Prepare release v0.1.0 (#6)

* add changelog

* update diagram

* add dev notes to README

* add pypi deploy cicd workflow

* ensure tests run on main

.github/workflows/ci-pypi-deploy.yml

@@ -0,0 +1,24 @@
+name: package-release
+
+on:
+  workflow_dispatch:
+  pull_request:
+  push:
+    branches:
+     - main
+  release:
+    types:
+      - published
+
+jobs:
+  build:
+    name: build and upload release to pypi
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v2
+      - uses: actions/setup-python@v2
+      - uses: casperdcl/deploy-pypi@v2
+        with:
+          password: ${{ secrets.PYPI_TOKEN }}
+          pip: wheel -w dist/ --no-deps .
+          upload: ${{ github.event_name == 'release' && github.event.action == 'published' }}

.github/workflows/python-package-pip.yml

@@ -3,10 +3,10 @@ name: pytest
 on:
  push:
     branches:
-    - master
+    - main
  pull_request:
     branches:
-    - master
+    - main
 
 jobs:
   test:

CHANGELOG.md

@@ -0,0 +1,16 @@
+# Changelog
+
+All notable changes to this project will be documented in this file.
+
+The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.1.0/),
+and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
+
+## [v0.1.0](https://github.com/mllam/mllam-data-prep/releases/tag/v0.1.0)
+
+First tagged release of `mllam-data-prep` which includes functionality to
+declaratively (in a yaml-config file) describe how the variables and
+coordinates of a set of zarr-based source datasets are mapped to a new set of
+variables with new coordinates to single a training dataset and write this
+resulting single dataset to a new zarr dataset. This explicit mapping gives the
+flexibility to target different different model architectures (which may
+require different inputs with different shapes between architectures).

README.md

@@ -12,14 +12,38 @@ The full configuration file specification is given in [mllam_data_prep/config/sp
 
 ## Installation
 
-The easiest way to install the package is to clone the repository and install it using pip:
+To simply use `mllam-data-prep` you can install the most recent tagged version from pypi with pip:
 
 ```bash
-git clone https://github.com/mllam/mllam-data-prep
+python -m pip install mllam-data-prep
+```
+
+## Developing `mllam-data-prep`
+
+To work on developing `mllam-data-prep` it easiest to install and manage the dependencies with [pdm](https://pdm.fming.dev/). To get started clone your fork of [the main repo](https://github.com/mllam/mllam-data-prep) locally:
+
+```bash
+git clone https://github.com/<your-github-username>/mllam-data-prep
 cd mllam-data-prep
-pip install .
 ```
 
+Use pdm to create and use a virtualenv:
+
+```bash
+pdm venv create
+pdm use --venv in-project
+pdm install
+```
+
+All the linting is handelled by `pre-commit` which can be setup to automatically be run on each `git commit` by installing the git commit hook:
+
+```bash
+pdm run pre-commit install
+```
+
+The branch, commit, push and make a pull-request :)
+
+
 ## Usage
 
 The package is designed to be used as a command-line tool. The main command is `mllam-data-prep` which takes a configuration file as input and outputs a training dataset in the form of a `.zarr` dataset named from the config file (e.g. `example.danra.yaml` produces `example.danra.zarr`).