Tudor
5eff9af69c
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- Backend: replace INNER JOIN ks2 with UNION ALL (ks2 + ks4) so primary and secondary schools both appear in the main DataFrame - Backend: add /api/national-averages endpoint computing means from live data, replacing the hardcoded NATIONAL_AVG constant on the frontend - Backend: add phase filter param to /api/schools; return phases from /api/filters; fix hardcoded "phase": "Primary" in school detail endpoint - Backend: add KS4 metric definitions (Attainment 8, Progress 8, EBacc, English & Maths pass rates) to METRIC_DEFINITIONS and RANKING_COLUMNS - Frontend: SchoolDetailView is now phase-aware — secondary schools show a GCSE Results section (Att8, P8, E&M, EBacc) instead of SATs; phonics tab hidden for secondary; admissions says Year 7 instead of Year 3; history table shows KS4 columns; chart datasets switch for secondary - Frontend: new MetricTooltip component (CSS-only ⓘ icon) backed by METRIC_EXPLANATIONS — added to RWM, GPS, SEN, EAL, IDACI, progress scores and all KS4 metrics throughout SchoolDetailView and SchoolCard - Frontend: METRIC_EXPLANATIONS extended with KS4 terms (Attainment 8, Progress 8, EBacc) and previously missing terms (SEN, EHCP, EAL, IDACI) - Frontend: SchoolCard expands "RWM" to "Reading, Writing & Maths" and shows Attainment 8 / English & Maths Grade 4+ for secondary schools - Frontend: FilterBar adds Phase dropdown (Primary / Secondary / All-through) - Frontend: HomeView hero copy updated; compact list shows phase-aware metric - Global metadata updated to remove "primary only" framing Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
542 lines
17 KiB
Python
542 lines
17 KiB
Python
"""
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Data loading module — reads from marts.* tables built by dbt.
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Provides efficient queries with caching.
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"""
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import pandas as pd
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import numpy as np
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from typing import Optional, Dict, Tuple, List
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import requests
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from sqlalchemy import text
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from sqlalchemy.orm import Session
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from .config import settings
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from .database import SessionLocal, engine
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from .models import (
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DimSchool, DimLocation, KS2Performance,
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FactOfstedInspection, FactParentView, FactAdmissions,
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FactDeprivation, FactFinance,
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)
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from .schemas import SCHOOL_TYPE_MAP
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_postcode_cache: Dict[str, Tuple[float, float]] = {}
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_typesense_client = None
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def _get_typesense_client():
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global _typesense_client
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if _typesense_client is not None:
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return _typesense_client
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url = settings.typesense_url
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key = settings.typesense_api_key
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if not url or not key:
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return None
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try:
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import typesense
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host = url.split("//")[-1]
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host_part, _, port_str = host.partition(":")
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port = int(port_str) if port_str else 8108
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_typesense_client = typesense.Client({
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"nodes": [{"host": host_part, "port": str(port), "protocol": "http"}],
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"api_key": key,
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"connection_timeout_seconds": 2,
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})
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return _typesense_client
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except Exception:
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return None
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def search_schools_typesense(query: str, limit: int = 250) -> List[int]:
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"""Search Typesense. Returns URNs in relevance order, or [] if unavailable."""
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client = _get_typesense_client()
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if client is None:
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return []
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try:
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result = client.collections["schools"].documents.search({
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"q": query,
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"query_by": "school_name,local_authority,postcode",
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"per_page": min(limit, 250),
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"typo_tokens_threshold": 1,
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})
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return [int(h["document"]["urn"]) for h in result.get("hits", [])]
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except Exception:
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return []
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def normalize_school_type(school_type: Optional[str]) -> Optional[str]:
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"""Convert cryptic school type codes to user-friendly names."""
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if not school_type:
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return None
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code = school_type.strip().upper()
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if code in SCHOOL_TYPE_MAP:
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return SCHOOL_TYPE_MAP[code]
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return school_type
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def geocode_single_postcode(postcode: str) -> Optional[Tuple[float, float]]:
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"""Geocode a single postcode using postcodes.io API."""
