Programming-Resources

Programming Resources

Our simulations use several difference tools and programming languages. All of us were new to at least some of these when we first started in the group. This is a list of resources that we found helpful for getting started. Please help us add more resources to this list if you find any that are especially useful!

[Last Update: November 2023]

This guide was compiled by Rob Campbell.

Contents

1. Foundational CS Skills (i.e. Command Line, VIM, Git, Markdown, and other general skills)
2. Python
3. R
4. C/C++
5. Fortran
6. Customizing VMD
7. Parallel Computing
8. HPC
9. Best Practices for Scientific Computing
10. Best Practices for Collaborating on Code

Foundational CS Skills

• The Missing Semester of Your CS Education
  Recorded lectures from MIT CSAIL's course on using the shell, VIM, Command Line, Git, etc.

Command Line

• Terminal Cheat Sheet (PDF)

VIM

• Graphical vi-vim Cheat Sheet and Tutorial

Git

• The Github Guide in the getting-started reposirory

• Git Cheat Sheet (PDF)

• Understanding the GitHub flow

• Learn Git Branching
  An interactive set of tutorials for learning Git.

• Pro Git Book
  Comprehensive free book for learning to use Git repositories.

• Reproducible research: Goals, Guidelines and Git
  Slides from a 2019 Princeton workshop with an overview of reproducible research best practices and a guide to setting up Git.

• .gitignore Templates

• Setting Up a Github Repository for Your Lab
  A guide for how to manage a research lab's Github organizational account. Aimed at ecology and evolutionary biology research, but includes many broadly applicable best practices.

• Scientific Collaboration and Project Management in GitHub
  Blog post about Github for scientific research project management

• Cookiecutter Science Project
  An example template for reproducible science projects (uses Conda)

Markdown

• Markdown Basic Syntax

• Getting Started with writing and formatting on Github

• Complete list of github markdown emoji markup

Python

• Jupyter Notebooks and Jupyter Lab

• For help inheriting some of the analysis code originally written as modules:

  • Formatting strings to automatically call variables

  • Runnign Modules with __name__ = __main__

R

• R for Data Science course on LinkedIn Learning (LinkedIn Learning)

• R Essential Training: Wrangling and Visualizing Data (LinkedIn Learning)

• R Essential Training Part 2: Modeling Data (LinkedIn Learning)

• A note on for-loops in R (StackOverflow)

• RStudio Cheat Sheets

• RStudio Cheat Sheet: ggplot2 (a more powerful data visualization package than base R's plot function)

• R for everyone : advanced analytics and graphics by Jared Lander (available as an E-book from the NU Library)

• R for Reproducible Scientific Analysis (Software Carpentry)

• 10 tips for making your R graphics look their best

C/C++

• C++ Language Tutorials/Overview

• Headers and Includes Overview (why use .cc and .h files)

Fortran

• Quickstart and other Fortran tutorials

• Understanding "precision" and "kind"

  • Best practices for declaring precision of variables
  • 1d0 = 1.0d0

• Rounding in Fortran

• Interfacing Fortran and Python (f2py)

  • f2py and alternatives
  • f2py has limited KIND parameters
  • example using Python/C++/Fortran together

• Input/Output Formatting

Customizing VMD

• How to change the default window layout

• Modifying the init script to change default display settings

• Change the default VMD movie maker settings

Parallel Computing

MPI

• OpenMPI FAQ: General

HPC

Slurm

• Slurm Workload Manager website (with links to tutorials and a quick start guide)

• Slurm Commands Summary (PDF)

Best Practices for Scientific Computing

• Good Enough Practices for Scientific Computing
  2016 follow up to "Best Practices for Scientific Computing" as 6 best practices specifically geared towards people who are just getting started with scientific computing.

• Best Practices for Scientific Computing
  2014 paper outlining 8 best practices for scientific computing.

Best Practices for Collaborating on Code

• Stealing Google's Coding Practices for Academia
  A 2016 blogpost on the differences between academic code and production code, making an argument for best practices for collaborative programming in academic research: style guides, tooling, code review, pair programming, and open source.

• Reproducible research: Goals, Guidelines and Git
  Slides from a 2019 Princeton workshop with an overview of reproducible research best practices and a guide to setting up Git (geared towards bioinformatics, but still useful)

• Making READMEs Readable
  Best practices for open-source READMEs/documentation