import pytest

from codegreen_core.data import entsoe,energy,carbon_intensity

from codegreen_core.utilities.message import CodegreenDataError

from datetime import datetime

import pandas as pd

class TestEnergyData:

def test_valid_country(self):

with pytest.raises(ValueError):

energy(91,datetime(2024,1,1),datetime(2024,1,2))

def test_valid_starttime(self):

with pytest.raises(ValueError):

energy("DE","2024,1,1",datetime(2024,1,2))

def test_valid_endtime(self):

with pytest.raises(ValueError):

energy("DE",datetime(2024,1,2),"2024,1,1")

def test_valid_type(self):

with pytest.raises(ValueError):

energy("DE",datetime(2024,1,1),datetime(2024,1,2),"magic")

def test_country_no_vaild_energy_source(self):

with pytest.raises(CodegreenDataError):

energy("IN",datetime(2024,1,1),datetime(2024,1,2))

def test_entsoe_generation_data(self):

cases = [

{

"country":"DE",

"start":datetime(2024,2,1),

"end":datetime(2024,2,2),

"dtype": 'generation' ,

"file": "data/generation_DE_24_downloaded.csv",

"interval60": False

},

{

"country":"DE",

"start":datetime(2024,3,20),

"end":datetime(2024,3,24),

"dtype": 'generation' ,

"file": "data/generation_DE_24_downloaded.csv",

"interval60": False

},

# {

# "country":"DE",

# "start":datetime(2024,1,1),

# "end":datetime(2024,1,5),

# "dtype": 'generation' ,

# "file": "data/DE_24_generation_downloaded.csv",

# "interval60": False,

# "note":"this has issues,Hydro Pumped Storage values do not match "

# },

{

"country":"GR",

"start":datetime(2024,3,20),

"end":datetime(2024,3,24),

"dtype": 'generation' ,

"file": "data/generation_GR_24_downloaded.csv",

"interval60": True

},

{

"country":"GR",

"start":datetime(2024,1,25),

"end":datetime(2024,1,28),

"dtype": 'generation' ,

"file": "data/generation_GR_24_downloaded.csv",

"interval60": True

}

]

for case in cases:

# intervals = int((case["end"].replace(minute=0, second=0, microsecond=0) - case["start"].replace(minute=0, second=0, microsecond=0)).total_seconds() // 3600)

# print(intervals)

if case["dtype"]=="generation":

data = energy(case["country"],case["start"],case["end"],case["dtype"],case["interval60"])

data_verify = pd.read_csv(case["file"])

data_verify['start_date'] = data_verify['MTU'].str.split(' - ').str[0]

data_verify['end_date'] = data_verify['MTU'].str.split(' - ').str[1].str.replace(' (UTC)', '', regex=False)

data_verify['start_date'] = pd.to_datetime(data_verify['start_date'], format='%d.%m.%Y %H:%M')

data_verify['end_date'] = pd.to_datetime(data_verify['end_date'], format='%d.%m.%Y %H:%M')

start_utc = pd.to_datetime(case["start"]) # case["start"].astimezone(pd.Timestamp.now(tz='UTC').tzinfo) if case["start"].tzinfo is None else case["start"]

end_utc = pd.to_datetime(case["end"]) #case["end"].astimezone(pd.Timestamp.now(tz='UTC').tzinfo) if case["end"].tzinfo is None else case["end"]

filtered_df = data_verify[(data_verify['start_date'] >= start_utc) & (data_verify['start_date'] < end_utc)]

allCols = data.columns.tolist()

renPresent = list(set(allCols).intersection(entsoe.renewableSources))

for e in renPresent:

difference = filtered_df[e+" - Actual Aggregated [MW]"] - data[e]

sum_of_differences = difference.sum()

print(e)

print(sum_of_differences)

print(filtered_df[e+" - Actual Aggregated [MW]"].to_list())

print(data[e].to_list())

print(difference.to_list())

print("===")

assert sum_of_differences == 0.0

# else :

# print("")

""" todo

- test cases where some data is missing and has to be replaced with average

"""