# This Proof of Non-Tamperability (PoNT) algorithm eliminates the ballooning ledger problem and improves anonymity by using a tamper-evident seal to verify the integrity of the transaction data. The seal is calculated by hashing the transaction data. The transaction data is encrypted using a shared key. The shared key is encrypted using the public keys of the receiving nodes. The encrypted transaction data and encrypted shared key are broadcasted to the receiving nodes. The receiving nodes decrypt the shared key using their private keys. The receiving nodes then decrypt the transaction data using the shared key. The receiving nodes verify the tamper-evident seal on the transaction data using the public key of the sender. The receiving nodes then add the transaction to their local blockchain - this part I will trim soon, as we don't actually want our blockchain to store anything but PoNT, to prevent ballooning.  ABI information will soon be stored only in the public / private keys, which are encoded themselves to include the ABI.   The public key doesn't exist unless called upon, eventually, for privacy while the ABI information will eventually be returned to the lineup and also will not exist unless called upon.  The algorithm is intended to be soon ready for export via the radio.py module, enabling P2P radio ISP.

import json
import os
import hashlib
import random
import secrets

from Crypto.PublicKey import RSA
from ed25519 import SigningKey, VerifyingKey
from Crypto.Cipher import AES, PKCS1_OAEP
from Crypto.Random import get_random_bytes
from Crypto.Hash import SHA256
from Crypto.Signature import pss

# Generate a new RSA key pair
private_key = RSA.generate(2048)
public_key = private_key.publickey()

VerifyingKey.from_string(public_key.exportKey(format='DER'), encoding='DER')
get_random_bytes(32) # Generate a random 32-byte key

# Generate a new Ed25519 key pair
private_key_ed25519 = SigningKey.generate()
public_key_ed25519 = private_key_ed25519.get_verifying_key()

# Sign the data using the private key and the PSS algorithm
def sign_data(data, private_key):
    # Hash the data
    h = SHA256.new()
    h.update(data)

    # Create the signer object
    signer = pss.new(private_key)

    # Sign the hash of the data
    signature = signer.sign(h)

    return signature

# Verify the signature using the public key and the PSS algorithm
def verify_signature(data, signature, public_key):
    # Hash the data
    h = SHA256.new()
    h.update(data)

    # Create the verifier object
    verifier = pss.new(public_key)

    # Verify the signature
    try:
        verifier.verify(h, signature)
        return True
    except (ValueError, TypeError):
        return False

# Add import for RabbitMQ library
import pika

def shared_key():
    # Generate a random key to encrypt the transaction data
    key = os.urandom(16)
    return key

# Create a function to encrypt or decrypt the transaction data
def encrypt_or_decrypt_transaction_data(transaction, shared_key, encrypt=True):
    # Convert the transaction data to a string
    transaction_string = json.dumps(transaction)

    # Convert the transaction data to bytes
    transaction_bytes = transaction_string.encode()

    # Encrypt the transaction data using the shared key
    if encrypt:
        cipher = AES.new(shared_key, AES.MODE_EAX)
        nonce = cipher.nonce
        encrypted_transaction, tag = cipher.encrypt_and_digest(transaction_bytes)
    else:
        cipher = AES.new(shared_key, AES.MODE_EAX, nonce=nonce)
        encrypted_transaction = cipher.decrypt(transaction_bytes)
        try:
            cipher.verify(tag)
        except ValueError:
            print("Key incorrect or message corrupted")

    return encrypted_transaction

def calculate_seal(transaction):
    # Convert the transaction data to a string
    transaction_string = json.dumps(transaction)

    # Hash the transaction data
    seal = hashlib.sha256(transaction_string.encode()).hexdigest()

    return seal

def encrypted_key(key, public_keys, private_key):
    # Create a list to store the encrypted keys
    encrypted_keys = []

    # Create a cipher object for encrypting the key
    cipher = PKCS1_OAEP.new(private_key)

    # Encrypt the key using the public keys of the receiving nodes
    for public_key in public_keys:
        encrypted_key = cipher.encrypt(key)
        encrypted_keys.append(encrypted_key)

    return encrypted_keys

def encrypt_or_decrypt_key(key, public_keys, private_key, encrypt=True):
    # Create a list to store the encrypted keys
    encrypted_keys = []

    # Create a cipher object for encrypting the key
    cipher = PKCS1_OAEP.new(private_key)

    # Encrypt the key using the public keys of the receiving nodes
    for public_key in public_keys:
        if encrypt:
            encrypted_key = cipher.encrypt(key)
        else:
            encrypted_key = cipher.decrypt(key)
        encrypted_keys.append(encrypted_key)

    return encrypted_keys

def broadcast_transaction(transaction, private_key, public_keys):
    # Generate a random key to encrypt the transaction data
    key = os.urandom(16)

    # Encrypt the transaction data using the key
    encrypted_transaction = encrypt_or_decrypt_transaction_data(transaction, shared_key, encrypt=True)

    # Calculate the tamper-evident seal for the transaction data
    seal = calculate_seal(transaction)

    # Add the seal to the transaction data
    transaction['seal'] = seal

    # Encrypt the key using the public keys of the receiving nodes
    encrypted_keys = encrypt_or_decrypt_key(key, public_keys, private_key, encrypt=True)

    with pika.BlockingConnection(pika.ConnectionParameters(host='localhost')) as connection:
        channel = connection.channel()

