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Fonction histogram2d - module numpy

Signature de la fonction histogram2d

def histogram2d(x, y, bins=10, range=None, normed=None, weights=None, density=None) 

Description

histogram2d.__doc__

    Compute the bi-dimensional histogram of two data samples.

    Parameters
    ----------
    x : array_like, shape (N,)
        An array containing the x coordinates of the points to be
        histogrammed.
    y : array_like, shape (N,)
        An array containing the y coordinates of the points to be
        histogrammed.
    bins : int or array_like or [int, int] or [array, array], optional
        The bin specification:

          * If int, the number of bins for the two dimensions (nx=ny=bins).
          * If array_like, the bin edges for the two dimensions
            (x_edges=y_edges=bins).
          * If [int, int], the number of bins in each dimension
            (nx, ny = bins).
          * If [array, array], the bin edges in each dimension
            (x_edges, y_edges = bins).
          * A combination [int, array] or [array, int], where int
            is the number of bins and array is the bin edges.

    range : array_like, shape(2,2), optional
        The leftmost and rightmost edges of the bins along each dimension
        (if not specified explicitly in the `bins` parameters):
        ``[[xmin, xmax], [ymin, ymax]]``. All values outside of this range
        will be considered outliers and not tallied in the histogram.
    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_area``.
    normed : bool, optional
        An alias for the density argument that behaves identically. To avoid
        confusion with the broken normed argument to `histogram`, `density`
        should be preferred.
    weights : array_like, shape(N,), optional
        An array of values ``w_i`` weighing each sample ``(x_i, y_i)``.
        Weights are normalized to 1 if `normed` is True. If `normed` 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, shape(nx, ny)
        The bi-dimensional histogram of samples `x` and `y`. Values in `x`
        are histogrammed along the first dimension and values in `y` are
        histogrammed along the second dimension.
    xedges : ndarray, shape(nx+1,)
        The bin edges along the first dimension.
    yedges : ndarray, shape(ny+1,)
        The bin edges along the second dimension.

    See Also
    --------
    histogram : 1D histogram
    histogramdd : Multidimensional histogram

    Notes
    -----
    When `normed` is True, then the returned histogram is the sample
    density, defined such that the sum over bins of the product
    ``bin_value * bin_area`` is 1.

    Please note that the histogram does not follow the Cartesian convention
    where `x` values are on the abscissa and `y` values on the ordinate
    axis.  Rather, `x` is histogrammed along the first dimension of the
    array (vertical), and `y` along the second dimension of the array
    (horizontal).  This ensures compatibility with `histogramdd`.

    Examples
    --------
    >>> from matplotlib.image import NonUniformImage
    >>> import matplotlib.pyplot as plt

    Construct a 2-D histogram with variable bin width. First define the bin
    edges:

    >>> xedges = [0, 1, 3, 5]
    >>> yedges = [0, 2, 3, 4, 6]

    Next we create a histogram H with random bin content:

    >>> x = np.random.normal(2, 1, 100)
    >>> y = np.random.normal(1, 1, 100)
    >>> H, xedges, yedges = np.histogram2d(x, y, bins=(xedges, yedges))
    >>> H = H.T  # Let each row list bins with common y range.

    :func:`imshow <matplotlib.pyplot.imshow>` can only display square bins:

    >>> fig = plt.figure(figsize=(7, 3))
    >>> ax = fig.add_subplot(131, title='imshow: square bins')
    >>> plt.imshow(H, interpolation='nearest', origin='lower',
    ...         extent=[xedges[0], xedges[-1], yedges[0], yedges[-1]])
    <matplotlib.image.AxesImage object at 0x...>

    :func:`pcolormesh <matplotlib.pyplot.pcolormesh>` can display actual edges:

    >>> ax = fig.add_subplot(132, title='pcolormesh: actual edges',
    ...         aspect='equal')
    >>> X, Y = np.meshgrid(xedges, yedges)
    >>> ax.pcolormesh(X, Y, H)
    <matplotlib.collections.QuadMesh object at 0x...>

    :class:`NonUniformImage <matplotlib.image.NonUniformImage>` can be used to
    display actual bin edges with interpolation:

    >>> ax = fig.add_subplot(133, title='NonUniformImage: interpolated',
    ...         aspect='equal', xlim=xedges[[0, -1]], ylim=yedges[[0, -1]])
    >>> im = NonUniformImage(ax, interpolation='bilinear')
    >>> xcenters = (xedges[:-1] + xedges[1:]) / 2
    >>> ycenters = (yedges[:-1] + yedges[1:]) / 2
    >>> im.set_data(xcenters, ycenters, H)
    >>> ax.images.append(im)
    >>> plt.show()