#!/usr/bin/python

import os, csv, sys
from GotermEnriched import *

#Example of using class GotermEnrich

#Generate an object of the GotermEnrich class
weightGographData = 'newWeightedPubMedGO.xml'
geneGotermAssociationData = 'gene_association.sgd'
G = GotermEnrich(weightGographData,geneGotermAssociationData)

#two list needed to be processed
list1= ['FUS3', 'RAD6', 'STE24', 'DIG2', 'DIG1', 'STE11', 'STE12', 'STE7', 'STE4', 'STE5', 'STE18', 'KSS1']
list2= ['YER044C', 'ERG3', 'CMD1', 'ERG11', 'YMR031W-A', 'SWI4', 'TUNICAMYCIN']


#call the Go term enriched function (Parameters: list1--a list of genes; 0.05--the PValue threshold;
#        2--number of top score (PValue) Go terms that are outputed. 5--Minimum number of genes in the Go terms of solutions)
#The return value of Goterm_enrich() is a list, where each element is also a list 
#               [goterm ID, a list of genes annotated with this goterm, PValue, the level of go term in the Ontology structure].
result1 = G.Goterm_enrich(list1,0.05,2,5)
result2 = G.Goterm_enrich(list2,0.05,2,3)


#print the result1
print 'The output of result1:'
for item in result1:
	print item

print '-----------------------------------'
print 'the output of result2:'
for item in result2:
	print item