school_compare/docs/superpowers/plans/2026-07-13-compare-api-enrichment.md

# Compare API Enrichment (Backend PR) Implementation Plan

> **For agentic workers:** REQUIRED SUB-SKILL: Use superpowers:subagent-driven-development (recommended) or superpowers:executing-plans to implement this plan task-by-task. Steps use checkbox (`- [ ]`) syntax for tracking.

**Goal:** Expose the PR #32 data through the API so the redesigned compare screen can be built: enrich `/api/compare` with supplementary blocks + national averages + computed benchmarks, translate Ofsted report-card codes to labels, and surface the new mart columns (spec §6, §8 of `docs/superpowers/specs/2026-07-11-compare-screen-redesign-design.md`).

**Architecture:** All changes are additive API fields — existing consumers keep working. One small dbt change rides along: `fact_performance` (the combined KS2+KS4 mart the backend's `_MAIN_QUERY` reads) enumerates columns explicitly and was not extended in PR #32, so the new KS2 CI and KS4 banding columns must be threaded through it here. Everything else is backend Python: `models.py` mappings, `data_loader` query/supplementary additions, an Ofsted label dictionary (gias_codes pattern), and `/api/compare` composition.

**Tech Stack:** FastAPI, SQLAlchemy, pandas; dbt (one model); pytest via `python -m pytest backend/tests -q` (CI installs `requirements.txt pytest "httpx<0.28"`; locally use `uv run --with-requirements requirements.txt --with pytest --with "httpx==0.27.0" python -m pytest backend/tests -q`).

## Global Constraints

- **Never push to `main`.** Branch: `feat/compare-api-enrichment`.
- **Additive only** to API responses; never rename/remove existing fields (frontend + e2e depend on them).
- **Report-card scale labels are the live-sampled vocabulary** (evidence in `pipeline/scripts/diagnose_compare_gaps.py`): `1=Exceptional, 2=Strong standard, 3=Expected standard, 4=Needs attention, 5=Urgent improvement`. Never "Attention needed". Safeguarding is boolean met/not-met, never counted as a graded area.
- **Ofsted links** are always the provider page `https://reports.ofsted.gov.uk/provider/21/{urn}` (spec §5) labelled as the school's Ofsted page.
- **Benchmark provenance** (spec §8.6): computed values are "state-school average (computed from our dataset)" — the API must expose them under a `benchmarks` key, clearly separate from official `national_averages`.
- TDD: each behaviour lands with a failing test first, in `backend/tests/` following the `test_school_details.py` pattern (pandas fixture + monkeypatched `load_school_data` + `TestClient`).
- Deploy note for the PR body: the new API fields return NULL/empty until prod's DAGs have run post-promotion.

---

### Task 0: Branch

- [ ] `git checkout main && git pull && git checkout -b feat/compare-api-enrichment` (commit this plan file on the branch).

---

### Task 1: Thread PR #32 columns through `fact_performance`

**Files:**
- Modify: `pipeline/transform/models/marts/fact_performance.sql`
- Modify: `pipeline/transform/models/marts/_marts_schema.yml` (fact_performance block, if it has one — add the columns wherever the model's other columns are listed; if the model has no column list there, skip the yml)

**Interfaces:**
- Produces (for `_MAIN_QUERY` in Task 4): `ks2.*` CI columns and `ks4.progress_8_banding`, `ks4.attainment_8_disadvantage_gap`, `ks4.progress_8_disadvantage_gap` on `marts.fact_performance`.

