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Jan 12, 2018
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DOC: fix doctests for numpy 1.14 array printing. #591

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6 changes: 3 additions & 3 deletions doc/source/coordinate_systems.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -104,7 +104,7 @@ middle voxel in the EPI data array like this:
(26, 30, 16)
>>> center_vox_value = epi_img_data[center_i, center_j, center_k]
>>> center_vox_value
81.549287796020508
81.5492877960205...

The values (26, 30, 16) are indices into the data array ``epi_img_data``. (26,
30, 16) is therefore a 'voxel coordinate' - a coordinate into the voxel array.
Expand Down Expand Up @@ -776,7 +776,7 @@ center of the EPI image?
:nofigs:

>>> apply_affine(epi_vox2anat_vox, epi_vox_center)
array([ 28.364, 31.562, 36.165])
array([28.364, 31.562, 36.165])

The voxel coordinate of the center voxel of the anatomical image is:

Expand All @@ -786,7 +786,7 @@ The voxel coordinate of the center voxel of the anatomical image is:

>>> anat_vox_center = (np.array(anat_img_data.shape) - 1) / 2.
>>> anat_vox_center
array([ 28. , 33. , 27.5])
array([28. , 33. , 27.5])

The voxel location in the anatomical image that matches the center voxel of
the EPI image is nearly exactly half way across the first axis, a voxel or two
Expand Down
8 changes: 4 additions & 4 deletions doc/source/image_orientation.rst
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Expand Up @@ -53,10 +53,10 @@ Here is the affine (to two digits decimal precision):

>>> np.set_printoptions(precision=2, suppress=True)
>>> img.affine
array([[ -2. , 0. , 0. , 117.86],
[ -0. , 1.97, -0.36, -35.72],
[ 0. , 0.32, 2.17, -7.25],
[ 0. , 0. , 0. , 1. ]])
array([[ -2. , 0. , 0. , 117.86],
[ -0. , 1.97, -0.36, -35.72],
[ 0. , 0.32, 2.17, -7.25],
[ 0. , 0. , 0. , 1. ]])

What are the orientations of the voxel axes here?

Expand Down
2 changes: 1 addition & 1 deletion doc/source/neuro_radio_conventions.rst
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Expand Up @@ -61,7 +61,7 @@ scanner RAS+ (see :doc:`coordinate_systems`):
... [0, 0, 0, 1]])
>>> ijk = [1, 0, 0] # moving one unit on the first voxel axis
>>> apply_affine(diag_affine, ijk)
array([ 3., 0., 0.])
array([3., 0., 0.])

In this case the voxel axes are aligned to the output axes, in the sense that
moving in a positive direction on the first voxel axis results in increasing
Expand Down
10 changes: 5 additions & 5 deletions doc/source/nibabel_images.rst
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Expand Up @@ -62,10 +62,10 @@ array to coordinates in some RAS+ world coordinate system
>>> np.set_printoptions(precision=2, suppress=True)

>>> img.affine
array([[ -2. , 0. , 0. , 117.86],
[ -0. , 1.97, -0.36, -35.72],
[ 0. , 0.32, 2.17, -7.25],
[ 0. , 0. , 0. , 1. ]])
array([[ -2. , 0. , 0. , 117.86],
[ -0. , 1.97, -0.36, -35.72],
[ 0. , 0.32, 2.17, -7.25],
[ 0. , 0. , 0. , 1. ]])

``header`` contains the metadata for this inage. In this case it is
specifically NIfTI metadata:
Expand Down Expand Up @@ -148,7 +148,7 @@ The header of any image will normally have the following methods:
* ``get_zooms()`` to get the voxel sizes in millimeters:

>>> print(header.get_zooms())
(2.0, 2.0, 2.1999991, 2000.0)
(2.0, 2.0, 2.19999..., 2000.0)

