docstring

src/ibldsp/waveform_extraction.py

@@ -477,6 +477,34 @@ def extract_wfs_cbin(
 
 class WaveformsLoader:
 
+    """
+    Interface to the output of `extract_wfs_cbin`. Requires the following four files to
+    exist in `data_dir`:
+
+    - waveforms.traces.npy: `(num_units, max_wf, nc, spike_length_samples)`
+        This file contains the lightly processed waveforms indexed by cluster in the first
+        dimension. By default `max_wf=256, nc=40, spike_length_samples=128`.
+
+    - waveforms.templates.npy: `(num_units, nc, spike_length_samples)`
+        This file contains the median across individual waveforms for each unit.
+
+    - waveforms.channels.npz: `(num_units * max_wf, nc)`
+        The i'th row contains the ordered indices of the `nc`-channel neighborhood used
+        to extract the i'th waveform. A NaN means the waveform is missing because the
+        unit it was supposed to come from has less than `max_wf` spikes total in the
+        recording.
+
+    - waveforms.table.pqt: `num_units * max_wf` rows
+        For each waveform, gives the absolute sample number from the recording (i.e.
+        where to find it in `spikes.samples`), peak channel, cluster, and linear index.
+        A row of -1s implies that the waveform is missing because the unit is was supposed
+        to come from has less than `max_wf` spikes total.
+
+    WaveformsLoader.load_waveforms() and random_waveforms() allow selection of a subset of
+        waveforms.
+
+    """
+
     def __init__(
         self,
         data_dir,
@@ -528,8 +556,28 @@ def __repr__(self):
 
         return s1 + s2 + s3
 
-    def load_waveforms(self, labels=None, indices=None, return_info=True, flatten=False):
+    @property
+    def wf_counts(self):
+        """
+        pandas Series containing number of (non-NaN) waveforms for each label.
+        """
+        return self.table.groupby("cluster").count()["sample"].rename("num_wfs")
 
+    def load_waveforms(self, labels=None, indices=None, return_info=True, flatten=False):
+        """
+        Returns a specified subset of waveforms from the dataset.
+
+        :param labels: (list, NumPy array) Label ids (usually clusters) from which to get waveforms.
+        :param indices: (list, NumPy array) Waveform indices to grab for each waveform.
+            Can be 1D in which case the same indices are returned for each waveform, or
+            2D with first dimension == len(labels) to return a specific set of indices for
+            each waveform.
+        :param return_info: If True, returns waveforms, table, channels, where table is a DF containing
+            information about the waveforms returned, and channels is the channel map for each waveform.
+        :param flatten: If True, returns all waveforms stacked along dimension zero, otherwise returns
+            array of shape (num_labels, num_indices_per_label, num_channels, spike_length_samples)
+
+        """
         if labels is None:
             labels = self.labels
         if indices is None:
@@ -577,7 +625,20 @@ def random_waveforms(
         seed=None,
         flatten=False
     ):
-
+        """
+        Returns a random subset of waveforms from the dataset.
+
+        :param labels: (list, NumPy array) Label ids (usually clusters) from which to get waveforms.
+            If None, 10 random labels are chosen.
+        :param num_random_labels: If set, this number of random labels are chosen
+        :param num_random_waveforms: If set, this number of random waveforms are chosen for each label.
+            If None, 10 random waveforms are chosen for each label.
+        :param return_info: If True, returns waveforms, table, channels, where table is a DF containing
+            information about the waveforms returned, and channels is the channel map for each waveform.
+        :param flatten: If True, returns all waveforms stacked along dimension zero, otherwise returns
+            array of shape (num_labels, num_indices_per_label, num_channels, spike_length_samples)
+
+        """
         rg = np.random.default_rng(seed=seed)
 
         if labels is None: