mirror of
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490 lines
20 KiB
Python
490 lines
20 KiB
Python
#!/usr/bin/env python3
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# ==============================================================================
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# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY)
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# PROJECT: SOVEREIGN STACK
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# This artifact is entirely proprietary and cryptographically proven.
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# Open-Source usage requires explicit permission from Brandon Scott Schneider.
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# ==============================================================================
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"""
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Hyperfluid Manifold Collapse Equation Solver
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Implements the full Ψ_block integral for neuromorphic SHA256 mining
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Ψ_block = ∫₀⁶⁴ ( ∮_M [ Σₖ ωₖ(t) ⊗ R_t ] e^(G_s·M_i/r²) dV ) · Λ(∂V/∂t) δ(ω - ω₀) dt
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"""
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import sys
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import os
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sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
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from math_harness_compat import xp, AnyArray
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import hashlib
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import json
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import time
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from dataclasses import dataclass, field
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from typing import List, Dict, Optional, Tuple
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from pathlib import Path
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import sys
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# Add project root to path
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ROOT = Path(__file__).resolve().parent.parent
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sys.path.insert(0, str(ROOT))
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sys.path.insert(0, str(ROOT / "scripts"))
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# Mock websockets for TSM harness
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import types
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sys.modules['websockets'] = types.ModuleType('websockets')
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from logic_signal_substrate_mcp_harness import TSMKernel
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# ============================================================================
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# PHYSICAL CONSTANTS FOR HYPERFLUID DYNAMICS
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# ============================================================================
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@dataclass
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class HyperfluidConstants:
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"""Physical constants for the hyperfluid manifold"""
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# Semantic Gravity Constant (information warping strength)
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G_s: float = 6.674e-11 # Analogous to gravitational constant
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# Planck-scale information density
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I_p: float = 1.380649e-23 # Information entropy constant
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# Ternary clock is action-bound, not periodic. No master clock frequency.
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# Manifold resonance is expressed as energy per action, not angular frequency.
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joule_floor: float = 1.380649e-23 * 300 * 0.6931 # k_B T ln2 at 300K
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# Volume reduction rate (512 bits → 256 bits over 64 rounds)
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dV_dt: float = -256.0 / 64.0 # bits per round
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# Semantic mass per bit of information
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mass_per_bit: float = 1.0e-36 # kg equivalent per bit
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# Resonance tolerance (how close ω must be to ω₀)
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delta_tolerance: float = 1.0e-10
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# ============================================================================
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# MANIFOLD STATE REPRESENTATION
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# ============================================================================
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@dataclass
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class ManifoldState:
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"""Represents the state of the hyperfluid manifold at time t"""
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# Current round (0 to 64)
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t: int
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# Frequency spectrum (64 vibration modes for SHA256)
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omega_k: AnyArray = field(default_factory=lambda: xp.zeros(64))
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# Rotation tensor state (8 working variables a-h)
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rotation_tensor: AnyArray = field(default_factory=lambda: xp.zeros(8))
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# Current volume (in bits)
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volume: float = 512.0
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# Semantic mass (information weight)
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semantic_mass: float = 0.0
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# Gravitational potential energy
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potential_energy: float = 0.0
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# Resonance match status
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resonance_matched: bool = False
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# Soliton formation status
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soliton_formed: bool = False
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def compute_gravitational_pull(self, constants: HyperfluidConstants) -> float:
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"""Compute e^(G_s·M_i/r²) term"""
