






import numpy as np

def 						snake_case      (UpperCAmelCase__			,					UpperCAmelCase__						) ->       Optional[Any]:
		UpperCamelCase_:      Dict					   =       np.array(__A						)
		UpperCamelCase_:      List[str]					   =       np.array(__A						)

		UpperCamelCase_:      Optional[int]					   =       abs(predict - actual						)
		UpperCamelCase_:      Dict					   =       difference.mean()

		return score

def 						snake_case      (UpperCAmelCase__			,					UpperCAmelCase__						) ->       Any:
		UpperCamelCase_:      Any					   =       np.array(__A						)
		UpperCamelCase_:      Optional[int]					   =       np.array(__A						)

		UpperCamelCase_:      Union[str, Any]					   =       predict - actual
		UpperCamelCase_:      Dict					   =       np.square(__A						)

		UpperCamelCase_:      Union[str, Any]					   =       square_diff.mean()
		return score

def 						snake_case      (UpperCAmelCase__			,					UpperCAmelCase__						) ->       Any:
		UpperCamelCase_:      Optional[Any]					   =       np.array(__A						)
		UpperCamelCase_:      Optional[Any]					   =       np.array(__A						)

		UpperCamelCase_:      Any					   =       predict - actual
		UpperCamelCase_:      Optional[Any]					   =       np.square(__A						)
		UpperCamelCase_:      Tuple					   =       square_diff.mean()
		UpperCamelCase_:      List[str]					   =       np.sqrt(__A						)
		return score

def 						snake_case      (UpperCAmelCase__			,					UpperCAmelCase__						) ->       List[Any]:
		UpperCamelCase_:      List[str]					   =       np.array(__A						)
		UpperCamelCase_:      Optional[int]					   =       np.array(__A						)

		UpperCamelCase_:      int					   =       np.log(predict + 1						)
		UpperCamelCase_:      Optional[int]					   =       np.log(actual + 1						)

		UpperCamelCase_:      Dict					   =       log_predict - log_actual
		UpperCamelCase_:      Tuple					   =       np.square(__A						)
		UpperCamelCase_:      Tuple					   =       square_diff.mean()

		UpperCamelCase_:      Any					   =       np.sqrt(__A						)

		return score

def 						snake_case      (UpperCAmelCase__			,					UpperCAmelCase__						) ->       List[str]:
		UpperCamelCase_:      Any					   =       np.array(__A						)
		UpperCamelCase_:      Optional[int]					   =       np.array(__A						)

		UpperCamelCase_:      Dict					   =       predict - actual
		UpperCamelCase_:      Any					   =       np.sum(__A						) / len(__A						)
		UpperCamelCase_:      int					   =       np.sum(__A						) / len(__A						)
		# print(numerator, denumerator)
		UpperCamelCase_:      str					   =       float(__A						) / denumerator * 1_0_0

		return score

def 						snake_case      (UpperCAmelCase__			,					UpperCAmelCase__						) ->       str:
		return np.mean(np.array(__A						) == np.array(__A						)						)







from datetime import date

import requests

A_	:	Any				=				'https://api.carbonintensity.org.uk/intensity'

def 						snake_case      () ->       str:
  UpperCamelCase_:      Any					   =       requests.get(UpperCAmelCase__						).json()['data'][0]
  return last_half_hour["intensity"]["actual"]

def 						snake_case      (UpperCAmelCase__			,					UpperCAmelCase__						) ->       list:
  return requests.get(F'''{BASE_URL}/{start}/{end}'''						).json()["data"]


if __name__ == "__main__":
   for entry in fetch_from_to(start=date(2020, 10, 1), end=date(2020, 10, 3)):
      print('from {from} to {to}: {intensity[actual]}'.format(**entry))
   print(F'''{fetch_last_half_hour() = }''')