Merge develop into main.

.github/workflows/ci.yml

@@ -117,11 +117,7 @@ jobs:
     - name: Run tests
       timeout-minutes: 20
       run: |
-         echo "Amici installed (1):"
-         pip list | grep amici
-         tox -e petab -vvv
-         echo "Amici installed (2):"
-         pip list | grep amici
+         tox -e petab
 
     - name: Coverage
       uses: codecov/codecov-action@v4
@@ -244,6 +240,9 @@ jobs:
       run: |
         .github/workflows/install_deps.sh doc
         pip install tox
+        pip install pypandoc
+        python .github/workflows/generate_readme_rst.py
+
 
     - name: Run quality checks
       timeout-minutes: 5

.github/workflows/generate_readme_rst.py

@@ -0,0 +1,12 @@
+import os
+
+try:
+    import pypandoc
+    content = pypandoc.convert_file('README.md', 'rst')
+except (ImportError, OSError):
+    with open('README.md', encoding='utf-8') as f:
+        content = f.read()
+
+with open('README.rst', 'w', encoding='utf-8') as f:
+    f.write(content)
+

README.md

@@ -0,0 +1,26 @@
+# pyABC
+
+<p align="left">
+  <img src="https://raw.githubusercontent.com/ICB-DCM/pyABC/main/doc/logo/logo.svg" alt="pyABC logo" width="30%">
+</p>
+
+[![CI](https://github.com/ICB-DCM/pyABC/workflows/CI/badge.svg)](https://github.com/ICB-DCM/pyABC/actions)
+[![Docs](https://readthedocs.org/projects/pyabc/badge/?version=latest)](https://pyabc.readthedocs.io/en/latest/)
+[![Codecov](https://codecov.io/gh/ICB-DCM/pyABC/branch/develop/graph/badge.svg)](https://codecov.io/gh/ICB-DCM/pyABC)
+[![PyPI](https://badge.fury.io/py/pyabc.svg)](https://badge.fury.io/py/pyabc)
+[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.3257587.svg)](https://doi.org/10.5281/zenodo.3257587)
+[![Python](https://img.shields.io/badge/Python-%3E%3D3.10-blue?logo=python&logoColor=green)](https://python.org)
+[![Codestyle](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
+
+**pyABC** is a massively parallel, distributed, and scalable ABC-SMC (Approximate Bayesian Computation - Sequential Monte Carlo) framework for parameter estimation of complex stochastic models. It provides numerous state-of-the-art algorithms for efficient, accurate, robust likelihood-free inference, described in the documentation and illustrated in example notebooks. Written in Python, with support for integration with R and Julia.
+
+---
+
+### Resources
+
+- 📖 **Documentation**: [https://pyabc.rtfd.io](https://pyabc.rtfd.io)
+- 💡 **Examples**: [https://pyabc.rtfd.io/en/latest/examples.html](https://pyabc.rtfd.io/en/latest/examples.html)
+- 💬 **Contact**: [https://pyabc.rtfd.io/en/latest/about.html](https://pyabc.rtfd.io/en/latest/about.html)
+- 🐛 **Bug Reports**: [https://github.com/icb-dcm/pyabc/issues](https://github.com/icb-dcm/pyabc/issues)
+- 💻 **Source Code**: [https://github.com/icb-dcm/pyabc](https://github.com/icb-dcm/pyabc)
+- 📄 **Cite**: [https://pyabc.rtfd.io/en/latest/cite.html](https://pyabc.rtfd.io/en/latest/cite.html)