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""" |
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Run searches and write files. |
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""" |
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from __future__ import annotations |
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from dataclasses import dataclass |
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from pathlib import Path |
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from typing import Callable, Optional, Sequence, Mapping, Set |
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import pandas as pd |
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from pocketutils.core.dot_dict import NestedDotDict |
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from pocketutils.tools.common_tools import CommonTools |
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from typeddfs import TypedDfs |
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from mandos import logger |
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from mandos.entries.api_singletons import Apis |
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from mandos.entries.paths import EntryPaths |
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from mandos.model import CompoundNotFoundError |
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from mandos.model.apis.chembl_support.chembl_utils import ChemblUtils |
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from mandos.model.apis.pubchem_api import PubchemApi |
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from mandos.model.hits import HitFrame |
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from mandos.model.searches import Search |
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from mandos.search.chembl import ChemblSearch |
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from mandos.search.pubchem import PubchemSearch |
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def _get_structure(df) -> Optional[Sequence[str]]: |
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if "inchi" in df.columns: |
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return df["inchi"].values |
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if "smiles" in df.columns: |
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return df["smiles"].values |
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return None |
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def _fix_cols(df): |
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return df.rename(columns={s: s.lower() for s in df.columns}) |
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InputFrame = ( |
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TypedDfs.typed("InputFrame") |
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.require("inchikey") |
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.reserve("inchi", "smiles", "compound_id", dtype=str) |
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.post(_fix_cols) |
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.strict(index=True, cols=False) |
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).build() |
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InputFrame.get_structures = _get_structure |
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IdMatchFrame = ( |
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TypedDfs.typed("IdMatchFrame") |
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.reserve("inchikey", dtype=str) |
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.reserve("inchi", "smiles", "compound_id", dtype=str) |
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.reserve("chembl_id", "pubchem_id", "hmdb_id", dtype=str) |
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.reserve("origin_inchikey", "origin_smiles", dtype=str) |
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.reserve("library", dtype=str) |
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.strict(index=True, cols=False) |
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).build() |
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@dataclass(frozen=True, repr=True) |
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class ChemFinder: |
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what: str |
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how: Callable[[str], str] |
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@classmethod |
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def chembl(cls) -> ChemFinder: |
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def how(inchikey: str) -> str: |
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return ChemblUtils(Apis.Chembl).get_compound(inchikey).chid |
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return ChemFinder("ChEMBL", how) |
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@classmethod |
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def pubchem(cls) -> ChemFinder: |
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def how(inchikey: str) -> str: |
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api: PubchemApi = Apis.Pubchem |
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return str(api.find_id(inchikey)) |
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return ChemFinder("PubChem", how) |
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def find(self, inchikey: str) -> Optional[str]: |
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try: |
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return self.how(inchikey) |
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except CompoundNotFoundError: |
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logger.info(f"NOT FOUND: {self.what.rjust(8)} ] {inchikey}") |
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logger.debug(f"Did not find {self.what} {inchikey}", exc_info=True) |
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return None |
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class SearcherUtils: |
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@classmethod |
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def dl( |
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cls, |
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inchikeys: Sequence[str], |
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pubchem: bool = True, |
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chembl: bool = True, |
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hmdb: bool = True, |
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) -> IdMatchFrame: |
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df = IdMatchFrame([pd.Series(dict(inchikey=c)) for c in inchikeys]) |
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if chembl: |
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df["chembl_id"] = df["inchikey"].map(ChemFinder.chembl().find) |
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if pubchem: |
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df["pubchem_id"] = df["inchikey"].map(ChemFinder.pubchem().find) |
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return df |
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@classmethod |
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def read(cls, input_path: Path) -> InputFrame: |
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df = InputFrame.read_file(input_path) |
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logger.info(f"Read {len(df)} input compounds") |
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return df |
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class CompoundIdFiller: |
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@classmethod |
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def fill( |
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cls, |
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df: IdMatchFrame, |
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) -> IdMatchFrame: |
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matchable = {"inchikey", "pubchem_id", "chembl_id"} |
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sources = {s for s in matchable if s in df.columns and not df[s].isnull().all()} |
