





import numpy as np






def  SCREAMING_SNAKE_CASE__							(				UpperCamelCase__:							str    ,      UpperCamelCase__:							Tuple    ,      UpperCamelCase__:							Any			):
		SCREAMING_SNAKE_CASE__								=					n_draws * class_counts / class_counts.sum()
		# floored means we don't overshoot n_samples, but probably undershoot
		SCREAMING_SNAKE_CASE__								=					np.floor(UpperCamelCase__			)
		# we add samples according to how much "left over" probability
		# they had, until we arrive at n_samples
		SCREAMING_SNAKE_CASE__								=					int(n_draws - floored.sum()			)
		if need_to_add > 0:
				SCREAMING_SNAKE_CASE__								=					continuous - floored
				SCREAMING_SNAKE_CASE__								=					np.sort(np.unique(UpperCamelCase__			)			)[::-1]
				# add according to remainder, but break ties
				# randomly to avoid biases
				for value in values:
						((SCREAMING_SNAKE_CASE__)		,  )								=					np.where(remainder == value			)
						# if we need_to_add less than what's in inds
						# we draw randomly from them.
						# if we need to add more, we add them all and
						# go to the next value
						SCREAMING_SNAKE_CASE__								=					min(len(UpperCamelCase__			)    ,      UpperCamelCase__			)
						SCREAMING_SNAKE_CASE__								=					rng.choice(UpperCamelCase__    ,      size=UpperCamelCase__    ,      replace=UpperCamelCase__			)
						floored[inds] += 1
						need_to_add -= add_now
						if need_to_add == 0:
								break
		return floored.astype(np.intaa			)






def  SCREAMING_SNAKE_CASE__							(				UpperCamelCase__:							List[Any]    ,      UpperCamelCase__:							Dict    ,      UpperCamelCase__:							Union[str, Any]    ,      UpperCamelCase__:							Any    ,      UpperCamelCase__:							str=10			):
		SCREAMING_SNAKE_CASE__		,  SCREAMING_SNAKE_CASE__								=					np.unique(UpperCamelCase__    ,      return_inverse=UpperCamelCase__			)
		SCREAMING_SNAKE_CASE__								=					classes.shape[0]
		SCREAMING_SNAKE_CASE__								=					np.bincount(UpperCamelCase__			)
		if np.min(UpperCamelCase__			) < 2:
				raise ValueError("""Minimum class count error"""			)
		if n_train < n_classes:
				raise ValueError(
				    """The train_size = %d should be greater or """ """equal to the number of classes = %d""" % (n_train, n_classes)			)
		if n_test < n_classes:
				raise ValueError(
				    """The test_size = %d should be greater or """ """equal to the number of classes = %d""" % (n_test, n_classes)			)
		SCREAMING_SNAKE_CASE__								=					np.split(np.argsort(UpperCamelCase__    ,      kind="""mergesort"""			)    ,      np.cumsum(UpperCamelCase__			)[:-1]			)
		for _ in range(UpperCamelCase__			):
				SCREAMING_SNAKE_CASE__								=					approximate_mode(UpperCamelCase__    ,      UpperCamelCase__    ,      UpperCamelCase__			)
				SCREAMING_SNAKE_CASE__								=					class_counts - n_i
				SCREAMING_SNAKE_CASE__								=					approximate_mode(UpperCamelCase__    ,      UpperCamelCase__    ,      UpperCamelCase__			)

				SCREAMING_SNAKE_CASE__								=					[]
				SCREAMING_SNAKE_CASE__								=					[]

				for i in range(UpperCamelCase__			):
						SCREAMING_SNAKE_CASE__								=					rng.permutation(class_counts[i]			)
						SCREAMING_SNAKE_CASE__								=					class_indices[i].take(UpperCamelCase__    ,      mode="""clip"""			)
						train.extend(perm_indices_class_i[: n_i[i]]			)
						test.extend(perm_indices_class_i[n_i[i] : n_i[i] + t_i[i]]			)
				SCREAMING_SNAKE_CASE__								=					rng.permutation(UpperCamelCase__			)
				SCREAMING_SNAKE_CASE__								=					rng.permutation(UpperCamelCase__			)

				yield train, test






from datetime import date

import requests

_lowerCamelCase       =	'https://api.carbonintensity.org.uk/intensity'






def  SCREAMING_SNAKE_CASE__							(				):
		SCREAMING_SNAKE_CASE__								=					requests.get(UpperCamelCase__			).json()["""data"""][0]
		return last_half_hour["intensity"]["actual"]






def  SCREAMING_SNAKE_CASE__							(				UpperCamelCase__:							Dict    ,      UpperCamelCase__:							Optional[int]			):
		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() = }''')