diff --git a/fsl/data/dtifit.py b/fsl/data/dtifit.py
new file mode 100644
index 0000000000000000000000000000000000000000..bbfeea9fa9676bdf020c4afe05de82df54a01503
--- /dev/null
+++ b/fsl/data/dtifit.py
@@ -0,0 +1,201 @@
+#!/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(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:
+ """
+
+ # 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[:, 0, 2] = data[..., 2].flat
+ matrices[:, 1, 0] = data[..., 1].flat
+ matrices[:, 1, 1] = data[..., 3].flat
+ matrices[:, 1, 2] = data[..., 4].flat
+ matrices[:, 2, 0] = data[..., 2].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]
+
+ l1 = vals[:, 0] .reshape(shape)
+ l2 = vals[:, 1] .reshape(shape)
+ l3 = vals[:, 2] .reshape(shape)
+ v1 = vecs[:, 0, :].reshape(vecShape)
+ v2 = vecs[:, 1, :].reshape(vecShape)
+ v3 = vecs[:, 2, :].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 = '{}[tensor]'.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
diff --git a/fsl/data/tensorimage.py b/fsl/data/tensorimage.py
deleted file mode 100644
index 013d1df5403621161f1a556708b87c5b49efd2ab..0000000000000000000000000000000000000000
--- a/fsl/data/tensorimage.py
+++ /dev/null
@@ -1,149 +0,0 @@
-#!/usr/bin/env python
-#
-# tensorimage.py - The TensorImage class.
-#
-# Author: Paul McCarthy <pauldmccarthy@gmail.com>
-#
-"""This module provides the :class:`.TensorImage` class, which encapsulates
-the diffusion tensor data generated by the FSL ``dtifit`` tool.
-"""
-
-
-import logging
-import re
-import glob
-import os.path as op
-
-import six
-
-from . import image as fslimage
-
-
-log = logging.getLogger(__name__)
-
-
-def getTensorDataPrefix(path):
- """Returns the prefix used for the DTI file names in the given
- directory, or ``None`` if the DTI 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.*'))
- fas = glob.glob(op.join(path, '*_FA.*'))
- mds = glob.glob(op.join(path, '*_MD.*'))
- files = [v1s, v2s, v3s, l1s, l2s, l3s, fas, mds]
-
- # Gather all of the existing file
- # prefixes into a dictionary of
- # prefix : [file list] mappings.
- pattern = '^(.*)_(?:V1|V2|V3|L1|L2|L3|FA|MD).*$'
- 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) != 8:
- 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(prefixes[0])
-
-
-def isPathToTensorData(path):
- """Returns ``True`` if the given directory path looks like it contains
- ``dtifit`` data, ``False`` otherwise.
- """
-
- return getTensorDataPrefix(path) is not None
-
-
-class TensorImage(fslimage.Nifti):
- """The ``TensorImage`` class is able to load and encapsulate the diffusion
- tensor data generated by the FSL ``dtifit`` tool.
- """
-
-
- def __init__(self, path):
- """Create a ``TensorImage``.
-
- :arg path: A path to a ``dtifit`` directory. Alternately, the ``path``
- may be a dictionary with keys
- ``{'v1', 'v2', 'v3', 'l1', 'l2', 'l3'}``, which specify
- paths to images containing the tensor eigenvectors and
- eigenvalues.
- """
-
- dtifitDir = isinstance(path, six.string_types)
-
- if dtifitDir:
-
- prefix = getTensorDataPrefix(path)
-
- if prefix is None:
- raise ValueError('Invalid path: {}'.format(path))
-
- v1 = op.join(path, '{}_V1'.format(prefix))
- v2 = op.join(path, '{}_V2'.format(prefix))
- v3 = op.join(path, '{}_V3'.format(prefix))
- l1 = op.join(path, '{}_L1'.format(prefix))
- l2 = op.join(path, '{}_L2'.format(prefix))
- l3 = op.join(path, '{}_L3'.format(prefix))
-
- paths = {'v1' : v1, 'v2' : v2, 'v3' : v3,
- 'l1' : l1, 'l2' : l2, 'l3' : l3}
-
- else:
- paths = path
-
-
- self.__v1 = fslimage.Image(paths['v1'])
- self.__v2 = fslimage.Image(paths['v2'])
- self.__v3 = fslimage.Image(paths['v3'])
- self.__l1 = fslimage.Image(paths['l1'])
- self.__l2 = fslimage.Image(paths['l2'])
- self.__l3 = fslimage.Image(paths['l3'])
-
- fslimage.Nifti.__init__(self, self.__l1.header)
-
- l1dir = op.abspath(op.dirname(paths['l1']))
-
- self.dataSource = l1dir
- self.name = '{}[tensor]'.format(op.basename(l1dir))
-
-
- 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