The last value of ``header.get_zooms()`` is the time between scans in
milliseconds; this is the equivalent of voxel size on the time axis.
Expand Down
102 changes: 51 additions & 51 deletions doc/source/nifti_images.rst
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Expand Up @@ -151,10 +151,10 @@ You can get and set individual fields in the header using dict (mapping-type)
item access. For example:

>>> n1_header['cal_max']
array(1162.0, dtype=float32)
array(1162., dtype=float32)
>>> n1_header['cal_max'] = 1200
>>> n1_header['cal_max']
array(1200.0, dtype=float32)
array(1200., dtype=float32)

Check the attributes of the header for ``get_`` / ``set_`` methods to get and
set various combinations of NIfTI header fields.
Expand All @@ -173,10 +173,10 @@ Like other nibabel image types, NIfTI images have an affine relating the voxel
coordinates to world coordinates in RAS+ space:

>>> n1_img.affine
array([[ -2. , 0. , 0. , 117.86],
[ -0. , 1.97, -0.36, -35.72],
[ 0. , 0.32, 2.17, -7.25],
[ 0. , 0. , 0. , 1. ]])
array([[ -2. , 0. , 0. , 117.86],
[ -0. , 1.97, -0.36, -35.72],
[ 0. , 0.32, 2.17, -7.25],
[ 0. , 0. , 0. , 1. ]])

Unlike other formats, the NIfTI header format can specify this affine in one
of three ways |--| the *sform* affine, the *qform* affine and the *fall-back
Expand All @@ -199,16 +199,16 @@ the image or the header.
For example:

>>> print(n1_header['srow_x'])
[ -2. 0. 0. 117.86]
[ -2. 0. 0. 117.86]
>>> print(n1_header['srow_y'])
[ -0. 1.97 -0.36 -35.72]
>>> print(n1_header['srow_z'])
[ 0. 0.32 2.17 -7.25]
>>> print(n1_header.get_sform())
[[ -2. 0. 0. 117.86]
[ -0. 1.97 -0.36 -35.72]
[ 0. 0.32 2.17 -7.25]
[ 0. 0. 0. 1. ]]
[[ -2. 0. 0. 117.86]
[ -0. 1.97 -0.36 -35.72]
[ 0. 0.32 2.17 -7.25]
[ 0. 0. 0. 1. ]]

This affine is valid only if the ``sform_code`` is not zero.

Expand All @@ -234,29 +234,29 @@ You can get the affine and the code using the ``coded=True`` argument to
``get_sform()``:

>>> print(n1_header.get_sform(coded=True))
(array([[ -2. , 0. , 0. , 117.86],
[ -0. , 1.97, -0.36, -35.72],
[ 0. , 0.32, 2.17, -7.25],
[ 0. , 0. , 0. , 1. ]]), 1)
(array([[ -2. , 0. , 0. , 117.86],
[ -0. , 1.97, -0.36, -35.72],
[ 0. , 0.32, 2.17, -7.25],
[ 0. , 0. , 0. , 1. ]]), 1)

You can set the sform with the ``set_sform()`` method of the header and
the image.

>>> n1_header.set_sform(np.diag([2, 3, 4, 1]))
>>> n1_header.get_sform()
array([[ 2., 0., 0., 0.],
[ 0., 3., 0., 0.],
[ 0., 0., 4., 0.],
[ 0., 0., 0., 1.]])
array([[2., 0., 0., 0.],
[0., 3., 0., 0.],
[0., 0., 4., 0.],
[0., 0., 0., 1.]])

Set the affine and code using the ``code`` parameter to ``set_sform()``:

>>> n1_header.set_sform(np.diag([3, 4, 5, 1]), code='mni')
>>> n1_header.get_sform(coded=True)
(array([[ 3., 0., 0., 0.],
[ 0., 4., 0., 0.],
[ 0., 0., 5., 0.],
[ 0., 0., 0., 1.]]), 4)
(array([[3., 0., 0., 0.],
[0., 4., 0., 0.],
[0., 0., 5., 0.],
[0., 0., 0., 1.]]), 4)

The qform affine
================
Expand All @@ -273,10 +273,10 @@ You can get and set the qform affine using the equivalent methods to those for
the sform: ``get_qform()``, ``set_qform()``.

