/usr/lib/python3/dist-packages/nibabel/spm99analyze.py is in python3-nibabel 2.0.2-2.
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#
# See COPYING file distributed along with the NiBabel package for the
# copyright and license terms.
#
### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ##
''' Read / write access to SPM99 version of analyze image format '''
import warnings
import numpy as np
from .externals.six import BytesIO
from .spatialimages import HeaderDataError, HeaderTypeError
from .batteryrunners import Report
from . import analyze # module import
from .keywordonly import kw_only_meth
''' Support subtle variations of SPM version of Analyze '''
header_key_dtd = analyze.header_key_dtd
# funused1 in dime subfield is scalefactor
image_dimension_dtd = analyze.image_dimension_dtd[:]
image_dimension_dtd[
image_dimension_dtd.index(('funused1', 'f4'))
] = ('scl_slope', 'f4')
# originator text field used as image origin (translations)
data_history_dtd = analyze.data_history_dtd[:]
data_history_dtd[
data_history_dtd.index(('originator', 'S10'))
] = ('origin', 'i2', (5,))
# Full header numpy dtype combined across sub-fields
header_dtype = np.dtype(header_key_dtd +
image_dimension_dtd +
data_history_dtd)
class SpmAnalyzeHeader(analyze.AnalyzeHeader):
''' Basic scaling Spm Analyze header '''
# Copies of module level definitions
template_dtype = header_dtype
# data scaling capabilities
has_data_slope = True
has_data_intercept = False
@classmethod
def default_structarr(klass, endianness=None):
''' Create empty header binary block with given endianness '''
hdr_data = super(SpmAnalyzeHeader, klass).default_structarr(endianness)
hdr_data['scl_slope'] = 1
return hdr_data
def get_slope_inter(self):
''' Get scalefactor and intercept
If scalefactor is 0.0 return None to indicate no scalefactor. Intercept
is always None because SPM99 analyze cannot store intercepts.
'''
slope = self._structarr['scl_slope']
# Return invalid slopes as None
if np.isnan(slope) or slope in (0, -np.inf, np.inf):
return None, None
return slope, None
def set_slope_inter(self, slope, inter=None):
''' Set slope and / or intercept into header
Set slope and intercept for image data, such that, if the image
data is ``arr``, then the scaled image data will be ``(arr *
slope) + inter``
The SPM Analyze header can't save an intercept value, and we raise an
error unless `inter` is None, NaN or 0
Parameters
----------
slope : None or float
If None, implies `slope` of NaN. NaN is a signal to the image
writing routines to rescale on save. 0, Inf, -Inf are invalid and
cause a HeaderDataError
inter : None or float, optional
intercept. Must be None, NaN or 0, because SPM99 cannot store
intercepts.
'''
if slope is None:
slope = np.nan
if slope in (0, -np.inf, np.inf):
raise HeaderDataError('Slope cannot be 0 or infinite')
self._structarr['scl_slope'] = slope
if inter in (None, 0) or np.isnan(inter):
return
raise HeaderTypeError('Cannot set non-zero intercept '
'for SPM headers')
class Spm99AnalyzeHeader(SpmAnalyzeHeader):
''' Class for SPM99 variant of basic Analyze header
SPM99 variant adds the following to basic Analyze format:
* voxel origin;
* slope scaling of data.
'''
def get_origin_affine(self):
''' Get affine from header, using SPM origin field if sensible
The default translations are got from the ``origin``
field, if set, or from the center of the image otherwise.
Examples
--------
>>> hdr = Spm99AnalyzeHeader()
>>> hdr.set_data_shape((3, 5, 7))
>>> hdr.set_zooms((3, 2, 1))
>>> hdr.default_x_flip
True
>>> hdr.get_origin_affine() # from center of image
array([[-3., 0., 0., 3.],
[ 0., 2., 0., -4.],
[ 0., 0., 1., -3.],
[ 0., 0., 0., 1.]])
>>> hdr['origin'][:3] = [3,4,5]
>>> hdr.get_origin_affine() # using origin
array([[-3., 0., 0., 6.],
[ 0., 2., 0., -6.],
[ 0., 0., 1., -4.],
[ 0., 0., 0., 1.]])
>>> hdr['origin'] = 0 # unset origin
>>> hdr.set_data_shape((3, 5, 7))
>>> hdr.get_origin_affine() # from center of image
array([[-3., 0., 0., 3.],
[ 0., 2., 0., -4.],
[ 0., 0., 1., -3.],
[ 0., 0., 0., 1.]])
'''
hdr = self._structarr
zooms = hdr['pixdim'][1:4].copy()
if self.default_x_flip:
zooms[0] *= -1
# Get translations from origin, or center of image
# Remember that the origin is for matlab (1-based indexing)
origin = hdr['origin'][:3]
dims = hdr['dim'][1:4]
if (np.any(origin) and
np.all(origin > -dims) and np.all(origin < dims*2)):
origin = origin-1
else:
origin = (dims-1) / 2.0
aff = np.eye(4)
aff[:3, :3] = np.diag(zooms)
aff[:3, -1] = -origin * zooms
return aff
get_best_affine = get_origin_affine
def set_origin_from_affine(self, affine):
''' Set SPM origin to header from affine matrix.
The ``origin`` field was read but not written by SPM99 and 2. It was
used for storing a central voxel coordinate, that could be used in
aligning the image to some standard position - a proxy for a full
translation vector that was usually stored in a separate matlab .mat
file.
