Function gen_reactions has a Cognitive Complexity of 192 (exceeds 5 allowed). Consider refactoring.

Open

    def gen_reactions(self):
        """ Generate reactions associated with submodel 
        
        Exchange reactions for components in the media will be be created if they do 
        not exist. The maximum and minimum flux bounds for exchange reactions will also be set.

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Function impute_kinetic_constant has a Cognitive Complexity of 118 (exceeds 5 allowed). Consider refactoring.

Open

    def impute_kinetic_constant(self, bound_values):
        """ Impute the values of kcat that have not been measured.

        Args:
            bound_values (:obj:`dict`): Keys are reaction IDs and values are tuples 

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File metabolism.py has 921 lines of code (exceeds 250 allowed). Consider refactoring.

Open

""" Generator for metabolism submodel for eukaryotes

:Author: Yin Hoon Chew <yinhoon.chew@mssm.edu>
:Date: 2020-01-21
:Copyright: 2020, Karr Lab

Function determine_bounds has a Cognitive Complexity of 46 (exceeds 5 allowed). Consider refactoring.

Open

    def determine_bounds(self):
        """ Determine the minimum and maximum bounds for each reaction. The bounds will be
            scaled according to the provided scale factor.

        Returns:

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Function relax_bounds has a Cognitive Complexity of 32 (exceeds 5 allowed). Consider refactoring.

Open

    def relax_bounds(self, target, lower_bound_adjustable, upper_bound_adjustable):
        """ Relax bounds to achieve set target flux(es) while minimizing the total necessary adjustment
            to the flux bounds
        
        Args:

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Function gen_reactions has 60 lines of code (exceeds 25 allowed). Consider refactoring.

Open

    def gen_reactions(self):
        """ Generate reactions associated with submodel 
        
        Exchange reactions for components in the media will be be created if they do 
        not exist. The maximum and minimum flux bounds for exchange reactions will also be set.

Function calibrate_submodel has a Cognitive Complexity of 16 (exceeds 5 allowed). Consider refactoring.

Open

    def calibrate_submodel(self):
        """ Calibrate the submodel by adjusting measured kinetic constants to achieve 
            the measured growth rate while minimizing the total necessary adjustment. 
            Kinetic constants that have no measured values are then imputed based on 
            values determined by Flux Variability Analysis.

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Function flux_variability_analysis has a Cognitive Complexity of 14 (exceeds 5 allowed). Consider refactoring.

Open

    def flux_variability_analysis(self, conv_model, fraction_of_objective=1.0, 
            fixed_values=None, target_reactions=None):
        """ Conduct flux variability analysis by:
            1) Optimizing the model by maximizing the objective function
            2) Setting the objective function to the optimal value

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Function clean_and_validate_options has 28 lines of code (exceeds 25 allowed). Consider refactoring.

Open

    def clean_and_validate_options(self):
        """ Apply default options and validate options """
        options = self.options

        recycled_metabolites = options.get('recycled_metabolites', {})

Function relax_bounds has 27 lines of code (exceeds 25 allowed). Consider refactoring.

Open

    def relax_bounds(self, target, lower_bound_adjustable, upper_bound_adjustable):
        """ Relax bounds to achieve set target flux(es) while minimizing the total necessary adjustment
            to the flux bounds
        
        Args:

Avoid deeply nested control flow statements.

Open

                        for kcat in law.expression.parameters:                                                     
                            kcat.value = kcat.value*value/effective_vmax
                            kcat.comments = 'Measured value adjusted to relax bound'
                elif all(numpy.isnan(kcat.value) for kcat in law.expression.parameters):

Avoid deeply nested control flow statements.

Open

                        if comp.distribution_init_concentration.mean!=0.0:
                            kcat.value = shared_vmax/comp.distribution_init_concentration.mean*\
                                kcat_adjustment_factor                                    
                            kcat.comments = 'Value imputed based on FVA bound value ' +\
                                'and adjusted with a factor of {}'.format(kcat_adjustment_factor) 

Avoid deeply nested control flow statements.

Open

                        for part in prot_deg_reaction.participants:
                            if part.species.id in met_requirement:
                                met_requirement[part.species.id] += -part.coefficient * prot_concentration * \
                                    doubling_time / prot_half_life

Avoid deeply nested control flow statements.

