Learning Resources

Learning resources that you can go through on your own time. Or take a look at the instructor-led training catalog as well as our upcoming training schedule.

Prerequisites of this course include familiarity with Emme Desktop and Emme Modeller and some basic Python knowledge.

View the course notebooks in your browser, or download the notebooks and follow along on your own with demonstration data. These materials are developed for Emme 4.5.0 and later, but may be adapted on previous versions.

Lesson 0: Python Basics This is a simple tutorial covering the basics of the Python programming language.

Lesson 1: Modeller API - Running Tools and Workflows Learn how to run a script execution of any Emme Modeller tool. Work effectively with tool specifications, implement model workflows using iterative or conditional control flow, and write comments to the Logbook for auditing.
4 hours

Lesson 2: Network API - Read, Write and Process Network Data Learn how to read, manipulate and publish Emme network data using the Emme Network API.
2 hours

Lesson 3: Matrix API - Read, Write and Process Matrix Data Learn how to access, manipulate, import and export matrix data using the Matrix API.
2 hours

Lesson 4: Desktop API - Automate Charts and Reports Learn how to automate Emme Desktop using the Desktop API.
2 hours

Lesson 5: Running Emme Workflows outside Emme Desktop Learn how to start Emme from a simple Python call to achieve Emme workflows without having to use the software interactively. Useful to automate workflows from A to Z, to implement distributed model computing etc.
2 hours

Lesson 6: Building Modeller Tools and Modules Learn how to create and distribute our own toolboxes and tools.
8 hours