Introduction to Scientific Computation Using Python

by Arun Prasaad Gunasekaran

Table of Contents

• Introduction to Scientific Computation Using Python
  • Table of Contents
  • What is this playlist about?
  • Why am I making this? - Part 1
  • Why am I making this? - Part 2
  • Reference Books
  • Note about references
  • Software Requirements
    • My setup
  • Ideology
  • We’ll be dealing with
  • Final note before we get started,
  • Are you ready?

What is this playlist about?

• A reference guide for people who wants to learn and use Python for scientific computation.
• A personal initiative to address a lot of concepts and make programming feasible to a lot of students.

Why am I making this? - Part 1

• To help people break their fear of programming.
• To equip people with sufficient tools for their scientific or engineering or mathematical requirements.

Why am I making this? - Part 2

• A challenge to myself to write and practice code for scientific computation.
• An attempt for me to go back to the basics and re-emerge to the advanced concepts with a lot of applications.
• To break my fear of failure.

Reference Books

For now, a majority of the ideas will be inspired from the two books below:

• Computational Physics - Problem Solving with Python, Third Edition, Rubin H. Landau, Manuel J, Paez, and Cristian C. Boudeianu, Wiley Publications
• A Primer on Scientific Programming with Python, Third Edition, Hans Petter Langtangen, Springer

Note about references

• In the future, I’ll add more references and examples.
• You need not have the references! :D. If you have it, then its a bonus!
• I’ll provide any additional references if content were taken outside of these books.

Software Requirements

• Must have a working installation of Python 3 installed in Windows/Linux/MacOS.

Personal Recommendation: Python from Anaconda/Miniconda or from Python from Enthought Canopy Distributions.

These distributions of python are designed specifically for scientific computation and have a collection of many commonly used packages and software at your disposal.

• One good Interactive Development Environment (IDE) for coding.

Personal Recommendation: Spyder, Pycharm

My setup

• Linux - Ubuntu 18.04 LTS 64-bit
• Python - Anaconda Python Distribution - Python 3.7+
• IDE - PyCharm

Ideology

We’ll be dealing with

• Data Visualisation,
• Making simple models,
• Running simulations,
• Numerical Experiments,
• Analysis of Experimental data,
• Inferences from the data

Final note before we get started,

• Do experiment!
• Point-out mistakes if I made!
• Feel free to give suggestions (I’ll try my best to incorporate them)

Are you ready?

Let’s start then! :D