Estimating the cost of generate clustered data in GR participant data via DeCartography

calculate_economics.py

import json

def load_data(file_path):
    with open(file_path, 'r') as f:
        return json.load(f)

def calculate_tasks(data, file_path):
    total_address = len(data)
    print(f"Analyzing {total_address:,} addresses... (Target file: '{file_path.split('/')[-1]}')")

    count_of_initial_task = total_address / 6
    count_of_subsequent_task = total_address / 6
    count_of_additional_task = count_of_initial_task / 2

    total_require_count_analyze = round(count_of_initial_task + count_of_subsequent_task + count_of_additional_task)
    print(f"Need to solve {total_require_count_analyze:,} tasks")

    session_per_task = 10
    number_of_require_session = total_require_count_analyze / session_per_task
    budget_per_session_USD = 0.5
    print(f"If you solve {session_per_task} tasks per session and pay {budget_per_session_USD}$ as a reward...")


    total_budget = number_of_require_session * budget_per_session_USD
    number_of_required_send_transaction = total_require_count_analyze / session_per_task
    print(f"A budget of {round(total_budget):,} $ is required. This requires {round(number_of_required_send_transaction):,} transactions. (Gas fee for sending is required, reduction is possible with batch operation)")


file_path = '/Users/tkgshn/Desktop/decartography/backend/backend_app/static/gr15_addresses.json' # or, mvp_addresses30file.json
data = load_data(file_path)

calculate_tasks(data, file_path)