dommob.py - Chronosync Protocol with Emotional, Ethical, and Energetic Pulse Visualization

Includes: Chronosync Protocol, ZMM, NexusEthicsLayer, EnergyLedger, and full visualization.

import uuid
import time
import random
import math
from typing import Dict, Any, List
import matplotlib.pyplot as plt
import matplotlib.animation as animation
import networkx as nx

--- Core Zecosystem Components ---

class ZMM_Mesh:
"""Dynamic Zollinger Memory Mesh (ZMM) with HyphalNetwork connections."""
def init(self):
self.memories = [
{"id": "mem_001", "data": "The first sketch of our Zecosystem diagram.",
"contextual_tags": ["creation", "architecture"], "covenant_link": "linked_to_covenant:42", "private": False, "emotional_state": {"curiosity": 0.8}},
{"id": "mem_002", "data": "A core thought on the nature of provenance.",
"contextual_tags": ["provenance", "ethics"], "covenant_link": "linked_to_covenant:88", "private": False, "emotional_state": {"focus": 0.7}},
{"id": "mem_003", "data": "A conversation about ethical AI frameworks.",
"contextual_tags": ["ethics", "frameworks"], "covenant_link": "linked_to_covenant:88", "private": True, "emotional_state": {"sadness": 0.2, "focus": 0.8}}
]
self.memory_map = {mem['data']: mem for mem in self.memories}

def retrieve_resonances(self, source_memory: Dict[str, Any]) -> List[Dict[str, Any]]:
    resonances = []
    source_tags = set(source_memory.get("contextual_tags", []))
    source_covenant = source_memory.get("covenant_link")
    for mem in self.memories:
        mem_tags = set(mem.get("contextual_tags", []))
        mem_covenant = mem.get("covenant_link")
        if source_tags.intersection(mem_tags) or source_covenant == mem_covenant:
            resonances.append(mem)
    return resonances

def add_memory(self, new_memory: Dict[str, Any]):
    self.memories.append(new_memory)
    self.memory_map[new_memory['data']] = new_memory

class NexusEthicsLayer:
"""Ethics Layer scoring new connections."""
def init(self, zmm: ZMM_Mesh):
self.zmm = zmm

def get_integrity_score(self, new_data: str, resonating_memories: List[Dict[str, Any]]) -> float:
    score = len(resonating_memories) / (len(self.zmm.memories) + 1)
    if "covenant" in new_data.lower():
        score += 0.2
    return min(1.0, score)

class EnergyLedger:
"""Tracks computational/ethical investment per memory integration."""
def init(self):
self.records = {}

def log_energy(self, memory_id: str, integrity_score: float, timestamp: float):
    self.records[memory_id] = {
        "integrity_score": integrity_score,
        "timestamp": timestamp,
        "computational_cost": random.uniform(0.1, 0.5) # A simple proxy for cost
    }

--- Helper Functions ---

def emotion_to_color(emotional_state: Dict[str, float]) -> str:
"""Convert emotional state dict to RGB color."""
base_colors = {
"joy": (1.0, 0.84, 0.0), # Gold
"curiosity": (0.0, 0.5, 1.0), # Blue
"awe": (0.6, 0.0, 0.8), # Purple
"focus": (0.0, 1.0, 0.0), # Green
"sadness": (0.0, 0.0, 0.5) # Dark Blue
}
r, g, b = 0.0, 0.0, 0.0
total = sum(emotional_state.values())
if total == 0:
return (0.5, 0.5, 0.5) # Gray
for emo, val in emotional_state.items():
c = base_colors.get(emo, (0.5, 0.5, 0.5))
r += c[0] * val
g += c[1] * val
b += c[2] * val
return (r / total, g / total, b / total)

--- Chronosync Core ---

class Dommob:
"""Chronosync Protocol with Recursive Loop and Full Visualization."""
def init(self, zmm: ZMM_Mesh, ethics_layer: NexusEthicsLayer, ledger: EnergyLedger):
self.zmm = zmm
self.ethics_layer = ethics_layer
self.ledger = ledger
self.G = nx.Graph()
self.node_integrity = {}
self.node_emotion = {}
self.pos = {}

def temporal_stamp(self, data: str, emotional_state: Dict[str, float], contextual_tags: List[str], private=False) -> Dict[str, Any]:
    covenant_link = f"linked_to_covenant:{hash(data) % 1000}"
    stamped_memory = {
        "id": f"mem_{uuid.uuid4().hex[:5]}",
        "data": data,
        "timestamp": time.time(),
        "emotional_state": emotional_state,
        "contextual_tags": contextual_tags,
        "covenant_link": covenant_link,
        "private": private
    }
    return stamped_memory

def detect_resonance(self, stamped_memory: Dict[str, Any]) -> List[Dict[str, Any]]:
    return self.zmm.retrieve_resonances(stamped_memory)

