text: """
  Copyright (C) 2019  Patrick Schwab, ETH Zurich
  
  Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
  documentation files (the "Software"), to deal in the Software without restriction, including without limitation
  the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software,
  and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
  
  The above copyright notice and this permission notice shall be included in all copies or substantial portions
   of the Software.
  
  THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED
  TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL
  THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF
  CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
  DEALINGS IN THE SOFTWARE.
  """
  from __future__ import print_function
  
  import numpy as np
  import pandas as pd
  
  
  class ArrayIndexableList(list):
      def __getitem__(self, keys):
          if isinstance(keys, (int, slice)):
              return list.__getitem__(self, keys)
          return [self[k] for k in keys]
  
  
  def get_list_in_equally_sized_chunks(list_object, num_chunks):
      for i in range(0, len(list_object), num_chunks):
          yield list_object[i:i + num_chunks]
  
  
  # Transforms a dataset __x__ into the format required by the tsfresh library.
  def collect_tsfresh_dataframe(x):
      x = map(lambda xx: xx.T, x)
  
      all_df = {}
      num_time_series = len(x[0])
      for i in range(num_time_series):
          full_df = pd.DataFrame(columns=["id", "time", "value"])
          for sample in range(len(x)):
              len_time_series = len(x[sample][i])
              id = [sample] * len_time_series
              timestamps = range(len_time_series)
              values = np.squeeze(x[sample][i])
              df = pd.DataFrame(data=np.array((id, timestamps, values)).T,
                                columns=["id", "time", "value"])
              full_df = full_df.append(df)
          all_df[str(i)] = full_df
  
      return all_df
  
  
  def make_tsfresh_transform_dataframe(x_vals):
      return pd.DataFrame(data=np.expand_dims(range(len(x_vals)), axis=-1), columns=["id"])
  