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""" |
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PubChem querying API. |
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""" |
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from __future__ import annotations |
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import abc |
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import logging |
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from pathlib import Path |
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from typing import Optional, Sequence, Union, FrozenSet, Mapping |
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import io |
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import gzip |
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import orjson |
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import pandas as pd |
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from pocketutils.core.dot_dict import NestedDotDict |
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from mandos.model.pubchem_api import PubchemCompoundLookupError, PubchemApi |
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from mandos.model.pubchem_support.pubchem_data import PubchemData |
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from mandos.model.querying_pubchem_api import QueryingPubchemApi |
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logger = logging.getLogger("mandos") |
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class CachingPubchemApi(PubchemApi): |
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def __init__( |
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self, cache_dir: Path, querier: Optional[QueryingPubchemApi], compress: bool = True |
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): |
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self._cache_dir = cache_dir |
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self._querier = querier |
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self._compress = compress |
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def fetch_data(self, inchikey: str) -> Optional[PubchemData]: |
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path = self.data_path(inchikey) |
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if path.exists(): |
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logger.info(f"Found cached PubChem data at {path.absolute()}") |
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elif self._querier is None: |
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raise PubchemCompoundLookupError(f"Key {inchikey} not found cached at {path}") |
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else: |
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logger.info(f"Downloading PubChem data for {inchikey} ...") |
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data = self._querier.fetch_data(inchikey) |
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path.parent.mkdir(parents=True, exist_ok=True) |
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encoded = data.to_json() |
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self._write_json(encoded, path) |
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logger.info(f"Wrote PubChem data to {path.absolute()}") |
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return data |
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read = self._read_json(path) |
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return PubchemData(read) |
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def data_path(self, inchikey: str): |
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ext = ".json.gz" if self._compress else ".json" |
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return self._cache_dir / "data" / f"{inchikey}{ext}" |
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def similarity_path(self, inchikey: str): |
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ext = ".tab.gz" if self._compress else ".tab" |
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return self._cache_dir / "similarity" / f"{inchikey}{ext}" |
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def _write_json(self, encoded: str, path: Path) -> None: |
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if self._compress: |
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path.write_bytes(gzip.compress(encoded.encode(encoding="utf8"))) |
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else: |
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path.write_text(encoded, encoding="utf8") |
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def _read_json(self, path: Path) -> NestedDotDict: |
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if self._compress: |
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deflated = gzip.decompress(path.read_bytes()) |
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read = orjson.loads(deflated) |
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else: |
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read = orjson.loads(path.read_text(encoding="utf8")) |
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return NestedDotDict(read) |
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def find_similar_compounds(self, inchi: Union[int, str], min_tc: float) -> FrozenSet[int]: |
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path = self.similarity_path(inchi) |
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if not path.exists(): |
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df = None |
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existing = set() |
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else: |
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df = pd.read_csv(path, sep="\t") |
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df = df[df["min_tc"] < min_tc] |
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existing = set(df["cid"].values) |
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if len(existing) == 0: |
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found = self._querier.find_similar_compounds(inchi, min_tc) |
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path.parent.mkdir(parents=True, exist_ok=True) |
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new_df = pd.DataFrame([pd.Series(dict(cid=cid, min_tc=min_tc)) for cid in found]) |
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if df is not None: |
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new_df = pd.concat([df, new_df]) |
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new_df.to_csv(path, sep="\t") |
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return frozenset(existing.union(found)) |
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else: |
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return frozenset(existing) |
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__all__ = ["CachingPubchemApi"] |
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dmyersturnbull /
mandos
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