#!/usr/bin/env python

# -*- coding: utf-8 -*-

'''

Please see this before you run this file!!!!!!

NOTICE:

    Be advised that this “Extract-Raw-Data-Into-Matlab-Files.py” Python File

    should only be executed under the Python 2 Environment.

    I highly recommend to execute the file under the Python 2.7 Environment

    because I have passed the test.

    However, if you are using Python 3 Environment to run this file,

    I'm afraid there will be an error and the generated labels will be wrong.

    If you have any question, please be sure to let me know.

    My email is shuyuej@ieee.org.

    Thanks a lot.

'''

import numpy as np

import io

import os

import pyedflib

import scipy.io as sio

from scipy.signal import butter, filtfilt, iirnotch, lfilter

MOVEMENT_START = 1 * 160  # MI starts 1s after trial begin

MOVEMENT_END   = 5 * 160  # MI lasts 4 seconds

NOISE_LEVEL    = 0.01

PHYSIONET_ELECTRODES = {

    1:  "FC5",  2: "FC3",  3: "FC1",  4: "FCz",  5: "FC2",  6: "FC4",

    7:  "FC6",  8: "C5",   9: "C3",  10: "C1",  11: "Cz",  12: "C2",

    13: "C4",  14: "C6",  15: "CP5", 16: "CP3", 17: "CP1", 18: "CPz",

    19: "CP2", 20: "CP4", 21: "CP6", 22: "Fp1", 23: "Fpz", 24: "Fp2",

    25: "AF7", 26: "AF3", 27: "AFz", 28: "AF4", 29: "AF8", 30: "F7",

    31: "F5",  32: "F3",  33: "F1",  34: "Fz",  35: "F2",  36: "F4",

    37: "F6",  38: "F8",  39: "FT7", 40: "FT8", 41: "T7",  42: "T8",

    43: "T9",  44: "T10", 45: "TP7", 46: "TP8", 47: "P7",  48: "P5",

    49: "P3",  50: "P1",  51: "Pz",  52: "P2",  53: "P4",  54: "P6",

    55: "P8",  56: "PO7", 57: "PO3", 58: "POz", 59: "PO4", 60: "PO8",

    61: "O1",  62: "Oz",  63: "O2",  64: "Iz"}

def load_edf_signals(path):

    try:

        sig = pyedflib.EdfReader(path)

        n = sig.signals_in_file

        signal_labels = sig.getSignalLabels()

        sigbuf = np.zeros((n, sig.getNSamples()[0]))

        for j in np.arange(n):

            sigbuf[j, :] = sig.readSignal(j)

        # (n,3) annotations: [t in s, duration, type T0/T1/T2]

        annotations = sig.read_annotation()

    except KeyboardInterrupt:

        # prevent memory leak and access problems of unclosed buffers

        sig._close()

        raise

    sig._close()

    del sig

    return sigbuf.transpose(), annotations

def get_physionet_electrode_positions():

    refpos = get_electrode_positions()

    return np.array([refpos[PHYSIONET_ELECTRODES[idx]] for idx in range(1, 65)])

def projection_2d(loc):

    """

    Azimuthal equidistant projection (AEP) of 3D carthesian coordinates.

    Preserves distance to origin while projecting to 2D carthesian space.

    loc: N x 3 array of 3D points

    returns: N x 2 array of projected 2D points

    """

    x, y, z = loc[:, 0], loc[:, 1], loc[:, 2]

    theta = np.arctan2(y, x)  # theta = azimuth

    rho = np.pi / 2 - np.arctan2(z, np.hypot(x, y))  # rho = pi/2 - elevation

    return np.stack((np.multiply(rho, np.cos(theta)), np.multiply(rho, np.sin(theta))), 1)

def get_electrode_positions():

    """

    Returns a dictionary (Name) -> (x,y,z) of electrode name in the extended

    10-20 system and its carthesian coordinates in unit sphere.

    """

    positions = dict()

    with io.open("electrode_positions.txt", "r") as pos_file:

        for line in pos_file:

            parts = line.split()

            positions[parts[0]] = tuple([float(part) for part in parts[1:]])

    return positions

def load_physionet_data(subject_id, num_classes=4, long_edge=False):

    """

    subject_id: ID (1-109) for the subject to be loaded from file

    num_classes: number of classes (2, 3 or 4) for L/R, L/R/0, L/R/0/F

    long_edge: if False include 1s before and after MI, if True include 3s

    returns (X, y, pos, fs)

        X: Trials with shape (N_subjects, N_trials, N_samples, N_channels)

        y: labels with shape (N_subjects, N_trials, N_classes)

        pos: 2D projected electrode positions

        fs: sample rate

    """

    SAMPLE_RATE  = 160

    EEG_CHANNELS = 64

    BASELINE_RUN = 1

    MI_RUNS = [4, 8, 12]  # l/r fist

    if num_classes >= 4:

        MI_RUNS += [6, 10, 14]  # feet (& fists)

    # total number of samples per long run

    RUN_LENGTH = 125 * SAMPLE_RATE

    # length of single trial in seconds

    TRIAL_LENGTH = 4 if not long_edge else 6

    NUM_TRIALS   = 21 * num_classes

    n_runs = len(MI_RUNS)

    X = np.zeros((n_runs, RUN_LENGTH, EEG_CHANNELS))

    events = []

    base_path = 'S%03dR%02d.edf'

    for i_run, current_run in enumerate(MI_RUNS):

