myreze 0.0.9

Placeholder for Myreze data and visualization package

Myreze

Myreze is a data passing, processing and visualization toolkit for handling geospatial data and rendering it through different visualization engines. It provides a unified interface for managing, validating, and visualizing geospatial data with time components. The packages are designed to be versatile enough to represent a wide range of data and visualization options, encode semantic information for agentic LLM systems, while being serializeable so it can be passed as a response to a HTTP request.

Enhanced with AI Integration: Myreze includes extended AI-generated docstrings and intelligent data validation to simplify integration with AI agents and automated workflows.

Installation

pip install -e .

Core Concepts

Myreze is built around these core concepts:

• Data Packages: Container for geospatial data with time information
• Time: Flexible time representation (timestamps, spans, series)
• Stores & Products: Data distribution system for creating and serving data packages
• Renderers: Visualization schemas for different platforms (might choose a different name for these in the future)
• Visualization Types: Metadata indicating how data should be interpreted and rendered
• Agent Context: Extendable metadata for LLM-based interpretation

Quick Start

Creating Your First Data Package

from myreze.data import MyrezeDataPackage, Time
import numpy as np

# Create time information
time_data = Time.timestamp("2023-01-01T12:00:00Z")

# Create a simple weather data package
weather_data = {
    "grid": np.random.rand(50, 50),  # Temperature values
    "bounds": [-74.0, 40.7, -73.9, 40.8],  # NYC area [west, south, east, north]
    "units": "celsius"
}

data_package = MyrezeDataPackage(
    id="my-weather-data",
    data=weather_data,
    time=time_data,
    visualization_type="heatmap",  # Tells receivers how to visualize this data
    metadata={
        "description": "Temperature data for NYC area",
        "source": "weather_station"
    }
)

# Export to JSON for sharing
json_data = data_package.to_json()
print(f"Created package: {data_package.id}")

Understanding Visualization Types

The visualization_type field is crucial - it tells anyone receiving your data package how to interpret and visualize it. Think of it as instructions for the visualization engine.

Common Visualization Types

Type	Use Case	Data Structure
"flat_overlay"	Weather radar, satellite imagery	2D grid or image data
"png_overlay"	-	-

Example: Weather Radar Overlay

# Weather radar data showing precipitation across a region
import numpy as np

radar_data = {
    "grid": np.random.rand(100, 100),  # Precipitation intensity (0-1)
    "bounds": [-74.5, 40.5, -73.5, 41.0],  # NYC area [west, south, east, north]
    "resolution": 0.01,  # degrees per grid cell
    "units": "mm/hour"
}

radar_package = MyrezeDataPackage(
    id="precipitation-radar",
    data=radar_data,
    time=Time.timestamp("2023-06-15T14:30:00Z"),
    visualization_type="flat_overlay",  # 2D map overlay
    metadata={
        "parameter": "precipitation_intensity",
        "colormap": "blues",
        "opacity": 0.7,
        "data_source": "weather_radar"
    }
)

Creating Data Stores and Products

Myreze includes a system for creating data services that can generate and serve data packages on demand.

Basic Store Setup

from myreze.store.product import Product
from myreze.store.provider import ProductProvider
from myreze.store.server import StoreServer
from myreze.data import MyrezeDataPackage, Time
import numpy as np

class WeatherProduct(Product):
    """A product that generates weather data on demand."""
    
    async def generate_package(self, spatial_region, temporal_region, visualization=None):
        # Generate or fetch data based on the requested region
        weather_data = {
            "grid": np.random.rand(100, 100) * 30,  # Temperature 0-30°C
            "bounds": spatial_region["coordinates"],
            "units": "celsius"
        }
        
        return MyrezeDataPackage(
            id=f"weather-{temporal_region['value']}",
            data=weather_data,
            time=Time.from_dict(temporal_region),
            visualization_type="heatmap",
            metadata={"generated_at": "2023-01-01T12:00:00Z"}
        )

class MyWeatherProvider(ProductProvider):
    """Provider offering weather products."""
    
