Blink_Final

import os
import numpy as np
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.model_selection import cross_val_score
 
from sklearn.model_selection import train_test_split
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.metrics import accuracy_score, confusion_matrix
import seaborn as sns
import matplotlib.pyplot as plt
 
data_dictionary_1blink={}
# Path to the folder containing the files
folder_path = r'C:\Users\skelil\Documents\OpenBCI_GUI\Recordings\OpenBCISession_1_Blink'
 
counter=1
# Iterate over all files in the folder
for filename in os.listdir(folder_path):
    if filename.endswith(".txt"):  # Check if the file has a .txt extension
        # Construct the full file path
        file_path = os.path.join(folder_path, filename)
 
        # Your existing code
        with open(file_path, 'r') as file:
            lines = file.readlines()
 
        if lines:
            lines = lines[770:2045]
 
        my_2d_array = np.genfromtxt(lines, delimiter=',', usemask=False)
 
        columns_to_remove = [0, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24]
        result_array = np.delete(my_2d_array, columns_to_remove, axis=1)
        # Calculate the mean for each row
        row_means_b1 = np.mean(result_array, axis=1)
        data_dictionary_1blink[counter]=row_means_b1
        counter=counter+1

 
data_dictionary_3blinks={}
# Path to the folder containing the files
folder_path = r'C:\Users\skelil\Documents\OpenBCI_GUI\Recordings\OpenBCISession_3_Blinks'
 
counter=1
# Iterate over all files in the folder
for filename in os.listdir(folder_path):
    if filename.endswith(".txt"):  # Check if the file has a .txt extension
        # Construct the full file path
        file_path = os.path.join(folder_path, filename)
 
        # Your existing code
        with open(file_path, 'r') as file:
            lines = file.readlines()
 
        if lines:
            lines = lines[770:2045]
 
        my_2d_array = np.genfromtxt(lines, delimiter=',', usemask=False)
 
        columns_to_remove = [0, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24]
        result_array = np.delete(my_2d_array, columns_to_remove, axis=1)
        # Calculate the mean for each row
        row_means_b3 = np.mean(result_array, axis=1)
        data_dictionary_3blinks[counter]=row_means_b3
        counter=counter+1
 
feature_vector=[]
for key1,data1 in data_dictionary_1blink.items():
    feature_vector.append(data1)
for key2,data2 in data_dictionary_3blinks.items():
    feature_vector.append(data2)    
label_y=[]
for i in range (0,30):
    label_y.append(0)
for i in range (30,60):
    label_y.append(1)
 
# Create the LinearDiscriminantAnalysis classifier
clf = LinearDiscriminantAnalysis()
 
# Train the classifier on the training set
clf.fit(feature_vector, label_y)

############## TESTING
file_path_testing = r'C:\Users\skelil\Documents\OpenBCI_GUI\Recordings\OpenBCISession_2023-12-26_14-45-14 - 1 Blink - S\OpenBCI-RAW-2023-12-26_14-49-03.txt'

with open(file_path_testing, 'r') as file:
    lines = file.readlines()
 
if lines:
    lines = lines[770:2045]
 
my_2d_array_testing = np.genfromtxt(lines, delimiter=',', usemask=False)
 
columns_to_remove = [0, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24]
result_array = np.delete(my_2d_array_testing, columns_to_remove, axis=1)
# Calculate the mean for each row
row_means_testing = np.mean(result_array, axis=1)

y_pred = clf.predict(row_means_testing.reshape(1, -1) )

if y_pred==0:
    num_blinks=1
else:
    num_blinks=3
print("Number of blinks is:",num_blinks)