/usr/lib/python2.7/dist-packages/nibabel/trackvis.py is in python-nibabel 2.0.2-2.
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"""
from __future__ import division, print_function
import warnings
import struct
import itertools
import numpy as np
import numpy.linalg as npl
from .py3k import asstr
from .volumeutils import (native_code, swapped_code, endian_codes, rec2dict)
from .volumeutils import BinOpener
from .orientations import aff2axcodes
from .affines import apply_affine
try:
basestring
except NameError: # python 3
basestring = str
# Definition of trackvis header structure.
# See http://www.trackvis.org/docs/?subsect=fileformat
# See https://docs.scipy.org/doc/numpy/reference/arrays.dtypes.html
header_1_dtd = [
('id_string', 'S6'),
('dim', 'h', 3),
('voxel_size', 'f4', 3),
('origin', 'f4', 3),
('n_scalars', 'h'),
('scalar_name', 'S20', 10),
('n_properties', 'h'),
('property_name', 'S20', 10),
('reserved', 'S508'),
('voxel_order', 'S4'),
('pad2', 'S4'),
('image_orientation_patient', 'f4', 6),
('pad1', 'S2'),
('invert_x', 'S1'),
('invert_y', 'S1'),
('invert_z', 'S1'),
('swap_xy', 'S1'),
('swap_yz', 'S1'),
('swap_zx', 'S1'),
('n_count', 'i4'),
('version', 'i4'),
('hdr_size', 'i4'),
]
# Version 2 adds a 4x4 matrix giving the affine transformtation going
# from voxel coordinates in the referenced 3D voxel matrix, to xyz
# coordinates (axes L->R, P->A, I->S). IF (0 based) value [3, 3] from
# this matrix is 0, this means the matrix is not recorded.
header_2_dtd = [
('id_string', 'S6'),
('dim', 'h', 3),
('voxel_size', 'f4', 3),
('origin', 'f4', 3),
('n_scalars', 'h'),
('scalar_name', 'S20', 10),
('n_properties', 'h'),
('property_name', 'S20', 10),
('vox_to_ras', 'f4', (4,4)), # new field for version 2
('reserved', 'S444'),
('voxel_order', 'S4'),
('pad2', 'S4'),
('image_orientation_patient', 'f4', 6),
('pad1', 'S2'),
('invert_x', 'S1'),
('invert_y', 'S1'),
('invert_z', 'S1'),
('swap_xy', 'S1'),
('swap_yz', 'S1'),
('swap_zx', 'S1'),
('n_count', 'i4'),
('version', 'i4'),
('hdr_size', 'i4'),
]
# Full header numpy dtypes
header_1_dtype = np.dtype(header_1_dtd)
header_2_dtype = np.dtype(header_2_dtd)
# affine to go from DICOM LPS to MNI RAS space
DPCS_TO_TAL = np.diag([-1, -1, 1, 1])
class HeaderError(Exception):
""" Error in trackvis header
"""
class DataError(Exception):
""" Error in trackvis data
"""
def read(fileobj, as_generator=False, points_space=None):
''' Read trackvis file, return streamlines, header
Parameters
----------
fileobj : string or file-like object
If string, a filename; otherwise an open file-like object
pointing to trackvis file (and ready to read from the beginning
of the trackvis header data)
as_generator : bool, optional
Whether to return tracks as sequence (False, default) or as a generator
(True).
points_space : {None, 'voxel', 'rasmm'}, optional
The coordinates in which you want the points in the *output* streamlines
expressed. If None, then return the points exactly as they are stored
in the trackvis file. The points will probably be in trackviz voxmm
space - see Notes for ``write`` function. If 'voxel', we convert the
points to voxel space simply by dividing by the recorded voxel size. If
'rasmm' we'll convert the points to RAS mm space (real space). For
'rasmm' we check if the affine is set and matches the voxel sizes and
voxel order.
Returns
-------
streamlines : sequence or generator
Returns sequence if `as_generator` is False, generator if True. Value is
sequence or generator of 3 element sequences with elements:
#. points : ndarray shape (N,3)
where N is the number of points
#. scalars : None or ndarray shape (N, M)
where M is the number of scalars per point
#. properties : None or ndarray shape (P,)
where P is the number of properties
hdr : structured array
structured array with trackvis header fields
Notes
-----
The endianness of the input data can be deduced from the endianness
of the returned `hdr` or `streamlines`
Points are in trackvis *voxel mm*. Each track has N points, each with 3
coordinates, ``x, y, z``, where ``x`` is the floating point voxel coordinate
along the first image axis, multiplied by the voxel size for that axis.
