1 why to become a Pyologist Perl is for plumbers – Python is for biologists Stefan Maetschke Teasdale Group

2 why   Biologists suffer for no good reason Perl is difficult to write and read Perl gives weak error feedback Perl obscures basic concepts   Limited understanding of principles   Low productivity   Reduced research scope   Perl is for plumbers - Python is for scientists   I want to have an easy life why, why, why …

3 plumbers and others sys admin  plumbing  vi  awk/Perl  grep/diff SW developer   designing   Emacs/IDE   C/C++/Java   UML/Unit test spectrum of tasks, tools and roles scientist Python

4 equals(, ) Cross-platform, open-source, scripting language, multi-paradigm, dynamic typing, statement ratio: 6 There should be one wayThere’s more than one way Guido van RossumLarry Wall Python Perl EasyDifficult

5 you must be joking! = ('a', 'b', 'c'); my %hash; $hash{‘letters'} = print list = ['a', 'b', 'c'] hash = {} hash[‘letters'] = list print hash[‘letters'] package Person; use strict; sub new { my $class = shift; my $age = shift or die "Must pass age"; my $rSelf = {'age' => $age}; bless ($rSelf, $class); return $rSelf; } class Person: def __init__(self, age): self.age = = ( [‘a’, ’b’, ’c’], [1, 2, 3] ); print list = [ [‘a’, ’b’, ’c’], [1, 2, 3] ] print list[0]

6 More Perl bashing… sub add { $_[0] + $_[1]; } def add(a, b): return a + b sub add { my ($a, $b) = return $a + $b; } sub add { my $a = shift; my $b = shift; return $a + $b; } def diff(a, b): return len(a) - len(b) sub diff { my ($aref, $bref) = my my return + sub add($, $) { local ($a, $b) = return $a + $b; }

7 complexity wall simple scripts ≈ 100 lines => fun stops Higher order concepts Data structures Functions Classes => Python allows you to break through the complexity wall everything you can do in Python you can do in Perl but you don’t

8 googliness  C53,0001,  Java7,7602,  C++ 1,290 3,  C#1,  Perl1,  Python  Ruby  Scala  Haskell X languageX load file kilo-hits, May 2008 X bioinformatics

9 and the winner is… <- without Psyco

10 damn lies and stats sourceforge projects   Perl declining, Python increasing ?   May 2008, keyword search : Perl 3474, Python 4063

11 see the light… classify Iris plants Fisher, R.A. "The use of multiple measurements in taxonomic problems" Annual Eugenics, 7, Part II, (1936) Three species: Iris setosa Iris versicolor Iris virginica Four attributes: sepal length sepal width petal length petal width

12 Iris – convert data

13 Iris – correlation

14 Iris – do stats

15 Iris – linear regression

16 Iris – plot data

17 libs for life science  Scientific computing: SciPy, NumPy, matplotlib  Bioinformatics: BioPython  Phylogenetic trees: Mavric, Plone, P4, Newick  Microarrays: SciGraph, CompClust  Molecular modeling: MMTK, OpenBabel, CDK, RDKit, cinfony, mmLib  Dynamic systems modeling: PyDSTools  Protein structure visualization: PyMol, UCSF Chimera  Networks/Graphs: NetworkX, PyGraphViz  Symbolic math: SymPy, Sage  Wrapper for C/C++ code: SWIG, Pyrex, Cython  R/SPlus interface: RSPython, RPy  Java interface: Jython  Fortran to Python: F2PY  … Check also out: and:

18 last words  Perl perfect for plumbing  Python excellent for scientific programming Easy to learn, write and maintain Suited for scripting and mid-size projects Huge number of scientific libraries  Python is an attractive alternative to Matlab/R  Easy integration of Java, C/C++ or Fortran code

19 questions Interest: Python Course? isn’t Python lovely…

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21 links  Wikipedia – Python  Instant Python  How to think like a computer scientist  Dive into Python  Python course in bioinformatics  Beginning Python for bioinformatics  SciPy Cookbook Matplotlib Cookbook  Biopython tutorial and cookbook  Huge collection of Python tutorial  What’s wrong with Perl  20 Stages of Perl to Python conversion  Why Python

22 some papers  Bassi S. (2007) A Primer on Python for Life Science Researchers. PLoS Comput Biol 3(11): e199. doi: /journal.pcbi  Mangalam H. (2002) The Bio* toolkits--a brief overview. Brief Bioinform. 3(3):  Fourment M., Gillings MR. (2008) A comparison of common programming languages used in bioinformatics. BMC Bioinformatics 9:82.

23 to whom it may concern  NPs who don’t use Perl yet  NPs who want to see the light  NPs who want to give their code away without being rightfully ashamed  Matlab aficionados NP = Non-Programmer

24 one of ten Perl myths “…Perl works the way you do…” “…That's one, fairly natural way to think about it…” while (<>) { s/(.*):(.*)/$2:$1/; print; } Swap two sections of a string: “aaa:bbb” -> “bbb:aaa” for line in file: line = line.strip() first, second = line.split(‘:’) print second+’:’+first while (<>) { chomp; ($first, $second) = split /:/; print $second, ":", $first, "\n"; } “…we can happily consign the idea that ‘Perl is hard’ to mythology.” from re import sub for line in file: print sub(‘(.*):(.*)’, r’\2:\1’, line)

25 camel chaos  does not scale well  complex syntax  cryptic commands  does not encourage clear code  difficult to read/maintain  hard to understand the principles  error prone no check of subroutine arguments variables are global by default …

26 why Python  overcome the complexity wall  many, excellent scientific libraries  clear, easy to learn syntax  hard to do it wrong  does not require prior suffering/experience

27 my bias  R&D: C/C++ -> applied ML in robotics, image processing, quality control  SW Development: Java -> Speech Processing, Data Mining  Computational Biology: Java, Python  Other languages I played with: Ada, APL, Basic, MatLab, Modula, Pascal, Perl, Prolog, R, Groovy, Forth, Fortran, Scala, Assembly code