A collection of plotting functions

version: 2.3.1

This repository collects plotting modules written on top of matplotlib. The functions describe plotting archetypes intended to set up light-touch, illustrations that can be customised using the standard matplotlib interface via axes and figures. Because the implementation is matplotlib-first, the API is consistent with matplotlib conventions, and users already familiar with the library will find the learning curve minimal.

The functionality is geared towards illustrations commonly used in biomedical research:

• Bar charts
• Bubble charts
• Forest plots (with optional side-tables)
• Heatmaps (with optional annotations)
• Incidence matrix plots
• Machine learning plots (calibration, feature importance, net benefit)
• Pie charts
• Survival plots (with optional survival table)
• Tree/compatibility plots
• Volcano plots

Please consult the documentation for plot-misc.

Citation

If you use this package in your research please cite the paper:

@article{Schmidt:2026,
  author  = {Schmidt, Amand Floriaan and Hukerikar, Nikita and
             Finan, Chris and van Vugt, Marion},
  title   = {Effective visualization of biomedical data using plot-misc},
  journal = {Bioinformatics Advances},
  volume  = {6},
  number  = {1},
  pages   = {vbag184},
  doi     = {10.1093/bioadv/vbag184},
  year    = {2026}
}

Installation

The package is available on PyPI, and conda, with the latest source code available on gitlab.

Installation using PyPI

To install the package from PyPI, run:

pip install plot-misc

This installs the latest stable release along with its dependencies.

Installation using conda

A Conda package is maintained in my personal Conda channel. To install from this channel, run:

conda install afschmidt::plot-misc

Installation using gitlab

If you require the latest updates, potentially not yet formally released, you can install the package directly from GitLab.

First, clone the repository and move into its root directory:

git clone git@gitlab.com:SchmidtAF/plot-misc.git
cd plot-misc

Install the dependencies:

# From the root of the repository
conda env create --file ./resources/conda/envs/conda_create.yaml

To add to an existing environment use:

# From the root of the repository
conda env update --file ./resources/conda/envs/conda_update.yaml

Next the package can be installed:

make install

Development

For development work, install the package in editable mode with Git commit hooks configured:

make install-dev

This command installs the package in editable mode and configures Git commit hooks, allowing you to run git pull to update the repository or switch branches without reinstalling.

Alternatively, you can install manually:

python -m pip install -e .
python .githooks/.setup_git_hooks.py

Git Hooks Configuration

When setting up a development environment, the setup-hooks command configures Git hooks to enforce conventional commit message formatting and spell check using codespell.

To view the commit message format requirements, run:

./.githooks/commit-msg -help

For frequent use, add this function to your shell configuration (~/.bashrc or ~/.zshrc):

commit-format-help() {
    local git_root
    git_root=$(git rev-parse --show-toplevel 2>/dev/null)
    
    if [ -z "$git_root" ]; then
        echo "Error: Not inside a git repository"
        return 1
    fi
    
    local hook_path="$git_root/.githooks/commit-msg"
    
    if [ ! -f "$hook_path" ]; then
        echo "Error: commit-msg hook not found"
        return 1
    fi
    
    "$hook_path" --help
}

Validating the package

After installing the package from GitLab, you may wish to run the test suite to confirm everything is working as expected:

# From the root of the repository
pytest tests

Usage

Please have a look at the examples in resources for some possible recipes.

Icon

The icon at the top of this README is a mandarinfish, widely regarded as one of the most colourful animals on the planet.