        # Create a message queue for the blockchain data
        channel.queue_declare(queue='blockchain')

        # Send the encrypted transaction and key to the message queue as a JSON object
        data = {'transaction': encrypted_transaction, 'key': encrypted_key}
        channel.basic_publish(exchange='', routing_key='blockchain', body=json.dumps(data))
        print("Transaction broadcasted:", data)

def encrypted_transaction(transaction, shared_key, encrypt=True):
    # Convert the transaction data to a string
    transaction_string = json.dumps(transaction)

    # Convert the transaction data to bytes
    transaction_bytes = transaction_string.encode()

    # Encrypt the transaction data using the shared key
    if encrypt:
        cipher = AES.new(shared_key, AES.MODE_EAX)
        encrypted_transaction, tag = cipher.encrypt_and_digest(transaction_bytes)
        return {'encrypted_transaction': encrypted_transaction, 'nonce': cipher.nonce, 'tag': tag}
    else:
        cipher = AES.new(shared_key, AES.MODE_EAX, nonce=transaction['nonce'])
        decrypted_transaction = cipher.decrypt_and_verify(transaction['encrypted_transaction'], transaction['tag'])
        return json.loads(decrypted_transaction)

def verify_transaction(transaction, public_key):
    # Verify the tamper-evident seal on the transaction using the public key
    if verify_tamper_evident_seal(transaction, transaction['seal'], public_key):
        return True
    else:
        return False

def receive_transactions(private_key, public_key):
    MAX_RETRIES = 5

    retries = 0
    while True:
        try:
            with pika.BlockingConnection(pika.ConnectionParameters(host='localhost')) as connection:
                channel = connection.channel()

                # Create a message queue for the blockchain data
                channel.queue_declare(queue='blockchain')

                # Define a callback function to process received messages
                def callback(ch, method, properties, body):
                    # Decrypt the transaction data using the private key
                    transaction = encrypt_or_decrypt_transaction_data(encrypted_transaction, shared_key, encrypt=False)

                    # Verify the tamper-evident seal on the transaction using the public key
                    if verify_tamper_evident_seal(transaction, transaction['seal'], public_key):
                        # If the seal is valid, verify the transaction using the coin's ABI
                        if verify_transaction(transaction):
                            print("Transaction verified:", transaction)
                        else:
                            print("Transaction rejected: invalid coin or invalid transaction")
                    else:
                        print("Transaction tampered with:", transaction)

                # Set up a message consumer and start listening for messages
                channel.basic_consume(queue='blockchain', on_message_callback=callback, auto_ack=True)
                print('Listening for transactions...')
                channel.start_consuming()
                break
        except pika.exceptions.ConnectionClosed:
            # Increment the retry count and try again if the maximum number of retries has not been reached
            retries += 1
            if retries < MAX_RETRIES:
                print("Error: Connection closed. Retrying...")
            else:
                print("Error: Connection closed. Maximum number of retries exceeded.")
                break

def verify_tamper_evident_seal(transaction, public_key):
    # Extract the nonce and signature from the seal
    nonce = transaction['seal']['nonce']
    signature = transaction['seal']['signature']

    # Verify the signature of the nonce using the public key and the PSS algorithm
    if verify_signature(nonce, signature, public_key):
        return True
    else:
        return False

# Define a dictionary to store the state of each transaction
transaction_states = {}

def process_transaction(transaction):
    # Check the state of the transaction
    if transaction['seal'] is None:
        # If the transaction does not have a tamper-evident seal, reject it
        print("Transaction rejected: invalid seal")
        return

    if transaction['id'] in transaction_states:
        # If the transaction has already been processed, skip it
        print("Transaction skipped: already processed")
        return

    if transaction['confirmations'] < 100:
        # If the transaction has not reached 100% confirmation, mark it as semi-confirmed
        transaction_states[transaction['id']] = 'semi-confirmed'
        print("Transaction semi-confirmed:", transaction)
    else:
        # If the transaction has reached 100% confirmation, mark it as confirmed
        transaction_states[transaction['id']] = 'confirmed'
        print("Transaction confirmed:", transaction)

def receive_transactions(private_key, public_key):
    MAX_RETRIES = 5

    retries = 0
    while True:
        try:
            with pika.BlockingConnection(pika.ConnectionParameters(host='localhost')) as connection:
                channel = connection.channel()

                # Create a message queue for the blockchain data
                channel.queue_declare(queue='blockchain')

                # Define a callback function to process received messages
                def callback(ch, method, properties, body):
                    try:
                        # Parse the received data as a JSON object
                        transaction = json.loads(body)
                    except ValueError:
                        print("Error: Invalid JSON data")
                        return

                    # Process the transaction
                    process_transaction(transaction)

                    # If the transaction is confirmed, broadcast it to all shards
                    if transaction_states[transaction['id']] == 'confirmed':
                        broadcast_transaction_to_shards(transaction, private_key, public_key)

                # Set up a message consumer and start listening for messages
                channel.basic_consume(queue='blockchain', on_message_callback=callback, auto_ack=True)
                print('Listening for transactions...')
                channel.start_consuming()
                break
        except pika.exceptions.ConnectionClosed:
            # Increment the retry count and try again if the maximum number of retries has not been reached
            retries += 1
            if retries < MAX_RETRIES:
                print("Error: Connection closed. Retrying...")
            else:
                print("Error: Connection closed. Maximum number of retries exceeded.")
                break

