code: import os
  import copy
  import numpy as np
  import matplotlib
  import matplotlib.pyplot as plt
  from matplotlib.lines import Line2D
  from matplotlib.colors import LinearSegmentedColormap, Normalize
  from matplotlib.cm import ScalarMappable
  import matplotlib.transforms as mtransforms
  from matplotlib.widgets import Button, Slider
  
  from IPython import embed
  
  from pypeit.par import pypeitpar
  from pypeit.core.wavecal import wv_fitting, waveio, wvutils
  from pypeit import utils, msgs
  from astropy.io import ascii as ascii_io
  from astropy.table import Table
  
  operations = dict({'cursor': "Select lines (LMB click)\n" +
                      "         Select regions (LMB drag = add, RMB drag = remove)\n" +
                      "         Navigate (LMB drag = pan, RMB drag = zoom)",
                     'left'  : "Advance the line list slider to the left by one",
                     'right' : "Advance the line list slider to the right by one",
                     'p' : "Toggle pan/zoom with the cursor",
                     'q' : "Close Identify window and continue PypeIt reduction",
                     'a' : "Automatically identify lines using current solution",
                     'c' : "Clear automatically identified lines",
                     'd' : "Delete all line identifications (start from scratch)",
                     'f' : "Fit the wavelength solution",
                     'g' : "Toggle ghost solution (show predicted line positions when wavelength is on the x-axis)",
                     'h' : "Reset ghost parameters",
                     'l' : "Load saved line IDs from file (waveids.ascii in local directory)",
                     'm' : "Select a line",
                     'r' : "Refit a line",
                     's' : "Save current line IDs to a file",
                     'w' : "Toggle wavelength/pixels on the x-axis of the main panel",
                     'z' : "Delete a single line identification",
                     '+/-' : "Raise/Lower the order of the fitting polynomial"
                     })
  
  
  class Identify(object):
      """
      GUI to interactively identify arc lines. The GUI can be run within
      PypeIt during data reduction, or as a standalone script outside of
      PypeIt. To initialise the GUI, call the initialise() function in this
      file.
      """
  
      def __init__(self, canvas, axes, spec, specres, detns, line_lists, par, lflag_color,
                   slit=0, spatid='0', wv_calib=None, pxtoler=None):
          """Controls for the Identify task in PypeIt.
  
          The main goal of this routine is to interactively identify arc lines
          to be used for wavelength calibration.
  
          Parameters
          ----------
          canvas : Matploltib figure canvas
              The canvas on which all axes are contained
          axes : dict
              Dictionary of four Matplotlib axes instances (Main spectrum panel, two for residuals, one for information)
          spec : Matplotlib.Line2D
              Matplotlib Line2D instance which contains plotting information of the plotted arc spectrum
          specres : dict
              Three element list of Matplotlib Line2D/path instances, used for residuals plotting
          detns : ndarray
              Detections from the arc spectrum
          line_lists : astropy.Table
              Contains information about the line list to be used for wavelength calibration
          par : class
              WavelengthSolutionPar Calibration parameters
          lflag_color : list
              List of colors used for plotting
          slit : int
              The slit to be used for wavelength calibration
          spatid : str
              Spatial ID corresponding to slit
          wv_calib : :obj:`dict`, None, optional
              If a best-fitting solution exists, and you wish to load it, provide the wv_calib dictionary.
          pxtoler : float, optional
              Tolerance in pixels for adding lines with the auto option
          """
          # Store the axes
          self.axes = axes
          # Initialise the spectrum properties
          self.spec = spec
          self.specres = specres   # Residual information
          self.specdata = spec.get_ydata()
          self.specx = np.arange(self.specdata.size)
          self.plotx = self.specx.copy()
          # Detections, linelist, line IDs, and fitting params
          self._slit = slit
          self._spatid = spatid
          self._detns = detns
          self._detnsy = self.get_ann_ypos()  # Get the y locations of the annotations
          self._line_lists = line_lists
          self._lines = np.sort(line_lists['wave'].data)  # Remove mask (if any) and then sort
          self._lineids = np.zeros(self._detns.size, dtype=np.float)
          self._lineflg = np.zeros(self._detns.size, dtype=np.int)  # Flags: 0=no ID, 1=user ID, 2=auto ID, 3=flag reject
          self._lflag_color = lflag_color
          self.par = par
          # Auto ID
          self.pxtoler = 0.1 if pxtoler is None else pxtoler
          # Fitting properties
          self._fitdict = dict(polyorder=1,
                               scale=self.specdata.size-1,
                               coeff=None,
                               fitc=None,
                               full_fit=None,
                               res_stats=[]
                               )
          # Initialise the residuals colormap
          residcmap = LinearSegmentedColormap.from_list("my_list", ['grey', 'blue', 'orange', 'red'], N=4)
          self.residmap = ScalarMappable(norm=Normalize(vmin=0, vmax=3), cmap=residcmap)
          # Initialise the annotations
          self.annlines = []
          self.anntexts = []
  
          # Unset some of the matplotlib keymaps
          matplotlib.pyplot.rcParams['keymap.fullscreen'] = ''        # toggling fullscreen (Default: f, ctrl+f)
          matplotlib.pyplot.rcParams['keymap.home'] = ''              # home or reset mnemonic (Default: h, r, home)
          matplotlib.pyplot.rcParams['keymap.back'] = ''              # forward / backward keys to enable (Default: left, c, backspace)
          matplotlib.pyplot.rcParams['keymap.forward'] = ''           # left handed quick navigation (Default: right, v)
          #matplotlib.pyplot.rcParams['keymap.pan'] = ''              # pan mnemonic (Default: p)
          matplotlib.pyplot.rcParams['keymap.zoom'] = ''              # zoom mnemonic (Default: o)
          matplotlib.pyplot.rcParams['keymap.save'] = ''              # saving current figure (Default: s)
          matplotlib.pyplot.rcParams['keymap.quit'] = ''              # close the current figure (Default: ctrl+w, cmd+w)
          matplotlib.pyplot.rcParams['keymap.grid'] = ''              # switching on/off a grid in current axes (Default: g)
          matplotlib.pyplot.rcParams['keymap.yscale'] = ''            # toggle scaling of y-axes ('log'/'linear') (Default: l)
          matplotlib.pyplot.rcParams['keymap.xscale'] = ''            # toggle scaling of x-axes ('log'/'linear') (Default: L, k)
          matplotlib.pyplot.rcParams['keymap.all_axes'] = ''          # enable all axes (Default: a)
  
