Custom functions to improve the look of matplotlib plots for scientific visualisation.
sciplotlib is just a set of simple functions and stylesheets for more professional looking plots
There are two main properties that make plots in papers look the way they do:
- default style properties such as typeface, color scheme,
- specific choices in terms of the placement of tick marks, or additional elements that are added to the plot such as shading or shadows
sciplotlib aims to make (1) easier by providing stylesheets that aims to mimic the style properties found in scientific papers:
We can compare the default matplotlib style with a style that mimics scatter plots found in articles from the Nature publishing group:
import matplotlib.pyplot as plt import numpy as np def make_plot(): fig, ax = plt.subplots() num_categories = 10 num_points = 10 for category in np.arange(num_categories): x = np.random.normal(size=num_points) y = np.random.normal(size=num_points) ax.scatter(x, y) return fig, ax fig, ax = make_plot() ax.set_title('Default matplotlib style')
Applying the most basic style is just one line of code
from sciplotlib import style as spstyle with plt.style.context(spstyle.get_style('nature-reviews')): fig, ax = make_plot() ax.set_title('Nature reviews style')
Modifying figure properties
sciplotlib also aims to make (2) easier by providing functions that automically add elements found in scientific plots. For example, in many scientific journals it is common for the axis to extend only from and up to the last tick mark, and in figures found in Nature review articles, it is also common that shading will be added to plots, these are implemented by functions that simpy takes in the figure handles and return them:
from sciplotlib import style as spstyle from sciplotlib import polish as sppolish with plt.style.context(spstyle.get_style('nature-reviews')): fig, ax = make_plot() fig, ax = sppolish.set_bounds(fig, ax) sppolish.apply_gradient(ax, extent=None, direction=0.3, cmap_range=(0.1, 0), cmap='Greys') ax.set_title('Nature reviews style with bells and whistles')
pip install sciplotlib
sciplotlib is built on top of matplotlib. To cite matplotlib in your publications, cite:
J. D. Hunter, "Matplotlib: A 2D Graphics Environment", Computing in Science & Engineering, vol. 9, no. 3, pp. 90-95, 2007
Other projects that is also built on the idea of providing stylesheets / wrappers for scientific plots include:
Color palettes of scientific papers are obtained from the wonderful
Do contact me if you are interested in adding new functions or templates to this repository.
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