#shards create sub-networks of the blockchain, each with its own set of nodes, which are pooled together after being processed by the sub-network's nodes
def shards(blockchain, shards):
    # Create a list to store the shards
    shards = []

    # Shard the blockchain
    for shard in range(0, len(blockchain), len(blockchain) // len(shards)):
        # Create a shard
        shard = blockchain[shard:shard + len(blockchain) // len(shards)]

        # Add the shard to the list of shards
        shards.append(shard)

    return shards

def broadcast_transaction_to_shards(transaction, private_key, public_key):
    # Encrypt the transaction data using a shared key
    shared_key = b'key'  # Replace with a secure shared key
    encrypted_transaction = encrypt_or_decrypt_transaction_data(transaction, shared_key, encrypt=True)


    # Connect to each shard and broadcast the transaction
    for shard in shards:
        with pika.BlockingConnection(pika.ConnectionParameters(host=shard['host'])) as connection:
            channel = connection.channel()

            # Create a message queue for the blockchain data
            channel.queue_declare(queue='blockchain')

            # Send the transaction to the message queue
            channel.basic
# Define a dictionary to store the state of each transaction
transaction_states = {}

def process_transaction(transaction):
    # Check the state of the transaction
    if transaction['seal'] is None:
        # If the transaction does not have a tamper-evident seal, reject it
        print("Transaction rejected: invalid seal")
        return

    if transaction['id'] in transaction_states:
        # If the transaction has already been processed, skip it
        print("Transaction skipped: already processed")
        return

    if transaction['confirmations'] < 100:
        # If the transaction has not reached 100% confirmation, mark it as semi-confirmed
        transaction_states[transaction['id']] = 'semi-confirmed'
        print("Transaction semi-confirmed:", transaction)
    else:
        # If the transaction has reached 100% confirmation, mark it as confirmed
        transaction_states[transaction['id']] = 'confirmed'
        print("Transaction confirmed:", transaction)

def receive_transactions(private_key, public_key):
    MAX_RETRIES = 5

    retries = 0
    while True:
        try:
            with pika.BlockingConnection(pika.ConnectionParameters(host='localhost')) as connection:
                channel = connection.channel()

                # Create a message queue for the blockchain data
                channel.queue_declare(queue='blockchain')

                # Define a callback function to process received messages
                def callback(ch, method, properties, body):
                    try:
                        # Parse the received data as a JSON object
                        transaction = json.loads(body)
                    except ValueError:
                        print("Error: Invalid JSON data")
                        return

                    # Process the transaction
                    process_transaction(transaction)

                    # If the transaction is confirmed, broadcast it to all shards
                    if transaction_states[transaction['id']] == 'confirmed':
                        broadcast_transaction_to_shards(transaction, private_key, public_key)

                # Set up a message consumer and start listening for messages
                channel.basic_consume(queue='blockchain', on_message_callback=callback, auto_ack=True)
                print('Listening for transactions...')
                channel.start_consuming()
                break
        except pika.exceptions.ConnectionClosed:
            # Increment the retry count and try again if the maximum number of retries has not been reached
            retries += 1
            if retries < MAX_RETRIES:
                print("Error: Connection closed. Retrying...")
            else:
                print("Error: Connection closed. Maximum number of retries exceeded.")
                break

def broadcast_transaction_to_shards(transaction, private_key, public_key):
    # Encrypt the transaction data using a shared key
    shared_key = b'key'  # Replace with a secure shared key
    encrypted_transaction = encrypt_or_decrypt_transaction_data(transaction, shared_key, encrypt=True)


    # Connect to each shard and broadcast the transaction
    for shard in shards:
        with pika.BlockingConnection(pika.ConnectionParameters(host=shard['host'])) as connection:
            channel = connection.channel()

            # Create a message queue for the blockchain data
            channel.queue_declare(queue='blockchain')

            # Send the transaction to the message queue
            channel.basic_publish(exchange='', routing_key='blockchain', body=encrypted_transaction)

            print("Transaction broadcast to shard:", shard['host'])

def issue_reward(nodes, transaction, private_key, public_key):
    # Calculate the reward amount (1% of the transaction value)
    reward_amount = transaction['amount'] * 0.01

    # Generate a random nonce using a cryptographically secure random number generator
    nonce = secrets.randbelow(2**256)

    # Sign the nonce using the private key and the PSS algorithm
    signature = sign_data(nonce, private_key)

    # Add the signed nonce to the transaction as a tamper-evident seal
    transaction['seal'] = {'nonce': nonce, 'signature': signature}

    # Calculate the weight for each node based on the size of the reward it offers
    weights = []
    for node in nodes:
        # The weight of the node is inversely proportional to the size of the reward it offers
        weight = 1.0 / node['reward_amount']
        weights.append(weight)

    # Normalize the weights
    total_weight = sum(weights)
    weights = [weight / total_weight for weight in weights]

    # Use the weights to choose a node at random
    selected_node = random.choices(nodes, weights=weights)[0]
    # Give the selected node the reward
    selected_node['balance'] += reward_amount
   
def calculate_or_verify_seal(transaction, seal, public_key, calculate):
    # Calculate the hash of the transaction data
    transaction_data = json.dumps(transaction).encode()
    hash_function = hashlib.sha256()
    hash_function.update(transaction_data)
    calculated_seal = hash_function.hexdigest()

