#!/usr/bin/env python
# coding: utf-8


import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import os
import csv

def generate_min_df(mins = 15):
    end = 60/mins * 24
    start_date = '2023-01-01'
    end_date = '2023-12-31'

    all_dates = pd.date_range(start=start_date, end=end_date, freq='D')
    all_times = pd.timedelta_range(start='0 min', end=f'1435 min', freq=f'{mins} min')

    date_times = [pd.Timestamp(date) + time for date in all_dates for time in all_times]

    time_frame = pd.DataFrame({
        'Time': date_times
    })
    return time_frame

def save_csv(df, filename, columns):
    with open(filename, 'w', newline='') as file:
        writer = csv.writer(file)
        writer.writerow(['Time', 'PV yield[kW/kWp]'])
        for index, row in df.iterrows():
            time_formatted = index.strftime('%H:%M')
            writer.writerow([time_formatted, row[columns[1]]])
            
        print(f'The file is written to {filename}')

def read_csv(filename):
    skip_rows = list(range(1, 17))
    data = pd.read_csv(filename, sep=';', skiprows=skip_rows)
    return data

def process(file_name):
    df = read_csv(file_name)
    city = file_name.split('_')[0]

    remain_column = ['Time','PV energy (AC) minus standby use ']
    energy_row_name = remain_column[1]

    df = df[remain_column]
    df[energy_row_name] = df[energy_row_name].str.replace(',','.').astype(float)

    sum_energy = df[energy_row_name].sum()
    group_size = 15
    df['group_id'] = df.index // group_size

    sums = df.groupby('group_id')[energy_row_name].sum()
    sums_df = sums.reset_index(drop=True).to_frame(name = 'Energy')

    pv_energy_column_name = 'PV yield[kW/kWp]'
    sums_df = sums_df.rename(columns={'Energy': pv_energy_column_name})

    time_frame = generate_min_df(15)
    sums_df = pd.concat([time_frame, sums_df], axis=1)
    # sums_df.set_index('Time', inplace=True)
    # max_value = sums_df[pv_energy_column_name].max()
    sums_df[pv_energy_column_name] = sums_df[pv_energy_column_name] / 390.
    sums_df[pv_energy_column_name] = sums_df[pv_energy_column_name].round(4)
    sums_df[pv_energy_column_name].replace(0.0, -0.0)

    sums_df.to_csv(f'{city}.csv')
    # save_csv(sums_df, f'{city}.csv', ['Time', 'Energy'])

if __name__ == '__main__':
    city_list = ['Riyahd', 'Cambodge', 'Berlin', 'Serbia']
    for city in city_list:
        print(f'Processing {city}')
        file_name = f'{city}_raw.csv'
        process(file_name)
        print(f'Processing {city} is done\n')