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)
print transformationCenter[6]
redundantDict,patternDictList = extractRedundantContext(rules,transformationCenter,transformationContext)
print redundantDict[(('Prot(iMod~U)',), ('Prot(egfr)', 'EGFR(prot)'))]
#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 createDataStructures(bnglContent):
'''
create an atomized biomodels in a temporary file to obtain relevant
bng information
'''
pointer = tempfile.mkstemp(suffix='.bngl', text=True)
with open(pointer[1], 'w') as f:
f.write(bnglContent)
retval = os.getcwd()
os.chdir(tempfile.tempdir)
consoleCommands.bngl2xml(pointer[1])
xmlfilename = '.'.join(pointer[1].split('.')[0:-1]) + '.xml'
os.chdir(retval)
return readBNGXML.parseXML(xmlfilename)
def createDataStructures(bnglContent):
'''
create an atomized biomodels in a temporary file to obtain relevant
bng information
'''
pointer = tempfile.mkstemp(suffix='.bngl',text=True)
with open(pointer[1],'w') as f:
f.write(bnglContent)
retval = os.getcwd()
os.chdir(tempfile.tempdir)
consoleCommands.bngl2xml(pointer[1])
xmlfilename = '.'.join(pointer[1].split('.')[0:-1]) + '.xml'
os.chdir(retval)
return readBNGXML.parseXML(xmlfilename)
def createDataStructures(bnglContent):
"""
create an atomized biomodels in a temporary file to obtain relevant
bng information
"""
pointer = tempfile.mkstemp(suffix=".bngl", text=True)
with open(pointer[1], "w") as f:
f.write(bnglContent)
retval = os.getcwd()
os.chdir(tempfile.tempdir)
consoleCommands.bngl2xml(pointer[1])
xmlfilename = ".".join(pointer[1].split(".")[0:-1]) + ".xml"
os.chdir(retval)
return readBNGXML.parseXML(xmlfilename)
def spaceCoveredCDF():
spaceCoveredArray = []
atomizationDict = {}
with open("sortedD.dump", "rb") as f:
atomizationStats = pickle.load(f)
for element in atomizationStats:
try:
atomizationDict[element["index"]] = element["atomization"]
except:
continue
for element in range(1, 491):
try:
if element in atomizationDict and atomizationDict[element] > 0:
console.bngl2xml("complex/output{0}.bngl".format(element), timeout=10)
species, _, _ = readBNGXML.parseXML("output{0}.xml".format(element))
observablesLen = readBNGXML.getNumObservablesXML("output{0}.xml".format(element))
try:
spaceCoveredArray.append([atomizationDict[element], spaceCovered(species, observablesLen)])
print element, spaceCoveredArray[-1]
except CycleError:
spaceCoveredArray.append([atomizationDict[element], 0])
print element, -1
except (IOError, IndexError):
print
continue
with open("spaceCovered.dump", "wb") as f:
pickle.dump(spaceCoveredArray, f)
atomization, space = zip(*spaceCoveredArray)
heatmap, xedges, yedges = np.histogram2d(space, atomization, bins=8)
extent = [xedges[0], xedges[-1], yedges[0], yedges[-1]]
plt.clf()
plt.imshow(heatmap, extent=extent)
plt.xlabel("space")
plt.ylabel("atomization")
plt.show()
plt.savefig("spaceCovered.png")
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 main():
with open('linkArray.dump', 'rb') as f:
linkArray = pickle.load(f)
with open('xmlAnnotationsExtended.dump','rb') as f:
annotations = pickle.load(f)
speciesEquivalence = {}
onlyDicts = [ x for x in listdir('./complex')]
onlyDicts = [x for x in onlyDicts if '.bngl.dict' in x]
for x in onlyDicts:
with open('complex/{0}'.format(x),'rb') as f:
speciesEquivalence[int(x.split('.')[0][6:])] = pickle.load(f)
for cidx,cluster in enumerate(linkArray):
#FIXME:only do the first cluster
cidx = 1
cluster = linkArray[1]
if len(cluster) == 1:
continue
annotationsDict = {idx:x for idx,x in enumerate(annotations)}
annotationsIncidence = {}
#get the model intersection points
for element in annotationsDict:
for species in annotationsDict[element]:
key = annotationsDict[element][species][1]
if key not in annotationsIncidence:
annotationsIncidence[key] = set([])
annotationsIncidence[key].add(element)
#number = 19
graph = nx.Graph()
nodeDict = {}
superNodeDict = {}