          # Initialise the main canvas tools
          canvas.mpl_connect('draw_event', self.draw_callback)
          canvas.mpl_connect('button_press_event', self.button_press_callback)
          canvas.mpl_connect('key_press_event', self.key_press_callback)
          canvas.mpl_connect('button_release_event', self.button_release_callback)
          canvas.mpl_connect('motion_notify_event', self.motion_notify_event)
          self.canvas = canvas
          self.background = self.canvas.copy_from_bbox(self.axes['main'].bbox)
  
          # Interaction variables
          self._detns_idx = -1
          self._fitr = None  # Matplotlib shaded fit region (for refitting lines)
          self._fitregions = np.zeros(self.specdata.size, dtype=np.int)  # Mask of the pixels to be included in a fit
          self._addsub = 0   # Adding a region (1) or removing (0)
          self._msedown = False  # Is the mouse button being held down (i.e. dragged)
          self._respreq = [False, None]  # Does the user need to provide a response before any other operation will be permitted? Once the user responds, the second element of this array provides the action to be performed.
          self._qconf = False  # Confirm quit message
          self._changes = False
          self._wavepix = 1   # Show wavelength (0) or pixels (1) on the x-axis of the main panel
          # Setup ghost properties
          # The ghost params correspond to the central wavelength and dispersion, as measured at the middle pixel of the display
          self._ghosttrans = mtransforms.blended_transform_factory(self.axes['main'].transData, self.axes['main'].transAxes)
          self._ghostmode = False  # Display a ghost wavelength solution
          self._ghostdown = False
          self._ghostparam = [0.0, 1.0]  # Ghost params [shift, scale] = [wavecen, disp]
          self.gstlines = []
          self.gsttexts = []
  
          # Setup slider for the linelist
          self._slideval = 0  # Default starting point for the linelist slider
          self.linelist_init()
  
  
          # If an initial solution is available, load it
          if wv_calib is not None:
              self.load_IDs(wv_calib=wv_calib)
              self.fitsol_fit()
  
          # Draw the spectrum
          self.replot()
  
      @classmethod
      def initialise(cls, arccen, slits, slit=0, par=None, wv_calib_all=None,
                     wavelim=None, nonlinear_counts=None, test=False,
                     pxtoler=0.1, fwhm=4.):
          """Initialise the 'Identify' window for real-time wavelength calibration
  
          .. todo::
  
              * Implement multislit functionality
  
          Parameters
          ----------
          arccen : ndarray
              Arc spectrum
          slits : :class:`SlitTraceSet`
              Data container with slit trace information
          slit : int, optional
              The slit to be used for wavelength calibration
          par : :obj:`int`, optional
              The slit to be used for wavelength calibration
          wv_calib_all : :obj:`dict`, None, optional
              If a best-fitting solution exists, and you wish to load it, provide the wv_calib dictionary.
          wavelim : :obj:`list`, None, optional
              A two element list containing the desired minimum and maximum wavelength of the linelist
          test : bool, optional
              If True, this is a unit test
          nonlinear_counts : float, optional
              Counts where the arc is presumed to go non-linear
              Passed to arc_lines_from_spec()
          fwhm : float, optional
              FWHM of arc lines in pixels
          pxtoler : float, optional
              Tolerance in pixels for adding lines with the auto option
  
  
          Returns
          -------
          object : :class:`Identify`
              Returns an instance of the :class:`Identify` class, which contains the results of the fit
          """
  
          # Double check that a WavelengthSolutionPar was input
          par = pypeitpar.WavelengthSolutionPar() if par is None else par
  
          # If a wavelength calibration has been performed already, load it:
          msgs.info("Slit ID = {0:d}  (SPAT ID = {1:d})".format(slit, slits.spat_id[slit]))
          wv_calib = wv_calib_all[str(slits.spat_id[slit])] if wv_calib_all is not None else None
  
          # Extract the lines that are detected in arccen
          thisarc = arccen[:, slit]
          tdetns, _, _, icut, _ = wvutils.arc_lines_from_spec(thisarc,
                                                              fwhm=fwhm,
                                                              sigdetect=par['sigdetect'],
                                                              nonlinear_counts=nonlinear_counts)
          detns = tdetns[icut]
  
          # Load line lists
          if 'ThAr' in par['lamps']:
              line_lists_all = waveio.load_line_lists(par['lamps'])
              line_lists = line_lists_all[np.where(line_lists_all['ion'] != 'UNKNWN')]
          else:
              line_lists = waveio.load_line_lists(par['lamps'])
  
          # Trim the wavelength scale if requested
          if wavelim is not None:
              ww = np.ones(len(line_lists), dtype=bool)
              if wavelim[0] is not None:
                  ww &= line_lists['wave'] > wavelim[0]
              if wavelim[1] is not None:
                  ww &= line_lists['wave'] < wavelim[1]
              line_lists = line_lists[ww]
  
          # Create a Line2D instance for the arc spectrum
          spec = Line2D(np.arange(thisarc.size), thisarc,
                        linewidth=1, linestyle='solid', color='k',
                        drawstyle='steps-mid', animated=True)
  
          # Add the main figure axis
          fig, ax = plt.subplots(figsize=(16, 9), facecolor="white")
          plt.subplots_adjust(bottom=0.05, top=0.85, left=0.05, right=0.65)
          ax.add_line(spec)
          ax.set_ylim((0.0, 1.1 * spec.get_ydata().max()))
  
          # Add two residual fitting axes
          axfit = fig.add_axes([0.7, .5, .28, 0.35])
          axres = fig.add_axes([0.7, .1, .28, 0.35])
          # Residuals
          lflag_color = ['grey', 'blue', 'yellow', 'red']
          residcmap = LinearSegmentedColormap.from_list("my_list", lflag_color, N=len(lflag_color))
          resres = axres.scatter(detns, np.zeros(detns.size), marker='x',
                                 c=np.zeros(detns.size), cmap=residcmap, norm=Normalize(vmin=0.0, vmax=3.0))
          axres.axhspan(-1*pxtoler, pxtoler, alpha=0.5, color='grey')
          axres.axhline(0.0, color='r', linestyle='-')  # Zero level
          axres.set_xlim((0, thisarc.size - 1))
          axres.set_ylim((-0.3, 0.3))
          axres.set_xlabel('Pixel')
          axres.set_ylabel('Residuals (Pix)')
  
