/usr/lib/python2.7/dist-packages/dipy/reconst/tests/test_dti.py is in python-dipy 0.10.1-1.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 | """ Testing DTI
"""
from __future__ import division, print_function, absolute_import
import numpy as np
from nose.tools import (assert_true, assert_equal,
assert_almost_equal, assert_raises)
import numpy.testing as npt
from numpy.testing import (assert_array_equal, assert_array_almost_equal,
assert_)
import nibabel as nib
import scipy.optimize as opt
import dipy.reconst.dti as dti
from dipy.reconst.dti import (axial_diffusivity, color_fa,
fractional_anisotropy, from_lower_triangular,
geodesic_anisotropy, lower_triangular,
mean_diffusivity, radial_diffusivity,
TensorModel, trace, linearity, planarity,
sphericity)
from dipy.io.bvectxt import read_bvec_file
from dipy.data import get_data, dsi_voxels, get_sphere
from dipy.core.subdivide_octahedron import create_unit_sphere
import dipy.core.gradients as grad
import dipy.core.sphere as dps
from dipy.sims.voxel import single_tensor
def test_roll_evals():
"""
"""
# Just making sure this never passes through
weird_evals = np.array([1, 0.5])
npt.assert_raises(ValueError, dti._roll_evals, weird_evals)
def test_tensor_algebra():
"""
Test that the computation of tensor determinant and norm is correct
"""
test_arr = np.random.rand(10, 3, 3)
t_det = dti.determinant(test_arr)
t_norm = dti.norm(test_arr)
for i, x in enumerate(test_arr):
assert_almost_equal(np.linalg.det(x), t_det[i])
assert_almost_equal(np.linalg.norm(x), t_norm[i])
def test_tensor_model():
fdata, fbval, fbvec = get_data('small_25')
data1 = nib.load(fdata).get_data()
gtab1 = grad.gradient_table(fbval, fbvec)
data2, gtab2 = dsi_voxels()
for data, gtab in zip([data1, data2], [gtab1, gtab2]):
dm = dti.TensorModel(gtab, 'LS')
dtifit = dm.fit(data[0, 0, 0])
assert_equal(dtifit.fa < 0.9, True)
dm = dti.TensorModel(gtab, 'WLS')
dtifit = dm.fit(data[0, 0, 0])
assert_equal(dtifit.fa < 0.9, True)
assert_equal(dtifit.fa > 0, True)
sphere = create_unit_sphere(4)
assert_equal(len(dtifit.odf(sphere)), len(sphere.vertices))
# Check that the multivoxel case works:
dtifit = dm.fit(data)
# Check that it works on signal that has already been normalized to S0:
dm_to_relative = dti.TensorModel(gtab)
if np.any(gtab.b0s_mask):
relative_data = (data[0, 0, 0]/np.mean(data[0, 0, 0,
gtab.b0s_mask]))
dtifit_to_relative = dm_to_relative.fit(relative_data)
npt.assert_almost_equal(dtifit.fa[0, 0, 0], dtifit_to_relative.fa,
decimal=3)
# And smoke-test that all these operations return sensibly-shaped arrays:
assert_equal(dtifit.fa.shape, data.shape[:3])
assert_equal(dtifit.ad.shape, data.shape[:3])
assert_equal(dtifit.md.shape, data.shape[:3])
assert_equal(dtifit.rd.shape, data.shape[:3])
assert_equal(dtifit.trace.shape, data.shape[:3])
assert_equal(dtifit.mode.shape, data.shape[:3])
assert_equal(dtifit.linearity.shape, data.shape[:3])
assert_equal(dtifit.planarity.shape, data.shape[:3])
assert_equal(dtifit.sphericity.shape, data.shape[:3])
# Test for the shape of the mask
assert_raises(ValueError, dm.fit, np.ones((10, 10, 3)), np.ones((3, 3)))
# Make some synthetic data
b0 = 1000.
