if one_point_b1_inside_b2 or one_point_b2_inside_b1:
return numpy.vstack((numpy.vstack(
(b1[0], b2[0])).max(0), numpy.vstack((b1[1], b2[1])).min(0)))
else:
return None
fiberDelta = lambda f: numpy.sqrt(((f[1:] - f[:-1])**2).sum(1))
def _constant(x, val=0):
"""docstring for parabolic_fun"""
return numpy.zeros(x.shape[:-1], dtype=float) + val
cov_thinPlate3D = _GP.covariance_function_bundle(
'thinplate3d_mat', 'tractographyGP.bounded_thinPlate', {'r': 'r'})
cov_thinPlate2D = _GP.covariance_function_bundle(
'thinplate2d_mat', 'tractographyGP.bounded_thinPlate', {'r': 'r'})
class fiberGP:
_k = 0
_precomputedVariance = None
_precomputedMean = None
_scaleFactor = 1
_mean = None
_cov = None
_alpha = None
_inv_cov = None
# Clindmri import
from .bounded_thinplate import innerproduct_thinplate3d
# Define 'pymc' new covariance using 'covariance_function_bundles' method:
# Init method takes three arguments:
# 1- cov_fun_name: The name of the covariance function.
# 2- cov_fun_module: The name of the module in which the covariance
# function can be found. This must be somewhere on your PYTHONPATH.
# 3- extra_cov_params: A dictionary whose keys are the extra parameters
# the covariance function takes, and whose values are brief sentences
# explaining the roles of those parameters. This dictionary will be used
# to generate the docstring of the bundle.
cov_thinplate3d = gp.covariance_function_bundle(
"thinplate3d_mat", "clindmri.tractography.bounded_thinplate",
{"R": ("the radius of a shpere in R3 inside which the soultion is "
"calculated.")})
# Lambda function to compute the fiber sampling deltas: if a determinist
# tractography with constant integration step has been used to produce the
# fiber, all the deltas are the same and are equal to the integration step.
fiber_deltas = lambda f: numpy.sqrt(((f[1:] - f[:-1]) ** 2).sum(1))
class FiberGP(object):
""" Single fiber as a gaussian process (GP).
The GP of a fiber ~ the blurred indicator function (BIF) of a fiber is
characterized by the BIF for a smooth trajectory as a GP with covariance
Cs (geometric) and by the anisotrpoic blurring of a smooth function as a
GP with covariance Cd (diffusion).
one_point_b1_inside_b2 = len(((b1>=b2[0])*(b1<=b2[1])).prod(1).nonzero()[0])>0
one_point_b2_inside_b1 = len(((b2>=b1[0])*(b2<=b1[1])).prod(1).nonzero()[0])>0
if one_point_b1_inside_b2 or one_point_b2_inside_b1:
return numpy.vstack((numpy.vstack((b1[0],b2[0])).max(0),numpy.vstack((b1[1],b2[1])).min(0)))
else:
return None
fiberDelta = lambda f: numpy.sqrt(((f[1:]-f[:-1])**2).sum(1))
def _constant(x, val = 0):
"""docstring for parabolic_fun"""
return numpy.zeros(x.shape[:-1],dtype=float) + val
cov_thinPlate3D = _GP.covariance_function_bundle( 'thinplate3d_mat', 'tractographyGP.bounded_thinPlate', {'r':'r'} )
cov_thinPlate2D = _GP.covariance_function_bundle( 'thinplate2d_mat', 'tractographyGP.bounded_thinPlate', {'r':'r'} )
class fiberGP:
_k = 0
_precomputedVariance = None
_precomputedMean = None
_scaleFactor = 1
_mean = None
_cov = None
_alpha = None
_inv_cov = None
def __init__(self,fiber, resolution=.5, rFactor=1,epsilonFactor=1,scaleFactor=1):
