semvec/pypi-packages huggingface dataset:
calico_lib 0.1.20
CALICO lib for all your problem writing needs

# CALICOlib
CALICOlib is a framework to help facilitate problem creation on DOMjudge. Currently, the library helps with test generation and creating problem zip, which is based on the ICPC problem package specification

## Installing
```
python -m pip install calico_lib
```
Alternatively, install the development version using flit.
```
brew install flit # or another package manager like pip or pipx
git clone https://github.com/calico-team/CALICOlib.git
cd CALICOlib
flit install --symlink
```

## Quick Start
See examples/add. Also see https://github.com/calico-team/CALICOlib/blob/main/examples/add/main.py

## Development
Bump version number in `__init__.py` and run `flit publish` or another build tool. See [documentation for flit](https://flit.pypa.io/en/stable/).

## Roadmap
Problem / Test Generation / Test Verification:
- [ ] Remove problem dir thingy, just cd
- [ ] Support test case from file
- [x] Rethink API (Subproblem should be Problem and Problem should be MultipartProblem)
- I thought about it, it's a bad idea

Other stuff:
- [ ] Upload problem to testing contest
- [ ] Create contest
- [ ] Create contest.zip

## Similar tools
https://github.com/RagnarGrootKoerkamp/BAPCtools
griptape-cloud-client 0.1.1
Python client for Griptape Cloud API
Requires Python: <3.13,>=3.12.0

# Griptape Cloud Python Client

Auto-generated Python client for the [Griptape Cloud API](https://docs.griptape.ai/stable/griptape-cloud/).

## About Griptape Cloud

Griptape Cloud is a managed platform for building and scaling AI applications, providing:

- **RAG Pipelines**: Connect data sources, manage data lakes, and build knowledge bases
- **Code Hosting**: Deploy Python code from Griptape Framework or other LLM frameworks as "Structures"
- **Agent Tools**: Extend agent capabilities with custom tools for service calls and logic
- **Configuration Management**: Control LLM behavior with Rules, Rulesets, and persistent conversation Threads
- **Full API Access**: Comprehensive REST API for all platform features

## Installation

### Using uv

Add to your `pyproject.toml`:

```toml
[tool.uv.sources]
griptape-cloud-client = { git = "https://github.com/griptape-ai/griptape-cloud-python-client", rev = "main" }
```

### Using pip

```bash
pip install git+https://github.com/griptape-ai/griptape-cloud-python-client.git@main
```

## Usage

```python
from griptape_cloud_python_client import AuthenticatedClient

client = AuthenticatedClient(base_url="https://cloud.griptape.ai/api", token="<your_api_key>")

# Use the client to interact with Griptape Cloud APIs
# See API Reference for available endpoints
```

## Documentation

- [Griptape Cloud Documentation](https://docs.griptape.ai/stable/griptape-cloud/)
- [API Reference](https://docs.griptape.ai/stable/griptape-cloud/api/api-reference/)

## Development

This client is automatically generated from the Griptape Cloud OpenAPI specification.

### Regenerating the Client

```bash
make gen/sdk
```

## License

See [LICENSE](LICENSE) file for details.
griptape_cloud_client 0.1.0
Python client for Griptape Cloud API
Requires Python: <3.13,>=3.12.0

# Griptape Cloud Python Client

Auto-generated Python client for the [Griptape Cloud API](https://docs.griptape.ai/stable/griptape-cloud/).

