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
|
from __future__ import print_function
import os.path as op
import numpy as np
from numpy.testing import (assert_array_almost_equal, assert_equal,
assert_allclose, assert_array_equal)
from scipy import sparse
from nose.tools import assert_true, assert_raises
import copy
import warnings
from mne.datasets import testing
from mne.label import read_label, label_sign_flip
from mne.event import read_events
from mne.epochs import Epochs
from mne.source_estimate import read_source_estimate, VolSourceEstimate
from mne import (read_cov, read_forward_solution, read_evokeds, pick_types,
pick_types_forward, make_forward_solution,
convert_forward_solution, Covariance)
from mne.io import read_raw_fif, Info
from mne.minimum_norm.inverse import (apply_inverse, read_inverse_operator,
apply_inverse_raw, apply_inverse_epochs,
make_inverse_operator,
write_inverse_operator,
compute_rank_inverse,
prepare_inverse_operator)
from mne.tests.common import assert_naming
from mne.utils import _TempDir, run_tests_if_main, slow_test
from mne.externals import six
test_path = testing.data_path(download=False)
s_path = op.join(test_path, 'MEG', 'sample')
fname_fwd = op.join(s_path, 'sample_audvis_trunc-meg-eeg-oct-4-fwd.fif')
# Four inverses:
fname_full = op.join(s_path, 'sample_audvis_trunc-meg-eeg-oct-6-meg-inv.fif')
fname_inv = op.join(s_path, 'sample_audvis_trunc-meg-eeg-oct-4-meg-inv.fif')
fname_inv_fixed_nodepth = op.join(s_path,
'sample_audvis_trunc-meg-eeg-oct-4-meg'
'-nodepth-fixed-inv.fif')
fname_inv_meeg_diag = op.join(s_path,
'sample_audvis_trunc-'
'meg-eeg-oct-4-meg-eeg-diagnoise-inv.fif')
fname_data = op.join(s_path, 'sample_audvis_trunc-ave.fif')
fname_cov = op.join(s_path, 'sample_audvis_trunc-cov.fif')
fname_raw = op.join(s_path, 'sample_audvis_trunc_raw.fif')
fname_event = op.join(s_path, 'sample_audvis_trunc_raw-eve.fif')
fname_label = op.join(s_path, 'labels', '%s.label')
fname_vol_inv = op.join(s_path,
'sample_audvis_trunc-meg-vol-7-meg-inv.fif')
# trans and bem needed for channel reordering tests incl. forward computation
fname_trans = op.join(s_path, 'sample_audvis_trunc-trans.fif')
s_path_bem = op.join(test_path, 'subjects', 'sample', 'bem')
fname_bem = op.join(s_path_bem, 'sample-320-320-320-bem-sol.fif')
src_fname = op.join(s_path_bem, 'sample-oct-4-src.fif')
snr = 3.0
lambda2 = 1.0 / snr ** 2
last_keys = [None] * 10
def read_forward_solution_meg(*args, **kwargs):
"""Read MEG forward."""
fwd = read_forward_solution(*args, **kwargs)
fwd = pick_types_forward(fwd, meg=True, eeg=False)
return fwd
def read_forward_solution_eeg(*args, **kwargs):
"""Read EEG forward."""
fwd = read_forward_solution(*args, **kwargs)
fwd = pick_types_forward(fwd, meg=False, eeg=True)
return fwd
def _get_evoked():
"""Get evoked data."""
evoked = read_evokeds(fname_data, condition=0, baseline=(None, 0))
evoked.crop(0, 0.2)
return evoked
def _compare(a, b):
"""Compare two python objects."""