- [ ] **Step 1:** In `fact_performance.sql`, after `ks2.reading_progress,` add `ks2.reading_progress_lower_ci,` and `ks2.reading_progress_upper_ci,`; after `ks2.writing_progress,` add `ks2.writing_progress_lower_ci,`, `ks2.writing_progress_upper_ci,`, `ks2.writing_working_towards_pct,`; after `ks2.maths_progress,` add `ks2.maths_progress_lower_ci,`, `ks2.maths_progress_upper_ci,`. In the KS4 section, after the `ks4.progress_8_upper_ci`-equivalent line (locate the Progress 8 block) add:

```sql
    ks4.progress_8_banding,
    ks4.attainment_8_disadvantage_gap,
    ks4.progress_8_disadvantage_gap,
```

- [ ] **Step 2:** Parse gate: `cd pipeline/transform && uv run --with dbt-postgres python -m dbt.cli.main parse --profiles-dir .` → exit 0.

- [ ] **Step 3:** Commit: `feat(pipeline): thread compare-foundation columns through fact_performance`

---

### Task 2: ORM mappings for the new mart columns

**Files:**
- Modify: `backend/models.py` (`KS2Performance` after `maths_progress`; `FactAdmissions` after `first_preference_offers`)
- Test: none (declarative mappings; covered by Task 4's query tests)

**Interfaces:**
- Produces attributes used by Task 4: `KS2Performance.reading_progress_lower_ci` … `maths_progress_upper_ci`, `writing_working_towards_pct` (Float); `FactAdmissions.total_offers`, `.second_preference_offers`, `.third_preference_offers`, `.cross_la_applications`, `.cross_la_offers` (Integer).

- [ ] **Step 1:** Add to `KS2Performance` (next to the existing progress columns):

```python
    reading_progress_lower_ci = Column(Float)
    reading_progress_upper_ci = Column(Float)
    writing_progress_lower_ci = Column(Float)
    writing_progress_upper_ci = Column(Float)
    writing_working_towards_pct = Column(Float)
    maths_progress_lower_ci = Column(Float)
    maths_progress_upper_ci = Column(Float)
```

Add to `FactAdmissions` (after `first_preference_offers`):

```python
    total_offers = Column(Integer)
    second_preference_offers = Column(Integer)
    third_preference_offers = Column(Integer)
    cross_la_applications = Column(Integer)
    cross_la_offers = Column(Integer)
```

(`FactOfstedInspection` already maps all `rc_*` columns with the right types — verify, don't change.)

- [ ] **Step 2:** Commit: `feat(api): map compare-foundation mart columns`

---

### Task 3: Ofsted label dictionary + provider URL (TDD)

**Files:**
- Create: `backend/ofsted_codes.py`
- Test: `backend/tests/test_ofsted_codes.py`

**Interfaces:**
- Produces for Task 4: `REPORT_CARD_GRADE_NAMES: dict[int, str]`, `report_card_labels(ofsted: dict) -> dict` (returns `{area_key: {"code": int, "label": str}}` for the non-null `rc_*` grade fields, excluding safeguarding), `ofsted_page_url(urn: int) -> str`.

- [ ] **Step 1: Failing tests**

```python
"""Report-card code translation uses the live-sampled Ofsted vocabulary
(pipeline/scripts/diagnose_compare_gaps.py TASK 7 VALUE SAMPLE):
Exceptional / Strong standard / Expected standard / Needs attention /
Urgent improvement — never the consultation draft's 'Attention needed'."""
from backend.ofsted_codes import (
    REPORT_CARD_GRADE_NAMES, report_card_labels, ofsted_page_url,
)


def test_scale_is_sampled_vocabulary():
    assert REPORT_CARD_GRADE_NAMES == {
        1: "Exceptional",
        2: "Strong standard",
        3: "Expected standard",
        4: "Needs attention",
        5: "Urgent improvement",
    }


def test_labels_only_for_populated_areas_and_never_safeguarding():
    ofsted = {
        "rc_achievement": 2,
        "rc_inclusion": 3,
        "rc_attendance_behaviour": 4,
        "rc_early_years": None,
        "rc_safeguarding_met": True,
        "overall_effectiveness": None,
    }
    labels = report_card_labels(ofsted)
    assert labels == {
        "rc_achievement": {"code": 2, "label": "Strong standard"},
        "rc_inclusion": {"code": 3, "label": "Expected standard"},
        "rc_attendance_behaviour": {"code": 4, "label": "Needs attention"},
    }


def test_unknown_code_is_skipped_not_crashed():
    assert report_card_labels({"rc_achievement": 9}) == {}


def test_provider_url():
    assert ofsted_page_url(138690) == "https://reports.ofsted.gov.uk/provider/21/138690"
```

