import time
import json
import gzip
import os
import shutil

import boto3
import botocore.exceptions

import pandas as pd
import matplotlib.pyplot as plt
from tqdm.auto import trange

    
def plot_forecasts(fcsts, exact, freq = '1H', forecastHorizon=24, time_back = 80):
    p10 = pd.DataFrame(fcsts['Forecast']['Predictions']['p10'])
    p50 = pd.DataFrame(fcsts['Forecast']['Predictions']['p50'])
    p90 = pd.DataFrame(fcsts['Forecast']['Predictions']['p90'])
    pred_int = p50['Timestamp'].apply(lambda x: pd.Timestamp(x))
    fcst_start_date = pred_int.iloc[0]
    fcst_end_date = pred_int.iloc[-1]
    time_int = exact['timestamp'].apply(lambda x: pd.Timestamp(x))
    plt.plot(time_int[-time_back:],exact['target'].values[-time_back:], color = 'r')
    plt.plot(pred_int, p50['Value'].values, color = 'k')
    plt.fill_between(pred_int, 
                     p10['Value'].values,
                     p90['Value'].values,
                     color='b', alpha=0.3);
    plt.axvline(x=pd.Timestamp(fcst_start_date), linewidth=3, color='g', ls='dashed')
    plt.axvline(x=pd.Timestamp(fcst_end_date), linewidth=3, color='g', ls='dashed')
    plt.xticks(rotation=30)
    plt.legend(['Target', 'Forecast'], loc = 'lower left')


def extract_gz( src, dst ):
    
    print( f"Extracting {src} to {dst}" )    

    with open(dst, 'wb') as fd_dst:
        with gzip.GzipFile( src, 'rb') as fd_src:
            data = fd_src.read()
            fd_dst.write(data)

    print("Done.")