Repository navigation
Expand file tree
/
Copy pathutils.py
More file actions
635 lines (524 loc) · 23 KB
/
Copy pathutils.py
File metadata and controls
635 lines (524 loc) · 23 KB
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
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
# Copyright 2016 Intel Corporation, Princeton University
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
import re
import warnings
import os.path
from .fmrisim import generate_stimfunction, double_gamma_hrf
"""
Some utility functions that can be used by different algorithms
"""
def from_tri_2_sym(tri, dim):
"""convert a upper triangular matrix in 1D format
to 2D symmetric matrix
Parameters
----------
tri: 1D array
Contains elements of upper triangular matrix
dim : int
The dimension of target matrix.
Returns
-------
symm : 2D array
Symmetric matrix in shape=[dim, dim]
"""
symm = np.zeros((dim, dim))
symm[np.triu_indices(dim)] = tri
return symm
def from_sym_2_tri(symm):
"""convert a 2D symmetric matrix to an upper
triangular matrix in 1D format
Parameters
----------
symm : 2D array
Symmetric matrix
Returns
-------
tri: 1D array
Contains elements of upper triangular matrix
"""
inds = np.triu_indices_from(symm)
tri = symm[inds]
return tri
def sumexp_stable(data):
"""Compute the sum of exponents for a list of samples
Parameters
----------
data : array, shape=[features, samples]
A data array containing samples.
Returns
-------
result_sum : array, shape=[samples,]
The sum of exponents for each sample divided by the exponent
of the maximum feature value in the sample.
max_value : array, shape=[samples,]
The maximum feature value for each sample.
result_exp : array, shape=[features, samples]
The exponent of each element in each sample divided by the exponent
of the maximum feature value in the sample.
Note
----
This function is more stable than computing the sum(exp(v)).
It useful for computing the softmax_i(v)=exp(v_i)/sum(exp(v)) function.
"""
max_value = data.max(axis=0)
result_exp = np.exp(data - max_value)
result_sum = np.sum(result_exp, axis=0)
return result_sum, max_value, result_exp
def concatenate_not_none(l, axis=0):
"""Construct a numpy array by stacking not-None arrays in a list
Parameters
----------
data : list of arrays
The list of arrays to be concatenated. Arrays have same shape in all
but one dimension or are None, in which case they are ignored.
axis : int, default = 0
Axis for the concatenation
Returns
-------
data_stacked : array
The resulting concatenated array.
"""
# Get the indexes of the arrays in the list
mask = []
for i in range(len(l)):
if l[i] is not None:
mask.append(i)
# Concatenate them
l_stacked = np.concatenate([l[i] for i in mask], axis=axis)
return l_stacked
def cov2corr(cov):
"""Calculate the correlation matrix based on a
covariance matrix
Parameters
----------
cov: 2D array
Returns
-------
corr: 2D array
correlation converted from the covarince matrix
"""
assert cov.ndim == 2, 'covariance matrix should be 2D array'
inv_sd = 1 / np.sqrt(np.diag(cov))
corr = cov * inv_sd[None, :] * inv_sd[:, None]
return corr
class ReadDesign:
"""A class which has the ability of reading in design matrix in .1D file,
generated by AFNI's 3dDeconvolve.
Parameters
----------
fname: string, the address of the file to read.
include_orth: Boollean, whether to include "orthogonal" regressors in
the nuisance regressors which are usually head motion parameters.
All the columns of design matrix are still going to be read in,
but the attribute cols_used will reflect whether these orthogonal
regressors are to be included for furhter analysis.
Note that these are not entered into design_task attribute which
include on
8000
ly regressors related to task conditions.
include_pols: Boollean, whether to include polynomial regressors in
the nuisance regressors which are used to capture slow drift of
signals.
Attributes
----------
design: 2d array. The design matrix read in from the csv file.
design_task: 2d array. The part of design matrix corresponding to
task conditions.
n_col: number of total columns in the design matrix.
column_types: 1d array. the types of each column in the design matrix.