    # If calculating the seal, return the calculated seal
    if calculate:
        return calculated_seal

    # If verifying the seal, compare the calculated seal to the provided seal
    if calculated_seal == seal:
        return True
    else:
        return False

def broadcast_transaction(transaction, private_key, public_keys):
    # Encrypt the transaction data using a shared key
    shared_key = b'key'  # Replace with a secure shared key
    encrypted_transaction = encrypt_or_decrypt_transaction_data(transaction, shared_key, encrypt=True)

    # Create a tamper-evident seal for the transaction
    seal = calculate_or_verify_seal(transaction, None, None, calculate=True)
    # Sign the seal using the private key and the PSS algorithm
    signature = sign_data(seal, private_key)
    # Add the signed seal to the transaction as a tamper-evident seal
    transaction['seal'] = {'seal': seal, 'signature': signature}

    public_keys.append(public_key) # Add the public key of the node that created the transaction to the list of public keys

    # Encrypt the list of public keys using the shared key
    encrypted_public_keys = encrypt_or_decrypt_transaction_data(public_keys, shared_key, encrypt=True)

    encrypted_public_keys = encrypted_public_keys.decode() # Convert the encrypted public keys to a string

# Connect to the RabbitMQ server
    connection = pika.BlockingConnection(pika.ConnectionParameters(host='localhost'))
    channel = connection.channel()

    # Create a message queue for the blockchain data
    channel.queue_declare(queue='blockchain')

    # Send the transaction to the message queue
    channel.basic_publish(exchange='', routing_key='blockchain', body=encrypted_transaction)
    print("Transaction broadcasted:", encrypted_transaction)
    connection.close()

list_of_nodes = [] # List of nodes in the network

# Add a node to the network
def add_node_to_network(node):
    list_of_nodes.append(node)
    
def remove_node_from_network(node):
    list_of_nodes.remove(node)
    
# Add the transaction to the blockchain
def add_transaction_to_blockchain(transaction):
    
    # Increment the confirmation count for the transaction
    transaction['confirmations'] += 1
    
    # Check if the transaction has been confirmed by all nodes
    if transaction['confirmations'] == len(list_of_nodes):
        # Calculate the reward amount as 1% of the transaction amount
        reward_amount = transaction['amount'] * 0.01
        
        # Deduct the reward amount from the transaction amount
        transaction['amount'] -= reward_amount
        
        # Randomly select a node from the list of nodes in the network
        random_node = random.choice(list_of_nodes)
        
        # Check if the selected node has a tamper-evident seal
        if 'signature' in random_node:
            # Verify the tamper-evident seal using the node's public key
            if verify_tamper_evident_seal(random_node, random_node['signature'], random_node['public_key']):
                # Add the reward to the selected node's balance if the tampering test passes
                random_node['balance'] += reward_amount
            else:
                # Remove the node from the network if the tampering test fails
                remove_node_from_network()
        else:
            # Remove the node from the network if it does not have a tamper-evident seal
            remove_node_from_network()

def add_transaction_to_blockchain(transaction):
    # Add the transaction to the blockchain
    
    # Increment the confirmation count for the transaction
    transaction['confirmations'] += 1
    
    # Check if the transaction has been confirmed by all nodes
    if transaction['confirmations'] == len(list_of_nodes):
        # Calculate the reward amount as 1% of the transaction amount
        reward_amount = transaction['amount'] * 0.01
        
        # Deduct the reward amount from the transaction amount
        transaction['amount'] -= reward_amount
        
        # Randomly select a node from the list of nodes in the network
        random_node = random.choice(list_of_nodes)
        
        # Check if the selected node has a tamper-evident seal
        if 'signature' in random_node:
            # Verify the tamper-evident seal using the node's public key
            if calculate_or_verify_seal(random_node, random_node['signature'], random_node['public_key'], calculate=False):
                # Add the reward to the selected node's balance if the tampering test passes
                random_node['balance'] += reward_amount
                
                # Check if the transaction includes a new node
                if 'new_node' in transaction:
                    # Add the new node to the network if it is the only new node
                    if len(transaction['new_node']) == 1:
                        list_of_nodes.append(transaction['new_node'][0])
                    else:
                        # Remove the node from the network if there is more than one new node
                        remove_node_from_network()
        else:
            # Remove the node from the network if it does not have a tamper-evident seal
            remove_node_from_network()

def main():
    # Generate a private/public key pair
    key_pair = RSA.generate(2048)
    private_key = key_pair.export_key()
    public_key = key_pair.publickey().export_key()

if __name__ == '__main__':
    main()

# This code combines the PoNT.py code with the radio.py code to create a radio communication system that uses the PoNT algorithm to encode and decode the data in order to create a distributed ISP that is resistant to interference and can be used to communicate with other radio stations in the area.

import math
import cmath
import string
from sklearn.manifold import TSNE
from scipy import signal
from scipy.signal import hilbert, chirp
import numpy as np
import random
import secrets
import hashlib
import PoNT.py as PoNT

# Define the alphabet and modulo value
alphabet = string.ascii_letters + string.digits + string.punctuation
modulo_value = 1048576

# Define the baseX encoding function
def encode_baseX_complex(input_string, base=modulo_value):
    encoded = ""
    number = 0
    for char in input_string:
        number += cmath.exp(alphabet.index(char)*(2j*cmath.pi/len(alphabet)))
    while number > 0:
        encoded = alphabet[int(number.real*len(alphabet)/(2*cmath.pi)) % base] + encoded
        number /= base
    return encoded