'''
fileobj = BinOpener(fileobj)
hdr_str = fileobj.read(header_2_dtype.itemsize)
# try defaulting to version 2 format
hdr = np.ndarray(shape=(),
dtype=header_2_dtype,
buffer=hdr_str)
if np.asscalar(hdr['id_string'])[:5] != b'TRACK':
raise HeaderError('Expecting TRACK as first '
'5 characters of id_string')
if hdr['hdr_size'] == 1000:
endianness = native_code
else:
hdr = hdr.newbyteorder()
if hdr['hdr_size'] != 1000:
raise HeaderError('Invalid hdr_size of %s'
% hdr['hdr_size'])
endianness = swapped_code
# Check version and adapt structure accordingly
version = hdr['version']
if version not in (1, 2):
raise HeaderError('Reader only supports versions 1 and 2')
if version == 1: # make a new header with the same data
hdr = np.ndarray(shape=(),
dtype=header_1_dtype,
buffer=hdr_str)
if endianness == swapped_code:
hdr = hdr.newbyteorder()
# Do points_space checks
_check_hdr_points_space(hdr, points_space)
# prepare transforms for later use
if points_space == 'voxel':
zooms = hdr['voxel_size'][None,:].astype('f4')
elif points_space == 'rasmm':
zooms = hdr['voxel_size']
affine = hdr['vox_to_ras']
tv2vx = np.diag((1. / zooms).tolist() + [1])
tv2mm = np.dot(affine, tv2vx).astype('f4')
n_s = hdr['n_scalars']
n_p = hdr['n_properties']
f4dt = np.dtype(endianness + 'f4')
pt_cols = 3 + n_s
pt_size = int(f4dt.itemsize * pt_cols)
ps_size = int(f4dt.itemsize * n_p)
i_fmt = endianness + 'i'
stream_count = hdr['n_count']
if stream_count < 0:
raise HeaderError('Unexpected negative n_count')
def track_gen():
n_streams = 0
# For case where there are no scalars or no properties
scalars = None
ps = None
while True:
n_str = fileobj.read(4)
if len(n_str) < 4:
if stream_count:
raise HeaderError(
'Expecting %s points, found only %s' % (
stream_count, n_streams))
break
n_pts = struct.unpack(i_fmt, n_str)[0]
pts_str = fileobj.read(n_pts * pt_size)
pts = np.ndarray(
shape = (n_pts, pt_cols),
dtype = f4dt,
buffer = pts_str)
if n_p:
ps_str = fileobj.read(ps_size)
ps = np.ndarray(
shape = (n_p,),
dtype = f4dt,
buffer = ps_str)
xyz = pts[:,:3]
if points_space == 'voxel':
xyz = xyz / zooms
elif points_space == 'rasmm':
xyz = apply_affine(tv2mm, xyz)
if n_s:
scalars = pts[:,3:]
yield (xyz, scalars, ps)
n_streams += 1
# deliberately misses case where stream_count is 0
if n_streams == stream_count:
fileobj.close_if_mine()
raise StopIteration
fileobj.close_if_mine()
streamlines = track_gen()
if not as_generator:
streamlines = list(streamlines)
return streamlines, hdr
def write(fileobj, streamlines, hdr_mapping=None, endianness=None,
points_space=None):
''' Write header and `streamlines` to trackvis file `fileobj`
The parameters from the streamlines override conflicting parameters
in the `hdr_mapping` information. In particular, the number of
streamlines, the number of scalars, and the number of properties are
written according to `streamlines` rather than `hdr_mapping`.
Parameters
----------
fileobj : filename or file-like
If filename, open file as 'wb', otherwise `fileobj` should be an
open file-like object, with a ``write`` method.
streamlines : iterable
iterable returning 3 element sequences with elements:
#. points : ndarray shape (N,3)
where N is the number of points
#. scalars : None or ndarray shape (N, M)
where M is the number of scalars per point
#. properties : None or ndarray shape (P,)
where P is the number of properties
If `streamlines` has a ``len`` (for example, it is a list or a tuple),
then we can write the number of streamlines into the header. Otherwise
we write 0 for the number of streamlines (a valid trackvis header) and
write streamlines into the file until the iterable is exhausted.
