"""
Data loading module — reads from marts.* tables built by dbt.
Provides efficient queries with caching.
"""

import pandas as pd
import numpy as np
from typing import Optional, Dict, Tuple, List
import requests
from sqlalchemy import text
from sqlalchemy.orm import Session

from .config import settings
from .database import SessionLocal, engine
from .models import (
    DimSchool, DimLocation, KS2Performance,
    FactOfstedInspection, FactParentView, FactAdmissions,
    FactDeprivation, FactFinance,
)
from .schemas import SCHOOL_TYPE_MAP

_postcode_cache: Dict[str, Tuple[float, float]] = {}
_typesense_client = None


def _get_typesense_client():
    global _typesense_client
    if _typesense_client is not None:
        return _typesense_client
    url = settings.typesense_url
    key = settings.typesense_api_key
    if not url or not key:
        return None
    try:
        import typesense
        host = url.split("//")[-1]
        host_part, _, port_str = host.partition(":")
        port = int(port_str) if port_str else 8108
        _typesense_client = typesense.Client({
            "nodes": [{"host": host_part, "port": str(port), "protocol": "http"}],
            "api_key": key,
            "connection_timeout_seconds": 2,
        })
        return _typesense_client
    except Exception:
        return None


def search_schools_typesense(query: str, limit: int = 250) -> List[int]:
    """Search Typesense. Returns URNs in relevance order, or [] if unavailable."""
    client = _get_typesense_client()
    if client is None:
        return []
    try:
        result = client.collections["schools"].documents.search({
            "q": query,
            "query_by": "school_name,local_authority,postcode",
            "per_page": min(limit, 250),
            "typo_tokens_threshold": 1,
        })
        return [int(h["document"]["urn"]) for h in result.get("hits", [])]
    except Exception:
        return []


def normalize_school_type(school_type: Optional[str]) -> Optional[str]:
    """Convert cryptic school type codes to user-friendly names."""
    if not school_type:
        return None
    code = school_type.strip().upper()
    if code in SCHOOL_TYPE_MAP:
        return SCHOOL_TYPE_MAP[code]
    return school_type


def geocode_single_postcode(postcode: str) -> Optional[Tuple[float, float]]:
    """Geocode a single postcode using postcodes.io API."""
    if not postcode:
        return None
    postcode = postcode.strip().upper()
    if postcode in _postcode_cache:
        return _postcode_cache[postcode]
    try:
        response = requests.get(
            f"https://api.postcodes.io/postcodes/{postcode}",
            timeout=10,
        )
        if response.status_code == 200:
            data = response.json()
            if data.get("result"):
                lat = data["result"].get("latitude")
                lon = data["result"].get("longitude")
                if lat and lon:
                    _postcode_cache[postcode] = (lat, lon)
                    return (lat, lon)
    except Exception:
        pass
    return None


def haversine_distance(lat1: float, lon1: float, lat2: float, lon2: float) -> float:
    """Calculate great-circle distance between two points (miles)."""
    from math import radians, cos, sin, asin, sqrt
    lat1, lon1, lat2, lon2 = map(radians, [lat1, lon1, lat2, lon2])
    dlat = lat2 - lat1
    dlon = lon2 - lon1
    a = sin(dlat / 2) ** 2 + cos(lat1) * cos(lat2) * sin(dlon / 2) ** 2
    return 2 * asin(sqrt(a)) * 3956


# =============================================================================
# MAIN DATA LOAD — joins dim_school + dim_location + fact_ks2_performance
# =============================================================================

_MAIN_QUERY = text("""
    -- Branch 1: Primary schools (KS2 data; KS4 columns NULL)
    SELECT
        s.urn,
        s.school_name,
        s.phase,
        s.school_type,
        s.academy_trust_name   AS trust_name,
        s.academy_trust_uid    AS trust_uid,
        s.religious_character  AS religious_denomination,
        s.gender,
        s.age_range,
        s.capacity,
        s.headteacher_name,
        s.website,
        s.ofsted_grade,
        s.ofsted_date,
        s.ofsted_framework,
        l.local_authority_name AS local_authority,
        l.local_authority_code,
        l.address_line1        AS address1,
        l.address_line2        AS address2,
        l.town,
        l.postcode,
        l.latitude,
        l.longitude,
        k.year,
        k.source_urn,
        k.total_pupils,
        k.eligible_pupils,
        -- KS2 columns
        k.rwm_expected_pct,
        k.rwm_high_pct,
        k.reading_expected_pct,
        k.reading_high_pct,
        k.reading_avg_score,
        k.reading_progress,
        k.writing_expected_pct,
        k.writing_high_pct,
        k.writing_progress,
        k.maths_expected_pct,
        k.maths_high_pct,
        k.maths_avg_score,
        k.maths_progress,
        k.gps_expected_pct,
        k.gps_high_pct,
        k.gps_avg_score,
        k.science_expected_pct,
        k.reading_absence_pct,
        k.writing_absence_pct,
        k.maths_absence_pct,
        k.gps_absence_pct,
        k.science_absence_pct,
        k.rwm_expected_boys_pct,
        k.rwm_high_boys_pct,
        k.rwm_expected_girls_pct,
        k.rwm_high_girls_pct,
        k.rwm_expected_disadvantaged_pct,
        k.rwm_expected_non_disadvantaged_pct,
        k.disadvantaged_gap,
        k.disadvantaged_pct,
        k.eal_pct,
        k.sen_support_pct,
        k.sen_ehcp_pct,
        k.stability_pct,
        -- KS4 columns (NULL for primary)
        NULL::numeric AS attainment_8_score,
        NULL::numeric AS progress_8_score,
        NULL::numeric AS progress_8_lower_ci,
        NULL::numeric AS progress_8_upper_ci,
        NULL::numeric AS progress_8_english,
        NULL::numeric AS progress_8_maths,
        NULL::numeric AS progress_8_ebacc,
        NULL::numeric AS progress_8_open,
        NULL::numeric AS english_maths_strong_pass_pct,
        NULL::numeric AS english_maths_standard_pass_pct,
        NULL::numeric AS ebacc_entry_pct,
        NULL::numeric AS ebacc_strong_pass_pct,
        NULL::numeric AS ebacc_standard_pass_pct,
        NULL::numeric AS ebacc_avg_score,
        NULL::numeric AS gcse_grade_91_pct,
        NULL::numeric AS prior_attainment_avg
    FROM marts.dim_school s
    JOIN marts.dim_location l ON s.urn = l.urn
    JOIN marts.fact_ks2_performance k ON s.urn = k.urn

    UNION ALL

    -- Branch 2: Secondary schools (KS4 data; KS2 columns NULL)
    SELECT
        s.urn,
        s.school_name,
        s.phase,
        s.school_type,
        s.academy_trust_name   AS trust_name,
        s.academy_trust_uid    AS trust_uid,
        s.religious_character  AS religious_denomination,
        s.gender,
        s.age_range,
        s.capacity,
        s.headteacher_name,
        s.website,
        s.ofsted_grade,
        s.ofsted_date,
        s.ofsted_framework,
        l.local_authority_name AS local_authority,
        l.local_authority_code,
        l.address_line1        AS address1,
        l.address_line2        AS address2,
        l.town,
        l.postcode,
        l.latitude,
        l.longitude,
        k4.year,
        k4.source_urn,
        k4.total_pupils,
        k4.eligible_pupils,
        -- KS2 columns (NULL for secondary)
        NULL::numeric AS rwm_expected_pct,
        NULL::numeric AS rwm_high_pct,
        NULL::numeric AS reading_expected_pct,
        NULL::numeric AS reading_high_pct,
        NULL::numeric AS reading_avg_score,
        NULL::numeric AS reading_progress,
        NULL::numeric AS writing_expected_pct,
        NULL::numeric AS writing_high_pct,
        NULL::numeric AS writing_progress,
        NULL::numeric AS maths_expected_pct,
        NULL::numeric AS maths_high_pct,
        NULL::numeric AS maths_avg_score,
        NULL::numeric AS maths_progress,
        NULL::numeric AS gps_expected_pct,
        NULL::numeric AS gps_high_pct,
        NULL::numeric AS gps_avg_score,
        NULL::numeric AS science_expected_pct,
        NULL::numeric AS reading_absence_pct,
        NULL::numeric AS writing_absence_pct,
        NULL::numeric AS maths_absence_pct,
        NULL::numeric AS gps_absence_pct,
        NULL::numeric AS science_absence_pct,
        NULL::numeric AS rwm_expected_boys_pct,
        NULL::numeric AS rwm_high_boys_pct,
        NULL::numeric AS rwm_expected_girls_pct,
        NULL::numeric AS rwm_high_girls_pct,
        NULL::numeric AS rwm_expected_disadvantaged_pct,
        NULL::numeric AS rwm_expected_non_disadvantaged_pct,
        NULL::numeric AS disadvantaged_gap,
        NULL::numeric AS disadvantaged_pct,
        NULL::numeric AS eal_pct,
        k4.sen_support_pct,
        k4.sen_ehcp_pct,
        NULL::numeric AS stability_pct,
        -- KS4 columns
        k4.attainment_8_score,
        k4.progress_8_score,
        k4.progress_8_lower_ci,
        k4.progress_8_upper_ci,
        k4.progress_8_english,
        k4.progress_8_maths,
        k4.progress_8_ebacc,
        k4.progress_8_open,
        k4.english_maths_strong_pass_pct,
        k4.english_maths_standard_pass_pct,
        k4.ebacc_entry_pct,
        k4.ebacc_strong_pass_pct,
        k4.ebacc_standard_pass_pct,
        k4.ebacc_avg_score,
        k4.gcse_grade_91_pct,
        k4.prior_attainment_avg
    FROM marts.dim_school s
    JOIN marts.dim_location l ON s.urn = l.urn
    JOIN marts.fact_ks4_performance k4 ON s.urn = k4.urn

    ORDER BY school_name, year
""")


def load_school_data_as_dataframe() -> pd.DataFrame:
    """Load all school + KS2 data as a pandas DataFrame."""
    try:
        df = pd.read_sql(_MAIN_QUERY, engine)
    except Exception as exc:
        print(f"Warning: Could not load school data from marts: {exc}")
        return pd.DataFrame()

    if df.empty:
        return df

    # Build address string
    df["address"] = df.apply(
        lambda r: ", ".join(
            p for p in [r.get("address1"), r.get("address2"), r.get("town"), r.get("postcode")]
            if p and str(p) != "None"
        ),
        axis=1,
    )

    # Normalize school type
    df["school_type"] = df["school_type"].apply(normalize_school_type)

    return df


# Cache for DataFrame
_df_cache: Optional[pd.DataFrame] = None


def load_school_data() -> pd.DataFrame:
    """Load school data with caching."""
    global _df_cache
    if _df_cache is not None:
        return _df_cache
    print("Loading school data from marts...")
    _df_cache = load_school_data_as_dataframe()
    if not _df_cache.empty:
        print(f"Total records loaded: {len(_df_cache)}")
        print(f"Unique schools: {_df_cache['urn'].nunique()}")
        print(f"Years: {sorted(_df_cache['year'].unique())}")
    else:
        print("No data found in marts (EES data may not have been loaded yet)")
    return _df_cache


def clear_cache():
    """Clear all caches."""
    global _df_cache
    _df_cache = None


# =============================================================================
# METADATA QUERIES
# =============================================================================

def get_available_years(db: Session = None) -> List[int]:
    close_db = db is None
    if db is None:
        db = SessionLocal()
    try:
        result = db.query(KS2Performance.year).distinct().order_by(KS2Performance.year).all()
        return [r[0] for r in result]
    except Exception:
        return []
    finally:
        if close_db:
            db.close()


def get_available_local_authorities(db: Session = None) -> List[str]:
    close_db = db is None
    if db is None:
        db = SessionLocal()
    try:
        result = (
            db.query(DimLocation.local_authority_name)
            .filter(DimLocation.local_authority_name.isnot(None))
            .distinct()
            .order_by(DimLocation.local_authority_name)
            .all()
        )
        return [r[0] for r in result if r[0]]
    except Exception:
        return []
    finally:
        if close_db:
            db.close()


def get_schools_count(db: Session = None) -> int:
    close_db = db is None
    if db is None:
        db = SessionLocal()
    try:
        return db.query(DimSchool).count()
    except Exception:
        return 0
    finally:
        if close_db:
            db.close()


def get_data_info(db: Session = None) -> dict:
    close_db = db is None
    if db is None:
        db = SessionLocal()
    try:
        school_count = get_schools_count(db)
        years = get_available_years(db)
        local_authorities = get_available_local_authorities(db)
        return {
            "total_schools": school_count,
            "years_available": years,
            "local_authorities_count": len(local_authorities),
            "data_source": "PostgreSQL (marts)",
        }
    finally:
        if close_db:
            db.close()


# =============================================================================
# SUPPLEMENTARY DATA — per-school detail page
# =============================================================================

def get_supplementary_data(db: Session, urn: int) -> dict:
    """Fetch all supplementary data for a single school URN."""
    result = {}

    def safe_query(model, pk_field, latest_field=None):
        try:
            q = db.query(model).filter(getattr(model, pk_field) == urn)
            if latest_field:
                q = q.order_by(getattr(model, latest_field).desc())
            return q.first()
        except Exception as e:
            import logging
            logging.getLogger(__name__).error("safe_query failed for %s: %s", model.__name__, e)
            db.rollback()
            return None

    # Latest Ofsted inspection
    o = safe_query(FactOfstedInspection, "urn", "inspection_date")
    result["ofsted"] = (
        {
            "framework": o.framework,
            "inspection_date": o.inspection_date.isoformat() if o.inspection_date else None,
            "inspection_type": o.inspection_type,
            "overall_effectiveness": o.overall_effectiveness,
            "quality_of_education": o.quality_of_education,
            "behaviour_attitudes": o.behaviour_attitudes,
            "personal_development": o.personal_development,
            "leadership_management": o.leadership_management,
            "early_years_provision": o.early_years_provision,
            "sixth_form_provision": o.sixth_form_provision,
            "previous_overall": None,  # Not available in new schema
            "rc_safeguarding_met": o.rc_safeguarding_met,
            "rc_inclusion": o.rc_inclusion,
            "rc_curriculum_teaching": o.rc_curriculum_teaching,
            "rc_achievement": o.rc_achievement,
            "rc_attendance_behaviour": o.rc_attendance_behaviour,
            "rc_personal_development": o.rc_personal_development,
            "rc_leadership_governance": o.rc_leadership_governance,
            "rc_early_years": o.rc_early_years,
            "rc_sixth_form": o.rc_sixth_form,
            "report_url": o.report_url,
        }
        if o
        else None
    )

    # Parent View
    pv = safe_query(FactParentView, "urn")
    result["parent_view"] = (
        {
            "survey_date": pv.survey_date.isoformat() if pv.survey_date else None,
            "total_responses": pv.total_responses,
            "q_happy_pct": pv.q_happy_pct,
            "q_safe_pct": pv.q_safe_pct,
            "q_behaviour_pct": pv.q_behaviour_pct,
            "q_bullying_pct": pv.q_bullying_pct,
            "q_communication_pct": pv.q_communication_pct,
            "q_progress_pct": pv.q_progress_pct,
            "q_teaching_pct": pv.q_teaching_pct,
            "q_information_pct": pv.q_information_pct,
            "q_curriculum_pct": pv.q_curriculum_pct,
            "q_future_pct": pv.q_future_pct,
            "q_leadership_pct": pv.q_leadership_pct,
            "q_wellbeing_pct": pv.q_wellbeing_pct,
            "q_recommend_pct": pv.q_recommend_pct,
        }
        if pv
        else None
    )

    # Census (fact_pupil_characteristics — minimal until census columns are verified)
    result["census"] = None

    # Admissions (latest year)
    a = safe_query(FactAdmissions, "urn", "year")
    result["admissions"] = (
        {
            "year": a.year,
            "school_phase": a.school_phase,
            "published_admission_number": a.published_admission_number,
            "total_applications": a.total_applications,
            "first_preference_applications": a.first_preference_applications,
            "first_preference_offers": a.first_preference_offers,
            "first_preference_offer_pct": a.first_preference_offer_pct,
            "oversubscribed": a.oversubscribed,
        }
        if a
        else None
    )

    # SEN detail — not available in current marts
    result["sen_detail"] = None

    # Phonics — no school-level data on EES
    result["phonics"] = None

    # Deprivation
    d = safe_query(FactDeprivation, "urn")
    result["deprivation"] = (
        {
            "lsoa_code": d.lsoa_code,
            "idaci_score": d.idaci_score,
            "idaci_decile": d.idaci_decile,
        }
        if d
        else None
    )

    # Finance (latest year)
    f = safe_query(FactFinance, "urn", "year")
    result["finance"] = (
        {
            "year": f.year,
            "per_pupil_spend": f.per_pupil_spend,
            "staff_cost_pct": f.staff_cost_pct,
            "teacher_cost_pct": f.teacher_cost_pct,
            "support_staff_cost_pct": f.support_staff_cost_pct,
            "premises_cost_pct": f.premises_cost_pct,
        }
        if f
        else None
    )

    return result