-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathdistance_calculation.py
177 lines (160 loc) · 7.52 KB
/
distance_calculation.py
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
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
import json
import gudhi
import gudhi.wasserstein
import numpy as np
from utils import get_dataset, timer
from mds_calculation import get_mds
import os
import argparse
def get_barcodes_distance(dgm_1, dgm_2, distance_method='ws'):
if distance_method == 'ws':
return gudhi.wasserstein.wasserstein_distance(dgm_1, dgm_2,
order=1., internal_p=2.)
elif distance_method == 'bn':
return gudhi.bottleneck_distance(dgm_1, dgm_2)
@timer
def get_mds_matrix(subject_id, json_directory):
data_path = f'{json_directory}/subject_{subject_id}.json'
dissimilarity_matrix = np.array(json.loads(open(data_path, "r").read()))
mds_matrix = get_mds(dissimilarity_matrix)
return json.dumps(mds_matrix.tolist())
@timer
def get_distance_matrix(data_dir, subject_number, timeslots,
normalize_file_prefix, distance_method='ws'):
dissimilarity_matrix = np.array([[0.0 for j in range(timeslots)] for i in
range(timeslots)])
barcodes = {}
for i in range(1, timeslots + 1):
time_1 = f"time_{i}"
if time_1 not in barcodes:
filepath_1 = f'{data_dir}/{normalize_file_prefix}{subject_number}_time_{i}.txt'
adjacency_matrix_1 = get_dataset(filename=filepath_1, fmri=True)
rips_complex_1 = gudhi.RipsComplex(
distance_matrix=adjacency_matrix_1)
pd_1 = rips_complex_1.create_simplex_tree(
max_dimension=1).persistence()[1:]
barcodes_1 = np.array([pair[1] for pair in pd_1])
barcodes[time_1] = barcodes_1
else:
barcodes_1 = barcodes[time_1]
for j in range(1, i):
time_2 = f"time_{j}"
if time_2 not in barcodes:
filepath_2 = f'{data_dir}/{normalize_file_prefix}{subject_number}_time_{j}.txt'
adjacency_matrix_2 = get_dataset(filename=filepath_2,
fmri=True)
rips_complex_2 = gudhi.RipsComplex(
distance_matrix=adjacency_matrix_2)
pd_2 = rips_complex_2.create_simplex_tree(
max_dimension=1).persistence()[1:]
barcodes_2 = np.array([pair[1] for pair in pd_2])
barcodes[time_2] = barcodes_2
else:
barcodes_2 = barcodes[time_2]
distance = get_barcodes_distance(barcodes_1,
barcodes_2,
distance_method=distance_method)
distance = round(distance, 3)
dissimilarity_matrix[i - 1][j - 1] = distance
dissimilarity_matrix[j - 1][i - 1] = distance
return dissimilarity_matrix
@timer
def generate_distance_matrix(data_dir, distance_directory,
total_subjects, total_timeslots,
normalize_file_prefix, start_subject=None,
end_subject=None, distance_method='ws'):
if not os.path.exists(distance_directory):
os.makedirs(distance_directory)
if start_subject == None:
start_subject = 1
end_subject = total_subjects
for subject_number in range(start_subject, end_subject + 1):
print(f"Generating distance matrix for Subject {subject_number}")
generated_json = f'{distance_directory}/subject_{subject_number}.json'
dissimilarity_matrix = get_distance_matrix(data_dir,
subject_number,
total_timeslots,
normalize_file_prefix,
distance_method)
with open(generated_json, "w") as f:
json.dump(dissimilarity_matrix.tolist(), f)
print(f"{distance_method} distance JSON created for Subject {subject_number}: {generated_json}")
print("Done generating the {distance_method} distance matrix JSON files")
@timer
def generate_mds(mds_directory, json_directory, total_subjects,
start_subject=None,
end_subject=None, distance_method='ws'
):
if not os.path.exists(mds_directory):
os.makedirs(mds_directory)
if start_subject == None:
start_subject = 1
end_subject = total_subjects
for subject_number in range(start_subject, end_subject + 1):
generated_mds = f'{mds_directory}/subject_{subject_number}.json'
mds_matrix = get_mds_matrix(subject_number, json_directory)
with open(generated_mds, "w") as f:
json.dump(mds_matrix, f)
print(f"MDS JSON created for Subject {subject_number}: {generated_mds}")
print("Done generating the MDS JSON files")
def get_user_input():
parser = argparse.ArgumentParser()
parser.add_argument('--data', '-d',
help='Enter one of the DFC dataset (645, 1400, 2500)')
parser.add_argument('--method', '-m',
help='Enter one of the distance method (ws, bn)')
parser.add_argument('--start', '-s',
help='Enter start subject (1, 316)')
parser.add_argument('--end', '-e',
help='Enter end subject (1, 316)')
parser.add_argument('--distance', '-p',
help='To calculate distance matrix (y or n)')
parser.add_argument('--mds', '-q',
help='To calculate MDS (y or n)')
args = parser.parse_args()
if args.data:
main(int(args.data), args.method, start_subject=int(args.start),
end_subject=int(args.end),
distance_calculation=args.distance,
mds_calculation=args.mds)
return
parser.print_help()
@timer
def main(dataset, method, start_subject=1, end_subject=316,
distance_calculation='y', mds_calculation='y'):
if dataset == 2500:
# DFC 2500
data_directory = "../dfc_2500_normal"
# data_directory = "fmri_data"
distance_matrix_directory = "../dfc_2500_subjects_distance_matrix_" + method
mds_directory = "../dfc_2500_subjects_mds_" + method
normalize_file_prefix = 'normalize_dfc_2500_subject_'
total_subjects = 316
total_timeslots = 86
elif dataset == 1400:
# DFC 1400
data_directory = "../dfc_1400_normal"
distance_matrix_directory = "../dfc_1400_subjects_distance_matrix_" + method
mds_directory = "../dfc_1400_subjects_mds_" + method
normalize_file_prefix = 'normalize_dfc_1400_subject_'
total_subjects = 316
total_timeslots = 336
elif dataset == 645:
# DFC 645
data_directory = "../dfc_645_normal"
distance_matrix_directory = "../dfc_645_subjects_distance_matrix_" + method
mds_directory = "../dfc_645_subjects_mds_" + method
normalize_file_prefix = 'normalize_dfc_645_subject_'
total_subjects = 316
total_timeslots = 754
if distance_calculation == 'y':
generate_distance_matrix(data_directory, distance_matrix_directory,
total_subjects, total_timeslots,
normalize_file_prefix, start_subject,
end_subject,
distance_method=method)
if mds_calculation == 'y':
generate_mds(mds_directory, distance_matrix_directory, total_subjects,
1, total_subjects, distance_method=method)
if __name__ == "__main__":
get_user_input()