Matplotlib styles for scientific plotting

This repo has Matplotlib styles to format your plots for scientific papers, presentations and theses.

The easiest way to install SciencePlots is using pip:

# for lastest release (from PyPI)
pip install SciencePlots

# for latest version (from GitHub)
pip install git+https://github.com/garrettj403/SciencePlots.git

The pip installation will automatically move all of the *.mplstyle files into the appropriate directory. Please see the FAQ for more information and troubleshooting.

The SciencePlots package also requires Latex to be installed on your machine.

• On MacOS, I recommend MacTex. Make sure that this Latex installation is added to your path enviroment variable. See this issue for more troubleshooting tips.

• On Windows, I recommend MikTex. Again, you have to add Latex to your path enviroment variable. See this issue for more troubleshooting tips.

• On Linux (Ubuntu), you can install Tex Live and the other requirements using:

  sudo apt-get install dvipng texlive-latex-extra texlive-fonts-recommended cm-super

Please see Matplotlib's guide to using Latex for more troubleshooting tips.

science.mplstyle is the main style from this repo. Whenever you want to use it, simply add the following to the top of your python script:

import matplotlib.pyplot as plt

plt.style.use('science')

You can also combine multiple styles together by:

plt.style.use(['science','ieee'])

In this case, the ieee style will override some of the parameters from the science style in order to configure the plot for IEEE papers (column width, fontsizes, etc.).

To use any of the styles temporarily, you can use:

with plt.style.context(['science', 'ieee']):
    plt.figure()
    plt.plot(x, y)
    plt.show()

The science style:

The science + grid styles:

The science + ieee styles for IEEE papers:

• IEEE requires figures to be readable when printed in black and white. The ieee style also sets the figure width to fit within one column of an IEEE paper.

The science + scatter styles for scatter plots:

The science + notebook styles for Jupyter notebooks:

You can also combine these styles with the other styles that come with Matplotlib. For example, the dark_background + science + high-vis styles:

Note: See the examples/ directory for more!

Traditional Chinese (science + cjk-tc-font + no-latex):

Simplified Chinese (science + cjk-sc-font + no-latex):

Japanese (science + cjk-jp-font + no-latex):

See the FAQ for information on installing CJK fonts.

The bright color cycle (7 colors):

The vibrant color cycle (7 colors):

The muted color cycle (10 colors):

The high-contrast color cycle (3 colors):

The light color cycle (9 colors):

Note: These color cycles are from Paul Tol's website.

The high-vis color cycle:

The retro color cycle:

Please feel free to contribute to the SciencePlots repo! For example, it would be good to add new styles for different journals and add new color cycles. Before starting a new style or making any changes, please create an issue through the GitHub issue tracker. That way we can discuss if the changes are necessary and the best approach.

If you need any help with SciencePlots, please first check the FAQ and search through the previous GitHub issues. If you can't find an answer, create a new issue through the GitHub issue tracker.

You can checkout Matplotlib's documentation for more information on plotting settings.

1. Errors related to Latex:

   • If you get an error saying RuntimeError: Failed to process string with tex because latex could not be found, this means that you do not have Latex installed on your computer (or at least Python can't find it). You have three options: (1) install Latex, (2) fix the path to Latex, or (3) disable Latex using the no-latex option:

     plt.style.use(['science','no-latex'])

   • See "Installing Latex" for more information.

2. Manual installation:

   • If you like, you can install the *.mplstyle files manually. First, clone the repository and then copy all of the *.mplstyle files into your Matplotlib style directory. If you're not sure where this is, in an interactive python console type:

     import matplotlib
     print(matplotlib.get_configdir())

   • You should get back something like /home/garrett/.matplotlib. You would then put the *.mplstyle files in /home/garrett/.matplotlib/stylelib/ (you may need to create the stylelib directory):

     cp styles/*/*.mplstyle ~/.matplotlib/stylelib/

3. Using different fonts:

   • SciencePlots uses the default serif font. If you would like to specify a different font, you can use:

     import matplotlib.pyplot as plt 
     plt.style.use('science')
     plt.rcParams.update({
         "font.family": "serif",   # specify font family here
         "font.serif": ["Times"],  # specify font here
         "font.size":11})          # specify font size here

   • If you would like to use Times New Roman specifically, please see the discussion in this issue.

4. Using CJK fonts:

   • To use CJK fonts, you first need to install Noto CJK Fonts. You can download and install these fonts from the given link or you can install them with a package manager:

     # Ubuntu / Debian
     $ sudo apt update
     $ sudo apt install fonts-noto-cjk
     
     # macOS
     $ brew tap homebrew/cask-fonts
     $ brew cask install font-noto-serif-cjk-tc
     $ brew cask install font-noto-serif-cjk-sc
     $ brew cask install font-noto-serif-cjk-jp
     $ brew cask install font-noto-serif-cjk-kr
     
     # archlinux
     $ sudo pacman -S noto-fonts-cjk

     Note that matplotlib may not find the fonts correctly. You can refresh the font cache by running:

     import matplotlib.font_manager as fm
     fm._rebuild()

   • You should use the parameter backend='pgf' for the savefig() to use XeLaTeX instead of PdfLaTeX (this LaTeX engine doesn't work with CJK fonts properly). Besides, it can't export the jpg format when you use the parameter backend='pgf' for the savefig().

   • See this issue for more information.

5. Installing SciencePlots within Google Colab, IPython, Jupyter Notebooks, etc.:

   • After installing SciencePlots within one of these environments, you may need to reload the Matplotlib style library. For example:

     !pip install SciencePlots
     import matplotlib.pyplot as plt
     plt.style.reload_library()
     plt.style.use('science')

The following papers use SciencePlots:

• Jegannathan, G., et al., "Current-Assisted SPAD with Improved p-n Junction and Enhanced NIR Performance", Sensors, Dec 2020. (open access)

• H. Tian, et al., "ivis Dimensionality Reduction Framework for Biomacromolecular Simulations", J. Chem. Inf. Model., Aug 2020. (open access)

• P. Stoltz, et al., "A new simple algorithm for space charge limited emission," Phys. Plasmas, vol. 27, no. 9, pp. 093103, Sep. 2020. (open access)

• J. Garrett, et al., "A Nonlinear Transmission Line Model for Simulating Distributed SIS Frequency Multipliers," IEEE Trans. THz Sci. Technol., vol. 10, no. 3, pp. 246-255, May 2020. (open access)

• J. Garrett, et al., "Simulating the Behavior of a 230 GHz SIS Mixer Using Multi-Tone Spectral Domain Analysis," IEEE Trans. THz Sci. Technol., vol. 9, no. 9, pp. 540-548, Nov. 2019. (open access)

• J. Garrett, et al., "A Compact and Easy to Fabricate E-plane Waveguide Bend," IEEE Microw. Wireless Compon. Lett., vol. 29, no. 8, pp. 529-531, Aug. 2019. (open access)

• J. Garrett, "A 230 GHz Focal Plane Array Using a Wide IF Bandwidth SIS Receiver," DPhil thesis, University of Oxford, Oxford, UK, 2018. (open access)

If you use SciencePlots in your paper/thesis, feel free to add it to the list!

You don't have to cite SciencePlots if you use it, but it's nice if you do:

@article{SciencePlots,
  author       = {J. D. Garrett},
  title        = {{SciencePlots (v1.0.6)}},
  month        = oct,
  year         = 2020,
  publisher    = {Zenodo},
  doi          = {10.5281/zenodo.4106650},
  url          = {http://doi.org/10.5281/zenodo.4106650}
}