"""
Telemetry Shell - Agent Telemetry and State Prediction
HEX_ID: 50483354 (PH3T - Telemetry)

PURE PYTHON VERSION - NO OLLAMA, NO LLM DEPENDENCIES

This is a standalone telemetry shell that provides:
- Pattern-based message interpretation
- Anomaly detection
- State prediction via SRL trajectory
- Semantic routing

INPUTS:
    - Natural language messages
    - Hex IDs (8-char strings)
    - Agent coordinates (x, y, z in 0-47)
    - Basin numbers (0-130)
    - K11 levels (-11 to +11)
    - I-Logic states (DIRECT, INVERSE, SPIRAL, ANTI_SPIRAL, ABSORBED)

OUTPUTS:
    - TelemetryEvent: event_type, hex_id, timestamp_ns, basin, layer, i_logic, k11_level, payload
    - AgentTelemetry: hex_id, coords, basin, layer, srl_state, k11_level, phi_norm, coherence
    - StatePredictor results: predicted states with probabilities
    - AnomalyDetector results: detected anomalies with severity
    - SemanticRouter results: shell, command, args, basin, layer, confidence

COMMANDS:
    --status        System status
    --interpret MSG Interpret natural language message
    --predict HEX   Predict state trajectory for hex ID
    --route MSG     Route message to appropriate shell
    --anomalies     Get detected anomalies
    --health        Get health summary

I-LOGIC STATES (SRL 4-state):
    DIRECT (1)      : Identity preservation
    INVERSE (-1)    : Polarity flip / healing
    SPIRAL (+i)     : Recursive expansion
    ANTI_SPIRAL (-i): Convergence / collapse
    ABSORBED (0)    : Neutral state

NO LLM INTEGRATION - Pure pattern-based interpretation.
"""

import hashlib
import math
import time
from collections import deque
from dataclasses import dataclass, field
from enum import Enum, IntEnum
from typing import Dict, List, Optional, Any, Tuple
import threading
import json

# Import from cubesquare core
from .._core.constants import PHI, K5, K11, PRIME_131, MATRIX_SIZE

# Import I-Logic states
from ..i_logic.states import ILogicState


# ==============================================================================
# CONSTANTS
# ==============================================================================

PHI_INVERSE = 0.618033988749895
MATRIX_CELLS = MATRIX_SIZE ** 3  # 110,592
BASE_FREQUENCY = 562.0  # Hz
MEMBRANE_FREQUENCY = 0x83  # 131 Hz
TELEMETRY_HEX = "50483354"


# ==============================================================================
# DATA STRUCTURES
# ==============================================================================

class SRLState(Enum):
    """SRL Four-State System."""
    DIRECT = (1, "DIRECT", [1, 0, 0, 0], "Identity preservation")
    INVERSE = (-1, "INVERSE", [0, 1, 0, 0], "Polarity flip / healing")
    SPIRAL = (2, "SPIRAL", [0, 0, 1, 0], "Recursive expansion")
    CONVERGENT = (3, "CONVERGENT", [0, 0, 0, 1], "Basin attraction")
    ABSORBED = (0, "ABSORBED", [0, 0, 0, 0], "Neutral state")

    def __init__(self, i_val: int, name: str, vector: List[int], desc: str):
        self._i_value = i_val
        self._state_name = name
        self._vector = vector
        self._description = desc

    @property
    def i_value(self) -> int:
        return self._i_value

    @property
    def state_name(self) -> str:
        return self._state_name

    @property
    def vector(self) -> List[int]:
        return self._vector.copy()

    @property
    def description(self) -> str:
        return self._description


class TelemetryEventType(IntEnum):
    """Telemetry event types."""
    MESSAGE = 0x01
    ANOMALY = 0x02
    STATE_CHANGE = 0x03
    PREDICTION = 0x04
    ROUTE = 0x05
    COUNCIL_VOTE = 0x06
    AGENT_SPAWN = 0x07
    AGENT_TERMINATE = 0x08
    COHERENCE_DROP = 0x09
    HEALING = 0x0A
    LAYER_TRANSITION = 0x0B


