Deckgl example

# DeckGL Map Example This notebook demonstrates the DeckGLMap widget for high-performance data visualization in Jupyter environments. DeckGL is a WebGL-powered framework for visual exploratory data analysis of large datasets.

In [1]:

import anymap
import numpy as np
import random

import anymap import numpy as np import random

## Basic DeckGL Map Create a basic DeckGL map widget with default settings.

In [2]:

# Create a basic DeckGL map
deck_map = anymap.DeckGLMap(
    center=[37.7749, -122.4194], zoom=10, width="100%", height="600px"  # San Francisco
)

deck_map

# Create a basic DeckGL map deck_map = anymap.DeckGLMap( center=[37.7749, -122.4194], zoom=10, width="100%", height="600px" # San Francisco ) deck_map

Out[2]:

## Scatterplot Layer Add a scatterplot layer with random points around San Francisco.

In [3]:

# Generate random data points around San Francisco
num_points = 1000
base_lat, base_lng = 37.7749, -122.4194

scatterplot_data = []
for i in range(num_points):
    lat = base_lat + (random.random() - 0.5) * 0.1
    lng = base_lng + (random.random() - 0.5) * 0.1
    scatterplot_data.append(
        {
            "position": [lng, lat],
            "radius": random.randint(20, 100),
            "color": [
                random.randint(0, 255),
                random.randint(0, 255),
                random.randint(0, 255),
                200,
            ],
        }
    )

# Add scatterplot layer
deck_map.add_scatterplot_layer(
    layer_id="scatterplot",
    data=scatterplot_data,
    get_position="position",
    get_radius="radius",
    get_color="color",
    radius_scale=1,
    pickable=True,
)

print(f"Added {len(scatterplot_data)} points to the scatterplot layer")

# Generate random data points around San Francisco num_points = 1000 base_lat, base_lng = 37.7749, -122.4194 scatterplot_data = [] for i in range(num_points): lat = base_lat + (random.random() - 0.5) * 0.1 lng = base_lng + (random.random() - 0.5) * 0.1 scatterplot_data.append( { "position": [lng, lat], "radius": random.randint(20, 100), "color": [ random.randint(0, 255), random.randint(0, 255), random.randint(0, 255), 200, ], } ) # Add scatterplot layer deck_map.add_scatterplot_layer( layer_id="scatterplot", data=scatterplot_data, get_position="position", get_radius="radius", get_color="color", radius_scale=1, pickable=True, ) print(f"Added {len(scatterplot_data)} points to the scatterplot layer")

Added 1000 points to the scatterplot layer

## Hexagon Layer Add a hexagon layer for spatial aggregation visualization. This demonstrates DeckGL's ability to aggregate point data into hexagonal bins.

In [4]:

# Generate data for hexagon layer
num_hex_points = 2000
hex_data = []

for i in range(num_hex_points):
    lat = base_lat + (random.random() - 0.5) * 0.2
    lng = base_lng + (random.random() - 0.5) * 0.2
    hex_data.append({"position": [lng, lat], "weight": random.random() * 10})

# Add hexagon layer
deck_map.add_hexagon_layer(
    layer_id="hexagons",
    data=hex_data,
    get_position="position",
    get_weight="weight",
    radius=500,
    elevation_scale=4,
    pickable=True,
    extruded=True,
)

print(f"Added hexagon layer with {len(hex_data)} data points")

# Generate data for hexagon layer num_hex_points = 2000 hex_data = [] for i in range(num_hex_points): lat = base_lat + (random.random() - 0.5) * 0.2 lng = base_lng + (random.random() - 0.5) * 0.2 hex_data.append({"position": [lng, lat], "weight": random.random() * 10}) # Add hexagon layer deck_map.add_hexagon_layer( layer_id="hexagons", data=hex_data, get_position="position", get_weight="weight", radius=500, elevation_scale=4, pickable=True, extruded=True, ) print(f"Added hexagon layer with {len(hex_data)} data points")

Added hexagon layer with 2000 data points

## Layer Management Demonstrate layer management capabilities like listing and controlling layers.

In [5]:

# List all layers
layers = deck_map.get_layers()
print("Current layers:")
for layer_id in layers.keys():
    print(f"- {layer_id}")

# Demonstrate map controls
deck_map.set_pitch(45)  # Tilt the map for 3D effect
deck_map.set_bearing(30)  # Rotate the map

print("\nSet pitch to 45° and bearing to 30° for a 3D perspective view")

# List all layers layers = deck_map.get_layers() print("Current layers:") for layer_id in layers.keys(): print(f"- {layer_id}") # Demonstrate map controls deck_map.set_pitch(45) # Tilt the map for 3D effect deck_map.set_bearing(30) # Rotate the map print("\nSet pitch to 45° and bearing to 30° for a 3D perspective view")

Current layers:
- scatterplot
- hexagons

Set pitch to 45° and bearing to 30° for a 3D perspective view

## Summary This notebook demonstrated the key features of the DeckGLMap widget: 1. **Basic map creation** with center, zoom, and size configuration 2. **Scatterplot layer** for visualizing point data with custom colors and sizes 3. **Hexagon layer** for spatial aggregation and 3D visualization 4. **Layer management** for listing and controlling layers 5. **3D controls** for setting pitch and bearing to create perspective views The DeckGLMap widget provides high-performance WebGL-based visualization capabilities suitable for large datasets and complex geospatial visualizations. It's particularly effective for: - Large-scale data visualization (millions of points) - Interactive data exploration - 3D geospatial visualization - Real-time data streaming visualization - Custom data aggregation and analysis To test the map functionality, run all cells above and interact with the map by panning, zooming, and clicking on the visualization layers.