old_contents: import numpy
  import pandas
  
  class PandasTimeSeries(object):
      """docstring for PandasTimeSeries"""
      def __init__(self):
          super(PandasTimeSeries, self).__init__()
          self.series = None
  
      def __len__(self):
          return len(self.series)
  
      def __str__(self):
          return str(self.series)
  
      def load_csv(self, csv_path):
          self.series = pandas.read_csv(csv_path, index_col=0, header=None,
                                             squeeze=True)
  
      def write_csv(self, csv_path):
          self.series.to_csv(csv_path, header=False)
  
      def load_npy_arrays(self, time_array, signal_array):
          self.series = pandas.Series(signal_array, index=time_array)
  
      def make_copy(self):
          my_copy = PandasTimeSeries()
          my_copy.series = self.series.copy()
          return my_copy
  
      def set_signal_from_scalar(self, scalar, index_list=None):
          if index_list:
              scalar_list = [scalar] * len(index_list)
              self.set_values_at_indices(scalar_list, index_list)
          else:
              self.series.fill(scalar)
  
      def is_similar_to(self, other_time_series, atol=1e-1):
          return numpy.allclose(self.series, other_time_series.series, atol=atol)
  
      def get_values_at_indices(self, index_list):
          sub_series = self.series.ix[index_list]
          sub_ts = PandasTimeSeries()
          sub_ts.series = sub_series
          return sub_ts
  
      def set_values_at_indices(self, new_values, index_list):
          self.series.ix[index_list] = new_values
  
      def isfinite(self):
          return numpy.isfinite(self.series).all()
  
new_contents: import numpy
  import pandas
  
  class PandasTimeSeries(object):
      """docstring for PandasTimeSeries"""
      def __init__(self):
          super(PandasTimeSeries, self).__init__()
          self.series = None
  
      def __len__(self):
          return len(self.series)
  
      def __str__(self):
          return str(self.series)
  
      def load_csv(self, csv_path):
          self.series = pandas.read_csv(csv_path, index_col=0, header=None,
                                             squeeze=True)
  
      def write_csv(self, csv_path):
          self.series.to_csv(csv_path, header=False)
  
      def load_npy_arrays(self, time_array, signal_array):
          self.series = pandas.Series(signal_array, index=time_array)
  
      def make_copy(self):
          my_copy = PandasTimeSeries()
          my_copy.series = self.series.copy()
          return my_copy
  
      def set_signal_from_scalar(self, scalar, index_list=None):
          if index_list is None:
              self.series.fill(scalar)
          elif len(index_list) > 0:
              scalar_list = [scalar] * len(index_list)
              self.set_values_at_indices(scalar_list, index_list)
          else:
              return
  
      def is_similar_to(self, other_time_series, atol=1e-1):
          return numpy.allclose(self.series, other_time_series.series, atol=atol)
  
      def get_values_at_indices(self, index_list):
          sub_series = self.series.ix[index_list]
          sub_ts = PandasTimeSeries()
          sub_ts.series = sub_series
          return sub_ts
  
      def set_values_at_indices(self, new_values, index_list):
          self.series.ix[index_list] = new_values
  
      def isfinite(self):
          return numpy.isfinite(self.series).all()
  
      def get_indices_where_geq(self, threshold):
          bool_series = (self.series >= threshold)
          series_where_true = self.series[bool_series]
          inds = series_where_true.index
          return inds
  
subject: Implement method to find indices of values that are greater than a given threshold.