semvec/pypi-packages huggingface dataset:
uxu 0.1.1
Server-side interactive HTML engine.
Requires Python: >=3.9

# uxu

[![PyPI - Version](https://img.shields.io/pypi/v/uxu.svg)](https://pypi.org/project/uxu)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/uxu.svg)](https://pypi.org/project/uxu)

Make little user interfaces without fussing around with JavaScript.

This library is very WIP.

## Installation

```console
pip install uxu
```

## License

`uxu` is distributed under the terms of the [MIT](https://spdx.org/licenses/MIT.html) license.

## Development

To develop the javascript portion of the library, please do

```sh
cd web
# to build
node build.mjs

# to build and watch and live-reload
node server.mjs
```

## Todos

- server: only connect websocket if interactivity is needed
- server: have checksum on patches to make sure state doesn't get messed up.
- server: timeouts on websocket connections
- server: auth
- server: live reloading
- components: implement forms
- components: spinners while waiting for patcher roundtrips
unitn-course-scraper 0.1.6
Interactive scraper for your UniTN courses
Requires Python: >=3.7
Keywords: unitn,courses,scraper,@unitn.it,moodle,dol,gestionecorsi,didatticaonline

# UniTN Courses Scraper

Interactive scraper for your unitn courses.

It allows you to get resources from the courses that you are takeing and the courses you can enroll in. Download everything in an organized way and more.

## Getting started

- Install a fresh version of Python 3.11 to your local machine (with your virtual environment management of choice)
- Install the requirements with `pip install -r requirements.txt`
- Add a copy of the `.env` file to the root of the project (instructions are found in the `.env.example` file)
- Do not use "studenti" inside your username, just @unitn.it
- Run the `start.sh` file to get started
shilver 0.0.1
An outstandingly fast Shingoki solver.
Requires Python: >=3.9
Keywords: answer set programming,puzzles,shingoki

# Shingoki solver

Práctica RCRA 2022-2023

## Setup

```sh
python -m install clingo clingraph
```

## Usage

```sh
# Solve example: clingo 0 solver.lp <examplefile>
clingo 0 solver.lp tests/6x6easy.lp

# Solve and draw solution:
clingo 0 tests/8x8normal.lp solver.lp --outf=2 | clingraph --viz-encoding=viz.lp --engine=neato --out=render --view

# Remove 'solver.lp' for drawing initial state
clingo 0 tests/8x8normal.lp --outf=2 | clingraph --viz-encoding=viz.lp --engine=neato --out=render --view
```
llama-rs-python 0.0.2
Unofficial python bindings for llama-rs. 🐍❤️🦀
Requires Python: >=3.7

# llama-rs-python
Unofficial python bindings for [llama-rs](https://github.com/rustformers/llama-rs) created with [PyO3](https://github.com/PyO3/pyo3). 🐍❤️🦀

This package gives access to the basic functionality of the llama-rs project.

GGML converted models can be loaded and executed.

## Installation

Simply install it via pip: `pip install llama-rs-python`

## Usage

The package is typehinted for easy usage.

A usage example could look like this:

```python 
from llama_rs_python import Model

#load the model
model = Model("path/to/model.bin")

#generate
print(model.generate("The meaning of life is"))
```

The package also supports callbacks to get each token as it is generated.
The callback-function also supports canceling the generation by returning a `True` value from the pytohn side.

```python 
from llama_rs_python import Model
import sys
from typing import Optional

#load the model
model = Model("path/to/model.bin")

#define the callback
def callback(token:str)->Optional[bool]:
    print(token,end="")
    sys.stdout.flush()
    # (return True here to cancel the generation)

#start generation
model.generate("The meaning of life is",callback=callback)
```

The configuration of the generation is handled by the `GenerationConfig` class.

```python 
from llama_rs_python import Model, GenerationConfig

#load the model
model = Model("path/to/model.bin")

#create a config
config = GenerationConfig(top_p=0.9,seed=1441,max_new_tokens=1024)

#generate
print(model.generate("The meaning of life is",generation_config=config))
```

To configure model specific settings the `SessionConfig` class can be used.

```python
from llama_rs_python import Model, SessionConfig

#define the session
session_config = SessionConfig(threads=8,context_length=512)

#load the model
model = Model("path/to/model.bin",session_config=session_config)
```
videonoise 0.3
Videonoise - util for generate a white noise video with audio.
Requires Python: >=3.6

# Videonoise

Videonise is a simple Python script to generate a white noise video with audio.

## Build package

To build the package, run:

```bash
make build
```

## Installation

To install, run:

```bash
pip install videonoise
```
## Usage
To generate a white noise video, run:
```bash
videonoise output.mp4 -w 640 -t 480 -f 30 -d 10
```
dnptools 0.0.4
Python utilities for working with DENOPTIM (https://github.com/denoptim-project/DENOPTIM)
Requires Python: >=3.7

# DENOPTIM's Python tools (dnptools)
Repository for python utilities related with DENOPTIM: the software for De Novo OPTimization of In/organic Molecules (see https://github.com/denoptim-project/DENOPTIM).

## Tools
Here is an overview of the tools included here:
### Socket server running a scoring service
`scoringservice` run a socket server that provides scores to DENOPTIM's <a href="https://github.com/denoptim-project/DENOPTIM/blob/93a58661c9b4a7b71393c32986d55e008fa36f85/src/main/java/denoptim/fitness/descriptors/SocketProvidedDescriptor.java">SocketProvidedDescriptor</a>. The sever allows low-latency communication between DENOPTIM and scoring functions written in python. In particular, it allows to by-pass the overhead needed to startup a python process from within DENOPTIM's <a href="https://denoptim-project.github.io/DENOPTIM/#FitnessEvaluation">fitness providers</a>. 
Example of usage:

``` (python)
from dnptools import scoringservice

def scoring_function(json_msg):
    smiles_string = json_msg[scoringservice.JSON_KEY_SMILES]
    score = ...do something to get the score from processing smiles_string...
    return score
    
scoringservice.start(scoring_function, 'localhost', 3863)
```
Note that `localhost` and port number 3863 are just parameters that can be choosen freely, but should be consistent with the settings of any client that wants to communicate with such server. The server is a threading server that can deal with multiple clients, like parallel threads running a fitness providing task each.
When you do not need the scoring service any more, use the following to stop the server:
```
scoringservice.stop('localhost',3863)
```
Logging can be controlled as follows:
```
scoringservice.logger.setLevel(logging.DEBUG)
```

## Install
The package is available on <a href="https://pypi.org/project/dnptools/">pypi</a> and <a href="https://anaconda.org/denoptim-project/dnptools">anaconda</a>, so install it with 
```
pip install dnptools
```
or
```
conda install -c denoptim-project dnptools
```

## License
GNU Affero General Public License v3 or later (AGPLv3+)

## Acknowledgments
The Research Council of Norway is acknowledge for funding.