The Python programming language is a powerful language which has gained popularity over the past several years. It is widely used in the scientific community, and there are numerous tools readily available for common data analysis tasks. If you are not convinced yet, here is an article you should read!
At UiO there is a repository for Modules developed internally. The pages herein provide important information on working with Python on the UiO servers and also with your own PC.
Key Topics / Tips for Python at UiO
Python on the Servers
By Anne Fouilloux Published May 22, 2014 09:59 AM - Last modified May 22, 2014 10:17 AM
Python is an interpreted, interactive, object-oriented programming language. It incorporates modules, exceptions, dynamic typing, very high level dynamic data types, and classes. Python combines remarkable power with very clear syntax. It has interfaces to many system calls and libraries, as well as to various window systems, and is extensible in C or C++.
UiO Geosciences IT provides users with two different python distributions to work with:
Both are available on: Red Hat 6 linux servers and desktops, use e.g sverdrup.uio.no
How to start:
If you wish to use Enthought Python Distribution:
module load python/canopy
or for Anaconda Python Distribution:
Before using Anaconda Python Distribution, you would need to get your own academic license. Academic licenses are free and can be generated for/by each user from:
Follow instructions given at https://www.continuum.io/anaconda-academic-subscriptions-available and make sure you use your university email address to register (otherwise you won't get your license for free!).
Once you receive the license file just place it in the $HOME/.continuum folder. Don't forget "." in front of the folder name (this folder is hidden); if it does not exist, you can create it:
The email accompanying the license will have more detailed instructions. You may also reference the online anaconda documentation for further detail.
Then to set-up your environment:
module load python/anaconda
If you have problems with the module command please follow this link.
Then for any of these two distributions:
At the command line.
Once you have connected to the servers, in order to use Python it is important to know where the modules are, and how to set up your environment. Many of the commonly used modules are available already, and installed in a directory that has been added to your
For more information see:
Let's start with some 'best practices' for programming with Python.
First and foremost, one should become familiar with the PEP8. This is a 'Python Enhancement Proposal' (PEP). In Python this is the equivalent of a detailed featured request. PEP8 lays the foundation for how you should format your code.
One important feature of python to highlight are Documentation Strings:
Conventions for writing good documentation strings (a.k.a. "docstrings") are immortalized in PEP 257 . - Write docstrings for all public modules, functions, classes, and methods. Docstrings are not necessary for non-public methods, but you should have a comment that describes what the method does. This comment should appear after the "def" line. - PEP 257 describes good docstring conventions. Note that most importantly, the """ that ends a multiline docstring should be on a line by itself, and preferably preceded by a blank line, e.g.: """Return a foobang Optional plotz says to frobnicate the bizbaz first. """ - For one liner docstrings, it's okay to keep the closing """ on the same line.
Import Statements & Namespaces
In python, almost all code will be preceded with some statements such as:
import this (try that, by the way). These are 'import statements' which pull in other modules to use within your own code.
You may find some examples, particularly when learning matplotlib and working with 'pylab', where the tutorial uses:
from pylab import *
This is NOT recommended. This will contaminate your local namespace, meaning that the module from which you import everything indicated by the '*', may overwrite some of your own functions or even python builtins.
The RECOMMENDED way to import modules is to:
import pylab as plb
Then you can still use the example, but you may simply need to prefix some of the function calls with 'plb.', which refers to the 'pylab' namespace.
Specific for Matplotlib
There will be some pages dedicated to Matplotlib, but in the meantime, the 'best practice' for working with Matplotlib is to use the 'object oriented' approach:
import matplotlib.pyplot as plt import numpy as np x = np.arange(100) y = np.sin(x) fig = plt.figure() ax = fig.add_subplot(111) ax.plot(x, y) plt.show()
Here we have shown an example of both the import of matplotlib.pyplot into the 'plt.' namespace, and numpy into the 'np.' namespace.
Useful Software / Modules
- Plone -- Old notes for reference