@dataclass
class TelemetryEvent:
    """Single telemetry event with CST token."""
    event_type: TelemetryEventType
    hex_id: str
    timestamp_ns: int
    basin: int
    layer: int
    i_logic: SRLState
    k11_level: int
    payload: Dict[str, Any]
    cst_token: str = ""

    def __post_init__(self):
        if not self.cst_token:
            self.cst_token = self._generate_token()

    def _generate_token(self) -> str:
        """Generate deterministic 16-char CST token."""
        content = f"{self.hex_id}:{self.event_type}:{self.timestamp_ns}"
        h = hashlib.sha256(content.encode()).digest()
        x, y, z = h[0] % MATRIX_SIZE, h[1] % MATRIX_SIZE, h[2] % MATRIX_SIZE
        diagonal = x + y + z
        basin = int.from_bytes(h[:4], 'little') % PRIME_131
        phi_depth = int(diagonal / PHI)
        radial = int.from_bytes(h[4:8], 'little') % 134
        packed = (diagonal << 48) | (basin << 40) | (phi_depth << 32) | (radial << 16)
        return f"{packed:016X}"

    def to_dict(self) -> Dict:
        """Convert to dictionary."""
        return {
            'event_type': self.event_type.name,
            'event_code': int(self.event_type),
            'hex_id': self.hex_id,
            'timestamp_ns': self.timestamp_ns,
            'basin': self.basin,
            'layer': self.layer,
            'i_logic': self.i_logic.state_name,
            'i_vector': self.i_logic.vector,
            'k11_level': self.k11_level,
            'cst_token': self.cst_token,
            'payload': self.payload
        }


@dataclass
class AgentTelemetry:
    """Telemetry state for an agent."""
    hex_id: str
    morton_level: int  # 1-4 in hierarchy
    agent_type: str
    coords: Tuple[int, int, int]
    basin: int
    layer: int
    srl_state: SRLState
    k11_level: int
    phi_norm: float
    em_coupling: float
    coherence: float
    last_event: Optional[TelemetryEvent] = None
    event_history: List[TelemetryEvent] = field(default_factory=list)

    @classmethod
    def from_hex(cls, hex_id: str, morton_level: int = 2, agent_type: str = "worker"):
        """Create agent telemetry from hex ID."""
        h = int(hex_id, 16)
        x = (h >> 24) % MATRIX_SIZE
        y = (h >> 16) % MATRIX_SIZE
        z = (h >> 8) % MATRIX_SIZE
        basin = h % PRIME_131
        layer = (h % 9) + 1
        k11 = ((x + y + z) % 23) - 11

        # Map K11 to SRL state
        level_mod = ((k11 % 4) + 4) % 4
        i_state_val = [0, 1, -1, 2][level_mod]
        srl_map = {0: SRLState.ABSORBED, 1: SRLState.DIRECT, -1: SRLState.INVERSE, 2: SRLState.SPIRAL}
        srl = srl_map.get(i_state_val, SRLState.DIRECT)

        # Phi norm (distance from center normalized by PHI)
        center = (MATRIX_SIZE - 1) / 2.0
        dx, dy, dz = x - center, y - center, z - center
        dist = math.sqrt(dx*dx + dy*dy + dz*dz)
        max_dist = math.sqrt(3.0 * center * center)
        phi_norm = (dist / max_dist) * PHI

        # EM coupling
        dist_factor = 1.0 + phi_norm / MATRIX_SIZE
        em_coupling = K5 * K11 * dist_factor

        return cls(
            hex_id=hex_id,
            morton_level=morton_level,
            agent_type=agent_type,
            coords=(x, y, z),
            basin=basin,
            layer=layer,
            srl_state=srl,
            k11_level=k11,
            phi_norm=phi_norm,
            em_coupling=em_coupling,
            coherence=0.7
        )


# ==============================================================================
# MESSAGE INTERPRETER (Pattern-Based, No LLM)
# ==============================================================================

class MessageInterpreter:
    """
    Pattern-based Natural Language -> Hex Protocol translator.

