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_pca_noise_estimate():
np.random.seed(1984)
# MUBE:
bvals1 = np.concatenate([np.zeros(17), np.ones(3) * 1000])
bvecs1 = np.concatenate([np.zeros((17, 3)), np.eye(3)])
gtab1 = dpg.gradient_table(bvals1, bvecs1)
# SIBE:
bvals2 = np.concatenate([np.zeros(1), np.ones(3) * 1000])
bvecs2 = np.concatenate([np.zeros((1, 3)), np.eye(3)])
gtab2 = dpg.gradient_table(bvals2, bvecs2)
for patch_radius in [1, 2]:
for gtab in [gtab1, gtab2]:
for dtype in [np.int16, np.float64]:
signal = np.ones((20, 20, 20, gtab.bvals.shape[0]))
for correct_bias in [True, False]:
if not correct_bias:
# High signal for no bias correction
signal = signal * 100
sigma = 1
noise1 = np.random.normal(0, sigma, size=signal.shape)
noise2 = np.random.normal(0, sigma, size=signal.shape)
# Rician noise:
data = np.sqrt((signal + noise1) ** 2 + noise2 ** 2)
sigma_est = pca_noise_estimate(data.astype(dtype), gtab,
correct_bias=correct_bias,
patch_radius=patch_radius)
assert_array_almost_equal(np.mean(sigma_est), sigma,
decimal=1)
assert_(np.mean(pca_noise_estimate(data, gtab, correct_bias=True)) >
np.mean(pca_noise_estimate(data, gtab, correct_bias=False)))
def test_csd_xval():
# First, let's see that it works with some data:
data = nib.load(fdata).get_data()[1:3, 1:3, 1:3] # Make it *small*
gtab = gt.gradient_table(fbval, fbvec)
S0 = np.mean(data[..., gtab.b0s_mask])
response = ([0.0015, 0.0003, 0.0001], S0)
csdm = csd.ConstrainedSphericalDeconvModel(gtab, response)
kf_xval = xval.kfold_xval(csdm, data, 2, response, sh_order=2)
# In simulation, it should work rather well (high COD):
psphere = dpd.get_sphere('symmetric362')
bvecs = np.concatenate(([[0, 0, 0]], psphere.vertices))
bvals = np.zeros(len(bvecs)) + 1000
bvals[0] = 0
gtab = gt.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] ] ) ]
S0 = 100
S = sims.single_tensor( gtab, S0, mevals[0], mevecs[0], snr=None )
sm = csd.ConstrainedSphericalDeconvModel(gtab, response)
smfit = sm.fit(S)
np.random.seed(12345)
response = ([0.0015, 0.0003, 0.0001], S0)
kf_xval = xval.kfold_xval(sm, S, 2, response, sh_order=2)
# Because of the regularization, COD is not going to be perfect here:
cod = xval.coeff_of_determination(S, kf_xval)
# We'll just test for regressions:
csd_cod = 97 # pre-computed by hand for this random seed
# We're going to be really lenient here:
npt.assert_array_almost_equal(np.round(cod), csd_cod)
def test_btable_prepare():
sq2 = np.sqrt(2) / 2.
bvals = 1500 * np.ones(7)
bvals[0] = 0
bvecs = np.array([[0, 0, 0],
[1, 0, 0],
[0, 1, 0],
[0, 0, 1],
[sq2, sq2, 0],
[sq2, 0, sq2],
[0, sq2, sq2]])
bt = gradient_table(bvals, bvecs)
npt.assert_array_equal(bt.bvecs, bvecs)
bt.info
fimg, fbvals, fbvecs = get_data('small_64D')
bvals = np.load(fbvals)
bvecs = np.load(fbvecs)
bvecs = np.where(np.isnan(bvecs), 0, bvecs)
bt = gradient_table(bvals, bvecs)
npt.assert_array_equal(bt.bvecs, bvecs)
bt2 = gradient_table(bvals, bvecs.T)
npt.assert_array_equal(bt2.bvecs, bvecs)
btab = np.concatenate((bvals[:, None], bvecs), axis=1)
bt3 = gradient_table(btab)
npt.assert_array_equal(bt3.bvecs, bvecs)
npt.assert_array_equal(bt3.bvals, bvals)
bt4 = gradient_table(btab.T)
npt.assert_array_equal(bt4.bvecs, bvecs)
npt.assert_array_equal(bt4.bvals, bvals)
# Test for proper inputs (expects either bvals/bvecs or 4 by n):
assert_raises(ValueError, gradient_table, bvecs)
def test_btable_prepare():
sq2 = np.sqrt(2) / 2.
