From 177014de4b92995c60b7c9a65d64769ad95c905e Mon Sep 17 00:00:00 2001 From: vaitkusd Date: Tue, 27 Jul 2021 23:37:22 +0200 Subject: [PATCH 01/12] AUT-866 Full atom mapping module added, including main functions, example notebook, and unit tests. feat: INCA_input_parser() now returns metabolite annotations. --- BFAIR/atom_mapping/__init__.py | 23 + BFAIR/atom_mapping/atom_mapping.py | 682 + BFAIR/mfa/INCA/INCA_input_parser.py | 1 + .../atom_mapping_example_notebook.ipynb | 296 + .../data/atom_mapping_Data/e_coli_core.json | 14786 ++++++++++++++++ requirements.txt | 1 + setup.py | 3 +- tests/test_atom_mapping.py | 168 + .../atom_mapping_Data/e_coli_core.json | 14786 ++++++++++++++++ .../generate_atom_mapping_test_data.py | 112 + .../test_data/atom_mapping_Data/test_data.obj | Bin 0 -> 70143 bytes 11 files changed, 30857 insertions(+), 1 deletion(-) create mode 100644 BFAIR/atom_mapping/__init__.py create mode 100644 BFAIR/atom_mapping/atom_mapping.py create mode 100644 docs/examples/atom_mapping_example_notebook.ipynb create mode 100644 docs/examples/data/atom_mapping_Data/e_coli_core.json create mode 100644 tests/test_atom_mapping.py create mode 100644 tests/test_data/atom_mapping_Data/e_coli_core.json create mode 100644 tests/test_data/atom_mapping_Data/generate_atom_mapping_test_data.py create mode 100644 tests/test_data/atom_mapping_Data/test_data.obj diff --git a/BFAIR/atom_mapping/__init__.py b/BFAIR/atom_mapping/__init__.py new file mode 100644 index 00000000..87a4e1d1 --- /dev/null +++ b/BFAIR/atom_mapping/__init__.py @@ -0,0 +1,23 @@ +from BFAIR.atom_mapping.atom_mapping import ( + MolfileDownloader, + write_rxn_files, + obtain_atom_mappings, + parse_reaction_mappings, + parse_metabolite_mappings, + generate_INCA_mapping_input, + check_symmetry, + clean_output, +) + +__version__ = '0.0.1' + +__all__ = [ + 'MolfileDownloader', + 'write_rxn_files', + 'obtain_atom_mappings', + 'parse_reaction_mappings', + 'parse_metabolite_mappings', + 'generate_INCA_mapping_input', + 'check_symmetry', + 'clean_output', +] diff --git a/BFAIR/atom_mapping/atom_mapping.py b/BFAIR/atom_mapping/atom_mapping.py new file mode 100644 index 00000000..36bedf2e --- /dev/null +++ b/BFAIR/atom_mapping/atom_mapping.py @@ -0,0 +1,682 @@ +"""Atom Mapping. +Module that automatically performs atom-atom mapping of reactions, +when provided a metabolic model with suitable annotations. +Relies heavily on: +Reaction Decoder Tool (RDT) (https://github.com/asad/ReactionDecoder) +to map the reactions; +CADD Group Chemoinformatics Tools and User Services (https://cactus.nci.nih.gov/chemical/structure) +to resolve InChI strings from given InChI keys. """ + +import requests +import os +import glob +import pandas as pd +from rdkit import Chem +from pymatgen.symmetry import analyzer +from pymatgen.core import structure + +__version__ = "0.0.1" + +class MolfileDownloader: + def __init__(self, metabolite_data, db_preference = (0,1,2,3)): + """ Class to find and download metabolite structures in + Molfile format. + + Parameters + ---------- + metabolite_data: pandas.DataFrame + Dataframe that contains information about metabolites + the model. Obtain from INCA_input_parser module. + db_preference: tuple of integers + Four integers specify the order of preference of + databases to obtain the metabolite structures from: + 0: Using InChI key > InChI string conversion + 1: KEGG Compound database + 2: HMDB database + 3: CHEBI database + """ + self.database_dict = {0: 'get_from_inchi_key', + 1: 'get_from_kegg', + 2: 'get_from_hmdb', + 3: 'get_from_chebi'} + + self.metabolite_data = metabolite_data + self.db_preference = db_preference + + def generate_molfile_database(self): + """ Main method that calls other methods and performs + most sanity checks on obtained files. + + Outputs + ------- + Molfiles: .mol files + """ + print('Fetching metabolite structures...') + + if not os.path.isdir('metabolites'): + os.mkdir('metabolites') + + for i, met in self.metabolite_data.iterrows(): + self.filename = met.met_id + '.mol' + + get_from_inchi_keyBool = False + get_from_keggBool = False + get_from_hmdbBool = False + get_from_chebiBool = False + + # Check for annotations and set bool values for those + # present. + if 'inchi_key' in met.annotations: + self.inchi_key = met.annotations['inchi_key'][0] + get_from_inchi_keyBool = True + if 'kegg.compound' in met.annotations: + self.keggIDs = [keggID for keggID in met.annotations['kegg.compound']] + get_from_keggBool = True + if 'hmdb' in met.annotations: + self.hmdbIDnums = ['0' * (7-len(hmdbID[4:])) + hmdbID[4:] for hmdbID in met.annotations['hmdb']] + get_from_hmdbBool = True + if 'chebi' in met.annotations: + self.chebiIDs = [chebiID for chebiID in met.annotations['chebi']] + get_from_chebiBool = True + + # Call helper functions according to order of preference + # and available references (specified by previous bool values). + for opt in self.db_preference: + if eval(self.database_dict.get(opt) + 'Bool'): + getattr(self, self.database_dict.get(opt))() + else: + continue + + try: + # Rewrites the .mol file. This removes hydrogens from + # structure, lowkey standardizes molecules, and works + # as a failswitch to see if file is of correct format. + m = Chem.MolFromMolFile(f'metabolites/{self.filename}') + with open(f'metabolites/{self.filename}', "w+") as f: + print(Chem.MolToMolBlock(m), file=f) + + # Add metabolite ID to the first line of Molfile + with open(f'metabolites/{self.filename}', 'r') as f: + lines = f.readlines() + lines.insert(0, self.filename[:-4]) + with open(f'metabolites/{self.filename}', 'w') as wf: + wf.writelines(lines) + break + except: + os.system(f'rm metabolites/{self.filename}') + continue + + print( + f"Successfully fetched {len(os.listdir('metabolites'))}/{self.metabolite_data.shape[0]} metabolites") + + + def get_from_inchi_key(self): + """ Helper method to obtain InChI string from InChI key, + and generate the Molfile from the string. """ + url = f'https://cactus.nci.nih.gov/chemical/structure/{self.inchi_key}/stdinchi' + r = requests.get(url, allow_redirects=False) + inchi_string = r.text + + try: + molfile = Chem.inchi.MolFromInchi(inchi_string) + with open(f'metabolites/{self.filename}', "w+") as f: + print(Chem.MolToMolBlock(molfile), file=f) + except: + return + + def get_from_kegg(self): + """ Helper method to obtain Molfile from KEGG Compound database + """ + for keggID in self.keggIDs: + url = f'https://www.genome.jp/dbget-bin/www_bget?-f+m+compound+{keggID}' + r = requests.get(url, allow_redirects=False) + open(f'metabolites/{self.filename}', 'wb').write(r.content) + if os.path.getsize(f'metabolites/{self.filename}') != 0: + break + + def get_from_hmdb(self): + """ Helper method to obtain Molfile from HMDB database + """ + for hmdbID in self.hmdbIDnums: + url = f'https://hmdb.ca/structures/metabolites/HMDB{hmdbID}.mol' + r = requests.get(url, allow_redirects=False) + open(f'metabolites/{self.filename}', 'wb').write(r.content) + if os.path.getsize(f'metabolites/{self.filename}') != 0: + break + + def get_from_chebi(self): + """ Helper method to obtain Molfile from CHEBI database + """ + for chebiID in self.chebiIDs: + url = f'https://www.ebi.ac.uk/chebi/saveStructure.do?defaultImage=true&chebiId={chebiID}&imageId=0' + r = requests.get(url, allow_redirects=False) + open(f'metabolites/{self.filename}', 'wb').write(r.content) + if os.path.getsize(f'metabolites/{self.filename}') != 0: + break + + +def write_rxn_files(rxn_data): + """ Generates RXN files in RDT suitable format. + Requires Molfiles of all metabolites to be present + in the working directory/metabolites folder. + + Parameters + ---------- + rxn_data: pandas.DataFrame + Dataframe that contains information about reactions + in the model. Obtained from INCA_input_parser module. + + Outputs + ------- + RXN files: .rxn files + """ + met_filter = ['h_e', 'h_c', 'h_p', 'h2_e', 'h2_c', 'h2_p'] + biomass_filter = ['Biomass', 'biomass', 'BIOMASS'] + + if not os.path.isdir('unmappedRxns'): + os.mkdir('unmappedRxns') + + path = os.path.join(os.getcwd(), 'unmappedRxns') + + for i, rxn in rxn_data.iterrows(): + # Filter out biomass reaction + if any(biomass_id in rxn.rxn_id for biomass_id in biomass_filter): + print(f'Excluded {rxn.rxn_id} reaction from mapping') + continue + + rxn_filename = rxn.rxn_id + '.rxn' + # Use copies to avoid messing up the original dataframe + reactants = rxn.reactants_ids.copy() + reactants_st = [int(abs(s)) for s in rxn.reactants_stoichiometry.copy()] + products = rxn.products_ids.copy() + products_st = [int(abs(s)) for s in rxn.products_stoichiometry.copy()] + + # Filter out unwanted molecules + react_indexes = [] + prod_indexes = [] + for i, met in enumerate(reactants): + if met in met_filter: + react_indexes.append(i) + if len(react_indexes) != 0: + for i in sorted(react_indexes, reverse=True): + del reactants[i] + del reactants_st[i] + + for i, met in enumerate(products): + if met in met_filter: + prod_indexes.append(i) + if len(prod_indexes) != 0: + for i in sorted(prod_indexes, reverse=True): + del products[i] + del products_st[i] + + metabolites = reactants + products + metabolites_st = reactants_st + products_st + + # Check if all metabolite structures are present + if not all([os.path.isfile(f'metabolites/{met}.mol') for met in metabolites]): + print( + f"Metabolite structures missing for reaction {rxn.rxn_id}") + continue + else: + with open(os.path.join(path, rxn_filename), "w") as f: + # Write first three lines, including reaction equation + f.write(f"$RXN\n{rxn.rxn_id}\n\n{rxn.equation}\n") + + # Write export reactions (1 reactant) + if not products_st and abs(int(sum(reactants_st))) == 1: + f.write(f'{abs(int(sum(reactants_st)))} {int(sum(reactants_st))}\n') + met = metabolites[0] + with open(f'metabolites/{met}.mol', 'r') as wf: + structure = wf.read() + f.write(f'$MOL\n{structure}') + f.write(f'$MOL\n{structure}') + + # Write all the other reactions with at least 1 metabolite on each side + else: + f.write(f'{abs(int(sum(reactants_st)))} {int(sum(products_st))}\n') + for s, met in zip(metabolites_st, metabolites): + with open(f'metabolites/{met}.mol', 'r') as wf: + structure = wf.read() + # Repeat structure based on stoichiometry + for i in range(s): + f.write(f'$MOL\n{structure}') + print(f"Generated {len(os.listdir('unmappedRxns'))}/{rxn_data.shape[0]}") + + +def obtain_atom_mappings(max_time=120): + """ Performs atom mapping by using RDT. + Only maps reactions that are available in .rxn format, + in the working_directory/unmappedRxns folder. + + Parameters + ---------- + max_time: int, optional + Specifies time limit for single reaction mapping + in seconds. Default: 120s. + + Outputs + ------- + mapped RXN files: .rxn files + mapped TXT files: .txt files + Mappings in SMILES format + pictures of mappings: .png files + """ + # Check if Java is installed + if os.system('java -version') != 0: + raise RuntimeError('Java installation not found') + + print('Mapping reactions...') + # Set the original working dir + owd = os.getcwd() + + # Check if RDT is present in working dir, download if not + if not os.path.isfile('RDT.jar'): + url = 'https://github.com/asad/ReactionDecoder/releases/download/v2.4.1/rdt-2.4.1-jar-with-dependencies.jar' + r = requests.get(url) + open(os.getcwd() + '/RDT.jar', 'wb').write(r.content) + + # Check if required directories are present + if not os.path.isdir('mappedRxns'): + os.makedirs('mappedRxns/rxnFiles') + os.makedirs('mappedRxns/txtFiles') + os.makedirs('mappedRxns/pngFiles') + + rxn_list = os.listdir('unmappedRxns') + + # Change working dir to keep the output organized + os.chdir('mappedRxns') + try: + for rxnFile in rxn_list: + # Check if reaction is mapped, and run RDT with specified time limit if not + if not os.path.isfile(f'rxnFiles/{rxnFile}'): + os.system( + f'timeout {max_time} java -jar ../RDT.jar -Q RXN -q ../unmappedRxns/{rxnFile} -g -j AAM -f TEXT') + + # Obtain filenames of generated files and simplify them to respective reaction IDs + for name in glob.glob('ECBLAST*'): + os.rename(name, name[8:-8]+name[-4:]) + + # Move all generated files to different directories, in respect to their filetype + os.system('mv *.png ./pngFiles') + os.system('mv *.rxn ./rxnFiles') + os.system('mv *.txt ./txtFiles') + + print( + f"Reactions mapped in total: {len(os.listdir('rxnFiles'))}/{len(rxn_list)}") + except: + # Make sure that wd is back to normal no matter what + os.chdir(owd) + + # Change working dir back to original + os.chdir(owd) + + # Remove RDT.jar from working dir + os.system('rm RDT.jar') + + +def parse_reaction_mappings(): + """ Parses reaction mappings from mapped RXN files + to a dataframe in suitable format for INCA. Requires + all mapped RXN files to be present in the working_dir/ + mappedRxns/rxnFiles folder. For unmapped reactions, + data is picked from working_dir/unmappedRxns folder + and all mapping data is represented as blanks. + + Returns + ------- + mapping_data: pandas.DataFrame + Reaction mapping data. + """ + if not os.path.isdir('mappedRxns'): + raise RuntimeError("'mappedRxns' directory not present in current working directory") + + rxn_list = os.listdir('unmappedRxns') + # Compile list of reactions that do not have any mapping + unmapped_list = list(set(os.listdir('unmappedRxns')) - set(os.listdir('mappedRxns/rxnFiles'))) + + keys = ['Unnamed: 0', + 'Unnamed: 0.1', + 'id', + 'mapping_id', + 'rxn_id', + 'rxn_description', + 'reactants_stoichiometry_tracked', + 'products_stoichiometry_tracked', + 'reactants_ids_tracked', + 'products_ids_tracked', + 'reactants_mapping', + 'products_mapping', + 'rxn_equation', + 'used_', + 'comment_', + 'reactants_elements_tracked', + 'products_elements_tracked', + 'reactants_positions_tracked', + 'products_positions_tracked' + ] + mapping_dict_tmp = {} + + for i, rxn in enumerate(rxn_list): + mapping_dict = {k: [] for k in keys} + met_mapping = [] + react_cnt = 0 + prod_cnt = 0 + productBool = False + + if rxn not in unmapped_list: + # Extract info from mapped .rxn files + with open(f'mappedRxns/rxnFiles/{rxn}', 'r') as f: + lines = f.readlines() + for j, line in enumerate(lines): + if line.rstrip() == '$RXN': + # Extract number of reactants + react_lim = int(lines[j+4].split()[0]) + # Extract number of products + prod_lim = int(lines[j+4].split()[1]) + if line.rstrip() == '$MOL': + met_id = lines[j+1].rstrip() + + # Hard-coded, since 16 columns is standard for Molfile atom rows, + # and 15 can occur if we have >100 atoms on one side (cols merge) + if len(line.split()) in (15,16) : + atom_row = line.split() + if atom_row[3] == 'C': + # Split columns if they get merged + if atom_row[-3][0] == '0': + atom_row[-3] = atom_row[-3][1:] + met_mapping.append(atom_row[-3]) + + # Check if reached the last atom row + if len(lines[j+1].split()) not in (15,16): + # Check if current metabolite is reactant or product + if not productBool: + # Check if any carbons are present + if met_mapping: + c_tracked = ['C' for atom in met_mapping] + pos_tracked = list(range(len(met_mapping))) + mapping_dict['reactants_ids_tracked'].append(met_id) + mapping_dict['reactants_mapping'].append(met_mapping) + mapping_dict['reactants_elements_tracked'].append(c_tracked) + mapping_dict['reactants_positions_tracked'].append(pos_tracked) + react_cnt += 1 + if react_cnt == react_lim: + productBool = True + + # Assign metabolite to products if reached reactant limit + else: + if met_mapping: + c_tracked = ['C' for atom in met_mapping] + pos_tracked = list(range(len(met_mapping))) + mapping_dict['products_ids_tracked'].append(met_id) + mapping_dict['products_mapping'].append(met_mapping) + mapping_dict['products_elements_tracked'].append(c_tracked) + mapping_dict['products_positions_tracked'].append(pos_tracked) + prod_cnt += 1 + + if prod_cnt == prod_lim: + react_stoich = ['-1' for met in range(len(mapping_dict['reactants_mapping']))] + prod_stoich = ['1' for met in range(len(mapping_dict['products_mapping']))] + mapping_dict['reactants_stoichiometry_tracked'] = react_stoich + mapping_dict['products_stoichiometry_tracked'] = prod_stoich + + met_mapping = [] + + mapping_dict['rxn_id'] = rxn[:-4] + mapping_dict['used_'] = True + # Fill all empty fields + mapping_dict['Unnamed: 0'] = 'NULL' + mapping_dict['Unnamed: 0.1'] = 'NULL' + mapping_dict['id'] = 'NULL' + mapping_dict['mapping_id'] = 'NULL' + mapping_dict['rxn_description'] = 'NULL' + mapping_dict['rxn_equation'] = 'NULL' + mapping_dict['comment_'] = 'NULL' + + mapping_dict_tmp[i] = mapping_dict + + mapping_data = pd.DataFrame.from_dict(mapping_dict_tmp, 'index') + + # alphabet for number-letter matching. Max capacity is 63 characters, + # which is a limit set in INCA for this format. + alphabet = list(map(chr, range(97, 123))) + list(map(chr, range(65,91))) + \ + list(map(chr, range(48, 58))) + ['_'] + + # Loop through all reactions + for i, rxn in mapping_data.iterrows(): + try: + # Convert number mappings to letters + carbons_list = [atom for met in rxn['reactants_mapping'] for atom in met] + carbon_map_dict = dict(zip(carbons_list, alphabet)) + + # Compile alphabetical mapping in curly bracket format + carbon_str = '{' + for j, met in enumerate(rxn['reactants_mapping']): + if j != 0: + carbon_str += ',' + for atom in met: + carbon_str += carbon_map_dict[atom] + carbon_str += '}' + mapping_data.at[i, 'reactants_mapping'] = carbon_str + + carbon_str = '{' + for j, met in enumerate(rxn['products_mapping']): + if j != 0: + carbon_str += ',' + for atom in met: + carbon_str += carbon_map_dict[atom] + carbon_str += '}' + mapping_data.at[i, 'products_mapping'] = carbon_str + + except KeyError: + if len(carbons_list) > 63: + print(f'Reaction {rxn["rxn_id"]} contains more than 63 carbon atoms') + else: + # Mostly happens when one of the metabolites has (R) + # group in the Molfile, and other has a C in that spot + print(f'{rxn["rxn_id"]} has unmapped carbon(-s)') + mapping_data.at[i, 'reactants_mapping'] = '{}' + mapping_data.at[i, 'products_mapping'] = '{}' + + # Convert metabolite lists/stoichiometries to strings in curly brackets + metabolite_str = '{%s}' % (','.join(rxn['reactants_ids_tracked'])) + mapping_data.at[i, 'reactants_ids_tracked'] = metabolite_str + + metabolite_str = '{%s}' % (','.join(rxn['products_ids_tracked'])) + mapping_data.at[i, 'products_ids_tracked'] = metabolite_str + + stoich_str = '{%s}' % (','.join(rxn['reactants_stoichiometry_tracked'])) + mapping_data.at[i, 'reactants_stoichiometry_tracked'] = stoich_str + + stoich_str = '{%s}' % (','.join(rxn['products_stoichiometry_tracked'])) + mapping_data.at[i, 'products_stoichiometry_tracked'] = stoich_str + + return mapping_data + + +def parse_metabolite_mappings(): + """ Parses metabolite mapping and symmetry data into + INCA suitable format. Requires all Molfiles to be present + in the working_dir/metabolites directory. + + Returns + ------- + metabolite_data: pandas.DataFrame + Dataframe containing mapped metabolite data. + """ + metabolite_list = os.listdir('metabolites') + keys = ['mapping_id', + 'met_id', + 'met_elements', + 'met_atompositions', + 'met_symmetry_elements', + 'met_symmetry_atompositions', + 'used_', + 'comment_', + 'met_mapping', + 'base_met_ids', + 'base_met_elements', + 'base_met_atompositions', + 'base_met_symmetry_elements', + 'base_met_symmetry_atompositions', + 'base_met_indices' + ] + metabolite_dict_tmp = {} + + # Works in a similar fashion to parse_reaction_mappings() + for i, met in enumerate(metabolite_list): + met_dict = {k:'NULL' for k in keys} + with open(f'metabolites/{met}', 'r') as f: + lines = f.readlines() + carbon_count = 0 + for j, line in enumerate(lines): + if j == 0: + met_dict['met_id'] = line.rstrip() + if len(line.split()) == 16: + atom_row = line.split() + if atom_row[3] == 'C': + carbon_count += 1 + # Generate carbon atom lists/mappings + carbon_count_list = ['C' for x in range(carbon_count)] + carbon_count_string = '{%s}' % (','.join(carbon_count_list)) + carbon_count_range = '{%s}' % (','.join([str(x) for x in range(carbon_count)])) + + met_dict['met_elements'] = carbon_count_string + met_dict['met_atompositions'] = carbon_count_range + # Check if the metabolite is symmetrical + if check_symmetry(met): + carbon_count_range_rev = '{%s}' % (','.join([str(x) for x in range(carbon_count-1,-1,-1)])) + met_dict["met_symmetry_elements"] = carbon_count_string + met_dict['met_symmetry_atompositions'] = carbon_count_range_rev + + metabolite_dict_tmp[i] = met_dict + + metabolite_data = pd.DataFrame.from_dict(metabolite_dict_tmp, 'index') + + return metabolite_data + + +def generate_INCA_mapping_input(reaction_df, metabolite_df): + """ Function to export reaction and metabolite mapping dataframes + to CSV files. + + Parameters + ---------- + reaction_df: pandas.DataFrame + Dataframe that contains reaction mapping data. + metabolite_df: pandas.DataFrame + Dataframe that contains metabolite mapping data. + + Outputs + ------- + MappingReactions: .csv file + MappingMetabolites: .csv file + """ + # Would be a good idea to make the filenames more informative. + reaction_df.to_csv(path_or_buf = os.path.join(os.getcwd(), f'MappingReactions.csv')) + metabolite_df.to_csv(path_or_buf = os.path.join(os.getcwd(), f'MappingMetabolites.csv')) + + +def check_symmetry(met_filename): + """ Function that checks if the given metabolite is symmetric. + Uses pymatgen package for symmetry related operations, and + RDKit for Molfile conversion to XYZ format. Requires Molfiles + of the metabolites to be present in the working_dir/metabolites + folder. + Current criterion for symmetricity is for every carbon except one + (central, if molecule consists of odd number of carbons) to have + at least one equivalent carbon in the structure. + + Parameters + ---------- + met_filename: string + Filename of the specific Molfile + + Returns + ------- + symmetrical: bool + True if metabolite is symmetric, False if not. + + """ + symmetrical = False + # Counter for non symmetrical carbon atoms + non_eq_carbons = 0 + carbons = 0 + # Convert Molfile to XYZ string + molecule = Chem.MolFromMolFile(f'metabolites/{met_filename}') + molecule_xyz = Chem.rdmolfiles.MolToXYZBlock(molecule) + + # Create IMolecule object to analyze its' symmetricity + try: + molecule_obj = structure.IMolecule.from_str(molecule_xyz, fmt='xyz') + if len(molecule_obj) == 1: + return symmetrical + # Initialize point group analyzer + pg_analyzer = analyzer.PointGroupAnalyzer(molecule_obj) + + except (IndexError, ValueError): + # '*' is unrecognized in particular + print(f'{met_filename} contains unrecognized symbols') + return symmetrical + # Extract equal atom sets + eq_atoms = pg_analyzer.get_equivalent_atoms() + for i in eq_atoms['eq_sets'].keys(): + if str(molecule_obj[i].specie) == 'C': + carbons += 1 + if len(eq_atoms['eq_sets'].get(i)) == 1: + non_eq_carbons += 1 + if non_eq_carbons > 1: + return symmetrical + + # Molecule has more than 1 carbon, and at most 1 non-symmetrical carbon + if carbons > 1: + symmetrical = True + + return symmetrical + + +def clean_output(metabolites = True, + reactions = True, + mappings = True, + csv = True): + """ Utility function. + Deletes all possible output of other mapping functions. + Deletes everything by default. + + Parameters + ---------- + metabolites: bool, optional + Removes /metabolites folder recursively. + reactions: bool, optional + Removes /unmappedRxns folder recursively. + mappings: bool, optional + Removes /mappedRxns folder recursively. + csv: bool, optional + Removes MappingReactions.csv and MappingMetabolites.csv. + """ + if metabolites: + os.system('rm -r metabolites') + if reactions: + os.system('rm -r unmappedRxns') + if mappings: + os.system('rm -r mappedRxns') + if csv: + os.system('rm MappingReactions.csv MappingMetabolites.csv') + + +# molfile_downloader_descr = MolfileDownloader() +# """A class to find and fetch metabolite structures in Molfile +# format. + +# Examples +# -------- +# >>> downloader = MolfileDownloader(met_df) +# >>> downloader.generate_molfile_database() +# The database will be generated in working_dir/metabolites folder + +# To change the order of databases for fetching, pass a tuple of +# integers when initiating an instance, f.e.: +# >>> downloader = MolfileDownloader(met_df, (2,1,0,3)) +# >>> downloader.generate_molfile_database() + +# For full workflow check, the example notebook. """ diff --git a/BFAIR/mfa/INCA/INCA_input_parser.py b/BFAIR/mfa/INCA/INCA_input_parser.py index 1f4e2e77..7d5b8aaa 100644 --- a/BFAIR/mfa/INCA/INCA_input_parser.py +++ b/BFAIR/mfa/INCA/INCA_input_parser.py @@ -158,6 +158,7 @@ def _parse_json_sbml_cobra_model( "charge": met.charge, "compartment": met.compartment, "bound": met._bound, + "annotations": met.annotation, "used_": True, } metabolite_data_tmp[cnt] = metabolite_data_dict diff --git a/docs/examples/atom_mapping_example_notebook.ipynb b/docs/examples/atom_mapping_example_notebook.ipynb new file mode 100644 index 00000000..3e98a0c0 --- /dev/null +++ b/docs/examples/atom_mapping_example_notebook.ipynb @@ -0,0 +1,296 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "8ed0c5fa", + "metadata": {}, + "source": [ + "# Automatic atom mapping - example" + ] + }, + { + "cell_type": "markdown", + "id": "5aefa4d6", + "metadata": {}, + "source": [ + "This notebook demonstrates how the `atom_mapping` module works in practice. It's purpose is to reduce the workload when preparing input data for MFA analysis on INCA. " + ] + }, + { + "cell_type": "markdown", + "id": "5b362b76", + "metadata": {}, + "source": [ + "The only input required is the COBRA model that contains all reaction data, and, most importantly, references for metabolite structures in KEGG Compound, HMDB, CHEBI databases, or an InChI key." + ] + }, + { + "cell_type": "markdown", + "id": "21edabd6", + "metadata": {}, + "source": [ + "#### First, import required modules:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "75aa4c2e", + "metadata": {}, + "outputs": [], + "source": [ + "from BFAIR import atom_mapping\n", + "from BFAIR.mfa.INCA import INCA_input_parser" + ] + }, + { + "cell_type": "markdown", + "id": "9b99ccdd", + "metadata": {}, + "source": [ + "#### Prepare input dataframes" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "2e216f79", + "metadata": {}, + "outputs": [], + "source": [ + "model, reaction_data, metabolite_data = INCA_input_parser.parse_cobra_model('data/atom_mapping_Data/e_coli_core.json', 'e_coli_core', '2021-07-15')" + ] + }, + { + "cell_type": "markdown", + "id": "fd095c49", + "metadata": {}, + "source": [ + "Workflow continues by fetching and storing all of the available metabolite structures in Molfile format." + ] + }, + { + "cell_type": "markdown", + "id": "006b1329", + "metadata": {}, + "source": [ + "#### Initialise MolfileDownloader and fetch the structures" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "4bdc27aa", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fetching metabolite structures...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "RDKit WARNING: [23:06:57] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [23:06:57] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [23:06:57] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [23:06:57] WARNING: not removing hydrogen atom without neighbors\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Successfully fetched 72/72 metabolites\n" + ] + } + ], + "source": [ + "downloader = atom_mapping.MolfileDownloader(metabolite_data)\n", + "downloader.generate_molfile_database()" + ] + }, + { + "cell_type": "markdown", + "id": "e2fd2df9", + "metadata": {}, + "source": [ + "Note: MolfileDownloader takes a second optional argument that allows user to specify preference of databases to search first. By default, 'InChI key -> InChI string -> structure' approach is preferred, and then databases are used for search. Check documentation for more information." + ] + }, + { + "cell_type": "markdown", + "id": "e807e9a6", + "metadata": {}, + "source": [ + "#### Write reactions in RXN format" + ] + }, + { + "cell_type": "markdown", + "id": "23e9e707", + "metadata": {}, + "source": [ + "Here, all of the obtained compound structure files are used to write reactions in RXN format, using `reaction_data` dataframe as a reference." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "df313fe0", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Excluded BIOMASS_Ecoli_core_w_GAM reaction from mapping\n", + "Generated 94/95\n" + ] + } + ], + "source": [ + "atom_mapping.write_rxn_files(reaction_data)" + ] + }, + { + "cell_type": "markdown", + "id": "39bfd6e0", + "metadata": {}, + "source": [ + "#### Run RDT to obtain atom mappings" + ] + }, + { + "cell_type": "markdown", + "id": "5e24a2e5", + "metadata": {}, + "source": [ + "RDT algorithm is downloaded and stored in the working directory (deleted after function is done). " + ] + }, + { + "cell_type": "markdown", + "id": "459d6b6e", + "metadata": {}, + "source": [ + "**NOTE**: Java is required to run the algorithm, please make sure it's installed on your computer." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "f7d50194", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mapping reactions...