- [ ] **Step 2:** Run `uv run --with-requirements requirements.txt --with pytest --with "httpx==0.27.0" python -m pytest backend/tests/test_ofsted_codes.py -q` → FAIL (module missing).

- [ ] **Step 3: Implement `backend/ofsted_codes.py`**

```python
"""Ofsted renewed-framework (Nov 2025) report-card code translation.

Scale labels are the live-sampled vocabulary from the Ofsted MI file
(see pipeline/scripts/diagnose_compare_gaps.py, TASK 7 VALUE SAMPLE) —
verified against real data, not the consultation draft.
"""

REPORT_CARD_GRADE_NAMES = {
    1: "Exceptional",
    2: "Strong standard",
    3: "Expected standard",
    4: "Needs attention",
    5: "Urgent improvement",
}

# Graded evaluation areas only — safeguarding is a separate boolean
# judgement and must never appear in grade counts or label maps.
_RC_AREA_KEYS = (
    "rc_inclusion",
    "rc_curriculum_teaching",
    "rc_achievement",
    "rc_attendance_behaviour",
    "rc_personal_development",
    "rc_leadership_governance",
    "rc_early_years",
    "rc_sixth_form",
)


def report_card_labels(ofsted: dict) -> dict:
    """{area_key: {code, label}} for populated, known-valued rc_* areas."""
    out = {}
    for key in _RC_AREA_KEYS:
        code = ofsted.get(key)
        label = REPORT_CARD_GRADE_NAMES.get(code)
        if code is not None and label is not None:
            out[key] = {"code": code, "label": label}
    return out


def ofsted_page_url(urn: int) -> str:
    """The school's page on ofsted.gov.uk (all its reports live there —
    we never deep-link an individual report; spec §5)."""
    return f"https://reports.ofsted.gov.uk/provider/21/{urn}"
```

- [ ] **Step 4:** Re-run the test file → 4 passed. Run the full suite (same command, `backend/tests -q`) → all pass.

- [ ] **Step 5:** Commit: `feat(api): Ofsted report-card labels and provider-page URL`

---

### Task 4: data_loader — query columns + richer supplementary blocks (TDD)

**Files:**
- Modify: `backend/data_loader.py` (`_MAIN_QUERY` ~line 153; `get_supplementary_data` ~line 460)
- Test: `backend/tests/test_supplementary_enrichment.py`

**Interfaces:**
- `_MAIN_QUERY` additionally selects (KS2 block, after `p.maths_progress`): `p.reading_progress_lower_ci, p.reading_progress_upper_ci, p.writing_progress_lower_ci, p.writing_progress_upper_ci, p.writing_working_towards_pct, p.maths_progress_lower_ci, p.maths_progress_upper_ci`; (KS4 block, after the Progress 8 CI columns): `p.progress_8_banding, p.attainment_8_disadvantage_gap, p.progress_8_disadvantage_gap`. Note `_MAIN_QUERY_NO_SIXTH_FORM`/`_MAIN_QUERY_LEGACY_NAMES` are string-derived from `_MAIN_QUERY` (lines 259-270) and inherit automatically — verify the assertions there still hold.
- `get_supplementary_data(db, urn)["admissions"]` rows additionally carry: `total_offers`, `second_preference_offers`, `third_preference_offers`, `cross_la_applications`, `cross_la_offers` (add to `_admissions_row`).
- `get_supplementary_data(db, urn)["ofsted"]` additionally carries: `report_card` (the `report_card_labels(...)` dict, `{}` when no rc data), `ofsted_page_url`, and `grade_source`: `"graded"` when `overall_effectiveness` came from the graded column, `"ungraded_carried_forward"` when the fallback `ungraded_grade` supplied it, `None` when neither.

