FA-68766 / Toll and parking fee computation / Open access
Demand-responsive meter rate adjustment: the lower band uses the quietest hour · case 01
Demand-responsive meter rate adjustment returns a wrong result when the lower band uses the quietest hour.
ROOT CAUSE
A mean of exactly the target minimum lowers the rate.
VERIFIED REPAIR
Restore the lower occupancy band rule so that the step reads `elif avg < x['lo']:`.
Unsuccessful approach: A single quiet hour lowers the rate even when the mean is in band.
Case contract
Input {rate, occupancy: hourly percentages, hi, lo, step, min_rate, max_rate, min_samples}. With fewer than min_samples samples the rate is unchanged. Otherwise the exact mean occupancy above hi raises the rate by step, below lo lowers it by step; the result is always clamped to [min_rate, max_rate].
Why this case matters
Fee engines bill customers in integer cents; a wrong boundary, rounding stage or cap scope silently over- or under-charges.
1 / The failure
Exit 1"""Failure Map reference implementation. Python standard library only."""
import json
from fractions import Fraction
N = 1
observations = []
def solve(x):
occ = x['occupancy']
if len(occ) < x['min_samples']:
return x['rate']
avg = Fraction(sum(occ), len(occ))
r = x['rate']
if avg > x['hi']:
r += x['step']
elif avg <= x['lo']:
r -= x['step']
return max(x['min_rate'], min(x['max_rate'], r))
def check(label, actual, expected):
observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[({'rate': 375, 'occupancy': [60], 'hi': 80, 'lo': 60, 'step': 50, 'min_rate': 50, 'max_rate': 700, 'min_samples': 2}, 375), ({'rate': 775, 'occupancy': [80, 53, 81], 'hi': 80, 'lo': 60, 'step': 50, 'min_rate': 100, 'max_rate': 700, 'min_samples': 3}, 700), ({'rate': 300, 'occupancy': [81, 51, 71, 73, 57], 'hi': 80, 'lo': 60, 'step': 50, 'min_rate': 50, 'max_rate': 700, 'min_samples': 3}, 300), ({'rate': 300, 'occupancy': [80, 80], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 300), ({'rate': 300, 'occupancy': [60, 60, 60], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 300), ({'rate': 125, 'occupancy': [60, 60], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 125), ({'rate': 300, 'occupancy': [90, 90], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 325), ({'rate': 325, 'occupancy': [83, 59, 59, 60], 'hi': 80, 'lo': 60, 'step': 50, 'min_rate': 100, 'max_rate': 600, 'min_samples': 2}, 325)], [({'rate': 300, 'occupancy': [90, 90], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 325), ({'rate': 425, 'occupancy': [53, 59, 80, 81], 'hi': 80, 'lo': 60, 'step': 50, 'min_rate': 100, 'max_rate': 700, 'min_samples': 3}, 425), ({'rate': 300, 'occupancy': [60, 59], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 275), ({'rate': 800, 'occupancy': [70, 70, 70], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 3}, 600), ({'rate': 400, 'occupancy': [80, 59, 91, 81, 59], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 400), ({'rate': 600, 'occupancy': [80, 78], 'hi': 80, 'lo': 60, 'step': 50, 'min_rate': 100, 'max_rate': 700, 'min_samples': 3}, 600), ({'rate': 300, 'occupancy': [60, 60, 60], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 300), ({'rate': 75, 'occupancy': [59, 56, 81, 80, 60], 'hi': 80, 'lo': 60, 'step': 50, 'min_rate': 100, 'max_rate': 700, 'min_samples': 2}, 100)], [({'rate': 300, 'occupancy': [60, 60, 60], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 300), ({'rate': 800, 'occupancy': [70, 70, 70], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 3}, 600), ({'rate': 300, 'occupancy': [80, 81], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 325), ({'rate': 300, 'occupancy': [90, 90], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 325), ({'rate': 525, 'occupancy': [40, 81, 59], 'hi': 80, 'lo': 60, 'step': 50, 'min_rate': 100, 'max_rate': 700, 'min_samples': 3}, 525), ({'rate': 325, 'occupancy': [60, 59, 80, 59, 59, 60], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 700, 'min_samples': 3}, 325), ({'rate': 425, 'occupancy': [59, 60, 60, 57, 81, 80], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 3}, 425), ({'rate': 600, 'occupancy': [], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 700, 'min_samples': 2}, 600)], [({'rate': 300, 'occupancy': [60, 59], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 275), ({'rate': 225, 'occupancy': [], 'hi': 80, 'lo': 60, 'step': 50, 'min_rate': 50, 'max_rate': 700, 'min_samples': 2}, 225), ({'rate': 600, 'occupancy': [80, 83, 60, 59, 81], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 100, 'max_rate': 600, 'min_samples': 2}, 600), ({'rate': 325, 'occupancy': [62, 59, 81], 'hi': 80, 'lo': 60, 'step': 50, 'min_rate': 50, 'max_rate': 700, 'min_samples': 2}, 325), ({'rate': 300, 'occupancy': [80, 80], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 300), ({'rate': 300, 'occupancy': [60, 60, 60], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 300), ({'rate': 300, 'occupancy': [80, 81], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 325), ({'rate': 775, 'occupancy': [60, 59, 81], 'hi': 80, 'lo': 60, 'step': 50, 'min_rate': 50, 'max_rate': 700, 'min_samples': 2}, 700)], [({'rate': 400, 'occupancy': [59, 60, 81, 59, 45], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 100, 'max_rate': 700, 'min_samples': 2}, 400), ({'rate': 475, 'occupancy': [60, 60], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 700, 'min_samples': 2}, 475), ({'rate': 425, 'occupancy': [60, 60], 'hi': 80, 'lo': 60, 'step': 50, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 425), ({'rate': 375, 'occupancy': [59, 59, 67, 60, 60], 'hi': 80, 'lo': 60, 'step': 50, 'min_rate': 50, 'max_rate': 600, 'min_samples': 3}, 375), ({'rate': 300, 'occupancy': [60, 60, 60], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 300), ({'rate': 300, 'occupancy': [80, 81], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 325), ({'rate': 300, 'occupancy': [80, 80], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 300), ({'rate': 800, 'occupancy': [70, 70, 70], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 3}, 600)]]
for i, (args, expected) in enumerate(fixtures[N-1]):
check('fee oracle' + ' %d' % i, solve(args), expected)
print(json.dumps({"observations": observations, "passed": all(x["passed"] for x in observations)}, ensure_ascii=False))
raise SystemExit(0 if all(x["passed"] for x in observations) else 1)
| Boundary fixture | Actual | Expected | Outcome |
|---|---|---|---|
| fee oracle 0 | 375 | 375 | Passed |
| fee oracle 1 | 700 | 700 | Passed |
| fee oracle 2 | 300 | 300 | Passed |
| fee oracle 3 | 300 | 300 | Passed |
| fee oracle 4 | 275 | 300 | Failed |
| fee oracle 5 | 100 | 125 | Failed |
| fee oracle 6 | 325 | 325 | Passed |
| fee oracle 7 | 325 | 325 | Passed |
SHA-256 / b5e6cc166630d15d95f69a285ea037ed13cd12bd3bf743ce6a5fc7fcab925156
2 / The unsuccessful fix
Exit 1"""Failure Map reference implementation. Python standard library only."""
