semvec/pypi-packages huggingface dataset:
acceptlang 0.0.5
A package for parsing Accept-Language tag received from (untrusted) client context.
Requires Python: <4,>=3.7

# Simple package for parsing Accept-Language tag.

This package can be used in the server environment to parse the Accept-Language
header.

Test cases and code copied from Django source code enhanced with typing.

Purpose of this code is to be able to use a battle-tested Accept-Language
parsing logic in Python code without need to install Django package.

## Installation

You can install the package via pip:

```sh
pip install acceptlang
```

## Usage

```python
from acceptlang import parse_accept_lang_header

result = parse_accept_lang_header("en-us,en;q=0.5,de;q=0.3")
assert result == (('en-us', 1.0), ('en',0.5), ('de',0.3))

result = parse_accept_lang_header("en-us,*;q=0.5")
assert result == (('en-us', 1.0), ('*',0.5))
```

Check [tests](tests/test_parse.py) for more examples.

# Local development

```sh
python3 -m venv venv
. venv/bin/activate
python3 -m pip install -r requirements.dev.txt
```

# Build and publish

```sh
python3 -m build --wheel
twine check dist/**
twine upload dist/
```
datrool-lib 1.0.5
Common library for the datrool project
Requires Python: >=3.10

# datrool-lib

Common library for the datrool project.


## Installation

Install package from PYPI repository
```sh
python -m pip install datrool-lib
```

## Contains
- Simple templating validator for complex `dict` and `list` stuctures
- Time based task scheduling class

## License

Created by Patrik Katreňák and released under General Public License v3.0.
etrainee-m4-utils 0.2.2
A package containing utilities for classification exercises in module 4 of e-trainee.
Requires Python: >=3.7

# E-TRAINEE_M4_UTILS
A package containing utilities for classification exercises in module 4 of [E-TRAINEE](https://3dgeo-heidelberg.github.io/etrainee/module4/module4.html).

Includes tools for preprocessing gdal-compatible rasters and ingesting them into the classification workflows,
combining classified tiles after deep learning, visualising the process and exporting results. Also included are
Spectral Angle Mapper (SAM) and Spectral Information Divergence (SID) classifiers (implementation based on pysptools).

## Installation
Install using pip:

```
pip install etrainee_m4_utils
```
last-asr 0.0.4
The LAttice-based Speech Transducer (LAST) library
Requires Python: >=3.9
Keywords: speech, jax

# LAttice-based Speech Transducer (LAST)

[![Unittests](https://github.com/google-research/last/actions/workflows/pytest_and_autopublish.yml/badge.svg)](https://github.com/google-research/last/actions/workflows/pytest_and_autopublish.yml)
[![PyPI version](https://badge.fury.io/py/last-asr.svg)](https://badge.fury.io/py/last-asr)

*This is not an officially supported Google product.*

The LAttice-based Speech Transducer (LAST) is a JAX library for building
lattice-based speech transducer models, such as
[GNAT](https://arxiv.org/abs/2205.13674).

To start using LAST, simply run `pip install --upgrade last-asr`. For more
information, check out https://last-asr.readthedocs.io.