global last_keys
skip_types = ['whitener', 'proj', 'reginv', 'noisenorm', 'nchan',
'command_line', 'working_dir', 'mri_file', 'mri_id']
try:
if isinstance(a, (dict, Info)):
assert_true(isinstance(b, (dict, Info)))
for k, v in six.iteritems(a):
if k not in b and k not in skip_types:
raise ValueError('First one had one second one didn\'t:\n'
'%s not in %s' % (k, b.keys()))
if k not in skip_types:
last_keys.pop()
last_keys = [k] + last_keys
_compare(v, b[k])
for k, v in six.iteritems(b):
if k not in a and k not in skip_types:
raise ValueError('Second one had one first one didn\'t:\n'
'%s not in %s' % (k, a.keys()))
elif isinstance(a, list):
assert_true(len(a) == len(b))
for i, j in zip(a, b):
_compare(i, j)
elif isinstance(a, sparse.csr.csr_matrix):
assert_array_almost_equal(a.data, b.data)
assert_equal(a.indices, b.indices)
assert_equal(a.indptr, b.indptr)
elif isinstance(a, np.ndarray):
assert_array_almost_equal(a, b)
else:
assert_true(a == b)
except Exception as exptn:
print(last_keys)
raise exptn
def _compare_inverses_approx(inv_1, inv_2, evoked, rtol, atol,
check_depth=True):
"""Compare inverses."""
# depth prior
if check_depth:
if inv_1['depth_prior'] is not None:
assert_array_almost_equal(inv_1['depth_prior']['data'],
inv_2['depth_prior']['data'], 5)
else:
assert_true(inv_2['depth_prior'] is None)
# orient prior
if inv_1['orient_prior'] is not None:
assert_array_almost_equal(inv_1['orient_prior']['data'],
inv_2['orient_prior']['data'])
else:
assert_true(inv_2['orient_prior'] is None)
# source cov
assert_array_almost_equal(inv_1['source_cov']['data'],
inv_2['source_cov']['data'])
# These are not as close as we'd like XXX
assert_array_almost_equal(np.abs(inv_1['eigen_fields']['data']),
np.abs(inv_2['eigen_fields']['data']), 0)
assert_array_almost_equal(np.abs(inv_1['eigen_leads']['data']),
np.abs(inv_2['eigen_leads']['data']), 0)
stc_1 = apply_inverse(evoked, inv_1, lambda2, "dSPM")
stc_2 = apply_inverse(evoked, inv_2, lambda2, "dSPM")
assert_true(stc_1.subject == stc_2.subject)
assert_equal(stc_1.times, stc_2.times)
assert_allclose(stc_1.data, stc_2.data, rtol=rtol, atol=atol)
assert_true(inv_1['units'] == inv_2['units'])
def _compare_io(inv_op, out_file_ext='.fif'):
"""Compare inverse IO."""
tempdir = _TempDir()
if out_file_ext == '.fif':
out_file = op.join(tempdir, 'test-inv.fif')
elif out_file_ext == '.gz':
out_file = op.join(tempdir, 'test-inv.fif.gz')
else:
raise ValueError('IO test could not complete')
# Test io operations
inv_init = copy.deepcopy(inv_op)
write_inverse_operator(out_file, inv_op)
read_inv_op = read_inverse_operator(out_file)
_compare(inv_init, read_inv_op)
_compare(inv_init, inv_op)
@testing.requires_testing_data
def test_warn_inverse_operator():
"""Test MNE inverse warning without average EEG projection."""
