docstr: dense network are more likely to be non stationary. we check that the simulator is still able to provide a
  stationary time-deries in that case.
  
  If df contain only ones it means that the generator failed to obtain a stationary structure
docstr_tok: ["dense","network","are","more","likely","to","be","non","stationary",".","we","check","that","the","simulator","is","still","able","to","provide","a","stationary","time-deries","in","that","case",".","If","df","contain","only","ones","it","means","that","the","generator","failed","to","obtain","a","stationary","structure"]
code: def test_dense_networks(self):
      """dense network are more likely to be non stationary. we check that the simulator is still able to provide a
      stationary time-deries in that case.
  
      If df contain only ones it means that the generator failed to obtain a stationary structure
      """
      np.random.seed(4)
      _, df, _, _ = gen_stationary_dyn_net_and_df(
          n_samples=1000,
          p=1,
          w_min_inter=0.2,
          w_max_inter=0.5,
          max_data_gen_trials=10,
          degree_intra=4,
          degree_inter=7,
      )
      assert np.any(np.ones(df.shape) != df)
  
imports: import re
  import string
  from itertools import product
  import numpy
  import pandas
  import pytest
  from scipy import stats
  from sklearn.gaussian_process.kernels import RBF
  from causalnex.structure import StructureModel
  from causalnex.structure.data_generators import gen_stationary_dyn_net_and_df, generate_binary_data, generate_binary_dataframe, generate_categorical_dataframe, generate_continuous_data, generate_continuous_dataframe, generate_count_dataframe, generate_dataframe_dynamic, generate_structure, generate_structure_dynamic
  from tests.structure.data_generators.test_core import calculate_proba
label_desc: Use image node_id 4 for calling the TestGenerateStationaryDynamicStructureAndSamples obj's underlying member method code with example usage: obj.test_dense_networks() without return types