    NO LLM INTEGRATION - Pure pattern matching.
    """

    # Hex protocol patterns
    HEX_PATTERNS = {
        '?': 'query',      # ?HEX -> Query topic
        '@': 'reference',  # @HEX -> Reference (no expand)
        '+': 'batch',      # +HEX,HEX -> Batch query
        '!': 'response',   # !HEX:K# -> Response at K-level
        '$': 'encode',     # $TEXT -> Text to hex
        '^': 'heal',       # ^HEX -> Heal token
        '*': 'observe',    # *HEX -> Collapse state
    }

    # Intent to basin mapping
    INTENT_BASIN_MAP = {
        'gene': 23, 'genetics': 23, 'breed': 23, 'reproduce': 23,
        'morton': 47, 'spatial': 47, 'coordinate': 47, 'position': 47,
        'mcp': 89, 'agent': 89, 'tool': 89, 'communication': 89,
        'cst': 65, 'layer': 65, 'structure': 65, 'token': 65,
        'beehive': 19, 'index': 19, 'documentation': 19,
        'memory': 101, 'orchestration': 101, 'task': 101,
        'identity': 127, 'membrane': 127, 'boundary': 127,
        'performance': 12, 'c_backend': 12, 'optimization': 12,
        'healing': 78, 'coherence': 78, 'fernandez': 78,
        'stuck': 99, 'blocked': 99, 'waiting': 99, 'timeout': 99,
        'error': 110, 'failed': 110, 'broken': 110, 'corrupted': 110,
    }

    # I-Logic vocabulary for NL generation
    I_LOGIC_VOCAB = {
        SRLState.DIRECT: {
            'verbs': ['maintain', 'preserve', 'keep', 'stabilize'],
            'adjectives': ['stable', 'consistent', 'reliable'],
            'tone': 'assertive'
        },
        SRLState.INVERSE: {
            'verbs': ['reverse', 'disconnect', 'remove', 'heal'],
            'adjectives': ['isolated', 'orphaned', 'broken'],
            'tone': 'cautionary'
        },
        SRLState.SPIRAL: {
            'verbs': ['expand', 'grow', 'elevate', 'abstract'],
            'adjectives': ['growing', 'hierarchical', 'evolving'],
            'tone': 'enthusiastic'
        },
        SRLState.CONVERGENT: {
            'verbs': ['consolidate', 'merge', 'unify', 'cluster'],
            'adjectives': ['related', 'grouped', 'connected'],
            'tone': 'focused'
        },
        SRLState.ABSORBED: {
            'verbs': ['observe', 'note', 'accept', 'acknowledge'],
            'adjectives': ['neutral', 'balanced', 'centered'],
            'tone': 'neutral'
        }
    }

    def __init__(self):
        pass

    def nl_to_hex(self, message: str) -> Dict[str, Any]:
        """
        Convert natural language to hex protocol command.

        Returns:
            {
                'hex_command': '?97038FBF',
                'intent': 'query',
                'basin': 23,
                'layer': 4,
                'confidence': 0.7,
                'method': 'pattern'
            }
        """
        return self._pattern_nl_to_hex(message)

    def _pattern_nl_to_hex(self, message: str) -> Dict[str, Any]:
        """Pattern-based NL to Hex conversion."""
        message_lower = message.lower()

        # Detect intent
        intent = 'query'  # Default
        if any(w in message_lower for w in ['what is', 'show', 'get', 'find']):
            intent = 'query'
        elif any(w in message_lower for w in ['fix', 'heal', 'repair']):
            intent = 'heal'
        elif any(w in message_lower for w in ['reference', 'link', 'point to']):
            intent = 'reference'
        elif any(w in message_lower for w in ['create', 'generate', 'spawn']):
            intent = 'encode'

        # Find basin from keywords
        basin = 0
        confidence = 0.5
        for keyword, b in self.INTENT_BASIN_MAP.items():
            if keyword in message_lower:
                basin = b
                confidence = 0.7
                break

        # Generate hex ID from message hash
        h = hashlib.md5(message.encode()).hexdigest()[:8].upper()

        # Build command
        prefix = {'query': '?', 'heal': '^', 'reference': '@', 'encode': '$'}.get(intent, '?')
        hex_command = f"{prefix}{h}"

        return {
            'hex_command': hex_command,
            'intent': intent,
            'basin': basin,
            'layer': (basin % 9) + 1,
            'confidence': confidence,
            'method': 'pattern'
        }

    def hex_to_nl(self, hex_command: str, context: Dict = None) -> str:
        """
        Convert hex protocol command to natural language.

