def extractCenterContext(rules, excludeReverse=False):
transformationCenter = []
transformationContext = []
transformationProduct = []
atomicArray = []
actionNames = []
label = []
doubleModificationRules = []
for idx, rule in enumerate(rules):
tatomicArray, ttransformationCenter, ttransformationContext, \
tproductElements, tactionNames, tlabelArray = extractAtomic.extractTransformations(
[rule], True)
if excludeReverse and '_reverse_' in rule[0].label and \
len([x for x in tactionNames if 'ChangeCompartment' not in x]) > 1:
continue
label.append(rule[0].label)
if len([x for x in tactionNames if 'ChangeCompartment' not in x]) > 1:
doubleModificationRules.append(rule[0].label)
transformationCenter.append(ttransformationCenter)
transformationContext.append(ttransformationContext)
actionNames.append(tactionNames)
atomicArray.append(tatomicArray)
transformationProduct.append(tproductElements)
return label, transformationCenter, transformationContext, \
transformationProduct, atomicArray, actionNames, doubleModificationRules
def extractCenterContext(rules, excludeReverse=False):
transformationCenter = []
transformationContext = []
transformationProduct = []
atomicArray = []
actionNames = []
label = []
doubleModificationRules = []
for idx, rule in enumerate(rules):
tatomicArray, ttransformationCenter, ttransformationContext, \
tproductElements, tactionNames, tlabelArray = extractAtomic.extractTransformations(
[rule], True)
if excludeReverse and '_reverse_' in rule[0].label and \
len([x for x in tactionNames if 'ChangeCompartment' not in x]) > 1:
continue
label.append(rule[0].label)
if len([x for x in tactionNames if 'ChangeCompartment' not in x]) > 1:
doubleModificationRules.append(rule[0].label)
transformationCenter.append(ttransformationCenter)
transformationContext.append(ttransformationContext)
actionNames.append(tactionNames)
atomicArray.append(tatomicArray)
transformationProduct.append(tproductElements)
return label, transformationCenter, transformationContext, \
transformationProduct, atomicArray, actionNames, doubleModificationRules
def askQuestions(inputfile, molecule, center, context=None):
_, rules, _ = readBNGXML.parseXML(inputfile)
transformationCenter = []
transformationContext = []
transformationProduct = []
atomicArray = []
actionNames = []
for idx, rule in enumerate(rules):
tatomicArray, ttransformationCenter, ttransformationContext, \
tproductElements, tactionNames, tlabelArray = extractAtomic.extractTransformations(
[rule],True)
if any([
molecule in y and center in y for x in ttransformationCenter
for y in x
]):
if context:
print str(rule[0]).split(':')[0]
print[
y for x in ttransformationContext for y in x
if context in y and molecule in y
]
else:
print rule
def main():
fileName = 'complex/output19.bngl'
console.bngl2xml(fileName)
species, rules, par = readBNGXML.parseXML('output19.xml')
#print rules
transformationCenter = []
transformationContext = []
#extract the context of such reactions
for idx, rule in enumerate(rules):
tatomicArray, ttransformationCenter, ttransformationContext, \
tproductElements,tactionNames,tlabelArray = extractAtomic.extractTransformations([rule])
#atomicArray.append(tatomicArray)
transformationCenter.append(ttransformationCenter)
transformationContext.append(ttransformationContext)
redundantDict, patternDictList = extractRedundantContext(
rules, transformationCenter, transformationContext)
#print redundantDict
#construct rule based patterns based on the reaction patterns they have to match and
#the ones they have to discriminate
for center in patternDictList:
for rate in patternDictList[center]:
match = patternDictList[center][rate]
notContext = []
for cRate in [x for x in patternDictList[center] if x != rate]:
notContext.append([
transformationContext[x]
for x in redundantDict[center][cRate]
])
ruleSet = [rules[x] for x in redundantDict[center][rate]]
createMetaRule(ruleSet, match)
newRules = range(0, len(rules))
for center in redundantDict:
for context in redundantDict[center]:
for element in range(1, len(redundantDict[center][context])):
newRules.remove(redundantDict[center][context][element])
#for element in newRules:
# print str(rules[element][0])
newRulesArray = []
for element in newRules:
newRulesArray.append('{0}\n'.format(str(rules[element][0])))
lines = readFile(fileName)
startR = lines.index('begin reaction rules\n')
endR = lines.index('end reaction rules\n')
startP = lines.index('begin parameters\n')
endP = lines.index('end parameters\n')
