semvec/pypi-packages huggingface dataset:
tamer-bordro-hesaplama 0.0.1
2026 Türkiye parametreleriyle çalışan, teşvik destekli Net-Brüt ve Brüt-Net maaş hesaplama motoru.
Requires Python: >=3.9

\# Maaş Dönüşüm Kütüphanesi (2026)



Bu kütüphane, Türkiye 2026 parametreleriyle çalışır. \*\*Çalışanın maaşını aylık ve yıllık seçenekleriyle brütten nete ve netten brüte dönüştürür.\*\*



\## Özellikler

\- \*\*Brütten Nete:\*\* Maaşın net karşılığını ve vergi kesintilerini hesaplar.

\- \*\*Netten Brüte:\*\* İstenilen net maaş için gereken brüt tutarı hesaplar (Tersine hesaplama).

\- \*\*Aylık ve Yıllık:\*\* Tek satır kodla ister tek ayın, ister tüm yılın dökümünü alabilirsiniz.

\- \*\*Hata Toleransı:\*\* Yanlış sigorta kodu girilse bile varsayılan ayarlarla çalışmaya devam eder.



\## Kurulum

```bash

pip install tamer-bordro-hesaplama

```



\## Kullanım Örnekleri



\### 1. Maaşı Brütten Nete Dönüştürme

```python

from bordro import brut\_net\_aylik



\# Ocak ayı için hesapla

sonuc = brut\_net\_aylik(60000, ay=1)

print(f"Net Maaş: {sonuc\['net']} TL")

```



\### 2. Maaşı Netten Brüte Dönüştürme

```python

from bordro import net\_brut\_aylik



\# Şubat ayı için hesapla

hesap = net\_brut\_aylik(45000, ay=2)

print(f"Gereken Brüt Maaş: {hesap\['brut']} TL")

```



\### 3. Emekli Çalışanın Yıllık Maaş Tablosu

```python

from bordro import brut\_net\_yillik



\# 75.000 TL Brüt alan Emekli çalışan (Kod: "02")

tablo = brut\_net\_yillik(75000, sigorta\_tipi\_kodu="02")



print(tablo)

```
cobotar-protocol 0.13.3
Protocol and more for the cobotar project
Requires Python: >=3.8
Keywords: ar, cobot, protocol

# CobotAR-Protocol
A collection of protobuf messages for the CobotAR-project

Current version: 0.13.3

## Documentation
Find it here: [documentation/README.md](documentation/README.md)

## New version
1. Make changes
2. `make publish bump={patch,minor,major}`
pyligent 0.2.5
Diligent framework for python
Requires Python: >=3.12
Keywords: reasoning, llm, transformers, ml

# Pyligent

![Python](https://img.shields.io/badge/python-3.13-blue.svg)
[![PyPI version](https://badge.fury.io/py/pyligent.svg)](https://badge.fury.io/py/pyligent)

**Pyligent** is a framework for reinforcement learning through search-based reasoning, implementing concepts from [From Reasoning to Super-Intelligence: A Search-Theoretic Perspective](https://arxiv.org/abs/2507.15865).

## Installation

**Note:** Pyligent requires [PyTorch](https://pytorch.org/). Please install the version appropriate for your hardware before installing Pyligent.

### 1. Install PyTorch

**Using `uv`:**

Check the [official guide](https://docs.astral.sh/uv/guides/integration/pytorch/#using-a-pytorch-index)


**For CPU-only environments:**

```bash
pip install torch --index-url https://download.pytorch.org/whl/cpu
```

**For CUDA 12.8 environments:**

```bash
pip install torch --index-url https://download.pytorch.org/whl/cu128
```

*(For other CUDA versions, please refer to the [PyTorch Get Started](https://pytorch.org/get-started/locally/) guide.)*

### 2. Install Pyligent

Once PyTorch is installed:

```bash
pip install pyligent
```

or

```bash
uv add pyligent
```

## Quick Start

```python
import pyligent

# Your quick start example here
```

## Key Features

- Search-based reasoning for transformer models
- PyTorch-based implementation with PEFT support
- GPU acceleration support (via CUDA-enabled PyTorch)
- Fine-tuning and evaluation pipelines

## Requirements

- Python ≥ 3.13
- PyTorch ≥ 2.8.0 (must be installed manually, see Installation above)

<!-- ## Documentation

For complete documentation, examples, and usage guides, visit our [GitHub repository](https://github.com/crogs-foundation/diligent-learner).

