def processFunctions(functions, sbmlfunctions, artificialObservables, tfunc):
'''
this method goes through the list of functions and removes all
sbml elements that are extraneous to bngl
'''
for idx in range(0, len(functions)):
for sbml in sbmlfunctions:
if sbml in functions[idx]:
functions[idx] = writer.extendFunction(functions[idx], sbml,
sbmlfunctions[sbml])
functions[idx] = re.sub(r'(\W|^)(time)(\W|$)', r'\1time()\3',
functions[idx])
functions[idx] = re.sub(r'(\W|^)(Time)(\W|$)', r'\1time()\3',
functions[idx])
functions[idx] = re.sub(r'(\W|^)(t)(\W|$)', r'\1time()\3',
functions[idx])
#functions.extend(sbmlfunctions)
dependencies2 = {}
for idx in range(0, len(functions)):
dependencies2[functions[idx].split(' = ')[0].split('(')
[0].strip()] = []
for key in artificialObservables:
oldfunc = functions[idx]
functions[idx] = (re.sub(r'(\W|^)({0})([^\w(]|$)'.format(key),
r'\1\2()\3', functions[idx]))
if oldfunc != functions[idx]:
dependencies2[functions[idx].split(' = ')[0].split('(')
[0]].append(key)
for element in sbmlfunctions:
oldfunc = functions[idx]
key = element.split(' = ')[0].split('(')[0]
if re.search('(\W|^){0}(\W|$)'.format(key),
functions[idx].split(' = ')[1]) != None:
dependencies2[functions[idx].split(' = ')[0].split('(')
[0]].append(key)
for element in tfunc:
key = element.split(' = ')[0].split('(')[0]
if key in functions[idx].split(' = ')[1]:
dependencies2[functions[idx].split(' = ')[0].split('(')
[0]].append(key)
'''
for counter in range(0,3):
for element in dependencies2:
if len(dependencies2[element]) > counter:
dependencies2[element].extend(dependencies2[dependencies2[element][counter]])
'''
fd = []
for function in functions:
#print function,dependencies2[function.split(' = ' )[0].split('(')[0]],function.split(' = ' )[0].split('(')[0],0
fd.append([
function,
resolveDependencies(dependencies2,
function.split(' = ')[0].split('(')[0], 0)
])
fd = sorted(fd, key=lambda rule: rule[1])
functions = [x[0] for x in fd]
return functions
def processFunctions(functions,sbmlfunctions,artificialObservables,tfunc):
'''
this method goes through the list of functions and removes all
sbml elements that are extraneous to bngl
'''
for idx in range(0,len(functions)):
for sbml in sbmlfunctions:
if sbml in functions[idx]:
functions[idx] = writer.extendFunction(functions[idx],sbml,sbmlfunctions[sbml])
functions[idx] =re.sub(r'(\W|^)(time)(\W|$)',r'\1time()\3',functions[idx])
functions[idx] =re.sub(r'(\W|^)(Time)(\W|$)',r'\1time()\3',functions[idx])
functions[idx] =re.sub(r'(\W|^)(t)(\W|$)',r'\1time()\3',functions[idx])
#functions.extend(sbmlfunctions)
dependencies2 = {}
for idx in range(0,len(functions)):
dependencies2[functions[idx].split(' = ')[0].split('(')[0].strip()] = []
for key in artificialObservables:
oldfunc = functions[idx]
functions[idx] = (re.sub(r'(\W|^)({0})([^\w(]|$)'.format(key),r'\1\2()\3',functions[idx]))
if oldfunc != functions[idx]:
dependencies2[functions[idx].split(' = ')[0].split('(')[0]].append(key)
for element in sbmlfunctions:
oldfunc = functions[idx]
key = element.split(' = ')[0].split('(')[0]
if re.search('(\W|^){0}(\W|$)'.format(key),functions[idx].split(' = ')[1]) != None:
dependencies2[functions[idx].split(' = ')[0].split('(')[0]].append(key)
for element in tfunc:
key = element.split(' = ')[0].split('(')[0]
if key in functions[idx].split(' = ')[1]:
dependencies2[functions[idx].split( ' = ')[0].split('(')[0]].append(key)
'''
for counter in range(0,3):
for element in dependencies2:
if len(dependencies2[element]) > counter:
dependencies2[element].extend(dependencies2[dependencies2[element][counter]])
'''
fd = []
for function in functions:
#print function,dependencies2[function.split(' = ' )[0].split('(')[0]],function.split(' = ' )[0].split('(')[0],0
fd.append([function,resolveDependencies(dependencies2,function.split(' = ' )[0].split('(')[0],0)])
fd = sorted(fd,key= lambda rule:rule[1])
functions = [x[0] for x in fd]
return functions
def analyzeHelper(document,reactionDefinitions,useID,outputFile,speciesEquivalence,atomize,translator,bioGrid = False):
'''
taking the atomized dictionary and a series of data structure, this method
does the actual string output.
