mandos.model.apis.querying_pubchem_api.QueryingPubchemApi._query() - Code Metrics - Inspection of "build(deps): bump selenium from 3.141.0 to 4.0.0" - dmyersturnbull/mandos - Measure and Improve Code Quality continuously with Scrutinizer

Passed

Push — dependabot/pip/selenium-4.0.0 ( 707494...3d45c5 )

unknown

created 2021-11-02 21:49 UTC

QueryingPubchemApi._query() A

↳ Parent: mandos.model.apis.querying_pubchem_api

Complexity

Conditions

Size

Total Lines	7
Code Lines	7

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
cc	1
eloc	7
nop	2
dl	0
loc	7
rs	10
c	0
b	0
f	0

"""
PubChem querying API.
"""
from __future__ import annotations

import io
import time
from datetime import datetime, timezone
from typing import Any, List, Mapping, Optional, Sequence, Tuple, Union
from urllib.error import HTTPError

import orjson

import pandas as pd

import regex

from pocketutils.core.dot_dict import NestedDotDict

from pocketutils.core.exceptions import (

    DataIntegrityError,
    DownloadError,
    LookupFailedError,
)
from pocketutils.core.query_utils import QueryExecutor


from mandos.model.apis.pubchem_api import PubchemApi, PubchemCompoundLookupError
from mandos.model.apis.pubchem_support.pubchem_data import PubchemData
from mandos.model.settings import QUERY_EXECUTORS, SETTINGS
from mandos.model.utils.setup import logger


class QueryingPubchemApi(PubchemApi):

    def __init__(

        self,

        chem_data: bool = True,

        extra_tables: bool = False,

        classifiers: bool = False,

        extra_classifiers: bool = False,

        executor: QueryExecutor = QUERY_EXECUTORS.pubchem,

    ):
        self._use_chem_data = chem_data
        self._use_extra_tables = extra_tables
        self._use_classifiers = classifiers
        self._use_extra_classifiers = extra_classifiers
        self._executor = executor

    _pug = "https://pubchem.ncbi.nlm.nih.gov/rest/pug"
    _pug_view = "https://pubchem.ncbi.nlm.nih.gov/rest/pug_view"
    _sdg = "https://pubchem.ncbi.nlm.nih.gov/sdq/sdqagent.cgi"
    _classifications = "https://pubchem.ncbi.nlm.nih.gov/classification/cgi/classifications.fcgi"
    _link_db = "https://pubchem.ncbi.nlm.nih.gov/link_db/link_db_server.cgi"

    def find_inchikey(self, cid: int) -> str:

        # return self.fetch_data(cid).names_and_identifiers.inchikey
        props = self.fetch_properties(cid)
        return props["InChIKey"]

    def find_id(self, inchikey: str) -> Optional[int]:

        # we have to scrape to get the parent anyway,
        # so just download it
        # TODO: there's a faster way

        try:
            return self.fetch_data(inchikey).cid
        except PubchemCompoundLookupError:
            logger.debug(f"Could not find pubchem ID for {inchikey}", exc_info=True)
            return None

    def fetch_properties(self, cid: int) -> Mapping[str, Any]:

        url = f"{self._pug}/compound/cid/{cid}/JSON"
        #
        try:
            matches: NestedDotDict = self._query_json(url)
        except HTTPError:
            raise PubchemCompoundLookupError(f"Failed finding pubchem compound {cid}")
        props = matches["PC_Compounds"][0]["props"]
        props = {NestedDotDict(p).get("urn.label"): p.get("value") for p in props}

        def _get_val(v):

            v = NestedDotDict(v)
            for t in ["ival", "fval", "sval"]:

                if t in v.keys():
                    return v[t]

        props = {k: _get_val(v) for k, v in props.items() if k is not None and v is not None}
        logger.debug(f"DLed properties for {cid}")
        return props

