semvec/pypi-packages huggingface dataset:
yapl-py 0.1.0
Python port of YAPL (Yet Another Prompt Language) renderer.
Requires Python: >=3.8
Keywords: yapl, prompt, templating, llm, renderer

# yapl-py

Python port of YAPL (Yet Another Prompt Language) renderer.

## Installation

- From PyPI (once published):
  pip install yapl-py

- From source (editable):
  pip install -e .

## Usage

See tests in `tests/` for examples of loading and rendering templates programmatically.

Basic example:

```python
from yapl_py import YAPL, YAPLOptions, WhitespaceOptions

yapl = YAPL(YAPLOptions(
    baseDir="/path/to/prompts",
    strictPaths=True,
    whitespace=WhitespaceOptions(trimBlocks=True, lstripBlocks=True, dedentBlocks=True),
))

prompt = yapl.render("prompts/examples/conditional-agent.md.yapl", {
    "user_type": "expert",
    "domain": "TypeScript",
    "include_examples": True,
})
print(prompt.content)
```

## Development

- Run tests:
  pytest -q

- Build the package locally:
  python -m pip install build
  python -m build

Artifacts will be generated under `dist/`.

## Releasing to PyPI

This repository includes a GitHub Actions workflow at `.github/workflows/publish.yml` that builds and publishes the package to PyPI when you push a Git tag that starts with `v` (for example, `v0.1.0`).

Steps:
1) Create a PyPI API token (User settings -> API tokens) with project-wide or scoped permissions.
2) In your GitHub repository settings, add a repository secret named `PYPI_API_TOKEN` and paste the token value.
3) Create and push a tag:
   git tag v0.1.0
   git push origin v0.1.0

The workflow will:
- Check out the repository
- Build sdist and wheel
- Publish to PyPI using `pypa/gh-action-pypi-publish`

## License

Specify your license here, e.g. MIT.
gsemgr 3.6.1
gsemgr tool to communicate with GSE device
Requires Python: >=3.12

# gsemgr

GSE's version of [mcumgr](https://docs.zephyrproject.org/latest/services/device_mgmt/mcumgr.html). Faster and with more functionality:

- Sane multi-core firmware updates.
- SMP extensions.
- Run Python code on the target and check its output.
gse-utils 2.7.0
GSE utility functions
Requires Python: >=3.8

# gse-utils

Common utility functions used by various GSE scripts.

The various packaging operations (adding and checking dependencies, building wheels/sdists and so on) are done with [rye](https://rye.astral.sh/) because it simplifies the workflow. That is not a requirement though, the usual Python tools should be usable without issues.
elva 0.35
A suite of local-first TUI realtime collaboration apps based on CRDTs.
Requires Python: >=3.11
Keywords: real time, real-time, realtime, collaboration, sync, crdt, y-crdt, yjs, yrs, suite, local, local-first, tui, text, terminal

<h1 align="center">
  <img src="./logo/logo.svg" alt="ELVA Logo" width="200"/>
  <p>ELVA</p>
</h1>

<h6 align="center">
    <p style="font-style: italic;">Norwegian: "the river", German: "Evaluation von Lösungen für verteilte Arbeit"</p>
</h6>


# Welcome

This is the source code repository of ELVA, a suite of apps enabling real-time collaboration locally, with CRDTs at its heart ❤️.

- If you want to get started using ELVA, head over to our [user guide](https://elva.mintgruen.tu-berlin.de/developer-guide/user-guide.md).
- You want to run a server? Check out the [server guide](https://elva.mintgruen.tu-berlin.de/developer-guide/server-guide.md).
- To realize your own app, have a look the our [app guide](https://elva.mintgruen.tu-berlin.de/developer-guide/app-guide.md).
- Looking for how to contribute? We have some notes for you in our [developer guide](https://elva.mintgruen.tu-berlin.de/developer-guide/developer-guide.md).

## Licensing

The ELVA source code is dsitributed under the GNU Affero General Public License (AGPL) 3.0, *except* for the `logo/` directory and its contents, which are licensed under the [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/).
wfmeta_darshan 1.0.0
The WFMeta tool to read and aggregate Darshan logs.
Requires Python: >=3.10

# wfmeta-darshan

Python module that, given a directory of Darshan logs, reads them into Log objects and aggregates the modules across independent Darshan log files. It then writes these aggregated module statistics into csv files using Pandas DataFrames, as well as a file describing metadata about the collected files themselves.

## Usage

```
usage: wfmeta_darshan [-h] [-d] input output

positional arguments:
  input        Relative directory containing the darshan logs to parse and
               aggregate.
  output       Relative directory to write the aggregated data.

options:
  -h, --help   show this help message and exit
  -d, --debug  If true, prints additional debug messages during runtime.
```