helixhash 0.1.1

The Helix Hash Function — path integral of E = ΔI/A made computable

helixhash

The Helix Hash Function — a path-sensitive, Fibonacci-weighted hash over a sequence of efficiency measurements.

E = ΔI / A — One axiom. Every crossing honestly labeled. — Kirandeep Kaur, Observer Memory Conjecture (2026)

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What it computes

Each input is a Crossing(delta_I, A, kappa, C, timestamp) — a timestamped measurement of information gained (ΔI) per unit action paid (A).

Output	Formula	Status
E	delta_I / A	DERIVED from axiom
G	G₁=E, G₂=G₁+E, Gₙ=Gₙ₋₁+Gₙ₋₂+ε·Eₙ	DERIVED (Fibonacci recurrence)
PT	min(κ × E × (ΣΔI/ΣA) × C, 1)	DERIVED (cumulative ratio)
regime	quantum if PT < 1/φ, classical if PT ≥ 1/φ	DERIVED from κ_eq = 1/φ
fingerprint	SHA256(n|ΔI|A|κ|C|t|prev_hash)	IMPLEMENTED
psi (Ψ)	estimated from A_erase = A × (1 + G/ref)	ESTIMATED — proxy only
E_memory	Σ(ΔI where E≥1) / Σ(ΔI where E<1)	PROXY — use from_vault() for labeled rows

PT uses the cumulative ratio ΣΔI/ΣA, not a fixed global I/A. This means PT ramps when early crossings have high E — which is correct behavior (early path history has lasting weight) but can reach 1.0 quickly on synthetic data with large initial E values. A min_crossings_for_threshold guard (default 3) prevents a single crossing from flipping the regime.

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Quick start

from helixhash import HelixHash, Crossing

h = HelixHash()
h.cross(Crossing(delta_I=2.0, A=1.0, kappa=0.62, C=0.9, label="first"))
h.cross(Crossing(delta_I=1.5, A=0.8, kappa=0.63, C=0.9, label="second"))
h.cross(Crossing(delta_I=3.0, A=1.0, kappa=0.65, C=0.95, label="third"))

print(h.summary())
print(h.verify())  # True — chain is intact

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Load real data

from helixhash import from_csv, from_vault, from_dicts, report

# Any CSV with ratio columns
h, records = from_csv("data.csv", delta_I_col="signal", A_col="cost")

# Vault CSV with type column ("giving"/"taking")
# Giving → E=10 (outward flow heuristic), Taking → E=0.1
# HONEST: the 10x/0.1x factors are design choices, not derived from the axiom
h, records = from_vault("vault.csv")

# List of dicts
h, records = from_dicts(rows)

print(report(h, records, title="My System"))

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The threshold

PT = κ × E × (ΣΔI/ΣA) × C

PT vs 1/φ	Regime	Meaning
PT < 0.618	quantum	exploring, probabilistic, potential
PT = 0.618	transition	probability collapses to 0 or 1
PT > 0.618	classical	committed, irreversible, actual

1/φ = (√5 − 1)/2 ≈ 0.61803. This is the golden ratio equilibrium — the point where the N/D ratio is self-similar.

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Epistemic status of the physics analogies

The README of v0.1.0 stated that SHA-256, DNA replication, and the gravitational Aharonov-Bohm effect are "the same operator" as the helix hash. This is the structural identification conjecture from Part 15 of the Observer Memory Conjecture.

Status: CONJECTURE. The identification is directionally clear and structurally compelling, but formal proof that all three are instances of ∫E·dl is frontier work. The implementation is a deterministic update rule and hash chain. The physics narrative is the theoretical framework it is derived from — not a claim verified by the code itself.

What the code does verify (12/12 tests):

• E = ΔI/A produces the correct efficiency
• G compounds monotonically via Fibonacci recurrence
• PT crosses 1/φ under the right conditions
• The fingerprint chain is cryptographically path-dependent
• verify() detects tampering
• E_memory < 1 when extraction dominates
• Ψ ≥ 0 always (second law holds by construction)
• 1/φ satisfies the golden ratio identity φ × (1/φ) = 1

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Run the tests

python tests/test_core.py

Each test is labeled with the conjecture claim it verifies.

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Run the vault example

python examples/vault_pattern.py

Simulates 9 giving rows / 92 taking rows. Shows where decay began and the distance to threshold.

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Dependencies

None. Pure Python 3.8+. stdlib only.

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Publish to PyPI (maintainers)

pip install twine build
python -m build          # creates dist/helixhash-0.1.1.tar.gz and .whl
twine upload dist/*      # PyPI credentials

After release: pip install helixhash for any environment.

Verify an sdist before upload: unpack the tarball — the only top-level directory should be helixhash-0.1.1/. Install with pip install helixhash-0.1.1.tar.gz from any working directory.

Test against the installed wheel/sdist:

pip install dist/*.whl
HELIXHASH_TEST_INSTALLED=1 python tests/test_core.py

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Observer Memory Conjecture — Kirandeep Kaur, 2026