import json
from fractions import Fraction
N = 1
observations = []
def solve(x):
occ = x['occupancy']
if len(occ) < x['min_samples']:
return x['rate']
avg = Fraction(sum(occ), len(occ))
r = x['rate']
if avg > x['hi']:
r += x['step']
elif min(occ) < x['lo']:
r -= x['step']
return max(x['min_rate'], min(x['max_rate'], r))
def check(label, actual, expected):
observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[({'rate': 375, 'occupancy': [60], 'hi': 80, 'lo': 60, 'step': 50, 'min_rate': 50, 'max_rate': 700, 'min_samples': 2}, 375), ({'rate': 775, 'occupancy': [80, 53, 81], 'hi': 80, 'lo': 60, 'step': 50, 'min_rate': 100, 'max_rate': 700, 'min_samples': 3}, 700), ({'rate': 300, 'occupancy': [81, 51, 71, 73, 57], 'hi': 80, 'lo': 60, 'step': 50, 'min_rate': 50, 'max_rate': 700, 'min_samples': 3}, 300), ({'rate': 300, 'occupancy': [80, 80], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 300), ({'rate': 300, 'occupancy': [60, 60, 60], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 300), ({'rate': 125, 'occupancy': [60, 60], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 125), ({'rate': 300, 'occupancy': [90, 90], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 325), ({'rate': 325, 'occupancy': [83, 59, 59, 60], 'hi': 80, 'lo': 60, 'step': 50, 'min_rate': 100, 'max_rate': 600, 'min_samples': 2}, 325)], [({'rate': 300, 'occupancy': [90, 90], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 325), ({'rate': 425, 'occupancy': [53, 59, 80, 81], 'hi': 80, 'lo': 60, 'step': 50, 'min_rate': 100, 'max_rate': 700, 'min_samples': 3}, 425), ({'rate': 300, 'occupancy': [60, 59], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 275), ({'rate': 800, 'occupancy': [70, 70, 70], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 3}, 600), ({'rate': 400, 'occupancy': [80, 59, 91, 81, 59], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 400), ({'rate': 600, 'occupancy': [80, 78], 'hi': 80, 'lo': 60, 'step': 50, 'min_rate': 100, 'max_rate': 700, 'min_samples': 3}, 600), ({'rate': 300, 'occupancy': [60, 60, 60], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 300), ({'rate': 75, 'occupancy': [59, 56, 81, 80, 60], 'hi': 80, 'lo': 60, 'step': 50, 'min_rate': 100, 'max_rate': 700, 'min_samples': 2}, 100)], [({'rate': 300, 'occupancy': [60, 60, 60], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 300), ({'rate': 800, 'occupancy': [70, 70, 70], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 3}, 600), ({'rate': 300, 'occupancy': [80, 81], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 325), ({'rate': 300, 'occupancy': [90, 90], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 325), ({'rate': 525, 'occupancy': [40, 81, 59], 'hi': 80, 'lo': 60, 'step': 50, 'min_rate': 100, 'max_rate': 700, 'min_samples': 3}, 525), ({'rate': 325, 'occupancy': [60, 59, 80, 59, 59, 60], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 700, 'min_samples': 3}, 325), ({'rate': 425, 'occupancy': [59, 60, 60, 57, 81, 80], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 3}, 425), ({'rate': 600, 'occupancy': [], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 700, 'min_samples': 2}, 600)], [({'rate': 300, 'occupancy': [60, 59], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 275), ({'rate': 225, 'occupancy': [], 'hi': 80, 'lo': 60, 'step': 50, 'min_rate': 50, 'max_rate': 700, 'min_samples': 2}, 225), ({'rate': 600, 'occupancy': [80, 83, 60, 59, 81], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 100, 'max_rate': 600, 'min_samples': 2}, 600), ({'rate': 325, 'occupancy': [62, 59, 81], 'hi': 80, 'lo': 60, 'step': 50, 'min_rate': 50, 'max_rate': 700, 'min_samples': 2}, 325), ({'rate': 300, 'occupancy': [80, 80], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 300), ({'rate': 300, 'occupancy': [60, 60, 60], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 300), ({'rate': 300, 'occupancy': [80, 81], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 325), ({'rate': 775, 'occupancy': [60, 59, 81], 'hi': 80, 'lo': 60, 'step': 50, 'min_rate': 50, 'max_rate': 700, 'min_samples': 2}, 700)], [({'rate': 