    def fetch_data(self, inchikey: Union[str, int]) -> [PubchemData]:
        # Dear God this is terrible
        # Here are the steps:
        # 1. Download HTML for the InChI key and scrape the CID
        # 2. Download the "display" JSON data from the CID
        # 3. Look for a Parent-type related compound. If it exists, download its display data
        # 4. Download the structural data and append it
        # 5. Download the external table CSVs and append them
        # 6. Download the link sets and append them
        # 7. Download the classifiers (hierarchies) and append them
        # 8. Attach metadata about how we found this.
        # 9. Return the stupid, stupid result as a massive JSON struct.
        logger.info(f"Downloading PubChem data for {inchikey}")
        if isinstance(inchikey, int):
            cid = inchikey
            # note: this might not be the parent
            # that's ok -- we're about to fix that
            inchikey = self.find_inchikey(cid)
            logger.debug(f"Matched CID {cid} to {inchikey}")
        else:
            cid = self._scrape_cid(inchikey)
            logger.debug(f"Matched inchikey {inchikey} to CID {cid} (scraped)")
        stack = []
        data = self._fetch_data(cid, inchikey, stack)
        logger.debug(f"DLed raw data for {cid}/{inchikey}")
        data = self._get_parent(cid, inchikey, data, stack)
        logger.debug(f"DLed PubChem compound {cid}")
        return data

    def _scrape_cid(self, inchikey: str) -> int:
        # This is awful
        # Every attempt to get the actual, correct, unique CID corresponding to the inchikey
        # failed with every proper PubChem API
        # We can't use <pug_view>/data/compound/<inchikey> -- we can only use a CID there
        # I found it with a PUG API
        # https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/CID/GJSURZIOUXUGAL-UHFFFAOYSA-N/record/JSON
        # But that returns multiple results!!
        # There's no apparent way to find out which one is real
        # I tried then querying each found CID, getting the display data, and looking at their parents

        # Unfortunately, we end up with multiple contradictory parents
        # Plus, that's insanely slow -- we have to get the full JSON data for each parent
        # Every worse -- the PubChem API docs LIE!!
        # Using ?cids_type=parent DOES NOT GIVE THE PARENT compound
        # Ex: https://pubchem.ncbi.nlm.nih.gov/compound/656832
        # This is cocaine HCl, which has cocaine (446220) as a parent
        # https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/cid/656832/JSON
        # gives 656832 back again
        # same thing when querying by inchikey
        # Ultimately, I found that I can get HTML containing the CID from an inchikey
        # From there, we'll just have to download its "display" data and get the parent, then download that data

        url = f"https://pubchem.ncbi.nlm.nih.gov/compound/{inchikey}"
        pat = regex.compile(
            r'<meta property="og:url" content="https://pubchem\.ncbi\.nlm\.nih\.gov/compound/(\d+)">',

            flags=regex.V1,
        )
        try:
            for i in range(SETTINGS.pubchem_n_tries):

                try:
                    html = self._query(url)
                except ConnectionAbortedError:
                    logger.warning(f"Connection aborted for {inchikey} [url: {url}]", exc_info=True)
                    continue
        except HTTPError:
            raise PubchemCompoundLookupError(
                f"Failed finding pubchem compound (HTML) from {inchikey} [url: {url}]"
            )
        match = pat.search(html)

        if match is None:
            raise DataIntegrityError(
                f"Something is wrong with the HTML from {url}; og:url not found"
            )
        return int(match.group(1))

    def _get_parent(
        self, cid: int, inchikey: str, data: PubchemData, stack: List[Tuple[int, str]]

    ) -> PubchemData:
        # guard with is not None: we're not caching, so don't do it twice
        p = data.parent_or_none

        if p is None:
            logger.info(f"{cid}/{inchikey} is its own parent")
            return data
        try:
            logger.info(f"{cid}/{inchikey} has parent {p}")
            del data
            return self._fetch_data(p, inchikey, stack)
        except HTTPError:
            raise PubchemCompoundLookupError(
                f"Failed finding pubchem parent compound (JSON)"
                f"for cid {p}, child cid {cid}, inchikey {inchikey}"
            )

    def _fetch_data(self, cid: int, inchikey: str, stack: List[Tuple[int, str]]) -> PubchemData:
        when_started = datetime.now(timezone.utc).astimezone()
        t0 = time.monotonic()

