"""
SQLAlchemy database models for school data.
Normalized schema with separate tables for schools and yearly results.
"""

from sqlalchemy import (
    Column, Integer, String, Float, ForeignKey, Index, UniqueConstraint,
    Text, Boolean
)
from sqlalchemy.orm import relationship
from .database import Base


class School(Base):
    """
    Core school information - relatively static data.
    """
    __tablename__ = "schools"
    
    id = Column(Integer, primary_key=True, autoincrement=True)
    urn = Column(Integer, unique=True, nullable=False, index=True)
    school_name = Column(String(255), nullable=False)
    local_authority = Column(String(100))
    local_authority_code = Column(Integer)
    school_type = Column(String(100))
    school_type_code = Column(String(10))
    religious_denomination = Column(String(100))
    age_range = Column(String(20))
    
    # Address
    address1 = Column(String(255))
    address2 = Column(String(255))
    town = Column(String(100))
    postcode = Column(String(20), index=True)
    
    # Geocoding (cached)
    latitude = Column(Float)
    longitude = Column(Float)
    
    # Relationships
    results = relationship("SchoolResult", back_populates="school", cascade="all, delete-orphan")
    
    def __repr__(self):
        return f"<School(urn={self.urn}, name='{self.school_name}')>"
    
    @property
    def address(self):
        """Combine address fields into single string."""
        parts = [self.address1, self.address2, self.town, self.postcode]
        return ", ".join(p for p in parts if p)


class SchoolResult(Base):
    """
    Yearly KS2 results for a school.
    Each school can have multiple years of results.
    """
    __tablename__ = "school_results"
    
    id = Column(Integer, primary_key=True, autoincrement=True)
    school_id = Column(Integer, ForeignKey("schools.id", ondelete="CASCADE"), nullable=False)
    year = Column(Integer, nullable=False, index=True)
    
    # Pupil numbers
    total_pupils = Column(Integer)
    eligible_pupils = Column(Integer)
    
    # Core KS2 metrics - Expected Standard
    rwm_expected_pct = Column(Float)
    reading_expected_pct = Column(Float)
    writing_expected_pct = Column(Float)
    maths_expected_pct = Column(Float)
    gps_expected_pct = Column(Float)
    science_expected_pct = Column(Float)
    
    # Higher Standard
    rwm_high_pct = Column(Float)
    reading_high_pct = Column(Float)
    writing_high_pct = Column(Float)
    maths_high_pct = Column(Float)
    gps_high_pct = Column(Float)
    
    # Progress Scores
    reading_progress = Column(Float)
    writing_progress = Column(Float)
    maths_progress = Column(Float)
    
    # Average Scores
    reading_avg_score = Column(Float)
    maths_avg_score = Column(Float)
    gps_avg_score = Column(Float)
    
    # School Context
    disadvantaged_pct = Column(Float)
    eal_pct = Column(Float)
    sen_support_pct = Column(Float)
    sen_ehcp_pct = Column(Float)
    stability_pct = Column(Float)

    # Pupil Absence from Tests
    reading_absence_pct = Column(Float)
    gps_absence_pct = Column(Float)
    maths_absence_pct = Column(Float)
    writing_absence_pct = Column(Float)
    science_absence_pct = Column(Float)

    # Gender Breakdown
    rwm_expected_boys_pct = Column(Float)
    rwm_expected_girls_pct = Column(Float)
    rwm_high_boys_pct = Column(Float)
    rwm_high_girls_pct = Column(Float)
    
    # Disadvantaged Performance
    rwm_expected_disadvantaged_pct = Column(Float)
    rwm_expected_non_disadvantaged_pct = Column(Float)
    disadvantaged_gap = Column(Float)
    
    # 3-Year Averages
    rwm_expected_3yr_pct = Column(Float)
    reading_avg_3yr = Column(Float)
    maths_avg_3yr = Column(Float)
    
    # Relationship
    school = relationship("School", back_populates="results")
    
    # Constraints
    __table_args__ = (
        UniqueConstraint('school_id', 'year', name='uq_school_year'),
        Index('ix_school_results_school_year', 'school_id', 'year'),
    )
    
    def __repr__(self):
        return f"<SchoolResult(school_id={self.school_id}, year={self.year})>"


# Mapping from CSV columns to model fields
SCHOOL_FIELD_MAPPING = {
    'urn': 'urn',
    'school_name': 'school_name',
    'local_authority': 'local_authority',
    'local_authority_code': 'local_authority_code',
    'school_type': 'school_type',
    'school_type_code': 'school_type_code',
    'religious_denomination': 'religious_denomination',
    'age_range': 'age_range',
    'address1': 'address1',
    'address2': 'address2',
    'town': 'town',
    'postcode': 'postcode',
}

RESULT_FIELD_MAPPING = {
    'year': 'year',
    'total_pupils': 'total_pupils',
    'eligible_pupils': 'eligible_pupils',
    # Expected Standard
    'rwm_expected_pct': 'rwm_expected_pct',
    'reading_expected_pct': 'reading_expected_pct',
    'writing_expected_pct': 'writing_expected_pct',
    'maths_expected_pct': 'maths_expected_pct',
    'gps_expected_pct': 'gps_expected_pct',
    'science_expected_pct': 'science_expected_pct',
    # Higher Standard
    'rwm_high_pct': 'rwm_high_pct',
    'reading_high_pct': 'reading_high_pct',
    'writing_high_pct': 'writing_high_pct',
    'maths_high_pct': 'maths_high_pct',
    'gps_high_pct': 'gps_high_pct',
    # Progress
    'reading_progress': 'reading_progress',
    'writing_progress': 'writing_progress',
    'maths_progress': 'maths_progress',
    # Averages
    'reading_avg_score': 'reading_avg_score',
    'maths_avg_score': 'maths_avg_score',
    'gps_avg_score': 'gps_avg_score',
    # Context
    'disadvantaged_pct': 'disadvantaged_pct',
    'eal_pct': 'eal_pct',
    'sen_support_pct': 'sen_support_pct',
    'sen_ehcp_pct': 'sen_ehcp_pct',
    'stability_pct': 'stability_pct',
    # Absence
    'reading_absence_pct': 'reading_absence_pct',
    'gps_absence_pct': 'gps_absence_pct',
    'maths_absence_pct': 'maths_absence_pct',
    'writing_absence_pct': 'writing_absence_pct',
    'science_absence_pct': 'science_absence_pct',
    # Gender
    'rwm_expected_boys_pct': 'rwm_expected_boys_pct',
    'rwm_expected_girls_pct': 'rwm_expected_girls_pct',
    'rwm_high_boys_pct': 'rwm_high_boys_pct',
    'rwm_high_girls_pct': 'rwm_high_girls_pct',
    # Disadvantaged
    'rwm_expected_disadvantaged_pct': 'rwm_expected_disadvantaged_pct',
    'rwm_expected_non_disadvantaged_pct': 'rwm_expected_non_disadvantaged_pct',
    'disadvantaged_gap': 'disadvantaged_gap',
    # 3-Year
    'rwm_expected_3yr_pct': 'rwm_expected_3yr_pct',
    'reading_avg_3yr': 'reading_avg_3yr',
    'maths_avg_3yr': 'maths_avg_3yr',
}