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school_compare/backend/models.py

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2026-01-06 17:22:39 +00:00
"""
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)
# 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',
# 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',
}