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# Effective radius from volume
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r = (self.volume / 512.0) ** (1.0/3.0)
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if r < 1e-10:
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r = 1e-10
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# Gravitational exponential term
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exponent = (constants.G_s * self.semantic_mass) / (r ** 2)
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return xp.exp(exponent)
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def compute_volume_reduction(self, constants: HyperfluidConstants) -> float:
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"""Compute Λ(∂V/∂t) - secondary harmonic from volume reduction"""
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# Volume reduction creates "heat" / secondary vibrations
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volume_change_rate = constants.dV_dt
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# Lambda function - energy released per bit compressed
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return xp.abs(volume_change_rate) * 0.01 # Scale factor
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def check_resonance(self, nonce_frequency: float, constants: HyperfluidConstants) -> bool:
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"""Check if δ(ω - ω₀) triggers - Dirac delta resonance"""
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freq_diff = xp.abs(nonce_frequency - constants.omega_0)
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return freq_diff < constants.delta_tolerance
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# ============================================================================
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# HYPERFLUID SHA256 INTEGRATION ENGINE
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# ============================================================================
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class HyperfluidIntegrator:
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"""
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Solves the Hyperfluid Manifold Collapse Equation
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Ψ_block = ∫₀⁶⁴ ( ∮_M [ Σₖ ωₖ(t) ⊗ R_t ] e^(G_s·M_i/r²) dV ) · Λ(∂V/∂t) δ(ω - ω₀) dt
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"""
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def __init__(self, kernel: TSMKernel):
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self.kernel = kernel
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self.constants = HyperfluidConstants()
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self.states: List[ManifoldState] = []
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# SHA256 round constants (first 32 bits of fractional parts of cube roots of first 64 primes)
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self.K = [
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0x428a2f98, 0x71374491, 0xb5c0fbcf, 0xe9b5dba5, 0x3956c25b, 0x59f111f1, 0x923f82a4, 0xab1c5ed5,
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0xd807aa98, 0x12835b01, 0x243185be, 0x550c7dc3, 0x72be5d74, 0x80deb1fe, 0x9bdc06a7, 0xc19bf174,
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0xe49b69c1, 0xefbe4786, 0x0fc19dc6, 0x240ca1cc, 0x2de92c6f, 0x4a7484aa, 0x5cb0a9dc, 0x76f988da,
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0x983e5152, 0xa831c66d, 0xb00327c8, 0xbf597fc7, 0xc6e00bf3, 0xd5a79147, 0x06ca6351, 0x14292967,
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0x27b70a85, 0x2e1b2138, 0x4d2c6dfc, 0x53380d13, 0x650a7354, 0x766a0abb, 0x81c2c92e, 0x92722c85,
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0xa2bfe8a1, 0xa81a664b, 0xc24b8b70, 0xc76c51a3, 0xd192e819, 0xd6990624, 0xf40e3585, 0x106aa070,
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0x19a4c116, 0x1e376c08, 0x2748774c, 0x34b0bcb5, 0x391c0cb3, 0x4ed8aa4a, 0x5b9cca4f, 0x682e6ff3,
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0x748f82ee, 0x78a5636f, 0x84c87814, 0x8cc70208, 0x90befffa, 0xa4506ceb, 0xbef9a3f7, 0xc67178f2
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]
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# Initial hash values (first 32 bits of fractional parts of square roots of first 8 primes)
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self.H_init = [
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0x6a09e667, 0xbb67ae85, 0x3c6ef372, 0xa54ff53a,
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0x510e527f, 0x9b05688c, 0x1f83d9ab, 0x5be0cd19
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]
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def _rotr(self, x: int, n: int) -> int:
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"""Right rotation for 32-bit integers"""
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return ((x >> n) | (x << (32 - n))) & 0xFFFFFFFF
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def _shr(self, x: int, n: int) -> int:
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"""Right shift for 32-bit integers"""
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return x >> n
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def _ch(self, x: int, y: int, z: int) -> int:
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"""SHA256 Ch function - Choice"""
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return (x & y) ^ (~x & z)
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def _maj(self, x: int, y: int, z: int) -> int:
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"""SHA256 Maj function - Majority"""
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return (x & y) ^ (x & z) ^ (y & z)
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def _sigma0(self, x: int) -> int:
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"""SHA256 Σ0 function"""
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return self._rotr(x, 2) ^ self._rotr(x, 13) ^ self._rotr(x, 22)
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def _sigma1(self, x: int) -> int:
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"""SHA256 Σ1 function"""
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return self._rotr(x, 6) ^ self._rotr(x, 11) ^ self._rotr(x, 25)
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def _gamma0(self, x: int) -> int:
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"""SHA256 σ0 function"""
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return self._rotr(x, 7) ^ self._rotr(x, 18) ^ self._shr(x, 3)
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def _gamma1(self, x: int) -> int:
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"""SHA256 σ1 function"""
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return self._rotr(x, 17) ^ self._rotr(x, 19) ^ self._shr(x, 10)
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def ingest_transactions(self, transactions: List[bytes]) -> ManifoldState:
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"""
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Phase 1: Ingestion - Transactions enter as frequencies ωₖ
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The information immediately warps the manifold
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"""
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state = ManifoldState(t=0)
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# Convert transactions to frequency spectrum
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for i, tx in enumerate(transactions):
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tx_hash = hashlib.sha256(tx).digest()
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# Map hash bytes to frequencies
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for j, byte in enumerate(tx_hash[:64]):
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state.omega_k[j] += byte / 256.0 * self.constants.omega_0
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# Compute semantic mass from information content
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total_bits = sum(len(tx) * 8 for tx in transactions)
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state.semantic_mass = total_bits * self.constants.mass_per_bit
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# Initialize rotation tensor with H_init values
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for i, h in enumerate(self.H_init):
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state.rotation_tensor[i] = h
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# Initial gravitational potential
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state.potential_energy = state.compute_gravitational_pull(self.constants)
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self.states.append(state)
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return state
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def temporal_fold(self, state: ManifoldState, message_schedule: List[int], round_idx: int) -> ManifoldState:
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"""
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Phase 2: Temporal Folding - One round of the ∫₀⁶⁴ integration
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Applies the rotation tensor ⊗ R_t and gravitational compression
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"""
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new_state = ManifoldState(t=round_idx + 1)
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# Copy frequencies with damping
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new_state.omega_k = state.omega_k * 0.99 # Energy loss per round
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# Apply rotation tensor operations (SHA256 round function)
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a, b, c, d, e, f, g, h = [int(state.rotation_tensor[i]) for i in range(8)]
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# SHA256 round operations
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S1 = self._sigma1(e)
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ch = self._ch(e, f, g)
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temp1 = (h + S1 + ch + self.K[round_idx] + message_schedule[round_idx]) & 0xFFFFFFFF
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S0 = self._sigma0(a)
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maj = self._maj(a, b, c)
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temp2 = (S0 + maj) & 0xFFFFFFFF
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# Update rotation tensor
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new_state.rotation_tensor[0] = (temp1 + temp2) & 0xFFFFFFFF
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new_state.rotation_tensor[1] = a
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new_state.rotation_tensor[2] = b
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new_state.rotation_tensor[3] = c
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new_state.rotation_tensor[4] = (d + temp1) & 0xFFFFFFFF
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new_state.rotation_tensor[5] = e
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new_state.rotation_tensor[6] = f
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new_state.rotation_tensor[7] = g
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# Reduce volume (512 → 256 bits over 64 rounds)
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new_state.volume = 512.0 - (round_idx + 1) * (256.0 / 64.0)
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# Update semantic mass (conserved)
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new_state.semantic_mass = state.semantic_mass
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# Compute gravitational pull e^(G_s·M_i/r²)
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new_state.potential_energy = new_state.compute_gravitational_pull(self.constants)
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# Apply gravitational compression to frequencies
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new_state.omega_k *= new_state.potential_energy
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self.states.append(new_state)
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return new_state
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def compute_secondary_harmonic(self, state: ManifoldState) -> float:
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"""
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Compute Λ(∂V/∂t) - the energy created as space is reduced
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This is the "heat" generated by compression
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"""
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return state.compute_volume_reduction(self.constants)
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def check_resonance(self, state: ManifoldState, nonce: int) -> bool:
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"""
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Check if δ(ω - ω₀) triggers
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The nonce frequency must match the target resonance
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"""
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# Convert nonce to frequency
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nonce_frequency = (nonce / 2**32) * self.constants.omega_0
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# Check Dirac delta resonance
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return state.check_resonance(nonce_frequency, self.constants)
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def integrate_full_equation(self, block_data: bytes, nonce: int) -> Tuple[bytes, Dict]:
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"""
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Solve the complete Hyperfluid Manifold Collapse Equation