        Args:
            hex_command: e.g., "?97038FBF" or "!2A9E0100:K4|DIRECT"
            context: Optional context for richer interpretation

        Returns:
            Natural language interpretation
        """
        if not hex_command:
            return "Empty command received."

        prefix = hex_command[0]
        cmd_type = self.HEX_PATTERNS.get(prefix, 'unknown')

        # Parse hex ID
        hex_id = hex_command[1:9] if len(hex_command) > 1 else ""

        srl = self._detect_srl_from_hex(hex_id)
        vocab = self.I_LOGIC_VOCAB.get(srl, {})

        # Build interpretation
        if cmd_type == 'query':
            verb = vocab.get('verbs', ['query'])[0]
            return f"Requesting to {verb} information about token {hex_id}."
        elif cmd_type == 'heal':
            return f"Initiating healing process for token {hex_id}."
        elif cmd_type == 'reference':
            return f"Creating reference to token {hex_id} (no expansion)."
        elif cmd_type == 'response':
            parts = hex_command.split(':')
            k_level = parts[1] if len(parts) > 1 else "K0"
            return f"Response at {k_level} compression for token {hex_id}."
        elif cmd_type == 'batch':
            hex_ids = hex_command[1:].split(',')
            return f"Batch query for {len(hex_ids)} tokens: {', '.join(hex_ids[:3])}..."
        else:
            return f"Unknown command type '{prefix}' for {hex_id}."

    def _detect_srl_from_hex(self, hex_id: str) -> SRLState:
        """Detect SRL state from hex ID."""
        try:
            h = int(hex_id, 16)
            x = (h >> 24) % MATRIX_SIZE
            y = (h >> 16) % MATRIX_SIZE
            z = (h >> 8) % MATRIX_SIZE
            k11 = ((x + y + z) % 23) - 11
            level_mod = ((k11 % 4) + 4) % 4
            i_state_val = [0, 1, -1, 2][level_mod]
            return {0: SRLState.ABSORBED, 1: SRLState.DIRECT, -1: SRLState.INVERSE, 2: SRLState.SPIRAL}.get(i_state_val, SRLState.DIRECT)
        except:
            return SRLState.DIRECT


# ==============================================================================
# ANOMALY DETECTOR
# ==============================================================================

class AnomalyDetector:
    """
    Monitors telemetry streams for unusual patterns.

    Detects:
    - Coherence drops (< 0.5 threshold)
    - SRL state stuck (same state > N events)
    - Polarity imbalance (DIRECT vs INVERSE ratio)
    - Unusual basin clustering
    - Agent spawn/terminate rate anomalies
    """

    COHERENCE_THRESHOLD = 0.5
    STUCK_STATE_THRESHOLD = 10
    POLARITY_RATIO_THRESHOLD = 5.0  # 5:1 ratio
    SPAWN_RATE_THRESHOLD = 10  # per second

    def __init__(self, window_size: int = 100):
        self.window_size = window_size
        self.event_window: deque = deque(maxlen=window_size)
        self.state_counts: Dict[SRLState, int] = {s: 0 for s in SRLState}
        self.basin_counts: Dict[int, int] = {}
        self.coherence_history: deque = deque(maxlen=window_size)
        self.anomalies: List[Dict] = []
        self.lock = threading.Lock()

    def record_event(self, event: TelemetryEvent) -> List[Dict]:
        """
        Record telemetry event and check for anomalies.

