Migrate Amazon Connect TEI study to the Mercury Notebook Pattern

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>

studies/202602_TEI_Amazon_Connect/tests/test_scenarios.py

@@ -0,0 +1,75 @@
+"""Scenario pins — hand-checked composite results per scenario, clamp
+behaviour, and copy semantics."""
+
+import pytest
+
+from teicalc import (
+    BENEFITS_VERBATIM,
+    COSTS_VERBATIM,
+    SCENARIOS,
+    apply_scenario,
+    compute_summary,
+)
+
+
+def _summary(scenario):
+    return compute_summary(
+        apply_scenario(BENEFITS_VERBATIM, scenario),
+        apply_scenario(COSTS_VERBATIM, scenario),
+        0.10,
+    )
+
+
+def test_scenario_definitions():
+    assert SCENARIOS == {
+        "conservative": {"adoption": 0.80, "risk_delta": 0.10},
+        "moderate": {"adoption": 1.00, "risk_delta": 0.00},
+        "aggressive": {"adoption": 1.15, "risk_delta": -0.05},
+    }
+
+
+def test_moderate_is_identity():
+    got = _summary("moderate")
+    want = compute_summary(BENEFITS_VERBATIM, COSTS_VERBATIM, 0.10)
+    assert got["benefits_pv"] == pytest.approx(want["benefits_pv"], abs=0.01)
+    assert got["costs_pv"] == pytest.approx(want["costs_pv"], abs=0.01)
+
+
+def test_conservative_pins():
+    s = _summary("conservative")
+    assert s["benefits_pv"] == pytest.approx(71_672_867.65, abs=1)
+    assert s["costs_pv"] == pytest.approx(17_437_916.34, abs=1)
+    assert s["npv"] == pytest.approx(54_234_951.31, abs=1)
+    assert s["roi_pct"] == pytest.approx(311.02, abs=0.01)
+    assert s["payback_months"] == pytest.approx(0.763, abs=0.001)
+
+
+def test_aggressive_pins():
+    s = _summary("aggressive")
+    assert s["benefits_pv"] == pytest.approx(123_911_705.40, abs=1)
+    assert s["costs_pv"] == pytest.approx(27_682_368.61, abs=1)
+    assert s["npv"] == pytest.approx(96_229_336.79, abs=1)
+    assert s["roi_pct"] == pytest.approx(347.62, abs=0.01)
+    assert s["payback_months"] == pytest.approx(0.705, abs=0.001)
+
+
+def test_risk_delta_clamps_at_zero():
+    """Conservative subtracts 0.10 from cost risk; usage (0.05) clamps to 0."""
+    rows = apply_scenario(COSTS_VERBATIM, "conservative")
+    usage = next(r for r in rows if r["field_key"] == "amazon_connect_usage")
+    assert usage["risk_adjustment"] == 0.0
+    impl = next(r for r in rows if r["field_key"] == "implementation_migration")
+    assert impl["risk_adjustment"] == pytest.approx(0.0)          # 0.10 − 0.10
+    assert impl["initial"] == pytest.approx(1_087_500 * 0.80)     # adoption scales initial
+
+
+def test_unknown_scenario_raises():
+    with pytest.raises(KeyError):
+        apply_scenario(BENEFITS_VERBATIM, "wildly_optimistic")
+
+
+def test_inputs_not_mutated():
+    apply_scenario(BENEFITS_VERBATIM, "aggressive")
+    apply_scenario(COSTS_VERBATIM, "conservative")
+    assert BENEFITS_VERBATIM[0]["year_values"]["1"] == 13_911_040
+    assert COSTS_VERBATIM[1]["initial"] == 1_087_500