Some useful extensions for Matplotlib.

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

sh pip install matplotx[all]

and use in Python with

python import matplotx

See below for what matplotx can do.

Clean line plots (dufte)

matplotlib	matplotx.styles.dufte, matplotx.ylabeltop, matplotx.linelabels	matplotx.styles.duftify(matplotx.styles.dracula)

The middle plot is created with

```python 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.ylabeltop("voltage [V]") # move ylabel to the top, rotate matplotx.linelabels() # 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

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

Further reading and other styles:

• Remove to improve: data-ink ratio

• Cereblog, Remove to improve: Line Graph Edition
• Show the Data - Maximize the Data Ink Ratio
• Randal S. Olson's blog entry
• prettyplotlib
• Wikipedia: Chartjunk

Clean bar plots

matplotlib	dufte	dufte with matplotx.showbarvalues()

The right plot is created with

```python 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.duftebar): plt.bar(xpos, vals) plt.xticks(xpos, labels) matplotx.showbar_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.

```python 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:

• John Garrett, Science Plots
• Dominik Haitz, Cyberpunk style
• Dominik Haitz, Matplotlib stylesheets
• Carlos da Costa, The Grand Budapest Hotel
• Danny Antaki, vaporwave aesthetics
• QuantumBlack Labs, QuantumBlack

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.

```python 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), logscaling=True, cmap="viridis", outline="white", ) plt.gca().setaspect("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.

```python 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], maxjump=1.0) matplotx.discontour(X, Y, vals, minjump=1.0, linestyle=":", color="r")

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

Relevant discussions:

• matplotlib/issues/21348

spy plots (betterspy)

Show sparsity patterns of sparse matrices or write them to image files.

Example:

```python import matplotx from scipy import sparse

A = sparse.rand(20, 20, density=0.1)

show the matrix

plt = matplotx.spy( A, # borderwidth=2, # bordercolor="red", # colormap="viridis" ) plt.show()

or save it as png

matplotx.spy(A, filename="out.png") ```

| | | | :----------------------------------------------------------: | :------------------------------------------------------------: | | no colormap | viridis |

There is a command-line tool that can be used to show matrix-market or Harwell-Boeing files:

matplotx spy msc00726.mtx [out.png]

See matplotx spy -h for all options.

License

This software is published under the MIT license.