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RF: move load/save logic out of SpatialImage #364

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Merged
merged 10 commits into from
Oct 23, 2015
Merged

RF: move load/save logic out of SpatialImage #364

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097d3b1
RF: Create abstract FileBasedImage / FileBasedHeader classes.
Sep 12, 2015
f5e5b22
RF: combine BaseHeader/Image with FileBasedHeader/Image.
Oct 7, 2015
249e77a
RF: Header=>SpatialHeader
Oct 7, 2015
11bd626
get_header => header; deprecate get_header
Oct 21, 2015
0226db3
Fix filebasedimage docstring.
Oct 21, 2015
3ea27d1
basestring => six.string_types
Oct 21, 2015
a34220a
Remove any mention of affine or dataobj from FileBasedImage
Oct 21, 2015
6b40777
make from_image an abstract method
Oct 21, 2015
308bbf8
Syncing up with code changes to spatialimages.py that occurred while …
Oct 22, 2015
975495e
STY: PEP8 cleanup
effigies Oct 23, 2015
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12 changes: 6 additions & 6 deletions doc/source/old/examples.txt
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Original file line number Diff line number Diff line change
Expand Up @@ -32,7 +32,7 @@ and we have made a temporary directory for the files we are going to write:
and we've got the path to the nifti example data:

>>> from nibabel.testing import data_path as example_data_path

Loading and saving NIfTI files
==============================

Expand Down Expand Up @@ -77,7 +77,7 @@ The relevant header information is extracted from the NumPy array. If you
query the header information about the dimensionality of the image, it returns
the desired values:

>>> print nim.get_header()['dim']
>>> print nim.header['dim']
[ 4 32 32 16 100 1 1 1]

First value shows the number of dimensions in the datset: 4 (good, that's what
Expand Down Expand Up @@ -110,8 +110,8 @@ preserving as much header information as possible

>>> nim2 = nib.Nifti1Image(nim.get_data()[..., :10],
... nim.get_affine(),
... nim.get_header())
>>> print nim2.get_header()['dim']
... nim.header)
>>> print nim2.header['dim']
[ 4 32 32 16 10 1 1 1]
>>> # a filename in our temporary directory
>>> fname = pjoin(tmpdir, 'part.hdr.gz')
Expand All @@ -136,7 +136,7 @@ will first create a NIfTI image with just a single voxel and 50 timepoints
>>> nim = nib.Nifti1Image(
... (np.linspace(0,100) + np.random.randn(50)).reshape(1,1,1,50),
... np.eye(4))
>>> print nim.get_header()['dim']
>>> print nim.header['dim']
[ 4 1 1 1 50 1 1 1]

Depending on the datatype of the input image the detrending process might
Expand All @@ -154,4 +154,4 @@ source image.

>>> nim_detrended = nib.Nifti1Image(data_detrended,
... nim.get_affine(),
... nim.get_header())
... nim.header)
4 changes: 2 additions & 2 deletions doc/source/old/orientation.txt
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Expand Up @@ -6,7 +6,7 @@

Every image in ``nibabel`` has an orientation. The orientation is the
relationship between the voxels in the image array, and millimeters in
some space.
some space.

Affines as orientation
----------------------
Expand Down Expand Up @@ -85,7 +85,7 @@ the affine after loading, as in::
img = nibabel.load('some_image.img')
aff = img.get_affine()
x_flipper = np.diag([-1,1,1,1])
lr_img = nibabel.Nifti1Image(img.get_data, np.dot(x_flipper, aff), img.get_header())
lr_img = nibabel.Nifti1Image(img.get_data, np.dot(x_flipper, aff), img.header)

Affines for Analyze, SPM analyze, and NIFTI
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Expand Down
4 changes: 2 additions & 2 deletions nibabel/ecat.py
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Expand Up @@ -929,7 +929,7 @@ def to_file_map(self, file_map=None):
# It appears to be necessary to load the data before saving even if the
# data itself is not used.
self.get_data()
hdr = self.get_header()
hdr = self.header
mlist = self._mlist
subheaders = self.get_subheaders()
dir_pos = 512
Expand All @@ -944,7 +944,7 @@ def to_file_map(self, file_map=None):
hdr.write_to(hdrf)

# Write every frames
for index in range(0, self.get_header()['num_frames']):
for index in range(0, self.header['num_frames']):
# Move to subheader offset
frame_offset = subheaders._get_frame_offset(index) - 512
imgf.seek(frame_offset)
Expand Down
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