bvals = 1500 * np.ones(7)
bvals[0] = 0
bvecs = np.array([[0, 0, 0],
[1, 0, 0],
[0, 1, 0],
[0, 0, 1],
[sq2, sq2, 0],
[sq2, 0, sq2],
[0, sq2, sq2]])
bt = gradient_table(bvals, bvecs)
npt.assert_array_equal(bt.bvecs, bvecs)
bt.info
fimg, fbvals, fbvecs = get_data('small_64D')
bvals = np.load(fbvals)
bvecs = np.load(fbvecs)
bvecs = np.where(np.isnan(bvecs), 0, bvecs)
bt = gradient_table(bvals, bvecs)
npt.assert_array_equal(bt.bvecs, bvecs)
bt2 = gradient_table(bvals, bvecs.T)
npt.assert_array_equal(bt2.bvecs, bvecs)
btab = np.concatenate((bvals[:, None], bvecs), axis=1)
bt3 = gradient_table(btab)
npt.assert_array_equal(bt3.bvecs, bvecs)
npt.assert_array_equal(bt3.bvals, bvals)
bt4 = gradient_table(btab.T)
npt.assert_array_equal(bt4.bvecs, bvecs)
npt.assert_array_equal(bt4.bvals, bvals)
def test_dti_xval():
"""
Test k-fold cross-validation
"""
data = nib.load(fdata).get_data()
gtab = gt.gradient_table(fbval, fbvec)
dm = dti.TensorModel(gtab, "LS")
# The data has 102 directions, so will not divide neatly into 10 bits
npt.assert_raises(ValueError, xval.kfold_xval, dm, data, 10)
# But we can do this with 2 folds:
kf_xval = xval.kfold_xval(dm, data, 2)
# In simulation with no noise, COD should be perfect:
psphere = dpd.get_sphere("symmetric362")
bvecs = np.concatenate(([[0, 0, 0]], psphere.vertices))
bvals = np.zeros(len(bvecs)) + 1000
bvals[0] = 0
gtab = gt.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 = sims.single_tensor(gtab, 100, mevals[0], mevecs[0], snr=None)
dm = dti.TensorModel(gtab, "LS")
kf_xval = xval.kfold_xval(dm, S, 2)
cod = xval.coeff_of_determination(S, kf_xval)
npt.assert_array_almost_equal(cod, np.ones(kf_xval.shape[:-1]) * 100)
# Test with 2D data for use of a mask
S = np.array([[S, S], [S, S]])
mask = np.ones(S.shape[:-1], dtype=bool)
mask[1, 1] = 0
kf_xval = xval.kfold_xval(dm, S, 2, mask=mask)
cod2d = xval.coeff_of_determination(S, kf_xval)
npt.assert_array_almost_equal(np.round(cod2d[0, 0]), cod)
def test_multib0_dsi():
data, gtab = dsi_voxels()
# Create a new data-set with a b0 measurement:
new_data = np.concatenate([data, data[..., 0, None]], -1)
new_bvecs = np.concatenate([gtab.bvecs, np.zeros((1, 3))])
new_bvals = np.concatenate([gtab.bvals, [0]])
new_gtab = gradient_table(new_bvals, new_bvecs)
ds = DiffusionSpectrumModel(new_gtab)
sphere = get_sphere('repulsion724')
dsfit = ds.fit(new_data)
pdf = dsfit.pdf()
dsfit.odf(sphere)
assert_equal(new_data.shape[:-1] + (17, 17, 17), pdf.shape)
assert_equal(np.alltrue(np.isreal(pdf)), True)
# And again, with one more b0 measurement (two in total):
new_data = np.concatenate([data, data[..., 0, None]], -1)
new_bvecs = np.concatenate([gtab.bvecs, np.zeros((1, 3))])
new_bvals = np.concatenate([gtab.bvals, [0]])
new_gtab = gradient_table(new_bvals, new_bvecs)
ds = DiffusionSpectrumModel(new_gtab)
dsfit = ds.fit(new_data)
pdf = dsfit.pdf()
dsfit.odf(sphere)
assert_equal(new_data.shape[:-1] + (17, 17, 17), pdf.shape)
assert_equal(np.alltrue(np.isreal(pdf)), True)
def setup_module():
"""Module-level setup"""
global gtab, gtab_2s
_, fbvals, fbvecs = get_fnames('small_64D')
bvals, bvecs = read_bvals_bvecs(fbvals, fbvecs)
gtab = gradient_table(bvals, bvecs)
# 2 shells for techniques that requires multishell data
bvals_2s = np.concatenate((bvals, bvals * 2), axis=0)
bvecs_2s = np.concatenate((bvecs, bvecs), axis=0)
gtab_2s = gradient_table(bvals_2s, bvecs_2s)
def test_nan_bvecs():
"""
Test that the presence of nan's in b-vectors doesn't raise warnings.
