original_docstring: 
          Function to blank regions of the spectrum that have already been
          assigned. This function takes the frequencies of assignments, and
          uses the noise statistics to generate white noise to replace the
          peak. This is to let one focus on unidentified features, rather than
          be distracted by the assignments with large amplitudes.
  
          Parameters
          ----------
          window: float, optional
              Frequency value in MHz to blank. The region corresponds to the
              frequency +/- this value.
          
docstring: Function to blank regions of the spectrum that have already been
  assigned. This function takes the frequencies of assignments, and
  uses the noise statistics to generate white noise to replace the
  peak. This is to let one focus on unidentified features, rather than
  be distracted by the assignments with large amplitudes.
  
  Parameters
  
  float, optional
  Frequency value in MHz to blank. The region corresponds to the
  frequency +/- this value.
docstring_tokens: Function to blank regions of the spectrum that have already been assigned . This function takes the frequencies of assignments and uses the noise statistics to generate white noise to replace the peak . This is to let one focus on unidentified features rather than be distracted by the assignments with large amplitudes . Parameters float optional Frequency value in MHz to blank . The region corresponds to the frequency + / - this value .
code: def clean_spectral_assignments(self, window=1.0):
          """
          Function to blank regions of the spectrum that have already been
          assigned. This function takes the frequencies of assignments, and
          uses the noise statistics to generate white noise to replace the
          peak. This is to let one focus on unidentified features, rather than
          be distracted by the assignments with large amplitudes.
  
          Parameters
          ----------
          window: float, optional
              Frequency value in MHz to blank. The region corresponds to the
              frequency +/- this value.
          """
          # Make a back up of the full spectrum
          if "Full" not in self.data.columns:
              self.data["Full"] = self.data[self.int_col].copy()
          # If the backup exists, restore the backup first before blanking.
          else:
              self.data[self.int_col] = self.data["Full"].copy()
          assignments = self.line_lists["Peaks"].get_assignments()
          frequencies = np.array([transition.frequency for transition in assignments])
          self.logger.info(f"Blanking spectrum over {frequencies} windows.")
          if self.session.noise_region == "als":
              baseline = np.mean(self.session.baseline)
              baseline /= baseline
              rms = np.std(baseline)
          else:
              baseline = self.session.baseline
              rms = self.session.noise_rms
          self.data[self.int_col] = analysis.blank_spectrum(
              self.data,
              frequencies,
              baseline,
              rms,
              freq_col=self.freq_col,
              int_col=self.int_col,
              window=window,
              df=False,
          )
code_tokens: def clean_spectral_assignments ( self , window = 1.0 ) : if "Full" not in self . data . columns : self . data [ "Full" ] = self . data [ self . int_col ] . copy ( ) else : self . data [ self . int_col ] = self . data [ "Full" ] . copy ( ) assignments = self . line_lists [ "Peaks" ] . get_assignments ( ) frequencies = np . array ( [ transition . frequency for transition in assignments ] ) self . logger . info ( f"Blanking spectrum over {frequencies} windows." ) if self . session . noise_region == "als" : baseline = np . mean ( self . session . baseline ) baseline /= baseline rms = np . std ( baseline ) else : baseline = self . session . baseline rms = self . session . noise_rms self . data [ self . int_col ] = analysis . blank_spectrum ( self . data , frequencies , baseline , rms , freq_col = self . freq_col , int_col = self . int_col , window = window , df = False , )
short_docstring: Function to blank regions of the spectrum that have already been
  assigned.
short_docstring_tokens: Function to blank regions of the spectrum that have already been assigned .
comment: """
          Function to blank regions of the spectrum that have already been
          assigned. This function takes the frequencies of assignments, and
          uses the noise statistics to generate white noise to replace the
          peak. This is to let one focus on unidentified features, rather than
          be distracted by the assignments with large amplitudes.
  
          Parameters
          ----------
          window: float, optional
              Frequency value in MHz to blank. The region corresponds to the
              frequency +/- this value.
          """$SEP$# Make a back up of the full spectrum$SEP$# If the backup exists, restore the backup first before blanking.