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
numpy.histogram2d()
.Original docstring below.
- Parameters:
x (array_like, shape (N,)) β An array containing the x coordinates of the points to be histogrammed.
y (array_like, shape (N,)) β An array containing the y coordinates of the points to be histogrammed.
bins (int or array_like or [int, int] or [array, array], optional) β
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) β 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) β 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) β An array of values
w_i
weighing each sample(x_i, y_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.
- Return type:
- Returns:
H (ndarray, shape(nx, ny)) β 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,)) β The bin edges along the first dimension.
yedges (ndarray, shape(ny+1,)) β The bin edges along the second dimension.