def uniform_seed_grid():
#read bvals,gradients and data
fimg, fbvals, fbvecs = get_data('small_64D')
bvals = np.load(fbvals)
gradients = np.load(fbvecs)
img = ni.load(fimg)
data = img.get_data()
x, y, z, g = data.shape
M = np.mgrid[.5:x - .5:np.complex(0, x), .5:y - .5:np.complex(0, y),
.5:z - .5:np.complex(0, z)]
M = M.reshape(3, x * y * z).T
print(M.shape)
print(M.dtype)
for m in M:
print(m)
gqs = GeneralizedQSampling(data, bvals, gradients)
iT = iter(EuDX(gqs.QA, gqs.IN, seeds=M))
T = []
for t in iT:
T.append(i)
print('lenT', len(T))
assert_equal(len(T), 1221)
def test_eudx():
#read bvals,gradients and data
fimg, fbvals, fbvecs = get_data('small_64D')
bvals = np.load(fbvals)
gradients = np.load(fbvecs)
img = ni.load(fimg)
data = img.get_data()
print(data.shape)
gqs = GeneralizedQSampling(data, bvals, gradients)
ten = Tensor(data, bvals, gradients, thresh=50)
seed_list = np.dot(np.diag(np.arange(10)), np.ones((10, 3)))
iT = iter(EuDX(gqs.qa(), gqs.ind(), seeds=seed_list))
T = []
for t in iT:
T.append(t)
iT2 = iter(EuDX(ten.fa(), ten.ind(), seeds=seed_list))
T2 = []
for t in iT2:
T2.append(t)
print('length T ', sum([length(t) for t in T]))
print('length T2', sum([length(t) for t in T2]))
print(gqs.QA[1, 4, 8, 0])
print(gqs.QA.ravel()[ndarray_offset(np.array([1, 4, 8, 0]),
np.array(gqs.QA.strides), 4, 8)])
assert_almost_equal(
gqs.QA[1, 4, 8, 0],
gqs.QA.ravel()[ndarray_offset(np.array([1, 4, 8, 0]),
np.array(gqs.QA.strides), 4, 8)])
assert_almost_equal(sum([length(t) for t in T]),
70.999996185302734,
places=3)
assert_almost_equal(sum([length(t) for t in T2]),
56.999997615814209,
places=3)