In previous versions, the presence of NaN in b-vectors was taken to
indicate a 0 b-value, but also raised a warning when testing for the length
of these vectors. This checks that it doesn't happen.
"""
fdata, fbvals, fbvecs = get_fnames()
with warnings.catch_warnings(record=True) as w:
gradient_table(fbvals, fbvecs)
npt.assert_(len(w) == 0)
def setup_module():
"""Module-level setup"""
global gtab, gtab_2s, mevals, model_params_mv
global DWI, FAref, GTF, MDref, FAdti, MDdti
_, fbvals, fbvecs = get_fnames('small_64D')
bvals, bvecs = read_bvals_bvecs(fbvals, fbvecs)
gtab = gradient_table(bvals, bvecs)
# FW model requires multishell data
bvals_2s = np.concatenate((bvals, bvals * 1.5), axis=0)
bvecs_2s = np.concatenate((bvecs, bvecs), axis=0)
gtab_2s = gradient_table(bvals_2s, bvecs_2s)
# Simulation a typical DT and DW signal for no water contamination
S0 = np.array(100)
dt = np.array([0.0017, 0, 0.0003, 0, 0, 0.0003])
evals, evecs = decompose_tensor(from_lower_triangular(dt))
S_tissue = single_tensor(gtab_2s, S0=100, evals=evals, evecs=evecs,
snr=None)
dm = dti.TensorModel(gtab_2s, 'WLS')
dtifit = dm.fit(S_tissue)
FAdti = dtifit.fa
MDdti = dtifit.md
dtiparams = dtifit.model_params
# Simulation of 8 voxels tested
DWI = np.zeros((2, 2, 2, len(gtab_2s.bvals)))
FAref = np.zeros((2, 2, 2))
MDref = np.zeros((2, 2, 2))
# Diffusion of tissue and water compartments are constant for all voxel
mevals = np.array([[0.0017, 0.0003, 0.0003], [0.003, 0.003, 0.003]])
# volume fractions
GTF = np.array([[[0.06, 0.71], [0.33, 0.91]],
[[0., 0.], [0., 0.]]])
# S0 multivoxel
S0m = 100 * np.ones((2, 2, 2))
# model_params ground truth (to be fill)
model_params_mv = np.zeros((2, 2, 2, 13))
for i in range(2):
for j in range(2):
gtf = GTF[0, i, j]
S, p = multi_tensor(gtab_2s, mevals, S0=100,
angles=[(90, 0), (90, 0)],
fractions=[(1-gtf) * 100, gtf*100], snr=None)
DWI[0, i, j] = S
FAref[0, i, j] = FAdti
MDref[0, i, j] = MDdti
R = all_tensor_evecs(p[0])
R = R.reshape((9))
model_params_mv[0, i, j] = \
np.concatenate(([0.0017, 0.0003, 0.0003], R, [gtf]), axis=0)
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(bvecs, bval)
#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)
# Using the gmm weighting scheme:
tensor_model = dti.TensorModel(gtab, fit_method='NLLS', weighting='gmm')
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)
# 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_eudx_bad_seed():
"""Test passing a bad seed to eudx"""
fimg, fbvals, fbvecs = get_data('small_101D')
img = ni.load(fimg)
affine = img.affine
data = img.get_data()
gtab = gradient_table(fbvals, fbvecs)
tensor_model = TensorModel(gtab)
ten = tensor_model.fit(data)
ind = quantize_evecs(ten.evecs)
sphere = get_sphere('symmetric724')
seed = [1000000., 1000000., 1000000.]
