class_docstr: The unit tests in this module make sure if the time series features are created properly
class_docstr_tok: ["The","unit","tests","in","this","module","make","sure","if","the","time","series","features","are","created","properly"]
code: def test_extract_features_for_one_time_series(self):
      # todo: implement more methods and test more aspects
      df = self.create_test_data_sample()
      settings = ComprehensiveFCParameters()
  
      extracted_features = extract_features(
          df,
          default_fc_parameters=settings,
          column_value="val",
          column_id="id",
          column_kind="kind",
          column_sort="sort",
      )
  
      self.assertIsInstance(extracted_features, pd.DataFrame)
      self.assertTrue(
          np.all(
              extracted_features.b__sum_values == np.array([757, 695])
          )
      )
      self.assertTrue(
          np.all(extracted_features.b__minimum == np.array([3, 1]))
      )
      self.assertTrue(
          np.all(
              extracted_features.b__abs_energy
              == np.array([36619, 35483])
          )
      )
      self.assertTrue(
          np.all(extracted_features.b__mean == np.array([37.85, 34.75]))
      )
      self.assertTrue(
          np.all(extracted_features.b__median == np.array([39.5, 28.0]))
      )
  
      df_sts = self.create_one_valued_time_series()
      extracted_features_sts = extract_features(
          df_sts,
          default_fc_parameters=settings,
          column_value="val",
          column_id="id",
          column_kind="kind",
          column_sort="sort",
      )
  
      self.assertIsInstance(extracted_features_sts, pd.DataFrame)
      self.assertTrue(
          np.all(
              extracted_features_sts.a__maximum == np.array([1.0, 6.0])
          )
      )
      self.assertTrue(
          np.all(
              extracted_features_sts.a__sum_values
              == np.array([1.0, 11.0])
          )
      )
      self.assertTrue(
          np.all(
              extracted_features_sts.a__count_above_mean
              == np.array([0, 1])
          )
      )
  
imports: import os
  import tempfile
  import numpy
  import pandas
  from mock import Mock
  from tests.fixtures import DataTestCase
  from tsfresh.feature_extraction.extraction import extract_features
  from tsfresh.feature_extraction.settings import ComprehensiveFCParameters, PickableSettings
  from tsfresh.utilities.distribution import IterableDistributorBaseClass, MapDistributor
label_desc: Use image node_id 4 for calling the ExtractionTestCase obj's underlying member method code with example usage: obj.test_extract_features_for_one_time_series() without return types