'''
useArtificialRules = False
parser =SBML2BNGL(document.getModel(),useID)
database = structures.Databases()
#translator,log,rdf = m2c.transformMolecules(parser,database,reactionDefinitions,speciesEquivalence)
#try:
#bioGridDict = {}
#if biogrid:
# bioGridDict = biogrid()
#if atomize:
# translator = mc.transformMolecules(parser,database,reactionDefinitions,speciesEquivalence,bioGridDict)
#else:
# translator={}
parser =SBML2BNGL(document.getModel(),useID)
#except:
# print 'failure'
# return None,None,None,None
#translator = {}
param,zparam = parser.getParameters()
molecules,initialConditions,observables,speciesDict = parser.getSpecies(translator,[x.split(' ')[0] for x in param])
#finally, adjust parameters and initial concentrations according to whatever initialassignments say
param,zparam,initialConditions = parser.getInitialAssignments(translator,param,zparam,molecules,initialConditions)
compartments = parser.getCompartments()
functions = []
assigmentRuleDefinedParameters = []
reactionParameters,rules,rateFunctions = parser.getReactions(translator,len(compartments)>1,atomize=atomize)
functions.extend(rateFunctions)
aParameters,aRules,nonzparam,artificialRules,removeParams,artificialObservables = parser.getAssignmentRules(zparam,param,molecules)
for element in nonzparam:
param.append('{0} 0'.format(element))
param = [x for x in param if x not in removeParams]
tags = '@{0}'.format(compartments[0].split(' ')[0]) if len(compartments) == 1 else '@cell'
molecules.extend([x.split(' ')[0] for x in removeParams])
if len(molecules) == 0:
compartments = []
observables.extend('Species {0} {0}'.format(x.split(' ')[0]) for x in removeParams)
for x in removeParams:
initialConditions.append(x.split(' ')[0] + tags + ' ' + x.split(' ')[1])
##Comment out those parameters that are defined with assignment rules
##TODO: I think this is correct, but it may need to be checked
tmpParams = []
for idx,parameter in enumerate(param):
for key in artificialObservables:
if re.search('^{0}\s'.format(key),parameter)!= None:
assigmentRuleDefinedParameters.append(idx)
tmpParams.extend(artificialObservables)
tmpParams.extend(removeParams)
tmpParams = set(tmpParams)
correctRulesWithParenthesis(rules,tmpParams)
for element in assigmentRuleDefinedParameters:
param[element] = '#' + param[element]
deleteMolecules = []
deleteMoleculesFlag = True
for key in artificialObservables:
flag = -1
for idx,observable in enumerate(observables):
if 'Species {0} {0}()'.format(key) in observable:
flag = idx
if flag != -1:
observables.pop(flag)
functions.append(artificialObservables[key])
flag = -1
if '{0}()'.format(key) in molecules:
flag = molecules.index('{0}()'.format(key))
if flag != -1:
if deleteMoleculesFlag:
deleteMolecules.append(flag)
else:
deleteMolecules.append(key)
#result =validateReactionUsage(molecules[flag],rules)
#if result != None:
# logMess('ERROR','Pseudo observable {0} in reaction {1}'.format(molecules[flag],result))
#molecules.pop(flag)
flag = -1
for idx,specie in enumerate(initialConditions):
if ':{0}('.format(key) in specie:
flag = idx
if flag != -1:
initialConditions[flag] = '#' + initialConditions[flag]
for flag in sorted(deleteMolecules,reverse=True):
if deleteMoleculesFlag:
logMess('WARNING:Simulation','{0} reported as function, but usage is ambiguous'.format(molecules[flag]) )
result =validateReactionUsage(molecules[flag],rules)
if result != None:
logMess('ERROR:Simulation','Pseudo observable {0} in reaction {1}'.format(molecules[flag],result))
molecules.pop(flag)
else:
logMess('WARNING:Simulation','{0} reported as species, but usage is ambiguous.'.format(flag) )
artificialObservables.pop(flag)
functions.extend(aRules)
sbmlfunctions = parser.getSBMLFunctions()
processFunctions(functions,sbmlfunctions,artificialObservables,rateFunctions)
for interation in range(0,3):
for sbml2 in sbmlfunctions:
for sbml in sbmlfunctions:
if sbml == sbml2:
continue
if sbml in sbmlfunctions[sbml2]:
sbmlfunctions[sbml2] = writer.extendFunction(sbmlfunctions[sbml2],sbml,sbmlfunctions[sbml])
functions = reorderFunctions(functions)
functions = changeNames(functions,aParameters)
# print [x for x in functions if 'functionRate60' in x]
functions = unrollFunctions(functions)
rules = changeRates(rules,aParameters)
if len(compartments) > 1 and 'cell 3 1.0' not in compartments:
compartments.append('cell 3 1.0')
#sbml always has the 'cell' default compartment, even when it
#doesn't declare it
elif len(compartments) == 0 and len(molecules) != 0:
compartments.append('cell 3 1.0')
if len(artificialRules) + len(rules) == 0:
logMess('ERROR:Simulation','The file contains no reactions')
if useArtificialRules or len(rules) == 0:
rules =['#{0}'.format(x) for x in rules]
evaluate = evaluation(len(observables),translator)
artificialRules.extend(rules)
rules = artificialRules
else:
artificialRules =['#{0}'.format(x) for x in artificialRules]
evaluate = evaluation(len(observables),translator)
rules.extend(artificialRules)
commentDictionary = {}
if atomize:
commentDictionary['notes'] = "'This is an atomized translation of an SBML model created on {0}.".format(time.strftime("%d/%m/%Y"))
else:
commentDictionary['notes'] = "'This is a plain translation of an SBML model created on {0}.".format(time.strftime("%d/%m/%Y"))
commentDictionary['notes'] += " The original model has {0} molecules and {1} reactions. The translated model has {2} molecules and {3} rules'".format(parser.model.getNumSpecies(),parser.model.getNumReactions(),len(molecules),len(set(rules)))
meta = parser.getMetaInformation(commentDictionary)
from collections import OrderedDict
finalString = writer.finalText(meta,param+reactionParameters,molecules,initialConditions,list(OrderedDict.fromkeys(observables)),list(OrderedDict.fromkeys(rules)),functions,compartments,outputFile)
#print outputFile
logMess('INFO:Summary','File contains {0} molecules out of {1} original SBML species'.format(len(molecules),len(observables)))
#store a logfile
try:
if len(logMess.log) > 0:
with open(outputFile + '.log', 'w') as f:
for element in logMess.log:
f.write(element + '\n')
except AttributeError:
print "error"
except IOError:
pass
#print ""
#rate of each classified rule
evaluate2 = 0 if len(observables) == 0 else len(molecules)*1.0/len(observables)
return len(rules),len(observables),evaluate,evaluate2,len(compartments), parser.getSpeciesAnnotation(),finalString,speciesDict
'''
def analyzeFile(bioNumber,reactionDefinitions,useID,outputFile,speciesEquivalence=None):
useArtificialRules = False
reader = libsbml.SBMLReader()
document = reader.readSBMLFromFile('XMLExamples/curated/BIOMD%010i.xml' % bioNumber)
parser =SBML2BNGL(document.getModel(),useID)
database = structures.Databases()
try:
translator,log = m2c.transformMolecules(parser,database,reactionDefinitions,speciesEquivalence)
#translator={}
except:
print 'failure'
return None,None
#translator = {}
param,zparam = parser.getParameters()
molecules,species,observables = parser.getSpecies(translator)
compartments = parser.getCompartments()
functions = []
idxArray = []
_,rules,tfunc = parser.getReactions(translator,True)
functions.extend(tfunc)
aParameters,aRules,nonzparam,artificialRules,removeParams,artificialObservables = parser.getAssignmentRules(zparam,param,molecules)
for element in nonzparam:
param.append('{0} 0'.format(element))
param = [x for x in param if x not in removeParams]
tags = '@{0}'.format(compartments[0].split(' ')[0]) if len(compartments) == 1 else '@cell'
molecules.extend([x.split(' ')[0] for x in removeParams])
if len(molecules) == 0:
compartments = []
observables.extend('Species {0} {0}'.format(x.split(' ')[0]) for x in removeParams)
for x in removeParams:
species.append(x.split(' ')[0] + tags + ' ' + x.split(' ')[1])
##Comment out those parameters that are defined with assignment rules
##TODO: I think this is correct, but it may need to be checked
for idx,parameter in enumerate(param):
for key in artificialObservables:
if re.search('^{0}\s'.format(key),parameter)!= None:
idxArray.append(idx)
for element in idxArray:
param[element] = '#' + param[element]
for key in artificialObservables:
flag = -1
for idx,observable in enumerate(observables):
if 'Species {0} {0}()'.format(key) in observable:
flag = idx
if flag != -1:
observables.pop(flag)
functions.append(artificialObservables[key])
flag = -1
if '{0}()'.format(key) in molecules:
flag = molecules.index('{0}()'.format(key))
if flag != -1:
molecules.pop(flag)
flag = -1
for idx,specie in enumerate(species):
if ':{0}('.format(key) in specie:
flag = idx
if flag != -1:
species[flag] = '#' + species[flag]
functions.extend(aRules)
sbmlfunctions = parser.getSBMLFunctions()
for interation in range(0,3):
for sbml2 in sbmlfunctions:
for sbml in sbmlfunctions:
if sbml == sbml2:
continue
if sbml in sbmlfunctions[sbml2]:
sbmlfunctions[sbml2] = writer.extendFunction(sbmlfunctions[sbml2],sbml,sbmlfunctions[sbml])
for idx in range(0,len(functions)):
for sbml in sbmlfunctions:
if sbml in functions[idx]:
functions[idx] = writer.extendFunction(functions[idx],sbml,sbmlfunctions[sbml])
#functions.extend(sbmlfunctions)
dependencies2 = {}
for idx in range(0,len(functions)):
dependencies2[functions[idx].split(' = ')[0].split('(')[0].strip()] = []
for key in artificialObservables:
oldfunc = functions[idx]
functions[idx] = (re.sub(r'(\W|^)({0})([^\w(]|$)'.format(key),r'\1\2()\3',functions[idx]))
if oldfunc != functions[idx]:
dependencies2[functions[idx].split(' = ')[0].split('(')[0]].append(key)
for element in sbmlfunctions:
oldfunc = functions[idx]
key = element.split(' = ')[0].split('(')[0]
if re.search('(\W|^){0}(\W|$)'.format(key),functions[idx].split(' = ')[1]) != None:
dependencies2[functions[idx].split(' = ')[0].split('(')[0]].append(key)
for element in tfunc:
key = element.split(' = ')[0].split('(')[0]
if key in functions[idx].split(' = ')[1]:
dependencies2[functions[idx].split( ' = ')[0].split('(')[0]].append(key)
'''
for counter in range(0,3):
for element in dependencies2:
if len(dependencies2[element]) > counter:
dependencies2[element].extend(dependencies2[dependencies2[element][counter]])
'''
fd = []
for function in functions:
fd.append([function,resolveDependencies(dependencies2,function.split(' = ' )[0].split('(')[0],0)])
fd = sorted(fd,key= lambda rule:rule[1])
functions = [x[0] for x in fd]
if len(param) == 0:
param.append('dummy 0')
#functions.extend(aRules)
if len(compartments) > 1 and 'cell 3 1.0' not in compartments:
compartments.append('cell 3 1.0')
if len(artificialRules) + len(rules) == 0:
logMess('ERROR','The file contains no reactions')
if useArtificialRules or len(artificialRules) > 0:
rules =['#{0}'.format(x) for x in rules]
evaluate = evaluation(len(artificialRules),translator)
artificialRules.extend(rules)
writer.finalText(param,molecules,species,observables,artificialRules,functions,compartments,outputFile)
else:
artificialRules =['#{0}'.format(x) for x in artificialRules]
evaluate = evaluation(len(rules),translator)
rules.extend(artificialRules)
writer.finalText(param,molecules,species,observables,rules,functions,compartments,outputFile)
print outputFile
if len(logMess.log) > 0:
with open(outputFile + '.log', 'w') as f:
for element in logMess.log:
f.write(element + '\n')
return len(rules), evaluate
def analyzeHelper(document,
reactionDefinitions,
useID,
outputFile,
speciesEquivalence,
atomize,
translator,
bioGrid=False):
'''
taking the atomized dictionary and a series of data structure, this method
does the actual string output.
