Metadata-Version: 2.4
Name: highdimensionsmap
Version: 0.1.0
Summary: High Dimensions Map: lightweight non-linear geometric scanning and phase-orbit indexing
Author: Marta Reinhardt
License: Apache-2.0
Keywords: geometry,high-dimensions,embeddings,manifold-learning,kinematics,spectral
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy>=1.20.0
Requires-Dist: fastapi>=0.110.0
Requires-Dist: uvicorn>=0.30.0
Dynamic: license-file

# HighDimensionsMap

High Dimensions Map is a lightweight Python library for generating and analyzing high-dimensional data with spectral signatures and similarity queries.

It is designed to work like a regular scientific Python package: import it, create a scanner, generate data, transform vectors, and run nearest-neighbor queries directly from Python, notebooks, or Colab.

## Installation

```bash
pip install git+https://github.com/reinhardtmarta/highdimensionsmap.git
```

Or from a local checkout:

```bash
cd highdimensionsmap
pip install -e .
```

## Quick start

```python
from highdimensionsmap import HDMScanner, MotionNoiseTracker

scanner = HDMScanner(input_dim=128, latent_modes=32, steps=48, seed=42)

dataset = scanner.generate_dataset(20)
print(dataset.shape)

signatures = scanner.transform(dataset)
print(signatures.shape)

tracker = MotionNoiseTracker(scanner)
metrics = tracker.track(dataset)
print(metrics["velocity"][:5])
```

## API overview

### HDMScanner

```python
scanner = HDMScanner(input_dim=128, latent_modes=32, steps=48, seed=42)
```

Methods:

- `generate_dataset(n_samples=1, noise=0.05, drift=0.1)`
- `transform(X)`
- `query(dataset_signatures, query_vector, k=5)`

### MotionNoiseTracker

```python
tracker = MotionNoiseTracker(scanner)
result = tracker.track(trajectory)
```

Returns a dictionary with:

- `signatures`
- `velocity`
- `noise`

## Colab usage

```python
!pip install git+https://github.com/reinhardtmarta/highdimensionsmap.git

from highdimensionsmap import HDMScanner

scanner = HDMScanner(input_dim=64, latent_modes=16, steps=32, seed=7)
dataset = scanner.generate_dataset(10)
signatures = scanner.transform(dataset)
print(signatures.shape)
```

## Notes

This package is designed as a Python library first. The FastAPI app is optional and can be used as an auxiliary interface, but the main workflow is direct Python use.
