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feat: add data utility functions #31

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feat: add data utility functions #31

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28 changes: 28 additions & 0 deletions data_utils.py
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Original file line number Diff line number Diff line change
@@ -0,0 +1,28 @@
def normalize(values):
mn = min(values)
mx = max(values)
return [(v - mn) / (mx - mn) for v in values]
Comment on lines +1 to +4

@sonar-review-dev18 sonar-review-dev18 Bot Apr 23, 2026

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Missing test coverage: normalize() has two unguarded crash paths that no test currently exercises:

  • Empty list — min([]) raises ValueError. There is no guard before calling min/max.
  • All-identical values — when every element is the same, mx - mn == 0, causing ZeroDivisionError on the return line.

Both will blow up in production on valid-looking inputs. The uniform-values case is especially sneaky because a caller has no obvious reason to expect a crash.

A minimal fix for the zero-division case:

Suggested change
def normalize(values):
mn = min(values)
mx = max(values)
return [(v - mn) / (mx - mn) for v in values]
def normalize(values):
mn = min(values)
mx = max(values)
if mx == mn:
return [0.0] * len(values)
return [(v - mn) / (mx - mn) for v in values]
  • Mark as noise



def flatten(nested):
result = []
for item in nested:
if isinstance(item, list):
result.extend(flatten(item))
else:
result.append(item)
return result


def chunk(lst, size):

@sonar-review-dev18 sonar-review-dev18 Bot Apr 23, 2026

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Missing test coverage: chunk(lst, 0) raises ValueError: range() arg 3 must not be zero with no helpful message. Since size comes from the caller, a zero or negative value is a plausible mistake. Add a guard and a clear error:

if size <= 0:
    raise ValueError(f"size must be a positive integer, got {size}")
  • Mark as noise

return [lst[i:i + size] for i in range(0, len(lst), size)]


def merge_dicts(a, b):
out = dict(a)
for k, v in b.items():
if k in out and isinstance(out[k], dict) and isinstance(v, dict):
out[k] = merge_dicts(out[k], v)
else:
out[k] = v
return out
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