@@ -1021,8 +1021,12 @@ def compute_amplitude_cutoffs(
10211021 amplitudes_bins_min_ratio ,
10221022 )
10231023
1024- if np .any (np .isnan (list (all_fraction_missing .values ()))):
1025- warnings .warn (f"Some units have too few spikes : amplitude_cutoff is set to NaN" )
1024+ units_with_few_spikes = [unit_id for unit_id , amp_cutoff in all_fraction_missing .items () if np .isnan (amp_cutoff )]
1025+ if len (units_with_few_spikes ) > 0 :
1026+ min_num_spikes = amplitudes_bins_min_ratio * num_histogram_bins
1027+ warnings .warn (
1028+ f"Amplitude cutoff set to NaN for units { units_with_few_spikes } : too few spikes (< { min_num_spikes } )."
1029+ )
10261030
10271031 return all_fraction_missing
10281032
@@ -1031,7 +1035,7 @@ class AmplitudeCutoff(BaseMetric):
10311035 metric_name = "amplitude_cutoff"
10321036 metric_function = compute_amplitude_cutoffs
10331037 metric_params = {
1034- "num_histogram_bins" : 200 ,
1038+ "num_histogram_bins" : 100 ,
10351039 "histogram_smoothing_value" : 3 ,
10361040 "amplitudes_bins_min_ratio" : 5 ,
10371041 }
@@ -1634,7 +1638,7 @@ def isi_violations(spike_trains, total_duration_s, isi_threshold_s=0.0015, min_i
16341638
16351639def amplitude_cutoff (
16361640 amplitudes ,
1637- num_histogram_bins = 500 ,
1641+ num_histogram_bins = 100 ,
16381642 histogram_smoothing_value = 3 ,
16391643 amplitudes_bins_min_ratio = 5 ,
16401644):
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