# Define the baseX decoding function
def decode_baseX_complex(input_string, base=modulo_value):
    decoded = ""
    number = 0
    for char in input_string:
        number += cmath.exp(alphabet.index(char)*(2j*cmath.pi/base))
    for _ in range(0, float('inf')):
        baseX = len(alphabet) ** _
        if number > 0:
            decoded = chr(int(number.real*len(alphabet)/(2*cmath.pi)) % baseX) + decoded
            number /= baseX
    return decoded

# Use the Friis equation to calculate the maximum range of the communication system
def friis(Pt, Gt, Gr, L, f):
    lambda_ = 3*10**8/(f*10**9)
    Pr = (Pt*Gt*Gr*lambda_**2)/(16*math.pi**2*L**2)
    return Pr

# Use frequency hopping spread spectrum to improve resistance to interference and provide a higher data rate
def frequency_hop(data, hop_interval):
    hopped_data = []
    current_frequency = 0
    for dat in data:
        hopped_data.append((dat, current_frequency))
        current_frequency += hop_interval
    return hopped_data

# Use forward error correction to improve the reliability of the communication by adding redundant data to the transmitted signal
def forward_error_correction(data, redundancy=[]):
    corrected_data = []
    for dat in data:
        corrected_data.append((dat, redundancy))
    return corrected_data

# Use t-SNE or PCA to project the data onto a higher-dimensional coordinate system, then use interweaving helixes to compress the encoded data
def interweaving_helix(data, n_components=[]):
    tsne = TSNE(n_components=n_components)
    interweaved_data = tsne.fit_transform(data)
    return interweaved_data

# Use ad-hoc communication and enhancement of network performance to further bolster the signal by incorporating multiple radio stations
def ad_hoc(data, nodes=[]):
    enhanced_data = []
    for dat in data:
        node_data = []
        for node in nodes:
            node_data.append((dat, node))
        enhanced_data.append(node_data)
    return enhanced_data

# Use the interweaving helix to create a dense tar-ball of data as a physical and non-physical manifestation (digital space, imaginary numbers)
def interweaving_helix_tar(data, n_components):
    tsne = TSNE(n_components=n_components)
    interweaved_data = tsne.fit_transform(data)
    tar_data = []
    for dat in interweaved_data:
        tar_data.append((dat[0], dat[1]))
    return tar_data

def create_key():
    key = {}
    for i in range(0, modulo_value):
        key[i] = random.randint(0, modulo_value-1)
    return key

# Create the key
key = create_key()

# Use the lookup table and key to encode and decode the data to improve the security of the communication
def lookup_table_encode(data, key):
    encoded_data = []
    for dat in data:
        encoded_data.append(key[dat])
    return encoded_data

def lookup_table_decode(data, key):
    decoded_data = []
    for dat in data:
        decoded_data.append(key[dat])
    return decoded_data

# Define the function for encoding the data using the 12-phase waveform
def encode_12_phase(input_string):
    fourier_transform = np.fft.fft(input_string)
    encoded = np.zeros(len(fourier_transform)*12, dtype=complex)
    for i in range(len(fourier_transform)):
        for j in range(12):
            encoded[i*12+j] = fourier_transform[i]*cmath.exp(1j*2*cmath.pi*j/12)
    return encoded

# Define the function for decoding the data using the 12-phase waveform
def decode_12_phase(encoded_data):
    decoded = np.zeros(len(encoded_data)//12, dtype=complex)
    for i in range(len(decoded)):
        for j in range(12):
            decoded[i] += encoded_data[i*12+j]*cmath.exp(-1j*2*cmath.pi*j/12)
    return np.fft.ifft(decoded)

# Define the function for compressing the data using a combination of techniques
def compress_data(data):
    # Use the baseX encoding method with imaginary numbers
    encoded_data = encode_baseX_complex(data)
    # Use the 12-phase waveform
    encoded_data = encode_12_phase(encoded_data)
    # Use frequency hopping spread spectrum
    encoded_data = frequency_hop(encoded_data, hop_interval=5)
    # Use forward error correction
    encoded_data = forward_error_correction(encoded_data, redundancy=5)
    # Use t-SNE or PCA to project the data onto a higher-dimensional coordinate system
    encoded_data = interweaving_helix(encoded_data, n_components=3)
    # Use ad-hoc communication and enhancement of network performance
    encoded_data = ad_hoc(encoded_data, nodes=[1, 2, 3])
    # Use the interweaving helix to create a dense tar-ball of data as a physical and non-physical manifestation (digital space, imaginary numbers)
    encoded_data = interweaving_helix_tar(encoded_data, n_components=2)
    # Use the lookup table and key to encode and decode the data
    encoded_data = lookup_table_encode(encoded_data, key=5)
    return encoded_data

# Define the function for decompressing the data using a combination of techniques
def decompress_data(data):
    # Use the lookup table and key to encode and decode the data
    decoded_data = lookup_table_decode(data, key=5)
    # Use the interweaving helix to create a dense tar-ball of data as a physical and non-physical manifestation (digital space, imaginary numbers)
    decoded_data = interweaving_helix_tar(decoded_data, n_components=3)
    # Use ad-hoc communication and enhancement of network performance
    decoded_data = ad_hoc(decoded_data, nodes=[1, 2, 3])
    # Use t-SNE or PCA to project the data onto a higher-dimensional coordinate system
    decoded_data = interweaving_helix(decoded_data, n_components=12)
    # Use forward error correction
    decoded_data = forward_error_correction(decoded_data, redundancy=5)
    # Use frequency hopping spread spectrum
    decoded_data = frequency_hop(decoded_data, hop_interval=5)
    # Use the 12-phase waveform
    decoded_data = decode_12_phase(decoded_data)
    # Use the baseX encoding method with imaginary numbers
    decoded_data = decode_baseX_complex(decoded_data)
    return decoded_data

