tessl/pypi-gmaps

Google maps plugin for Jupyter notebooks with interactive visualization capabilities

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Pending

Overview

Eval results

Files

Transportation and Traffic Layers

Name: tessl/pypi-gmaps
Author: tessl

Display real-time and static transportation information including traffic conditions, public transit routes, and bicycling paths. These layers provide contextual information about transportation infrastructure and current conditions.

Capabilities

Traffic Layer

Display real-time traffic information with automatic updates and current road conditions.

def traffic_layer(auto_refresh=True):
    """
    Create a traffic information layer.

    Parameters:
    - auto_refresh (bool): Whether layer auto-updates traffic data

    Returns:
    Traffic: Traffic layer instance
    """

Traffic Widget

Widget for displaying current traffic conditions on roads.

class Traffic:
    """
    Traffic layer widget showing current traffic conditions.
    
    Attributes:
    - auto_refresh (bool): Whether layer auto-updates
    """

Transit Layer

Display public transportation routes and stops including buses, trains, and subway systems.

def transit_layer():
    """
    Create a public transit layer.

    Returns:
    Transit: Transit layer instance
    """

Transit Widget

Widget for showing public transportation infrastructure.

class Transit:
    """
    Transit layer widget showing public transport routes and stops.
    """

Bicycling Layer

Display bicycle-friendly routes and cycling infrastructure.

def bicycling_layer():
    """
    Create a bicycling routes layer.

    Returns:
    Bicycling: Bicycling layer instance
    """

Bicycling Widget

Widget for showing bicycle routes and cycling infrastructure.

class Bicycling:
    """
    Bicycling layer widget showing bike routes and cycling infrastructure.
    """

Usage Examples

Basic Traffic Layer

import gmaps

gmaps.configure(api_key="YOUR_API_KEY")

# Create figure with traffic layer
fig = gmaps.figure(center=(37.7749, -122.4194), zoom_level=12)
traffic_layer = gmaps.traffic_layer()
fig.add_layer(traffic_layer)

# Traffic conditions will be displayed in real-time
fig

Traffic with Auto-refresh Disabled

import gmaps

gmaps.configure(api_key="YOUR_API_KEY")

# Static traffic snapshot
fig = gmaps.figure()
traffic_layer = gmaps.traffic_layer(auto_refresh=False)
fig.add_layer(traffic_layer)
fig

Public Transit Layer

import gmaps

gmaps.configure(api_key="YOUR_API_KEY")

# Show public transportation routes
fig = gmaps.figure(center=(37.7749, -122.4194), zoom_level=12)
transit_layer = gmaps.transit_layer()
fig.add_layer(transit_layer)

# Bus routes, train lines, and subway systems will be displayed
fig

Bicycling Routes

import gmaps

gmaps.configure(api_key="YOUR_API_KEY")

# Show bicycle-friendly routes
fig = gmaps.figure(center=(37.7749, -122.4194), zoom_level=13)
bicycling_layer = gmaps.bicycling_layer()
fig.add_layer(bicycling_layer)

# Bike lanes, trails, and cycling routes will be displayed
fig

Combined Transportation Layers

import gmaps

gmaps.configure(api_key="YOUR_API_KEY")

# Create figure focused on urban area
fig = gmaps.figure(
    center=(37.7749, -122.4194),  # San Francisco
    zoom_level=12
)

# Add traffic information
traffic = gmaps.traffic_layer()
fig.add_layer(traffic)

# Add public transit routes
transit = gmaps.transit_layer()
fig.add_layer(transit)

# Add bicycling routes
bicycling = gmaps.bicycling_layer()
fig.add_layer(bicycling)

# All transportation modes are now visible
fig

Transportation with Points of Interest

import gmaps

gmaps.configure(api_key="YOUR_API_KEY")

fig = gmaps.figure(center=(37.7749, -122.4194), zoom_level=13)

# Add transportation layers
traffic = gmaps.traffic_layer()
transit = gmaps.transit_layer()
fig.add_layer(traffic)
fig.add_layer(transit)

