def list2predict2(l, pids, uids=None, us=None, yids=None, c=None, p0=None):
"""
pred = list2predict(['exp(-p1*1)+exp(-p2*1)', 'exp(-p1*2)+exp(-p2*2)', 'exp(-p1*1)-exp(-p2*1)'],
pids=['p1','p2'], p0=None)
pred = list2predict(['(k1f*C1-k1r*X1)-(k2f*X1-k2r*X2)',
'(k2f*X1-k2r*X2)-(k3f*X2-k3r*C2)'],
uids=['X1','X2'],
us=butil.get_product([1,2,3],[1,2,3]),
pids=['k1f','k1r','k2f','k2r','k3f','k3r'],
c={'C1':2,'C2':1})
Input:
c: a mapping
"""
if c is not None:
l = [exprmanip.sub_for_vars(s, c) for s in l]
ystr = str(l).replace("'", "")
ycode = compile(ystr, '', 'eval')
def f(p):
return np.array(eval(ycode, dict(zip(pids, p)), mathsubs))
jaclist = []
for s in l:
jacrow = [exprmanip.simplify_expr(exprmanip.diff_expr(s, pid))
for pid in pids]
jaclist.append(jacrow)
if us is not None:
jaclist = [[[exprmanip.sub_for_vars(jacentry, dict(zip(uids, u)))
for jacentry in jacrow]
for jacrow in jaclist]
for u in us]
jaclist = butil.flatten(jaclist, depth=1)
jacstr = str(jaclist).replace("'", "")
jaccode = compile(jacstr, '', 'eval')
def Df(p):
return np.array(eval(jaccode, dict(zip(pids, p)), mathsubs))
if p0 is None:
p0 = [1] * len(pids)
if yids is None:
yids = ['y%d'%i for i in range(1, len(l)+1)]
if us is not None:
uids = ['u%d'%i for i in range(1, len(us)+1)]
yids = butil.get_product(yids, uids)
return Predict(f=f, Df=Df, p0=p0, pids=pids, yids=yids)
def _sub_var_names(self, input):
mapping_dict = {}
for id in ExprManip.extract_vars(input):
# convert it back to something key_column will recognize
# had to use a form dynVarName__derivWRT__optParamName for the
# sensitivity variable because otherwise,
# extract_vars gets confused
splitId = id.split('__derivWRT__')
if len(splitId) == 1:
idname = splitId[0]
elif len(splitId) == 2:
idname = tuple(splitId)
else:
raise 'Problem with id %s in Trajectory._sub_var_names' % id
if idname in self.key_column.keys():
mapping = 'values[start:end, %i]' % self.key_column.get(idname)
elif idname in self.const_var_values.keys():
# Don't substitute for constant variable names. Those will
# be taken care of earlier in the method.
continue
elif idname == 'time':
mapping = 'times[start:end]'
else:
raise 'Problem with idname %s in Trajectory._sub_var_names' % id
mapping_dict[id] = mapping
input = ExprManip.sub_for_vars(input, mapping_dict)
return input
def test_sub_for_vars(self):
cases = [
('x', {
'x': 'y'
}, 'y'),
('x + 1', {
'x': 'y'
}, 'y + 1'),
('f(x)**2 - y + z', {
'x': 'y*z/(x-2)'
}, 'f(y*z/(x-2))**2 - y + z'),
('x**2', {
'x': 'y+2'
}, '(y+2)**2'),
('p[0]**x', {
'x': 'y+2'
}, 'p[0]**(y+2)'),
# In these cases substituting one-by-one will fail
('x*y', {
'x': 'y',
'y': 'z'
}, 'y*z'),
('z*y', {
'z': 'y',
'y': 'z'
}, 'y*z'),
]
for expr, mapping, answer in cases:
subbed = ExprManip.sub_for_vars(expr, mapping)
assert eval(answer) == eval(subbed)
def doKwargsSubstitution(self, kwargs):
oldStoichiometry = self.stoichiometry
self.stoichiometry = {}
for base in oldStoichiometry:
self.stoichiometry[kwargs[base]] = oldStoichiometry[base]
self.kineticLaw = ExprManip.sub_for_vars(self.kineticLaw, kwargs)
def doKwargsSubstitution(self, kwargs):
oldStoichiometry = self.stoichiometry
self.stoichiometry = {}
for base in oldStoichiometry:
self.stoichiometry[kwargs[base]] = oldStoichiometry[base]
self.kineticLaw = ExprManip.sub_for_vars(self.kineticLaw, kwargs)
def get_Ep_str(net):
"""
"""
Ep = []
for rxnid in net.rxnids:
ratelaw = exprmanip.sub_for_vars(net.rxns[rxnid].kineticLaw,
net.asgrules.to_od())
Ep_rxn = []
for pid in net.pids:
Ep_rxn.append(exprmanip.simplify_expr(exprmanip.diff_expr(ratelaw, pid)))
Ep.append(Ep_rxn)
Ep_str = str(Ep).replace("'", "")
Ep_code = compile(Ep_str, '', 'eval') # compile to code object
net.Ep_str, net.Ep_code = Ep_str, Ep_code
return Ep_str, Ep_code
def str2predict(s, pids, uids, us, c=None, p0=None, yids=None):
"""
Input:
us: a list of u's where each u has the same length as uids and has the
same order
c: if given, a mapping from convarid to convarval
"""
if c is not None:
s = exprmanip.sub_for_vars(s, c)
ystr = str([exprmanip.sub_for_vars(s, dict(zip(uids, u))) for u in us]).\
replace("'", "")
ycode = compile(ystr, '', 'eval')
def f(p):
return np.array(eval(ycode, dict(zip(pids, p)), mathsubs))
jaclist = []
for u in us:
s_u = exprmanip.sub_for_vars(s, dict(zip(uids, u)))
jacrow = [exprmanip.simplify_expr(exprmanip.diff_expr(s_u, pid))
for pid in pids]
jaclist.append(jacrow)
jacstr = str(jaclist).replace("'", "")
jaccode = compile(jacstr, '', 'eval')
def Df(p):
return np.array(eval(jaccode, dict(zip(pids, p)), mathsubs))
if p0 is None:
p0 = [1] * len(pids)
if yids is None:
yids = ['u=%s'%str(list(u)) for u in us]
return Predict(f=f, Df=Df, p0=p0, pids=pids, yids=yids)
def get_concentration_elasticity_string(net):
"""
"""
Ex = []
for rxnid in net.rxnids:
ratelaw = exprmanip.sub_for_vars(net.rxns[rxnid].kineticLaw,
net.asgrules.to_dict())
Ex_rxn = []
for xid in net.xids:
Ex_rxn.append(
exprmanip.simplify_expr(exprmanip.diff_expr(ratelaw, xid)))
Ex.append(Ex_rxn)
Ex_str = str(Ex).replace("'", "")
Ex_code = compile(Ex_str, '', 'eval') # compile to code object
net.Ex_str, net.Ex_code = Ex_str, Ex_code
return Ex_str, Ex_code
def fromSBMLString(sbmlStr, id=None, duplicate_rxn_params=False):
r = libsbml.SBMLReader()
d = r.readSBMLFromString(sbmlStr)
if d.getNumErrors():
message = 'libSBML reported errors in SBML file. Try running file '\
'through the online validator: '\
'http://www.sbml.org/Facilities/Validator . Specific errors '\
'noted are: '
errors = []
for ii in range(d.getNumErrors()):
pm = d.getError(ii)
errors.append(pm.getMessage())
print(message + '; '.join(errors))
m = d.getModel()
modelId = m.getId()
if (id is None) and (modelId == ''):
raise ValueError('Network id not specified in SBML or passed in.')
elif id is not None:
modelId = id
rn = Network_mod.Network(id=modelId, name=m.getName())
for f in m.getListOfFunctionDefinitions():
id, name = f.getId(), f.getName()
math = f.getMath()
variables = []
for ii in range(math.getNumChildren() - 1):
variables.append(formula_to_py(math.getChild(ii)))
math = formula_to_py(math.getRightChild())
rn.addFunctionDefinition(id, variables, math)
for c in m.getListOfCompartments():
id, name = c.getId(), c.getName()
size = c.getSize()
isConstant = c.getConstant()
rn.addCompartment(id=id, size=size, isConstant=isConstant, name=name)
for s in m.getListOfSpecies():
id, name = s.getId(), s.getName()
compartment = s.getCompartment()
if s.isSetInitialConcentration():
iC = s.getInitialConcentration()
elif s.isSetInitialAmount():
iC = s.getInitialAmount()
else:
iC = 1
isBC, isConstant = s.getBoundaryCondition(), s.getConstant()
xml_text = s.toSBML()
uniprot_ids = set([
entry[1:].split('"')[0] for entry in xml_text.split('uniprot')[1:]
])
rn.addSpecies(id=id,
compartment=compartment,
initialConcentration=iC,
isConstant=isConstant,
is_boundary_condition=isBC,
name=name,
uniprot_ids=uniprot_ids)
for p in m.getListOfParameters():
parameter = createNetworkParameter(p)
rn.addVariable(parameter)
for rxn in m.getListOfReactions():
id, name = rxn.getId(), rxn.getName()
kL = rxn.getKineticLaw()
kLFormula = kL.getFormula()
substitution_dict = {}
# Deal with parameters defined within reactions
for p in kL.getListOfParameters():
parameter = createNetworkParameter(p)
# If a parameter with this name already exists, **and it has a
# different value than this parameter** we rename this parameter
# instance by prefixing it with the rxn name so there isn't a
# clash.
if parameter.id in list(rn.variables.keys()):
logger.warn('Parameter %s appears in two different reactions '
'in SBML file.' % parameter.id)
if parameter.value != rn.variables.get(parameter.id).value or\
duplicate_rxn_params:
oldId = parameter.id
parameter.id = id + '_' + parameter.id
substitution_dict[oldId] = parameter.id
logger.warn('It has different values in the two positions '
'so we are creating a new parameter %s.' %
(parameter.id))
else:
logger.warn('It has the same value in the two positions '
'so we are only defining one parameter %s. '
'This behavior can be changed with the option '
'duplicate_rxn_params = True' % (parameter.id))
if parameter.id not in list(rn.variables.keys()):
rn.addVariable(parameter)
kLFormula = ExprManip.sub_for_vars(kLFormula, substitution_dict)
# Assemble the stoichiometry. SBML has the annoying trait that
# species can appear as both products and reactants and 'cancel out'
# For each species appearing in the reaction, we build up a string
# representing the stoichiometry. Then we'll simplify that string and
# see whether we ended up with a float value in the end.
stoichiometry = {}
reactant_stoichiometry = {}
product_stoichiometry = {}
for reactant in rxn.getListOfReactants():
species = reactant.getSpecies()
stoichiometry.setdefault(species, '0')
stoich = reactant.getStoichiometryMath()
stoich = stoichToString(reactant, stoich)
stoichiometry[species] += '-(%s)' % stoich
if species in reactant_stoichiometry:
reactant_stoichiometry[species].append(stoich)
else:
reactant_stoichiometry[species] = [stoich]
for product in rxn.getListOfProducts():
species = product.getSpecies()
stoichiometry.setdefault(species, '0')
stoich = product.getStoichiometryMath()
stoich = stoichToString(product, stoich)
stoichiometry[species] += '+(%s)' % stoich
if species in product_stoichiometry:
product_stoichiometry[species].append(stoich)
else:
product_stoichiometry[species] = [stoich]
for species, stoich in list(stoichiometry.items()):
stoich = ExprManip.simplify_expr(stoich)
try:
# Try converting the string to a float.
stoich = float(stoich)
except ValueError:
pass
stoichiometry[species] = stoich
for modifier in rxn.getListOfModifiers():
stoichiometry.setdefault(modifier.getSpecies(), 0)
rn.addReaction(id=id,
stoichiometry=stoichiometry,
kineticLaw=kLFormula,
reactant_stoichiometry=reactant_stoichiometry,
product_stoichiometry=product_stoichiometry)
for ii, r in enumerate(m.getListOfRules()):
if r.getTypeCode() == libsbml.SBML_ALGEBRAIC_RULE:
math = formula_to_py(r.getMath())
rn.add_algebraic_rule(math)
else:
variable = r.getVariable()
math = formula_to_py(r.getMath())
if r.getTypeCode() == libsbml.SBML_ASSIGNMENT_RULE:
rn.addAssignmentRule(variable, math)
elif r.getTypeCode() == libsbml.SBML_RATE_RULE:
rn.addRateRule(variable, math)
for ii, e in enumerate(m.getListOfEvents()):
id, name = e.getId(), e.getName()
if id == '':
id = 'event%i' % ii
try:
# For libSBML 3.0
trigger_math = e.getTrigger().getMath()
except AttributeError:
# For older versions
trigger_math = e.getTrigger()
trigger = formula_to_py(trigger_math)
if e.getDelay() is not None:
try:
# For libSBML 3.0
delay_math = e.getDelay().getMath()
except AttributeError:
# For older versions
delay_math = e.getDelay()
delay = formula_to_py(delay_math)
else:
delay = 0
timeUnits = e.getTimeUnits()
eaDict = KeyedList()
for ea in e.getListOfEventAssignments():
ea_formula = formula_to_py(ea.getMath())
ea_formula = ea_formula.replace('or(', 'or_func(')
ea_formula = ea_formula.replace('and(', 'and_func(')
eaDict.set(ea.getVariable(), ea_formula)
rn.addEvent(id=id,
trigger=trigger,
eventAssignments=eaDict,
delay=delay,
name=name)
for ii, con in enumerate(m.getListOfConstraints()):
id, name = con.getId(), con.getName()
if id == '':
id = 'constraint%i' % ii
trigger_math = con.getMath()
trigger = formula_to_py(trigger_math)
if con.isSetMessage():
message = con.getMessage()
else:
message = None
rn.addConstraint(id=id, trigger=trigger, message=message, name=name)
return rn
def fromSBMLString(sbmlStr, id = None, duplicate_rxn_params=False):
r = libsbml.SBMLReader()
d = r.readSBMLFromString(sbmlStr)
if d.getNumErrors():
message = 'libSBML reported errors in SBML file. Try running file '\
'through the online validator: '\
'http://www.sbml.org/Facilities/Validator . Specific errors '\
'noted are: '
errors = []
for ii in range(d.getNumErrors()):
pm = d.getError(ii)
errors.append(pm.getMessage())
raise ValueError(message + '; '.join(errors))
m = d.getModel()
modelId = m.getId()
if (id == None) and (modelId == ''):
raise ValueError('Network id not specified in SBML or passed in.')
elif id is not None:
modelId = id
rn = Network_mod.Network(id = modelId, name = m.getName())
for f in m.getListOfFunctionDefinitions():
id, name = f.getId(), f.getName()
math = f.getMath()
variables = []
for ii in range(math.getNumChildren() - 1):
variables.append(libsbml.formulaToString(math.getChild(ii)))
math = libsbml.formulaToString(math.getRightChild())
rn.addFunctionDefinition(id, variables, math)
for c in m.getListOfCompartments():
id, name = c.getId(), c.getName()
size = c.getSize()
isConstant = c.getConstant()
rn.addCompartment(id = id, size = size,
isConstant = isConstant,
name = name)
for s in m.getListOfSpecies():
id, name = s.getId(), s.getName()
compartment = s.getCompartment()
if s.isSetInitialConcentration():
iC = s.getInitialConcentration()
elif s.isSetInitialAmount():
iC = s.getInitialAmount()
else:
iC = 1
isBC, isConstant = s.getBoundaryCondition(), s.getConstant()
xml_text = s.toSBML()
uniprot_ids = set([entry[1:].split('"')[0]
for entry in xml_text.split('uniprot')[1:]])
rn.addSpecies(id = id, compartment = compartment,
initialConcentration = iC,
isConstant = isConstant,
is_boundary_condition = isBC,
name = name, uniprot_ids = uniprot_ids)
for p in m.getListOfParameters():
parameter = createNetworkParameter(p)
rn.addVariable(parameter)
for rxn in m.getListOfReactions():
id, name = rxn.getId(), rxn.getName()
kL = rxn.getKineticLaw()
kLFormula = kL.getFormula()
substitution_dict = {}
# Deal with parameters defined within reactions
for p in kL.getListOfParameters():
parameter = createNetworkParameter(p)
# If a parameter with this name already exists, **and it has a
# different value than this parameter** we rename this parameter
# instance by prefixing it with the rxn name so there isn't a
# clash.
if parameter.id in rn.variables.keys():
logger.warn('Parameter %s appears in two different reactions '
'in SBML file.' % parameter.id)
if parameter.value != rn.variables.get(parameter.id).value or\
duplicate_rxn_params:
oldId = parameter.id
parameter.id = id + '_' + parameter.id
substitution_dict[oldId] = parameter.id
logger.warn('It has different values in the two positions '
'so we are creating a new parameter %s.'