0 for orthogonal regressors (usually head motion parameters),
-1 for polynomial basis (capturing slow drift of signals),
values > 0 for stimulus conditions
n_basis: scalar. The number of polynomial bases in the designn matrix.
n_stim: scalar. The number of stimulus conditions.
n_orth: scalar. The number of orthogoanal regressors (usually head
motions)
StimLabels: list. The names of each column in the design matrix.
"""
def __init__(self, fname=None, include_orth=True, include_pols=True):
if fname is None:
# fname is the name of the file to read in the design matrix
self.design = np.zeros([0, 0])
self.n_col = 0
# number of columns (conditions) in the design matrix
self.column_types = np.ones(0)
self.n_basis = 0
self.n_stim = 0
self.n_orth = 0
self.StimLabels = []
else:
# isAFNI = re.match(r'.+[.](1D|1d|txt)$', fname)
filename, ext = os.path.splitext(fname)
# We assume all AFNI 1D files have extension of 1D or 1d or txt
if ext in ['.1D', '.1d', '.txt']:
self.read_afni(fname=fname)
self.include_orth = include_orth
self.include_pols = include_pols
# The two flags above dictates whether columns corresponding to
# baseline drift modeled by polynomial functions of time and
# columns corresponding to other orthogonal signals (usually motion)
# are included in nuisance regressors.
self.cols_task = np.where(self.column_types == 1)[0]
self.design_task = self.design[:, self.cols_task]
if np.ndim(self.design_task) == 1:
self.design_task = self.design_task[:, None]
# part of the design matrix related to task conditions.
self.n_TR = np.size(self.design_task, axis=0)
self.cols_nuisance = np.array([])
if self.include_orth:
self.cols_nuisance = np.int0(
np.sort(np.append(self.cols_nuisance,
np.where(self.column_types == 0)[0])))
if self.include_pols:
self.cols_nuisance = np.int0(
np.sort(np.append(self.cols_nuisance,
np.where(self.column_types == -1)[0])))
if np.size(self.cols_nuisance) > 0:
self.reg_nuisance = self.design[:, self.cols_nuisance]
if np.ndim(self.reg_nuisance) == 1:
self.reg_nuisance = self.reg_nuisance[:, None]
else:
self.reg_nuisance = None
# Nuisance regressors for motion, baseline, etc.
def read_afni(self, fname):
# Read design file written by AFNI
self.n_basis = 0
self.n_stim = 0
self.n_orth = 0
self.StimLabels = []
self.design = np.loadtxt(fname, ndmin=2)
with open(fname) as f:
all_text = f.read()
find_n_column = re.compile(
r'^#[ ]+ni_type[ ]+=[ ]+"(?P<n_col>\d+)[*]', re.MULTILINE)
n_col_found = find_n_column.search(all_text)
if n_col_found:
self.n_col = int(n_col_found.group('n_col'))
if self.n_col != np.size(self.design, axis=1):
warnings.warn(
'The number of columns in the design matrix'
+ 'does not match the header information')
self.n_col = np.size(self.design, axis=1)
else:
self.n_col = np.size(self.design, axis=1)
self.column_types = np.ones(self.n_col)
# default that all columns are conditions of interest
find_ColumnGroups = re.compile(
r'^#[ ]+ColumnGroups[ ]+=[ ]+"(?P<CGtext>.+)"', re.MULTILINE)
CG_found = find_ColumnGroups.search(all_text)
if CG_found:
CG_text = re.split(',', CG_found.group('CGtext'))
curr_idx = 0
for CG in CG_text:
split_by_at = re.split('@', CG)
if len(split_by_at) == 2:
# the first tells the number of columns in this condition
# the second tells the condition type
n_this_cond = int(split_by_at[0])
self.column_types[curr_idx:curr_idx + n_this_cond] = \
int(split_by_at[1])
curr_idx += n_this_cond
elif len(split_by_at) == 1 and \
not re.search('\..', split_by_at[0]):
# Just a number, and not the type like '1..4'
self.column_types[curr_idx] = int(split_by_at[0])
curr_idx += 1
8000
else: # must be a single stimulus condition
