Upload shibber.py

shibber.py

@@ -0,0 +1,207 @@
+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()