aboucaud / python-euclid2016

Material for the Euclid Python programming course - May 2016

Python Tutorial for Euclid Developers

This repository contains material associated with the basic and advanced Python tutorial.

Date: 26-27 May 2016

Instructors:

• Alexandre Boucaud [e-mail]
• Mher Kazandjian [e-mail]

Content:

• General installation
• Installation for Euclid dev.
• Getting the notebooks
• Beginner tutorial list
• Advanced tutorial list
• References and cheatsheet

System installation

This tutorial will be using Python version 3.4-3.5 with the following libraries

• NumPy (Numerical Python) for efficient manipulation of array-based data
• SciPy (Scientific Python) for optimization and other routines
• Matplotlib for scientific visualization
• AstroPy community library gathering astronomical routines

Optionally one might want to install these libraries

• IPython/Jupyter notebook as an interactive computing environment
• Seaborn a data visualization library

LODEEN Virtual Machine

For Euclid developers, a virtual machine with all the requisite libraries/packages has been setup and can be used on all operating systems. The VM is available at this link with a valid Cosmos Account.

If you use VirtualBox (recommended), go to File > Import appliance > /path/to/CppPythonSession.ova The VM credentials are ID: user / pwd: password Tip: In the VM, install the VirtualBox Guest Additions in order to enable the full screen mode.

The Python tutorial has already been integrated to the machine but you should make sure you have the latest version

cd ~/Desktop/python-euclid2016
git pull

Then launch the notebooks

jupyter-notebook notebooks

Get the tutorial material

If you have git installed, you can clone the material of this tutorial with

git clone https://github.com/aboucaud/python-euclid2016.git

If you can't or don't want to install git, there is a link above to download the contents of this repository as a zip file. We may make minor changes to the repository during the tutorial, so cloning the repository is the best option.

You can access a static view of the notebooks following the links below. To modify them, first download the tutorial repository, change to the notebooks directory, and run

jupyter-notebook

This will launch a page on web browser with the list of notebooks. For more information on the IPython/Jupyter notebook, see http://ipython.org/notebook.html or http://jupyter.org

Note that some of the code in these notebooks may not work outside the directory structure of this tutorial, so it is important to clone the full repository if possible.

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Beginner tutorial

1. General Python concepts

variables, collections, iteration, strings, logical operations, functions, classes, ipython, python2/3...

2. Scientific computing with Python: NumPy/SciPy

arrays creation, accessing elements, array operations, masked array, broadcasting...

3. Plotting with Python

2D plots, labeling, configuration, 3D plots, saving, Matplotlib/Seaborn

4. The AstroPy library

reading files (ascii/fits/..), physical constants, cosmological calculations, sky coordinates manipulation...

Advanced tutorial

5. Python coding guidelines

code formatting, naming conventions, docstring formatting, static analysis...

6. Project architecture

See also slides.

modules/submodules, __init__.py, imports, the __all__ variable, module documentation

7. Testing

See slides by F. Raison from the Dev. Workshop #2.

pytest, writing unit tests, fixtures, configuration, doctests, running tests ...

8. Tips & Tricks

ipython configuration, more on classes, things to avoid, quick optimization, debugging, extending python ...

References

A list of references can be found here and a useful cheatsheet here.