Percentiles need 0-100 and ad duinit_ids to syncrhony metrics

src/spikeinterface/qualitymetrics/misc_metrics.py

@@ -499,7 +499,7 @@ def compute_sliding_rp_violations(
 )
 
 
-def compute_synchrony_metrics(waveform_extractor, synchrony_sizes=(2, 4, 8), **kwargs):
+def compute_synchrony_metrics(waveform_extractor, synchrony_sizes=(2, 4, 8), unit_ids=None, **kwargs):
     """Compute synchrony metrics. Synchrony metrics represent the rate of occurrences of
     "synchrony_size" spikes at the exact same sample index.
 
@@ -509,6 +509,8 @@ def compute_synchrony_metrics(waveform_extractor, synchrony_sizes=(2, 4, 8), **k
         The waveform extractor object.
     synchrony_sizes : list or tuple, default: (2, 4, 8)
         The synchrony sizes to compute.
+    unit_ids : list or None, default: None
+        List of unit ids to compute the synchrony metrics. If None, all units are used.
 
     Returns
     -------
@@ -526,6 +528,9 @@ def compute_synchrony_metrics(waveform_extractor, synchrony_sizes=(2, 4, 8), **k
     sorting = waveform_extractor.sorting
     spikes = sorting.to_spike_vector(concatenated=False)
 
+    if unit_ids is None:
+        unit_ids = sorting.unit_ids
+
     # Pre-allocate synchrony counts
     synchrony_counts = {}
     for synchrony_size in synchrony_sizes:
@@ -538,20 +543,20 @@ def compute_synchrony_metrics(waveform_extractor, synchrony_sizes=(2, 4, 8), **k
         unique_spike_index, complexity = np.unique(spikes_in_segment["sample_index"], return_counts=True)
 
         # add counts for this segment
-        for unit_index in np.arange(len(sorting.unit_ids)):
+        for unit_id in unit_ids:
+            unit_index = sorting.unit_ids.index(unit_id)
             spikes_per_unit = spikes_in_segment[spikes_in_segment["unit_index"] == unit_index]
             # some segments/units might have no spikes
             if len(spikes_per_unit) == 0:
                 continue
             spike_complexity = complexity[np.in1d(unique_spike_index, spikes_per_unit["sample_index"])]
             for synchrony_size in synchrony_sizes:
-                synchrony_counts[synchrony_size][unit_index] += np.count_nonzero(spike_complexity >= synchrony_size)
+                synchrony_counts[synchrony_size][unit_id] += np.count_nonzero(spike_complexity >= synchrony_size)
 
     # add counts for this segment
     synchrony_metrics_dict = {
         f"sync_spike_{synchrony_size}": {
-            unit_id: synchrony_counts[synchrony_size][unit_index] / spike_counts[unit_id]
-            for unit_index, unit_id in enumerate(sorting.unit_ids)
+            unit_id: synchrony_counts[synchrony_size][unit_id] / spike_counts[unit_id] for unit_id in unit_ids
         }
         for synchrony_size in synchrony_sizes
     }
@@ -565,7 +570,7 @@ def compute_synchrony_metrics(waveform_extractor, synchrony_sizes=(2, 4, 8), **k
 _default_params["synchrony"] = dict(synchrony_sizes=(0, 2, 4))
 
 
-def compute_firing_ranges(waveform_extractor, bin_size_s=5, percentiles=(0.05, 0.95), unit_ids=None):
+def compute_firing_ranges(waveform_extractor, bin_size_s=5, percentiles=(5, 95), unit_ids=None, **kwargs):
     """Calculate firing range, the range between the 5th and 95th percentiles of the firing rates distribution
     computed in non-overlapping time bins.
 
@@ -575,7 +580,7 @@ def compute_firing_ranges(waveform_extractor, bin_size_s=5, percentiles=(0.05, 0
         The waveform extractor object.
     bin_size_s : float, default: 5
         The size of the bin in seconds.
-    percentiles : tuple, default: (0.05, 0.95)
+    percentiles : tuple, default: (5, 95)
         The percentiles to compute.
     unit_ids : list or None
         List of unit ids to compute the firing range. If None, all units are used.
@@ -617,13 +622,13 @@ def compute_firing_ranges(waveform_extractor, bin_size_s=5, percentiles=(0.05, 0
     return firing_ranges
 
 
-_default_params["firing_range"] = dict(bin_size_s=5, percentiles=(0.05, 0.95))
+_default_params["firing_range"] = dict(bin_size_s=5, percentiles=(5, 95))
 
 
 def compute_amplitude_cv_metrics(
     waveform_extractor,
     average_num_spikes_per_bin=50,
-    percentiles=(0.05, 0.95),
+    percentiles=(5, 95),
     min_num_bins=10,
     amplitude_extension="spike_amplitudes",
     unit_ids=None,
@@ -726,7 +731,7 @@ def compute_amplitude_cv_metrics(
 
 
 _default_params["amplitude_cv"] = dict(
-    average_num_spikes_per_bin=50, percentiles=(0.05, 0.95), min_num_bins=10, amplitude_extension="spike_amplitudes"
+    average_num_spikes_per_bin=50, percentiles=(5, 95), min_num_bins=10, amplitude_extension="spike_amplitudes"
 )