Inheritance diagram for nipy.io.imageformats.orientations:
Utilities for calculating and applying affine orientations
Apply transformations implied by ornt to the first n axes of the array arr
Parameters : | arr : array-like of data with ndim >= n ornt : (n,2) orientation array
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Returns : | t_arr : ndarray
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Flip contents of axis in array arr
flip_axis is the same transform as np.flipud, but for any axis. For example flip_axis(arr, axis=0) is the same transform as np.flipud(arr), and flip_axis(arr, axis=1) is the same transform as np.fliplr(arr)
Parameters : | arr : array-like axis : int, optional
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Returns : | farr : array
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Examples
>>> a = np.arange(6).reshape((2,3))
>>> a
array([[0, 1, 2],
[3, 4, 5]])
>>> flip_axis(a, axis=0)
array([[3, 4, 5],
[0, 1, 2]])
>>> flip_axis(a, axis=1)
array([[2, 1, 0],
[5, 4, 3]])
Orientation of input axes in terms of output axes for affine
Valid for an affine transformation from m dimensions to n dimensions (affine.shape == (n+1, m+1)).
The calculated orientations can be used to transform associated arrays to best match the output orientations. If n > m, then some of the output axes should be considered dropped in this orientation.
Parameters : | affine : (n+1,m+1) ndarray-like
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Returns : | orientations : (n,2) ndarray
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Affine transform resulting from transforms implied in ornt
Imagine you have an array arr of shape shape, and you apply the transforms implied by ornt (more below), to get tarr. tarr may have a different shape shape_prime. This routine returns the affine that will take a array coordinate for tarr and give you the corresponding array coordinate in arr.
Parameters : | ornt : (n,2) ndarray
shape : length n sequence
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Returns : | transformed_affine : (n+1,n+1) ndarray
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