dask_ndfilters package

dask_ndfilters.convolve(input, weights, mode='reflect', cval=0.0, origin=0)

Wrapped copy of “scipy.ndimage.filters.convolve”

Excludes the output parameter as it would not work with Dask arrays.

Original docstring:

Multidimensional convolution.

The array is convolved with the given kernel.

Parameters:
  • input (array_like) – Input array to filter.
  • weights (array_like) – Array of weights, same number of dimensions as input
  • mode ({'reflect','constant','nearest','mirror', 'wrap'}, optional) – the mode parameter determines how the array borders are handled. For ‘constant’ mode, values beyond borders are set to be cval. Default is ‘reflect’.
  • cval (scalar, optional) – Value to fill past edges of input if mode is ‘constant’. Default is 0.0
  • origin (array_like, optional) – The origin parameter controls the placement of the filter, relative to the centre of the current element of the input. Default of 0 is equivalent to (0,)*input.ndim.
Returns:

result – The result of convolution of input with weights.
Return type:

ndarray

See also

correlate()
Correlate an image with a kernel.

Notes

Each value in result is , where W is the weights kernel, j is the n-D spatial index over , I is the input and k is the coordinate of the center of W, specified by origin in the input parameters.

Examples

Perhaps the simplest case to understand is mode='constant', cval=0.0, because in this case borders (i.e. where the weights kernel, centered on any one value, extends beyond an edge of input.

>>> a = np.array([[1, 2, 0, 0],
...               [5, 3, 0, 4],
...               [0, 0, 0, 7],
...               [9, 3, 0, 0]])
>>> k = np.array([[1,1,1],[1,1,0],[1,0,0]])
>>> from scipy import ndimage
>>> ndimage.convolve(a, k, mode='constant', cval=0.0)
array([[11, 10,  7,  4],
       [10,  3, 11, 11],
       [15, 12, 14,  7],
       [12,  3,  7,  0]])

Setting cval=1.0 is equivalent to padding the outer edge of input with 1.0’s (and then extracting only the original region of the result).

>>> ndimage.convolve(a, k, mode='constant', cval=1.0)
array([[13, 11,  8,  7],
       [11,  3, 11, 14],
       [16, 12, 14, 10],
       [15,  6, 10,  5]])

With mode='reflect' (the default), outer values are reflected at the edge of input to fill in missing values.

>>> b = np.array([[2, 0, 0],
...               [1, 0, 0],
...               [0, 0, 0]])
>>> k = np.array([[0,1,0], [0,1,0], [0,1,0]])
>>> ndimage.convolve(b, k, mode='reflect')
array([[5, 0, 0],
       [3, 0, 0],
       [1, 0, 0]])

This includes diagonally at the corners.

>>> k = np.array([[1,0,0],[0,1,0],[0,0,1]])
>>> ndimage.convolve(b, k)
array([[4, 2, 0],
       [3, 2, 0],
       [1, 1, 0]])

With mode='nearest', the single nearest value in to an edge in input is repeated as many times as needed to match the overlapping weights.

>>> c = np.array([[2, 0, 1],
...               [1, 0, 0],
...               [0, 0, 0]])
>>> k = np.array([[0, 1, 0],
...               [0, 1, 0],
...               [0, 1, 0],
...               [0, 1, 0],
...               [0, 1, 0]])
>>> ndimage.convolve(c, k, mode='nearest')
array([[7, 0, 3],
       [5, 0, 2],
       [3, 0, 1]])
dask_ndfilters.correlate(input, weights, mode='reflect', cval=0.0, origin=0)

Wrapped copy of “scipy.ndimage.filters.correlate”

Excludes the output parameter as it would not work with Dask arrays.

Original docstring:

Multi-dimensional correlation.

The array is correlated with the given kernel.

Parameters:
  • input (array-like) – input array to filter
  • weights (ndarray) – array of weights, same number of dimensions as input
  • mode ({'reflect','constant','nearest','mirror', 'wrap'}, optional) – The mode parameter determines how the array borders are handled, where cval is the value when mode is equal to ‘constant’. Default is ‘reflect’
  • cval (scalar, optional) – Value to fill past edges of input if mode is ‘constant’. Default is 0.0
  • origin (scalar, optional) – The origin parameter controls the placement of the filter. Default 0

See also

convolve()
Convolve an image with a kernel.
dask_ndfilters.gaussian_filter(input, sigma, order=0, mode='reflect', cval=0.0, truncate=4.0)

Wrapped copy of “scipy.ndimage.filters.gaussian_filter”

Excludes the output parameter as it would not work with Dask arrays.