\n", + "Reactions mapped in total: 85/94\n" + ] + } + ], + "source": [ + "atom_mapping.obtain_atom_mappings(max_time=20) # specify time limit for single reaction" + ] + }, + { + "cell_type": "markdown", + "id": "29fd3bab", + "metadata": {}, + "source": [ + "#### Parse data from generated mappings to suitable format for INCA" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "841634e1", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "RDKit WARNING: [23:23:18] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [23:23:18] WARNING: not removing hydrogen atom without neighbors\n" + ] + } + ], + "source": [ + "reaction_mapping_df = atom_mapping.parse_reaction_mappings()\n", + "met_mapping_df = atom_mapping.parse_metabolite_mappings()" + ] + }, + { + "cell_type": "markdown", + "id": "0afc3974", + "metadata": {}, + "source": [ + "#### Generate CSV output of mapping data" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "92f7ec99", + "metadata": {}, + "outputs": [], + "source": [ + "atom_mapping.generate_INCA_mapping_input(reaction_mapping_df, met_mapping_df)" + ] + }, + { + "cell_type": "markdown", + "id": "ba2dd63b", + "metadata": {}, + "source": [ + "The generated CSV files can be used in general MFA workflow, as atom mapping inputs." + ] + }, + { + "cell_type": "markdown", + "id": "871a16f2", + "metadata": {}, + "source": [ + "#### Clear the working directory of generated output (optional)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "e8b0ba00", + "metadata": {}, + "outputs": [], + "source": [ + "atom_mapping.clean_output()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [conda env:test]", + "language": "python", + "name": "conda-env-test-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/examples/data/atom_mapping_Data/e_coli_core.json b/docs/examples/data/atom_mapping_Data/e_coli_core.json new file mode 100644 index 00000000..39db7732 --- /dev/null +++ b/docs/examples/data/atom_mapping_Data/e_coli_core.json @@ -0,0 +1,14786 @@ +{ +"metabolites":[ +{ +"id":"glc__D_e", +"name":"D-Glucose", +"compartment":"e", +"charge":0, +"formula":"C6H12O6", +"notes":{ +"original_bigg_ids":[ +"glc_D_e" +] +}, +"annotation":{ +"bigg.metabolite":[ +"glc__D" +], +"biocyc":[ +"META:Glucopyranose" +], +"chebi":[ +"CHEBI:12965", +"CHEBI:20999", +"CHEBI:4167", +"CHEBI:17634" +], +"hmdb":[ +"HMDB00122", +"HMDB06564" +], +"inchi_key":[ +"WQZGKKKJIJFFOK-GASJEMHNSA-N" +], +"kegg.compound":[ +"C00031" +], +"kegg.drug":[ +"D00009" +], +"metanetx.chemical":[ +"MNXM41" +], +"sabiork":[ +"1406", +"1407" +], +"sbo":"SBO:0000247", +"seed.compound":[ +"cpd26821", +"cpd00027" +] +} +}, +{ +"id":"gln__L_c", +"name":"L-Glutamine", +"compartment":"c", +"charge":0, +"formula":"C5H10N2O3", +"notes":{ +"original_bigg_ids":[ +"gln_L_c" +] +}, +"annotation":{ +"bigg.metabolite":[ +"gln__L" +], +"biocyc":[ +"META:GLN" +], +"chebi":[ +"CHEBI:42943", +"CHEBI:42899", +"CHEBI:32679", +"CHEBI:32678", +"CHEBI:58359", +"CHEBI:28300", +"CHEBI:42812", +"CHEBI:13110", +"CHEBI:18050", +"CHEBI:32666", +"CHEBI:6227", +"CHEBI:32665", +"CHEBI:21308", +"CHEBI:42814", +"CHEBI:5432", +"CHEBI:24316" +], +"hmdb":[ +"HMDB00641" +], +"inchi_key":[ +"ZDXPYRJPNDTMRX-VKHMYHEASA-N" +], +"kegg.compound":[ +"C00064", +"C00303" +], +"kegg.drug":[ +"D00015" +], +"metanetx.chemical":[ +"MNXM37" +], +"reactome.compound":[ +"113522", +"212615", +"29472" +], +"sabiork":[ +"2011", +"74" +], +"sbo":"SBO:0000247", +"seed.compound":[ +"cpd00253", +"cpd00053" +] +} +}, +{ +"id":"gln__L_e", +"name":"L-Glutamine", +"compartment":"e", +"charge":0, +"formula":"C5H10N2O3", +"notes":{ +"original_bigg_ids":[ +"gln_L_e" +] +}, +"annotation":{ +"bigg.metabolite":[ +"gln__L" +], +"biocyc":[ +"META:GLN" +], +"chebi":[ +"CHEBI:42943", +"CHEBI:42899", +"CHEBI:32679", +"CHEBI:32678", +"CHEBI:58359", +"CHEBI:28300", +"CHEBI:42812", +"CHEBI:13110", +"CHEBI:18050", +"CHEBI:32666", +"CHEBI:6227", +"CHEBI:32665", +"CHEBI:21308", +"CHEBI:42814", +"CHEBI:5432", +"CHEBI:24316" +], +"hmdb":[ +"HMDB00641" +], +"inchi_key":[ +"ZDXPYRJPNDTMRX-VKHMYHEASA-N" +], +"kegg.compound":[ +"C00064", +"C00303" +], +"kegg.drug":[ +"D00015" +], +"metanetx.chemical":[ +"MNXM37" +], 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+"seed.compound":[ +"cpd27177", +"cpd00023", +"cpd19002" +] +} +}, +{ +"id":"glu__L_e", +"name":"L-Glutamate", +"compartment":"e", +"charge":-1, +"formula":"C5H8NO4", +"notes":{ +"original_bigg_ids":[ +"glu_L_e" +] +}, +"annotation":{ +"bigg.metabolite":[ +"glu__L" +], +"biocyc":[ +"META:Glutamates", +"META:GLT" +], +"chebi":[ +"CHEBI:76051", +"CHEBI:21301", +"CHEBI:29985", +"CHEBI:42825", +"CHEBI:29987", +"CHEBI:18237", +"CHEBI:24314", +"CHEBI:16015", +"CHEBI:13107", +"CHEBI:5431", +"CHEBI:21304", +"CHEBI:6224", +"CHEBI:14321", +"CHEBI:29988" +], +"hmdb":[ +"HMDB00148", +"HMDB60475" +], +"inchi_key":[ +"WHUUTDBJXJRKMK-VKHMYHEASA-M" +], +"kegg.compound":[ +"C00025", +"C00302" +], +"kegg.drug":[ +"D00007", +"D04341" +], +"metanetx.chemical":[ +"MNXM89557" +], +"reactome.compound":[ +"428614", +"29404", +"113552", +"210382" +], +"sabiork":[ +"73", +"2010" +], +"sbo":"SBO:0000247", +"seed.compound":[ +"cpd27177", +"cpd00023", +"cpd19002" +] +} +}, +{ +"id":"glx_c", +"name":"Glyoxylate", +"compartment":"c", +"charge":-1, +"formula":"C2H1O3", +"notes":{ +"original_bigg_ids":[ +"glx_c" +] +}, +"annotation":{ +"bigg.metabolite":[ +"glx" +], +"biocyc":[ +"META:GLYOX" +], +"chebi":[ +"CHEBI:24420", +"CHEBI:36655", +"CHEBI:14368", +"CHEBI:24421", +"CHEBI:42767", +"CHEBI:16891", +"CHEBI:35977", +"CHEBI:5509" +], +"envipath":[ +"32de3cf4-e3e6-4168-956e-32fa5ddb0ce1/compound/cdffdb1a-3322-4cc1-9171-d857bfaa198a", +"4fd7f3e0-dd25-43ac-9453-dda3e52396e4/compound/aecbda66-6e98-4c11-aeaf-6a072f4f963c", +"5882df9c-dae1-4d80-a40e-db4724271456/compound/9dc0aa3b-447a-4b5d-8157-501b036f9626", +"650babc9-9d68-4b73-9332-11972ca26f7b/compound/43b74f4f-bc8a-4b8b-b587-c97d8e9eed48" +], +"hmdb":[ +"HMDB00119" +], +"inchi_key":[ +"HHLFWLYXYJOTON-UHFFFAOYSA-M" +], +"kegg.compound":[ +"C00048" +], +"metanetx.chemical":[ +"MNXM69" +], +"reactome.compound":[ +"904849", +"389678" +], +"sabiork":[ +"1838" +], +"sbo":"SBO:0000247", +"seed.compound":[ +"cpd00040" +] +} +}, +{ +"id":"h2o_c", 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+"948602" +], +"ncbigi":[ +"16131916" +], +"refseq_locus_tag":[ +"b4090" +], +"refseq_name":[ +"rpiB" +], +"refseq_synonym":[ +"JW4051", +"alsB", +"alsI", +"yjcA", +"ECK4083" +], +"sbo":"SBO:0000243", +"uniprot":[ +"P37351" +] +} +}, +{ +"id":"b0721", +"name":"sdhC", +"notes":{ +"original_bigg_ids":[ +"b0721" +] +}, +"annotation":{ +"asap":[ +"ABE-0002460" +], +"ecogene":[ +"EG10933" +], +"ncbigene":[ +"945316" +], +"ncbigi":[ +"16128696" +], +"refseq_locus_tag":[ +"b0721" +], +"refseq_name":[ +"sdhC" +], +"refseq_synonym":[ +"JW0711", +"ECK0710", +"cybA" +], +"sbo":"SBO:0000243", +"uniprot":[ +"P69054" +] +} +}, +{ +"id":"b0722", +"name":"sdhD", +"notes":{ +"original_bigg_ids":[ +"b0722" +] +}, +"annotation":{ +"asap":[ +"ABE-0002464" +], +"ecogene":[ +"EG10934" +], +"ncbigene":[ +"945322" +], +"ncbigi":[ +"16128697" +], +"refseq_locus_tag":[ +"b0722" +], +"refseq_name":[ +"sdhD" +], +"refseq_synonym":[ +"JW0712", +"ECK0711" +], +"sbo":"SBO:0000243", +"uniprot":[ +"P0AC44" +] +} +}, +{ +"id":"b0724", +"name":"sdhB", +"notes":{ +"original_bigg_ids":[ +"b0724" +] +}, +"annotation":{ +"asap":[ +"ABE-0002468" +], +"ecogene":[ +"EG10932" +], +"ncbigene":[ +"945300" +], +"ncbigi":[ +"16128699" +], +"refseq_locus_tag":[ +"b0724" +], +"refseq_name":[ +"sdhB" +], +"refseq_synonym":[ +"ECK0713", +"JW0714" +], +"sbo":"SBO:0000243", +"uniprot":[ +"P07014" +] +} +}, +{ +"id":"b0723", +"name":"sdhA", +"notes":{ +"original_bigg_ids":[ +"b0723" +] +}, +"annotation":{ +"asap":[ +"ABE-0002466" +], +"ecogene":[ +"EG10931" +], +"ncbigene":[ +"945402" +], +"ncbigi":[ +"16128698" +], +"refseq_locus_tag":[ +"b0723" +], +"refseq_name":[ +"sdhA" +], +"refseq_synonym":[ +"JW0713", +"ECK0712" +], +"sbo":"SBO:0000243", +"uniprot":[ +"P0AC41" +] +} +}, +{ +"id":"b0729", +"name":"sucD", +"notes":{ +"original_bigg_ids":[ +"b0729" +] +}, +"annotation":{ +"asap":[ +"ABE-0002485" +], +"ecogene":[ +"EG10982" +], +"ncbigene":[ +"945314" +], +"ncbigi":[ +"16128704" +], +"refseq_locus_tag":[ +"b0729" +], +"refseq_name":[ +"sucD" +], +"refseq_synonym":[ +"JW0718", +"ECK0717" +], +"sbo":"SBO:0000243", +"uniprot":[ +"P0AGE9" +] +} +}, +{ +"id":"b0728", +"name":"sucC", +"notes":{ +"original_bigg_ids":[ +"b0728" +] +}, +"annotation":{ +"asap":[ +"ABE-0002483" +], +"ecogene":[ +"EG10981" +], +"ncbigene":[ +"945312" +], +"ncbigi":[ +"16128703" +], +"refseq_locus_tag":[ +"b0728" +], +"refseq_name":[ +"sucC" +], +"refseq_synonym":[ +"JW0717", +"ECK0716" +], +"sbo":"SBO:0000243", +"uniprot":[ +"P0A836" +] +} +}, +{ +"id":"b0008", +"name":"talB", +"notes":{ +"original_bigg_ids":[ +"b0008" +] +}, +"annotation":{ +"asap":[ +"ABE-0000027" +], +"ecogene":[ +"EG11556" +], +"ncbigene":[ +"944748" +], +"ncbigi":[ +"16128002" +], +"refseq_locus_tag":[ +"b0008" +], +"refseq_name":[ +"talB" +], +"refseq_synonym":[ +"yaaK", +"JW0007", +"ECK0008" +], +"sbo":"SBO:0000243", +"uniprot":[ +"P0A870" +] +} +}, +{ +"id":"b2464", +"name":"talA", +"notes":{ +"original_bigg_ids":[ +"b2464" +] +}, +"annotation":{ +"asap":[ +"ABE-0008115" +], +"ecogene":[ +"EG11797" +], +"ncbigene":[ +"947006" +], +"ncbigi":[ +"16130389" +], +"refseq_locus_tag":[ +"b2464" +], +"refseq_name":[ +"talA" +], +"refseq_synonym":[ +"ECK2459", +"JW2448" +], +"sbo":"SBO:0000243", +"uniprot":[ +"P0A867" +] +} +}, +{ +"id":"b2465", +"name":"tktB", +"notes":{ +"original_bigg_ids":[ +"b2465" +] +}, +"annotation":{ +"asap":[ +"ABE-0008117" +], +"ecogene":[ +"EG12100" +], +"ncbigene":[ +"945865" +], +"ncbigi":[ +"16130390" +], +"refseq_locus_tag":[ +"b2465" +], +"refseq_name":[ +"tktB" +], +"refseq_synonym":[ +"JW2449", +"ECK2460" +], +"sbo":"SBO:0000243", +"uniprot":[ +"P33570" +] +} +}, +{ +"id":"b2935", +"name":"tktA", +"notes":{ +"original_bigg_ids":[ +"b2935" +] +}, +"annotation":{ +"asap":[ +"ABE-0009625" +], +"ecogene":[ +"EG11427" +], +"ncbigene":[ +"947420" +], +"ncbigi":[ +"49176286" +], +"refseq_locus_tag":[ +"b2935" +], +"refseq_name":[ +"tktA" +], +"refseq_synonym":[ +"JW5478", +"tkt", +"ECK2930" +], +"sbo":"SBO:0000243", +"uniprot":[ +"P27302" +] +} +}, +{ +"id":"b3919", +"name":"tpiA", +"notes":{ +"original_bigg_ids":[ +"b3919" +] +}, +"annotation":{ +"asap":[ +"ABE-0012799" +], +"ecogene":[ +"EG11015" +], +"ncbigene":[ +"948409" +], +"ncbigi":[ +"16131757" +], +"refseq_locus_tag":[ +"b3919" +], +"refseq_name":[ +"tpiA" +], +"refseq_synonym":[ +"JW3890", +"ECK3911", +"tpi" +], +"sbo":"SBO:0000243", +"uniprot":[ +"P0A858" +] +} +} +], +"id":"e_coli_core", +"compartments":{ +"c":"cytosol", +"e":"extracellular space" +}, +"version":"1" +} \ No newline at end of file diff --git a/requirements.txt b/requirements.txt index 2aea1a4d..214c574b 100644 --- a/requirements.txt +++ b/requirements.txt @@ -17,3 +17,4 @@ nbsphinx == 0.8.3 scipy == 1.6.0 seaborn == 0.11.1 scikit-learn == 0.24.2 +pymatgen == 2022.0.10 diff --git a/setup.py b/setup.py index 676b3000..4a8dce57 100644 --- a/setup.py +++ b/setup.py @@ -56,7 +56,8 @@ "nbsphinx == 0.8.3", "scipy == 1.6.0", "seaborn == 0.11.1", - "scikit-learn == 0.24.2" + "scikit-learn == 0.24.2", + "pymatgen == 2022.0.10" ], # dependency_links= include_package_data=True, diff --git a/tests/test_atom_mapping.py b/tests/test_atom_mapping.py new file mode 100644 index 00000000..87a567c7 --- /dev/null +++ b/tests/test_atom_mapping.py @@ -0,0 +1,168 @@ +import unittest +import pandas as pd +import pickle +import pathlib +import os +import shutil +from BFAIR.atom_mapping import (MolfileDownloader, + write_rxn_files, + obtain_atom_mappings, + parse_reaction_mappings, + parse_metabolite_mappings, + generate_INCA_mapping_input, + check_symmetry, + ) + +original_wd = os.getcwd() +current_dir = str(pathlib.Path(__file__).parent.absolute()) + +class test_methods(unittest.TestCase): + + # Create method to compare dataframes + def assertDataframeEqual(self, a, b, msg): + try: + pd.testing.assert_frame_equal(a, b) + except AssertionError as e: + raise self.failureException(msg) from e + + @classmethod + def setUpClass(cls): + # Working directory changed to make sure that all generated + # test data is contained properly. + os.chdir(current_dir) + file_obj = open(current_dir + '/test_data/atom_mapping_Data/test_data.obj', 'rb') + ( + metabolites, + unmapped_rxns, + mapped_rxns, + reaction_df, + metabolite_df, + reaction_df_csv, + metabolite_df_csv, + model_reaction_df, + model_metabolite_df, + ) = pickle.load(file_obj) + + file_obj.close() + + cls.metabolites = metabolites + cls.unmapped_rxns = unmapped_rxns + cls.mapped_rxns = mapped_rxns + cls.reaction_df = reaction_df + cls.metabolite_df = metabolite_df + cls.reaction_df_csv = reaction_df_csv + cls.metabolite_df_csv = metabolite_df_csv + cls.model_reaction_df = model_reaction_df + cls.model_metabolite_df = model_metabolite_df + + + @classmethod + def tearDownClass(cls): + shutil.rmtree(current_dir + '/metabolites') + shutil.rmtree(current_dir + '/unmappedRxns') + shutil.rmtree(current_dir + '/mappedRxns') + os.remove(current_dir + '/MappingReactions.csv') + os.remove(current_dir + '/MappingMetabolites.csv') + os.chdir(original_wd) + + + def setUp(self): + # Add the method to compare dataframes in the class + self.addTypeEqualityFunc(pd.DataFrame, self.assertDataframeEqual) + + + def test_a_MolfileDownloader(self): + """ Tests the MolfileDownloader class with all of its' methods. + Compares downloaded files to the ones downloaded previously """ + metabolites = self.metabolites + model_metabolite_df = self.model_metabolite_df + downloader = MolfileDownloader(model_metabolite_df) + downloader.generate_molfile_database() + + metabolites_ = os.listdir(current_dir + '/metabolites') + for i, molfile in enumerate(metabolites_): + with open(current_dir + f'/metabolites/{molfile}', 'r') as f: + metabolites_[i] = f.readlines() + + self.assertEqual(metabolites, metabolites_) + + + def test_b_write_rxn_files(self): + """ Tests write_rxn_files function. + Compares the resulting output to the previously generated results """ + unmapped_rxns = self.unmapped_rxns + model_reaction_df = self.model_reaction_df + write_rxn_files(model_reaction_df) + + unmapped_rxns_ = os.listdir(current_dir + '/unmappedRxns') + for i, rxn_file in enumerate(unmapped_rxns_): + with open(current_dir + f'/unmappedRxns/{rxn_file}', 'r') as f: + unmapped_rxns_[i] = f.readlines() + + self.assertEqual(unmapped_rxns, unmapped_rxns_) + + + def test_c_obtain_atom_mappings(self): + """ Tests obtain_atom_mapppings() function. + Compares the resulting output to the previously generated results """ + mapped_rxns = self.mapped_rxns + + obtain_atom_mappings() + + mapped_rxns_ = os.listdir(current_dir + '/mappedRxns/rxnFiles') + for i, rxn_file in enumerate(mapped_rxns_): + with open(current_dir + f'/mappedRxns/rxnFiles/{rxn_file}', 'r') as f: + lines = f.readlines() + atom_rows = [] + for j, line in enumerate(lines): + if len(line.split()) in (15,16): + # Only append rows containing atom mappings + atom_rows.append(line.split()) + mapped_rxns_[i] = atom_rows + + self.assertEqual(mapped_rxns, mapped_rxns_) + + + def test_d_parse_reaction_mappings(self): + """ Tests parse_reaction_mappings function. + Compares the resulting output to the previously generated results """ + reaction_df = self.reaction_df + reaction_df_ = parse_reaction_mappings() + self.assertEqual(reaction_df, reaction_df_) + + + def test_e_parse_metabolite_mappings(self): + """ Tests parse_metabolite_mappings() function. + Compares the resulting output to the previously generated results """ + metabolite_df = self.metabolite_df + metabolite_df_ = parse_metabolite_mappings() + self.assertEqual(metabolite_df, metabolite_df_) + + + def test_f_generate_INCA_mapping_input(self): + """ Tests generate_INCA_mapping_input() function + Compares the resulting output to the previously generated results """ + reaction_df_csv = self.reaction_df_csv + metabolite_df_csv = self.metabolite_df_csv + reaction_df_ = parse_reaction_mappings() + metabolite_df_ = parse_metabolite_mappings() + + generate_INCA_mapping_input(reaction_df_, metabolite_df_) + + reaction_df_csv_ = pd.read_csv(current_dir + '/MappingReactions.csv') + metabolite_df_csv_ = pd.read_csv(current_dir + '/MappingMetabolites.csv') + + self.assertEqual(reaction_df_csv, reaction_df_csv_) + self.assertEqual(metabolite_df_csv, metabolite_df_csv_) + + + def test_g_check_symmetry(self): + """ Tests check_symmetry() function + Makes sure that symmetric and non-symmetric compounds + are reported correctly """ + self.assertTrue(check_symmetry('succ_c.mol')) + self.assertFalse(check_symmetry('f6p_c.mol')) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/test_data/atom_mapping_Data/e_coli_core.json b/tests/test_data/atom_mapping_Data/e_coli_core.json new file mode 100644 index 00000000..39db7732 --- /dev/null +++ b/tests/test_data/atom_mapping_Data/e_coli_core.json @@ -0,0 +1,14786 @@ +{ +"metabolites":[ +{ +"id":"glc__D_e", +"name":"D-Glucose", +"compartment":"e", +"charge":0, +"formula":"C6H12O6", +"notes":{ +"original_bigg_ids":[ +"glc_D_e" +] +}, +"annotation":{ +"bigg.metabolite":[ +"glc__D" +], +"biocyc":[ +"META:Glucopyranose" +], +"chebi":[ +"CHEBI:12965", +"CHEBI:20999", +"CHEBI:4167", +"CHEBI:17634" +], +"hmdb":[ +"HMDB00122", +"HMDB06564" +], +"inchi_key":[ +"WQZGKKKJIJFFOK-GASJEMHNSA-N" +], +"kegg.compound":[ +"C00031" +], +"kegg.drug":[ +"D00009" +], +"metanetx.chemical":[ +"MNXM41" +], +"sabiork":[ +"1406", +"1407" +], +"sbo":"SBO:0000247", +"seed.compound":[ +"cpd26821", +"cpd00027" +] +} +}, +{ +"id":"gln__L_c", +"name":"L-Glutamine", +"compartment":"c", +"charge":0, +"formula":"C5H10N2O3", +"notes":{ +"original_bigg_ids":[ +"gln_L_c" +] +}, +"annotation":{ +"bigg.metabolite":[ +"gln__L" +], +"biocyc":[ +"META:GLN" +], +"chebi":[ +"CHEBI:42943", +"CHEBI:42899", +"CHEBI:32679", +"CHEBI:32678", +"CHEBI:58359", +"CHEBI:28300", +"CHEBI:42812", +"CHEBI:13110", +"CHEBI:18050", +"CHEBI:32666", +"CHEBI:6227", +"CHEBI:32665", +"CHEBI:21308", +"CHEBI:42814", +"CHEBI:5432", +"CHEBI:24316" +], +"hmdb":[ +"HMDB00641" +], +"inchi_key":[ +"ZDXPYRJPNDTMRX-VKHMYHEASA-N" +], +"kegg.compound":[ +"C00064", +"C00303" +], +"kegg.drug":[ +"D00015" +], +"metanetx.chemical":[ +"MNXM37" +], +"reactome.compound":[ +"113522", +"212615", +"29472" +], +"sabiork":[ +"2011", +"74" +], +"sbo":"SBO:0000247", +"seed.compound":[ +"cpd00253", +"cpd00053" +] +} +}, +{ +"id":"gln__L_e", +"name":"L-Glutamine", +"compartment":"e", +"charge":0, +"formula":"C5H10N2O3", +"notes":{ +"original_bigg_ids":[ +"gln_L_e" +] +}, +"annotation":{ +"bigg.metabolite":[ +"gln__L" +], +"biocyc":[ +"META:GLN" +], +"chebi":[ +"CHEBI:42943", +"CHEBI:42899", +"CHEBI:32679", +"CHEBI:32678", +"CHEBI:58359", +"CHEBI:28300", +"CHEBI:42812", +"CHEBI:13110", +"CHEBI:18050", +"CHEBI:32666", +"CHEBI:6227", +"CHEBI:32665", +"CHEBI:21308", +"CHEBI:42814", +"CHEBI:5432", +"CHEBI:24316" +], +"hmdb":[ +"HMDB00641" +], +"inchi_key":[ +"ZDXPYRJPNDTMRX-VKHMYHEASA-N" +], +"kegg.compound":[ +"C00064", +"C00303" +], +"kegg.drug":[ +"D00015" +], +"metanetx.chemical":[ +"MNXM37" +], +"reactome.compound":[ +"113522", +"212615", +"29472" +], +"sabiork":[ +"2011", +"74" +], +"sbo":"SBO:0000247", +"seed.compound":[ +"cpd00253", +"cpd00053" +] +} +}, +{ +"id":"glu__L_c", +"name":"L-Glutamate", +"compartment":"c", +"charge":-1, +"formula":"C5H8NO4", +"notes":{ +"original_bigg_ids":[ +"glu_L_c" +] +}, +"annotation":{ +"bigg.metabolite":[ +"glu__L" +], +"biocyc":[ +"META:Glutamates", +"META:GLT" +], +"chebi":[ +"CHEBI:76051", +"CHEBI:21301", +"CHEBI:29985", +"CHEBI:42825", +"CHEBI:29987", +"CHEBI:18237", +"CHEBI:24314", +"CHEBI:16015", +"CHEBI:13107", +"CHEBI:5431", +"CHEBI:21304", +"CHEBI:6224", +"CHEBI:14321", +"CHEBI:29988" +], +"hmdb":[ +"HMDB00148", +"HMDB60475" +], +"inchi_key":[ +"WHUUTDBJXJRKMK-VKHMYHEASA-M" +], +"kegg.compound":[ +"C00025", +"C00302" +], +"kegg.drug":[ +"D00007", +"D04341" +], +"metanetx.chemical":[ +"MNXM89557" +], +"reactome.compound":[ +"428614", +"29404", +"113552", +"210382" 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import (MolfileDownloader, + write_rxn_files, + obtain_atom_mappings, + parse_reaction_mappings, + parse_metabolite_mappings, + generate_INCA_mapping_input, + check_symmetry, + clean_output, + ) + +original_wd = os.getcwd() +current_dir = str(pathlib.Path(__file__).parent.absolute()) +os.chdir(current_dir) + +# Load e_coli_core model +model, reaction_data, metabolite_data = INCA_input_parser.parse_cobra_model('e_coli_core.json', 'e_coli_core', '2021-07-15') + +# Subset handpicked reactions +model_reaction_df = pd.DataFrame() +model_reaction_df = model_reaction_df.append(reaction_data[reaction_data['rxn_id'] == 'PFK']) +model_reaction_df = model_reaction_df.append(reaction_data[reaction_data['rxn_id'] == 'BIOMASS_Ecoli_core_w_GAM']) +model_reaction_df = model_reaction_df.append(reaction_data[reaction_data['rxn_id'] == 'EX_pyr_e']) +model_reaction_df = model_reaction_df.append(reaction_data[reaction_data['rxn_id'] == 'ICL']) + +# And metabolites from these reactions +model_metabolite_df = pd.DataFrame() +model_metabolite_df = model_metabolite_df.append(metabolite_data[metabolite_data['met_id'] == 'atp_c']) +model_metabolite_df = model_metabolite_df.append(metabolite_data[metabolite_data['met_id'] == 'f6p_c']) +model_metabolite_df = model_metabolite_df.append(metabolite_data[metabolite_data['met_id'] == 'adp_c']) +model_metabolite_df = model_metabolite_df.append(metabolite_data[metabolite_data['met_id'] == 'fdp_c']) +model_metabolite_df = model_metabolite_df.append(metabolite_data[metabolite_data['met_id'] == 'h_c']) +model_metabolite_df = model_metabolite_df.append(metabolite_data[metabolite_data['met_id'] == 'pyr_e']) +model_metabolite_df = model_metabolite_df.append(metabolite_data[metabolite_data['met_id'] == 'icit_c']) +model_metabolite_df = model_metabolite_df.append(metabolite_data[metabolite_data['met_id'] == 'succ_c']) +model_metabolite_df = model_metabolite_df.append(metabolite_data[metabolite_data['met_id'] == 'glx_c']) + +# Obtain all required files +# Metabolite Molfiles +downloader = MolfileDownloader(met_df) +downloader.generate_molfile_database() + +# Rxn files +write_rxn_files(rxn_df) + +# Mapped Rxn files +obtain_atom_mappings() + +# Parsed dataframes of mappings +reaction_df = parse_reaction_mappings() +metabolite_df = parse_metabolite_mappings() + +# CSV outputs of these dataframes +generate_INCA_mapping_input(reaction_df, metabolite_df) + + +# Load all the generated files in Python +# Molfiles in a single list +metabolites = os.listdir('metabolites') +for i, molfile in enumerate(metabolites): + with open(f'metabolites/{molfile}', 'r') as f: + metabolites[i] = f.readlines() + +# Rxn files in a single list +unmapped_rxns = os.listdir('unmappedRxns') +for i, rxn_file in enumerate(unmapped_rxns): + with open(f'unmappedRxns/{rxn_file}', 'r') as f: + unmapped_rxns[i] = f.readlines() + +# Mapped Rxn files in a single list +mapped_rxns = os.listdir('mappedRxns/rxnFiles') +for i, rxn_file in enumerate(mapped_rxns): + with open(f'mappedRxns/rxnFiles/{rxn_file}', 'r') as f: + lines = f.readlines() + atom_rows = [] + for j, line in enumerate(lines): + if len(line.split()) in (15,16): + # Only append rows containing atom mappings + atom_rows.append(line.split()) + mapped_rxns[i] = atom_rows + +# CSV outputs of parsed mapping data +reaction_df_csv = pd.read_csv('MappingReactions.csv') +metabolite_df_csv = pd.read_csv('MappingMetabolites.csv') + +# Pickle all the variables using pickle +filehandler = open("test_data.obj", "wb") + +pickle.dump( + [ + metabolites, + unmapped_rxns, + mapped_rxns, + reaction_df, + metabolite_df, + reaction_df_csv, + metabolite_df_csv, + model_reaction_df, + model_metabolite_df, + ], + filehandler + ) + +filehandler.close() + +clean_output() + +os.chdir(original_wd) + diff --git a/tests/test_data/atom_mapping_Data/test_data.obj b/tests/test_data/atom_mapping_Data/test_data.obj new file mode 100644 index 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+++++++++++++++++++------------------ 1 file changed, 62 insertions(+), 59 deletions(-) diff --git a/tests/test_atom_mapping.py b/tests/test_atom_mapping.py index 87a567c7..f729e384 100644 --- a/tests/test_atom_mapping.py +++ b/tests/test_atom_mapping.py @@ -71,36 +71,49 @@ def setUp(self): self.addTypeEqualityFunc(pd.DataFrame, self.assertDataframeEqual) - def test_a_MolfileDownloader(self): - """ Tests the MolfileDownloader class with all of its' methods. - Compares downloaded files to the ones downloaded previously """ - metabolites = self.metabolites - model_metabolite_df = self.model_metabolite_df - downloader = MolfileDownloader(model_metabolite_df) - downloader.generate_molfile_database() + # Currently, tests are supposed to be run in alphabetical order + # i.e. "test_a_" first, then "test_b_", because their output + # depends on the output of other tests. + def test_g_check_symmetry(self): + """ Tests check_symmetry() function + Makes sure that symmetric and non-symmetric compounds + are reported correctly """ + self.assertTrue(check_symmetry('succ_c.mol')) + self.assertFalse(check_symmetry('f6p_c.mol')) - metabolites_ = os.listdir(current_dir + '/metabolites') - for i, molfile in enumerate(metabolites_): - with open(current_dir + f'/metabolites/{molfile}', 'r') as f: - metabolites_[i] = f.readlines() - - self.assertEqual(metabolites, metabolites_) + def test_f_generate_INCA_mapping_input(self): + """ Tests generate_INCA_mapping_input() function + Compares the resulting output to the previously generated results """ + reaction_df_csv = self.reaction_df_csv + metabolite_df_csv = self.metabolite_df_csv + reaction_df_ = parse_reaction_mappings() + metabolite_df_ = parse_metabolite_mappings() + + generate_INCA_mapping_input(reaction_df_, metabolite_df_) + + reaction_df_csv_ = pd.read_csv(current_dir + '/MappingReactions.csv') + metabolite_df_csv_ = pd.read_csv(current_dir + '/MappingMetabolites.csv') + + self.assertEqual(reaction_df_csv, reaction_df_csv_) + self.assertEqual(metabolite_df_csv, metabolite_df_csv_) + - def test_b_write_rxn_files(self): - """ Tests write_rxn_files function. + def test_e_parse_metabolite_mappings(self): + """ Tests parse_metabolite_mappings() function. Compares the resulting output to the previously generated results """ - unmapped_rxns = self.unmapped_rxns - model_reaction_df = self.model_reaction_df - write_rxn_files(model_reaction_df) + metabolite_df = self.metabolite_df + metabolite_df_ = parse_metabolite_mappings() + self.assertEqual(metabolite_df, metabolite_df_) + - unmapped_rxns_ = os.listdir(current_dir + '/unmappedRxns') - for i, rxn_file in enumerate(unmapped_rxns_): - with open(current_dir + f'/unmappedRxns/{rxn_file}', 'r') as f: - unmapped_rxns_[i] = f.readlines() - - self.assertEqual(unmapped_rxns, unmapped_rxns_) - + def test_d_parse_reaction_mappings(self): + """ Tests parse_reaction_mappings function. + Compares the resulting output to the previously generated results """ + reaction_df = self.reaction_df + reaction_df_ = parse_reaction_mappings() + self.assertEqual(reaction_df, reaction_df_) + def test_c_obtain_atom_mappings(self): """ Tests obtain_atom_mapppings() function. @@ -122,46 +135,36 @@ def test_c_obtain_atom_mappings(self): self.assertEqual(mapped_rxns, mapped_rxns_) - - def test_d_parse_reaction_mappings(self): - """ Tests parse_reaction_mappings function. - Compares the resulting output to the previously generated results """ - reaction_df = self.reaction_df - reaction_df_ = parse_reaction_mappings() - self.assertEqual(reaction_df, reaction_df_) - - - def test_e_parse_metabolite_mappings(self): - """ Tests parse_metabolite_mappings() function. - Compares the resulting output to the previously generated results """ - metabolite_df = self.metabolite_df - metabolite_df_ = parse_metabolite_mappings() - self.assertEqual(metabolite_df, metabolite_df_) - - def test_f_generate_INCA_mapping_input(self): - """ Tests generate_INCA_mapping_input() function + def test_b_write_rxn_files(self): + """ Tests write_rxn_files function. Compares the resulting output to the previously generated results """ - reaction_df_csv = self.reaction_df_csv - metabolite_df_csv = self.metabolite_df_csv - reaction_df_ = parse_reaction_mappings() - metabolite_df_ = parse_metabolite_mappings() - - generate_INCA_mapping_input(reaction_df_, metabolite_df_) + unmapped_rxns = self.unmapped_rxns + model_reaction_df = self.model_reaction_df + write_rxn_files(model_reaction_df) - reaction_df_csv_ = pd.read_csv(current_dir + '/MappingReactions.csv') - metabolite_df_csv_ = pd.read_csv(current_dir + '/MappingMetabolites.csv') + unmapped_rxns_ = os.listdir(current_dir + '/unmappedRxns') + for i, rxn_file in enumerate(unmapped_rxns_): + with open(current_dir + f'/unmappedRxns/{rxn_file}', 'r') as f: + unmapped_rxns_[i] = f.readlines() + + self.assertEqual(unmapped_rxns, unmapped_rxns_) - self.assertEqual(reaction_df_csv, reaction_df_csv_) - self.assertEqual(metabolite_df_csv, metabolite_df_csv_) + def test_a_MolfileDownloader(self): + """ Tests the MolfileDownloader class with all of its' methods. + Compares downloaded files to the ones downloaded previously """ + metabolites = self.metabolites + model_metabolite_df = self.model_metabolite_df + downloader = MolfileDownloader(model_metabolite_df) + downloader.generate_molfile_database() - def test_g_check_symmetry(self): - """ Tests check_symmetry() function - Makes sure that symmetric and non-symmetric compounds - are reported correctly """ - self.assertTrue(check_symmetry('succ_c.mol')) - self.assertFalse(check_symmetry('f6p_c.mol')) + metabolites_ = os.listdir(current_dir + '/metabolites') + for i, molfile in enumerate(metabolites_): + with open(current_dir + f'/metabolites/{molfile}', 'r') as f: + metabolites_[i] = f.readlines() + + self.assertEqual(metabolites, metabolites_) if __name__ == "__main__": From e8db23a3ccb0094c972d9f18265014a4a4171abc Mon Sep 17 00:00:00 2001 From: vaitkusd Date: Mon, 2 Aug 2021 16:56:36 +0200 Subject: [PATCH 03/12] fix: adaptation to run in Windows Added platform and subprocess modules to help correctly run different shell commands in all OS's --- BFAIR/atom_mapping/atom_mapping.py | 49 +++++++++++++++++++----------- 1 file changed, 32 insertions(+), 17 deletions(-) diff --git a/BFAIR/atom_mapping/atom_mapping.py b/BFAIR/atom_mapping/atom_mapping.py index 36bedf2e..ce05e4f7 100644 --- a/BFAIR/atom_mapping/atom_mapping.py +++ b/BFAIR/atom_mapping/atom_mapping.py @@ -5,11 +5,14 @@ Reaction Decoder Tool (RDT) (https://github.com/asad/ReactionDecoder) to map the reactions; CADD Group Chemoinformatics Tools and User Services (https://cactus.nci.nih.gov/chemical/structure) -to resolve InChI strings from given InChI keys. """ +to resolve InChI strings from given InChI keys; +Java, to run RDT""" import requests import os import glob +import platform +import subprocess import pandas as pd from rdkit import Chem from pymatgen.symmetry import analyzer @@ -103,7 +106,7 @@ def generate_molfile_database(self): wf.writelines(lines) break except: - os.system(f'rm metabolites/{self.filename}') + os.remove(f'metabolites/{self.filename}') continue print( @@ -289,30 +292,41 @@ def obtain_atom_mappings(max_time=120): try: for rxnFile in rxn_list: # Check if reaction is mapped, and run RDT with specified time limit if not - if not os.path.isfile(f'rxnFiles/{rxnFile}'): - os.system( - f'timeout {max_time} java -jar ../RDT.jar -Q RXN -q ../unmappedRxns/{rxnFile} -g -j AAM -f TEXT') - + try: + if not os.path.isfile(f'rxnFiles/{rxnFile}'): + subprocess.run(['java', '-jar', + '../RDT.jar', '-Q', + 'RXN', '-q', + f'../unmappedRxns/{rxnFile}', + '-g', '-j', + 'AAM', '-f', + 'TEXT'], timeout=max_time) + except: + continue # Obtain filenames of generated files and simplify them to respective reaction IDs for name in glob.glob('ECBLAST*'): os.rename(name, name[8:-8]+name[-4:]) # Move all generated files to different directories, in respect to their filetype - os.system('mv *.png ./pngFiles') - os.system('mv *.rxn ./rxnFiles') - os.system('mv *.txt ./txtFiles') + if platform.system() == 'Windows': + os.system('move *.png pngFiles') + os.system('move *.rxn rxnFiles') + os.system('move *.txt txtFiles') + else: + os.system('mv *.png ./pngFiles') + os.system('mv *.rxn ./rxnFiles') + os.system('mv *.txt ./txtFiles') - print( - f"Reactions mapped in total: {len(os.listdir('rxnFiles'))}/{len(rxn_list)}") except: # Make sure that wd is back to normal no matter what os.chdir(owd) - + + print(f"Reactions mapped in total: {len(os.listdir('rxnFiles'))}/{len(rxn_list)}") # Change working dir back to original os.chdir(owd) # Remove RDT.jar from working dir - os.system('rm RDT.jar') + os.remove('RDT.jar') def parse_reaction_mappings(): @@ -655,13 +669,14 @@ def clean_output(metabolites = True, Removes MappingReactions.csv and MappingMetabolites.csv. """ if metabolites: - os.system('rm -r metabolites') + os.rmdir('metabolites') if reactions: - os.system('rm -r unmappedRxns') + os.rmdir('unmappedRxns') if mappings: - os.system('rm -r mappedRxns') + os.rmdir('mappedRxns') if csv: - os.system('rm MappingReactions.csv MappingMetabolites.csv') + os.remove('MappingReactions.csv') + os.remove('MappingMetabolites.csv') # molfile_downloader_descr = MolfileDownloader() From 31a16ad2bbda6063132f9137a7dde4dc0deeda7e Mon Sep 17 00:00:00 2001 From: vaitkusd Date: Mon, 2 Aug 2021 17:01:04 +0200 Subject: [PATCH 04/12] tests: fix item sorting Sorted os.listdir output alphabetically to standardize file reading order --- tests/test_atom_mapping.