## Citation

If you use Pyligent in your research, please cite:

```bibtex
@article{yourpaper2026,
  title={Your Paper Title},
  author={Your Name},
  journal={arXiv preprint},
  year={2026}
}
```

## Support

- 📝 [Documentation](https://github.com/crogs-foundation/diligent-learner)
- 🐛 [Issue Tracker](https://github.com/crogs-foundation/diligent-learner/issues)
- 💬 [Discussions](https://github.com/crogs-foundation/diligent-learner/discussions) -->
ds-provider-grasp-py-lib 0.1.0a2
A Python package from the DS library collection
Requires Python: >=3.11
Keywords: ds, python

# ds-provider-grasp-py-lib

A Python package from the ds-common library collection.

## Installation

Install the package using pip:

```bash
pip install ds-provider-grasp-py-lib
```

Or using uv (recommended):

```bash
uv pip install ds-provider-grasp-py-lib
```

## Quick Start

```python
from ds_provider_grasp_py_lib import __version__

print(f"ds-provider-grasp-py-lib version: {__version__}")
```

## Features

- **GRASP Provider**: Provider for the GRASP platform
- **AWS Provider**: Provider for the AWS platform

## Usage

```python
import uuid
import logging
from os import environ

from ds_common_logger_py_lib import Logger
from ds_provider_aws_py_lib.linked_service.aws import AWSLinkedService, AWSLinkedServiceSettings
from ds_provider_grasp_py_lib.dataset.cart import GraspCartDataset, GraspCartDatasetSettings
Logger.configure(level=logging.DEBUG)
logger = Logger.get_logger(__name__)


dataset = GraspCartDataset(
    id=uuid.uuid4(),
    name="cart-dataset",
    version="1.0.0",
    linked_service=AWSLinkedService(
        id=uuid.uuid4(),
        name="aws-linked-service",
        version="1.0.0",
        settings=AWSLinkedServiceSettings(
            access_key_id="****************",
            access_key_secret="****************",
            region="eu-north-1",
            account_id="****************",
        ),
    ),
    settings=GraspCartDatasetSettings(
        owner_id="****************",
        product_group_name="****************",
        product_name="****************",
        version="****************",
        include_history=False,
    ),
)

dataset.linked_service.connect()
environ["TENANT_ID"] = "****************"
dataset.read()
logger.debug(f"Dataset: {dataset.output}")
```

## Requirements

- Python 3.11 or higher

## Documentation

Full documentation is available at:

- [GitHub Repository](https://github.com/grasp-labs/ds-provider-grasp-py-lib)
- [Documentation Site](https://grasp-labs.github.io/ds-provider-grasp-py-lib/)

## Development

To contribute or set up a development environment:

```bash
# Clone the repository
git clone https://github.com/grasp-labs/ds-provider-grasp-py-lib.git
cd ds-provider-grasp-py-lib

# Install development dependencies
uv sync --all-extras --dev

# Run tests
make test
```

See the [README][readme] for more information.

[readme]: https://github.com/grasp-labs/ds-provider-grasp-py-lib#readme

## License

This package is licensed under the Apache License 2.0.
See the [LICENSE-APACHE](https://github.com/grasp-labs/ds-provider-grasp-py-lib/blob/main/LICENSE-APACHE)
file for details.

## Support

- **Issues**: [GitHub Issues](https://github.com/grasp-labs/ds-provider-grasp-py-lib/issues)
- **Releases**: [GitHub Releases](https://github.com/grasp-labs/ds-provider-grasp-py-lib/releases)