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Jammy2211 merged 1 commit into from
Jul 25, 2026
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chore: normalise all tracked text files to LF + enforce via .gitattributes #1419

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7 changes: 5 additions & 2 deletions .gitattributes
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# Shebang'd, directly-runnable module — keep LF so execution works on HPC/Linux.
autofit/aggregator/aggregator.py eol=lf
# All files use Unix line endings (LF) — see AGENTS.md. text=auto lets git
# detect binaries; eol=lf normalises every text file on checkout and checkin,
# subsuming the earlier targeted shebang/script rules. Tree-wide CRLF
# normalisation landed alongside this file (2026-07-25).
* text=auto eol=lf
614 changes: 307 additions & 307 deletions CODE_OF_CONDUCT.md
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254 changes: 127 additions & 127 deletions CONTRIBUTING.md
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# AI-Assisted Development
This project uses an AI-first development workflow. Most features, bug fixes, and improvements are implemented through AI coding agents (Claude Code, GitHub Copilot, OpenAI Codex) working from structured issue descriptions.
## How It Works
1. **Issues are the starting point** — Every task begins as a GitHub issue with a structured format: an overview, a human-readable plan, a detailed implementation plan (in a collapsible block), and optionally the original prompt that generated the issue.
2. **AI agents pick up issues** — Issues can be assigned to AI coding agents (e.g. GitHub Copilot) which read the issue description, `AGENTS.md`, and `CLAUDE.md` for context, then implement the changes autonomously.
3. **Human review** — All AI-generated pull requests are reviewed by maintainers before merging.
## Maintainer Workflow
Maintainer-driven dev work starts as a prompt file in
**[PyAutoPrompt](https://github.com/PyAutoLabs/PyAutoPrompt)** — the public
workflow repo that hosts the PyAuto task registry and the prompt-coupled
Claude Code skills. The pipeline:
1. Write the task as `PyAutoPrompt/<category>/<name>.md` (free-form markdown
describing what to do, with `@RepoName/path/to/file.py` references).
2. `/start_dev <category>/<name>.md` — reads the prompt, audits the code,
drafts the GitHub issue you see in this repo, and files it.
3. `/start_library` or `/start_workspace` — opens a feature worktree under
`~/Code/PyAutoLabs-wt/<task-name>/`.
4. `/ship_library` / `/ship_workspace` — runs tests, opens the PR, and
tracks state in `PyAutoPrompt/active.md`.
External contributors don't need PyAutoPrompt access — open an issue using
the templates in this repo and the same machinery handles it on our end.
## Creating an Issue
When opening an issue, please use the provided issue templates. The **Feature / Task Request** template follows our standard format:
- **Overview** — What and why, in 2-4 sentences
- **Plan** — High-level bullet points (human-readable)
- **Detailed implementation plan** — File paths, steps, key files (in a collapsible block)
- **Original Prompt** — If you used an AI to help draft the issue, include the original prompt
If your feature involves a specific calculation, algorithm, or small piece of functionality — **include example code**. Even a rough script, a working prototype, or a snippet showing the existing behaviour you want to change makes a huge difference. Code examples give AI agents and human contributors concrete context to work from, and dramatically reduce misunderstandings about what you're asking for.
This structure ensures that both human contributors and AI agents can understand and act on the issue effectively.
## Contributing Without AI
Traditional contributions are equally welcome! If you prefer to work without AI tools, simply follow the development setup and pull request guidelines below. The issue templates are helpful for any contributor, AI or human.
---
# Contributing
Contributions are welcome and greatly appreciated!
## Types of Contributions
### Report Bugs
Report bugs at https://github.com/PyAutoLabs/PyAutoFit/issues
If you are playing with the PyAutoFit library and find a bug, please
reporting it including:
* Your operating system name and version.
* Any details about your Python environment.
* Detailed steps to reproduce the bug.
### Propose New `NonLinearSearch` or Features
The best way to send feedback is to open an issue at
https://github.com/PyAutoLabs/PyAutoFit/issues
with tag *enhancement*.
If you are proposing a new `NonLinearSearch` or a new feature:
* Explain in detail how it should work.
* Keep the scope as narrow as possible, to make it easier to implement.
### Implement `NonLinearSearch` or Features
Look through the Git issues for operator or feature requests.
Anything tagged with *enhancement* is open to whoever wants to
implement it.
### Add Examples or improve Documentation
Writing new features is not the only way to get involved and
contribute. Create examples with existing non-linear searches as well
as improving the documentation of existing operators is as important
as making new non-linear searches and very much encouraged.
## Getting Started to contribute
Ready to contribute?
1. Follow the installation instructions for installing **PyAutoFit** from source root on our
[readthedocs](https://pyautofit.readthedocs.io/en/latest/general/installation.html#forking-cloning>).
2. Create a branch for local development:
```
git checkout -b name-of-your-branch
```
Now you can make your changes locally.
3. When you're done making changes, check that old and new tests pass
succesfully:
```
cd PyAutoFit/test_autofit
python3 -m pytest
```
4. Commit your changes and push your branch to GitLab::
```
git add .
git commit -m "Your detailed description of your changes."
git push origin name-of-your-branch
```
Remember to add ``-u`` when pushing the branch for the first time.
5. Submit a pull request through the GitHub website.
### Pull Request Guidelines
Before you submit a pull request, check that it meets these guidelines:
1. The pull request should include new tests for all the core routines that have been developed.
2. If the pull request adds functionality, the docs should be updated accordingly.
# AI-Assisted Development

This project uses an AI-first development workflow. Most features, bug fixes, and improvements are implemented through AI coding agents (Claude Code, GitHub Copilot, OpenAI Codex) working from structured issue descriptions.

