This commit is contained in:
Tudor Sitaru
190
backend/models.py
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190
backend/models.py
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"""
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SQLAlchemy database models for school data.
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Normalized schema with separate tables for schools and yearly results.
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"""
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from sqlalchemy import (
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Column, Integer, String, Float, ForeignKey, Index, UniqueConstraint,
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Text, Boolean
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)
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from sqlalchemy.orm import relationship
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from .database import Base
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class School(Base):
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"""
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Core school information - relatively static data.
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"""
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__tablename__ = "schools"
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id = Column(Integer, primary_key=True, autoincrement=True)
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urn = Column(Integer, unique=True, nullable=False, index=True)
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school_name = Column(String(255), nullable=False)
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local_authority = Column(String(100))
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local_authority_code = Column(Integer)
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school_type = Column(String(100))
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school_type_code = Column(String(10))
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religious_denomination = Column(String(100))
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age_range = Column(String(20))
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# Address
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address1 = Column(String(255))
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address2 = Column(String(255))
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town = Column(String(100))
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postcode = Column(String(20), index=True)
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# Geocoding (cached)
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latitude = Column(Float)
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longitude = Column(Float)
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# Relationships
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results = relationship("SchoolResult", back_populates="school", cascade="all, delete-orphan")
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def __repr__(self):
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return f"<School(urn={self.urn}, name='{self.school_name}')>"
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@property
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def address(self):
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"""Combine address fields into single string."""
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parts = [self.address1, self.address2, self.town, self.postcode]
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return ", ".join(p for p in parts if p)
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class SchoolResult(Base):
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"""
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Yearly KS2 results for a school.
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Each school can have multiple years of results.
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"""
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__tablename__ = "school_results"
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id = Column(Integer, primary_key=True, autoincrement=True)
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school_id = Column(Integer, ForeignKey("schools.id", ondelete="CASCADE"), nullable=False)
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year = Column(Integer, nullable=False, index=True)
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# Pupil numbers
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total_pupils = Column(Integer)
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eligible_pupils = Column(Integer)
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# Core KS2 metrics - Expected Standard
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rwm_expected_pct = Column(Float)
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reading_expected_pct = Column(Float)
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writing_expected_pct = Column(Float)
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maths_expected_pct = Column(Float)
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gps_expected_pct = Column(Float)
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science_expected_pct = Column(Float)
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# Higher Standard
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rwm_high_pct = Column(Float)
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reading_high_pct = Column(Float)
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writing_high_pct = Column(Float)
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maths_high_pct = Column(Float)
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gps_high_pct = Column(Float)
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# Progress Scores
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reading_progress = Column(Float)
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writing_progress = Column(Float)
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maths_progress = Column(Float)
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# Average Scores
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reading_avg_score = Column(Float)
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maths_avg_score = Column(Float)
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gps_avg_score = Column(Float)
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# School Context
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disadvantaged_pct = Column(Float)
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eal_pct = Column(Float)
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sen_support_pct = Column(Float)
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sen_ehcp_pct = Column(Float)
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stability_pct = Column(Float)
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# Gender Breakdown
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rwm_expected_boys_pct = Column(Float)
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rwm_expected_girls_pct = Column(Float)
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rwm_high_boys_pct = Column(Float)
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rwm_high_girls_pct = Column(Float)
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# Disadvantaged Performance
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rwm_expected_disadvantaged_pct = Column(Float)
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rwm_expected_non_disadvantaged_pct = Column(Float)
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disadvantaged_gap = Column(Float)
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# 3-Year Averages
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rwm_expected_3yr_pct = Column(Float)
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reading_avg_3yr = Column(Float)
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maths_avg_3yr = Column(Float)
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# Relationship
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school = relationship("School", back_populates="results")
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# Constraints
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__table_args__ = (
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UniqueConstraint('school_id', 'year', name='uq_school_year'),
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Index('ix_school_results_school_year', 'school_id', 'year'),
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)
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def __repr__(self):
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return f"<SchoolResult(school_id={self.school_id}, year={self.year})>"
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# Mapping from CSV columns to model fields
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SCHOOL_FIELD_MAPPING = {
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'urn': 'urn',
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'school_name': 'school_name',
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'local_authority': 'local_authority',
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'local_authority_code': 'local_authority_code',
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'school_type': 'school_type',
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'school_type_code': 'school_type_code',
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'religious_denomination': 'religious_denomination',
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'age_range': 'age_range',
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'address1': 'address1',
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'address2': 'address2',
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'town': 'town',
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'postcode': 'postcode',
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}
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RESULT_FIELD_MAPPING = {
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'year': 'year',
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'total_pupils': 'total_pupils',
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'eligible_pupils': 'eligible_pupils',
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# Expected Standard
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'rwm_expected_pct': 'rwm_expected_pct',
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'reading_expected_pct': 'reading_expected_pct',
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'writing_expected_pct': 'writing_expected_pct',
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'maths_expected_pct': 'maths_expected_pct',
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'gps_expected_pct': 'gps_expected_pct',
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'science_expected_pct': 'science_expected_pct',
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# Higher Standard
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'rwm_high_pct': 'rwm_high_pct',
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'reading_high_pct': 'reading_high_pct',
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'writing_high_pct': 'writing_high_pct',
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'maths_high_pct': 'maths_high_pct',
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'gps_high_pct': 'gps_high_pct',
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# Progress
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'reading_progress': 'reading_progress',
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'writing_progress': 'writing_progress',
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'maths_progress': 'maths_progress',
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# Averages
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'reading_avg_score': 'reading_avg_score',
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'maths_avg_score': 'maths_avg_score',
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'gps_avg_score': 'gps_avg_score',
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# Context
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'disadvantaged_pct': 'disadvantaged_pct',
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'eal_pct': 'eal_pct',
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'sen_support_pct': 'sen_support_pct',
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'sen_ehcp_pct': 'sen_ehcp_pct',
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'stability_pct': 'stability_pct',
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# Gender
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'rwm_expected_boys_pct': 'rwm_expected_boys_pct',
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'rwm_expected_girls_pct': 'rwm_expected_girls_pct',
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'rwm_high_boys_pct': 'rwm_high_boys_pct',
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'rwm_high_girls_pct': 'rwm_high_girls_pct',
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# Disadvantaged
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'rwm_expected_disadvantaged_pct': 'rwm_expected_disadvantaged_pct',
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'rwm_expected_non_disadvantaged_pct': 'rwm_expected_non_disadvantaged_pct',
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'disadvantaged_gap': 'disadvantaged_gap',
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# 3-Year
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'rwm_expected_3yr_pct': 'rwm_expected_3yr_pct',
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'reading_avg_3yr': 'reading_avg_3yr',
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'maths_avg_3yr': 'maths_avg_3yr',
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}
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