newPar = findNewParameters(lines[startP + 1:endP], par)
newLines = lines[0:endP] + newPar + lines[
endP:startR + 1] + newRulesArray + lines[endR:len(lines)]
f = open(fileName + 'reduced.bngl', 'w')
f.writelines(newLines)
'''
def main():
fileName = 'complex/output19.bngl'
console.bngl2xml(fileName)
species,rules,par= readBNGXML.parseXML('output19.xml')
#print rules
transformationCenter = []
transformationContext = []
#extract the context of such reactions
for idx,rule in enumerate(rules):
tatomicArray, ttransformationCenter, ttransformationContext, \
tproductElements,tactionNames,tlabelArray = extractAtomic.extractTransformations([rule])
#atomicArray.append(tatomicArray)
transformationCenter.append(ttransformationCenter)
transformationContext.append(ttransformationContext)
redundantDict,patternDictList = extractRedundantContext(rules,transformationCenter,transformationContext)
#print redundantDict
#construct rule based patterns based on the reaction patterns they have to match and
#the ones they have to discriminate
for center in patternDictList:
for rate in patternDictList[center]:
match = patternDictList[center][rate]
notContext = []
for cRate in [x for x in patternDictList[center] if x != rate]:
notContext.append([transformationContext[x] for x in redundantDict[center][cRate]])
ruleSet = [rules[x] for x in redundantDict[center][rate]]
createMetaRule(ruleSet,match)
newRules = range(0,len(rules))
for center in redundantDict:
for context in redundantDict[center]:
for element in range(1,len(redundantDict[center][context])):
newRules.remove(redundantDict[center][context][element])
#for element in newRules:
# print str(rules[element][0])
newRulesArray = []
for element in newRules:
newRulesArray.append('{0}\n'.format(str(rules[element][0])))
lines = readFile(fileName)
startR = lines.index('begin reaction rules\n')
endR = lines.index('end reaction rules\n')
startP = lines.index('begin parameters\n')
endP = lines.index('end parameters\n')
newPar = findNewParameters(lines[startP+1:endP],par)
newLines = lines[0:endP] + newPar + lines[endP:startR+1] + newRulesArray + lines[endR:len(lines)]
f = open(fileName + 'reduced.bngl','w')
f.writelines(newLines)
'''
def askQuestions(inputfile, molecule, center, context=None):
_, rules, _ = readBNGXML.parseXML(inputfile)
ruleArray = []
contextArray = []
for idx, rule in enumerate(rules):
tatomicArray, ttransformationCenter, ttransformationContext, \
tproductElements, tactionNames, tlabelArray = extractAtomic.extractTransformations(
[rule], True)
if any([molecule in y and center in y for x in ttransformationCenter for y in x]):
if context:
ruleArray.append(str(rule[0]).split(':')[0])
contextArray.append([y for x in ttransformationContext for y in x if context in y and molecule in y])
else:
print rule
return ruleArray, contextArray
def extractCenterContext(rules):
transformationCenter = []
transformationContext = []
transformationProduct = []
atomicArray = []
actionNames = []
for idx, rule in enumerate(rules):
tatomicArray, ttransformationCenter, ttransformationContext, \
tproductElements, tactionNames, tlabelArray = extractAtomic.extractTransformations(
[rule], True)
transformationCenter.append(ttransformationCenter)
transformationContext.append(ttransformationContext)
actionNames.append(tactionNames)
atomicArray.append(tatomicArray)
transformationProduct.append(tproductElements)
return transformationCenter, transformationContext, \
transformationProduct, atomicArray, actionNames
def extractCenterContext(rules):
transformationCenter = []
transformationContext = []
transformationProduct = []
atomicArray = []
actionNames = []
for idx, rule in enumerate(rules):
tatomicArray, ttransformationCenter, ttransformationContext, \
tproductElements, tactionNames, tlabelArray = extractAtomic.extractTransformations(
[rule], True)
transformationCenter.append(ttransformationCenter)
transformationContext.append(ttransformationContext)
actionNames.append(tactionNames)
atomicArray.append(tatomicArray)
transformationProduct.append(tproductElements)
return transformationCenter, transformationContext, \
transformationProduct, atomicArray, actionNames
def extractStatistics():
number = 151
console.bngl2xml('complex/output{0}.bngl'.format(number))
species, rules, parameterDict = readBNGXML.parseXML(
'output{0}.xml'.format(number))
#print rules
transformationCenter = []
transformationContext = []
k = []
actions = Counter()
actionSet = Counter()
for idx, rule in enumerate(rules):
