Avoid deeply nested control flow statements.

Open

                        for i_monomer in range(monomer2.start_position, monomer2.end_position + 1):
                            if form.seq[i_monomer - 1] == monomers[seq[i_monomer - 1]]:
                                monomer2.base_monomers = [bpforms.protein_alphabet.monomers.get(seq[i_monomer - 1])]
                                form.seq[i_monomer - 1] = monomer2
                                set_monomer = True

Function create_kinetic_law_from_sbml has 6 arguments (exceeds 4 allowed). Consider refactoring.

Open

    def create_kinetic_law_from_sbml(self, id, sbml, root_species, specie_properties, functions, units):

Function normalize_parameter_value has 6 arguments (exceeds 4 allowed). Consider refactoring.

Open

    def normalize_parameter_value(self, name, type, value, error, units, enzyme_molecular_weight):

Function normalize_kinetic_laws has a Cognitive Complexity of 8 (exceeds 5 allowed). Consider refactoring.

Open

    def normalize_kinetic_laws(self, new_ids):
        """ Normalize parameter values.

        Args:
            new_ids (:obj:`list` of :obj:`int`): list of IDs of kinetic laws to normalize

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Avoid deeply nested control flow statements.

Open

                    if characteristic.category.lower() == 'organism':
                        organism = characteristic.value
                    if characteristic.category.lower() in SYNONYMS_PRIORITY_LIST[i]:

Avoid deeply nested control flow statements.

Open

                        if monomer['mod']:
                            codes.add(monomer['mod'].get_canonical_code(monomer_codes))
                        msg = 'Modified monomeric form {} potentially inconsistent with residue {} != {}'.format(

Avoid deeply nested control flow statements.

Open

                        if start_position == 0:
                            protein['modified_errors'].append('Sequence does not contain residue {} for modification {}'.format(
                                canonical_code, modification['monomer']))
                        else:
                            monomer2.start_position = start_position

Avoid deeply nested control flow statements.

Open

                        for base in origin:
                            start_pos = seq.find(base) + 1
                            if start_pos > 0:
                                start_position = min(start_position, start_pos)

Avoid deeply nested control flow statements.

Open

                        if position >= processing['start'] and position <= processing['end']:
                            processed_position = seq_len + position - processing['start'] + 1
                            break
                        seq_len += processing['end'] - processing['start'] + 1

Function load_ko_from_uniprot has a Cognitive Complexity of 8 (exceeds 5 allowed). Consider refactoring.

Open

    def load_ko_from_uniprot(self):
        """loading ko number from uniprot collection into
        aggregate collection (one shot after building new uniprot collection)
        """
        query = {'ko_number': {'$exists': False}}

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Function find_protein_gene has a Cognitive Complexity of 8 (exceeds 5 allowed). Consider refactoring.

Open

    def find_protein_gene(self, interactor, alias):
        """ Parse the protein and gene identifiers from key-value pairs of interactors and their aliases

        Args:
            interactor (:obj:`str`): key-value pairs of interactor

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Function get_processed_data_samples has a Cognitive Complexity of 8 (exceeds 5 allowed). Consider refactoring.

Open

def get_processed_data_samples(samples, output_directory, temp_directory):
    for sample in samples:
        if sample.ensembl_info and sample.fastq_urls:
            download_cdna(sample.ensembl_info[0].ref_genome, sample.ensembl_info[0].organism_strain, sample.ensembl_info[0].url, temp_directory)
            fastq_urls = ""

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Function gen_bpform has 6 arguments (exceeds 4 allowed). Consider refactoring.

Open

    def gen_bpform(self, protein, pro_ids_to_bpform_monomers, monomer_codes,

Avoid deeply nested control flow statements.

Open

                        if form.seq[position - 1] == monomers[seq[position - 1]]:
                            if monomer not in bpforms.protein_alphabet.monomers.values():
                                monomer.base_monomers = [form.seq[position - 1]]
                            form.seq[position - 1] = monomer
                        else:

Avoid deeply nested control flow statements.

Open

                        for k in range(len(cur_compound_compound_structure_list)):
                            compound_structure__id = cur_compound_compound_structure_list[
                                k]['compound_structure__id']
                            cur_compound_structure_dict = next(
                                item for item in compound_structure_list if item['_id'] == compound_structure__id)

Function __init__ has 6 arguments (exceeds 4 allowed). Consider refactoring.

Open

    def __init__(self, MongoDB, db, entity_query_collection, taxon_query_collection, username, password):

Function fill_rna_halflife has a Cognitive Complexity of 8 (exceeds 5 allowed). Consider refactoring.

Open

    def fill_rna_halflife(self, df, start=0):
        """Fill rna_halflife collection with information parsed
        from the publication
        
        Args:

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Avoid deeply nested control flow statements.

Open

                        if val_type == 'ID':
                            ec_data = self.parse_ec_code(data, val_content)
                        else:
                            self.parse_content(ec_code, ec_data, val_type, val_content)

Function get_specie_reference_from_sbml has a Cognitive Complexity of 8 (exceeds 5 allowed). Consider refactoring.

Open

    def get_specie_reference_from_sbml(self, specie_id):
        """ Get the compound/enzyme associated with an SBML species by its ID

        Args:
            specie_id (:obj:`str`): ID of an SBML species

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Avoid deeply nested control flow statements.

Open

                        for k in range(len(cur_entry_synonym_list)):
                            cur_entry_synonym_dict = cur_entry_synonym_list[k]
                            synonym__id = cur_entry_synonym_dict['synonym__id']
                            synonyms.append(
                                synonym_list[synonym__id-1]['name'])