import pandas as pd

def df_column_switch(df: pd.DataFrame, column1, column2):

    i = list(df.columns)

    a, b = i.index(column1), i.index(column2)

    i[b], i[a] = i[a], i[b]

    df = df[i]

    return df

def calculate_data_existing_length(data):

    res = 0

    for i in data:

        if not pd.isna(i):

            res += 1

    return res

# elements in data are sorted in time ascending order

def fill_missing_value(data, to_fill_value=0):

    data_len = len(data)

    data_exist_len = calculate_data_existing_length(data)

    if data_len == data_exist_len:

        return data

    elif data_exist_len == 0:

        # data = [to_fill_value for _ in range(data_len)]

        for i in range(data_len):

            data[i] = to_fill_value

        return data

    if pd.isna(data[0]):

        # find the first non-nan value's position

        not_na_pos = 0

        for i in range(data_len):

            if not pd.isna(data[i]):

                not_na_pos = i

                break

        # fill element before the first non-nan value with median

        for i in range(not_na_pos):

            data[i] = to_fill_value

    # fill element after the first non-nan value

    for i in range(1, data_len):

        if pd.isna(data[i]):

            data[i] = data[i - 1]

    return data

def forward_fill_pipeline(

    df: pd.DataFrame,

    default_fill: pd.DataFrame,

    demographic_features: list[str],

    labtest_features: list[str],

):

    grouped = df.groupby("PatientID")

    all_x = []

    all_y = []

    all_pid = []

    for name, group in grouped:

        sorted_group = group.sort_values(by=["RecordTime"], ascending=True)

        patient_x = []

        patient_y = []

        for f in ["Age"] + labtest_features:

            to_fill_value = default_fill[f]

            # take median patient as the default to-fill missing value

            fill_missing_value(sorted_group[f].values, to_fill_value)

        for _, v in sorted_group.iterrows():

            patient_y.append([v["Outcome"], v["LOS"]])

            x = []

            for f in demographic_features + labtest_features:

                x.append(v[f])

            patient_x.append(x)

        all_x.append(patient_x)

        all_y.append(patient_y)

        all_pid.append(name)

    return all_x, all_y, all_pid

def normalize_dataframe(train_df, val_df, test_df, normalize_features):

    # Calculate the quantiles

    q_low = train_df[normalize_features].quantile(0.05)

    q_high = train_df[normalize_features].quantile(0.95)

    # Filter the DataFrame based on the quantiles

    filtered_df = train_df[(train_df[normalize_features] > q_low) & (train_df[normalize_features] < q_high)]

    # Calculate the mean and standard deviation and median of the filtered data, also the default fill value

    train_mean = filtered_df[normalize_features].mean()

    train_std = filtered_df[normalize_features].std()

    train_median = filtered_df[normalize_features].median()

    default_fill: pd.DataFrame = (train_median-train_mean)/(train_std+1e-12)

    # LOS info

    LOS_info = {"mean": train_mean["LOS"], "std": train_std["LOS"], "median": train_median["LOS"]}

    # Z-score normalize the train, val, and test sets with train_mean and train_std

    train_df[normalize_features] = (train_df[normalize_features] - train_mean) / (train_std+1e-12)

    val_df[normalize_features] = (val_df[normalize_features] - train_mean) / (train_std+1e-12)

    test_df[normalize_features] = (test_df[normalize_features] - train_mean) / (train_std+1e-12)

    return train_df, val_df, test_df, default_fill, LOS_info