"""Database and utility functions for phenotypes."""
import logging
import tempfile
from pathlib import Path
from functools import reduce
from datetime import datetime
from typing import Optional, Iterable

import MySQLdb as mdb
from MySQLdb.cursors import Cursor, DictCursor

from gn_libs.mysqldb import debug_query

from functional_tools import take

logger = logging.getLogger(__name__)


__PHENO_DATA_TABLES__ = {
    "PublishData": {
        "table": "PublishData", "valueCol": "value", "DataIdCol": "Id"},
    "PublishSE": {
        "table": "PublishSE", "valueCol": "error", "DataIdCol": "DataId"},
    "NStrain": {
        "table": "NStrain", "valueCol": "count", "DataIdCol": "DataId"}
}


def datasets_by_population(
        conn: mdb.Connection,
        species_id: int,
        population_id: int
) -> tuple[dict, ...]:
    """Retrieve all of a population's phenotype studies."""
    with conn.cursor(cursorclass=DictCursor) as cursor:
        cursor.execute(
            "SELECT s.SpeciesId, pf.* FROM Species AS s "
            "INNER JOIN InbredSet AS iset ON s.Id=iset.SpeciesId "
            "INNER JOIN PublishFreeze AS pf ON iset.Id=pf.InbredSetId "
            "WHERE s.Id=%s AND iset.Id=%s;",
                       (species_id, population_id))
        return tuple(dict(row) for row in cursor.fetchall())


def dataset_by_id(conn: mdb.Connection,
                  species_id: int,
                  population_id: int,
                  dataset_id: int) -> dict:
    """Fetch dataset details by identifier"""
    with conn.cursor(cursorclass=DictCursor) as cursor:
        cursor.execute(
            "SELECT Species.SpeciesId, PublishFreeze.* FROM Species "
            "INNER JOIN InbredSet ON Species.Id=InbredSet.SpeciesId "
            "INNER JOIN PublishFreeze ON InbredSet.Id=PublishFreeze.InbredSetId "
            "WHERE Species.Id=%s AND InbredSet.Id=%s AND PublishFreeze.Id=%s",
            (species_id, population_id, dataset_id))
        return dict(cursor.fetchone())


def phenotypes_count(conn: mdb.Connection,
                     population_id: int,
                     dataset_id: int) -> int:
    """Count the number of phenotypes in the dataset."""
    with conn.cursor(cursorclass=DictCursor) as cursor:
        cursor.execute(
            "SELECT COUNT(*) AS total_phenos FROM Phenotype AS pheno "
            "INNER JOIN PublishXRef AS pxr ON pheno.Id=pxr.PhenotypeId "
            "INNER JOIN PublishFreeze AS pf ON pxr.InbredSetId=pf.InbredSetId "
            "WHERE pxr.InbredSetId=%s AND pf.Id=%s",
        (population_id, dataset_id))
        return int(cursor.fetchone()["total_phenos"])


def phenotype_publication_data(conn, phenotype_id) -> Optional[dict]:
    """Retrieve the publication data for a phenotype if it exists."""
    with conn.cursor(cursorclass=DictCursor) as cursor:
        cursor.execute(
            "SELECT DISTINCT pxr.PhenotypeId, pub.* FROM PublishXRef AS pxr "
            "INNER JOIN Publication as pub ON pxr.PublicationId=pub.Id "
            "WHERE pxr.PhenotypeId=%s",
            (phenotype_id,))
        res = cursor.fetchone()
        if res is None:
            return res
        return dict(res)


def dataset_phenotypes(conn: mdb.Connection,
                       population_id: int,
                       dataset_id: int,
                       offset: int = 0,
                       limit: Optional[int] = None) -> tuple[dict, ...]:
    """Fetch the actual phenotypes."""
    _query = (
        "SELECT pheno.*, pxr.Id AS xref_id, pxr.InbredSetId, ist.InbredSetCode "
        "FROM Phenotype AS pheno "
        "INNER JOIN PublishXRef AS pxr ON pheno.Id=pxr.PhenotypeId "
        "INNER JOIN PublishFreeze AS pf ON pxr.InbredSetId=pf.InbredSetId "
        "INNER JOIN InbredSet AS ist ON pf.InbredSetId=ist.Id "
        "WHERE pxr.InbredSetId=%s AND pf.Id=%s") + (
            f" LIMIT {limit} OFFSET {offset}" if bool(limit) else "")
    with conn.cursor(cursorclass=DictCursor) as cursor:
        cursor.execute(_query, (population_id, dataset_id))
        debug_query(cursor, logger)
        return tuple(dict(row) for row in cursor.fetchall())


