#!/usr/bin/env python
# coding: utf-8

# # Analyze Electrooculography EOG data (eye blinks, saccades, etc.)

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# This is temporary to load the dev version of NK, needs to be removed once it's in master
import os
os.chdir('D:/Dropbox/RECHERCHE/N/NeuroKit/')
# -----------------------
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import neurokit2 as nk

plt.rcParams['figure.figsize'] = [14, 8]  # Increase plot size


# ## Explore the EOG signal

# Let's load the example dataset corresponding to a vertical EOG signal.

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eog_signal = pd.read_csv("data/eog_100hz.csv")
eog_signal = nk.as_vector(eog_signal)  # Extract the only column as a vector
nk.signal_plot(eog_signal)


# Let's zoom in to some areas where clear blinks are present.

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nk.signal_plot(eog_signal[100:1700])


# ## Clean the signal

# We can now filter the signal to remove some noise and trends.

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eog_cleaned = nk.eog_clean(eog_signal, sampling_rate=100, method='neurokit')


# Let's visualize the same chunk and compare the clean version with the original signal.

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nk.signal_plot([eog_signal[100:1700], eog_cleaned[100:1700]])


# ## Detect and visualize eye blinks

# We will nor run a peak detection algorithm to detect peaks location.

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blinks = nk.eog_findpeaks(eog_cleaned, sampling_rate=100, method="mne")
blinks


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events = nk.epochs_create(eog_cleaned, blinks, sampling_rate=100, epochs_start=-0.3, epochs_end=0.7)


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data = nk.epochs_to_array(events)  # Convert to 2D array
data = nk.standardize(data)  # Rescale so that all the blinks are on the same scale

# Plot with their median (used here as a robust average)  
plt.plot(data, linewidth=0.4)
plt.plot(np.median(data, axis=1), linewidth=3, linestyle='--', color="black")


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