## About Griptape Cloud

Griptape Cloud is a managed platform for building and scaling AI applications, providing:

- **RAG Pipelines**: Connect data sources, manage data lakes, and build knowledge bases
- **Code Hosting**: Deploy Python code from Griptape Framework or other LLM frameworks as "Structures"
- **Agent Tools**: Extend agent capabilities with custom tools for service calls and logic
- **Configuration Management**: Control LLM behavior with Rules, Rulesets, and persistent conversation Threads
- **Full API Access**: Comprehensive REST API for all platform features

## Installation

### Using uv

Add to your `pyproject.toml`:

```toml
[tool.uv.sources]
griptape-cloud-client = { git = "https://github.com/griptape-ai/griptape-cloud-python-client", rev = "main" }
```

### Using pip

```bash
pip install git+https://github.com/griptape-ai/griptape-cloud-python-client.git@main
```

## Usage

```python
from griptape_cloud_python_client import AuthenticatedClient

client = AuthenticatedClient(base_url="https://cloud.griptape.ai/api", token="<your_api_key>")

# Use the client to interact with Griptape Cloud APIs
# See API Reference for available endpoints
```

## Documentation

- [Griptape Cloud Documentation](https://docs.griptape.ai/stable/griptape-cloud/)
- [API Reference](https://docs.griptape.ai/stable/griptape-cloud/api/api-reference/)

## Development

This client is automatically generated from the Griptape Cloud OpenAPI specification.

### Regenerating the Client

```bash
make gen/sdk
```

## License

See [LICENSE](LICENSE) file for details.
wurst 0.5.3
Wurst is a python package for linking and modifying industrial ecology models
Requires Python: >=3.9

# Wurst

[![PyPi Version](https://img.shields.io/pypi/v/wurst.svg)](https://pypi.org/project/wurst/) [![Conda Version](https://img.shields.io/conda/vn/conda-forge/wurst.svg)](https://anaconda.org/conda-forge/wurst) [![Build Status](https://travis-ci.org/IndEcol/wurst.svg?branch=master)](https://travis-ci.org/IndEcol/wurst) [![Coverage Status](https://coveralls.io/repos/github/IndEcol/wurst/badge.svg?branch=master)](https://coveralls.io/github/IndEcol/wurst?branch=master) [![Docs](https://readthedocs.org/projects/wurst/badge/?version=latest)](https://wurst.readthedocs.io/)

Show how the sausage is made!

Wurst is a python package for linking and modifying industrial ecology models, with a focus on sparse matrices in life cycle assessment. It provides the following:

* Helper functions to filter activities and exchanges
* Helper functions to link exchanges
* Transformation functions to change markets, change input efficiencies, and change emissions
* Data IO with [Brightway2](https://brightwaylca.org/)
* Logging framework and a log browser

See also the separate [wurst examples](https://github.com/IndEcol/wurst-examples) repository.

## Installation

Download and install [miniconda](https://conda.io/miniconda.html), create and activate a [new environment](https://conda.io/docs/user-guide/tasks/manage-environments.html), and then install::

    conda install -c conda-forge brightway2 jupyter wurst

## License

BSD 2-clause license. Contributions are welcome!

## Authors

* Chris Mutel
* Brian Cox

## TODO

* Review BW2 IO code to make sure all needed fields are present in newly-created and modified databases
* Parameterized exchanges (e.g. electricity sector)
* Check logging on all transformation functions
* Log browser web app
no-requests 0.1.0
A lightweight, dependency-free, requests-like HTTP client with safe defaults
Requires Python: >=3.6
Keywords: http, requests, standard library, no dependencies, lightweight

# no-requests

**Author:** gatopeich ~ https://github.com/gatopeich/no-requests

A lightweight, safe, dependency-free HTTP client for Python 3, inspired by `requests`, but with no dependencies outside the standard library.

## Features

- Only the most-used API: `get`, `post`, `put`, `delete`
- No dependencies outside Python 3 standard library
- Default timeout is 9 seconds
- Warns (but does not fail) on non-SSL (non-HTTPS) requests
- Simple `Response` object with `.status_code`, `.headers`, `.url`, `.content`, `.text`, `.json()`

## Usage

```python
import norequests as requests

resp = requests.get('https://httpbin.org/get')
print(resp.status_code)
print(resp.json())
```

## How to use no-requests in place of requests

If you want to replace the `requests` module in your project with `no-requests`, you can do so by adding the following line to your `requirements.txt`:

```
requests @ git+https://github.com/gatopeich/no-requests.git
```

This will install `no-requests` as if it were `requests`, letting you import it as usual:

```python
import requests

resp = requests.get('https://example.com')
print(resp.status_code)
```

**Note:**  
This works because the package provides a `requests`-like API and will be installed as the `requests` package in your environment, overriding the original. Be sure to remove any other lines referring to `requests` in your `requirements.txt` to avoid conflicts.

