• If you have concerns about the course, please email to me or open the github issue. I value all suggestions.

  • Email: [email protected].

This course is intended to train students majored in thermal energy engineering in good software skills for producing code.

We will cover:

• writing clean, testable, high quality code in Python
• interactive analysis and literate programming with the IPython Notebook
• a useful set of algorithmic and problem reduction techniques
• computational tools to model and understand data(numpy, matplotlib, scipy)
• debug programs using a standardized approach
• write unit tests and evaluate software quality
• use version control
• C/C++ progamming with GCC

• A laptop computer will be needed in the classroom.

• John V. Guttag. Introduction to Computation and Programming Using Python. Revised and expanded edition. MIT Press. 2013.08.

• Guttag, John. Introduction to Computation and Programming Using Python: With Application to Understanding Data. MIT Press, 2016. ISBN: 9780262529624.

  • https://mitpress.mit.edu/index.php?q=books/introduction-computation-and-programming-using-python-0

  • 梁杰译. 编程导论. 人民邮电出版社(第1版) . 2015.03

  • Accompanying Python3 Code：https://mitpress.mit.edu/sites/all/modules/patched/pubdlcnt/pubdlcnt.php?file=/sites/default/files/code-978-0-262-52962-4_0.zip&nid=321887

• Python 3 documentation. https://docs.python.org/3/

• Lectures on scientific computing with python https://github.com/jrjohansson/scientific-python-lectures

• Michael J . Mora. Fundamentals of Engineering Thermodynamics(7th Edition). John Wiley & Sons, Inc. 2011

• An Introduction to GCC http://www.network-theory.co.uk/docs/gccintro/index.html.

• Scott Chacon，Ben Straub. Pro Git. https://git-scm.com/book/en/v2/Getting-Started-About-Version-Control

• Lectures in Jupyter Notebook

  Please install Jupyter to read and interactive with the notebook.

  Online read-only versions：

  http://nbviewer.ipython.org/github/PySEE/home/tree/S2018/notebook/

• Guide

  1. Building Software Environment

  2. Introduction to Markdown

The Course graded on an 100 point scale and then weighted according to the following distribution:

• In-class Exercises: 20%
• Practices(5):60%, Bonus Points: +5
• Final Exam: 20%

Please Visit Practices for details: https://github.com/PySEE/Practices/

1. Github(5)：Github、Git

2. Python and Interactive Computing(15)：The Simple Simulator of Rankine cycle

3. The Object-oriented Programming(15)： The General Simulator of Rankine cycle

4. Data Analysis(15)：Statistics, regression and visualization

5. Unit Test(10)：IAPWS-IF97 physical properties calculation and unit test

6. Bonus Points(+5): C/C++ Programming with GCC, Ubuntu

This repository contain all files of the course. You can manually download these files,

but we recommend that you use git to clone and update this repository.

If you have installed git, you can use any GUI git client to update this repository, for example: GitHub Desktop, Visual Studio Code.

You may also use the following commands:

When you clone a repository you set up a copy on your computer. Run:

git clone https://github.com/PySEE/home.git

This will create a folder home on your computer, with the files in subdirectories.

As we release new files, or if we update an already released files, you'll have to update your repository.

You can do this by changing into the home directory and executing:

git pull

That's it - you'll have the latest version of the repository.

We highly recommend you practice coding whenever you have a few minutes.

Even if you are just modifying available code, it will be incredibly beneficial.

You NEED to get your hands dirty and practice