class_docstr: Iterable/indexable class for time series data with context data
  Numpy arrays are sufficient time series data alone is needed
  
  Parameters
  ----------
  ts_data : array-like, shape (N, )
      time series data
  context_data : array-like (N, )
      contextual data
code: def __init__(self, ts_data, context_data):
      N = len(ts_data)
      if isinstance(ts_data, (list, tuple)):
          ts_data = np.array(ts_data, dtype=object)
      self.ts_data = np.atleast_1d(ts_data)
      self.context_data = np.atleast_1d(context_data)
      self.index = 0
      self.N = N
      self.shape = [N]
  
file_path: seglearn/seglearn/base.py
imports: import numpy
label_desc: Use image node_id 1 to create a new TS_Data object from inherited base classes: object with example: obj = TS_Data(ts_data, context_data)