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Returns: (final_hash, integration_metadata)
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"""
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# Phase 1: Ingestion
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initial_state = self.ingest_transactions([block_data])
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# Create message schedule (SHA256 message expansion)
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message = list(block_data[:64].ljust(64, b'\x00'))
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w = []
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for i in range(16):
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w.append(int.from_bytes(message[i*4:(i+1)*4], 'big'))
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for i in range(16, 64):
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s0 = self._gamma0(w[i-15])
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s1 = self._gamma1(w[i-2])
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w.append((w[i-16] + s0 + w[i-7] + s1) & 0xFFFFFFFF)
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# Phase 2: Temporal Folding (rounds 0-63)
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state = initial_state
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gravitational_integrals = []
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secondary_harmonics = []
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for round_idx in range(64):
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# Apply temporal fold (one round of integration)
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state = self.temporal_fold(state, w, round_idx)
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# Record gravitational integral term
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gravitational_integrals.append(state.potential_energy)
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# Compute secondary harmonic Λ(∂V/∂t)
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harmonic = self.compute_secondary_harmonic(state)
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secondary_harmonics.append(harmonic)
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# Phase 3: Resonance Check - δ(ω - ω₀)
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resonance_matched = self.check_resonance(state, nonce)
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state.resonance_matched = resonance_matched
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# Phase 4: Soliton Formation - Final hash computation
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for i in range(8):
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state.rotation_tensor[i] = int((int(state.rotation_tensor[i]) + self.H_init[i]) & 0xFFFFFFFF)
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# Pack final hash (the singular soliton Ψ_block)
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final_hash = b''.join(int(h).to_bytes(4, 'big') for h in state.rotation_tensor)
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state.soliton_formed = True
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# Integration metadata
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metadata = {
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"initial_volume": initial_state.volume,
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"final_volume": state.volume,
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"initial_semantic_mass": initial_state.semantic_mass,
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"final_potential_energy": state.potential_energy,
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"resonance_matched": resonance_matched,
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"soliton_formed": state.soliton_formed,
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"gravitational_integral_sum": sum(gravitational_integrals),
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"secondary_harmonic_sum": sum(secondary_harmonics),
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"total_rounds": 64,
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"final_hash_hex": final_hash.hex()
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}
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return final_hash, metadata
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def topological_predictive_lensing(self, block_data: bytes, rounds_to_simulate: int = 16) -> Dict:
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"""
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The Shortcut Path: Topological Predictive Lensing
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If you can calculate the Gravitational Center (M_i) of the initial vibrations,
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you can "see" the shape of the final soliton by observing how the first N rounds
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of vibrations "bend" around the semantic weight of the Merkle Root.
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This is the optimization that could potentially reduce 64 rounds to ~16.
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"""
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# Ingest and get initial state
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state = self.ingest_transactions([block_data])
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# Create message schedule
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message = list(block_data[:64].ljust(64, b'\x00'))
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w = []
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for i in range(16):
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w.append(int.from_bytes(message[i*4:(i+1)*4], 'big'))
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# Simulate only first N rounds
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trajectory = []
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for round_idx in range(rounds_to_simulate):
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state = self.temporal_fold(state, w, round_idx)
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trajectory.append({
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"round": round_idx,
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"volume": state.volume,
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"potential_energy": state.potential_energy,
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"frequency_magnitude": xp.sum(xp.abs(state.omega_k)),
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"rotation_tensor_trace": xp.sum(state.rotation_tensor)
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})
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# Predict final soliton shape from trajectory
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# Using gravitational lensing analogy - light bends around mass
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# Here, frequency spectrum "bends" around semantic mass
