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def generic_filter(input, function, size=None, footprint=None, output=None, mode='reflect', cval=0.0, origin=0, extra_arguments=(), extra_keywords=None, *, axes=None)
Calculate a multidimensional 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 1-D array of double values.
Parameters
----------
input : array_like
The input array.
function : {callable, scipy.LowLevelCallable}
Function to apply at each element.
size : scalar or tuple, optional
See footprint, below. Ignored if footprint is given.
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). When `footprint` is given, `size` is ignored.
output : array or dtype, optional
The array in which to place the output, or the dtype of the
returned array. By default an array of the same dtype as input
will be created.
mode : {'reflect', 'constant', 'nearest', 'mirror', 'wrap'}, optional
The `mode` parameter determines how the input array is extended
beyond its boundaries. Default is 'reflect'. Behavior for each valid
value is as follows:
'reflect' (`d c b a | a b c d | d c b a`)
The input is extended by reflecting about the edge of the last
pixel. This mode is also sometimes referred to as half-sample
symmetric.
'constant' (`k k k k | a b c d | k k k k`)
The input is extended by filling all values beyond the edge with
the same constant value, defined by the `cval` parameter.
'nearest' (`a a a a | a b c d | d d d d`)
The input is extended by replicating the last pixel.
'mirror' (`d c b | a b c d | c b a`)
The input is extended by reflecting about the center of the last
pixel. This mode is also sometimes referred to as whole-sample
symmetric.
'wrap' (`a b c d | a b c d | a b c d`)
The input is extended by wrapping around to the opposite edge.
For consistency with the interpolation functions, the following mode
names can also be used:
'grid-mirror'
This is a synonym for 'reflect'.
'grid-constant'
This is a synonym for 'constant'.
'grid-wrap'
This is a synonym for 'wrap'.
cval : scalar, optional
Value to fill past edges of input if `mode` is 'constant'. Default
is 0.0.
origin : int or sequence, optional
Controls the placement of the filter on the input array's pixels.
A value of 0 (the default) centers the filter over the pixel, with
positive values shifting the filter to the left, and negative ones
to the right. By passing a sequence of origins with length equal to
the number of dimensions of the input array, different shifts can
be specified along each axis.
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.
axes : tuple of int or None, optional
If None, `input` is filtered along all axes. Otherwise,
`input` is filtered along the specified axes. When `axes` is
specified, any tuples used for `size` or `origin` must match the length
of `axes`. The ith entry in any of these tuples corresponds to the ith
entry in `axes`.
Returns
-------
generic_filter : ndarray
Filtered array. Has the same shape as `input`.
Notes
-----
This function also accepts low-level callback functions with one of
the following signatures and wrapped in `scipy.LowLevelCallable`:
.. code:: c
int callback(double *buffer, npy_intp filter_size,
double *return_value, void *user_data)
int callback(double *buffer, intptr_t filter_size,
double *return_value, void *user_data)
The calling function iterates over the elements of the input and
output arrays, calling the callback function at each element. The
elements within the footprint of the filter at the current element are
passed through the ``buffer`` parameter, and the number of elements
within the footprint through ``filter_size``. The calculated value is
returned in ``return_value``. ``user_data`` is the data pointer provided
to `scipy.LowLevelCallable` as-is.
The callback function must return an integer error status that is zero
if something went wrong and one otherwise. If an error occurs, you should
normally set the python error status with an informative message
before returning, otherwise a default error message is set by the
calling function.
In addition, some other low-level function pointer specifications
are accepted, but these are for backward compatibility only and should
not be used in new code.
Examples
--------
Import the necessary modules and load the example image used for
filtering.
>>> import numpy as np
>>> from scipy import datasets
>>> from scipy.ndimage import zoom, generic_filter
>>> import matplotlib.pyplot as plt
>>> ascent = zoom(datasets.ascent(), 0.5)
Compute a maximum filter with kernel size 5 by passing a simple NumPy
aggregation function as argument to `function`.
>>> maximum_filter_result = generic_filter(ascent, np.amax, [5, 5])
While a maximum filter could also directly be obtained using
`maximum_filter`, `generic_filter` allows generic Python function or
`scipy.LowLevelCallable` to be used as a filter. Here, we compute the
range between maximum and minimum value as an example for a kernel size
of 5.
>>> def custom_filter(image):
... return np.amax(image) - np.amin(image)
>>> custom_filter_result = generic_filter(ascent, custom_filter, [5, 5])
Plot the original and filtered images.
>>> fig, axes = plt.subplots(3, 1, figsize=(3, 9))
>>> plt.gray() # show the filtered result in grayscale
>>> top, middle, bottom = axes
>>> for ax in axes:
... ax.set_axis_off() # remove coordinate system
>>> top.imshow(ascent)
>>> top.set_title("Original image")
>>> middle.imshow(maximum_filter_result)
>>> middle.set_title("Maximum filter, Kernel: 5x5")
>>> bottom.imshow(custom_filter_result)
>>> bottom.set_title("Custom filter, Kernel: 5x5")
>>> fig.tight_layout()
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