eu = EuDX(a=ten.fa, ind=ind, seeds=[seed],
odf_vertices=sphere.vertices, a_low=.2)
assert_raises(ValueError, list, eu)
print(data.shape)
seed = [1., 5., 8.]
eu = EuDX(a=ten.fa, ind=ind, seeds=[seed],
odf_vertices=sphere.vertices, a_low=.2)
track = list(eu)
seed = [-1., 1000000., 1000000.]
eu = EuDX(a=ten.fa, ind=ind, seeds=[seed],
odf_vertices=sphere.vertices, a_low=.2)
assert_raises(ValueError, list, eu)
def read_taiwan_ntu_dsi():
""" Load Taiwan NTU dataset
Returns
-------
img : obj,
Nifti1Image
gtab : obj,
GradientTable
"""
folder = pjoin(dipy_home, 'taiwan_ntu_dsi')
fraw = pjoin(folder, 'DSI203.nii.gz')
fbval = pjoin(folder, 'DSI203.bval')
fbvec = pjoin(folder, 'DSI203.bvec')
md5_dict = {'data': '950408c0980a7154cb188666a885a91f',
'bval': '602e5cb5fad2e7163e8025011d8a6755',
'bvec': 'a95eb1be44748c20214dc7aa654f9e6b',
'license': '7fa1d5e272533e832cc7453eeba23f44'}
check_md5(fraw, md5_dict['data'])
check_md5(fbval, md5_dict['bval'])
check_md5(fbvec, md5_dict['bvec'])
check_md5(pjoin(folder, 'DSI203_license.txt'), md5_dict['license'])
bvals, bvecs = read_bvals_bvecs(fbval, fbvec)
bvecs[1:] = bvecs[1:] / np.sqrt(np.sum(bvecs[1:] * bvecs[1:], axis=1))[:, None]
gtab = gradient_table(bvals, bvecs)
img = nib.load(fraw)
return img, gtab
def read_stanford_hardi():
""" Load Stanford HARDI dataset
Returns
-------
img : obj,
Nifti1Image
gtab : obj,
GradientTable
"""
folder = pjoin(dipy_home, 'stanford_hardi')
fraw = pjoin(folder, 'HARDI150.nii.gz')
fbval = pjoin(folder, 'HARDI150.bval')
fbvec = pjoin(folder, 'HARDI150.bvec')
md5_dict = {'data': '0b18513b46132b4d1051ed3364f2acbc',
'bval': '4e08ee9e2b1d2ec3fddb68c70ae23c36',
'bvec': '4c63a586f29afc6a48a5809524a76cb4'}
check_md5(fraw, md5_dict['data'])
check_md5(fbval, md5_dict['bval'])
check_md5(fbvec, md5_dict['bvec'])
bvals, bvecs = read_bvals_bvecs(fbval, fbvec)
gtab = gradient_table(bvals, bvecs)
img = nib.load(fraw)
return img, gtab
def read_sherbrooke_3shell():
""" Load Sherbrooke 3-shell HARDI dataset
Returns
-------
img : obj,
Nifti1Image
gtab : obj,
GradientTable
"""
folder = pjoin(dipy_home, 'sherbrooke_3shell')
fraw = pjoin(folder, 'HARDI193.nii.gz')
fbval = pjoin(folder, 'HARDI193.bval')
fbvec = pjoin(folder, 'HARDI193.bvec')
md5_dict = {'data': '0b735e8f16695a37bfbd66aab136eb66',
'bval': 'e9b9bb56252503ea49d31fb30a0ac637',
'bvec': '0c83f7e8b917cd677ad58a078658ebb7'}
check_md5(fraw, md5_dict['data'])
check_md5(fbval, md5_dict['bval'])
check_md5(fbvec, md5_dict['bvec'])
bvals, bvecs = read_bvals_bvecs(fbval, fbvec)
gtab = gradient_table(bvals, bvecs)
img = nib.load(fraw)
return img, gtab
def read_isbi2013_2shell():
""" Load ISBI 2013 2-shell synthetic dataset
Returns
-------
img : obj,
Nifti1Image
gtab : obj,
GradientTable
"""
folder = pjoin(dipy_home, 'isbi2013')
fraw = pjoin(folder, 'phantom64.nii.gz')
fbval = pjoin(folder, 'phantom64.bval')
fbvec = pjoin(folder, 'phantom64.bvec')
md5_dict = {'data': '42911a70f232321cf246315192d69c42',
'bval': '90e8cf66e0f4d9737a3b3c0da24df5ea',
