class_docstr: An internal class to manage filters/algorithms for uniform grid datasets.
class_docstr_tok: ["An","internal","class","to","manage","filters\/algorithms","for","uniform","grid","datasets","."]
docstr: Apply a reverse fast Fourier transform (RFFT) to the active scalars.
  
  The input can be real or complex data, but the output is always
  :attr:`numpy.complex128`. The filter is fastest for images that have power
  of two sizes.
  
  The filter uses a butterfly diagram for each prime factor of the
  dimension. This makes images with prime number dimensions (i.e. 17x17)
  much slower to compute. FFTs of multidimensional meshes (i.e volumes)
  are decomposed so that each axis executes serially.
  
  The frequencies of the input assume standard order: along each axis
  first positive frequencies are assumed from 0 to the maximum, then
  negative frequencies are listed from the largest absolute value to
  smallest. This implies that the corners of the grid correspond to low
  frequencies, while the center of the grid corresponds to high
  frequencies.
  
  Parameters
  ----------
  output_scalars_name : str, optional
      The name of the output scalars. By default, this is the same as the
      active scalars of the dataset.
  
  progress_bar : bool, default: False
      Display a progress bar to indicate progress.
  
  Returns
  -------
  pyvista.ImageData
      :class:`pyvista.ImageData` with the applied reverse FFT.
  
  See Also
  --------
  fft : The direct transform.
  low_pass : Low-pass filtering of FFT output.
  high_pass : High-pass filtering of FFT output.
  
  Examples
  --------
  Apply reverse FFT to an example image.
  
  >>> from pyvista import examples
  >>> image = examples.download_moonlanding_image()
  >>> fft_image = image.fft()
  >>> image_again = fft_image.rfft()
  >>> image_again.point_data  # doctest:+SKIP
  pyvista DataSetAttributes
  Association     : POINT
  Active Scalars  : PNGImage
  Active Vectors  : None
  Active Texture  : None
  Active Normals  : None
  Contains arrays :
      PNGImage                complex128 (298620,)            SCALARS
  
  See :ref:`image_fft_example` for a full example using this filter.
docstr_tok: ["Apply","a","reverse","fast","Fourier","transform","(","RFFT",")","to","the","active","scalars",".","The","input","can","be","real","or","complex","data",",","but","the","output","is","always",":","attr",":","`","numpy.complex128","`",".","The","filter","is","fastest","for","images","that","have","power","of","two","sizes",".","The","filter","uses","a","butterfly","diagram","for","each","prime","factor","of","the","dimension",".","This","makes","images","with","prime","number","dimensions","(","i.e",".","17x17",")","much","slower","to","compute",".","FFTs","of","multidimensional","meshes","(","i.e","volumes",")","are","decomposed","so","that","each","axis","executes","serially",".","The","frequencies","of","the","input","assume","standard","order",":","along","each","axis","first","positive","frequencies","are","assumed","from","0","to","the","maximum",",","then","negative","frequencies","are","listed","from","the","largest","absolute","value","to","smallest",".","This","implies","that","the","corners","of","the","grid","correspond","to","low","frequencies",",","while","the","center","of","the","grid","corresponds","to","high","frequencies",".","Parameters","--","--","--","--","--","output_scalars_name",":","str",",","optional","The","name","of","the","output","scalars",".","By","default",",","this","is","the","same","as","the","active","scalars","of","the","dataset",".","progress_bar",":","bool",",","default",":","False","Display","a","progress","bar","to","indicate","progress",".","Returns","--","--","--","-","pyvista.ImageData",":","class",":","`","pyvista.ImageData","`","with","the","applied","reverse","FFT",".","See","Also","--","--","--","--","fft",":","The","direct","transform",".","low_pass",":","Low-pass","filtering","of","FFT","output",".","high_pass",":","High-pass","filtering","of","FFT","output",".","Examples","--","--","--","--","Apply","reverse","FFT","to","an","example","image",".",">",">",">","from","pyvista","import","examples",">",">",">","image","=","examples.download_moonlanding_image","(",")",">",">",">","fft_image","=","image.fft","(",")",">",">",">","image_again","=","fft_image.rfft","(",")",">",">",">","image_again.point_data","#","doctest",":","+SKIP","pyvista","DataSetAttributes","Association",":","POINT","Active","Scalars",":","PNGImage","Active","Vectors",":","None","Active","Texture",":","None","Active","Normals",":","None","Contains","arrays",":","PNGImage","complex128","(","298620",",",")","SCALARS","See",":","ref",":","`","image_fft_example","`","for","a","full","example","using","this","filter","."]
code: def rfft(self, output_scalars_name=None, progress_bar=False):
      """Apply a reverse fast Fourier transform (RFFT) to the active scalars.
  
      The input can be real or complex data, but the output is always
      :attr:`numpy.complex128`. The filter is fastest for images that have power
      of two sizes.
  
      The filter uses a butterfly diagram for each prime factor of the
      dimension. This makes images with prime number dimensions (i.e. 17x17)
      much slower to compute. FFTs of multidimensional meshes (i.e volumes)
      are decomposed so that each axis executes serially.
  
      The frequencies of the input assume standard order: along each axis
      first positive frequencies are assumed from 0 to the maximum, then
      negative frequencies are listed from the largest absolute value to
      smallest. This implies that the corners of the grid correspond to low
      frequencies, while the center of the grid corresponds to high
      frequencies.
  
      Parameters
      ----------
      output_scalars_name : str, optional
          The name of the output scalars. By default, this is the same as the
          active scalars of the dataset.
  
      progress_bar : bool, default: False
          Display a progress bar to indicate progress.
  
      Returns
      -------
      pyvista.ImageData
          :class:`pyvista.ImageData` with the applied reverse FFT.
  
      See Also
      --------
      fft : The direct transform.
      low_pass : Low-pass filtering of FFT output.
      high_pass : High-pass filtering of FFT output.
  
      Examples
      --------
      Apply reverse FFT to an example image.
  
      >>> from pyvista import examples
      >>> image = examples.download_moonlanding_image()
      >>> fft_image = image.fft()
      >>> image_again = fft_image.rfft()
      >>> image_again.point_data  # doctest:+SKIP
      pyvista DataSetAttributes
      Association     : POINT
      Active Scalars  : PNGImage
      Active Vectors  : None
      Active Texture  : None
      Active Normals  : None
      Contains arrays :
          PNGImage                complex128 (298620,)            SCALARS
  
      See :ref:`image_fft_example` for a full example using this filter.
  
      """
      self._check_fft_scalars()
      alg = _vtk.vtkImageRFFT()
      alg.SetInputDataObject(self)
      _update_alg(
          alg,
          progress_bar,
          "Performing Reverse Fast Fourier Transform.",
      )
      output = _get_output(alg)
      self._change_fft_output_scalars(
          output,
          self.point_data.active_scalars_name,
          output_scalars_name,
      )
      return output
  
imports: import collections.abc
  from typing import Literal, Optional, cast
  import numpy
  import pyvista
  from pyvista.core import _vtk_core
  from pyvista.core.errors import AmbiguousDataError, MissingDataError
  from pyvista.core.filters import _get_output, _update_alg
  from pyvista.core.filters.data_set import DataSetFilters
  from pyvista.core.utilities.arrays import FieldAssociation, set_default_active_scalars
  from pyvista.core.utilities.helpers import wrap
  from pyvista.core.utilities.misc import abstract_class
label_desc: Use image node_id 7 for calling the ImageDataFilters obj's underlying member method code with example usage: obj.rfft(output_scalars_name, progress_bar) and returns: output