"""
Estimates the forecasted storm impacts based on the forecasted water level and dune crest/toe.
"""

import logging.config
import os

import pandas as pd

logging.config.fileConfig("./src/logging.conf", disable_existing_loggers=False)
logger = logging.getLogger(__name__)


def forecasted_impacts(df_profile_features, df_forecasted_twl):
    """
    Combines our profile features (containing dune toes and crests) with water levels, to get the forecasted storm
    impacts.
    :param df_profile_features:
    :param df_forecasted_twl:
    :return:
    """
    logger.info("Getting forecasted storm regimes")

    df_forecasted_impacts = pd.DataFrame(index=df_profile_features.index)

    # For each site, find the maximum R_high value and the corresponding R_low value.
    idx = df_forecasted_twl.groupby(level=["site_id"])["R_high"].idxmax().dropna()
    df_r_vals = df_forecasted_twl.loc[idx, ["R_high", "R_low"]].reset_index(["datetime"])
    df_forecasted_impacts = df_forecasted_impacts.merge(df_r_vals, how="left", left_index=True, right_index=True)

    # Join with df_profile features to find dune toe and crest elevations
    df_forecasted_impacts = df_forecasted_impacts.merge(
        df_profile_features[["dune_toe_z", "dune_crest_z"]], how="left", left_index=True, right_index=True
    )

    # Compare R_high and R_low wirth dune crest and toe elevations
    df_forecasted_impacts = storm_regime(df_forecasted_impacts)

    return df_forecasted_impacts


def storm_regime(df_forecasted_impacts):
    """
    Returns the dataframe with an additional column of storm impacts based on the Storm Impact Scale. Refer to
    Sallenger (2000) for details.
    :param df_forecasted_impacts:
    :return:
    """
    logger.info("Getting forecasted storm regimes")
    df_forecasted_impacts.loc[
        df_forecasted_impacts.R_high <= df_forecasted_impacts.dune_toe_z, "storm_regime"
    ] = "swash"
    df_forecasted_impacts.loc[
        df_forecasted_impacts.dune_toe_z <= df_forecasted_impacts.R_high, "storm_regime"
    ] = "collision"
    df_forecasted_impacts.loc[
        (df_forecasted_impacts.dune_crest_z <= df_forecasted_impacts.R_high)
        & (df_forecasted_impacts.R_low <= df_forecasted_impacts.dune_crest_z),
        "storm_regime",
    ] = "overwash"
    df_forecasted_impacts.loc[
        (df_forecasted_impacts.dune_crest_z <= df_forecasted_impacts.R_low)
        & (df_forecasted_impacts.dune_crest_z <= df_forecasted_impacts.R_high),
        "storm_regime",
    ] = "inundation"

    return df_forecasted_impacts


if __name__ == "__main__":
    logger.info("Importing existing data")
    data_folder = "./data/interim"
    df_profiles = pd.read_csv(os.path.join(data_folder, "profiles.csv"), index_col=[0, 1, 2])
    df_profile_features = pd.read_csv(os.path.join(data_folder, "profile_features.csv"), index_col=[0])
    df_forecasted_twl = pd.read_csv(os.path.join(data_folder, "twl_mean_slope_sto06.csv"), index_col=[0, 1])

    df_forecasted_impacts = forecasted_impacts(df_profile_features, df_forecasted_twl)
    df_forecasted_impacts.to_csv(os.path.join(data_folder, "impacts_forecasted_mean_slope_sto06.csv"))