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Module « scipy.ndimage »

Fonction minimum_position - module scipy.ndimage

Signature de la fonction minimum_position

def minimum_position(input, labels=None, index=None) 

Description

help(scipy.ndimage.minimum_position)

Find the positions of the minimums of the values of an array at labels.

Parameters
----------
input : array_like
    Array_like of values.
labels : array_like, optional
    An array of integers marking different regions over which the
    position of the minimum value of `input` is to be computed.
    `labels` must have the same shape as `input`. If `labels` is not
    specified, the location of the first minimum over the whole
    array is returned.

    The `labels` argument only works when `index` is specified.
index : array_like, optional
    A list of region labels that are taken into account for finding the
    location of the minima. If `index` is None, the ``first`` minimum
    over all elements where `labels` is non-zero is returned.

    The `index` argument only works when `labels` is specified.

Returns
-------
output : list of tuples of ints
    Tuple of ints or list of tuples of ints that specify the location
    of minima of `input` over the regions determined by `labels` and
    whose index is in `index`.

    If `index` or `labels` are not specified, a tuple of ints is
    returned specifying the location of the first minimal value of `input`.

See Also
--------
label, minimum, median, maximum_position, extrema, sum, mean, variance,
standard_deviation

Examples
--------
>>> import numpy as np
>>> a = np.array([[10, 20, 30],
...               [40, 80, 100],
...               [1, 100, 200]])
>>> b = np.array([[1, 2, 0, 1],
...               [5, 3, 0, 4],
...               [0, 0, 0, 7],
...               [9, 3, 0, 0]])

>>> from scipy import ndimage

>>> ndimage.minimum_position(a)
(2, 0)
>>> ndimage.minimum_position(b)
(0, 2)

Features to process can be specified using `labels` and `index`:

>>> label, pos = ndimage.label(a)
>>> ndimage.minimum_position(a, label, index=np.arange(1, pos+1))
[(2, 0)]

>>> label, pos = ndimage.label(b)
>>> ndimage.minimum_position(b, label, index=np.arange(1, pos+1))
[(0, 0), (0, 3), (3, 1)]

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