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/tests/test_atom_mapping.py b/tests/test_atom_mapping.py index f729e384..a251035e 100644 --- a/tests/test_atom_mapping.py +++ b/tests/test_atom_mapping.py @@ -122,7 +122,7 @@ def test_c_obtain_atom_mappings(self): obtain_atom_mappings() - mapped_rxns_ = os.listdir(current_dir + '/mappedRxns/rxnFiles') + mapped_rxns_ = sorted(os.listdir(current_dir + '/mappedRxns/rxnFiles')) for i, rxn_file in enumerate(mapped_rxns_): with open(current_dir + f'/mappedRxns/rxnFiles/{rxn_file}', 'r') as f: lines = f.readlines() @@ -143,12 +143,12 @@ def test_b_write_rxn_files(self): model_reaction_df = self.model_reaction_df write_rxn_files(model_reaction_df) - unmapped_rxns_ = os.listdir(current_dir + '/unmappedRxns') + unmapped_rxns_ = sorted(os.listdir(current_dir + '/unmappedRxns')) for i, rxn_file in enumerate(unmapped_rxns_): with open(current_dir + f'/unmappedRxns/{rxn_file}', 'r') as f: unmapped_rxns_[i] = f.readlines() - self.assertEqual(unmapped_rxns, unmapped_rxns_) + self.assertEqual(unmapped_rxns, unmapped_rxns_) def test_a_MolfileDownloader(self): @@ -159,11 +159,11 @@ def test_a_MolfileDownloader(self): downloader = MolfileDownloader(model_metabolite_df) downloader.generate_molfile_database() - metabolites_ = os.listdir(current_dir + '/metabolites') + metabolites_ = sorted(os.listdir(current_dir + '/metabolites')) for i, molfile in enumerate(metabolites_): with open(current_dir + f'/metabolites/{molfile}', 'r') as f: metabolites_[i] = f.readlines() - + self.assertEqual(metabolites, metabolites_) From 447be0095ec1956de2f6c9a4f294a735b706c1ee Mon Sep 17 00:00:00 2001 From: vaitkusd Date: Wed, 4 Aug 2021 11:12:57 +0200 Subject: [PATCH 05/12] tests: update MFA_visualization tests Changed assertEqual to assertAlmostEqual in test_reshape_fluxes_escher() --- tests/test_MFA_visualization.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/test_MFA_visualization.py b/tests/test_MFA_visualization.py index a72a32af..f1b76424 100644 --- a/tests/test_MFA_visualization.py +++ b/tests/test_MFA_visualization.py @@ -71,7 +71,7 @@ def test_reshape_fluxes_escher(self): self.assertEqual(self.fluxes_relaxed, fluxes_relaxed_) relaxed_fluxes_sampling_ = reshape_fluxes_escher( self.relaxed_sampled_fluxes) - self.assertEqual( + self.assertAlmostEqual( self.relaxed_fluxes_sampling, relaxed_fluxes_sampling_) def test_sampled_fluxes_minrange(self): From 2e86a7ae39974f82d73ce4d3fcc2efed5e1c037e Mon Sep 17 00:00:00 2001 From: vaitkusd Date: Sun, 8 Aug 2021 16:02:54 +0200 Subject: [PATCH 06/12] tests: added AlmostEqual dict assertion New method added to compare whether two dictionaries are almost equal --- tests/test_MFA_visualization.py | 42 +++++++++++++++++++++++++++++++-- 1 file changed, 40 insertions(+), 2 deletions(-) diff --git a/tests/test_MFA_visualization.py b/tests/test_MFA_visualization.py index f1b76424..cf43cb10 100644 --- a/tests/test_MFA_visualization.py +++ b/tests/test_MFA_visualization.py @@ -4,6 +4,7 @@ import cobra import matplotlib import pandas as pd +from numpy import long, ndarray from BFAIR.mfa.visualization import ( reshape_fluxes_escher, sampled_fluxes_minrange, @@ -24,7 +25,44 @@ class test_methods(unittest.TestCase): maxDiff = None - + + # Add method for dictionary AlmostEqual assertion + def assertDeepAlmostEqual(self, expected, actual, *args, **kwargs): + """ + Assert that two complex structures have almost equal contents. + + Compares lists, dicts and tuples recursively. Checks numeric values + using test_case's :py:meth:`unittest.TestCase.assertAlmostEqual` and + checks all other values with :py:meth:`unittest.TestCase.assertEqual`. + Accepts additional positional and keyword arguments and pass those + intact to assertAlmostEqual() (that's how you specify comparison + precision). + """ + is_root = not '__trace' in kwargs + trace = kwargs.pop('__trace', 'ROOT') + try: + if isinstance(expected, (int, float, long, complex)): + self.assertAlmostEqual(expected, actual, *args, **kwargs) + elif isinstance(expected, (list, tuple, ndarray)): + self.assertEqual(len(expected), len(actual)) + for index in range(len(expected)): + v1, v2 = expected[index], actual[index] + self.assertDeepAlmostEqual(v1, v2, + __trace=repr(index), *args, **kwargs) + elif isinstance(expected, dict): + self.assertEqual(set(expected), set(actual)) + for key in expected: + self.assertDeepAlmostEqual(expected[key], actual[key], + __trace=repr(key), *args, **kwargs) + else: + self.assertEqual(expected, actual) + except AssertionError as exc: + exc.__dict__.setdefault('traces', []).append(trace) + if is_root: + trace = ' -> '.join(reversed(exc.traces)) + exc = AssertionError("%s\nTRACE: %s" % (exc.message, trace)) + raise exc + # Create method to compare dataframes def assertDataframeEqual(self, a, b, msg): try: @@ -71,7 +109,7 @@ def test_reshape_fluxes_escher(self): self.assertEqual(self.fluxes_relaxed, fluxes_relaxed_) relaxed_fluxes_sampling_ = reshape_fluxes_escher( self.relaxed_sampled_fluxes) - self.assertAlmostEqual( + self.assertDeepAlmostEqual( self.relaxed_fluxes_sampling, relaxed_fluxes_sampling_) def test_sampled_fluxes_minrange(self): From df86760169d0a1a00d7690f9297a7e78f464903b Mon Sep 17 00:00:00 2001 From: vaitkusd Date: Sun, 8 Aug 2021 16:13:30 +0200 Subject: [PATCH 07/12] fix: fixed docstring formatting --- BFAIR/atom_mapping/atom_mapping.py | 45 +++++++++++++++--------------- 1 file changed, 23 insertions(+), 22 deletions(-) diff --git a/BFAIR/atom_mapping/atom_mapping.py b/BFAIR/atom_mapping/atom_mapping.py index ce05e4f7..e16b82ce 100644 --- a/BFAIR/atom_mapping/atom_mapping.py +++ b/BFAIR/atom_mapping/atom_mapping.py @@ -27,10 +27,10 @@ def __init__(self, metabolite_data, db_preference = (0,1,2,3)): Parameters ---------- - metabolite_data: pandas.DataFrame + metabolite_data : pandas.DataFrame Dataframe that contains information about metabolites the model. Obtain from INCA_input_parser module. - db_preference: tuple of integers + db_preference : tuple of int, optional Four integers specify the order of preference of databases to obtain the metabolite structures from: 0: Using InChI key > InChI string conversion @@ -52,7 +52,7 @@ def generate_molfile_database(self): Outputs ------- - Molfiles: .mol files + Molfiles : .mol files """ print('Fetching metabolite structures...') @@ -115,7 +115,8 @@ def generate_molfile_database(self): def get_from_inchi_key(self): """ Helper method to obtain InChI string from InChI key, - and generate the Molfile from the string. """ + and generate the Molfile from the string. + """ url = f'https://cactus.nci.nih.gov/chemical/structure/{self.inchi_key}/stdinchi' r = requests.get(url, allow_redirects=False) inchi_string = r.text @@ -165,13 +166,13 @@ def write_rxn_files(rxn_data): Parameters ---------- - rxn_data: pandas.DataFrame + rxn_data : pandas.DataFrame Dataframe that contains information about reactions in the model. Obtained from INCA_input_parser module. Outputs ------- - RXN files: .rxn files + RXN files : .rxn files """ met_filter = ['h_e', 'h_c', 'h_p', 'h2_e', 'h2_c', 'h2_p'] biomass_filter = ['Biomass', 'biomass', 'BIOMASS'] @@ -254,16 +255,16 @@ def obtain_atom_mappings(max_time=120): Parameters ---------- - max_time: int, optional + max_time : int, optional Specifies time limit for single reaction mapping in seconds. Default: 120s. Outputs ------- - mapped RXN files: .rxn files - mapped TXT files: .txt files + mapped RXN files : .rxn files + mapped TXT files : .txt files Mappings in SMILES format - pictures of mappings: .png files + pictures of mappings : .png files """ # Check if Java is installed if os.system('java -version') != 0: @@ -339,7 +340,7 @@ def parse_reaction_mappings(): Returns ------- - mapping_data: pandas.DataFrame + mapping_data : pandas.DataFrame Reaction mapping data. """ if not os.path.isdir('mappedRxns'): @@ -515,7 +516,7 @@ def parse_metabolite_mappings(): Returns ------- - metabolite_data: pandas.DataFrame + metabolite_data : pandas.DataFrame Dataframe containing mapped metabolite data. """ metabolite_list = os.listdir('metabolites') @@ -576,15 +577,15 @@ def generate_INCA_mapping_input(reaction_df, metabolite_df): Parameters ---------- - reaction_df: pandas.DataFrame + reaction_df : pandas.DataFrame Dataframe that contains reaction mapping data. - metabolite_df: pandas.DataFrame + metabolite_df : pandas.DataFrame Dataframe that contains metabolite mapping data. Outputs ------- - MappingReactions: .csv file - MappingMetabolites: .csv file + MappingReactions : .csv file + MappingMetabolites : .csv file """ # Would be a good idea to make the filenames more informative. reaction_df.to_csv(path_or_buf = os.path.join(os.getcwd(), f'MappingReactions.csv')) @@ -603,12 +604,12 @@ def check_symmetry(met_filename): Parameters ---------- - met_filename: string + met_filename : str Filename of the specific Molfile Returns ------- - symmetrical: bool + symmetrical : bool True if metabolite is symmetric, False if not. """ @@ -659,13 +660,13 @@ def clean_output(metabolites = True, Parameters ---------- - metabolites: bool, optional + metabolites : bool, optional Removes /metabolites folder recursively. - reactions: bool, optional + reactions : bool, optional Removes /unmappedRxns folder recursively. - mappings: bool, optional + mappings : bool, optional Removes /mappedRxns folder recursively. - csv: bool, optional + csv : bool, optional Removes MappingReactions.csv and MappingMetabolites.csv. """ if metabolites: From e5a58bdc67125386f148179d94e05b7fafbe8e41 Mon Sep 17 00:00:00 2001 From: vaitkusd Date: Sun, 8 Aug 2021 16:21:11 +0200 Subject: [PATCH 08/12] tests: added method for list AlmostEqual assertion Issue: Different OS's generate -0 instead of 0 in some molecule coordinates. Solution: converted all numeric values in lists to floats instead of strings, and used new method to assertAlmostEqual on lists. --- tests/test_atom_mapping.py | 61 +++++++++++++++++- .../generate_atom_mapping_test_data.py | 31 +++++++-- .../test_data/atom_mapping_Data/test_data.obj | Bin 70143 -> 93831 bytes 3 files changed, 82 insertions(+), 10 deletions(-) diff --git a/tests/test_atom_mapping.py b/tests/test_atom_mapping.py index a251035e..92900d06 100644 --- a/tests/test_atom_mapping.py +++ b/tests/test_atom_mapping.py @@ -4,6 +4,7 @@ import pathlib import os import shutil +from numpy import long, ndarray from BFAIR.atom_mapping import (MolfileDownloader, write_rxn_files, obtain_atom_mappings, @@ -18,6 +19,44 @@ class test_methods(unittest.TestCase): + # Add method for list AlmostEqual assertion + def assertDeepAlmostEqual(self, expected, actual, *args, **kwargs): + """ + Assert that two complex structures have almost equal contents. + + Compares lists, dicts and tuples recursively. Checks numeric values + using test_case's :py:meth:`unittest.TestCase.assertAlmostEqual` and + checks all other values with :py:meth:`unittest.TestCase.assertEqual`. + Accepts additional positional and keyword arguments and pass those + intact to assertAlmostEqual() (that's how you specify comparison + precision). + """ + is_root = not '__trace' in kwargs + trace = kwargs.pop('__trace', 'ROOT') + try: + if isinstance(expected, (int, float, long, complex)): + self.assertAlmostEqual(expected, actual, *args, **kwargs) + elif isinstance(expected, (list, tuple, ndarray)): + self.assertEqual(len(expected), len(actual)) + for index in range(len(expected)): + v1, v2 = expected[index], actual[index] + self.assertDeepAlmostEqual(v1, v2, + __trace=repr(index), *args, **kwargs) + elif isinstance(expected, dict): + self.assertEqual(set(expected), set(actual)) + for key in expected: + self.assertDeepAlmostEqual(expected[key], actual[key], + __trace=repr(key), *args, **kwargs) + else: + self.assertEqual(expected, actual) + except AssertionError as exc: + exc.__dict__.setdefault('traces', []).append(trace) + if is_root: + trace = ' -> '.join(reversed(exc.traces)) + exc = AssertionError("%s\nTRACE: %s" % (exc.message, trace)) + raise exc + + # Create method to compare dataframes def assertDataframeEqual(self, a, b, msg): try: @@ -143,12 +182,20 @@ def test_b_write_rxn_files(self): model_reaction_df = self.model_reaction_df write_rxn_files(model_reaction_df) + # Store all file contents in one list, and convert all numerics to floats unmapped_rxns_ = sorted(os.listdir(current_dir + '/unmappedRxns')) for i, rxn_file in enumerate(unmapped_rxns_): with open(current_dir + f'/unmappedRxns/{rxn_file}', 'r') as f: - unmapped_rxns_[i] = f.readlines() + unmapped_rxns_[i] = f.readlines() + for j, line in enumerate(unmapped_rxns_[i]): + unmapped_rxns_[i][j] = unmapped_rxns_[i][j].split() + for k, val in enumerate(unmapped_rxns_[i][j]): + try: + unmapped_rxns_[i][j][k] = float(val) + except: + pass - self.assertEqual(unmapped_rxns, unmapped_rxns_) + self.assertDeepAlmostEqual(unmapped_rxns, unmapped_rxns_) def test_a_MolfileDownloader(self): @@ -159,12 +206,20 @@ def test_a_MolfileDownloader(self): downloader = MolfileDownloader(model_metabolite_df) downloader.generate_molfile_database() + # Store all file contents in one list, and convert all numerics to floats metabolites_ = sorted(os.listdir(current_dir + '/metabolites')) for i, molfile in enumerate(metabolites_): with open(current_dir + f'/metabolites/{molfile}', 'r') as f: metabolites_[i] = f.readlines() + for j, met in enumerate(metabolites_[i]): + metabolites_[i][j] = metabolites_[i][j].split() + for k, val in enumerate(metabolites_[i][j]): + try: + metabolites_[i][j][k] = float(val) + except: + pass - self.assertEqual(metabolites, metabolites_) + self.assertDeepAlmostEqual(metabolites, metabolites_) if __name__ == "__main__": diff --git a/tests/test_data/atom_mapping_Data/generate_atom_mapping_test_data.py b/tests/test_data/atom_mapping_Data/generate_atom_mapping_test_data.py index 390590de..307d3eeb 100644 --- a/tests/test_data/atom_mapping_Data/generate_atom_mapping_test_data.py +++ b/tests/test_data/atom_mapping_Data/generate_atom_mapping_test_data.py @@ -1,7 +1,8 @@ import pickle import os import pandas as pd -from BFAIR.INCA import INCA_input_parser +import pathlib +from BFAIR.mfa.INCA import INCA_input_parser from BFAIR.atom_mapping import (MolfileDownloader, write_rxn_files, obtain_atom_mappings, @@ -40,11 +41,11 @@ # Obtain all required files # Metabolite Molfiles -downloader = MolfileDownloader(met_df) +downloader = MolfileDownloader(model_metabolite_df) downloader.generate_molfile_database() # Rxn files -write_rxn_files(rxn_df) +write_rxn_files(model_reaction_df) # Mapped Rxn files obtain_atom_mappings() @@ -58,17 +59,33 @@ # Load all the generated files in Python -# Molfiles in a single list +# Molfiles in a single list. +# All numerics converted to floats. metabolites = os.listdir('metabolites') for i, molfile in enumerate(metabolites): with open(f'metabolites/{molfile}', 'r') as f: metabolites[i] = f.readlines() - -# Rxn files in a single list + for j, met in enumerate(metabolites[i]): + metabolites[i][j] = metabolites[i][j].split() + for k, val in enumerate(metabolites[i][j]): + try: + metabolites[i][j][k] = float(val) + except: + pass + +# Rxn files in a single list. +# All numerics converted to floats. unmapped_rxns = os.listdir('unmappedRxns') for i, rxn_file in enumerate(unmapped_rxns): with open(f'unmappedRxns/{rxn_file}', 'r') as f: - unmapped_rxns[i] = f.readlines() + unmapped_rxns[i] = f.readlines() + for j, line in enumerate(unmapped_rxns[i]): + unmapped_rxns[i][j] = unmapped_rxns[i][j].split() + for k, val in enumerate(unmapped_rxns[i][j]): + try: + unmapped_rxns[i][j][k] = float(val) + except: + pass # Mapped Rxn files in a single list mapped_rxns = os.listdir('mappedRxns/rxnFiles') diff --git 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z=kuJGs-f=!oI8CNg38N9O!xF%47!