alpha release

.github/workflows/pypi-publish.yml

@@ -0,0 +1,39 @@
+name: PyPI publisher
+
+on:
+  release:
+    types: [published]
+
+jobs:
+  pypi-publish:
+    name: Build and upload release to PyPI
+    runs-on: ubuntu-latest
+    environment:
+      name: pypi
+      url: https://pypi.org/project/rxmc/
+    permissions:
+      id-token: write  
+    steps:
+    - uses: actions/checkout@v3
+    - name: Set up Python
+      uses: actions/setup-python@v4
+      with:
+        python-version: "3.x"
+    - name: Install pypa/build
+      run: >-
+        python3 -m
+        pip install
+        build
+        --user
+    - name: Build a binary wheel and a source tarball
+      run: >-
+        python3 -m
+        build
+        --sdist
+        --wheel
+        --outdir dist/
+        .
+    - name: Publish distribution 📦 to PyPI
+      uses: pypa/gh-action-pypi-publish@release/v1
+      with:
+        password: ${{ secrets.PYPI_ACCESS_TOKEN }}

.github/workflows/python-package.yml

@@ -0,0 +1,70 @@
+# This workflow will install Python dependencies, run tests and lint with a variety of Python versions
+# For more information see: https://docs.github.com/en/actions/automating-builds-and-tests/building-and-testing-python
+
+name: Python package
+
+on:
+  push:
+    branches: [ "main" ]
+  pull_request:
+    branches: [ "main" ]
+
+jobs:
+  build:
+
+    permissions:
+      # Give the default GITHUB_TOKEN write permission to commit and push the changed files back to the repository.
+      contents: write
+
+    runs-on: ubuntu-latest
+
+    strategy:
+      fail-fast: false
+      matrix:
+        python-version: ["3.11", "3.12", "3.13", "3.14"]
+
+    steps:
+    - uses: actions/checkout@v3
+      with:
+        ref: ${{ github.head_ref }}
+
+    - name: black
+      uses: lgeiger/black-action@v1.0.1
+      with:
+        args: .
+
+    - name: Check for modified files
+      id: git-check
+      run: echo "modified=$(if git diff-index --quiet HEAD --; then echo "false"; else echo "true"; fi)" >> $GITHUB_OUTPUT
+
+    - name: Push changes
+      if: steps.git-check.outputs.modified == 'true'
+      uses: stefanzweifel/git-auto-commit-action@v4
+
+    - name: Set up Python ${{ matrix.python-version }}
+      uses: actions/setup-python@v3
+      with:
+        python-version: ${{ matrix.python-version }}
+
+    - name: Install package and dependencies
+      run: |
+        python -m pip install --upgrade pip
+        python -m pip install flake8
+        pip install -r requirements.txt
+        pip install .
+
+    - name: Lint with flake8
+      run: |
+        # stop the build if there are Python syntax errors or undefined names
+        flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
+        # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
+        flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
+
+    - name: Run tests
+      run: |
+        pytest -v 
+
+    - name: run jupyter notebook tests
+      run: |
+        pip install -r examples/requirements.txt
+        pytest --nbmake examples/ -v

