util.py

import os
from datetime import datetime
import pandas as pd

EU_country_codes = [
    "AT",  # Austria
    "BE",  # Belgium
    "BG",  # Bulgaria
    "CY",  # Cyprus
    "CZ",  # Czech Republic
    "DE",  # Germany
    "DK",  # Denmark
    "EE",  # Estonia
    "ES",  # Spain
    "FI",  # Finland
    "FR",  # France
    "GR",  # Greece
    "HR",  # Croatia
    "HU",  # Hungary
    "IE",  # Ireland
    "IT",  # Italy
    "LT",  # Lithuania
    "LU",  # Luxembourg
    "LV",  # Latvia
    "MT",  # Malta
    "NL",  # Netherlands
    "PL",  # Poland
    "PT",  # Portugal
    "RO",  # Romania
    "SE",  # Sweden
    "SI",  # Slovenia
    "SK",  # Slovakia
]
renewableSources = ["Biomass","Geothermal", "Hydro Pumped Storage", "Hydro Run-of-river and poundage",
                        "Hydro Water Reservoir", "Marine", "Other renewable", "Solar", "Waste", "Wind Offshore", "Wind Onshore"]
windSolarOnly = ["Solar", "Wind Offshore", "Wind Onshore"]
nonRenewableSources = [ "Fossil Brown coal/Lignite", "Fossil Coal-derived gas", "Fossil Gas",
                           "Fossil Hard coal", "Fossil Oil", "Fossil Oil shale", "Fossil Peal", "Nuclear", "Other"]


energy_type = {
    "Wind":["Wind Offshore", "Wind Onshore"],
    "Solar":["Solar"],
    "Nuclear": ["Nuclear"],
    "Hydroelectricity":[ "Hydro Pumped Storage", "Hydro Run-of-river and poundage", "Hydro Water Reservoir"],
    "Geothermal":["Geothermal"],
    "Natural Gas": ["Fossil Coal-derived gas", "Fossil Gas"],
    "Petroleum":["Fossil Oil", "Fossil Oil shale"],
    "Coal":["Fossil Brown coal/Lignite","Fossil Hard coal","Fossil Peal"],
    "Biomass":["Biomass"]
}

DATA_folder_location= "data"


def get_available_country_list(data_folder="data"):
    """
    Generate lists of country codes for actual and forecast data from CSV filenames in the data folder.

    Parameters:
    data_folder (str): The path to the folder containing the CSV files.

    Returns:
    tuple: A tuple containing two lists:
        - countryActual (list): List of country codes for actual data.
        - countryForecast (list): List of country codes for forecast data.
    """
    countryActual = []
    countryForecast = []

    # Function to extract country code and data type from file name
    def extract_info(filename):
        parts = filename.split('-')
        country_code = parts[0]
        data_type = parts[1]
        return country_code, data_type

    # Iterate over files in the data folder
    for filename in os.listdir(data_folder):
        if filename.endswith(".csv") and filename.endswith("-60.csv"):
            country_code, data_type = extract_info(filename)
            if data_type == "actual":
                countryActual.append(country_code)
            elif data_type == "forecast":
                countryForecast.append(country_code)

    return countryActual, countryForecast


def get_today_starting_date():
    # Get today's date
  today = datetime.today()
  # Format the date in yyyymmddhhmm format
  formatted_date = today.strftime("%Y%m%d") + "0000"
  return formatted_date



def read_actual_data_file(ccode,interval="60"):
  file = DATA_folder_location+"/"+ccode+"-actual-"+interval+".csv"
  currentData = pd.read_csv(file)
  currentData = currentData.drop(currentData.columns[0], axis=1)
  # currentData['startTime'] = currentData['startTime'].astype(str)
  # currentData['endTime'] = currentData['endTime'].astype(str)
  currentData['startTime'] = pd.to_datetime(currentData['startTime'], format='%Y%m%d%H%M')
  currentData['endTime'] = pd.to_datetime(currentData['endTime'], format='%Y%m%d%H%M')
  currentData['startTime'] = currentData['startTime'].dt.strftime('%Y%m%d%H%M')
  currentData['endTime'] = currentData['endTime'].dt.strftime('%Y%m%d%H%M')
  return currentData


def calculate_energy_values(table):
    """
    """
    allAddkeys = ["Wind","Solar","Nuclear","Hydroelectricity","Geothermal","Natural Gas","Petroleum","Coal","Biomass"]
    colsToDel = ["renewableTotal","renewableTotalWS","nonRenewableTotal","total","percentRenewable","percentRenewableWS","ci1_kgCO2perkWh"]
    for a in allAddkeys:
        fname = a+"_per"
        if a not in table.columns:
            colsToDel.append(fname)
    for c in colsToDel:
        if c in table.columns:
            del table[c]

    allCols = table.columns.tolist()
    # find out which columns are present in the data out of all the possible columns in both the categories
    renPresent = list(set(allCols).intersection(renewableSources))
    renPresentWS = list(set(allCols).intersection(windSolarOnly))
    nonRenPresent = list(set(allCols).intersection(nonRenewableSources))
    # find total renewable, total non renewable and total energy values
    table["renewableTotal"] = table[renPresent].sum(axis=1)
    table["renewableTotalWS"] = table[renPresentWS].sum(axis=1)
    table["nonRenewableTotal"] = table[nonRenPresent].sum(axis=1)
    table["total"] = table["nonRenewableTotal"] + table["renewableTotal"]
    # calculate percent renewable
    table["percentRenewable"] = (
        table["renewableTotal"] / table["total"]) * 100
    # refine percentage values : replacing missing values with 0 and converting to integer
    table['percentRenewable'].fillna(0, inplace=True)
    table["percentRenewable"] = table["percentRenewable"].round().astype(int)
    table["percentRenewableWS"] = (
        table["renewableTotalWS"] / table["total"]) * 100
    table['percentRenewableWS'].fillna(0, inplace=True)
    table["percentRenewableWS"] = table["percentRenewableWS"].round().astype(int)

    # individual energy source percentage calculation 

    # energy_type_available = {}
    for ky in allAddkeys:
        keys_available =  list(set(allCols).intersection(energy_type[ky]))   
        #print(keys_available)  
        fieldName = ky+"_per"  
        # print(fieldName)
        table[fieldName] = table[keys_available].sum(axis=1)
        table[fieldName] = (table[fieldName]/table["total"])*100
        table[fieldName].fillna(0, inplace=True)
        # print(table[fieldName])        
        table[fieldName] =  table[fieldName].astype(int)
    return table