nanshe.imp.renorm module

The renorm module provides ways to standardize frames of an image stack.

Overview

The renorm (or renormalization) module provides a few simple utility methods for standardizing a set of images. The first is to recompute each frame so that its mean is 0. The second is to normalize each frame using an appropriate L^p norm. Both have the ability of working in-place.

API

nanshe.imp.renorm.renormalized_images(*args, **kwargs)

Takes and divide each image by its norm. Where each image is new_numpy_array[i] with some index i.

Parameters:

• new_numpy_array (numpy.ndarray) – array images with time as the first index
• ord (int) – Which norm to use. (L_2 or Euclidean is default)
• output_array (numpy.ndarray) – provides a location to store the result (optional)

Returns:

The same array with each images

normalized.

Return type:

result(numpy.ndarray)

Examples

>>> renormalized_images(numpy.array([[0.,1.],[1.,0.]]))
array([[ 0.,  1.],
       [ 1.,  0.]])

>>> renormalized_images(
...     numpy.array([[0,1],[1,0]])
... ) #doctest: +ELLIPSIS
Traceback (most recent call last):
    ...
AssertionError

>>> renormalized_images(
...     numpy.array([[0,1],[1,0]], dtype=numpy.float32)
... )
array([[ 0.,  1.],
       [ 1.,  0.]], dtype=float32)

>>> renormalized_images(numpy.array([[0.,2.],[1.,0.]]))
array([[ 0.,  1.],
       [ 1.,  0.]])

>>> renormalized_images(numpy.array([[2.,2.],[1.,0.]]))
array([[ 0.70710678,  0.70710678],
       [ 1.        ,  0.        ]])

>>> renormalized_images(numpy.array([[1.,2.],[3.,4.]]))
array([[ 0.4472136 ,  0.89442719],
       [ 0.6       ,  0.8       ]])

>>> renormalized_images(numpy.array([[1.,2.],[3.,4.]]), ord=1)
array([[ 0.33333333,  0.66666667],
       [ 0.42857143,  0.57142857]])

>>> a = numpy.array([[1.,2.],[3.,4.]])
>>> numpy.all(a != renormalized_images(a))
True

>>> a = numpy.array([[1.,2.],[3.,4.]])
>>> numpy.all(a == renormalized_images(a, output_array=a))
True

>>> a = numpy.array([[1.,2.],[3.,4.]]); b = numpy.zeros_like(a)
>>> numpy.all(b == renormalized_images(a, output_array=b))
True

>>> renormalized_images(numpy.zeros((2,3,)))
array([[ 0.,  0.,  0.],
       [ 0.,  0.,  0.]])

nanshe.imp.renorm.zeroed_mean_images(*args, **kwargs)

Takes and finds the mean for each image. Where each image is new_numpy_array[i] with some index i.

Parameters:

• new_numpy_array (numpy.ndarray) – array images with time as the first index
• output_array (numpy.ndarray) – provides a location to store the result (optional)

Returns:

The same array with each images

mean removed. Where means[i] = mean(new_numpy_array[i])

Return type:

result(numpy.ndarray)

Examples

>>> zeroed_mean_images(numpy.array([[0.,0.],[0.,0.]]))
array([[ 0.,  0.],
       [ 0.,  0.]])

>>> zeroed_mean_images(numpy.array([[6.,0.],[0.,0.]]))
array([[ 3., -3.],
       [ 0.,  0.]])

>>> zeroed_mean_images(numpy.array([[0.,0.],[0.,4.]]))
array([[ 0.,  0.],
       [-2.,  2.]])

>>> zeroed_mean_images(numpy.array([[6.,0],[0.,4.]]))
array([[ 3., -3.],
       [-2.,  2.]])

>>> zeroed_mean_images(numpy.array([[1.,2.],[3.,4.]]))
array([[-0.5,  0.5],
       [-0.5,  0.5]])

>>> zeroed_mean_images(
...     numpy.array([[1,2],[3,4]])
... ) #doctest: +ELLIPSIS
Traceback (most recent call last):
    ...
AssertionError

>>> a = numpy.array([[1.,2.],[3.,4.]])
>>> zeroed_mean_images(a, output_array=a)
array([[-0.5,  0.5],
       [-0.5,  0.5]])
>>> a
array([[-0.5,  0.5],
       [-0.5,  0.5]])

>>> a = numpy.array([[1.,2.],[3.,4.]]); b = numpy.zeros_like(a)
>>> zeroed_mean_images(a, output_array=b)
array([[-0.5,  0.5],
       [-0.5,  0.5]])
>>> b
array([[-0.5,  0.5],
       [-0.5,  0.5]])

>>> zeroed_mean_images(
...     numpy.array([[1,2],[3,4]]).astype(numpy.float32)
... )
array([[-0.5,  0.5],
       [-0.5,  0.5]], dtype=float32)

>>> a = numpy.array([[1.,2.],[3.,4.]])
>>> numpy.all(a != zeroed_mean_images(a))
True

>>> a = numpy.array([[1.,2.],[3.,4.]])
>>> numpy.all(a == zeroed_mean_images(a, output_array=a))
True