bad_info = copy.deepcopy(_get_evoked().info)
bad_info['projs'] = list()
fwd_op = read_forward_solution(fname_fwd, surf_ori=True)
noise_cov = read_cov(fname_cov)
with warnings.catch_warnings(record=True) as w:
make_inverse_operator(bad_info, fwd_op, noise_cov)
assert_equal(len(w), 1)
@slow_test
@testing.requires_testing_data
def test_make_inverse_operator():
"""Test MNE inverse computation (precomputed and non-precomputed)
"""
# Test old version of inverse computation starting from forward operator
evoked = _get_evoked()
noise_cov = read_cov(fname_cov)
inverse_operator = read_inverse_operator(fname_inv)
fwd_op = read_forward_solution_meg(fname_fwd, surf_ori=True)
my_inv_op = make_inverse_operator(evoked.info, fwd_op, noise_cov,
loose=0.2, depth=0.8,
limit_depth_chs=False)
_compare_io(my_inv_op)
assert_true(inverse_operator['units'] == 'Am')
_compare_inverses_approx(my_inv_op, inverse_operator, evoked, 1e-2, 1e-2,
check_depth=False)
# Test MNE inverse computation starting from forward operator
my_inv_op = make_inverse_operator(evoked.info, fwd_op, noise_cov,
loose=0.2, depth=0.8)
_compare_io(my_inv_op)
_compare_inverses_approx(my_inv_op, inverse_operator, evoked, 1e-2, 1e-2)
assert_true('dev_head_t' in my_inv_op['info'])
assert_true('mri_head_t' in my_inv_op)
@slow_test
@testing.requires_testing_data
def test_inverse_operator_channel_ordering():
"""Test MNE inverse computation is immune to channel reorderings
"""
# These are with original ordering
evoked = _get_evoked()
noise_cov = read_cov(fname_cov)
fwd_orig = make_forward_solution(evoked.info, fname_trans, src_fname,
fname_bem, eeg=True, mindist=5.0)
fwd_orig = convert_forward_solution(fwd_orig, surf_ori=True)
inv_orig = make_inverse_operator(evoked.info, fwd_orig, noise_cov,
loose=0.2, depth=0.8,
limit_depth_chs=False)
stc_1 = apply_inverse(evoked, inv_orig, lambda2, "dSPM")
# Assume that a raw reordering applies to both evoked and noise_cov,
# so we don't need to create those from scratch. Just reorder them,
# then try to apply the original inverse operator
new_order = np.arange(len(evoked.info['ch_names']))
randomiser = np.random.RandomState(42)
randomiser.shuffle(new_order)
evoked.data = evoked.data[new_order]
evoked.info['chs'] = [evoked.info['chs'][n] for n in new_order]
evoked.info._update_redundant()
evoked.info._check_consistency()
cov_ch_reorder = [c for c in evoked.info['ch_names']
if (c in noise_cov.ch_names)]
new_order_cov = [noise_cov.ch_names.index(name) for name in cov_ch_reorder]
noise_cov['data'] = noise_cov.data[np.ix_(new_order_cov, new_order_cov)]
noise_cov['names'] = [noise_cov['names'][idx] for idx in new_order_cov]
fwd_reorder = make_forward_solution(evoked.info, fname_trans, src_fname,
fname_bem, eeg=True, mindist=5.0)
fwd_reorder = convert_forward_solution(fwd_reorder, surf_ori=True)
inv_reorder = make_inverse_operator(evoked.info, fwd_reorder, noise_cov,
loose=0.2, depth=0.8,
limit_depth_chs=False)
stc_2 = apply_inverse(evoked, inv_reorder, lambda2, "dSPM")
assert_equal(stc_1.subject, stc_2.subject)
assert_array_equal(stc_1.times, stc_2.times)
assert_allclose(stc_1.data, stc_2.data, rtol=1e-5, atol=1e-5)
assert_true(inv_orig['units'] == inv_reorder['units'])
# Reload with original ordering & apply reordered inverse
evoked = _get_evoked()
noise_cov = read_cov(fname_cov)
stc_3 = apply_inverse(evoked, inv_reorder, lambda2, "dSPM")
assert_allclose(stc_1.data, stc_3.data, rtol=1e-5, atol=1e-5)
@slow_test
@testing.requires_testing_data
def test_apply_inverse_operator():
"""Test MNE inverse application
"""
inverse_operator = read_inverse_operator(fname_full)
evoked = _get_evoked()