M - the number of scalars - has to be the same for each streamline in
`streamlines`. Similarly for P. See `points_space` and Notes for more
detail on the coordinate system for ``points`` above.
hdr_mapping : None, ndarray or mapping, optional
Information for filling header fields. Can be something
dict-like (implementing ``items``) or a structured numpy array
endianness : {None, '<', '>'}, optional
Endianness of file to be written. '<' is little-endian, '>' is
big-endian. None (the default) is to use the endianness of the
`streamlines` data.
points_space : {None, 'voxel', 'rasmm'}, optional
The coordinates in which the points in the input streamlines are
expressed. If None, then assume the points are as you want them
(probably trackviz voxmm space - see Notes). If 'voxel', the points are
in voxel space, and we will transform them to trackviz voxmm space. If
'rasmm' the points are in RAS mm space (real space). We transform them
to trackvis voxmm space. If 'voxel' or 'rasmm' we insist that the voxel
sizes and ordering are set to non-default values. If 'rasmm' we also
check if the affine is set and matches the voxel sizes
Returns
-------
None
Examples
--------
>>> from io import BytesIO
>>> file_obj = BytesIO()
>>> pts0 = np.random.uniform(size=(10,3))
>>> pts1 = np.random.uniform(size=(10,3))
>>> streamlines = ([(pts0, None, None), (pts1, None, None)])
>>> write(file_obj, streamlines)
>>> _ = file_obj.seek(0) # returns 0 in python 3
>>> streams, hdr = read(file_obj)
>>> len(streams)
2
If there are too many streamlines to fit in memory, you can pass an iterable
thing instead of a list
>>> file_obj = BytesIO()
>>> def gen():
... yield (pts0, None, None)
... yield (pts0, None, None)
>>> write(file_obj, gen())
>>> _ = file_obj.seek(0)
>>> streams, hdr = read(file_obj)
>>> len(streams)
2
Notes
-----
Trackvis (the application) expects the ``points`` in the streamlines be in
what we call *trackviz voxmm* coordinates. If we have a point (x, y, z) in
voxmm coordinates, and ``voxel_size`` has the voxel sizes for each of the 3
dimensions, then x, y, z refer to mm in voxel space. Thus if i, j, k is a
point in voxel coordinates, then ``x = i * voxel_size[0]; y = j *
voxel_size[1]; z = k * voxel_size[2]``. The spatial direction of x, y and
z are defined with the "voxel_order" field. For example, if the original
image had RAS voxel ordering then "voxel_order" would be "RAS". RAS here
refers to the spatial direction of the voxel axes: "R" means that moving
along first voxel axis moves from left to right in space, "A" -> second axis
goes from posterior to anterior, "S" -> inferior to superior. If
"voxel_order" is empty we assume "LPS".
This information comes from some helpful replies on the trackviz forum about
`interpreting point coordiantes
<http://trackvis.org/blog/forum/diffusion-toolkit-usage/interpretation-of-track-point-coordinates>`_
'''
stream_iter = iter(streamlines)
try:
streams0 = next(stream_iter)
except StopIteration: # empty sequence or iterable
# write header without streams
hdr = _hdr_from_mapping(None, hdr_mapping, endianness)
with BinOpener(fileobj, 'wb') as fileobj:
fileobj.write(hdr.tostring())
return
if endianness is None:
endianness = endian_codes[streams0[0].dtype.byteorder]
# fill in a new header from mapping-like
hdr = _hdr_from_mapping(None, hdr_mapping, endianness)
