Tudor
1c49a135c4
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Ofsted replaced single overall grades with Report Cards from Nov 2025. Both systems are retained during the transition period. - DB: new framework + 9 RC columns on ofsted_inspections (schema v4) - Integrator: auto-detect OEIF vs Report Card from CSV column headers; parse 5-level RC grades and safeguarding met/not-met - API: expose all new fields in the ofsted response dict - Frontend: branch on framework='ReportCard' to show safeguarding badge + 8-category grid; fall back to legacy OEIF layout otherwise; always show inspection date in both layouts - CSS: rcGrade1–5 and safeguardingMet/NotMet classes Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
409 lines
14 KiB
Python
409 lines
14 KiB
Python
"""
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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 datetime import datetime
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from sqlalchemy import (
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Column, Integer, String, Float, ForeignKey, Index, UniqueConstraint,
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Text, Boolean, DateTime, Date
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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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# GIAS enrichment fields
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website = Column(String(255))
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headteacher_name = Column(String(200))
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capacity = Column(Integer)
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trust_name = Column(String(255))
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trust_uid = Column(String(20))
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gender = Column(String(20)) # Mixed / Girls / Boys
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nursery_provision = Column(Boolean)
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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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# Pupil Absence from Tests
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reading_absence_pct = Column(Float)
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gps_absence_pct = Column(Float)
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maths_absence_pct = Column(Float)
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writing_absence_pct = Column(Float)
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science_absence_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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class SchemaVersion(Base):
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"""
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Tracks database schema version for automatic migrations.
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Single-row table that stores the current schema version.
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"""
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__tablename__ = "schema_version"
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id = Column(Integer, primary_key=True, default=1)
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version = Column(Integer, nullable=False)
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migrated_at = Column(DateTime, default=datetime.utcnow, onupdate=datetime.utcnow)
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def __repr__(self):
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return f"<SchemaVersion(version={self.version}, migrated_at={self.migrated_at})>"
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# ---------------------------------------------------------------------------
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# Supplementary data tables (populated by the Kestra data integrator)
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# ---------------------------------------------------------------------------
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class OfstedInspection(Base):
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"""Latest Ofsted inspection judgement per school."""
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__tablename__ = "ofsted_inspections"
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urn = Column(Integer, primary_key=True)
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inspection_date = Column(Date)
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publication_date = Column(Date)
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inspection_type = Column(String(100)) # Section 5 / Section 8 etc.
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# Which inspection framework was used: 'OEIF' or 'ReportCard'
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framework = Column(String(20))
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# --- OEIF grades (old framework, pre-Nov 2025) ---
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# 1=Outstanding 2=Good 3=Requires improvement 4=Inadequate
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overall_effectiveness = Column(Integer)
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quality_of_education = Column(Integer)
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behaviour_attitudes = Column(Integer)
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personal_development = Column(Integer)
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leadership_management = Column(Integer)
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early_years_provision = Column(Integer) # nullable — not all schools
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previous_overall = Column(Integer) # for trend display
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# --- Report Card grades (new framework, from Nov 2025) ---
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# 1=Exceptional 2=Strong 3=Expected standard 4=Needs attention 5=Urgent improvement
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rc_safeguarding_met = Column(Boolean) # True=Met, False=Not met
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rc_inclusion = Column(Integer)
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rc_curriculum_teaching = Column(Integer)
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rc_achievement = Column(Integer)
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rc_attendance_behaviour = Column(Integer)
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rc_personal_development = Column(Integer)
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rc_leadership_governance = Column(Integer)
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rc_early_years = Column(Integer) # nullable — not all schools
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rc_sixth_form = Column(Integer) # nullable — secondary only
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def __repr__(self):
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return f"<OfstedInspection(urn={self.urn}, framework={self.framework}, overall={self.overall_effectiveness})>"
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class OfstedParentView(Base):
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"""Ofsted Parent View survey — latest per school. 14 questions, % saying Yes."""
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__tablename__ = "ofsted_parent_view"
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urn = Column(Integer, primary_key=True)
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survey_date = Column(Date)
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total_responses = Column(Integer)
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q_happy_pct = Column(Float) # My child is happy at this school
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q_safe_pct = Column(Float) # My child feels safe at this school
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q_bullying_pct = Column(Float) # School deals with bullying well
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q_communication_pct = Column(Float) # School keeps me informed
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q_progress_pct = Column(Float) # My child does well / good progress
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q_teaching_pct = Column(Float) # Teaching is good
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q_information_pct = Column(Float) # I receive valuable info about progress
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q_curriculum_pct = Column(Float) # Broad range of subjects taught
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q_future_pct = Column(Float) # Prepares child well for the future
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q_leadership_pct = Column(Float) # Led and managed effectively
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q_wellbeing_pct = Column(Float) # Supports wider personal development
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q_behaviour_pct = Column(Float) # Pupils are well behaved
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q_recommend_pct = Column(Float) # I would recommend this school
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q_sen_pct = Column(Float) # Good information about child's SEN (where applicable)
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def __repr__(self):
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return f"<OfstedParentView(urn={self.urn}, responses={self.total_responses})>"
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class SchoolCensus(Base):
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"""Annual school census snapshot — class sizes and ethnicity breakdown."""
