Skip to main content

A graph-based routing library for dynamic routing.

Project description

Multi Modal Router

The Multi Modal Router is a graph-based routing engine that allows you to build and query any hub-based network. It supports multiple transport modes like driving, flying, or shipping, and lets you optimize routes by distance, time, or custom metrics. It can be expanded to any n-dimensional space making it versatile in any coordinate space

NOTE: This project is a work in progress and features might be added and or changed

In depth Documentation

installation guide

graph module documentation

code examples

command line interface documentation

utilities documentation

Features

Building Freedom / Unlimited Usecases

The graph can be build from any data aslong as the required fields are present (example). Whether your data contains real world places or you are working in a more abstract spaces with special coordinates and distance metrics the graph will behave the same (with minor limitations due to dynamic distance calculation, but not a problem when distances are already precomputed. solutions).

Example Usecases

  • real world flight router

    • uses data with real flight data and actuall airport coordinates
    • builds a graph with airport Hubs
    • connects airports based on flight routes
    • finds the shortest flights or multi leg routes to get from A to B
    • simple example implementation here
  • social relation ship graph

    • uses user data like a social network where users are connected through others via a group of other users
    • builds a graph with users as Hubs
    • connects users based on know interactions or any other connection meric
    • finds users that are likely to share; interests, friends, a social circle, etc.
  • coordinate based game AI and pathfinding

    • uses a predefined path network (e.g. a simple maze)
    • builds the garph representation of the network
    • finds the shortest way to get from any point A to any other point B in the network
    • you can checkout a simple example implementation for a maze pathfinder here

example from the maze solver

graph visualizations

Use the build-in visualization tool to plot any 2D or 3D Graph.

example plot of flight paths

Important considerations for your usecase

Depending on your usecase and datasets some features may not be usable see solutions below

potential problems based on use case

Please check your data for the following

distance present coordinate format unusable features special considerations
YES degrees None None
YES not degrees runtime distance calculations set drivingEnabled = False or do this
NO degrees None distances must be calculated when preprocessing
NO not degrees ALL U cant build the graph with neither distances or supported coordinates! solution

example dataframe with the required fields

License

see here

dependencies

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

multimodalrouter-0.1.12.tar.gz (331.8 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

multimodalrouter-0.1.12-py3-none-any.whl (24.0 kB view details)

Uploaded Python 3

File details

Details for the file multimodalrouter-0.1.12.tar.gz.

File metadata

  • Download URL: multimodalrouter-0.1.12.tar.gz
  • Upload date:
  • Size: 331.8 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.13.3

File hashes

Hashes for multimodalrouter-0.1.12.tar.gz
Algorithm Hash digest
SHA256 501c4fd1d38ab9bcdce9835ff87b6aa3e03e001133ea441175907dfc51a73202
MD5 315bf7cf301d6fe30426cf3c526976f2
BLAKE2b-256 ae09ba80bea6a6af814e0f3ebbd89e053a639d15110494446c0a0299dee61d69

See more details on using hashes here.

File details

Details for the file multimodalrouter-0.1.12-py3-none-any.whl.

File metadata

File hashes

Hashes for multimodalrouter-0.1.12-py3-none-any.whl
Algorithm Hash digest
SHA256 fe1f1bb3ac12ac6c12a5a09b7d298e76fbed93906c70f4f00203b9df43b7f098
MD5 8edc6fb9a96fc4f07544c39fdff3a01e
BLAKE2b-256 4ff3535da41464f04a8d0c8a5cd4a5cf3468262edd782b12b448c8963baf1556

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page