def set_kinetic_law(model, reaction, formula): """ Sets the kinetic law in reaction based on given formula. """ law = reaction.createKineticLaw() ast_node = libsbml.parseL3FormulaWithModel(formula, model) if ast_node is None: logging.error(libsbml.getLastParseL3Error()) check(law.setMath(ast_node), 'set math in kinetic law') return law
def set_kinetic_law(model, reaction, formula): """ Sets the kinetic law in reaction based on given formula. """ law = reaction.createKineticLaw() ast_node = libsbml.parseL3FormulaWithModel(formula, model) if ast_node is None: logging.error(libsbml.getLastParseL3Error()) check(law.setMath(ast_node), 'set math in kinetic law') return law
def formula_to_astnode(formula: str) -> libsbml.ASTNode: """Convert formula string to ASTNode. :param formula: SBML formula string :return: libsbml.ASTNode """ astnode = libsbml.parseL3Formula(formula) if not astnode: logging.error(f"Formula could not be parsed: '{formula}'") logging.error(libsbml.getLastParseL3Error()) return astnode
def ast_node_from_formula(model, formula): """ Parses the ASTNode from given formula string with model. :param model: SBMLModel instance :param formula: formula str :return: """ ast_node = libsbml.parseL3FormulaWithModel(formula, model) if not ast_node: logging.error("Formula could not be parsed: '{}'".format(formula)) logging.error(libsbml.getLastParseL3Error()) return ast_node
def ast_node_from_formula(model, formula): """ Parses the ASTNode from given formula string with model. :param model: SBMLModel instance :param formula: formula str :return: """ ast_node = libsbml.parseL3FormulaWithModel(formula, model) if not ast_node: logging.error("Formula could not be parsed: '{}'".format(formula)) logging.error(libsbml.getLastParseL3Error()) return ast_node
def distrib_all(): """ Create simple distrib model. :return: """ doc = _distrib_doc() model = doc.createModel() # type: libsbml.Model # extended math approach formulas_data = [ ('p_normal_1', 'normal(0, 1)'), ('p_normal_2', 'normal(0, 1, 0, 10)'), ('p_uniform', 'uniform(5, 10)'), ('p_bernoulli', 'bernoulli(0.4)'), ('p_binomial_1', 'binomial(100, 0.3)'), ('p_binomial_2', 'binomial(100, 0.3, 0, 2)'), ('p_cauchy_1', 'cauchy(0, 1)'), ('p_cauchy_2', 'cauchy(0, 1, 0, 5)'), ('p_chisquare_1', 'chisquare(10)'), ('p_chisquare_2', 'chisquare(10, 0, 10)'), ('p_exponential_1', 'exponential(1.0)'), ('p_exponential_2', 'exponential(1.0, 0, 10)'), ('p_gamma_1', 'gamma(0, 1)'), ('p_gamma_2', 'gamma(0, 1, 0, 10)'), ('p_laplace_1', 'laplace(0, 1)'), ('p_laplace_2', 'laplace(0, 1, 0, 10)'), ('p_lognormal_1', 'lognormal(0, 1)'), ('p_lognormal_2', 'lognormal(0, 1, 0, 10)'), ('p_poisson_1', 'poisson(0.5)'), ('p_poisson_2', 'poisson(0.5, 0, 10)'), ('p_raleigh_1', 'rayleigh(0.5)'), ('p_raleigh_2', 'rayleigh(0.5, 0, 10)'), ] # create parameters with distribution assignments for pid, formula in formulas_data: print("{} = {}".format(pid, formula)) p = _create_parameter(pid, model=model) # type: libsbml.Parameter assignment = model.createInitialAssignment( ) # type: libsbml.InitialAssignment assignment.setSymbol(pid) ast_node = libsbml.parseL3FormulaWithModel(formula, model) if ast_node is None: raise IOError("{}, {}".format(formula, libsbml.getLastParseL3Error())) assignment.setMath(ast_node), "setting math" return doc
def ast_node_from_formula(model, formula): """Parses the ASTNode from given formula string with model. :param model: SBMLModel instance :param formula: formula str :return: """ # sanitize formula (allow double and int assignments) if not isinstance(formula, str): formula = str(formula) ast_node = libsbml.parseL3FormulaWithModel(formula, model) if not ast_node: logger.error("Formula could not be parsed: '{}'".format(formula)) logger.error(libsbml.getLastParseL3Error()) return ast_node
def distrib_all(): """ Create simple distrib model. :return: """ doc = _distrib_doc() model = doc.createModel() # type: libsbml.Model # extended math approach formulas_data = [ ('p_normal_1', 'normal(0, 1)'), ('p_normal_2', 'normal(0, 1, 0, 10)'), ('p_uniform', 'uniform(5, 10)'), ('p_bernoulli', 'bernoulli(0.4)'), ('p_binomial_1', 'binomial(100, 0.3)'), ('p_binomial_2', 'binomial(100, 0.3, 0, 2)'), ('p_cauchy_1', 'cauchy(0, 1)'), ('p_cauchy_2', 'cauchy(0, 1, 0, 5)'), ('p_chisquare_1', 'chisquare(10)'), ('p_chisquare_2', 'chisquare(10, 0, 10)'), ('p_exponential_1', 'exponential(1.0)'), ('p_exponential_2', 'exponential(1.0, 0, 10)'), ('p_gamma_1', 'gamma(0, 1)'), ('p_gamma_2', 'gamma(0, 1, 0, 10)'), ('p_laplace_1', 'laplace(0, 1)'), ('p_laplace_2', 'laplace(0, 1, 0, 10)'), ('p_lognormal_1', 'lognormal(0, 1)'), ('p_lognormal_2', 'lognormal(0, 1, 0, 10)'), ('p_poisson_1', 'poisson(0.5)'), ('p_poisson_2', 'poisson(0.5, 0, 10)'), ('p_raleigh_1', 'rayleigh(0.5)'), ('p_raleigh_2', 'rayleigh(0.5, 0, 10)'), ] # create parameters with distribution assignments for pid, formula in formulas_data: print("{} = {}".format(pid, formula)) p = _create_parameter(pid, model=model) # type: libsbml.Parameter assignment = model.createInitialAssignment() # type: libsbml.InitialAssignment assignment.setSymbol(pid) ast_node = libsbml.parseL3FormulaWithModel(formula, model) if ast_node is None: raise IOError("{}, {}".format(formula, libsbml.getLastParseL3Error())) assignment.setMath(ast_node), "setting math" return doc
