diff --git a/HiPRGen/lmdb_dataset.py b/HiPRGen/lmdb_dataset.py new file mode 100644 index 0000000..f56192e --- /dev/null +++ b/HiPRGen/lmdb_dataset.py @@ -0,0 +1,329 @@ +#give dgl graphs, reaction features, meta. write them into lmdb file. +#1. check expend lmdb reasonably + +#give dgl graphs, reaction features, meta. write them into lmdb file. +#1. check expend lmdb reasonably + +from torch.utils.data import Dataset +from pathlib import Path +import numpy as np +import pickle +import lmdb +from torch.utils.data import random_split +import multiprocessing as mp +import os +import pickle +from tqdm import tqdm +import glob + + +class LmdbDataset(Dataset): + """ + Dataset class to + 1. write Reaction networks objecs to lmdb + 2. load lmdb files + """ + def __init__(self, config, transform=None): + super(LmdbDataset, self).__init__() + + self.config = config + self.path = Path(self.config["src"]) + + #Get metadata in case + #self.metadata_path = self.path.parent / "metadata.npz" + self.env = self.connect_db(self.path) + + # If "length" encoded as ascii is present, use that + # If there are additional properties, there must be length. + length_entry = self.env.begin().get("length".encode("ascii")) + if length_entry is not None: + num_entries = pickle.loads(length_entry) + else: + # Get the number of stores data from the number of entries + # in the LMDB + num_entries = self.env.stat()["entries"] + + self._keys = list(range(num_entries)) + self.num_samples = num_entries + + #Get portion of total dataset + self.sharded = False + if "shard" in self.config and "total_shards" in self.config: + self.sharded = True + self.indices = range(self.num_samples) + # split all available indices into 'total_shards' bins + self.shards = np.array_split( + self.indices, self.config.get("total_shards", 1) + ) + # limit each process to see a subset of data based off defined shard + self.available_indices = self.shards[self.config.get("shard", 0)] + self.num_samples = len(self.available_indices) + + #TODO + self.transform = transform + + def __len__(self): + return self.num_samples + + def __getitem__(self, idx): + # if sharding, remap idx to appropriate idx of the sharded set + if self.sharded: + idx = self.available_indices[idx] + + #!CHECK, _keys should be less then total numbers of keys as there are more properties. + datapoint_pickled = self.env.begin().get( + f"{self._keys[idx]}".encode("ascii") + ) + + data_object = pickle.loads(datapoint_pickled) + + #TODO + if self.transform is not None: + data_object = self.transform(data_object) + + return data_object + + def connect_db(self, lmdb_path=None): + env = lmdb.open( + str(lmdb_path), + subdir=False, + readonly=True, + lock=False, + readahead=True, + meminit=False, + max_readers=1, + ) + return env + + def close_db(self): + if not self.path.is_file(): + for env in self.envs: + env.close() + else: + self.env.close() + + def get_metadata(self, num_samples=100): + pass + + @property + def dtype(self): + dtype = self.env.begin().get("dtype".encode("ascii")) + return pickle.loads(dtype) + + @property + def feature_size(self): + feature_size = self.env.begin().get("feature_size".encode("ascii")) + return pickle.loads(feature_size) + + @property + def feature_name(self): + feature_name = self.env.begin().get("feature_name".encode("ascii")) + return pickle.loads(feature_name) + + @property + def mean(self): + mean = self.env.begin().get("mean".encode("ascii")) + return pickle.loads(mean) + + @property + def std(self): + std = self.env.begin().get("std".encode("ascii")) + return pickle.loads(std) + + +def divide_to_list(a, b): + quotient = a // b + remainder = a % b + + result = [] + for i in range(b): + increment = 1 if i < remainder else 0 + result.append(quotient + increment) + + return result + +def cleanup_lmdb_files(directory, pattern): + """ + Cleans up files matching the given pattern in the specified directory. + """ + file_list = glob.glob(os.path.join(directory, pattern)) + + for file_path in file_list: + try: + os.remove(file_path) + print(f"Deleted file: {file_path}") + except OSError as e: + print(f"Error deleting file: {file_path}. {str(e)}") + +def CRNs2lmdb( dtype, + feature_size, + feature_name, + mean, + std, + lmdb_dir, + lmdb_name + ): + + #os.makedirs(os.path.join(lmdb_dir, exist_ok=True)) + os.makedirs(lmdb_dir, exist_ok=True) + + db_paths = os.path.join(lmdb_dir, "_tmp_data.%04d.lmdb") + + meta_keys = { + "dtype" : dtype, + "feature_size": feature_size, + "feature_name": feature_name, + "mean" : mean, + "std" : std + } + + # Merge LMDB files + merge_lmdbs(db_paths, lmdb_dir, lmdb_name) + cleanup_lmdb_files(lmdb_dir, "_tmp_data*") + + + +def write_crns_to_lmdb(mp_args): + #pid is idx of workers. + db_path, samples, pid, meta_keys = mp_args + + db = lmdb.open( + db_path, + map_size=1099511627776 * 2, + subdir=False, + meminit=False, + map_async=True, + ) + + pbar = tqdm( + total=len(samples), + position=pid, + desc=f"Worker {pid}: Writing CRNs Objects into LMDBs", + ) + + #write indexed samples + idx = 0 + for sample in samples: + txn=db.begin(write=True) + txn.put( + f"{idx}".encode("ascii"), + pickle.dumps(sample, protocol=-1), + ) + idx += 1 + pbar.update(1) + txn.commit() + + #write properties + txn=db.begin(write=True) + txn.put("length".encode("ascii"), pickle.dumps(len(samples), protocol=-1)) + txn.commit() + + for key, value in meta_keys.items(): + txn=db.begin(write=True) + txn.put(key.encode("ascii"), pickle.dumps(value, protocol=-1)) + txn.commit() + + db.sync() + db.close() + + +def merge_lmdbs(db_paths, out_path, output_file): + """ + merge lmdb files and reordering indexes. + """ + env_out = lmdb.open( + os.path.join(out_path, output_file), + map_size=1099511627776 * 2, + subdir=False, + meminit=False, + map_async=True, + ) + + + idx = 0 + for db_path in db_paths: + env_in = lmdb.open( + str(db_path), + subdir=False, + readonly=True, + lock=False, + readahead=True, + meminit=False, + ) + + #should set indexes so that properties do not writtent down as well. + with env_out.begin(write=True) as txn_out, env_in.begin(write=False) as txn_in: + cursor = txn_in.cursor() + for key, value in cursor: + #write indexed samples + try: + int(key.decode("ascii")) + txn_out.put( + f"{idx}".encode("ascii"), + value, + ) + idx+=1 + #print(idx) + #write properties + except ValueError: + txn_out.put( + key, + value + ) + env_in.close() + + #update length + txn_out=env_out.begin(write=True) + txn_out.put("length".encode("ascii"), pickle.dumps(idx, protocol=-1)) + txn_out.commit() + + env_out.sync() + env_out.close() + + + +def write_to_lmdb(new_samples, current_length, lmdb_update, db_path): + + # #pid is idx of workers. + # db_path, samples, pid, meta_keys = mp_args + db = lmdb.open( + db_path, + map_size=1099511627776 * 2, + subdir=False, + meminit=False, + map_async=True, + ) + + pbar = tqdm( + total=len(new_samples), + desc=f"Adding new samples into LMDBs", + ) + + #write indexed samples + idx = current_length + for sample in new_samples: + txn=db.begin(write=True) + txn.put( + f"{idx}".encode("ascii"), + pickle.dumps(sample, protocol=-1), + ) + idx += 1 + pbar.update(1) + txn.commit() + + #write properties + + total_length = current_length + len(new_samples) + + txn=db.begin(write=True) + txn.put("length".encode("ascii"), pickle.dumps(total_length, protocol=-1)) + txn.commit() + + #write mean, std, feature_size, feature_name. dtype etc. + for key, value in lmdb_update.items(): + txn=db.begin(write=True) + txn.put(key.encode("ascii"), pickle.dumps(value, protocol=-1)) + txn.commit() + + db.sync() + db.close() \ No newline at end of file diff --git a/HiPRGen/mol_entry.py b/HiPRGen/mol_entry.py index 0fdc045..6bb92bd 100644 --- a/HiPRGen/mol_entry.py +++ b/HiPRGen/mol_entry.py @@ -7,19 +7,172 @@ from pymatgen.analysis.local_env import OpenBabelNN, metal_edge_extender#, oxygen_edge_extender from pymatgen.core.structure import Molecule from networkx.algorithms.graph_hashing import weisfeiler_lehman_graph_hash +import networkx.algorithms.isomorphism as iso from HiPRGen.constants import ROOM_TEMP, metals from itertools import permutations, product +class FragmentObject: + def __init__( + self, + fragment_hash, + atom_ids, + neighborhood_hashes, + graph, + hot_atoms, + compressed_graph + ): + self.fragment_hash = fragment_hash + self.atom_ids = atom_ids + self.neighborhood_hashes = neighborhood_hashes + self.graph = graph + self.hot_atoms = hot_atoms + self.compressed_graph = compressed_graph + +def sym_iterator(n): + return permutations(range(n), r=n) + + +def find_fragment_atom_mappings(fragment_1, fragment_2, return_one=True): + groups_by_hash = {} + + hot_f1_neighborhood_hashes = list(fragment_1.hot_atoms.keys()) + hot_f1_indices = [] + match_hot_atoms = [] + hot_f2_indices = [] + for hot_nbh_hash in hot_f1_neighborhood_hashes: + if hot_nbh_hash in fragment_2.hot_atoms: + match_hot_atoms.append(hot_nbh_hash) + hot_f2_indices.append(fragment_2.hot_atoms[hot_nbh_hash]) + hot_f1_indices.append(fragment_1.hot_atoms[hot_nbh_hash]) + + + for left_index in fragment_1.compressed_graph.nodes(): + + neighborhood_hash = fragment_1.neighborhood_hashes[left_index] + if neighborhood_hash in match_hot_atoms and left_index in hot_f1_indices: + neighborhood_hash = neighborhood_hash + "hot" + if neighborhood_hash not in groups_by_hash: + groups_by_hash[neighborhood_hash] = ([],[]) + + groups_by_hash[neighborhood_hash][0].append(left_index) + + + for right_index in fragment_2.compressed_graph.nodes(): + + neighborhood_hash = fragment_2.neighborhood_hashes[right_index] + if neighborhood_hash in match_hot_atoms and right_index in hot_f2_indices: + neighborhood_hash = neighborhood_hash + "hot" + if neighborhood_hash not in groups_by_hash: + groups_by_hash[neighborhood_hash] = ([],[]) + + groups_by_hash[neighborhood_hash][1].append(right_index) + + groups = list(groups_by_hash.values()) + + product_sym_iterator = product(*[sym_iterator(len(p[0])) for p in groups]) + + mappings = [] + + for product_perm in product_sym_iterator: + mapping = {} + for perm, vals in zip(product_perm, groups): + for i, j in enumerate(perm): + mapping[vals[0][i]] = vals[1][j] + + isomorphism = True + for edge in fragment_1.compressed_graph.edges: + u = mapping[edge[0]] + v = mapping[edge[1]] + if not fragment_2.compressed_graph.has_edge(u,v): + isomorphism = False + break + + + if isomorphism: + + for hot_nbh_hash in hot_f1_neighborhood_hashes: + if hot_nbh_hash not in match_hot_atoms: + hot_f1_index = fragment_1.hot_atoms[hot_nbh_hash] + new_hot_f2_index = mapping[hot_f1_index] + assert fragment_2.neighborhood_hashes[new_hot_f2_index] == hot_nbh_hash + fragment_2.hot_atoms[hot_nbh_hash] = new_hot_f2_index + + uncompressed_mapping = copy.deepcopy(mapping) + for frag1_idx in mapping: + frag2_idx=mapping[frag1_idx] + for element in fragment_1.compressed_graph.nodes()[frag1_idx]["compressed"]: + for ii, idx in enumerate(fragment_1.compressed_graph.nodes()[frag1_idx]["compressed"][element]): + uncompressed_mapping[idx] = fragment_2.compressed_graph.nodes()[frag2_idx]["compressed"][element][ii] + + for edge in fragment_1.graph.edges: + u = uncompressed_mapping[edge[0]] + v = uncompressed_mapping[edge[1]] + assert fragment_2.graph.has_edge(u,v) + + mappings.append(uncompressed_mapping) + + if return_one: + return mappings + + if len(mappings) == 0: + raise RuntimeError("No isomorphic mapping found?!?! Exiting...") + return mappings + + +def build_compressed_graph(graph, to_compress=["Br", "Cl", "F", "H"]): + comp_graph = nx.Graph(copy.deepcopy(graph)) + indices_to_save = [] + for idx in comp_graph.nodes(): + if comp_graph.nodes()[idx]["specie"] not in to_compress: + indices_to_save.append(idx) + if "compressed" not in comp_graph.nodes()[idx]: + comp_graph.nodes()[idx]["compressed"] = {} + for idx in indices_to_save: + indices_to_remove = [] + for n_idx in comp_graph.neighbors(idx): + if comp_graph.nodes()[n_idx]["specie"] in to_compress: + if comp_graph.nodes()[n_idx]["specie"] not in comp_graph.nodes()[idx]["compressed"]: + comp_graph.nodes()[idx]["compressed"][comp_graph.nodes()[n_idx]["specie"]] = [n_idx] + else: + comp_graph.nodes()[idx]["compressed"][comp_graph.nodes()[n_idx]["specie"]].append(n_idx) + indices_to_remove.append(n_idx) + comp_graph.remove_nodes_from(indices_to_remove) + to_append = "" + for element in to_compress: + if element in comp_graph.nodes()[idx]["compressed"]: + to_append += element + str(len(comp_graph.nodes()[idx]["compressed"][element])) + comp_graph.nodes()[idx]["specie"] = comp_graph.nodes()[idx]["specie"] + to_append + return comp_graph + + +def extend_mapping(full_mapping, reactant_index, reactant_fragment_mapping, product_index, product_fragment_mapping): + inverted_product_fragment_mapping = {} + for product_atom_ind in product_fragment_mapping: + master_atom_ind = product_fragment_mapping[product_atom_ind] + inverted_product_fragment_mapping[master_atom_ind] = product_atom_ind + + for reactant_atom_ind in reactant_fragment_mapping: + master_atom_ind = reactant_fragment_mapping[reactant_atom_ind] + product_atom_ind = inverted_product_fragment_mapping[master_atom_ind] + full_mapping[(reactant_index, reactant_atom_ind)] = (product_index, product_atom_ind) + + return full_mapping + + + + class FragmentComplex: def __init__( - self, number_of_fragments, number_of_bonds_broken, bonds_broken, fragment_hashes + self, number_of_fragments, number_of_bonds_broken, bonds_broken, fragment_hashes, fragment_objects=None, ): self.number_of_fragments = number_of_fragments self.number_of_bonds_broken = number_of_bonds_broken self.bonds_broken = bonds_broken self.fragment_hashes = fragment_hashes + self.fragment_objects = fragment_objects + self.fragment_mappings = [] class MoleculeEntry: @@ -78,6 +231,7 @@ def __init__( self.molecule = self.mol_graph.molecule self.graph = self.mol_graph.graph.to_undirected() + self.species = [str(s) for s in self.molecule.species] self.m_inds = [i for i, x in enumerate(self.species) if x in metals] @@ -90,6 +244,10 @@ def __init__( self.covalent_graph = copy.deepcopy(self.graph) self.covalent_graph.remove_nodes_from(self.m_inds) + # self.to_compress = ["Br", "Cl", "F", "H"] + + self.compressed_graph = build_compressed_graph(self.covalent_graph)#, self.to_compress) + self.formula = self.molecule.composition.alphabetical_formula self.charge = self.molecule.charge self.num_atoms = len(self.molecule) @@ -102,6 +260,26 @@ def __init__( i for i in range(self.num_atoms) if self.species[i] not in metals ] + self.uncompressed_atoms = list(self.compressed_graph.nodes()) + + # if self.entry_id == "libe-120767": + + # for idx in self.covalent_graph.nodes(): + # print(idx, self.covalent_graph.nodes()[idx]) + # print() + + # print(self.covalent_graph.edges()) + + # for idx in self.compressed_graph.nodes(): + # print(idx, self.compressed_graph.nodes()[idx]) + # print() + + # new_cg = build_compressed_graph(self.covalent_graph) + # for idx in new_cg.nodes(): + # print(idx, new_cg.nodes()[idx]) + # print() + + @classmethod def from_dataset_entry( cls, diff --git a/HiPRGen/reaction_filter.py b/HiPRGen/reaction_filter.py index 8dae7ed..5e672f1 100644 --- a/HiPRGen/reaction_filter.py +++ b/HiPRGen/reaction_filter.py @@ -1,4 +1,5 @@ from mpi4py import MPI +from HiPRGen.rxn_networks_graph import rxn_networks_graph from itertools import permutations, product from HiPRGen.report_generator import ReportGenerator import sqlite3 @@ -14,6 +15,7 @@ run_decision_tree ) + """ Phases 3 & 4 run in parallel using MPI @@ -104,6 +106,8 @@ def log_message(*args, **kwargs): def dispatcher( mol_entries, + dgl_molecules_dict, + grapher_features, dispatcher_payload ): @@ -131,6 +135,17 @@ def dispatcher( rn_cur.execute(create_reactions_table) rn_con.commit() + #### HY + ## initialize preprocess data + + rxn_networks_g = rxn_networks_graph( + mol_entries, + dgl_molecules_dict, + grapher_features, + dispatcher_payload.bondnet_test + ) + #### + log_message("initializing report generator") # since MPI processes spin lock, we don't want to have the dispathcer @@ -225,6 +240,7 @@ def dispatcher( reaction['is_redox'] )) + rxn_networks_g.create_rxn_networks_graph(reaction, reaction_index) reaction_index += 1 if reaction_index % dispatcher_payload.commit_frequency == 0: rn_con.commit() @@ -249,7 +265,7 @@ def dispatcher( reaction_index) ) - + report_generator.finished() rn_con.commit() bucket_con.close() @@ -333,6 +349,13 @@ def worker( reaction, dest=DISPATCHER_RANK, tag=NEW_REACTION_DB) + + # rxn_networks_g.create_rxn_networks_graph(reaction, reaction_index) + # reaction_index += 1 + # if reaction_index % dispatcher_payload.commit_frequency == 0: + # rn_con.commit() + + if run_decision_tree(reaction, diff --git a/HiPRGen/reaction_filter_payloads.py b/HiPRGen/reaction_filter_payloads.py index 516565e..f316314 100644 --- a/HiPRGen/reaction_filter_payloads.py +++ b/HiPRGen/reaction_filter_payloads.py @@ -13,6 +13,7 @@ def __init__( bucket_db_file, reaction_network_db_file, report_file, + bondnet_test, commit_frequency = 1000, checkpoint_interval = 10): @@ -21,6 +22,7 @@ def __init__( self.report_file = report_file self.commit_frequency = commit_frequency self.checkpoint_interval = checkpoint_interval + self.bondnet_test = bondnet_test class WorkerPayload(MSONable): diff --git a/HiPRGen/reaction_questions.py b/HiPRGen/reaction_questions.py index ca12510..698b50d 100644 --- a/HiPRGen/reaction_questions.py +++ b/HiPRGen/reaction_questions.py @@ -1,5 +1,5 @@ import math -from HiPRGen.mol_entry import MoleculeEntry +from HiPRGen.mol_entry import MoleculeEntry, find_fragment_atom_mappings, sym_iterator, extend_mapping from functools import partial import itertools import copy @@ -7,6 +7,10 @@ from networkx.algorithms.graph_hashing import weisfeiler_lehman_graph_hash from HiPRGen.constants import Terminal, ROOM_TEMP, KB, PLANCK, m_formulas from monty.json import MSONable +from pymatgen.analysis.local_env import OpenBabelNN +from pymatgen.core.structure import Molecule +from pymatgen.analysis.graphs import MoleculeGraph +import os """ The reaction decision tree: @@ -66,6 +70,11 @@ class Terminal(Enum): KEEP = 1 DISCARD = -1 carbon_graph.add_node(0, specie="C") carbon_hash = weisfeiler_lehman_graph_hash(carbon_graph, node_attr="specie") +co3_mol = Molecule.from_file("xyz_files/co3.xyz") +co3_mg = MoleculeGraph.with_local_env_strategy(co3_mol, OpenBabelNN()) +co3_g = co3_mg.graph.to_undirected() +co3_hash = weisfeiler_lehman_graph_hash(co3_g, node_attr="specie") + def run_decision_tree( reaction, mol_entries, params, decision_tree, decision_pathway=None @@ -508,6 +517,33 @@ def __call__(self, reaction, mol_entries, params): return False +class map_redox_reaction(MSONable): + def __init__(self): + pass + + def __str__(self): + return "map redox reaction" + + def __call__(self, reaction, mol_entries, params): + assert reaction["number_of_reactants"] == 1 and reaction["number_of_products"] == 1 + full_mapping = {} + reactant_fragment_complex = mol_entries[reaction["reactants"][0]].fragment_data[0] + product_fragment_complex = mol_entries[reaction["products"][0]].fragment_data[0] + assert len(reactant_fragment_complex.fragment_mappings) == 1 + assert len(reactant_fragment_complex.fragment_mappings[0]) == 1 + assert len(product_fragment_complex.fragment_mappings) == 1 + assert len(product_fragment_complex.fragment_mappings[0]) == 1 + full_mappping = extend_mapping( + full_mapping, + 0, + reactant_fragment_complex.fragment_mappings[0][0], + 0, + product_fragment_complex.fragment_mappings[0][0] + ) + reaction["atom_map"] = full_mapping + return False + + class reaction_is_charge_transfer(MSONable): def __init__(self): pass @@ -535,12 +571,58 @@ def __call__(self, reaction, mol_entries, params): product_hash_list.append(product.covalent_hash) if len(reactant_total_hashes.intersection(product_total_hashes)) == 2: + full_mapping = {} + for reactant_index in range(reaction["number_of_reactants"]): + reactant = mol_entries[reaction["reactants"][reactant_index]] + for product_index in range(reaction["number_of_products"]): + product = mol_entries[reaction["products"][product_index]] + if reactant.covalent_hash == product.covalent_hash: + reactant_fragment_complex = reactant.fragment_data[0] + product_fragment_complex = product.fragment_data[0] + if reactant_fragment_complex.fragment_mappings != []: + assert len(reactant_fragment_complex.fragment_mappings) == 1 + assert len(reactant_fragment_complex.fragment_mappings[0]) == 1 + assert len(product_fragment_complex.fragment_mappings) == 1 + assert len(product_fragment_complex.fragment_mappings[0]) == 1 + full_mappping = extend_mapping( + full_mapping, + reactant_index, + reactant_fragment_complex.fragment_mappings[0][0], + product_index, + product_fragment_complex.fragment_mappings[0][0] + ) + if full_mapping != {}: + reaction["atom_map"] = full_mapping + reaction["is_redox"] = True return True elif len(reactant_total_hashes.intersection(product_total_hashes)) == 1 and reactant_hash_list[0] == reactant_hash_list[1] and product_hash_list[0] == product_hash_list[1]: + full_mapping = {} + for rctpro_index in range(reaction["number_of_reactants"]): + reactant = mol_entries[reaction["reactants"][rctpro_index]] + product = mol_entries[reaction["products"][rctpro_index]] + reactant_fragment_complex = reactant.fragment_data[0] + product_fragment_complex = product.fragment_data[0] + if reactant_fragment_complex.fragment_mappings != []: + assert len(reactant_fragment_complex.fragment_mappings) == 1 + assert len(reactant_fragment_complex.fragment_mappings[0]) == 1 + assert len(product_fragment_complex.fragment_mappings) == 1 + assert len(product_fragment_complex.fragment_mappings[0]) == 1 + full_mappping = extend_mapping( + full_mapping, + rctpro_index, + reactant_fragment_complex.fragment_mappings[0][0], + rctpro_index, + product_fragment_complex.fragment_mappings[0][0] + ) + if full_mapping != {}: + reaction["atom_map"] = full_mapping + reaction["is_redox"] = True return True else: + assert reaction["is_redox"] == False return False + assert reaction["is_redox"] == False return False @@ -857,7 +939,11 @@ def __call__(self, reaction, mol_entries, params): product_bonds_broken = [] reactant_hashes = dict() - for reactant_index, frag_complex_index in enumerate(reactant_fragment_indices): + reactant_fragment_objects = dict() + reactant_fragment_mappings = dict() + for reactant_index, frag_complex_index in enumerate( + reactant_fragment_indices + ): fragment_complex = mol_entries[ #pulls out a fragment_complex whose index matches the above reaction["reactants"][reactant_index] ].fragment_data[frag_complex_index] @@ -875,7 +961,18 @@ def __call__(self, reaction, mol_entries, params): else: reactant_hashes[tag] = 1 + reactant_fragment_objects[reactant_index] = fragment_complex.fragment_objects + reactant_fragment_mappings[reactant_index] = {} + for ii, mappings in enumerate(fragment_complex.fragment_mappings): + if fragment_complex.fragment_hashes[ii] in reactant_fragment_mappings[reactant_index]: + assert len(mappings) == 1 + reactant_fragment_mappings[reactant_index][fragment_complex.fragment_hashes[ii]].append(mappings[0]) + else: + reactant_fragment_mappings[reactant_index][fragment_complex.fragment_hashes[ii]] = copy.deepcopy(mappings) + product_hashes = dict() + product_fragment_objects = dict() + product_fragment_mappings = dict() for product_index, frag_complex_index in enumerate( product_fragment_indices ): @@ -885,7 +982,9 @@ def __call__(self, reaction, mol_entries, params): ].fragment_data[frag_complex_index] for bond in fragment_complex.bonds_broken: - product_bonds_broken.append([(product_index, x) for x in bond]) + product_bonds_broken.append( + [(product_index, x) for x in bond] + ) for i in range(fragment_complex.number_of_fragments): product_fragment_count += 1 @@ -895,6 +994,15 @@ def __call__(self, reaction, mol_entries, params): else: product_hashes[tag] = 1 + product_fragment_objects[product_index] = fragment_complex.fragment_objects + product_fragment_mappings[product_index] = {} + for ii, mappings in enumerate(fragment_complex.fragment_mappings): + if fragment_complex.fragment_hashes[ii] in product_fragment_mappings[product_index]: + assert len(mappings) == 1 + product_fragment_mappings[product_index][fragment_complex.fragment_hashes[ii]].append(mappings[0]) + else: + product_fragment_mappings[product_index][fragment_complex.fragment_hashes[ii]] = copy.deepcopy(mappings) + # don't consider fragmentations with both a ring opening and closing if ( reaction["number_of_reactants"] == 2 @@ -904,6 +1012,34 @@ def __call__(self, reaction, mol_entries, params): ): continue + elif ( + reaction["number_of_reactants"] == 2 + and reaction["number_of_products"] == 2 + and reactant_fragment_count == 3 + and product_fragment_count == 3 + and len(list(reactant_hashes.keys())) == 3 + ): + # if each reactant is isomorphic to one of the products, then the reaction must + # be a fragment transfer of the form AX + A -> A + AX, where charges must differ + # on the left and right species. In some contexts, we will assume this is an + # electron transfer, but in others we will assume its a fragment transfer. In the + # case of a fragment transfer, the three fragments getting matched must include + # two of the same fragment, e.g. A, A, and X. Otherwise, we can have a situation + # where the same bond is broken on e.g. A on both sides of the reaction which is + # unphysical and incorrect. + if set([reactant_0.covalent_hash, reactant_1.covalent_hash]) == set([product_0.covalent_hash, product_1.covalent_hash]): + continue + else: + # If one reactant is isomorphic to one product, then the reaction is of the form + # A + B -> A + C, where charges must differ on the left and right species else + # the reaction will have been filtered out as covalently decomposable. If the + # reaction truly is not covalently decomposable, then A must be the species to + # include the bond breaking on both sides of the reaction. In other words, A must + # not also be one of the fragments. + overlap = list(set([reactant_0.covalent_hash, reactant_1.covalent_hash]).intersection(set([product_0.covalent_hash, product_1.covalent_hash]))) + if len(overlap) == 1 and overlap[0] in reactant_hashes: + continue + if reactant_hashes == product_hashes: if hydrogen_hash in reactant_hashes: reaction["reactant_bonds_broken"] = reactant_bonds_broken @@ -911,6 +1047,10 @@ def __call__(self, reaction, mol_entries, params): reaction["hashes"] = reactant_hashes reaction["reactant_fragment_count"] = reactant_fragment_count reaction["product_fragment_count"] = product_fragment_count + reaction["reactant_fragment_objects"] = reactant_fragment_objects + reaction["reactant_fragment_mappings"] = reactant_fragment_mappings + reaction["product_fragment_objects"] = product_fragment_objects + reaction["product_fragment_mappings"] = product_fragment_mappings return True else: tmp = {} @@ -919,6 +1059,10 @@ def __call__(self, reaction, mol_entries, params): tmp["hashes"] = reactant_hashes tmp["reactant_fragment_count"] = reactant_fragment_count tmp["product_fragment_count"] = product_fragment_count + tmp["reactant_fragment_objects"] = reactant_fragment_objects + tmp["reactant_fragment_mappings"] = reactant_fragment_mappings + tmp["product_fragment_objects"] = product_fragment_objects + tmp["product_fragment_mappings"] = product_fragment_mappings viable_fragment_matches.append(tmp) if len(viable_fragment_matches) > 0: @@ -942,9 +1086,6 @@ def __call__(self, reaction, mol_entries, params): connected_components = nx.algorithms.components.connected_components(hot_reactant_graph) for c in connected_components: subgraph = hot_reactant_graph.subgraph(c) - # if subgraph.number_of_nodes() == 1: - # for node in subgraph: - # print(nx.get_node_attributes(hot_reactant_graph, "specie")[node]) if subgraph.number_of_nodes() < min_frag_size: min_frag_size = subgraph.number_of_nodes() best_matching = copy.deepcopy(viable_match) @@ -953,11 +1094,301 @@ def __call__(self, reaction, mol_entries, params): reaction["hashes"] = best_matching["hashes"] reaction["reactant_fragment_count"] = best_matching["reactant_fragment_count"] reaction["product_fragment_count"] = best_matching["product_fragment_count"] + reaction["reactant_fragment_objects"] = best_matching["reactant_fragment_objects"] + reaction["reactant_fragment_mappings"] = best_matching["reactant_fragment_mappings"] + reaction["product_fragment_objects"] = best_matching["product_fragment_objects"] + reaction["product_fragment_mappings"] = best_matching["product_fragment_mappings"] return True return False +class mapping_with_reaction_center_not_found(MSONable): + def __init__(self): + pass + + def __str__(self): + return "determine atom mapping" + + def __call__(self, reaction, mol_entries, params): + + full_mapping = {} + + if len(reaction["hashes"].keys()) == 3: + # This must be A+B -> C+D where all three fragments are different + assert reaction["reactant_fragment_count"] == 3 + + for reactant_index in range(reaction["number_of_reactants"]): + reactant_frag_hash_set = set(reaction["reactant_fragment_mappings"][reactant_index].keys()) + for product_index in range(reaction["number_of_products"]): + product_frag_hash_set = set(reaction["product_fragment_mappings"][product_index].keys()) + overlapping_hash_list = list(reactant_frag_hash_set.intersection(product_frag_hash_set)) + assert len(overlapping_hash_list) == 0 or len(overlapping_hash_list) == 1 + for overlapping_hash in overlapping_hash_list: + assert len(reaction["reactant_fragment_mappings"][reactant_index][overlapping_hash]) == 1 + assert len(reaction["product_fragment_mappings"][product_index][overlapping_hash]) == 1 + full_mappping = extend_mapping( + full_mapping, + reactant_index, + reaction["reactant_fragment_mappings"][reactant_index][overlapping_hash][0], + product_index, + reaction["product_fragment_mappings"][product_index][overlapping_hash][0] + ) + + elif len(reaction["hashes"].keys()) == 2: + if reaction["number_of_reactants"] == 1 and reaction["number_of_products"] == 1: + # A -> B e.g. intramolecular H-transfer + assert reaction["reactant_fragment_count"] == 2 + + for reactant_index in range(reaction["number_of_reactants"]): + reactant_frag_hash_set = set(reaction["reactant_fragment_mappings"][reactant_index].keys()) + for product_index in range(reaction["number_of_products"]): + product_frag_hash_set = set(reaction["product_fragment_mappings"][product_index].keys()) + overlapping_hash_list = list(reactant_frag_hash_set.intersection(product_frag_hash_set)) + assert len(overlapping_hash_list) == 2 + for overlapping_hash in overlapping_hash_list: + assert len(reaction["reactant_fragment_mappings"][reactant_index][overlapping_hash]) == 1 + assert len(reaction["product_fragment_mappings"][product_index][overlapping_hash]) == 1 + full_mappping = extend_mapping( + full_mapping, + reactant_index, + reaction["reactant_fragment_mappings"][reactant_index][overlapping_hash][0], + product_index, + reaction["product_fragment_mappings"][product_index][overlapping_hash][0] + ) + + elif reaction["number_of_reactants"] + reaction["number_of_products"] == 3: + # A -> B+C aka break 1 or A+B -> C aka form 1 + assert reaction["reactant_fragment_count"] == 2 + + for reactant_index in range(reaction["number_of_reactants"]): + reactant_frag_hash_set = set(reaction["reactant_fragment_mappings"][reactant_index].keys()) + for product_index in range(reaction["number_of_products"]): + product_frag_hash_set = set(reaction["product_fragment_mappings"][product_index].keys()) + overlapping_hash_list = list(reactant_frag_hash_set.intersection(product_frag_hash_set)) + assert len(overlapping_hash_list) == 1 + for overlapping_hash in overlapping_hash_list: + assert len(reaction["reactant_fragment_mappings"][reactant_index][overlapping_hash]) == 1 + assert len(reaction["product_fragment_mappings"][product_index][overlapping_hash]) == 1 + full_mappping = extend_mapping( + full_mapping, + reactant_index, + reaction["reactant_fragment_mappings"][reactant_index][overlapping_hash][0], + product_index, + reaction["product_fragment_mappings"][product_index][overlapping_hash][0] + ) + + elif reaction["number_of_reactants"] == 2 and reaction["number_of_products"] == 2: + # A+B -> C+D where fragments are of the form 2X, Y + # more specifically, two cases: + # AA + B -> A + AB + # AB + A -> A + BA (where AB != BA) - this looks like it should be covalently decomposable, but + # once you account for charges, it can be not, e.g. AH- + A -> A- + HA + assert reaction["reactant_fragment_count"] == 3 + + for frag_hash in reaction["hashes"]: + if reaction["hashes"][frag_hash] == 2: + double_hash = frag_hash + elif reaction["hashes"][frag_hash] == 1: + single_hash = frag_hash + + double_hash_on_same_specie = False + for ind in range(reaction["number_of_reactants"]): + if double_hash in reaction["reactant_fragment_mappings"][ind]: + if len(reaction["reactant_fragment_mappings"][ind][double_hash]) == 2: + assert double_hash_on_same_specie == False + double_hash_on_same_specie = True + if double_hash in reaction["product_fragment_mappings"][ind]: + if len(reaction["product_fragment_mappings"][ind][double_hash]) == 2: + assert double_hash_on_same_specie == False + double_hash_on_same_specie = True + + if not double_hash_on_same_specie: + for reactant_index in range(reaction["number_of_reactants"]): + reactant_frag_hash_set = set(reaction["reactant_fragment_mappings"][reactant_index].keys()) + for product_index in range(reaction["number_of_products"]): + product_frag_hash_set = set(reaction["product_fragment_mappings"][product_index].keys()) + if len(reactant_frag_hash_set) == 2 and len(product_frag_hash_set) == 2: + full_mappping = extend_mapping( + full_mapping, + reactant_index, + reaction["reactant_fragment_mappings"][reactant_index][single_hash][0], + product_index, + reaction["product_fragment_mappings"][product_index][single_hash][0] + ) + elif len(reactant_frag_hash_set) + len(product_frag_hash_set) == 3: + full_mappping = extend_mapping( + full_mapping, + reactant_index, + reaction["reactant_fragment_mappings"][reactant_index][double_hash][0], + product_index, + reaction["product_fragment_mappings"][product_index][double_hash][0] + ) + else: + double_ind = 0 + for reactant_index in range(reaction["number_of_reactants"]): + reactant_frag_hash_set = set(reaction["reactant_fragment_mappings"][reactant_index].keys()) + for product_index in range(reaction["number_of_products"]): + product_frag_hash_set = set(reaction["product_fragment_mappings"][product_index].keys()) + overlapping_hash_list = list(reactant_frag_hash_set.intersection(product_frag_hash_set)) + for overlapping_hash in overlapping_hash_list: + if len(reactant_frag_hash_set) == 1 and len(reaction["reactant_fragment_mappings"][reactant_index][overlapping_hash]) == 2: + assert overlapping_hash == double_hash + full_mappping = extend_mapping( + full_mapping, + reactant_index, + reaction["reactant_fragment_mappings"][reactant_index][double_hash][double_ind], + product_index, + reaction["product_fragment_mappings"][product_index][double_hash][0] + ) + double_ind += 1 + elif len(product_frag_hash_set) == 1 and len(reaction["product_fragment_mappings"][product_index][overlapping_hash]) == 2: + assert overlapping_hash == double_hash + full_mappping = extend_mapping( + full_mapping, + reactant_index, + reaction["reactant_fragment_mappings"][reactant_index][double_hash][0], + product_index, + reaction["product_fragment_mappings"][product_index][double_hash][double_ind] + ) + double_ind += 1 + elif len(reactant_frag_hash_set) + len(product_frag_hash_set) == 3: + assert overlapping_hash == single_hash + full_mappping = extend_mapping( + full_mapping, + reactant_index, + reaction["reactant_fragment_mappings"][reactant_index][single_hash][0], + product_index, + reaction["product_fragment_mappings"][product_index][single_hash][0] + ) + + else: + raise RuntimeError("Number of reactants and number of products must both be either 1 or 2! Exiting...") + + elif len(reaction["hashes"].keys()) == 1: + if reaction["number_of_reactants"] == 1 and reaction["number_of_products"] == 1: + # This would have to be a ring opening or ring closing + assert reaction["reactant_fragment_count"] == 1 + + for reactant_index in range(reaction["number_of_reactants"]): + reactant_frag_hash_set = set(reaction["reactant_fragment_mappings"][reactant_index].keys()) + for