## License

MIT
d-augmentation 0.1.2
Comprehensive image data augmentation library with geometric, color, noise, and occlusion techniques
Requires Python: >=3.7

# Data_Augmentation

A complete Python library for advanced image data augmentation â€” including geometric, color, noise, and occlusion-based transformations.

### Features
âœ… Flip, Rotate, Scale, Shear, Crop, Perspective, Elastic Deformation  
âœ… Brightness, Contrast, Hue, Gamma, Solarize, Posterize  
âœ… Gaussian, Speckle, Motion, Defocus, Salt & Pepper Noise  
âœ… Cutout, Hide-and-Seek, GridMask, Mixup, CutMix, FMix  

### Installation
```bash
pip install -e .
chemsource 1.1.17
Tool to classify novel drugs and other health-related chemicals by origin
Requires Python: >=3.8

# chemsource

[![Documentation Status](https://readthedocs.org/projects/chemsource/badge/?version=latest)](https://chemsource.readthedocs.io/en/latest/?badge=latest)
[![PyPI version](https://badge.fury.io/py/chemsource.svg)](https://badge.fury.io/py/chemsource)
[![Python 3.8+](https://img.shields.io/badge/python-3.8+-blue.svg)](https://www.python.org/downloads/)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

chemsource is a Python tool for exposomics research that classifies chemical compounds based on their exposure sources. It retrieves information from Wikipedia and PubMed, then uses large language models to classify chemicals into user-defined categories.


## Quick Start

```python
from chemsource import ChemSource

# Initialize with your OpenAI API key
chem = ChemSource(model_api_key="your_openai_api_key")

# Classify a compound
info, classification = chem.chemsource("aspirin")
print(f"Classification: {classification}")
```

## Installation

Install from PyPI:

```bash
pip install chemsource
```

## Documentation

For detailed documentation, tutorials, and API reference, visit:

**[Read the Docs](https://chemsource.readthedocs.io/)**

The documentation includes:
- Installation and setup instructions
- Comprehensive API reference
- Usage examples and tutorials
- Configuration options
- Error handling guides

## Requirements

- Python 3.8+
- OpenAI (or other LLM) API key (for classification)
- NCBI API key (optional, for enhanced PubMed access)

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

## Citation

If you use chemsource in your research, please cite:
(Preprint coming soon)

```
```

## Support

- [Documentation](https://chemsource.readthedocs.io/)
- [Issue Tracker](https://github.com/prajitrr/chemsource/issues)
- [Contact](mailto:prajkumar@ucsd.edu)

---

**Note**: chemsource uses OpenAI's API services which incur costs based on usage. New users receive $5 in free credits, sufficient for testing purposes. Other lower-cost endpoints are also available for classification (e.g. DeepSeek, gpt-oss, etc.). In particular, as of the creation of this package, Google Gemini provides a reasonable amount of free API credits.
mdabojbot 0.1.1
Тот самый токсичный бот в телеграм группе.
Requires Python: >=3.12

# mdabojbot

Тот самый токсичный бот в телеграм группе - [начать тут](https://t.me/mdabojbot).

> ⚠️ Он только родился (в бета версии) - не судите строго

Если найдете какой либо баг или есть какие либо пожелания - прошу в issue, также буду рад PR-ам ❤️

## 🤗 Автор

Сделано с любовью [@skv0zsneg](https://github.com/skv0zsneg)