% (parameter.id))
else:
logger.warn('It has the same value in the two positions '
'so we are only defining one parameter %s. '
'This behavior can be changed with the option '
'duplicate_rxn_params = True' % (parameter.id))
if parameter.id not in rn.variables.keys():
rn.addVariable(parameter)
kLFormula = ExprManip.sub_for_vars(kLFormula, substitution_dict)
# Assemble the stoichiometry. SBML has the annoying trait that
# species can appear as both products and reactants and 'cancel out'
# For each species appearing in the reaction, we build up a string
# representing the stoichiometry. Then we'll simplify that string and
# see whether we ended up with a float value in the end.
stoichiometry = {}
reactant_stoichiometry = {}
product_stoichiometry = {}
for reactant in rxn.getListOfReactants():
species = reactant.getSpecies()
stoichiometry.setdefault(species, '0')
stoich = reactant.getStoichiometryMath()
stoich = stoichToString(reactant, stoich)
stoichiometry[species] += '-(%s)' % stoich
if species in reactant_stoichiometry:
reactant_stoichiometry[species].append(stoich)
else:
reactant_stoichiometry[species] = [stoich]
for product in rxn.getListOfProducts():
species = product.getSpecies()
stoichiometry.setdefault(species, '0')
stoich = product.getStoichiometryMath()
stoich = stoichToString(product, stoich)
stoichiometry[species] += '+(%s)' % stoich
if species in product_stoichiometry:
product_stoichiometry[species].append(stoich)
else:
product_stoichiometry[species] = [stoich]
for species, stoich in stoichiometry.items():
stoich = ExprManip.simplify_expr(stoich)
try:
# Try converting the string to a float.
stoich = float(stoich)
except ValueError:
pass
stoichiometry[species] = stoich
for modifier in rxn.getListOfModifiers():
stoichiometry.setdefault(modifier.getSpecies(), 0)
rn.addReaction(id = id, stoichiometry = stoichiometry,
kineticLaw = kLFormula,
reactant_stoichiometry = reactant_stoichiometry,
product_stoichiometry = product_stoichiometry)
for ii, r in enumerate(m.getListOfRules()):
if r.getTypeCode() == libsbml.SBML_ALGEBRAIC_RULE:
math = libsbml.formulaToString(r.getMath())
rn.add_algebraic_rule(math)
else:
variable = r.getVariable()
math = libsbml.formulaToString(r.getMath())
if r.getTypeCode() == libsbml.SBML_ASSIGNMENT_RULE:
rn.addAssignmentRule(variable, math)
elif r.getTypeCode() == libsbml.SBML_RATE_RULE:
rn.addRateRule(variable, math)
for ii, e in enumerate(m.getListOfEvents()):
id, name = e.getId(), e.getName()
if id == '':
id = 'event%i' % ii
try:
# For libSBML 3.0
trigger_math = e.getTrigger().getMath()
except AttributeError:
# For older versions
trigger_math = e.getTrigger()
trigger = libsbml.formulaToString(trigger_math)
trigger = trigger.replace('or(','or_func(')
trigger = trigger.replace('and(','and_func(')
if e.getDelay() is not None:
try:
# For libSBML 3.0
delay_math = e.getDelay().getMath()
except AttributeError:
# For older versions
delay_math = e.getDelay()
delay = libsbml.formulaToString(delay_math)
else:
delay = 0
timeUnits = e.getTimeUnits()
eaDict = KeyedList()
for ea in e.getListOfEventAssignments():
ea_formula = libsbml.formulaToString(ea.getMath())
ea_formula = ea_formula.replace('or(','or_func(')
ea_formula = ea_formula.replace('and(','and_func(')
eaDict.set(ea.getVariable(), ea_formula)
rn.addEvent(id = id, trigger = trigger, eventAssignments = eaDict,
delay = delay, name = name)
for ii, con in enumerate(m.getListOfConstraints()):
id, name = con.getId(), con.getName()
if id == '':
id = 'constraint%i' % ii
trigger_math = con.getMath()
trigger = libsbml.formulaToString(trigger_math)
if con.isSetMessage():
message = con.getMessage()
else:
message = None
rn.addConstraint(id = id, trigger = trigger, message = message,
name = name)
return rn
