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Module « numpy.matlib »

Fonction full_like - module numpy.matlib

Signature de la fonction full_like

def full_like(a, fill_value, dtype=None, order='K', subok=True, shape=None, *, device=None) 

Description

help(numpy.matlib.full_like)

Return a full array with the same shape and type as a given array.

Parameters
----------
a : array_like
    The shape and data-type of `a` define these same attributes of
    the returned array.
fill_value : array_like
    Fill value.
dtype : data-type, optional
    Overrides the data type of the result.
order : {'C', 'F', 'A', or 'K'}, optional
    Overrides the memory layout of the result. 'C' means C-order,
    'F' means F-order, 'A' means 'F' if `a` is Fortran contiguous,
    'C' otherwise. 'K' means match the layout of `a` as closely
    as possible.
subok : bool, optional.
    If True, then the newly created array will use the sub-class
    type of `a`, otherwise it will be a base-class array. Defaults
    to True.
shape : int or sequence of ints, optional.
    Overrides the shape of the result. If order='K' and the number of
    dimensions is unchanged, will try to keep order, otherwise,
    order='C' is implied.
device : str, optional
    The device on which to place the created array. Default: None.
    For Array-API interoperability only, so must be ``"cpu"`` if passed.

    .. versionadded:: 2.0.0

Returns
-------
out : ndarray
    Array of `fill_value` with the same shape and type as `a`.

See Also
--------
empty_like : Return an empty array with shape and type of input.
ones_like : Return an array of ones with shape and type of input.
zeros_like : Return an array of zeros with shape and type of input.
full : Return a new array of given shape filled with value.

Examples
--------
>>> import numpy as np
>>> x = np.arange(6, dtype=int)
>>> np.full_like(x, 1)
array([1, 1, 1, 1, 1, 1])
>>> np.full_like(x, 0.1)
array([0, 0, 0, 0, 0, 0])
>>> np.full_like(x, 0.1, dtype=np.double)
array([0.1, 0.1, 0.1, 0.1, 0.1, 0.1])
>>> np.full_like(x, np.nan, dtype=np.double)
array([nan, nan, nan, nan, nan, nan])

>>> y = np.arange(6, dtype=np.double)
>>> np.full_like(y, 0.1)
array([0.1, 0.1, 0.1, 0.1, 0.1, 0.1])

>>> y = np.zeros([2, 2, 3], dtype=int)
>>> np.full_like(y, [0, 0, 255])
array([[[  0,   0, 255],
        [  0,   0, 255]],
       [[  0,   0, 255],
        [  0,   0, 255]]])

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