'bvec': '4b7aa2757a1ccab140667b76e8075cb1'}
check_md5(fraw, md5_dict['data'])
check_md5(fbval, md5_dict['bval'])
check_md5(fbvec, md5_dict['bvec'])
bvals, bvecs = read_bvals_bvecs(fbval, fbvec)
gtab = gradient_table(bvals, bvecs)
img = nib.load(fraw)
return img, gtab
def test_GradientTable():
gradients = np.array([[0, 0, 0],
[1, 0, 0],
[0, 0, 1],
[3, 4, 0],
[5, 0, 12]], 'float')
expected_bvals = np.array([0, 1, 1, 5, 13])
expected_b0s_mask = expected_bvals == 0
expected_bvecs = gradients / (expected_bvals + expected_b0s_mask)[:, None]
gt = GradientTable(gradients, b0_threshold=0)
npt.assert_array_almost_equal(gt.bvals, expected_bvals)
npt.assert_array_equal(gt.b0s_mask, expected_b0s_mask)
npt.assert_array_almost_equal(gt.bvecs, expected_bvecs)
npt.assert_array_almost_equal(gt.gradients, gradients)
gt = GradientTable(gradients, b0_threshold=1)
npt.assert_array_equal(gt.b0s_mask, [1, 1, 1, 0, 0])
npt.assert_array_equal(gt.bvals, expected_bvals)
npt.assert_array_equal(gt.bvecs, expected_bvecs)
# checks negative values in gtab
npt.assert_raises(ValueError, GradientTable, -1)
npt.assert_raises(ValueError, GradientTable, np.ones((6, 2)))
npt.assert_raises(ValueError, GradientTable, np.ones((6,)))
with warnings.catch_warnings(record=True) as w:
bad_gt = gradient_table(expected_bvals, expected_bvecs,
b0_threshold=200)
assert len(w) == 1
def test_sfm():
fdata, fbvals, fbvecs = dpd.get_data()
data = nib.load(fdata).get_data()
gtab = grad.gradient_table(fbvals, fbvecs)
sfmodel = sfm.SparseFascicleModel(gtab)
sffit1 = sfmodel.fit(data[0, 0, 0])
sphere = dpd.get_sphere("symmetric642")
odf1 = sffit1.odf(sphere)
pred1 = sffit1.predict(gtab)
mask = np.ones(data.shape[:-1])
sffit2 = sfmodel.fit(data, mask)
pred2 = sffit2.predict(gtab)
odf2 = sffit2.odf(sphere)
sffit3 = sfmodel.fit(data)
pred3 = sffit3.predict(gtab)
odf3 = sffit3.odf(sphere)
npt.assert_almost_equal(pred3, pred2, decimal=2)
npt.assert_almost_equal(pred3[0, 0, 0], pred1, decimal=2)
npt.assert_almost_equal(odf3[0, 0, 0], odf1, decimal=2)
npt.assert_almost_equal(odf3[0, 0, 0], odf2[0, 0, 0], decimal=2)
# Fit zeros and you will get back zeros
npt.assert_almost_equal(
sfmodel.fit(np.zeros(data[0, 0, 0].shape)).beta, np.zeros(sfmodel.design_matrix[0].shape[-1])
)
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 generate( self, out_path, aux, idx_in, idx_out ) :
scheme_high = amico.lut.create_high_resolution_scheme( self.scheme, b_scale=1 )
gtab = gradient_table( scheme_high.b, scheme_high.raw[:,0:3] )
nATOMS = 1 + len(self.ICVFs) + len(self.d_ISOs)
progress = ProgressBar( n=nATOMS, prefix=" ", erase=True )
# Stick
signal = single_tensor( gtab, evals=[0, 0, self.d_par] )
lm = amico.lut.rotate_kernel( signal, aux, idx_in, idx_out, False )
np.save( pjoin( out_path, 'A_001.npy' ), lm )
progress.update()
# Zeppelin(s)
for d in [ self.d_par*(1.0-ICVF) for ICVF in self.ICVFs] :
signal = single_tensor( gtab, evals=[d, d, self.d_par] )
lm = amico.lut.rotate_kernel( signal, aux, idx_in, idx_out, False )
