plot

named_arrays.plt.plot(*args, ax=None, axis=None, where=True, transformation=None, components=None, **kwargs)

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

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

Parameters:

• args (AbstractScalar) – Same signature as matplotlib.axes.Axes.plot(). If ax is a 2D plot, *args should be y or x, y. If ax is a 3D plot, *args should be x, y or x, y, z.

• 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

• transformation (None | AbstractTransformation) – A callable that is applied to args before plotting

• components (None | tuple[str, ...]) – The component names of *args to plot, helpful if *args are an instance of named_arrays.AbstractVectorArray.

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

Return type:

An array of artists that were plotted

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)
y = np.sin(x)

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

Plot an array of scalars

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

y = np.sin(x - z)

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

Plot an uncertain scalar

ux = na.NormalUncertainScalarArray(x, width=0.2)
uy = np.sin(ux)

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

Broadcast an array of scalars against an array of matplotlib.axes.Axes

fig, ax = na.plt.subplots(axis_rows="z", nrows=z.shape["z"], sharex=True)

na.plt.plot(x, y, ax=ax, axis="x");

Plot a 2D Cartesian vector

v = na.Cartesian2dVectorArray(x, np.sin(x))

plt.figure()
na.plt.plot(v)

ScalarArray(
    ndarray=<matplotlib.lines.Line2D object at 0x79a6481b5ee0>,
    axes=(),
)