#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue May 16 10:37:35 2023

This script extracts monthly fuel prices of oil, gas, coal and lignite,
as well as CO2 prices


Inputs
------
- ``data/energy-price-trends-xlsx-5619002.xlsx``: energy price index of fossil fuels
- ``emission-spot-primary-market-auction-report-2019-data.xls``: CO2 Prices spot primary auction


Outputs
-------

- ``data/validation/monthly_fuel_price.csv``
- ``data/validation/CO2_price_2019.csv``


Description
-----------

The rule :mod:`build_monthly_prices` collects monthly fuel prices and CO2 prices
and translates them from different input sources to pypsa syntax

Data sources:
    [1] Fuel price index. Destatis
    https://www.destatis.de/EN/Home/_node.html
    [2] average annual import price (coal, gas, oil) Agora, slide 22
    https://static.agora-energiewende.de/fileadmin/Projekte/2022/2022-10_DE_JAW2022/2023-02-20_Praesentation_Agora_Jahresauswertung.pdf
    [3] average annual fuel price lignite, ENTSO-E
    https://2020.entsos-tyndp-scenarios.eu/fuel-commodities-and-carbon-prices/
    [4] CO2 Prices, Emission spot primary auction, EEX
    https://www.eex.com/en/market-data/environmental-markets/eua-primary-auction-spot-download


Data was accessed at 16.5.2023
"""

import pandas as pd
import logging
from _helpers import configure_logging

logger = logging.getLogger(__name__)

validation_year = 2019

# sheet names to pypsa syntax
sheet_name_map = {"5.1 Hard coal and lignite": "coal",
               "5.2 Mineral oil" : "oil",
               "5.3.1 Natural gas - indices":"gas"}

# keywords in datasheet
keywords = {"coal": " GP09-051 Hard coal",
            "lignite": " GP09-052 Lignite and lignite briquettes",
            "oil": " GP09-0610 10 Mineral oil, crude",
            "gas": "GP09-062 Natural gas"
            }

# import fuel price 2015 in Eur/MWh
# source for coal, oil, gas, Agora, slide 22 [2]
# source lignite, price for 2020, scaled by price index, ENTSO-E [3]
price_2015 = {"coal": 8,
              "oil": 31,
              "gas": 21,
              "lignite": 3.8}  # 2020 3.96/1.04 

def get_fuel_price():
    fuel_price = pd.read_excel(snakemake.input.fuel_price_raw,
                               sheet_name=list(sheet_name_map.keys()))
    fuel_price = {sheet_name_map[key]: value for key, value in fuel_price.items()
     if key in sheet_name_map}
    # lignite and hard coal are on the same sheet
    fuel_price["lignite"] = fuel_price["coal"]
    
    def extract_df(sheet, keyword):
        # Create a DatetimeIndex for the first day of each month of a given year
        dti = pd.date_range(start=f'{validation_year}-01-01',
                            end=f'{validation_year}-12-01', freq='MS')
        # Extract month names
        month_list = dti.month
        start = fuel_price[sheet].index[(fuel_price[sheet] == keyword).any(axis=1)]
        df = fuel_price[sheet].loc[start[0]:start[0]+18,:]
        df.dropna(axis=0, inplace=True)
        df.iloc[:,0] = df.iloc[:,0].apply(lambda x: int(x.replace(" ...", "")))
        df.set_index(df.columns[0], inplace=True)
        df = df.iloc[:, :12]
        df.columns = month_list
        return df
    
    m_price = {}
    for carrier, keyword in keywords.items():
        df = extract_df(carrier, keyword).loc[validation_year]
        m_price[carrier] = df.mul(price_2015[carrier]/100)
    
    pd.concat(m_price, axis=1).to_csv(snakemake.output.fuel_price)

def get_co2_price():
    # emission price
    CO2_price = pd.read_excel(snakemake.input.co2_price_raw, index_col=1,
                              header=5)
    CO2_price["Auction Price €/tCO2"].to_csv(snakemake.output.co2_price)


if __name__ == "__main__":
    if "snakemake" not in globals():
        from _helpers import mock_snakemake
        snakemake = mock_snakemake("build_monthly_prices")
        
    configure_logging(snakemake)
    
    get_fuel_price()
    get_co2_price()