Dave Boyd
9e2c55c568
Item 1 — QCEW county/industry detail: - new bls_client/qcew.py: thin client over the QCEW Open Data CSV service (area/industry/size slices; no API key, no quota), with filter_rows helper - covers the county/industry detail the timeseries API can't reach Item 2 — ECEC benefit breakdown: - new series.ecec() builder from the authoritative cm.estimate decode table - real helpers: wages, total benefits, paid leave, supplemental pay, health insurance, retirement & savings, legally required + ecec_dashboard() - FIX: ecec_total_benefits was CMU1036... (education/health industries only); correct all-civilian total benefits is CMU1030000000000D Item 3 — caching + typed errors: - bls_client/cache.py FileCache (on-disk, TTL); BLSClient(cache=True), queries_used counter (cache hits don't spend quota) - bls_client/errors.py: BLSError / BLSQuotaError / BLSRequestError; quota exhaustion now raises a clear BLSQuotaError instead of a generic RuntimeError Tests: 119 offline (+24) incl. ECEC goldens, QCEW fixture parse, cache/quota behavior; live smoke extended to ECEC + QCEW. Helper sweep: 96/96 live. README: caching/QCEW usage, updated coverage + limitations. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
102 lines
5.0 KiB
Python
102 lines
5.0 KiB
Python
"""Unit tests for the low-level series-ID builders in bls_client.series.
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These lock the exact encodings against known-good BLS series IDs. They run
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offline (no network) and are the regression guard for the encoding bugs that
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were fixed in June 2026 (JOLTS width, OES area code, ECI component/estimate,
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productivity sector/measure widths, QCEW form).
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"""
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import pytest
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from bls_client import series as S
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# (callable, kwargs) -> exact expected series ID. Every value here has been
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# confirmed to return data from the live BLS API.
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GOLDEN = [
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# LAUS — 20 chars
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(S.laus_state, dict(state_fips=11, measure="rate"), "LAUST110000000000003"),
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(S.laus_state, dict(state_fips=11, measure="rate", seasonal=True), "LASST110000000000003"),
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(S.laus_county, dict(state_fips=11, county_fips=1, measure="rate"), "LAUCN110010000000003"),
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(S.laus_msa, dict(state_fips=11, cbsa_code=47900, measure="rate"),"LAUMT114790000000003"),
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# CES national / state
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(S.ces_national, dict(), "CES0000000001"),
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(S.ces_national, dict(industry="manufacturing"), "CES3000000001"),
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(S.ces_state, dict(state_fips=11), "SMS11000000000000001"),
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# CPI / PPI
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(S.cpi, dict(), "CUUR0000SA0"),
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(S.cpi, dict(item="core", seasonal=True), "CUSR0000SA0L1E"),
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(S.ppi_commodity,dict(), "WPU00000000"),
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# OES — 25 chars, national area 0000000 (was the 0000400 bug)
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(S.oes_national, dict(), "OEUN000000000000000000001"),
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(S.oes_national, dict(occupation_code="151252", data_type="annual_median"),
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"OEUN000000000000015125213"),
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# JOLTS — 21 chars / 15 zeros (was 18 chars / 12 zeros)
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(S.jolts, dict(), "JTS000000000000000JOL"),
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(S.jolts, dict(element="quits", rate_level="R"), "JTS000000000000000QUR"),
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(S.jolts, dict(element="hires", seasonal=False), "JTU000000000000000HIL"),
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# ECI — 17 chars, unadjusted, component 10/20/30, estimate A
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(S.eci, dict(), "CIU1010000000000A"),
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(S.eci, dict(component="20"), "CIU1020000000000A"),
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(S.eci, dict(component="30"), "CIU1030000000000A"),
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(S.eci, dict(owner="20"), "CIU2010000000000A"),
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# Productivity — 11 chars, 4-digit sector + 4-digit measure
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(S.productivity, dict(), "PRS85006092"),
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(S.productivity, dict(sector="manufacturing", measure="unit_labor_cost"), "PRS30006112"),
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(S.productivity, dict(sector="business"), "PRS84006092"),
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# ECEC — 17 chars, CMU + owner(1) + component(2) + 10 zeros + datatype
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(S.ecec, dict(), "CMU1010000000000D"),
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(S.ecec, dict(component="health_insurance"), "CMU1150000000000D"),
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(S.ecec, dict(component="retirement_and_savings"), "CMU1180000000000D"),
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(S.ecec, dict(component="total_benefits", owner="private"), "CMU2030000000000D"),
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(S.ecec, dict(component="total_compensation", owner="state_local_gov"), "CMU3010000000000D"),
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]
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@pytest.mark.parametrize("fn,kwargs,expected", GOLDEN, ids=[f"{f.__name__}-{e}" for f, _, e in GOLDEN])
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def test_builder_exact_id(fn, kwargs, expected):
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assert fn(**kwargs) == expected
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# --- Format invariants (catch width regressions even for un-goldened inputs) ---
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def test_jolts_is_21_chars():
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for elem in ("job_openings", "hires", "quits", "layoffs", "total_separations"):
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sid = S.jolts(elem)
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assert len(sid) == 21, f"{sid} should be 21 chars"
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assert sid.startswith("JTS")
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def test_oes_is_25_chars_national():
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sid = S.oes_national()
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assert len(sid) == 25
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assert sid.startswith("OEUN0000000"), "national area code must be 0000000"
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def test_eci_is_17_chars_and_unadjusted_by_default():
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sid = S.eci()
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assert len(sid) == 17
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assert sid.startswith("CIU"), "12-month % change ECI is published unadjusted (CIU)"
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def test_productivity_is_11_chars():
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sid = S.productivity()
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assert len(sid) == 11
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assert sid.startswith("PRS")
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def test_ecec_is_17_chars_cost_per_hour():
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for comp in ("total_compensation", "health_insurance", "retirement_and_savings"):
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sid = S.ecec(comp)
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assert len(sid) == 17
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assert sid.startswith("CMU") and sid.endswith("D")
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def test_laus_state_is_20_chars():
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assert len(S.laus_state(48, "rate")) == 20
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def test_seasonal_flag_flips_adjustment_char():
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assert S.cpi(seasonal=False).startswith("CUU")
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assert S.cpi(seasonal=True).startswith("CUS")
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assert S.jolts(seasonal=False).startswith("JTU")
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assert S.jolts(seasonal=True).startswith("JTS")
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