semvec/pypi-packages huggingface dataset:
flake8-author 2.2.0
Flake8 __author__ checker
Requires Python: >=3.9
Keywords: flake8, __author__

=========================
Flake8 __author__ Checker
=========================

|PyPI Version| |Python Versions|

``flake8-author`` is a `Flake8 <http://flake8.pycqa.org/>`_ extension that
checks Python modules for module-level ``__author__`` attributes.

There is no official standard on ``__author__`` usage. It is used largely by
convention. Guido had this to say `back in 2001`__:

    Anothor source of discomfort is that there's absolutely no standard for
    this kind of meta-data variables.  We've got __version__, and I believe we
    once agreed on that (in 1994 or so :-).  But __author__?  __credits__?
    What next -- __cute_signoff__?

__ https://mail.python.org/pipermail/python-dev/2001-March/013328.html

This extension can therefore be configured to explicitly require or forbid
``__author__`` attributes. By default, ``__author__`` is considered optional.

If the ``__author__`` attribute is allowed, its value can also be validated
using a configurable regular expression pattern.

The ``__author__`` attribute is generally assigned to a string, but multiple
authors can be listed using a list (or tuple or set) of strings.

.. |PyPI Version| image:: https://img.shields.io/pypi/v/flake8-author.svg
   :target: https://pypi.python.org/pypi/flake8-author
.. |Python Versions| image:: https://img.shields.io/pypi/pyversions/flake8-author.svg
   :target: https://pypi.python.org/pypi/flake8-author


Installation
------------

Install from PyPI using ``pip``:

.. code-block:: sh

    $ pip install flake8-author

The extension will be activated automatically by ``flake8``. You can verify
that it has been loaded by inspecting the ``flake8 --version`` string.

.. code-block:: sh

    $ flake8 --version
    6.1.0 (author: 2.1.0, ...) CPython 3.11.5 on Darwin


Error Codes
-----------

This extension adds three new `error codes`__ (using the ``A40`` prefix):

- ``A400``: a module-level ``__author__`` attribute is required
- ``A401``: ``__author__`` attributes are not allowed
- ``A402``: ``__author__`` attribute value does not match *pattern*

__ http://flake8.pycqa.org/en/latest/user/error-codes.html

Configuration
-------------

Configuration values are specified in the ``[flake8]`` section of your `config
file`_ or as command line arguments (e.g. ``--author-attribute=required``).

- ``author-attribute``: "optional", "required", "forbidden" (default: optional)
- ``author-pattern``: ``__author__`` validation `re`_ pattern (default: ``''``)

.. _config file: http://flake8.pycqa.org/en/latest/user/configuration.html
.. _re: https://docs.python.org/library/re.html
liblinea 2.2.1
The Core Module(s) for Linea Programming Language
Requires Python: >=3.12

# The Linea Core Library

The Linea Programming Language Core Library

## What's new in LibLinea 2.2.1 'Mango'?

* Updated the `liblinea_ai` module to include more features [UNSTABLE].

## What was new in LibLinea 2.2.0 'Mango'?

* Introduced new dataframe support.
* Introduced new data handling features with `liblinea_data` module [UNSTABLE].
* Introduced new AI/ML features with `liblinea_ai` module [UNSTABLE].

## What was new in Linea 2.1.0 'Coconut'?

* Revaped functions.
* New syntax for functions and methods.
* Included network module `liblinea_network`.
py-ccm15 0.1.2
A package to control Midea CCM15 data converter modules
Requires Python: >=3.7
Keywords: Midea, CCM15, data converter

# py-ccm15

Python Library to access a Midea CCM15 data converter

This package provides an asynchronous interface to communicate with Midea CCM15 data converter modules. It allows you to control and monitor air conditioning units via the CCM15 gateway using Python.

## Features

- Read and set temperature
- Control fan mode and AC mode
- Async support for non-blocking operations
- Communicate with CCM15 over HTTP

## Installation

```bash
pip install py-ccm15
```

## Usage

```python
import asyncio
from py_ccm15 import CCM15Client

async def main():
    client = CCM15Client(host="192.168.1.100", token="your_token_here")
    status = await client.get_status()
    print(status)

    await client.set_state(ac_mode="cool", fan_mode="auto", temperature=24)

asyncio.run(main())
```

## Requirements

- Python 3.7+
- httpx>=0.24.1
- xmltodict>=0.13.0
- aiohttp>=3.8.5

## Documentation

For full API reference and advanced usage, visit the [GitHub repository](https://github.com/ocalvo/py-ccm15).

## Contributing

Pull requests are welcome. If you find a bug or have a feature request, feel free to open an issue.

## License

This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.