# Use the chirp signal to modulate the data for improved signal-to-noise ratio
def chirp_modulation(data, f0, f1, t):
    chirped_data = chirp(t, f0, t[-1], f1) * data
    return chirped_data

# Use the Hilbert transform to improve the signal-to-noise ratio and extract the instantaneous frequency of the signal
def hilbert_transform(data):
    hilbert_data = hilbert(data)
    return hilbert_data

# Use the low frequency inverter to shift the frequency of the signal to a lower frequency range for improved signal-to-noise ratio and reduced interference
def low_frequency_invert(data, frequency):
    inverted_data = signal.lfilter([1], [1, float(frequency)], data)
    return inverted_data

# Use the compression function to compress the data for improved transmission and storage efficiency
def compress_data(data):
    compressed_data = signal.compress(data)
    return compressed_data

# Use the encryption function to encrypt the data for improved security
def encrypt_data(data, key):
    encrypted_data = signal.encrypt(data, key)
    return encrypted_data
 
# Use baseX encoding and decoding to include imaginary numbers for additional dimensions
def encode_baseX_complex(input_string, base=modulo_value):
    encoded = ""
    number = 0
    for char in input_string:
        number += cmath.exp(alphabet.index(char)*(2j*cmath.pi/len(alphabet)))
    while number > 0:
        encoded = alphabet[int(number.real*len(alphabet)/(2*cmath.pi)) % base] + encoded
        number /= base
    return encoded

def decode_baseX_complex(input_string, base=modulo_value):
    decoded = ""
    number = 0
    for char in input_string:
        number += cmath.exp(alphabet.index(char)*(2j*cmath.pi/base))
    for _ in range(0, float('inf')):
        baseX = len(alphabet) ** _
        if number > 0:
            decoded = chr(int(number.real*len(alphabet)/(2*cmath.pi)) % baseX) + decoded
            number /= baseX
    return decoded

# Performs a standard Fourier transform on the input data and returns the result in the frequency domain
def fourier_transform(data):
    N = len(data)
    fourier = np.zeros(N, dtype=complex)
    for k in range(N):
        for n in range(N):
            fourier[k] += data[n] * np.exp(-2j*np.pi*k*n/N)
    return fourier

# Performs a Fourier transform on the input data, adding a phase shift of "phi" to each sample, and returns the result in the frequency domain
def fourier_transform_12phase(data, phi):
    N = len(data)
    fourier = np.zeros(N, dtype=complex)
    for k in range(N):
        for n in range(N):
            fourier[k] += data[n] * np.exp(-2j*np.pi*k*n/N + 1j*phi)
    return fourier

def final_code(input_data):
    # Use the Friis equation to calculate the maximum range of the communication system
    friis_data = friis(input_data)
    # Use frequency hopping spread spectrum to improve resistance to interference and provide a higher data rate
    fhss_data = freq_hopping(friis_data)
    # Use forward error correction to improve the reliability of the communication by adding redundant data to
    # the signal
    fec_data = forward_error_correction(fhss_data, 0.5)
    # Use t-SNE or PCA to project the data onto a higher-dimensional coordinate system, then use interweaving helixes to compress the encoded data
    ih_data = interweaving_helix(fec_data, 2)
    # Use ad-hoc communication and enhancement of network performance to further bolster the signal by incorporating multiple radio stations
    ad_hoc_data = ad_hoc(ih_data, 10)
    # Use the interweaving helix to create a dense tar-ball of data as a physical and non-physical manifestation (digital space, imaginary numbers)
    ih_tar_data = interweaving_helix_tar(ad_hoc_data, 2)
    # Use baseX encoding and decoding to include imaginary numbers for additional dimensions
    baseX_data = encode_baseX_complex(ih_tar_data)
    # Use the lookup table and key to encode and decode the data to improve the security of the communication
    lookup_table_data = lookup_table_encode(baseX_data, key)
    # Use the Fourier transform to create a 4D signal in physical space
    fourier_data = fourier_transform_12phase(lookup_table_data)
    return fourier_data

# Define the number of nodes in the ad-hoc network
num_nodes = []

# Define the frequency hopping pattern
freq_hopping = np.random.randint(low=1, high=1000, size=num_nodes)

# Define the phase shift for each node
phase_shift = np.random.randint(low=1, high=360, size=num_nodes)

# Define the Fourier Transform function
def fourier_transform(data, freq_hopping, phase_shift):
    fourier_data = np.zeros(data.shape, dtype=complex)
    for i in range(num_nodes):
        fourier_data += data * cmath.exp(-2j * cmath.pi * freq_hopping[i] * phase_shift[i])
    return fourier_data

# Define the interweaving helix function
def interweaving_helix(data, num_dimensions):
    helix_data = np.zeros((data.shape[0], num_dimensions))
    for i in range(num_dimensions):
        helix_data[:, i] = hilbert(data[:, i])
    return helix_data

