/usr/lib/python2.7/dist-packages/dipy/tracking/interfaces.py is in python-dipy 0.10.1-1.
This file is owned by root:root, with mode 0o644.
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#############################################################################
# Remove this when the module becomes functional again
class ThisIsBroken(SkipTest):
pass
raise ThisIsBroken("this module is undergoing a major overhaul as therefore "
"does not currently work")
#############################################################################
import pickle
import string
import os.path as path
import numpy as np
from scipy.ndimage import convolve
# Import traits as optional package
try:
import traits.api as T
except ImportError:
from ..utils.optpkg import OptionalImportError
raise OptionalImportError("You must have traits to use this module")
import nibabel as nib
from nibabel.trackvis import write, empty_header
from ..reconst.shm import (SlowAdcOpdfModel, MonoExpOpdfModel, QballOdfModel,
normalize_data, ClosestPeakSelector,
ResidualBootstrapWrapper, hat, lcr_matrix,
bootstrap_data_array, NND_ClosestPeakSelector)
from ..reconst.interpolate import (TriLinearInterpolator,
NearestNeighborInterpolator)
from ..tracking.integration import (BoundryIntegrator, FixedStepIntegrator,
generate_streamlines)
from ..tracking.utils import (seeds_from_mask, target, merge_streamlines,
density_map)
from ..io.bvectxt import (read_bvec_file, orientation_to_string,
reorient_vectors)
nifti_file = T.File(filter=['Nifti Files', '*.nii.gz',
'Nifti Pair or Analyze Files', '*.img.gz',
'All Files', '*'])
def read_roi(file, threshold=0, shape=None):
img = nib.load(file)
if shape is not None:
if shape != img.shape:
raise IOError('The roi image does not have the right shape, '+
'expecting '+str(shape)+' got '+str(img.shape))
img_data = img.get_data()
if img_data.max() > 1:
raise ValueError('this does not seem to be a mask')
mask = img_data > threshold
return mask
class InputData(T.HasTraits):
dwi_images = nifti_file
fa_file = nifti_file
bvec_file = T.File(filter=['*.bvec'])
bvec_orientation = T.String('IMG', minlen=3, maxlen=3)
min_signal = T.Float(1)
@T.on_trait_change('dwi_images')
def update_files(self):
dir, file = path.split(self.dwi_images)
base = string.split(file, path.extsep, 1)[0]
if self.fa_file == '':
self.fa_file = path.join(dir, base+'_fa.nii.gz')
if self.bvec_file == '':
self.bvec_file = path.join(dir, base+'.bvec')
def read_data(self):
data_img = nib.load(self.dwi_images)
affine = data_img.get_affine()
voxel_size = data_img.get_header().get_zooms()
voxel_size = voxel_size[:3]
fa_img = nib.load(self.fa_file)
assert data_img.shape[:-1] == fa_img.shape
bvec, bval = read_bvec_file(self.bvec_file)
data_ornt = nib.io_orientation(affine)
if self.bvec_orientation != 'IMG':
bvec = reorient_vectors(bvec, self.bvec_orientation, data_ornt)
fa = fa_img.get_data()
data = data_img.get_data()
return data, voxel_size, affine, fa, bvec, bval
class GausianKernel(T.HasTraits):
sigma = T.Float(1, label='sigma (in voxels)')
shape = T.Array('int', shape=(3,), value=[1,1,1],
label='shape (in voxels)')
def get_kernel(self):
raise NotImplementedError
#will get to this soon
class BoxKernel(T.HasTraits):
shape = T.Array('int', shape=(3,), value=[3,3,3],
label='shape (in voxels)')
def get_kernel(self):
kernel = np.ones(self.shape)/self.shape.prod()
kernel.shape += (1,)
return kernel
def lazy_index(index):
"""Produces a lazy index
Returns a slice that can be used for indexing an array, if no slice can be
made index is returned as is.
