TudorandClaude Fable 5
4b75152ee0
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Closes the deploy window flagged by CI review — the backend now works against both the old (name) and new (code) mart schemas. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
97 lines
3.3 KiB
Python
97 lines
3.3 KiB
Python
"""API-boundary translation: marts now carry GIAS codes; the DataFrame the
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rest of the backend sees must carry today's name strings."""
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import numpy as np
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import pandas as pd
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from backend.data_loader import translate_gias_code_columns
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from backend.gias_codes import ESTABLISHMENT_STATUS, PHASE_OF_EDUCATION
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def _code_for(mapping, name):
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return next(c for c, n in mapping.items() if n == name)
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def test_codes_become_todays_names():
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df = pd.DataFrame([{
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"urn": 1,
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"phase_code": float(_code_for(PHASE_OF_EDUCATION, "Primary")),
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"school_type_code": np.nan,
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"status_code": float(_code_for(ESTABLISHMENT_STATUS, "Open, but proposed to close")),
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"religious_character_code": np.nan,
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"admissions_policy_code": np.nan,
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}])
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out = translate_gias_code_columns(df)
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row = out.iloc[0]
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assert row["phase"] == "Primary"
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assert row["status"] == "Open, but proposed to close"
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assert row["school_type"] is None
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assert row["religious_denomination"] is None
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assert row["admissions_policy"] is None
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def test_unknown_code_degrades_not_blanks():
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df = pd.DataFrame([{"urn": 1, "phase_code": 9999.0}])
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out = translate_gias_code_columns(df)
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assert out.iloc[0]["phase"] == "Unknown (9999)"
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def test_missing_code_columns_are_a_noop():
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"""Old-schema DataFrames (tests, pre-pipeline DBs) pass through untouched."""
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df = pd.DataFrame([{"urn": 1, "phase": "Primary", "status": "Open"}])
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out = translate_gias_code_columns(df)
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assert out.iloc[0]["phase"] == "Primary"
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assert out.iloc[0]["status"] == "Open"
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def test_load_school_data_survives_premigration_marts(monkeypatch):
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"""Real prod state until the nightly pipeline first rebuilds the mart with
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the GIAS code columns: marts.dim_school still has the old name columns
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(phase, school_type, religious_character, status, admissions_policy)
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instead of the new *_code columns. The first query raises UndefinedColumn
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on s.phase_code; load_school_data_as_dataframe must retry with the
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legacy name-column query rather than swallow the error and return (and
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then have load_school_data cache) an empty DataFrame."""
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import sqlalchemy.exc
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from backend import data_loader
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data_loader._df_cache = None
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data_loader._df_latest_cache = None
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good_df = pd.DataFrame(
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[
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{
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"urn": 1,
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"school_name": "Legacy School",
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"phase": "Primary",
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"school_type": "Academy",
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"status": "Open",
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}
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]
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)
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calls = []
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def fake_read_sql(query, con):
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calls.append(query)
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if len(calls) == 1:
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raise sqlalchemy.exc.ProgrammingError(
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"(psycopg2.errors.UndefinedColumn) column s.phase_code does not exist",
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None,
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None,
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)
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return good_df.copy()
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monkeypatch.setattr(data_loader.pd, "read_sql", fake_read_sql)
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try:
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df = data_loader.load_school_data_as_dataframe()
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finally:
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data_loader._df_cache = None
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data_loader._df_latest_cache = None
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assert len(calls) == 2, "must retry with the legacy name-column query variant"
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assert calls[1] is data_loader._MAIN_QUERY_LEGACY_NAMES
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assert not df.empty
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assert df["phase"].iloc[0] == "Primary"
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assert df["status"].iloc[0] == "Open"
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