transform_ILI.py

import pandas as pd
from datetime import datetime


if __name__ == "__main__":
    df = pd.read_csv("data/Flu_USA/ILINet.csv")

    df = df[df['REGION TYPE'] == 'National']
    df = df[df['WEEK'] != 53]

    df['date'] = pd.to_datetime(df['YEAR'].astype(str) + '-W' + df['WEEK'].astype(str) + '-1', format='%Y-W%U-%w')

    result = []
    for i in range(len(df) - 1):
        result.append(df.iloc[i])  

        if (df.iloc[i + 1]['date'] - df.iloc[i]['date']).days == 14:
            new_row = df.iloc[i].copy()
            new_row['date'] = df.iloc[i]['date'] + pd.Timedelta(days=7)
            result.append(new_row)
    
    result.append(df.iloc[-1])
    df = pd.DataFrame(result)

    df = df.drop(columns=['YEAR', 'WEEK'])
    gaps = df['date'].diff().dropna().unique()
    print("Unique time intervals:", gaps)
    df['time_diff'] = df['date'].diff()

    rows_with_14_days = df[df['time_diff'] == pd.Timedelta(days=14)]
    print(rows_with_14_days)
    df = df.drop(columns=['time_diff'])
    infered_freq = pd.infer_freq(df['date'])
    print(f"Infered frequency: {infered_freq}")

    df.to_csv("data/Flu_USA/Flu_USA.csv", index=False)

    print("Data saved to output.csv")