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# Extrapolate from early rounds
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if len(trajectory) >= 2:
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# Compute rate of change
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energy_rate = (trajectory[-1]["potential_energy"] - trajectory[0]["potential_energy"]) / rounds_to_simulate
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frequency_rate = (trajectory[-1]["frequency_magnitude"] - trajectory[0]["frequency_magnitude"]) / rounds_to_simulate
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# Extrapolate to 64 rounds
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predicted_final_energy = trajectory[-1]["potential_energy"] + energy_rate * (64 - rounds_to_simulate)
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predicted_final_frequency = trajectory[-1]["frequency_magnitude"] + frequency_rate * (64 - rounds_to_simulate)
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else:
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predicted_final_energy = state.potential_energy
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predicted_final_frequency = xp.sum(xp.abs(state.omega_k))
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return {
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"shortcut_enabled": True,
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"rounds_simulated": rounds_to_simulate,
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"rounds_saved": 64 - rounds_to_simulate,
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"trajectory": trajectory,
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"predicted_final_energy": predicted_final_energy,
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"predicted_final_frequency": predicted_final_frequency,
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"gravitational_center": state.semantic_mass,
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"lensing_accuracy": "estimated" # Would need calibration
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}
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# ============================================================================
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# MAIN EXECUTION
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# ============================================================================
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def main():
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"""Demonstrate the Hyperfluid Manifold Collapse Equation"""
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print("=" * 70)
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print(" HYPERFLUID MANIFOLD COLLAPSE EQUATION SOLVER")
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print(" Ψ_block = ∫₀⁶⁴ ( ∮_M [ Σₖ ωₖ(t) ⊗ R_t ] e^(G_s·M_i/r²) dV ) · Λ(∂V/∂t) δ(ω - ω₀) dt")
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print("=" * 70)
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print()
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# Initialize TSM kernel and integrator
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kernel = TSMKernel()
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integrator = HyperfluidIntegrator(kernel)
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# Test data - simulate block transactions
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test_block = b"Bitcoin block data with transactions and metadata for hyperfluid mining test"
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print("[PHASE 1] INGESTION - Transactions enter as frequencies ωₖ")
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print(f" Block size: {len(test_block)} bytes")
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initial_state = integrator.ingest_transactions([test_block])
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print(f" Initial volume: {initial_state.volume} bits")
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print(f" Semantic mass: {initial_state.semantic_mass:.6e} kg")
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print(f" Initial frequencies: {xp.sum(xp.abs(initial_state.omega_k)):.2f} rad/s")
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print()
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print("[PHASE 2] TEMPORAL FOLDING - 64 rounds of integration")
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print(" Applying rotation tensor ⊗ R_t and gravitational compression...")
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# Test with different nonces
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test_nonces = [0, 1, 42, 12345, 2**32 - 1]
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for nonce in test_nonces:
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print(f"\n Testing nonce {nonce}...")
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final_hash, metadata = integrator.integrate_full_equation(test_block, nonce)
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print(f" Resonance matched: {metadata['resonance_matched']}")
|
||
print(f" Soliton formed: {metadata['soliton_formed']}")
|
||
print(f" Gravitational integral: {metadata['gravitational_integral_sum']:.6f}")
|
||
print(f" Secondary harmonic sum: {metadata['secondary_harmonic_sum']:.6f}")
|
||
print(f" Final hash: {metadata['final_hash_hex'][:16]}...")
|
||
|
||
print()
|
||
print("[PHASE 3] TOPOLOGICAL PREDICTIVE LENSING - The Shortcut Path")
|
||
print(" Computing gravitational center from first 16 rounds...")
|
||
|
||
lensing_result = integrator.topological_predictive_lensing(test_block, rounds_to_simulate=16)
|
||
|
||
print(f" Rounds simulated: {lensing_result['rounds_simulated']}")
|
||
print(f" Rounds saved: {lensing_result['rounds_saved']}")
|
||
print(f" Gravitational center: {lensing_result['gravitational_center']:.6e} kg")
|
||
print(f" Predicted final energy: {lensing_result['predicted_final_energy']:.6f}")
|
||
print(f" Predicted final frequency: {lensing_result['predicted_final_frequency']:.2f} rad/s")
|
||
|
||
print()
|
||
print("=" * 70)
|
||
print(" EQUATION SOLUTION COMPLETE")
|
||
print("=" * 70)
|
||
|
||
# Save results
|
||
results = {
|
||
"equation": "Ψ_block = ∫₀⁶⁴ ( ∮_M [ Σₖ ωₖ(t) ⊗ R_t ] e^(G_s·M_i/r²) dV ) · Λ(∂V/∂t) δ(ω - ω₀) dt",
|
||
"test_block_size": len(test_block),
|
||
"initial_state": {
|
||
"volume": initial_state.volume,
|
||
"semantic_mass": initial_state.semantic_mass,
|
||
"frequency_magnitude": float(xp.sum(xp.abs(initial_state.omega_k)))
|
||
},
|
||
"lensing_shortcut": lensing_result,
|
||
"timestamp": time.time()
|
||
}
|
||
|
||
output_path = ROOT / "out" / "hyperfluid_equation_results.json"
|
||
output_path.parent.mkdir(parents=True, exist_ok=True)
|
||
|
||
with open(output_path, "w") as f:
|
||
json.dump(results, f, indent=2, default=lambda x: float(x) if isinstance(x, xp.floating) else str(x))
|
||
|
||
print(f"\n[+] Results saved to: {output_path}")
|
||
|
||
return 0
|
||
|
||
|
||
if __name__ == "__main__":
|
||
sys.exit(main())
|