docs: 📝 name change & add docstring

Author: JaerongA

element_array_ephys/ephys_report.py

@@ -102,7 +102,7 @@ def make(self, key):
 
         from .plotting.unit_level import (
             plot_waveform,
-            plot_correlogram,
+            plot_auto_correlogram,
             plot_depth_waveforms,
         )
 
@@ -119,7 +119,7 @@ def make(self, key):
             waveform=peak_electrode_waveform, sampling_rate=sampling_rate
         )
 
-        correlogram_fig = plot_correlogram(
+        correlogram_fig = plot_auto_correlogram(
             spike_times=spike_times, bin_size=0.001, window_size=1
         )
 

element_array_ephys/plotting/acorr.py element_array_ephys/plotting/corr.pyelement_array_ephys/plotting/acorr.py renamed to element_array_ephys/plotting/corr.py

@@ -1,25 +1,12 @@
-"""autocorrelation code adapted from ibllib
-https://github.com/int-brain-lab/ibllib
+"""Code adapted from International Brain Laboratory, T. (2021). ibllib [Computer software]. https://github.com/int-brain-lab/ibllib
 """
 
 import numpy as np
 
 
-def _index_of(arr, lookup):
-    """Replace scalars in an array by their indices in a lookup table.
+def _index_of(arr: np.ndarray, lookup: np.ndarray):
+    """Replace scalars in an array by their indices in a lookup table."""
 
-    Implicitly assume that:
-
-    * All elements of arr and lookup are non-negative integers.
-    * All elements or arr belong to lookup.
-
-    This is not checked for performance reasons.
-
-    """
-    # Equivalent of np.digitize(arr, lookup) - 1, but much faster.
-    # TODO: assertions to disable in production for performance reasons.
-    # TODO: np.searchsorted(lookup, arr) is faster on small arrays with large
-    # values
     lookup = np.asarray(lookup, dtype=np.int32)
     m = (lookup.max() if len(lookup) else 0) + 1
     tmp = np.zeros(m + 1, dtype=int)
@@ -32,7 +19,9 @@ def _index_of(arr, lookup):
 
 def _increment(arr, indices):
     """Increment some indices in a 1D vector of non-negative integers.
-    Repeated indices are taken into account."""
+    Repeated indices are taken into account.
+    """
+
     bbins = np.bincount(indices)
     arr[: len(bbins)] += bbins
     return arr
@@ -63,24 +52,22 @@ def _symmetrize_correlograms(correlograms):
     return np.dstack((sym, correlograms))
 
 
-def xcorr(spike_times, spike_clusters, bin_size=None, window_size=None):
+def xcorr(
+    spike_times: np.ndarray,
+    spike_clusters: np.ndarray,
+    bin_size: float,
+    window_size: int,
+) -> np.ndarray:
     """Compute all pairwise cross-correlograms among the clusters appearing in `spike_clusters`.
 
-    Parameters
-    ----------
-
-    :param spike_times: Spike times in seconds.
-    :type spike_times: array-like
-    :param spike_clusters: Spike-cluster mapping.
-    :type spike_clusters: array-like
-    :param bin_size: Size of the bin, in seconds.
-    :type bin_size: float
-    :param window_size: Size of the window, in seconds.
-    :type window_size: float
-
-    Returns an `(n_clusters, n_clusters, winsize_samples)` array with all pairwise
-    cross-correlograms.
+    Args:
+        spike_times (np.ndarray): Spike times in seconds.
+        spike_clusters (np.ndarray): Spike-cluster mapping.
+        bin_size (float): Size of the time bin in seconds.
+        window_size (int): Size of the correlogram window in seconds.
 
+    Returns:
+         np.ndarray: cross-correlogram array
     """
     assert np.all(np.diff(spike_times) >= 0), "The spike times must be increasing."
     assert spike_times.ndim == 1
@@ -140,21 +127,16 @@ def xcorr(spike_times, spike_clusters, bin_size=None, window_size=None):
     return _symmetrize_correlograms(correlograms)
 
 
-def acorr(spike_times, bin_size=None, window_size=None):
-    """Compute the auto-correlogram of a neuron.
-
-    Parameters
-    ----------
-
-    :param spike_times: Spike times in seconds.
-    :type spike_times: array-like
-    :param bin_size: Size of the bin, in seconds.
-    :type bin_size: float
-    :param window_size: Size of the window, in seconds.
-    :type window_size: float
+def acorr(spike_times: np.ndarray, bin_size: float, window_size: int) -> np.ndarray:
+    """Compute the auto-correlogram of a unit.
 
-    Returns an `(winsize_samples,)` array with the auto-correlogram.
+    Args:
+        spike_times (np.ndarray): Spike times in seconds.
+        bin_size (float, optional): Size of the time bin in seconds.
+        window_size (int, optional): Size of the correlogram window in seconds.
 
+    Returns:
+        np.ndarray: auto-correlogram array (winsize_samples,)
     """
     xc = xcorr(
         spike_times,

element_array_ephys/plotting/unit_level.py

@@ -3,12 +3,20 @@
 import numpy as np
 import pandas as pd
 import plotly.graph_objs as go
-
+from typing import Any
 from .. import probe
 
 
 def plot_waveform(waveform: np.ndarray, sampling_rate: float) -> go.Figure:
+    """Plot unit waveform.
+
+    Args:
+        waveform (np.ndarray): Amplitude of a spike waveform in μV.
+        sampling_rate (float): Sampling rate in kHz.
 
+    Returns:
+        go.Figure: Plotly figure object.
+    """
     waveform_df = pd.DataFrame(data={"waveform": waveform})
     waveform_df["timestamp"] = waveform_df.index / sampling_rate
 
@@ -33,11 +41,20 @@ def plot_waveform(waveform: np.ndarray, sampling_rate: float) -> go.Figure:
     return fig
 
 
-def plot_correlogram(
+def plot_auto_correlogram(
     spike_times: np.ndarray, bin_size: float = 0.001, window_size: int = 1
 ) -> go.Figure:
+    """Plot the auto-correlogram of a unit.
+
+    Args:
+        spike_times (np.ndarray): Spike timestamps in seconds
+        bin_size (float, optional): Size of the time bin (lag) in seconds. Defaults to 0.001.
+        window_size (int, optional): Size of the correlogram window in seconds. Defaults to 1.
 
-    from .acorr import acorr
+    Returns:
+        go.Figure: Plotly figure object.
+    """
+    from .corr import acorr
 
     correlogram = acorr(
         spike_times=spike_times, bin_size=bin_size, window_size=window_size
@@ -76,9 +93,19 @@ def plot_correlogram(
 
 def plot_depth_waveforms(
     ephys: Module,
-    unit_key: dict,
+    unit_key: dict[str, Any],
     y_range: float = 60,
 ) -> go.Figure:
+    """Plot waveforms
+
+    Args:
+        ephys (Module): Imported ephys module.
+        unit_key (dict[str, Any]): Key dictionary from ephys.CuratedClustering.Unit table.
+        y_range (float, optional): Vertical range to show waveforms relative to the peak waveform in μm. Defaults to 60.
+
+    Returns:
+        go.Figure: Plotly figure object.
+    """
 
     sampling_rate = (ephys.EphysRecording & unit_key).fetch1(
         "sampling_rate"