          # pixel vs wavelength
          respts = axfit.scatter(detns, np.zeros(detns.size), marker='x',
                                 c=np.zeros(detns.size), cmap=residcmap, norm=Normalize(vmin=0.0, vmax=3.0))
          resfit = Line2D(np.arange(thisarc.size), np.zeros(thisarc.size), linewidth=1, linestyle='-', color='r')
          axfit.add_line(resfit)
          axfit.set_xlim((0, thisarc.size - 1))
          axfit.set_ylim((-0.3, 0.3))  # This will get updated as lines are identified
          axfit.set_xlabel('Pixel')
          axfit.set_ylabel('Wavelength')
  
          # Add an information GUI axis
          axinfo = fig.add_axes([0.15, .92, .7, 0.07])
          axinfo.get_xaxis().set_visible(False)
          axinfo.get_yaxis().set_visible(False)
          axinfo.text(0.5, 0.5, "Press '?' to list the available options", transform=axinfo.transAxes,
                      horizontalalignment='center', verticalalignment='center')
          axinfo.set_xlim((0, 1))
          axinfo.set_ylim((0, 1))
          specres = dict(pixels=respts, model=resfit, resid=resres)
  
          axes = dict(main=ax, fit=axfit, resid=axres, info=axinfo)
          # Initialise the identify window and display to screen
          fig.canvas.set_window_title('PypeIt - Identify')
          ident = Identify(fig.canvas, axes, spec, specres, detns, line_lists, par, lflag_color, slit=slit,
                           spatid=str(slits.spat_id[slit]), wv_calib=wv_calib, pxtoler=pxtoler)
  
          if not test:
              plt.show()
  
          # Now return the results
          return ident
  
      def print_help(self):
          """Print the keys and descriptions that can be used for Identification
          """
          keys = operations.keys()
          print("===============================================================")
          print(" Colored lines in main panels:")
          print("   gray   : wavelength has not been assigned to this detection")
          print("   red    : currently selected line")
          print("   blue   : user has assigned wavelength to this detection")
          print("   yellow : detection has been automatically assigned")
          print(" Colored symbols in residual panels:")
          print("   gray   : wavelength has not been assigned to this detection")
          print("   blue   : user has assigned wavelength to this detection")
          print("   yellow : detection has been automatically assigned")
          print("   red    : automatically assigned wavelength was rejected")
          print("---------------------------------------------------------------")
          print("       IDENTIFY OPERATIONS")
          for key in keys:
              print("{0:6s} : {1:s}".format(key, operations[key]))
          print("---------------------------------------------------------------")
  
      def replot(self):
          """Redraw the entire canvas
          """
          # First set the xdata to be shown
          self.canvas.restore_region(self.background)
          self.toggle_wavepix()
          self.draw_residuals()
          self.draw_lines()
          self.draw_ghost()
          self.canvas.draw()
  
      def linelist_update(self, val):
          """For a given detection, set the linelist value to be the best guess based on the wavelength solution
  
          When a user selects a detection, reset the current value of the linelist
          to reflect the best candidate wavelength for that detection (given the current
          wavelength solution)
  
          Args:
              val (int): The index corresponding to the closest match
          """
          val = int(val)
          self._slidell.label.set_text("{0:.4f}".format(self._lines[val]))
          self._slideval = val
  
      def linelist_select(self, event):
          """Assign a wavelength to a detection
  
          Note, only the LMB works.
  
          Args:
              event (Event): A matplotlib event instance
          """
          if event.button == 1:
              self.update_line_id()
              self._detns_idx = -1
              # Try to perform a fit
              self.fitsol_fit()
              # Now replot everything
              self.replot()
  
      def linelist_init(self):
          """Initialise the linelist Slider (used to assign a line to a detection)
          """
          axcolor = 'lightgoldenrodyellow'
          # Slider
          self.axl = plt.axes([0.15, 0.87, 0.7, 0.04], facecolor=axcolor)
          self._slidell = Slider(self.axl, "{0:.4f}".format(self._lines[self._slideval]), self._slideval,
                                 self._lines.size-1, valinit=0, valstep=1)
          self._slidell.valtext.set_visible(False)
          self._slidell.on_changed(self.linelist_update)
          # Select button
          selax = plt.axes([0.86, 0.87, 0.1, 0.04])
          self._select = Button(selax, 'Assign Line', color=axcolor, hovercolor='y')
          self._select.on_clicked(self.linelist_select)
  
      def toggle_wavepix(self, toggled=False):
          if toggled:
              self._wavepix = 1 - self._wavepix
          self.plotx = self.specx.copy()  # Plot pixels on the x-axis
          if self._wavepix == 0:
              # Check that a wavelength solution exists
              if self._fitdict['coeff'] is None:
                  self.update_infobox(message="Unable to show wavelength until a guess at the solution is available",
                                      yesno=False)
              else:
                  self.plotx = self._fitdict['wave_soln'].copy()
          # Update the x-axis data and axis range
          self.spec.set_xdata(self.plotx)
          if toggled:
              self.axes['main'].set_xlim([self.plotx.min(), self.plotx.max()])
  
      def draw_ghost(self):
          """Draw tick marks at the location of the ghost
          """
          for i in self.gstlines:
              try:
                  i.remove()
              except TypeError:
                  i[0].remove()
          for i in self.gsttexts:
              i.remove()
          self.gstlines = []
          self.gsttexts = []
          # Must have ghost mode on, plotting in wavelength, and have an estimated wavelength solution
          if not self._ghostmode or self._wavepix != 0 or self._fitdict['fitc'] is None:
              return
  
          xmn, xmx = self.axes['main'].get_xlim()
          cent = 0.5*(xmn+xmx)
          plotx = cent + (self._lines + self._ghostparam[0] - cent)*self._ghostparam[1]
  
