jax.numpy.histogram2d¶

jax.numpy.histogram2d(x, y, bins=10, range=None, weights=None, density=None)[source]¶

Compute the bi-dimensional histogram of two data samples.

LAX-backend implementation of histogram2d(). Original docstring below.

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:
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.
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.

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...>

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...>

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()