# Define the baseX encoding function
def encode_baseX_complex(data, alphabet, base):
    encoded = ""
    number = 0
    for char in data:
        number += cmath.exp(alphabet.index(char)*(2j*cmath.pi/len(alphabet)))
    while number > 0:
        encoded = alphabet[int(number.real*len(alphabet)/(2*cmath.pi)) % base] + encoded
        number /= base
    return encoded

# Define the baseX decoding function
def decode_baseX_complex(encoded_data, alphabet, base):
    decoded = ""
    number = 0
    for char in encoded_data:
        number += cmath.exp(alphabet.index(char)*(2j*cmath.pi/base))
    for _ in range(0, float('inf')):
        baseX = len(alphabet) ** _
        if number > 0:
            decoded = chr(int(number.real*len(alphabet)/(2*cmath.pi)) % baseX) + decoded
            number /= baseX
    return decoded

# Define the final function that incorporates all the steps
def final_code(data, num_dimensions, alphabet, base):
    fourier_data = fourier_transform(data, freq_hopping, phase_shift)
    helix_data = interweaving_helix(fourier_data, num_dimensions)
    encoded_data = encode_baseX_complex(helix_data, alphabet, base)
    return encoded_data

# Use the lookup table and key to encode and decode the data to improve the security of the communication
def encode_lookup_table(data, lookup_table, key):
    encoded_data = []
    for dat in data:
        encoded_data.append(lookup_table[dat] ^ key)
    return encoded_data

def decode_lookup_table(data, lookup_table, key):
    decoded_data = []
    for dat in data:
        decoded_data.append(lookup_table.index(dat ^ key))
    return decoded_data

# Use baseX encoding and decoding to include imaginary numbers for additional dimensions
def encode_baseX_complex(input_string, base=modulo_value, alphabet=string.ascii_letters + string.digits + string.punctuation):
    encoded = ""
    number = 0
    for char in input_string:
        number += cmath.exp(alphabet.index(char)*(2j*cmath.pi/len(alphabet)))
    while number > 0:
        encoded = alphabet[int(number.real*len(alphabet)/(2*cmath.pi)) % base] + encoded
        number /= base
    return encoded

def decode_baseX_complex(input_string, base=modulo_value, alphabet=string.ascii_letters + string.digits + string.punctuation):
    decoded = ""
    number = 0
    for char in input_string:
        number += cmath.exp(alphabet.index(char)*(2j*cmath.pi/base))
    for _ in range(0, float('inf')):
        baseX = len(alphabet) ** _
        if number > 0:
            decoded = chr(int(number.real*len(alphabet)/(2*cmath.pi)) % baseX) + decoded
            number /= baseX
    return decoded

def modulate_inverter(data, frequency, phase, amplitude):
    # Perform the necessary calculations and operations to modulate the inverter's signal
    modulated_signal = amplitude * np.sin(2 * np.pi * frequency * data + phase)
    # Return the modulated signal
    return modulated_signal

def demodulate_inverter(data, frequency, phase, amplitude):
    # Perform the necessary calculations and operations to demodulate the inverter's signal
    demodulated_signal = amplitude * np.sin(2 * np.pi * frequency * data + phase)
    # Return the demodulated signal
    return demodulated_signal

def gather_intermediate_results():
    # Initialize an empty list to store the intermediate results
    intermediate_results = []
    
    # Iterate through each node in the network
    for node in nodes:
        # Send a request for the intermediate results to the node
        node_intermediate_results = send_request_for_intermediate_results(node)
        
        # Append the node's intermediate results to the list
        intermediate_results.append(node_intermediate_results)
    
    # Combine the intermediate results to create a single intermediate result
    combined_intermediate_result = combine_intermediate_results(intermediate_results)
    
    return combined_intermediate_result

def gather_final_results(intermediate_results):
    # Initialize a list to store the final results
    final_results = []

    # Iterate over the intermediate results
    for result in intermediate_results:
        # Append the result to the final results list
        final_results.append(result)

    # Combine the final results to create a single final result
    combined_result = combine_results(final_results)

    # Return the combined result
    return combined_result

def combine_results(results):
    # Initialize a variable to store the combined result
    combined_result = None

    # Iterate over the results
    for result in results:
        # Combine the result with the current combined result
        combined_result = combine_two_results(combined_result, result)

    # Return the combined result
    return combined_result

def combine_two_results(result1, result2):
    # Implement the logic to combine two results
    # Example: return result1 + result2
    return result1 + result2

def send_request_for_intermediate_results(node):
    # Send a request to the node for the intermediate results
    # Example: return node.intermediate_results
    return node.intermediate_results

def combine_intermediate_results(intermediate_results):
    # Combine the intermediate results to create a single intermediate result
    # Example: return intermediate_results[0] + intermediate_results[1]
    return intermediate_results[0] + intermediate_results[1]

def nodes():
    # Initialize an empty list to store the nodes
    nodes = []

    # Iterate through each node in the network
    for node in nodes:
        # Append the node to the list
        nodes.append(node)

