import pandas as pd
import os
from utils.df_utils import slice_df


def load_raw_metadata() -> pd.DataFrame:
    """
    Load metadata.csv
    :return:
    """
    with open("../data_raw/metadata.csv") as f:
        return pd.read_csv(f, index_col="file")


def load_raw_measure(filename: str) -> pd.DataFrame:
    """
    Load a given measure session
    :param filename:
    :return:
    """
    with open(f"../data_raw/{filename}") as f:
        df_experiment = pd.DataFrame([x.split() for x in f.readlines()])
        df_experiment.columns = df_experiment.iloc[0]
        df_experiment = df_experiment[1:][["#Wave", "#Intensity"]].astype(float)
    return df_experiment


def get_raw_measure_with_metadata(file: pd.Series) -> tuple[pd.DataFrame, list[pd.DataFrame]]:
    """
    Load a given measure, slice it and give the corresponding metadata
    :param file:
    :return: dataframe containing the metadata with one row per sliced measure
    :return: list of sliced measure dataframes
    """
    df_experiment = load_raw_measure(file.name)
    sliced_experiments = slice_df(df_experiment, df_experiment[df_experiment["#Wave"].diff() > 0].index)
    sliced_experiments = [exp.set_index("#Wave") for exp in sliced_experiments]
    file = pd.DataFrame(file).transpose()
    metadata = pd.DataFrame(file.loc[file.index.repeat(len(sliced_experiments))])
    return metadata, sliced_experiments


def load_raw_data() -> tuple[pd.DataFrame, list[pd.DataFrame]]:
    """
    Load all the available data, slice it into individual measures and give the corresponding metadata
    :return: dataframe containing the metadata with one row per sliced measure
    :return: list of sliced measure dataframes
    """
    metadata, sliced_experiments = pd.DataFrame(), []
    raw_metadata = load_raw_metadata()
    for _, row in raw_metadata.iterrows():
        temp_metadata, temp_sliced_experiments = get_raw_measure_with_metadata(row)
        metadata = pd.concat([metadata, temp_metadata])
        sliced_experiments.extend(temp_sliced_experiments)
    return metadata, sliced_experiments


def load_data(name: str, path: os.path = os.path.join("data")) -> tuple[pd.DataFrame, pd.DataFrame]:
    metadata = pd.read_csv(os.path.join(path, name, "metadata.csv"))
    experiments = pd.read_csv(os.path.join(path, name, "experiments.csv"))
    return metadata, experiments