- [ ] **Step 1: Failing tests** — construct a fake Ofsted row object (simple `types.SimpleNamespace` with the model's attributes) and call the block-building logic via `get_supplementary_data` with a stubbed session (follow how existing tests stub the db; if none do, factor the ofsted-dict construction into a pure helper `_ofsted_block(o, urn)` and test that directly — preferred):

```python
import types
from backend.data_loader import _ofsted_block


def _row(**kw):
    base = dict(
        framework="RC", inspection_date=None, inspection_type=None,
        overall_effectiveness=None, quality_of_education=None,
        behaviour_attitudes=None, personal_development=None,
        leadership_management=None, early_years_provision=None,
        sixth_form_provision=None, ungraded_outcome=None, ungraded_grade=None,
        rc_safeguarding_met=None, rc_inclusion=None, rc_curriculum_teaching=None,
        rc_achievement=None, rc_attendance_behaviour=None,
        rc_personal_development=None, rc_leadership_governance=None,
        rc_early_years=None, rc_sixth_form=None, report_url=None,
    )
    base.update(kw)
    return types.SimpleNamespace(**base)


def test_report_card_block_and_provider_url():
    o = _row(rc_achievement=2, rc_inclusion=3, rc_safeguarding_met=True)
    block = _ofsted_block(o, urn=100140)
    assert block["report_card"]["rc_achievement"]["label"] == "Strong standard"
    assert "rc_safeguarding_met" not in block["report_card"]
    assert block["rc_safeguarding_met"] is True
    assert block["ofsted_page_url"] == "https://reports.ofsted.gov.uk/provider/21/100140"


def test_grade_source_graded_vs_carried_forward():
    assert _ofsted_block(_row(overall_effectiveness=1), urn=1)["grade_source"] == "graded"
    carried = _ofsted_block(_row(ungraded_grade=2), urn=1)
    assert carried["grade_source"] == "ungraded_carried_forward"
    assert carried["overall_effectiveness"] == 2
    assert _ofsted_block(_row(), urn=1)["grade_source"] is None


def test_admissions_row_new_fields():
    from backend.data_loader import _admissions_row_dict
    a = types.SimpleNamespace(
        year=202627, school_phase="Primary", places_offered=80,
        total_applications=185, first_preference_applications=74,
        first_preference_offers=74, first_preference_offer_pct=100.0,
        oversubscription_ratio=0.925, oversubscribed=False,
        total_offers=80, second_preference_offers=4, third_preference_offers=2,
        cross_la_applications=12, cross_la_offers=3,
    )
    d = _admissions_row_dict(a)
    for k in ("total_offers", "second_preference_offers", "third_preference_offers",
              "cross_la_applications", "cross_la_offers"):
        assert d[k] == getattr(a, k)
```

- [ ] **Step 2:** Run → FAIL (helpers don't exist).

- [ ] **Step 3: Implement.** Refactor the existing inline ofsted-dict construction in `get_supplementary_data` into a module-level `_ofsted_block(o, urn)` that produces the existing keys **unchanged** plus the three new ones (`report_card` via `report_card_labels(...)` from Task 3, `ofsted_page_url` via `ofsted_page_url(urn)`, `grade_source` per the interface rule — derived from which source supplied `overall_effectiveness`). Rename/extract the local `_admissions_row` into module-level `_admissions_row_dict(a)` and append the five new fields. Add the ten new columns to `_MAIN_QUERY` exactly as the interface lists them. `get_supplementary_data` calls both helpers; its external shape gains only additive keys.

- [ ] **Step 4:** Full suite → all pass (existing `test_school_details.py` etc. must not break; if a fixture enumerates yearly-data columns, extend it with the new NaN columns as needed).