        try:
            data = self._fetch_core_data(cid, stack)
        except HTTPError:
            raise PubchemCompoundLookupError(
                f"Failed finding pubchem compound (JSON) from cid {cid}, inchikey {inchikey}"
            )
        t1 = time.monotonic()

        when_finished = datetime.now(timezone.utc).astimezone()
        logger.trace(f"Downloaded {cid} in {t1-t0} s")
        data["meta"] = self._get_metadata(inchikey, when_started, when_finished, t0, t1)
        self._strip_by_key_in_place(data, "DisplayControls")
        stack.append((cid, inchikey))
        logger.trace(f"Stack: {stack}")
        return PubchemData(NestedDotDict(data))

    def _fetch_core_data(self, cid: int, stack: List[Tuple[int, str]]) -> dict:
        return dict(
            record=self._fetch_display_data(cid),
            linked_records=self._get_linked_records(cid, stack),
            structure=self._fetch_structure_data(cid),
            external_tables=self._fetch_external_tables(cid),
            link_sets=self._fetch_external_linksets(cid),
            classifications=self._fetch_hierarchies(cid),
            properties=NestedDotDict(self.fetch_properties(cid)),
        )

    def _get_metadata(self, inchikey: str, started: datetime, finished: datetime, t0: int, t1: int):
class Foo:
    def some_method(self, x, y):
        return x + y;
        return dict(
            timestamp_fetch_started=started.isoformat(),
            timestamp_fetch_finished=finished.isoformat(),
            from_lookup=inchikey,
            fetch_nanos_taken=str(t1 - t0),
        )

    def _get_linked_records(self, cid: int, stack: List[Tuple[int, str]]) -> NestedDotDict:
        url = f"{self._pug}/compound/cid/{cid}/cids/JSON?cids_type=same_parent_stereo"
        data = self._query_json(url).sub("IdentifierList")
        logger.debug(f"DLed {len(data.get('CID', []))} linked records for {cid}")
        results = {
            "CID": [*data.get("CID", []), *[s for s, _ in stack]],
            "inchikey": [i for _, i in stack],
        }
        logger.debug(f"Linked records are: {results}")
        return NestedDotDict(results)

    def _fetch_display_data(self, cid: int) -> Optional[NestedDotDict]:
        url = f"{self._pug_view}/data/compound/{cid}/JSON/?response_type=display"
        data = self._query_json(url)["Record"]
        logger.debug(f"DLed display data for {cid}")
        return data

    def _fetch_structure_data(self, cid: int) -> NestedDotDict:
        if not self._use_chem_data:
            return NestedDotDict({})
        url = f"{self._pug}/compound/cid/{cid}/JSON"
        data = self._query_json(url)["PC_Compounds"][0]
        del data["props"]  # redundant with props section in record
        logger.debug(f"DLed structure for {cid}")
        return data

    def _fetch_external_tables(self, cid: int) -> Mapping[str, str]:
        x = {

            ext_table: self._fetch_external_table(cid, ext_table)
            for ext_table in self._tables_to_use.values()
        }
        logger.debug(f"DLed {len(self._tables_to_use)} external tables for {cid}")
        return x

    def _fetch_external_linksets(self, cid: int) -> Mapping[str, str]:
        x = {

            table: self._fetch_external_linkset(cid, table)
            for table in self._linksets_to_use.values()
        }
        logger.debug(f"DLed {len(self._linksets_to_use)} external linksets for {cid}")
        return x

    def _fetch_hierarchies(self, cid: int) -> NestedDotDict:
        build_up = {}
        for hname, hid in self._hierarchies_to_use.items():
            try:
                build_up[hname] = self._fetch_hierarchy(cid, hname, hid)
            except (HTTPError, KeyError, LookupError) as e:

                logger.debug(f"No data for classifier {hid}, compound {cid}: {e}")
        # These list all of the child nodes for each node
        # Some of them are > 1000 items -- they're HUGE
        # We don't expect to need to navigate to children
        self._strip_by_key_in_place(build_up, "ChildID")
        logger.debug(f"DLed {len(self._hierarchies_to_use)} hierarchies for {cid}")
        return NestedDotDict(build_up)

    def _fetch_external_table(self, cid: int, table: str) -> Sequence[dict]:
        url = self._external_table_url(cid, table)
        data = self._query(url)
        df: pd.DataFrame = pd.read_csv(io.StringIO(data)).reset_index()

        logger.debug(f"DLed table {table} with {len(df)} rows for {cid}")
        return list(df.to_dict(orient="records"))

    def _fetch_external_linkset(self, cid: int, table: str) -> NestedDotDict:
        url = f"{self._link_db}?format=JSON&type={table}&operation=GetAllLinks&id_1={cid}"
        data = self._query(url)
        logger.debug(f"DLed linkset {table} rows for {cid}")
        return NestedDotDict(orjson.loads(data))

    def _fetch_hierarchy(self, cid: int, hname: str, hid: int) -> Sequence[dict]:
        url = f"{self._classifications}?format=json&hid={hid}&search_uid_type=cid&search_uid={cid}&search_type=list&response_type=display"

        data: Sequence[dict] = orjson.loads(self._query(url))["Hierarchies"]
        # underneath Hierarchies is a list of Hierarchy
        logger.debug(f"Found data for classifier {hid}, compound {cid}")
        if len(data) == 0:
            raise LookupFailedError(f"Failed getting hierarchy {hid}")
        logger.debug(f"DLed hierarchy {hname} ({hid}) for {cid}")
        return data

    @property
    def _tables_to_use(self) -> Mapping[str, str]:
        dct = {
            "drug:clinicaltrials.gov:clinical_trials": "clinicaltrials",
            "pharm:pubchem:reactions": "pathwayreaction",
            "uses:cpdat:uses": "cpdat",
            "tox:chemidplus:acute_effects": "chemidplus",
            "dis:ctd:associated_disorders_and_diseases": "ctd_chemical_disease",
            "lit:pubchem:depositor_provided_pubmed_citations": "pubmed",
            "bio:dgidb:drug_gene_interactions": "dgidb",
            "bio:ctd:chemical_gene_interactions": "ctdchemicalgene",
            "bio:drugbank:drugbank_interactions": "drugbank",
            "bio:drugbank:drug_drug_interactions": "drugbankddi",
            "bio:pubchem:bioassay_results": "bioactivity",
        }
        if self._use_extra_tables:
            dct.update(
                {
                    "patent:depositor_provided_patent_identifiers": "patent",
                    "bio:rcsb_pdb:protein_bound_3d_structures": "pdb",
                    "related:pubchem:related_compounds_with_annotation": "compound",
                }
            )
        return dct

    @property
    def _linksets_to_use(self) -> Mapping[str, str]:
        return {
            "lit:pubchem:chemical_cooccurrences_in_literature": "ChemicalNeighbor",
            "lit:pubchem:gene_cooccurrences_in_literature": "ChemicalGeneSymbolNeighbor",
            "lit:pubchem:disease_cooccurrences_in_literature": "ChemicalDiseaseNeighbor",
        }

    @property
    def _hierarchies_to_use(self) -> Mapping[str, int]:
        if not self._use_classifiers:
            return {}
        dct = {
            "MeSH Tree": 1,
            "ChEBI Ontology": 2,
            "WHO ATC Classification System": 79,
            "Guide to PHARMACOLOGY Target Classification": 92,
            "ChEMBL Target Tree": 87,
        }
        if self._use_extra_classifiers:
            dct.update(
                {
                    "KEGG: Phytochemical Compounds": 5,
                    "KEGG: Drug": 14,
                    "KEGG: USP": 15,
                    "KEGG: Major components of natural products": 69,
                    "KEGG: Target-based Classification of Drugs": 22,
                    "KEGG: OTC drugs": 25,
                    "KEGG: Drug Classes": 96,
                    "CAMEO Chemicals": 86,
                    "EPA CPDat Classification": 99,
                    "FDA Pharm Classes": 78,
                    "ChemIDplus": 84,
                }
            )
        return dct

    def _external_table_url(self, cid: int, collection: str) -> str:
        return (
            self._sdg
            + "?infmt=json"
            + "&outfmt=csv"
            + "&query={ download : * , collection : "
            + collection
            + " , where :{ ands :[{ cid : "
            + str(cid)
            + " }]}}"
        ).replace(" ", "%22")

    def _query_json(self, url: str) -> NestedDotDict:
        data = self._query(url)
        data = NestedDotDict(orjson.loads(data))
        if "Fault" in data:
            raise DownloadError(
                f"PubChem query failed ({data.get('Code')}) on {url}: {data.get('Message')}"
            )
        logger.trace(
            f"Fetched JSON has {data.n_bytes_total()} bytes and {data.n_elements_total()} elements"
        )
        return data

    def _query(self, url: str):
        data = self._executor(url)
        tt = self._executor.last_time_taken

        wt, qt = tt.wait.total_seconds(), tt.query.total_seconds()

        bts = int(len(data) * 8 / 1024)
        logger.trace(f"Queried {bts} kb from {url} in {qt:.1} s with {wt:.1} s of wait")
        return data

    def _strip_by_key_in_place(self, data: Union[dict, list], bad_key: str) -> None:
        if isinstance(data, list):
            for x in data:

                self._strip_by_key_in_place(x, bad_key)
        elif isinstance(data, dict):
            for k, v in list(data.items()):

                if k == bad_key:
                    del data[k]
                elif isinstance(v, (list, dict)):
                    self._strip_by_key_in_place(v, bad_key)


__all__ = ["QueryingPubchemApi"]


1			"""
2			PubChem querying API.
3			"""
4			from __future__ import annotations
5
6			import io
7			import time
8			from datetime import datetime, timezone
9			from typing import Any, List, Mapping, Optional, Sequence, Tuple, Union
10			from urllib.error import HTTPError
11
12			import orjson
			0 ignored issues – show introduced 2021-06-30 04:51 UTC by Report Bug Copy Issue Report Unable to import 'orjson' Loading history...
13			import pandas as pd
			0 ignored issues – show introduced 2021-03-22 20:05 UTC by Report Bug Copy Issue Report Unable to import 'pandas' Loading history...
14			import regex
			0 ignored issues – show introduced 2021-08-07 00:15 UTC by Report Bug Copy Issue Report Unable to import 'regex' Loading history...
15			from pocketutils.core.dot_dict import NestedDotDict
			0 ignored issues – show introduced 2021-06-30 04:51 UTC by Report Bug Copy Issue Report Unable to import 'pocketutils.core.dot_dict' Loading history...
16			from pocketutils.core.exceptions import (
			0 ignored issues – show introduced 2021-09-23 03:01 UTC by Report Bug Copy Issue Report Unable to import 'pocketutils.core.exceptions' Loading history...
17			DataIntegrityError,
18			DownloadError,
19			LookupFailedError,
20			)
21			from pocketutils.core.query_utils import QueryExecutor
			0 ignored issues – show introduced 2021-06-30 04:51 UTC by Report Bug Copy Issue Report Unable to import 'pocketutils.core.query_utils' Loading history...
22
23			from mandos.model.apis.pubchem_api import PubchemApi, PubchemCompoundLookupError
24			from mandos.model.apis.pubchem_support.pubchem_data import PubchemData
25			from mandos.model.settings import QUERY_EXECUTORS, SETTINGS
26			from mandos.model.utils.setup import logger
27
28
29			class QueryingPubchemApi(PubchemApi):
			0 ignored issues – show introduced 2021-01-25 23:06 UTC by Report Bug Copy Issue Report Missing class docstring Loading history...
30			def __init__(
			0 ignored issues – show best-practice introduced 2021-03-10 02:41 UTC by Report Bug Copy Issue Report Too many arguments (6/5) Loading history...
31			self,
			0 ignored issues – show Coding Style introduced 2021-03-10 02:41 UTC by Report Bug Copy Issue Report Wrong hanging indentation before block (add 4 spaces). Loading history...

dmyersturnbull / mandos

Push — dependabot/pip/selenium-4.0.0 ( 707494...3d45c5 )

QueryingPubchemApi._query() A

Complexity

Size

Duplication

Importance

Duplication Side-by-Side

Filter issues like