          # Plot the lines
          w = np.where((plotx > xmn) & (plotx < xmx))[0]
          for i in range(w.size):
              self.gstlines.append(self.axes['main'].plot([plotx[w[i]], plotx[w[i]]], [0.45, 0.55],
                                                          color='g', transform=self._ghosttrans))
              txt = "{0:.2f}".format(self._lines[w[i]])
              self.gsttexts.append(
                  self.axes['main'].annotate(txt, (plotx[w[i]], 0.6), rotation=90.0, alpha=0.5,
                                   color='g', ha='center', xycoords=self._ghosttrans))
  
      def draw_lines(self):
          """Draw the lines and annotate with their IDs
          """
          for i in self.annlines:
              i.remove()
          for i in self.anntexts:
              i.remove()
          self.annlines = []
          self.anntexts = []
          # Decide if pixels or wavelength is being plotted
          plotx = self._detns
          if self._wavepix == 0 and self._fitdict['fitc'] is not None:
              # Plot wavelength
              pixel_fit = self._detns
              xnorm = self._fitdict['xnorm']
  
              # Calculate the estimated wavelength of the detections
              plotx = self._fitdict['full_fit'].eval(pixel_fit / xnorm)
              #plotx = utils.func_val(self._fitdict['fitc'],
              #                       pixel_fit / xnorm,
              #                       self._fitdict["function"],
              #                       minx=self._fitdict['fmin'],
              #                       maxx=self._fitdict['fmax'])
          # Plot the lines
          xmn, xmx = self.axes['main'].get_xlim()
          w = np.where((plotx > xmn) & (plotx < xmx))[0]
          for i in range(w.size):
              if self._lineflg[w[i]] in [0, 3]:
                  if w[i] == self._detns_idx:
                      self.annlines.append(self.axes['main'].axvline(plotx[w[i]], color='r'))
                  else:
                      self.annlines.append(self.axes['main'].axvline(plotx[w[i]], color='grey', alpha=0.5))
                  continue
              else:
                  if w[i] == self._detns_idx:
                      self.annlines.append(self.axes['main'].axvline(plotx[w[i]], color='r'))
                  else:
                      self.annlines.append(self.axes['main'].axvline(plotx[w[i]],
                                                                     color=self._lflag_color[self._lineflg[w[i]]]))
                  txt = "{0:.2f}".format(self._lineids[w[i]])
                  self.anntexts.append(
                      self.axes['main'].annotate(txt, (plotx[w[i]], self._detnsy[w[i]]), rotation=90.0,
                                       color='b', ha='right', va='bottom'))
  
      def draw_residuals(self):
          """Update the subplots that show the residuals
          """
          if self._fitdict["coeff"] is None:
              nid = np.where((self._lineflg == 1) | (self._lineflg == 2))[0].size
              msg = "Cannot plot residuals until more lines have been identified\n" +\
                    "Polynomial order = {0:d}, Number of line IDs = {1:d}".format(self._fitdict["polyorder"], nid)
              self.update_infobox(message=msg, yesno=False)
          else:
              # Remove the annotated residual statistics
              for i in self._fitdict["res_stats"]:
                  i.remove()
              self._fitdict["res_stats"] = []
  
              # Update the line IDs
              for ii in range(self._fitdict['pixel_fit'].size):
                  idx = np.argmin(np.abs(self._detns-self._fitdict['pixel_fit'][ii]))
                  self._lineids[idx] = self._fitdict['wave_fit'][ii]
  
              # Extract the fitting info
              wave_soln = self._fitdict['wave_soln']
              pixel_fit = self._detns
              wave_fit = self._lineids
              xnorm = self._fitdict['xnorm']
              ymin, ymax = np.min(wave_soln[wave_soln != 0.0]) * .95, np.max(wave_soln) * 1.05
  
              # Calculate some stats
              wave_soln_fit = self._fitdict['full_fit'].eval(pixel_fit / xnorm)
              dwv_pix = np.median(np.abs(wave_soln - np.roll(wave_soln, 1)))
              resvals = (wave_fit - wave_soln_fit) / dwv_pix
  
              # Pixel vs wavelength
              self.specres['pixels'].set_offsets(np.c_[pixel_fit, wave_fit])
              self.specres['model'].set_ydata(wave_soln)
              self.axes['fit'].set_ylim((ymin, ymax))
              self.specres['pixels'].set_color(self.residmap.to_rgba(self._lineflg))
  
              # Pixel residuals
              self.specres['resid'].set_offsets(np.c_[pixel_fit, resvals])
              self.axes['resid'].set_ylim((-1.0, 1.0))
              self.specres['resid'].set_color(self.residmap.to_rgba(self._lineflg))
  
              # Write some statistics on the plot
              disptxt = r'$\Delta\lambda$={:.3f}$\AA$ (per pix)'.format(dwv_pix)
              rmstxt = 'RMS={:.3f} (pixels)'.format(self._fitdict['rms'])
              self._fitdict["res_stats"].append(self.axes['fit'].text(0.1 * self.specdata.size,
                                                                      ymin + 0.90 * (ymax - ymin),
                                                                      disptxt, size='small'))
              self._fitdict["res_stats"].append(self.axes['fit'].text(0.1 * self.specdata.size,
                                                                      ymin + 0.80 * (ymax - ymin),
                                                                      rmstxt, size='small'))
  
      def draw_callback(self, event):
          """Draw the lines and annotate with their IDs
  
          Args:
              event (Event): A matplotlib event instance
          """
          # Get the background
          self.background = self.canvas.copy_from_bbox(self.axes['main'].bbox)
          # Set the axis transform
          trans = mtransforms.blended_transform_factory(self.axes['main'].transData, self.axes['main'].transAxes)
          self.draw_fitregions(trans)
          self.axes['main'].draw_artist(self.spec)
          self.draw_lines()
          self.draw_ghost()
  
      def draw_fitregions(self, trans):
          """Refresh the fit regions
  
          Args:
              trans (AxisTransform): A matplotlib axis transform from data to axes coordinates
          """
          if self._fitr is not None:
              self._fitr.remove()
          # Find all regions
          regwhr = np.copy(self._fitregions == 1)
          # Fudge to get the leftmost pixel shaded in too
          regwhr[np.where((self._fitregions[:-1] == 0) & (self._fitregions[1:] == 1))] = True
          self._fitr = self.axes['main'].fill_between(self.plotx, 0, 1, where=regwhr, facecolor='green',
                                            alpha=0.5, transform=trans)
  
      def get_ann_ypos(self, scale=1.02):
          """Calculate the y locations of the annotated IDs
  
