update docstring

Author: JaerongA

element_array_ephys/plotting/probe_level.py

@@ -12,7 +12,7 @@ def plot_raster(units: np.ndarray, spike_times: np.ndarray) -> matplotlib.figure
         spike_times (np.ndarray): Spike timestamps in seconds.
 
     Returns:
-        matplotlib.figure.Figure: matplotlib figure object.
+        matplotlib.figure.Figure: matplotlib figure object showing spikes rasters over time (x-axis in seconds). Each row (y-axis) indicates a single unit.
     """
     units = np.arange(1, len(units) + 1)
     x = np.hstack(spike_times)
@@ -42,7 +42,7 @@ def plot_driftmap(
         colormap (str, optional): Colormap. Defaults to "gist_heat_r".
 
     Returns:
-        matplotlib.figure.Figure: matplotlib figure object.
+        matplotlib.figure.Figure: matplotlib figure object for showing population activity for all units over time (x-axis in seconds) according to the spatial depths of the spikes (y-axis in μm).
     """
 
     spike_times = np.hstack(spike_times)

element_array_ephys/plotting/unit_level.py

@@ -16,7 +16,7 @@ def plot_waveform(waveform: np.ndarray, sampling_rate: float) -> go.Figure:
         sampling_rate (float): Sampling rate in kHz.
 
     Returns:
-        go.Figure: Plotly figure object.
+        go.Figure: Plotly figure object for showing the amplitude of a waveform (y-axis in μV) over time (x-axis).
     """
     waveform_df = pd.DataFrame(data={"waveform": waveform})
     waveform_df["timestamp"] = waveform_df.index / sampling_rate
@@ -50,10 +50,11 @@ def plot_auto_correlogram(
     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.
+        window_size (int, optional): Size of the correlogram window in seconds. Defaults to 1 (± 500ms)
 
     Returns:
-        go.Figure: Plotly figure object.
+        go.Figure: Plotly figure object for showing
+        counts (y-axis) over time lags (x-axis).
     """
     from .corr import acorr