def test_gradient_table_from_qvals_bvecs():
qvals = 30. * np.ones(7)
big_delta = .03 # pulse separation of 30ms
small_delta = 0.01 # pulse duration of 10ms
qvals[0] = 0
sq2 = np.sqrt(2) / 2
bvecs = np.array([[0, 0, 0], [1, 0, 0], [0, 1, 0], [0, 0, 1],
[sq2, sq2, 0], [sq2, 0, sq2], [0, sq2, sq2]])
gt = gradient_table_from_qvals_bvecs(qvals, bvecs, big_delta, small_delta)
bvals_expected = (qvals * 2 * np.pi)**2 * (big_delta - small_delta / 3.)
gradient_strength_expected = qvals * 2 * np.pi /\
(small_delta * WATER_GYROMAGNETIC_RATIO)
npt.assert_almost_equal(gt.gradient_strength, gradient_strength_expected)
npt.assert_almost_equal(gt.bvals, bvals_expected)
def generate_gtab4D(number_of_tau_shells=4, delta=0.01):
"""Generates testing gradient table for 4D qt-dMRI scheme"""
gtab = get_gtab_taiwan_dsi()
qvals = np.tile(gtab.bvals / 100., number_of_tau_shells)
bvecs = np.tile(gtab.bvecs, (number_of_tau_shells, 1))
pulse_separation = []
for ps in np.linspace(0.02, 0.05, number_of_tau_shells):
pulse_separation = np.append(pulse_separation,
np.tile(ps, gtab.bvals.shape[0]))
pulse_duration = np.tile(delta, qvals.shape[0])
gtab_4d = gradient_table_from_qvals_bvecs(qvals=qvals, bvecs=bvecs,
big_delta=pulse_separation,
small_delta=pulse_duration,
b0_threshold=0)
return gtab_4d
def generate_gtab4D(number_of_tau_shells=4, delta=0.01):
"""Generates testing gradient table for 4D qt-dMRI scheme"""
gtab = get_gtab_taiwan_dsi()
qvals = np.tile(gtab.bvals / 100., number_of_tau_shells)
bvecs = np.tile(gtab.bvecs, (number_of_tau_shells, 1))
pulse_separation = []
for ps in np.linspace(0.02, 0.05, number_of_tau_shells):
pulse_separation = np.append(pulse_separation,
np.tile(ps, gtab.bvals.shape[0]))
pulse_duration = np.tile(delta, qvals.shape[0])
gtab_4d = gradient_table_from_qvals_bvecs(qvals=qvals, bvecs=bvecs,
big_delta=pulse_separation,
small_delta=pulse_duration,
b0_threshold=0)
return gtab_4d
def test_gradient_table_from_qvals_bvecs():
qvals = 30. * np.ones(7)
big_delta = .03 # pulse separation of 30ms
small_delta = 0.01 # pulse duration of 10ms
qvals[0] = 0
sq2 = np.sqrt(2) / 2
bvecs = np.array([[0, 0, 0],
[1, 0, 0],
[0, 1, 0],
[0, 0, 1],
[sq2, sq2, 0],
[sq2, 0, sq2],
[0, sq2, sq2]])
gt = gradient_table_from_qvals_bvecs(qvals, bvecs,
big_delta, small_delta)
bvals_expected = (qvals * 2 * np.pi) ** 2 * (big_delta - small_delta / 3.)
gradient_strength_expected = qvals * 2 * np.pi /\
(small_delta * WATER_GYROMAGNETIC_RATIO)
npt.assert_almost_equal(gt.gradient_strength, gradient_strength_expected)
npt.assert_almost_equal(gt.bvals, bvals_expected)