          Args:
              scale (float): Scale the location relative to the maximum value of the spectrum
  
          Returns:
              ypos (ndarray): y locations of the annotations
          """
          ypos = np.zeros(self._detns.size)
          for xx in range(self._detns.size):
              wmin = np.argmin(np.abs(self.specx-self._detns[xx]))
              ypos[xx] = scale * np.max(self.specdata[wmin-1:wmin+2])
          return ypos
  
      def get_detns(self):
          """Get the index of the detection closest to the cursor
          """
          return np.argmin(np.abs(self._detns-self.specx[self._end]))
  
      def get_ind_under_point(self, event):
          """Get the index of the line closest to the cursor
  
          Args:
              event (Event): Matplotlib event instance containing information about the event
  
          Returns:
              ind (int): Index of the spectrum where the event occurred
          """
          ind = np.argmin(np.abs(self.plotx - event.xdata))
          return ind
  
      def get_axisID(self, event):
          """Get the ID of the axis where an event has occurred
  
          Args:
              event (Event): Matplotlib event instance containing information about the event
  
          Returns:
              axisID (int, None): Axis where the event has occurred
          """
          if event.inaxes == self.axes['main']:
              return 0
          elif event.inaxes == self.axes['resid']:
              return 1
          elif event.inaxes == self.axes['fit']:
              return 2
          elif event.inaxes == self.axes['info']:
              return 3
          return None
  
      def get_results(self):
          """Perform the final wavelength calibration
  
          Using the line IDs perform the final fit according
          to the wavelength calibration parameters set by the
          user. This routine must be called after the user has
          manually identified all lines.
  
          Returns:
              wvcalib (dict): Dict of wavelength calibration solutions
          """
          wvcalib = {}
          # Check that a result exists:
          if self._fitdict['coeff'] is None:
              wvcalib[str(self._slit)] = None
          else:
              # Perform an initial fit to the user IDs
              self.fitsol_fit()
              # Now perform a detailed fit
              gd_det = np.where((self._lineflg == 1) | (self._lineflg == 2))[0]
              bdisp = self.fitsol_deriv(self.specdata.size/2) # Angstroms/pixel at the centre of the spectrum
              try:
                  #n_final = wvutils.parse_param(self.par, 'n_final', self._slit)
                  final_fit = wv_fitting.iterative_fitting(self.specdata, self._detns, gd_det,
                                                        self._lineids[gd_det], self._line_lists, bdisp,
                                                        verbose=False, n_first=self._fitdict["polyorder"],
                                                        match_toler=self.par['match_toler'],
                                                        func=self.par['func'],
                                                        n_final=self._fitdict["polyorder"], input_only=True,
                                                        sigrej_first=self.par['sigrej_first'],
                                                        sigrej_final=self.par['sigrej_final'])
              except TypeError:
                  wvcalib = None
              else:
                  wvcalib = copy.deepcopy(final_fit)
          return wvcalib
  
      def store_solution(self, final_fit, master_dir, binspec, rmstol=0.15,
                         force_save=False, wvcalib=None):
          """Check if the user wants to store this solution in the reid arxiv
  
          Parameters
          ----------
  
          final_fit : dict
              Dict of wavelength calibration solutions (see self.get_results())
          master_dir : str
              Master directory -- NOT USED
          binspec : int
              Spectral binning
          rmstol : float
              RMS tolerance allowed for the wavelength solution to be stored in the archive
          force_save : bool
              Force save
          wvcalib : :class:`pypeit.wavecalib.WaveCalib`
              Wavelength solution
  
          """
          # Line IDs
          ans = ''
          if not force_save:
              while ans != 'y' and ans != 'n':
                  ans = input("Would you like to store the line IDs? (y/n): ")
          else:
              ans = 'y'
          if ans == 'y':
              self.save_IDs()
          # Solution
          if 'rms' not in final_fit.keys():
              msgs.warn("No wavelength solution available")
              return
          elif final_fit['rms'] < rmstol:
              ans = ''
              if not force_save:
                  while ans != 'y' and ans != 'n':
                      ans = input("Would you like to write this wavelength solution to disk? (y/n): ")
              else:
                  ans = 'y'
              if ans == 'y':
                  # Arxiv solution
                  #outroot = templates.pypeit_identify_record(final_fit, binspec, specname, gratname, dispangl, outdir=master_dir)
                  wavelengths = self._fitdict['full_fit'].eval(np.arange(self.specdata.size) /
                                                               (self.specdata.size - 1))
                  wvutils.write_template(wavelengths, self.specdata, binspec,
                                           './', 'wvarxiv.fits')
                  msgs.info("\nYour arxiv solution has been written to wvarxiv.fits")
                  #msgs.info("\nYour wavelength solution has been stored here:" + msgs.newline() +
                  #          os.path.join(master_dir, outroot) + msgs.newline() + msgs.newline() +
                  #          "If you would like to move this to the PypeIt database, please move this file into the directory:" +
                  #          msgs.newline() + templates.outpath + msgs.newline() + msgs.newline() +
                  #          "Please consider sending your solution to the PypeIt team!" + msgs.newline())
                  #
                  if wvcalib is not None:
                      wvcalib.to_file('wvcalib.fits')
                      msgs.info("\nA WaveCalib container was written to wvcalib.fits")
                  msgs.info("\nPlease consider sending your solution to the PypeIt team!" + msgs.newline())
          else:
              print("Final fit RMS: {0:0.3f} is larger than the allowed tolerance: {1:0.3f}".format(final_fit['rms'], rmstol))
              print("Set the variable --rmstol on the command line to allow a more flexible RMS tolerance")
              ans = ''
  
      def button_press_callback(self, event):
          """What to do when the mouse button is pressed
  
