def updateHeader(self, hdrdict):
"""Deprecated method only here for backward compatibility.
Please refer to NiftiFormat.updateFromDict()
"""
warn(
"updateHeader function is deprecated and will be removed with "
"PyNIfTI 1.0. Please use updateFromDict instead.",
DeprecationWarning,
)
NiftiFormat.updateFromDict(self, hdrdict)
def can_load(self):
loadable = False
try:
NiftiFormat(self._filename)
loadable = True
except:
pass
return loadable
def __init__(self, source, header=None, load=False, **kwargs):
"""
:Parameters:
source: str | ndarray
If source is a string, it is assumed to be a filename and an
attempt will be made to open the corresponding NIfTI file.
In case of an ndarray the array data will be used for the to be
created nifti image and a matching nifti header is generated.
If an object of a different type is supplied as 'source' a
ValueError exception will be thrown.
header: dict
Additional header data might be supplied. However,
dimensionality and datatype are determined from the ndarray and
not taken from a header dictionary.
load: Boolean
If set to True the image data will be loaded into memory. This
is only useful if loading a NIfTI image from file.
This flag is almost useless, as the data will be loaded
automatically whenever it is accessed.
**kwargs:
Additional stuff is passed to :class:`~nifti.format.NiftiFormat`.
"""
# setup all nifti header related stuff
NiftiFormat.__init__(self, source, header, **kwargs)
# where the data will go to
self._data = None
# load data
if type(source) == N.ndarray:
# assign data from source array
self._data = source[:]
elif type(source) in (str, unicode):
# only load image data from file if requested
if load:
self.load()
else:
raise ValueError, "Unsupported source type. Only NumPy arrays and filename " + "string are supported."
def __del__(self):
self.unload()
# it is required to call base class destructors!
NiftiFormat.__del__(self)
def getFilename(self):
"""Please see :meth:`nifti.format.NiftiFormat.getFilename`
for the documentation."""
return NiftiFormat.getFilename(self)
def __del__(self):
self._data.flush()
# it is required to call base class destructors!
NiftiFormat.__del__(self)
def load_metadata(self, index=0):
format = NiftiFormat(self._filename)
metadata = {}
# Get the number of dimensions in the image from the extent
nb_dimensions = len(format.extent)
for i in range(max(len(format.extent) - 3, 0)):
if format.extent[-1] == 1:
nb_dimensions -= 1
else:
break
# Get spacing and origin for those dimensions. Reverse to keep the order
# of the numpy array
metadata["spacing"] = format.asDict()["pixdim"][1:nb_dimensions + 1]
metadata["spacing"].reverse()
metadata["spacing"] = numpy.asarray(metadata["spacing"])
if format.asDict()["scl_slope"] != 0:
metadata["slope"] = format.asDict()["scl_slope"]
metadata["shift"] = format.asDict()["scl_inter"]
metadata["header"] = format.asDict()
metadata["annotations"] = ObservableList()
#########################
# Diffusion information #
#########################
# Load gradient direction file
base_name = os.path.splitext(self._filename)[0]
if base_name.endswith(".nii"):
base_name = os.path.splitext(base_name)[0]
gradient_candidates = [
base_name + ".bvecs", # /foo/bar/image.bvecs
base_name + ".bvec", # /foo/bar/image.bvec
os.path.join(os.path.dirname(self._filename),
"bvecs"), # /foo/bar/bvecs
os.path.join(os.path.dirname(self._filename),
"bvec") # /foo/bar/bvec
]
gradient_file = None
for candidate in gradient_candidates:
if os.path.isfile(candidate):
gradient_file = candidate
break
# Load b-values file
bvalue_candidates = [
base_name + ".bvals", # /foo/bar/image.bvals
base_name + ".bval", # /foo/bar/image.bval
os.path.join(os.path.dirname(self._filename),
"bval"), # /foo/bar/bvals
os.path.join(os.path.dirname(self._filename),
"bvals") # /foo/bar/bval
]
bvalue_file = None
for candidate in bvalue_candidates:
if os.path.isfile(candidate):
bvalue_file = candidate
break
if None not in [gradient_file, bvalue_file]:
gradients = numpy.loadtxt(gradient_file, dtype=numpy.single)
bvalues = numpy.loadtxt(bvalue_file, dtype=numpy.single)
gradients = gradients.T
mr_diffusion_sequence = []
for index, gradient in enumerate(gradients):
dataset = medipy.io.dicom.DataSet()
dataset.diffusion_directionality = "DIRECTIONAL"
dataset.diffusion_bvalue = bvalues[index]
gradient_dataset = medipy.io.dicom.DataSet()
gradient_dataset.diffusion_gradient_orientation = gradient
dataset.diffusion_gradient_direction_sequence = [
gradient_dataset
]
mr_diffusion_sequence.append(dataset)
metadata["mr_diffusion_sequence"] = mr_diffusion_sequence
return metadata
