Robert Helewka
a420af230b
studies/202602_AmazonConnect -> studies/202602_TEI_Amazon_Connect, rebuilt as pattern Variant 4 (TEI composite reproduction): - teicalc/ self-contained engine (stdlib-only): Forrester's tables as the never-edited verbatim anchor, NPV/ROI/payback + risk adjustment transplanted from core/calculations, ClientDrivers overlay (contacts/ agents/fixed driver map, growth re-base, identity at composite scale), scenario stress with core-identical semantics - one deliverable notebook (business_case.ipynb): widget-pair sidebar drivers, published-vs-overlay KPI columns, cash-flow/waterfall/scenario charts, verification gate, backstage JSON data appendix - gate + tests reproduce the published totals within PDF rounding: NPV $78.7M / ROI 342% / payback <6 months (engine $78,713,492 / 342.48% / 0.7 months); 27 study tests, headless nbconvert green, stage simulation leak-free, exports carry the appendix - old Athena workflow (00_provision..04_export, config.py, seed_data.py) deleted; git history preserves it; root test fixture repointed to teicalc.anchor - docs: study README rewritten; root README points new studies at template/MercuryNotebook; pattern doc stale ctm-token-calculator paths now cite studies/202607_CTM_GenesysCX; Variant 4 cites this study as its realized reference Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
97 lines
3.6 KiB
Python
97 lines
3.6 KiB
Python
"""Client-overlay pins — identity at the composite, linear per-driver
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scaling, growth re-basing, and copy semantics."""
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import dataclasses
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import pytest
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from teicalc import (
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BENEFIT_DRIVERS,
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BENEFITS_VERBATIM,
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COMPOSITE,
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COST_DRIVERS,
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COSTS_VERBATIM,
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ClientDrivers,
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compute_summary,
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growth_multiplier,
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overlay_rows,
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scale_factor,
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)
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def _row(rows, key):
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return next(r for r in rows if r["field_key"] == key)
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def test_identity_at_composite():
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"""overlay_rows(COMPOSITE) reproduces the verbatim study to the cent."""
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ob, oc = overlay_rows(COMPOSITE)
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got = compute_summary(ob, oc, 0.10)
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want = compute_summary(BENEFITS_VERBATIM, COSTS_VERBATIM, 0.10)
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assert got["benefits_pv"] == pytest.approx(want["benefits_pv"], abs=0.01)
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assert got["costs_pv"] == pytest.approx(want["costs_pv"], abs=0.01)
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assert got["npv"] == pytest.approx(want["npv"], abs=0.01)
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def test_driver_map_covers_every_row():
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assert set(BENEFIT_DRIVERS) == {r["field_key"] for r in BENEFITS_VERBATIM}
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assert set(COST_DRIVERS) == {r["field_key"] for r in COSTS_VERBATIM}
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def test_scale_factor():
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d = ClientDrivers(agents_fte=1_000, annual_contacts_y1=40_000_000)
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assert scale_factor("agents", d) == pytest.approx(0.5)
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assert scale_factor("contacts", d) == pytest.approx(2.0)
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assert scale_factor("fixed", d) == 1.0
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with pytest.raises(KeyError):
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scale_factor("revenue", d)
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def test_half_agents_halves_agent_rows_only():
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ob, oc = overlay_rows(ClientDrivers(agents_fte=1_000))
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assert _row(ob, "ai_forecasting_supervision")["year_values"]["1"] == \
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pytest.approx(6_651_680 / 2)
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assert _row(ob, "legacy_solution_savings")["year_values"]["1"] == \
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pytest.approx(6_177_600 / 2)
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# Contact-driven and fixed rows unmoved.
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assert _row(ob, "ai_contact_resolution")["year_values"]["1"] == \
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pytest.approx(13_911_040)
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assert _row(oc, "amazon_connect_usage")["year_values"]["1"] == \
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pytest.approx(6_456_448)
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assert _row(oc, "implementation_migration")["initial"] == 1_087_500
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def test_double_contacts_doubles_usage_only():
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_, oc = overlay_rows(ClientDrivers(annual_contacts_y1=40_000_000))
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assert _row(oc, "amazon_connect_usage")["year_values"]["1"] == \
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pytest.approx(6_456_448 * 2)
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assert _row(oc, "implementation_migration")["year_values"]["1"] == \
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pytest.approx(188_333)
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assert _row(oc, "ongoing_management")["year_values"]["1"] == \
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pytest.approx(256_200)
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def test_growth_rebase():
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assert growth_multiplier(1, 0.0) == 1.0 # Y1 always 1.0
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assert growth_multiplier(2, 0.0) == pytest.approx(1 / 1.3)
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assert growth_multiplier(3, 0.0) == pytest.approx((1 / 1.3) ** 2)
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assert growth_multiplier(3, 0.30) == 1.0 # composite growth = identity
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ob, oc = overlay_rows(ClientDrivers(growth_rate=0.0))
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row = _row(ob, "ai_contact_resolution")
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assert row["year_values"]["1"] == pytest.approx(13_911_040)
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assert row["year_values"]["2"] == pytest.approx(23_932_480 / 1.3)
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assert row["year_values"]["3"] == pytest.approx(37_797_760 / 1.3**2)
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# Fixed rows ignore the growth re-base too.
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assert _row(oc, "ongoing_management")["year_values"]["2"] == pytest.approx(187_200)
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def test_drivers_frozen_and_rows_are_copies():
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with pytest.raises(dataclasses.FrozenInstanceError):
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COMPOSITE.agents_fte = 1 # type: ignore[misc]
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ob, oc = overlay_rows(COMPOSITE)
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ob[0]["year_values"]["1"] = -1
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oc[0]["year_values"]["1"] = -1
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assert BENEFITS_VERBATIM[0]["year_values"]["1"] == 13_911_040
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assert COSTS_VERBATIM[0]["year_values"]["1"] == 6_456_448
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