-
Notifications
You must be signed in to change notification settings - Fork 1
/
generate_all.py
184 lines (149 loc) · 7.63 KB
/
generate_all.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
178
179
180
181
182
183
184
"""
master script to generate all data.
"""
import copy
import os.path as op
from ping.ping.data import prefixes
from ping.scripts.brain import do_roygbiv
from ping.scripts.scatter import do_scatter
from ping.scripts.similarity import do_similarity
def generate_all_brains(data_dir=op.join('generated', 'data'),
output_dir=op.join('generated', 'data')):
"""Generate all VTK files used by roygbiv (2D surface plot)"""
for measure in ['area', 'thickness', 'volume']:
for atlas in ['desikan']: # skip destrieux
for surface_type in ['pial', 'inflated']:
for subject in ['fsaverage']:
for hemi in ['lh', 'rh']:
kwargs = dict(prefix=prefixes[atlas][measure],
surface_type=surface_type, hemi=hemi,
atlas=atlas, subject=subject,
key='AI:mean',
output_format='json',
sample_rate=0.1, force=False,
data_dir=data_dir,
output_dir=output_dir)
do_roygbiv(**kwargs)
def generate_manhattan(data_dir=op.join('generated', 'data'),
output_dir=op.join('generated', 'data')):
"""Generate genetic metadata and JSON for manhattan plot."""
from ping.scripts.gwas import do_gwas
do_gwas(action='display', measures='MRI_cort_area_ctx_frontalpole_AI',
covariates=['Age_At_IMGExam'], data_dir=data_dir,
output_dir=output_dir, output_format='json')
def generate_scatter_bokeh(data_dir=op.join('generated', 'data'),
output_dir=op.join('generated', 'plots')):
"""Various scatter plots"""
for atlas, measures in prefixes.items():
# Generate area vs. thickness plots
if atlas.lower() == 'destrieux':
continue
# Thickness vs. area
do_scatter(atlas=atlas, prefixes=[op.commonprefix(measures.values())],
x_key='%s:AI:mean' % measures['area'],
y_key='%s:AI:mean' % measures['thickness'],
title="Area vs. thickness",
data_dir=data_dir,
output_dir=output_dir,
output_format='bokeh')
for measure, prefix in measures.items():
# Generate scatter plot for given dataset / data point
do_scatter(atlas=atlas, prefixes=[prefix], x_key='AI:mean',
y_key='AI:std', size_key='LH_PLUS_RH:mean',
data_dir=data_dir,
output_dir=output_dir,
output_format='bokeh')
def generate_similarity_bokeh(data_dir=op.join('generated', 'data'),
output_dir=op.join('generated', 'plots',
'similarity')):
"""Asymmetry partial correlation matrix"""
for atlas, measures in prefixes.items():
if atlas == 'destrieux': # skip destrieux
continue
for measure, prefix in measures.items():
# Generate similarity matrix for given dataset / data point
do_similarity(atlas=atlas, prefixes=[prefix],
metric='partial-correlation',
measures=['Asymmetry Index'],
data_dir=data_dir,
output_dir=output_dir, output_format='bokeh')
def generate_similarity_json(data_dir=op.join('generated', 'data'),
output_dir=op.join('generated', 'data')):
"""Asymmetry partial correlation data as json overlay for roygbiv"""
from ping.ping.data import prefixes
from ping.scripts.similarity import do_similarity
for atlas, measures in prefixes.items():
if atlas == 'destrieux': # skip destrieux
continue
for measure, prefix in measures.items():
do_similarity(atlas=atlas, prefixes=[prefix],
metric='partial-correlation',
measures=['Asymmetry Index'],
data_dir=data_dir,
output_dir=op.join(output_dir, 'fsaverage', atlas),
output_format='json')
def generate_multivariate(data_dir=op.join('generated', 'data'),
output_dir=op.join('generated', 'data')):
"""PCA overlay for roygbiv"""
from ping.ping.data import prefixes
from ping.scripts.multivariate import do_multivariate
for atlas, measures in prefixes.items():
if atlas == 'destrieux': # skip destrieux
continue
for measure, prefix in measures.items():
do_multivariate(prefixes=[prefix], atlas=atlas,
data_dir=data_dir,
output_dir=op.join(output_dir, 'fsaverage', atlas),
output_format='json',
verbose=0, pc_thresh=0.05)
def generate_regressions(data_dir=op.join('generated', 'data'),
output_dir=op.join('generated', 'plots',
'regression')):
"""Regression between age and value, grouped by gender/handedness"""
from ping.ping.data import prefixes
from ping.scripts.grouping import do_grouping
for atlas, measures in prefixes.items():
if atlas == 'destrieux': # skip destrieux
continue
for measure, prefix in measures.items():
for grouping_key in ['Gender', 'FDH_23_Handedness_Prtcpnt']:
do_grouping(prefixes=[prefix], grouping_keys=[grouping_key],
xaxis_key='Age_At_IMGExam',
plots='regressions', atlas='desikan',
data_dir=data_dir,
output_dir=output_dir,
output_type='matplotlib')
if __name__ == '__main__':
from argparse import ArgumentParser
commands = ('brain', 'manhattan', 'scatter', 'similarity',
'multivariate', 'regression', 'all')
parser = ArgumentParser()
parser.add_argument('command', default='all', choices=commands)
parser.add_argument('--data-dir', nargs='?',
default=op.join('generated', 'data'))
parser.add_argument('--output-dir', nargs='?',
default=op.join('generated'))
args = parser.parse_args()
args_dict = copy.deepcopy(vars(args))
command = args.command
del args_dict['command']
if command == 'all' or command == 'brain':
args_dict['output_dir'] = op.join(args.output_dir, 'data')
generate_all_brains(**args_dict)
if command == 'all' or command == 'manhattan':
args_dict['output_dir'] = op.join(args.output_dir, 'data')
generate_manhattan(**args_dict)
if command == 'all' or command == 'scatter':
args_dict['output_dir'] = op.join(args.output_dir, 'plots', command)
generate_scatter_bokeh(**args_dict)
if command == 'all' or command == 'similarity':
args_dict['output_dir'] = op.join(args.output_dir, 'plots', command)
generate_similarity_bokeh(**args_dict)
args_dict['output_dir'] = op.join(args.output_dir, 'data')
generate_similarity_json(**args_dict)
if command == 'all' or command == 'multivariate':
args_dict['output_dir'] = op.join(args.output_dir, 'data')
generate_multivariate(**args_dict)
if command == 'all' or command == 'regression':
args_dict['output_dir'] = op.join(args.output_dir, 'plots', command)
generate_regressions(**args_dict)