Made by [Oscar Calvo](https://github.com/ocalvo)
ordinalcorr 0.7.0
correlation coefficients for ordinal-scaled variables
Requires Python: >=3.10
Keywords: correlation, discrete, ordinal, statistics

# ordinalcorr: correlation coefficients for ordinal variables

[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/ordinalcorr)](https://pypi.org/project/ordinalcorr/)
[![PyPI version](https://img.shields.io/pypi/v/ordinalcorr.svg)](https://pypi.org/project/ordinalcorr/)
[![License](https://img.shields.io/pypi/l/ordinalcorr)](https://github.com/nigimitama/ordinalcorr/blob/main/LICENSE)

`ordinalcorr` is a Python package designed to compute correlation coefficients tailored for ordinal-scale data (e.g., Likert items).
It supports polychoric correlation coefficients and other coefficients for ordinal data.

## ✨ Features

### 1️⃣ Correlation Coefficients

This package provides several correlation coefficients (e.g. Polyserial and Polychoric)

| Variable X            | Variable Y            | Method                 | Function     |
| --------------------- | --------------------- | ---------------------- | ------------ |
| continuous            | ordinal (discretized) | Polyserial correlation | `polyserial` |
| ordinal (discretized) | ordinal (discretized) | Polychoric correlation | `polychoric` |

Here is an example:

```python
>>> from ordinalcorr import polychoric
>>> x = [1, 1, 2, 2, 3, 3]
>>> y = [0, 0, 0, 1, 1, 1]
>>> polychoric(x, y)
0.9986287922233864
```

### 2️⃣ Heterogeneous Correlation Matrix

A function for computing the _heterogeneous correlation matrix_—a correlation matrix that includes both continuous and ordinal variables—is also provided.

```python
>>> from ordinalcorr import hetcor
>>> import pandas as pd
>>> data = pd.DataFrame({
...     "continuous": [0.1, 0.1, 0.2, 0.2, 0.3, 0.3],
...     "dichotomous": [0, 0, 0, 1, 1, 1],
...     "polytomous": [1, 1, 3, 3, 2, 2],
... })
>>> hetcor(data)
             continuous  dichotomous  polytomous
continuous     1.000000     0.989335    0.514870
dichotomous    0.989335     1.000000    0.549231
polytomous     0.514870     0.549231    1.000000
```

## 📦 Installation

ordinalcorr is available at the [PyPI](https://pypi.org/project/ordinalcorr/)

```bash
pip install ordinalcorr
```

### Requirements

- Python 3.10 or later
- Dependencies:
  - numpy >= 1.23.0
  - scipy >= 1.8.0

## 📒 Documentation

[ordinalcorr documentation](https://nigimitama.github.io/ordinalcorr/index.html)

## ⚖️ License

This project is licensed under the MIT License. See the [LICENSE](./LICENSE) file for details.
mozapkpublisher 11.0.0
Scripts to get and push Firefox for Android to Google Play Store
Requires Python: >=3.11

# MozApkPublisher

Scripts to publish Firefox for Android on Google Play Store.

## Setup and run

1. :warning: You need Python >= 3.6 to run this set of scripts. Python 2 isn't supported starting version 0.5.0. Python 3.5 was removed in version 3.0.0.
1. `uv venv`
1. `uv pip install -e .`
1. If using push_aab.py, download `bundletool` from https://github.com/google/bundletool/releases and set environment variable `BUNDLETOOL_PATH=path/to/bundletool.jar`
1. Execute either `mozapkpublisher/push_apk.py`, or `mozapkpublisher/push_aab.py`, or `mozapkpublisher/update_apk_description.py`
1. Run `--help` to each of these script to know how to call them.

### Running tests

1. `uv tool install tox --with tox-uv`
1. `uv tool run tox -e py39`

### Preparing a release

1. `uv tool run hatch build`

## What to do when pushapk_scriptworker doesn't work?

> A guide to manually publish APKs onto Google Play Store

1. Generate a Google Play Store json certificate. This certificate needs to have write access to the app you want to publish. In this context, "app" means Fennec, Fennec Beta or Fennec Nightly.
1. Execute the steps defined in the section above.
1. Download the latest [signed builds](https://treeherder.mozilla.org/jobs?repo=mozilla-central&searchStr=signing-bundle-fenix-nightly)
1. 
```sh
uv run python ./mozapkpublisher/push_apk.py --no-gp-string-update --track beta --credentials /path/to/your/googleplay/creds.json x86.apk arm.apk
```

  * Note `beta` track on Google Play, that's our way to show to people on Play Store that it's not a finished product. We don't use the "production" track for Nightly, unlike beta and release.
1. If all goes well, add `--commit` to the command line and rerun it.
LN-Studio 1.1.1
LN-Studio: QT based GUI for livenodes projects.

[![Format and Test](https://github.com/pyLiveNodes/LN-Studio/actions/workflows/format_test.yml/badge.svg)](https://github.com/pyLiveNodes/LN-Studio/actions/workflows/format_test.yml)
[![Publish](https://github.com/pyLiveNodes/LN-Studio/actions/workflows/publish.yml/badge.svg)](https://github.com/pyLiveNodes/LN-Studio/actions/workflows/publish.yml)

# LN-Studio

LN-Studio is a GUI Application to create, run and debug [LiveNode graphs](https://livenodes.pages.csl.uni-bremen.de/livenodes/).
It enables live sensor recording, processing and machine learning for interactive low-latency research applications.

