def test_interp_rbf():
from dipy.core.sphere import Sphere, interp_rbf
from dipy.core.subdivide_octahedron import create_unit_hemisphere
import numpy as np
s0 = create_unit_hemisphere(2)
s1 = create_unit_hemisphere(3)
data = np.cos(s0.theta) + np.sin(s0.phi)
expected = np.cos(s1.theta) + np.sin(s1.phi)
interp_data_en = interp_rbf(data, s0, s1, norm="euclidean_norm")
interp_data_a = interp_rbf(data, s0, s1, norm="angle")
nt.assert_(np.mean(np.abs(interp_data_en - expected)) < 0.1)
nt.assert_(np.mean(np.abs(interp_data_a - expected)) < 0.1)
def test_interp_rbf():
from dipy.core.sphere import Sphere, interp_rbf
from dipy.core.subdivide_octahedron import create_unit_hemisphere
import numpy as np
s0 = create_unit_hemisphere(2)
s1 = create_unit_hemisphere(3)
data = np.cos(s0.theta) + np.sin(s0.phi)
expected = np.cos(s1.theta) + np.sin(s1.phi)
interp_data_en = interp_rbf(data, s0, s1, norm = "euclidean_norm")
interp_data_a = interp_rbf(data, s0, s1, norm = "angle")
nt.assert_(np.mean(np.abs(interp_data_en - expected)) < 0.1)
nt.assert_(np.mean(np.abs(interp_data_a - expected)) < 0.1)
def odf(self, sphere):
## Override the odf method to subclass OdfFit
bvec = self.model.bvec[self.model.bval > 0]
data = self.data[self.model.bval > 0]
origin_sphere = Sphere(xyz=bvec)
discrete_odf = 1. / data
return interp_rbf(discrete_odf, origin_sphere, sphere)
def test_interp_rbf():
def data_func(s, a, b):
return a * np.cos(s.theta) + b * np.sin(s.phi)
from dipy.core.sphere import Sphere, interp_rbf
import numpy as np
s0 = create_unit_sphere(3)
s1 = create_unit_sphere(4)
for a, b in zip([1, 2, 0.5], [1, 0.5, 2]):
data = data_func(s0, a, b)
expected = data_func(s1, a, b)
interp_data_a = interp_rbf(data, s0, s1, norm="angle")
nt.assert_(np.mean(np.abs(interp_data_a - expected)) < 0.1)
# Test that using the euclidean norm raises a warning
# (following https://docs.python.org/2/library/warnings.html#testing-warnings)
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
interp_data_en = interp_rbf(data, s0, s1, norm ="euclidean_norm")
nt.assert_(len(w) == 1)
nt.assert_(issubclass(w[-1].category, DeprecationWarning))
nt.assert_("deprecated" in str(w[-1].message))
def test_interp_rbf():
def data_func(s, a, b):
return a * np.cos(s.theta) + b * np.sin(s.phi)
from dipy.core.sphere import Sphere, interp_rbf
import numpy as np
s0 = create_unit_sphere(3)
s1 = create_unit_sphere(4)
for a, b in zip([1, 2, 0.5], [1, 0.5, 2]):
data = data_func(s0, a, b)
expected = data_func(s1, a, b)
interp_data_a = interp_rbf(data, s0, s1, norm="angle")
nt.assert_(np.mean(np.abs(interp_data_a - expected)) < 0.1)
# Test that using the euclidean norm raises a warning
# (following
# https://docs.python.org/2/library/warnings.html#testing-warnings)
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
interp_data_en = interp_rbf(data, s0, s1, norm="euclidean_norm")
nt.assert_(len(w) == 1)
nt.assert_(issubclass(w[-1].category, DeprecationWarning))
nt.assert_("deprecated" in str(w[-1].message))
def odf(self, sphere, interp_kwargs=dict(function='multiquadric', smooth=0)):
"""
Interpolate the fiber odf into a provided sphere class instance (from
dipy)
"""
s0 = dps.Sphere(xyz=self.bvecs[:, self.b_idx].T)
s1 = sphere
params_flat = self.model_params[self.mask]
out_flat = np.empty((self._n_vox, len(sphere.x)))
if self._n_vox==1:
this_params = params_flat
this_params[np.isnan(this_params)] = 0
out = dps.interp_rbf(this_params, s0, s1, **interp_kwargs)
return np.squeeze(out)
else:
for vox in range(self._n_vox):
this_params = params_flat[vox]
this_params[np.isnan(this_params)] = 0
out_flat[vox] = dps.interp_rbf(this_params, s0, s1,
**interp_kwargs)
out = ozu.nans(self.model_params.shape[:3] + (len(sphere.x),))
out[self.mask] = out_flat
return out
def plot_signal_interp(bvecs, signal, origin=[0,0,0], maya=True, cmap='jet',
file_name=None, colorbar=False, figure=None, vmin=None,
vmax=None, offset=0, azimuth=60, elevation=90, roll=0,
points=False, cmap_points=None, scale_points=False,
non_neg=False,
interp_kwargs=dict(function='thin_plate', smooth=0)):
"""
Interpolate a measured signal, using RBF interpolation.
