Programming with Python for Experiments and Simulations in ...
Programming with Python for Experiments and Simulations in Psychology
This is a 10-day mini-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. Free downloadable copy at
Day 1 - Basics
What is Python and what can I do with it?
Obtaining and installing Python
Python resources
Fundamentals of programming languages (Chap 1)
Variables, expressions, and statements (Chap 2)
Void Functions (Chap 3)
Day 2 – Program design and flow
Program design (Chap 4)
Planning and writing with pseudo-code
Conditionals and recursion (Chap 5)
Functions that return values (Chap 6)
Day 3 – Algorithms and strings
Iteration (Chap 7)
Strings (Chap 8) [Includes the for loop]
Case study: Word play (Chap 9)
Day 4 – Data structures: Lists, dictionaries, and tuples
Lists (Chap 10)
Dictionaries (Chap 11)
Tuples (Chap 12)
Case study: data structure selection (Chap 13)
Day 5 – Reading and writing data files
Files (Chap 14)
Using text files to share data with Excel, SAS, and MATLAB
Hands-on project: Using Python for statistical analyses
Day 6 – Beginning object-oriented programming (OOP)
The object-oriented thought process
Classes and objects (Chap 15)
Classes and functions (Chap 16)
Classes and methods (Chap 17)
Day 7 – More advanced OOP + Graphic interfaces
Inheritance (Chap 18)
Case study: Tkinter (Chap 19)
Day 8 – Numerical and scientific computing with Python
NumPy
SciPy
matplotlib
Hands on project: Using NumPy for statistical analyses
Day 9 – More about GUIs
Tkinter
wxPython
Hands on project: Programming an Experiment
Day 10 – 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
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