          Args:
              event (Event): Matplotlib event instance containing information about the event
          """
          if event.inaxes is None:
              return
          if self.canvas.toolbar.mode != "":
              return
          if event.button == 1:
              self._addsub = 1
          elif event.button == 3:
              self._addsub = 0
          if event.inaxes == self.axes["main"]:
              self._msedown = True
          axisID = self.get_axisID(event)
          self._start = self.get_ind_under_point(event)
          self._startdata = event.xdata
          self._oldghostscl = self._ghostparam[1]
  
      def motion_notify_event(self, event):
          if event.inaxes is None:
              return
          self._middata = event.xdata
          if self._ghostmode and self._msedown:
              self.update_ghosts()
              # Now plot
              trans = mtransforms.blended_transform_factory(self.axes['main'].transData, self.axes['main'].transAxes)
              self.canvas.restore_region(self.background)
              self.draw_fitregions(trans)
              # Now replot everything
              self.replot()
  
      def button_release_callback(self, event):
          """What to do when the mouse button is released
  
          Args:
              event (Event): Matplotlib event instance containing information about the event
  
          Returns:
              None
          """
          self._msedown = False
          if event.inaxes is None:
              return
          if event.inaxes == self.axes['info']:
              if (event.xdata > 0.8) and (event.xdata < 0.9):
                  answer = "y"
              elif event.xdata >= 0.9:
                  answer = "n"
              else:
                  return
              self.operations(answer, -1)
              self.update_infobox(default=True)
              return
          elif self._respreq[0]:
              # The user is trying to do something before they have responded to a question
              return
          if self.canvas.toolbar.mode != "":
              return
          # Draw an actor
          axisID = self.get_axisID(event)
          if axisID is not None:
              if axisID <= 2:
                  self._end = self.get_ind_under_point(event)
                  if self._end == self._start:
                      # The mouse button was pressed (not dragged)
                      self.operations('m', axisID, event)
                  elif self._end != self._start:
                      # The mouse button was dragged
                      if axisID == 0:
                          if not self._ghostmode:
                              if self._start > self._end:
                                  tmp = self._start
                                  self._start = self._end
                                  self._end = tmp
                              self.update_regions()
          # Now plot
          trans = mtransforms.blended_transform_factory(self.axes['main'].transData, self.axes['main'].transAxes)
          self.canvas.restore_region(self.background)
          self.draw_fitregions(trans)
          # Now replot everything
          self.replot()
  
      def key_press_callback(self, event):
          """What to do when a key is pressed
  
          Args:
              event (Event): Matplotlib event instance containing information about the event
  
          Returns:
              None
          """
          # Check that the event is in an axis...
          if not event.inaxes:
              return
          # ... but not the information box!
          if event.inaxes == self.axes['info']:
              return
          axisID = self.get_axisID(event)
          self.operations(event.key, axisID, event)
  
      def operations(self, key, axisID, event):
          """Canvas operations
  
          Args:
              key (str): Which key has been pressed
              axisID (int): The index of the axis where the key has been pressed (see get_axisID)
          """
          # Check if the user really wants to quit
          if key == 'q' and self._qconf:
              if self._changes:
                  self.update_infobox(message="WARNING: There are unsaved changes!!\nPress q again to exit", yesno=False)
                  self._qconf = True
              else:
                  msgs.bug("Need to change this to kill and return the results to PypeIt")
                  plt.close()
          elif self._qconf:
              self.update_infobox(default=True)
              self._qconf = False
  
          # Manage responses from questions posed to the user.
          if self._respreq[0]:
              if key != "y" and key != "n":
                  return
              else:
                  # Switch off the required response
                  self._respreq[0] = False
                  # Deal with the response
                  if self._respreq[1] == "write":
                      # First remove the old file, and save the new one
                      msgs.work("Not implemented yet!")
                      self.write()
                  else:
                      return
              # Reset the info box
              self.update_infobox(default=True)
              return
  
          if key == '?':
              self.print_help()
          elif key == 'left':
              widx = self._slideval - 1
              if widx < 0:
                  widx = self._lines.size-1
              self.linelist_update(widx)
          elif key == 'right':
              widx = self._slideval + 1
              if widx >= self._lines.size:
                  widx = 0
              self.linelist_update(widx)
          elif key == 'a':
              if self._fitdict['coeff'] is not None:
                  self.auto_id()
              else:
                  msgs.info("You must identify a few lines first")
          elif key == 'c':
              wclr = np.where((self._lineflg == 2) | (self._lineflg == 3))
              self._lineflg[wclr] = 0
              self.replot()
          elif key == 'd':
              self._lineflg *= 0
              self._lineids *= 0.0
              self._fitdict['coeff'] = None
              self.replot()
          elif key == 'f':
              self.fitsol_fit()
              self.replot()
          elif key == 'l':
              self.load_IDs()
          elif key == 'm':
              self._end = self.get_ind_under_point(event)
              self._detns_idx = self.get_detns()
              # Estimate the wavelength, if a solution is available
              if self._fitdict['coeff'] is not None:
                  # Find closest line
                  waveest = self.fitsol_value(idx=self._detns_idx)
                  widx = np.argmin(np.abs(waveest - self._lines))
                  self.linelist_update(widx)
                  self._slidell.set_val(self._slideval)
                  # Print to the information panel
                  self.update_infobox(message="Pixel position = {0:.1f}  Estimated wavelength = {1:.3f}".format(
                      self._detns[self._detns_idx], waveest), yesno=False)
              self.replot()
          elif key == 'q':
              if self._changes:
                  self.update_infobox(message="WARNING: There are unsaved changes!!\nPress q again to exit", yesno=False)
                  self._qconf = True
              else:
                  plt.close()
          elif key == 'r':
              if self._detns_idx == -1:
                  msgs.info("You must select a line first")
              elif self._fitr is None:
                  msgs.info("You must select a fitting region first")
              else:
                  msgs.work("Feature not yet implemented")
          elif key == 's':
              self.save_IDs()
          elif key == 'w':
              self.toggle_wavepix(toggled=True)
              self.replot()
          elif key == 'z':
              self.delete_line_id()
              self.operations('f', axisID, event)
          elif key == '+':
              if self._fitdict["polyorder"] < 10:
                  self._fitdict["polyorder"] += 1
                  self.update_infobox(message="Polynomial order = {0:d}".format(self._fitdict["polyorder"]), yesno=False)
                  self.fitsol_fit()
                  self.replot()
              else:
                  self.update_infobox(message="Polynomial order must be <= 10", yesno=False)
          elif key == '-':
              if self._fitdict["polyorder"] > 1:
                  self._fitdict["polyorder"] -= 1
                  self.update_infobox(message="Polynomial order = {0:d}".format(self._fitdict["polyorder"]), yesno=False)
                  self.fitsol_fit()
                  self.replot()
              else:
                  self.update_infobox(message="Polynomial order must be >= 1", yesno=False)
          elif key == 'g':
              if self._wavepix == 0:
                  self._ghostmode = not self._ghostmode
                  self.replot()
              else:
                  self.update_infobox(message="To enable ghost mode, you need to identify some lines.\nYou also need to set wavelength as the x-axis scale", yesno=False)
          elif key == 'h':
              self._ghostparam = [0.0, 1.0]
              self.replot()
          self.canvas.draw()
  
