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feat(portfolio): fold in RAILIANCE-WP-0016 apps-pg evidence

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First delegated evidence from RESOURCE-WP-0003-T04 to land. railiance-platform
delivered apps-pg capacity, utilization, consumers, and the apps-pg-dbbytes-v1
allocation driver, and correctly delivered no EUR.

- data/resources/apps-pg.json: real capacity; allocation unattributed -> shared
  under apps-pg-dbbytes-v1; second consumer vergabe-teilnahme registered
- data/control-cycle/apps-pg-2026-09-base.json: first operational control-cycle
  record in the repository
- examples/control-cycle/apps-pg-*.json retired; the invented fixture collided
  with the real record's identifier
- data/portfolio-coverage-2026-08-14.json: gap marked delivered with three
  residual unknowns still open

The real evidence exposed a design gap in the T05 schema: v0.1 required a number
for every cost field, so recording genuine usage without a booked cost meant
inventing one. Schema 0.2 permits null costs, null unattributed_eur, a technical
unattributed_share, and null measurements. Null is unknown, never zero; an
unknown component makes the total null rather than the sum of the known parts;
and the comparator classifies unknown amounts as data_quality instead of
computing a variance. Existing 0.1 records are not rewritten.

apps-pg is now measured (idle at 5.8% of volume) and attributed, and remains
unpriced: delivered technical evidence does not create a booked cost.

86 tests pass.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
This commit is contained in:
tegwick 2026-08-14 09:36:57 +02:00
parent b64b5683df
commit 17de8b831e
16 changed files with 908 additions and 71 deletions
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13
tools/control_cycle.py
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@ -11,7 +11,11 @@ COST_FIELDS = ("infrastructure", "internal_labor", "external_labor", "total")
ATTRIBUTIONS = {"demand", "provider_price", "allocation", "labor", "model", "data_quality"}
def delta(forecast: float, actual: float) -> dict:
def delta(forecast: float | None, actual: float | None) -> dict:
# A missing amount is unknown, not zero: subtracting against it would
# manufacture a variance the evidence does not support.
if forecast is None or actual is None:
return {"forecast": forecast, "actual": actual, "status": "unknown"}
error = actual - forecast
return {
"forecast": forecast,
@ -50,7 +54,12 @@ def compare(forecast: dict, actual: dict) -> dict:
costs = {}
for name in COST_FIELDS:
default_category = "labor" if "labor" in name else "provider_price"
costs[name] = {**delta(forecast["costs"][name], actual["costs"][name]), "currency": "EUR", "category": attribution.get(f"costs.{name}", default_category)}
result = delta(forecast["costs"][name], actual["costs"][name])
# An unknown amount is a data-quality gap, not a price or labour movement.
category = "data_quality" if result.get("status") == "unknown" else attribution.get(
f"costs.{name}", default_category
)
costs[name] = {**result, "currency": "EUR", "category": category}
if forecast["allocation"] != actual["allocation"]:
costs["allocation_method"] = {"status": "changed", "category": attribution.get("allocation", "allocation")}