Original docstring:

Multidimensional Gaussian filter.

Parameters:
  • input (array_like) – Input array to filter.
  • sigma (scalar or sequence of scalars) – Standard deviation for Gaussian kernel. The standard deviations of the Gaussian filter are given for each axis as a sequence, or as a single number, in which case it is equal for all axes.
  • order ({0, 1, 2, 3} or sequence from same set, optional) – The order of the filter along each axis is given as a sequence of integers, or as a single number. An order of 0 corresponds to convolution with a Gaussian kernel. An order of 1, 2, or 3 corresponds to convolution with the first, second or third derivatives of a Gaussian. Higher order derivatives are not implemented
  • mode ({'reflect', 'constant', 'nearest', 'mirror', 'wrap'}, optional) – The mode parameter determines how the array borders are handled, where cval is the value when mode is equal to ‘constant’. Default is ‘reflect’
  • cval (scalar, optional) – Value to fill past edges of input if mode is ‘constant’. Default is 0.0
  • truncate (float) – Truncate the filter at this many standard deviations. Default is 4.0.
Returns:

gaussian_filter – Returned array of same shape as input.
Return type:

ndarray

Notes

The multidimensional filter is implemented as a sequence of one-dimensional convolution filters. The intermediate arrays are stored in the same data type as the output. Therefore, for output types with a limited precision, the results may be imprecise because intermediate results may be stored with insufficient precision.

Examples

>>> from scipy.ndimage import gaussian_filter
>>> a = np.arange(50, step=2).reshape((5,5))
>>> a
array([[ 0,  2,  4,  6,  8],
       [10, 12, 14, 16, 18],
       [20, 22, 24, 26, 28],
       [30, 32, 34, 36, 38],
       [40, 42, 44, 46, 48]])
>>> gaussian_filter(a, sigma=1)
array([[ 4,  6,  8,  9, 11],
       [10, 12, 14, 15, 17],
       [20, 22, 24, 25, 27],
       [29, 31, 33, 34, 36],
       [35, 37, 39, 40, 42]])
dask_ndfilters.gaussian_gradient_magnitude(input, sigma, mode='reflect', cval=0.0, truncate=4.0, **kwargs)

Wrapped copy of “scipy.ndimage.filters.gaussian_gradient_magnitude”

Excludes the output parameter as it would not work with Dask arrays.

Original docstring:

Multidimensional gradient magnitude using Gaussian derivatives.

Parameters:
  • input (array_like) – Input array to filter.
  • sigma (scalar or sequence of scalars) – The standard deviations of the Gaussian filter are given for each axis as a sequence, or as a single number, in which case it is equal for all axes..
  • mode ({'reflect', 'constant', 'nearest', 'mirror', 'wrap'}, optional) – The mode parameter determines how the array borders are handled, where cval is the value when mode is equal to ‘constant’. Default is ‘reflect’
  • cval (scalar, optional) – Value to fill past edges of input if mode is ‘constant’. Default is 0.0
  • keyword arguments will be passed to gaussian_filter() (Extra) –
dask_ndfilters.gaussian_laplace(input, sigma, mode='reflect', cval=0.0, truncate=4.0, **kwargs)

Wrapped copy of “scipy.ndimage.filters.gaussian_laplace”

Excludes the output parameter as it would not work with Dask arrays.

Original docstring:

Multidimensional Laplace filter using gaussian second derivatives.

Parameters:
  • input (array_like) – Input array to filter.
  • sigma (scalar or sequence of scalars) – The standard deviations of the Gaussian filter are given for each axis as a sequence, or as a single number, in which case it is equal for all axes.
  • mode ({'reflect', 'constant', 'nearest', 'mirror', 'wrap'}, optional) – The mode parameter determines how the array borders are handled, where cval is the value when mode is equal to ‘constant’. Default is ‘reflect’
  • cval (scalar, optional) – Value to fill past edges of input if mode is ‘constant’. Default is 0.0
  • keyword arguments will be passed to gaussian_filter() (Extra) –

Examples

>>> from scipy import ndimage, misc
>>> import matplotlib.pyplot as plt
>>> ascent = misc.ascent()