Nifti uses the space occupied by the SPM ``origin`` field for important
other information (the transform codes), so writing the origin will make
the header a confusing Nifti file. If you work with both Analyze and
Nifti, you should probably avoid doing this.
Parameters
----------
affine : array-like, shape (4,4)
Affine matrix to set
Returns
-------
None
Examples
--------
>>> hdr = Spm99AnalyzeHeader()
>>> hdr.set_data_shape((3, 5, 7))
>>> hdr.set_zooms((3,2,1))
>>> hdr.get_origin_affine()
array([[-3., 0., 0., 3.],
[ 0., 2., 0., -4.],
[ 0., 0., 1., -3.],
[ 0., 0., 0., 1.]])
>>> affine = np.diag([3,2,1,1])
>>> affine[:3,3] = [-6, -6, -4]
>>> hdr.set_origin_from_affine(affine)
>>> np.all(hdr['origin'][:3] == [3,4,5])
True
>>> hdr.get_origin_affine()
array([[-3., 0., 0., 6.],
[ 0., 2., 0., -6.],
[ 0., 0., 1., -4.],
[ 0., 0., 0., 1.]])
'''
if affine.shape != (4, 4):
raise ValueError('Need 4x4 affine to set')
hdr = self._structarr
RZS = affine[:3, :3]
Z = np.sqrt(np.sum(RZS * RZS, axis=0))
T = affine[:3, 3]
# Remember that the origin is for matlab (1-based) indexing
hdr['origin'][:3] = -T / Z + 1
@classmethod
def _get_checks(klass):
checks = super(Spm99AnalyzeHeader, klass)._get_checks()
return checks + (klass._chk_origin,)
@staticmethod
def _chk_origin(hdr, fix=False):
rep = Report(HeaderDataError)
origin = hdr['origin'][0:3]
dims = hdr['dim'][1:4]
if (not np.any(origin) or
(np.all(origin > -dims) and np.all(origin < dims*2))):
return hdr, rep
rep.problem_level = 20
rep.problem_msg = 'very large origin values relative to dims'
if fix:
rep.fix_msg = 'leaving as set, ignoring for affine'
return hdr, rep
class Spm99AnalyzeImage(analyze.AnalyzeImage):
""" Class for SPM99 variant of basic Analyze image
"""
header_class = Spm99AnalyzeHeader
files_types = (('image', '.img'),
('header', '.hdr'),
('mat','.mat'))
@classmethod
@kw_only_meth(1)
def from_file_map(klass, file_map, mmap=True):
''' class method to create image from mapping in `file_map ``
Parameters
----------
file_map : dict
Mapping with (kay, value) pairs of (``file_type``, FileHolder
instance giving file-likes for each file needed for this image
type.
mmap : {True, False, 'c', 'r'}, optional, keyword only
`mmap` controls the use of numpy memory mapping for reading image
array data. If False, do not try numpy ``memmap`` for data array.
If one of {'c', 'r'}, try numpy memmap with ``mode=mmap``. A `mmap`
value of True gives the same behavior as ``mmap='c'``. If image
data file cannot be memory-mapped, ignore `mmap` value and read
array from file.
Returns
-------
img : Spm99AnalyzeImage instance
'''
ret = super(Spm99AnalyzeImage, klass).from_file_map(file_map, mmap=mmap)
try:
matf = file_map['mat'].get_prepare_fileobj()
except IOError:
return ret
# Allow for possibility of empty file -> no update to affine
with matf:
contents = matf.read()
if len(contents) == 0:
return ret
import scipy.io as sio
mats = sio.loadmat(BytesIO(contents))
if 'mat' in mats: # this overrides a 'M', and includes any flip
mat = mats['mat']
if mat.ndim > 2:
warnings.warn('More than one affine in "mat" matrix, '
'using first')
mat = mat[:, :, 0]
ret._affine = mat
elif 'M' in mats: # the 'M' matrix does not include flips
hdr = ret._header
if hdr.default_x_flip:
ret._affine = np.dot(np.diag([-1, 1, 1, 1]), mats['M'])
else:
ret._affine = mats['M']
else:
raise ValueError('mat file found but no "mat" or "M" in it')
# Adjust for matlab 1,1,1 voxel origin
to_111 = np.eye(4)
to_111[:3,3] = 1
ret._affine = np.dot(ret._affine, to_111)
return ret
def to_file_map(self, file_map=None):
''' Write image to `file_map` or contained ``self.file_map``
Extends Analyze ``to_file_map`` method by writing ``mat`` file
Parameters
----------
file_map : None or mapping, optional
files mapping. If None (default) use object's ``file_map``
attribute instead
'''
if file_map is None:
file_map = self.file_map
super(Spm99AnalyzeImage, self).to_file_map(file_map)
mat = self._affine
if mat is None:
return
import scipy.io as sio
hdr = self._header
if hdr.default_x_flip:
M = np.dot(np.diag([-1, 1, 1, 1]), mat)
else:
M = mat
# Adjust for matlab 1,1,1 voxel origin
from_111 = np.eye(4)
from_111[:3,3] = -1
M = np.dot(M, from_111)
mat = np.dot(mat, from_111)
# use matlab 4 format to allow gzipped write without error
with file_map['mat'].get_prepare_fileobj(mode='wb') as mfobj:
sio.savemat(mfobj, {'M': M, 'mat': mat}, format='4')
load = Spm99AnalyzeImage.load
save = Spm99AnalyzeImage.instance_to_filename
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