# Inverse has 306 channels - 4 proj = 302
assert_true(compute_rank_inverse(inverse_operator) == 302)
# Inverse has 306 channels - 4 proj = 302
assert_true(compute_rank_inverse(inverse_operator) == 302)
stc = apply_inverse(evoked, inverse_operator, lambda2, "MNE")
assert_true(stc.subject == 'sample')
assert_true(stc.data.min() > 0)
assert_true(stc.data.max() < 10e-9)
assert_true(stc.data.mean() > 1e-11)
# test if using prepared and not prepared inverse operator give the same
# result
inv_op = prepare_inverse_operator(inverse_operator, nave=evoked.nave,
lambda2=lambda2, method="MNE")
stc2 = apply_inverse(evoked, inv_op, lambda2, "MNE")
assert_array_almost_equal(stc.data, stc2.data)
assert_array_almost_equal(stc.times, stc2.times)
stc = apply_inverse(evoked, inverse_operator, lambda2, "sLORETA")
assert_true(stc.subject == 'sample')
assert_true(stc.data.min() > 0)
assert_true(stc.data.max() < 10.0)
assert_true(stc.data.mean() > 0.1)
stc = apply_inverse(evoked, inverse_operator, lambda2, "dSPM")
assert_true(stc.subject == 'sample')
assert_true(stc.data.min() > 0)
assert_true(stc.data.max() < 35)
assert_true(stc.data.mean() > 0.1)
# test without using a label (so delayed computation is used)
label = read_label(fname_label % 'Aud-lh')
stc = apply_inverse(evoked, inv_op, lambda2, "MNE")
stc_label = apply_inverse(evoked, inv_op, lambda2, "MNE",
label=label)
assert_equal(stc_label.subject, 'sample')
label_stc = stc.in_label(label)
assert_true(label_stc.subject == 'sample')
assert_array_almost_equal(stc_label.data, label_stc.data)
# Test we get errors when using custom ref or no average proj is present
evoked.info['custom_ref_applied'] = True
assert_raises(ValueError, apply_inverse, evoked, inv_op, lambda2, "MNE")
evoked.info['custom_ref_applied'] = False
evoked.info['projs'] = [] # remove EEG proj
assert_raises(ValueError, apply_inverse, evoked, inv_op, lambda2, "MNE")
@testing.requires_testing_data
def test_make_inverse_operator_fixed():
"""Test MNE inverse computation (fixed orientation)
"""
fwd_1 = read_forward_solution_meg(fname_fwd, surf_ori=False,
force_fixed=False)
fwd_2 = read_forward_solution_meg(fname_fwd, surf_ori=False,
force_fixed=True)
evoked = _get_evoked()
noise_cov = read_cov(fname_cov)
# can't make depth-weighted fixed inv without surf ori fwd
assert_raises(ValueError, make_inverse_operator, evoked.info, fwd_1,
noise_cov, depth=0.8, loose=None, fixed=True)
# can't make fixed inv with depth weighting without free ori fwd
assert_raises(ValueError, make_inverse_operator, evoked.info, fwd_2,
noise_cov, depth=0.8, loose=None, fixed=True)
# now compare to C solution
# note that the forward solution must not be surface-oriented
# to get equivalency (surf_ori=True changes the normals)
inv_op = make_inverse_operator(evoked.info, fwd_2, noise_cov, depth=None,
loose=None, fixed=True)
inverse_operator_nodepth = read_inverse_operator(fname_inv_fixed_nodepth)
_compare_inverses_approx(inverse_operator_nodepth, inv_op, evoked, 0, 1e-2)
# Inverse has 306 channels - 6 proj = 302
assert_true(compute_rank_inverse(inverse_operator_nodepth) == 302)
@testing.requires_testing_data
def test_make_inverse_operator_free():
"""Test MNE inverse computation (free orientation)
"""
fwd_op = read_forward_solution_meg(fname_fwd, surf_ori=True)
fwd_1 = read_forward_solution_meg(fname_fwd, surf_ori=False,
force_fixed=False)
fwd_2 = read_forward_solution_meg(fname_fwd, surf_ori=False,
force_fixed=True)
evoked = _get_evoked()
noise_cov = read_cov(fname_cov)
# can't make free inv with fixed fwd
assert_raises(ValueError, make_inverse_operator, evoked.info, fwd_2,
noise_cov, depth=None)
# for free ori inv, loose=None and loose=1 should be equivalent
inv_1 = make_inverse_operator(evoked.info, fwd_op, noise_cov, loose=None)
