MPS224
Scientific computing

Lecturers:
Sam Dolan and Wei Xing

Module information

Most module information will appear here. There is also a Blackboard page for the course, with a discussion board etc.

Arrangements

There will be one lecture and one lab session each week.
In lectures, we will write code live and talk about it. We may also distribute tidier and more complete versions of that code, possibly with associated YouTube videos.
In the lab sessions, you will be asked to complete various tasks, which will partially overlap with tasks covered in the lecture.
For some of these tasks, you will be asked to paste code into an online test system. These tests will count for 10% of your grade for the course. There is a page of general instructions which you should read before attempting any tests.
There will be three assignments, each of which will account for 30% of your overall grade.
For each assignment, you will be asked to submit a Jupyter notebook containing a mixture of Python code and text with mathematics written in LaTeX (via Markdown).
Most of the credit will be for intelligent and accurate analysis of relevant mathematical issues and how they interact with software issues. Only a smaller part of the credit will be for the code itself.
You are welcome to send me email with questions, but you should first read the page asking for help for advice on how to ask questions effectively.

Setting up the environment

We will use Visual Studio Code as the main way to interact with Python. This is already installed on the university open access PCs, but you need to carry out a couple of small steps before you can use it. You can also install VS Code and other required software on your own PC or laptop. Instructions are given below:

University PC setup:	Web page	YouTube video
Personal PC setup:	Web page	YouTube video
Personal Mac setup:	Web page

Schedule

Details of the lectures and labs will appear here as the course progresses. The weekly schedule is:

Week 1: Python revision and an introduction to Visual Studio Code.
Week 2: Arrays, linear algebra and vectorization.
Week 3: Plotting with matplotlib.
Week 4: Solving differential equations.
Week 5: Integration and root finding.
Week 6: The Python ecosystem.
Week 7: Version control, testing and debugging.
Week 8: Data analysis and curve fitting with Pandas.
Week 9: Signal processing.
Week 10: 3D Plotting and animations.

Lectures and labs

Week 1:	Introduction to VS Code and revision	Lecture	Lab	Test	Solutions
Week 2:	`numpy` arrays, linear algebra and vectorization	Lecture	Lab	Test	Solutions
Week 3:	Plotting with `matplotlib`	Lecture	Lab	Test	Solutions
Week 4:	Solving differential equations	Lecture	Lab	Test	Solutions
Week 5:	Integration and PDEs	Lecture	Lab	Test	Solutions
Week 6:	The Python ecosystem	Lecture	Lab	Test	Solutions
Week 7:	Version control, testing and debugging	Lecture	Lab	(no test)	Solutions
Week 8:	Data analysis and curve fitting	Lecture	Lab	Test	Solutions
Week 9:	Signal processing	Lecture	Lab	Test	Solutions
Week 10:	3D plotting and animations	Lecture	Lab	Test	Solutions

Assignments

There will be three assignments, each counting for approximately 30% of your overall grade. Details of the assignments will appear on Blackboard as the course progresses.