def __phenotype_se__(cursor: Cursor, xref_id, dataids_and_strainids):
    """Fetch standard-error values (if they exist) for a phenotype."""
    paramstr = ", ".join(["(%s, %s)"] * len(dataids_and_strainids))
    flat = tuple(item for sublist in dataids_and_strainids for item in sublist)
    cursor.execute("SELECT * FROM PublishSE WHERE (DataId, StrainId) IN "
                   f"({paramstr})",
                   flat)
    debug_query(cursor, logger)
    _se = {
        (row["DataId"], row["StrainId"]): {
            "DataId": row["DataId"],
            "StrainId": row["StrainId"],
            "error": row["error"]
        }
        for row in cursor.fetchall()
    }

    cursor.execute("SELECT * FROM NStrain WHERE (DataId, StrainId) IN "
                   f"({paramstr})",
                   flat)
    debug_query(cursor, logger)
    _n = {
        (row["DataId"], row["StrainId"]): {
            "DataId": row["DataId"],
            "StrainId": row["StrainId"],
            "count": row["count"]
        }
        for row in cursor.fetchall()
    }

    keys = set(tuple(_se.keys()) + tuple(_n.keys()))
    return {
        key: {"xref_id": xref_id, **_se.get(key,{}), **_n.get(key,{})}
        for key in keys
    }

def __organise_by_phenotype__(pheno, row):
    """Organise disparate data rows into phenotype 'objects'."""
    _pheno = pheno.get(row["Id"])
    return {
        **pheno,
        row["Id"]: {
            "Id": row["Id"],
            "Pre_publication_description": row["Pre_publication_description"],
            "Post_publication_description": row["Post_publication_description"],
            "Original_description": row["Original_description"],
            "Units": row["Units"],
            "Pre_publication_abbreviation": row["Pre_publication_abbreviation"],
            "Post_publication_abbreviation": row["Post_publication_abbreviation"],
            "xref_id": row["pxr.Id"],
            "DataId": row["DataId"],
            "data": {
                **(_pheno["data"] if bool(_pheno) else {}),
                (row["DataId"], row["StrainId"]): {
                    "DataId": row["DataId"],
                    "StrainId": row["StrainId"],
                    "mean": row["mean"],
                    "Locus": row["Locus"],
                    "LRS": row["LRS"],
                    "additive": row["additive"],
                    "Sequence": row["Sequence"],
                    "comments": row["comments"],
                    "value": row["value"],
                    "StrainName": row["Name"],
                    "StrainName2": row["Name2"],
                    "StrainSymbol": row["Symbol"],
                    "StrainAlias": row["Alias"]
                }
            }
        }
    }


def __merge_pheno_data_and_se__(data, sedata) -> dict:
    """Merge phenotype data with the standard errors."""
    return {
        key: {**value, **sedata.get(key, {})}
        for key, value in data.items()
    }


def phenotype_by_id(
        conn: mdb.Connection,
        species_id: int,
        population_id: int,
        dataset_id: int,
        xref_id
) -> Optional[dict]:
    """Fetch a specific phenotype."""
    _dataquery = ("SELECT pheno.*, pxr.*, pd.*, str.*, iset.InbredSetCode "
                  "FROM Phenotype AS pheno "
                  "INNER JOIN PublishXRef AS pxr ON pheno.Id=pxr.PhenotypeId "
                  "INNER JOIN PublishData AS pd ON pxr.DataId=pd.Id "
                  "INNER JOIN Strain AS str ON pd.StrainId=str.Id "
                  "INNER JOIN StrainXRef AS sxr ON str.Id=sxr.StrainId "
                  "INNER JOIN PublishFreeze AS pf ON sxr.InbredSetId=pf.InbredSetId "
                  "INNER JOIN InbredSet AS iset ON pf.InbredSetId=iset.InbredSetId "
                  "WHERE "
                  "(str.SpeciesId, pxr.InbredSetId, pf.Id, pxr.Id)=(%s, %s, %s, %s)")
    with conn.cursor(cursorclass=DictCursor) as cursor:
        cursor.execute(_dataquery,
                       (species_id, population_id, dataset_id, xref_id))
        _pheno: dict = reduce(__organise_by_phenotype__, cursor.fetchall(), {})
        if bool(_pheno) and len(_pheno.keys()) == 1:
            _pheno = tuple(_pheno.values())[0]
            return {
                **_pheno,
                "data": tuple(__merge_pheno_data_and_se__(
                    _pheno["data"],
                    __phenotype_se__(
                        cursor, xref_id, tuple(_pheno["data"].keys()))
                ).values())
            }
        if bool(_pheno) and len(_pheno.keys()) > 1:
            raise Exception(# pylint: disable=[broad-exception-raised]
                "We found more than one phenotype with the same identifier!")