        Returns list of detected anomalies (if any).
        """
        with self.lock:
            self.event_window.append(event)
            self.state_counts[event.i_logic] = self.state_counts.get(event.i_logic, 0) + 1
            self.basin_counts[event.basin] = self.basin_counts.get(event.basin, 0) + 1

            if 'coherence' in event.payload:
                self.coherence_history.append(event.payload['coherence'])

            detected = []

            # Check coherence drop
            coherence = event.payload.get('coherence', 0.7)
            if coherence < self.COHERENCE_THRESHOLD:
                anomaly = {
                    'type': 'COHERENCE_DROP',
                    'severity': 'HIGH' if coherence < 0.3 else 'MEDIUM',
                    'hex_id': event.hex_id,
                    'value': coherence,
                    'threshold': self.COHERENCE_THRESHOLD,
                    'timestamp': event.timestamp_ns,
                    'recommendation': f'Run healing: ^{event.hex_id}'
                }
                detected.append(anomaly)
                self.anomalies.append(anomaly)

            # Check state stuck
            detected.extend(self._check_state_stuck(event))

            # Check polarity imbalance
            detected.extend(self._check_polarity_imbalance())

            return detected

    def _check_state_stuck(self, event: TelemetryEvent) -> List[Dict]:
        """Check if SRL state is stuck."""
        recent = list(self.event_window)[-self.STUCK_STATE_THRESHOLD:]
        if len(recent) < self.STUCK_STATE_THRESHOLD:
            return []

        states = [e.i_logic for e in recent]
        if len(set(states)) == 1:
            return [{
                'type': 'STATE_STUCK',
                'severity': 'MEDIUM',
                'hex_id': event.hex_id,
                'state': states[0].state_name,
                'count': len(states),
                'timestamp': event.timestamp_ns,
                'recommendation': 'Apply SPIRAL transition to break stuck state'
            }]
        return []

    def _check_polarity_imbalance(self) -> List[Dict]:
        """Check for polarity imbalance."""
        direct = self.state_counts.get(SRLState.DIRECT, 0)
        inverse = self.state_counts.get(SRLState.INVERSE, 0)

        if inverse == 0:
            inverse = 1  # Avoid division by zero

        ratio = direct / inverse
        if ratio > self.POLARITY_RATIO_THRESHOLD or ratio < 1/self.POLARITY_RATIO_THRESHOLD:
            return [{
                'type': 'POLARITY_IMBALANCE',
                'severity': 'LOW',
                'direct_count': direct,
                'inverse_count': inverse,
                'ratio': ratio,
                'timestamp': time.time_ns(),
                'recommendation': 'Balance DIRECT/INVERSE states for system stability'
            }]
        return []

    def get_health_summary(self) -> Dict:
        """Get overall system health summary."""
        avg_coherence = sum(self.coherence_history) / len(self.coherence_history) if self.coherence_history else 0.7

        return {
            'event_count': len(self.event_window),
            'state_distribution': {s.state_name: c for s, c in self.state_counts.items()},
            'basin_distribution': dict(self.basin_counts),
            'average_coherence': avg_coherence,
            'anomaly_count': len(self.anomalies),
            'recent_anomalies': self.anomalies[-5:] if self.anomalies else [],
            'health_score': avg_coherence * (1 - len(self.anomalies) / max(len(self.event_window), 1) * 0.1)
        }


# ==============================================================================
# STATE PREDICTOR
# ==============================================================================

class StatePredictor:
    """
    Predicts future states via SRL trajectory analysis.

    Uses:
    - SRL hop distance (t = sqrt(d))
    - Phi-harmonic resonance
    - Basin affinity
    - Historical patterns
    """

    def __init__(self):
        self.trajectory_history: Dict[str, List[Tuple[int, int, int]]] = {}

    def predict_next_state(self, agent: AgentTelemetry, steps: int = 5) -> List[Dict]:
        """
        Predict next N states for an agent.

        Returns list of predicted states with probabilities.
        """
        predictions = []
        x, y, z = agent.coords
        current_srl = agent.srl_state

        for step in range(steps):
            # Calculate state transition probabilities
            probs = self._transition_probabilities(current_srl, agent.k11_level)

            # Select most likely next state
            next_srl = max(probs, key=probs.get)

            # Calculate next coordinates via SRL
            dx, dy, dz = self._srl_displacement(next_srl)
            x = (x + dx) % MATRIX_SIZE
            y = (y + dy) % MATRIX_SIZE
            z = (z + dz) % MATRIX_SIZE