# Try and get number of streams from streamlines. If this is an iterable,
# we don't have a len, so we write 0 for length. The 0 is a valid trackvis
# value with meaning - keep reading until you run out of data.
try:
n_streams = len(streamlines)
except TypeError: # iterable; we don't know the number of streams
n_streams = 0
hdr['n_count'] = n_streams
# Get number of scalars and properties
pts, scalars, props = streams0
# calculate number of scalars
if not scalars is None:
n_s = scalars.shape[1]
else:
n_s = 0
hdr['n_scalars'] = n_s
# calculate number of properties
if not props is None:
n_p = props.size
hdr['n_properties'] = n_p
else:
n_p = 0
# do points_space checks
_check_hdr_points_space(hdr, points_space)
# prepare transforms for later use
if points_space == 'voxel':
zooms = hdr['voxel_size'][None,:].astype('f4')
elif points_space == 'rasmm':
zooms = hdr['voxel_size']
affine = hdr['vox_to_ras']
vx2tv = np.diag(zooms.tolist() + [1])
mm2vx = npl.inv(affine)
mm2tv = np.dot(vx2tv, mm2vx).astype('f4')
# write header
fileobj = BinOpener(fileobj, mode='wb')
fileobj.write(hdr.tostring())
# track preliminaries
f4dt = np.dtype(endianness + 'f4')
i_fmt = endianness + 'i'
# Add back the read first streamline to the sequence
for pts, scalars, props in itertools.chain([streams0], stream_iter):
n_pts, n_coords = pts.shape
if n_coords != 3:
raise ValueError('pts should have 3 columns')
fileobj.write(struct.pack(i_fmt, n_pts))
if points_space == 'voxel':
pts = pts * zooms
elif points_space == 'rasmm':
pts = apply_affine(mm2tv, pts)
# This call ensures that the data are 32-bit floats, and that
# the endianness is OK.
if pts.dtype != f4dt:
pts = pts.astype(f4dt)
if n_s == 0:
if not (scalars is None or len(scalars) == 0):
raise DataError('Expecting 0 scalars per point')
else:
if scalars.shape != (n_pts, n_s):
raise DataError('Scalars should be shape (%s, %s)'
% (n_pts, n_s))
if scalars.dtype != f4dt:
scalars = scalars.astype(f4dt)
pts = np.c_[pts, scalars]
fileobj.write(pts.tostring())
if n_p == 0:
if not (props is None or len(props) == 0):
raise DataError('Expecting 0 properties per point')
else:
if props.size != n_p:
raise DataError('Properties should be size %s' % n_p)
if props.dtype != f4dt:
props = props.astype(f4dt)
fileobj.write(props.tostring())
fileobj.close_if_mine()
def _check_hdr_points_space(hdr, points_space):
""" Check header `hdr` for consistency with transform `points_space`
Parameters
----------
hdr : ndarray
trackvis header as structured ndarray
points_space : {None, 'voxmm', 'voxel', 'rasmm'
nature of transform that we will (elsewhere) apply to streamlines paired
with `hdr`. None or 'voxmm' means pass through with no futher checks.
'voxel' checks for all ``hdr['voxel_sizes'] being <= zero (error) or any
being zero (warning). 'rasmm' checks for presence of non-zeros affine
in ``hdr['vox_to_ras']``, and that the affine therein corresponds to
``hdr['voxel_order']`` and ''hdr['voxe_sizes']`` - and raises an error
otherwise.
Returns
-------
None
Notes
-----
"""
if points_space is None or points_space == 'voxmm':
return
if points_space == 'voxel':
voxel_size = hdr['voxel_size']
if np.any(voxel_size < 0):
raise HeaderError('Negative voxel sizes %s not valid for voxel - '
'voxmm conversion' % voxel_size)
if np.all(voxel_size == 0):
raise HeaderError('Cannot convert between voxels and voxmm when '
'"voxel_sizes" all 0')
if np.any(voxel_size == 0):
warnings.warn('zero values in "voxel_size" - %s' % voxel_size)
return
elif points_space == 'rasmm':
try:
affine = hdr['vox_to_ras']
except ValueError:
raise HeaderError('Need "vox_to_ras" field to get '
'affine with which to convert points; '
'this is present for headers >= version 2')
if np.all(affine == 0) or affine[3,3] == 0:
raise HeaderError('Need non-zero affine to convert between '
'rasmm points and voxmm')
zooms = hdr['voxel_size']
aff_zooms = np.sqrt(np.sum(affine[:3,:3]**2,axis=0))
if not np.allclose(aff_zooms, zooms):
raise HeaderError('Affine zooms %s differ from voxel_size '
'field value %s' % (aff_zooms, zooms))
aff_order = ''.join(aff2axcodes(affine))
voxel_order = asstr(np.asscalar(hdr['voxel_order']))
if voxel_order == '':
voxel_order = 'LPS' # trackvis default
if not voxel_order == aff_order:
raise HeaderError('Affine implies voxel_order %s but '
'header voxel_order is %s' %
(aff_order, voxel_order))
else:
raise ValueError('Painfully confusing "points_space" value of "%s"'
% points_space)
def _hdr_from_mapping(hdr=None, mapping=None, endianness=native_code):
''' Fill `hdr` from mapping `mapping`, with given endianness '''
if hdr is None:
# passed a valid mapping as header? Copy and return
if isinstance(mapping, np.ndarray):
test_dtype = mapping.dtype.newbyteorder('=')
if test_dtype in (header_1_dtype, header_2_dtype):
return mapping.copy()
# otherwise make a new empty header. If no version specified,
# go for default (2)
if mapping is None:
version = 2
else:
version = mapping.get('version', 2)
hdr = empty_header(endianness, version)
if mapping is None:
return hdr
if isinstance(mapping, np.ndarray):
mapping = rec2dict(mapping)
for key, value in mapping.items():
hdr[key] = value
# check header values
if np.asscalar(hdr['id_string'])[:5] != b'TRACK':
raise HeaderError('Expecting TRACK as first '
'5 characaters of id_string')
if hdr['version'] not in (1, 2):
raise HeaderError('Reader only supports version 1')
if hdr['hdr_size'] != 1000:
raise HeaderError('hdr_size should be 1000')
return hdr
def empty_header(endianness=None, version=2):
''' Empty trackvis header
Parameters
----------
endianness : {'<','>'}, optional
Endianness of empty header to return. Default is native endian.
