import threading
import requests

class BitcoinPrivateKeyGenerator:
    """
    Class to generate Bitcoin private keys and check their balance.

    Attributes:
    - keys_with_balance: list
        List to store the generated private keys with balance above 0.00001 BTC.
    """

    def __init__(self):
        """
        Constructor to instantiate the BitcoinPrivateKeyGenerator class.

        Attributes:
        - keys_with_balance: list
            Initialize an empty list to store the generated private keys with balance above 1 BTC.
        """

        self.keys_with_balance = []

    def generate_private_key(self):
        """
        Generates a Bitcoin private key.

        Returns:
        - str:
            The generated Bitcoin private key.
        """

        # Code to generate a Bitcoin private key
        # ...

        return private_key

    def check_balance(self, private_key):
        """
        Checks the balance of a Bitcoin address associated with the given private key.

        Parameters:
        - private_key: str
            The Bitcoin private key to check the balance for.

        Returns:
        - float:
            The balance of the Bitcoin address associated with the private key.

        Raises:
        - ValueError:
            Raises an error if there is an issue with checking the balance.
        """

        # Code to check the balance of a Bitcoin address
        # ...

        return balance

    def save_keys_with_balance(self):
        """
        Saves the generated private keys with balance above 1 BTC to a file.

        Raises:
        - ValueError:
            Raises an error if there are no private keys with balance above 1 BTC.
        """

        # Check if there are private keys with balance above 1 BTC
        if len(self.keys_with_balance) == 0:
            raise ValueError("No private keys with balance above 1 BTC to save.")

        # Code to save the private keys to a file
        # ...

    def generate_and_check_keys(self, num_keys):
        """
        Generates and checks the balance of the specified number of Bitcoin private keys.

        Parameters:
        - num_keys: int
            The number of Bitcoin private keys to generate and check.

        Raises:
        - ValueError:
            Raises an error if the specified number of keys is less than or equal to 0.
        """

        # Check if the specified number of keys is valid
        if num_keys <= 0:
            raise ValueError("Number of keys should be greater than 0.")

        # Generate and check the balance of the specified number of keys
        for _ in range(num_keys):
            private_key = self.generate_private_key()
            balance = self.check_balance(private_key)

            # Check if the balance is above 1 BTC
            if balance > 1:
                self.keys_with_balance.append(private_key)

# Example usage of the BitcoinPrivateKeyGenerator class:

# Create an instance of the BitcoinPrivateKeyGenerator class
generator = BitcoinPrivateKeyGenerator()

# Generate and check the balance of 10 Bitcoin private keys (using threading for parallel execution)
num_keys = 10
threads = []
for _ in range(num_keys):
    thread = threading.Thread(target=generator.generate_and_check_keys, args=(1,))
    threads.append(thread)
    thread.start()

# Wait for all threads to finish
for thread in threads:
    thread.join()

# Save the private keys with balance above 1 BTC to a file
try:
    generator.save_keys_with_balance()
    print("Private keys with balance above 1 BTC saved successfully.")
except ValueError as e:
    print(f"Error while saving private keys: {e}") 

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About Python

Python is a very popular general-purpose programming language which was created by Guido van Rossum, and released in 1991. It is very popular for web development and you can build almost anything like mobile apps, web apps, tools, data analytics, machine learning etc. It is designed to be simple and easy like english language. It's is highly productive and efficient making it a very popular language.

Tutorial & Syntax help

Loops

1. If-Else:

When ever you want to perform a set of operations based on a condition IF-ELSE is used.

if conditional-expression
    #code
elif conditional-expression
    #code
else:
    #code

Note:

Indentation is very important in Python, make sure the indentation is followed correctly

2. For:

For loop is used to iterate over arrays(list, tuple, set, dictionary) or strings.

Example:

mylist=("Iphone","Pixel","Samsung")
for i in mylist:
    print(i)

3. While:

While is also used to iterate a set of statements based on a condition. Usually while is preferred when number of iterations are not known in advance.

while condition  
    #code 

Collections

There are four types of collections in Python.

1. List:

List is a collection which is ordered and can be changed. Lists are specified in square brackets.

Example:

mylist=["iPhone","Pixel","Samsung"]
print(mylist)

2. Tuple:

Tuple is a collection which is ordered and can not be changed. Tuples are specified in round brackets.

Example:

myTuple=("iPhone","Pixel","Samsung")
print(myTuple)

Below throws an error if you assign another value to tuple again.

myTuple=("iPhone","Pixel","Samsung")
print(myTuple)
myTuple[1]="onePlus"
print(myTuple)

3. Set:

Set is a collection which is unordered and unindexed. Sets are specified in curly brackets.

Example:

myset = {"iPhone","Pixel","Samsung"}
print(myset)

4. Dictionary:

Dictionary is a collection of key value pairs which is unordered, can be changed, and indexed. They are written in curly brackets with key - value pairs.

Example:

mydict = {
    "brand" :"iPhone",
    "model": "iPhone 11"
}
print(mydict)

Supported Libraries

Following are the libraries supported by OneCompiler's Python compiler

NameDescription
NumPyNumPy python library helps users to work on arrays with ease
SciPySciPy is a scientific computation library which depends on NumPy for convenient and fast N-dimensional array manipulation
SKLearn/Scikit-learnScikit-learn or Scikit-learn is the most useful library for machine learning in Python
PandasPandas is the most efficient Python library for data manipulation and analysis
DOcplexDOcplex is IBM Decision Optimization CPLEX Modeling for Python, is a library composed of Mathematical Programming Modeling and Constraint Programming Modeling