split_by_dots = re.split('\..', CG)
n_this_cond = int(split_by_dots[1])
self.column_types[curr_idx:curr_idx + n_this_cond] = 1
curr_idx += n_this_cond
self.n_basis = np.sum(self.column_types == -1)
self.n_stim = np.sum(self.column_types > 0)
self.n_orth = np.sum(self.column_types == 0)
find_StimLabels = re.compile(
r'^#[ ]+StimLabels[ ]+=[ ]+"(?P<SLtext>.+)"', re.MULTILINE)
StimLabels_found = find_StimLabels.search(all_text)
if StimLabels_found:
self.StimLabels = \
re.split(r'[ ;]+', StimLabels_found.group('SLtext'))
else:
self.StimLabels = []
def gen_design(stimtime_files, scan_duration, TR, style='FSL',
temp_res=0.01,
hrf_para={'response_delay': 6, 'undershoot_delay': 12,
'response_dispersion': 0.9,
'undershoot_dispersion': 0.9,
'undershoot_scale': 0.035}):
""" Generate design matrix based on a list of names of stimulus
timing files. The function will read each file, and generate
a numpy array of size [time_points \* condition], where
time_points equals duration / TR, and condition is the size of
stimtime_filenames. Each column is the hypothetical fMRI response
based on the stimulus timing in the corresponding file
of stimtime_files.
This function uses generate_stimfunction and double_gamma_hrf
of brainiak.utils.fmrisim.
Parameters
----------
stimtime_files: a string or a list of string.
Each string is the name of the file storing
the stimulus timing information of one task condition.
The contents in the files will be interpretated
based on the style parameter.
Details are explained under the style parameter.
scan_duration: float or a list (or a 1D numpy array) of numbers.
Total duration of each fMRI scan, in unit of seconds.
If there are multiple runs, the duration should be
a list (or 1-d numpy array) of numbers.
If it is a list, then each number in the list
represents the duration of the corresponding scan
in the stimtime_files.
If only a number is provided, it is assumed that
there is only one fMRI scan lasting for scan_duration.
TR: float.
The sampling period of fMRI, in unit of seconds.
style: string, default: 'FSL'
Acceptable inputs: 'FSL', 'AFNI'
The formating style of the stimtime_files.
'FSL' style has one line for each event of the same condition.
Each line contains three numbers. The first number is the onset
of the event relative to the onset of the first scan,
in units of seconds.
(Multiple scans should be treated as a concatenated long scan
for the purpose of calculating onsets.
However, the design matrix from one scan won't leak into the next).
The second number is the duration of the event,
in unit of seconds.
The third number is the amplitude modulation (or weight)
of the response.
It is acceptable to not provide the weight,
or not provide both duration and weight.
In such cases, these parameters will default to 1.0.
'AFNI' style has one line for each scan (run).
Each line has a few triplets in the format of
stim_onsets*weight:duration
(or simpler, see below), separated by spaces.
For example, 3.2\*2.0:1.5 means that one event starts at 3.2s,
modulated by weight of 2.0 and lasts for 1.5s.
If some run does not include a single event
of a condition (stimulus type), then you can put \*,
or a negative number, or a very large number in that line.
Either duration or weight can be neglected. In such
cases, they will default to 1.0.
For example, 3.0, 3.0\*1.0, 3.0:1.0 and 3.0\*1.0:1.0 all
means an event starting at 3.0s, lasting for 1.0s, with
amplitude modulation of 1.0.
temp_res: float, default: 0.01
Temporal resolution of fMRI, in second.
hrf_para: dictionary
The parameters of the double-Gamma hemodynamic response function.
