semvec/pypi-packages huggingface dataset:
lib-toggl 0.2.4
Asynchronous Python library for the Toggl API.
Requires Python: <4.0,>=3.13

# `lib-toggl`

`lib-toggl` is a Python library for interacting with the [Toggl](https://developers.track.toggl.com/docs/api) API.
It exists because I figured it'd be easier to start fresh instead of update [TogglPy](https://github.com/matthewdowney/TogglPy) to use the new `v9` API and the other changes needed for better Home Assistant integration.

> [!NOTE]
> `lib-toggl` is currently in a very early stage of development and is meant primarily for use in with [`ha-toggl-track`](https://github.com/kquinsland/ha-toggl-track).
> There are other, better Python based API clients for toggl track.
> You should probably use one of those instead.

**Seriously, the number of `#TODO:` comments in this code is ridiculous.**

As this library is mostly meant for use with Home Assistant, it's written to be compatible with Python 3.11+ and is mostly `async` based with strong-ish typing.
It is _absolutely_ not meant to be a complete implementation of the Toggl API; it does just enough for[`ha-toggl-track`](https://github.com/kquinsland/ha-toggl-track) and that's about it.

- [`lib-toggl`](#lib-toggl)
  - [Using](#using)
  - [Dev](#dev)
    - [pre-commit](#pre-commit)
    - [`doctoc`](#doctoc)
  - [TODO](#todo)

## Using

More formal docs [to be added](#todo).
Tests are also needed!

For now, see the `scripts` directory for a quick example of how to use the library.

## Dev

I use VSCode for development so there's a [`.vscode`](./.vscode) directory with some settings that I use.
Most of the extensions and configuration directives there should be easy to port to your preferred editor / IDE as needed.

Pretty simple setup; it's all [`uv`](https://github.com/astral-sh/uv) driven...

Create a virtual environment for the project.

```shell
❯ uv venv --python 3.13
Using CPython 3.13.0 interpreter at: /opt/homebrew/opt/python@3.13/bin/python3.13
Creating virtual environment at: .venv
Activate with: source .venv/bin/activate
❯ source .venv/bin/activate
```

Use [autoenv](https://github.com/Tarrasch/zsh-autoenv) to automatically activate the virtual environment when you enter the project directory.

```shell
❯ cd TogglPy
Switching virtualenv: .venv [🐍Python 3.11.6]
❯ which python
/home/karl/projects/ha-dev/TogglPy/.venv/bin/python
```

Don't forget to [install `pre-commit` hooks.](https://pre-commit.com/)

### pre-commit

Assuming you've [installed the `pre-commit` tool](https://pre-commit.com/#install), just run the following to install the git hooks:

```shell
❯ pre-commit install
pre-commit installed at .git/hooks/pre-commit
```

### `doctoc`

The table of contents in `readme.md` is updated with [`doctoc`](https://github.com/thlorenz/doctoc)

```shell
❯ docker run --rm -v "$(pwd)":/app peterdavehello/npm-doctoc doctoc /app/README.md
<...>
```

## TODO

- [ ] pre-commit hooks
- [ ] Tests
- [ ] CI/CD/GHA automation for testing and pypi release
- [ ] Devcontainers (for VSCode) so the dev environment is more portable / easier to set up
excelsior-fast 0.12.0
Blazingly fast append to xlsx file
Requires Python: >=3.10
Keywords: excel, xlsx, rust, append

# excelsior
[![Rust](https://github.com/krakotay/excelsior/actions/workflows/rust.yml/badge.svg)](https://github.com/krakotay/excelsior/actions/workflows/rust.yml)
[![Build maturin wheels](https://github.com/krakotay/excelsior/actions/workflows/release.yml/badge.svg?branch=master)](https://github.com/krakotay/excelsior/actions/workflows/release.yml)

A project for fast and predictable `.xlsx` editing from Rust and Python.
It consists of two crates:

* **rust-core** – the core library that works directly with spreadsheet XML.
* **python-bindings** – Python wrapper built with `pyo3` and `maturin`.

The library supports:

* opening existing workbooks,
* creating an empty workbook from scratch,
* editing cells/tables/styles/column widths,
* saving changes back to disk without loading the full workbook model.

For detailed usage examples see [docs/usage.md](docs/usage.md).
qoco 0.2.3
QOCO: Quadratic Objective Conic Optimizer
Requires Python: >=3.8

# QOCO Python
<p align="center">
  <img src="https://github.com/user-attachments/assets/7bd44fa7-d198-4739-bb79-a5c15e04a8de" alt="drawing" width="500"/>
</p>

<p align="center">
  <a href=https://github.com/qoco-org/qoco-python/actions/workflows/unit_tests.yml/badge.svg"><img src="https://github.com/qoco-org/qoco-python/actions/workflows/unit_tests.yml/badge.svg"/></a>
  <a href="https://img.shields.io/pypi/dm/qoco.svg?label=Pypi%20downloads"><img src="https://img.shields.io/pypi/dm/qoco.svg?label=Pypi%20downloads" alt="PyPI Downloads" /></a>
  <a href="https://arxiv.org/abs/2503.12658"><img src="http://img.shields.io/badge/arXiv-2503.12658-B31B1B.svg"/></a>
  <a href="https://qoco-org.github.io/qoco/"><img src="https://img.shields.io/badge/docs-online-brightgreen?logo=read-the-docs&style=flat" alt="Documentation" /></a>
  <a href="https://opensource.org/licenses/BSD-3-Clause"><img src="https://img.shields.io/badge/License-BSD_3--Clause-green.svg" alt="License" /></a>
</p>

This repository contains the python wrapper for [QOCO](https://github.com/qoco-org/qoco).