>>> fig = plt.figure()
>>> plt.gray()  # show the filtered result in grayscale
>>> ax1 = fig.add_subplot(121)  # left side
>>> ax2 = fig.add_subplot(122)  # right side

>>> result = ndimage.gaussian_laplace(ascent, sigma=1)
>>> ax1.imshow(result)

>>> result = ndimage.gaussian_laplace(ascent, sigma=3)
>>> ax2.imshow(result)
>>> plt.show()
dask_ndfilters.generic_filter(input, function, size=None, footprint=None, mode='reflect', cval=0.0, origin=0, extra_arguments=(), extra_keywords={})

Wrapped copy of “scipy.ndimage.filters.generic_filter”

Excludes the output parameter as it would not work with Dask arrays.

Original docstring:

Calculates a multi-dimensional filter using the given function.

At each element the provided function is called. The input values within the filter footprint at that element are passed to the function as a 1D array of double values.

Parameters:
  • input (array_like) – Input array to filter.
  • function (callable) – Function to apply at each element.
  • size (scalar or tuple, optional) – See footprint, below
  • footprint (array, optional) – Either size or footprint must be defined. size gives the shape that is taken from the input array, at every element position, to define the input to the filter function. footprint is a boolean array that specifies (implicitly) a shape, but also which of the elements within this shape will get passed to the filter function. Thus size=(n,m) is equivalent to footprint=np.ones((n,m)). We adjust size to the number of dimensions of the input array, so that, if the input array is shape (10,10,10), and size is 2, then the actual size used is (2,2,2).
  • mode ({'reflect', 'constant', 'nearest', 'mirror', 'wrap'}, optional) – The mode parameter determines how the array borders are handled, where cval is the value when mode is equal to ‘constant’. Default is ‘reflect’
  • cval (scalar, optional) – Value to fill past edges of input if mode is ‘constant’. Default is 0.0
  • origin (scalar, optional) – The origin parameter controls the placement of the filter. Default 0.0.
  • extra_arguments (sequence, optional) – Sequence of extra positional arguments to pass to passed function
  • extra_keywords (dict, optional) – dict of extra keyword arguments to pass to passed function
dask_ndfilters.laplace(input, mode='reflect', cval=0.0)

Wrapped copy of “scipy.ndimage.filters.laplace”

Excludes the output parameter as it would not work with Dask arrays.

Original docstring:

N-dimensional Laplace filter based on approximate second derivatives.

Parameters:
  • input (array_like) – Input array to filter.
  • mode ({'reflect', 'constant', 'nearest', 'mirror', 'wrap'}, optional) – The mode parameter determines how the array borders are handled, where cval is the value when mode is equal to ‘constant’. Default is ‘reflect’
  • cval (scalar, optional) – Value to fill past edges of input if mode is ‘constant’. Default is 0.0

Examples

>>> from scipy import ndimage, misc
>>> import matplotlib.pyplot as plt
>>> ascent = misc.ascent()
>>> result = ndimage.laplace(ascent)
>>> plt.gray()  # show the filtered result in grayscale
>>> plt.imshow(result)
dask_ndfilters.maximum_filter(input, size=None, footprint=None, mode='reflect', cval=0.0, origin=0)

Wrapped copy of “scipy.ndimage.filters.maximum_filter”

Excludes the output parameter as it would not work with Dask arrays.

Original docstring:

Calculates a multi-dimensional maximum filter.

Parameters:
  • input (array_like) – Input array to filter.
  • size (scalar or tuple, optional) – See footprint, below
  • footprint (array, optional) – Either size or footprint must be defined. size gives the shape that is taken from the input array, at every element position, to define the input to the filter function. footprint is a boolean array that specifies (implicitly) a shape, but also which of the elements within this shape will get passed to the filter function. Thus size=(n,m) is equivalent to footprint=np.ones((n,m)). We adjust size to the number of dimensions of the input array, so that, if the input array is shape (10,10,10), and size is 2, then the actual size used is (2,2,2).
  • mode ({'reflect', 'constant', 'nearest', 'mirror', 'wrap'}, optional) – The mode parameter determines how the array borders are handled, where cval is the value when mode is equal to ‘constant’. Default is ‘reflect’
  • cval (scalar, optional) – Value to fill past edges of input if mode is ‘constant’. Default is 0.0
  • origin (scalar, optional) – The origin parameter controls the placement of the filter. Default 0.0.
dask_ndfilters.median_filter(input, size=None, footprint=None, mode='reflect', cval=0.0, origin=0)

Wrapped copy of “scipy.ndimage.filters.median_filter”

Excludes the output parameter as it would not work with Dask arrays.

