DARNet/myutils.py at main · fchest/DARNet · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
from __future__ import print_function

import random

import numpy as np
import math

import data_loader

np.set_printoptions(suppress=True)
import os
import time
import torch
from sklearn.preprocessing import scale
from scipy.interpolate import griddata
from scipy.io import loadmat


def cart2sph(x, y, z):
    """
    Transform Cartesian coordinates to spherical
    :param x: X coordinate
    :param y: Y coordinate
    :param z: Z coordinate
    :return: radius, elevation, azimuth
    """
    x2_y2 = x ** 2 + y ** 2
    r = math.sqrt(x2_y2 + z ** 2)  # r
    elev = math.atan2(z, math.sqrt(x2_y2))  # Elevation
    az = math.atan2(y, x)  # Azimuth
    return r, elev, az


def pol2cart(theta, rho):
    """
    Transform polar coordinates to Cartesian
    :param theta: angle value
    :param rho: radius value
    :return: X, Y
    """
    return rho * math.cos(theta), rho * math.sin(theta)


def makePath(path):
    if not os.path.isdir(path):
        os.makedirs(path)
    return path


def monitor(process, multiple, second):
    while True:
        sum = 0
        for ps in process:
            if ps.is_alive():
                sum += 1
        if sum < multiple:
            break
        else:
            time.sleep(second)


def save_load_name(args, name=''):
    name = name if len(name) > 0 else 'default_model'
    return name


def save_model(args, model, name=''):
    name = save_load_name(args, name)
    torch.save(model, f'pre_trained_models/{name}.pt')


def load_model(args, name=''):
    name = save_load_name(args, name)
    model = torch.load(f'pre_trained_models/{name}.pt')
    return model


def getData(name="S1", time_len=1, dataset="DTU"):
    DTU_document_path = "E:/EEG_data/DTU/DATA_preproc"
    KUL_document_path = "E:/EEG_data/KUL_single_siongle3"

    if dataset == 'DTU':
        return data_loader.get_DTU_data(name, time_len, DTU_document_path)
    elif dataset == 'KUL':
        return data_loader.get_KUL_data(name, time_len, KUL_document_path)


def setup_seed(seed):
    torch.manual_seed(seed)
    torch.cuda.manual_seed(seed)
    torch.cuda.manual_seed_all(seed)
    np.random.seed(seed)
    random.seed(seed)


def azim_proj(pos):
    """
    Computes the Azimuthal Equidistant Projection of input point in 3D Cartesian Coordinates.
    Imagine a plane being placed against (tangent to) a globe. If
    a light source inside the globe projects the graticule onto
    the plane the result would be a planar, or azimuthal, map
    projection.

    :param pos: position in 3D Cartesian coordinates
    :return: projected coordinates using Azimuthal Equidistant Projection
    """
    [r, elev, az] = cart2sph(pos[0], pos[1], pos[2])
    return pol2cart(az, math.pi / 2 - elev)


def gen_images(data, args):
    locs = loadmat('locs_orig.mat')
    locs_3d = locs['data']
    locs_2d = []
    for e in locs_3d:
        locs_2d.append(azim_proj(e))

    locs_2d_final = np.array(locs_2d)
    grid_x, grid_y = np.mgrid[
                     min(np.array(locs_2d)[:, 0]):max(np.array(locs_2d)[:, 0]):args.image_size * 1j,
                     min(np.array(locs_2d)[:, 1]):max(np.array(locs_2d)[:, 1]):args.image_size * 1j]

    images = []
    for i in range(data.shape[0]):
        images.append(griddata(locs_2d_final, data[i, :], (grid_x, grid_y), method='cubic', fill_value=np.nan))
    images = np.stack(images, axis=0)

    images[~np.isnan(images)] = scale(images[~np.isnan(images)])
    images = np.nan_to_num(images)
    return images