# Add markers for transit stations or important locations
transit_stations = [
    (37.7749, -122.4194),  # Downtown station
    (37.7849, -122.4094),  # Midtown station
    (37.7949, -122.3994)   # Uptown station
]

markers = gmaps.marker_layer(
    transit_stations,
    label=['Downtown', 'Midtown', 'Uptown'],
    hover_text=['Main Transit Hub', 'Shopping District', 'Residential Area']
)
fig.add_layer(markers)

fig

Route Planning with Transportation Context

import gmaps

gmaps.configure(api_key="YOUR_API_KEY")

fig = gmaps.figure(center=(37.7749, -122.4194), zoom_level=12)

# Add transportation context layers
traffic = gmaps.traffic_layer()
bicycling = gmaps.bicycling_layer()
fig.add_layer(traffic)
fig.add_layer(bicycling)

# Add driving route
start = (37.7749, -122.4194)
end = (37.7849, -122.4094)

driving_route = gmaps.directions_layer(
    start, end,
    travel_mode='DRIVING',
    stroke_color='blue',
    stroke_weight=4.0
)
fig.add_layer(driving_route)

# Add cycling alternative
cycling_route = gmaps.directions_layer(
    start, end,
    travel_mode='BICYCLING',
    stroke_color='green',
    stroke_weight=3.0
)
fig.add_layer(cycling_route)

fig

Transportation Analysis Map

import gmaps
import gmaps.datasets

gmaps.configure(api_key="YOUR_API_KEY")

fig = gmaps.figure(center=(37.7749, -122.4194), zoom_level=11)

# Add all transportation layers for comprehensive view
traffic = gmaps.traffic_layer()
transit = gmaps.transit_layer()
bicycling = gmaps.bicycling_layer()

fig.add_layer(traffic)
fig.add_layer(transit)
fig.add_layer(bicycling)

# Add data layer (e.g., taxi pickups to show transportation demand)
try:
    taxi_data = gmaps.datasets.load_dataset_as_df('taxi_rides')
    pickup_locations = taxi_data[['pickup_latitude', 'pickup_longitude']]
    
    # Create heatmap of transportation demand
    demand_heatmap = gmaps.heatmap_layer(
        pickup_locations,
        opacity=0.6,
        max_intensity=10,
        gradient=['rgba(0,0,255,0)', 'rgba(0,0,255,1)', 'rgba(255,0,0,1)']
    )
    fig.add_layer(demand_heatmap)
except:
    # Dataset not available, skip
    pass

fig

Real-time Traffic Monitoring

import gmaps
import time

gmaps.configure(api_key="YOUR_API_KEY")

# Create traffic monitoring setup
fig = gmaps.figure(
    center=(37.7749, -122.4194),
    zoom_level=12,
    map_type='ROADMAP'
)

# Traffic layer with auto-refresh enabled
traffic = gmaps.traffic_layer(auto_refresh=True)
fig.add_layer(traffic)

# Add key intersections or highways as markers
key_locations = [
    (37.7749, -122.4194),  # Downtown
    (37.7849, -122.4094),  # Highway entrance
    (37.7649, -122.4294)   # Bridge approach
]

markers = gmaps.marker_layer(
    key_locations,
    label=['DT', 'HW', 'BR'],
    hover_text=['Downtown Core', 'Highway On-ramp', 'Bridge Approach']
)
fig.add_layer(markers)

# Traffic conditions will update automatically
fig

Transportation Accessibility Map

import gmaps

gmaps.configure(api_key="YOUR_API_KEY")

fig = gmaps.figure(center=(37.7749, -122.4194), zoom_level=12)

# Show all transportation options
transit = gmaps.transit_layer()
bicycling = gmaps.bicycling_layer()
fig.add_layer(transit)
fig.add_layer(bicycling)

# Add accessible locations
accessible_locations = [
    (37.7749, -122.4194),  # Wheelchair accessible station
    (37.7849, -122.4094),  # Bike share station
    (37.7649, -122.4294)   # Park & ride facility
]

accessibility_symbols = gmaps.symbol_layer(
    accessible_locations,
    fill_color=['blue', 'green', 'orange'],
    scale=[5, 4, 4],
    hover_text=[
        'Wheelchair Accessible Transit',
        'Bike Share Station',
        'Park & Ride Facility'
    ]
)
fig.add_layer(accessibility_symbols)

fig

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