def createContactMap(fileName):
rules = extractRulesfromBNGL(fileName)
transformationCenter = []
transformationContext = []
transformationProduct = []
actionNames = []
atomicArray = []
for idx, rule in enumerate(rules):
tatomicArray, ttransformationCenter, ttransformationContext, tproductElements, tactionNames, tlabelArray = extractAtomic.extractTransformations(
[rule]
)
transformationCenter.append(ttransformationCenter)
transformationContext.append(ttransformationContext)
actionNames.append(tactionNames)
atomicArray.append(tatomicArray)
transformationProduct.append(tproductElements)
# tatomicArray, ttransformationCenter, ttransformationContext, \
# tproductElements,tactionNames,tlabelArray = extractAtomic.extractTransformations(rules)
redundantDict, patternDictList = contextAnalyzer.extractRedundantContext(
rules, transformationCenter, transformationContext
)
reverseRedundantDict = reverseLookup(redundantDict)
for idx in range(0, len(rules)):
redundantPattern = []
if idx in reverseRedundantDict:
for key in reverseRedundantDict[idx]:
redundantPattern.append(patternDictList[key[0]][key[1]])
redundantPatternCounter = dictionaryToCounter(redundantPattern)
createMap(rules[idx], transformationProduct[idx], redundantPatternCounter, "conImg/contatMap{0}".format(idx))
def createBipartiteGraph(fileName):
rules = extractRulesfromBNGL(fileName)
transformationCenter, transformationContext, transformationProduct, atomicArray, actionNames = extractCenterContext(
rules
)
redundantDict, patternDictList = contextAnalyzer.extractRedundantContext(
rules, transformationCenter, transformationContext
)
reverseRedundantDict = reverseLookup(redundantDict)
idx = 0
for rule, center, context, product, action, atom in zip(
rules, transformationCenter, transformationContext, transformationProduct, actionNames, atomicArray
):
redundantPattern = []
if idx in reverseRedundantDict:
for key in reverseRedundantDict[idx]:
redundantPattern.append(patternDictList[key[0]][key[1]])
redundantPatternCounter = dictionaryToCounter(redundantPattern)
createRuleBiPartite(
rule, center, context, product, action, atom, "img/bipartite{0}".format(idx), redundantPatternCounter
)
idx += 1
def cooperativityAnalysis2(fileName):
species, rules, par = extractRulesfromBNGL(fileName)
transformationCenter, transformationContext, transformationProduct, atomicArray, actionNames = extractCenterContext(
rules
)
# group by reaction center
redundantDict, patternDictList = contextAnalyzer.extractRedundantContext(
rules, transformationCenter, transformationContext
)
totalProcesses = 0
groupedProcesses = 0
redundantProcesses = 0
reverseRedundantDict = reverseLookup(redundantDict)
totalSpaceCovered = []
both = hetero = h**o = 0
for center in redundantDict:
processCount = []
reactionInCenter = len([y for x in redundantDict[center] for y in redundantDict[center][x]])
if reactionInCenter > 1 and len([x for x in center if x not in ["Add", "Delete"]]) > 1:
totalSpace = analyzeSpace(center, species)
totalSpaceCovered.append((totalSpace, reactionInCenter))
for rateKey in redundantDict[center]:
if len([x for x in center if x not in ["Add", "Delete"]]) > 1:
processCount.append(len(redundantDict[center][rateKey]))
totalProcesses += len(redundantDict[center][rateKey])
if len(processCount) > 1 and any([x > 1 for x in processCount]):
both += sum(processCount)
elif len(processCount) > 1:
hetero += sum(processCount)
elif sum(processCount) > 1:
h**o += sum(processCount)
# totalhetero = hetero*1.0/totalProcesses if totalProcesses > 0 else 0
# totalhomo = h**o*1.0/totalProcesses if totalProcesses > 0 else 0
# totalboth = both*1.0/totalProcesses if totalProcesses > 0 else 0
# totalall = (hetero+h**o+both)*1.0/totalProcesses if totalProcesses > 0 else 0
return hetero, h**o, both, totalProcesses, totalSpaceCovered
