Why Python:
We have decided to use Python as the primary programming language for this class. To quote Landau, Páez, & Bordeianu in their Computational Physics book, "Python is free, robust (not easily broken), portable (program run without modifications on various devices), universal (available for most every computer system), has a clean syntax that lets students learn the language quickly, has dynamic typing and high-level, built-in data types that enable getting programs to work quickly without having to declare data types or arrays, count matching braces, or use separate visualization programs. Because Python is interpreted, students can learn the language by executing and analyzing individual commands within an interactive shell, or by running the entire program in one fell swoop. Furthermore, Python brings to scientific computing the availability of a myriad of free packages supporting numerical algorithms, state-of the art, or simple, visualizations and specialized toolkits that rival those in Matlab and Mathematica/Maple. And did we mention, all of this is free?"
A Python primer (+ other chapters) from Mark Newman's book
We have decided to use Python as the primary programming language for this class. To quote Landau, Páez, & Bordeianu in their Computational Physics book, "Python is free, robust (not easily broken), portable (program run without modifications on various devices), universal (available for most every computer system), has a clean syntax that lets students learn the language quickly, has dynamic typing and high-level, built-in data types that enable getting programs to work quickly without having to declare data types or arrays, count matching braces, or use separate visualization programs. Because Python is interpreted, students can learn the language by executing and analyzing individual commands within an interactive shell, or by running the entire program in one fell swoop. Furthermore, Python brings to scientific computing the availability of a myriad of free packages supporting numerical algorithms, state-of the art, or simple, visualizations and specialized toolkits that rival those in Matlab and Mathematica/Maple. And did we mention, all of this is free?"
A Python primer (+ other chapters) from Mark Newman's book
Books on Computational Physics (Python Based):
1. Computational Physics by Mark Newman.
2. Computational Physics, 3rd Ed, Problem Solving with Python by Rubin H Landau, Manuel J Paez & Cristian Bordeianu.
1. Computational Physics by Mark Newman.
2. Computational Physics, 3rd Ed, Problem Solving with Python by Rubin H Landau, Manuel J Paez & Cristian Bordeianu.
Books on Numerical Methods:
1. Numerical Mathematical Analysis by James B. Scarborough
2. Applied Numerical Analysis by Curtis F. Gerald and Patrick O. Wheatley
3. Numerical Methods by Sukehndu Dey and Shishir Gupta (McGraw Hill)
1. Numerical Mathematical Analysis by James B. Scarborough
2. Applied Numerical Analysis by Curtis F. Gerald and Patrick O. Wheatley
3. Numerical Methods by Sukehndu Dey and Shishir Gupta (McGraw Hill)
Fortran manuals
fortran_shortmanual.pdf | |
File Size: | 211 kb |
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prof77.pdf | |
File Size: | 505 kb |
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fortran.pdf | |
File Size: | 61 kb |
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fortran_interactive.pdf | |
File Size: | 1443 kb |
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Learning Basic Unix Commands:
Searching "unix commands" on google works pretty well. Here's a list of some links which seem relatively short and manageable.
http://mally.stanford.edu/~sr/computing/basic-unix.html
www.math.utah.edu/lab/unix/unix-commands.html
https://kb.iu.edu/d/afsk
Learn the following first. Then slowly learn some more.
mkdir, cd, ls, cp, mv, rm (be careful about rm), pwd, ls -l, cat, wc, more.
Searching "unix commands" on google works pretty well. Here's a list of some links which seem relatively short and manageable.
http://mally.stanford.edu/~sr/computing/basic-unix.html
www.math.utah.edu/lab/unix/unix-commands.html
https://kb.iu.edu/d/afsk
Learn the following first. Then slowly learn some more.
mkdir, cd, ls, cp, mv, rm (be careful about rm), pwd, ls -l, cat, wc, more.
Slides: How Computers Store and Manipulate Numbers
number_represent.pdf | |
File Size: | 3622 kb |
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Fortran in high-performance scientific computing: https://arstechnica.com/science/2014/05/scientific-computings-future-can-any-coding-language-top-a-1950s-behemoth/2/