tatomicArray, ttransformationCenter, ttransformationContext, \
tproductElements,tactionNames,tlabelArray = extractAtomic.extractTransformations([rule])
#atomicArray.append(tatomicArray)
transformationCenter.append(ttransformationCenter)
transformationContext.append(ttransformationContext)
k.append(len(rule[0].actions))
#if len(rule[0].actions) > 3:
# print rule[0].reactants
actions.update([x.action for x in rule[0].actions])
tmp = [x.action for x in rule[0].actions]
tmp.sort()
actionSet.update([tuple(tmp)])
#print actions
#print actionSet
print 'number of species', len(species)
print 'avg number o actions', np.average(k), np.std(k)
centerDict = groupByReactionCenter(transformationCenter)
print 'singletons', len(
{x: centerDict[x]
for x in centerDict if len(centerDict[x]) == 1})
tmp = [[tuple(set(x)), len(centerDict[x])] for x in centerDict]
#reactionCenterGraph(species,tmp)
#tmp.sort(key=lambda x:x[1],reverse=True)
print 'number of reaction centers', len(centerDict.keys())
print 'number of rules', len(rules)
#print 'unique',[x for x in centerDict[element] if len(centerDict[element]) == 1]
redundantDict = groupByReactionCenterAndRateAndActions(rules, centerDict)
#print redundantDict
tmp2 = [('$\\tt{{{0}}}$'.format(tuple(set(x))), tuple(set(y[:-1])), y[-1],
len(redundantDict[x][y])) for x in redundantDict
for y in redundantDict[x] if 'kr' not in y[-1]]
tmp2 = set(tmp2)
tmp2 = list(tmp2)
tmp2.sort(key=lambda x: x[3], reverse=True)
tmp2.sort(key=lambda x: x[0], reverse=True)
tmp2 = [
'{0} & {1} & {2} & {3}\\\\\n'.format(element[0], element[1],
element[2], element[3])
for element in tmp2
]
with open('table.tex', 'w') as f:
f.write('\\begin{tabular}{|cccc|}\n')
f.write('\\hline\n')
f.write('Reaction Centers & Action & Score\\\\\\hline\n')
for element in tmp2:
f.write(element)
f.write('\\hline\n')
f.write('\\end{tabular}\n')
#print redundantDict
x = [len(centerDict[x]) for x in centerDict]
#122,138
print 'average number of reactions with same rate and reaction cneter', np.average(
x), np.std(x)
print 'total number of clusters', len(x)
print x
def extractStatistics():
number = 151
console.bngl2xml('complex/output{0}.bngl'.format(number))
species,rules,parameterDict= readBNGXML.parseXML('output{0}.xml'.format(number))
#print rules
transformationCenter = []
transformationContext = []
k = []
actions = Counter()
actionSet = Counter()
for idx,rule in enumerate(rules):
tatomicArray, ttransformationCenter, ttransformationContext, \
tproductElements,tactionNames,tlabelArray = extractAtomic.extractTransformations([rule])
#atomicArray.append(tatomicArray)
transformationCenter.append(ttransformationCenter)
transformationContext.append(ttransformationContext)
k.append(len(rule[0].actions))
#if len(rule[0].actions) > 3:
# print rule[0].reactants
actions.update([x.action for x in rule[0].actions])
tmp = [x.action for x in rule[0].actions]
tmp.sort()
actionSet.update([tuple(tmp)])
#print actions
#print actionSet
print 'number of species',len(species)
print 'avg number o actions',np.average(k),np.std(k)
centerDict = groupByReactionCenter(transformationCenter)
print 'singletons',len({x:centerDict[x] for x in centerDict if len(centerDict[x]) == 1})
tmp = [[tuple(set(x)),len(centerDict[x])] for x in centerDict]
#reactionCenterGraph(species,tmp)
#tmp.sort(key=lambda x:x[1],reverse=True)
print 'number of reaction centers',len(centerDict.keys())
print 'number of rules',len(rules)
#print 'unique',[x for x in centerDict[element] if len(centerDict[element]) == 1]
redundantDict = groupByReactionCenterAndRateAndActions(rules,centerDict)
#print redundantDict
tmp2 = [('$\\tt{{{0}}}$'.format(tuple(set(x))),tuple(set(y[:-1])),y[-1],len (redundantDict[x][y])) for x in redundantDict for y in redundantDict[x] if 'kr' not in y[-1]]
tmp2 = set(tmp2)
tmp2 = list(tmp2)
tmp2.sort(key=lambda x:x[3],reverse=True)
tmp2.sort(key=lambda x:x[0],reverse=True)
tmp2 = ['{0} & {1} & {2} & {3}\\\\\n'.format(element[0],element[1],element[2],element[3]) for element in tmp2]
with open('table.tex','w') as f:
f.write('\\begin{tabular}{|cccc|}\n')
f.write('\\hline\n')
f.write('Reaction Centers & Action & Score\\\\\\hline\n')
for element in tmp2:
f.write(element)
f.write('\\hline\n')
f.write('\\end{tabular}\n')
#print redundantDict
x = [len(centerDict[x]) for x in centerDict]
#122,138
print 'average number of reactions with same rate and reaction cneter',np.average(x),np.std(x)
print 'total number of clusters',len(x)
print x