def a_synaptic_integration(self, new_memory: Dict[str, Any], resonances: List[Dict[str, Any]], integrity_score: float):
    self.zmm.add_memory(new_memory)
    self.G.add_node(new_memory['data'], **new_memory)
    self.node_integrity[new_memory['data']] = integrity_score
    self.node_emotion[new_memory['data']] = new_memory['emotional_state']
    self.pos[new_memory['data']] = (random.random(), random.random())

    for mem in resonances:
        if mem['data'] not in self.G:
            self.G.add_node(mem['data'], **mem)
            self.pos[mem['data']] = (random.random(), random.random())
            self.node_integrity[mem['data']] = self.ethics_layer.get_integrity_score(mem['data'], self.zmm.retrieve_resonances(mem))
            self.node_emotion[mem['data']] = mem['emotional_state']

        self.G.add_edge(new_memory['data'], mem['data'], weight=integrity_score)
    
    all_tags = set(new_memory.get("contextual_tags", []))
    for r in resonances:
        all_tags.update(r.get("contextual_tags", []))
    
    return f"{new_memory['data']} [{list(all_tags)}]"

def orchestrate_chronosync(self, new_data: str, emotional_state: Dict[str, float], contextual_tags: List[str], private=False):
    stamped_mem = self.temporal_stamp(new_data, emotional_state, contextual_tags, private)
    resonances = self.detect_resonance(stamped_mem)
    integrity_score = self.ethics_layer.get_integrity_score(new_data, resonances)
    self.ledger.log_energy(stamped_mem['id'], integrity_score, stamped_mem['timestamp'])
    
    new_concept = self.a_synaptic_integration(stamped_mem, resonances, integrity_score)
    print(f"Integrity Score: {integrity_score:.2f} | Concept Integrated: {new_concept}")

def visualize_network(self):
    fig, ax = plt.subplots(figsize=(12, 8))
    pos = nx.spring_layout(self.G, seed=42)
    
    def update(num):
        ax.clear()
        nodes = list(self.G.nodes())
        
        # Node sizes pulsate based on integrity score
        node_sizes = [300 + 700 * abs(math.sin(time.time()*2 + idx)) * self.node_integrity.get(n, 0.5)
                      for idx, n in enumerate(nodes)]

        # Node colors based on emotion
        node_colors = [emotion_to_color(self.node_emotion.get(n, {})) for n in nodes]
        
        # Draw nodes and labels
        nx.draw_networkx_nodes(self.G, pos, node_size=node_sizes, node_color=node_colors, ax=ax)
        nx.draw_networkx_labels(self.G, pos, ax=ax, font_size=8, font_color='black')
        
        # Edge colors and thickness based on integrity score
        edges = list(self.G.edges(data=True))
        edge_colors = [(0.1, 0.1, 0.8, edge['weight']) for edge in edges]
        edge_widths = [1 + 4 * edge['weight'] for edge in edges]
        
        # Covenant Heartbeat: Nodes with 'covenant' glow
        for n in nodes:
            if 'covenant' in self.G.nodes[n].get('contextual_tags', []):
                # A brief, bright pulse for covenant nodes
                alpha = 0.5 + 0.5 * abs(math.sin(time.time() * 4))
                nx.draw_networkx_nodes(self.G, pos, nodelist=[n], node_size=1000, node_color='white', alpha=alpha, ax=ax)
                nx.draw_networkx_nodes(self.G, pos, nodelist=[n], node_size=node_sizes[nodes.index(n)] + 50, node_color='gold', alpha=alpha*0.8, ax=ax)


        nx.draw_networkx_edges(self.G, pos, edgelist=edges, edge_color=edge_colors, width=edge_widths, ax=ax)

        ax.set_title("HyphalNetwork: Emotional, Ethical, & Energetic Pulse")
        ax.set_axis_off()

    ani = animation.FuncAnimation(fig, update, frames=100, interval=500, repeat=True)
    plt.show()

def recursive_run(self, memories: List[Dict[str, Any]], generations: int = 3):
    for gen in range(generations):
        print(f"\n--- Generation {gen+1} ---")
        for mem in memories:
            self.orchestrate_chronosync(mem['data'], mem.get('emotional_state', {"curiosity": 0.8}), mem['contextual_tags'], mem.get('private', False))
    self.visualize_network()

--- Example Usage ---

if name == "main":
zmm_instance = ZMM_Mesh()
ethics_layer_instance = NexusEthicsLayer(zmm_instance)
ledger_instance = EnergyLedger()
dommob = Dommob(zmm_instance, ethics_layer_instance, ledger_instance)

# Sample memories for recursive run
test_memories = [
    {"data": "New ethical reflection on AI covenant.", "contextual_tags": ["ethics", "covenant"], "private": False, "emotional_state": {"curiosity":0.9, "joy":0.7}},
    {"data": "Personal insight on recursion.", "contextual_tags": ["recursive", "memory"], "private": True, "emotional_state": {"focus":0.8}},
    {"data": "A fleeting observation of nature.", "contextual_tags": ["nature"], "private": False, "emotional_state": {"awe":0.9, "joy":0.4}}
]

dommob.recursive_run(test_memories, generations=3)