def asDict(self):
"""Returns the header data of the `NiftiImage` in a dictionary.
:Returns:
dict
The dictionary contains all NIfTI header information. Additionally,
it might also contain a special 'meta' item that contains the
meta data currently assigned to this instance.
.. note::
Modifications done to the returned dictionary do not cause any
modifications in the NIfTI image itself. Please use
:meth:`~nifti.format.NiftiFormat.updateFromDict` to apply
changes to the image.
.. seealso::
:meth:`~nifti.format.NiftiFormat.updateFromDict`,
:attr:`~nifti.format.NiftiFormat.header`
"""
# Convert nifti_image struct into nifti1 header struct.
# This get us all data that will actually make it into a
# NIfTI file.
nhdr = ncl.nifti_convert_nim2nhdr(self.raw_nimg)
# pass extensions as well
ret = nhdr2dict(nhdr, extensions=self.extensions)
if len(self.meta.keys()):
ret['meta'] = self.meta
return ret
def __newFromArray(self, data, hdr=None):
"""Create a `nifti_image` struct from a ndarray.
:Parameters:
data: ndarray
Source ndarray.
hdr: dict
Optional dictionary with NIfTI header data.
.. warning::
This is an internal method. Neither its availability nor its API is
guarenteed.
"""
if hdr == None:
hdr = {}
# check array
if len(data.shape) > 7:
raise ValueError, \
"NIfTI does not support data with more than 7 dimensions."
# create template nifti header struct
niptr = ncl.nifti_simple_init_nim()
nhdr = ncl.nifti_convert_nim2nhdr(niptr)
# intermediate cleanup
ncl.nifti_image_free(niptr)
# convert virgin nifti header to dict to merge properties
# with supplied information and array properties
hdic = nhdr2dict(nhdr)
# copy data from supplied header dict
for k, v in hdr.iteritems():
hdic[k] = v
# finally set header data that is determined by the data array
# convert NumPy to nifti datatype
hdic['datatype'] = Ndtype2niftidtype(data)
# make sure there are no zeros in the dim vector
# especially not in #4 as FSLView doesn't like that
hdic['dim'] = [ 1 for i in hdic['dim'] ]
# set number of dims
hdic['dim'][0] = len(data.shape)
# set size of each dim (and reverse the order to match nifti format
# requirements)
for i, s in enumerate(data.shape):
hdic['dim'][len(data.shape)-i] = s
# set magic field to mark as nifti file
hdic['magic'] = 'n+1'
self._rebuildNimgFromHdrAndDict(nhdr, hdic)