            # Calculate new properties
            morton = self._morton_encode(x, y, z)
            new_basin = morton % PRIME_131
            new_k11 = ((x + y + z) % 23) - 11

            # Phi norm
            center = (MATRIX_SIZE - 1) / 2.0
            dx_c, dy_c, dz_c = x - center, y - center, z - center
            dist = math.sqrt(dx_c*dx_c + dy_c*dy_c + dz_c*dz_c)
            max_dist = math.sqrt(3.0 * center * center)
            phi_norm = (dist / max_dist) * PHI

            predictions.append({
                'step': step + 1,
                'srl_state': next_srl.state_name,
                'probability': probs[next_srl],
                'coords': (x, y, z),
                'morton': morton,
                'basin': new_basin,
                'k11_level': new_k11,
                'phi_norm': phi_norm
            })

            current_srl = next_srl

        return predictions

    def _transition_probabilities(self, current: SRLState, k11: int) -> Dict[SRLState, float]:
        """Calculate SRL state transition probabilities."""
        base = {
            SRLState.DIRECT: {SRLState.DIRECT: 0.5, SRLState.SPIRAL: 0.3, SRLState.CONVERGENT: 0.15, SRLState.INVERSE: 0.05},
            SRLState.INVERSE: {SRLState.INVERSE: 0.3, SRLState.DIRECT: 0.4, SRLState.ABSORBED: 0.2, SRLState.SPIRAL: 0.1},
            SRLState.SPIRAL: {SRLState.SPIRAL: 0.4, SRLState.CONVERGENT: 0.3, SRLState.DIRECT: 0.2, SRLState.INVERSE: 0.1},
            SRLState.CONVERGENT: {SRLState.CONVERGENT: 0.35, SRLState.DIRECT: 0.35, SRLState.SPIRAL: 0.2, SRLState.INVERSE: 0.1},
            SRLState.ABSORBED: {SRLState.DIRECT: 0.4, SRLState.SPIRAL: 0.3, SRLState.CONVERGENT: 0.2, SRLState.INVERSE: 0.1}
        }

        probs = base.get(current, base[SRLState.DIRECT]).copy()

        # Modify by K11 level
        if k11 > 5:
            # High K11 favors SPIRAL
            probs[SRLState.SPIRAL] = probs.get(SRLState.SPIRAL, 0) + 0.1
        elif k11 < -5:
            # Low K11 favors INVERSE
            probs[SRLState.INVERSE] = probs.get(SRLState.INVERSE, 0) + 0.1

        # Normalize
        total = sum(probs.values())
        return {k: v/total for k, v in probs.items()}

    def _srl_displacement(self, srl: SRLState) -> Tuple[int, int, int]:
        """Get coordinate displacement for SRL state."""
        displacements = {
            SRLState.DIRECT: (0, 0, 0),
            SRLState.INVERSE: (-1, -1, -1),
            SRLState.SPIRAL: (1, 1, 0),
            SRLState.CONVERGENT: (0, 1, 1),
            SRLState.ABSORBED: (0, 0, 0)
        }
        return displacements.get(srl, (0, 0, 0))

    def _morton_encode(self, x: int, y: int, z: int) -> int:
        """Morton encode (x,y,z)."""
        def spread_bits(v):
            v = v & 0x3FF
            v = (v | (v << 16)) & 0x030000FF
            v = (v | (v << 8)) & 0x0300F00F
            v = (v | (v << 4)) & 0x030C30C3
            v = (v | (v << 2)) & 0x09249249
            return v
        return spread_bits(x) | (spread_bits(y) << 1) | (spread_bits(z) << 2)

    def predict_trajectory(self, hex_id: str, steps: int = 10) -> Dict:
        """
        Compute epicycle orbital trajectory for a hex ID.

