semvec/pypi-packages huggingface dataset:
voss-taren 1.1.0
Effet Matrix authentique en Python
Requires Python: >=3.6

# Voss-Taren

Effet Matrix authentique en Python avec Tkinter.

## Installation
```bash
pip install voss-taren
```

## Utilisation

Tapez simplement dans votre terminal :
```bash
vosstaren
```

Appuyez sur Echap ou Q pour quitter.
d4k-ms-base 0.4.0
A python package containing utility classes for d4k microservices
Requires Python: >=3.10

# Micorservices Base

## Description

A set of simple utility classes for d4k microservices.

## Installation

```bash
pip install d4k-ms-base
```

## Building package

Use pip to install build and twine. Use the following commands to build

```python3 -m build --sdist --wheel``` 

and upload to pypi.org using the command

```twine upload dist/*```
nolam 1.0.0
Action Model Learning from Noisy Traces: a Probabilistic Approach.
Requires Python: >=3.8
Keywords: action model learning, domain model learning, action model acquisition

# Action Model Learning from Noisy Traces: a Probabilistic Approach
[![License: MIT](https://img.shields.io/badge/License-MIT-green.svg)](https://opensource.org/licenses/MIT)

This repository contains the official code of the Noisy Offline Learning of Action Models ([NOLAM](https://doi.org/10.1609/icaps.v34i1.31493)) algorithm.


### Installation
```
pip install nolam
```

### Example usage
```
from nolam.algorithm.Learner import Learner
noise_rate = 0.1
model = Learner().learn('path/to/domain.pddl', ['path/to/trace0', 'path/to/trace1'], e=noise_rate)
print(model)
```

## Custom domain learning

The NOLAM algorithm can be run for learning from traces with noisy states with an observation noise varying from 0 to 1. 
For running NOLAM on a custom domain, you need to provide an input domain file `'path/to/domain.pddl'`, a 
list of plan trace files `['path/to/trace0', 'path/to/trace1', etc.]`, and the (possibly estimated) observation noise. 
The input planning domain must contain the predicates, object types, and operator signatures. Note NOLAM does not 
yet exploit input knowledge in terms of preconditions and effects, hence providing such input domain knowledge 
does not currently affect the learning process. NOLAM can learn a planning domain from 
plan traces of different environments (e.g. it is possible to learn a planning domain from small environments 
and exploit the learned domain in large environments). 


## Citations
If you find this repository useful, please consider citing the related paper.
```
@article{lamanna2024action,
  title={Lifted Action Models Learning from Partial Traces},
  author={Lamanna, Leonardo and Serafini, Luciano},
  booktitle={Proceedings of the International Conference on Automated Planning and Scheduling},
  volume={34},
  pages={342--350},
  year={2024}
}
```

## License
This project is licensed under the MIT License - see the [LICENSE](/LICENSE) file for details.
score-loader 0.0.1
Python implementation of the score.dev specification, allowing to load workload definition and applying a first pass of evaluation for ${metadata...} placeholders.
Requires Python: >=3.12

# Score.dev Python loader

Python implementation of the [score.dev](https://docs.score.dev/docs/) specification, allowing to load workload definition and applying a first pass of evaluation for `${metadata...}` placeholders.

`${resources...}` placeholders must be evaluated after provisioning of the resources, cannot be done while loading the workload definition.

## Unit tests

`uv run pytest`
django-online-issues 0.6
A reusable Django app for collecting and reporting user-submitted issues (tickets). It provides a view and a form to submit tickets and forwards them to configurable backends like GitLab, GitHub, email, or simply the development console.
Requires Python: >=3.9

# Django Online Issues


[![Test](https://github.com/saxix/django-online-issues/actions/workflows/test.yml/badge.svg)](https://github.com/saxix/django-online-issues/actions/workflows/test.yml)
[![Lint](https://github.com/saxix/django-online-issues/actions/workflows/lint.yml/badge.svg)](https://github.com/saxix/django-online-issues/actions/workflows/lint.yml)
[![codecov](https://codecov.io/github/saxix/django-online-issues/branch/develop/graph/badge.svg?token=3ZmxTFfYra)](https://codecov.io/github/saxix/django-online-issues)
[![Documentation](https://github.com/saxix/django-online-issues/actions/workflows/docs.yml/badge.svg)](https://saxix.github.io/django-online-issues/)
[![Pypi](https://badge.fury.io/py/django-online-issues.svg)](https://badge.fury.io/py/saxix-django-online-issues)


**Django Online Issues** is a reusable Django app for collecting and reporting user-submitted issues (tickets).
It provides a view and a form to submithe development console.

