def to_files(self, files=None):
''' Write image to files passed, or self._files
'''
if files is None:
files = self._files
if files is None:
raise ValueError('Need files to write data')
data = self.get_data()
# Adapt header to possible two<->one file difference
is_pair = files['header'] != files['image']
hdr = self.get_header().for_file_pair(is_pair)
slope, inter, mn, mx = adapt_header(hdr, data)
hdrf = allopen(files['header'], 'wb')
hdr.write_to(hdrf)
if is_pair:
imgf = allopen(files['image'], 'wb')
else: # single file for header and image
imgf = hdrf
# streams like bz2 do not allow seeks, even forward. We
# check where to go, and write zeros up until the data part
# of the file
offset = hdr.get_data_offset()
diff = offset-hdrf.tell()
if diff > 0:
hdrf.write('\x00' * diff)
write_data(hdr, data, imgf, inter, slope, mn, mx)
self._header = hdr
self._files = files
def from_filespec(klass, filespec):
ret = super(Spm99AnalyzeImage, klass).from_filespec(filespec)
import scipy.io as sio
matf = ret._files['mat']
try:
matf = allopen(matf)
except IOError:
return ret
mats = sio.loadmat(matf)
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
return ret
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')
return ret
def from_files(klass, files):
fname = files['header']
header = klass._header_maker.from_fileobj(allopen(fname))
affine = header.get_best_affine()
ret = klass(None, affine, header)
ret._files = files
return ret
def get_data(self):
''' Lazy load of data '''
if not self._data is None:
return self._data
if not self._files:
return None
try:
fname = self._files['image']
except KeyError:
return None
self._data = read_data(self._header, allopen(fname))
return self._data
def to_files(self, files=None):
super(Spm99AnalyzeImage, self).to_files(files)
if self._affine is None:
return
import scipy.io as sio
matfname = self._files['mat']
mfobj = allopen(matfname, 'wb')
mat = self._affine
hdr = self._header
if hdr.default_x_flip:
M = np.dot(np.diag([-1,1,1,1]), mat)
else:
M = mat
# use matlab 4 format to allow gzipped write without error
sio.savemat(mfobj, {'M': M, 'mat': mat}, format='4')
mfobj.close()
def load(filespec, *args, **kwargs):
''' Load file given filespec, guessing at file type
Parameters
----------
filespec : string or file-like
specification of filename or file to load
*args
**kwargs
arguments to pass to image load function
Returns
-------
img : ``SpatialImage``
Image of guessed type
'''
# Try and guess file type from filename
if isinstance(filespec, basestring):
fname = filespec
for ending in ('.gz', '.bz2'):
if filespec.endswith(ending):
fname = fname[:-len(ending)]
break
if fname.endswith('.nii'):
return nifti1.load(filespec, *args, **kwargs)
if fname.endswith('.mnc'):
return minc.load(filespec, *args, **kwargs)
# Not a string, or not recognized as nii or mnc
try:
files = nifti1.Nifti1Image.filespec_to_files(filespec)
except ValueError:
raise RuntimeError('Cannot work out file type of "%s"' %
filespec)
hdr = nifti1.Nifti1Header.from_fileobj(
vu.allopen(files['header']),
check=False)
magic = hdr['magic']
if magic in ('ni1', 'n+1'):
return nifti1.load(filespec, *args, **kwargs)
return spm2.load(filespec, *args, **kwargs)
