Add numba check to sd_ratio

src/spikeinterface/curation/curation_tools.py

@@ -127,10 +127,10 @@ def find_duplicated_spikes(
         assert seed is not None, "The 'seed' has to be provided if method=='random'"
         return _find_duplicated_spikes_random(spike_train, censored_period, seed)
     elif method == "keep_first_iterative":
-        assert HAVE_NUMBA, "'keep_first' method requires numba. Install it with >>> pip install numba"
+        assert HAVE_NUMBA, "'keep_first_iterative' method requires numba. Install it with >>> pip install numba"
         return _find_duplicated_spikes_keep_first_iterative(spike_train.astype(np.int64), censored_period)
     elif method == "keep_last_iterative":
-        assert HAVE_NUMBA, "'keep_last' method requires numba. Install it with >>> pip install numba"
+        assert HAVE_NUMBA, "'keep_last_iterative' method requires numba. Install it with >>> pip install numba"
         return _find_duplicated_spikes_keep_last_iterative(spike_train.astype(np.int64), censored_period)
     else:
         raise ValueError(f"Method '{method}' isn't a valid method for find_duplicated_spikes. Use one of {_methods}")

src/spikeinterface/qualitymetrics/misc_metrics.py

@@ -1469,7 +1469,8 @@ def compute_sd_ratio(
     num_spikes : dict
         The number of spikes, across all segments, for each unit ID.
     """
-    from spikeinterface.curation.curation_tools import _find_duplicated_spikes_keep_first_iterative
+
+    from spikeinterface.curation.curation_tools import find_duplicated_spikes
 
     kwargs, job_kwargs = split_job_kwargs(kwargs)
     job_kwargs = fix_job_kwargs(job_kwargs)
@@ -1487,9 +1488,15 @@ def compute_sd_ratio(
         )
         return {unit_id: np.nan for unit_id in unit_ids}
 
+    if not HAVE_NUMBA:
+        warnings.warn(
+            "'sd_ratio' metric computation requires numba. Install it with >>> pip install numba. "
+            "SD ratio metric will be set to NaN"
+        )
+        return {unit_id: np.nan for unit_id in unit_ids}
+
     if sorting_analyzer.has_extension("spike_amplitudes"):
         amplitudes_ext = sorting_analyzer.get_extension("spike_amplitudes")
-        # spike_amplitudes = amplitudes_ext.get_data(outputs="by_unit")
         spike_amplitudes = amplitudes_ext.get_data()
     else:
         warnings.warn(
@@ -1516,18 +1523,17 @@ def compute_sd_ratio(
         spk_amp = []
 
         for segment_index in range(sorting_analyzer.get_num_segments()):
-            # spike_train = sorting_analyzer.sorting.get_unit_spike_train(unit_id, segment_index=segment_index).astype(
-            #     np.int64, copy=False
-            # )
+
             spike_mask = (spikes["unit_index"] == unit_index) & (spikes["segment_index"] == segment_index)
             spike_train = spikes[spike_mask]["sample_index"].astype(np.int64, copy=False)
             amplitudes = spike_amplitudes[spike_mask]
 
-            censored_indices = _find_duplicated_spikes_keep_first_iterative(
+            censored_indices = find_duplicated_spikes(
                 spike_train,
                 censored_period,
+                method="keep_first_iterative",
             )
-            # spk_amp.append(np.delete(spike_amplitudes[segment_index][unit_id], censored_indices))
+
             spk_amp.append(np.delete(amplitudes, censored_indices))
 
         spk_amp = np.concatenate([spk_amp[i] for i in range(len(spk_amp))])