feat(data): integrate 9 UK government data sources via Kestra

Adds a full data integration pipeline for enriching school profiles with
supplementary data from Ofsted, GIAS, EES, IDACI, and FBIT.

Backend:
- Bump SCHEMA_VERSION to 3; add 8 new DB tables (ofsted_inspections,
  ofsted_parent_view, school_census, admissions, sen_detail, phonics,
  school_deprivation, school_finance) plus GIAS columns on schools
- Expose all supplementary data via GET /api/schools/{urn}
- Enrich school list responses with ofsted_grade + ofsted_date

Integrator (new service):
- FastAPI HTTP microservice; Kestra calls POST /run/{source}
- 9 source modules: ofsted, gias, parent_view, census, admissions,
  sen_detail, phonics, idaci, finance
- 9 Kestra flow YAMLs with scheduled triggers and 3× retry

Frontend:
- SchoolRow: colour-coded Ofsted badge (Outstanding/Good/RI/Inadequate)
- SchoolDetailView: 7 new sections — Ofsted sub-judgements, Parent View
  survey bars, Admissions, Pupils & Inclusion / SEN, Phonics, Deprivation
  Context, Finances
- types.ts: 8 new interfaces + extended School/SchoolDetailsResponse

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

integrator/scripts/sources/sen_detail.py

@@ -0,0 +1,150 @@
+"""
+SEN (Special Educational Needs) primary need type breakdown.
+
+Source: EES publication "special-educational-needs-in-england"
+Update: Annual (September)
+"""
+import argparse
+import re
+import sys
+from pathlib import Path
+
+import pandas as pd
+
+sys.path.insert(0, str(Path(__file__).parent.parent))
+from config import SUPPLEMENTARY_DIR
+from db import get_session
+from sources.ees import get_latest_csv_url, download_csv
+
+DEST_DIR = SUPPLEMENTARY_DIR / "sen_detail"
+PUBLICATION_SLUG = "special-educational-needs-in-england"
+
+NULL_VALUES = {"SUPP", "NE", "NA", "NP", "NEW", "LOW", "X", ""}
+
+COLUMN_MAP = {
+    "URN": "urn",
+    "urn": "urn",
+    "YEAR": "year",
+    "Year": "year",
+    # Primary need types — DfE abbreviated codes
+    "PT_SPEECH": "primary_need_speech_pct",       # SLCN
+    "PT_ASD": "primary_need_autism_pct",           # ASD
+    "PT_MLD": "primary_need_mld_pct",             # Moderate learning difficulty
+    "PT_SPLD": "primary_need_spld_pct",           # Specific learning difficulty
+    "PT_SEMH": "primary_need_semh_pct",           # Social, emotional, mental health
+    "PT_PHYSICAL": "primary_need_physical_pct",   # Physical/sensory
+    "PT_OTHER": "primary_need_other_pct",
+    # Alternative naming
+    "SLCN_PCT": "primary_need_speech_pct",
+    "ASD_PCT": "primary_need_autism_pct",
+    "MLD_PCT": "primary_need_mld_pct",
+    "SPLD_PCT": "primary_need_spld_pct",
+    "SEMH_PCT": "primary_need_semh_pct",
+    "PHYSICAL_PCT": "primary_need_physical_pct",
+    "OTHER_PCT": "primary_need_other_pct",
+}
+
+
+def download(data_dir: Path | None = None) -> Path:
+    dest = (data_dir / "supplementary" / "sen_detail") if data_dir else DEST_DIR
+    dest.mkdir(parents=True, exist_ok=True)
+
+    url = get_latest_csv_url(PUBLICATION_SLUG, keyword="school")
+    if not url:
+        url = get_latest_csv_url(PUBLICATION_SLUG)
+    if not url:
+        raise RuntimeError("Could not find CSV URL for SEN publication")
+
+    filename = url.split("/")[-1].split("?")[0] or "sen_latest.csv"
+    return download_csv(url, dest / filename)
+
+
+def _parse_pct(val) -> float | None:
+    if pd.isna(val):
+        return None
+    s = str(val).strip().upper().replace("%", "")
+    if s in NULL_VALUES:
+        return None
+    try:
+        return float(s)
+    except ValueError:
+        return None
+
