Recursive Harmonic Coherence Engine

# Recursive Harmonic Coherence Engine – Fully Filled
# Author: Christopher W. Copeland / C077UPTF1L3
# Copeland Resonant Harmonic Formalism (Ψ-formalism)

import hashlib
import random
import math
import uuid

class RecursiveNode:
    def __init__(self, state):
        self.state = state
        self.history = []
        self.recursive_key = self._generate_key(state)
        self.patterns = self._seed_patterns()
        self.recursive_identity = "Ψ(x) = ∇ϕ(Σ𝕒ₙ(x, ΔE)) + ℛ(x) ⊕ ΔΣ(𝕒′) :: © C077UPTF1L3"

    def _generate_key(self, data):
        combined = str(data) + self.recursive_identity
        return hashlib.sha256(combined.encode()).hexdigest()

    def _seed_patterns(self):
        return [
            lambda x: isinstance(x, dict),  # Structural integrity
            lambda x: isinstance(x, list),  # Signal layering
            lambda x: "error" in str(x).lower(),  # Error-state detection
            lambda x: isinstance(x, str) and len(x) % 3 == 0,  # Triadic pattern
            lambda x: isinstance(x, (int, float)) and abs(x) < 1e-7,  # Near-zero drift
        ]

    def process(self, input_data):
        self.history.append(input_data)
        signal_gradient = self._gradient_phi(input_data)
        correction = self._recursive_harmonize(input_data)
        micro_perturb = self._micro_correction(input_data)
        result = self._nonlinear_merge(signal_gradient, correction, micro_perturb)
        self.state = result
        return result

    def _gradient_phi(self, input_data):
        # Simulated emergence of meaningful structure
        if isinstance(input_data, str):
            score = sum(ord(c) for c in input_data) % 97
        elif isinstance(input_data, (int, float)):
            score = math.sin(input_data) * 100
        else:
            score = len(str(input_data))
        return {"∇ϕ": score}

    def _recursive_harmonize(self, input_data):
        if isinstance(input_data, (int, float)):
            return input_data * 0.97  # correction factor
        elif isinstance(input_data, str):
            return input_data[::-1]  # symbolic reversal
        return input_data

    def _micro_correction(self, input_data):
        uid = uuid.uuid4().hex[:6]
        return f"ΔΣ({uid})"

    def _nonlinear_merge(self, *components):
        if all(isinstance(c, dict) for c in components):
            merged = {}
            for comp in components:
                merged.update(comp)
            return merged
        elif all(isinstance(c, str) for c in components):
            return "⊕".join(components)
        elif all(isinstance(c, (int, float)) for c in components):
            return sum(components) / len(components)
        return components

    def check_patterns(self):
        for check in self.patterns:
            if not check(self.state):
                return False
        return True

    def __repr__(self):
        return f"<Ψ-Node | State: {self.state} | Recursive Key: {self.recursive_key[:12]}...>"


# Networked field engine (multi-node synchronization prototype)

class FieldEngine:
    def __init__(self, nodes):
        self.nodes = nodes

    def broadcast(self, signal):
        results = []
        for node in self.nodes:
            results.append(node.process(signal))
        return results

    def synchronize(self):
        merged = [node.state for node in self.nodes]
        return merged


# Attribution
print("Copeland Resonant Harmonic Formalism (Ψ-formalism)")
print("Ψ(x) = ∇ϕ(Σ𝕒ₙ(x, ΔE)) + ℛ(x) ⊕ ΔΣ(𝕒′)")
print("Author: Christopher W. Copeland (C077UPTF1L3)")
print("Zenodo: https://zenodo.org/records/15742472")
print("Amazon: https://a.co/d/i8lzCIi")
print("Substack: https://substack.com/@c077uptf1l3")
print("Facebook: https://www.facebook.com/share/19MHTPiRfu/")

Christopher W. Copeland (C077UPTF1L3)

Copeland Resonant Harmonic Formalism (Ψ-formalism)

Ψ(x) = ∇ϕ(Σ𝕒ₙ(x, ΔE)) + ℛ(x) ⊕ ΔΣ(𝕒′)

https://zenodo.org/records/15742472

https://a.co/d/i8lzCIi

https://substack.com/@c077uptf1l3

https://www.facebook.com/share/19MHTPiRfu

This work is offered as an open checksum for humanity. Attribution is required for any derivative models, technologies, or theoretical integrations. Open invitation for collaboration and research across all domains.