original_docstring: 
          Blanks a spectrum based on the lines already previously assigned. The required arguments are the average
          and standard deviation of the noise, typically estimated by picking a region free of spectral features.
  
          The spectra are sequentially blanked - online catalogs first, followed by literature species, finally the
          private assignments.
  
          Parameters
          ----------
          noise - float
              Average noise value for the spectrum. Typically measured by choosing a region void of spectral lines.
          noise_std - float
              Standard deviation for the spectrum noise.
          window - float
              Value to use for the range to blank. This region blanked corresponds to frequency+/-window.
          
docstring: Blanks a spectrum based on the lines already previously assigned. The required arguments are the average
  and standard deviation of the noise, typically estimated by picking a region free of spectral features.
  
  The spectra are sequentially blanked - online catalogs first, followed by literature species, finally the
  private assignments.
  
  Parameters
  
  float
  Average noise value for the spectrum. Typically measured by choosing a region void of spectral lines.
  noise_std - float
  Standard deviation for the spectrum noise.
  window - float
  Value to use for the range to blank. This region blanked corresponds to frequency+/-window.
docstring_tokens: Blanks a spectrum based on the lines already previously assigned . The required arguments are the average and standard deviation of the noise typically estimated by picking a region free of spectral features . The spectra are sequentially blanked - online catalogs first followed by literature species finally the private assignments . Parameters float Average noise value for the spectrum . Typically measured by choosing a region void of spectral lines . noise_std - float Standard deviation for the spectrum noise . window - float Value to use for the range to blank . This region blanked corresponds to frequency + / - window .
code: def blank_spectrum(self, noise=0.0, noise_std=0.05, window=1.0):
          """
          Blanks a spectrum based on the lines already previously assigned. The required arguments are the average
          and standard deviation of the noise, typically estimated by picking a region free of spectral features.
  
          The spectra are sequentially blanked - online catalogs first, followed by literature species, finally the
          private assignments.
  
          Parameters
          ----------
          noise - float
              Average noise value for the spectrum. Typically measured by choosing a region void of spectral lines.
          noise_std - float
              Standard deviation for the spectrum noise.
          window - float
              Value to use for the range to blank. This region blanked corresponds to frequency+/-window.
          """
          sources = ["CDMS/JPL", "Literature", "New"]
          slices = [
              self.table.loc[self.table["source"] == "CDMS/JPL"],
              self.table.loc[
                  (self.table["source"] != "CDMS/JPL") & (self.table["public"] == True)
              ],
              self.table.loc[
                  (self.table["source"] != "CDMS/JPL") & (self.table["public"] == False)
              ],
          ]
          last_source = "Catalog"
          for index, (df, source) in enumerate(zip(slices, sources)):
              try:
                  if len(df) > 0:
                      if index == 0:
                          reference = self.int_col
                      else:
                          reference = last_source
                      blanked_spectrum = analysis.blank_spectrum(
                          self.data,
                          df["frequency"].values,
                          noise,
                          noise_std,
                          self.freq_col,
                          reference,
                          window,
                          df=False,
                      )
                      self.data[source] = blanked_spectrum
                      last_source = source
              except (KeyError, ValueError):
                  self.logger.warning(
                      f"Could not blank spectrum {last_source} with {source}."
                  )
code_tokens: def blank_spectrum ( self , noise = 0.0 , noise_std = 0.05 , window = 1.0 ) : sources = [ "CDMS/JPL" , "Literature" , "New" ] slices = [ self . table . loc [ self . table [ "source" ] == "CDMS/JPL" ] , self . table . loc [ ( self . table [ "source" ] != "CDMS/JPL" ) & ( self . table [ "public" ] == True ) ] , self . table . loc [ ( self . table [ "source" ] != "CDMS/JPL" ) & ( self . table [ "public" ] == False ) ] , ] last_source = "Catalog" for index , ( df , source ) in enumerate ( zip ( slices , sources ) ) : try : if len ( df ) > 0 : if index == 0 : reference = self . int_col else : reference = last_source blanked_spectrum = analysis . blank_spectrum ( self . data , df [ "frequency" ] . values , noise , noise_std , self . freq_col , reference , window , df = False , ) self . data [ source ] = blanked_spectrum last_source = source except ( KeyError , ValueError ) : self . logger . warning ( f"Could not blank spectrum {last_source} with {source}." )
short_docstring: Blanks a spectrum based on the lines already previously assigned.
short_docstring_tokens: Blanks a spectrum based on the lines already previously assigned .
comment: """
          Blanks a spectrum based on the lines already previously assigned. The required arguments are the average
          and standard deviation of the noise, typically estimated by picking a region free of spectral features.
  
          The spectra are sequentially blanked - online catalogs first, followed by literature species, finally the
          private assignments.
  
          Parameters
          ----------
          noise - float
              Average noise value for the spectrum. Typically measured by choosing a region void of spectral lines.
          noise_std - float
              Standard deviation for the spectrum noise.
          window - float
              Value to use for the range to blank. This region blanked corresponds to frequency+/-window.
          """