Introductory description

This module develops programming in the Python programming language and follows from PX150 Physics Programming Workshop

Module web page

Module aims

Outline syllabus

This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.

1. Vectorized programming in Python using Numpy
2. Handling, processing and analysing physics data: plotting distributions, data fitting, hypothesis testing
3. Monte Carlo simulation for physics modelling: Different types of random numbers, generation of random numbers according to specific distributions. Brownian motion and diffusion
4. Digital Signal Processing: the Fourier transform and convolution method, digital filters
5. Numerical solutions of ordinary differential equations: the Verlet algorithm for many coupled ODE’s
6. Speeding up Python: why, when and what again is a compiler

Learning outcomes

By the end of the module, students should be able to:

• Explain how computers can be used to solve physics problems
• Model physics problems using a computer
• Design algorithms and implement them.
• Handle and analyse physics data

Indicative reading list

M. Newman, Computational Physics, CreateSpace Independent Publishing Platform, ISBN: 978-
1480145511 (2012).

H.P. Langtangen, A Primer on scientific programming with Python, Springer e-books (2012):
http://link.springer.com/book/10.1007%2F978-3-642-18366-9

Ch. Hill, Learning Scientific Programming with Python, CUP (2016) (e-book)

• Python documentation: http://www.python.org/doc/

• Scientific Python: http://docs.scipy.org/doc/scipy/reference/

View reading list on Talis Aspire

Subject specific skills

Knowledge of programming. Skills in numerical modelling.

Transferable skills

IT skills, analytical, communication, problem-solving, self-study