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Module « scipy.ndimage »

Fonction value_indices - module scipy.ndimage

Signature de la fonction value_indices

def value_indices(arr, *, ignore_value=None) 

Description

help(scipy.ndimage.value_indices)

Find indices of each distinct value in given array.

Parameters
----------
arr : ndarray of ints
    Array containing integer values.
ignore_value : int, optional
    This value will be ignored in searching the `arr` array. If not
    given, all values found will be included in output. Default
    is None.

Returns
-------
indices : dictionary
    A Python dictionary of array indices for each distinct value. The
    dictionary is keyed by the distinct values, the entries are array
    index tuples covering all occurrences of the value within the
    array.

    This dictionary can occupy significant memory, usually several times
    the size of the input array.

See Also
--------
label, maximum, median, minimum_position, extrema, sum, mean, variance,
standard_deviation, numpy.where, numpy.unique

Notes
-----
For a small array with few distinct values, one might use
`numpy.unique()` to find all possible values, and ``(arr == val)`` to
locate each value within that array. However, for large arrays,
with many distinct values, this can become extremely inefficient,
as locating each value would require a new search through the entire
array. Using this function, there is essentially one search, with
the indices saved for all distinct values.

This is useful when matching a categorical image (e.g. a segmentation
or classification) to an associated image of other data, allowing
any per-class statistic(s) to then be calculated. Provides a
more flexible alternative to functions like ``scipy.ndimage.mean()``
and ``scipy.ndimage.variance()``.

Some other closely related functionality, with different strengths and
weaknesses, can also be found in ``scipy.stats.binned_statistic()`` and
the `scikit-image <https://scikit-image.org/>`_ function
``skimage.measure.regionprops()``.

Note for IDL users: this provides functionality equivalent to IDL's
REVERSE_INDICES option (as per the IDL documentation for the
`HISTOGRAM <https://www.l3harrisgeospatial.com/docs/histogram.html>`_
function).

.. versionadded:: 1.10.0

Examples
--------
>>> import numpy as np
>>> from scipy import ndimage
>>> a = np.zeros((6, 6), dtype=int)
>>> a[2:4, 2:4] = 1
>>> a[4, 4] = 1
>>> a[:2, :3] = 2
>>> a[0, 5] = 3
>>> a
array([[2, 2, 2, 0, 0, 3],
       [2, 2, 2, 0, 0, 0],
       [0, 0, 1, 1, 0, 0],
       [0, 0, 1, 1, 0, 0],
       [0, 0, 0, 0, 1, 0],
       [0, 0, 0, 0, 0, 0]])
>>> val_indices = ndimage.value_indices(a)

The dictionary `val_indices` will have an entry for each distinct
value in the input array.

>>> val_indices.keys()
dict_keys([np.int64(0), np.int64(1), np.int64(2), np.int64(3)])

The entry for each value is an index tuple, locating the elements
with that value.

>>> ndx1 = val_indices[1]
>>> ndx1
(array([2, 2, 3, 3, 4]), array([2, 3, 2, 3, 4]))

This can be used to index into the original array, or any other
array with the same shape.

>>> a[ndx1]
array([1, 1, 1, 1, 1])

If the zeros were to be ignored, then the resulting dictionary
would no longer have an entry for zero.

>>> val_indices = ndimage.value_indices(a, ignore_value=0)
>>> val_indices.keys()
dict_keys([np.int64(1), np.int64(2), np.int64(3)])

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