# -*- coding: utf-8 -*-
"""
Created on Fri May 22 12:15:10 2020

@author: rugve
"""
# import codecs
# import csv
# import urllib
from datetime import date
import itertools
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.metrics import mean_squared_error
from math import sqrt
#from statsmodels.tsa.api import ExponentialSmoothing, Holt
import statsmodels.api as sm
import warnings
warnings.filterwarnings("ignore")


def data_for_country(country,data,column):
    data = data[["location","date",column]]
    data = data[data["location"] == country]
    data = data[data[column] != 0]
    data.reset_index(inplace = True)
    data.Timestamp = pd.to_datetime(data.date,format='%Y-%m-%d')
    data.index = data.Timestamp
    data=data.drop('index',1)
    data=data.drop('location',1)
    data=data.drop('date',1)
    data = data.resample('D').mean()
    data.iloc[:,0]=  data.iloc[:,0].fillna(method='bfill').fillna(method='ffill')
    return data

def plot_Data(df):
    ts = df.iloc[:,0]
    ts.plot(figsize=(15,8), title= 'Daily Cases', fontsize=14)
    plt.show()


# def make_predictions_HL(df):
#     no_of_days = int(input("Please enter the number of days you want to predict for HL:"))
#     ts = df['total_cases']
#     fit1 = Holt(ts).fit(smoothing_level = 0.4,smoothing_slope = 0.8)
#     prediction= fit1.forecast(no_of_days).astype(int)
#     df2 = pd.DataFrame({'prediction': prediction })
#     return df2

# def make_predictions_HW_add(df):
#     no_of_days = int(input("Please enter the number of days you want to predict for HL:"))
#     ts = df['total_cases']
#     fit1 = ExponentialSmoothing(ts ,trend='add').fit()
#     prediction= fit1.forecast(no_of_days).astype(int)
#     df2 = pd.DataFrame({'prediction': prediction })
#     return df2


# def make_predictions_HW_mul(df):
#     no_of_days = int(input("Please enter the number of days you want to predict for HL:"))
#     ts = df['total_cases']
#     fit1 = ExponentialSmoothing(ts ,trend='mul').fit()
#     prediction= fit1.forecast(no_of_days).astype(int)
#     df2 = pd.DataFrame({'prediction': prediction })
#     return df2

def select_prams_for_arima(train,test):
    p = d = q = range(0, 4)
    pdq = list(itertools.product(p, d, q))
    seasonal_pdq = [(0,0,0,0)]
    params = []
    rms_arimas =[]
    for param in pdq:
        params.append(param)
        for param_seasonal in seasonal_pdq:
            try:
                y_hat_avg = test.copy()
                mod = sm.tsa.statespace.SARIMAX(train.iloc[:,0],order=param,
                                                seasonal_order=param_seasonal,
                                                enforce_stationarity=False,
                                                enforce_invertibility=False)
                results = mod.fit()
                y_hat_avg['SARIMA'] = results.predict(start=test.index[0],
                                                      end=test.index[-1], dynamic=True)
                rms_arimas.append(sqrt(mean_squared_error(test.iloc[:,0], y_hat_avg.SARIMA)))
            except:
                continue
    data_tuples = list(zip(params,rms_arimas))
    rms = pd.DataFrame(data_tuples, columns=['Parameters','RMS value'])
    minimum = int(rms[['RMS value']].idxmin())
    parameters = params[minimum]
    return parameters

#df = data
def make_predictions_arima(df,parameters,colummn):
    no_of_days = int(input("Please enter the number of days you want to predict :"))
    fit1 = sm.tsa.statespace.SARIMAX(df.iloc[:,0], order=parameters).fit()
    prediction = fit1.forecast(no_of_days).astype(int)
    df2 = pd.DataFrame({'prediction': prediction })

    plt.figure(figsize=(16,8))
    plt.plot(df.iloc[:,0], label='Original Data')
    #plt.plot(df2['prediction'], label='Predicted data')

    #calc the confidence intervals
    fcast = fit1.get_forecast(no_of_days)
    pred_ci = fcast.conf_int()
    print(fcast.predicted_mean)
    print('Confidence intervals:')
    print(pred_ci)

    # Make as pandas series
    upper_series = pd.Series(pred_ci.iloc[:, 1])
    lower_series = pd.Series(pred_ci.iloc[:, 0])
    # lower series shouldnt be less than original series
    # if colummn is 'total_deaths', then lower series shouldnt be less than the max value of original series
    if colummn == 'total_deaths':
        last_value = df.iloc[:,0].iat[-1] #Basically get the last value of df series (lower_series[lower_series < 30057] = 30057)
        lower_series = lower_series.clip(lower=last_value)

    # join the two series
    forecast = pd.concat([lower_series, fcast.predicted_mean, upper_series], axis=1)
    forecast.columns = ['lower_series', 'predicted_mean', 'upper_series']
    # plot the forecast
    plt.plot(df2['prediction'], label='Forecast')  # or plt.plot(forecast.predicted_mean, label='Forecast')   to get the mean value
    plt.fill_between(forecast.index, forecast.lower_series, forecast.upper_series, color='k', alpha=.15)
    plt.title('Forecast of '+str(colummn)+' for '+str(no_of_days)+' days')
    plt.legend(loc='best')
    plt.show()
    print("ARIMAX model prediction")
    print(df2)

    #pd.DataFrame(df2, columns=['Date','prediction']).to_csv(r'C:\Users\rugve\Desktop\Rucha\3k\Covid19Data\Result1s.csv',index=True)

def train_test_split(data, colummn):
    today = date.today()
    today = str(today)
    today = today.replace(today[:8], '')
    today = int(today)
    split_index = len(data) - today
    train=data[0:split_index]
    test=data[split_index:]
    parameters = select_prams_for_arima(train,test)
    make_predictions_arima(data,parameters, colummn)


# def get_data_from_url(url):
#     ftpstream = urllib.request.urlopen(url)
#     csvfile = csv.reader(codecs.iterdecode(ftpstream, 'utf-8'))
#     data = [ ]
#     for line in csvfile:
#         data.append(line)
#     column_names = data.pop(0)
#     full_data = pd.DataFrame(data,columns=column_names)
#     retrun full_data

def main():
    #url = 'https://covid.ourworldindata.org/data/owid-covid-data.csv'
    url = r"owid-covid-data.csv"
    full_data = pd.read_csv(url)
    coutries = (list(set(full_data["location"])))
    print("Predictions for COVID19")
    print(coutries)
    #cont = input("Enter a Country for which You want to make predictions from the above list:")
    cont = "Greece"
    columns  = ["total_cases","new_cases","total_deaths","new_deaths","hosp_patients", "icu_patients"]
    print(columns)
    colummn = input("Print the one value from above list for which  you want to  make prediction: ")
    data = data_for_country(cont,full_data,colummn)
    #plot_Data(data)
    #data = data.iloc[:103]
    train_test_split(data, colummn)


if __name__ == '__main__':
    main()