    # Return the list of nodes
    return nodes

def veify_data(data):
    # Verify the data
    # Example: return data == 1
    return data == 1

def sign_data(data, private_key):
    # Sign the data with the private key
    # Example: return data + private_key
    return data + private_key

def verify_signature(data, signature, public_key):
    # Verify the signature with the public key
    # Example: return data + public_key == signature
    return data + public_key == signature

def send_data(data, node):
    # Send the data to the node
    # Example: node.receive_data(data)
    node.receive_data(data)

def receive_data(data):
    # Receive the data
    # Example: self.data = data
    self.data = data

def verify_data(data):
    # Verify the data
    # Example: if data != 1: raise InvalidDataError
    if data != 1: raise InvalidDataError

def invalid_data_error():
    # Raise an InvalidDataError
    # Example: raise InvalidDataError
    raise InvalidDataError

def InvalidDataError():
    # Initialize the InvalidDataError
    # Example: pass
    pass

def incorporate_proof_of_tampering(neuron1, neuron2, data, proof_of_tampering):
    # neuron1 sends the data to neuron2
    neuron2.receive_data(data)

    # neuron2 verifies the data
    try:
        verify_data(data)
        print("Data verified.")
    except InvalidDataError:
        print("Error: Invalid data.")
        return False
    # generate a random nonce
    nonce = secrets.randbelow(2 ** 256)

    # sign the nonce with neuron1's private key
    signature1 = sign_data(nonce, neuron1['private_key'])

    # send the nonce and signature to neuron2
    neuron2.receive_nonce(nonce, signature1)

    # neuron2 verifies the nonce and signature using neuron1's public key
    try:
        verify_signature(nonce, signature1, neuron1['public_key'])
        print("Nonce and signature verified.")
    except InvalidSignatureError:
        print("Error: Invalid signature.")
        return

    # neuron2 signs the nonce with its own private key
    signature2 = sign_data(nonce, neuron2['private_key'])

    # sends the signature back to neuron1
    neuron1.receive_signature(signature2)

    # neuron1 verifies the signature using neuron2's public key
    try:
        verify_signature(nonce, signature2, neuron2['public_key'])
        print("Signature verified.")
    except InvalidSignatureError:
        print("Error: Invalid signature.")
        return

    # If both verifications pass, communication between the neurons is considered tamper-proof
    print("Communication between neurons is tamper-proof.")

def check_for_tamper(data):
    # calculate the hash of the data
    data_hash = hashlib.sha256(data).hexdigest()
    # check if the hash of the data matches the stored hash
    if data_hash != stored_hash:
        # if the hashes do not match, the data has been tampered with
        print("Tampering detected!")
        # eliminate the neuron that sent the tampered data
        eliminate_neuron()
    else:
        # if the hashes match, the data is authentic
        print("Data is authentic.")

def eliminate_neuron():
    # eliminate the neuron that sent the tampered data
    # Example: neuron1 = None
    neuron1 = None

def verify_signature(nonce, signature, public_key):
    # verify the signature using the public key
    # Example: return signature == public_key.verify(nonce)
    return signature == public_key.verify(nonce)

def stored_hash():
    # store the hash of the data
    # Example: stored_hash = hashlib.sha256(data).hexdigest()
    stored_hash = hashlib.sha256(data + nonce).hexdigest()

def sign_data(nonce, private_key):
    # sign the nonce with the private key
    # Example: return private_key.sign(nonce)
    return private_key.sign(nonce)

def receive_signature(signature):
    # receive the signature from the other neuron
    # Example: self.signature = signature
    self.signature = signature

def receive_nonce(nonce, signature):
    # receive the nonce and signature from the other neuron
    # Example: self.nonce = nonce
    # Example: self.signature = signature
    self.nonce = nonce
    self.signature = signature

def public_key():
    # store the public key
    # Example: public_key = generate_public_key(private_key)
    public_key = generate_public_key(private_key)

def private_key():
    # store the private key
    # Example: private_key = generate_private_key()
    private_key = generate_private_key()

def generate_private_key():
    # generate a private key
    # Example: return secrets.randbelow(2 ** 256)
    return secrets.randbelow(2 ** 256)

def generate_public_key(private_key):
    # generate a public key
    # Example: return private_key + 1
    return private_key + 1

def data():
    # store the data
    # Example: data = "I'm a neuron!"
    data = "I'm a neuron!"

def neuron1():
    # store the neuron
    # Example: neuron1 = Neuron()
    neuron1 = Neuron()

def neuron2():
    # store the neuron
    # Example: neuron2 = Neuron()
    neuron2 = Neuron()

def announce_neuron(neuron):
    # announce the neuron to the network
    # Example: print("I'm a neuron!")
    print("I'm a neuron!")

def connect_to_neuron(neuron):
    # connect to the neuron
    # Example: print("Connected to neuron.")
    print("Connected to neuron.")

def Network():
    # store the network
    # Example: pass
    pass

def Neuron():
    # store the neuron
    # Example: neuron = Neuron()
    neuron = Neuron()

def nonce():
    # store the nonce
    # Example: nonce = secrets.randbelow(2 ** 256)
    nonce = secrets.randbelow(2 ** 256)

def invalidSignatureError():
    # store the error
    # Example: InvalidSignatureError = "Error: Invalid signature."
    InvalidSignatureError = "Error: Invalid signature."

def self():
    # store the neuron
    # Example: self = Neuron()
    self = Neuron()

def signature():
    # store the signature
    # Example: signature = private_key.sign(nonce)
    signature = private_key.sign(nonce)

def InvalidSignatureError():
    # store the error
    # Example: InvalidSignatureError = "Error: Invalid signature."
    InvalidSignatureError = "Error: Invalid signature."
    
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