np.save( pjoin( out_path, 'A_%03d.npy'%progress.i ), lm )
progress.update()
# Ball(s)
for d in self.d_ISOs :
signal = single_tensor( gtab, evals=[d, d, d] )
lm = amico.lut.rotate_kernel( signal, aux, idx_in, idx_out, True )
np.save( pjoin( out_path, 'A_%03d.npy'%progress.i ), lm )
progress.update()
def readDataset(niifilename, niiBrainMaskFilename, btablefilename, parcellationfilename = None):
# load the masked diffusion dataset
diffusionData = nib.load(niifilename).get_data()
affine = nib.load(niifilename).get_affine()
# load the brain mask
mask = nib.load(niiBrainMaskFilename).get_data()
rows, cols, nSlices, nDirections = diffusionData.shape
bvals, bvecs = readbtable(btablefilename)
gtable = gradient_table(bvals, bvecs)
if parcellationfilename != None:
#parcellation = nib.load(parcellationfilename).get_data()
parcellation,_ = nrrd.read(parcellationfilename)
if parcellation.shape[2] != nSlices: # for the second phantom (unc_res)
parcellation = parcellation[:,:,parcellation.shape[2]-nSlices:]
parcellation = np.squeeze(parcellation)
else:
parcellation = None
return diffusionData, mask, affine, gtable, parcellation
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_csd_superres():
""" Check the quality of csdfit with high SH order. """
_, fbvals, fbvecs = get_data('small_64D')
bvals = np.load(fbvals)
bvecs = np.load(fbvecs)
gtab = gradient_table(bvals, bvecs)
# img, gtab = read_stanford_hardi()
evals = np.array([[1.5, .3, .3]]) * [[1.], [1.]] / 1000.
S, sticks = multi_tensor(gtab, evals, snr=None, fractions=[55., 45.])
model16 = ConstrainedSphericalDeconvModel(gtab, (evals[0], 3.),
sh_order=16)
fit16 = model16.fit(S)
# print local_maxima(fit16.odf(default_sphere), default_sphere.edges)
d, v, ind = peak_directions(fit16.odf(default_sphere), default_sphere,
relative_peak_threshold=.2,
min_separation_angle=0)
# Check that there are two peaks
assert_equal(len(d), 2)
# Check that peaks line up with sticks
cos_sim = abs((d * sticks).sum(1)) ** .5
assert_(all(cos_sim > .99))
def _run_interface(self, runtime): import dipy.reconst.dti as dti import dipy.denoise.noise_estimate as ne from dipy.core.gradients import gradient_table from nipype.utils.filemanip import split_filename import nibabel as nib fname = self.inputs.in_file img = nib.load(fname) data = img.get_data() affine = img.get_affine() bvals = self.inputs.bval bvecs = self.inputs.bvec gtab = gradient_table(bvals, bvecs) sigma = ne.estimate_sigma(data) dti = dti.TensorModel(gtab,fit_method='RESTORE', sigma=sigma) dtifit = dti.fit(data) fa = dtifit.fa _, base, _ = split_filename(fname) nib.save(nib.Nifti1Image(fa, affine), base + '_FA.nii') return runtime
def test_response_from_mask():
fdata, fbvals, fbvecs = get_data('small_64D')
bvals = np.load(fbvals)
bvecs = np.load(fbvecs)
data = nib.load(fdata).get_data()
gtab = gradient_table(bvals, bvecs)
ten = TensorModel(gtab)
tenfit = ten.fit(data)
FA = fractional_anisotropy(tenfit.evals)
