doc: fix warnings

docs/api/_toctree/VisualizationAPI/miv.visualization.causality.spike_triggered_average_plot.rst

@@ -0,0 +1,6 @@
+miv.visualization.causality.spike\_triggered\_average\_plot
+===========================================================
+
+.. currentmodule:: miv.visualization.causality
+
+.. autofunction:: spike_triggered_average_plot

docs/api/visualization.rst

@@ -45,6 +45,7 @@ Causality Analysis
       :toctree: _toctree/VisualizationAPI
 
       pairwise_causality_plot
+      spike_triggered_average_plot
 
 Burst Analysis
 ------------------
@@ -59,20 +60,6 @@ Burst Analysis
 
       plot_burst
 
-Spike Triggered Average
------------------------
-
-.. currentmodule:: miv.visualization
-
-.. automodule:: miv.visualization.spike_triggered_avg
-
-   .. autosummary::
-      :nosignatures:
-      :toctree: _toctree/VisualizationAPI
-
-      spike_triggered_avg
-
-
 
 Useful External Packages
 ========================

docs/index.rst

@@ -59,7 +59,6 @@ If you are interested in contributing to this project, we prepared contribution
    :caption: Examples
 
    examples/sample_datasets
-.. examples/spike_detection
 
 .. toctree::
    :maxdepth: 2

miv/visualization/__init__.py

@@ -1,5 +1,4 @@
 from miv.visualization.causality import *
 from miv.visualization.event import *
 from miv.visualization.fft_domain import *
-from miv.visualization.spike_triggered_avg import *
 from miv.visualization.waveform import *

miv/visualization/causality.py

@@ -1,4 +1,4 @@
-# Pairwise Granger Causality Plot
+__all__ = ["pairwise_causality_plot", "spike_triggered_average_plot"]
 
 import os
 
@@ -8,7 +8,8 @@
 from viziphant.spike_train_correlation import plot_corrcoef
 
 from miv.statistics import pairwise_causality
-from miv.typing import SignalType
+from miv.statistics.spiketrain_statistics import binned_spiketrain
+from miv.typing import SignalType, SpikestampsType
 
 
 def pairwise_causality_plot(signal: SignalType, start: float, end: float):
@@ -20,9 +21,9 @@ def pairwise_causality_plot(signal: SignalType, start: float, end: float):
     signal : SignalType
         Input signal
     start : float
-        starting point from signal
+        Starting point of the signal
     end : float
-        End point from signal
+        End point of the signal
 
     Returns
     -------
@@ -31,7 +32,8 @@ def pairwise_causality_plot(signal: SignalType, start: float, end: float):
         instantaneous causality between X,Y, and total causality. X and Y
         represents electrodes
     axes : matplotlib.axes
-        Figure axes
+        axes parameters for plot modification
+
     """
 
     # Causality
@@ -60,3 +62,68 @@ def pairwise_causality_plot(signal: SignalType, start: float, end: float):
     axes[1, 1].set_title("Total interdependence of X,Y")
 
     return fig, axes
+
+
+def spike_triggered_average_plot(
+    signal: SignalType,
+    channel_x: int,
+    spiketrains: SpikestampsType,
+    channel_y: int,
+    sampling_freq: float,
+    window_length: float,
+):
+    """
+    Plots the spike-triggered average of Local Field Potential (LFP) from channel X
+    corresponding to spiketrain from channel Y. The spiketrain from channel Y
+    can be replaced with stimulation signal to understand stimulus dependent
+    LFP on channel X, but take care in providing stimualtion as SpikestampsType.
+
+    Parameters
+    ----------
+    signal : SignalType
+        LFP signal recorded from the electrodes
+    channel_x : float
+        Channel to consider for LFP data
+    spiketrains : SpikestampsType
+        Single spike-stamps
+    channel_y : float
+        Channel to consider for spiketrain data
+    sampling_freq : float
+        sampling frequency for LFP recordings
+    window_length : float
+        window length to consider before and after spike
+
+    Returns
+    -------
+    figure : matplotlib.pyplot.Figure
+        matplot figure plotting spike triggered average of channel X in the provided window
+    axes : matplotlib.axes.Axes
+        axes parameters for plot modification
+
+    """
+
+    # Spike Triggered Average
+    dt = 1 / sampling_freq
+    n = np.shape(signal[:, channel_x])[0] / sampling_freq
+    assert (
+        window_length < np.shape(signal[:, channel_x])[0] / 2
+    ), "Window cannot be longer than signal length"
+    spike = binned_spiketrain(spiketrains, channel_x, 0, n, dt)
+    lfp = signal[:, channel_x]
+    spike_times = np.where(spike == 1)[0]
+    spike_len = np.shape(spike_times)[0]
+    sta = np.zeros([2 * window_length + 1])
+
+    for i in np.arange(spike_len):
+        sta += lfp[spike_times[i] - window_length : spike_times[i] + window_length + 1]
+
+    spike_triggered_average = sta / spike_len
+    lags = np.arange(-window_length, window_length + 1) * dt * 1000
+
+    # Plotting
+    fig, ax = plt.subplots()
+    plt.plot(lags, spike_triggered_average)
+    ax.set_xlabel("Time (ms)")
+    ax.set_ylabel("Voltage (\u03bcV)")
+
+    return fig, ax

tests/visualization/test_spike_triggered_average.py

@@ -2,11 +2,10 @@
 import pytest
 from neo.core import AnalogSignal, Segment, SpikeTrain
 
-from miv.visualization import spike_triggered_avg
-
 
 def test_spike_triggered_avg_output():
     from miv.statistics import burst
+    from miv.visualization import spike_triggered_average_plot
 
     # Initialize the spiketrain as below
     seg = Segment(index=1)
@@ -20,5 +19,5 @@ def test_spike_triggered_avg_output():
     seg.spiketrains.append(train0)
 
     with np.testing.assert_raises(AssertionError):
-        spike_triggered_avg(signal, 0, seg.spiketrains, 0, 2000, 10000000)
+        spike_triggered_average_plot(signal, 0, seg.spiketrains, 0, 2000, 10000000)
     # The function above should throw an error since the window is greater than signal length