indapa

#!/usr/bin/env python
import numpy as np
from Factor import *
from FactorOperations import *

#example from figure 4.3 in Koller and Friedman textbook, 'Probabilistic Graphical Models: Principles and Techniques'
a1=Factor([  1, 2], [ 3, 2], [ 0, 0, 0, 0, 0, 0, 0, 0 ] )
b1=Factor( [2,3], [2,2], [ 0,0] )

assignmentA=IndexToAssignment( np.arange(0, np.prod( a1.getCard() )), a1.getCard() )

indapa

import numpy as np

indices= [ 9,8,7,6,5,4,3,2,1,0]
values=np.random.rand(10).tolist()
zeros=np.zeros(10) #get an array of zeros
zeros[indices]=values # then assign the indices the values

indapa

val=randn(1,10); % get 10 random values
indx=[10 9 8 7 6 5 4 3 2 1] % vector representing indices
value(indx)=val % set the indices of value to value

 

indapa

from itertools import *

ploidys=[2,3,4]

for p in ploidys:
    l=[ combo for combo in combinations_with_replacement(['A','C','G','T'],p) ]
    for g in l:
         print "".join( list(g) ), "ploidy: ", p
    print

indapa

#plot the empirical CDF

x=rnorm(100) # 100 random draws from standard normal
f=ecdf(x) # ecdf returns a function
percentiles=f(x) # return the percentiles of the data points
plot(percentiles, x)

#see ?ecdf for more information

indapa

import numpy as np
from pylab import *
import sys
file=sys.argv[1]
fh=open(file,'r')
headerline=fh.readline()
As=[]
Cs=[]
Gs=[]
Ts=[]

indapa

"""http://docs.python.org/library/itertools.html#itertools.izip"""
#thanks Brent Pedersen	


vcf_gen1=vcfobj1.yieldVcfRecordwithGenotypes(vcfh1)
vcf_gen2=vcfobj1.yieldVcfRecordwithGenotypes(vcfh2)

  
for vrec1, vrec2 in itertools.izip(vcf_gen1, vcf_gen2):
    print vrec1.toStringwithGenotypes()

indapa

#I'm taking the STanford on line course Probabilistic Graphical Models <pgm-class.org>
# They have a simple  example of a Bayesian network for a Student letter of reference
# the code below was originally posted by a fellow classmate taking the class
# it really helped me in understanding how to do computations on conditional probability tables (CPDs) that 
# form the heart of Bayesian networks

from numpy import *
P_I = array([
[0.7,0.3]
])

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library(reshape)

#suppose I read a file with the header lines of sample and 13 target ids
# the values represent median target coverage from an exome capture experiment
median <- read.table("median.txt", header=T)
> median
  sample t1 t2 t3 t4 t5 t6 t7 t8  t9 t10 t11 t12 t13
1     s1 10 34 31 66 34 61 40 46 117  29  55  96  33
2     s2 20 29 25 36 27 53 34 26  89  28  40  70  26
3     s3  8 31 31 66 33 57 31 36  93  36  40  74  27

indapa

#!/usr/bin/env python
from itertools import *




#print the number of possible genotypes with various ploidies
ploidys=[2,3,4,5,6,7,8,9,10]
for p in ploidys:
    print p, len([combo  for combo in combinations_with_replacement(['A','C','G','T'],p) ])