Pre 2024 DS4CG updates

.github/workflows/python_package.yml

@@ -12,7 +12,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: ["3.10", "3.11"]
+        python-version: ["3.10", "3.11", "3.12"]
 
     steps:
       - uses: actions/checkout@v3

CHANGELOG.md

@@ -6,13 +6,17 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 You should also add project tags for each release in Github, see [Managing releases in a repository](https://docs.github.com/en/repositories/releasing-projects-on-github/managing-releases-in-a-repository).
 
-## [Unreleased]
+## [2.0.0] - 2024-05-29
 ### Added
 - Added example auto-built Sphinx documentation in the `docs` folder
 - Github workflow for running ruff linter
+- A note about conda dependencies to README
+- A note about using docker containers to README
+- Ruff as a linter for development
 ### Changed
 - All build and packaging switched to use only pyproject.toml
 - Minimum python version changed to 3.10
+- Github workflow checks python versions 3.10, 3.11, 3.12
 ### Removed
 - Removed setup.cfg
 

README.md

@@ -9,7 +9,7 @@ This template draws a lot of inspiration from [Cookiecutter Data Science](https:
 # Getting Started
 ## Installing Dependencies and Packages
 Use these steps for setting up a development environment to install and work with code in this template:
-1) Set up a Python 3 virtual environment using [Conda](https://docs.conda.io/projects/conda/en/latest/user-guide/install/index.html#) or [Virtualenv](https://virtualenv.pypa.io/en/latest/index.html). Read [Python Virtual Environments: A Primer](https://realpython.com/python-virtual-environments-a-primer/#the-virtualenv-project) for details on how to get started with virtual environments and why you need them.
+1) Set up a Python 3 virtual environment using [Conda](https://docs.conda.io/projects/conda/en/latest/user-guide/install/index.html#) or [Virtualenv](https://virtualenv.pypa.io/en/latest/index.html). Read [Python Virtual Environments: A Primer](https://realpython.com/python-virtual-environments-a-primer/#the-virtualenv-project) for details on how to get started with virtual environments and why you need them. For a _really detailed_ explanation, see [An unbiased evaluation of environment management and packaging tools](https://alpopkes.com/posts/python/packaging_tools/). 
 2) Activate your virtual environment.
 
 3) Install the package.
@@ -51,11 +51,17 @@ Collecting numpy
 
 ## Specifying Requirements
 In order for users to install your package and all the libraries it depends on by running `pip install`, you need to provide a `pyproject.toml` file. This has two important sections:
-- `project`: List project metadata and version information and all library requirements/dependencies, including for testing or development environments. This is the main file you will work with and add requirements to.
-- `build-system`: Define the build tool that is used to package and distribute your code. For this project, we use [SetupTools](https://setuptools.pypa.io/en/latest/userguide/quickstart.html), but we also recommend [Poetry](https://python-poetry.org/docs/).
+- `project`: List project metadata and version information and all library requirements/dependencies, including for testing or development environments. This is the main file you will work with and add requirements to. Some dependencies 
+- `build-system`: Define the build tool that is used to package and distribute your code. For this project, we use [SetupTools](https://setuptools.pypa.io/en/latest/userguide/quickstart.html).
 
 If you'd like to learn more about python packaging, refer to [the Python Packaging User Guide](https://packaging.python.org/en/latest/) or [PEP 517](https://peps.python.org/pep-0517/#build-requirements).
 
+### Requirements via conda environment files
+[Anaconda](https://www.anaconda.com/download/) and its bare bones counterpart, [Miniconda](https://docs.anaconda.com/free/miniconda/index.html), are especially useful if your project depends on libraries that are difficult to install in the standard pythonic way, such as [GPU libraries](https://docs.anaconda.com/free/working-with-conda/packages/gpu-packages/). If this is the case, you should also share a [Conda environment file](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#creating-an-environment-file-manually) with your code. The conda virtual environment will need to be created and activated before any `pip install` steps. Installations with conda dependencies are usually a little more complicated, so make sure you include step-by-step instructions in documentation. 
+
+### Containerized applications
+In cases when its important that your software work exactly the same on every operating system or you want to abstract away difficult installation steps for end user, you can consider creating a [Docker container](https://www.docker.com/resources/what-container/). This is often appropriate deploying services in the cloud or providing an application for a tech-savvy person to use on their own. However, it's not necessary for most of our projects. 
+
 
 ## Directory Structure
 So what does each file in this repository do?
@@ -111,7 +117,6 @@ The README, CHANGELOG and docstrings are just as important.
 Read [Real Python's Documenting Python Code: A Complete Guide](https://realpython.com/documenting-python-code/) for more ideas about effectively documenting code. The `.md` files are written using [Markdown](https://www.markdownguide.org/), a handy formatting language that is automatically rendered in Github.
 
 # Tests
-
 Although it's [impossible to generally prove that your code is bug-free](https://en.wikipedia.org/wiki/Undecidable_problem), automated testing is a valuable tool. It provides:
 - Proof that your code works as intended in most common examples and important edge cases
 - Instant feedback on whether changes to the code broke its functionality

src/cdstemplate/word_count.py

@@ -1,5 +1,5 @@
 """An example of an module with functions and a class that can be imported once the package is installed.
-This module provides operations for tokenization and tracking cumulative word counts in a set of docuents.
+This module provides operations for tokenization and tracking cumulative word counts in a set of documents.
 """
 from collections import Counter
 import logging