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targets = {s for s in matchable if s not in df.columns or df[s].isnull().all()} |
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# noinspection PyUnresolvedReferences |
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logger.notice(f"Copying {sources} to {targets}") |
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source = next(iter(sources)) |
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# watch out! these are simply in order, nothing more |
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remapped = {t: [] for t in targets} |
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for source_val in df[source].values: |
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matches = cls._matches(source, source_val, targets) |
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for target, target_val in matches.items(): |
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remapped[target].append(target_val) |
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remapped.update(matches) |
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for target in targets: |
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df[target] = remapped[target] |
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@classmethod |
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def _matches(cls, source: str, source_val: str, targets: Set[str]) -> Mapping[str, str]: |
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if source == "pubchem_id": |
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inchikey = Apis.Pubchem.find_inchikey(int(source_val)) |
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elif source == "chembl_id": |
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# TODO |
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# get_compound wants an inchikey, |
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# but we're secretly passing a CHEMBLxxxx ID instead |
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# we just know that that works |
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inchikey = ChemblUtils(Apis.Chembl).get_compound(source_val).inchikey |
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elif source == "inchikey": |
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inchikey = source |
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else: |
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raise AssertionError(source) |
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matched = {} if source == "inchikey" else dict(inchikey=inchikey) |
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if "pubchem_id" in targets: |
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pubchem_id = ChemFinder.pubchem().find(inchikey) |
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if pubchem_id is not None: |
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matched["pubchem_id"] = str(pubchem_id) |
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if "chembl_id" in targets: |
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chembl_id = ChemFinder.chembl().find(inchikey) |
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if chembl_id is not None: |
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matched["chembl_id"] = chembl_id |
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return matched |
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class Searcher: |
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""" |
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Executes one or more searches and saves the results to CSV files. |
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Create and use once. |
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""" |
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def __init__(self, searches: Sequence[Search], to: Sequence[Path], input_path: Path): |
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""" |
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Constructor. |
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Args: |
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searches: |
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input_path: Path to the input file of one of the formats: |
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- .txt containing one InChI Key per line |
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- .csv, .tsv, .tab, csv.gz, .tsv.gz, .tab.gz, or .feather containing a column called inchikey |
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""" |
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self.what = searches |
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self.input_path: Optional[Path] = input_path |
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self.input_df: InputFrame = None |
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self.output_paths = { |
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what.key: EntryPaths.output_path_of(what, input_path, path) |
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for what, path in CommonTools.zip_list(searches, to) |
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} |
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def search(self) -> Searcher: |
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""" |
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Performs the search, and writes data. |
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""" |
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if self.input_df is not None: |
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raise ValueError(f"Already ran a search") |
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self.input_df = SearcherUtils.read(self.input_path) |
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inchikeys = self.input_df["inchikey"].unique() |
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has_pubchem = any((isinstance(what, PubchemSearch) for what in self.what)) |
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has_chembl = any((isinstance(what, ChemblSearch) for what in self.what)) |
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# find the compounds first so the user knows what's missing before proceeding |
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SearcherUtils.dl(inchikeys, pubchem=has_pubchem, chembl=has_chembl) |
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for what in self.what: |
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output_path = self.output_paths[what.key] |
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metadata_path = output_path.with_suffix(".json.metadata") |
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df = what.find_to_df(inchikeys) |
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# keep all of the original extra columns from the input |
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# e.g. if the user had 'inchi' or 'smiles' or 'pretty_name' |
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for extra_col in [c for c in self.input_df.columns if c != "inchikey"]: |
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extra_mp = self.input_df.set_index("inchikey")[extra_col].to_dict() |
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df[extra_col] = df["lookup"].map(extra_mp.get) |
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# write the (intermediate) file |
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df.write_file(output_path) |
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# write metadata |
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params = {k: str(v) for k, v in what.get_params().items() if k not in {"key", "api"}} |
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metadata = NestedDotDict(dict(key=what.key, search=what.search_class, params=params)) |
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metadata.write_json(metadata_path) |
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logger.info(f"Wrote {what.key} to {output_path}") |
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return self |
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__all__ = ["Searcher", "IdMatchFrame", "SearcherUtils", "CompoundIdFiller", "InputFrame"] |
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dmyersturnbull /
mandos
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