>>> n1_header.get_qform(coded=True)
(array([[ -2. , 0. , 0. , 117.86],
[ -0. , 1.97, -0.36, -35.72],
[ 0. , 0.32, 2.17, -7.25],
[ 0. , 0. , 0. , 1. ]]), 1)
(array([[ -2. , 0. , 0. , 117.86],
[ -0. , 1.97, -0.36, -35.72],
[ 0. , 0.32, 2.17, -7.25],
[ 0. , 0. , 0. , 1. ]]), 1)

The qform also has a corresponding ``qform_code`` with the same interpretation
as the `sform_code`.
Expand All @@ -295,10 +295,10 @@ Again like SPM, we prefer to assume LAS+ voxel orientation by default.
You can always get the fall-back affine with ``get_base_affine()``:

>>> n1_header.get_base_affine()
array([[ -2. , 0. , 0. , 127. ],
[ 0. , 2. , 0. , -95. ],
[ 0. , 0. , 2.2, -25.3],
[ 0. , 0. , 0. , 1. ]])
array([[ -2. , 0. , 0. , 127. ],
[ 0. , 2. , 0. , -95. ],
[ 0. , 0. , 2.2, -25.3],
[ 0. , 0. , 0. , 1. ]])

.. _choosing-image-affine:

Expand Down Expand Up @@ -329,10 +329,10 @@ The sform and qform codes will be initialised to 2 (aligned) and 0 (unknown)
respectively:

>>> img.get_sform(coded=True) # doctest: +NORMALIZE_WHITESPACE
(array([[ 2., 0., 0., 0.],
[ 0., 2., 0., 0.],
[ 0., 0., 2., 0.],
[ 0., 0., 0., 1.]]), 2)
(array([[2., 0., 0., 0.],
[0., 2., 0., 0.],
[0., 0., 2., 0.],
[0., 0., 0., 1.]]), 2)
>>> img.get_qform(coded=True)
(None, 0)

Expand Down Expand Up @@ -434,21 +434,21 @@ image is written. We can do this by setting the fields directly, or with
>>> array_header.get_slope_inter()
(2.0, 10.0)
>>> array_header['scl_slope']
array(2.0, dtype=float32)
array(2., dtype=float32)
>>> array_header['scl_inter']
array(10.0, dtype=float32)
array(10., dtype=float32)

Setting the scale factors in the header has no effect on the image data before
we save and load again:

>>> array_img.get_data()
array([[[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]],
>>> array_img.get_fdata()
array([[[ 0., 1., 2., 3.],
[ 4., 5., 6., 7.],
[ 8., 9., 10., 11.]],
<BLANKLINE>
[[12, 13, 14, 15],
[16, 17, 18, 19],
[20, 21, 22, 23]]], dtype=int16)
[[12., 13., 14., 15.],
[16., 17., 18., 19.],
[20., 21., 22., 23.]]])

Now we save the image and load it again:

Expand All @@ -457,14 +457,14 @@ Now we save the image and load it again:

The data array has the scaling applied:

>>> scaled_img.get_data()
...([[[ 10., 12., 14., 16.],
[ 18., 20., 22., 24.],
[ 26., 28., 30., 32.]],
>>> scaled_img.get_fdata()
array([[[10., 12., 14., 16.],
[18., 20., 22., 24.],
[26., 28., 30., 32.]],
<BLANKLINE>
[[ 34., 36., 38., 40.],
[ 42., 44., 46., 48.],
[ 50., 52., 54., 56.]]])
[[34., 36., 38., 40.],
[42., 44., 46., 48.],
[50., 52., 54., 56.]]])

The header for the loaded image has had the scaling reset to undefined, to
mark the fact that the scaling has been "consumed" by the load:
Expand Down