This repository contains the main resources of a workshop serving as a crash-course introduction to Python for natural scientists. More specifically, for chemists and pharmaceutical scientists of masters and graduate level.
If you are completely new to programming and want to play with the examples used in the Jupyter Notebooks, we recommend to check out the provided Binder (icon next to the title of this section).
When you click on the Binder icon, a page is going to open where you can immediately browse the notebooks from this repository. It may take a while for Binder to set up the required environment, but this is the easiest way to use and play with the provided notebooks.
The repository contains a file named environment.yml. It specifies all requirements for the
conda environment to run the Jupyter notebooks and everything you need to work on the exercises.
The following steps are necessary to set up your own conda environment:
- install Anaconda/Miniconda
- create a conda environment
- activate the environment
- start the Jupyter notebook server
Anaconda and Miniconda are environment and package managers for Python. The difference between the two is that Anaconda ships with a lot of commonly used packages out of the box, whereas Miniconda only ships with the bare minimum required for environment and package management. The official websites offer install guides for every operating system:
Since installing either of them on a Linux system requires some additional dependencies, we'll walk through an example setup procedure of Anaconda for Debian and derived distributions like Ubuntu.
Download the anaconda_requirements.txt from this repository. Afterwards, open a terminal and enter
the following:
sudo apt-get install < anaconda_requirements.txtThis will install any system packages required by Anaconda. If you run a different distribution, you will have to look up the correct package names for your package manager.
Get the installer for Linux here.
Assuming you downloaded the installer verson Anaconda3-2019.10 to the standard Downloads folder,
type the following in your terminal (replace either the download folder or installer version with
the one you downloaded if necessary):
bash ~/Downloads/Anaconda3-2019.10-Linux-x86_64.shA few prompts will appear. You can type yes in all cases. But be careful, one of them will modify
the behavior of your bash scripts if you have any. If you select no, you can still let
Anaconda change the shell variables later with:
source <path to conda>/bin/activate
conda init <type of shell> # bash, zsh, sh, ...
If you accepted all prompts, you can now source your bashrc file:
source ~/.bashrcand initialize Anaconda with:
conda initFor more information, other operating systems or troubleshooting, please refer to the Anaconda installer site.
Now the provided environment.yml file comes into play. It lists all necessary packages needed to
use the Jupyter notebooks and the Python files in the Exercises and Solutions folders. Assuming you
set up your conda installation properly, you can create a new environment with all required packages
like this:
conda env create -f environment.ymlAs soon as the virtual environment is created properly (see last step) you can activate it with:
conda activate introductiontopythonAll the necessary packages should now be available.
Last but not least, you will have to start the Jupyter notebook server with:
jupyter notebookThe workshop that resides in this repository is only the beginning of your journey. There are many resources online that are a good reference point to dive deeper into programming with Python. We have tried to compile a short, opinionated list of some resources we think are a good start.
Software carpentry is a volunteer project aiming to teach researchers the computing skills they need to get more done in less time and with less pain.
MOOCs are online platforms that are designed to be like university courses with lectures and assignments (sometimes graded).
Codecademy is an online resource for many programming languages and concepts. They provide an interface with three columns (task | code | output) that makes coding fun and is easy to use. Unfortunately, learning Python3 is a pro feature and Python2 is officially deprecated since January 2020. Nonetheless, it is a good starting point, and the differences between the two language versions can be learned pretty quickly. Just make sure to not start a project in Python2 anymore!
The MolSSI has prepared a Python course as well. It goes beyond the material that is part of our workshop. It is a good location to start learning about best practices, unit testing and version control.
Is a teaching platform for computer-aided drug design (CADD), using open source packages and data. Python is only a first step to computational chemistry. Besides, data crunching, chemical and biological models are widely used to predict experimental outcomes or aid in exploring chemical mechanisms.
Chemotion aims for the development of a freely available infrastructure for research data management through the development of two independent IT systems namely an electronic laboratory notebook (ELN), and a chemistry web repository. We include it in this repository since it is a great platform that furthers the cause to create fully integrated and programmable pipelines from bench to predictive models.