def add_interpolator_to_model(interpolator: "Interpolator", model: libsbml.Model) -> None: """Add interpolator to model. The parameters, formulas and rules have to be added to the SBML model. :param interpolator: :param model: Model :return: """ # create parameter pid = interpolator.yid # if parameter exists remove it if model.getParameter(pid): logger.warning( "Model contains parameter: {}. Parameter is removed.".format( pid)) model.removeParameter(pid) # if assignment rule exists remove it for rule in model.getListOfRules(): if rule.isAssignment(): if rule.getVariable() == pid: model.removeRule(rule) break p = model.createParameter() p.setId(pid) p.setName(pid) p.setConstant(False) # create rule rule = model.createAssignmentRule() rule.setVariable(pid) formula = interpolator.formula() ast_node = libsbml.parseL3FormulaWithModel(formula, model) if ast_node is None: logger.warning(libsbml.getLastParseL3Error()) else: rule.setMath(ast_node)
def formula_to_astnode( formula: str, model: Optional[libsbml.Model] = None ) -> libsbml.ASTNode: """Convert formula string to ASTNode. :param formula: SBML formula string :param model: libsbml.Model :return: libsbml.ASTNode """ if model: astnode = libsbml.parseL3FormulaWithModel(formula, model) else: astnode = libsbml.parseL3Formula(formula) if not astnode: logger.error(f"Formula could not be parsed: '{formula}'") logger.error(libsbml.getLastParseL3Error()) raise ValueError( f"Formula could not be parsed: '{formula}'.\n" f"{libsbml.getLastParseL3Error()}" ) return astnode
def create_sbml(self, sbase): """ Create libsbml Uncertainty. :param model: :return: """ sbase_distrib = sbase.getPlugin( "distrib") # type: libsbml.DistribSBasePlugin uncertainty = sbase_distrib.createUncertainty( ) # type: libsbml.Uncertainty for uncertParameter in self.uncertParameters: up = None if uncertParameter.type in [ libsbml.DISTRIB_UNCERTTYPE_INTERQUARTILERANGE, libsbml.DISTRIB_UNCERTTYPE_CREDIBLEINTERVAL, libsbml.DISTRIB_UNCERTTYPE_CONFIDENCEINTERVAL, libsbml.DISTRIB_UNCERTTYPE_RANGE, ]: up = uncertainty.createUncertSpan() # type: libsbml.UncertSpan up.setType(uncertParameter.type) if uncertParameter.valueLower is not None: up.setValueLower(uncertParameter.valueLower) if uncertParameter.valueUpper is not None: up.setValueUpper(uncertParameter.valueUpper) if uncertParameter.varLower is not None: up.setVarLower(uncertParameter.varLower) if uncertParameter.varUpper is not None: up.setValueLower(uncertParameter.varUpper) elif uncertParameter.type in [ libsbml.DISTRIB_UNCERTTYPE_COEFFIENTOFVARIATION, libsbml.DISTRIB_UNCERTTYPE_KURTOSIS, libsbml.DISTRIB_UNCERTTYPE_MEAN, libsbml.DISTRIB_UNCERTTYPE_MEDIAN, libsbml.DISTRIB_UNCERTTYPE_MODE, libsbml.DISTRIB_UNCERTTYPE_SAMPLESIZE, libsbml.DISTRIB_UNCERTTYPE_SKEWNESS, libsbml.DISTRIB_UNCERTTYPE_STANDARDDEVIATION, libsbml.DISTRIB_UNCERTTYPE_STANDARDERROR, libsbml.DISTRIB_UNCERTTYPE_VARIANCE, ]: up = uncertainty.createUncertParameter( ) # type: libsbml.UncertParameter up.setType(uncertParameter.type) if uncertParameter.value is not None: up.setValue(uncertParameter.value) if uncertParameter.var is not None: up.setValue(uncertParameter.var) else: logging.error( "Unsupported UncertParameter or UncertSpan type: %s", uncertParameter.type) if up and uncertParameter.unit: up.setUnits(Unit.get_unit_string(uncertParameter.unit)) # create a distribution uncertainty if self.formula: model = sbase.getModel() up = uncertainty.createUncertParameter( ) # type: libsbml.UncertParameter up.setType(libsbml.DISTRIB_UNCERTTYPE_DISTRIBUTION) for key in [ "normal", "uniform", "bernoulli", "binomial", "cauchy", "chisquare", "exponential", "gamma", "laplace", "lognormal", "poisson", "raleigh" ]: if key in self.formula: up.setDefinitionURL( "http://www.sbml.org/sbml/symbols/distrib/{}".format( key)) ast = libsbml.parseL3FormulaWithModel(self.formula, model) if ast is None: logging.error(libsbml.getLastParseL3Error()) else: check(up.setMath(ast), 'set math in distrib formula') return uncertainty