scripts/pull_data.py

import logging
import os
import pickle
from datetime import datetime
from concurrent.futures import ThreadPoolExecutor
from tqdm import tqdm
from web3 import Web3
import pandas as pd
from pathlib import Path
from functools import partial
from markets import (
    etl as mkt_etl,
    DEFAULT_FILENAME as MARKETS_FILENAME,
)
from tools import (
    DEFAULT_FILENAME as TOOLS_FILENAME,
    generate_tools_file,
)
from profitability import run_profitability_analysis
from utils import get_question, current_answer, RPC
from get_mech_info import (
    get_mech_events_last_60_days,
)
from update_tools_accuracy import compute_tools_accuracy
import gc

logging.basicConfig(level=logging.INFO)

SCRIPTS_DIR = Path(__file__).parent
ROOT_DIR = SCRIPTS_DIR.parent
DATA_DIR = ROOT_DIR / "data"


def block_number_to_timestamp(block_number: int, web3: Web3) -> str:
    """Convert a block number to a timestamp."""
    block = web3.eth.get_block(block_number)
    timestamp = datetime.utcfromtimestamp(block["timestamp"])
    return timestamp.strftime("%Y-%m-%d %H:%M:%S")


def parallelize_timestamp_conversion(df: pd.DataFrame, function: callable) -> list:
    """Parallelize the timestamp conversion."""
    block_numbers = df["request_block"].tolist()
    with ThreadPoolExecutor(max_workers=10) as executor:
        results = list(
            tqdm(executor.map(function, block_numbers), total=len(block_numbers))
        )
    return results


def add_current_answer():
    # Get currentAnswer from FPMMS
    fpmms = pd.read_parquet(DATA_DIR / MARKETS_FILENAME)
    tools = pd.read_parquet(DATA_DIR / TOOLS_FILENAME)

    # Get the question from the tools
    logging.info("Getting the question and current answer for the tools")
    tools["title"] = tools["prompt_request"].apply(lambda x: get_question(x))
    tools["currentAnswer"] = tools["title"].apply(lambda x: current_answer(x, fpmms))

    tools["currentAnswer"] = tools["currentAnswer"].str.replace("yes", "Yes")
    tools["currentAnswer"] = tools["currentAnswer"].str.replace("no", "No")
    # Save the tools data after the updates on the content
    tools.to_parquet(DATA_DIR / TOOLS_FILENAME, index=False)
    del fpmms


def updating_timestamps(rpc: str):
    web3 = Web3(Web3.HTTPProvider(rpc))

    tools = pd.read_parquet(DATA_DIR / TOOLS_FILENAME)

    # Convert block number to timestamp
    logging.info("Converting block number to timestamp")
    t_map = pickle.load(open(DATA_DIR / "t_map.pkl", "rb"))
    tools["request_time"] = tools["request_block"].map(t_map)

    no_data = tools["request_time"].isna().sum()
    logging.info(f"Total rows with no request time info = {no_data}")

    # Identify tools with missing request_time and fill them
    missing_time_indices = tools[tools["request_time"].isna()].index
    if not missing_time_indices.empty:
        partial_block_number_to_timestamp = partial(
            block_number_to_timestamp, web3=web3
        )
        missing_timestamps = parallelize_timestamp_conversion(
            tools.loc[missing_time_indices], partial_block_number_to_timestamp
        )

        # Update the original DataFrame with the missing timestamps
        for i, timestamp in zip(missing_time_indices, missing_timestamps):
            tools.at[i, "request_time"] = timestamp

    tools["request_month_year"] = pd.to_datetime(tools["request_time"]).dt.strftime(
        "%Y-%m"
    )
    tools["request_month_year_week"] = (
        pd.to_datetime(tools["request_time"]).dt.to_period("W").astype(str)
    )

    # Save the tools data after the updates on the content
    tools.to_parquet(DATA_DIR / TOOLS_FILENAME, index=False)

    # Update t_map with new timestamps
    new_timestamps = (
        tools[["request_block", "request_time"]]
        .dropna()
        .set_index("request_block")
        .to_dict()["request_time"]
    )
    t_map.update(new_timestamps)

    with open(DATA_DIR / "t_map.pkl", "wb") as f:
        pickle.dump(t_map, f)

    # clean and release all memory
    del tools
    del t_map
    gc.collect()


def weekly_analysis():
    """Run weekly analysis for the FPMMS project."""
    rpc = RPC
    # Run markets ETL
    logging.info("Running markets ETL")
    mkt_etl(MARKETS_FILENAME)
    logging.info("Markets ETL completed")

    # New tools ETL
    logging.info("Generating the mech json files")
    get_mech_events_last_60_days()
    logging.info("Finished generating the mech json files")

    # Run tools ETL
    logging.info("Generate and parse the tools content")
    generate_tools_file()
    logging.info("Tools ETL completed")

    # Run profitability analysis
    logging.info("Running profitability analysis")
    if os.path.exists(DATA_DIR / "fpmmTrades.parquet"):
        os.remove(DATA_DIR / "fpmmTrades.parquet")
    run_profitability_analysis(
        rpc=rpc,
    )

    logging.info("Profitability analysis completed")
    add_current_answer()
    try:
        updating_timestamps(rpc)
    except Exception as e:
        logging.error("Error while updating timestamps of tools")
        print(e)

    compute_tools_accuracy()

    logging.info("Weekly analysis files generated and saved")


if __name__ == "__main__":
    weekly_analysis()
    # rpc = RPC
    # updating_timestamps(rpc)
    # compute_tools_accuracy()