Migrate Genesys CX Cloud TEI study to the pattern; retire Streamlit app

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>
This commit is contained in:
2026-07-09 16:38:51 -04:00
parent a420af230b
commit e88449d15a
54 changed files with 8462 additions and 6427 deletions

View File

@@ -0,0 +1,267 @@
"""
Finance engine — the single source of truth for every number in the notebook.
Transplanted from the retired shared ``core/calculations`` and
``core/export/report_data.py`` so the study is self-contained (Mercury
Notebook Pattern, Required §2/§7). Conventions match the Forrester TEI
methodology:
* The *Initial* investment is **not** discounted — it occurs at time zero.
* Year-N cash flows are discounted at the end of the year:
``PV = CF_n / (1 + r) ** n``.
* Benefits are risk-adjusted *down* (``×(1rf)``), costs *up* (``×(1+rf)``).
* Payback runs on risk-adjusted **undiscounted** flows (the PDF's
"<6 months" uses the Cash Flow Analysis table's nominal RA rows).
Everything this module returns for display is keyed by **calendar year**
(Forrester Year 1/2/3 → 2026/2027/2028); ``initial`` stays a Year-0 scalar
and never appears inside a ``*_by_year`` dict.
This module is stdlib-only on purpose — the repo-root test suite imports it
without the study's venv.
"""
from __future__ import annotations
import math
from collections.abc import Iterable, Sequence
from copy import deepcopy
# ── Timeline ─────────────────────────────────────────────────────────
YEARS: list[int] = [2026, 2027, 2028] # Forrester Year 1/2/3; window opens Jan 2026
YEAR_INDEX: dict[int, int] = {y: i for i, y in enumerate(YEARS, start=1)}
X_LABELS: list[str] = ["Initial"] + [str(y) for y in YEARS]
_MONTHS = ["Jan", "Feb", "Mar", "Apr", "May", "Jun",
"Jul", "Aug", "Sep", "Oct", "Nov", "Dec"]
def month_label(m: int) -> str:
"""Calendar label for a 1-indexed month from Jan of YEARS[0]."""
return f"{_MONTHS[(m - 1) % 12]} {YEARS[0] + (m - 1) // 12}"
def by_calendar(year_values: dict[str, float]) -> dict[int, float]:
"""Map Forrester's ``{"1": v, …}`` year-index keys to calendar years."""
return {YEARS[int(k) - 1]: float(v or 0) for k, v in year_values.items()}
# ── Discounting primitives ───────────────────────────────────────────
def discount_factor(year_index: int, discount_rate: float) -> float:
"""``1 / (1 + r) ** n``. Year 0 → 1.0 (no discount)."""
if year_index < 0:
raise ValueError("year_index must be >= 0")
return 1.0 / ((1.0 + discount_rate) ** year_index)
def present_value(amount: float, year_index: int, discount_rate: float) -> float:
"""Discount ``amount`` from end-of-year ``year_index`` to present."""
return amount * discount_factor(year_index, discount_rate)
def npv(cashflows: Iterable[float], discount_rate: float,
initial: float = 0.0) -> float:
"""``initial + Σ CF_n / (1 + r)^n`` — initial undiscounted (TEI)."""
return initial + sum(
present_value(float(cf), n, discount_rate)
for n, cf in enumerate(cashflows, start=1)
)
def roi_pct(benefits_pv: float, costs_pv: float) -> float:
"""``(Benefits Costs) / Costs`` as a percentage; 0 when costs ≤ 0."""
if costs_pv <= 0:
return 0.0
return (benefits_pv - costs_pv) / costs_pv * 100.0
# ── Payback ──────────────────────────────────────────────────────────
def payback_years(initial_cost: float,
yearly_net: Sequence[float]) -> float | None:
"""
Years until cumulative net benefits cover the initial cost, with linear
interpolation inside the crossing year. ``None`` if never reached.
"""
remaining = float(initial_cost)
if remaining <= 0:
return 0.0
for i, cf in enumerate(yearly_net):
cf = float(cf)
if cf <= 0:
remaining += -cf # a net-loss year widens the gap
continue
if cf >= remaining:
return i + remaining / cf
remaining -= cf
return None
def payback_months(initial_cost: float,
yearly_net: Sequence[float]) -> float | None:
"""Same as :func:`payback_years`, in months."""
yrs = payback_years(initial_cost, yearly_net)
return yrs * 12.0 if yrs is not None else None
def payback_label(months: float | None) -> str:
"""Human label: ``"0.7 months (~Jan 2026)"`` / ``"immediate"`` / ``"beyond 2028"``."""
if months is None:
return f"beyond {YEARS[-1]}"
if months <= 0:
return "immediate"
return f"{months:.1f} months (~{month_label(max(1, math.ceil(months)))})"
# ── Risk adjustment (TEI: benefits down, costs up) ───────────────────
def risk_adjust_benefit(amount: float, risk_factor: float) -> float:
"""``amount × (1 rf)``, rf clamped to [0, 1]."""
