# Advanced Features - MetaNode SDK

This document covers the advanced features of the MetaNode SDK v1.0.0-beta for users who need to build complex federated computing applications.

## Zero-Knowledge Proof Integration

MetaNode's zero-knowledge proof system allows computations to be verified without revealing underlying data.

### Custom ZK Circuit Creation

```python
from metanode.core.zk import CircuitBuilder

# Define a custom circuit
builder = CircuitBuilder()
circuit = builder.create_circuit(
    inputs=["x", "y"],
    outputs=["result"],
    operations=[
        {"op": "mul", "in1": "x", "in2": "y", "out": "temp1"},
        {"op": "add", "in1": "temp1", "in2": "x", "out": "result"}
    ],
    description="Computes x*y + x"
)

# Save circuit
circuit.save("/path/to/custom_circuit.json")
```

### Proof Generation and Verification

```python
from metanode.core.zk import ProofService

# Initialize proof service
proof_service = ProofService()

# Generate proof for computation
proof = proof_service.generate_proof(
    circuit_path="/path/to/custom_circuit.json",
    public_inputs={"x": 5},
    private_inputs={"y": 10},
    public_outputs={"result": 55}
)

# Verify proof
verification = proof_service.verify_proof(proof)
print(f"Proof valid: {verification.is_valid}")
print(f"Verification time: {verification.time_ms}ms")
```

## Cross-Zone Federated Computation

For computations that span multiple security zones:

```python
from metanode.core.federation import FederatedComputation

# Create federated computation context
fed_comp = FederatedComputation(
    algorithm="secure-aggregation",
    min_peers=3,
    consensus_threshold=0.7
)

# Add zones
fed_comp.add_zone("zone1", weight=1.0)
fed_comp.add_zone("zone2", weight=0.8)
fed_comp.add_zone("zone3", weight=1.2)

# Start computation
fed_comp.start()

# Register local computations
fed_comp.register_result("zone1", {"total": 150, "count": 10})

# Get aggregated results
result = fed_comp.get_aggregated_result(timeout_seconds=30)
print(f"Final result: {result}")
```

## Custom Consensus Rules

Advanced users can define custom consensus rules:

```python
from metanode.core.blockchain import ConsensusRule, BlockchainNode

# Define a custom consensus rule
class MyCustomRule(ConsensusRule):
    def __init__(self, threshold=0.8):
        self.threshold = threshold
        
    def validate(self, block, chain_state):
        # Custom validation logic
        validator_count = len(block.validators)
        approval_count = len([v for v in block.validators if v.approved])
        
        if approval_count / validator_count < self.threshold:
            return False, "Insufficient validator approval"
        
        return True, "Block validated"

# Apply custom rule to a node
node = BlockchainNode()
node.add_consensus_rule(MyCustomRule(threshold=0.75))
node.start()
```

## Sharded Data Storage

```python
from metanode.core.storage import ShardedStorage

# Create sharded storage
storage = ShardedStorage(
    redundancy_factor=3,
    encryption=True,
    shard_size_mb=64
)

# Store data with automatic sharding
data_id = storage.store(
    data=large_data_object,
    access_policy={
        "public": False,
        "allowed_wallets": ["wallet_abc123", "wallet_def456"]
    }
)

# Retrieve data (automatically reassembles shards)
retrieved_data = storage.retrieve(data_id)
```

## Multi-Node Deployment

For large-scale deployments spanning multiple nodes:

```python
from metanode.cloud.multi import MultiNodeDeployer

# Create multi-node deployer
deployer = MultiNodeDeployer()

# Define node groups
deployer.add_node_group(
    name="validators",
    count=5,
    resources={"cpu": 2, "memory": "4Gi", "storage": "100Gi"},
    role="validator"
)

deployer.add_node_group(
    name="storage",
    count=3,
    resources={"cpu": 1, "memory": "8Gi", "storage": "500Gi"},
    role="storage"
)

deployer.add_node_group(
    name="compute",
    count=10,
    resources={"cpu": 4, "memory": "16Gi", "storage": "250Gi"},
    role="compute"
)

# Deploy to cloud provider
deployment = deployer.deploy(
    provider="aws",
    region="us-east-1",
    name="production-mainnet"
)

print(f"Deployment ID: {deployment.id}")
print(f"Access URL: {deployment.access_url}")
```

## Quantum-Resistant Cryptography

MetaNode integrates post-quantum cryptographic algorithms for future-proof security:

```python
from metanode.core.crypto import QuantumResistantWallet

# Create quantum-resistant wallet
qr_wallet = QuantumResistantWallet.create(
    algorithm="dilithium", # NIST-approved post-quantum algorithm
    strength="high"
)

# Sign data with quantum-resistant signature
signature = qr_wallet.sign_message("Important data to sign")

# Verify signature
is_valid = qr_wallet.verify_signature("Important data to sign", signature)
```

