# MSKit — Mini Simulation Kit

[![PyPI](https://img.shields.io/pypi/v/mskit)](https://pypi.org/project/mskit/)
[![Dataset](https://img.shields.io/badge/🤗%20Dataset-MegaBites--AI%2FAW3D30--DEM--Tiles-blue)](https://huggingface.co/datasets/MegaBites-AI/AW3D30-DEM-Tiles)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](LICENSE)

**MSKit** is a lightweight Python library for running terrain-based simulations backed by **real-world elevation data** from JAXA's AW3D30 30m Digital Elevation Model.

Tiles are streamed on-demand from the [`MegaBites-AI/AW3D30-DEM-Tiles`](https://huggingface.co/datasets/MegaBites-AI/AW3D30-DEM-Tiles) dataset hosted on Hugging Face — no manual data download required.

---

## Installation

```bash
pip install mskit
```

With optional visualisation support:
```bash
pip install mskit[viz]
```

---

## Quick Start

```python
from mskit import DEMLoader, RandomWalk, Projectile, WaterFlow, TerrainAgent

# Create a loader — tiles are fetched automatically as needed
loader = DEMLoader()

# ── Random Walk ──────────────────────────────────────────────────────────────
rw = RandomWalk(loader, start_lat=35.68, start_lon=139.69, step_m=300)
path = rw.run(steps=500)
print(f"Distance: {rw.total_distance_km():.2f} km")
print(f"Elevation gain: {rw.elevation_gain_m():.0f} m")

# ── Projectile ───────────────────────────────────────────────────────────────
proj = Projectile(loader, lat=36.0, lon=137.5,
                  azimuth_deg=45, elevation_deg=30, speed_ms=80)
traj = proj.run()
print(f"Range: {proj.range_km():.3f} km | Flight time: {proj.flight_time():.1f} s")

# ── Water Flow ───────────────────────────────────────────────────────────────
wf = WaterFlow(loader, patch_km=10)
flow = wf.run(lat=35.6, lon=137.5)
print(f"Flow length: {len(flow)} steps | Descent: {wf.total_descent_m():.0f} m")

# ── Terrain-Navigating AI Agent ──────────────────────────────────────────────
agent = TerrainAgent(loader,
    start_lat=35.60,  start_lon=139.70,
    target_lat=35.65, target_lon=139.75)
dataset = agent.generate_episode(max_steps=300, policy="mixed")
arr = agent.to_numpy()   # shape (N, 7): [step, lat, lon, elev, action, reward, done]
print(f"Episode: {len(dataset)} steps | Reached target: {agent.reached_target()}")
```

---

## Simulations

| Class | Description |
|---|---|
| `DEMLoader` | Multi-tile loader with LRU cache. Core data access layer. |
| `DEMTile` | Single 1°×1° elevation tile with spatial query methods. |
| `RandomWalk` | Slope-biased 2D random walk on real terrain. |
| `Projectile` | Ballistic trajectory simulation that terminates on terrain impact. |
| `WaterFlow` | D8 water runoff routing on real elevation grids. |
| `TerrainAgent` | RL-ready navigation agent producing training trajectories. |

---

## Dataset

Elevation tiles are served from:

> **[`MegaBites-AI/AW3D30-DEM-Tiles`](https://huggingface.co/datasets/MegaBites-AI/AW3D30-DEM-Tiles)**

- Source: JAXA ALOS World 3D 30m (AW3D30 v3.2)
- Coverage: Global
- Resolution: 30 m/pixel, 1°×1° tiles (3600×3600 px)
- Format: Compressed NumPy `.npy` (int16, metres)
- Cache: Tiles are cached locally at `~/.cache/mskit/dem/`

---

## Uploading Tiles

To upload tiles to the dataset (MegaBites team only):

```bash
# Upload Japan tiles
python scripts/upload_tiles.py --region japan --token $HF_TOKEN

# Upload a custom bounding box
python scripts/upload_tiles.py --lat-range 30 45 --lon-range 130 145 --token $HF_TOKEN

# Dry run first
python scripts/upload_tiles.py --region japan --dry-run
```

---

## License

- **MSKit code:** MIT License
- **AW3D30 data:** © JAXA, CC-BY-4.0

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*Produced by [MegaBites AI](https://huggingface.co/MegaBites-AI)*