        # load from file

        path = base_path % (subject_id, current_run)

        signals, annotations = load_edf_signals(path)

        X[i_run, :signals.shape[0], :] = signals

        # read annotations

        current_event = [i_run, 0, 0, 0]  # run, class (l/r), start, end

        for annotation in annotations:

            t = int(annotation[0] * SAMPLE_RATE * 1e-7)

            action = int(annotation[2][1])

            if action == 0 and current_event[1] != 0:

                # make 6 second runs by extending snippet

                length = TRIAL_LENGTH * SAMPLE_RATE

                pad = (length - (t - current_event[2])) / 2

                current_event[2] -= pad + (t - current_event[2]) % 2

                current_event[3] = t + pad

                if (current_run - 6) % 4 != 0 or current_event[1] == 2:

                    if (current_run - 6) % 4 == 0:

                        current_event[1] = 3

                    events.append(current_event)

            elif action > 0:

                current_event = [i_run, action, t, 0]

    # split runs into trials

    num_mi_trials = len(events)

    trials = np.zeros((NUM_TRIALS, TRIAL_LENGTH * SAMPLE_RATE, EEG_CHANNELS))

    labels = np.zeros((NUM_TRIALS, num_classes))

    for i, ev in enumerate(events):

        trials[i, :, :] = X[ev[0], ev[2]:ev[3]]

        labels[i, ev[1] - 1] = 1.

    if num_classes < 3:

        return (trials[:num_mi_trials, ...],

                labels[:num_mi_trials, ...],

                projection_2d(get_physionet_electrode_positions()),

                SAMPLE_RATE)

    else:

        # baseline run

        path = base_path % (subject_id, BASELINE_RUN)

        signals, annotations = load_edf_signals(path)

        SAMPLES = TRIAL_LENGTH * SAMPLE_RATE

        for i in range(num_mi_trials, NUM_TRIALS):

            offset = np.random.randint(0, signals.shape[0] - SAMPLES)

            trials[i, :, :] = signals[offset: offset+SAMPLES, :]

            labels[i, -1] = 1.

        return trials, labels, projection_2d(get_physionet_electrode_positions()), SAMPLE_RATE

def load_raw_data(electrodes, subject=None, num_classes=4, long_edge=False):

    # load from file

    trials = []

    labels = []

    if subject == None:

        subject_ids = range(11, 15)

    else:

        try:

            subject_ids = [int(subject)]

        except:

            subject_ids = subject

    for subject_id in subject_ids:

        try:

            t, l, loc, fs = load_physionet_data(subject_id, num_classes, long_edge=long_edge)

            if num_classes == 2 and t.shape[0] != 42:

                # drop subjects with less trials

                continue

            trials.append(t[:, :, electrodes])

            labels.append(l)

        except:

            pass

    return np.array(trials, dtype=np.float64).reshape((len(trials),) + trials[0].shape + (1,)), \

           np.array(labels, dtype=np.float64)

def butter_bandpass(lowcut, highcut, fs, order=5):

    nyq = 0.5 * fs

    low = lowcut / nyq

    high = highcut / nyq

    b, a = butter(order, [low, high], btype='band')

    return b, a

def butter_bandpass_filter(data, lowcut, highcut, fs, order=5):

    b, a = butter_bandpass(lowcut, highcut, fs, order=order)

    y = filtfilt(b, a, data)

    return y

print("Start to save the Files!")

# Sample rate and desired cutoff frequencies (in Hz).

fs      = 160.0

lowcut  = 5.0

highcut = 30.0

# Save Dataset of 4 clases

nclasses = 4

# Save 20 subjects' dataset

SAVE = '20-Subjects'

if not os.path.exists(SAVE):

    os.mkdir(SAVE)

subject = range(1, 21)

# Save 64 electrodes

for i in range(0, 64):

    electrodes = [i]

    X = 'X_' + str(i)

    Y = 'Y_' + str(i)

    X, Y = load_raw_data(electrodes=electrodes, subject=subject, num_classes=nclasses)

    X = np.squeeze(X)

    sio.savemat(SAVE + 'Dataset_%d.mat' % int(i+1), {'Dataset': X})

    sio.savemat(SAVE + 'Labels_%d.mat' % int(i+1), {'Labels': Y})

    print("Finished saving %d electrodes" % int(i+1))

# SAVE = '100-Subjects/'

#

# if not os.path.exists(SAVE):

#     os.mkdir(SAVE)

#

# subject = range(1, 102)

#

# for i in range(0, 64):

#     electrodes = [i]

#     X = 'X_' + str(i)

#     Y = 'Y_' + str(i)

#     X, Y = load_raw_data(electrodes=electrodes, subject=subject, num_classes=nclasses)

#     X = np.squeeze(X)

#

#     # # Notch Filter

#     # b, a = iirnotch(w0=60.0, Q=30.0, fs=fs)

#     # X = lfilter(b, a, X)

#     #

#     # # Butterworth Band-pass filter

#     # X = butter_bandpass_filter(X, lowcut, highcut, fs, order=4)

#

#     sio.savemat(SAVE + 'Dataset_%d.mat' % int(i+1), {'Dataset': X})

#     sio.savemat(SAVE + 'Labels_%d.mat' % int(i+1), {'Labels': Y})

#

#     print("Finished saving %d electrodes" % int(i+1))