class_docstr: Sklearn-compatible estimator, for calculating and adding many features calculated from a given time series
  to the data. It is basically a wrapper around :func:`~tsfresh.feature_extraction.extract_features`.
  
  The features include basic ones like min, max or median, and advanced features like fourier
  transformations or statistical tests. For a list of all possible features, see the module
  :mod:`~tsfresh.feature_extraction.feature_calculators`. The column name of each added feature contains the name
  of the function of that module, which was used for the calculation.
  
  For this estimator, two datasets play a crucial role:
  
  1. the time series container with the timeseries data. This container (for the format see :ref:`data-formats-label`)
     contains the data which is used for calculating the
     features. It must be groupable by ids which are used to identify which feature should be attached to which row
     in the second dataframe.
  
  2. the input data X, where the features will be added to. Its rows are identifies by the index and each index in
     X must be present as an id in the time series container.
  
  Imagine the following situation: You want to classify 10 different financial shares and you have their development
  in the last year as a time series. You would then start by creating features from the metainformation of the
  shares, e.g. how long they were on the market etc. and filling up a table - the features of one stock in one row.
  This is the input array X, which each row identified by e.g. the stock name as an index.
  
  >>> df = pandas.DataFrame(index=["AAA", "BBB", ...])
  >>> # Fill in the information of the stocks
  >>> df["started_since_days"] = ... # add a feature
  
  You can then extract all the features from the time development of the shares, by using this estimator.
  The time series container must include a column of ids, which are the same as the index of X.
  
  >>> time_series = read_in_timeseries() # get the development of the shares
  >>> from tsfresh.transformers import FeatureAugmenter
  >>> augmenter = FeatureAugmenter(column_id="id")
  >>> augmenter.set_timeseries_container(time_series)
  >>> df_with_time_series_features = augmenter.transform(df)
  
  The settings for the feature calculation can be controlled with the settings object.
  If you pass ``None``, the default settings are used.
  Please refer to :class:`~tsfresh.feature_extraction.settings.ComprehensiveFCParameters` for
  more information.
  
  This estimator does not select the relevant features, but calculates and adds all of them to the DataFrame. See the
  :class:`~tsfresh.transformers.relevant_feature_augmenter.RelevantFeatureAugmenter` for calculating and selecting
  features.
  
  For a description what the parameters column_id, column_sort, column_kind and column_value mean, please see
  :mod:`~tsfresh.feature_extraction.extraction`.
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docstr: Set the timeseries, with which the features will be calculated. For a format of the time series container,
  please refer to :mod:`~tsfresh.feature_extraction.extraction`. The timeseries must contain the same indices
  as the later DataFrame, to which the features will be added (the one you will pass to :func:`~transform`). You
  can call this function as often as you like, to change the timeseries later (e.g. if you want to extract for
  different ids).
  
  :param timeseries_container: The timeseries as a pandas.DataFrame or a dict. See
      :mod:`~tsfresh.feature_extraction.extraction` for the format.
  :type timeseries_container: pandas.DataFrame or dict
  :return: None
  :rtype: None
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code: def set_timeseries_container(self, timeseries_container):
      """
      Set the timeseries, with which the features will be calculated. For a format of the time series container,
      please refer to :mod:`~tsfresh.feature_extraction.extraction`. The timeseries must contain the same indices
      as the later DataFrame, to which the features will be added (the one you will pass to :func:`~transform`). You
      can call this function as often as you like, to change the timeseries later (e.g. if you want to extract for
      different ids).
  
      :param timeseries_container: The timeseries as a pandas.DataFrame or a dict. See
          :mod:`~tsfresh.feature_extraction.extraction` for the format.
      :type timeseries_container: pandas.DataFrame or dict
      :return: None
      :rtype: None
      """
      self.timeseries_container = timeseries_container
  
file_path: tsfresh/tsfresh/transformers/feature_augmenter.py
imports: import pandas
  from sklearn.base import BaseEstimator, TransformerMixin
  import tsfresh.defaults
  from tsfresh.feature_extraction import extract_features
  from tsfresh.utilities.dataframe_functions import restrict_input_to_index
label_desc: Use image node_id 2 for calling the FeatureAugmenter obj's underlying member method code with example usage: obj.set_timeseries_container(timeseries_container) without return types