Open

                        for comp, kcat in comp_kcat.items():                            
                            if comp.distribution_init_concentration.mean!=0.0:
                                kcat.value = shared_vmax/comp.distribution_init_concentration.mean*\
                                    kcat_adjustment_factor
                                kcat.comments = 'Value imputed based on FVA bound value ' +\

Avoid deeply nested control flow statements.

Open

                        for part in translocation_reaction.participants:
                            if part.species.id in met_requirement:
                                met_requirement[part.species.id] += -part.coefficient * prot_concentration * \
                                    (1 + doubling_time / prot_half_life)
                    

Avoid deeply nested control flow statements.

Open

                        if numpy.isnan(kcat.value):
                            kcat.value = median_kcat
                            kcat.comments = 'Value imputed as the median of measured k_cat values'         
                elif not any(numpy.isnan(kcat.value) for kcat in law.expression.parameters):

Avoid deeply nested control flow statements.

Open

                        if part.species.id in met_requirement:
                            met_requirement[part.species.id] += -part.coefficient * mean_concentration * \
                                (1 + doubling_time / half_life)
                # Transcription elongation
                transcription_el_reaction = model.reactions.get_one(id='transcription_elongation_{}'.format(rna_kb.id))

Avoid deeply nested control flow statements.

Open

                        for comp, kcat in comp_kcat.items():                            
                            kcat.value = median_kcat
                            kcat.comments = 'Value imputed as the median of measured k_cat values'
            else:

Avoid deeply nested control flow statements.

Open

                        if part.species.id in met_requirement:
                            met_requirement[part.species.id] += -part.coefficient * total_concentration * \
                                (1 + doubling_time / prot_half_life)
                    # Translation elongation
                    translation_el_reaction = model.reactions.get_one(id='translation_elongation_{}'.format(rna_kb.id))                

Avoid deeply nested control flow statements.

Open

                        if numpy.isnan(kcat.value):                    
                            kcat.value = value/conc*kcat_adjustment_factor
                            kcat.comments = 'Value imputed based on FVA bound value ' +\
                                'and adjusted with a factor of {}'.format(kcat_adjustment_factor)
                        elif (value - kcat.value*conc)/value > 0.01:

Avoid deeply nested control flow statements.

Open

                        if part.species.id in met_requirement:
                            met_requirement[part.species.id] += -part.coefficient * mean_concentration * \
                                (1 + doubling_time / half_life)
            

Avoid deeply nested control flow statements.

Open

                        if sp.distribution_init_concentration:
                            if sp.distribution_init_concentration.mean > 0.:
                                compartment = sp.compartment
                                model_met_id = met_id + f'[{compartment.id}]'
                                if model_met_id in met_requirement:

Avoid deeply nested control flow statements.

Open

                        if part.species.id in met_requirement:
                            met_requirement[part.species.id] += -part.coefficient * total_concentration * \
                                (1 + doubling_time / prot_half_life)
                

Avoid deeply nested control flow statements.

Open

                        if unknown_vmax_count > 0:
                            shared_vmax = (value - known_vmax)/unknown_vmax_count
                        for comp, kcat in comp_kcat.items():                            

Avoid deeply nested control flow statements.

Open

                        if not numpy.isnan(kcat.value):
                            known_vmax += kcat.value*comp.distribution_init_concentration.mean
                        else:
                            comp_kcat[comp] = kcat    
                    if known_vmax < value and (value - known_vmax)/value > 0.01:

Function conv_for_optim has a Cognitive Complexity of 7 (exceeds 5 allowed). Consider refactoring.

Open

    def conv_for_optim(self):
        """ Convert metabolism reactions into an optimization problem model

        Returns:
            :obj:`conv_opt.Model`: a conv_opt model for optimization

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Function gen_rate_laws has a Cognitive Complexity of 6 (exceeds 5 allowed). Consider refactoring.

Open

    def gen_rate_laws(self):
        """ Generate rate laws for carbohydrate and lipid formation reactions. High
            rates are assumed so that the macromolecules are formed as soon as the
            components are available.        
        """

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