Livenodes are small units of computation for digital signal processing in python. They are connected multiple synced channels to create complex graphs for real-time applications. Each node may provide a GUI or Graph for live interaction and visualization.

Any contribution is welcome! These projects take more time than I can muster, so feel free to create issues for everything that you think might work better and feel free to create a MR for them as well!

Have fun and good coding!

Yale

## Citation

If you use LN-Studio in your research, please cite it as follows:

As of 2024 there is no dedicated paper to LiveNodes yet. I'm working on it. But for now, please cite the following paper:
```
@inproceedings{hartmann2022demo,
  title = {Interactive and Interpretable Online Human Activity Recognition},
  author = {Hartmann, Yale and Liu, Hui and Schultz, Tanja},
  booktitle = {PERCOM 2022 - 20th IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops)},
  year = {2022},
  pages = {109--111},
  doi = {10.1109/PerComWorkshops53856.2022.9767207},
  url = {https://www.csl.uni-bremen.de/cms/images/documents/publications/HartmannLiuSchultz_PERCOM2022.pdf},
}
```

## Quickstart

I recommend basing your code on the [example project repo](https://gitlab.csl.uni-bremen.de/livenodes/example-project) and adjusting what you need. The project also includes a guide on how to setup LN-Studio.

To install LN-Studio:
1. Install LN-Studio via pip (or conda if you like): `pip install ln_studio `.
2. Run `ln_studio` or `lns` in your terminal to start the application.
3. Select your livenodes folder (or create a new one).
4. Have fun!

For Development:
1. install LN-Studio via pip (or conda if you like): `pip install -e . `.

## Migration from 0.9.4

Moving from 0.9 to 0.10 includes refactoring of the project structure. The following steps are necessary to migrate your project:
In your **project folder (the one where ln_studio_state.json is located)**, run `ln_studio_migrate` to migrate your project to the new structure.`

### Docs

You can find the docs [here](https://livenodes.pages.csl.uni-bremen.de/LN-Studio/index.html).

### Restrictions

None, I switched the conda forge PyQtAds bindings to the [pure python implementation](https://github.com/klauer/qtpydocking/tree/master) of Ken Lauer so that we can use ln_studio with pure pip.
CatastroInspireDownloader 0.3.1
A CLI tool to download data from Spanish Cadastre Atom Service
Requires Python: >=3.9

# Catastro Inspire Downloader

Herramienta para descargar datos del servicio ATOM [Inspire de Catastro](http://www.catastro.minhap.es/webinspire/index.html) y convertirlo en [GeoPackage](https://www.geopackage.org/).

> Necesita disponer de GDAL >= 2.3.3. y Python 3

## Usar la CLI

Para instalar:

```bash
pip install CatastroInspireDownloader
```

Para ejecutar:

```bash
$ cidownloader --help

Usage: cidownloader [OPTIONS]

  Catastro Inspire Downloader.

  Utilidad que permite descargar cartografía del  servicio inspire de la
  Dirección General de Catastro.

  Genera un fichero GeoPackage.

Options:
  -p, --provincia INTEGER         Código Gerencia Catastro. Si no se indica descarga todas las provincias.
  -m, --municipio INTEGER         Código Municipio Catastro. Si no se indica descarga todos los municipios.
  --srs INTEGER                   Código EPSG final. Si no se indica, se mantendrá el de origen.
  --tipo [all|parcels|buildings|addresses] Tipo Cartografía a descargar. Default: 'all'
  --filename TEXT                 Nombre Geopackage sin la extensión
  -s, --separar_salida            Separar salida a un GeoPackage por Provincia
  --version                       Show the version and exit.
  --help                          Show this message and exit.
```

Si se ejecuta sin opciones descarga todos los municipios de España y genera un GeoPackage llamado `buildings.gpkg` de unos 25 Gb.

Un ejemplo de uso sería

```bash
cidownloader -p 36 -m 62 --srs 25829
```

## Generar MVTs desde el GeoPackage

Se puede generar un fichero mbtiles de [Mapbox Vector Tiles](https://docs.mapbox.com/vector-tiles/reference/) con las construcciones de catastro:

```bash
ogr2ogr -f "MVT" -dsco MINZOOM=14 -dsco MAXZOOM=16 -lco "NAME=buildingparts" -sql "SELECT localID AS id, substr(localid, 0, 15) as parcel, numberOfFloorsAboveGround AS floors, geom FROM BuildingPart WHERE numberOfFloorsAboveGround > 0" buildings.mbtiles buildings.gpkg
```


## Build

```bash
python3 -m pip install --upgrade build
python3 -m build
```

## Publish

```bash
python3 -m pip install --upgrade twine
python3 -m twine upload dist/*
```