






import warnings

import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.metrics import pairwise_distances

warnings.filterwarnings('ignore')

a 			=							'K-MEANS-CLUST/ '
def    a__    (	A		,					A		,					A=None       ):
   if seed is not None:  # useful for obtaining consistent results
      np.random.seed(A       )
   lowercase__    :						str       =			data.shape[0]  # number of data points

   # Pick K indices from range [0, N).
   lowercase__    :						Any       =			np.random.randint(0		,					A		,					A       )

   # Keep centroids as dense format, as many entries will be nonzero due to averaging.
   # As long as at least one document in a cluster contains a word,
   # it will carry a nonzero weight in the TF-IDF vector of the centroid.
   lowercase__    :						List[Any]       =			data[rand_indices, :]

   return centroids
def    a__    (	A		,					A       ):
   return pairwise_distances(A		,					A		,					metric="""euclidean"""       )
def    a__    (	A		,					A       ):
   # Compute distances between each data point and the set of centroids:
   # Fill in the blank (RHS only)
   lowercase__    :						Any       =			centroid_pairwise_dist(A		,					A       )

   # Compute cluster assignments for each data point:
   # Fill in the blank (RHS only)
   lowercase__    :						List[Any]       =			np.argmin(A		,					axis=1       )

   return cluster_assignment
def    a__    (	A		,					A		,					A       ):
   lowercase__    :						Optional[int]       =			[]
   for i in range(A       ):
      # Select all data points that belong to cluster i. Fill in the blank (RHS only)
      lowercase__    :						List[Any]       =			data[cluster_assignment == i]
      # Compute the mean of the data points. Fill in the blank (RHS only)
      lowercase__    :						List[str]       =			member_data_points.mean(axis=0       )
      new_centroids.append(A       )
   lowercase__    :						Tuple       =			np.array(A       )

   return new_centroids
def    a__    (	A		,					A		,					A		,					A       ):
   lowercase__    :						Union[str, Any]       =			0.0
   for i in range(A       ):
      # Select all data points that belong to cluster i. Fill in the blank (RHS only)
      lowercase__    :						Tuple       =			data[cluster_assignment == i, :]

      if member_data_points.shape[0] > 0:  # check if i-th cluster is non-empty
         # Compute distances from centroid to data points (RHS only)
         lowercase__    :						Union[str, Any]       =			pairwise_distances(
             A		,					[centroids[i]]		,					metric="""euclidean"""       )
         lowercase__    :						List[Any]       =			distances**2
         heterogeneity += np.sum(A       )

   return heterogeneity
def    a__    (	A		,					A       ):
   plt.figure(figsize=(7, 4)       )
   plt.plot(A		,					linewidth=4       )
   plt.xlabel("""# Iterations"""       )
   plt.ylabel("""Heterogeneity"""       )
   plt.title(F'''Heterogeneity of clustering over time, K={k:d}'''       )
   plt.rcParams.update({"""font.size""": 16}       )
   plt.show()
def    a__    (	A		,					A		,					A		,					A=500		,					A=None		,					A=False       ):
   lowercase__    :						List[str]       =			initial_centroids[:]
   lowercase__    :						str       =			None

   for itr in range(A       ):
      if verbose:
         print(A		,					end=""""""       )

      # 1. Make cluster assignments using nearest centroids
      lowercase__    :						List[Any]       =			assign_clusters(A		,					A       )

      # 2. Compute a new centroid for each of the k clusters, averaging all data
      #    points assigned to that cluster.
      lowercase__    :						str       =			revise_centroids(A		,					A		,					A       )

      # Check for convergence: if none of the assignments changed, stop
      if (
          prev_cluster_assignment is not None
          and (prev_cluster_assignment == cluster_assignment).all()
      ):
         break

      # Print number of new assignments
      if prev_cluster_assignment is not None:
         lowercase__    :						int       =			np.sum(prev_cluster_assignment != cluster_assignment       )
         if verbose:
            print(
                F'''    {num_changed:5d} elements changed their cluster assignment.'''       )

        # Record heterogeneity convergence metric
      if record_heterogeneity is not None:
         # YOUR CODE HERE
         lowercase__    :						str       =			compute_heterogeneity(A		,					A		,					A		,					A       )
         record_heterogeneity.append(A       )

      lowercase__    :						List[Any]       =			cluster_assignment[:]

