Programming with Python for Experiments and Simulations in Psychology

Course overview

This is a 40 hour training course on how to use the Python programming language to create custom programs for behavioral experiments, computational models of psychological processes, data processing, and statistical analyses. The course is targeted toward advanced undergraduate students, graduate students, and faculty in experimental psychology. No prior programming experience is assumed, but some experience in scripting languages such as SAS, MATLAB, or E-Basic will help.

Main text:
Downey, A. (2008). Think Python: An introduction to software design. Needham, MA: Green Tea Press.

Course outline

Module 1 - Basics

• Lesson 1 - Getting started
• Lesson 2 - Fundamentals of programming languages (Chap 1)
• Lesson 3 - Variables, expressions, and statements (Chap 2)
• Lesson 4 - Void Functions (Chap 3)

Module 2 - Program design and flow

• Lesson 5 - Function interfaces (Chap 4)
• Lesson 6 - Program design
• Lesson 7 - Conditionals and loops (Chap 5)
• Lesson 8 - Fruitful functions (Chap 6)
• Source code for Module 2 exercises

Module 3 - Algorithms and strings

• Lesson 9 - Iteration: Recursion (Chap 6, 7)
• Lesson 10 - Iteration: Loops (Chap 7)
• Lesson 11 - Using strings and sequences (Chap 8)
• Lesson 12 - More about strings
• Source code for Module 3 exercises

Module 4 - Data structures: Lists, dictionaries, and tuples

• Lesson 13 - Case study: Word play (Chap 9)
• Text file for Word Play (Chap 9) exercises
• Lesson 14 - Lists (Chap 10)
• Lesson 15 - Tuples (Chap 12)
• Lesson 16 - Dictionaries (Chap 11)
• Source code for Module 4 exercises

Module 5 - Reading and writing data files

• Lesson 17 - Files (Chap 14)
• Lesson 18 - Sharing data with Excel, SAS, and MATLAB
• Lesson 19 - Handling exceptions
• Lesson 20 - Case Study: Data structure selection (Chap 13)
• Source code for Module 5 exercises

Module 6 - Numeric and scientific computing with Python

• Lesson 21 - NumPy
• Lesson 22 - SciPy
• Lesson 23 - Plotting and graphing with matplotlib
• Lesson 24 - Hands on project
• Source code for Module 6 exercises

Module 7 - Object-oriented programming (OOP)

• Lesson 25 - The object-oriented thought process
• Lesson 26 - Classes and objects (Chap 15)
• Lesson 27 - Classes and functions (Chap 16)
• Lesson 28 - Classes and methods (Chap 17)
• Lesson 29 - Inheritance (Chap 18)
• Source code for Module 7 exercises

Module 8 - Graphic Interfaces

• Lesson 30 - Case study: Tkinter (Chap 19)
• Lesson 31 - More about Tkinter
• Lesson 32 - Other GUI tools
• Hands on project: Programming an Experiment

Module 9 - Other Python libraries for behavioral scientists

• PyEPL - The Python Experimental Programming Library
• SymPy - A Python Computer Algebra System (CAS)
• PyDSTool - simulation package for dynamical systems
• Imaging
  • PIL - The Python Imaging Library
  • Visual Python
• Interfaces to hardware and other software ackages
  • PyLink - Interface to EyeLink eye tracking hardware
  • RPy - Interface to the R programming language
  • RSPython - Another interface to R
  • PyMat - Interface to MATLAB
  • PYML - Interface to Mathematica

Python websites

Python Software Foundation (python.org)

Python Tutorial by Guido van Rossum

Python Language Reference Manual

Numpy Example List with Documentation

The University of Oklahoma
Human Learning and Memory Lab
Department of Psychology, 455 W. Lindsey, Room 705, Norman, OK 73019
Phone 405.325.2599 | Fax 405.325.4737 | dkimball@ou.edu

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Questions about this site? Email: dkimball@ou.edu