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(),seed_list=seed_list))
T=[]
for t in iT:
T.append(t)
iT2=iter(EuDX(ten.fa(),ten.ind(),seed_list=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_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_equal, sum([length(t) for t in T ]) , 77.999996662139893
#assert_equal, sum([length(t) for t in T2]) , 63.499998092651367
assert_equal(sum([length(t) for t in T ]) , 75.214988201856613)
assert_equal(sum([length(t) for t in T2]) , 60.202986091375351)
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)
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)
def test_offset():
# Test ndarray_offset function
for dt in (np.int32, np.float64):
index = np.array([1, 1], dtype=np.intp)
A = np.array([[1, 0, 0], [0, 2, 0], [0, 0, 3]], dtype=dt)
strides = np.array(A.strides, np.intp)
i_size = A.dtype.itemsize
assert_equal(ndarray_offset(index, strides, 2, i_size), 4)
assert_equal(A.ravel()[4], A[1, 1])
# Index and strides arrays must be C-continuous. Test this is enforced
# by using non-contiguous versions of the input arrays.
assert_raises(ValueError, ndarray_offset, stepped_1d(index), strides,
2, i_size)
assert_raises(ValueError, ndarray_offset, index, stepped_1d(strides),
2, i_size)
def test_offset():
# Test ndarray_offset function
for dt in (np.int32, np.float64):
index = np.array([1, 1], dtype=np.intp)
A = np.array([[1, 0, 0], [0, 2, 0], [0, 0, 3]], dtype=dt)
strides = np.array(A.strides, np.intp)
i_size = A.dtype.itemsize
assert_equal(ndarray_offset(index, strides, 2, i_size), 4)
assert_equal(A.ravel()[4], A[1, 1])
# Index and strides arrays must be C-continuous. Test this is enforced
# by using non-contiguous versions of the input arrays.
assert_raises(ValueError, ndarray_offset,
stepped_1d(index), strides, 2, i_size)
assert_raises(ValueError, ndarray_offset,
index, stepped_1d(strides), 2, i_size)