"""
index = np.asarray(index)
assert index.ndim == 1
if index.dtype == np.bool:
index = index.nonzero()[0]
if len(index) == 1:
return slice(index[0], index[0] + 1)
step = np.unique(np.diff(index))
if len(step) != 1 or step[0] == 0:
return index
else:
return slice(index[0], index[-1] + 1, step[0])
def closest_start(seeds, peak_finder, best_start):
starts = np.empty(seeds.shape)
best_start = np.asarray(best_start, 'float')
best_start /= np.sqrt((best_start*best_start).sum())
for i in xrange(len(seeds)):
try:
starts[i] = peak_finder.next_step(seeds[i], best_start)
except StopIteration:
starts[i] = best_start
return starts
all_kernels = {None:None,'Box':BoxKernel,'Gausian':GausianKernel}
all_interpolators = {'NearestNeighbor':NearestNeighborInterpolator,
'TriLinear':TriLinearInterpolator}
all_shmodels = {'QballOdf':QballOdfModel, 'SlowAdcOpdf':SlowAdcOpdfModel,
'MonoExpOpdf':MonoExpOpdfModel}
all_integrators = {'Boundry':BoundryIntegrator, 'FixedStep':FixedStepIntegrator}
class ShmTrackingInterface(T.HasStrictTraits):
dwi_images = T.DelegatesTo('all_inputs')
all_inputs = T.Instance(InputData, args=())
min_signal = T.DelegatesTo('all_inputs')
seed_roi = nifti_file
seed_density = T.Array(dtype='int', shape=(3,), value=[1,1,1])
smoothing_kernel_type = T.Enum(None, all_kernels.keys())
smoothing_kernel = T.Instance(T.HasTraits)
@T.on_trait_change('smoothing_kernel_type')
def set_smoothing_kernel(self):
if self.smoothing_kernel_type is not None:
kernel_factory = all_kernels[self.smoothing_kernel_type]
self.smoothing_kernel = kernel_factory()
else:
self.smoothing_kernel = None
interpolator = T.Enum('NearestNeighbor', all_interpolators.keys())
model_type = T.Enum('SlowAdcOpdf', all_shmodels.keys())
sh_order = T.Int(4)
Lambda = T.Float(0, desc="Smoothing on the odf")
sphere_coverage = T.Int(5)
min_peak_spacing = T.Range(0.,1,np.sqrt(.5), desc="as a dot product")
min_relative_peak = T.Range(0.,1,.25)
probabilistic = T.Bool(False, label='Probabilistic (Residual Bootstrap)')
bootstrap_input = T.Bool(False)
bootstrap_vector = T.Array(dtype='int', value=[])
#integrator = Enum('Boundry', all_integrators.keys())
seed_largest_peak = T.Bool(False, desc="Ignore sub-peaks and start follow "
"the largest peak at each seed")
start_direction = T.Array(dtype='float', shape=(3,), value=[0,0,1],
desc="Prefered direction from seeds when "
"multiple directions are available. "
"(Mostly) doesn't matter when 'seed "
"largest peak' and 'track two directions' "
"are both True",
label="Start direction (RAS)")
track_two_directions = T.Bool(False)
fa_threshold = T.Float(1.0)
max_turn_angle = T.Range(0.,90,0)
stop_on_target = T.Bool(False)
targets = T.List(nifti_file, [])
#will be set later
voxel_size = T.Array(dtype='float', shape=(3,))
affine = T.Array(dtype='float', shape=(4,4))
shape = T.Tuple((0,0,0))
#set for io
save_streamlines_to = T.File('')
save_counts_to = nifti_file
#io methods
def save_streamlines(self, streamlines, save_streamlines_to):
trk_hdr = empty_header()
voxel_order = orientation_to_string(nib.io_orientation(self.affine))
trk_hdr['voxel_order'] = voxel_order
trk_hdr['voxel_size'] = self.voxel_size
trk_hdr['vox_to_ras'] = self.affine
trk_hdr['dim'] = self.shape
trk_tracks = ((ii,None,None) for ii in streamlines)
write(save_streamlines_to, trk_tracks, trk_hdr)
pickle.dump(self, open(save_streamlines_to + '.p', 'wb'))