@WOUS47U8*u(MnH8yU5}+nHwc-NAF-@Bw{a!@1LUC#byKg(GhGxSMSHhL5k~)ZFmF+ZxOb zANLR)lzUa8y5WOY%e~ty#^AC!ZnL-#40*X9M=Wj+IPP^Z2t97RUX5=~A5q+oYVOAz_ts% Date: Sun, 8 Aug 2021 17:25:17 +0200 Subject: [PATCH 09/12] fix: standardized formatting --- BFAIR/atom_mapping/atom_mapping.py | 390 ++++++++++-------- tests/test_MFA_visualization.py | 16 +- tests/test_atom_mapping.py | 131 +++--- .../generate_atom_mapping_test_data.py | 65 +-- 4 files changed, 328 insertions(+), 274 deletions(-) diff --git a/BFAIR/atom_mapping/atom_mapping.py b/BFAIR/atom_mapping/atom_mapping.py index e16b82ce..f67f22ce 100644 --- a/BFAIR/atom_mapping/atom_mapping.py +++ b/BFAIR/atom_mapping/atom_mapping.py @@ -14,17 +14,18 @@ import platform import subprocess import pandas as pd -from rdkit import Chem +from rdkit import Chem, RDLogger from pymatgen.symmetry import analyzer from pymatgen.core import structure __version__ = "0.0.1" + class MolfileDownloader: - def __init__(self, metabolite_data, db_preference = (0,1,2,3)): + def __init__(self, metabolite_data, db_preference=(0, 1, 2, 3)): """ Class to find and download metabolite structures in Molfile format. - + Parameters ---------- metabolite_data : pandas.DataFrame @@ -39,65 +40,70 @@ def __init__(self, metabolite_data, db_preference = (0,1,2,3)): 3: CHEBI database """ self.database_dict = {0: 'get_from_inchi_key', - 1: 'get_from_kegg', - 2: 'get_from_hmdb', - 3: 'get_from_chebi'} - + 1: 'get_from_kegg', + 2: 'get_from_hmdb', + 3: 'get_from_chebi'} + self.metabolite_data = metabolite_data self.db_preference = db_preference - + def generate_molfile_database(self): - """ Main method that calls other methods and performs + """ Main method that calls other methods and performs most sanity checks on obtained files. - + Outputs ------- Molfiles : .mol files """ print('Fetching metabolite structures...') - + if not os.path.isdir('metabolites'): os.mkdir('metabolites') - + # Disable RDKit warnings + RDLogger.DisableLog('rdApp.*') + for i, met in self.metabolite_data.iterrows(): self.filename = met.met_id + '.mol' - + get_from_inchi_keyBool = False get_from_keggBool = False get_from_hmdbBool = False get_from_chebiBool = False - + # Check for annotations and set bool values for those - # present. + # present. if 'inchi_key' in met.annotations: self.inchi_key = met.annotations['inchi_key'][0] get_from_inchi_keyBool = True if 'kegg.compound' in met.annotations: - self.keggIDs = [keggID for keggID in met.annotations['kegg.compound']] + self.keggIDs = [ + keggID for keggID in met.annotations['kegg.compound']] get_from_keggBool = True if 'hmdb' in met.annotations: - self.hmdbIDnums = ['0' * (7-len(hmdbID[4:])) + hmdbID[4:] for hmdbID in met.annotations['hmdb']] + self.hmdbIDnums = ['0' * (7 - len(hmdbID[4:])) + hmdbID[4:] + for hmdbID in met.annotations['hmdb']] get_from_hmdbBool = True if 'chebi' in met.annotations: - self.chebiIDs = [chebiID for chebiID in met.annotations['chebi']] + self.chebiIDs = [ + chebiID for chebiID in met.annotations['chebi']] get_from_chebiBool = True - - # Call helper functions according to order of preference + + # Call helper functions according to order of preference # and available references (specified by previous bool values). for opt in self.db_preference: if eval(self.database_dict.get(opt) + 'Bool'): getattr(self, self.database_dict.get(opt))() - else: + else: continue - + try: # Rewrites the .mol file. This removes hydrogens from - # structure, lowkey standardizes molecules, and works + # structure, lowkey standardizes molecules, and works # as a failswitch to see if file is of correct format. m = Chem.MolFromMolFile(f'metabolites/{self.filename}') with open(f'metabolites/{self.filename}', "w+") as f: print(Chem.MolToMolBlock(m), file=f) - + # Add metabolite ID to the first line of Molfile with open(f'metabolites/{self.filename}', 'r') as f: lines = f.readlines() @@ -105,17 +111,16 @@ def generate_molfile_database(self): with open(f'metabolites/{self.filename}', 'w') as wf: wf.writelines(lines) break - except: + except BaseException: os.remove(f'metabolites/{self.filename}') continue - + print( f"Successfully fetched {len(os.listdir('metabolites'))}/{self.metabolite_data.shape[0]} metabolites") - def get_from_inchi_key(self): """ Helper method to obtain InChI string from InChI key, - and generate the Molfile from the string. + and generate the Molfile from the string. """ url = f'https://cactus.nci.nih.gov/chemical/structure/{self.inchi_key}/stdinchi' r = requests.get(url, allow_redirects=False) @@ -125,9 +130,9 @@ def get_from_inchi_key(self): molfile = Chem.inchi.MolFromInchi(inchi_string) with open(f'metabolites/{self.filename}', "w+") as f: print(Chem.MolToMolBlock(molfile), file=f) - except: + except BaseException: return - + def get_from_kegg(self): """ Helper method to obtain Molfile from KEGG Compound database """ @@ -137,7 +142,7 @@ def get_from_kegg(self): open(f'metabolites/{self.filename}', 'wb').write(r.content) if os.path.getsize(f'metabolites/{self.filename}') != 0: break - + def get_from_hmdb(self): """ Helper method to obtain Molfile from HMDB database """ @@ -147,36 +152,36 @@ def get_from_hmdb(self): open(f'metabolites/{self.filename}', 'wb').write(r.content) if os.path.getsize(f'metabolites/{self.filename}') != 0: break - + def get_from_chebi(self): """ Helper method to obtain Molfile from CHEBI database """ for chebiID in self.chebiIDs: - url = f'https://www.ebi.ac.uk/chebi/saveStructure.do?defaultImage=true&chebiId={chebiID}&imageId=0' + url = f'https://www.ebi.ac.uk/chebi/saveStructure.do?defaultImage=true&chebiId={chebiID}&imageId=0' r = requests.get(url, allow_redirects=False) open(f'metabolites/{self.filename}', 'wb').write(r.content) if os.path.getsize(f'metabolites/{self.filename}') != 0: break - - + + def write_rxn_files(rxn_data): """ Generates RXN files in RDT suitable format. Requires Molfiles of all metabolites to be present in the working directory/metabolites folder. - + Parameters ---------- rxn_data : pandas.DataFrame Dataframe that contains information about reactions in the model. Obtained from INCA_input_parser module. - + Outputs ------- - RXN files : .rxn files - """ + RXN files : .rxn files + """ met_filter = ['h_e', 'h_c', 'h_p', 'h2_e', 'h2_c', 'h2_p'] biomass_filter = ['Biomass', 'biomass', 'BIOMASS'] - + if not os.path.isdir('unmappedRxns'): os.mkdir('unmappedRxns') @@ -187,14 +192,15 @@ def write_rxn_files(rxn_data): if any(biomass_id in rxn.rxn_id for biomass_id in biomass_filter): print(f'Excluded {rxn.rxn_id} reaction from mapping') continue - + rxn_filename = rxn.rxn_id + '.rxn' # Use copies to avoid messing up the original dataframe reactants = rxn.reactants_ids.copy() - reactants_st = [int(abs(s)) for s in rxn.reactants_stoichiometry.copy()] + reactants_st = [int(abs(s)) + for s in rxn.reactants_stoichiometry.copy()] products = rxn.products_ids.copy() products_st = [int(abs(s)) for s in rxn.products_stoichiometry.copy()] - + # Filter out unwanted molecules react_indexes = [] prod_indexes = [] @@ -205,20 +211,21 @@ def write_rxn_files(rxn_data): for i in sorted(react_indexes, reverse=True): del reactants[i] del reactants_st[i] - + for i, met in enumerate(products): if met in met_filter: prod_indexes.append(i) if len(prod_indexes) != 0: - for i in sorted(prod_indexes, reverse=True): + for i in sorted(prod_indexes, reverse=True): del products[i] del products_st[i] - + metabolites = reactants + products metabolites_st = reactants_st + products_st - + # Check if all metabolite structures are present - if not all([os.path.isfile(f'metabolites/{met}.mol') for met in metabolites]): + if not all( + [os.path.isfile(f'metabolites/{met}.mol') for met in metabolites]): print( f"Metabolite structures missing for reaction {rxn.rxn_id}") continue @@ -226,39 +233,42 @@ def write_rxn_files(rxn_data): with open(os.path.join(path, rxn_filename), "w") as f: # Write first three lines, including reaction equation f.write(f"$RXN\n{rxn.rxn_id}\n\n{rxn.equation}\n") - + # Write export reactions (1 reactant) if not products_st and abs(int(sum(reactants_st))) == 1: - f.write(f'{abs(int(sum(reactants_st)))} {int(sum(reactants_st))}\n') + f.write( + f'{abs(int(sum(reactants_st)))} {int(sum(reactants_st))}\n') met = metabolites[0] with open(f'metabolites/{met}.mol', 'r') as wf: structure = wf.read() f.write(f'$MOL\n{structure}') f.write(f'$MOL\n{structure}') - - # Write all the other reactions with at least 1 metabolite on each side + + # Write all the other reactions with at least 1 metabolite on + # each side else: - f.write(f'{abs(int(sum(reactants_st)))} {int(sum(products_st))}\n') + f.write( + f'{abs(int(sum(reactants_st)))} {int(sum(products_st))}\n') for s, met in zip(metabolites_st, metabolites): with open(f'metabolites/{met}.mol', 'r') as wf: structure = wf.read() # Repeat structure based on stoichiometry for i in range(s): - f.write(f'$MOL\n{structure}') + f.write(f'$MOL\n{structure}') print(f"Generated {len(os.listdir('unmappedRxns'))}/{rxn_data.shape[0]}") - - + + def obtain_atom_mappings(max_time=120): """ Performs atom mapping by using RDT. Only maps reactions that are available in .rxn format, in the working_directory/unmappedRxns folder. - + Parameters ---------- max_time : int, optional - Specifies time limit for single reaction mapping + Specifies time limit for single reaction mapping in seconds. Default: 120s. - + Outputs ------- mapped RXN files : .rxn files @@ -269,7 +279,7 @@ def obtain_atom_mappings(max_time=120): # Check if Java is installed if os.system('java -version') != 0: raise RuntimeError('Java installation not found') - + print('Mapping reactions...') # Set the original working dir owd = os.getcwd() @@ -292,23 +302,26 @@ def obtain_atom_mappings(max_time=120): os.chdir('mappedRxns') try: for rxnFile in rxn_list: - # Check if reaction is mapped, and run RDT with specified time limit if not + # Check if reaction is mapped, and run RDT with specified time + # limit if not try: - if not os.path.isfile(f'rxnFiles/{rxnFile}'): + if not os.path.isfile(f'rxnFiles/{rxnFile}'): subprocess.run(['java', '-jar', - '../RDT.jar', '-Q', - 'RXN', '-q', - f'../unmappedRxns/{rxnFile}', - '-g', '-j', - 'AAM', '-f', + '../RDT.jar', '-Q', + 'RXN', '-q', + f'../unmappedRxns/{rxnFile}', + '-g', '-j', + 'AAM', '-f', 'TEXT'], timeout=max_time) - except: + except BaseException: continue - # Obtain filenames of generated files and simplify them to respective reaction IDs + # Obtain filenames of generated files and simplify them to respective + # reaction IDs for name in glob.glob('ECBLAST*'): - os.rename(name, name[8:-8]+name[-4:]) + os.rename(name, name[8:-8] + name[-4:]) - # Move all generated files to different directories, in respect to their filetype + # Move all generated files to different directories, in respect to + # their filetype if platform.system() == 'Windows': os.system('move *.png pngFiles') os.system('move *.rxn rxnFiles') @@ -318,38 +331,41 @@ def obtain_atom_mappings(max_time=120): os.system('mv *.rxn ./rxnFiles') os.system('mv *.txt ./txtFiles') - except: + except BaseException: # Make sure that wd is back to normal no matter what os.chdir(owd) - - print(f"Reactions mapped in total: {len(os.listdir('rxnFiles'))}/{len(rxn_list)}") + + print( + f"Reactions mapped in total: {len(os.listdir('rxnFiles'))}/{len(rxn_list)}") # Change working dir back to original os.chdir(owd) - + # Remove RDT.jar from working dir os.remove('RDT.jar') - - + + def parse_reaction_mappings(): - """ Parses reaction mappings from mapped RXN files - to a dataframe in suitable format for INCA. Requires + """ Parses reaction mappings from mapped RXN files + to a dataframe in suitable format for INCA. Requires all mapped RXN files to be present in the working_dir/ - mappedRxns/rxnFiles folder. For unmapped reactions, + mappedRxns/rxnFiles folder. For unmapped reactions, data is picked from working_dir/unmappedRxns folder - and all mapping data is represented as blanks. - + and all mapping data is represented as blanks. + Returns ------- mapping_data : pandas.DataFrame Reaction mapping data. """ if not os.path.isdir('mappedRxns'): - raise RuntimeError("'mappedRxns' directory not present in current working directory") - + raise RuntimeError( + "'mappedRxns' directory not present in current working directory") + rxn_list = os.listdir('unmappedRxns') # Compile list of reactions that do not have any mapping - unmapped_list = list(set(os.listdir('unmappedRxns')) - set(os.listdir('mappedRxns/rxnFiles'))) - + unmapped_list = list(set(os.listdir('unmappedRxns')) - + set(os.listdir('mappedRxns/rxnFiles'))) + keys = ['Unnamed: 0', 'Unnamed: 0.1', 'id', @@ -358,9 +374,9 @@ def parse_reaction_mappings(): 'rxn_description', 'reactants_stoichiometry_tracked', 'products_stoichiometry_tracked', - 'reactants_ids_tracked', - 'products_ids_tracked', - 'reactants_mapping', + 'reactants_ids_tracked', + 'products_ids_tracked', + 'reactants_mapping', 'products_mapping', 'rxn_equation', 'used_', @@ -371,72 +387,85 @@ def parse_reaction_mappings(): 'products_positions_tracked' ] mapping_dict_tmp = {} - + for i, rxn in enumerate(rxn_list): mapping_dict = {k: [] for k in keys} met_mapping = [] react_cnt = 0 prod_cnt = 0 productBool = False - - if rxn not in unmapped_list: + + if rxn not in unmapped_list: # Extract info from mapped .rxn files with open(f'mappedRxns/rxnFiles/{rxn}', 'r') as f: lines = f.readlines() for j, line in enumerate(lines): if line.rstrip() == '$RXN': # Extract number of reactants - react_lim = int(lines[j+4].split()[0]) + react_lim = int(lines[j + 4].split()[0]) # Extract number of products - prod_lim = int(lines[j+4].split()[1]) + prod_lim = int(lines[j + 4].split()[1]) if line.rstrip() == '$MOL': - met_id = lines[j+1].rstrip() + met_id = lines[j + 1].rstrip() # Hard-coded, since 16 columns is standard for Molfile atom rows, - # and 15 can occur if we have >100 atoms on one side (cols merge) - if len(line.split()) in (15,16) : + # and 15 can occur if we have >100 atoms on one side (cols + # merge) + if len(line.split()) in (15, 16): atom_row = line.split() if atom_row[3] == 'C': # Split columns if they get merged if atom_row[-3][0] == '0': atom_row[-3] = atom_row[-3][1:] met_mapping.append(atom_row[-3]) - + # Check if reached the last atom row - if len(lines[j+1].split()) not in (15,16): - # Check if current metabolite is reactant or product + if len(lines[j + 1].split()) not in (15, 16): + # Check if current metabolite is reactant or + # product if not productBool: # Check if any carbons are present if met_mapping: c_tracked = ['C' for atom in met_mapping] pos_tracked = list(range(len(met_mapping))) - mapping_dict['reactants_ids_tracked'].append(met_id) - mapping_dict['reactants_mapping'].append(met_mapping) - mapping_dict['reactants_elements_tracked'].append(c_tracked) - mapping_dict['reactants_positions_tracked'].append(pos_tracked) + mapping_dict['reactants_ids_tracked'].append( + met_id) + mapping_dict['reactants_mapping'].append( + met_mapping) + mapping_dict['reactants_elements_tracked'].append( + c_tracked) + mapping_dict['reactants_positions_tracked'].append( + pos_tracked) react_cnt += 1 - if react_cnt == react_lim: + if react_cnt == react_lim: productBool = True - - # Assign metabolite to products if reached reactant limit - else: + + # Assign metabolite to products if reached reactant + # limit + else: if met_mapping: c_tracked = ['C' for atom in met_mapping] pos_tracked = list(range(len(met_mapping))) - mapping_dict['products_ids_tracked'].append(met_id) - mapping_dict['products_mapping'].append(met_mapping) - mapping_dict['products_elements_tracked'].append(c_tracked) - mapping_dict['products_positions_tracked'].append(pos_tracked) + mapping_dict['products_ids_tracked'].append( + met_id) + mapping_dict['products_mapping'].append( + met_mapping) + mapping_dict['products_elements_tracked'].append( + c_tracked) + mapping_dict['products_positions_tracked'].append( + pos_tracked) prod_cnt += 1 - - if prod_cnt == prod_lim: - react_stoich = ['-1' for met in range(len(mapping_dict['reactants_mapping']))] - prod_stoich = ['1' for met in range(len(mapping_dict['products_mapping']))] + + if prod_cnt == prod_lim: + react_stoich = [ + '-1' for met in range(len(mapping_dict['reactants_mapping']))] + prod_stoich = ['1' for met in range( + len(mapping_dict['products_mapping']))] mapping_dict['reactants_stoichiometry_tracked'] = react_stoich mapping_dict['products_stoichiometry_tracked'] = prod_stoich - + met_mapping = [] - + mapping_dict['rxn_id'] = rxn[:-4] mapping_dict['used_'] = True # Fill all empty fields @@ -447,27 +476,28 @@ def parse_reaction_mappings(): mapping_dict['rxn_description'] = 'NULL' mapping_dict['rxn_equation'] = 'NULL' mapping_dict['comment_'] = 'NULL' - + mapping_dict_tmp[i] = mapping_dict - + mapping_data = pd.DataFrame.from_dict(mapping_dict_tmp, 'index') - + # alphabet for number-letter matching. Max capacity is 63 characters, # which is a limit set in INCA for this format. - alphabet = list(map(chr, range(97, 123))) + list(map(chr, range(65,91))) + \ - list(map(chr, range(48, 58))) + ['_'] - + alphabet = list(map(chr, range(97, 123))) + list(map(chr, + range(65, 91))) + list(map(chr, range(48, 58))) + ['_'] + # Loop through all reactions for i, rxn in mapping_data.iterrows(): try: # Convert number mappings to letters - carbons_list = [atom for met in rxn['reactants_mapping'] for atom in met] + carbons_list = [atom for met in rxn['reactants_mapping'] + for atom in met] carbon_map_dict = dict(zip(carbons_list, alphabet)) # Compile alphabetical mapping in curly bracket format carbon_str = '{' for j, met in enumerate(rxn['reactants_mapping']): - if j != 0: + if j != 0: carbon_str += ',' for atom in met: carbon_str += carbon_map_dict[atom] @@ -476,44 +506,46 @@ def parse_reaction_mappings(): carbon_str = '{' for j, met in enumerate(rxn['products_mapping']): - if j != 0: + if j != 0: carbon_str += ',' for atom in met: carbon_str += carbon_map_dict[atom] carbon_str += '}' mapping_data.at[i, 'products_mapping'] = carbon_str - + except KeyError: if len(carbons_list) > 63: - print(f'Reaction {rxn["rxn_id"]} contains more than 63 carbon atoms') + print( + f'Reaction {rxn["rxn_id"]} contains more than 63 carbon atoms') else: # Mostly happens when one of the metabolites has (R) - # group in the Molfile, and other has a C in that spot + # group in the Molfile, and other has a C in that spot print(f'{rxn["rxn_id"]} has unmapped carbon(-s)') mapping_data.at[i, 'reactants_mapping'] = '{}' mapping_data.at[i, 'products_mapping'] = '{}' - + # Convert metabolite lists/stoichiometries to strings in curly brackets metabolite_str = '{%s}' % (','.join(rxn['reactants_ids_tracked'])) mapping_data.at[i, 'reactants_ids_tracked'] = metabolite_str - + metabolite_str = '{%s}' % (','.join(rxn['products_ids_tracked'])) mapping_data.at[i, 'products_ids_tracked'] = metabolite_str - - stoich_str = '{%s}' % (','.join(rxn['reactants_stoichiometry_tracked'])) + + stoich_str = '{%s}' % (','.join( + rxn['reactants_stoichiometry_tracked'])) mapping_data.at[i, 'reactants_stoichiometry_tracked'] = stoich_str - + stoich_str = '{%s}' % (','.join(rxn['products_stoichiometry_tracked'])) mapping_data.at[i, 'products_stoichiometry_tracked'] = stoich_str - + return mapping_data - + def parse_metabolite_mappings(): """ Parses metabolite mapping and symmetry data into INCA suitable format. Requires all Molfiles to be present - in the working_dir/metabolites directory. - + in the working_dir/metabolites directory. + Returns ------- metabolite_data : pandas.DataFrame @@ -535,12 +567,12 @@ def parse_metabolite_mappings(): 'base_met_symmetry_elements', 'base_met_symmetry_atompositions', 'base_met_indices' - ] + ] metabolite_dict_tmp = {} - + # Works in a similar fashion to parse_reaction_mappings() for i, met in enumerate(metabolite_list): - met_dict = {k:'NULL' for k in keys} + met_dict = {k: 'NULL' for k in keys} with open(f'metabolites/{met}', 'r') as f: lines = f.readlines() carbon_count = 0 @@ -551,84 +583,94 @@ def parse_metabolite_mappings(): atom_row = line.split() if atom_row[3] == 'C': carbon_count += 1 - # Generate carbon atom lists/mappings + # Generate carbon atom lists/mappings carbon_count_list = ['C' for x in range(carbon_count)] carbon_count_string = '{%s}' % (','.join(carbon_count_list)) - carbon_count_range = '{%s}' % (','.join([str(x) for x in range(carbon_count)])) - + carbon_count_range = '{%s}' % ( + ','.join([str(x) for x in range(carbon_count)])) + met_dict['met_elements'] = carbon_count_string met_dict['met_atompositions'] = carbon_count_range # Check if the metabolite is symmetrical if check_symmetry(met): - carbon_count_range_rev = '{%s}' % (','.join([str(x) for x in range(carbon_count-1,-1,-1)])) + carbon_count_range_rev = '{%s}' % ( + ','.join([str(x) for x in range(carbon_count - 1, -1, -1)])) met_dict["met_symmetry_elements"] = carbon_count_string met_dict['met_symmetry_atompositions'] = carbon_count_range_rev - + metabolite_dict_tmp[i] = met_dict - + metabolite_data = pd.DataFrame.from_dict(metabolite_dict_tmp, 'index') - + return metabolite_data - - + + def generate_INCA_mapping_input(reaction_df, metabolite_df): """ Function to export reaction and metabolite mapping dataframes - to CSV files. - + to CSV files. + Parameters ---------- reaction_df : pandas.DataFrame Dataframe that contains reaction mapping data. metabolite_df : pandas.DataFrame Dataframe that contains metabolite mapping data. - + Outputs ------- MappingReactions : .csv file MappingMetabolites : .csv file """ # Would be a good idea to make the filenames more informative. - reaction_df.to_csv(path_or_buf = os.path.join(os.getcwd(), f'MappingReactions.csv')) - metabolite_df.to_csv(path_or_buf = os.path.join(os.getcwd(), f'MappingMetabolites.csv')) + reaction_df.to_csv( + path_or_buf=os.path.join( + os.getcwd(), + f'MappingReactions.csv')) + metabolite_df.to_csv( + path_or_buf=os.path.join( + os.getcwd(), + f'MappingMetabolites.csv')) + - def check_symmetry(met_filename): """ Function that checks if the given metabolite is symmetric. Uses pymatgen package for symmetry related operations, and - RDKit for Molfile conversion to XYZ format. Requires Molfiles + RDKit for Molfile conversion to XYZ format. Requires Molfiles of the metabolites to be present in the working_dir/metabolites folder. Current criterion for symmetricity is for every carbon except one (central, if molecule consists of odd number of carbons) to have at least one equivalent carbon in the structure. - + Parameters ---------- met_filename : str Filename of the specific Molfile - + Returns ------- symmetrical : bool True if metabolite is symmetric, False if not. - + """ symmetrical = False + # Disable RDKit warnings + RDLogger.DisableLog('rdApp.*') # Counter for non symmetrical carbon atoms non_eq_carbons = 0 carbons = 0 # Convert Molfile to XYZ string molecule = Chem.MolFromMolFile(f'metabolites/{met_filename}') molecule_xyz = Chem.rdmolfiles.MolToXYZBlock(molecule) - + # Create IMolecule object to analyze its' symmetricity try: molecule_obj = structure.IMolecule.from_str(molecule_xyz, fmt='xyz') if len(molecule_obj) == 1: return symmetrical - # Initialize point group analyzer + # Initialize point group analyzer pg_analyzer = analyzer.PointGroupAnalyzer(molecule_obj) - + except (IndexError, ValueError): # '*' is unrecognized in particular print(f'{met_filename} contains unrecognized symbols') @@ -640,24 +682,24 @@ def check_symmetry(met_filename): carbons += 1 if len(eq_atoms['eq_sets'].get(i)) == 1: non_eq_carbons += 1 - if non_eq_carbons > 1: + if non_eq_carbons > 1: return symmetrical - + # Molecule has more than 1 carbon, and at most 1 non-symmetrical carbon if carbons > 1: symmetrical = True - + return symmetrical -def clean_output(metabolites = True, - reactions = True, - mappings = True, - csv = True): +def clean_output(metabolites=True, + reactions=True, + mappings=True, + csv=True): """ Utility function. Deletes all possible output of other mapping functions. Deletes everything by default. - + Parameters ---------- metabolites : bool, optional @@ -679,7 +721,7 @@ def clean_output(metabolites = True, os.remove('MappingReactions.csv') os.remove('MappingMetabolites.csv') - + # molfile_downloader_descr = MolfileDownloader() # """A class to find and fetch metabolite structures in Molfile # format. @@ -690,7 +732,7 @@ def clean_output(metabolites = True, # >>> downloader.generate_molfile_database() # The database will be generated in working_dir/metabolites folder -# To change the order of databases for fetching, pass a tuple of +# To change the order of databases for fetching, pass a tuple of # integers when initiating an instance, f.e.: # >>> downloader = MolfileDownloader(met_df, (2,1,0,3)) # >>> downloader.generate_molfile_database() diff --git a/tests/test_MFA_visualization.py b/tests/test_MFA_visualization.py index cf43cb10..c63b2fe5 100644 --- a/tests/test_MFA_visualization.py +++ b/tests/test_MFA_visualization.py @@ -4,7 +4,7 @@ import cobra import matplotlib import pandas as pd -from numpy import long, ndarray +from numpy import ndarray from BFAIR.mfa.visualization import ( reshape_fluxes_escher, sampled_fluxes_minrange, @@ -25,7 +25,7 @@ class test_methods(unittest.TestCase): maxDiff = None - + # Add method for dictionary AlmostEqual assertion def assertDeepAlmostEqual(self, expected, actual, *args, **kwargs): """ @@ -38,22 +38,22 @@ def assertDeepAlmostEqual(self, expected, actual, *args, **kwargs): intact to assertAlmostEqual() (that's how you specify comparison precision). """ - is_root = not '__trace' in kwargs + is_root = '__trace' not in kwargs trace = kwargs.pop('__trace', 'ROOT') try: - if isinstance(expected, (int, float, long, complex)): + if isinstance(expected, (int, float, complex)): self.assertAlmostEqual(expected, actual, *args, **kwargs) elif isinstance(expected, (list, tuple, ndarray)): self.assertEqual(len(expected), len(actual)) for index in range(len(expected)): v1, v2 = expected[index], actual[index] - self.assertDeepAlmostEqual(v1, v2, - __trace=repr(index), *args, **kwargs) + self.assertDeepAlmostEqual(v1, v2, + __trace=repr(index), *args, **kwargs) elif isinstance(expected, dict): self.assertEqual(set(expected), set(actual)) for key in expected: self.assertDeepAlmostEqual(expected[key], actual[key], - __trace=repr(key), *args, **kwargs) + __trace=repr(key), *args, **kwargs) else: self.assertEqual(expected, actual) except AssertionError as exc: @@ -62,7 +62,7 @@ def assertDeepAlmostEqual(self, expected, actual, *args, **kwargs): trace = ' -> '.join(reversed(exc.traces)) exc = AssertionError("%s\nTRACE: %s" % (exc.message, trace)) raise exc - + # Create method to compare dataframes def assertDataframeEqual(self, a, b, msg): try: diff --git a/tests/test_atom_mapping.py b/tests/test_atom_mapping.py index 92900d06..448fcd84 100644 --- a/tests/test_atom_mapping.py +++ b/tests/test_atom_mapping.py @@ -4,7 +4,7 @@ import pathlib import os import shutil -from numpy import long, ndarray +from numpy import ndarray from BFAIR.atom_mapping import (MolfileDownloader, write_rxn_files, obtain_atom_mappings, @@ -12,13 +12,14 @@ parse_metabolite_mappings, generate_INCA_mapping_input, check_symmetry, - ) + ) original_wd = os.getcwd() current_dir = str(pathlib.Path(__file__).parent.absolute()) + class test_methods(unittest.TestCase): - + # Add method for list AlmostEqual assertion def assertDeepAlmostEqual(self, expected, actual, *args, **kwargs): """ @@ -31,22 +32,22 @@ def assertDeepAlmostEqual(self, expected, actual, *args, **kwargs): intact to assertAlmostEqual() (that's how you specify comparison precision). """ - is_root = not '__trace' in kwargs + is_root = '__trace' not in kwargs trace = kwargs.pop('__trace', 'ROOT') try: - if isinstance(expected, (int, float, long, complex)): + if isinstance(expected, (int, float, complex)): self.assertAlmostEqual(expected, actual, *args, **kwargs) elif isinstance(expected, (list, tuple, ndarray)): self.assertEqual(len(expected), len(actual)) for index in range(len(expected)): v1, v2 = expected[index], actual[index] - self.assertDeepAlmostEqual(v1, v2, - __trace=repr(index), *args, **kwargs) + self.assertDeepAlmostEqual( + v1, v2, __trace=repr(index), *args, **kwargs) elif isinstance(expected, dict): self.assertEqual(set(expected), set(actual)) for key in expected: - self.assertDeepAlmostEqual(expected[key], actual[key], - __trace=repr(key), *args, **kwargs) + self.assertDeepAlmostEqual( + expected[key], actual[key], __trace=repr(key), *args, **kwargs) else: self.assertEqual(expected, actual) except AssertionError as exc: @@ -55,9 +56,9 @@ def assertDeepAlmostEqual(self, expected, actual, *args, **kwargs): trace = ' -> '.join(reversed(exc.traces)) exc = AssertionError("%s\nTRACE: %s" % (exc.message, trace)) raise exc - - + # Create method to compare dataframes + def assertDataframeEqual(self, a, b, msg): try: pd.testing.assert_frame_equal(a, b) @@ -69,21 +70,24 @@ def setUpClass(cls): # Working directory changed to make sure that all generated # test data is contained properly. os.chdir(current_dir) - file_obj = open(current_dir + '/test_data/atom_mapping_Data/test_data.obj', 'rb') + file_obj = open( + current_dir + + '/test_data/atom_mapping_Data/test_data.obj', + 'rb') ( - metabolites, - unmapped_rxns, - mapped_rxns, - reaction_df, - metabolite_df, - reaction_df_csv, - metabolite_df_csv, - model_reaction_df, - model_metabolite_df, + metabolites, + unmapped_rxns, + mapped_rxns, + reaction_df, + metabolite_df, + reaction_df_csv, + metabolite_df_csv, + model_reaction_df, + model_metabolite_df, ) = pickle.load(file_obj) file_obj.close() - + cls.metabolites = metabolites cls.unmapped_rxns = unmapped_rxns cls.mapped_rxns = mapped_rxns @@ -93,9 +97,8 @@ def setUpClass(cls): cls.metabolite_df_csv = metabolite_df_csv cls.model_reaction_df = model_reaction_df cls.model_metabolite_df = model_metabolite_df - - - @classmethod + + @classmethod def tearDownClass(cls): shutil.rmtree(current_dir + '/metabolites') shutil.rmtree(current_dir + '/unmappedRxns') @@ -103,24 +106,22 @@ def tearDownClass(cls): os.remove(current_dir + '/MappingReactions.csv') os.remove(current_dir + '/MappingMetabolites.csv') os.chdir(original_wd) - - + def setUp(self): # Add the method to compare dataframes in the class self.addTypeEqualityFunc(pd.DataFrame, self.assertDataframeEqual) - - + # Currently, tests are supposed to be run in alphabetical order - # i.e. "test_a_" first, then "test_b_", because their output + # i.e. "test_a_" first, then "test_b_", because their output # depends on the output of other tests. + def test_g_check_symmetry(self): - """ Tests check_symmetry() function + """ Tests check_symmetry() function