Compute a 1-dimensional histogram.
JAX implementation of numpy.histogram().
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). bins may 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 with a. If not specified, each data point is weighted equally.
density (bool | None) – If True, return the normalized histogram in units of counts per unit length. If False (default) return the (weighted) counts per bin.
A tuple of arrays (histogram, bin_edges), where histogram contains the aggregated data, and bin_edges specifies the boundaries of the bins.
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.]
Using density=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)
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