- [ ] **Step 5:** Commit: `feat(api): expose progress CIs, KS4 banding/gaps, admissions detail, report-card labels`

---

### Task 5: Computed benchmarks helper (TDD)

**Files:**
- Modify: `backend/data_loader.py` (new function)
- Test: `backend/tests/test_benchmarks.py`

**Interfaces:**
- Produces for Task 6: `compute_benchmarks(df) -> dict` — pure function over the main dataframe (latest year, state schools), shape:

```python
{
  "source": "state-school average (computed from our dataset)",
  "year": 202425,
  "primary": {
      "disadvantaged_rwm_expected_pct": 46.1,   # weighted by eligible_pupils
      "eal_pct": 22.3,                           # median
      "sen_support_pct": 14.0,                   # median
      "disadvantaged_pct": 24.8,                 # median (FSM6 proxy)
      "median_pupils": 281,                      # median school size
  },
  "secondary": { "median_pupils": 1024, "eal_pct": ..., "sen_support_pct": ..., "disadvantaged_pct": ... },
}
```

- [ ] **Step 1: Failing tests** — build a small synthetic df (6 primary rows with known eligible_pupils/rwm_expected_disadvantaged_pct so the weighted average is hand-checkable; a couple of secondary rows flagged by non-null `attainment_8_score`), assert: weighted disadvantaged average matches hand computation (not the unweighted mean), medians ignore NaN, secondary block lacks the disadvantaged-RWM key, latest-year filtering (rows from an older year must not affect results), and empty df → `{}`.

- [ ] **Step 2:** Run → FAIL.

- [ ] **Step 3: Implement** in `data_loader.py`:

```python
def compute_benchmarks(df: pd.DataFrame) -> dict:
    """State-school benchmarks computed from our dataset (spec §5/§8.6).
    These are NOT official DfE figures — consumers must label them
    'state-school average (computed from our dataset)'."""
    if df.empty or "year" not in df.columns:
        return {}
    latest_year = df["year"].max()
    d = df[df["year"] == latest_year]
    if d.empty:
        return {}
    is_secondary = d["attainment_8_score"].notna() if "attainment_8_score" in d.columns else pd.Series(False, index=d.index)
    prim, sec = d[~is_secondary], d[is_secondary]

    def _median(sub, col):
        if col not in sub.columns:
            return None
        v = sub[col].median()
        return round(float(v), 1) if pd.notna(v) else None

    def _weighted_disadvantaged(sub):
        if not {"rwm_expected_disadvantaged_pct", "eligible_pupils"} <= set(sub.columns):
            return None
        s = sub.dropna(subset=["rwm_expected_disadvantaged_pct", "eligible_pupils"])
        if s.empty or s["eligible_pupils"].sum() == 0:
            return None
        w = (s["rwm_expected_disadvantaged_pct"] * s["eligible_pupils"]).sum() / s["eligible_pupils"].sum()
        return round(float(w), 1)

    def _block(sub, with_disadvantaged):
        block = {
            "eal_pct": _median(sub, "eal_pct"),
            "sen_support_pct": _median(sub, "sen_support_pct"),
            "disadvantaged_pct": _median(sub, "disadvantaged_pct"),
            "median_pupils": int(sub["total_pupils"].median()) if "total_pupils" in sub.columns and pd.notna(sub["total_pupils"].median()) else None,
        }
        if with_disadvantaged:
            block["disadvantaged_rwm_expected_pct"] = _weighted_disadvantaged(sub)
        return block

    return {
        "source": "state-school average (computed from our dataset)",
        "year": int(latest_year),
        "primary": _block(prim, with_disadvantaged=True),
        "secondary": _block(sec, with_disadvantaged=False),
    }
```

(Adapt column presence to the real df — `sen_support_pct` reaches the df via `_MAIN_QUERY`; confirm and add it there if the KS2 block doesn't already select it, mirroring Task 4's additions.)