   return centroids, cluster_assignment


# Mock test below
if False:  # change to true to run this test case.
 from sklearn import datasets as ds

 a 			=							ds.load_iris()
 a 			=							3
 a 			=							[]
 a 			=							get_initial_centroids(dataset['data'], k, seed=0)
 a			,   a 			=							kmeans(
     dataset['data'],
     k,
     initial_centroids,
     maxiter=400,
     record_heterogeneity=heterogeneity,
     verbose=True,
 )
 plot_heterogeneity(heterogeneity, k)
def    a__    (	A		,					A		,					A=None       ):
   # Fill missing values with given rules
   if fill_missing_report:
      lowercase__    :						Optional[Any]       =			df.fillna(value=A       )
   lowercase__    :						Any       =			1
   lowercase__    :						Union[str, Any]       =			df.select_dtypes(np.number       ).columns
   lowercase__    :						str       =			(
       df.groupby(["""Cluster"""]       )[  # construct report dataframe
           numeric_cols
       ]  # group by cluster number
       .agg(
           [
               ("""sum""", np.sum),
               ("""mean_with_zeros""", lambda A       : np.mean(np.nan_to_num(A       )       )),
               ("""mean_without_zeros""", lambda A       : x.replace(0		,					np.NaN       ).mean()),
               (
                   """mean_25-75""",
                   lambda A       : np.mean(
                       np.nan_to_num(
                           sorted(A       )[
                               round(len(A       ) * 25 / 100       ) : round(len(A       ) * 75 / 100       )
                           ]       )       ),
               ),
               ("""mean_with_na""", np.mean),
               ("""min""", lambda A       : x.min()),
               ("""5%""", lambda A       : x.quantile(0.0_5       )),
               ("""25%""", lambda A       : x.quantile(0.2_5       )),
               ("""50%""", lambda A       : x.quantile(0.5_0       )),
               ("""75%""", lambda A       : x.quantile(0.7_5       )),
               ("""95%""", lambda A       : x.quantile(0.9_5       )),
               ("""max""", lambda A       : x.max()),
               ("""count""", lambda A       : x.count()),
               ("""stdev""", lambda A       : x.std()),
               ("""mode""", lambda A       : x.mode()[0]),
               ("""median""", lambda A       : x.median()),
               ("""# > 0""", lambda A       : (x > 0).sum()),
           ]       )
       .T.reset_index()
       .rename(index=A		,					columns={"""level_0""": """Features""", """level_1""": """Type"""}       )
   )  # rename columns
   # calculate the size of cluster(count of clientID's)
   lowercase__    :						Dict       =			report[
       (report["""Features"""] == """dummy""") & (report["""Type"""] == """count""")
   ].copy()  # avoid SettingWithCopyWarning
   lowercase__    :						Any       =			(
       """ClusterSize"""  # rename created cluster df to match report column names
   )
   lowercase__    :						Any       =			"""# of Customers"""
   lowercase__    :						Dict       =			pd.DataFrame(
       clustersize.iloc[:, 2:].values
       / clustersize.iloc[:, 2:].values.sum()       )
   lowercase__    :						Dict       =			"""% of Customers"""  # rename created cluster df to match report column names
   lowercase__    :						Dict       =			"""ClusterProportion"""
   lowercase__    :						Union[str, Any]       =			clusterproportion.columns.tolist()
   lowercase__    :						int       =			cols[-2:] + cols[:-2]
   lowercase__    :						Union[str, Any]       =			clusterproportion[cols]  # rearrange columns to match report
   lowercase__    :						str       =			report.columns
   lowercase__    :						Any       =			pd.DataFrame(
       abs(
           report[report["""Type"""] == """count"""].iloc[:, 2:].values
           - clustersize.iloc[:, 2:].values       )       )  # generating df with count of nan values
   lowercase__    :						Tuple       =			0
   lowercase__    :						Any       =			"""# of nan"""
   lowercase__    :						Tuple       =			report[
       report["""Type"""] == """count"""
   ].Features.tolist()  # filling values in order to match report
   lowercase__    :						int       =			a.columns.tolist()
   lowercase__    :						Optional[int]       =			cols[-2:] + cols[:-2]
   lowercase__    :						Tuple       =			a[cols]  # rearrange columns to match report
   lowercase__    :						int       =			report.columns  # rename columns to match report
   lowercase__    :						List[str]       =			report.drop(
       report[report.Type == """count"""].index       )  # drop count values except cluster size
   lowercase__    :						Optional[Any]       =			pd.concat(
       [report, a, clustersize, clusterproportion]		,					axis=0       )  # concat report with clustert size and nan values
   lowercase__    :						Any       =			report["""Features"""].isin(A       )
   lowercase__    :						Union[str, Any]       =			report.columns.tolist()
   lowercase__    :						List[str]       =			cols[0:2] + cols[-1:] + cols[2:-1]
   lowercase__    :						Optional[Any]       =			report[cols]
   lowercase__    :						List[Any]       =			{
       """ClusterSize""": 9,
       """ClusterProportion""": 8,
       """mean_with_zeros""": 7,
       """mean_with_na""": 6,
       """max""": 5,
       """50%""": 4,
       """min""": 3,
       """25%""": 2,
       """75%""": 1,
       """# of nan""": 0,
       """# > 0""": -1,
       """sum_with_na""": -2,
   }
   lowercase__    :						Any       =			(
       report.assign(
           Sortera=lambda A       : x.Type.map(A       )		,					Sortera=lambda A       : list(reversed(range(len(A       )       )       )       )		,					)
       .sort_values(["""Sorter1""", """Mark""", """Sorter2"""]		,					ascending=A       )
       .drop(["""Sorter1""", """Sorter2"""]		,					axis=1       )
   )
   lowercase__    :						Optional[int]       =			""""""
   lowercase__    :						Optional[int]       =			report.reset_index()
   lowercase__    :						Optional[int]       =			report.drop(columns=["""index"""]       )
   return report


if __name__ == "__main__":
 import doctest

 doctest.testmod()










from datetime import date

import requests

a 			=							'https://api.carbonintensity.org.uk/intensity'
def    a__    (	):
   lowercase__    :						Union[str, Any]       =			requests.get(A       ).json()["""data"""][0]
   return last_half_hour["intensity"]["actual"]
def    a__    (	A		,					A       ):
   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() = }')