## Features

-   **Easy Integration**: Seamlessly add a ticketing system to your Django project.
-   **Multiple Backends**: Send tickets to various platforms. Built-in backends include:
    -   Azure DevOps
    -   Console (default)
    -   Email
    -   GitLab
    -   GitHub
    -   (Others can be easily added)
-   **Configurable**: Customize the backend and its options through Django's settings.
-   **Extensible**: Create your own custom backends to integrate with any issue-tracking system.
-   **Screenshot Capture**: Users can attach a screenshot of the current page to the ticket.


### Planned (contributions are welcome)

- Jira
- Redmine
- Odoo Helpdesk
- ...

## Screenshot

#### In App popup (with ap screenshot)
<img src="docs/src/images/popup.png" alt="Popup Screenshot" width="600"/>

#### GitLab Ticket
<img src="docs/src/images/gitlab.png" alt="Popup Screenshot" width="600"/>

#### Github Ticket
<img src="docs/src/images/github.png" alt="Popup Screenshot" width="600"/>

#### Azure Ticket
<img src="docs/src/images/azure.png" alt="Popup Screenshot" width="600"/>


## Note on Development Process

This project has been developed with the assistance of the Gemini CLI, serving as a personal experimental exploration into the capabilities of AI in software development. While Gemini aided in various development tasks and execution, the overarching design (patterns, modularisation, naming convention) and core implementation decisions were originated and guided by human intuition and expertise.
lekinpy 0.1.0
A Python library for job shop scheduling compatible with LEKIN and JSON formats.
Requires Python: >=3.9
Keywords: scheduling, job shop, lekin, optimization


# lekinpy

A Python library for job shop scheduling, compatible with LEKIN file formats and JSON. Easily extensible for new algorithms and open source contributions.

## Installation

```bash
pip install lekinpy
```
Or for local development:
```bash
pip install .
```

## Features
- Parse and write `.job`, `.mch`, `.seq`, and `.json` files
- Add jobs and machines programmatically or via files
- Run scheduling algorithms (FCFS, SPT, EDD, WSPT)
- Output schedules in LEKIN-compatible or JSON format
- Plot Gantt charts (requires `matplotlib`)

## Example Usage
```python
from lekinpy.system import System
from lekinpy.job import Job, Operation
from lekinpy.machine import Machine, Workcenter
from lekinpy.algorithms.fcfs import FCFSAlgorithm

# Create a new scheduling system
system = System()

# Define a machine and assign it to a workcenter
machine = Machine("M1", release=0, status="A")
workcenter = Workcenter("WC1", release=0, status="A", machines=[machine])
system.add_workcenter(workcenter)

# Define a job with one operation assigned to WC1
job = Job("J1", release=0, due=10, weight=1, operations=[Operation("WC1", 5, "A")])
system.add_job(job)

# Choose the scheduling algorithm (First-Come First-Served)
algo = FCFSAlgorithm()

# Generate the schedule for the system
schedule = algo.schedule(system)

# Attach the computed schedule to the system
system.set_schedule(schedule)

# Print schedule details as a dictionary
print(system.schedule.to_dict())

# Plot a Gantt chart of the schedule (requires matplotlib)
schedule.plot_gantt_chart(system)
```

## API Reference
📚 **Full API Reference:** [docs/API_REFERENCE.md](docs/API_REFERENCE.md)

- `System`, `Job`, `Operation`, `Machine`, `Workcenter`
- Algorithms: `FCFSAlgorithm`, `SPTAlgorithm`, `EDDAlgorithm`, `WSPTAlgorithm`
- IO: `export_jobs_to_jobfile`, `export_workcenters_to_mchfile`, etc.

## Contributing

- Fork, branch, and submit pull requests for new algorithms or features.
- See `tests/` for unit test examples.

## License

MIT

## Contact

Author: Ruturaj Vasant  
Email: rvt2018@nyu.edu