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>
76 lines
2.6 KiB
Python
76 lines
2.6 KiB
Python
"""Scenario pins — hand-checked composite results per scenario, clamp
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behaviour, and copy semantics."""
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import pytest
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from teicalc import (
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BENEFITS_VERBATIM,
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COSTS_VERBATIM,
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SCENARIOS,
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apply_scenario,
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compute_summary,
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)
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def _summary(scenario):
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return compute_summary(
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apply_scenario(BENEFITS_VERBATIM, scenario),
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apply_scenario(COSTS_VERBATIM, scenario),
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0.10,
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)
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def test_scenario_definitions():
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assert SCENARIOS == {
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"conservative": {"adoption": 0.80, "risk_delta": 0.10},
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"moderate": {"adoption": 1.00, "risk_delta": 0.00},
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"aggressive": {"adoption": 1.15, "risk_delta": -0.05},
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}
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def test_moderate_is_identity():
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got = _summary("moderate")
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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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def test_conservative_pins():
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s = _summary("conservative")
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assert s["benefits_pv"] == pytest.approx(71_672_867.65, abs=1)
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assert s["costs_pv"] == pytest.approx(17_437_916.34, abs=1)
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assert s["npv"] == pytest.approx(54_234_951.31, abs=1)
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assert s["roi_pct"] == pytest.approx(311.02, abs=0.01)
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assert s["payback_months"] == pytest.approx(0.763, abs=0.001)
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def test_aggressive_pins():
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s = _summary("aggressive")
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assert s["benefits_pv"] == pytest.approx(123_911_705.40, abs=1)
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assert s["costs_pv"] == pytest.approx(27_682_368.61, abs=1)
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assert s["npv"] == pytest.approx(96_229_336.79, abs=1)
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assert s["roi_pct"] == pytest.approx(347.62, abs=0.01)
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assert s["payback_months"] == pytest.approx(0.705, abs=0.001)
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def test_risk_delta_clamps_at_zero():
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"""Conservative subtracts 0.10 from cost risk; usage (0.05) clamps to 0."""
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rows = apply_scenario(COSTS_VERBATIM, "conservative")
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usage = next(r for r in rows if r["field_key"] == "amazon_connect_usage")
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assert usage["risk_adjustment"] == 0.0
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impl = next(r for r in rows if r["field_key"] == "implementation_migration")
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assert impl["risk_adjustment"] == pytest.approx(0.0) # 0.10 − 0.10
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assert impl["initial"] == pytest.approx(1_087_500 * 0.80) # adoption scales initial
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def test_unknown_scenario_raises():
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with pytest.raises(KeyError):
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apply_scenario(BENEFITS_VERBATIM, "wildly_optimistic")
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def test_inputs_not_mutated():
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apply_scenario(BENEFITS_VERBATIM, "aggressive")
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apply_scenario(COSTS_VERBATIM, "conservative")
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assert BENEFITS_VERBATIM[0]["year_values"]["1"] == 13_911_040
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assert COSTS_VERBATIM[1]["initial"] == 1_087_500
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