bvecs, bvals = read_bvec_file(get_data('55dir_grad.bvec'))
gtab = grad.gradient_table_from_bvals_bvecs(bvals, bvecs.T)
# The first b value is 0., so we take the second one:
B = bvals[1]
# Scale the eigenvalues and tensor by the B value so the units match
D = np.array([1., 1., 1., 0., 0., 1., -np.log(b0) * B]) / B
evals = np.array([2., 1., 0.]) / B
md = evals.mean()
tensor = from_lower_triangular(D)
A_squiggle = tensor - (1 / 3.0) * np.trace(tensor) * np.eye(3)
mode = (3 * np.sqrt(6) * np.linalg.det(A_squiggle /
np.linalg.norm(A_squiggle)))
evals_eigh, evecs_eigh = np.linalg.eigh(tensor)
# Sort according to eigen-value from large to small:
evecs = evecs_eigh[:, np.argsort(evals_eigh)[::-1]]
# Check that eigenvalues and eigenvectors are properly sorted through
# that previous operation:
for i in range(3):
assert_array_almost_equal(np.dot(tensor, evecs[:, i]),
evals[i] * evecs[:, i])
# Design Matrix
X = dti.design_matrix(gtab)
# Signals
Y = np.exp(np.dot(X, D))
assert_almost_equal(Y[0], b0)
Y.shape = (-1,) + Y.shape
# Test fitting with different methods:
for fit_method in ['OLS', 'WLS', 'NLLS']:
tensor_model = dti.TensorModel(gtab,
fit_method=fit_method)
tensor_fit = tensor_model.fit(Y)
assert_true(tensor_fit.model is tensor_model)
assert_equal(tensor_fit.shape, Y.shape[:-1])
assert_array_almost_equal(tensor_fit.evals[0], evals)
# Test that the eigenvectors are correct, one-by-one:
for i in range(3):
# Eigenvectors have intrinsic sign ambiguity
# (see
# http://prod.sandia.gov/techlib/access-control.cgi/2007/076422.pdf)
# so we need to allow for sign flips. One of the following should
# always be true:
assert_(
np.all(np.abs(tensor_fit.evecs[0][:, i] -
evecs[:, i]) < 10e-6) or
np.all(np.abs(-tensor_fit.evecs[0][:, i] -
evecs[:, i]) < 10e-6))
# We set a fixed tolerance of 10e-6, similar to array_almost_equal
err_msg = "Calculation of tensor from Y does not compare to "
err_msg += "analytical solution"
assert_array_almost_equal(tensor_fit.quadratic_form[0], tensor,
err_msg=err_msg)
assert_almost_equal(tensor_fit.md[0], md)
assert_array_almost_equal(tensor_fit.mode, mode, decimal=5)
assert_equal(tensor_fit.directions.shape[-2], 1)
assert_equal(tensor_fit.directions.shape[-1], 3)
# Test error-handling:
assert_raises(ValueError,
dti.TensorModel,
gtab,
fit_method='crazy_method')
# Test custom fit tensor method
try:
model = dti.TensorModel(gtab, fit_method=lambda *args, **kwargs: 42)
fit = model.fit_method()
except Exception as exc:
assert False, "TensorModel should accept custom fit methods: %s" % exc
assert fit == 42, "Custom fit method for TensorModel returned %s." % fit
# Test multi-voxel data
data = np.zeros((3, Y.shape[1]))
# Normal voxel
data[0] = Y
# High diffusion voxel, all diffusing weighted signal equal to zero
data[1, gtab.b0s_mask] = b0
data[1, ~gtab.b0s_mask] = 0
# Masked voxel, all data set to zero
data[2] = 0.
tensor_model = dti.TensorModel(gtab)
fit = tensor_model.fit(data)
assert_array_almost_equal(fit[0].evals, evals)
# Evals should be high for high diffusion voxel
assert_(all(fit[1].evals > evals[0] * .9))
# Evals should be zero where data is masked
assert_array_almost_equal(fit[2].evals, 0.)
