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import hashlib
import json
import requests
import time
import numpy as np
import threading
import logging
import yaml
from web3 import Web3
from bayes_opt import BayesianOptimization
from deap import base, creator, tools
import smtplib
from email.mime.text import MIMEText
# Configure logging
logging.basicConfig(filename='shib_miner.log', level=logging.INFO,
format='%(asctime)s - %(levelname)s - %(message)s')
# Email notification function
def send_email(subject, body):
"""Send email notifications."""
sender_email = "[email protected]"
receiver_email = "[email protected]"
password = "your_email_password" # Use environment variables for security
msg = MIMEText(body)
msg['Subject'] = subject
msg['From'] = sender_email
msg['To'] = receiver_email
with smtplib.SMTP('smtp.gmail.com', 587) as server:
server.starttls()
server.login(sender_email, password)
server.send_message(msg)
logging.info("Notification email sent.")
# SHIB Miner Class
class ShibMiner:
def __init__(self, wallet_address, mining_pool_url, eth_provider_url, config):
self.wallet_address = wallet_address
self.mining_pool_url = mining_pool_url
self.web3 = Web3(Web3.HTTPProvider(eth_provider_url))
self.difficulty = None
self.nonce = 0
self.best_solution = None
self.hash_rates = []
self.max_threads = config['max_threads']
self.thread_lock = threading.Lock()
def get_pool_data(self):
"""Fetch pool data including difficulty."""
try:
response = requests.get(f'{self.mining_pool_url}/getDifficulty')
if response.status_code == 200:
self.difficulty = response.json()['difficulty']
logging.info(f"Retrieved difficulty: {self.difficulty}")
else:
logging.error("Could not retrieve pool data")
except Exception as e:
logging.error(f"Error fetching pool data: {e}")
def sha256(self, data):
return hashlib.sha256(data.encode('utf-8')).hexdigest()
def mine(self):
"""Start the mining process."""
self.get_pool_data()
block_data = f"{self.wallet_address}"
while True:
with self.thread_lock:
block_hash = self.sha256(f"{block_data}{self.nonce}")
if int(block_hash, 16) < self.difficulty:
logging.info(f'Mined a block: {block_hash} with nonce: {self.nonce}')
self.submit_work(block_hash, self.nonce)
break
self.nonce += 1
def submit_work(self, block_hash, nonce):
"""Submit mined work to the pool."""
payload = {
'wallet': self.wallet_address,
'blockHash': block_hash,
'nonce': nonce,
}
try:
response = requests.post(f'{self.mining_pool_url}/submitWork', json=payload)
if response.status_code == 200:
logging.info("Work submitted successfully.")
send_email("Mining Success", f"Successfully mined block: {block_hash}")
else:
logging.error(f"Error submitting work: {response.json()}")
# Retry logic
self.retry_submission(payload)
except Exception as e:
logging.error(f"Error submitting work: {e}")
def retry_submission(self, payload, attempts=3):
"""Retry submission of mined work."""
for attempt in range(attempts):
try:
response = requests.post(f'{self.mining_pool_url}/submitWork', json=payload)
if response.status_code == 200:
logging.info("Work submitted successfully on retry.")
send_email("Mining Success", f"Successfully mined block on retry: {payload['blockHash']}")
return
except Exception as e:
logging.error(f"Retry attempt {attempt + 1} failed: {e}")
time.sleep(5) # Wait before retrying
logging.error("All retry attempts failed.")
def optimize_parameters(self, iterations=10):
"""Optimize mining parameters using Bayesian Optimization."""
def black_box_function(param1, param2):
# Simulated hash rate based on parameters
return np.sin(param1) * np.cos(param2) + 0.5 # Simulated function
pbounds = {'param1': (0, 10), 'param2': (0, 10)}
optimizer = BayesianOptimization(
f=black_box_function,
pbounds=pbounds,
random_state=1,
)
optimizer.maximize(init_points=5, n_iter=iterations)
# Get the best parameters
self.best_solution = optimizer.max
logging.info("Best parameters from Bayesian Optimization: %s", self.best_solution)
def genetic_algorithm(self, iterations=10):
"""Optimize using a Genetic Algorithm."""
creator.create("FitnessMax", base.Fitness, weights=(1.0,))
creator.create("Individual", list, fitness=creator.FitnessMax)
toolbox = base.Toolbox()
toolbox.register("attr_float", np.random.uniform, 0, 10)
toolbox.register("individual", tools.initRepeat, creator.Individual, toolbox.attr_float, n=2)
toolbox.register("population", tools.initRepeat, list, toolbox.individual)
def eval_individual(individual):
return (np.sin(individual[0]) * np.cos(individual[1]) + 0.5,)
toolbox.register("evaluate", eval_individual)
toolbox.register("mate", tools.cxBlend, alpha=0.5)
toolbox.register("mutate", tools.mutGaussian, mu=0, sigma=1, indpb=0.2)
toolbox.register("select", tools.selTournament, tournsize=3)
population = toolbox.population(n=50)
for gen in range(iterations):
offspring = toolbox.select(population, len(population))
offspring = list(map(toolbox.clone, offspring))
for child1, child2 in zip(offspring[::2], offspring[1::2]):
if np.random.rand() < 0.5:
toolbox.mate(child1, child2)
del child1.fitness.values
del child2.fitness.values
for mutant in offspring:
if np.random.rand() < 0.2:
toolbox.mutate(mutant)
del mutant.fitness.values
invalid_ind = [ind for ind in offspring if not ind.fitness.valid]
fitnesses = map(toolbox.evaluate, invalid_ind)
for ind, fit in zip(invalid_ind, fitnesses):
ind.fitness.values = fit
population[:] = offspring
fits = [ind.fitness.values[0] for ind in population]
self.best_solution = population[np.argmax(fits)]
logging.info("Best parameters from Genetic Algorithm: %s", self.best_solution)
def trinary_qubit_optimization(self):
"""Simulated Trinary Qubit Optimization."""
# A placeholder for trinary qubit logic
qubits = [np.random.choice([0, 1, 2]) for _ in range(10)]
logging.info("Simulated Trinary Qubits: %s", qubits)
def start_mining_threads(self):
"""Start multiple mining threads."""
threads = []
for _ in range(self.max_threads):
thread = threading.Thread(target=self.mine)
thread.start()
threads.append(thread)
for thread in threads:
thread.join()
if __name__ == "__main__":
wallet_address = config['wallet_address']
mining_pool_url = config['mining_pool_url']
eth_provider_url = config['eth_provider_url']
miner = ShibMiner(wallet_address, mining_pool_url, eth_provider_url, config)
# Perform optimizations
miner.optimize_parameters(iterations=config['bayesian_iterations'])
miner.genetic_algorithm(iterations=config['ga_iterations'])
miner.trinary_qubit_optimization()
# Start mining with multiple threads
miner.start_mining_threads() |