version : int, optional
Header version. 1 or 2. Default is 2
Returns
-------
hdr : structured array
structured array containing empty trackvis header
Examples
--------
>>> hdr = empty_header()
>>> print(hdr['version'])
2
>>> np.asscalar(hdr['id_string']) == b'TRACK'
True
>>> endian_codes[hdr['version'].dtype.byteorder] == native_code
True
>>> hdr = empty_header(swapped_code)
>>> endian_codes[hdr['version'].dtype.byteorder] == swapped_code
True
>>> hdr = empty_header(version=1)
>>> print(hdr['version'])
1
Notes
-----
The trackvis header can store enough information to give an affine
mapping between voxel and world space. Often this information is
missing. We make no attempt to fill it with sensible defaults on
the basis that, if the information is missing, it is better to be
explicit.
'''
if version == 1:
dt = header_1_dtype
elif version == 2:
dt = header_2_dtype
else:
raise HeaderError('Header version should be 1 or 2')
if endianness:
dt = dt.newbyteorder(endianness)
hdr = np.zeros((), dtype=dt)
hdr['id_string'] = 'TRACK'
hdr['version'] = version
hdr['hdr_size'] = 1000
return hdr
def aff_from_hdr(trk_hdr, atleast_v2=None):
''' Return voxel to mm affine from trackvis header
Affine is mapping from voxel space to Nifti (RAS) output coordinate
system convention; x: Left -> Right, y: Posterior -> Anterior, z:
Inferior -> Superior.
Parameters
----------
trk_hdr : mapping
Mapping with trackvis header keys ``version``. If ``version == 2``, we
also expect ``vox_to_ras``.
atleast_v2 : None or bool
If None, currently defaults to False. This will change to True in
future versions. If True, require that there is a valid 'vox_to_ras'
affine, raise HeaderError otherwise. If False, look for valid
'vox_to_ras' affine, but fall back to best guess from version 1 fields
otherwise.
Returns
-------
aff : (4,4) array
affine giving mapping from voxel coordinates (affine applied on
the left to points on the right) to millimeter coordinates in the
RAS coordinate system
Notes
-----
Our initial idea was to try and work round the deficiencies of the version 1
format by using the DICOM orientation fields to store the affine. This
proved difficult in practice because trackvis (the application) doesn't
allow negative voxel sizes (needed for recording axis flips) and sets the
origin field to 0. In future, we'll raise an error rather than try and
estimate the affine from version 1 fields
'''
if atleast_v2 is None:
warnings.warn('Defaulting to `atleast_v2` of False. Future versions '
'will default to True',
FutureWarning,
stacklevel=2)
atleast_v2 = False
if trk_hdr['version'] == 2:
aff = trk_hdr['vox_to_ras']
if aff[3,3] != 0:
return aff
if atleast_v2:
raise HeaderError('Requiring version 2 affine and this affine is '
'not valid')
# Now we are in the dark world of the DICOM fields. We might have made this
# one ourselves, in which case the origin might be set, and it might have
# negative voxel sizes
aff = np.eye(4)
# The IOP field has only two of the three columns we need
iop = trk_hdr['image_orientation_patient'].reshape(2,3).T
# R might be a rotation matrix (and so completed by the cross product of the
# first two columns), or it might be an orthogonal matrix with negative
# determinant. We try pure rotation first
R = np.c_[iop, np.cross(*iop.T)]
vox = trk_hdr['voxel_size']
aff[:3,:3] = R * vox
aff[:3,3] = trk_hdr['origin']
aff = np.dot(DPCS_TO_TAL, aff)