inv_2 = make_inverse_operator(evoked.info, fwd_op, noise_cov, loose=1)
_compare_inverses_approx(inv_1, inv_2, evoked, 0, 1e-2)
# for depth=None, surf_ori of the fwd should not matter
inv_3 = make_inverse_operator(evoked.info, fwd_op, noise_cov, depth=None,
loose=None)
inv_4 = make_inverse_operator(evoked.info, fwd_1, noise_cov, depth=None,
loose=None)
_compare_inverses_approx(inv_3, inv_4, evoked, 0, 1e-2)
@testing.requires_testing_data
def test_make_inverse_operator_diag():
"""Test MNE inverse computation with diagonal noise cov
"""
evoked = _get_evoked()
noise_cov = read_cov(fname_cov).as_diag()
fwd_op = read_forward_solution(fname_fwd, surf_ori=True)
inv_op = make_inverse_operator(evoked.info, fwd_op, noise_cov,
loose=0.2, depth=0.8)
_compare_io(inv_op)
inverse_operator_diag = read_inverse_operator(fname_inv_meeg_diag)
# This one's only good to zero decimal places, roundoff error (?)
_compare_inverses_approx(inverse_operator_diag, inv_op, evoked, 0, 1e0)
# Inverse has 366 channels - 6 proj = 360
assert_true(compute_rank_inverse(inverse_operator_diag) == 360)
@testing.requires_testing_data
def test_inverse_operator_noise_cov_rank():
"""Test MNE inverse operator with a specified noise cov rank
"""
fwd_op = read_forward_solution_meg(fname_fwd, surf_ori=True)
evoked = _get_evoked()
noise_cov = read_cov(fname_cov)
inv = make_inverse_operator(evoked.info, fwd_op, noise_cov, rank=64)
assert_true(compute_rank_inverse(inv) == 64)
fwd_op = read_forward_solution_eeg(fname_fwd, surf_ori=True)
inv = make_inverse_operator(evoked.info, fwd_op, noise_cov,
rank=dict(eeg=20))
assert_true(compute_rank_inverse(inv) == 20)
@testing.requires_testing_data
def test_inverse_operator_volume():
"""Test MNE inverse computation on volume source space
"""
tempdir = _TempDir()
evoked = _get_evoked()
inverse_operator_vol = read_inverse_operator(fname_vol_inv)
assert_true(repr(inverse_operator_vol))
stc = apply_inverse(evoked, inverse_operator_vol, lambda2, "dSPM")
assert_true(isinstance(stc, VolSourceEstimate))
# volume inverses don't have associated subject IDs
assert_true(stc.subject is None)
stc.save(op.join(tempdir, 'tmp-vl.stc'))
stc2 = read_source_estimate(op.join(tempdir, 'tmp-vl.stc'))
assert_true(np.all(stc.data > 0))
assert_true(np.all(stc.data < 35))
assert_array_almost_equal(stc.data, stc2.data)
assert_array_almost_equal(stc.times, stc2.times)
@slow_test
@testing.requires_testing_data
def test_io_inverse_operator():
"""Test IO of inverse_operator
"""
tempdir = _TempDir()
inverse_operator = read_inverse_operator(fname_inv)
x = repr(inverse_operator)
assert_true(x)
assert_true(isinstance(inverse_operator['noise_cov'], Covariance))
# just do one example for .gz, as it should generalize
_compare_io(inverse_operator, '.gz')
# test warnings on bad filenames
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
inv_badname = op.join(tempdir, 'test-bad-name.fif.gz')
write_inverse_operator(inv_badname, inverse_operator)
read_inverse_operator(inv_badname)
assert_naming(w, 'test_inverse.py', 2)
# make sure we can write and read
inv_fname = op.join(tempdir, 'test-inv.fif')
args = (10, 1. / 9., 'dSPM')
inv_prep = prepare_inverse_operator(inverse_operator, *args)
write_inverse_operator(inv_fname, inv_prep)
inv_read = read_inverse_operator(inv_fname)
_compare(inverse_operator, inv_read)
inv_read_prep = prepare_inverse_operator(inv_read, *args)
_compare(inv_prep, inv_read_prep)
inv_prep_prep = prepare_inverse_operator(inv_prep, *args)
_compare(inv_prep, inv_prep_prep)
@testing.requires_testing_data
def test_apply_mne_inverse_raw():
"""Test MNE with precomputed inverse operator on Raw."""
