56 lines
1.7 KiB
Python
56 lines
1.7 KiB
Python
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"""Decimal-string discipline (docs/11 §3.3) at the skill boundary.
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Contracts carry money/energy/prices as fixed-point decimal *strings*. Inside a
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skill we compute with numpy floats, then quantize back to strings with an
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explicit scale — and any headline figure (revenue, energy) is re-derived with
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``decimal.Decimal`` from the already-quantized curves, so what a report cites
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is exactly reproducible from the curves it cites.
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"""
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from __future__ import annotations
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from decimal import ROUND_HALF_EVEN, Decimal
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from typing import Iterable
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import numpy as np
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INTERVALS = 96
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INTERVAL_HOURS = Decimal("0.25")
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SCALE_MW = 3
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SCALE_MWH = 3
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SCALE_PRICE = 2
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SCALE_MONEY = 2
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def to_array(values: Iterable[str]) -> np.ndarray:
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return np.array([float(Decimal(v)) for v in values], dtype=float)
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def quantize(x: float | Decimal, scale: int) -> str:
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q = Decimal(1).scaleb(-scale)
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d = x if isinstance(x, Decimal) else Decimal(repr(float(x)))
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out = d.quantize(q, rounding=ROUND_HALF_EVEN)
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if out == 0:
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out = abs(out) # normalise "-0.000"
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return format(out, "f")
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def curve(values: np.ndarray | list[str], date: str, scale: int) -> dict:
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if isinstance(values, np.ndarray):
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vals = [quantize(float(v), scale) for v in values.tolist()]
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else:
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vals = list(values)
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if len(vals) != INTERVALS:
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raise ValueError(f"curve must have {INTERVALS} values, got {len(vals)}")
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return {"interval_minutes": 15, "date": date, "values": vals}
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def dsum(values: Iterable[str]) -> Decimal:
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return sum((Decimal(v) for v in values), Decimal(0))
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def dot(a: Iterable[str], b: Iterable[str]) -> Decimal:
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"""Exact Σ a_i·b_i over decimal strings."""
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return sum((Decimal(x) * Decimal(y) for x, y in zip(a, b, strict=True)), Decimal(0))
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