Computational Laboratory in Physics (Physics 102)

Announcements:

• Grade histogram at bottom of page.

Instructor: Professor Jim Crutchfield (Physics and CSC)
Assistant: Alec Boyd (Physics and CSC)
WWW: http://csc.ucdavis.edu/~chaos/courses/clab/

Upper Division catalog number: Physics 102
CRNs: 36231 (section A01), 36232 (section A02), 36233 (section A03)
Units: 1
Lecture Time and Location: Tu 0310-0400 PM: 148 Physics
Lab (3 hours per week) Times and Location: 106 Physics
   Catalog lists Mo, Tu, Th 0410-0700 PM, but
   Walk-in, no need to attend any particular time.
Office hours:
   Crutchfield: Wed 0300-0400 PM, 197 Physics
   Boyd: Fri 1030 AM - 1230 PM, 106 Physics
Computer Access:
   Accounts provided on Linux machines in Physics Computer Lab (106 Physics).
   Or, you may use your own system.

Description: The course is a brief introduction to computational physics methods and the required computational infrastructure—editors, debuggers, version control, documentation tools, and Physics Department computing resources.

At a minimum, it will prepare you for programming assignments required in other upper division physics classes.

The goal, though, is for you to learn how to build powerful computing tools that help you do physics.

With some initiative, you can learn how to design and build interactive tools for simulating and visualizing many complex physical systems.

We will pursue these using a modern interpreted language called Python.

Outline: (Course Syllabus [PDF] [HTML])

• Week 1: Introduction, Python and the iPython Environment
• Weeks 2-3: Data Types and Calculating
• Weeks 4-5: Strings, Lists, Tuples, Loops, Conditionals, File I/O
• Weeks 5-6: Dictionaries, Arrays, Functions, and Modules
• Weeks 7-8: Statistics, Linear Algebra, and Plotting
• Weeks 9-10: One-Dimensional Dynamics and Numerical Integration

Physical systems to be analyzed:

• Mechanics
• Low-dimensional dynamical systems
• Chaos and routes to chaos
• Spatially extended systems, as time and interest permit

Prerequisites: Mathematics 21D, 22A, 22B, Computer Science Engineering 30, Physics course 9D or 9HD, Physics course 104A concurrently. Not open for credit to students who have completed Physics course 104B or 105AL.

Audience: Upper division undergraduates in physics, mathematics, computer science, engineering, mathematical biology, and theoretical neuroscience. Others also welcome.

Reference materials:

• Books (suggested):
  • Numerical Methods for Physics by Alejandro Garcia.
  • Dive into Python, Mark Pilgram (2004). Out of date, but free!
  • Learning Python, M. Lutz, Fourth Edition, O'Reilly Media, New York (2009). This edition covers Python 2.6 and Python 3.X. We'll use Python 2.7 in the course, which is very close to Python 2.6. An electronic version is on reserve at the library. Go there, select Course Reserves Catalog. Select Instructor, enter Crutchfield. This will bring up a page with a link to the electronic version. Here is a pdf.
  • But there are many many others.
• Lecture Notes.
• Python Programming Labs.
• Python Installation.
• Computing Tutorials.