histogram2d

named_arrays.histogram2d(x, y, bins, axis=None, min=None, max=None, density=False, weights=None)

A thin wrapper around numpy.histogram2d() which adds an axis argument.

Parameters:

• x (AbstractScalarArray) – An array containing the x coordinates of the points to be sampled.

• y (AbstractScalarArray) – An array containing the y coordinates of the points to be sampled.

• bins (dict[str, int] | AbstractCartesian2dVectorArray) –

  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 exactly two keys.

  • If bins is a 2D Cartesian vector, each component of the vector represents the bin edges in each dimension.

• 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 x and y.

• min (None | AbstractScalarArray | AbstractCartesian2dVectorArray) – The lower boundary of the histogram along each dimension. If None (the default), the minimum of x and y is used.

• max (None | AbstractScalarArray | AbstractCartesian2dVectorArray) – The upper boundary of the histogram along each dimension. If None (the default), the maximum of x and y 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 | AbstractScalarArray) – An optional array weighting each sample.

Return type:

FunctionArray[Cartesian2dVectorArray, 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, y=5)

# Define random points to collect into a histogram
x = na.random.normal(0, 2, shape_random=dict(h=101))
y = na.random.normal(0, 3, shape_random=dict(h=101))

# Compute the 2D histogram
hist = na.histogram2d(x, y, bins=bins)

# Plot the resulting histogram
fig, ax = plt.subplots()
na.plt.pcolormesh(C=hist);

Construct a 2D histogram with variable bin width.

import matplotlib.pyplot as plt
import named_arrays as na

# Define the bin edges
bins = na.Cartesian2dVectorArray(
    x=np.square(na.linspace(0, 2, axis="x", num=6)),
    y=np.square(na.linspace(0, 2, axis="y", num=5)),
)

# Define random points to collect into a histogram
x = na.random.normal(0, 2, shape_random=dict(h=101))
y = na.random.normal(0, 3, shape_random=dict(h=101))

# Compute the 2D histogram
hist = na.histogram2d(x, y, bins=bins)

# Plot the resulting histogram
fig, ax = plt.subplots()
na.plt.pcolormesh(C=hist);