class_docstr: Class to extract persistence diagrams from time series.
  
  Args:
      takens_embedding_dim: Dimension of the Takens embedding.
      takens_embedding_delay: Delay of the Takens embedding.
      homology_dimensions: Homology dimensions to compute.
      filtering: Whether to filter the persistence diagrams.
      filtering_dimensions: Homology dimensions to filter.
      parallel: Whether to parallelize the computation.
class_docstr_tok: ["Class","to","extract","persistence","diagrams","from","time","series",".","Args",":","takens_embedding_dim",":","Dimension","of","the","Takens","embedding",".","takens_embedding_delay",":","Delay","of","the","Takens","embedding",".","homology_dimensions",":","Homology","dimensions","to","compute",".","filtering",":","Whether","to","filter","the","persistence","diagrams",".","filtering_dimensions",":","Homology","dimensions","to","filter",".","parallel",":","Whether","to","parallelize","the","computation","."]
code: def __init__(
      self,
      takens_embedding_dim: int,
      takens_embedding_delay: int,
      homology_dimensions: tuple,
      filtering: bool = False,
      filtering_dimensions: tuple = (1, 2),
  ):
      self.takens_embedding_dim_ = takens_embedding_dim
      self.takens_embedding_delay_ = takens_embedding_delay
      self.homology_dimensions_ = homology_dimensions
      self.filtering_ = filtering
      self.filtering_dimensions_ = filtering_dimensions
  
imports: from abc import ABC
  import numpy
  import pandas
  from gtda.diagrams import Scaler, Filtering, PersistenceEntropy, PersistenceLandscape, BettiCurve
  from gtda.homology import VietorisRipsPersistence
label_desc: Use image node_id 1 to create a new PersistenceDiagramsExtractor object  with example: obj = PersistenceDiagramsExtractor(takens_embedding_dim, takens_embedding_delay, homology_dimensions, filtering, filtering_dimensions)