To set different parameters, supply a dictionary with
the same set of keys as the default, and replace the corresponding
values with the new values.
Returns
-------
design: 2D numpy array
design matrix. Each time row represents one TR
(fMRI sampling time point) and each column represents
one experiment condition, in the order in stimtime_files
"""
if np.ndim(scan_duration) == 0:
scan_duration = [scan_duration]
scan_duration = np.array(scan_duration)
assert np.all(scan_duration > TR), \
'scan duration should be longer than a TR'
if type(stimtime_files) is str:
stimtime_files = [stimtime_files]
assert TR > 0, 'TR should be positive'
assert style == 'FSL' or style == 'AFNI', 'style can only be FSL or AFNI'
n_C = len(stimtime_files) # number of conditions
n_S = np.size(scan_duration) # number of scans
if n_S > 1:
design = [np.empty([int(np.floor(duration / TR)), n_C])
for duration in scan_duration]
else:
design = [np.empty([int(np.floor(scan_duration / TR)), n_C])]
scan_onoff = np.insert(np.cumsum(scan_duration), 0, 0)
if style == 'FSL':
design_info = _read_stimtime_FSL(stimtime_files, n_C, n_S, scan_onoff)
elif style == 'AFNI':
design_info = _read_stimtime_AFNI(stimtime_files, n_C, n_S, scan_onoff)
response_delay = hrf_para['response_delay']
undershoot_delay = hrf_para['undershoot_delay']
response_dispersion = hrf_para['response_dispersion']
undershoot_dispersion = hrf_para['undershoot_dispersion']
undershoot_scale = hrf_para['undershoot_scale']
# generate design matrix
for i_s in range(n_S):
for i_c in range(n_C):
stimfunction = generate_stimfunction(
>
event_durations=design_info[i_s][i_c]['duration'],
total_time=scan_duration[i_s],
weights=design_info[i_s][i_c]['weight'],
temporal_resolution=1.0/temp_res)
design[i_s][:, i_c] = double_gamma_hrf(
stimfunction, TR, response_delay=response_delay,
undershoot_delay=undershoot_delay,
response_dispersion=response_dispersion,
undershoot_dispersion=undershoot_dispersion,
undershoot_scale=undershoot_scale, scale_function=0,
temporal_resolution=1.0/temp_res) * temp_res
# We multiply the resulting design matrix with
# the temporal resolution to normalize it.
# We do not use the internal normalization
# in double_gamma_hrf because it does not guarantee
# normalizing with the same constant.
return np.concatenate(design, axis=0)
def _read_stimtime_FSL(stimtime_files, n_C, n_S, scan_onoff):
""" Utility called by gen_design. It reads in one or more
stimulus timing file comforming to FSL style,
and return a list (size of [#run \* #condition])
of dictionary including onsets, durations and weights of each event.
Parameters
----------
stimtime_files: a string or a list of string.
Each string is the name of the file storing the stimulus
timing information of one task condition.
The contents in the files should follow the style of FSL
stimulus timing files, refer to gen_design.
n_C: integer, number of task conditions
n_S: integer, number of scans
scan_onoff: list of numbers.
The onset of each scan after concatenating all scans,
together with the offset of the last scan.
For example, if 3 scans of duration 100s, 150s, 120s are run,
scan_onoff is [0, 100, 250, 370]
Returns
-------
design_info: list of stimulus information
The first level of the list correspond to different scans.
The second level of the list correspond to different conditions.
Each item in the list is a dictiornary with keys "onset",
"duration" and "weight". If one condition includes no event
in a scan, the values of these keys in that scan of the condition
are empty lists.
