def get_pmf(self, point):
"""Produces an ODF from a SH bootstrap sample"""
where_dwi = self.where_dwi
bootdata = trilinear_interpolate4d(self.data, point)
dwidata = bootdata[where_dwi]
dwidata = shm.bootstrap_data_voxel(dwidata, self.H, self.R)
bootdata[where_dwi] = dwidata
fit = self.model.fit(bootdata)
return fit.odf(self.sphere)
def test_trilinear_interpolate():
a, b, c = np.random.random(3)
def linear_function(x, y, z):
return a * x + b * y + c * z
N = 6
x, y, z = np.mgrid[:N, :N, :N]
data = np.empty((N, N, N, 2))
data[..., 0] = linear_function(x, y, z)
data[..., 1] = 99.
# Use a point not near the edges
point = np.array([2.1, 4.8, 3.3])
out = trilinear_interpolate4d(data, point)
expected = [linear_function(*point), 99.]
npt.assert_array_almost_equal(out, expected)
# Pass in out ourselves
out[:] = -1
trilinear_interpolate4d(data, point, out)
npt.assert_array_almost_equal(out, expected)
# use a point close to an edge
point = np.array([-.1, -.1, -.1])
expected = [0., 99.]
out = trilinear_interpolate4d(data, point)
npt.assert_array_almost_equal(out, expected)
# different edge
point = np.array([2.4, 5.4, 3.3])
# On the edge 5.4 get treated as the max y value, 5.
expected = [linear_function(point[0], 5., point[2]), 99.]
out = trilinear_interpolate4d(data, point)
npt.assert_array_almost_equal(out, expected)
# Test index errors
point = np.array([2.4, 5.5, 3.3])
npt.assert_raises(IndexError, trilinear_interpolate4d, data, point)
point = np.array([2.4, -1., 3.3])
npt.assert_raises(IndexError, trilinear_interpolate4d, data, point)
def test_trilinear_interpolate():
a, b, c = np.random.random(3)
def linear_function(x, y, z):
return a * x + b * y + c * z
N = 6
x, y, z = np.mgrid[:N, :N, :N]
data = np.empty((N, N, N, 2))
data[..., 0] = linear_function(x, y, z)
data[..., 1] = 99.
# Use a point not near the edges
point = np.array([2.1, 4.8, 3.3])
out = trilinear_interpolate4d(data, point)
expected = [linear_function(*point), 99.]
npt.assert_array_almost_equal(out, expected)
# Pass in out ourselves
out[:] = -1
trilinear_interpolate4d(data, point, out)
npt.assert_array_almost_equal(out, expected)
# use a point close to an edge
point = np.array([-.1, -.1, -.1])
expected = [0., 99.]
out = trilinear_interpolate4d(data, point)
npt.assert_array_almost_equal(out, expected)
# different edge
point = np.array([2.4, 5.4, 3.3])
# On the edge 5.4 get treated as the max y value, 5.
expected = [linear_function(point[0], 5., point[2]), 99.]
out = trilinear_interpolate4d(data, point)
npt.assert_array_almost_equal(out, expected)
# Test index errors
point = np.array([2.4, 5.5, 3.3])
npt.assert_raises(IndexError, trilinear_interpolate4d, data, point)
point = np.array([2.4, -1., 3.3])
npt.assert_raises(IndexError, trilinear_interpolate4d, data, point)
def get_pmf(self, point):
coeff = trilinear_interpolate4d(self.shcoeff, point)
pmf = np.dot(self._B, coeff)
pmf.clip(0, out=pmf)
return pmf
def get_pmf(self, point):
return trilinear_interpolate4d(self.pmf_array, point)
def get_pmf(self, point):
coeff = trilinear_interpolate4d(self.shcoeff, point)
pmf = np.dot(self._B, coeff)
pmf.clip(0, out=pmf)
return pmf
def get_pmf(self, point):
return trilinear_interpolate4d(self.pmf_array, point)
def pmf_no_boot(self, point):
data = trilinear_interpolate4d(self.data, point)
fit = self.model.fit(data)
return fit.odf(self.sphere)