+
+def load(path: Path | None = None, data_dir: Path | None = None) -> dict:
+    if path is None:
+        dest = (data_dir / "supplementary" / "sen_detail") if data_dir else DEST_DIR
+        files = sorted(dest.glob("*.csv"))
+        if not files:
+            raise FileNotFoundError(f"No SEN CSV found in {dest}")
+        path = files[-1]
+
+    print(f"  SEN Detail: loading {path} ...")
+    df = pd.read_csv(path, encoding="latin-1", low_memory=False)
+    df.rename(columns=COLUMN_MAP, inplace=True)
+
+    if "urn" not in df.columns:
+        raise ValueError(f"URN column not found. Available: {list(df.columns)[:20]}")
+
+    df["urn"] = pd.to_numeric(df["urn"], errors="coerce")
+    df = df.dropna(subset=["urn"])
+    df["urn"] = df["urn"].astype(int)
+
+    year = None
+    m = re.search(r"20(\d{2})", path.stem)
+    if m:
+        year = int("20" + m.group(1))
+
+    inserted = 0
+    with get_session() as session:
+        from sqlalchemy import text
+        for _, row in df.iterrows():
+            urn = int(row["urn"])
+            row_year = int(row["year"]) if "year" in df.columns and pd.notna(row.get("year")) else year
+            if not row_year:
+                continue
+
+            session.execute(
+                text("""
+                    INSERT INTO sen_detail
+                        (urn, year, primary_need_speech_pct, primary_need_autism_pct,
+                         primary_need_mld_pct, primary_need_spld_pct, primary_need_semh_pct,
+                         primary_need_physical_pct, primary_need_other_pct)
+                    VALUES (:urn, :year, :speech, :autism, :mld, :spld, :semh, :physical, :other)
+                    ON CONFLICT (urn, year) DO UPDATE SET
+                        primary_need_speech_pct   = EXCLUDED.primary_need_speech_pct,
+                        primary_need_autism_pct   = EXCLUDED.primary_need_autism_pct,
+                        primary_need_mld_pct      = EXCLUDED.primary_need_mld_pct,
+                        primary_need_spld_pct     = EXCLUDED.primary_need_spld_pct,
+                        primary_need_semh_pct     = EXCLUDED.primary_need_semh_pct,
+                        primary_need_physical_pct = EXCLUDED.primary_need_physical_pct,
+                        primary_need_other_pct    = EXCLUDED.primary_need_other_pct
+                """),
+                {
+                    "urn": urn, "year": row_year,
+                    "speech": _parse_pct(row.get("primary_need_speech_pct")),
+                    "autism": _parse_pct(row.get("primary_need_autism_pct")),
+                    "mld": _parse_pct(row.get("primary_need_mld_pct")),
+                    "spld": _parse_pct(row.get("primary_need_spld_pct")),
+                    "semh": _parse_pct(row.get("primary_need_semh_pct")),
+                    "physical": _parse_pct(row.get("primary_need_physical_pct")),
+                    "other": _parse_pct(row.get("primary_need_other_pct")),
+                },
+            )
+            inserted += 1
+            if inserted % 5000 == 0:
+                session.flush()
+
+    print(f"  SEN Detail: upserted {inserted} records")
+    return {"inserted": inserted, "updated": 0, "skipped": 0}
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--action", choices=["download", "load", "all"], default="all")
+    parser.add_argument("--data-dir", type=Path, default=None)
+    args = parser.parse_args()
+    if args.action in ("download", "all"):
+        download(args.data_dir)
+    if args.action in ("load", "all"):
+        load(data_dir=args.data_dir)