Robert Helewka
e88449d15a
studies/202512_GenesysCX -> studies/202512_TEI_Genesys_CX_Cloud, rebuilt as pattern Variant 4 (TEI composite reproduction): - teicalc/ self-contained engine: Forrester's tables as the never-edited verbatim anchor (incl. the p.14 typo note and the $0 AI-token line), generic model/scenarios/staging carried over from the Amazon Connect study, ClientDrivers overlay (agents / weekly interactions / revenue, flat composite so no growth re-base) with ai_tokens_annual as a direct input for the token line the published study models at $0 - one deliverable notebook (business_case.ipynb): widget-pair sidebar drivers incl. the AI-token price, published-vs-overlay KPI columns, cash-flow/waterfall/scenario charts, verification gate, backstage JSON data appendix - gate + tests reproduce the published totals within $2: NPV $10.8M / ROI 266% (engine $10,783,466 / 265.79%; payback 3.3 months, not headlined in the PDF); 29 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, PALLADIUM_GENESYSCX_* keys, ATHENA_EXPECTED reconciliation) deleted; git history preserves it With the last legacy study migrated, the retirement lands too: - app/ (Streamlit UI) and core/notebook_helpers deleted; nothing else imported them - streamlit stripped from pyproject extras, requirements.txt, Makefile; .env.example reduced to the Athena keys; 00_setup.ipynb and core/bootstrap.py repointed at the pattern studies - root README reworked: self-contained studies + slim core/ Athena toolkit (tei_client, calculations, export, cli) All suites green: Genesys 29, Amazon Connect 27, CTM 55, template 7, root 58. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
75 lines
2.5 KiB
Python
75 lines
2.5 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(10_543_493.91, abs=1)
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assert s["costs_pv"] == pytest.approx(3_026_631.40, abs=1)
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assert s["npv"] == pytest.approx(7_516_862.51, abs=1)
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assert s["roi_pct"] == pytest.approx(248.36, abs=0.01)
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assert s["payback_months"] == pytest.approx(3.464, 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(18_021_962.25, abs=1)
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assert s["costs_pv"] == pytest.approx(4_883_285.09, abs=1)
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assert s["npv"] == pytest.approx(13_138_677.16, abs=1)
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assert s["roi_pct"] == pytest.approx(269.05, abs=0.01)
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assert s["payback_months"] == pytest.approx(3.294, 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; every cost rf clamps to 0
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(licenses 0.05, implementation 0.10, ongoing 0.10, tokens 0.0)."""
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rows = apply_scenario(COSTS_VERBATIM, "conservative")
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assert all(r["risk_adjustment"] == 0.0 for r in rows)
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impl = next(r for r in rows if r["field_key"] == "implementation")
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assert impl["initial"] == pytest.approx(1_190_000 * 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"] == 680_000
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assert COSTS_VERBATIM[1]["initial"] == 1_190_000
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