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if not postcode:
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return None
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postcode = postcode.strip().upper()
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if postcode in _postcode_cache:
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return _postcode_cache[postcode]
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try:
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response = requests.get(
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f"https://api.postcodes.io/postcodes/{postcode}",
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timeout=10,
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)
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if response.status_code == 200:
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data = response.json()
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if data.get("result"):
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lat = data["result"].get("latitude")
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lon = data["result"].get("longitude")
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if lat and lon:
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_postcode_cache[postcode] = (lat, lon)
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return (lat, lon)
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except Exception:
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pass
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return None
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def haversine_distance(lat1: float, lon1: float, lat2: float, lon2: float) -> float:
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"""Calculate great-circle distance between two points (miles)."""
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from math import radians, cos, sin, asin, sqrt
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lat1, lon1, lat2, lon2 = map(radians, [lat1, lon1, lat2, lon2])
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dlat = lat2 - lat1
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dlon = lon2 - lon1
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a = sin(dlat / 2) ** 2 + cos(lat1) * cos(lat2) * sin(dlon / 2) ** 2
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return 2 * asin(sqrt(a)) * 3956
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# =============================================================================
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# MAIN DATA LOAD — joins dim_school + dim_location + fact_ks2_performance
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# =============================================================================
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_MAIN_QUERY = text("""
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-- Branch 1: Primary schools (KS2 data; KS4 columns NULL)
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SELECT
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s.urn,
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s.school_name,
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s.phase,
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s.school_type,
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s.academy_trust_name AS trust_name,
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s.academy_trust_uid AS trust_uid,
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s.religious_character AS religious_denomination,
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s.gender,
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s.age_range,
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s.capacity,
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s.headteacher_name,
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s.website,
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s.ofsted_grade,
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s.ofsted_date,
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s.ofsted_framework,
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l.local_authority_name AS local_authority,
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l.local_authority_code,
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l.address_line1 AS address1,
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l.address_line2 AS address2,
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l.town,
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l.postcode,
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l.latitude,
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l.longitude,
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k.year,
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k.source_urn,
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k.total_pupils,
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k.eligible_pupils,
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-- KS2 columns
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k.rwm_expected_pct,
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k.rwm_high_pct,
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k.reading_expected_pct,
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k.reading_high_pct,
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k.reading_avg_score,
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k.reading_progress,
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k.writing_expected_pct,
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k.writing_high_pct,
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k.writing_progress,
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k.maths_expected_pct,
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k.maths_high_pct,
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k.maths_avg_score,
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k.maths_progress,
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k.gps_expected_pct,
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k.gps_high_pct,
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k.gps_avg_score,
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k.science_expected_pct,
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k.reading_absence_pct,
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k.writing_absence_pct,
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k.maths_absence_pct,
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k.gps_absence_pct,
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k.science_absence_pct,
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k.rwm_expected_boys_pct,
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k.rwm_high_boys_pct,
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k.rwm_expected_girls_pct,
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k.rwm_high_girls_pct,
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k.rwm_expected_disadvantaged_pct,
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k.rwm_expected_non_disadvantaged_pct,
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k.disadvantaged_gap,
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k.disadvantaged_pct,
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k.eal_pct,
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k.sen_support_pct,
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k.sen_ehcp_pct,
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k.stability_pct,
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-- KS4 columns (NULL for primary)
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NULL::numeric AS attainment_8_score,
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NULL::numeric AS progress_8_score,
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NULL::numeric AS progress_8_lower_ci,
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NULL::numeric AS progress_8_upper_ci,
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NULL::numeric AS progress_8_english,
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NULL::numeric AS progress_8_maths,
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NULL::numeric AS progress_8_ebacc,
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NULL::numeric AS progress_8_open,
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NULL::numeric AS english_maths_strong_pass_pct,
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NULL::numeric AS english_maths_standard_pass_pct,
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NULL::numeric AS ebacc_entry_pct,
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NULL::numeric AS ebacc_strong_pass_pct,
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NULL::numeric AS ebacc_standard_pass_pct,
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NULL::numeric AS ebacc_avg_score,
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NULL::numeric AS gcse_grade_91_pct,
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NULL::numeric AS prior_attainment_avg
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FROM marts.dim_school s
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JOIN marts.dim_location l ON s.urn = l.urn
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JOIN marts.fact_ks2_performance k ON s.urn = k.urn
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UNION ALL
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-- Branch 2: Secondary schools (KS4 data; KS2 columns NULL)
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SELECT
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s.urn,
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s.school_name,
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s.phase,
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s.school_type,
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s.academy_trust_name AS trust_name,
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s.academy_trust_uid AS trust_uid,
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s.religious_character AS religious_denomination,
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s.gender,
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s.age_range,
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s.capacity,
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s.headteacher_name,
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s.website,
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s.ofsted_grade,
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s.ofsted_date,
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s.ofsted_framework,
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l.local_authority_name AS local_authority,
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l.local_authority_code,
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l.address_line1 AS address1,
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l.address_line2 AS address2,
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l.town,
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l.postcode,
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l.latitude,
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l.longitude,
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k4.year,
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k4.source_urn,
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k4.total_pupils,
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k4.eligible_pupils,
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-- KS2 columns (NULL for secondary)
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NULL::numeric AS rwm_expected_pct,
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NULL::numeric AS rwm_high_pct,
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NULL::numeric AS reading_expected_pct,
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NULL::numeric AS reading_high_pct,
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NULL::numeric AS reading_avg_score,
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NULL::numeric AS reading_progress,
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NULL::numeric AS writing_expected_pct,
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NULL::numeric AS writing_high_pct,
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NULL::numeric AS writing_progress,
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NULL::numeric AS maths_expected_pct,
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NULL::numeric AS maths_high_pct,
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NULL::numeric AS maths_avg_score,
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NULL::numeric AS maths_progress,
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NULL::numeric AS gps_expected_pct,
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NULL::numeric AS gps_high_pct,
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NULL::numeric AS gps_avg_score,
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NULL::numeric AS science_expected_pct,
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NULL::numeric AS reading_absence_pct,
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NULL::numeric AS writing_absence_pct,
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NULL::numeric AS maths_absence_pct,
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NULL::numeric AS gps_absence_pct,
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NULL::numeric AS science_absence_pct,
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NULL::numeric AS rwm_expected_boys_pct,
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NULL::numeric AS rwm_high_boys_pct,
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NULL::numeric AS rwm_expected_girls_pct,
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NULL::numeric AS rwm_high_girls_pct,
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NULL::numeric AS rwm_expected_disadvantaged_pct,
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NULL::numeric AS rwm_expected_non_disadvantaged_pct,
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NULL::numeric AS disadvantaged_gap,
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NULL::numeric AS disadvantaged_pct,
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NULL::numeric AS eal_pct,
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k4.sen_support_pct,
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k4.sen_ehcp_pct,
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NULL::numeric AS stability_pct,
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-- KS4 columns
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k4.attainment_8_score,
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k4.progress_8_score,
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k4.progress_8_lower_ci,
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k4.progress_8_upper_ci,
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k4.progress_8_english,
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k4.progress_8_maths,
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k4.progress_8_ebacc,
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k4.progress_8_open,
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k4.english_maths_strong_pass_pct,
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k4.english_maths_standard_pass_pct,
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k4.ebacc_entry_pct,
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k4.ebacc_strong_pass_pct,
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k4.ebacc_standard_pass_pct,
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k4.ebacc_avg_score,
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k4.gcse_grade_91_pct,
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k4.prior_attainment_avg
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FROM marts.dim_school s
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JOIN marts.dim_location l ON s.urn = l.urn
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JOIN marts.fact_ks4_performance k4 ON s.urn = k4.urn
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ORDER BY school_name, year
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""")
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def load_school_data_as_dataframe() -> pd.DataFrame:
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"""Load all school + KS2 data as a pandas DataFrame."""
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try:
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df = pd.read_sql(_MAIN_QUERY, engine)
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except Exception as exc:
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print(f"Warning: Could not load school data from marts: {exc}")
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return pd.DataFrame()
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if df.empty:
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return df
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# Build address string
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df["address"] = df.apply(
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lambda r: ", ".join(
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p for p in [r.get("address1"), r.get("address2"), r.get("town"), r.get("postcode")]
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if p and str(p) != "None"
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),
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axis=1,
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)
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# Normalize school type
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df["school_type"] = df["school_type"].apply(normalize_school_type)
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return df
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# Cache for DataFrame
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_df_cache: Optional[pd.DataFrame] = None
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def load_school_data() -> pd.DataFrame:
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"""Load school data with caching."""
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global _df_cache
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if _df_cache is not None:
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return _df_cache
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print("Loading school data from marts...")
