semvec/pypi-packages huggingface dataset:
IntrinsicDimEstimator 0.3.2
Intrinsic Dimensionality Estimation with idPettis Method
Keywords: intrinsic dimensionality,dimensionality estimation,data analysis,machine learning

A package for estimating the intrinsic dimensionality of a dataset. Supports multiple estimation methods including Correlation Dimension, Nearest Neighbor Dimension, Packing Numbers, Geodesic Minimum Spanning Tree, Eigenvalue Analysis, Maximum Likelihood Estimation, and the newly added idPettis method. The idPettis method provides an innovative approach for dimensionality estimation, enhancing the package’s utility and accuracy in analyzing complex datasets.
langtrace-trace-attributes 0.1.0
LangTrace - Trace Attributes
Requires Python: >=3.6

# LangTrace - Trace Attributes

This repository hosts the JSON schema definitions and the generated model code for both Python and TypeScript. It's designed to streamline the development process across different programming languages, ensuring consistency in data structure and validation logic. The repository includes tools for automatically generating model code from JSON schema definitions, simplifying the task of keeping model implementations synchronized with schema changes.

## Repository Structure

```
/
├── schemas/                      # JSON schema definitions
│   └── openai_span_attributes.json
├── scripts/                      # Shell scripts for model generation
│   └── generate_python.sh
├── generated/                    # Generated model code
│   ├── python/                   # Python models
│   └── typescript/               # TypeScript interfaces
├── package.json
├── requirements.txt
├── README.md
└── .gitignore
```

## Prerequisites

Before you begin, make sure you have the following installed on your system:
- Node.js and npm
- Python and pip
- `ts-node` for running TypeScript scripts directly (install globally via `npm install -g ts-node`)
- `datamodel-code-generator` for Python model generation (install via `pip install datamodel-code-generator`)

## Generating Models

### Python Models

To generate Python models from a JSON schema, use the `generate_python.sh` script located in the `scripts` directory. This script takes the path to a JSON schema file as an argument and generates a Python model in the `generated/python` directory.

```sh
./scripts/generate_python.sh schemas/openai_span_attributes.json
```

### TypeScript Interfaces

To generate TypeScript interfaces from a JSON schema, use the `schema_to_interface.ts` script located in the `src/models` directory. This script also takes the path to a JSON schema file as an argument and generates a TypeScript interface in the `generated/typescript` directory.

```sh
ts-node src/models/schema_to_interface.ts schemas/openai_span_attributes.json
```

### Build instructions

## Contributing

Contributions are welcome! If you'd like to add a new schema or improve the existing model generation process, please follow these steps:

1. Fork the repository.
2. Create a new branch for your feature or fix.
3. Make your changes.
4. Test your changes to ensure the generated models are correct.
5. Submit a pull request with a clear description of your changes.

## License

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

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This README provides a comprehensive overview of the repository, including its purpose, structure, prerequisites for generating models, and guidelines for contributing. Adjust any paths, installation commands, or other details as necessary to fit your project's specific setup and requirements.