scgraph 1.2.0

Determine an approximate route between two points on earth

scgraph

Supply chain graph package for Python

Documentation

Getting Started: https://github.com/connor-makowski/scgraph

Low Level: https://connor-makowski.github.io/scgraph/core.html

Key Features

• Calculate the shortest path between two points on earth using a latitude / longitude pair
  • Inputs:
    • A latitude / longitude pair for the origin
    • A latitude / longitude pair for the destination
  • Calculation:
    • Algorithms:
      • Dijkstra's algorithm (Modified for sparse networks)
        • Modified to support sparse network data structures
      • Makowski's Modified Sparse Dijkstra algorithm
        • Modified for O(n) performance on particularly sparse networks
      • Possible future support for other algorithms
    • Distances:
      • Uses the haversine formula to calculate the distance between two points on earth
  • Returns:
    • path:
      • A list of dictionaries (latitude and longitude) that make up the shortest path
    • length:
      • The distance in kilometers between the two points
• Antimeridian support
• Arbitrary start and end points
• Arbitrary network data sets

Setup

Make sure you have Python 3.6.x (or higher) installed on your system. You can download it here.

Installation

pip install scgraph

Use with Google Colab

See the example here

Getting Started

Basic Usage

Get the shortest path between two points on earth using a latitude / longitude pair In this case, calculate the shortest maritime path between Shanghai, China and Savannah, Georgia, USA.

# Use a maritime network geograph
from scgraph.geographs.marnet import marnet_geograph

# Get the shortest path between 
output = marnet_geograph.get_shortest_path(
    origin_node={"latitude": 31.23,"longitude": 121.47}, 
    destination_node={"latitude": 32.08,"longitude": -81.09}
)
print('Length: ',output['length']) #=> Length:  19596.4653

In the above example, the output variable is a dictionary with three keys: length, path and coordinate_path. - length: The distance in kilometers between the two points - path: A list of keys (from the network data set) that make up the shortest path - coordinate_path: A list of dictionaries (latitude and longitude) that make up the shortest path

To get the latitude / longitude pairs that make up the shortest path, as a list of lists, you could do something like the following:

# Use a maritime network geograph
from scgraph.geographs.marnet import marnet_geograph

# Get the shortest path between 
output = marnet_geograph.get_shortest_path(
    origin_node={"latitude": 31.23,"longitude": 121.47}, 
    destination_node={"latitude": 32.08,"longitude": -81.09}
)
print(str([[i['latitude'],i['longitude']] for i in output['coordinate_path']]))

Advanced Usage

You can specify your own custom graphs for direct access to the solving algorithms. This requires the use of the low level Graph class

from scgraph import Graph

# Define a graph
# See the graph definitions here: 
# https://connor-makowski.github.io/scgraph/core.html
graph = {
    0:{1: 5, 2: 1},
    1:{0: 5, 2: 2, 3: 1},
    2:{0: 1, 1: 2, 3: 4, 4: 8},
    3:{1: 1, 2: 4, 4: 3, 5: 6},
    4:{2: 8, 3: 3},
    5:{3: 6}
}

# Optional: Validate your graph
Graph.validate_graph(graph=graph)

# Get the shortest path between 0 and 5
output = Graph.dijkstra_makowski(graph=graph, origin_id=0, destination_id=5)
#=> {'path': [0, 2, 1, 3, 5], 'length': 10}

You can also use a slightly higher level GeoGraph class to work with latitude / longitude pairs

from scgraph import GeoGraph

# Define nodes
# See the nodes definitions here: 
# https://connor-makowski.github.io/scgraph/core.html
nodes = {
    0: {"latitude": 0, "longitude": 0},
    1: {"latitude": 0, "longitude": 1},
    2: {"latitude": 1, "longitude": 0},
    3: {"latitude": 1, "longitude": 1},
    4: {"latitude": 1, "longitude": 2},
    5: {"latitude": 2, "longitude": 1}
}
# Define a graph
# See the graph definitions here: 
# https://connor-makowski.github.io/scgraph/core.html
graph = {
    0:{1: 5, 2: 1},
    1:{0: 5, 2: 2, 3: 1},
    2:{0: 1, 1: 2, 3: 4, 4: 8},
    3:{1: 1, 2: 4, 4: 3, 5: 6},
    4:{2: 8, 3: 3},
    5:{3: 6}
}

# Create a GeoGraph object
my_geograph = GeoGraph(nodes=nodes, graph=graph)

# Optional: Validate your graph
my_geograph.validate_graph()

# Optional: Validate your nodes
my_geograph.validate_nodes()

# Get the shortest path between two points
output = my_geograph.get_shortest_path(
    origin_node = {'latitude': 0, 'longitude': 0},
    destination_node = {'latitude': 2, 'longitude': 1}
)
#=>
# {
#     "path": [6, 0, 2, 1, 3, 5, 7],
#     "coordinate_path": [
#         {'latitude': 0, 'longitude': 0},
#         {'latitude': 0, 'longitude': 0},
#         {'latitude': 1, 'longitude': 0},
#         {'latitude': 0, 'longitude': 1},
#         {'latitude': 1, 'longitude': 1},
#         {'latitude': 2, 'longitude': 1},
#         {'latitude': 2, 'longitude': 1}
#     ],
#     "length": 10
# }

Included GeoGraphs

• marnet_geograph:
  • What: A maritime network data set from searoute
  • Use: from scgraph.geographs.marnet import marnet_geograph
  • Attribution: searoute
  • Length Measurement: Kilometers
  • Geograph Picture

    
• oak_ridge_maritime_geograph:
  • What: A maritime data set from the Oak Ridge National Laboratory campus
  • Use: from scgraph.geographs.oak_ridge_maritime import oak_ridge_maritime_geograph
  • Attribution: Oak Ridge National Laboratory with data from Geocommons
  • Length Measurement: Kilometers
  • Geograph Picture

    
• north_america_rail_geograph:
  • What: Class 1 Rail network for North America
  • Use: from scgraph.geographs.north_america_rail import north_america_rail_geograph
  • Attribution: U.S. Department of Transportation: ArcGIS Online
  • Length Measurement: Kilometers
  • Geograph Picture

    
• us_freeway_geograph:
  • What: Freeway network for the United States
  • Use: from scgraph.geographs.us_freeway import us_freeway_geograph
  • Attribution: U.S. Department of Transportation: ArcGIS Online
  • Length Measurement: Kilometers
  • Geograph Picture

    

Attributions and Thanks

Originally inspired by searoute including the use of one of their datasets that has been modified to work properly with this package.