histogram

named_arrays.histogram(a, bins, axis=None, min=None, max=None, density=False, weights=None)

A thin wrapper around numpy.histogram() which adds an axis argument.

Parameters:

• a (AbstractArray) – The input data over which to compute the histogram.

• bins (dict[str, int] | AbstractArray) –

  The bin specification of the histogram:

  • If bins is a dictionary, the keys are interpreted as the axis names and the values are the number of bins along each axis. This dictionary must have only one key per coordinate.

  • If bins is an array, it represents the bin edges.

• axis (None | str | Sequence[str]) – The logical axes along which to histogram the data points. If None (the default), the histogram will be computed along all the axes of a.

• min (None | float | AbstractArray) – The lower boundary of the histogram. If None (the default), the minimum of a is used.

• max (None | float | AbstractArray) – The upper boundary of the histogram. If None (the default), the maximum of a is used.

• density (bool) – If False (the default), returns the number of samples in each bin. If True, returns the probability density in each bin.

• weights (None | AbstractArray) – An optional array weighting each sample.

Return type:

FunctionArray[AbstractArray, ScalarArray]

Examples

Construct a 2D histogram with constant bin width.

import numpy as np
import matplotlib.pyplot as plt
import named_arrays as na

# Define the bin edges
bins = dict(x=6)

# Define random points to collect into a histogram
a = na.random.normal(0, 2, shape_random=dict(h=101))

# Compute the histogram
hist = na.histogram(a, bins=bins)

# Plot the resulting histogram
fig, ax = plt.subplots()
na.plt.stairs(hist.inputs, hist.outputs);