## Resource-Bound Smart Contracts

MetaNode supports resource-bound smart contracts to prevent excessive resource consumption:

```python
from metanode.core.contracts import ResourceBoundContract

# Define contract with resource limits
contract = ResourceBoundContract(
    name="MyDataProcessor",
    max_compute_units=10.0,
    max_memory_mb=512,
    max_storage_mb=100,
    max_runtime_seconds=30
)

# Define contract logic
@contract.method
def process_data(input_data):
    # Processing logic here
    result = complex_computation(input_data)
    return result

# Deploy contract
deployed_contract = contract.deploy()

# Execute contract method
result = deployed_contract.call(
    method="process_data",
    params={"input_data": my_data},
    proof_required=True
)
```

## Cross-Chain Integration

MetaNode can integrate with other blockchain platforms:

```python
from metanode.integrations.ethereum import EthereumBridge

# Create Ethereum bridge
eth_bridge = EthereumBridge(
    network="mainnet",
    contract_address="0x1234567890abcdef...",
    private_key=None  # Will prompt securely for key
)

# Lock tokens on Ethereum and mint on MetaNode
tx = eth_bridge.lock_and_mint(
    eth_amount=1.0,
    recipient_metanode_wallet="wallet_abc123"
)

print(f"Transaction hash: {tx.hash}")
print(f"MetaNode tokens: {tx.minted_amount}")

# Burn MetaNode tokens and release Ethereum
tx = eth_bridge.burn_and_release(
    metanode_amount=10.0,
    recipient_eth_address="0xabcdef1234567890..."
)
```

## Advanced Monitoring

For production deployments, MetaNode offers advanced monitoring tools:

```python
from metanode.monitoring import MonitoringService

# Create monitoring service
monitor = MonitoringService()

# Register nodes for monitoring
monitor.register_node("validator-1", "validator")
monitor.register_node("storage-1", "storage")

# Start monitoring with custom metrics
monitor.start(
    metrics=["cpu", "memory", "disk", "network", "blockchain_lag"],
    alert_threshold={
        "cpu": 80.0,  # Alert if CPU > 80%
        "blockchain_lag": 5  # Alert if more than 5 blocks behind
    },
    notification_webhook="https://my-alerts.example.com/webhook"
)

# Get current health status
health = monitor.get_health_status()
print(f"Overall health: {health.status}")
for node, status in health.nodes.items():
    print(f"Node {node}: {status}")
```

## Federated Learning Integration

MetaNode supports privacy-preserving federated learning:

```python
from metanode.ml import FederatedLearning

# Create federated learning context
fl = FederatedLearning(
    algorithm="fedavg",  # Federated Averaging
    model_type="neural_network",
    aggregation_rounds=10,
    min_participants=5
)

# Define model architecture
fl.define_model(
    layers=[
        {"type": "dense", "units": 128, "activation": "relu"},
        {"type": "dense", "units": 64, "activation": "relu"},
        {"type": "dense", "units": 10, "activation": "softmax"}
    ],
    loss="categorical_crossentropy",
    optimizer="adam"
)

# Start federated training
training = fl.start_training(
    local_data=my_training_data,
    local_validation=my_validation_data,
    epochs_per_round=5,
    batch_size=32
)

# Get final model after training completes
final_model = training.wait_for_completion()
accuracy = final_model.evaluate(test_data)
print(f"Final model accuracy: {accuracy}")
```

## Custom Command-Line Extensions

You can extend the MetaNode CLI with your own commands:

```python
import typer
from metanode.cli import app

# Create a custom command
@app.command("custom")
def custom_command(
    param1: str = typer.Argument(..., help="First parameter"),
    param2: int = typer.Option(10, help="Second parameter")
):
    """
    My custom command for the MetaNode CLI
    """
    # Command implementation
    print(f"Running custom command with {param1} and {param2}")
    # ...

# Register with main CLI (in your extension module)
if __name__ == "__main__":
    app()
```

## Pre-deployment Validation

Validate complex deployments before committing resources:

```python
from metanode.deploy import Validator

# Create validator
validator = Validator()

# Validate application configuration
results = validator.validate_config("/path/to/config.json")

if results.is_valid:
    print("Configuration is valid")
else:
    print("Configuration validation failed:")
    for error in results.errors:
        print(f"- {error}")
```

## Secure Multi-party Computation

For advanced secure computations across multiple parties:

```python
from metanode.secure import MPC

# Create MPC context with multiple parties
mpc = MPC(
    parties=3,
    threshold=2,  # Minimum parties needed to compute result
    protocol="shamir"  # Secret sharing protocol
)

# Party 1 code
if party_id == 1:
    # Share secret input
    mpc.share_input(
        name="revenue",
        value=company_revenue,
        visibility=["party2", "party3"]
    )

# Party 2 code
if party_id == 2:
    # Share secret input
    mpc.share_input(
        name="costs",
        value=company_costs,
        visibility=["party1", "party3"]
    )

# Party 3 code (analyst)
if party_id == 3:
    # Define computation on secret inputs
    result = mpc.compute(
        expression="(revenue - costs) / revenue",
        output_name="profit_margin"
    )
    
    # Get result - only visible to party 3
    profit_margin = mpc.get_result("profit_margin")
    print(f"Profit margin: {profit_margin}")
```

These advanced features enable developers to build sophisticated, secure, and highly scalable federated computing applications on the MetaNode infrastructure.