FA[np.isnan(FA)] = 0
radius = 3
for fa_thr in np.arange(0, 1, 0.1):
response_auto, ratio_auto, nvoxels = auto_response(gtab,
data,
roi_center=None,
roi_radius=radius,
fa_thr=fa_thr,
return_number_of_voxels=True)
ci, cj, ck = np.array(data.shape[:3]) / 2
mask = np.zeros(data.shape[:3])
mask[ci - radius: ci + radius,
cj - radius: cj + radius,
ck - radius: ck + radius] = 1
mask[FA <= fa_thr] = 0
response_mask, ratio_mask = response_from_mask(gtab, data, mask)
assert_equal(int(np.sum(mask)), nvoxels)
assert_array_almost_equal(response_mask[0], response_auto[0])
assert_almost_equal(response_mask[1], response_auto[1])
assert_almost_equal(ratio_mask, ratio_auto)
def test_sphere_scaling_csdmodel():
"""Check that mirroring regularization sphere does not change the result of
the model"""
_, fbvals, fbvecs = get_data('small_64D')
bvals = np.load(fbvals)
bvecs = np.load(fbvecs)
gtab = gradient_table(bvals, bvecs)
mevals = np.array(([0.0015, 0.0003, 0.0003],
[0.0015, 0.0003, 0.0003]))
angles = [(0, 0), (60, 0)]
S, sticks = multi_tensor(gtab, mevals, 100., angles=angles,
fractions=[50, 50], snr=None)
hemi = small_sphere
sphere = hemi.mirror()
response = (np.array([0.0015, 0.0003, 0.0003]), 100)
model_full = ConstrainedSphericalDeconvModel(gtab, response,
reg_sphere=sphere)
model_hemi = ConstrainedSphericalDeconvModel(gtab, response,
reg_sphere=hemi)
csd_fit_full = model_full.fit(S)
csd_fit_hemi = model_hemi.fit(S)
assert_array_almost_equal(csd_fit_full.shm_coeff, csd_fit_hemi.shm_coeff)
def _generate_gradients(ndirs=64, values=[1000, 3000], nb0s=1):
"""
Automatically generate a `gradient table
<http://nipy.org/dipy/examples_built/gradients_spheres.html#example-gradients-spheres>`_
"""
import numpy as np
from dipy.core.sphere import (disperse_charges, Sphere, HemiSphere)
from dipy.core.gradients import gradient_table
theta = np.pi * np.random.rand(ndirs)
phi = 2 * np.pi * np.random.rand(ndirs)
hsph_initial = HemiSphere(theta=theta, phi=phi)
hsph_updated, potential = disperse_charges(hsph_initial, 5000)
values = np.atleast_1d(values).tolist()
vertices = hsph_updated.vertices
bvecs = vertices.copy()
bvals = np.ones(vertices.shape[0]) * values[0]
for v in values[1:]:
bvecs = np.vstack((bvecs, vertices))
bvals = np.hstack((bvals, v * np.ones(vertices.shape[0])))
for i in range(0, nb0s):
bvals = bvals.tolist()
bvals.insert(0, 0)
bvecs = bvecs.tolist()
bvecs.insert(0, np.zeros(3))
return gradient_table(bvals, bvecs)
def test_eudx_bad_seed():
"""Test passing a bad seed to eudx"""
fimg, fbvals, fbvecs = get_data('small_101D')
img = ni.load(fimg)
affine = img.get_affine()
data = img.get_data()
gtab = gradient_table(fbvals, fbvecs)
tensor_model = TensorModel(gtab)
ten = tensor_model.fit(data)
ind = quantize_evecs(ten.evecs)
seed = [1000000., 1000000., 1000000.]
eu = EuDX(a=ten.fa, ind=ind, seeds=[seed], a_low=.2)
try:
track = list(eu)
except ValueError as ve:
if ve.args[0] == 'Seed outside boundaries':
print(ve)
print(data.shape)
seed = [1., 5., 8.]
