<p align="center"> <a href="https://github.com/nschloe/matplotx"><img alt="matplotx" src="https://nschloe.github.io/matplotx/matplotx-logo.svg" width="55%"></a> <p align="center">Some useful extensions for <a href="https://matplotlib.org/">Matplotlib</a>.</p> </p>

This package includes some useful or beautiful extensions to Matplotlib. 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

import matplotx

See below for what matplotx can do.

Clean line plots (dufte)

<a href="tests/dufte_comparison.py"> <table width="100%"> <tr> <td width="33%"><img src="https://nschloe.github.io/matplotx/ex1-mpl.svg"/></td> <td width="33%"><img src="https://nschloe.github.io/matplotx/ex1-dufte.svg"/></td> <td width="33%"><img src="https://nschloe.github.io/matplotx/ex1-dufte-dracula.svg"/></td> </tr> <tr> <td>matplotlib</td> <td> <code>matplotx.styles.dufte</code>, <code>matplotx.ylabel_top</code>, <code>matplotx.line_labels</code> </td> <td> <code>matplotx.styles.duftify(matplotx.styles.dracula)</code> </td> </tr> </table> </a>

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

<a href="tests/dufte_comparison.py"> <table width="100%"> <tr> <td width="33%"><img src="https://nschloe.github.io/matplotx/bars-mpl.svg"/></td> <td width="33%"><img src="https://nschloe.github.io/matplotx/bars-dufte1.svg"/></td> <td width="33%"><img src="https://nschloe.github.io/matplotx/bars-dufte2.svg"/></td> </tr> <tr> <td>matplotlib</td> <td>dufte</td> <td>dufte with <code>matplotx.show_bar_values()</code></td> </tr> </table> </a>

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, and Solarized, 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

| <img src="https://nschloe.github.io/matplotx/aura-dark-soft.svg" width="100%"> | | :----------------------------------------------------------------------------------: | | <img src="https://nschloe.github.io/matplotx/dracula.svg" width="100%"> | | <img src="https://nschloe.github.io/matplotx/gruvbox-dark.svg" width="100%"> | | <img src="https://nschloe.github.io/matplotx/pitaya-smoothie-dark.svg" width="100%"> |

<details> <summary>Many more styles here...</summary>

| <img src="https://nschloe.github.io/matplotx/aura-dark.svg" width="100%"> | :-----------------------------------------------------------------------------------: | | <img src="https://nschloe.github.io/matplotx/ayu-dark.svg" width="100%"> | <img src="https://nschloe.github.io/matplotx/ayu-light.svg" width="100%"> | <img src="https://nschloe.github.io/matplotx/ayu-mirage.svg" width="100%"> | <img src="https://nschloe.github.io/matplotx/challenger-deep.svg" width="100%"> | <img src="https://nschloe.github.io/matplotx/github-dark.svg" width="100%"> | <img src="https://nschloe.github.io/matplotx/github-dimmed.svg" width="100%"> | <img src="https://nschloe.github.io/matplotx/github-light.svg" width="100%"> | <img src="https://nschloe.github.io/matplotx/gruvbox-light.svg" width="100%"> | <img src="https://nschloe.github.io/matplotx/nord.svg" width="100%"> | <img src="https://nschloe.github.io/matplotx/one-dark.svg" width="100%"> | <img src="https://nschloe.github.io/matplotx/pacoty.svg" width="100%"> | <img src="https://nschloe.github.io/matplotx/pitaya-smoothie-light.svg" width="100%"><img src="https://nschloe.github.io/matplotx/solarized-dark.svg" width="100%"> | <img src="https://nschloe.github.io/matplotx/solarized-light.svg" width="100%"> | <img src="https://nschloe.github.io/matplotx/tableau-10.svg" width="100%"> | <img src="https://nschloe.github.io/matplotx/tableau-20.svg" width="100%"> | <img src="https://nschloe.github.io/matplotx/tokyo-night-day.svg" width="100%"> | <img src="https://nschloe.github.io/matplotx/tokyo-night-night.svg" width="100%"> | <img src="https://nschloe.github.io/matplotx/tokyo-night-storm.svg" width="100%"> | | | | | | | | | | | | --> | | | | | | | |

</details>

Other styles:

Smooth contours

| <img src="https://nschloe.github.io/matplotx/mpl-contourf.svg" width="100%"> | <img src="https://nschloe.github.io/matplotx/matplotx-contours.svg" width="100%"> | | :--------------------------------------------------------------------------: | :-------------------------------------------------------------------------------: | | 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

| <img src="https://nschloe.github.io/matplotx/contour-mpl.svg" width="100%"> | <img src="https://nsc

Matplotx

Install / Use

README

Clean line plots (dufte)

Clean bar plots

Extra styles

Smooth contours

Contour plots for functions with discontinuities