## Written and copyright by <NAME>
## Georgia Institute of Technology
## Email: <EMAIL>

import os
import numpy as np
from scipy.signal import *
import csv
import matplotlib.pyplot as plt

#Options to read: 'EEG-IO', 'EEG-VV', 'EEG-VR', 'EEG-MB'
data_folder = 'EEG-VR' 

# Parameters and bandpass filtering
fs = 250.0
def lowpass(sig, fc, fs, butter_filt_order):
    B,A = butter(butter_filt_order, np.array(fc)/(fs/2), btype='low')
    return lfilter(B, A, sig, axis=0)

# function to read stimulations
def decode_stim(data_path, file_stim):
    interval_corrupt = []
    blinks = []
    n_corrupt = 0
    with open(os.path.join(data_path,file_stim)) as csvfile:
        readCSV = csv.reader(csvfile, delimiter=',')
        for row in readCSV:
            if row[0]=="corrupt":
                n_corrupt = int(row[1])
            elif n_corrupt > 0:
                if float(row[1]) == -1:
                    t_end = data_sig[-1,0]
                else:
                    t_end = float(row[1])
                interval_corrupt.append([float(row[0]), t_end])
                n_corrupt = n_corrupt - 1
            elif row[0]=="blinks":
                #check that n_corrupt is 0
                if not n_corrupt==0:
                    print("!Error in parsing")
            else:
                blinks.append([float(row[0]), int(row[1])])
    blinks = np.array(blinks)

    return interval_corrupt, blinks 

# Reading data files
file_idx = 0
list_of_files = [f for f in os.listdir(data_folder) if os.path.isfile(os.path.join(data_folder, f)) and '_data' in f]
file_sig = list_of_files[file_idx]
file_stim = list_of_files[file_idx].replace('_data','_labels')
print("Reading: ", file_sig, file_stim)

# Loading data
if data_folder is 'EEG-IO' or data_folder is 'EEG-MB':
	data_sig = np.loadtxt(open(os.path.join(data_folder,file_sig), "rb"), delimiter=";", skiprows=1, usecols=(0,1,2))
elif data_folder is 'EEG-VR' or data_folder is 'EEG-VV':
	data_sig = np.loadtxt(open(os.path.join(data_folder,file_sig), "rb"), delimiter=",", skiprows=5, usecols=(0,1,2))
	data_sig = data_sig[0:(int(200*fs)+1),:]
	data_sig = data_sig[:,0:3]
	data_sig[:,0] = np.array(range(0,len(data_sig)))/fs

# Loading Stimulations
interval_corrupt, groundtruth_blinks = decode_stim(data_folder, file_stim)

# FIltering
data_sig[:,1] = lowpass(data_sig[:,1], 10, fs, 4)
data_sig[:,2] = lowpass(data_sig[:,2], 10, fs, 4)

# Visualizing data and ground truth
chan_id = 1
fig, ax1 = plt.subplots()
ax1.plot(data_sig[:,0], data_sig[:,chan_id])
for i in interval_corrupt:
    ax1.axvspan(i[0], i[1], alpha=0.5, color='red')
for d in groundtruth_blinks:
    if d[1] < 2:
        plt.axvline(x=d[0], color='green')
    elif d[1] == 2:
        plt.axvline(x=d[0], color='black')
plt.show()



## Written and copyright by <NAME>
## Georgia Institute of Technology
## Email: <EMAIL>

import os
import numpy as np
from scipy.signal import *
import csv
import matplotlib.pyplot as plt

#Options to read: 'EEG-IO', 'EEG-VV', 'EEG-VR', 'EEG-MB'
data_folder = 'EEG-VR' 

# Parameters and bandpass filtering
fs = 250.0
def lowpass(sig, fc, fs, butter_filt_order):
    B,A = butter(butter_filt_order, np.array(fc)/(fs/2), btype='low')
    return lfilter(B, A, sig, axis=0)

# function to read stimulations
def decode_stim(data_path, file_stim):
    interval_corrupt = []
    blinks = []
    n_corrupt = 0
    with open(os.path.join(data_path,file_stim)) as csvfile:
        readCSV = csv.reader(csvfile, delimiter=',')
        for row in readCSV:
            if row[0]=="corrupt":
                n_corrupt = int(row[1])
            elif n_corrupt > 0:
                if float(row[1]) == -1:
                    t_end = data_sig[-1,0]
                else:
                    t_end = float(row[1])
                interval_corrupt.append([float(row[0]), t_end])
                n_corrupt = n_corrupt - 1
            elif row[0]=="blinks":
                #check that n_corrupt is 0
                if not n_corrupt==0:
                    print("!Error in parsing")
            else:
                blinks.append([float(row[0]), int(row[1])])
    blinks = np.array(blinks)

    return interval_corrupt, blinks 

# Reading data files
file_idx = 0
list_of_files = [f for f in os.listdir(data_folder) if os.path.isfile(os.path.join(data_folder, f)) and '_data' in f]
file_sig = list_of_files[file_idx]
file_stim = list_of_files[file_idx].replace('_data','_labels')
print("Reading: ", file_sig, file_stim)

# Loading data
if data_folder is 'EEG-IO' or data_folder is 'EEG-MB':
	data_sig = np.loadtxt(open(os.path.join(data_folder,file_sig), "rb"), delimiter=";", skiprows=1, usecols=(0,1,2))
elif data_folder is 'EEG-VR' or data_folder is 'EEG-VV':
	data_sig = np.loadtxt(open(os.path.join(data_folder,file_sig), "rb"), delimiter=",", skiprows=5, usecols=(0,1,2))
	data_sig = data_sig[0:(int(200*fs)+1),:]
	data_sig = data_sig[:,0:3]
	data_sig[:,0] = np.array(range(0,len(data_sig)))/fs

# Loading Stimulations
interval_corrupt, groundtruth_blinks = decode_stim(data_folder, file_stim)

# FIltering
data_sig[:,1] = lowpass(data_sig[:,1], 10, fs, 4)
data_sig[:,2] = lowpass(data_sig[:,2], 10, fs, 4)

# Visualizing data and ground truth
chan_id = 1
fig, ax1 = plt.subplots()
ax1.plot(data_sig[:,0], data_sig[:,chan_id])
for i in interval_corrupt:
    ax1.axvspan(i[0], i[1], alpha=0.5, color='red')
for d in groundtruth_blinks:
    if d[1] < 2:
        plt.axvline(x=d[0], color='green')
    elif d[1] == 2:
        plt.axvline(x=d[0], color='black')
plt.show()



## Written and copyright by <NAME> ## Georgia Institute of Technology ## Email: <EMAIL> #Options to read: 'EEG-IO', 'EEG-VV', 'EEG-VR', 'EEG-MB' # Parameters and bandpass filtering # function to read stimulations #check that n_corrupt is 0 # Reading data files # Loading data # Loading Stimulations # FIltering # Visualizing data and ground truth