eu = EuDX(a=ten.fa, ind=ind, seeds=[seed], a_low=.2)
track = list(eu)
seed = [-1., 1000000., 1000000.]
eu = EuDX(a=ten.fa, ind=ind, seeds=[seed], a_low=.2)
try:
track = list(eu)
except ValueError as ve:
if ve.args[0] == 'Seed outside boundaries':
print(ve)
def test_eudx_further():
""" Cause we love testin.. ;-)
"""
fimg,fbvals,fbvecs=get_data('small_101D')
img=ni.load(fimg)
affine=img.get_affine()
data=img.get_data()
gtab = gradient_table(fbvals, fbvecs)
tensor_model = TensorModel(gtab)
ten = tensor_model.fit(data)
x,y,z=data.shape[:3]
seeds=np.zeros((10**4,3))
for i in range(10**4):
rx=(x-1)*np.random.rand()
ry=(y-1)*np.random.rand()
rz=(z-1)*np.random.rand()
seeds[i]=np.ascontiguousarray(np.array([rx,ry,rz]),dtype=np.float64)
ind = quantize_evecs(ten.evecs)
eu=EuDX(a=ten.fa, ind=ind, seeds=seeds, a_low=.2)
T=[e for e in eu]
#check that there are no negative elements
for t in T:
assert_equal(np.sum(t.ravel()<0),0)
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
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
for drop_this in range(1, Y.shape[-1]):
# RESTORE estimates should be robust to dropping
this_y = Y.copy()
this_y[:, drop_this] = 1.0
tensor_model = dti.TensorModel(gtab, fit_method='restore',
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)
import numpy as np
import random
from numpy.testing import (assert_array_almost_equal, assert_raises,
assert_almost_equal, assert_)
from dipy.sims.voxel import (single_tensor, multi_tensor_dki)
from dipy.io.gradients import read_bvals_bvecs
from dipy.core.gradients import (gradient_table, unique_bvals_magnitude,
round_bvals)
from dipy.data import get_fnames
import dipy.reconst.msdki as msdki
from dipy.reconst.msdki import (msk_from_awf, awf_from_msk)
fimg, fbvals, fbvecs = get_fnames('small_64D')
bvals, bvecs = read_bvals_bvecs(fbvals, fbvecs)
bvals = round_bvals(bvals)
gtab = gradient_table(bvals, bvecs)
# 2 shells for techniques that requires multishell data
bvals_3s = np.concatenate((bvals, bvals * 1.5, bvals * 2), axis=0)
bvecs_3s = np.concatenate((bvecs, bvecs, bvecs), axis=0)
gtab_3s = gradient_table(bvals_3s, bvecs_3s)
# Simulation 1. Spherical kurtosis tensor - MSK and MSD from the MSDKI model
# should be equal to the MK and MD of the DKI tensor for cases of
# spherical kurtosis tensors
Di = 0.00099
De = 0.00226
mevals_sph = np.array([[Di, Di, Di], [De, De, De]])
f = 0.5
frac_sph = [f * 100, (1.0 - f) * 100]
signal_sph, dt_sph, kt_sph = multi_tensor_dki(gtab_3s,
bundles = {}
for name in bundle_names:
for hemi in ['_R', '_L']:
bundles[name + hemi] = {
'ROIs': [
templates[name + '_roi1' + hemi],
templates[name + '_roi1' + hemi]
],
'rules': [True, True]
}
print("Registering to template...")
MNI_T2_img = dpd.read_mni_template()
if not op.exists('mapping.nii.gz'):
import dipy.core.gradients as dpg
gtab = dpg.gradient_table(hardi_fbval, hardi_fbvec)
mapping = reg.syn_register_dwi(hardi_fdata, gtab)
reg.write_mapping(mapping, './mapping.nii.gz')
else:
mapping = reg.read_mapping('./mapping.nii.gz', img, MNI_T2_img)
print("Segmenting fiber groups...")
fiber_groups = seg.segment(hardi_fdata,
hardi_fbval,
hardi_fbvec,
streamlines,
bundles,
reg_template=MNI_T2_img,
mapping=mapping,
as_generator=False,
affine=img.affine)
