original_docstring: 
              Generates a Plotly figure with the assignments overlaid
              on the experimental spectrum.
  
              Does not require any parameters, but requires that
              the assignments and peak finding functions have been
              run previously.
          
docstring: Generates a Plotly figure with the assignments overlaid
  on the experimental spectrum.
  
  Does not require any parameters, but requires that
  the assignments and peak finding functions have been
  run previously.
docstring_tokens: Generates a Plotly figure with the assignments overlaid on the experimental spectrum . Does not require any parameters but requires that the assignments and peak finding functions have been run previously .
code: def plot_assigned(self):
          """
              Generates a Plotly figure with the assignments overlaid
              on the experimental spectrum.
  
              Does not require any parameters, but requires that
              the assignments and peak finding functions have been
              run previously.
          """
          fig = go.FigureWidget()
          fig.layout["title"] = f"Experiment {self.session.experiment}"
          fig.layout["xaxis"]["title"] = "Frequency (MHz)"
          fig.layout["xaxis"]["tickformat"] = ",.2f"
  
          # Update the peaks table
          self.peaks = pd.DataFrame(
              data=[
                  [uline.frequency, uline.intensity]
                  for uline in self.line_lists["Peaks"].get_ulines()
              ],
              columns=["Frequency", "Intensity"],
          )
  
          fig.add_scattergl(
              x=self.data["Frequency"],
              y=self.data[self.int_col],
              name="Experiment",
              opacity=0.6,
          )
  
          fig.add_bar(
              x=self.table["catalog_frequency"],
              y=self.table["intensity"],
              width=1.0,
              hoverinfo="text",
              text=self.table["name"].astype(str)
              + "-"
              + self.table["r_qnos"].astype(str),
              name="Assignments",
          )
          ulines = np.array(
              [
                  [uline.intensity, uline.frequency]
                  for uline in self.line_lists["Peaks"].get_ulines()
              ]
          )
  
          fig.add_bar(x=ulines[:, 1], y=ulines[:, 0], width=1.0, name="U-lines")
          return fig
code_tokens: def plot_assigned ( self ) : fig = go . FigureWidget ( ) fig . layout [ "title" ] = f"Experiment {self.session.experiment}" fig . layout [ "xaxis" ] [ "title" ] = "Frequency (MHz)" fig . layout [ "xaxis" ] [ "tickformat" ] = ",.2f" self . peaks = pd . DataFrame ( data = [ [ uline . frequency , uline . intensity ] for uline in self . line_lists [ "Peaks" ] . get_ulines ( ) ] , columns = [ "Frequency" , "Intensity" ] , ) fig . add_scattergl ( x = self . data [ "Frequency" ] , y = self . data [ self . int_col ] , name = "Experiment" , opacity = 0.6 , ) fig . add_bar ( x = self . table [ "catalog_frequency" ] , y = self . table [ "intensity" ] , width = 1.0 , hoverinfo = "text" , text = self . table [ "name" ] . astype ( str ) + "-" + self . table [ "r_qnos" ] . astype ( str ) , name = "Assignments" , ) ulines = np . array ( [ [ uline . intensity , uline . frequency ] for uline in self . line_lists [ "Peaks" ] . get_ulines ( ) ] ) fig . add_bar ( x = ulines [ : , 1 ] , y = ulines [ : , 0 ] , width = 1.0 , name = "U-lines" ) return fig
short_docstring: Generates a Plotly figure with the assignments overlaid
  on the experimental spectrum.
short_docstring_tokens: Generates a Plotly figure with the assignments overlaid on the experimental spectrum .
comment: """
              Generates a Plotly figure with the assignments overlaid
              on the experimental spectrum.
  
              Does not require any parameters, but requires that
              the assignments and peak finding functions have been
              run previously.
          """$SEP$# Update the peaks table