The PySD Cookbook

Simple recipes for powerful analysis of system dynamics models

Quickstart

1. Download and install Anaconda Python 3.6+
2. Install PySD with pip using the command pip install pysd at your command prompt
3. Download this cookbook and unzip it to a working directory
4. Navigate your command prompt to the working directory just created
5. Launch ipython notebook with the command ipython notebook at your command prompt
6. In the ipython browser, open source\analyses\getting_started\Hello_World_Teacup.ipynb

How to use this cookbook

Every recipe in this cookbook is an executable ipython notebook. Because of this, I recommend that you download a copy of the cookbook, and follow along by executing the steps in each notebook as you read, playing with the parameters, etc.

If you want to implement a recipe, you can then make a copy of the notebook you are interested in, and modify it to analyze your own problem with your own data.

To download the cookbook in its entirity, use this link or visit the cookbook’s Github Page and select one of the options in the righthand panel.

Contents

• Getting Started
  • Introduction to the PySD Cookbook
  • Installation and Setup of Python and PySD
  • Getting Started with Python
  • Using the Jupyter Notebook
  • Working with git and github
  • Hello World: The Teacup Model
• Data Handling
  • Data handling with Pandas
  • Saving Simulation Results to a Database
• Visualization
  • Basic Visualization with matplotlib
  • Other Visualization Packages in Python
  • Phase Portraits
• Model Fitting
  • Fitting a model’s parameters with run-at-a-time optimization
  • Step-at-a-time optimization
  • Parallel Model Fitting
  • Fitting a model with Markov Chain Monte Carlo
• Geographic Analyses
  • Installation
  • Using SD to understand the SD Fever
  • Modify parameter values
  • Change Model settings
  • Geographical Information
  • Run the model for each country
  • Transpose simulation results for plotting
  • Comparative Analysis
  • Analysis of results
  • How Spatial Analysis Leads to Insight
  • Plotting simulation results on map with Ipywidgets
  • Using SD models to understand the differences between population measures at varying levels of geographic disagregation
• Surrogating Functions
  • Surrogating a function with a machine learning estimator
  • Surrogating a function with a machine learning estimator
• Realtime Data Incorporation
  • Nth Order Delay Demo
  • Feeding models with realtime streaming data
• Model Development Workflow
  • Unit Testing
• Data Used in this Cookbook
  • Baby Name Data
  • Collecting US decennial census data
  • Carbon Data Sources
  • Manufacturing Defects Synthetic Data
  • Ebola Data Loader
  • Parse gmail .mbox file
• Chapters to be Written
• End Notes
  • For instruction

Extra Resources

The PySD Documentation can answer most questions about how PySD itself should be used, with basic examples and function reference.

Indices and tables

• Index
• Module Index
• Search Page