def test_indexing_on_tensor_fit():
params = np.zeros([2, 3, 4, 12])
fit = dti.TensorFit(None, params)
# Should return a TensorFit of appropriate shape
assert_equal(fit.shape, (2, 3, 4))
fit1 = fit[0]
assert_equal(fit1.shape, (3, 4))
assert_equal(type(fit1), dti.TensorFit)
fit1 = fit[0, 0, 0]
assert_equal(fit1.shape, ())
assert_equal(type(fit1), dti.TensorFit)
fit1 = fit[[0], slice(None)]
assert_equal(fit1.shape, (1, 3, 4))
assert_equal(type(fit1), dti.TensorFit)
# Should raise an index error if too many indices are passed
assert_raises(IndexError, fit.__getitem__, (0, 0, 0, 0))
def test_fa_of_zero():
evals = np.zeros((4, 3))
fa = fractional_anisotropy(evals)
assert_array_equal(fa, 0)
def test_ga_of_zero():
evals = np.zeros((4, 3))
ga = geodesic_anisotropy(evals)
assert_array_equal(ga, 0)
def test_diffusivities():
psphere = get_sphere('symmetric362')
bvecs = np.concatenate(([[0, 0, 0]], psphere.vertices))
bvals = np.zeros(len(bvecs)) + 1000
bvals[0] = 0
gtab = grad.gradient_table(bvals, bvecs)
mevals = np.array(([0.0015, 0.0003, 0.0001], [0.0015, 0.0003, 0.0003]))
mevecs = [np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]]),
np.array([[0, 0, 1], [0, 1, 0], [1, 0, 0]])]
S = single_tensor(gtab, 100, mevals[0], mevecs[0], snr=None)
dm = dti.TensorModel(gtab, 'LS')
dmfit = dm.fit(S)
md = mean_diffusivity(dmfit.evals)
Trace = trace(dmfit.evals)
rd = radial_diffusivity(dmfit.evals)
ad = axial_diffusivity(dmfit.evals)
lin = linearity(dmfit.evals)
plan = planarity(dmfit.evals)
spher = sphericity(dmfit.evals)
assert_almost_equal(md, (0.0015 + 0.0003 + 0.0001) / 3)
assert_almost_equal(Trace, (0.0015 + 0.0003 + 0.0001))
assert_almost_equal(ad, 0.0015)
assert_almost_equal(rd, (0.0003 + 0.0001) / 2)
assert_almost_equal(lin, (0.0015 - 0.0003)/Trace)
assert_almost_equal(plan, 2 * (0.0003 - 0.0001)/Trace)
assert_almost_equal(spher, (3 * 0.0001)/Trace)
def test_color_fa():
data, gtab = dsi_voxels()
dm = dti.TensorModel(gtab, 'LS')
dmfit = dm.fit(data)
fa = fractional_anisotropy(dmfit.evals)
cfa = color_fa(fa, dmfit.evecs)
fa = np.ones((3, 3, 3))
# evecs should be of shape (fa, 3, 3)
evecs = np.zeros(fa.shape + (3, 2))
npt.assert_raises(ValueError, color_fa, fa, evecs)
evecs = np.zeros(fa.shape + (3, 3))
evecs[..., :, :] = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])
assert_equal(fa.shape, evecs[..., 0, 0].shape)
assert_equal((3, 3), evecs.shape[-2:])
# 3D test case
fa = np.ones((3, 3, 3))
evecs = np.zeros(fa.shape + (3, 3))
evecs[..., :, :] = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])
cfa = color_fa(fa, evecs)
cfa_truth = np.array([1, 0, 0])
true_cfa = np.reshape(np.tile(cfa_truth, 27), [3, 3, 3, 3])
assert_array_equal(cfa, true_cfa)
# 2D test case
fa = np.ones((3, 3))
evecs = np.zeros(fa.shape + (3, 3))
evecs[..., :, :] = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])
cfa = color_fa(fa, evecs)
cfa_truth = np.array([1, 0, 0])
true_cfa = np.reshape(np.tile(cfa_truth, 9), [3, 3, 3])
assert_array_equal(cfa, true_cfa)
# 1D test case
fa = np.ones((3))
evecs = np.zeros(fa.shape + (3, 3))
evecs[..., :, :] = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])
cfa = color_fa(fa, evecs)
cfa_truth = np.array([1, 0, 0])
true_cfa = np.reshape(np.tile(cfa_truth, 3), [3, 3])
assert_array_equal(cfa, true_cfa)
def test_wls_and_ls_fit():
"""
Tests the WLS and LS fitting functions to see if they returns the correct
eigenvalues and eigenvectors.