400, 'occupancy': [59, 60, 81, 59, 45], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 100, 'max_rate': 700, 'min_samples': 2}, 400), ({'rate': 475, 'occupancy': [60, 60], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 700, 'min_samples': 2}, 475), ({'rate': 425, 'occupancy': [60, 60], 'hi': 80, 'lo': 60, 'step': 50, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 425), ({'rate': 375, 'occupancy': [59, 59, 67, 60, 60], 'hi': 80, 'lo': 60, 'step': 50, 'min_rate': 50, 'max_rate': 600, 'min_samples': 3}, 375), ({'rate': 300, 'occupancy': [60, 60, 60], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 300), ({'rate': 300, 'occupancy': [80, 81], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 325), ({'rate': 300, 'occupancy': [80, 80], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 300), ({'rate': 800, 'occupancy': [70, 70, 70], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 3}, 600)]]
for i, (args, expected) in enumerate(fixtures[N-1]):
check('fee oracle' + ' %d' % i, solve(args), expected)
print(json.dumps({"observations": observations, "passed": all(x["passed"] for x in observations)}, ensure_ascii=False))
raise SystemExit(0 if all(x["passed"] for x in observations) else 1)
| Boundary fixture | Actual | Expected | Outcome |
|---|---|---|---|
| fee oracle 0 | 375 | 375 | Passed |
| fee oracle 1 | 700 | 700 | Passed |
| fee oracle 2 | 250 | 300 | Failed |
| fee oracle 3 | 300 | 300 | Passed |
| fee oracle 4 | 300 | 300 | Passed |
| fee oracle 5 | 125 | 125 | Passed |
| fee oracle 6 | 325 | 325 | Passed |
| fee oracle 7 | 275 | 325 | Failed |
SHA-256 / 77f855aecf6b32b7c309a8f118ffc6deb79e206a87ae48f1a125a79cd47d62c9
3 / The verified repair
Exit 0"""Failure Map reference implementation. Python standard library only."""
import json
from fractions import Fraction
N = 1
observations = []
def solve(x):
occ = x['occupancy']
if len(occ) < x['min_samples']:
return x['rate']
avg = Fraction(sum(occ), len(occ))
r = x['rate']
if avg > x['hi']:
r += x['step']
elif avg < x['lo']:
r -= x['step']
return max(x['min_rate'], min(x['max_rate'], r))
def check(label, actual, expected):
observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[({'rate': 375, 'occupancy': [60], 'hi': 80, 'lo': 60, 'step': 50, 'min_rate': 50, 'max_rate': 700, 'min_samples': 2}, 375), ({'rate': 775, 'occupancy': [80, 53, 81], 'hi': 80, 'lo': 60, 'step': 50, 'min_rate': 100, 'max_rate': 700, 'min_samples': 3}, 700), ({'rate': 300, 'occupancy': [81, 51, 71, 73, 57], 'hi': 80, 'lo': 60, 'step': 50, 'min_rate': 50, 'max_rate': 700, 'min_samples': 3}, 300), ({'rate': 300, 'occupancy': [80, 80], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 300), ({'rate': 300, 'occupancy': [60, 60, 60], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 300), ({'rate': 125, 'occupancy': [60, 60], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 125), ({'rate': 300, 'occupancy': [90, 90], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 325), ({'rate': 325, 'occupancy': [83, 59, 59, 60], 'hi': 80, 'lo': 60, 'step': 50, 'min_rate': 100, 'max_rate': 600, 'min_samples': 2}, 325)], [({'rate': 300, 'occupancy': [90, 90], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 325), ({'rate': 425, 'occupancy': [53, 59, 80, 81], 'hi': 80, 'lo': 60, 'step': 50, 'min_rate': 100, 'max_rate': 700, 'min_samples': 3}, 425), ({'rate': 300, 'occupancy': [60, 59], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 275), ({'rate': 800, 'occupancy': [70, 70, 70], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 3}, 600), ({'rate': 400, 'occupancy': [80, 59, 91, 81, 59], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 400), ({'rate': 600, 'occupancy': [80, 78], 'hi': 80, 'lo': 60, 'step': 50, 'min_rate': 100, 'max_rate': 700, 'min_samples': 3}, 600), ({'rate': 300, 'occupancy': [60, 60, 60], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 300), ({'rate': 75, 'occupancy': [59, 56, 81, 80, 60], 'hi': 80, 'lo': 60, 'step': 50, 'min_rate': 100, 'max_rate': 700, 'min_samples': 2}, 100)], [({'rate': 300, 'occupancy': [60, 60, 60], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 300), ({'rate': 800, 'occupancy': [70, 70, 70], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 3}, 600), ({'rate': 300, 'occupancy': [80, 81], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 325), ({'rate': 300, 'occupancy': [90, 90], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 325), ({'rate': 525, 'occupancy': [40, 81, 59], 'hi': 80, 'lo': 60, 'step': 50, 'min_rate': 100, 'max_rate': 700, 'min_samples': 3}, 525), ({'rate': 325, 'occupancy': [60, 59, 80, 59, 59, 60], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 700, 'min_samples': 3}, 325), ({'rate': 425, 'occupancy': [59, 60, 60, 57, 81, 80], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 3}, 425), ({'rate': 600, 'occupancy': [], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 700, 'min_samples': 2}, 600)], [({'rate': 300, 'occupancy': [60, 59], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 275), ({'rate': 225, 'occupancy': [], 'hi': 80, 'lo': 60, 'step': 50, 'min_rate': 50, 'max_rate': 700, 'min_samples': 2}, 225), ({'rate': 600, 'occupancy': [80, 83, 60, 59, 81], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 100, 'max_rate': 600, 'min_samples': 2}, 600), ({'rate': 325, 'occupancy': [62, 59, 81], 'hi': 80, 'lo': 60, 'step': 50, 'min_rate': 50, 'max_rate': 700, 'min_samples': 2}, 325), ({'rate': 300, 'occupancy': [80, 80], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 300), ({'rate': 300, 'occupancy': [60, 60, 60], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 300), ({'rate': 300, 'occupancy': [80, 81], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 325), ({'rate': 775, 'occupancy': [60, 59, 81], 'hi': 80, 'lo': 60, 'step': 50, 'min_rate': 50, 'max_rate': 700, 'min_samples': 2}, 700)], [({'rate': 400, 'occupancy': [59, 60, 81, 59, 45], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 100, 'max_rate': 700, 'min_samples': 2}, 400), ({'rate': 475, 'occupancy': [60, 60], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 700, 'min_samples': 2}, 475), ({'rate': 425, 'occupancy': [60, 60], 'hi': 80, 'lo': 60, 'step': 50, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 425), ({'rate': 375, 'occupancy': [59, 59, 67, 60, 60], 'hi': 80, 'lo': 60, 'step': 50, 'min_rate': 50, 'max_rate': 600, 'min_samples': 3}, 375), ({'rate': 300, 'occupancy': [60, 60, 60], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 300), ({'rate': 300, 'occupancy': [80, 81], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 325), ({'rate': 300, 'occupancy': [80, 80], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 300), ({'rate': 800, 'occupancy': [70, 70, 70], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 3}, 600)]]
for i, (args, expected) in enumerate(fixtures[N-1]):
check('fee oracle' + ' %d' % i, solve(args), expected)
print(json.dumps({"observations": observations, "passed": all(x["passed"] for x in observations)}, ensure_ascii=False))
raise SystemExit(0 if all(x["passed"] for x in observations) else 1)
| Boundary fixture | Actual | Expected | Outcome |
|---|---|---|---|
| fee oracle 0 | 375 | 375 | Passed |
| fee oracle 1 | 700 | 700 | Passed |
| fee oracle 2 | 300 | 300 | Passed |
| fee oracle 3 | 300 | 300 | Passed |
| fee oracle 4 | 300 | 300 | Passed |
| fee oracle 5 | 125 | 125 | Passed |
| fee oracle 6 | 325 | 325 | Passed |
| fee oracle 7 | 325 | 325 | Passed |
SHA-256 / 55746ae1ebf0bdc0eeed5b1fb1e788a88bd8acc9301555b58fa6e56a42f8346b
Verification & scope
A deterministic, bounded toy model with a stipulated contract; it makes no claim of conformance to any agency manual or standard. This reproducer isolates one failure mechanism. Results cover the supplied fixtures. Variants within a family share a test contract and should remain grouped when constructing evaluation splits. Related mechanisms with a shared evaluation_group must also remain together; these controlled models are not independent production incidents.
Observations recorded using Python 3.12.14 at 2026-09-29T14:48:05.330045+00:00.
Case digest / 2b684fe2d0178c6bdda8837292f5ac560531856fe41458615fb51e9a521f2814