See also
--------
gen_design
"""
design_info = [[{'onset': [], 'duration': [], 'weight': []}
for i_c in range(n_C)] for i_s in range(n_S)]
# Read stimulus timing files
for i_c in range(n_C):
with open(stimtime_files[i_c]) as f:
for line in f.readlines():
tmp = line.strip().split()
i_s = np.where(
np.logical_and(scan_onoff[:-1] <= float(tmp[0]),
scan_onoff[1:] > float(tmp[0])))[0]
if len(i_s) == 1:
i_s = i_s[0]
design_info[i_s][i_c]['onset'].append(float(tmp[0])
- scan_onoff[i_s])
if len(tmp) >= 2:
design_info[i_s][i_c]['duration'].append(float(tmp[1]))
else:
design_info[i_s][i_c]['duration'].append(1.0)
if len(tmp) >= 3:
design_info[i_s][i_c]['weight'].append(float(tmp[2]))
else:
design_info[i_s][i_c]['weight'].append(1.0)
return design_info
def _read_stimtime_AFNI(stimtime_files, n_C, n_S, scan_onoff):
""" Utility called by gen_design. It reads in one or more stimulus timing
file comforming to AFNI style, and return a list
(size of ``[number of runs \* number of conditions]``)
of dictionary including onsets, durations and weights of each event.
Parameters
----------
stimtime_files: a string or a list of string.
Each string is the name of the file storing the stimulus
timing information of one task condition.
The contents in the files should follow the style of AFNI
stimulus timing files, refer to gen_design.
n_C: integer, number of task conditions
n_S: integer, number of scans
scan_onoff: list of numbers.
The onset of each scan after concatenating all scans,
together with the offset of the last scan.
For example, if 3 scans of duration 100s, 150s, 120s are run,
scan_onoff is [0, 100, 250, 370]
Returns
-------
design_info: list of stimulus information
The first level of the list correspond to different scans.
The second level of the list correspond to different conditions.
Each item in the list is a dictiornary with keys "onset",
"duration" and "weight". If one condition includes no event
in a scan, the values of these keys in that scan of the condition
are empty lists.
See also
--------
gen_design
"""
design_info = [[{'onset': [], 'duration': [], 'weight': []}
for i_c in range(n_C)] for i_s in range(n_S)]
# Read stimulus timing files
for i_c in range(n_C):
with open(stimtime_files[i_c]) as f:
text = f.readlines()
assert len(text) == n_S, \
'Number of lines does not match number of runs!'
for i_s, line in enumerate(text):
events = line.strip().split()
if events[0] == '*':
continue
for event in events:
assert event != '*'
tmp = str.split(event, ':')
if len(tmp) == 2:
duration = float(tmp[1])
else:
duration = 1.0
tmp = str.split(tmp[0], '*')
if len(tmp) == 2:
weight = float(tmp[1])
else:
weight = 1.0
if (float(tmp[0]) >= 0
and float(tmp[0])
< scan_onoff[i_s + 1] - scan_onoff[i_s]):
design_info[i_s][i_c]['onset'].append(float(tmp[0]))
design_info[i_s][i_c]['duration'].append(duration)
design_info[i_s][i_c]['weight'].append(weight)
return design_info
def center_mass_exp(a, b, scale=1.0):
""" Calculate the center of mass of negative exponential distribution
p(x) = exp(-x / scale) / scale
in the interval of (a, b). scale is the same scale
parameter as scipy.stats.expon.pdf
Parameters
----------
a: float
The starting point of the interval in which the center of mass
is calculated for exponential distribution.
b: float
The ending point of the interval in which the center of mass
is calculated for exponential distribution.
scale: float
The scale parameter of the exponential distribution. See above.
Returns
-------
m: float
The center of mass in the interval of (a, b) for exponential
distribution.
"""
assert a < b, 'a must be smaller than b'
if b < np.inf:
return ((a + scale) * np.exp(-a / scale)
- (scale + b) * np.exp(-b / scale)) \
/ (np.exp(-a / scale) - np.exp(-b / scale))
else:
return a + scale