Add waveform_ptp_median metric

src/spikeinterface/qualitymetrics/misc_metrics.py

@@ -1107,6 +1107,48 @@ def compute_amplitude_medians(sorting_analyzer, peak_sign="neg", unit_ids=None):
 _default_params["amplitude_median"] = dict(peak_sign="neg")
 
 
+def compute_waveform_ptp_medians(sorting_analyzer, unit_ids=None):
+    """
+    Compute median of the peak-to-peak (PTP) values of the waveforms.
+
+    Parameters
+    ----------
+    sorting_analyzer : SortingAnalyzer
+        A SortingAnalyzer object.
+    unit_ids : list or None
+        List of unit ids to compute the waveform PTP medians. If None, all units are used.
+
+    Returns
+    -------
+    all_waveform_ptp_medians : dict
+        Estimated waveform PTP median for each unit ID.
+
+    References
+    ----------
+    Inspired by bombcell folks
+    """
+    if unit_ids is None:
+        unit_ids = sorting_analyzer.unit_ids
+
+    _has_required_extensions(sorting_analyzer, metric_name="waveform_ptp_median")
+
+    wfs_ext = sorting_analyzer.get_extension("waveforms")
+    extremum_channel_indices = get_template_extremum_channel(sorting_analyzer, outputs="index", mode="peak_to_peak")
+    all_waveform_ptp_medians = {}
+
+    for unit_id in unit_ids:
+        waveforms = wfs_ext.get_waveforms_one_unit(unit_id, force_dense=True)
+        waveform_max_channel = waveforms[:, :, extremum_channel_indices[unit_id]]
+        ptps = np.ptp(waveform_max_channel, axis=1)
+        median_ptp = np.median(ptps)
+        all_waveform_ptp_medians[unit_id] = median_ptp
+
+    return all_waveform_ptp_medians
+
+
+_default_params["waveform_ptp_median"] = dict()
+
+
 def compute_drift_metrics(
     sorting_analyzer,
     interval_s=60,

src/spikeinterface/qualitymetrics/quality_metric_list.py

@@ -11,6 +11,7 @@
     "drift": ["spike_locations"],
     "sd_ratio": ["templates", "spike_amplitudes"],
     "noise_cutoff": ["spike_amplitudes"],
+    "waveform_ptp_median": ["templates", "waveforms"],
 }
 
 
@@ -30,6 +31,7 @@
     compute_amplitude_cv_metrics,
     compute_sd_ratio,
     compute_noise_cutoffs,
+    compute_waveform_ptp_medians,
 )
 
 from .pca_metrics import (
@@ -64,6 +66,7 @@
     "drift": compute_drift_metrics,
     "sd_ratio": compute_sd_ratio,
     "noise_cutoff": compute_noise_cutoffs,
+    "waveform_ptp_median": compute_waveform_ptp_medians,
 }
 
 
@@ -96,6 +99,7 @@
     "silhouette": ["silhouette"],
     "silhouette_full": ["silhouette_full"],
     "noise_cutoff": ["noise_cutoff", "noise_ratio"],
+    "waveform_ptp_median": ["waveform_ptp_median"],
 }
 
 # this dict allows us to ensure the appropriate dtype of metrics rather than allow Pandas to infer them
@@ -133,4 +137,5 @@
     "silhouette_full": float,
     "noise_cutoff": float,
     "noise_ratio": float,
+    "waveform_ptp_median": float,
 }