    async def get_products(self):
        return [
            WeatherProduct(
                product_id="current-weather",
                name="Current Weather Data",
                description="Real-time temperature and precipitation data",
                spatial_coverage={
                    "type": "BoundingBox",
                    "coordinates": [[-180, -90], [180, 90]]
                },
                visualization_type="heatmap"
            )
        ]

# Start the server
provider = MyWeatherProvider()
server = StoreServer(provider)
server.run()  # Runs on http://localhost:8000

Using the Store API

Once your store is running, clients can discover and order data:

# List available products
curl http://localhost:8000/products

# Order specific data
curl -X POST http://localhost:8000/orders \
  -H "Content-Type: application/json" \
  -d '{
    "product_id": "current-weather",
    "spatial_region": {
      "type": "BoundingBox", 
      "coordinates": [[-74.1, 40.6], [-73.9, 40.9]]
    },
    "temporal_region": {
      "type": "Timestamp", 
      "value": "2023-01-01T12:00:00Z"
    }
  }'

You can test these enpoints in your browser when running the server (try it!).

Visualization with Different Engines

Ideally, we'd like to accomodate various rendering environments, but for now we are focusing on Unreal and ThreeJS.

Three.js (Web Visualization)

from myreze.viz import ThreeJSRenderer

# Add Three.js visualization to your package
data_package = MyrezeDataPackage(
    id="web-viz-example",
    data=your_data,
    time=your_time,
    threejs_visualization=ThreeJSRenderer(),
    visualization_type="heatmap"
)

# Generate web-ready visualization
web_viz = data_package.to_threejs()

Unreal Engine (3D Visualization)

from myreze.viz import UnrealRenderer

# Add Unreal Engine visualization
data_package = MyrezeDataPackage(
    id="3d-viz-example",
    data=terrain_data,
    time=your_time,
    unreal_visualization=UnrealRenderer(),
    visualization_type="terrain"
)

# Generate 3D visualization
unreal_viz = data_package.to_unreal(params={"quality": "high"})

Time Handling Made Simple

Myreze supports three types of time representations:

from myreze.data import Time

# Single point in time
timestamp = Time.timestamp("2023-01-01T12:00:00Z")

# Time range
timespan = Time.span("2023-01-01T00:00:00Z", "2023-01-02T00:00:00Z")

# Multiple time points (for time series data)
timeseries = Time.series([
    "2023-01-01T00:00:00Z",
    "2023-01-01T01:00:00Z",
    "2023-01-01T02:00:00Z"
])

Package Components

• myreze.data: Core data structures and validation
• myreze.store: Data distribution system (products, providers, servers)
• myreze.viz: Visualization renderers for various platforms
  • threejs: Web-based 3D visualizations
  • unreal: Unreal Engine visualizations
  • png: Static image export

Data Conventions

For consistency across all Myreze packages, follow these conventions:

• Coordinate System and projection: Use EPSG:4326 for geolocations.
• Orientation: Let the Y component point up in returned geometries

AI Integration Features

Myreze is enhanced with intelligent features for AI workflows:

• Extended Docstrings: All classes and methods include comprehensive AI-generated documentation. Try typing help(...) on any part of the module when having it loaded in IPython.
• Smart Validation: Automatic data structure validation with helpful error messages
• Type Discovery: Built-in methods to discover available visualization types and requirements
• Schema Information: Programmatic access to data structure requirements

# Discover available visualization types
viz_types = MyrezeDataPackage.get_available_visualization_types()

# Get schema requirements for a specific type
schema_info = package.get_schema_info()

Development

Update PyPI

(this is mostly for Audun)

python -m build
python -m twine upload dist/*

Dependencies

Check pyproject.toml

Documentation

See the API documentation and tutorial for more information.

Examples

Check out the examples/ directory for complete working examples:

• create_data_package.py: Comprehensive data package examples
• create_store.py: Setting up data stores and products
• enhanced_demo.py: Advanced features demonstration