jax.numpy.histogram#

jax.numpy.histogram(a,bins=10,range=None,weights=None,density=None)[source]#

Compute a 1-dimensional histogram.

JAX implementation ofnumpy.histogram().

Parameters:

• a (ArrayLike) – array of values to be binned. May be any size or dimension.

• bins (ArrayLike) – Specify the number of bins in the histogram (default: 10).binsmay also be an array specifying the locations of the bin edges.

• range (Sequence[ArrayLike]|None) – tuple of scalars. Specifies the range of the data. If not specified,the range is inferred from the data.

• weights (ArrayLike |None) – An optional array specifying the weights of the data points.Should be broadcast-compatible witha. If not specified, eachdata point is weighted equally.

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

Returns:

A tuple of arrays(histogram,bin_edges), wherehistogram containsthe aggregated data, andbin_edges specifies the boundaries of the bins.

Return type:

tuple[Array,Array]

See also

• jax.numpy.bincount(): Count the number of occurrences of each value in an array.

• jax.numpy.histogram2d(): Compute the histogram of a 2D 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

>>>a=jnp.array([1,2,3,10,11,15,19,25])>>>counts,bin_edges=jnp.histogram(a,bins=8)>>>print(counts)[3. 0. 0. 2. 1. 0. 1. 1.]>>>print(bin_edges)[ 1.  4.  7. 10. 13. 16. 19. 22. 25.]

Specifying the bin range:

>>>counts,bin_edges=jnp.histogram(a,range=(0,25),bins=5)>>>print(counts)[3. 0. 2. 2. 1.]>>>print(bin_edges)[ 0.  5. 10. 15. 20. 25.]

Specifying the bin edges explicitly:

>>>bin_edges=jnp.array([0,10,20,30])>>>counts,_=jnp.histogram(a,bins=bin_edges)>>>print(counts)[3. 4. 1.]

Usingdensity=True returns a normalized histogram:

>>>density,bin_edges=jnp.histogram(a,density=True)>>>dx=jnp.diff(bin_edges)>>>normed_sum=jnp.sum(density*dx)>>>jnp.allclose(normed_sum,1.0)Array(True, dtype=bool)