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Module « numpy.matlib »

Fonction histogramdd - module numpy.matlib

Signature de la fonction histogramdd

def histogramdd(sample, bins=10, range=None, density=None, weights=None) 

Description

help(numpy.matlib.histogramdd)

Compute the multidimensional histogram of some data.

Parameters
----------
sample : (N, D) array, or (N, D) array_like
    The data to be histogrammed.

    Note the unusual interpretation of sample when an array_like:

    * When an array, each row is a coordinate in a D-dimensional space -
      such as ``histogramdd(np.array([p1, p2, p3]))``.
    * When an array_like, each element is the list of values for single
      coordinate - such as ``histogramdd((X, Y, Z))``.

    The first form should be preferred.

bins : sequence or int, optional
    The bin specification:

    * A sequence of arrays describing the monotonically increasing bin
      edges along each dimension.
    * The number of bins for each dimension (nx, ny, ... =bins)
    * The number of bins for all dimensions (nx=ny=...=bins).

range : sequence, optional
    A sequence of length D, each an optional (lower, upper) tuple giving
    the outer bin edges to be used if the edges are not given explicitly in
    `bins`.
    An entry of None in the sequence results in the minimum and maximum
    values being used for the corresponding dimension.
    The default, None, is equivalent to passing a tuple of D None values.
density : bool, optional
    If False, the default, returns the number of samples in each bin.
    If True, returns the probability *density* function at the bin,
    ``bin_count / sample_count / bin_volume``.
weights : (N,) array_like, optional
    An array of values `w_i` weighing each sample `(x_i, y_i, z_i, ...)`.
    Weights are normalized to 1 if density is True. If density is False,
    the values of the returned histogram are equal to the sum of the
    weights belonging to the samples falling into each bin.

Returns
-------
H : ndarray
    The multidimensional histogram of sample x. See density and weights
    for the different possible semantics.
edges : tuple of ndarrays
    A tuple of D arrays describing the bin edges for each dimension.

See Also
--------
histogram: 1-D histogram
histogram2d: 2-D histogram

Examples
--------
>>> import numpy as np
>>> rng = np.random.default_rng()
>>> r = rng.normal(size=(100,3))
>>> H, edges = np.histogramdd(r, bins = (5, 8, 4))
>>> H.shape, edges[0].size, edges[1].size, edges[2].size
((5, 8, 4), 6, 9, 5)

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