GitHub - nschloe/matplotx: 📊 More styles and useful extensions for Matplotlib (original) (raw)

Some useful extensions for Matplotlib.

This package includes some useful or beautiful extensions toMatplotlib. Most of those features could be in Matplotlib itself, but I haven't had time to PR yet. If you're eager, let me know and I'll support the effort.

Install with

pip install matplotx[all]

and use in Python with

See below for what matplotx can do.

Clean line plots (dufte)

The middle plot is created with

import matplotlib.pyplot as plt import matplotx import numpy as np

create data

rng = np.random.default_rng(0) offsets = [1.0, 1.50, 1.60] labels = ["no balancing", "CRV-27", "CRV-27*"] x0 = np.linspace(0.0, 3.0, 100) y = [offset * x0 / (x0 + 1) + 0.1 * rng.random(len(x0)) for offset in offsets]

plot

with plt.style.context(matplotx.styles.dufte): for yy, label in zip(y, labels): plt.plot(x0, yy, label=label) plt.xlabel("distance [m]") matplotx.ylabel_top("voltage [V]") # move ylabel to the top, rotate matplotx.line_labels() # line labels to the right plt.show()

The three matplotx ingredients are:

matplotx.styles.dufte: A minimalistic style
matplotx.ylabel_top: Rotate and move the the y-label
matplotx.line_labels: Show line labels to the right, with the line color

You can also "duftify" any other style (see below) with

matplotx.styles.duftify(matplotx.styles.dracula)

Clean bar plots

The right plot is created with

import matplotlib.pyplot as plt import matplotx

labels = ["Australia", "Brazil", "China", "Germany", "Mexico", "United\nStates"] vals = [21.65, 24.5, 6.95, 8.40, 21.00, 8.55] xpos = range(len(vals))

with plt.style.context(matplotx.styles.dufte_bar): plt.bar(xpos, vals) plt.xticks(xpos, labels) matplotx.show_bar_values("{:.2f}") plt.title("average temperature [°C]") plt.show()

The two matplotx ingredients are:

matplotx.styles.dufte_bar: A minimalistic style for bar plots
matplotx.show_bar_values: Show bar values directly at the bars

Extra styles

matplotx contains numerous extra color schemes, e.g.,Dracula, Nord,gruvbox, andSolarized,the revised Tableau colors.

import matplotlib.pyplot as plt import matplotx

use everywhere:

plt.style.use(matplotx.styles.dracula)

use with context:

with plt.style.context(matplotx.styles.dracula): pass

Many more styles here...

Other styles:

Smooth contours


plt.contourf	matplotx.contours()

Sometimes, the sharp edges of contour[f] plots don't accurately represent the smoothness of the function in question. Smooth contours, contours(), serves as a drop-in replacement.

import matplotlib.pyplot as plt import matplotx

def rosenbrock(x): return (1.0 - x[0]) ** 2 + 100.0 * (x[1] - x[0] ** 2) ** 2

im = matplotx.contours( rosenbrock, (-3.0, 3.0, 200), (-1.0, 3.0, 200), log_scaling=True, cmap="viridis", outline="white", ) plt.gca().set_aspect("equal") plt.colorbar(im) plt.show()

Contour plots for functions with discontinuities


plt.contour	matplotx.contour(max_jump=1.0)

Matplotlib has problems with contour plots of functions that have discontinuities. The software has no way to tell discontinuities and very sharp, but continuous cliffs apart, and contour lines will be drawn along the discontinuity.

matplotx improves upon this by adding the parameter max_jump. If the difference between two function values in the grid is larger than max_jump, a discontinuity is assumed and no line is drawn. Similarly, min_jump can be used to highlight the discontinuity.

As an example, take the function imag(log(Z)) for complex values Z. Matplotlib's contour lines along the negative real axis are wrong.

import matplotlib.pyplot as plt import numpy as np

import matplotx

x = np.linspace(-2.0, 2.0, 100) y = np.linspace(-2.0, 2.0, 100)

X, Y = np.meshgrid(x, y) Z = X + 1j * Y

vals = np.imag(np.log(Z))

plt.contour(X, Y, vals, levels=[-2.0, -1.0, 0.0, 1.0, 2.0]) # draws wrong lines

matplotx.contour(X, Y, vals, levels=[-2.0, -1.0, 0.0, 1.0, 2.0], max_jump=1.0) matplotx.discontour(X, Y, vals, min_jump=1.0, linestyle=":", color="r")

plt.gca().set_aspect("equal") plt.show()

Relevant discussions: