gridspeccer

Plotting tool to make plotting with many subfigures easier, especially for publications. After installation gridspeccer can be used from the command line to create plots

gridspeccer [path]

Installation

You can install either from PyPi

pip install [--user] gridspeccer

or the current version from GitHub

pip install [--user] git+https://github.com/gridspeccer/gridspeccer/

For a debug version where local changes are automatically in effect, clone the repository and install it with the editable flag -e

git clone https://github.com/gridspeccer/gridspeccer
cd gridspeccer
pip install -e [--user] .

Usage

A standalone plot file that does not need data is examples/fig_setup.py, this is also used for testing, see actions.

• gridspeccer can be used on specific files gridspeccer fig_setup.py or on folders (no argument is equivalent to CWD .), in which files that satisfy fig*.py are searched for.
• with the optional argument --mplrc [file] one can specify a matplotlibrc to be used for plotting (two examples in gridspeccer/defaults/)
• default filetype of the plot is .pdf, for other filetypes specify, e.g., --filetype .png
• plots are saved to ../fig/ by default, can be specified by --output-folder FOLDER

Requirements

• python3
• matplotlib
• LaTeX (in case you want true latex and not matplotlib's Tex parser for mathtext

Notes

• Don't install using python setup.py install, as this will create an .egg, and the default matplotlibrcs will not be accessible.
• Many old examples that are not executable at the moment can be found in old_examples, to serve as inspiration for other plots.
• For an example using gridspeccer see JulianGoeltz/automised_latex_template. Recent papers utilising gridspeccer include
  • Göltz, J.∗, Kriener, L.∗, Baumbach, A., Billaudelle, S., Breitwieser, O., Cramer, B., ... & Petrovici, M. A. (2021). Fast and energy-efficient neuromorphic deep learning with first-spike times. Nature Machine Intelligence, 3(9), 823-835., 3(9), 823-835. preprint at https://arxiv.org/abs/1912.11443
  • Haider, P., Ellenberger, B., Kriener, L., Jordan, J., Senn, W., & Petrovici, M. A. (2021). Latent equilibrium: A unified learning theory for arbitrarily fast computation with arbitrarily slow neurons. Advances in Neural Information Processing Systems, 34, 17839-17851. preprint at https://arxiv.org/abs/2110.14549

Todos

• make true tex standard?
• Format code to satisfy linting, then update linting workflow