    return None


def phenotypes_data(conn: mdb.Connection,
                    population_id: int,
                    dataset_id: int,
                    offset: int = 0,
                    limit: Optional[int] = None) -> tuple[dict, ...]:
    """Fetch the data for the phenotypes."""
    # — Phenotype -> PublishXRef -> PublishData -> Strain -> StrainXRef -> PublishFreeze
    _query = ("SELECT pheno.*, pxr.*, pd.*, str.*, iset.InbredSetCode "
              "FROM Phenotype AS pheno "
              "INNER JOIN PublishXRef AS pxr ON pheno.Id=pxr.PhenotypeId "
              "INNER JOIN PublishData AS pd ON pxr.DataId=pd.Id "
              "INNER JOIN Strain AS str ON pd.StrainId=str.Id "
              "INNER JOIN StrainXRef AS sxr ON str.Id=sxr.StrainId "
              "INNER JOIN PublishFreeze AS pf ON sxr.InbredSetId=pf.InbredSetId "
              "INNER JOIN InbredSet AS iset ON pf.InbredSetId=iset.InbredSetId "
              "WHERE pxr.InbredSetId=%s AND pf.Id=%s") + (
                  f" LIMIT {limit} OFFSET {offset}" if bool(limit) else "")
    with conn.cursor(cursorclass=DictCursor) as cursor:
        cursor.execute(_query, (population_id, dataset_id))
        debug_query(cursor, logger)
        return tuple(dict(row) for row in cursor.fetchall())


def save_new_dataset(cursor: Cursor,
                     population_id: int,
                     dataset_name: str,
                     dataset_fullname: str,
                     dataset_shortname: str) -> dict:
    """Create a new phenotype dataset."""
    params = {
        "population_id": population_id,
        "dataset_name": dataset_name,
        "dataset_fullname": dataset_fullname,
        "dataset_shortname": dataset_shortname,
        "created": datetime.now().date().isoformat(),
        "public": 2,
        "confidentiality": 0,
        "users": None
    }
    cursor.execute(
        "INSERT INTO PublishFreeze(Name, FullName, ShortName, CreateTime, "
        "public, InbredSetId, confidentiality, AuthorisedUsers) "
        "VALUES(%(dataset_name)s, %(dataset_fullname)s, %(dataset_shortname)s, "
        "%(created)s, %(public)s, %(population_id)s, %(confidentiality)s, "
        "%(users)s)",
        params)
    debug_query(cursor, logger)
    return {**params, "Id": cursor.lastrowid}


def phenotypes_data_by_ids(
        conn: mdb.Connection,
        inbred_pheno_xref: dict[str, int]
) -> tuple[dict, ...]:
    """Fetch all phenotype data, filtered by the `inbred_pheno_xref` mapping."""
    _paramstr = ",".join(["(%s, %s, %s)"] * len(inbred_pheno_xref))
    _query = ("SELECT "
              "pub.PubMed_ID, pheno.*, pxr.*, pd.*, str.*, iset.InbredSetCode "
              "FROM Publication AS pub "
              "RIGHT JOIN PublishXRef AS pxr0 ON pub.Id=pxr0.PublicationId "
              "INNER JOIN Phenotype AS pheno ON pxr0.PhenotypeId=pheno.id "
              "INNER JOIN PublishXRef AS pxr ON pheno.Id=pxr.PhenotypeId "
              "INNER JOIN PublishData AS pd ON pxr.DataId=pd.Id "
              "INNER JOIN Strain AS str ON pd.StrainId=str.Id "
              "INNER JOIN StrainXRef AS sxr ON str.Id=sxr.StrainId "
              "INNER JOIN PublishFreeze AS pf ON sxr.InbredSetId=pf.InbredSetId "
              "INNER JOIN InbredSet AS iset ON pf.InbredSetId=iset.InbredSetId "
              f"WHERE (pxr.InbredSetId, pheno.Id, pxr.Id) IN ({_paramstr}) "
              "ORDER BY pheno.Id")
    with conn.cursor(cursorclass=DictCursor) as cursor:
        cursor.execute(_query, tuple(item for row in inbred_pheno_xref
                                     for item in (row["population_id"],
                                                  row["phenoid"],
                                                  row["xref_id"])))
        debug_query(cursor, logger)
        return tuple(
            reduce(__organise_by_phenotype__, cursor.fetchall(), {}).values())


def __pre_process_phenotype_data__(row):
    _desc = row.get("description", "")
    _pre_pub_desc = row.get("pre_publication_description", _desc)
    _orig_desc = row.get("original_description", _desc)
    _post_pub_desc = row.get("post_publication_description", _orig_desc)
    _pre_pub_abbr = row.get("pre_publication_abbreviation", row["id"])
    _post_pub_abbr = row.get("post_publication_abbreviation", _pre_pub_abbr)
    return {
        "pre_publication_description": _pre_pub_desc,
        "post_publication_description": _post_pub_desc,
        "original_description": _orig_desc,
        "units": row["units"],
        "pre_publication_abbreviation": _pre_pub_abbr,
        "post_publication_abbreviation": _post_pub_abbr
    }


def create_new_phenotypes(conn: mdb.Connection,
                          population_id: int,
                          publication_id: int,
                          phenotypes: Iterable[dict]) -> tuple[dict, ...]:
    """Add entirely new phenotypes to the database. WARNING: Not thread-safe."""
    _phenos = tuple()
    with conn.cursor(cursorclass=DictCursor) as cursor:
        def make_next_id(idcol, table):
            cursor.execute(f"SELECT MAX({idcol}) AS last_id FROM {table}")
            _last_id = int(cursor.fetchone()["last_id"])
            def __next_id__():
                _next_id = _last_id + 1
                while True:
                    yield _next_id
                    _next_id = _next_id + 1

            return __next_id__

        ### Bottleneck: Everything below makes this function not         ###
        ###   thread-safe because we have to retrieve the last IDs from  ###
        ###   the database and increment those to compute the next IDs.  ###
        ###   This is an unfortunate result from the current schema that ###
        ###   has a cross-reference table that requires that a phenotype ###
        ###   be linked to an existing publication, and have data IDs to ###
        ###   link to that phenotype's data.                             ###
        ###   The fact that the IDs are sequential also compounds the    ###
        ###   bottleneck.                                                ###
        ###
        ###   For extra safety, ensure the following tables are locked   ###
        ###   for `WRITE`:                                               ###
        ###   - PublishXRef                                              ###
        ###   - Phenotype                                                ###
        ###   - PublishXRef                                              ###
        __next_xref_id = make_next_id("Id", "PublishXRef")()
        __next_pheno_id__ = make_next_id("Id", "Phenotype")()
        __next_data_id__ = make_next_id("DataId", "PublishXRef")()

        def __build_params_and_prepubabbrevs__(acc, row):
            processed = __pre_process_phenotype_data__(row)
            return (
                acc[0] + ({
                    **processed,
                    "population_id": population_id,
                    "publication_id": publication_id,
                    "phenotype_id": next(__next_pheno_id__),
                    "xref_id": next(__next_xref_id),
                    "data_id": next(__next_data_id__)
                },),
                acc[1] + (processed["pre_publication_abbreviation"],))
        while True:
            batch = take(phenotypes, 1000)
            if len(batch) == 0:
                break

            params, abbrevs = reduce(__build_params_and_prepubabbrevs__,
                                     batch,
                                     (tuple(), tuple()))
            # Check for uniqueness for all "Pre_publication_description" values
            abbrevs_paramsstr = ", ".join(["%s"] * len(abbrevs))
            _query = ("SELECT PublishXRef.PhenotypeId, Phenotype.* "
                      "FROM PublishXRef "
                      "INNER JOIN Phenotype "
                      "ON PublishXRef.PhenotypeId=Phenotype.Id "
                      "WHERE PublishXRef.InbredSetId=%s "
                      "AND Phenotype.Pre_publication_abbreviation IN "
                      f"({abbrevs_paramsstr})")
            cursor.execute(_query,
                           ((population_id,) + abbrevs))
            existing = tuple(row["Pre_publication_abbreviation"]
                             for row in cursor.fetchall())
            if len(existing) > 0:
                # Narrow this exception, perhaps?
                raise Exception(
                    "Found already existing phenotypes with the following "
                    "'Pre-publication abbreviations':\n\t"
                    "\n\t".join(f"* {item}" for item in existing))

            cursor.executemany(
                (
                    "INSERT INTO "
                    "Phenotype("
                    "Id, "
                    "Pre_publication_description, "
                    "Post_publication_description, "
                    "Original_description, "
                    "Units, "
                    "Pre_publication_abbreviation, "
                    "Post_publication_abbreviation, "
                    "Authorized_Users"
                    ")"
                    "VALUES ("
                    "%(phenotype_id)s, "
                    "%(pre_publication_description)s, "
                    "%(post_publication_description)s, "
                    "%(original_description)s, "
                    "%(units)s, "
                    "%(pre_publication_abbreviation)s, "
                    "%(post_publication_abbreviation)s, "
                    "'robwilliams'"
                    ")"),
                params)
            _comments = f"Created at {datetime.now().isoformat()}"
            cursor.executemany(
                ("INSERT INTO PublishXRef("
                 "Id, "
                 "InbredSetId, "
                 "PhenotypeId, "
                 "PublicationId, "
                 "DataId, "
                 "comments"
                 ")"
                 "VALUES("
                 "%(xref_id)s, "
                 "%(population_id)s, "
                 "%(phenotype_id)s, "
                 "%(publication_id)s, "
                 "%(data_id)s, "
                 f"'{_comments}'"
                 ")"),
                params)
            _phenos = _phenos + params

    return _phenos


def save_phenotypes_data(
        conn: mdb.Connection,
        table: str,
        data: Iterable[dict]
) -> int:
    """Save new phenotypes data into the database."""
    _table_details = __PHENO_DATA_TABLES__[table]
    with conn.cursor(cursorclass=DictCursor) as cursor:
        _count = 0
        while True:
            batch = take(data, 100000)
            if len(batch) == 0:
                logger.warning("Got an empty batch. This needs investigation.")
                break

            logger.debug("Saving batch of %s items.", len(batch))
            cursor.executemany(
                (f"INSERT INTO {_table_details['table']}"
                 f"({_table_details['DataIdCol']}, StrainId, {_table_details['valueCol']}) "
                 "VALUES "
                 f"(%(data_id)s, %(sample_id)s, %(value)s) "),
                tuple(batch))
            debug_query(cursor, logger)
            _count = _count + len(batch)


    logger.debug("Saved a total of %s data rows", _count)
    return _count


def quick_save_phenotypes_data(
        conn: mdb.Connection,
        table: str,
        dataitems: Iterable[dict],
        tmpdir: Path
) -> int:
    """Save data items to the database, but using """
    _table_details = __PHENO_DATA_TABLES__[table]
    with (tempfile.NamedTemporaryFile(
            prefix=f"{table}_data", mode="wt", dir=tmpdir) as tmpfile,
          conn.cursor(cursorclass=DictCursor) as cursor):
        _count = 0
        logger.debug("Write data rows to text file.")
        for row in dataitems:
            tmpfile.write(
                f'{row["data_id"]}\t{row["sample_id"]}\t{row["value"]}\n')
            _count = _count + 1
        tmpfile.flush()

        logger.debug("Load text file into database (table: %s)",
                      _table_details["table"])
        cursor.execute(
            f"LOAD DATA LOCAL INFILE '{tmpfile.name}' "
            f"INTO TABLE {_table_details['table']} "
            "("
            f"{_table_details['DataIdCol']}, "
            "StrainId, "
            f"{_table_details['valueCol']}"
            ")")
        debug_query(cursor, logger)
        return _count