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Module « numpy.matlib »

Fonction intersect1d - module numpy.matlib

Signature de la fonction intersect1d

def intersect1d(ar1, ar2, assume_unique=False, return_indices=False) 

Description

help(numpy.matlib.intersect1d)

Find the intersection of two arrays.

Return the sorted, unique values that are in both of the input arrays.

Parameters
----------
ar1, ar2 : array_like
    Input arrays. Will be flattened if not already 1D.
assume_unique : bool
    If True, the input arrays are both assumed to be unique, which
    can speed up the calculation.  If True but ``ar1`` or ``ar2`` are not
    unique, incorrect results and out-of-bounds indices could result.
    Default is False.
return_indices : bool
    If True, the indices which correspond to the intersection of the two
    arrays are returned. The first instance of a value is used if there are
    multiple. Default is False.

Returns
-------
intersect1d : ndarray
    Sorted 1D array of common and unique elements.
comm1 : ndarray
    The indices of the first occurrences of the common values in `ar1`.
    Only provided if `return_indices` is True.
comm2 : ndarray
    The indices of the first occurrences of the common values in `ar2`.
    Only provided if `return_indices` is True.

Examples
--------
>>> import numpy as np
>>> np.intersect1d([1, 3, 4, 3], [3, 1, 2, 1])
array([1, 3])

To intersect more than two arrays, use functools.reduce:

>>> from functools import reduce
>>> reduce(np.intersect1d, ([1, 3, 4, 3], [3, 1, 2, 1], [6, 3, 4, 2]))
array([3])

To return the indices of the values common to the input arrays
along with the intersected values:

>>> x = np.array([1, 1, 2, 3, 4])
>>> y = np.array([2, 1, 4, 6])
>>> xy, x_ind, y_ind = np.intersect1d(x, y, return_indices=True)
>>> x_ind, y_ind
(array([0, 2, 4]), array([1, 0, 2]))
>>> xy, x[x_ind], y[y_ind]
(array([1, 2, 4]), array([1, 2, 4]), array([1, 2, 4]))

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