for element in annotationsIncidence:
if len(annotationsIncidence[element]) > 1:
tmpEdgeList = []
names= []
for idx in annotationsIncidence[element]:
for species in [x for x in annotationsDict[idx] if annotationsDict[idx][x][1] == element]:
try:
if isinstance(speciesEquivalence[idx][species],str):
tmpEdgeList.append('{0}_{1}'.format(idx,speciesEquivalence[idx][species][0:-2]))
names.append(speciesEquivalence[idx][species][0:-2])
else:
tmpEdgeList.append('{0}_{1}'.format(idx,speciesEquivalence[idx][species].molecules[0].name))
names.append(speciesEquivalence[idx][species].molecules[0].name)
except:
print 'error',idx,species
names = [x.lower() for x in names]
name = Counter(names).most_common(5)
name = [x for x in name if len(x[0])> 1]
if len(name) == 0:
name = Counter(names).most_common(1)
for edge in tmpEdgeList:
superNodeDict[edge] = name[0][0]
if len(name) > 0:
superNodeDict[name[0][0]] = len(tmpEdgeList)
#pairs = [(x,y,1) for x in tmpEdgeList for y in tmpEdgeList if x!= y]
#graph.add_weighted_edges_from(pairs)
print superNodeDict
for x in cluster:
number = x-1
try:
console.bngl2xml('complex/output{0}.bngl'.format(number))
species,rules,parameterDict,observableList= readBNGXML.parseXML('output{0}.xml'.format(number))
except:
continue
nodeDict[number] = simpleGraph(graph,species,observableList,number,superNodeDict)
nx.write_gml(graph,'cgraph_{0}.gml'.format(cidx))
break
def main():
with open('linkArray.dump', 'rb') as f:
linkArray = pickle.load(f)
with open('xmlAnnotationsExtended.dump', 'rb') as f:
annotations = pickle.load(f)
speciesEquivalence = {}
onlyDicts = [x for x in listdir('./complex')]
onlyDicts = [x for x in onlyDicts if '.bngl.dict' in x]
for x in onlyDicts:
with open('complex/{0}'.format(x), 'rb') as f:
speciesEquivalence[int(x.split('.')[0][6:])] = pickle.load(f)
for cidx, cluster in enumerate(linkArray):
#FIXME:only do the first cluster
cidx = 0
cluster = linkArray[0]
if len(cluster) == 1:
continue
annotationsDict = {idx: x for idx, x in enumerate(annotations)}
annotationsIncidence = {}
#get the model intersection points
for element in annotationsDict:
for species in annotationsDict[element]:
key = annotationsDict[element][species][1]
if key not in annotationsIncidence:
annotationsIncidence[key] = set([])
annotationsIncidence[key].add(element)
#number = 19
graph = nx.Graph()
nodeDict = {}
superNodeDict = {}
for element in annotationsIncidence:
if len(annotationsIncidence[element]) > 1:
tmpEdgeList = []
names = []
for idx in annotationsIncidence[element]:
for species in [
x for x in annotationsDict[idx]
if annotationsDict[idx][x][1] == element
]:
try:
print speciesEquivalence[idx]
if isinstance(speciesEquivalence[idx][species],
str):
tmpEdgeList.append('{0}_{1}'.format(
idx,
speciesEquivalence[idx][species][0:-2]))
names.append(
speciesEquivalence[idx][species][0:-2])
else:
tmpEdgeList.append('{0}_{1}'.format(
idx, speciesEquivalence[idx]
[species].molecules[0].name))
names.append(speciesEquivalence[idx]
[species].molecules[0].name)
except:
print '----', 'error', idx, species
pass
names = [x.lower() for x in names]
name = Counter(names).most_common(5)
name = [x for x in name if len(x[0]) > 1]
if len(name) == 0:
name = Counter(names).most_common(1)
for edge in tmpEdgeList:
superNodeDict[edge] = name[0][0]
if len(name) > 0:
superNodeDict[name[0][0]] = len(tmpEdgeList)
#pairs = [(x,y,1) for x in tmpEdgeList for y in tmpEdgeList if x!= y]
#graph.add_weighted_edges_from(pairs)
print superNodeDict
for x in cluster:
number = x - 1
try:
console.bngl2xml('complex/output{0}.bngl'.format(number))
species, rules, parameterDict, observableList = readBNGXML.parseXML(
'output{0}.xml'.format(number))
except:
continue
nodeDict[number] = simpleGraph(graph, species, observableList,
number, superNodeDict)
nx.write_gml(graph, 'cgraph_{0}.gml'.format(cidx))
break
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