      def auto_id(self):
          """Automatically assign lines based on a few lines identified by the user
  
          Using the current line IDs and approximate wavelength solution,
          automatically assign a wavelength to all line detections.
          """
  
          # If the IDs are within an acceptable tolerance, flag them as such
          wave_est = self._fitdict['full_fit'].eval(self._detns / self._fitdict['xnorm'])
          for wav in range(wave_est.size):
              if self._lineflg[wav] == 1:
                  # User has manually identified this line already
                  continue
              pixdiff = np.abs(wave_est[wav]-self._lines)
              amin = np.argmin(pixdiff)
              pxtst = pixdiff[amin]/self._fitdict['cen_disp']
              self._lineids[wav] = self._lines[amin]
              if pxtst < self.pxtoler:
                  # Acceptable
                  self._lineflg[wav] = 2
              else:
                  # Unacceptable
                  self._lineflg[wav] = 3
          # Now that we've automatically identified lines, update the canvas
          self.replot()
  
      def delete_line_id(self):
          """Remove an incorrect line ID
          """
          rmid = self.get_detns()
          self._lineids[rmid] = 0.0
          self._lineflg[rmid] = 0
  
      def fitsol_value(self, xfit=None, idx=None):
          """Calculate the wavelength at a pixel
  
          Parameters
          ----------
  
          xfit : ndarray, float
              Pixel values that the user wishes to evaluate the wavelength
          idx : ndarray, int
              Index of the arc line detections that the user wishes to evaluate the wavelength
  
          Returns
          -------
  
          disp : The wavelength (Angstroms) of the requested pixels
          """
          if xfit is None:
              xfit = self._detns
          if self._fitdict['coeff'] is not None:
              if idx is None:
                  return np.polyval(self._fitdict["coeff"], xfit / self._fitdict["scale"])
              else:
                  return np.polyval(self._fitdict["coeff"], xfit[idx] / self._fitdict["scale"])
          else:
              msgs.bug("Cannot predict wavelength value - no fit has been performed")
              return None
  
      def fitsol_deriv(self, xfit=None, idx=None):
          """Calculate the dispersion as a function of wavelength
  
          Args:
              xfit (ndarray, float): Pixel values that the user wishes to evaluate the wavelength
              idx (int): Index of the arc line detections that the user wishes to evaluate the wavelength
  
          Returns:
              disp (ndarray, float, None): The dispersion (Angstroms/pixel) as a function of wavelength
          """
          if xfit is None:
              xfit = self._detns
          if self._fitdict['coeff'] is not None:
              cder = np.polyder(self._fitdict["coeff"])
              if idx is None:
                  return np.polyval(cder, xfit / self._fitdict["scale"]) / self._fitdict["scale"]
              else:
                  return np.polyval(cder, xfit[idx] / self._fitdict["scale"]) / self._fitdict["scale"]
          else:
              msgs.bug("Cannot predict wavelength value - no fit has been performed")
              return None
  
      def fitsol_fit(self):
          """Perform a fit to the line identifications
          """
          # Calculate the dispersion
          # disp = (ids[-1] - ids[0]) / (tcent[idx_str[-1]] - tcent[idx_str[0]])
          # final_fit = fitting.iterative_fitting(censpec, tcent, idx_str, ids,
          #                                       llist, disp, verbose=False,
          #                                       n_first=2, n_final=self._fitdict["polyorder"])
          ord = self._fitdict["polyorder"]
          gd_det = np.where((self._lineflg == 1) | (self._lineflg == 2))  # Use the user IDs or acceptable auto IDs only!
          # Check if there are enough points to perform a fit
          if gd_det[0].size < ord+1:
              msg = "Polynomial order must be >= number of line IDs\n" +\
                    "Polynomial order = {0:d}, Number of line IDs = {1:d}".format(ord, gd_det[0].size)
              self.update_infobox(message=msg, yesno=False)
          else:
              # Start by performing a basic fit
              xpix = self._detns[gd_det] / self._fitdict["scale"]
              ylam = self._lineids[gd_det]
              self._fitdict["coeff"] = np.polyfit(xpix, ylam, ord)
              bdisp = self.fitsol_deriv(self.specdata.size / (2*self._fitdict["scale"]))  # Angstroms/pixel at the centre of the spectrum
              # Then try a detailed fit
              try:
                  final_fit = wv_fitting.iterative_fitting(
                      self.specdata, self._detns, gd_det[0],
                      self._lineids[gd_det[0]], self._line_lists, bdisp,
                      verbose=False, n_first=min(2, self._fitdict["polyorder"]),
                      match_toler=self.par['match_toler'],
                      func=self.par['func'], input_only=True,
                      n_final=self._fitdict["polyorder"],
                      sigrej_first=self.par['sigrej_first'],
                      sigrej_final=self.par['sigrej_final'])
                  final_fit.spat_id = self._slit
  