start = 3
stop = 10
raw = read_raw_fif(fname_raw, add_eeg_ref=False)
label_lh = read_label(fname_label % 'Aud-lh')
_, times = raw[0, start:stop]
inverse_operator = read_inverse_operator(fname_full)
inverse_operator = prepare_inverse_operator(inverse_operator, nave=1,
lambda2=lambda2, method="dSPM")
for pick_ori in [None, "normal"]:
stc = apply_inverse_raw(raw, inverse_operator, lambda2, "dSPM",
label=label_lh, start=start, stop=stop, nave=1,
pick_ori=pick_ori, buffer_size=None,
prepared=True)
stc2 = apply_inverse_raw(raw, inverse_operator, lambda2, "dSPM",
label=label_lh, start=start, stop=stop,
nave=1, pick_ori=pick_ori,
buffer_size=3, prepared=True)
if pick_ori is None:
assert_true(np.all(stc.data > 0))
assert_true(np.all(stc2.data > 0))
assert_true(stc.subject == 'sample')
assert_true(stc2.subject == 'sample')
assert_array_almost_equal(stc.times, times)
assert_array_almost_equal(stc2.times, times)
assert_array_almost_equal(stc.data, stc2.data)
@testing.requires_testing_data
def test_apply_mne_inverse_fixed_raw():
"""Test MNE with fixed-orientation inverse operator on Raw."""
raw = read_raw_fif(fname_raw, add_eeg_ref=False)
start = 3
stop = 10
_, times = raw[0, start:stop]
label_lh = read_label(fname_label % 'Aud-lh')
# create a fixed-orientation inverse operator
fwd = read_forward_solution_meg(fname_fwd, force_fixed=False,
surf_ori=True)
noise_cov = read_cov(fname_cov)
inv_op = make_inverse_operator(raw.info, fwd, noise_cov,
loose=None, depth=0.8, fixed=True)
inv_op2 = prepare_inverse_operator(inv_op, nave=1,
lambda2=lambda2, method="dSPM")
stc = apply_inverse_raw(raw, inv_op2, lambda2, "dSPM",
label=label_lh, start=start, stop=stop, nave=1,
pick_ori=None, buffer_size=None, prepared=True)
stc2 = apply_inverse_raw(raw, inv_op2, lambda2, "dSPM",
label=label_lh, start=start, stop=stop, nave=1,
pick_ori=None, buffer_size=3, prepared=True)
stc3 = apply_inverse_raw(raw, inv_op, lambda2, "dSPM",
label=label_lh, start=start, stop=stop, nave=1,
pick_ori=None, buffer_size=None)
assert_true(stc.subject == 'sample')
assert_true(stc2.subject == 'sample')
assert_array_almost_equal(stc.times, times)
assert_array_almost_equal(stc2.times, times)
assert_array_almost_equal(stc3.times, times)
assert_array_almost_equal(stc.data, stc2.data)
assert_array_almost_equal(stc.data, stc3.data)
@testing.requires_testing_data
def test_apply_mne_inverse_epochs():
"""Test MNE with precomputed inverse operator on Epochs."""