'''
useArtificialRules = False
parser = SBML2BNGL(document.getModel(), useID)
database = structures.Databases()
#translator,log,rdf = m2c.transformMolecules(parser,database,reactionDefinitions,speciesEquivalence)
#try:
#bioGridDict = {}
#if biogrid:
# bioGridDict = biogrid()
#if atomize:
# translator = mc.transformMolecules(parser,database,reactionDefinitions,speciesEquivalence,bioGridDict)
#else:
# translator={}
parser = SBML2BNGL(document.getModel(), useID)
#except:
# print 'failure'
# return None,None,None,None
#translator = {}
param, zparam = parser.getParameters()
molecules, initialConditions, observables, speciesDict = parser.getSpecies(
translator, [x.split(' ')[0] for x in param])
#finally, adjust parameters and initial concentrations according to whatever initialassignments say
param, zparam, initialConditions = parser.getInitialAssignments(
translator, param, zparam, molecules, initialConditions)
compartments = parser.getCompartments()
functions = []
assigmentRuleDefinedParameters = []
reactionParameters, rules, rateFunctions = parser.getReactions(
translator, len(compartments) > 1, atomize=atomize)
functions.extend(rateFunctions)
aParameters, aRules, nonzparam, artificialRules, removeParams, artificialObservables = parser.getAssignmentRules(
zparam, param, molecules)
for element in nonzparam:
param.append('{0} 0'.format(element))
param = [x for x in param if x not in removeParams]
tags = '@{0}'.format(
compartments[0].split(' ')[0]) if len(compartments) == 1 else '@cell'
molecules.extend([x.split(' ')[0] for x in removeParams])
if len(molecules) == 0:
compartments = []
observables.extend('Species {0} {0}'.format(x.split(' ')[0])
for x in removeParams)
for x in removeParams:
initialConditions.append(
x.split(' ')[0] + tags + ' ' + x.split(' ')[1])
##Comment out those parameters that are defined with assignment rules
##TODO: I think this is correct, but it may need to be checked
tmpParams = []
for idx, parameter in enumerate(param):
for key in artificialObservables:
if re.search('^{0}\s'.format(key), parameter) != None:
assigmentRuleDefinedParameters.append(idx)
tmpParams.extend(artificialObservables)
tmpParams.extend(removeParams)
tmpParams = set(tmpParams)
correctRulesWithParenthesis(rules, tmpParams)
for element in assigmentRuleDefinedParameters:
param[element] = '#' + param[element]
deleteMolecules = []
deleteMoleculesFlag = True
for key in artificialObservables:
flag = -1
for idx, observable in enumerate(observables):
if 'Species {0} {0}()'.format(key) in observable:
flag = idx
if flag != -1:
observables.pop(flag)
functions.append(artificialObservables[key])
flag = -1
if '{0}()'.format(key) in molecules:
flag = molecules.index('{0}()'.format(key))
if flag != -1:
if deleteMoleculesFlag:
deleteMolecules.append(flag)
else:
deleteMolecules.append(key)
#result =validateReactionUsage(molecules[flag],rules)
#if result != None:
# logMess('ERROR','Pseudo observable {0} in reaction {1}'.format(molecules[flag],result))
#molecules.pop(flag)
flag = -1
for idx, specie in enumerate(initialConditions):
if ':{0}('.format(key) in specie:
flag = idx
if flag != -1:
initialConditions[flag] = '#' + initialConditions[flag]
for flag in sorted(deleteMolecules, reverse=True):