product_index in range(reaction["number_of_products"]): + product_frag_hash_set = set(reaction["product_fragment_mappings"][product_index].keys()) + overlapping_hash_list = list(reactant_frag_hash_set.intersection(product_frag_hash_set)) + assert len(overlapping_hash_list) == 1 + for overlapping_hash in overlapping_hash_list: + assert len(reaction["reactant_fragment_mappings"][reactant_index][overlapping_hash]) == 1 + assert len(reaction["product_fragment_mappings"][product_index][overlapping_hash]) == 1 + full_mappping = extend_mapping( + full_mapping, + reactant_index, + reaction["reactant_fragment_mappings"][reactant_index][overlapping_hash][0], + product_index, + reaction["product_fragment_mappings"][product_index][overlapping_hash][0] + ) + + else: + # This would have to be A+A -> B (modulo charge) + assert reaction["number_of_reactants"] + reaction["number_of_products"] == 3 + assert reaction["reactant_fragment_count"] == 2 + + for frag_hash in reaction["hashes"]: + if reaction["hashes"][frag_hash] == 2: + double_hash = frag_hash + + double_hash_on_same_specie = False + for ind in range(reaction["number_of_reactants"]): + if double_hash in reaction["reactant_fragment_mappings"][ind]: + if len(reaction["reactant_fragment_mappings"][ind][double_hash]) == 2: + assert double_hash_on_same_specie == False + double_hash_on_same_specie = True + for ind in range(reaction["number_of_products"]): + if double_hash in reaction["product_fragment_mappings"][ind]: + if len(reaction["product_fragment_mappings"][ind][double_hash]) == 2: + assert double_hash_on_same_specie == False + double_hash_on_same_specie = True + + assert double_hash_on_same_specie + + double_ind = 0 + for reactant_index in range(reaction["number_of_reactants"]): + reactant_frag_hash_set = set(reaction["reactant_fragment_mappings"][reactant_index].keys()) + for product_index in range(reaction["number_of_products"]): + product_frag_hash_set = set(reaction["product_fragment_mappings"][product_index].keys()) + overlapping_hash_list = list(reactant_frag_hash_set.intersection(product_frag_hash_set)) + for overlapping_hash in overlapping_hash_list: + if len(reactant_frag_hash_set) == 1 and len(reaction["reactant_fragment_mappings"][reactant_index][overlapping_hash]) == 2: + assert overlapping_hash == double_hash + full_mappping = extend_mapping( + full_mapping, + reactant_index, + reaction["reactant_fragment_mappings"][reactant_index][double_hash][double_ind], + product_index, + reaction["product_fragment_mappings"][product_index][double_hash][0] + ) + double_ind += 1 + elif len(product_frag_hash_set) == 1 and len(reaction["product_fragment_mappings"][product_index][overlapping_hash]) == 2: + assert overlapping_hash == double_hash + full_mappping = extend_mapping( + full_mapping, + reactant_index, + reaction["reactant_fragment_mappings"][reactant_index][double_hash][0], + product_index, + reaction["product_fragment_mappings"][product_index][double_hash][double_ind] + ) + double_ind += 1 + else: + raise RuntimeError("Mapping should be length 2 on either reactant or product! Exiting...") + + # Map metals, if present: + reactant_m_inds = [] + for reactant_index in range(reaction["number_of_reactants"]): + for m_ind in mol_entries[reaction["reactants"][reactant_index]].m_inds: + reactant_m_inds.append((reactant_index, m_ind)) + + product_m_inds = [] + for product_index in range(reaction["number_of_products"]): + for m_ind in mol_entries[reaction["products"][product_index]].m_inds: + product_m_inds.append((product_index, m_ind)) + + assert len(reactant_m_inds) == len(product_m_inds) + + # For now, we will arbitrarily map metals. In the future, if there are multiple metal atoms present, + # it would be better to e.g. compare the graph distance of each metal from the reaction center and + # then have the closer metal to the reaction center in the reactants mapped to the closer metal to + # the reaction center in the products. That's just one idea, but there may be better approaches. + for ii, reactant_m_ind in enumerate(reactant_m_inds): + full_mapping[reactant_m_ind] = product_m_inds[ii] + + + reaction["atom_map"] = full_mapping + + if len(reaction["reactant_bonds_broken"]) + len(reaction["product_bonds_broken"]) < 2: + return False + + elif len(reaction["reactant_bonds_broken"]) == 1 and len(reaction["product_bonds_broken"]) == 1: + + hot_reactant_atoms = reaction["reactant_bonds_broken"][0] + hot_product_atoms = reaction["product_bonds_broken"][0] + + reaction["reaction_center"] = None + + for hot_reactant_atom in hot_reactant_atoms: + if reaction["atom_map"][hot_reactant_atom] in hot_product_atoms: + if reaction["reaction_center"] is not None: + raise RuntimeError("Should never find two reaction centers! Exiting...") + else: + reaction["reaction_center"] = (hot_reactant_atom, reaction["atom_map"][hot_reactant_atom]) + + if reaction["reaction_center"] is None: + return True + + else: + return False + else: + raise RuntimeError("Reaction center not implemented for " + str(len(reaction["reactant_bonds_broken"])) + " reactant bonds broken and " + str(len(reaction["product_bonds_broken"])) + " product bonds broken! Exiting...") + + class single_reactant_single_product_not_atom_transfer(MSONable): def __init__(self): pass @@ -1303,6 +1734,7 @@ def __call__(self, reaction, mol_entries, params): (dcharge_too_large(), Terminal.DISCARD), (reactant_and_product_not_isomorphic(), Terminal.DISCARD), (dG_above_threshold(0.0, "free_energy", 0.0), Terminal.DISCARD), + (map_redox_reaction(), Terminal.DISCARD), (reaction_default_true(), Terminal.KEEP), ], ), @@ -1320,12 +1752,49 @@ def __call__(self, reaction, mol_entries, params): [ (single_reactant_single_product_not_atom_transfer(), Terminal.DISCARD), (single_reactant_double_product_ring_close(), Terminal.DISCARD), + (mapping_with_reaction_center_not_found(), Terminal.DISCARD), (reaction_default_true(), Terminal.KEEP), ], ), (reaction_default_true(), Terminal.DISCARD), ] + +default_logging_decision_tree = [ + (metal_metal_reaction(), Terminal.DISCARD), + # redox branch + ( + is_redox_reaction(), + [ + (too_many_reactants_or_products(), Terminal.DISCARD), + (dcharge_too_large(), Terminal.DISCARD), + (reactant_and_product_not_isomorphic(), Terminal.DISCARD), + (dG_above_threshold(0.0, "free_energy", 0.0), Terminal.DISCARD), + (map_redox_reaction(), Terminal.DISCARD), + (reaction_default_true(), Terminal.DISCARD), + ], + ), + (dG_above_threshold(0.0, "solvation_free_energy", 0.0), Terminal.DISCARD), + # (single_reactant_with_ring_break_two(), Terminal.KEEP), + # (single_product_with_ring_form_two(), Terminal.KEEP), + (star_count_diff_above_threshold(6), Terminal.DISCARD), + (reaction_is_covalent_decomposable(), Terminal.DISCARD), + (concerted_metal_coordination(), Terminal.DISCARD), + (concerted_metal_coordination_one_product(), Terminal.DISCARD), + (concerted_metal_coordination_one_reactant(), Terminal.DISCARD), + (metal_coordination_passthrough(), Terminal.DISCARD), + ( + fragment_matching_found(), + [ + (single_reactant_single_product_not_atom_transfer(), Terminal.DISCARD), + (single_reactant_double_product_ring_close(), Terminal.DISCARD), + (mapping_with_reaction_center_not_found(), Terminal.KEEP), + (reaction_default_true(), Terminal.DISCARD), + ], + ), + (reaction_default_true(), Terminal.DISCARD), +] + co2_reaction_decision_tree = [ ( is_redox_reaction(), @@ -1359,6 +1828,7 @@ def __call__(self, reaction, mol_entries, params): (too_many_reactants_or_products(), Terminal.DISCARD), (dcharge_too_large(), Terminal.DISCARD), (reactant_and_product_not_isomorphic(), Terminal.DISCARD), + (map_redox_reaction(), Terminal.DISCARD), (add_electron_species(), Terminal.DISCARD), (dG_above_threshold(-float("inf"), "free_energy", 0.0), Terminal.KEEP), (reaction_default_true(), Terminal.DISCARD), @@ -1382,6 +1852,7 @@ def __call__(self, reaction, mol_entries, params): (not_h_transfer(), Terminal.DISCARD), (h_abstraction_from_closed_shell_reactant(), Terminal.DISCARD), (h_minus_abstraction(), Terminal.DISCARD), + (mapping_with_reaction_center_not_found(), Terminal.DISCARD), (dG_above_threshold(0.0, "free_energy", 0.0, 0.1), Terminal.KEEP), (reaction_default_true(), Terminal.DISCARD), ], @@ -1397,6 +1868,7 @@ def __call__(self, reaction, mol_entries, params): fragment_matching_found(), [ (single_reactant_double_product_ring_close(), Terminal.DISCARD), + (mapping_with_reaction_center_not_found(), Terminal.DISCARD), (dG_above_threshold(0.0, "free_energy", 0.0), Terminal.KEEP), (reaction_default_true(), Terminal.DISCARD), ], @@ -1406,13 +1878,14 @@ def __call__(self, reaction, mol_entries, params): ] -euvl_phase1_reaction_logging_tree = [ +euvl_phase1_logging_decision_tree = [ ( is_redox_reaction(), [ (too_many_reactants_or_products(), Terminal.DISCARD), (dcharge_too_large(), Terminal.DISCARD), (reactant_and_product_not_isomorphic(), Terminal.DISCARD), + (map_redox_reaction(), Terminal.DISCARD), (add_electron_species(), Terminal.DISCARD), (dG_above_threshold(-float("inf"), "free_energy", 0.0), Terminal.DISCARD), (reaction_default_true(), Terminal.DISCARD), @@ -1436,6 +1909,7 @@ def __call__(self, reaction, mol_entries, params): (not_h_transfer(), Terminal.DISCARD), (h_abstraction_from_closed_shell_reactant(), Terminal.DISCARD), (h_minus_abstraction(), Terminal.DISCARD), + (mapping_with_reaction_center_not_found(), Terminal.KEEP), (dG_above_threshold(0.0, "free_energy", 0.0, 0.1), Terminal.DISCARD), (reaction_default_true(), Terminal.DISCARD), ], @@ -1451,6 +1925,7 @@ def __call__(self, reaction, mol_entries, params): fragment_matching_found(), [ (single_reactant_double_product_ring_close(), Terminal.DISCARD), + (mapping_with_reaction_center_not_found(), Terminal.KEEP), (dG_above_threshold(0.0, "free_energy", 0.0), Terminal.DISCARD), (reaction_default_true(), Terminal.DISCARD), ], @@ -1464,29 +1939,31 @@ def __call__(self, reaction, mol_entries, params): (dG_above_threshold(0.0, "free_energy", 0.0), Terminal.DISCARD), (reactants_are_both_anions_or_both_cations(), Terminal.DISCARD), (reaction_is_charge_transfer(), Terminal.KEEP), - (reaction_is_covalent_charge_decomposable(), Terminal.DISCARD), - (reaction_is_coupled_electron_fragment_transfer(), Terminal.DISCARD), - (star_count_diff_above_threshold(6), Terminal.DISCARD), - ( - fragment_matching_found(), - [ - (single_reactant_single_product_not_atom_transfer(), Terminal.DISCARD), - (single_reactant_double_product_ring_close(), Terminal.DISCARD), - (reaction_is_hindered(), Terminal.DISCARD), - ( - reaction_is_covalent_decomposable(), - [ - (fragments_are_not_2A_B(), Terminal.DISCARD), - (reaction_default_true(), Terminal.KEEP), - ], - ), - (reaction_default_true(), Terminal.KEEP), - ], - ), + # (reaction_is_covalent_charge_decomposable(), Terminal.DISCARD), + # (reaction_is_coupled_electron_fragment_transfer(), Terminal.DISCARD), + # (star_count_diff_above_threshold(6), Terminal.DISCARD), + # ( + # fragment_matching_found(), + # [ + # (single_reactant_single_product_not_atom_transfer(), Terminal.DISCARD), + # (single_reactant_double_product_ring_close(), Terminal.DISCARD), + # (reaction_is_hindered(), Terminal.DISCARD), + # ( + # reaction_is_covalent_decomposable(), + # [ + # (fragments_are_not_2A_B(), Terminal.DISCARD), + # (mapping_with_reaction_center_not_found(), Terminal.DISCARD), + # (reaction_default_true(), Terminal.KEEP), + # ], + # ), + # (mapping_with_reaction_center_not_found(), Terminal.DISCARD), + # (reaction_default_true(), Terminal.KEEP), + # ], + # ), (reaction_default_true(), Terminal.DISCARD), ] -euvl_phase2_logging_tree = [ +euvl_phase2_logging_decision_tree = [ (is_redox_reaction(), Terminal.DISCARD), (dG_above_threshold(0.0, "free_energy", 0.0), Terminal.DISCARD), (reactants_are_both_anions_or_both_cations(), Terminal.DISCARD), @@ -1504,9 +1981,11 @@ def __call__(self, reaction, mol_entries, params): reaction_is_covalent_decomposable(), [ (fragments_are_not_2A_B(), Terminal.DISCARD), - (reaction_default_true(), Terminal.KEEP), + (mapping_with_reaction_center_not_found(), Terminal.KEEP), + (reaction_default_true(), Terminal.DISCARD), ], ), + (mapping_with_reaction_center_not_found(), Terminal.KEEP), (reaction_default_true(), Terminal.DISCARD), ], ), diff --git a/HiPRGen/report_generator.py b/HiPRGen/report_generator.py index 582142d..157eafd 100644 --- a/HiPRGen/report_generator.py +++ b/HiPRGen/report_generator.py @@ -138,6 +138,10 @@ def emit_initial_state(self, initial_state): self.emit_molecule(species_id) self.emit_newline() + def emit_atom_map(self, atom_map): + for key in atom_map: + self.emit_verbatim(str(key) + ": " + str(atom_map[key])) + def emit_reaction(self, reaction, label=None): reactants_filtered = [i for i in reaction["reactants"] if i != -1] @@ -180,6 +184,10 @@ def emit_reaction(self, reaction, label=None): self.f.write("$$") self.f.write("\n\n\n") + if "atom_map" in reaction: + self.f.write("atom map:") + self.emit_atom_map(reaction["atom_map"]) + def emit_bond_breakage(self, reaction): if "reactant_bonds_broken" in reaction: self.f.write("reactant bonds broken:") @@ -192,3 +200,5 @@ def emit_bond_breakage(self, reaction): self.emit_verbatim(str(bond)) self.f.write("\n\n\n") + + diff --git a/HiPRGen/rxn_networks_graph.py b/HiPRGen/rxn_networks_graph.py new file mode 100644 index 0000000..277731b --- /dev/null +++ b/HiPRGen/rxn_networks_graph.py @@ -0,0 +1,325 @@ +import torch +import math +import os +from pathlib import Path +import copy +from collections import defaultdict +from monty.serialization import dumpfn +from bondnet.data.utils import create_rxn_graph +from HiPRGen.lmdb_dataset import LmdbDataset +import lmdb +import tqdm +import pickle +from HiPRGen.lmdb_dataset import write_to_lmdb + +class rxn_networks_graph: + def __init__( + self, + mol_entries, + dgl_molecules_dict, + grapher_features, + report_file_path + ): + self.mol_entries = mol_entries + self.dgl_mol_dict = dgl_molecules_dict + self.grapher_features = grapher_features + self.report_file_path = report_file_path + + # initialize data + self.data = {} + + + def create_rxn_networks_graph(self, rxn, rxn_id): + + """ + To create a reaction graph (or a reaction networks graph) using a function from BonDNet, + we need to specify four inputs: + - reactants: a list of dgl graphs of reactants, + - products: a list of dgl graphs of products, + - mappings: detailed explanation in the below, + - has_bonds: a dictionary with a form of {'reactants': [True, True], 'products': [True]} as an example of two reactants and one product. + + mappings = { + "bond_map": a list of lists, + e.g.) For a reaction that has two reactants and two products, it has [[{}, {}], [{}, {}]] format. + The first inner list includes dictionaries of bond map for reactants. + The second inner list includes dictionaries of bond map for products. + A key of the dictionary represents a local bond index and the corresponding value represents + a global bond index from "total_bonds". + "atom_map": a list of lists, + e.g.) For a reaction that has two reactants and two products, it has [[{}, {}], [{}, {}]] format. + The first inner list includes dictionaries of atom map for reactants. + The second inner list includes dictionaries of atom map for products. + A key of the dictionary represetns a local atom index and the corresponding value represents + a global atom index. + "total_bonds": list of lists. It's an union of bonds in reactants and products + e.g.) [[0,1], [0,2], [0,3], [0, 4]] + "total_atoms": list of integer, + "num_bonds_total": integer == len(total_bonds), + "num_atoms_total": integer == len(total_atoms), + } + + The goal of this function is to create a reaction graph with a reaction filtered from HiPRGen, specifically, reaction_filter.py + """ + + + atom_map = rxn['atom_map'] + num_reactants = rxn['number_of_reactants'] + num_products = rxn['number_of_products'] + transformed_atom_map = [] + + # step 1: Transform atom map to "atom_map" format in mappings + + # find the number of atoms for reactant_0 + num_reactant0_atoms = self.mol_entries[rxn['reactants'][0]].num_atoms + + # transform atom map for reactants + reactants = [{} for _ in range(num_reactants)] + for ind, atom_i in atom_map.keys(): + reactants[ind][atom_i] = atom_i + ind*num_reactant0_atoms + transformed_atom_map.append(reactants) + + # transform atom map for products + products = [{} for _ in range(num_products)] + for r_tuple, p_tuple in atom_map.items(): + prod_ind, p_atom_i = p_tuple + if r_tuple == p_tuple: + products[prod_ind][p_atom_i] = reactants[prod_ind][p_atom_i] + else: + react_ind, r_atom_i = r_tuple + products[prod_ind][p_atom_i] = reactants[react_ind][r_atom_i] + transformed_atom_map.append(products) + # print(f"products: {products}") + # print(f"reactants: {reactants}") + # print(f"transformed_atom_map: {transformed_atom_map}") + + # check the conservation of mass in a reaction + assert sum([len(i) for i in reactants]) == sum([len(i) for i in products]) + assert num_reactants == len(reactants) + assert num_products == len(products) + + # Find "total_atoms" in mapping + num_tot_atoms = sum([len(i) for i in reactants]) + total_atoms = [i for i in range(num_tot_atoms)] + + + # step 2: Get total_bonds which are a union of bonds in reactants and products + + # define a function to find total bonds in reactants or products + def find_total_bonds(rxn, species, reactants, products): + """ Goal: find total bonds in reactants or products and fetch entry_ids of reactants or products + Inputs: + - rxn: a reaction from reaction_filter.py from HiPRGen + - species: (str) 'reactants' or 'products' + - reactants: a list of dictionaries of transformed atom map for reactants + - products: a list of dictionaries of transformed atom map for products + Outputs: + - species_entry_ids: a list of mol.entry_ids of reactants or products + - species_total_bonds: a set of tuples. Each tuple represents a bond between two atoms noted with global indexes + """ + species_entry_ids = [] + species_total_bonds = set() + + if species == 'reactants': + temp_species = reactants + else: + temp_species = products + for k, ind in enumerate(rxn[species]): + mol_reactant = self.mol_entries[ind] + networkx_graph = mol_reactant.graph + + # This is needed because there is a case where num_reactants != len(rxn['reactants']) or num_products != len(rxn['products']) + if len(temp_species) <= k: + break + species_entry_ids.append(self.mol_entries[ind].entry_id) + for i, j, weight in networkx_graph.edges: + + species_total_bonds.add(tuple(sorted([temp_species[k][i], temp_species[k][j]]))) + + return species_total_bonds, species_entry_ids + + reactants_total_bonds, reactants_entry_ids = find_total_bonds(rxn, 'reactants', reactants, products) + products_total_bonds, products_entry_ids = find_total_bonds(rxn, 'products', reactants, products) + + # find an union of bonds of reactants and products + set_total_bonds = reactants_total_bonds.union(products_total_bonds) + + # convert to the correct format in "total_bonds" in mappings + total_bonds = [[i,j] for i, j in set_total_bonds] + + # a dictionary, total_bonds_map, is used for creating bond_map for reactants and products in step 3 + total_bonds_map = {} + for ind, bonds in enumerate(total_bonds): + i, j = bonds + total_bonds_map[(i,j)] = ind + + if rxn['is_redox']: + assert len(set(reactants_total_bonds)) == len(set(products_total_bonds)) + + + + + # step 3: Get bond_mapping + bond_mapping = [] + + # bond mapping for reactants + bonds_in_reactants = [{} for _ in range(num_reactants)] + for k, ind in enumerate(rxn['reactants']): + mol_reactant = self.mol_entries[ind] + networkx_graph = mol_reactant.graph + # This is needed because there is a case where num_reactants != len(rxn['reactants']) or num_products != len(rxn['products']) + if len(reactants) <= k: + break + + for bond_ind, edges in enumerate(networkx_graph.edges): + i, j, _ = edges + bonds_in_reactants[k][bond_ind] = total_bonds_map[tuple(sorted([reactants[k][i],reactants[k][j]]))] + bond_mapping.append(bonds_in_reactants) + + # bond mapping for products + bonds_in_products = [{} for _ in range(num_products)] + for k, ind in enumerate(rxn['products']): + mol_reactant = self.mol_entries[ind] + networkx_graph = mol_reactant.graph + # This is needed because there is a case where num_reactants != len(rxn['reactants']) or num_products != len(rxn['products']) + if len(products) <= k: + break + for bond_ind, edges in enumerate(networkx_graph.edges): + i, j, _ = edges + bonds_in_products[k][bond_ind] = total_bonds_map[tuple(sorted([products[k][i],products[k][j]]))] + bond_mapping.append(bonds_in_products) + + # print(f'bonds_in_reactants: {bonds_in_reactants}') + # print(f'bonds_in_products: {bonds_in_products}') + # print(f'reactants: {reactants}') + # print(f'products: {products}') + assert len(bonds_in_reactants) == len(reactants) + assert len(bonds_in_products) == len(products) + + + + # step 4: get mapping + mappings = {} + mappings['bond_map'] = bond_mapping + mappings['atom_map'] = transformed_atom_map + mappings['total_bonds'] = total_bonds + mappings['total_atoms'] = total_atoms + mappings['num_bonds_total'] = len(total_bonds_map) + mappings['num_atoms_total'] = len(total_atoms) + # if rxn['is_redox']: + # print(f"mappings: {mappings}") + #print(f"mapping: {mappings}") + # print(f"atom_map: {atom_map}") + # print(f"reactants: {reactants}") + # print(f"products: {products}") + + # grab dgl graphs of reactants or products + reactants_dgl_graphs = [self.dgl_mol_dict[entry_i] for entry_i in reactants_entry_ids] + products_dgl_graphs = [self.dgl_mol_dict[entry_i] for entry_i in products_entry_ids] + # print(f"reactants_dgl_graphs: {reactants_dgl_graphs}") + # print(f"products_dgl_graphs: {products_dgl_graphs}") + + # create has_bonds in mappings + has_bonds = defaultdict(list) + for _ in range(len(reactants)): + has_bonds['reactants'].append(True) + for _ in range(len(products)): + has_bonds['products'].append(True) + + # print(f"has_bonds: {has_bonds}") + # print(f"mappings: {mappings}") + + # step 5: Create a reaction graphs and features + rxn_graph, features = create_rxn_graph( + reactants = reactants_dgl_graphs, + products = products_dgl_graphs, + mappings = mappings, + has_bonds = has_bonds, + device = None, + ntypes=("global", "atom", "bond"), + ft_name="feat", + reverse=False, + ) + + # print(f"rxn_graph: {rxn_graph}") + if rxn['is_redox']: + print(f"mappings: {mappings}") + print(f"features: {features}") + print(f"transformed_atom_map: {transformed_atom_map}") + print(f"atom_map: {atom_map}") + + # step 5: update reaction features to the reaction graph + for nt, ft in features.items(): + # print(f"nt: {nt}") + # print(f"ft: {ft}") + rxn_graph.nodes[nt].data.update({'ft': ft}) + + # step 6: save a reaction graph and dG + self.data[rxn_id] = {} # {'id': {}} + self.data[rxn_id]['rxn_graph'] = rxn_graph + self.data[rxn_id]['value'] = rxn['dG'] #torch.tensor([rxn['dG']]) + self.data[rxn_id]['reaction_features'] = features + + + #### Write LMDB #### + #1 load lmdb + + lmdb_path = "training_trial5.lmdb" + lmdb_file = Path(lmdb_path) + if lmdb_file.is_file(): + # file exists + current_lmdb = LmdbDataset({'src': lmdb_path}) + lmdb_update = { + "mean" : current_lmdb.mean, + "std": current_lmdb.std, + "feature_size": self.grapher_features['feature_size'], + "feature_name": self.grapher_features['feature_name'], + "dtype": "float32" + } + current_length = current_lmdb.num_samples + else: + current_lmdb = lmdb.open( + lmdb_path, + map_size=1099511627776 * 2, + subdir=False, + meminit=False, + map_async=True, + + ) + #2 initialize dict + lmdb_update = { + "mean" : 0, + "std": 0, + "feature_size": self.grapher_features['feature_size'], + "feature_name": self.grapher_features['feature_name'], + "dtype": "float32" + } + current_length = 0 + + + #3 update mean, std, feature_size, feature_name to dict_update + #3.1 update mean + prev_mean = lmdb_update["mean"] + #print(f"current_length: {current_length}") + n = current_length + 1 + current_y = rxn['dG'] + updated_mean = (current_y + (n-1)*prev_mean)/n + lmdb_update["mean"] = updated_mean + + #3.2 update std + prev_variance = (lmdb_update["std"])**2 + updated_variance = (n-1)/(n)*prev_variance + (n-1)/n*(prev_mean-updated_mean)**2 + (current_y - updated_mean)**2/n + lmdb_update["std"] = math.sqrt(updated_variance) + + + labels = {'value': torch.tensor([rxn['dG']]), 'value_rev': torch.tensor([0]), 'id': [str(rxn_id)], "reaction_type": ['']} + data = (self.data[rxn_id]['rxn_graph'], self.data[rxn_id]['reaction_features'], labels) + # print(f"data: {data}") + # print(f"lmdb_update: {lmdb_update}") + write_to_lmdb([data], current_length, lmdb_update, lmdb_path) + + + + def write_data(self): + # write a json file + dumpfn(self.data, self.report_file_path) \ No newline at end of file diff --git a/HiPRGen/species_filter.py b/HiPRGen/species_filter.py index e06fff7..751255d 100644 --- a/HiPRGen/species_filter.py +++ b/HiPRGen/species_filter.py @@ -1,8 +1,9 @@ -from HiPRGen.mol_entry import MoleculeEntry +from HiPRGen.mol_entry import MoleculeEntry, find_fragment_atom_mappings, build_compressed_graph from functools import partial from itertools import chain from monty.serialization import dumpfn import pickle +import copy from HiPRGen.species_questions import run_decision_tree from HiPRGen.constants import Terminal import networkx as nx @@ -14,6 +15,10 @@ from pymatgen.core.sites import Site from pymatgen.core.structure import Molecule from pymatgen.analysis.graphs import MoleculeGraph +from bondnet.model.training_utils import get_grapher +from bondnet.core.molwrapper import MoleculeWrapper +from bondnet.data.transformers import HeteroGraphFeatureStandardScaler + """ Phase 1: species filtering @@ -82,6 +87,8 @@ def log_message(string): def species_filter( dataset_entries, mol_entries_pickle_location, + dgl_mol_grphs_pickle_location, + grapher_features_pickle_location, species_report, species_decision_tree, coordimer_weight, @@ -120,7 +127,7 @@ def species_filter( log_message("applying local filters") mol_entries_filtered = [] - + elements = set() # note: it is important here that we are applying the local filters before # the non local ones. We remove some molecules which are lower energy # than other more realistic lithomers. @@ -132,6 +139,11 @@ def species_filter( mol_entries_filtered.append(mol) if run_decision_tree(mol, species_logging_decision_tree): + # create a set of elements + # log_message("create a set of elements") + for element in mol.species: + if element not in elements: + elements.add(element) report_generator.emit_verbatim( "\n".join([str(f) for f in decision_pathway]) @@ -188,6 +200,66 @@ def collapse_isomorphism_group(g): for i, e in enumerate(mol_entries): e.ind = i + + log_message("mapping fragments") + fragment_dict = {} + for mol in mol_entries: + # print(mol.entry_id) + for fragment_complex in mol.fragment_data: + for ii, fragment in enumerate(fragment_complex.fragment_objects): + hot_nbh_hashes = list(fragment.hot_atoms.keys()) + assert len(hot_nbh_hashes) == 0 or len(hot_nbh_hashes) == 1 or len(hot_nbh_hashes) == 2 + assert fragment.fragment_hash == fragment_complex.fragment_hashes[ii] + if fragment.fragment_hash not in fragment_dict: + fragment_dict[fragment.fragment_hash] = copy.deepcopy(fragment) + all_mappings = find_fragment_atom_mappings( + fragment, + fragment_dict[fragment.fragment_hash]) + fragment_complex.fragment_mappings.append(all_mappings) + assert len(fragment_complex.fragment_objects) == len(fragment_complex.fragment_mappings) + + + log_message(str(len(fragment_dict.keys())) + " unique fragments found") + + # Make DGL Molecule graphs via BonDNet functions + log_message("creating dgl molecule graphs") + dgl_molecules_dict = {} + dgl_molecules = [] + extra_keys = [] + + for mol in mol_entries: + # print(f"mol: {mol.mol_graph}") + molecule_grapher = get_grapher(extra_keys) + + non_metal_bonds = [ (i, j) for i, j, _ in mol.covalent_graph.edges.data()] + # print(f"non metal bonds: {non_metal_bonds}") + mol_wrapper = MoleculeWrapper(mol_graph = mol.mol_graph, free_energy = None, id = mol.entry_id, non_metal_bonds = non_metal_bonds) + feature = {'charge': mol.charge} + dgl_molecule_graph = molecule_grapher.build_graph_and_featurize(mol_wrapper, extra_feats_info = feature, dataset_species = elements) + dgl_molecules.append(dgl_molecule_graph) + for nt in ["global", "atom", "bond"]: + print(f"nt: {nt}") + fts = dgl_molecule_graph.nodes[nt].data["feat"] + print(f"features: {fts}") + dgl_molecules_dict[mol.entry_id] = mol.ind + grapher_features= {'feature_size':molecule_grapher.feature_size, 'feature_name': molecule_grapher.feature_name} + #mol_wrapper_dict[mol.entry_id] = mol_wrapper + + # Normalize DGL molecule graphs + scaler = HeteroGraphFeatureStandardScaler(mean = None, std = None) + normalized_graphs = scaler(dgl_molecules) + + # print(f"mean: {scaler._mean}") + # print(f"std: {scaler._std}") + + + # Create a dictionary where key is mol.entry_id and value is a normalized dgl molecule graph + for key in dgl_molecules_dict.keys(): + temp_index = dgl_molecules_dict[key] + dgl_molecules_dict[key] = normalized_graphs[temp_index] + #print(dgl_molecules_dict) + + log_message("creating molecule entry pickle") # ideally we would serialize mol_entries to a json # some of the auxilary_data we compute @@ -196,9 +268,15 @@ def collapse_isomorphism_group(g): with open(mol_entries_pickle_location, "wb") as f: pickle.dump(mol_entries, f) + with open(dgl_mol_grphs_pickle_location, "wb") as f: + pickle.dump(dgl_molecules_dict, f) + + with open(grapher_features_pickle_location, "wb") as f: + pickle.dump(grapher_features, f) + log_message("species filtering finished. " + str(len(mol_entries)) + " species") - return mol_entries + return mol_entries, dgl_molecules_dict def add_electron_species( diff --git a/HiPRGen/species_questions.py b/HiPRGen/species_questions.py index b28efcf..6f5e2c5 100644 --- a/HiPRGen/species_questions.py +++ b/HiPRGen/species_questions.py @@ -1,4 +1,4 @@ -from HiPRGen.mol_entry import MoleculeEntry, FragmentComplex +from HiPRGen.mol_entry import MoleculeEntry, FragmentComplex, FragmentObject, build_compressed_graph import networkx as nx from networkx.algorithms.graph_hashing import weisfeiler_lehman_graph_hash import copy @@ -130,23 +130,54 @@ def __call__(self, mol): class add_unbroken_fragment(MSONable): #aka adds unfragmented molecule as a "fragment complex" - def __init__(self): - pass + def __init__(self, neighborhood_width=None): + self.neighborhood_width = neighborhood_width def __call__(self, mol): if mol.formula in m_formulas: return False - fragment_complex = FragmentComplex(1, 0, [], [mol.covalent_hash]) + fragment_objects = [] + if self.neighborhood_width is not None: + neighborhood_hashes = {} + for i in mol.uncompressed_atoms: + hash_list = [] + for d in range(self.neighborhood_width): + neighborhood = nx.generators.ego.ego_graph( + mol.compressed_graph, + i, + d, + undirected=True) + + neighborhood_hash = weisfeiler_lehman_graph_hash( + neighborhood, + node_attr='specie') + + hash_list.append(neighborhood_hash) + + neighborhood_hashes[i] = str(hash(tuple(hash_list))) + fragment_object = FragmentObject( + fragment_hash=mol.covalent_hash, + atom_ids=mol.uncompressed_atoms, + neighborhood_hashes=neighborhood_hashes, + graph=mol.covalent_graph, + hot_atoms={}, + compressed_graph=mol.compressed_graph + ) + fragment_objects.append(fragment_object) + + fragment_complex = FragmentComplex(1, 0, [], [mol.covalent_hash], fragment_objects) - mol.fragment_data.append(fragment_complex) + if len(mol.fragment_data) == 0: + mol.fragment_data.append(fragment_complex) return False class add_single_bond_fragments(MSONable): #called for all species that have passed through filtration - def __init__(self, allow_ring_opening=True): + def __init__(self, allow_ring_opening=True, neighborhood_width=None): self.allow_ring_opening = allow_ring_opening + self.neighborhood_width = neighborhood_width def __call__(self, mol): @@ -155,17 +186,24 @@ def __call__(self, mol): for edge in mol.covalent_graph.edges: #iterates through each bond in a molecule graph by iterating through a list of tuples fragment_hashes = [] + fragment_objects = [] h = copy.deepcopy(mol.covalent_graph) h.remove_edge(*edge) #"breaks a bond" in the molecule graph connected_components = nx.algorithms.components.connected_components(h) #generates a set of nodes for each "fragment" + list_of_c_dicts = [] for c in connected_components: - + tmp = {} subgraph = h.subgraph(c) #generates a subgraph from one set of nodes (this is a fragment graph) fragment_hash = weisfeiler_lehman_graph_hash( #saves the hash of this graph subgraph, node_attr="specie" ) + tmp["c"] = copy.deepcopy(c) + tmp["subgraph"] = copy.deepcopy(subgraph) + tmp["fragment_hash"] = copy.deepcopy(fragment_hash) + list_of_c_dicts.append(tmp) + fragment_hashes.append(fragment_hash) #adds each fragment hash to the fragment hash list equivalent_fragments_already_found = False @@ -178,11 +216,58 @@ def __call__(self, mol): if len(fragment_hashes) == 1 and not self.allow_ring_opening: pass + else: + + if self.neighborhood_width is not None: + for entry in list_of_c_dicts: + c = entry["c"] + subgraph = entry["subgraph"] + fragment_hash = entry["fragment_hash"] + neighborhood_hashes = {} + compressed_subgraph = build_compressed_graph(subgraph) + for i in compressed_subgraph.nodes(): + hash_list = [] + for d in range(self.neighborhood_width): + neighborhood = nx.generators.ego.ego_graph( + compressed_subgraph, + i, + d, + undirected=True) + + neighborhood_hash = weisfeiler_lehman_graph_hash( + neighborhood, + node_attr='specie') + + hash_list.append(neighborhood_hash) + + neighborhood_hashes[i] = str(hash(tuple(hash_list))) + hot_atom_inds = [i for i in c if i in edge[0:2]] + assert len(hot_atom_inds) == 1 or len(hot_atom_inds) == 2 + hot_atoms = {} + # We basically ignore the case where we have two hot atoms with + # the same neighborhood hash. These have to be ring openings, + # and not only that, but probably very unlikely ring openings + # involving C-C bond breakages. I'm pretty sure that just taking + # one of the equivalent atoms as "hot" won't cause issues. + for hot_atom_ind in hot_atom_inds: + hot_atoms[neighborhood_hashes[hot_atom_ind]] = hot_atom_ind + fragment_object = FragmentObject( + fragment_hash=fragment_hash, + atom_ids=c, + neighborhood_hashes=neighborhood_hashes, + graph=subgraph, + hot_atoms=hot_atoms, + compressed_graph=compressed_subgraph + ) + fragment_objects.append(fragment_object) + + if self.neighborhood_width is not None: + assert len(fragment_objects) == len(fragment_hashes) - fragment_complex = FragmentComplex( #saves a FragmentComplex object after both fragment_hashes have been - len(fragment_hashes), 1, [edge[0:2]], fragment_hashes #added to the list of fragments with len(fragments) fragments, 1 bond broken, the identity - ) #of the bond broken (as a list containing one tuple), and the list of fragment hashes + fragment_complex = FragmentComplex( #saves a FragmentComplex object after both fragment_hashes have been + len(fragment_hashes), 1, [edge[0:2]], fragment_hashes, fragment_objects #added to the list of fragments with len(fragments) fragments, 1 bond broken, the identity + ) #of the bond broken (as a list containing one tuple), and the list of fragment hashes mol.fragment_data.append(fragment_complex) #append the above FragmentComplex object to the molecule's fragment_data list @@ -207,6 +292,7 @@ def __call__(self, mol): class covalent_ring_fragments(MSONable): + # NOTE: haven't added fragment neighborhood hashes here yet! def __init__(self): pass @@ -338,6 +424,7 @@ def __call__(self, mol): mol.graph.remove_edge(i, adjacent_atom) if adjacent_atom in mol.covalent_graph: mol.covalent_graph.remove_edge(i, adjacent_atom) + mol.compressed_graph = build_compressed_graph(mol.covalent_graph) return False @@ -464,6 +551,8 @@ def __call__(self, mol): # any species filter which modifies bonding has to come before # any filter checking for connectivity (which includes the metal-centric complex filter) +width = 6 + li_species_decision_tree = [ (fix_hydrogen_bonding(), Terminal.KEEP), (set_solvation_correction(li_ec), Terminal.KEEP), @@ -476,8 +565,10 @@ def __call__(self, mol): (metal_complex(), Terminal.DISCARD), (spin_multiplicity_filter(0.4), Terminal.DISCARD), (add_star_hashes(), Terminal.KEEP), - (add_unbroken_fragment(), Terminal.KEEP), - (add_single_bond_fragments(), Terminal.KEEP), + (add_unbroken_fragment(neighborhood_width=width), Terminal.KEEP), + (add_single_bond_fragments(neighborhood_width=width), Terminal.KEEP), + # (add_unbroken_fragment(), Terminal.KEEP), + # (add_single_bond_fragments(), Terminal.KEEP), # (has_covalent_ring(), [ # (covalent_ring_fragments(), Terminal.KEEP), # (species_default_true(), Terminal.KEEP) @@ -495,8 +586,10 @@ def __call__(self, mol): (mol_not_connected(), Terminal.DISCARD), (metal_complex(), Terminal.DISCARD), (add_star_hashes(), Terminal.KEEP), - (add_unbroken_fragment(), Terminal.KEEP), - (add_single_bond_fragments(), Terminal.KEEP), + (add_unbroken_fragment(neighborhood_width=width), Terminal.KEEP), + (add_single_bond_fragments(neighborhood_width=width), Terminal.KEEP), + # (add_unbroken_fragment(), Terminal.KEEP), + # (add_single_bond_fragments(), Terminal.KEEP), (species_default_true(), Terminal.KEEP), ] @@ -509,6 +602,7 @@ def __call__(self, mol): (species_default_true(), Terminal.KEEP), ] +width = 6 euvl_species_decision_tree = [ (fix_hydrogen_bonding(), Terminal.KEEP), @@ -516,7 +610,7 @@ def __call__(self, mol): (oh_plus_filter(), Terminal.DISCARD), (compute_graph_hashes, Terminal.KEEP), (add_star_hashes(), Terminal.KEEP), - (add_unbroken_fragment(), Terminal.KEEP), - (add_single_bond_fragments(allow_ring_opening=False), Terminal.KEEP), + (add_unbroken_fragment(neighborhood_width=width), Terminal.KEEP), + (add_single_bond_fragments(allow_ring_opening=False, neighborhood_width=width), Terminal.KEEP), (species_default_true(), Terminal.KEEP), ] diff --git a/HiPRGen/test_species.txt b/HiPRGen/test_species.txt index 22b431e..a6c3ff2 100644 --- a/HiPRGen/test_species.txt +++ b/HiPRGen/test_species.txt @@ -1,45 +1,14 @@ -PHS neutral-1d64d1a96b5db50b9fdf583dc18f90cf-C10H14O1-0-1 -tBA-0aade5ee5263fd1ad77490688fb37d0e-C10H20O2-0-1 -TPS-94be97269b03e361ba1b344f48f19e44-C18H15S1-1-1 -HONF-31c2af8f1605817c400250991fe7fefe-C4F9H1O3S1-0-1 -TPS CS fragment-bc3641376a32020a0345549009577712-C12H10S1-0-1 -tBaH+-2c137c7f62959d051173f4c02a854944-C10H21O2-1-1 -benzene-a502ffe15bce2adb300daebdd27f0aa0-C6H6-0-1 -TPS CS fragmentH+-2f65c8c819c26044f8a1b94755ad77b1-C12H11S1-1-1 -SO3-06ec8edf4209e7e492b8e563b71afbeb-O3S1-0-1 -ONFminus-f357352f5dd5488b54d50242d228ed6d-C4F9O3S1-m1-1 -PHSminusHrad-df1b0273442db364220e3662c74307eb-C10H13O1-0-2 -PHSminusOHrad-82ca46cecd8439a6d7fbd87eea2c54b9-C10H13-0-2 -TBOCminustertradical-0701a0edad7058c8168456de1c92e8e0-C6H11O2-0-2 -TBOCminustertradical+H+-a4c65f243bb0eef36b5ee0a359560871-C6H12O2-0-1 -556-b753e8d3829ebe4c1b93bf339690d319-C16H19S1-1-1 -222-affe6041ffe648433daa714b2d39dd72-C15H28O4-0-1 -524-00dc803a2a2dd2005acbb5d51b71a7d7-C9H17O2-0-2 -CH3dot-5f85470c393edc87393085bba5b03428-C1H3-0-2 -CO2-6abc9e860814179fef28aabbe09d1571-C1O2-0-1 -1260-aa3de124b222759aaea1463f6143873b-C23H26S1-0-1 -1258-93d061fd43eaf14c5fc8c73be52da15b-C23H26S1-0-1 -610-eea050c031d211549457abfc44b469a9-C4H10-0-1 --62e08d5119480b03357cc679f310168d-C10H14O1-0-1 --ace426ad373f83a93c83458cf66b8866-C6H5-1-1 --d7c3b045b53266f82e25ae3098e29d4f-C18H14S1-0-1 --2f4051ef4b3969cbf0d7a51a3d57959e-C4H9-1-1 --443639a41f47d07b41b9f1b4150e7e7b-C10H15O1-m1-1 --3bd4ab1135b39ff25d94beb51dd6c503-C13H13S1-1-1 --aab0ea89ff9b4e27da78126a6c80cbf0-H2O1-0-1 --367103e07e78cd55a4b80413679fda7e-H1O1-0-2 --3ce8bce066bf652195a8784f7b4f04af-C10H19O2-0-2 --edad4c3a7b053b636810e3cb94f17981-C10H15O1-1-1 --3464f250ed90621ea6a9588d1705d205-C6H12O1-0-1 --22ad4d4343667554c075f68e93c5c419-C4H9O1-0-2 --24bbd018781f482c1635b08033fdf846-C4H10O1-0-1 --414f98c42405835d32167a01608750ef-C4H8-m1-2 --2f7e19a6344da517348e59f408a1aa99-C4H8-0-1 --5d58a6a0200f85eff7a5f9f805a8e35f-C4H8-1-2 --206ec2213d7388c5e8bf2cff81dedac7-C3H6O1-m1-2 --9e7acc5b302a48411607db0a9234396f-C3H6O1-0-1 --f4194a8bb7a342bbab4eece7c3820fa8-C3H6O1-1-2 --ce487751a2c5c0b104116e3240cf5042-C6H12O1-m1-2 --9480763ced3710477010a038211b937f-C6H12O1-0-1 --a07777285e002c40f6d2107272819986-C6H12O1-1-2 --646e41d216c60e02a2f50b62e29b8b88-C18H16S1-0-1 +22ad4d4343667554c075f68e93c5c419-C4H9O1-0-2 +bc3641376a32020a0345549009577712-C12H10S1-0-1 +2f65c8c819c26044f8a1b94755ad77b1-C12H11S1-1-1 +94be97269b03e361ba1b344f48f19e44-C18H15S1-1-1 +53e9047861139aaf2a4fff70dc49d701-C4H9-0-2 +0701a0edad7058c8168456de1c92e8e0-C6H11O2-0-2 +c9636169ed9efe63dd613dad22c5b63f-C6H5-0-2 +5f85470c393edc87393085bba5b03428-C1H3-0-2 +3ce8bce066bf652195a8784f7b4f04af-C10H19O2-0-2 +0aade5ee5263fd1ad77490688fb37d0e-C10H20O2-0-1 +24bbd018781f482c1635b08033fdf846-C4H10O1-0-1 +2f7e19a6344da517348e59f408a1aa99-C4H8-0-1 +9e7acc5b302a48411607db0a9234396f-C3H6O1-0-1 +8a7e8693a6998655afc6c3cd13715281-C10H21O2-0-2 diff --git a/data/mini_euvl_test_set.json b/data/mini_euvl_test_set.json new file mode 100644 index 0000000..a39b03e --- /dev/null +++ b/data/mini_euvl_test_set.json @@ -0,0 +1 @@ +[{"_id": {"@module": "bson.objectid", "@class": "ObjectId", "oid": "63f8ccb43275e1179a48d7ad"}, "builder_meta": {"emmet_version": "0.44.1.dev6+g3ad3b8c", "pymatgen_version": "2023.1.20", "pull_request": null, "database_version": null, "build_date": {"@module": "datetime", "@class": "datetime", "string": "2023-02-24 13:43:12.118000"}}, "charge": -1, "spin_multiplicity": 1, "natoms": null, "elements": ["C", "H", "O"], "nelements": 3, "composition": {"O": 1.0, "C": 4.0, "H": 9.0}, "chemsys": "C-H-O", "symmetry": {"point_group": "C3v", "rotation_number": 3.0, "linear": false, "tolerance": 0.3, "eigen_tolerance": 0.01, "matrix_tolerance": 0.1}, "property_name": "summary", "property_id": "c4aa81109c75a513243543b9b09a60aa64ab41022bb3da37967d8d4cf4014e2ffb41f6eccf98837378e4f8d7ce2a083ce771d707720103237d20895161abd247", "molecule_id": "af0733dcda4cc3494970c99618faad2a-C4H9O1-m1-1", "deprecated": false, "deprecation_reasons": null, "level_of_theory": null, "solvent": null, "lot_solvent": null, "last_updated": {"@module": "datetime", "@class": "datetime", "string": "2023-02-24 13:43:12.118000"}, "origins": [], "warnings": [], "molecule": {"@module": "pymatgen.core.structure", "@class": "Molecule", "charge": -1, "spin_multiplicity": 1, "sites": [{"name": "O", "species": [{"element": "O", "occu": 1}], "xyz": [0.1081718803, 0.5015850131, 0.0492782803], "properties": {}}, {"name": "C", "species": [{"element": "C", "occu": 1}], "xyz": [-1.1766578405, 0.0744521021, 0.088796424], "properties": {}}, {"name": "C", "species": [{"element": "C", "occu": 1}], "xyz": [-1.3062991939, -1.2419082925, 0.8881070114], "properties": {}}, {"name": "C", "species": [{"element": "C", "occu": 1}], "xyz": [-2.0968855177, 1.1197827888, 0.7606606104], "properties": {}}, {"name": "C", "species": [{"element": "C", "occu": 1}], "xyz": [-1.7264859598, -0.1859698242, -1.3323125177], "properties": {}}, {"name": "H", "species": [{"element": "H", "occu": 1}], "xyz": [-0.9446267719, -1.0832598349, 1.911825254], "properties": {}}, {"name": "H", "species": [{"element": "H", "occu": 1}], "xyz": [-2.3379957566, -1.6250467044, 0.9436836444], "properties": {}}, {"name": "H", "species": [{"element": "H", "occu": 1}], "xyz": [-0.6766352786, -2.0128803272, 0.4255019759], "properties": {}}, {"name": "H", "species": [{"element": "H", "occu": 1}], "xyz": [-2.0288804489, 2.0689806693, 0.214246123], "properties": {}}, {"name": "H", "species": [{"element": "H", "occu": 1}], "xyz": [-1.754653868, 1.2996818498, 1.7875602085], "properties": {}}, {"name": "H", "species": [{"element": "H", "occu": 1}], "xyz": [-3.156296872, 0.8194526829, 0.7991726959], "properties": {}}, {"name": "H", "species": [{"element": 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[{"element": "H", "occu": 1}], "xyz": [-3.0681772447, 0.8775899692, 0.5904710873], "properties": {}}]}, "graphs": {"directed": true, "multigraph": true, "graph": [["edge_weight_name", null], ["edge_weight_units", null], ["name", "bonds"]], "nodes": [{"specie": "O", "coords": {"@module": "numpy", "@class": "array", "dtype": "float64", "data": [-0.3100934868, 0.3232444182, -0.4615031281]}, "properties": {}, "id": 0}, {"specie": "C", "coords": {"@module": "numpy", "@class": "array", "dtype": "float64", "data": [-1.1424459219, 0.097503871, 0.3909574798]}, "properties": {}, "id": 1}, {"specie": "C", "coords": {"@module": "numpy", "@class": "array", "dtype": "float64", "data": [-1.2223611326, -1.2776275834, 0.9687706198]}, "properties": {}, "id": 2}, {"specie": "C", "coords": {"@module": "numpy", "@class": "array", "dtype": "float64", "data": [-2.047409524, 1.2046947348, 0.8213877931]}, "properties": {}, "id": 3}, {"specie": "H", "coords": {"@module": "numpy", "@class": "array", "dtype": "float64", "data": [-1.0502197838, -1.1709331295, 2.0460724024]}, "properties": {}, "id": 4}, {"specie": "H", "coords": {"@module": "numpy", "@class": "array", "dtype": "float64", "data": [-2.251230329, -1.621748392, 0.8141039354]}, "properties": {}, "id": 5}, {"specie": "H", "coords": {"@module": "numpy", "@class": "array", "dtype": "float64", "data": [-0.4993769066, -1.9541261337, 0.5176067154]}, "properties": {}, "id": 6}, {"specie": "H", "coords": {"@module": "numpy", "@class": "array", "dtype": "float64", "data": [-1.8126700085, 2.1440551094, 0.3256157809]}, "properties": {}, "id": 7}, {"specie": "H", "coords": {"@module": "numpy", "@class": "array", "dtype": "float64", "data": [-1.9484608636, 1.2871384877, 1.9091573262]}, "properties": {}, "id": 8}, {"specie": "H", "coords": {"@module": "numpy", "@class": "array", "dtype": "float64", "data": [-3.0681772447, 0.8775899692, 0.5904710873]}, "properties": {}, "id": 9}], "adjacency": [[{"type": "covalent", "id": 1, "key": 0}], [{"type": "covalent", "id": 2, "key": 0}, {"type": "covalent", "id": 3, "key": 0}], [{"type": "covalent", "id": 6, "key": 0}, {"type": "covalent", "id": 4, "key": 0}, {"type": "covalent", "id": 5, "key": 0}], [{"type": "covalent", "id": 9, "key": 0}, {"type": "covalent", "id": 7, "key": 0}, {"type": "covalent", "id": 8, "key": 0}], [], [], [], [], [], []]}, "@version": null}, "OpenBabelNN + metal_edge_extender": {"@module": "pymatgen.analysis.graphs", "@class": "MoleculeGraph", "molecule": {"@module": "pymatgen.core.structure", "@class": "Molecule", "charge": 1, "spin_multiplicity": 2, "sites": [{"name": "O", "species": [{"element": "O", "occu": 1}], "xyz": [-0.3100934868, 0.3232444182, -0.4615031281], "properties": {}}, {"name": "C", "species": [{"element": "C", "occu": 1}], "xyz": [-1.1424459219, 0.097503871, 0.3909574798], "properties": {}}, {"name": "C", "species": [{"element": "C", "occu": 1}], "xyz": [-1.2223611326, -1.2776275834, 0.9687706198], "properties": {}}, {"name": "C", "species": [{"element": "C", "occu": 1}], "xyz": [-2.047409524, 1.2046947348, 0.8213877931], "properties": {}}, {"name": "H", "species": [{"element": "H", "occu": 1}], "xyz": [-1.0502197838, -1.1709331295, 2.0460724024], "properties": {}}, {"name": "H", "species": [{"element": "H", "occu": 1}], "xyz": [-2.251230329, -1.621748392, 0.8141039354], "properties": {}}, {"name": "H", "species": [{"element": "H", "occu": 1}], "xyz": [-0.4993769066, -1.9541261337, 0.5176067154], "properties": {}}, {"name": "H", "species": [{"element": "H", "occu": 1}], "xyz": [-1.8126700085, 2.1440551094, 0.3256157809], "properties": {}}, {"name": "H", "species": [{"element": "H", "occu": 1}], "xyz": [-1.9484608636, 1.2871384877, 1.9091573262], "properties": {}}, {"name": "H", "species": [{"element": "H", "occu": 1}], "xyz": [-3.0681772447, 0.8775899692, 0.5904710873], "properties": {}}]}, "graphs": {"directed": true, "multigraph": true, "graph": [["edge_weight_name", "weight"], ["edge_weight_units", ""], ["name", "bonds"]], "nodes": [{"specie": "O", "coords": {"@module": "numpy", "@class": "array", "dtype": "float64", "data": [-0.3100934868, 0.3232444182, -0.4615031281]}, "properties": {}, "id": 0}, {"specie": "C", "coords": {"@module": "numpy", "@class": "array", "dtype": "float64", "data": [-1.1424459219, 0.097503871, 0.3909574798]}, "properties": {}, "id": 1}, {"specie": "C", "coords": {"@module": "numpy", "@class": "array", "dtype": "float64", "data": [-1.2223611326, -1.2776275834, 0.9687706198]}, "properties": {}, "id": 