## How It Works

1. **Issues are the starting point** — Every task begins as a GitHub issue with a structured format: an overview, a human-readable plan, a detailed implementation plan (in a collapsible block), and optionally the original prompt that generated the issue.

2. **AI agents pick up issues** — Issues can be assigned to AI coding agents (e.g. GitHub Copilot) which read the issue description, `AGENTS.md`, and `CLAUDE.md` for context, then implement the changes autonomously.

3. **Human review** — All AI-generated pull requests are reviewed by maintainers before merging.

## Maintainer Workflow

Maintainer-driven dev work starts as a prompt file in
**[PyAutoPrompt](https://github.com/PyAutoLabs/PyAutoPrompt)** — the public
workflow repo that hosts the PyAuto task registry and the prompt-coupled
Claude Code skills. The pipeline:

1. Write the task as `PyAutoPrompt/<category>/<name>.md` (free-form markdown
describing what to do, with `@RepoName/path/to/file.py` references).
2. `/start_dev <category>/<name>.md` — reads the prompt, audits the code,
drafts the GitHub issue you see in this repo, and files it.
3. `/start_library` or `/start_workspace` — opens a feature worktree under
`~/Code/PyAutoLabs-wt/<task-name>/`.
4. `/ship_library` / `/ship_workspace` — runs tests, opens the PR, and
tracks state in `PyAutoPrompt/active.md`.

External contributors don't need PyAutoPrompt access — open an issue using
the templates in this repo and the same machinery handles it on our end.

## Creating an Issue

When opening an issue, please use the provided issue templates. The **Feature / Task Request** template follows our standard format:

- **Overview** — What and why, in 2-4 sentences
- **Plan** — High-level bullet points (human-readable)
- **Detailed implementation plan** — File paths, steps, key files (in a collapsible block)
- **Original Prompt** — If you used an AI to help draft the issue, include the original prompt

If your feature involves a specific calculation, algorithm, or small piece of functionality — **include example code**. Even a rough script, a working prototype, or a snippet showing the existing behaviour you want to change makes a huge difference. Code examples give AI agents and human contributors concrete context to work from, and dramatically reduce misunderstandings about what you're asking for.

This structure ensures that both human contributors and AI agents can understand and act on the issue effectively.

## Contributing Without AI

Traditional contributions are equally welcome! If you prefer to work without AI tools, simply follow the development setup and pull request guidelines below. The issue templates are helpful for any contributor, AI or human.

---

# Contributing

Contributions are welcome and greatly appreciated!

## Types of Contributions

### Report Bugs

Report bugs at https://github.com/PyAutoLabs/PyAutoFit/issues

If you are playing with the PyAutoFit library and find a bug, please
reporting it including:

* Your operating system name and version.
* Any details about your Python environment.
* Detailed steps to reproduce the bug.

### Propose New `NonLinearSearch` or Features

The best way to send feedback is to open an issue at
https://github.com/PyAutoLabs/PyAutoFit/issues
with tag *enhancement*.

If you are proposing a new `NonLinearSearch` or a new feature:

* Explain in detail how it should work.
* Keep the scope as narrow as possible, to make it easier to implement.

### Implement `NonLinearSearch` or Features
Look through the Git issues for operator or feature requests.
Anything tagged with *enhancement* is open to whoever wants to
implement it.

### Add Examples or improve Documentation
Writing new features is not the only way to get involved and
contribute. Create examples with existing non-linear searches as well
as improving the documentation of existing operators is as important
as making new non-linear searches and very much encouraged.


## Getting Started to contribute

Ready to contribute?

1. Follow the installation instructions for installing **PyAutoFit** from source root on our
[readthedocs](https://pyautofit.readthedocs.io/en/latest/general/installation.html#forking-cloning>).

2. Create a branch for local development:
```
git checkout -b name-of-your-branch
```
Now you can make your changes locally.

3. When you're done making changes, check that old and new tests pass
succesfully:
```
cd PyAutoFit/test_autofit
python3 -m pytest
```

4. Commit your changes and push your branch to GitLab::
```
git add .
git commit -m "Your detailed description of your changes."
git push origin name-of-your-branch
```
Remember to add ``-u`` when pushing the branch for the first time.

5. Submit a pull request through the GitHub website.


### Pull Request Guidelines

Before you submit a pull request, check that it meets these guidelines:

1. The pull request should include new tests for all the core routines that have been developed.
2. If the pull request adds functionality, the docs should be updated accordingly.
42 changes: 21 additions & 21 deletions LICENSE
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MIT License
Copyright (c) 2018 Richard Hayes
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
MIT License

Copyright (c) 2018 Richard Hayes

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
42 changes: 21 additions & 21 deletions MANIFEST.in
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# MANIFEST.in
exclude .gitignore
include README.md
include setup.cfg
include CITATIONS.md
include LICENSE
include requirements.txt
include optional_requirements.txt
prune .cache
prune .git
prune build
prune dist
recursive-exclude *.egg-info *
recursive-include autofit/config *
exclude docs
global-exclude test_autofit
# MANIFEST.in
exclude .gitignore
include README.md
include setup.cfg
include CITATIONS.md
include LICENSE
include requirements.txt
include optional_requirements.txt

prune .cache
prune .git
prune build
prune dist

recursive-exclude *.egg-info *

recursive-include autofit/config *

exclude docs

global-exclude test_autofit
recursive-exclude test_autofit *
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