stairs

named_arrays.plt.stairs(*args, ax=None, axis=None, where=True, **kwargs)

A thin wrapper around matplotlib.axes.Axes.stairs() for named arrays.

The main difference of this function from matplotlib.pyplot.stairs() is the addition of the axis parameter indicating along which axis the lines should be connected.

Another difference is that this function swaps the order of values and edges so that the signature matches plot().

Parameters:

• args (AbstractArray) – Either a single instance of AbstractScalar representing the step heights, or a pair of instances of AbstractScalar representing the edges and heights.

• ax (None | Axes | ScalarArray[ndarray[tuple[Any, ...], dtype[Axes]]]) – The instances of matplotlib.axes.Axes to use. If None, calls matplotlib.pyplot.gca() to get the current axes. If an instance of named_arrays.ScalarArray, ax.shape should be a subset of the broadcasted shape of *args.

• axis (None | str) – The name of the axis that the plot lines should be connected along. If None, the broadcasted shape of args should have only one element, otherwise a ValueError is raised.

• where (bool | AbstractScalar) – A boolean array that selects which elements to plot

• kwargs – Additional keyword arguments passed to matplotlib.axes.Axes.stairs(). These can be instances of named_arrays.AbstractArray.

Return type:

ScalarArray[ndarray[tuple[Any, …], dtype[None | Artist]]]

Examples

Plot a single scalar

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

x = na.linspace(0, 2 * np.pi, axis="x",  num=101)

a = na.linspace(0, 2 * np.pi, axis="x", num=100, centers=True)
y = np.sin(a)

plt.figure();
na.plt.stairs(x, y);

Plot an array of scalars

z = na.linspace(0, np.pi, axis="z", num=5)

y = np.sin(a - z)

plt.figure();
na.plt.stairs(x, y, axis="x");

Plot an uncertain scalar

ua = na.NormalUncertainScalarArray(a, width=0.2)
uy = np.sin(ua)

plt.figure();
na.plt.stairs(x, uy);