"""
(DEPRECATED) Functional implementation of Information-Driven Bars.

This logic is superseded by the `ImbalanceBars` and `RunBars` classes.
Note the typo in the filename ("infomation").
"""

from typing import Tuple
import numpy as np
import pandas as pd

# Assuming these helpers are available
from RiskLabAI.utils.utilities_lopez import (
    compute_thresholds, create_ohlcv_dataframe
)

def generate_information_driven_bars(
    tick_data: pd.DataFrame,
    bar_type: str = "volume",
    initial_expected_ticks: int = 2000,
) -> Tuple[pd.DataFrame, np.ndarray, np.ndarray]:
    """
    (DEPRECATED) Implements Information-Driven Bars.

    Parameters
    ----------
    tick_data : pd.DataFrame
        DataFrame of tick data with 'volume_labeled', 'label', 'dollar_labeled'.
    bar_type : str, default="volume"
        Type of bar: "tick", "volume", or "dollar".
    initial_expected_ticks : int, default=2000
        The initial value of expected ticks (E[T]).

    Returns
    -------
    Tuple[pd.DataFrame, np.ndarray, np.ndarray]
        - ohlcv_dataframe: The generated bars.
        - thetas_absolute: Array of absolute cumulative imbalances.
        - thresholds: Array of dynamic thresholds.
    """
    if bar_type == "volume":
        input_data = tick_data['volume_labeled']
    elif bar_type == "tick":
        input_data = tick_data['label']
    elif bar_type == "dollar":
        input_data = tick_data['dollar_labeled']
    else:
        raise ValueError("Invalid bar type. Choose 'tick', 'volume', or 'dollar'.")

    # E[b] = |mean(imbalance)|
    bar_expected_value = np.abs(input_data.mean())
    
    # Compute thresholds and grouping
    (
        times_delta,
        thetas_absolute,
        thresholds,
        times,
        thetas,
        grouping_id,
    ) = compute_thresholds(
        input_data.values, initial_expected_ticks, bar_expected_value
    )

    tick_grouped = tick_data.reset_index().assign(grouping_id=grouping_id)
    dates = tick_grouped.groupby('grouping_id', as_index=False).first()['dates']
    tick_data_grouped = tick_grouped.groupby('grouping_id')

    ohlcv_dataframe = create_ohlcv_dataframe(tick_data_grouped)
    ohlcv_dataframe.set_index(dates, drop=True, inplace=True)

    return ohlcv_dataframe, thetas_absolute, thresholds