Uses data/55dir_grad.bvec as the gradient table and 3by3by56.nii
as the data.
"""
# Defining Test Voxel (avoid nibabel dependency) ###
# Recall: D = [Dxx,Dyy,Dzz,Dxy,Dxz,Dyz,log(S_0)] and D ~ 10^-4 mm^2 /s
b0 = 1000.
bvec, bval = read_bvec_file(get_data('55dir_grad.bvec'))
B = bval[1]
# Scale the eigenvalues and tensor by the B value so the units match
D = np.array([1., 1., 1., 0., 0., 1., -np.log(b0) * B]) / B
evals = np.array([2., 1., 0.]) / B
md = evals.mean()
tensor = from_lower_triangular(D)
# Design Matrix
gtab = grad.gradient_table(bval, bvec)
X = dti.design_matrix(gtab)
# Signals
Y = np.exp(np.dot(X, D))
assert_almost_equal(Y[0], b0)
Y.shape = (-1,) + Y.shape
# Testing WLS Fit on Single Voxel
# If you do something wonky (passing min_signal<0), you should get an
# error:
npt.assert_raises(ValueError, TensorModel, gtab, fit_method='WLS',
min_signal=-1)
# Estimate tensor from test signals
model = TensorModel(gtab, fit_method='WLS')
tensor_est = model.fit(Y)
assert_equal(tensor_est.shape, Y.shape[:-1])
assert_array_almost_equal(tensor_est.evals[0], evals)
assert_array_almost_equal(tensor_est.quadratic_form[0], tensor,
err_msg="Calculation of tensor from Y does not "
"compare to analytical solution")
assert_almost_equal(tensor_est.md[0], md)
# Test that we can fit a single voxel's worth of data (a 1d array)
y = Y[0]
tensor_est = model.fit(y)
assert_equal(tensor_est.shape, tuple())
assert_array_almost_equal(tensor_est.evals, evals)
assert_array_almost_equal(tensor_est.quadratic_form, tensor)
assert_almost_equal(tensor_est.md, md)
assert_array_almost_equal(tensor_est.lower_triangular(b0), D)
# Test using fit_method='LS'
model = TensorModel(gtab, fit_method='LS')
tensor_est = model.fit(y)
assert_equal(tensor_est.shape, tuple())
assert_array_almost_equal(tensor_est.evals, evals)
assert_array_almost_equal(tensor_est.quadratic_form, tensor)
assert_almost_equal(tensor_est.md, md)
assert_array_almost_equal(tensor_est.lower_triangular(b0), D)
assert_array_almost_equal(tensor_est.linearity, linearity(evals))
assert_array_almost_equal(tensor_est.planarity, planarity(evals))
assert_array_almost_equal(tensor_est.sphericity, sphericity(evals))
def test_masked_array_with_tensor():
data = np.ones((2, 4, 56))
mask = np.array([[True, False, False, True],
[True, False, True, False]])
bvec, bval = read_bvec_file(get_data('55dir_grad.bvec'))
gtab = grad.gradient_table_from_bvals_bvecs(bval, bvec.T)
tensor_model = TensorModel(gtab)
tensor = tensor_model.fit(data, mask=mask)
assert_equal(tensor.shape, (2, 4))
assert_equal(tensor.fa.shape, (2, 4))
assert_equal(tensor.evals.shape, (2, 4, 3))
assert_equal(tensor.evecs.shape, (2, 4, 3, 3))
tensor = tensor[0]
assert_equal(tensor.shape, (4,))
assert_equal(tensor.fa.shape, (4,))
assert_equal(tensor.evals.shape, (4, 3))
assert_equal(tensor.evecs.shape, (4, 3, 3))
tensor = tensor[0]
assert_equal(tensor.shape, tuple())