2}, {"specie": "C", "coords": {"@module": "numpy", "@class": "array", "dtype": "float64", "data": [-2.047409524, 1.2046947348, 0.8213877931]}, "properties": {}, "id": 3}, {"specie": "H", "coords": {"@module": "numpy", "@class": "array", "dtype": "float64", "data": [-1.0502197838, -1.1709331295, 2.0460724024]}, "properties": {}, "id": 4}, {"specie": "H", "coords": {"@module": "numpy", "@class": "array", "dtype": "float64", "data": [-2.251230329, -1.621748392, 0.8141039354]}, "properties": {}, "id": 5}, {"specie": "H", "coords": {"@module": "numpy", "@class": "array", "dtype": "float64", "data": [-0.4993769066, -1.9541261337, 0.5176067154]}, "properties": {}, "id": 6}, {"specie": "H", "coords": {"@module": "numpy", "@class": "array", "dtype": "float64", "data": [-1.8126700085, 2.1440551094, 0.3256157809]}, "properties": {}, "id": 7}, {"specie": "H", "coords": {"@module": "numpy", "@class": "array", "dtype": "float64", "data": [-1.9484608636, 1.2871384877, 1.9091573262]}, "properties": {}, "id": 8}, {"specie": "H", "coords": {"@module": "numpy", "@class": "array", "dtype": "float64", "data": [-3.0681772447, 0.8775899692, 0.5904710873]}, "properties": {}, "id": 9}], "adjacency": [[{"weight": 2, "id": 1, "key": 0}], [{"weight": 1, "id": 3, "key": 0}, {"weight": 1, "id": 2, "key": 0}], [{"weight": 1, "id": 6, "key": 0}, {"weight": 1, "id": 5, "key": 0}, {"weight": 1, "id": 4, "key": 0}], [{"weight": 1, "id": 7, "key": 0}, {"weight": 1, "id": 9, "key": 0}, {"weight": 1, "id": 8, "key": 0}], [], [], [], [], [], []]}, "@version": null}}}, "bond_types": {"DIELECTRIC=3,00": {"nbo": {"C-O": [1.2126246158182228], "C-C": [1.4937339062022204, 1.493352264074198], "C-H": [1.0880741464791235, 1.0961731073388183, 1.095861641575062, 1.0964883913288042, 1.0877915432593985, 1.0953676857237402]}, "OpenBabelNN + metal_edge_extender": {"C-O": [1.2126246158182228], "C-C": [1.493352264074198, 1.4937339062022204], "C-H": [1.0880741464791235, 1.095861641575062, 1.0961731073388183, 1.0877915432593985, 1.0964883913288042, 1.0953676857237402]}}}, "bonds": {"DIELECTRIC=3,00": {"nbo": [[0, 1], [1, 2], [1, 3], [2, 6], [2, 4], [2, 5], [3, 9], [3, 7], [3, 8]], "OpenBabelNN + metal_edge_extender": [[0, 1], [1, 3], [1, 2], [2, 6], [2, 5], [2, 4], [3, 7], [3, 9], [3, 8]]}}, "bonds_nometal": {"DIELECTRIC=3,00": {"nbo": [[0, 1], [1, 2], [1, 3], [2, 6], [2, 4], [2, 5], [3, 9], [3, 7], [3, 8]], "OpenBabelNN + metal_edge_extender": [[0, 1], [1, 3], [1, 2], [2, 6], [2, 5], [2, 4], [3, 7], [3, 9], [3, 8]]}}, "electron_affinity": null, "ea_task_id": null, "ionization_energy": null, "ie_task_id": null, "reduction_free_energy": {"DIELECTRIC=3,00": -7.993629961692022}, "red_mpcule_id": {"DIELECTRIC=3,00": "9e7acc5b302a48411607db0a9234396f-C3H6O1-0-1"}, "oxidation_free_energy": null, "ox_mpcule_id": null, "reduction_potentials": {"DIELECTRIC=3,00": {"H": 3.553629961692022, "Li": 6.593629961692022, "Mg": 5.933629961692022, "Ca": 6.393629961692023}}, "oxidation_potentials": null, "has_props": ["bonding", "vibration", "thermo", "partial_spins", "redox", "orbitals", "partial_charges"], "_bt": {"@module": "datetime", "@class": "datetime", "string": "2023-03-29 19:23:25.167000"}}] \ No newline at end of file diff --git a/run_network_generation.py b/run_network_generation.py index a0887ba..42a055b 100644 --- a/run_network_generation.py +++ b/run_network_generation.py @@ -19,15 +19,23 @@ mol_entries_pickle_file = sys.argv[1] dispatcher_payload_json = sys.argv[2] worker_payload_json = sys.argv[3] +dgl_molecules_dict_pickle_file = sys.argv[4] +grapher_features_dict_pickle_file = sys.argv[5] with open(mol_entries_pickle_file, 'rb') as f: mol_entries = pickle.load(f) +with open(dgl_molecules_dict_pickle_file, 'rb') as f: + dgl_molecules_dict_pickle_file = pickle.load(f) +with open(grapher_features_dict_pickle_file, 'rb') as f: + grapher_features_dict_pickle_file = pickle.load(f) if rank == DISPATCHER_RANK: dispatcher_payload = loadfn(dispatcher_payload_json) dispatcher(mol_entries, + dgl_molecules_dict_pickle_file, + grapher_features_dict_pickle_file, dispatcher_payload ) diff --git a/test.py b/test.py index 252e19a..6bf38c9 100644 --- a/test.py +++ b/test.py @@ -4,6 +4,7 @@ import sqlite3 import pickle import copy +import time import matplotlib.colors as mcolors from HiPRGen.network_loader import NetworkLoader @@ -27,11 +28,12 @@ from HiPRGen.reaction_questions import ( default_reaction_decision_tree, + default_logging_decision_tree, co2_reaction_decision_tree, euvl_phase1_reaction_decision_tree, - euvl_phase1_reaction_logging_tree, + euvl_phase1_logging_decision_tree, euvl_phase2_reaction_decision_tree, - euvl_phase2_logging_tree + euvl_phase2_logging_decision_tree ) from HiPRGen.mc_analysis import ( @@ -151,7 +153,7 @@ def li_test(): folder + "/buckets.sqlite", default_reaction_decision_tree, params, - Terminal.DISCARD, + default_logging_decision_tree, ) # The dispatcher and worker payloads are passed through the MPI barrier @@ -295,7 +297,7 @@ def li_test(): tests_passed = False print("Number of reactions:", network_loader.number_of_reactions) - if network_loader.number_of_reactions == 4921: + if network_loader.number_of_reactions == 4649: print(bcolors.PASS + "li_test: correct number of reactions" + bcolors.ENDC) else: print(bcolors.FAIL + "li_test: correct number of reactions" + bcolors.ENDC) @@ -335,7 +337,7 @@ def mg_test(): folder + "/buckets.sqlite", default_reaction_decision_tree, params, - Terminal.DISCARD, + default_logging_decision_tree, ) dumpfn(dispatcher_payload, folder + "/dispatcher_payload.json") @@ -414,7 +416,7 @@ def mg_test(): tests_passed = False print("Number of reactions:", network_loader.number_of_reactions) - if network_loader.number_of_reactions == 788: + if network_loader.number_of_reactions == 766: print(bcolors.PASS + "mg_test: correct number of reactions" + bcolors.ENDC) else: print(bcolors.FAIL + "mg_test: correct number of reactions" + bcolors.ENDC) @@ -579,6 +581,7 @@ def euvl_phase1_test(): folder = "./scratch/euvl_phase1_test" subprocess.run(["mkdir", folder]) + # mol_json = "./data/mini_euvl_test_set.json" mol_json = "./data/euvl_test_set.json" database_entries = loadfn(mol_json) @@ -623,7 +626,7 @@ def euvl_phase1_test(): folder + "/buckets.sqlite", euvl_phase1_reaction_decision_tree, params, - euvl_phase1_reaction_decision_tree + euvl_phase1_logging_decision_tree ) dumpfn(dispatcher_payload, folder + "/dispatcher_payload.json") @@ -652,18 +655,18 @@ def euvl_phase1_test(): insert_initial_state(initial_state, mol_entries, folder + "/initial_state.sqlite") - subprocess.run( - [ - "GMC", - "--reaction_database=" + folder + "/rn.sqlite", - "--initial_state_database=" + folder + "/initial_state.sqlite", - "--number_of_simulations=1000", - "--base_seed=1000", - "--thread_count=" + number_of_threads, - "--step_cutoff=200", - "--energy_budget=92", - ] - ) + # subprocess.run( + # [ + # "GMC", + # "--reaction_database=" + folder + "/rn.sqlite", + # "--initial_state_database=" + folder + "/initial_state.sqlite", + # "--number_of_simulations=1000", + # "--base_seed=1000", + # "--thread_count=" + number_of_threads, + # "--step_cutoff=200", + # "--energy_budget=92", + # ] + # ) network_loader = NetworkLoader( folder + "/rn.sqlite", @@ -671,16 +674,16 @@ def euvl_phase1_test(): folder + "/initial_state.sqlite", ) - network_loader.load_initial_state_and_trajectories() + # network_loader.load_initial_state_and_trajectories() report_generator = ReportGenerator( network_loader.mol_entries, folder + "/dummy.tex", rebuild_mol_pictures=True ) - reaction_tally_report(network_loader, folder + "/reaction_tally.tex", cutoff=10) - species_report(network_loader, folder + "/species_report.tex") - simulation_replayer = SimulationReplayer(network_loader) - final_state_report(simulation_replayer, folder + "/final_state_report.tex") + # reaction_tally_report(network_loader, folder + "/reaction_tally.tex", cutoff=10) + # species_report(network_loader, folder + "/species_report.tex") + # simulation_replayer = SimulationReplayer(network_loader) + # final_state_report(simulation_replayer, folder + "/final_state_report.tex") tests_passed = True print("Number of species:", network_loader.number_of_species) @@ -691,7 +694,7 @@ def euvl_phase1_test(): tests_passed = False print("Number of reactions:", network_loader.number_of_reactions) - if network_loader.number_of_reactions == 563: + if network_loader.number_of_reactions == 560: print(bcolors.PASS + "euvl_phase_1_test: correct number of reactions" + bcolors.ENDC) else: print(bcolors.FAIL + "euvl_phase_1_test: correct number of reactions" + bcolors.ENDC) @@ -742,7 +745,7 @@ def euvl_phase2_test(): folder + "/buckets.sqlite", euvl_phase2_reaction_decision_tree, params, - euvl_phase2_logging_tree, + euvl_phase2_reaction_decision_tree, ) dumpfn(dispatcher_payload, folder + "/dispatcher_payload.json") @@ -874,13 +877,108 @@ def euvl_phase2_test(): return tests_passed +def euvl_bondnet_test(): + + start_time = time.time() + phase1_folder = "./euvl_phase1_test" + folder = "./scratch/euvl_phase2_test" + subprocess.run(["mkdir", folder]) + + mol_json = "./data/euvl_test_set.json" + database_entries = loadfn(mol_json) + + ## HY + bondnet_test_json = "./scratch/euvl_phase2_test/reaction_networks_graphs" + subprocess.run(["mkdir", bondnet_test_json]) + ## + + species_decision_tree = euvl_species_decision_tree + + params = { + "temperature": ROOM_TEMP+200.0, + "electron_free_energy": 0.0, + } + + mol_entries, dgl_molecules_dict = species_filter( + database_entries, + mol_entries_pickle_location=folder + "/mol_entries.pickle", + dgl_mol_grphs_pickle_location = folder + "/dgl_mol_graphs.pickle", + grapher_features_pickle_location= folder + "/grapher_features.pickle", + species_report=folder + "/unfiltered_species_report.tex", + species_decision_tree=species_decision_tree, + coordimer_weight=lambda mol: (mol.get_free_energy(params["temperature"])), + species_logging_decision_tree=species_decision_tree, + generate_unfiltered_mol_pictures=False, + ) + + print(len(mol_entries), "initial mol entries") + + bucket(mol_entries, folder + "/buckets.sqlite") + + print(len(mol_entries), "final mol entries") + + dispatcher_payload = DispatcherPayload( + folder + "/buckets.sqlite", + folder + "/rn.sqlite", + folder + "/reaction_report.tex", + bondnet_test_json + "/test.json" + ) + + worker_payload = WorkerPayload( + folder + "/buckets.sqlite", + euvl_phase2_reaction_decision_tree, + params, + euvl_phase2_reaction_decision_tree, + ) + + dumpfn(dispatcher_payload, folder + "/dispatcher_payload.json") + dumpfn(worker_payload, folder + "/worker_payload.json") + + subprocess.run( + [ + "mpirun", + "--use-hwthread-cpus", + "-n", + number_of_threads, + "python", + "run_network_generation.py", + folder + "/mol_entries.pickle", + folder + "/dispatcher_payload.json", + folder + "/worker_payload.json", + folder + "/dgl_mol_graphs.pickle", + folder + "/grapher_features.pickle" + ] + ) + + + tests_passed = True + execution_time = time.time() - start_time + print(f"Time taken: {execution_time}") + # Check the length + # print("Number of species:", network_loader.number_of_species) + # if network_loader.number_of_species == 103: + # print(bcolors.PASS + "euvl_phase_2_test: correct number of species" + bcolors.ENDC) + # else: + # print(bcolors.FAIL + "euvl_phase_2_test: correct number of species" + bcolors.ENDC) + # tests_passed = False + + # print("Number of reactions:", network_loader.number_of_reactions) + # if network_loader.number_of_reactions == 3912: + # print(bcolors.PASS + "euvl_phase_2_test: correct number of reactions" + bcolors.ENDC) + # else: + # print(bcolors.FAIL + "euvl_phase_2_test: correct number of reactions" + bcolors.ENDC) + # tests_passed = False + + return tests_passed + tests = [ # mg_test, # li_test, # flicho_test, # co2_test, # euvl_phase1_test, - euvl_phase2_test, + # euvl_phase2_test, + euvl_bondnet_test ] for test in tests: diff --git a/xyz_files/co3.xyz b/xyz_files/co3.xyz new file mode 100644 index 0000000..16f56dc --- /dev/null +++ b/xyz_files/co3.xyz @@ -0,0 +1,6 @@ +4 + +O 0.60173 0.50763 -0.37574 +C -0.56879 0.05546 -0.58675 +O -1.25942 0.59711 -1.59229 +O -1.13674 -0.86917 0.07778