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_df_cache = load_school_data_as_dataframe()
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if not _df_cache.empty:
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print(f"Total records loaded: {len(_df_cache)}")
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print(f"Unique schools: {_df_cache['urn'].nunique()}")
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print(f"Years: {sorted(_df_cache['year'].unique())}")
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else:
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print("No data found in marts (EES data may not have been loaded yet)")
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return _df_cache
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def clear_cache():
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"""Clear all caches."""
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global _df_cache
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_df_cache = None
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# =============================================================================
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# METADATA QUERIES
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# =============================================================================
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def get_available_years(db: Session = None) -> List[int]:
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close_db = db is None
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if db is None:
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db = SessionLocal()
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try:
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result = db.query(KS2Performance.year).distinct().order_by(KS2Performance.year).all()
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return [r[0] for r in result]
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except Exception:
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return []
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finally:
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if close_db:
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db.close()
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def get_available_local_authorities(db: Session = None) -> List[str]:
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close_db = db is None
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if db is None:
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db = SessionLocal()
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try:
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result = (
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db.query(DimLocation.local_authority_name)
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.filter(DimLocation.local_authority_name.isnot(None))
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.distinct()
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.order_by(DimLocation.local_authority_name)
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.all()
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)
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return [r[0] for r in result if r[0]]
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except Exception:
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return []
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finally:
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if close_db:
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db.close()
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def get_schools_count(db: Session = None) -> int:
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close_db = db is None
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if db is None:
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db = SessionLocal()
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try:
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return db.query(DimSchool).count()
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except Exception:
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return 0
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finally:
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if close_db:
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db.close()
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def get_data_info(db: Session = None) -> dict:
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close_db = db is None
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if db is None:
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db = SessionLocal()
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try:
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school_count = get_schools_count(db)
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years = get_available_years(db)
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local_authorities = get_available_local_authorities(db)
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return {
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"total_schools": school_count,
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"years_available": years,
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"local_authorities_count": len(local_authorities),
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"data_source": "PostgreSQL (marts)",
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}
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finally:
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if close_db:
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db.close()
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# =============================================================================
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# SUPPLEMENTARY DATA — per-school detail page
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# =============================================================================
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def get_supplementary_data(db: Session, urn: int) -> dict:
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"""Fetch all supplementary data for a single school URN."""
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result = {}
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def safe_query(model, pk_field, latest_field=None):
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try:
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q = db.query(model).filter(getattr(model, pk_field) == urn)
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if latest_field:
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q = q.order_by(getattr(model, latest_field).desc())
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return q.first()
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except Exception as e:
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import logging
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logging.getLogger(__name__).error("safe_query failed for %s: %s", model.__name__, e)
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db.rollback()
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return None
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# Latest Ofsted inspection
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o = safe_query(FactOfstedInspection, "urn", "inspection_date")
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result["ofsted"] = (
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{
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"framework": o.framework,
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"inspection_date": o.inspection_date.isoformat() if o.inspection_date else None,
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"inspection_type": o.inspection_type,
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"overall_effectiveness": o.overall_effectiveness,
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"quality_of_education": o.quality_of_education,
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"behaviour_attitudes": o.behaviour_attitudes,
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"personal_development": o.personal_development,
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"leadership_management": o.leadership_management,
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"early_years_provision": o.early_years_provision,
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"sixth_form_provision": o.sixth_form_provision,
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"previous_overall": None, # Not available in new schema
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"rc_safeguarding_met": o.rc_safeguarding_met,
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"rc_inclusion": o.rc_inclusion,
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"rc_curriculum_teaching": o.rc_curriculum_teaching,
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"rc_achievement": o.rc_achievement,
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"rc_attendance_behaviour": o.rc_attendance_behaviour,
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"rc_personal_development": o.rc_personal_development,
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"rc_leadership_governance": o.rc_leadership_governance,
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"rc_early_years": o.rc_early_years,
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"rc_sixth_form": o.rc_sixth_form,
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"report_url": o.report_url,
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}
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if o
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else None
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)
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# Parent View
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pv = safe_query(FactParentView, "urn")
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result["parent_view"] = (
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{
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"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,
|
|
"published_admission_number": a.published_admission_number,
|
|
"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,
|
|
"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
|