        Returns trajectory with Phi-harmonic resonance.
        """
        agent = AgentTelemetry.from_hex(hex_id)
        predictions = self.predict_next_state(agent, steps)

        # Calculate epicycle parameters
        x, y, z = agent.coords
        center = MATRIX_SIZE / 2
        orbital_radius = math.sqrt((x-center)**2 + (y-center)**2 + (z-center)**2)
        angular_velocity = (2 * math.pi) / (orbital_radius * PHI) if orbital_radius > 0 else 0

        # Phi-harmonic resonance
        phi_harmonic = 1.0 - abs(1.0 - (orbital_radius / (center * PHI)))

        return {
            'hex_id': hex_id,
            'start_coords': agent.coords,
            'start_srl': agent.srl_state.state_name,
            'orbital_radius': orbital_radius,
            'angular_velocity': angular_velocity,
            'phi_harmonic': phi_harmonic,
            'consciousness_level': agent.k11_level,
            'trajectory': predictions
        }


# ==============================================================================
# SEMANTIC ROUTER
# ==============================================================================

class SemanticRouter:
    """
    Routes messages to appropriate shell/agent based on intent.

    Pattern-based routing - no LLM dependencies.
    """

    # Basin to shell mapping
    BASIN_SHELL_MAP = {
        23: {'shell': 'gene', 'layer': 4, 'agent_type': 'worker'},
        47: {'shell': 'distance', 'layer': 3, 'agent_type': 'worker'},
        89: {'shell': 'agent', 'layer': 4, 'agent_type': 'cognitive'},
        65: {'shell': 'cst', 'layer': 2, 'agent_type': 'worker'},
        19: {'shell': 'content', 'layer': 2, 'agent_type': 'worker'},
        12: {'shell': 'performance', 'layer': 1, 'agent_type': 'tool'},
        78: {'shell': 'unified', 'layer': 6, 'agent_type': 'cognitive'},
        34: {'shell': 'world', 'layer': 4, 'agent_type': 'worker'},
        56: {'shell': 'vision', 'layer': 7, 'agent_type': 'worker'},
        101: {'shell': 'learning', 'layer': 5, 'agent_type': 'cognitive'},
        99: {'shell': 'unified', 'layer': 6, 'agent_type': 'healer'},
        110: {'shell': 'telemetry', 'layer': 7, 'agent_type': 'monitor'},
    }

    # Layer affinity for shells
    LAYER_SHELL_AFFINITY = {
        1: ['performance', 'ogawa'],
        2: ['content', 'cst', 'beehive'],
        3: ['distance', 'spatial'],
        4: ['gene', 'world', 'agent'],
        5: ['learning', 'data_analysis'],
        6: ['unified', 'language'],
        7: ['vision', 'time', 'telemetry'],
        8: ['mass', 'gravity'],
        9: ['phi', 'chat'],
    }

    def __init__(self):
        self.interpreter = MessageInterpreter()

    def route(self, message: str) -> Dict:
        """
        Route a message to the appropriate shell/agent.

        Returns:
            {
                'shell': 'gene',
                'command': 'generate',
                'args': ['TOKEN_NAME'],
                'basin': 23,
                'layer': 4,
                'agent_hex': '2A9E0100',
                'confidence': 0.8,
                'reasoning': 'Detected gene/breeding intent'
            }
        """
        interpretation = self.interpreter.nl_to_hex(message)

        basin = interpretation.get('basin', 0)
        layer = interpretation.get('layer', 4)
        intent = interpretation.get('intent', 'query')

        mapping = self.BASIN_SHELL_MAP.get(basin, {'shell': 'cg', 'layer': 4, 'agent_type': 'worker'})
        shell = mapping['shell']

        command_map = {
            'query': 'status',
            'heal': 'heal',
            'encode': 'generate',
            'reference': 'status',
        }
        command = command_map.get(intent, 'status')

        args = self._extract_args(message, intent)
        agent_hex = self._find_agent(basin, layer)

        return {
            'shell': shell,
            'command': command,
            'args': args,
            'basin': basin,
            'layer': layer,
            'agent_hex': agent_hex,
            'confidence': interpretation.get('confidence', 0.5),
            'reasoning': f"Routed to {shell} shell (basin {basin}, L{layer}) for {intent} intent",
            'hex_command': interpretation.get('hex_command', '')
        }

    def _extract_args(self, message: str, intent: str) -> List[str]:
        """Extract arguments from natural language message."""
        import re

        args = []

        # Quoted strings
        quoted = re.findall(r'"([^"]*)"', message)
        args.extend(quoted)

        # Hex IDs (8 char hex)
        hex_ids = re.findall(r'\b[0-9A-Fa-f]{8}\b', message)
        args.extend(hex_ids)

        return args

    def _find_agent(self, basin: int, layer: int) -> str:
        """Find appropriate agent for basin/layer."""
        hex_id = f"2A9E{basin % 256:02X}{layer:02X}"
        return hex_id


# ==============================================================================
# TELEMETRY SHELL (MAIN CLASS)
# ==============================================================================

class TelemetryShell:
    """
    Main Telemetry Shell class.