.gitignore

@@ -1,5 +1,6 @@
 #slurm
 *.out
+*.slurm
 
 # data
 *.h5

README.md

@@ -1,22 +1,34 @@
-# rxmc
-Bayesian calibration of reaction models with Markov-Chain Monte Carlo, with flexible and composable models for the likelihood and body of evidence.
+[![Python package](https://github.com/beykyle/rxmc/actions/workflows/python-package.yml/badge.svg)](https://github.com/beykyle/rxmc/actions/workflows/python-package.yml)
+
+
+# RxMC
+Bayesian calibration of reaction models, with flexible and composable models for the likelihood and body of evidence.
 - curate the corpus of experimental constraints (e.g. using [`exfor_tools`](https://github.com/beykyle/exfor_tools))
 - efficiently calculate your model's corresponding predictions for these observables using [`jitR`](https://github.com/beykyle/jitr)
 - choose from a variety of likelihood models, or extend the basic [`LikelihoodModel`](https://github.com/beykyle/rxmc/blob/main/src/rxmc/likelihood_model.py) class to implement your own.
-- package the constraints, physical model (and solver), and likelihood model together in a [`Evidence`](https://github.com/beykyle/rxmc/blob/main/src/rxmc/evidence.py) object which provides the likelihood of a given model parameter, for use in Bayesian calibration 
-- run Bayesian calibration using a [`Walker`](https://github.com/beykyle/rxmc/blob/main/src/rxmc/walker.py)
-
-An example of this code in use is in the development of the [East Lansing Model](https://github.com/beykyle/elm)
+- package the constraints, physics model, solver, and likelihood model together in a [`Evidence`](https://github.com/beykyle/rxmc/blob/main/src/rxmc/evidence.py) object which provides the likelihood of a given model parameter, for use in Bayesian calibration 
+- run Bayesian calibration using a [`Walker`](https://github.com/beykyle/rxmc/blob/main/src/rxmc/walker.py), or using a 3rd party MCMC sampler like [emcee](https://emcee.readthedocs.io/en/stable/user/sampler/).
 
 Check out the [`examples/` directory](https://github.com/beykyle/rxmc/blob/main/examples/).
 
-## documentation
-- TBD
 
 ## installation
+
+
 ### pypi
 
-- TBD
+```
+pip install rxmc
+```
+
+To also install the command line interface:
+
+```
+pip install rxm[cli]
+```
+
+The use of the CLI is demonstrated in `examples/rxmc_with_emcee_sampler/`.
+
 
 ### for development
 ```bash
@@ -70,18 +82,24 @@ This will require that your current python environment satisfies `requirements.t
 ## test
 
 ```
-python -m unittest discover ./test
+pytest
+```
+
+The examples and demos also serve as tests:
+
+```
+pip install -r examples/requirements.txt
+pytest --nbmake examples/
 ```
+
 ## examples, demos and tutorials
 
-check out the [`examples/` directory](https://github.com/beykyle/rxmc/blob/main/examples/)
+check out the [`examples/` directory](https://github.com/beykyle/rxmc/blob/main/examples/). First install example specific dependencies:
+
+```
+pip install -r examples/requirements.txt
+```
 
 In particular, the following notebooks are useful for getting started with `rxmc`: 
 - [`examples/linear_calibration_demo.ipynb`](https://github.com/beykyle/rxmc/blob/main/examples/linear_calibration_demo.ipynb) for an illustrative example of fitting a line to data, which serves as the basic `rxmc` tutorial.
-- [`systematic_err_demo.ipynb`](https://github.com/beykyle/rxmc/blob/main/examples/systematic_err_demo.ipynb) for a comparison of some of the likelihood models built into `rxmc`, and how to use them for situations involving systematic errors and multiple independent experimental constraints
 - [`examples/30s_optical_potential_calibration.ipynb`](https://github.com/beykyle/rxmc/blob/main/examples/30s_optical_potential_calibration.ipynb) for a demo of a full Bayesian calibration of a a local optical potential to real experimental data using `rxmc` and `jitR`, in only 30 seconds!
-
-## use with third party MCMC samplers
-
-The `Evidence` class in `rxmc` can be used with any MCMC sampler that requires a function which returns the log-likelihood of a given parameter set. `rxmc.config.CalibrationConfig` provides a convenient way to package together an `Evidence` object with MCMC sampler settings, and can be used to run MCMC sampling with third party samplers like [`emcee`](https://emcee.readthedocs.io/en/stable/) or [`pymc`](https://www.pymc.io/). A fully fledged example of setting up an inference problem
-with `rxmc`, and then using an `emcee` `EnsembleSampler` to sample from the posterior in a massively parallel MPI approach can be found in [`examples/emcee/`](https://github.com/beykyle/rxmc/blob/main/examples/emcee/).