class_docstr: Test behaviour of the learn_dynamic_structure of `from_pandas_dynamic`
class_docstr_tok: ["Test","behaviour","of","the","learn_dynamic_structure","of","`","from_pandas_dynamic","`"]
docstr: The result when having a point of discontinuity must be the same as if we cut the df in two (on the discont.
  point) and provide the two datasets as input.
  
  This is because, inside, the algorithm cuts the dfs into continuous datasets
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code: def test_discondinuity(self):
      """
      The result when having a point of discontinuity must be the same as if we cut the df in two (on the discont.
      point) and provide the two datasets as input.
  
      This is because, inside, the algorithm cuts the dfs into continuous datasets
      """
      np.random.seed(12)
      df = pd.DataFrame(
          np.random.random([100, 5]), columns=["a", "b", "c", "d", "e"]
      )
      df_2 = pd.DataFrame(
          np.random.random([100, 5]),
          columns=["a", "b", "c", "d", "e"],
          index=np.arange(200, 300),
      )
  
      sm = from_pandas_dynamic(
          pd.concat([df, df_2], axis=0), p=2, w_threshold=0.05
      )
      sm_1 = from_pandas_dynamic([df, df_2], p=2, w_threshold=0.05)
  
      assert [
          (u, v, round(w, 3)) for u, v, w in sm_1.edges(data="weight")
      ] == [(u, v, round(w, 3)) for u, v, w in sm.edges(data="weight")]
  
imports: import re
  import networkx
  import numpy
  import pandas
  import pytest
  from causalnex.structure.dynotears import from_numpy_dynamic, from_pandas_dynamic
label_desc: Use image node_id 19 for calling the TestFromPandasDynotears obj's underlying member method code with example usage: obj.test_discondinuity() without return types