I’m going out on a limb here and guessing you’re either new to programming, or you’re self taught and looking for ways to improve. New graduate students are typically met with the cold reality that they need to quickly learn how to program to keep up with their course work and/or research. They search for help online (thank you StackOverflow) and ask seasoned graduate students for help. This places an unnecessary burden on older graduate students, and can be frustrating and stressful for new students.
Inefficient programming habits formed while learning tend to hang around long after graduation because it takes time and effort to break habits. Free time is of short supply. Especially after graduation when real life begins.
I’m here to help.
In this series we will explore a framework and best practices to create reproducible science with Python that others can easily understand. You will learn from my many mistakes. I get it, my methods are not the only way to do things. The idea is to provide a starting point that you can adjust to fit your style.
My platform of choice is Anaconda. More specifically, Miniconda. Miniconda is a stripped-down version of Anaconda that will serve our purposes perfectly. Anaconda uses the Conda manager. Per the website, Conda is a ” Package, dependency and environment management for any language—Python, R, Ruby, Lua, Scala, Java, JavaScript, C/ C++, FORTRAN, and more“.
Outline of posts (updated: 2020-01-06)
- Installation
- Environments
- Project Organization
- Version Control
- Simple Script Framework
- Functions
- Classes
- Advanced Script Framework
