scatter

named_arrays.plt.scatter(*args, s=None, c=None, ax=None, where=True, transformation=None, components=None, **kwargs)

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

Parameters:

• args (AbstractScalar) – Same signature as matplotlib.axes.Axes.scatter(). 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.

• s (None | AbstractScalarArray) – The marker size in points**2

• c (None | AbstractScalarArray) – The color of the markers

• 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.

• 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.scatter(). 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=51)
y = np.sin(x)

plt.figure();
na.plt.scatter(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.scatter(x, y, c=z);

Plot an uncertain scalar

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

plt.figure();
na.plt.scatter(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.scatter(x, y, ax=ax);