assert_equal(tensor.fa.shape, tuple())
assert_equal(tensor.evals.shape, (3,))
assert_equal(tensor.evecs.shape, (3, 3))
assert_equal(type(tensor.model_params), np.ndarray)
def test_fit_method_error():
bvec, bval = read_bvec_file(get_data('55dir_grad.bvec'))
gtab = grad.gradient_table_from_bvals_bvecs(bval, bvec.T)
# This should work (smoke-testing!):
TensorModel(gtab, fit_method='WLS')
# This should raise an error because there is no such fit_method
assert_raises(ValueError, TensorModel, gtab, min_signal=1e-9,
fit_method='s')
def test_lower_triangular():
tensor = np.arange(9).reshape((3, 3))
D = lower_triangular(tensor)
assert_array_equal(D, [0, 3, 4, 6, 7, 8])
D = lower_triangular(tensor, 1)
assert_array_equal(D, [0, 3, 4, 6, 7, 8, 0])
assert_raises(ValueError, lower_triangular, np.zeros((2, 3)))
shape = (4, 5, 6)
many_tensors = np.empty(shape + (3, 3))
many_tensors[:] = tensor
result = np.empty(shape + (6,))
result[:] = [0, 3, 4, 6, 7, 8]
D = lower_triangular(many_tensors)
assert_array_equal(D, result)
D = lower_triangular(many_tensors, 1)
result = np.empty(shape + (7,))
result[:] = [0, 3, 4, 6, 7, 8, 0]
assert_array_equal(D, result)
def test_from_lower_triangular():
result = np.array([[0, 1, 3],
[1, 2, 4],
[3, 4, 5]])
D = np.arange(7)
tensor = from_lower_triangular(D)
assert_array_equal(tensor, result)
result = result * np.ones((5, 4, 1, 1))
D = D * np.ones((5, 4, 1))
tensor = from_lower_triangular(D)
assert_array_equal(tensor, result)
def test_all_constant():
bvecs, bvals = read_bvec_file(get_data('55dir_grad.bvec'))
gtab = grad.gradient_table_from_bvals_bvecs(bvals, bvecs.T)
fit_methods = ['LS', 'OLS', 'NNLS', 'RESTORE']
for fit_method in fit_methods:
dm = dti.TensorModel(gtab)
assert_almost_equal(dm.fit(100 * np.ones(bvals.shape[0])).fa, 0)
# Doesn't matter if the signal is smaller than 1:
assert_almost_equal(dm.fit(0.4 * np.ones(bvals.shape[0])).fa, 0)
def test_all_zeros():
bvecs, bvals = read_bvec_file(get_data('55dir_grad.bvec'))
gtab = grad.gradient_table_from_bvals_bvecs(bvals, bvecs.T)
fit_methods = ['LS', 'OLS', 'NNLS', 'RESTORE']
for fit_method in fit_methods:
dm = dti.TensorModel(gtab)
assert_array_almost_equal(dm.fit(np.zeros(bvals.shape[0])).evals, 0)
def test_mask():
data, gtab = dsi_voxels()
dm = dti.TensorModel(gtab, 'LS')
mask = np.zeros(data.shape[:-1], dtype=bool)
mask[0, 0, 0] = True
dtifit = dm.fit(data)
dtifit_w_mask = dm.fit(data, mask=mask)
# Without a mask it has some value
assert_(not np.isnan(dtifit.fa[0, 0, 0]))
# Where mask is False, evals, evecs and fa should all be 0
assert_array_equal(dtifit_w_mask.evals[~mask], 0)
assert_array_equal(dtifit_w_mask.evecs[~mask], 0)
assert_array_equal(dtifit_w_mask.fa[~mask], 0)
# Except for the one voxel that was selected by the mask:
assert_almost_equal(dtifit_w_mask.fa[0, 0, 0], dtifit.fa[0, 0, 0])
def test_nnls_jacobian_fucn():
b0 = 1000.