                  # Update the fitdict
                  #for key in final_fit:
                  #    self._fitdict[key] = final_fit[key]
                  self._fitdict['polyorder'] = final_fit.pypeitfit['order'][0]
                  self._fitdict['fitc'] = final_fit.pypeitfit['fitc']
                  self._fitdict['full_fit'] = final_fit.pypeitfit
                  self._fitdict['pixel_fit'] = final_fit.pixel_fit
                  self._fitdict['wave_fit'] = final_fit.wave_fit
                  self._fitdict['wave_soln'] = final_fit.wave_soln
                  self._fitdict['xnorm'] = final_fit.xnorm
                  self._fitdict['rms'] = final_fit.rms
                  self._fitdict['tcent'] = final_fit.tcent
                  self._fitdict['cen_disp'] = final_fit.cen_disp
                  self._fitdict['cen_wave'] = final_fit.cen_wave
                  self._fitdict['WaveFit'] = final_fit
  
              except TypeError:
                  # Just stick use the basic fit
                  self._fitdict["fitc"] = None
  
      def update_infobox(self, message="Press '?' to list the available options",
                         yesno=True, default=False):
          """Send a new message to the information window at the top of the canvas
  
          Args:
              message (str): Message to be displayed
          """
          self.axes['info'].clear()
          if default:
              self.axes['info'].text(0.5, 0.5, "Press '?' to list the available options", transform=self.axes['info'].transAxes,
                            horizontalalignment='center', verticalalignment='center')
              self.canvas.draw()
              return
          # Display the message
          self.axes['info'].text(0.5, 0.5, message, transform=self.axes['info'].transAxes,
                        horizontalalignment='center', verticalalignment='center')
          if yesno:
              self.axes['info'].fill_between([0.8, 0.9], 0, 1, facecolor='green', alpha=0.5, transform=self.axes['info'].transAxes)
              self.axes['info'].fill_between([0.9, 1.0], 0, 1, facecolor='red', alpha=0.5, transform=self.axes['info'].transAxes)
              self.axes['info'].text(0.85, 0.5, "YES", transform=self.axes['info'].transAxes,
                            horizontalalignment='center', verticalalignment='center')
              self.axes['info'].text(0.95, 0.5, "NO", transform=self.axes['info'].transAxes,
                            horizontalalignment='center', verticalalignment='center')
          self.axes['info'].set_xlim((0, 1))
          self.axes['info'].set_ylim((0, 1))
          self.canvas.draw()
  
      def update_line_id(self):
          """Find the nearest wavelength in the linelist
          """
          if self._detns_idx != -1:
              self._lineids[self._detns_idx] = self._lines[self._slideval]
              self._lineflg[self._detns_idx] = 1
  
      def update_regions(self):
          """Update the regions used to fit Gaussian
          """
          self._fitregions[self._start:self._end] = self._addsub
  
      def update_ghosts(self):
          """Update the ghosts
          """
          if self._addsub == 0:  # RMB
              # Stretching factor
              xmn, xmx = self.axes['main'].get_xlim()
              self._ghostparam[1] = self._oldghostscl*(1.0 + (self._middata - self._startdata) / (xmx - xmn))
          else:  # LMB
              if self._wavepix == 0:
                  # Plotting wavelength
                  self._ghostparam[0] = self._middata - self._startdata
              elif self._fitdict['fitc'] is not None:
                  # Plotting pixels and have a wavelength solution
                  xnorm = self._fitdict['xnorm']
  
                  # Calculate the estimated wavelength of the detections
                  specy = self._fitdict['full_fit'].eval(np.array([self._startdata, self._middata]) / xnorm)
                  self._ghostparam[0] = specy[1] - specy[0]
              else:
                  # Plotting pixels, but don't have a wavelength solution
                  scale = (np.max(self._lines) - np.min(self._lines))/self.specx.size # Angstroms per pixel
                  self._ghostparam[0] = (self._middata - self._startdata) * scale # Calculate the shift in Angstroms
              # grad_orig = self.specx.size / (np.max(self._lines) - np.min(self._lines))
              # plotx = self._ghostparam[1] * grad_orig * (self._lines - np.min(self._lines) + self._ghostparam[0])
  
      def load_IDs(self, wv_calib=None, fname='waveid.ascii'):
          """Load line IDs
          """
          if wv_calib is not None:
              for ii in range(wv_calib['pixel_fit'].size):
                  idx = np.argmin(np.abs(self._detns-wv_calib['pixel_fit'][ii]))
                  self._lineids[idx] = wv_calib['wave_fit'][ii]
                  self._lineflg[idx] = int(wv_calib['mask'][ii])
              self._fitdict['polyorder'] = len(wv_calib['fitc'])-1
              msgs.info("Loaded line IDs")
          elif os.path.exists(fname):
              data = ascii_io.read(fname, format='fixed_width')
              self._detns = data['pixel'].data
              self._lineids = data['wavelength'].data
              self._lineflg = data['flag'].data
              msgs.info("Loaded line IDs:" + msgs.newline() + fname)
              self.update_infobox(message="Loaded line IDs: {0:s}".format(fname), yesno=False)
          else:
              self.update_infobox(message="Could not find line IDs: {0:s}".format(fname), yesno=False)
  
      def save_IDs(self, fname='waveid.ascii'):
          """Save the current IDs
          """
          meta = dict(comments=["flags:",
                                "   0 = wavelength has not been assigned to this detection",
                                "   1 = user has assigned wavelength to this detection",
                                "   2 = detection has been automatically assigned",
                                "   3 = automatically assigned wavelength was rejected"])
          data = Table({'pixel' : self._detns,
                        'wavelength' : self._lineids,
                        'flag' : self._lineflg},
                       names=['pixel', 'wavelength', 'flag'],
                       meta=meta)
          ascii_io.write(data, fname, format='fixed_width')
          msgs.info("Line IDs saved as:" + msgs.newline() + fname)
          self.update_infobox(message="Line IDs saved as: {0:s}".format(fname), yesno=False)
  
apis: matplotlib.widgets.Button numpy.copy matplotlib.colors.LinearSegmentedColormap.from_list matplotlib.pyplot.axes matplotlib.pyplot.subplots_adjust numpy.polyfit numpy.polyval matplotlib.colors.Normalize numpy.argmin numpy.abs numpy.where numpy.zeros matplotlib.pyplot.subplots numpy.arange numpy.max numpy.min matplotlib.pyplot.close numpy.sort numpy.roll matplotlib.pyplot.show numpy.polyder numpy.array matplotlib.transforms.blended_transform_factory