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)