bvecs, bval = read_bvec_file(get_data('55dir_grad.bvec'))
gtab = grad.gradient_table(bval, bvecs)
B = bval[1]
# Scale the eigenvalues and tensor by the B value so the units match
D = np.array([1., 1., 1., 0., 0., 1., -np.log(b0) * B]) / B
# Design Matrix
X = dti.design_matrix(gtab)
# Signals
Y = np.exp(np.dot(X, D))
# Test Jacobian at D
args = [X, Y]
analytical = dti._nlls_jacobian_func(D, *args)
for i in range(len(X)):
args = [X[i], Y[i]]
approx = opt.approx_fprime(D, dti._nlls_err_func, 1e-8, *args)
assert_true(np.allclose(approx, analytical[i]))
# Test Jacobian at zero
D = np.zeros_like(D)
args = [X, Y]
analytical = dti._nlls_jacobian_func(D, *args)
for i in range(len(X)):
args = [X[i], Y[i]]
approx = opt.approx_fprime(D, dti._nlls_err_func, 1e-8, *args)
assert_true(np.allclose(approx, analytical[i]))
def test_nlls_fit_tensor():
"""
Test the implementation of NLLS and RESTORE
"""
b0 = 1000.
bvecs, bval = read_bvec_file(get_data('55dir_grad.bvec'))
gtab = grad.gradient_table(bval, bvecs)
B = bval[1]
# Scale the eigenvalues and tensor by the B value so the units match
D = np.array([1., 1., 1., 0., 0., 1., -np.log(b0) * B]) / B
evals = np.array([2., 1., 0.]) / B
md = evals.mean()
tensor = from_lower_triangular(D)
# Design Matrix
X = dti.design_matrix(gtab)
# Signals
Y = np.exp(np.dot(X, D))
Y.shape = (-1,) + Y.shape
# Estimate tensor from test signals and compare against expected result
# using non-linear least squares:
tensor_model = dti.TensorModel(gtab, fit_method='NLLS')
tensor_est = tensor_model.fit(Y)
assert_equal(tensor_est.shape, Y.shape[:-1])
assert_array_almost_equal(tensor_est.evals[0], evals)
assert_array_almost_equal(tensor_est.quadratic_form[0], tensor)
assert_almost_equal(tensor_est.md[0], md)
# You can also do this without the Jacobian (though it's slower):
tensor_model = dti.TensorModel(gtab, fit_method='NLLS', jac=False)
tensor_est = tensor_model.fit(Y)
assert_equal(tensor_est.shape, Y.shape[:-1])
assert_array_almost_equal(tensor_est.evals[0], evals)
assert_array_almost_equal(tensor_est.quadratic_form[0], tensor)
assert_almost_equal(tensor_est.md[0], md)
# Using the gmm weighting scheme:
tensor_model = dti.TensorModel(gtab, fit_method='NLLS', weighting='gmm')
tensor_est = tensor_model.fit(Y)
assert_equal(tensor_est.shape, Y.shape[:-1])
assert_array_almost_equal(tensor_est.evals[0], evals)
assert_array_almost_equal(tensor_est.quadratic_form[0], tensor)
assert_almost_equal(tensor_est.md[0], md)
# If you use sigma weighting, you'd better provide a sigma:
tensor_model = dti.TensorModel(gtab, fit_method='NLLS', weighting='sigma')
npt.assert_raises(ValueError, tensor_model.fit, Y)
# Use NLLS with some actual 4D data:
data, bvals, bvecs = get_data('small_25')
gtab = grad.gradient_table(bvals, bvecs)
tm1 = dti.TensorModel(gtab, fit_method='NLLS')
dd = nib.load(data).get_data()
tf1 = tm1.fit(dd)
tm2 = dti.TensorModel(gtab)
tf2 = tm2.fit(dd)
assert_array_almost_equal(tf1.fa, tf2.fa, decimal=1)
def test_restore():
"""
Test the implementation of the RESTORE algorithm
"""
b0 = 1000.