# Next we check against the 'voxel_order' field if present and not empty.
try:
voxel_order = asstr(np.asscalar(trk_hdr['voxel_order']))
except (KeyError, ValueError):
voxel_order = ''
if voxel_order == '':
return aff
# If the voxel_order conflicts with the affine by one flip, this may have
# been a negative determinant affine saved with positive voxel sizes
exp_order = ''.join(aff2axcodes(aff))
if voxel_order != exp_order:
# If first pass doesn't match, try flipping the (estimated) third column
aff[:,2] *= -1
exp_order = ''.join(aff2axcodes(aff))
if voxel_order != exp_order:
raise HeaderError('Estimate of header affine does not match '
'voxel_order of %s' % exp_order)
return aff
def aff_to_hdr(affine, trk_hdr, pos_vox=None, set_order=None):
''' Set affine `affine` into trackvis header `trk_hdr`
Affine is mapping from voxel space to Nifti RAS) output coordinate
system convention; x: Left -> Right, y: Posterior -> Anterior, z:
Inferior -> Superior. Sets affine if possible, and voxel sizes, and voxel
axis ordering.
Parameters
----------
affine : (4,4) array-like
Affine voxel to mm transformation
trk_hdr : mapping
Mapping implementing __setitem__
pos_vos : None or bool
If None, currently defaults to False - this will change in future
versions of nibabel. If False, allow negative voxel sizes in header to
record axis flips. Negative voxels cause problems for trackvis (the
application). If True, enforce positive voxel sizes.
set_order : None or bool
If None, currently defaults to False - this will change in future
versions of nibabel. If False, do not set ``voxel_order`` field in
`trk_hdr`. If True, calculcate ``voxel_order`` from `affine` and set
into `trk_hdr`.
Returns
-------
None
Notes
-----
version 2 of the trackvis header has a dedicated field for the nifti RAS
affine. In theory trackvis 1 has enough information to store an affine, with
the fields 'origin', 'voxel_size' and 'image_orientation_patient'.
Unfortunately, to be able to store any affine, we'd need to be able to set
negative voxel sizes, to encode axis flips. This is because
'image_orientation_patient' is only two columns of the 3x3 rotation matrix,
and we need to know the number of flips to reconstruct the third column
reliably. It turns out that negative flips upset trackvis (the
application). The application also ignores the origin field, and may not
use the 'image_orientation_patient' field.
'''
if pos_vox is None:
warnings.warn('Default for ``pos_vox`` will change to True in '
'future versions of nibabel',
FutureWarning,
stacklevel=2)
pos_vox = False
if set_order is None:
warnings.warn('Default for ``set_order`` will change to True in '
'future versions of nibabel',
FutureWarning,
stacklevel=2)
set_order = False
try:
version = trk_hdr['version']
except (KeyError, ValueError): # dict or structured array
version = 2
if version == 2:
trk_hdr['vox_to_ras'] = affine
if set_order:
trk_hdr['voxel_order'] = ''.join(aff2axcodes(affine))
# Now on dodgy ground with DICOM fields in header
# RAS to DPCS output
affine = np.dot(DPCS_TO_TAL, affine)
trans = affine[:3, 3]
# Get zooms
RZS = affine[:3, :3]
zooms = np.sqrt(np.sum(RZS * RZS, axis=0))
RS = RZS / zooms
# If you said we could, adjust zooms to make RS correspond (below) to a true
# rotation matrix. We need to set the sign of one of the zooms to deal with
# this. Trackvis (the application) doesn't like negative zooms at all, so
# you might want to disallow this with the pos_vox option.
if not pos_vox and npl.det(RS) < 0:
zooms[0] *= -1
RS[:,0] *= -1
# retrieve rotation matrix from RS with polar decomposition.
# Discard shears because we cannot store them.
P, S, Qs = npl.svd(RS)
R = np.dot(P, Qs)
# it's an orthogonal matrix
assert np.allclose(np.dot(R, R.T), np.eye(3))
# set into header
trk_hdr['origin'] = trans
trk_hdr['voxel_size'] = zooms
trk_hdr['image_orientation_patient'] = R[:,0:2].T.ravel()
class TrackvisFileError(Exception):
""" Error from TrackvisFile class
"""
class TrackvisFile(object):
''' Convenience class to encapsulate trackvis file information
Parameters
----------
streamlines : sequence
sequence of streamlines. This object does not accept generic iterables
as input because these can be consumed and make the object unusable.