Makes sure that symmetric and non-symmetric compounds are reported correctly """ self.assertTrue(check_symmetry('succ_c.mol')) self.assertFalse(check_symmetry('f6p_c.mol')) - - + def test_f_generate_INCA_mapping_input(self): """ Tests generate_INCA_mapping_input() function Compares the resulting output to the previously generated results """ @@ -128,76 +129,73 @@ def test_f_generate_INCA_mapping_input(self): metabolite_df_csv = self.metabolite_df_csv reaction_df_ = parse_reaction_mappings() metabolite_df_ = parse_metabolite_mappings() - + generate_INCA_mapping_input(reaction_df_, metabolite_df_) - + reaction_df_csv_ = pd.read_csv(current_dir + '/MappingReactions.csv') - metabolite_df_csv_ = pd.read_csv(current_dir + '/MappingMetabolites.csv') - + metabolite_df_csv_ = pd.read_csv( + current_dir + '/MappingMetabolites.csv') + self.assertEqual(reaction_df_csv, reaction_df_csv_) - self.assertEqual(metabolite_df_csv, metabolite_df_csv_) - - + self.assertEqual(metabolite_df_csv, metabolite_df_csv_) + def test_e_parse_metabolite_mappings(self): """ Tests parse_metabolite_mappings() function. Compares the resulting output to the previously generated results """ metabolite_df = self.metabolite_df metabolite_df_ = parse_metabolite_mappings() self.assertEqual(metabolite_df, metabolite_df_) - - + def test_d_parse_reaction_mappings(self): """ Tests parse_reaction_mappings function. Compares the resulting output to the previously generated results """ reaction_df = self.reaction_df reaction_df_ = parse_reaction_mappings() - self.assertEqual(reaction_df, reaction_df_) - - + self.assertEqual(reaction_df, reaction_df_) + def test_c_obtain_atom_mappings(self): """ Tests obtain_atom_mapppings() function. Compares the resulting output to the previously generated results """ mapped_rxns = self.mapped_rxns - + obtain_atom_mappings() - + mapped_rxns_ = sorted(os.listdir(current_dir + '/mappedRxns/rxnFiles')) for i, rxn_file in enumerate(mapped_rxns_): with open(current_dir + f'/mappedRxns/rxnFiles/{rxn_file}', 'r') as f: lines = f.readlines() atom_rows = [] for j, line in enumerate(lines): - if len(line.split()) in (15,16): + if len(line.split()) in (15, 16): # Only append rows containing atom mappings atom_rows.append(line.split()) mapped_rxns_[i] = atom_rows - + self.assertEqual(mapped_rxns, mapped_rxns_) - - + def test_b_write_rxn_files(self): """ Tests write_rxn_files function. Compares the resulting output to the previously generated results """ unmapped_rxns = self.unmapped_rxns model_reaction_df = self.model_reaction_df write_rxn_files(model_reaction_df) - - # Store all file contents in one list, and convert all numerics to floats + + # Store all file contents in one list, and convert all numerics to + # floats unmapped_rxns_ = sorted(os.listdir(current_dir + '/unmappedRxns')) for i, rxn_file in enumerate(unmapped_rxns_): with open(current_dir + f'/unmappedRxns/{rxn_file}', 'r') as f: unmapped_rxns_[i] = f.readlines() - for j, line in enumerate(unmapped_rxns_[i]): + for j, line in enumerate(unmapped_rxns_[i]): unmapped_rxns_[i][j] = unmapped_rxns_[i][j].split() for k, val in enumerate(unmapped_rxns_[i][j]): try: unmapped_rxns_[i][j][k] = float(val) - except: + except BaseException: pass - - self.assertDeepAlmostEqual(unmapped_rxns, unmapped_rxns_) - - + + self.assertDeepAlmostEqual(unmapped_rxns, unmapped_rxns_) + def test_a_MolfileDownloader(self): """ Tests the MolfileDownloader class with all of its' methods. Compares downloaded files to the ones downloaded previously """ @@ -205,22 +203,23 @@ def test_a_MolfileDownloader(self): model_metabolite_df = self.model_metabolite_df downloader = MolfileDownloader(model_metabolite_df) downloader.generate_molfile_database() - - # Store all file contents in one list, and convert all numerics to floats + + # Store all file contents in one list, and convert all numerics to + # floats metabolites_ = sorted(os.listdir(current_dir + '/metabolites')) for i, molfile in enumerate(metabolites_): with open(current_dir + f'/metabolites/{molfile}', 'r') as f: metabolites_[i] = f.readlines() - for j, met in enumerate(metabolites_[i]): + for j, met in enumerate(metabolites_[i]): metabolites_[i][j] = metabolites_[i][j].split() for k, val in enumerate(metabolites_[i][j]): try: metabolites_[i][j][k] = float(val) - except: + except BaseException: pass - + self.assertDeepAlmostEqual(metabolites, metabolites_) - - + + if __name__ == "__main__": unittest.main() diff --git a/tests/test_data/atom_mapping_Data/generate_atom_mapping_test_data.py b/tests/test_data/atom_mapping_Data/generate_atom_mapping_test_data.py index 307d3eeb..96bbd886 100644 --- a/tests/test_data/atom_mapping_Data/generate_atom_mapping_test_data.py +++ b/tests/test_data/atom_mapping_Data/generate_atom_mapping_test_data.py @@ -11,33 +11,47 @@ generate_INCA_mapping_input, check_symmetry, clean_output, - ) + ) original_wd = os.getcwd() current_dir = str(pathlib.Path(__file__).parent.absolute()) os.chdir(current_dir) # Load e_coli_core model -model, reaction_data, metabolite_data = INCA_input_parser.parse_cobra_model('e_coli_core.json', 'e_coli_core', '2021-07-15') +model, reaction_data, metabolite_data = INCA_input_parser.parse_cobra_model( + 'e_coli_core.json', 'e_coli_core', '2021-07-15') # Subset handpicked reactions model_reaction_df = pd.DataFrame() -model_reaction_df = model_reaction_df.append(reaction_data[reaction_data['rxn_id'] == 'PFK']) -model_reaction_df = model_reaction_df.append(reaction_data[reaction_data['rxn_id'] == 'BIOMASS_Ecoli_core_w_GAM']) -model_reaction_df = model_reaction_df.append(reaction_data[reaction_data['rxn_id'] == 'EX_pyr_e']) -model_reaction_df = model_reaction_df.append(reaction_data[reaction_data['rxn_id'] == 'ICL']) +model_reaction_df = model_reaction_df.append( + reaction_data[reaction_data['rxn_id'] == 'PFK']) +model_reaction_df = model_reaction_df.append( + reaction_data[reaction_data['rxn_id'] == 'BIOMASS_Ecoli_core_w_GAM']) +model_reaction_df = model_reaction_df.append( + reaction_data[reaction_data['rxn_id'] == 'EX_pyr_e']) +model_reaction_df = model_reaction_df.append( + reaction_data[reaction_data['rxn_id'] == 'ICL']) # And metabolites from these reactions model_metabolite_df = pd.DataFrame() -model_metabolite_df = model_metabolite_df.append(metabolite_data[metabolite_data['met_id'] == 'atp_c']) -model_metabolite_df = model_metabolite_df.append(metabolite_data[metabolite_data['met_id'] == 'f6p_c']) -model_metabolite_df = model_metabolite_df.append(metabolite_data[metabolite_data['met_id'] == 'adp_c']) -model_metabolite_df = model_metabolite_df.append(metabolite_data[metabolite_data['met_id'] == 'fdp_c']) -model_metabolite_df = model_metabolite_df.append(metabolite_data[metabolite_data['met_id'] == 'h_c']) -model_metabolite_df = model_metabolite_df.append(metabolite_data[metabolite_data['met_id'] == 'pyr_e']) -model_metabolite_df = model_metabolite_df.append(metabolite_data[metabolite_data['met_id'] == 'icit_c']) -model_metabolite_df = model_metabolite_df.append(metabolite_data[metabolite_data['met_id'] == 'succ_c']) -model_metabolite_df = model_metabolite_df.append(metabolite_data[metabolite_data['met_id'] == 'glx_c']) +model_metabolite_df = model_metabolite_df.append( + metabolite_data[metabolite_data['met_id'] == 'atp_c']) +model_metabolite_df = model_metabolite_df.append( + metabolite_data[metabolite_data['met_id'] == 'f6p_c']) +model_metabolite_df = model_metabolite_df.append( + metabolite_data[metabolite_data['met_id'] == 'adp_c']) +model_metabolite_df = model_metabolite_df.append( + metabolite_data[metabolite_data['met_id'] == 'fdp_c']) +model_metabolite_df = model_metabolite_df.append( + metabolite_data[metabolite_data['met_id'] == 'h_c']) +model_metabolite_df = model_metabolite_df.append( + metabolite_data[metabolite_data['met_id'] == 'pyr_e']) +model_metabolite_df = model_metabolite_df.append( + metabolite_data[metabolite_data['met_id'] == 'icit_c']) +model_metabolite_df = model_metabolite_df.append( + metabolite_data[metabolite_data['met_id'] == 'succ_c']) +model_metabolite_df = model_metabolite_df.append( + metabolite_data[metabolite_data['met_id'] == 'glx_c']) # Obtain all required files # Metabolite Molfiles @@ -51,7 +65,7 @@ obtain_atom_mappings() # Parsed dataframes of mappings -reaction_df = parse_reaction_mappings() +reaction_df = parse_reaction_mappings() metabolite_df = parse_metabolite_mappings() # CSV outputs of these dataframes @@ -65,12 +79,12 @@ for i, molfile in enumerate(metabolites): with open(f'metabolites/{molfile}', 'r') as f: metabolites[i] = f.readlines() - for j, met in enumerate(metabolites[i]): + for j, met in enumerate(metabolites[i]): metabolites[i][j] = metabolites[i][j].split() for k, val in enumerate(metabolites[i][j]): try: metabolites[i][j][k] = float(val) - except: + except BaseException: pass # Rxn files in a single list. @@ -79,14 +93,14 @@ for i, rxn_file in enumerate(unmapped_rxns): with open(f'unmappedRxns/{rxn_file}', 'r') as f: unmapped_rxns[i] = f.readlines() - for j, line in enumerate(unmapped_rxns[i]): + for j, line in enumerate(unmapped_rxns[i]): unmapped_rxns[i][j] = unmapped_rxns[i][j].split() for k, val in enumerate(unmapped_rxns[i][j]): try: unmapped_rxns[i][j][k] = float(val) - except: + except BaseException: pass - + # Mapped Rxn files in a single list mapped_rxns = os.listdir('mappedRxns/rxnFiles') for i, rxn_file in enumerate(mapped_rxns): @@ -94,11 +108,11 @@ lines = f.readlines() atom_rows = [] for j, line in enumerate(lines): - if len(line.split()) in (15,16): + if len(line.split()) in (15, 16): # Only append rows containing atom mappings atom_rows.append(line.split()) mapped_rxns[i] = atom_rows - + # CSV outputs of parsed mapping data reaction_df_csv = pd.read_csv('MappingReactions.csv') metabolite_df_csv = pd.read_csv('MappingMetabolites.csv') @@ -117,13 +131,12 @@ metabolite_df_csv, model_reaction_df, model_metabolite_df, - ], + ], filehandler - ) +) filehandler.close() clean_output() os.chdir(original_wd) - From 53bbcba544af0a9839d00f690e9bdbbd090a93c7 Mon Sep 17 00:00:00 2001 From: vaitkusd Date: Sun, 8 Aug 2021 19:58:16 +0200 Subject: [PATCH 10/12] fix: last reformat to please flake8 --- BFAIR/atom_mapping/atom_mapping.py | 17 ++++++++--------- tests/test_MFA_visualization.py | 2 +- tests/test_atom_mapping.py | 3 +-- .../generate_atom_mapping_test_data.py | 1 - 4 files changed, 10 insertions(+), 13 deletions(-) diff --git a/BFAIR/atom_mapping/atom_mapping.py b/BFAIR/atom_mapping/atom_mapping.py index f67f22ce..220c1095 100644 --- a/BFAIR/atom_mapping/atom_mapping.py +++ b/BFAIR/atom_mapping/atom_mapping.py @@ -61,7 +61,7 @@ def generate_molfile_database(self): os.mkdir('metabolites') # Disable RDKit warnings RDLogger.DisableLog('rdApp.*') - + for i, met in self.metabolite_data.iterrows(): self.filename = met.met_id + '.mol' @@ -74,19 +74,19 @@ def generate_molfile_database(self): # present. if 'inchi_key' in met.annotations: self.inchi_key = met.annotations['inchi_key'][0] - get_from_inchi_keyBool = True + get_from_inchi_keyBool = True # noqa: F841 if 'kegg.compound' in met.annotations: self.keggIDs = [ keggID for keggID in met.annotations['kegg.compound']] - get_from_keggBool = True + get_from_keggBool = True # noqa: F841 if 'hmdb' in met.annotations: self.hmdbIDnums = ['0' * (7 - len(hmdbID[4:])) + hmdbID[4:] for hmdbID in met.annotations['hmdb']] - get_from_hmdbBool = True + get_from_hmdbBool = True # noqa: F841 if 'chebi' in met.annotations: self.chebiIDs = [ chebiID for chebiID in met.annotations['chebi']] - get_from_chebiBool = True + get_from_chebiBool = True # noqa: F841 # Call helper functions according to order of preference # and available references (specified by previous bool values). @@ -363,8 +363,7 @@ def parse_reaction_mappings(): rxn_list = os.listdir('unmappedRxns') # Compile list of reactions that do not have any mapping - unmapped_list = list(set(os.listdir('unmappedRxns')) - - set(os.listdir('mappedRxns/rxnFiles'))) + unmapped_list = list(set(os.listdir('unmappedRxns')) - set(os.listdir('mappedRxns/rxnFiles'))) keys = ['Unnamed: 0', 'Unnamed: 0.1', @@ -625,11 +624,11 @@ def generate_INCA_mapping_input(reaction_df, metabolite_df): reaction_df.to_csv( path_or_buf=os.path.join( os.getcwd(), - f'MappingReactions.csv')) + 'MappingReactions.csv')) metabolite_df.to_csv( path_or_buf=os.path.join( os.getcwd(), - f'MappingMetabolites.csv')) + 'MappingMetabolites.csv')) def check_symmetry(met_filename): diff --git a/tests/test_MFA_visualization.py b/tests/test_MFA_visualization.py index c63b2fe5..c1a25b5f 100644 --- a/tests/test_MFA_visualization.py +++ b/tests/test_MFA_visualization.py @@ -47,7 +47,7 @@ def assertDeepAlmostEqual(self, expected, actual, *args, **kwargs): self.assertEqual(len(expected), len(actual)) for index in range(len(expected)): v1, v2 = expected[index], actual[index] - self.assertDeepAlmostEqual(v1, v2, + self.assertDeepAlmostEqual(v1, v2, __trace=repr(index), *args, **kwargs) elif isinstance(expected, dict): self.assertEqual(set(expected), set(actual)) diff --git a/tests/test_atom_mapping.py b/tests/test_atom_mapping.py index 448fcd84..d98eade5 100644 --- a/tests/test_atom_mapping.py +++ b/tests/test_atom_mapping.py @@ -71,8 +71,7 @@ def setUpClass(cls): # test data is contained properly. os.chdir(current_dir) file_obj = open( - current_dir + - '/test_data/atom_mapping_Data/test_data.obj', + current_dir + '/test_data/atom_mapping_Data/test_data.obj', 'rb') ( metabolites, diff --git a/tests/test_data/atom_mapping_Data/generate_atom_mapping_test_data.py b/tests/test_data/atom_mapping_Data/generate_atom_mapping_test_data.py index 96bbd886..59cbd30e 100644 --- a/tests/test_data/atom_mapping_Data/generate_atom_mapping_test_data.py +++ b/tests/test_data/atom_mapping_Data/generate_atom_mapping_test_data.py @@ -9,7 +9,6 @@ parse_reaction_mappings, parse_metabolite_mappings, generate_INCA_mapping_input, - check_symmetry, clean_output, ) From 9882150cc7d80f30496e2abe73d695155c852825 Mon Sep 17 00:00:00 2001 From: vaitkusd <70449549+vaitkusd@users.noreply.github.com> Date: Wed, 11 Aug 2021 10:52:46 +0200 Subject: [PATCH 11/12] fix: sorted file reading order --- BFAIR/atom_mapping/atom_mapping.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/BFAIR/atom_mapping/atom_mapping.py b/BFAIR/atom_mapping/atom_mapping.py index 220c1095..146155f3 100644 --- a/BFAIR/atom_mapping/atom_mapping.py +++ b/BFAIR/atom_mapping/atom_mapping.py @@ -361,7 +361,7 @@ def parse_reaction_mappings(): raise RuntimeError( "'mappedRxns' directory not present in current working directory") - rxn_list = os.listdir('unmappedRxns') + rxn_list = sorted(os.listdir('unmappedRxns')) # Compile list of reactions that do not have any mapping unmapped_list = list(set(os.listdir('unmappedRxns')) - set(os.listdir('mappedRxns/rxnFiles'))) @@ -550,7 +550,7 @@ def parse_metabolite_mappings(): metabolite_data : pandas.DataFrame Dataframe containing mapped metabolite data. """ - metabolite_list = os.listdir('metabolites') + metabolite_list = sorted(os.listdir('metabolites')) keys = ['mapping_id', 'met_id', 'met_elements', From 30b668b5ca566559e3f554ed01891f82c9e51fdc Mon Sep 17 00:00:00 2001 From: vaitkusd <70449549+vaitkusd@users.noreply.github.com> Date: Wed, 11 Aug 2021 10:55:21 +0200 Subject: [PATCH 12/12] build: updated config.yml to install Java --- .circleci/config.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.circleci/config.yml b/.circleci/config.yml index 76ae6899..b91c614b 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -2,6 +2,7 @@ version: 2.1 orbs: win: circleci/windows@2.2.0 + linux: cloudesire/openjdk-install@1.2.3 commands: install-conda: