school_compare/backend/data_loader.py

"""
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_performance
# fact_performance is a merged KS2+KS4 table (one row per URN per year).
# All-through schools have both KS2 and KS4 columns populated in the same row.
# =============================================================================

_MAIN_QUERY = text("""
    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.admissions_policy,
        s.capacity,
        s.total_pupils         AS gias_total_pupils,
        s.headteacher_name,
        s.website,
        foi.ofsted_grade,
        foi.ofsted_date,
        foi.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,
        p.year,
        p.source_urn,
        p.total_pupils,
        p.eligible_pupils,
        -- KS2 columns (NULL for pure secondary schools)
        p.rwm_expected_pct,
        p.rwm_high_pct,
        p.reading_expected_pct,
        p.reading_high_pct,
        p.reading_avg_score,
        p.reading_progress,
        p.writing_expected_pct,
        p.writing_high_pct,
        p.writing_progress,
        p.maths_expected_pct,
        p.maths_high_pct,
        p.maths_avg_score,
        p.maths_progress,
        p.gps_expected_pct,
        p.gps_high_pct,
        p.gps_avg_score,
        p.science_expected_pct,
        p.reading_absence_pct,
        p.writing_absence_pct,
        p.maths_absence_pct,
        p.gps_absence_pct,
        p.science_absence_pct,
        p.rwm_expected_boys_pct,
        p.rwm_high_boys_pct,
        p.rwm_expected_girls_pct,
        p.rwm_high_girls_pct,
        p.rwm_expected_disadvantaged_pct,
        p.rwm_expected_non_disadvantaged_pct,
        p.disadvantaged_gap,
        p.disadvantaged_pct,
        p.eal_pct,
        p.stability_pct,
        -- KS4 columns (NULL for pure primary schools)
        p.attainment_8_score,
        p.progress_8_score,
        p.progress_8_lower_ci,
        p.progress_8_upper_ci,
        p.progress_8_english,
        p.progress_8_maths,
        p.progress_8_ebacc,
        p.progress_8_open,
        p.english_maths_strong_pass_pct,
        p.english_maths_standard_pass_pct,
        p.ebacc_entry_pct,
        p.ebacc_strong_pass_pct,
        p.ebacc_standard_pass_pct,
        p.ebacc_avg_score,
        p.gcse_grade_91_pct,
        p.prior_attainment_avg,
        -- SEN (coalesced KS2+KS4 in fact_performance)
        p.sen_support_pct,
        p.sen_ehcp_pct
    FROM marts.dim_school s
    JOIN marts.dim_location l ON s.urn = l.urn
    LEFT JOIN marts.fact_performance p ON s.urn = p.urn
    LEFT JOIN (
        SELECT DISTINCT ON (urn)
            urn,
            overall_effectiveness AS ofsted_grade,
            inspection_date       AS ofsted_date,
            framework             AS ofsted_framework
        FROM marts.fact_ofsted_inspection
        ORDER BY urn, inspection_date DESC NULLS LAST
    ) foi ON s.urn = foi.urn
    ORDER BY s.school_name, p.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
# Pre-computed latest-year snapshot (one row per school, with prev-year trend columns)
_df_latest_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'].dropna().unique())}")
    else:
        print("No data found in marts (EES data may not have been loaded yet)")
    return _df_cache


def load_latest_school_data() -> pd.DataFrame:
    """Return a cached one-row-per-school DataFrame at the latest available year.

    The expensive groupby / merge / prev-year trend computation runs once at
    startup (or after a cache clear) rather than on every search request.
    Per-request filters (phase, gender, LA …) should be applied to the returned
    DataFrame's copy; they must NOT modify the cached object.
    """
    global _df_latest_cache
    if _df_latest_cache is not None:
        return _df_latest_cache

    df = load_school_data()
    if df.empty:
        return df

    # Schools that have no performance rows (PRUs, new schools, etc.)
    df_no_perf = df[df["year"].isna()].drop_duplicates(subset=["urn"])
    df_with_perf = df[df["year"].notna()]

    # Reduce to the latest year per school
    latest_year = df_with_perf.groupby("urn")["year"].max().reset_index()
    df_latest = df_with_perf.merge(latest_year, on=["urn", "year"])

    # Attach previous-year metrics for trend arrows (second-latest year per school)
    df_sorted = df_with_perf.sort_values(["urn", "year"], ascending=[True, False])
    df_prev = df_sorted.groupby("urn").nth(1).reset_index()
    if not df_prev.empty and "rwm_expected_pct" in df_prev.columns:
        prev_rwm = df_prev[["urn", "rwm_expected_pct"]].rename(
            columns={"rwm_expected_pct": "prev_rwm_expected_pct"}
        )
        if "attainment_8_score" in df_prev.columns:
            prev_rwm = prev_rwm.merge(
                df_prev[["urn", "attainment_8_score"]].rename(
                    columns={"attainment_8_score": "prev_attainment_8_score"}
                ),
                on="urn",
                how="outer",
            )
        df_latest = df_latest.merge(prev_rwm, on="urn", how="left")

    # Merge back schools with no performance data
    df_latest = pd.concat([df_latest, df_no_perf], ignore_index=True)

    print(f"Latest-snapshot cache built: {len(df_latest)} schools")
    _df_latest_cache = df_latest
    return _df_latest_cache


def clear_cache():
    """Clear all caches."""
    global _df_cache, _df_latest_cache
    _df_cache = None
    _df_latest_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,
            "places_offered": a.places_offered,
            "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,
            "oversubscription_ratio": a.oversubscription_ratio,
            "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