/usr/lib/python2.7/dist-packages/nibabel/minc2.py is in python-nibabel 2.2.1-1.
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#
# See COPYING file distributed along with the NiBabel package for the
# copyright and license terms.
#
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""" Preliminary MINC2 support
Use with care; I haven't tested this against a wide range of MINC files.
If you have a file that isn't read correctly, please send an example.
Test reading with something like::
import nibabel as nib
img = nib.load('my_funny.mnc')
data = img.get_data()
print(data.mean())
print(data.max())
print(data.min())
and compare against command line output of::
mincstats my_funny.mnc
"""
import numpy as np
from .optpkg import optional_package
h5py, have_h5py, setup_module = optional_package('h5py')
from .minc1 import Minc1File, MincHeader, Minc1Image, MincError
class Hdf5Bunch(object):
""" Make object for accessing attributes of variable
"""
def __init__(self, var):
for name, value in var.attrs.items():
setattr(self, name, value)
class Minc2File(Minc1File):
''' Class to wrap MINC2 format file
Although it has some of the same methods as a ``Header``, we use
this only when reading a MINC2 file, to pull out useful header
information, and for the method of reading the data out
'''
def __init__(self, mincfile):
self._mincfile = mincfile
minc_part = mincfile['minc-2.0']
# The whole image is the first of the entries in 'image'
image = minc_part['image']['0']
self._image = image['image']
self._dim_names = self._get_dimensions(self._image)
dimensions = minc_part['dimensions']
self._dims = [Hdf5Bunch(dimensions[s]) for s in self._dim_names]
# We don't currently support irregular spacing
# https://en.wikibooks.org/wiki/MINC/Reference/MINC2.0_File_Format_Reference#Dimension_variable_attributes
for dim in self._dims:
if dim.spacing != b'regular__':
raise ValueError('Irregular spacing not supported')
self._spatial_dims = [name for name in self._dim_names
if name.endswith('space')]
self._image_max = image['image-max']
self._image_min = image['image-min']
def _get_dimensions(self, var):
# Dimensions for a particular variable
# Differs for MINC1 and MINC2 - see:
# https://en.wikibooks.org/wiki/MINC/Reference/MINC2.0_File_Format_Reference#Associating_HDF5_dataspaces_with_MINC_dimensions
try:
dimorder = var.attrs['dimorder'].decode()
except KeyError: # No specified dimensions
return []
# The dimension name list must contain only as many entries
# as the variable has dimensions. This reduces errors when an
# unnecessary dimorder attribute is left behind.
return dimorder.split(',')[:len(var.shape)]
def get_data_dtype(self):
return self._image.dtype
def get_data_shape(self):
return self._image.shape
def _get_valid_range(self):
''' Return valid range for image data
The valid range can come from the image 'valid_range' or
failing that, from the data type range
'''
ddt = self.get_data_dtype()
info = np.iinfo(ddt.type)
try:
valid_range = self._image.attrs['valid_range']
except (AttributeError, KeyError):
valid_range = [info.min, info.max]
else:
if valid_range[0] < info.min or valid_range[1] > info.max:
raise ValueError('Valid range outside input '
'data type range')
return np.asarray(valid_range, dtype=np.float)
def _get_scalar(self, var):
""" Get scalar value from HDF5 scalar """
return var.value
def _get_array(self, var):
""" Get array from HDF5 array """
return np.asanyarray(var)
def get_scaled_data(self, sliceobj=()):
""" Return scaled data for slice definition `sliceobj`
Parameters
----------
sliceobj : tuple, optional
slice definition. If not specified, return whole array
Returns
-------
scaled_arr : array
array from minc file with scaling applied
"""
if sliceobj == ():
raw_data = np.asanyarray(self._image)
else: # Try slicing into the HDF array (maybe it's possible)
try:
raw_data = self._image[sliceobj]
except (ValueError, TypeError):
raw_data = np.asanyarray(self._image)[sliceobj]
else:
raw_data = np.asanyarray(raw_data)
return self._normalize(raw_data, sliceobj)
class Minc2Header(MincHeader):
@classmethod
def may_contain_header(klass, binaryblock):
return binaryblock[:4] == b'\211HDF'
class Minc2Image(Minc1Image):
''' Class for MINC2 images
The MINC2 image class uses the default header type, rather than a
specific MINC header type - and reads the relevant information from
the MINC file on load.
'''
# MINC2 does not do compressed whole files
_compressed_suffixes = ()
header_class = Minc2Header
@classmethod
def from_file_map(klass, file_map):
holder = file_map['image']
if holder.filename is None:
raise MincError('MINC2 needs filename for load')
minc_file = Minc2File(h5py.File(holder.filename, 'r'))
affine = minc_file.get_affine()
if affine.shape != (4, 4):
raise MincError('Image does not have 3 spatial dimensions')
data_dtype = minc_file.get_data_dtype()
shape = minc_file.get_data_shape()
zooms = minc_file.get_zooms()
header = klass.header_class(data_dtype, shape, zooms)
data = klass.ImageArrayProxy(minc_file)
return klass(data, affine, header, extra=None, file_map=file_map)
load = Minc2Image.load
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