def writeImage(self, filestem, format_type="analyze",
datatype="magnitude", **kwargs):
"""
Export the image object in a medical file format (ANALYZE or NIFTI).
format_type is one of the internal file format specifiers, which
are currently %s.
possible keywords are:
datatype -- a datatype identifier, supported by the given format
targetdim -- number of dimensions per file
filetype -- differentiates single + dual formats for NIFTI
suffix -- over-ride default suffix style (eg volume0001)
If necessary, a scaling is found for integer types
"""%(" ; ".join(available_writers))
new_dtype = recon_output2dtype.get(datatype.lower(), None)
if new_dtype is None:
raise ValueError("Unsupported data type: %s"%datatype)
# The image writing tool does scaling only to preserve dynamic range
# when saving an as integer data type. Therefore specifying a scale
# in the writeImage kwargs is not appropriate, since it would
# cause the image writing logic to "unscale" the data first--it
# is not to be used as a "gain" knob.
try:
kwargs.pop('scale')
except KeyError:
pass
if new_dtype in integer_ranges.keys():
scale = float(scale_data(self[:], new_dtype))
else:
scale = float(1.0)
_write(self, filestem, format_type, scale=scale,
dtype=new_dtype,**kwargs)
def load_image(self, filename, target_dtype, vrange):
# bytes per pixel
bytepix = self.bitpix / 8
numtype = datatype2dtype[self.datatype]
byteoffset = 0
if target_dtype is not None and \
not range_exceeds(target_dtype, numtype):
raise ValueError("the dynamic range of the desired datatype does "\
"not exceed that of the raw data")
# need to cook tdim if vrange is set
if self.tdim and vrange:
vend = (vrange[1]<0 or vrange[1]>=self.tdim) \
and self.tdim-1 or vrange[1]
vstart = (vrange[0] > vend) and vend or vrange[0]
self.tdim = vend - vstart + 1
byteoffset = vstart * bytepix * np.product(
(self.kdim, self.jdim, self.idim))
dims = self.tdim > 1 and (self.tdim, self.kdim, self.jdim, self.idim) \
or (self.kdim, self.jdim, self.idim)
datasize = bytepix * np.product(dims)
fp = file(filename, 'rb')
fp.seek(byteoffset, 1)
data = np.fromstring(fp.read(datasize), numtype).reshape(dims)
if self.swapped: data = data.byteswap()
if target_dtype is not None and target_dtype != numtype:
if target_dtype not in integer_ranges.keys():
scale = self.scale_factor or 1.0
self.data = (data * scale).astype(target_dtype)
else:
self.data = data.astype(target_dtype)
else:
self.data = data
fp.close()
def load_image(self, filename, target_dtype, vrange):
# bytes per pixel
bytepix = self.bitpix / 8
numtype = datatype2dtype[self.datatype]
byteoffset = 0
if target_dtype is not None and not range_exceeds(target_dtype, numtype):
raise ValueError("the dynamic range of the desired datatype does " "not exceed that of the raw data")
# need to cook tdim if vrange is set
if self.tdim and vrange:
vend = (vrange[1] < 0 or vrange[1] >= self.tdim) and self.tdim - 1 or vrange[1]
vstart = (vrange[0] > vend) and vend or vrange[0]
self.tdim = vend - vstart + 1
byteoffset = vstart * bytepix * np.product((self.kdim, self.jdim, self.idim))
dims = self.tdim > 1 and (self.tdim, self.kdim, self.jdim, self.idim) or (self.kdim, self.jdim, self.idim)
datasize = bytepix * np.product(dims)
fp = file(filename, "rb")
fp.seek(byteoffset, 1)
data = np.fromstring(fp.read(datasize), numtype).reshape(dims)
if self.swapped:
data = data.byteswap()
if target_dtype is not None and target_dtype != numtype:
if target_dtype not in integer_ranges.keys():
scale = self.scale_factor or 1.0
self.data = (data * scale).astype(target_dtype)
else:
self.data = data.astype(target_dtype)
else:
self.data = data
fp.close()
def writeImage(self,
filestem,
format_type="analyze",
datatype="magnitude",
**kwargs):
"""
Export the image object in a medical file format (ANALYZE or NIFTI).
format_type is one of the internal file format specifiers, which
are currently %s.
possible keywords are:
datatype -- a datatype identifier, supported by the given format
targetdim -- number of dimensions per file
filetype -- differentiates single + dual formats for NIFTI
suffix -- over-ride default suffix style (eg volume0001)
If necessary, a scaling is found for integer types
""" % (" ; ".join(available_writers))
new_dtype = recon_output2dtype.get(datatype.lower(), None)
if new_dtype is None:
raise ValueError("Unsupported data type: %s" % datatype)
# The image writing tool does scaling only to preserve dynamic range
# when saving an as integer data type. Therefore specifying a scale
# in the writeImage kwargs is not appropriate, since it would
# cause the image writing logic to "unscale" the data first--it
# is not to be used as a "gain" knob.
try:
kwargs.pop('scale')
except KeyError:
pass
if new_dtype in integer_ranges.keys():
scale = float(scale_data(self[:], new_dtype))
else:
scale = float(1.0)
_write(self,
filestem,
format_type,
scale=scale,
dtype=new_dtype,
**kwargs)
def load_data(self, filestem, target_dtype, vrange):
bytepix = self.bitpix/8
numtype = datatype2dtype[self.datatype]
byteoffset = 0
if target_dtype is not None and \
not range_exceeds(target_dtype, numtype):
raise ValueError("the dynamic range of the desired datatype does "\
"not exceed that of the raw data")
if self.filetype == 'single':
fp = open(filestem+".nii", 'rb')
fp.seek(self.vox_offset)
else:
fp = open(filestem+".img", 'rb')
# need to cook tdim if vrange is set
if self.tdim and vrange:
vend = (vrange[1]<0 or vrange[1]>=self.tdim) \
and self.tdim-1 or vrange[1]
vstart = (vrange[0] > vend) and vend or vrange[0]
self.tdim = vend-vstart+1
byteoffset = vstart*bytepix*N.product((self.kdim,
self.jdim,self.idim))
dims = self.tdim > 1 and (self.tdim, self.kdim, self.jdim, self.idim) \
or self.kdim > 1 and (self.kdim, self.jdim, self.idim) \
or (self.jdim, self.idim)
datasize = bytepix * N.product(dims)
fp.seek(byteoffset, 1)
data = N.fromstring(fp.read(datasize), numtype).reshape(dims)
if self.swapped: data = data.byteswap()
if target_dtype is not None and target_dtype != numtype:
if target_dtype not in integer_ranges.keys():
scaling = self.scl_slope or 1.0
yinter = self.scl_inter
self.data = (data*scaling+yinter).astype(target_dtype)
else:
self.data = data.astype(target_dtype)
else:
self.data = data
fp.close()
