minor refactoring

        #ax2.plot(remix_liquidity_btc, color='black', alpha=0.6, linestyle='--', label='Cummulative remix liquidity, MIX_ENTER - MIX_LEAVE - MIX_STAY')

        ax2.plot([0], [0], label=f'Average remix rate', color='brown', linewidth=1, linestyle='--', alpha=0.5)  # Fake plot to have correct legend record from other twinx

        PLOT_CHAINANALYSIS_TIMECUTOFF = False

        if PLOT_CHAINANALYSIS_TIMECUTOFF:

            ax2.plot([a - b for a, b in zip([item / SATS_IN_BTC for item in changing_liquidity_timecutoff], [item / SATS_IN_BTC for item in stay_liquidity_timecutoff])], color='red', alpha=0.6, linestyle='-.', label='Actively remixed liquidity (Changing - Unmoved)')

    burntime_histogram(mix_id, data)

def fix_ww2_for_fdnp_ww1(mix_id: str, target_path: Path):

def fix_ww2_for_fdnp_ww1(mix_id: str, target_path: str):

    """

    Detects and corrects all information of WW2 extracted from coinjoin_tx_info.json based on WW1 inflows.

    Process also subfolders with monthly intervals

    target_path = os.path.join(target_path, mix_id)  # Go into target ww2 folder

    paths_to_process = [target_path]  # Always process 'coinjoin_tx_info.json' with all transactions

    # paths_to_process = []  # Do not process large .json with all txs

    paths_to_process = []

    # Add subpaths for months if present

    files = os.listdir(target_path)

    for file_name in files:

        tx_json_file = os.path.join(target_base_path, f'coinjoin_tx_info.json')

        if os.path.isdir(target_base_path) and os.path.exists(tx_json_file):

            paths_to_process.append(target_base_path)

    # Always process 'coinjoin_tx_info.json' with all transactions

    paths_to_process.append(target_path)

    # Now fix all prepared paths

    ww2_data = {}

    for path in paths_to_process:

        logging.info(f'Processing {path}...')

        ww2_data = als.load_json_from_file(os.path.join(path, f'coinjoin_tx_info.json'))

        als.save_json_to_file(os.path.join(path, f'coinjoin_tx_info.json'), ww2_data)

    return ww2_data

def extract_flows_blocksci(flows: dict):

    start_year = 2019