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PHYS281: Scientific Programming and Modelling Project

Important

As of the 2022/23 academic year, the most up-to-date version of these course notes can be found at https://www.lancaster.ac.uk/staff/drummonn/PHYS281/. Please refer to those pages for details on the course, including dates and submission details for assignments.

This page will remain in place for posterity and can still be used as a general reference for Python programming.

This website provides a series of notes introducing the Python programming language as part of the PHYS281 course. These notes are available in both text and video formats. They can be accessed in any order, but it is recommended to cover them in the order presented in the table of contents sidebar. Concepts and terms will be covered in that order.

To get the most out of the notes it is recommended that you try writing and running the examples that are presented. A series of exercises are provided to test yourself on the practicalities of creating some code for a specific problem. These exercises are not part of the course assessment.

This website was created by Matthew Pitkin, based on earlier lecture notes by Iain Bertram, Robin Long and Brooke Simmons. Feedback to improve this website is very welcome, so if you have any questions or comments please contact the current module lecturers:

In this course we will use many jargon terms and unfortunately this is inevitable. In some cases certain terms will be used interchangeably as synonyms. We have created a glossary of many terms to try and help with this, but it will be incomplete. Please do ask us if there are any concepts or terms that you do not understand and remember that Google is your friend! It is worth noting that the aim of this course is to allow you to write and use Python code. However, this is not a computer science course and we do not expect you to understand the detailed reasons behind why something is done in a certain way. In the majority of cases you can treat what we show you as a recipe to follow without needing to know why a particular syntax or formatting is required.

Course aims

Programming is an important skill for scientists in the modern world. At the end of this module you should be able to:

  • understand the basic concepts involved in writing computer programs;
  • understand the main features of Python;
  • design and write simple programs in Python;
  • devise and use test procedures for your programs;
  • learn how to debug computer programs;
  • design and write programs to solve numerical problems.

Programming is a skill. While there is a syntax that you have to learn, the skill is mainly acquired through practice, i.e., actually writing and running code.

What is Python?

Python is a programming language, i.e., it is used to write instructions with a specific syntax that are interpreted and run by a computer. Python is an interpreted language rather than a compiled language. This means that you write a code that is then interpreted and run line-by-line. In a compiled language, such as C, the code you write has to be compiled by a different program to produce an executable file (basically one written in a more primitive machine language), that you then run.

Interpreted languages are often slightly slower at certain tasks than compiled languages, but have the advantage that they are easier for doing code development, and can even be run interactively, due to not having the extra compilation step.

Python is what is known as an Object Oriented Programming (OOP) language (other major languages that support the object oriented paradigm include C++ and Java). This means that the fundamental things that you use within a code are objects; these are things that can contain both data and functions that can perform specific tasks on that data.

There have been several versions of Python as the language gets updated and new features added. The latest major version is Python 3, with Python 3.10 being a recent stable release. While most Python code is fairly backwards compatible, i.e., can run with previous versions of the language, there are some specific features that are not. Broadly speaking, code written based on Python 3.5 or above should be compatible and in this course we will not use any syntax that does not work across these versions.

Python can be run in both an interactive mode, i.e., you can start an interactive Python terminal into which you can type and run commands immediately, or it can be used to run pre-written scripts. In this course we will describe both these modes of use.

Why Python?

There are many reasons why Python is a good choice as a programming language to learn:

  • Python is now becoming a popular language in school, so you are more likely to have experience that we can build on.
  • Python is currently one of the most used languages in industry (see, e.g., the IEEE Spectrum report).
  • There are a huge number of widely used and tested resources and packages out there for scientific computing (see, e.g., https://wiki.python.org/moin/NumericAndScientific), such as NumPy, SciPy and Matploltib.
  • Python is portable - Python programs are cross platform and run on most computer systems in use today.
  • Many modern data science and machine learning applications use Python as an interface, which is driving its growth.
  • Python is currently used by many academics in the department.

In the TIOBE index Python popularity and usage is growing:

Sep 2021 Sep 2020 Change Programming Language Ratings Change
1 1 C 11.83% -4.12%
2 3 Python 11.67% +1.20%
3 2 Java 11.12% -2.37%
4 4 C++ 7.13% +0.01%
5 5 C# 5.78% +1.20%
6 6 Visual Basic 4.62% +0.50%

Course overview

This course is assessed through coursework only. There is no exam.

