def write_hdr(self, filename):
"Write ANALYZE format header (.hdr) file."
image = self.image
# NOT IMPLEMENTING YET
# ReconImage r0 is the offset in xyz-space, but ANALYZE
# calls for an offset in vox-space
xform = image.orientation_xform.tomatrix()
orientation_name = canonical_orient(xform)
inv_xform = np.linalg.inv(xform)
r0 = np.array([image.x0, image.y0, image.z0])
dimscl = np.array([image.isize, image.jsize, image.ksize])
r0_ana = (-np.dot(inv_xform, r0) / dimscl).astype(np.int16)
dtype = datatype2dtype[self.datatype]
if dtype in np.sctypes['int']:
glmax = 2**(datatype2bitpix[self.datatype] - 1) - 1
elif dtype in np.sctypes['uint']:
glmax = 2**(datatype2bitpix[self.datatype]) - 1
else:
glmax = 1
imagevalues = {
'datatype': self.datatype,
'bitpix': datatype2bitpix[self.datatype],
# should be 4 for (z,y,x) and (t,z,y,x) ...
# should be 3 for (y,x) ??
'ndim': (image.ndim >= 3 and 4 or image.ndim),
'idim': image.idim,
'jdim': image.jdim,
'kdim': image.kdim,
'tdim': (image.tdim or 1),
'isize': image.isize,
'jsize': image.jsize,
'ksize': image.ksize,
'tsize': image.tsize,
'x0': r0_ana[0],
'y0': r0_ana[1],
'z0': r0_ana[2],
'scale_factor': self.scaling,
# SPM says that these are (0,2^bitpix-1) for integers,
# or (0,1) for floating points
'glmin': 0,
'glmax': glmax,
'cal_min': 0.0, # don't suggest display color mapping
'cal_max': 0.0,
'orient': orientname2orientcode.get(orientation_name, 255),
}
def fieldvalue(fieldname, fieldformat):
if imagevalues.has_key(fieldname):
return imagevalues[fieldname]
return self._default_field_value(fieldname, fieldformat)
fieldvalues = [fieldvalue(*field) for field in struct_fields.items()]
header = struct_pack(NATIVE, field_formats, fieldvalues)
file(filename, 'wb').write(header)
def make_hdr(self):
"Pack a NIFTI format header."
# The NIFTI Q-form field is helpful for keeping track of anatomical
# dimensions in the presence of rotations.
#
# (un)rotation is handled like this: take the image as transformed with
# 2 rotations, Qs, Rb (Qs represents a transform between scanner space
# and a right-handed interpretation of the data, Rb is the xform
# applied by slicing coronal-wise, sagital-wise, etc with the slice
# gradient).
# Then I_m = Qs*Rb(psi,theta,phi)*I_real
# The goal is to encode a quaternion which corrects this rotation:
# Rc = inv(Rb)*inv(Qs)
# so I_real = Rc*I_m
#
# The euler angles for slicing rotations are from procpar
# To avoid the chance of interpolation, these angles are "normalized"
# to the closest multiple of pi/2 (ie 22 degrees->0, 49 degrees->90)
#
image = self.image
Qform = image.orientation_xform.Q
Qform_mat = image.orientation_xform.tomatrix()
# hack alert!
## (pe_dim, fe_dim) = Qform_mat[0,0] == 0. and (2, 1) or (1, 2)
(pe_dim, fe_dim) = (2,1)
imagevalues = {
'dim_info': (3<<4 | pe_dim<<2 | fe_dim),
'datatype': self.datatype,
'bitpix': datatype2bitpix[self.datatype],
'ndim': image.ndim,
'idim': image.idim,
'jdim': image.jdim,
# including t,z info should probably depend on targetdims
'kdim': image.kdim,
'tdim': image.tdim,
'isize': image.isize,
'jsize': image.jsize,
'ksize': image.ksize,
'tsize': image.tsize,
'scl_slope': self.scaling,
'xyzt_units': (NIFTI_UNITS_MM | NIFTI_UNITS_SEC),
'qfac': image.orientation_xform.qfac,
'qform_code': NIFTI_XFORM_SCANNER_ANAT,
'quatern_b': Qform[0],
'quatern_c': Qform[1],
'quatern_d': Qform[2],
'qoffset_x': image.x0,
'qoffset_y': image.y0,
'qoffset_z': image.z0,
'slice_start': 0,
# THIS KDIM ASSUMPTION IS ONLY SAFE BECAUSE NOTHING ROTATES
# IMAGES OFF THE ORIGINAL SLICE AXIS (YET!!)
'slice_end': image.kdim-1,
# VARIAN ACQUIRES FROM +Z TO -Z (IN NEURO FRAME)
'slice_code': NIFTI_SLICE_SEQ_DEC,
'slice_duration': float(image.tsize) / image.kdim
}
if self.filetype=='single':
imagevalues['vox_offset'] = HEADER_SIZE + self.sizeof_extension
imagevalues['magic'] = 'n+1'
else:
imagevalues['magic'] = 'ni1'
def fieldvalue(fieldname, fieldformat):
return imagevalues.get(fieldname) or \
self._default_field_value(fieldname, fieldformat)
fieldvalues = [fieldvalue(*field) for field in struct_fields.items()]
return struct_pack(NATIVE, field_formats, fieldvalues)