## Installation and Usage

You can install `qoco-python` by running `pip install qoco`.

For instructions on using the wrapper for QOCO, refer to [API](https://qoco-org.github.io/qoco/api/matlab.html#matlab-interface), and [simple example](https://qoco-org.github.io/qoco/examples/simple_example.html#simple-example) for an example of solving a simple SOCP with the python wrapper.

## Tests
To run tests, first install cvxpy and pytest
```bash
pip install cvxpy pytest
```

and execute:

```bash
pytest
```

## Bug reports

File any issues or bug reports using the [issue tracker](https://github.com/qoco-org/qoco-python/issues).

## Citing
```
@misc{chari2025qoco,
  title         = {QOCO: A Quadratic Objective Conic Optimizer with Custom Solver Generation},
  author        = {Chari, Govind M and A{\c{c}}{\i}kme{\c{s}}e, Beh{\c{c}}et},
  year          = {2025},
  eprint        = {2503.12658},
  archiveprefix = {arXiv},
  primaryclass  = {math.OC},
  url           = {https://arxiv.org/abs/2503.12658}
}
```

## License
QOCO is licensed under the BSD-3-Clause license.
pcodec 1.0.1
Good compression for numerical sequences
Keywords: compression, numerical

<div style="text-align:center">
  <img alt="Pco logo: a pico-scale, compressed version of the Pyramid of Khafre in the palm of your hand" src="https://raw.githubusercontent.com/pcodec/pcodec/cac902e714077426d915f4fc397508b187c72380/images/logo.svg" width="160px">
</div>

[![pypi.org][pypi-badge]][pypi-url]

[pypi-badge]: https://img.shields.io/pypi/v/pcodec.svg

[pypi-url]: https://pypi.org/project/pcodec/

# Pcodec Python API

Pcodec is a codec for numerical sequences. Example usage:

```python
>>> from pcodec import standalone, ChunkConfig
>>> import numpy as np
>>> 
>>> np.random.seed(0)
>>> nums = np.random.normal(size=1000000)
>>> 
>>> # compress
>>> compressed = standalone.simple_compress(nums, ChunkConfig())
>>> print(f'compressed to {len(compressed)} bytes')
compressed to 6946280 bytes
>>> 
>>> # decompress
>>> recovered = standalone.simple_decompress(compressed)
>>> 
>>> np.testing.assert_array_equal(recovered, nums)

```

For pcodec's uses, design, and benchmarks, [see the main repo](https://github.com/pcodec/pcodec).

Documentation is served
[on readthedocs](https://pcodec.readthedocs.io/en/latest/).
It's sometimes incomplete, but the types and functions largely mirror those of
the better-documented Rust API.
cfde-kc-fetch 0.4.0
Dataset fetcher for the CFDE Knowledge Center API
Requires Python: >=3.9

# CFDE Knowledge Center Dataset Fetcher

A small CLI and library for downloading CFDE Knowledge Center single-cell artifacts and querying gene-level log-normalized expression.

This repository contains a brief README and a `docs/` directory with usage instructions, examples, and development notes. If you need detailed guidance, see `docs/usage.md` and `docs/examples.md`.

Quick links:

- Full usage: `docs/usage.md`
- Short examples: `docs/examples.md`
- API docs (Swagger UI): <https://cfde.hugeampkpnbi.org/docs>

Install from PyPI:

```bash
pip install cfde-kc-fetch
```

If you spot missing information or a confusing message in the tool, please open an issue with a minimal reproduction and the command you ran.

## Development & Testing

To run tests locally:

```bash
pip install -e ".[dev]"
pytest tests/
```

Project layout (essential):

- `cfde_kc_fetch/` — package code
- `tests/` — unit tests

## References

- CFDE Knowledge Center: <https://cfdeknowledge.org>
- API docs (Swagger UI): <https://cfde.hugeampkpnbi.org/docs>
- Single Cell Browser: <https://cfdeknowledge.org/single-cell/>

## License

MIT License - See LICENSE file for details.

## Contributing

Contributions are welcome! Please:

1. Fork the repository
2. Create a feature branch
3. Add tests for new functionality
4. Submit a pull request

## Citation

If you use this tool in your research, please cite:

```bibtex
@software{cfde_kc_fetch,
  title = {CFDE Knowledge Center Dataset Fetcher},
  author = {Zeyu Yao},
  year = {2026},
  url = {https://github.com/cytronicoder/cfde-kc-fetch}
}
```