Advanced Python notes - Structural Bioinformatics Group

import numpy
from scipy.cluster.vq import vq, kmeans, whiten
points = numpy.array([ [1.9, 2.3, 5.2], ..., [1.3, 0.2, 5.7] ])
points = whiten(points) # normalises points in each dimension
(means, labels) = kmeans2(points, 3)
# now label[i] is a cluster number for points[i]
# ... will be 0, 1 or 2 in this case
# More at: www.scipy.org/doc/api_docs/SciPy.cluster.vq.html
Integrating ODEs:
from scipy.integrate.ode import ode
def f(t, y):
return 0.1*y
print "y at time 1.0: %f" % ode(f).\
set_initial_value(200.0).integrate(1.0)
print "y at time 20.0: %f" % ode(f).\
set_initial_value(200.0).integrate(20.0)
# More at: www.scipy.org/doc/api_docs/SciPy.integrate.ode.html
Optimisation:
from scipy.optimize.minpack import fsolve
from scipy.optimize.optimize import fmin
def f(x):
return x ** 2.0 + x - 1.0
print "f(%0.3f) == 0.0" % fsolve(f, 0.0)[0]
minimisingX = fmin(f, 0.0)[0]
print "f(%0.3f) == %0.3f (local minimum)" % \
(minimisingX , f(minimisingX))
# More at: www.scipy.org/doc/api_docs/SciPy.optimize.html
It can image processing, Fourier transforms, provides sparse matrix data types, and has 100+ statistical
functions including central tendency (means etc.), other moments (skew, kutosis etc.), frequency statistics,
variability, correlation, inference (Mann-Whitney, chi-squared etc.), probability (P-value) calculations,
ANOVA
Three useful libraries for visualisation
In the end you have to see the results of your work. You can use other software for this, but it is
sometimes faster to generate the visualisation of your data in your own program.
matplotlib
An excellent and flexible library for data plotting and general visualisation. It will cover most of your needs
for plotting graphs. Example for plotting some randomly generated points:
from pylab import *
Advanced Python notes benjamin.jefferys03@imperial.ac.uk Page 6 of 16