Upload to pypi and bump to v1.2.0 (#42)

* Add yml to upload to pip

* Bump version: 1.1.0 → 1.1.1

* Switch to python 3.8 & 3.9

* Update README

* Update pypi_install.yml

* Bump version: 1.1.1 → 1.2.0

.bumpversion.cfg

@@ -1,5 +1,5 @@
 [bumpversion]
-current_version = 1.1.0
+current_version = 1.2.0
 files = setup.py blueice/__init__.py
 commit = True
 tag = True

.github/workflows/pypi_install.yml

@@ -0,0 +1,32 @@
+# Pipy upload blueice after a release (or manually).
+## Mostly based on https://github.com/pypa/gh-action-pypi-publish/blob/f9ed8ba9ad06d20b1ebb6002ffb93050ed9a1951/README.md
+name: PyPI
+
+on:
+  workflow_dispatch:
+  release:
+    types: [ created ]
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+    permissions:
+      id-token: write
+    steps:
+      # Setup steps
+      - name: Setup python
+        uses: actions/[email protected]
+        with:
+          python-version: '3.9'
+
+      - name: Checkout repo
+        uses: actions/checkout@v3
+
+      - name: Install dependencies
+        run: pip install wheel
+
+      - name: Build package
+        run: python setup.py sdist bdist_wheel
+
+      - name: Publish a Python distribution to PyPI
+        uses: pypa/gh-action-pypi-publish@release/v1

README.rst

@@ -20,7 +20,7 @@ This package allows you to do parametric inference using likelihood functions, i
 
 Especially when connected to a Monte Carlo, blueice lets you make likelihood functions which measure agreement between data and theory with flexibility: you choose which settings to vary (which parameters the likelihood functions has) and in which space the agreement is measured.
 
-This package contains only generic code: you'll need a few things to make it useful for a particular experiment. Originally this code was developed for XENON1T only; the XENON1T models have since been split off to the `laidbax <https://github.com/XENON1T/laidbax>`_ repository.
+This package contains only generic code: you'll need a few things to make it useful for a particular experiment. Originally this code was developed for XENON1T only; the XENON1T models have since been split off to the `laidbax <https://github.com/XENON1T/laidbax>`_ repository. XENONnT is still developing `alea <https://github.com/XENONnT/alea>`_ which is based on blueice.
 
 
 Contributors

blueice/__init__.py

@@ -8,4 +8,4 @@
 # from .inference import *   # Not needed, all the inference methods are added to LogLikelihood
 from .parallel import *
 
-__version__ = '1.1.0'
+__version__ = '1.2.0'

setup.py

@@ -12,7 +12,7 @@
 test_requirements = requirements + ['coverage']
 
 setup(name='blueice',
-      version='1.1.0',
+      version='1.2.0',
       description='Build Likelihoods Using Efficient Interpolations from monte-Carlo generated Events',
       long_description=readme + '\n\n' + history,
       author='Jelle Aalbers',
@@ -31,8 +31,10 @@
           'Intended Audience :: Developers',
           'License :: OSI Approved :: MIT License',
           'Natural Language :: English',
-          'Programming Language :: Python :: 3',
-          'Programming Language :: Python :: 3.3',
-          'Programming Language :: Python :: 3.4',
+          'Programming Language :: Python :: 3.8',
+          'Programming Language :: Python :: 3.9',
+          'Intended Audience :: Science/Research',
+          'Programming Language :: Python :: Implementation :: CPython',
+          'Topic :: Scientific/Engineering :: Physics',
       ],
 )