# from codegreen_core.tools.loadshift_location import predict_optimal_location,predict_optimal_location_now

# from datetime import datetime,timedelta

# import pandas as pd

# import pytz

# def test_location_now():

# a,b,c,d = predict_optimal_location_now(["DE","HU","AT","FR","AU","NO"],5,0,50,datetime(2024,9,13))

# print(a,b,c,d)

# # test_location_now()

# def fetch_data(month_no,countries):

# data = pd.read_csv("tests/data/prediction_testing_data.csv")

# forecast_data = {}

# for c in countries:

# filter = data["file_id"] == c+""+str(month_no)

# d = data[filter].copy()

# if(len(d)>0):

# forecast_data[c] = d

# return forecast_data

# def test_locations():

# cases = [

# {

# "month":1,

# "c":["DE","NO","SW","ES","IT"],

# "h":5,

# "m":0,

# "p":50,

# "s":"2024-01-05 02:00:00",

# "e": 10

# }

# ]

# for case in cases:

# data = fetch_data(case["month"],case["c"])

# start_utc = datetime.strptime(case["s"], '%Y-%m-%d %H:%M:%S')

# start_utc = pytz.UTC.localize(start_utc)

# start = start_utc.astimezone(pytz.timezone('Europe/Berlin'))

# end = (start + timedelta(hours=case["e"]))

# a,b,c,d = predict_optimal_location(data,case["h"],case["m"],case["p"],end,start)

# print(a,b,c,d)

# # test_locations()