     ax.set_ylabel("speed (s)")

src/spikeinterface/sortingcomponents/benchmark/benchmark_motion_interpolation.py

@@ -193,11 +193,15 @@ def run_sorters(self, skip_already_done=True):
             recording = self.recordings[case["recording"]]
             output_folder = self.folder / f"tmp_sortings_{label}"
             if output_folder.exists() and skip_already_done:
-                print('already done')
+                print("already done")
                 sorting = read_sorter_folder(output_folder)
             else:
                 sorting = run_sorter(
-                    sorter_name, recording, output_folder, **sorter_params, delete_output_folder=self.delete_output_folder
+                    sorter_name,
+                    recording,
+                    output_folder,
+                    **sorter_params,
+                    delete_output_folder=self.delete_output_folder,
                 )
             self.sortings[label] = sorting