semvec/pypi-packages huggingface dataset:
odd-or-even-albin 0.1.0
A simple library to check odd or even numbers

# odd_or_even

A simple Python library to check whether a number is odd or even.

## Installation
```bash
pip install odd_or_even_albin


from odd_or_even import is_even, is_odd, check

print(is_even(10))   # True
print(is_odd(7))     # True
print(check(15))     # "odd"
swiftly-api-python-client 0.17.0
Hopthru's Swiftly API Python Client
Requires Python: >=3.10

# Swiftly API Python Client

Hopthru's Python Client for the Swiftly API. 

## Building this package

- Create a virtual environment
    - `python -m venv venv`  
- Install the Python dependencies:
    - `python -m pip install --upgrade pip`
    - `python -m pip install -r test-requirements.txt`
- Build the distribution:
    - `python -m build`
- The distribution will be in the dist folder.
sigtekx 0.0.2
GPU-accelerated signal processing pipeline builder for real-time FFT analysis
Requires Python: >=3.11
Keywords: signal-processing, fft, cuda, gpu, real-time, stft, spectrogram

# SigTekX

**Package name reservation - Full release coming soon**

## About

**SigTekX** (Signal + Tekton "builder" + X "acceleration") is a high-performance GPU-accelerated signal processing library for building real-time FFT analysis pipelines.

This placeholder reserves the `sigtekx` package name on PyPI. The library is currently in development (v0.9.4) and will be published under this name in an upcoming release.

## Current Status

The library is in **active development** (Beta) with a working implementation featuring:

- **Sub-200μs latency** CUDA FFT engine
- **Python API** with C++ backend via pybind11
- **Multi-stream async execution** for throughput optimization
- **Research infrastructure** (Hydra, MLflow, DVC, Snakemake)
- **Professional benchmarking** suite with statistical analysis
- **NVTX profiling** support for NVIDIA tools

## Coming Soon

The rebranded `sigtekx` package will include:

- Complete CUDA-accelerated FFT processing engine
- Type-safe Python API with Pydantic configuration
- Batch and streaming execution modes
- Comprehensive benchmark and profiling tools
- Pre-configured presets for common use cases
- Full documentation and examples

## Repository

Development is happening at: https://github.com/SEAL-Embedded/ionosense-hpc-lib

**Note**: Repository will be renamed to `sigtekx` upon package release.

## Requirements (Future Release)

- Python 3.11+
- NVIDIA GPU with CUDA compute capability 6.0+ (Pascal or newer)
- CUDA Toolkit 13.0+
- Windows 11 or Linux

## Contact

**Author**: Kevin Rahsaz
**Email**: rahsaz.kevin@gmail.com

## License

MIT License
odd-or-even 0.1.0
A simple library to check if a number is odd or even.
Requires Python: >=3.6

# Odd or Even

A simple Python library to check if a number is odd or even.

## Installation

You can install the library via pip:

```bash
pip install odd-or-even


#usage
from odd_or_even import is_odd, is_even

print(is_odd(5))  # True
print(is_even(10))  # True
hatch-validator 0.8.0
Package validator for Hatch packages
Requires Python: >=3.7

# Hatch-Validator

A validation package for Hatch packages and dependencies.

## Features

- **Package Validation**: Validate Hatch packages against schema specifications
- **Dependency Resolution**: Resolve and validate package dependencies
- **Schema Management**: Automatically fetch and manage schema versions

## Installation

### From Source

```bash
# Install directly from the repository
pip install git+https://github.com/CrackingShells/Hatch-Validator.git

# Or install local copy
git clone https://github.com/CrackingShells/Hatch-Validator.git
cd Hatch-Validator
pip install /path/to/Hatch-Validator
```

## Usage

```python
from hatch_validator import HatchPackageValidator, DependencyResolver

# Initialize validator
validator = HatchPackageValidator()

# Validate a package
is_valid, results = validator.validate_package('/path/to/package')
if is_valid:
    print("Package is valid!")
else:
    print("Validation errors:", results)

# Initialize dependency resolver
resolver = DependencyResolver()

# Check for missing dependencies
missing_deps = resolver.get_missing_hatch_dependencies(dependencies)
```

## Contributing

We welcome contributions! Please see our [Contributing Guide](./CONTRIBUTING.md) for details.

### Quick Start

1. **Fork and clone** the repository
2. **Install dependencies**: `pip install -e .` and `npm install`
3. **Create a feature branch**: `git checkout -b feat/your-feature`
4. **Make changes** and add tests
5. **Use conventional commits**: `npm run commit` for guided commits
6. **Create a pull request**

### Commit Messages

We use [Conventional Commits](https://www.conventionalcommits.org/) for automated versioning:

```bash
feat: add new feature
fix: resolve bug
docs: update documentation
test: add tests
chore: maintenance tasks
```

Use `npm run commit` for guided commit messages.

For detailed guidelines, see [CONTRIBUTING.md](./CONTRIBUTING.md).

## License

AGPL v3: see [file](./LICENSE)
django-ainstein 0.1.1
AI-powered Django assistant with Claude integration
Requires Python: >=3.12
Keywords: ai, anthropic, assistant, claude, django

# Django AInstein

An AI agent that learns your schema and helps you reason about your data.

A reusable Django app powered by Anthropic's Claude that provides an intelligent agent for interacting with your Django application.

## Features

- **Claude Integration**: Built on Anthropic's Claude SDK for powerful AI capabilities
- **Django ORM Tools**: Query models, analyze data, and generate insights
- **File Management**: Create and edit files within project sandboxes
- **Code Execution**: Safe code execution in isolated environments
- **React Frontend**: Modern chat interface with streaming support
- **Extensible Tools**: Add custom tools for your specific use case

## Installation

```bash
pip install django-ainstein
```

## Quick Start

1. Add to your `INSTALLED_APPS`:

```python
INSTALLED_APPS = [
    ...
    'ainstein',
]
```

2. Add URL configuration:

```python
urlpatterns = [
    ...
    path('ainstein/', include('ainstein.urls')),
]
```

3. Configure settings:

```python
AINSTEIN = {
    "ANTHROPIC_API_KEY": "your-api-key",
    "DEFAULT_MODEL": "claude-sonnet-4-20250514",
}
```

4. Run migrations:

```bash
python manage.py migrate ainstein
```

## Configuration

| Setting | Description | Default |
|---------|-------------|---------|
| `ANTHROPIC_API_KEY` | Your Anthropic API key | Required |
| `DEFAULT_MODEL` | Claude model to use | `claude-sonnet-4-20250514` |
| `MAX_TOKENS` | Maximum response tokens | `16000` |
| `TEMPERATURE` | Response temperature | `1` |

## Development

```bash
# Clone the repository
git clone https://github.com/harpb/django-ainstein.git
cd django-ainstein

# Install dependencies
pip install -e ".[dev]"

# Run tests
pytest
```

## License

MIT License - see [LICENSE](LICENSE) for details.