bvecs, bval = read_bvec_file(get_data('55dir_grad.bvec'))
gtab = grad.gradient_table(bval, bvecs)
B = bval[1]
# Scale the eigenvalues and tensor by the B value so the units match
D = np.array([1., 1., 1., 0., 0., 1., -np.log(b0) * B]) / B
evals = np.array([2., 1., 0.]) / B
tensor = from_lower_triangular(D)
# Design Matrix
X = dti.design_matrix(gtab)
# Signals
Y = np.exp(np.dot(X, D))
Y.shape = (-1,) + Y.shape
for drop_this in range(1, Y.shape[-1]):
for jac in [True, False]:
# RESTORE estimates should be robust to dropping
this_y = Y.copy()
this_y[:, drop_this] = 1.0
for sigma in [67.0, np.ones(this_y.shape[-1]) * 67.0]:
tensor_model = dti.TensorModel(gtab, fit_method='restore',
jac=jac,
sigma=67.0)
tensor_est = tensor_model.fit(this_y)
assert_array_almost_equal(tensor_est.evals[0], evals,
decimal=3)
assert_array_almost_equal(tensor_est.quadratic_form[0], tensor,
decimal=3)
# If sigma is very small, it still needs to work:
tensor_model = dti.TensorModel(gtab, fit_method='restore', sigma=0.0001)
tensor_model.fit(Y.copy())
def test_adc():
"""
Test the implementation of the calculation of apparent diffusion
coefficient
"""
data, gtab = dsi_voxels()
dm = dti.TensorModel(gtab, 'LS')
mask = np.zeros(data.shape[:-1], dtype=bool)
mask[0, 0, 0] = True
dtifit = dm.fit(data)
# The ADC in the principal diffusion direction should be equal to the AD in
# each voxel:
pdd0 = dtifit.evecs[0, 0, 0, 0]
sphere_pdd0 = dps.Sphere(x=pdd0[0], y=pdd0[1], z=pdd0[2])
assert_array_almost_equal(dtifit.adc(sphere_pdd0)[0, 0, 0],
dtifit.ad[0, 0, 0], decimal=5)
# Test that it works for cases in which the data is 1D
dtifit = dm.fit(data[0, 0, 0])
sphere_pdd0 = dps.Sphere(x=pdd0[0], y=pdd0[1], z=pdd0[2])
assert_array_almost_equal(dtifit.adc(sphere_pdd0),
dtifit.ad, decimal=5)
def test_predict():
"""
Test model prediction API
"""
psphere = get_sphere('symmetric362')
bvecs = np.concatenate(([[1, 0, 0]], psphere.vertices))
bvals = np.zeros(len(bvecs)) + 1000
bvals[0] = 0
gtab = grad.gradient_table(bvals, bvecs)
mevals = np.array(([0.0015, 0.0003, 0.0001], [0.0015, 0.0003, 0.0003]))
mevecs = [np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]]),
np.array([[0, 0, 1], [0, 1, 0], [1, 0, 0]])]
S = single_tensor(gtab, 100, mevals[0], mevecs[0], snr=None)
dm = dti.TensorModel(gtab, 'LS')
dmfit = dm.fit(S)
assert_array_almost_equal(dmfit.predict(gtab, S0=100), S)
assert_array_almost_equal(dm.predict(dmfit.model_params, S0=100), S)
fdata, fbvals, fbvecs = get_data()
data = nib.load(fdata).get_data()
# Make the data cube a bit larger:
data = np.tile(data.T, 2).T
gtab = grad.gradient_table(fbvals, fbvecs)
dtim = dti.TensorModel(gtab)
dtif = dtim.fit(data)
S0 = np.mean(data[..., gtab.b0s_mask], -1)
p = dtif.predict(gtab, S0)
assert_equal(p.shape, data.shape)
def test_eig_from_lo_tri():
psphere = get_sphere('symmetric362')
bvecs = np.concatenate(([[0, 0, 0]], psphere.vertices))
bvals = np.zeros(len(bvecs)) + 1000
bvals[0] = 0
gtab = grad.gradient_table(bvals, bvecs)
mevals = np.array(([0.0015, 0.0003, 0.0001], [0.0015, 0.0003, 0.0003]))
mevecs = [np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]]),
np.array([[0, 0, 1], [0, 1, 0], [1, 0, 0]])]
S = np.array([[single_tensor(gtab, 100, mevals[0], mevecs[0], snr=None),
single_tensor(gtab, 100, mevals[0], mevecs[0], snr=None)]])
dm = dti.TensorModel(gtab, 'LS')
dmfit = dm.fit(S)
lo_tri = lower_triangular(dmfit.quadratic_form)
assert_array_almost_equal(dti.eig_from_lo_tri(lo_tri), dmfit.model_params)
|