magni.imaging._util module

Module providing the public functions of the magni.imaging subpackage.

magni.imaging._util.double_mirror(img, fftstyle=False)

Mirror image in both the vertical and horisontal axes.

The image is mirrored around its upper left corner first in the horizontal axis and then in the vertical axis such that an image of four times the size of the original is returned. If fftstyle is True, the image is constructed such it would represent a fftshifted version of the mirrored img such that entry (0, 0) is the DC component.

Parameters:

• img (ndarray) – The image to mirror.
• fftstyle (bool) – The flag that indicates if the fftstyle mirrored image is returned.

Returns:

mirrored_img (ndarray) – The mirrored image.

Examples

For example, mirror a very simple 2-by-3 pixel image.

>>> import numpy as np
>>> from magni.imaging._util import double_mirror
>>> img = np.arange(6).reshape(2, 3)
>>> img
array([[0, 1, 2],
       [3, 4, 5]])
>>> double_mirror(img)
array([[5, 4, 3, 3, 4, 5],
       [2, 1, 0, 0, 1, 2],
       [2, 1, 0, 0, 1, 2],
       [5, 4, 3, 3, 4, 5]])
>>> double_mirror(img, fftstyle=True)
array([[0, 0, 0, 0, 0, 0],
       [0, 5, 4, 3, 4, 5],
       [0, 2, 1, 0, 1, 2],
       [0, 5, 4, 3, 4, 5]])

magni.imaging._util.get_inscribed_masks(img, as_vec=False)

Return a set of inscribed masks covering the image.

Two masks are returned. One is the disc with radius equal to that of the inscribed circle for img. The other is the inscribed square of the first mask. If as_vec is True, the img must be a vector representation of the (matrix) image. In this case, the masks are also returned in vector representation.

Parameters:

• img (ndarray) – The square image of even height/width which the masks should cover.
• as_vec (bool) – The indicator of whether or not to treat img as a vector instead of an image (the default is False, which implies that img is treated as a matrix.

Returns:

• cicle_mask (ndarray) – The inscribed cicle mask.
• square_mask (ndarray) – The inscribed square mask.

Examples

For example, get the inscribed masks of an 8-by-8 image:

>>> import numpy as np
>>> from magni.imaging._util import get_inscribed_masks, mat2vec
>>> img = np.arange(64).reshape(8, 8)
>>> circle_mask, square_mask = get_inscribed_masks(img)
>>> np_printoptions = np.get_printoptions()
>>> np.set_printoptions(formatter={'bool': lambda x: str(int(x))})
>>> circle_mask
array([[0, 1, 1, 1, 1, 1, 1, 0],
       [1, 1, 1, 1, 1, 1, 1, 1],
       [1, 1, 1, 1, 1, 1, 1, 1],
       [1, 1, 1, 1, 1, 1, 1, 1],
       [1, 1, 1, 1, 1, 1, 1, 1],
       [1, 1, 1, 1, 1, 1, 1, 1],
       [1, 1, 1, 1, 1, 1, 1, 1],
       [0, 1, 1, 1, 1, 1, 1, 0]], dtype=bool)

>>> square_mask
array([[0, 0, 0, 0, 0, 0, 0, 0],
       [0, 1, 1, 1, 1, 1, 1, 0],
       [0, 1, 1, 1, 1, 1, 1, 0],
       [0, 1, 1, 1, 1, 1, 1, 0],
       [0, 1, 1, 1, 1, 1, 1, 0],
       [0, 1, 1, 1, 1, 1, 1, 0],
       [0, 1, 1, 1, 1, 1, 1, 0],
       [0, 0, 0, 0, 0, 0, 0, 0]], dtype=bool)

Or get the same masks based on a vector:

>>> img_vec = mat2vec(img)
>>> c_vec_mask, s_vec_mask = get_inscribed_masks(img_vec, as_vec=True)
>>> c_vec_mask
array([[0],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [0],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [0],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [0]], dtype=bool)
>>> np.set_printoptions(**np_printoptions)

magni.imaging._util.mat2vec(x)

Reshape x from matrix to vector by stacking columns.

Parameters:x (ndarray) – Matrix that should be reshaped to vector. Returns:ndarray – Column vector formed by stacking the columns of the matrix x.

See also

vec2mat()

The inverse operation

Notes

The returned column vector is C contiguous.

Examples

For example,

>>> import numpy as np
>>> from magni.imaging._util import mat2vec
>>> x = np.arange(4).reshape(2, 2)
>>> x
array([[0, 1],
       [2, 3]])
>>> mat2vec(x)
array([[0],
       [2],
       [1],
       [3]])

magni.imaging._util.vec2mat(x, mn_tuple)

Reshape x from column vector to matrix.

Parameters:

• x (ndarray) – Matrix that should be reshaped to vector.
• mn_tuple (tuple) – A tuple (m, n) containing the parameters m, n as listed below.
• m (int) – Number of rows in the resulting matrix.
• n (int) – Number of columns in the resulting matrix.

Returns:

ndarray – Matrix formed by taking n columns of lenght m from the column vector x.

See also

mat2vec()

The inverse operation

Notes

The returned matrix is C contiguous.

Examples

For example,

>>> import numpy as np
>>> from magni.imaging._util import vec2mat
>>> x = np.arange(4).reshape(4, 1)
>>> x
array([[0],
       [1],
       [2],
       [3]])
>>> vec2mat(x, (2, 2))
array([[0, 2],
       [1, 3]])