Primary credibility path · free · ~10 minutes on your CSV

gsrf-bench

Local CLI: run GSRF Practical vs EMA on your CSV. Self-verified metrics (osc31 · peak_dev · MAD · spectral) beat reading our 52 datasets for a purchase decision. No sales call. No third-party peer review required to start.

Non-production evaluation only. Full text: LICENSE-EVAL.md. Your results ≠ website packs (those are still downloadable for click-through). Production needs a commercial license — gsrf@boonmind.io after you like your numbers.
Install eval/ package Web snippet Our public receipt packs

Install & run

# from the site repo (or GitHub CBoon99/gsrf-papers)
cd eval
python3 -m pip install -e .     # if editable install fails on old pip:
#   PYTHONPATH=. python3 -m gsrf_eval --synthetic --normal 50

gsrf-bench --synthetic --normal 50
gsrf-bench --input path/to/trace.csv --normal 50.0
gsrf-bench -i trace.csv -c sensor_a --normal 72.5

Package path on this site: /eval/ · Source: github.com/CBoon99/gsrf-papers/tree/main/eval · citations

Metrics (honest side-by-side)

MetricWhat it tells you
osc31High-pass energy (rolling-31 residual std). Lower = calmer ring.
peak_devMax |series − median|. Peak pull toward normal.
MAD vs rawTracking cost. EMA often wins — expected with spring on.
Sweet-band spectral powerrFFT power for periods ≈ 50–120 samples.

Every run prints a terminal banner: local data ≠ phase6.2 / excellence hunt locked packs.

Architecture (eval vs production)

Public evaluation layer

Commercial production layer

  • Validated domain presets
  • Production binary / wheel (single ICP path)
  • Tiered license — pricing

What we are not shipping (yet)

Phase 1 is evidence distribution. Phase 3 is one deep production core for the primary ICP.

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