def save_counts(self, streamlines, save_counts_to):
counts = density_map(streamlines, self.shape, self.voxel_size)
if counts.max() < 2**15:
counts = counts.astype('int16')
nib.save(nib.Nifti1Image(counts, self.affine), save_counts_to)
#tracking methods
def track_shm(self, debug=False):
if self.sphere_coverage > 7 or self.sphere_coverage < 1:
raise ValueError("sphere coverage must be between 1 and 7")
verts, edges, faces = create_half_unit_sphere(self.sphere_coverage)
verts, pot = disperse_charges(verts, 10, .3)
data, voxel_size, affine, fa, bvec, bval = self.all_inputs.read_data()
self.voxel_size = voxel_size
self.affine = affine
self.shape = fa.shape
model_type = all_shmodels[self.model_type]
model = model_type(self.sh_order, bval, bvec, self.Lambda)
model.set_sampling_points(verts, edges)
data = np.asarray(data, dtype='float', order='C')
if self.smoothing_kernel is not None:
kernel = self.smoothing_kernel.get_kernel()
convolve(data, kernel, out=data)
normalize_data(data, bval, self.min_signal, out=data)
dmin = data.min()
data = data[..., lazy_index(bval > 0)]
if self.bootstrap_input:
if self.bootstrap_vector.size == 0:
n = data.shape[-1]
self.bootstrap_vector = np.random.randint(n, size=n)
H = hat(model.B)
R = lcr_matrix(H)
data = bootstrap_data_array(data, H, R, self.bootstrap_vector)
data.clip(dmin, out=data)
mask = fa > self.fa_threshold
targets = [read_roi(tgt, shape=self.shape) for tgt in self.targets]
if self.stop_on_target:
for target_mask in targets:
mask = mask & ~target_mask
seed_mask = read_roi(self.seed_roi, shape=self.shape)
seeds = seeds_from_mask(seed_mask, self.seed_density, voxel_size)
if self.interpolator == 'NearestNeighbor' and not self.probabilistic and not debug:
using_optimze = True
peak_finder = NND_ClosestPeakSelector(model, data, mask, voxel_size)
else:
using_optimze = False
interpolator_type = all_interpolators[self.interpolator]
interpolator = interpolator_type(data, voxel_size, mask)
peak_finder = ClosestPeakSelector(model, interpolator)
#Set peak_finder parameters for start steps
peak_finder.angle_limit = 90
model.peak_spacing = self.min_peak_spacing
if self.seed_largest_peak:
model.min_relative_peak = 1
else:
model.min_relative_peak = self.min_relative_peak
data_ornt = nib.io_orientation(self.affine)
best_start = reorient_vectors(self.start_direction, 'ras', data_ornt)
start_steps = closest_start(seeds, peak_finder, best_start)
if self.probabilistic:
interpolator = ResidualBootstrapWrapper(interpolator, model.B,
min_signal=dmin)
peak_finder = ClosestPeakSelector(model, interpolator)
elif using_optimze and self.seed_largest_peak:
peak_finder.reset_cache()
#Reset peak_finder parameters for tracking
peak_finder.angle_limit = self.max_turn_angle
model.peak_spacing = self.min_peak_spacing
model.min_relative_peak = self.min_relative_peak
integrator = BoundryIntegrator(voxel_size, overstep=.1)
streamlines = generate_streamlines(peak_finder, integrator, seeds,
start_steps)
if self.track_two_directions:
start_steps = -start_steps
streamlinesB = generate_streamlines(peak_finder, integrator, seeds,
start_steps)
streamlines = merge_streamlines(streamlines, streamlinesB)
for target_mask in targets:
streamlines = target(streamlines, target_mask, voxel_size)
return streamlines
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