The course assessment consists of two main components:

  • Five coursework assignments and quizzes over Weeks 1-5 of Michaelmas Term
  • A project over Weeks 6-10 of Michaelmas Term

The five coursework assignments and quizzes count for 30% of the overall grade (6% each), with the project counting for 70%.

Help and guidance for the exercises and project will be provided in weekly (Weeks 1-10) computer labs split over three sessions. All students will be assigned to one of these three sessions. Support can also be found via the course Teams channel during these same time slots. There will also be an "office hour" slot for additional help.

The assignments and quizzes must be submitted on Moodle by 14:00 on the Tuesday the week after the associated lab session (e.g., the Week 1 assignment should be submitted no later than 14:00 on the Tuesday of Week 2). The late submission deadline is 14:00 on the following Wednesday. If you require an extension or have a problem please contact Elisabetta Boella or Neil Drummond or Louise Crook via email or Teams. Submitted code will be tested and marked automatically using a Moodle plugin. To avoid losing marks it is highly recommended that you test your code on your own machine before submitting it on Moodle. Read the questions and expected inputs/outputs carefully, including making sure functions/classes are named exactly as specified (including letter case).

The project grade is split between several components:

  • 2 exercises to be submitted prior to the final project (5% each)
  • The written project report (30%)
  • The project code (30%)

The project aim is to create and test an \(n\)-body gravitationally interacting system (e.g., the Solar System). The two exercises provide a basis on which to build up to the full \(n\)-body simulation.

Prerequisites

Before getting started with the course please make sure you are familiar with some of the basics of the exploring the directory/folder structure of the computer operating system that you are using. You should know how to browse the directory structure (e.g., with "File Explorer" in Windows, or "Finder" on a Mac OS), be able to create new folders, and understand how file path structure is formatted, including drive if necessary (e.g., C:\User\username\Project\myfile.py).

Please also see the material in the "Course notes" -> "Getting started" menu for information on installing the software required for this course (Anaconda and Visual Studio Code) on your own machine or using AppsAnywhere on a machine on campus. The "Online Python environments" section below has some options for running Python, which can be used as a back-up alternative if you have initial installation problems.

Exercises

A selection of exercises have been created for you to try out and test your Python knowledge. These exercises are not marked and are voluntary. They will also be used for demonstration purposes during the course lecture slots.

Other material

There are a variety of useful Python tutorials freely available online:

  • The official Python documentation comes with a tutorial on getting started with Python, covering the majority of concepts required to become a proficient user.
  • The w3schools.com site also offers a Python tutorial which provides interactive demonstrations embedded in the browser, and includes an introduction to NumPy.
  • There is a very nice, and freely available, Software Carpentry course "Plotting and Programming in Python".

Ethical programming

Coding is done by people and the outputs of code can affect peoples lives. In some cases the code you produce may be part of publicly funded work. In others, it may be done within a private institution and bound by national laws or industry-specific regulations and guidelines. So coding cannot exist independently of ethics and scrutiny by others.

In general, it is good to think of the following practices when coding:

  • Do not use code for illegal or malicious activities.
  • Make sure you properly attribute any part of your code that uses code/algorithms from other sources. Many codes have open source licenses that allow you to use them or edit them provided proper credit is given. Consider giving an open source license to your own code (see, e.g., here), such as an MIT license or GPL license.
  • Coding is often done as part of a community. In such cases be kind and be inclusive. Take a look at, e.g., the NumFOCUS Code of Conduct.
  • If your code directly affects people or uses personal data, think about whether its design could contain or promote biases and if so whether it is possible to mitigate against them.
  • Try to document, curate and distribute your code, so that it is useful and usable to others. Results should be reproducible into the future. For example, consider hosting your code on a open repository such as Github (other repositories exist!) and providing online documentation, including practical examples of running the code, on sites such as Read the Docs.
  • Make sure any data that your code uses or produces is curated and handled in accordance with data protection and governance laws. This is particularly relevant to personal data, for which security of the data and compliance with laws and user agreed conditions of use are vital.

While these may not seem relevant to this particular course they are good things to keep in mind for the future.

Online Python environments

There are a variety of online Python environments that can be used as a back up. These generally require your to create an account, but there are generally free account options that provide a limited but usable range of functionality. For the first half of the course these should suffice for any coding needs, but may be more limited when attempting the project. The options suggested below seem to have NumPy, SciPy and Matplotlib available.

Let us know of any others.