Original docstring:

Calculates a multidimensional median filter.

Parameters:
  • input (array_like) – Input array to filter.
  • size (scalar or tuple, optional) – See footprint, below
  • footprint (array, optional) – Either size or footprint must be defined. size gives the shape that is taken from the input array, at every element position, to define the input to the filter function. footprint is a boolean array that specifies (implicitly) a shape, but also which of the elements within this shape will get passed to the filter function. Thus size=(n,m) is equivalent to footprint=np.ones((n,m)). We adjust size to the number of dimensions of the input array, so that, if the input array is shape (10,10,10), and size is 2, then the actual size used is (2,2,2).
  • mode ({'reflect', 'constant', 'nearest', 'mirror', 'wrap'}, optional) – The mode parameter determines how the array borders are handled, where cval is the value when mode is equal to ‘constant’. Default is ‘reflect’
  • cval (scalar, optional) – Value to fill past edges of input if mode is ‘constant’. Default is 0.0
  • origin (scalar, optional) – The origin parameter controls the placement of the filter. Default 0.0.
Returns:

median_filter – Return of same shape as input.
Return type:

ndarray
dask_ndfilters.minimum_filter(input, size=None, footprint=None, mode='reflect', cval=0.0, origin=0)

Wrapped copy of “scipy.ndimage.filters.minimum_filter”

Excludes the output parameter as it would not work with Dask arrays.

Original docstring:

Calculates a multi-dimensional minimum filter.

Parameters:
  • input (array_like) – Input array to filter.
  • size (scalar or tuple, optional) – See footprint, below
  • footprint (array, optional) – Either size or footprint must be defined. size gives the shape that is taken from the input array, at every element position, to define the input to the filter function. footprint is a boolean array that specifies (implicitly) a shape, but also which of the elements within this shape will get passed to the filter function. Thus size=(n,m) is equivalent to footprint=np.ones((n,m)). We adjust size to the number of dimensions of the input array, so that, if the input array is shape (10,10,10), and size is 2, then the actual size used is (2,2,2).
  • mode ({'reflect', 'constant', 'nearest', 'mirror', 'wrap'}, optional) – The mode parameter determines how the array borders are handled, where cval is the value when mode is equal to ‘constant’. Default is ‘reflect’
  • cval (scalar, optional) – Value to fill past edges of input if mode is ‘constant’. Default is 0.0
  • origin (scalar, optional) – The origin parameter controls the placement of the filter. Default 0.0.
dask_ndfilters.percentile_filter(input, percentile, size=None, footprint=None, mode='reflect', cval=0.0, origin=0)

Wrapped copy of “scipy.ndimage.filters.percentile_filter”

Excludes the output parameter as it would not work with Dask arrays.

Original docstring:

Calculates a multi-dimensional percentile filter.

Parameters:
  • input (array_like) – Input array to filter.
  • percentile (scalar) – The percentile parameter may be less then zero, i.e., percentile = -20 equals percentile = 80
  • size (scalar or tuple, optional) – See footprint, below
  • footprint (array, optional) – Either size or footprint must be defined. size gives the shape that is taken from the input array, at every element position, to define the input to the filter function. footprint is a boolean array that specifies (implicitly) a shape, but also which of the elements within this shape will get passed to the filter function. Thus size=(n,m) is equivalent to footprint=np.ones((n,m)). We adjust size to the number of dimensions of the input array, so that, if the input array is shape (10,10,10), and size is 2, then the actual size used is (2,2,2).
  • mode ({'reflect', 'constant', 'nearest', 'mirror', 'wrap'}, optional) – The mode parameter determines how the array borders are handled, where cval is the value when mode is equal to ‘constant’. Default is ‘reflect’
  • cval (scalar, optional) – Value to fill past edges of input if mode is ‘constant’. Default is 0.0
  • origin (scalar, optional) – The origin parameter controls the placement of the filter. Default 0.0.
dask_ndfilters.prewitt(input, axis=-1, mode='reflect', cval=0.0)

Wrapped copy of “scipy.ndimage.filters.prewitt”

Excludes the output parameter as it would not work with Dask arrays.

