#!/usr/bin/env python
#
# dtifit.py - The DTIFitTensor class, and some related utility functions.
#
# Author: Paul McCarthy <pauldmccarthy@gmail.com>
#
"""This module provides the :class:`.DTIFitTensor` class, which encapsulates
the diffusion tensor data generated by the FSL ``dtifit`` tool.
The following utility functions are also defined:
.. autosummary::
:nosignatures:
getDTIFitDataPrefix
isDTIFitPath
looksLikeTensorImage
decomposeTensorMatrix
"""
import logging
import re
import glob
import os.path as op
import numpy as np
import numpy.linalg as npla
from . import image as fslimage
log = logging.getLogger(__name__)
def getDTIFitDataPrefix(path):
"""Returns the prefix (a.k,a, base name) used for the ``dtifit`` file
names in the given directory, or ``None`` if the ``dtifit`` files could
not be identified.
"""
v1s = glob.glob(op.join(path, '*_V1.*'))
v2s = glob.glob(op.join(path, '*_V2.*'))
v3s = glob.glob(op.join(path, '*_V3.*'))
l1s = glob.glob(op.join(path, '*_L1.*'))
l2s = glob.glob(op.join(path, '*_L2.*'))
l3s = glob.glob(op.join(path, '*_L3.*'))
files = [v1s, v2s, v3s, l1s, l2s, l3s]
# Gather all of the existing file
# prefixes into a dictionary of
# prefix : [file list] mappings.
pattern = '^(.*)_(?:V1|V2|V3|L1|L2|L3).*$'
prefixes = {}
for f in [f for flist in files for f in flist]:
prefix = re.findall(pattern, f)[0]
if prefix not in prefixes: prefixes[prefix] = [f]
else: prefixes[prefix].append(f)
# Discard any prefixes which are
# not present for every file type.
for prefix, files in list(prefixes.items()):
if len(files) != 6:
prefixes.pop(prefix)
# Discard any prefixes which
# match any files that do
# not look like image files
for prefix, files in list(prefixes.items()):
if not all([fslimage.looksLikeImage(f) for f in files]):
prefixes.pop(prefix)
prefixes = list(prefixes.keys())
# No more prefixes remaining -
# this is probably not a dtifit
# directory
if len(prefixes) == 0:
return None
# If there's more than one remaining
# prefix, I don't know what to do -
# just return the first one.
if len(prefixes) > 1:
log.warning('Multiple dtifit prefixes detected: {}'.format(prefixes))
return op.basename(sorted(prefixes)[0])
def isDTIFitPath(path):
"""Returns ``True`` if the given directory path looks like it contains
``dtifit`` data, ``False`` otherwise.
"""
return getDTIFitDataPrefix(path) is not None
def looksLikeTensorImage(image):
"""Returns ``True`` if the given :class:`.Image` looks like it could
contain tensor matrix data, ``False`` otherwise.
"""
return len(image.shape) == 4 and image.shape[3] == 6
def decomposeTensorMatrix(data):
"""Decomposes the given ``numpy`` array into six separate arrays,
containing the eigenvectors and eigenvalues of the tensor matrix
decompositions.
:arg image: A 4D ``numpy`` array with 6 volumes, which contains
the unique elements of diffusion tensor matrices at
every voxel.
:returns: A tuple containing the principal eigenvectors and
eigenvalues of the tensor matrix.
"""
# The image contains 6 volumes, corresponding
# to the Dxx, Dxy, Dxz, Dyy, Dyz, Dzz elements
# of the tensor matrix, at each voxel.
#
# We need to re-organise this into a series of
# complete 3x3 tensor matrices, one for each
# voxel.
shape = data.shape[:3]
nvoxels = np.prod(shape)
matrices = np.zeros((nvoxels, 3, 3), dtype=np.float32)
# Copy the tensor matrix elements
# into their respective locations
matrices[:, 0, 0] = data[..., 0].flat
matrices[:, 0, 1] = data[..., 1].flat
matrices[:, 1, 0] = data[..., 1].flat
matrices[:, 0, 2] = data[..., 2].flat
matrices[:, 2, 0] = data[..., 2].flat
matrices[:, 1, 1] = data[..., 3].flat
matrices[:, 1, 2] = data[..., 4].flat
matrices[:, 2, 1] = data[..., 4].flat
matrices[:, 2, 2] = data[..., 5].flat
# Calculate the eigenvectors and
# values on all of those matrices
vals, vecs = npla.eig(matrices)
vecShape = list(shape) + [3]
# Grr, np.linalg.eig does not
# sort the eigenvalues/vectors,
# so we have to do it ourselves.
order = vals.argsort(axis=1)
i = np.arange(nvoxels)[:, np.newaxis]
vecs = vecs.transpose(0, 2, 1)
vals = vals[i, order]
vecs = vecs[i, order, :]
l1 = vals[:, 2] .reshape(shape)
l2 = vals[:, 1] .reshape(shape)
l3 = vals[:, 0] .reshape(shape)
v1 = vecs[:, 2, :].reshape(vecShape)
v2 = vecs[:, 1, :].reshape(vecShape)
v3 = vecs[:, 0, :].reshape(vecShape)
return v1, v2, v3, l1, l2, l3
class DTIFitTensor(fslimage.Nifti):
"""The ``DTIFitTensor`` class is able to load and encapsulate the diffusion
tensor data generated by the FSL ``dtifit`` tool. The ``DtiFitTensor``
class supports tensor model data generated by ``dtifit``, where the
eigenvectors and eigenvalues of the tensor matrices have been saved as six
separate NIFTI images.
"""
def __init__(self, path):
"""Create a ``DTIFitTensor``.
:arg path: A path to a ``dtifit`` directory.
"""
prefix = getDTIFitDataPrefix(path)
isDTIfitDir = prefix is not None
if not isDTIfitDir:
raise ValueError('{} does not look like a dtifit '
'output directory!'.format(path))
# DTIFit output directory with separate
# eigenvector/eigenvalue images
self.__v1 = fslimage.Image(op.join(path, '{}_V1'.format(prefix)))
self.__v2 = fslimage.Image(op.join(path, '{}_V2'.format(prefix)))
self.__v3 = fslimage.Image(op.join(path, '{}_V3'.format(prefix)))
self.__l1 = fslimage.Image(op.join(path, '{}_L1'.format(prefix)))
self.__l2 = fslimage.Image(op.join(path, '{}_L2'.format(prefix)))
self.__l3 = fslimage.Image(op.join(path, '{}_L3'.format(prefix)))
fslimage.Nifti.__init__(self, self.__l1.header)
self.dataSource = op.abspath(path)
self.name = '{}'.format(op.basename(path))
def V1(self):
return self.__v1
def V2(self):
return self.__v2
def V3(self):
return self.__v3
def L1(self):
return self.__l1
def L2(self):
return self.__l2
def L3(self):
return self.__l3