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Histogram edge ownership
BinOriole is a Windows AI coding client for examining histogram edge rules. Open the project, connect a compatible model and review where equal-edge observations and out-of-range values belong.
● ● ●BinOriole●
A concrete starting point · Histogram edge ownership ×
import bisect, math
def histogram(edges, values):
if type(edges) is not list or type(values) is not list or not 2 <= len(edges) <= 257 or len(values) > 4096:
raise ValueError("bins.shape")
if any(type(x) not in (int, float) or not -10**100 <= x <= 10**100 or not math.isfinite(x) for x in edges+values):
raise ValueError("bins.value")
if any(a >= b for a, b in zip(edges, edges[1:])):
raise ValueError("bins.edges")
counts, under, over = [0]*(len(edges)-1), 0, 0
for value in values:
if value < edges[0]:
under += 1
elif value > edges[-1]:
over += 1
else:
counts[min(bisect.bisect_right(edges, value)-1, len(counts)-1)] += 1
return counts, under, over
✳Histogram edge ownership
✓ ChangesReview before applying ↗
Illustrative workspace
Project
Test edge equality, empty observations and outliers, include the final right edge and report underflow and overflow without changing either list.
Review before applying
Compare actual behavior with the expected result. If a check fails, investigate it before applying more changes.
A concrete starting point
import bisect, math
def histogram(edges, values):
if type(edges) is not list or type(values) is not list or not 2 <= len(edges) <= 257 or len(values) > 4096:
raise ValueError("bins.shape")
if any(type(x) not in (int, float) or not -10**100 <= x <= 10**100 or not math.isfinite(x) for x in edges+values):
raise ValueError("bins.value")
if any(a >= b for a, b in zip(edges, edges[1:])):
raise ValueError("bins.edges")
counts, under, over = [0]*(len(edges)-1), 0, 0
for value in values:
if value < edges[0]:
under += 1
elif value > edges[-1]:
over += 1
else:
counts[min(bisect.bisect_right(edges, value)-1, len(counts)-1)] += 1
return counts, under, over
Make data boundaries visible before interpreting counts
A histogram can look credible while assigning boundary observations to the wrong group. BinOriole gives data-processing code a Windows AI coding workspace where those choices can be inspected before the results are interpreted. Open the project folder and discuss the bin definitions alongside the function that counts observations.
For example, ask the connected model to review ownership of values exactly equal to an edge. Provide a small list with an interior-edge value, the final edge and observations outside the range. State the expected counts and how underflow and overflow should be reported, so the proposed edit answers a concrete contract.
Trace those outputs into the chart or report that consumes them. A caller that drops out-of-range counts can present a misleading total even when the binning helper behaves as designed. Review proposed source changes and run fixtures that reconcile every observation with a reported category. Include rejected non-finite inputs and invalid edge order in the boundary review.
BinOriole supports OpenAI-compatible or Anthropic endpoint connections. You can select an uncensored AI model offered by your configured service, while actual behavior, context and tool support remain dependent on that setup. The conversation helps prepare a patch; your checked examples and application tests determine whether the counts are ready to use.
Make data boundaries visible before interpreting counts — questions before editing
Which values should appear in a binning fixture?
Include a value below the range, each relevant edge, an ordinary interior value and a value above the range. Write the expected ownership explicitly. This exposes endpoint decisions that a large random dataset can obscure.
Why check the final edge separately?
The last edge may be included even when interior bins use a different boundary convention. State that rule in the request and test it directly. Otherwise a refactor can move the maximum observation into overflow unintentionally.
How do I check that observations were not lost?
Reconcile bin counts and out-of-range counts with the accepted input count. Then verify individual assignments on a small fixture. A matching total alone can hide observations placed in the wrong categories.
Should malformed edges be sorted automatically?
Only if that transformation is part of the application contract. Sorting can conceal an upstream configuration problem or change category meaning. Review the existing validation path and how the caller reports failure before accepting such a proposal.
BinOriole · Updated 2026-10-25