semvec/pypi-packages huggingface dataset:
poolcontrolpy 0.2.16
Package for accessing nodejs-poolController
License: MIT
Requires Python: >=3.9,<4.0

# poolcontrolpy

Package for accessing nodejs-poolController for use with a planned Home Assistant integration.

## Installation

```bash
$ pip install poolcontrolpy
```

## Usage

Configure pool controller and confirm successful connection.

```python
import asyncio
import aiohttp

from poolcontrolpy.poolcontrolpy import Controller


async def checkconnection():

    async with aiohttp.ClientSession() as client:
        controller = Controller(client, "10.0.20.13", 4200)
        return await controller.checkconnect()

resp = asyncio.run(checkconnection())
print(resp)
```

*Note: This example uses 'asyncio.run' so it cannot be called when another asyncio event loop is running in the same thread.*

## Contributing

Interested in contributing? Check out the contributing guidelines. Please note that this project is released with a Code of Conduct. By contributing to this project, you agree to abide by its terms.

## License

`poolcontrolpy` was created by Kevin Robinson. It is licensed under the terms of the MIT license.

## Credits

`poolcontrolpy` was created with [`cookiecutter`](https://cookiecutter.readthedocs.io/en/latest/) and the `py-pkgs-cookiecutter` [template](https://github.com/py-pkgs/py-pkgs-cookiecutter).
EuroParl-Amendment-Extract 1.0.2
Extract amendments from European Parliament docx files
License: MIT

EuroParl-Amendment-Extract
===========================

This is a simple script to convert the amendments from the EP to JSON.
It follow the format define in that dataset https://zenodo.org/record/4709248#.YXesJS8itqs for the article War Of Word
https://github.com/indy-lab/war-of-words

## prerequisites

Build the MEPs dataset with the script 
`python3 meps.py`


## Debugger

To run the debugger we simply need to run 'streamlit run diff_visualizer.py', it will run the am labeler on the ep8 dataset and visualize the first error that it comes across. 


## Sequence matcher update

In diff.py there is now a extract_opcodes and extract_opcodes_v2. The v2 takes into account to merge consecutive 'replace' operations as well as 'delete' operations followed by a replace. We also display now at the end an accuracy metric that sort of roughly sketches the am labeler's performance. The accuracy is calculated by penalizing the algorithm for each edit that it gets wrong relative to the size / length of text that was attributed to the edit in question. With this evaluation metric we observed a high accuracy (99+ %) on the ep8 dataset. 


## Usage 

```python
from ep_amendments import get_amendments

get_amendments('file.docx')
```
amendments2json 1.0.1
Extract amendments from European Parliament docx files
License: MIT

Amendments2JSON
===============

This is a simple script to convert the amendments from the EP to JSON.
It follow the format define in that dataset https://zenodo.org/record/4709248#.YXesJS8itqs for the article War Of Word
https://github.com/indy-lab/war-of-words

## prerequisites

Build the MEPs dataset with the script 
`python3 meps.py`


## Debugger

To run the debugger we simply need to run 'streamlit run diff_visualizer.py', it will run the am labeler on the ep8 dataset and visualize the first error that it comes across. 


## Sequence matcher update

In diff.py there is now a extract_opcodes and extract_opcodes_v2. The v2 takes into account to merge consecutive 'replace' operations as well as 'delete' operations followed by a replace. We also display now at the end an accuracy metric that sort of roughly sketches the am labeler's performance. The accuracy is calculated by penalizing the algorithm for each edit that it gets wrong relative to the size / length of text that was attributed to the edit in question. With this evaluation metric we observed a high accuracy (99+ %) on the ep8 dataset.
amendements2json 1.0.0
Extract amendments from European Parliament docx files
License: MIT

Amendments2JSON
===============

This is a simple script to convert the amendments from the EP to JSON.
It follow the format define in that dataset https://zenodo.org/record/4709248#.YXesJS8itqs for the article War Of Word
https://github.com/indy-lab/war-of-words

## prerequisites

Build the MEPs dataset with the script 
`python3 meps.py`


## Debugger

To run the debugger we simply need to run 'streamlit run diff_visualizer.py', it will run the am labeler on the ep8 dataset and visualize the first error that it comes across. 


## Sequence matcher update

In diff.py there is now a extract_opcodes and extract_opcodes_v2. The v2 takes into account to merge consecutive 'replace' operations as well as 'delete' operations followed by a replace. We also display now at the end an accuracy metric that sort of roughly sketches the am labeler's performance. The accuracy is calculated by penalizing the algorithm for each edit that it gets wrong relative to the size / length of text that was attributed to the edit in question. With this evaluation metric we observed a high accuracy (99+ %) on the ep8 dataset.