jax.numpy.histogram2d#

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

Compute a 2-dimensional histogram.

JAX implementation ofnumpy.histogram2d().

Parameters:

• x (ArrayLike) – one-dimensional array of x-values for points to be binned.

• y (ArrayLike) – one-dimensional array of y-values for points to be binned.

• bins (ArrayLike |list[ArrayLike]) – Specify the number of bins in the histogram (default: 10).binsmay also be an array specifying the locations of the bin edges, or a pairof integers or pair of arrays specifying the number of bins in eachdimension.

• range (Sequence[None |Array |Sequence[ArrayLike]]|None) – Pair of arrays or lists of the form[[xmin,xmax],[ymin,ymax]]specifying the range of the data in each dimension. If not specified, therange is inferred from the data.

• weights (ArrayLike |None) – An optional array specifying the weights of the data points.Should be the same shape asx andy. If not specified, eachdata point is weighted equally.

• density (bool |None) – If True, return the normalized histogram in units of countsper unit area. If False (default) return the (weighted) counts per bin.

Returns:

A tuple of arrays(histogram,x_edges,y_edges), wherehistogramcontains the aggregated data, andx_edges andy_edges specify theboundaries of the bins.

Return type:

tuple[Array,Array,Array]

See also

• jax.numpy.histogram(): Compute the histogram of a 1D array.

• jax.numpy.histogramdd(): Compute the histogram of an N-dimensional array.

• jax.numpy.histogram_bin_edges(): Compute the bin edges for a histogram.

Examples

>>>x=jnp.array([1,2,3,10,11,15,19,25])>>>y=jnp.array([2,5,6,8,13,16,17,18])>>>counts,x_edges,y_edges=jnp.histogram2d(x,y,bins=8)>>>counts.shape(8, 8)>>>x_edgesArray([ 1.,  4.,  7., 10., 13., 16., 19., 22., 25.], dtype=float32)>>>y_edgesArray([ 2.,  4.,  6.,  8., 10., 12., 14., 16., 18.], dtype=float32)

Specifying the bin range:

>>>counts,x_edges,y_edges=jnp.histogram2d(x,y,range=[(0,25),(0,25)],bins=5)>>>counts.shape(5, 5)>>>x_edgesArray([ 0.,  5., 10., 15., 20., 25.], dtype=float32)>>>y_edgesArray([ 0.,  5., 10., 15., 20., 25.], dtype=float32)

Specifying the bin edges explicitly:

>>>x_edges=jnp.array([0,10,20,30])>>>y_edges=jnp.array([0,10,20,30])>>>counts,_,_=jnp.histogram2d(x,y,bins=[x_edges,y_edges])>>>countsArray([[3, 0, 0],       [1, 3, 0],       [0, 1, 0]], dtype=int32)

Usingdensity=True returns a normalized histogram:

>>>density,x_edges,y_edges=jnp.histogram2d(x,y,density=True)>>>dx=jnp.diff(x_edges)>>>dy=jnp.diff(y_edges)>>>normed_sum=jnp.sum(density*dx[:,None]*dy[None,:])>>>jnp.allclose(normed_sum,1.0)Array(True, dtype=bool)