rf = max(0.0, min(1.0, float(risk_factor)))
return amount * (1.0 - rf)
def risk_adjust_cost(amount: float, risk_factor: float) -> float:
"""``amount × (1 + rf)``, rf clamped to [0, 1]."""
rf = max(0.0, min(1.0, float(risk_factor)))
return amount * (1.0 + rf)
def risk_adjusted_rows(rows: list[dict], table: str) -> list[dict]:
"""Deep-copied rows with the per-row risk factor applied to every value."""
adjust = risk_adjust_benefit if table == "benefits" else risk_adjust_cost
out: list[dict] = []
for raw in rows:
row = deepcopy(raw)
rf = float(row.get("risk_adjustment") or 0.0)
row["year_values"] = {
k: adjust(float(v or 0), rf) for k, v in row["year_values"].items()
}
if row.get("initial"):
# Only costs carry an initial; TEI adjusts it upward like the years.
row["initial"] = risk_adjust_cost(float(row["initial"]), rf) \
if table == "costs" else float(row["initial"])
out.append(row)
return out
# ── Aggregation (calendar-keyed) ─────────────────────────────────────
def _totals_by_year(ra_rows: list[dict]) -> dict[int, float]:
totals = {y: 0.0 for y in YEARS}
for row in ra_rows:
for y, v in by_calendar(row["year_values"]).items():
totals[y] += v
return totals
def benefits_by_year(rows: list[dict]) -> dict[int, float]:
"""Risk-adjusted benefit totals per calendar year."""
return _totals_by_year(risk_adjusted_rows(rows, "benefits"))
def costs_by_year(rows: list[dict]) -> dict[int, float]:
"""Risk-adjusted cost totals per calendar year (excludes ``initial``)."""
return _totals_by_year(risk_adjusted_rows(rows, "costs"))
def initial_costs(rows: list[dict]) -> float:
"""Risk-adjusted Year-0 outlay (undiscounted)."""
return sum(
float(row.get("initial") or 0)
for row in risk_adjusted_rows(rows, "costs")
)
# ── Composite summary ────────────────────────────────────────────────
def compute_summary(benefits: list[dict], costs: list[dict],
discount_rate: float = 0.10) -> dict:
"""
The full business-case readout for one set of value rows.
Returns KPIs (``benefits_pv``/``costs_pv``/``npv``/``roi_pct``/
``payback_months``/``payback_label``/``initial_costs``/nominal totals),
calendar-keyed schedules (``benefits_by_year``/``costs_by_year``/
``net_by_year``/``cumulative_net_by_year`` — cumulative subtracts the
initial outlay), and a per-row breakdown under ``rows``.
"""
ben_ra = risk_adjusted_rows(benefits, "benefits")
cost_ra = risk_adjusted_rows(costs, "costs")
ben_by = _totals_by_year(ben_ra)
cost_by = _totals_by_year(cost_ra)
initial = sum(float(r.get("initial") or 0) for r in cost_ra)
benefits_pv = npv([ben_by[y] for y in YEARS], discount_rate)
costs_pv = npv([cost_by[y] for y in YEARS], discount_rate, initial=initial)
net_by = {y: ben_by[y] - cost_by[y] for y in YEARS}
cum, cum_by = -initial, {}
for y in YEARS:
cum += net_by[y]
cum_by[y] = cum
pb_months = payback_months(initial, [net_by[y] for y in YEARS])
def _row_breakdown(ra_rows: list[dict], table: str) -> list[dict]:
out = []
for row in ra_rows:
ra_by = by_calendar(row["year_values"])
init_ra = float(row.get("initial") or 0)
entry = {
"field_key": row["field_key"],
"label": row["label"],
"category": row["category"],
"risk_adjustment": row["risk_adjustment"],
"ra_by_year": ra_by,
"three_yr_ra": sum(ra_by.values()),
"pv": npv([ra_by[y] for y in YEARS], discount_rate,
initial=init_ra if table == "costs" else 0.0),
}
if table == "costs":
entry["initial_ra"] = init_ra
out.append(entry)
return out
return {
"discount_rate": discount_rate,
"benefits_pv": benefits_pv,
"costs_pv": costs_pv,
"npv": benefits_pv - costs_pv,
"roi_pct": roi_pct(benefits_pv, costs_pv),
"payback_months": pb_months,
"payback_label": payback_label(pb_months),
"initial_costs": initial,
"benefits_nominal": sum(ben_by.values()),
"costs_nominal": sum(cost_by.values()) + initial,
"benefits_by_year": ben_by,
"costs_by_year": cost_by,
"net_by_year": net_by,
"cumulative_net_by_year": cum_by,
"rows": {
"benefits": _row_breakdown(ben_ra, "benefits"),
"costs": _row_breakdown(cost_ra, "costs"),
},
}
# ── Display helpers ──────────────────────────────────────────────────
def money(v: float) -> str:
sign, a = ("-" if v < 0 else ""), abs(v)
return f"{sign}${a/1e6:,.1f}M" if a >= 1e6 else f"{sign}${a/1e3:,.0f}K"
def html_money(v: float) -> str:
"""Plotly text with two or more bare ``$`` triggers MathJax math mode —
annotations holding several amounts must use the HTML entity instead."""
return money(v).replace("$", "&#36;")