ASDM
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Agile System Dynamics Modelling
asdm
Agile System Dynamics Modelling
ASDM is a Python library that enables users to create and simulate System Dynamics (SD) models. It also supports SD models saved in the XMILE format, including advanced features such as arrays and conveyors. The support is being continuously improved.
ASDM's Contribution & Impact
Check out this presentation: Project Care Home Demand, given by Sally Thompson, Senior Healthcare Analyst at The Strategy Unit (part of NHS Midlands and Lancashire CSU). The presentation highlights the role of ASDM in developing an online SD model-based simulator.
Library Structure
asdm/asdm.pycontains the main functionalities, including the lexer, parser, and interpreter.asdm/utilities.pyprovides a data visualisation tool.asdm/inference/consists of tools for model calibration.asdm/simulator/provides a web-based simulation interface for easy model execution, result downloading, and visualisation.
Installation
Install from PyPi
pip install asdm
ASDM and its required dependencies will be automatically installed.
Basic Usage
To create a new SD model using ASDM:
from asdm import sdmodel
model = sdmodel()
sdmodel is the core class for System Dynamics models.
Alternatively, you can load an SD model saved in XMILE format, including .stmx models:
model = sdmodel(from_xmile='example_model.stmx')
Run the simulation:
model.simulate()
Export simulation results:
- As a pandas DataFrame:
result = model.export_simulation_result(format='df') - As a Python dictionary:
result = model.export_simulation_result(format='dict')
Web-Based Simulation Interface
ASDM now includes a web-based simulation interface that allows users to:
- Upload
.stmxor.xmilemodels for simulation. - Download simulation results as a CSV file.
- Select variables and visualise them on an interactive chart.

Quick Start
Run the ASDM web simulator with:
asdm simulator
By default, this starts a local server at http://127.0.0.1:8080. If port 8080 is unavailable, specify a different port, for example:
asdm simulator --port 8081
You can also bind to all network interfaces to allow access from others:
asdm simulator --host 0.0.0.0
Once started, the browser will automatically open the simulator page.
You can also provide a model file directly to run it automatically:
asdm simulator path/to/model.stmx
This will launch the simulator and automatically run the specified model, displaying results immediately.
Features
- Drag-and-drop file upload: Upload your
.stmxor.xmilemodel file. - Simulation results in a table: Automatically display after the model runs.
- CSV download: You can download simulation results as a CSV file.
- Interactive charting:
- Select variables from a dropdown list.
- Automatically detects the time column name (e.g., "Years", "Months", etc.).
- Uses Plotly.js to generate interactive line charts.
Functionalities
Please refer to Documentation for detailed function descriptions.
Tutorial Jupyter Notebooks
Jupyter Notebooks demonstrate ASDM's functionalities:
SD Modelling
- Creating an SD model from scratch:
- Adding stocks, flows, auxiliaries.
- Support for nonlinear and stochastic functions.
- Running simulations.
- Exporting and examining simulation results.
- Visualising results.
Support for .stmx Models
- Load and simulate
.stmxmodels. - Support for arrays.
- Modify equations and re-run simulations.
More tutorial notebooks will be added.
Feel free to contribute your own via pull requests—please ensure they do not contain sensitive data.
Licence
ASDM is open-source and released under the MIT licence.
Contributors
Wang Zhao (main author)
- Postgraduate research student & research assistant at University of Strathclyde, UK.
- Software engineer at Newcastle Marine Services, UK.
- Speaker at multiple conferences on SD modelling.
- Contact: [email protected]; [email protected]
- Conference talk: Watch Here on YouTube.
Matt Stammers (contributor)
- Consultant Gastroenterologist & open-source developer at University Hospital Southampton, UK.
- Developed Streamlit-powered web apps using ASDM for healthcare modelling.
- Part of the Really Useful Models initiative: Learn More.
- GitHub: Matt's Homepage.