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__tablename__ = "school_census"
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urn = Column(Integer, primary_key=True)
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year = Column(Integer, primary_key=True)
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class_size_avg = Column(Float)
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ethnicity_white_pct = Column(Float)
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ethnicity_asian_pct = Column(Float)
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ethnicity_black_pct = Column(Float)
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ethnicity_mixed_pct = Column(Float)
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ethnicity_other_pct = Column(Float)
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__table_args__ = (
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Index('ix_school_census_urn_year', 'urn', 'year'),
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)
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def __repr__(self):
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return f"<SchoolCensus(urn={self.urn}, year={self.year})>"
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class SchoolAdmissions(Base):
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"""Annual admissions statistics per school."""
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__tablename__ = "school_admissions"
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urn = Column(Integer, primary_key=True)
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year = Column(Integer, primary_key=True)
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published_admission_number = Column(Integer) # PAN
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total_applications = Column(Integer)
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first_preference_offers_pct = Column(Float) # % receiving 1st choice
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oversubscribed = Column(Boolean)
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__table_args__ = (
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Index('ix_school_admissions_urn_year', 'urn', 'year'),
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)
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def __repr__(self):
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return f"<SchoolAdmissions(urn={self.urn}, year={self.year})>"
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class SenDetail(Base):
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"""SEN primary need type breakdown — more granular than school_results context fields."""
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__tablename__ = "sen_detail"
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urn = Column(Integer, primary_key=True)
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year = Column(Integer, primary_key=True)
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primary_need_speech_pct = Column(Float) # SLCN
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primary_need_autism_pct = Column(Float) # ASD
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primary_need_mld_pct = Column(Float) # Moderate learning difficulty
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primary_need_spld_pct = Column(Float) # Specific learning difficulty (dyslexia etc.)
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primary_need_semh_pct = Column(Float) # Social, emotional, mental health
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primary_need_physical_pct = Column(Float) # Physical/sensory
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primary_need_other_pct = Column(Float)
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__table_args__ = (
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Index('ix_sen_detail_urn_year', 'urn', 'year'),
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)
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def __repr__(self):
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return f"<SenDetail(urn={self.urn}, year={self.year})>"
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class Phonics(Base):
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"""Phonics Screening Check pass rates."""
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__tablename__ = "phonics"
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urn = Column(Integer, primary_key=True)
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year = Column(Integer, primary_key=True)
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year1_phonics_pct = Column(Float) # % reaching expected standard in Year 1
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year2_phonics_pct = Column(Float) # % reaching standard in Year 2 (re-takers)
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__table_args__ = (
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Index('ix_phonics_urn_year', 'urn', 'year'),
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)
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def __repr__(self):
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return f"<Phonics(urn={self.urn}, year={self.year})>"
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class SchoolDeprivation(Base):
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"""IDACI deprivation index — derived via postcode → LSOA lookup."""
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__tablename__ = "school_deprivation"
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urn = Column(Integer, primary_key=True)
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lsoa_code = Column(String(20))
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idaci_score = Column(Float) # 0–1, higher = more deprived
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idaci_decile = Column(Integer) # 1 = most deprived, 10 = least deprived
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def __repr__(self):
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return f"<SchoolDeprivation(urn={self.urn}, decile={self.idaci_decile})>"
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class SchoolFinance(Base):
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"""FBIT financial benchmarking data."""
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__tablename__ = "school_finance"
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urn = Column(Integer, primary_key=True)
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year = Column(Integer, primary_key=True)
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per_pupil_spend = Column(Float) # £ total expenditure per pupil
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staff_cost_pct = Column(Float) # % of budget on all staff
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teacher_cost_pct = Column(Float) # % on teachers specifically
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support_staff_cost_pct = Column(Float)
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premises_cost_pct = Column(Float)
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__table_args__ = (
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Index('ix_school_finance_urn_year', 'urn', 'year'),
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)
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def __repr__(self):
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return f"<SchoolFinance(urn={self.urn}, 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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# Absence
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'reading_absence_pct': 'reading_absence_pct',
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'gps_absence_pct': 'gps_absence_pct',
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'maths_absence_pct': 'maths_absence_pct',
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'writing_absence_pct': 'writing_absence_pct',
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'science_absence_pct': 'science_absence_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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