# Difference between revisions of "Open writing projects/Sage and cython a brief introduction"

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== Sage notebook interface == | == Sage notebook interface == | ||

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A key feature of Sage is its notebook web-browser interface. | A key feature of Sage is its notebook web-browser interface. |

## Revision as of 13:50, 1 May 2008

### Work in progress

Please check back later for the final version...

## Abstract

This is a quick introduction to Sage, a powerful new computational platform that builds on the strengths of Python. This article was directly inspired by Julius B. Lucks' "Python: All A Scientist Needs"; I recommend reading it first as it explains some of the attractions of Python and biopython.

Sage is a free and open-source project for computation of all sorts that uses Python as its primary language and "glue". One of the goals of Sage is to provide a viable free and open-source alternative to Matlab, Maple, and Mathematica. Sage unifies a great deal of open-source mathematical and statistical software; it includes biopython as an optional package and the statistics language R by default.

## Sage notebook interface

A key feature of Sage is its notebook web-browser interface.

Jose Unpingco has made a good short introductory video on the notebook interface that may help get a sense of what its like.

The @interact command in the Sage notebook provides an easy way to make simple GUIs to explore data. In the example below, a user can enter the URL of a fasta-formatted protein file and a PROSITE-style regular expression. Using biopython and the "re" module of python we can search for and display matches to the pattern. For this screenshot, I used proteins from the malaria-causing Plasmodium falciparum and a fragment of the transthyretin pattern (Prosite PS00768). This is just for illustrative purposes - I do not claim any significance for these matches.

<syntax type="python"> def PStoRE(PrositePattern):

""" Converts a PROSITE regular expression to a python r.e. """ rePattern = PrositePattern rePattern = rePattern.replace('-',)rePattern = rePattern.replace(' ',)rePattern = rePattern.replace('x','.') rePattern = rePattern.replace('{','[^') rePattern = rePattern.replace('}',']') rePattern = rePattern.replace('(','{') rePattern = rePattern.replace(')','}') return rePattern

from Bio import Fasta import re import urllib2 as U @interact def re_scan(fasta_file_url = 'http://www.d.umn.edu/~mhampton/PlasProtsRef.fa', pat = input_box('G - x - P - [AG] - x(2) - [LIVM] - x - [IV] ', type = str, width = 60)):

re_pat = re.compile(PStoRE(pat)) parser = Fasta.RecordParser() prot_file = U.urlopen(fasta_file_url) fasta_iterator = Fasta.Iterator(prot_file, parser = parser) for record in fasta_iterator: matches = re_pat.findall(record.sequence) if len(matches) != 0: html(record.title) html(matches) print

Here is another example of the interact command, along with some of Sage's 2D plotting. We take a human mitochondrial DNA sequence and plot the fraction of CG dinucleotides in a window of variable size. This fraction is divided by 16, to normalize it against the expected value in the case of independently, uniformly distributed nucleotides (obviously a poor model in this case).

<syntax type = "python"> from Bio import SeqIO import urllib2 as U import scipy.stats as Stat f = U.urlopen('http://www.d.umn.edu/~mhampton/HomoSmito.fa') p = SeqIO.parse(f, 'fasta') hsmito = p.next().seq.tostring() f.close() display_f = RealField(16) @interact def cgplot(window_length = slider([2^i for i in range(4,12)],default = 2^9)):

avg = [16.0*hsmito[x-window_length: x+window_length].count('CG')/(2*window_length-1) for x in range(window_length, len(hsmito) - window_length)] mean = display_f(Stat.mean(avg)) std = display_f(Stat.std(avg)) html('Ratio of CG dinucleotides in a window of size ' + str(2*window_length) + ' to the expected fraction 1/16

in the human mitochondrion.

Mean value: ' + str(mean) + '; standard deviation: ' + str(std)) show(list_plot(avg, plotjoined=True), ymin = 0, ymax = max(avg))

## Cython

Sage initially used an alternative to SWIG (described in Julius's article) called Pyrex to compile Python code to C when performance concerns demanded it. Because they needed to extend Pyrex in various ways, they created a friendly fork of Pyrex called "Cython". I believe it is fair to say that Cython is the easiest way to create C code in Python.

(TODO: example of Cython usage)