    PURE PYTHON - No LLM dependencies.

    Usage:
        bridge = TelemetryShell()

        # Interpret message
        result = bridge.interpret_message("the healer is stuck")

        # Predict state trajectory
        prediction = bridge.predict_state("2A9E0100", steps=5)

        # Route to shell
        route = bridge.route_message("find gene documentation")

        # Get status
        status = bridge.get_status()
    """

    HEX_ID = TELEMETRY_HEX

    def __init__(self):
        self.interpreter = MessageInterpreter()
        self.anomaly_detector = AnomalyDetector()
        self.predictor = StatePredictor()
        self.router = SemanticRouter()

        self.event_queue: deque = deque(maxlen=1000)
        self.event_lock = threading.Lock()

        self.metrics = {
            'events_processed': 0,
            'messages_interpreted': 0,
            'anomalies_detected': 0,
            'predictions_made': 0,
            'routes_computed': 0,
            'start_time': time.time(),
        }

    def interpret_message(self, message: str) -> Dict:
        """Interpret a natural language message."""
        self.metrics['messages_interpreted'] += 1
        return self.interpreter.nl_to_hex(message)

    def predict_state(self, hex_id: str, steps: int = 5) -> Dict:
        """Predict state trajectory for a hex ID."""
        self.metrics['predictions_made'] += 1
        return self.predictor.predict_trajectory(hex_id, steps)

    def route_message(self, message: str) -> Dict:
        """Route message to appropriate shell."""
        self.metrics['routes_computed'] += 1
        return self.router.route(message)

    def record_event(self, event: TelemetryEvent) -> List[Dict]:
        """Record event and check for anomalies."""
        self.metrics['events_processed'] += 1
        with self.event_lock:
            self.event_queue.append(event)
        anomalies = self.anomaly_detector.record_event(event)
        self.metrics['anomalies_detected'] += len(anomalies)
        return anomalies

    def get_anomalies(self) -> List[Dict]:
        """Get detected anomalies."""
        return self.anomaly_detector.anomalies

    def get_health_summary(self) -> Dict:
        """Get health summary."""
        return self.anomaly_detector.get_health_summary()

    def get_status(self, json_output: bool = False) -> Dict:
        """Get telemetry shell status."""
        uptime = time.time() - self.metrics['start_time']

        status = {
            'hex_id': self.HEX_ID,
            'protocol': 'CG:HEX|V1|AGENT:50483354',
            'llm_available': False,  # Pure Python - no LLM
            'uptime_seconds': uptime,
            'events_processed': self.metrics['events_processed'],
            'messages_interpreted': self.metrics['messages_interpreted'],
            'predictions_made': self.metrics['predictions_made'],
            'routes_computed': self.metrics['routes_computed'],
            'anomalies_detected': self.metrics['anomalies_detected'],
            'health_summary': self.get_health_summary(),
        }

        if json_output:
            return json.dumps(status, indent=2)
        return status

    def run(self, command: str, *args, as_json: bool = False) -> Any:
        """Run a Telemetry shell command (CLI interface)."""
        commands = {
            'status': lambda: self.get_status(as_json),
            'interpret': lambda: self.interpret_message(args[0] if args else "test"),
            'predict': lambda: self.predict_state(args[0] if args else "2A9E0100"),
            'route': lambda: self.route_message(args[0] if args else "find documentation"),
            'health': lambda: self.get_health_summary(),
        }
        if command in commands:
            result = commands[command]()
            if as_json and isinstance(result, dict):
                return json.dumps(result, indent=2)
            return result
        return {'error': f'Unknown command: {command}', 'available': list(commands.keys())}