Please use the function interface to work with generators / iterables
mapping : None or mapping
Mapping defining header attributes
endianness : {None, '<', '>'}
Set here explicit endianness if required. Endianness otherwise inferred
from `streamlines`
filename : None or str, optional
filename
points_space : {None, 'voxel', 'rasmm'}, optional
Space in which streamline points are expressed in memory. Default
(None) means streamlines contain points in trackvis *voxmm* space (voxel
positions * voxel sizes). 'voxel' means points are in voxel space (and
need to be multiplied by voxel size for saving in file). 'rasmm' mean
the points are expressed in mm space according to the affine. See
``read`` and ``write`` function docstrings for more detail.
affine : None or (4,4) ndarray, optional
Affine expressing relationship of voxels in an image to mm in RAS mm
space. If 'points_space' is not None, you can use this to give the
relationship between voxels, rasmm and voxmm space (above).
'''
def __init__(self,
streamlines,
mapping=None,
endianness=None,
filename=None,
points_space=None,
affine = None,
):
try:
n_streams = len(streamlines)
except TypeError:
raise TrackvisFileError('Need sequence for streamlines input')
self.streamlines = streamlines
if endianness is None:
if n_streams > 0:
pts0 = streamlines[0][0]
endianness = endian_codes[pts0.dtype.byteorder]
else:
endianness = native_code
self.header = _hdr_from_mapping(None, mapping, endianness)
self.endianness = endianness
self.filename = filename
self.points_space = points_space
if not affine is None:
self.set_affine(affine, pos_vox=True, set_order=True)
@classmethod
def from_file(klass, file_like, points_space=None):
streamlines, header = read(file_like, points_space=points_space)
filename = (file_like if isinstance(file_like, basestring)
else None)
return klass(streamlines, header, None, filename, points_space)
def to_file(self, file_like):
write(file_like, self.streamlines, self.header, self.endianness,
points_space=self.points_space)
self.filename = (file_like if isinstance(file_like, basestring)
else None)
def get_affine(self, atleast_v2=None):
""" Get affine from header in object
Returns
-------
aff : (4,4) ndarray
affine from header
atleast_v2 : None or bool, optional
See ``aff_from_hdr`` docstring for detail. If True, require valid
affine in ``vox_to_ras`` field of header.
Notes
-----
This method currently works for trackvis version 1 headers, but we
consider it unsafe for version 1 headers, and in future versions of
nibabel we will raise an error for trackvis headers < version 2.
"""
if atleast_v2 is None:
warnings.warn('Defaulting to `atleast_v2` of False. Future versions '
'will default to True',
FutureWarning,
stacklevel=2)
atleast_v2 = False
return aff_from_hdr(self.header, atleast_v2)
def set_affine(self, affine, pos_vox=None, set_order=None):
""" Set affine `affine` into trackvis header
Affine is mapping from voxel space to Nifti RAS) output coordinate
system convention; x: Left -> Right, y: Posterior -> Anterior, z:
Inferior -> Superior. Sets affine if possible, and voxel sizes, and voxel
axis ordering.
Parameters
----------
affine : (4,4) array-like
Affine voxel to mm transformation
pos_vos : None or bool, optional
If None, currently defaults to False - this will change in future
versions of nibabel. If False, allow negative voxel sizes in header to
record axis flips. Negative voxels cause problems for trackvis (the
application). If True, enforce positive voxel sizes.
set_order : None or bool, optional
If None, currently defaults to False - this will change in future
versions of nibabel. If False, do not set ``voxel_order`` field in
`trk_hdr`. If True, calculcate ``voxel_order`` from `affine` and set
into `trk_hdr`.
Returns
-------
None
"""
if pos_vox is None:
warnings.warn('Default for ``pos_vox`` will change to True in '
'future versions of nibabel',
FutureWarning,
stacklevel=2)
pos_vox = False
if set_order is None:
warnings.warn('Default for ``set_order`` will change to True in '
'future versions of nibabel',
FutureWarning,
stacklevel=2)
set_order = False
return aff_to_hdr(affine, self.header, pos_vox, set_order)
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