Parameters
----------
signal:
bvecs: array (3,n)
the x,y,z locations where the signal was measured
offset : float
where to place the plotted voxel (on the z axis)
points : bool
whether to show the sampling points on the interpolated
surface. Default: False.
"""
bvecs_new = np.hstack([bvecs, -bvecs])
new_signal = np.hstack([signal, signal])
s0 = Sphere(xyz=bvecs_new.T)
s1 = create_unit_sphere(7)
signal[np.isnan(signal)] = 0
interp_signal = interp_rbf(new_signal, s0, s1, **interp_kwargs)
vertices = s1.vertices
if non_neg:
interp_signal[interp_signal<0] = 0
faces = s1.faces
x,y,z = vertices.T
r, phi, theta = geo.cart2sphere(x,y,z)
x_plot, y_plot, z_plot = geo.sphere2cart(interp_signal, phi, theta)
if points:
r, phi, theta = geo.cart2sphere(s0.x, s0.y, s0.z)
x_p, y_p, z_p = geo.sphere2cart(signal, phi, theta)
figure = _display_maya_voxel(x_p, y_p, z_p, faces,
signal, origin, cmap=cmap,
colorbar=colorbar, figure=figure,
vmin=vmin, vmax=vmax, file_name=file_name,
azimuth=azimuth, elevation=elevation,
points=True, cmap_points=cmap_points,
scale_points=scale_points)
# Call and return straightaway:
return _display_maya_voxel(x_plot, y_plot, z_plot, faces,
interp_signal, origin,
cmap=cmap, colorbar=colorbar, figure=figure,
vmin=vmin, vmax=vmax, file_name=file_name,
azimuth=azimuth, elevation=elevation)
def plot_signal_interp(bvecs,
signal,
origin=[0, 0, 0],
maya=True,
cmap='jet',
file_name=None,
colorbar=False,
figure=None,
vmin=None,
vmax=None,
offset=0,
azimuth=60,
elevation=90,
roll=0,
points=False,
cmap_points=None,
scale_points=False,
non_neg=False,
interp_kwargs=dict(function='thin_plate', smooth=0)):
"""
Interpolate a measured signal, using RBF interpolation.
Parameters
----------
signal:
bvecs: array (3,n)
the x,y,z locations where the signal was measured
offset : float
where to place the plotted voxel (on the z axis)
points : bool
whether to show the sampling points on the interpolated
surface. Default: False.
"""
bvecs_new = np.hstack([bvecs, -bvecs])
new_signal = np.hstack([signal, signal])
s0 = Sphere(xyz=bvecs_new.T)
s1 = create_unit_sphere(7)
signal[np.isnan(signal)] = 0
interp_signal = interp_rbf(new_signal, s0, s1, **interp_kwargs)
vertices = s1.vertices
if non_neg:
interp_signal[interp_signal < 0] = 0
faces = s1.faces
x, y, z = vertices.T
r, phi, theta = geo.cart2sphere(x, y, z)
x_plot, y_plot, z_plot = geo.sphere2cart(interp_signal, phi, theta)
if points:
r, phi, theta = geo.cart2sphere(s0.x, s0.y, s0.z)
x_p, y_p, z_p = geo.sphere2cart(signal, phi, theta)
figure = _display_maya_voxel(x_p,
y_p,
z_p,
faces,
signal,
origin,
cmap=cmap,
colorbar=colorbar,
figure=figure,
vmin=vmin,
vmax=vmax,
file_name=file_name,
azimuth=azimuth,
elevation=elevation,
points=True,
cmap_points=cmap_points,
scale_points=scale_points)
# Call and return straightaway:
return _display_maya_voxel(x_plot,
y_plot,
z_plot,
faces,
interp_signal,
origin,
cmap=cmap,
colorbar=colorbar,
figure=figure,
vmin=vmin,
vmax=vmax,
file_name=file_name,
azimuth=azimuth,
elevation=elevation)
