fix: replace numpy/scipy with pure Python in experiment analytics#6830
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Removes significant per-worker RSS overhead by replacing numpy and scipy with stdlib math and random modules. The experiment analytics endpoint is demo-only code, so the minor latency trade-off is well worth the memory savings. - Implement G-test via log-likelihood ratio + incomplete gamma function - Use random.betavariate for Bayesian Monte Carlo (10k samples) - Remove numpy and scipy from pyproject.toml and poetry.lock - Convert tests from class-based to function-based
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Codecov Report✅ All modified and coverable lines are covered by tests. Additional details and impacted files@@ Coverage Diff @@
## main #6830 +/- ##
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+ Coverage 98.30% 98.32% +0.02%
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Files 1331 1331
Lines 49332 49394 +62
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+ Hits 48494 48568 +74
+ Misses 838 826 -12 ☔ View full report in Codecov by Sentry. 🚀 New features to boost your workflow:
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Cover all new code paths through the public calculate_statistics API and mark unreachable numerical guards with pragma: no cover.
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Zaimwa9
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Mar 3, 2026
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Zaimwa9
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Thanks, that's a lot of maths for fake graphs 😄
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Changes
Removes numpy and scipy dependencies from the API, replacing them with pure Python (stdlib
mathandrandom) implementations. These libraries added significant per-worker RSS overhead but are only used by the demo experiment analytics endpoint.random.betavariatefor Bayesian Monte Carlo simulation (10k samples)pyproject.tomland regeneratepoetry.lockHow did you test this code?
pytest tests/unit/app_analytics/test_experiments.py)