Category: Program Dev-Languages
Python is a clear and powerful object-oriented programming language, comparable to Perl, Ruby, Scheme, or Java.
For more information see the Python home page .
Two versions of python, 2.7.1 and 3.1.1, are provided and can be used by
loading their corresponding module file. (see Usage
Python/2.7.1 installation also includes the following python packages. Please see the website for each of these packages (linked below) for their description:
A version of python (2.6) is available by default when you login.
However, this version does not have any python packages such as Numpy and
Scipy installed for it. If you wish to use python with these packages, you
may load the python/2.7.1 module (the default).
A newer version of python (3.1.1) is also available as module. This
version does not have as many python packages installed with it.
To view the versions available you can use:
module avail pythonIf you simply want to load the default version (i.e. python/2.7.1) use:
module load pythonif you want to load version 3.1.1 use:
module load python/3.1.1
NumPy is a general-purpose array-processing package designed to efficiently manipulate large multi-dimensional arrays of arbitrary records without sacrificing too much speed for small multi-dimensional arrays. NumPy is built on the Numeric code base and adds features introduced by numarray as well as an extended C-API and the ability to create arrays of arbitrary type which also makes NumPy suitable for interfacing with general-purpose data-base applications.
This package contains:
- a powerful N-dimensional array object
- sophisticated (broadcasting) functions
- basic linear algebra functions
- basic Fourier transforms
- sophisticated random number capabilities
- tools for integrating Fortran code
- tools for integrating C/C++ code
Numpy module can be invoked using the following statement inside python interpreter or script:
from numpy import *or
SciPy (pronounced "Sigh Pie") is open-source software for mathematics, science, and engineering. It is also the name of a very popular conference on scientific programming with Python. The SciPy library depends on NumPy, which provides convenient and fast N-dimensional array manipulation. The SciPy library is built to work with NumPy arrays, and provides many user-friendly and efficient numerical routines such as routines for numerical integration and optimization.
Scipy module can be invoked using the following statement inside python interpreter or script:
from scipy import *or
matplotlib is a python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms. matplotlib can be used in python scripts, the python and ipython shell (ala matlab or mathematica), web application servers, and six graphical user interface toolkits.
matplotlib tries to make easy things easy and hard things possible. You can generate plots, histograms, power spectra, bar charts, errorcharts, scatterplots, etc, with just a few lines of code. For a sampling, see the screenshots, thumbnail gallery, and examples directory
Matplotlib package is included in python/2.7.1 installation, Please see the matplotlib Matplotlib User Guide for more information
Note that the default matplotlib behavior is to defer drawing a plot until the end of a script. If you want to change this behavior, then create your own .matplotlibrc file with "interactive" set to True.
The interactive property of the pyplot interface controls whether a figure canvas is drawn on every pyplot command. If interactive is False (default setting), then the figure state is updated on every plot command, but will only be drawn on explicit calls to draw(). When interactive is True, then every pyplot command triggers a draw. The pyplot interface provides four commands that are useful for interactive control:
isinteractive() - returns the interactive setting True|False ion() - turns interactive mode on ioff() - turns interactive mode off draw() - forces a figure redraw
Example 1: To generate simple plot, do the following:
python >>> from pylab import * >>> ion() >>> plot([1,2,3])
Example 2: To generate 10,000 Gaussian random numbers and make a histogram plot binning the data into 100 bins, you simply need to type
python >>> from pylab import randn, hist >>> x = randn(10000) >>> hist(x,100)
This package has the following support level : Supported