Original docstring:

Calculate a Prewitt filter.

Parameters:
  • input (array_like) – Input array to filter.
  • axis (int, optional) – The axis of input along which to calculate. Default is -1.
  • mode ({'reflect', 'constant', 'nearest', 'mirror', 'wrap'}, optional) – The mode parameter determines how the array borders are handled, where cval is the value when mode is equal to ‘constant’. Default is ‘reflect’
  • cval (scalar, optional) – Value to fill past edges of input if mode is ‘constant’. Default is 0.0

Examples

>>> from scipy import ndimage, misc
>>> import matplotlib.pyplot as plt
>>> ascent = misc.ascent()
>>> result = ndimage.prewitt(ascent)
>>> plt.gray()  # show the filtered result in grayscale
>>> plt.imshow(result)
dask_ndfilters.rank_filter(input, rank, size=None, footprint=None, mode='reflect', cval=0.0, origin=0)

Wrapped copy of “scipy.ndimage.filters.rank_filter”

Excludes the output parameter as it would not work with Dask arrays.

Original docstring:

Calculates a multi-dimensional rank filter.

Parameters:
  • input (array_like) – Input array to filter.
  • rank (int) – The rank parameter may be less then zero, i.e., rank = -1 indicates the largest element.
  • size (scalar or tuple, optional) – See footprint, below
  • footprint (array, optional) – Either size or footprint must be defined. size gives the shape that is taken from the input array, at every element position, to define the input to the filter function. footprint is a boolean array that specifies (implicitly) a shape, but also which of the elements within this shape will get passed to the filter function. Thus size=(n,m) is equivalent to footprint=np.ones((n,m)). We adjust size to the number of dimensions of the input array, so that, if the input array is shape (10,10,10), and size is 2, then the actual size used is (2,2,2).
  • mode ({'reflect', 'constant', 'nearest', 'mirror', 'wrap'}, optional) – The mode parameter determines how the array borders are handled, where cval is the value when mode is equal to ‘constant’. Default is ‘reflect’
  • cval (scalar, optional) – Value to fill past edges of input if mode is ‘constant’. Default is 0.0
  • origin (scalar, optional) – The origin parameter controls the placement of the filter. Default 0.0.
dask_ndfilters.sobel(input, axis=-1, mode='reflect', cval=0.0)

Wrapped copy of “scipy.ndimage.filters.sobel”

Excludes the output parameter as it would not work with Dask arrays.

Original docstring:

Calculate a Sobel filter.

Parameters:
  • input (array_like) – Input array to filter.
  • axis (int, optional) – The axis of input along which to calculate. Default is -1.
  • mode ({'reflect', 'constant', 'nearest', 'mirror', 'wrap'}, optional) – The mode parameter determines how the array borders are handled, where cval is the value when mode is equal to ‘constant’. Default is ‘reflect’
  • cval (scalar, optional) – Value to fill past edges of input if mode is ‘constant’. Default is 0.0

Examples

>>> from scipy import ndimage, misc
>>> import matplotlib.pyplot as plt
>>> ascent = misc.ascent()
>>> result = ndimage.sobel(ascent)
>>> plt.gray()  # show the filtered result in grayscale
>>> plt.imshow(result)
dask_ndfilters.uniform_filter(input, size=3, mode='reflect', cval=0.0, origin=0)

Wrapped copy of “scipy.ndimage.filters.uniform_filter”

Excludes the output parameter as it would not work with Dask arrays.

Original docstring:

Multi-dimensional uniform filter.

Parameters:
  • input (array_like) – Input array to filter.
  • size (int or sequence of ints, optional) – The sizes of the uniform filter are given for each axis as a sequence, or as a single number, in which case the size is equal for all axes.
  • mode ({'reflect', 'constant', 'nearest', 'mirror', 'wrap'}, optional) – The mode parameter determines how the array borders are handled, where cval is the value when mode is equal to ‘constant’. Default is ‘reflect’
  • cval (scalar, optional) – Value to fill past edges of input if mode is ‘constant’. Default is 0.0
  • origin (scalar, optional) – The origin parameter controls the placement of the filter. Default 0.0.

Notes

The multi-dimensional filter is implemented as a sequence of one-dimensional uniform filters. The intermediate arrays are stored in the same data type as the output. Therefore, for output types with a limited precision, the results may be imprecise because intermediate results may be stored with insufficient precision.