if deleteMoleculesFlag:
logMess(
'WARNING:Simulation',
'{0} reported as function, but usage is ambiguous'.format(
molecules[flag]))
result = validateReactionUsage(molecules[flag], rules)
if result != None:
logMess(
'ERROR:Simulation',
'Pseudo observable {0} in reaction {1}'.format(
molecules[flag], result))
molecules.pop(flag)
else:
logMess(
'WARNING:Simulation',
'{0} reported as species, but usage is ambiguous.'.format(
flag))
artificialObservables.pop(flag)
functions.extend(aRules)
sbmlfunctions = parser.getSBMLFunctions()
processFunctions(functions, sbmlfunctions, artificialObservables,
rateFunctions)
for interation in range(0, 3):
for sbml2 in sbmlfunctions:
for sbml in sbmlfunctions:
if sbml == sbml2:
continue
if sbml in sbmlfunctions[sbml2]:
sbmlfunctions[sbml2] = writer.extendFunction(
sbmlfunctions[sbml2], sbml, sbmlfunctions[sbml])
functions = reorderFunctions(functions)
functions = changeNames(functions, aParameters)
# print [x for x in functions if 'functionRate60' in x]
functions = unrollFunctions(functions)
rules = changeRates(rules, aParameters)
if len(compartments) > 1 and 'cell 3 1.0' not in compartments:
compartments.append('cell 3 1.0')
#sbml always has the 'cell' default compartment, even when it
#doesn't declare it
elif len(compartments) == 0 and len(molecules) != 0:
compartments.append('cell 3 1.0')
if len(artificialRules) + len(rules) == 0:
logMess('ERROR:Simulation', 'The file contains no reactions')
if useArtificialRules or len(rules) == 0:
rules = ['#{0}'.format(x) for x in rules]
evaluate = evaluation(len(observables), translator)
artificialRules.extend(rules)
rules = artificialRules
else:
artificialRules = ['#{0}'.format(x) for x in artificialRules]
evaluate = evaluation(len(observables), translator)
rules.extend(artificialRules)
commentDictionary = {}
if atomize:
commentDictionary[
'notes'] = "'This is an atomized translation of an SBML model created on {0}.".format(
time.strftime("%d/%m/%Y"))
else:
commentDictionary[
'notes'] = "'This is a plain translation of an SBML model created on {0}.".format(
time.strftime("%d/%m/%Y"))
commentDictionary[
'notes'] += " The original model has {0} molecules and {1} reactions. The translated model has {2} molecules and {3} rules'".format(
parser.model.getNumSpecies(), parser.model.getNumReactions(),
len(molecules), len(set(rules)))
meta = parser.getMetaInformation(commentDictionary)
from collections import OrderedDict
finalString = writer.finalText(meta, param + reactionParameters, molecules,
initialConditions,
list(OrderedDict.fromkeys(observables)),
list(OrderedDict.fromkeys(rules)),
functions, compartments, outputFile)
#print outputFile
logMess(
'INFO:Summary',
'File contains {0} molecules out of {1} original SBML species'.format(
len(molecules), len(observables)))
#store a logfile
try:
if len(logMess.log) > 0:
with open(outputFile + '.log', 'w') as f:
for element in logMess.log:
f.write(element + '\n')
except AttributeError:
print "error"
except IOError:
pass
#print ""
#rate of each classified rule
evaluate2 = 0 if len(
observables) == 0 else len(molecules) * 1.0 / len(observables)
return len(rules), len(observables), evaluate, evaluate2, len(
compartments), parser.getSpeciesAnnotation(), finalString, speciesDict
'''