- [ ] **Step 4:** Full suite → pass. **Step 5:** Commit: `feat(api): computed state-school benchmarks`

---

### Task 6: Enrich `/api/compare` + expose GPS/science national averages (TDD)

**Files:**
- Modify: `backend/app.py` (`compare_schools` ~line 636; `get_national_averages` ~line 730)
- Test: `backend/tests/test_compare_enrichment.py`

**Interfaces (response additions, all additive):**
- `/api/compare` top level gains: `"national_averages"` (same payload the `/api/national-averages` endpoint returns — extract the endpoint body into a helper `_national_averages_payload(df)` and reuse; do not duplicate the logic) and `"benchmarks"` (Task 5's `compute_benchmarks(df)`).
- Each `comparison[urn]` gains: `"ofsted"`, `"census"`, `"admissions"`, `"admissions_history"`, `"deprivation"` from `get_supplementary_data` (one `SessionLocal()` for the whole request, closed in `finally`; on exception the five keys are `None`/`[]` — mirror the detail endpoint's defensive pattern at app.py:583-590).
- `get_national_averages`' KS2 metric list gains `"gps_expected_pct", "gps_high_pct", "science_expected_pct"` so the England ticks for GPS/science flow once the data exists.

- [ ] **Step 1: Failing tests** — monkeypatch `load_school_data` with a two-school primary df (reuse/extend the fixture style of `test_school_details.py`) and monkeypatch `get_supplementary_data` to a canned dict; assert on `TestClient(app).get("/api/compare?urns=...")`:
  - response keeps the existing shape (`comparison[urn]["school_info"]["rwm_expected_pct"]` etc.),
  - each school gains the five supplementary keys (canned values round-tripped),
  - top-level `national_averages` and `benchmarks` present; `benchmarks["source"]` is the exact provenance string,
  - a supplementary-layer exception (monkeypatched to raise) degrades to `ofsted: None` etc. with HTTP 200,
  - `/api/national-averages` includes `gps_expected_pct` in the primary block when the df/national table provides it (monkeypatch the national-averages source the endpoint reads).

- [ ] **Step 2:** Run → FAIL. **Step 3:** Implement per the interfaces. **Step 4:** Full suite → pass.

- [ ] **Step 5:** Commit: `feat(api): compare endpoint carries supplementary blocks, national averages and benchmarks`

---

### Task 7: PR + verification

- [ ] **Step 1:** Full suite one more time + `uv run --with pyyaml python3 -c "import yaml; yaml.safe_load(open('.gitea/workflows/deploy.yml'))"` sanity is NOT needed (no workflow changes) — instead run the dbt parse gate again (Task 1 file).
- [ ] **Step 2:** Push, open PR via the Gitea API (credential-helper basic auth). PR body: the new response shapes (one JSON sketch), the reused-not-duplicated national-averages helper, the provenance rule for benchmarks, deploy note (fields NULL until prod DAGs run post-promotion), and that no e2e change is needed (no user-facing behaviour changes — the compare UI still reads the old fields; the frontend PR carries the journey updates).
- [ ] **Step 3:** After merge + staging deploy: `curl -s https://stx.schoolcompare.co.uk/api/compare?urns=138690,100140 | python3 -m json.tool | head -80` — verify the new keys and that `benchmarks.primary.disadvantaged_rwm_expected_pct` is plausible (~45-47). Verify `/api/national-averages` now carries `gps_expected_pct`/`science_expected_pct` (values or honest nulls if DfE suppresses them at national level).

---

## Out of scope

- Frontend rebuild + e2e journeys (next PR — consumes everything this PR exposes).
- `schemas.py` METRIC_DEFINITIONS additions for the trends picker (frontend PR decides which of the new columns become picker metrics).
- CI-based progress banding logic (frontend computes Above/Average/Below from the CI columns; historical years only).