inverse_operator = read_inverse_operator(fname_full)
label_lh = read_label(fname_label % 'Aud-lh')
label_rh = read_label(fname_label % 'Aud-rh')
event_id, tmin, tmax = 1, -0.2, 0.5
raw = read_raw_fif(fname_raw, add_eeg_ref=False)
picks = pick_types(raw.info, meg=True, eeg=False, stim=True, ecg=True,
eog=True, include=['STI 014'], exclude='bads')
reject = dict(grad=4000e-13, mag=4e-12, eog=150e-6)
flat = dict(grad=1e-15, mag=1e-15)
events = read_events(fname_event)[:15]
epochs = Epochs(raw, events, event_id, tmin, tmax, picks=picks,
baseline=(None, 0), reject=reject, flat=flat,
add_eeg_ref=False)
stcs = apply_inverse_epochs(epochs, inverse_operator, lambda2, "dSPM",
label=label_lh, pick_ori="normal")
inverse_operator = prepare_inverse_operator(inverse_operator, nave=1,
lambda2=lambda2, method="dSPM")
stcs2 = apply_inverse_epochs(epochs, inverse_operator, lambda2, "dSPM",
label=label_lh, pick_ori="normal",
prepared=True)
# test if using prepared and not prepared inverse operator give the same
# result
assert_array_almost_equal(stcs[0].data, stcs2[0].data)
assert_array_almost_equal(stcs[0].times, stcs2[0].times)
assert_true(len(stcs) == 2)
assert_true(3 < stcs[0].data.max() < 10)
assert_true(stcs[0].subject == 'sample')
data = sum(stc.data for stc in stcs) / len(stcs)
flip = label_sign_flip(label_lh, inverse_operator['src'])
label_mean = np.mean(data, axis=0)
label_mean_flip = np.mean(flip[:, np.newaxis] * data, axis=0)
assert_true(label_mean.max() < label_mean_flip.max())
# test extracting a BiHemiLabel
stcs_rh = apply_inverse_epochs(epochs, inverse_operator, lambda2, "dSPM",
label=label_rh, pick_ori="normal",
prepared=True)
stcs_bh = apply_inverse_epochs(epochs, inverse_operator, lambda2, "dSPM",
label=label_lh + label_rh,
pick_ori="normal",
prepared=True)
n_lh = len(stcs[0].data)
assert_array_almost_equal(stcs[0].data, stcs_bh[0].data[:n_lh])
assert_array_almost_equal(stcs_rh[0].data, stcs_bh[0].data[n_lh:])
# test without using a label (so delayed computation is used)
stcs = apply_inverse_epochs(epochs, inverse_operator, lambda2, "dSPM",
pick_ori="normal", prepared=True)
assert_true(stcs[0].subject == 'sample')
label_stc = stcs[0].in_label(label_rh)
assert_true(label_stc.subject == 'sample')
assert_array_almost_equal(stcs_rh[0].data, label_stc.data)
@testing.requires_testing_data
def test_make_inverse_operator_bads():
"""Test MNE inverse computation given a mismatch of bad channels."""
fwd_op = read_forward_solution_meg(fname_fwd, surf_ori=True)
evoked = _get_evoked()
noise_cov = read_cov(fname_cov)
# test bads
bad = evoked.info['bads'].pop()
inv_ = make_inverse_operator(evoked.info, fwd_op, noise_cov, loose=None)
union_good = set(noise_cov['names']) & set(evoked.ch_names)
union_bads = set(noise_cov['bads']) & set(evoked.info['bads'])
evoked.info['bads'].append(bad)
assert_true(len(set(inv_['info']['ch_names']) - union_good) == 0)
assert_true(len(set(inv_['info']['bads']) - union_bads) == 0)
run_tests_if_main()
|