content: """
  These are  classes to support contour plotting and
  labelling for the axes class
  """
  from __future__ import division
  import warnings
  import matplotlib as mpl
  import numpy as np
  from numpy import ma
  import matplotlib._cntr as _cntr
  import matplotlib.path as path
  import matplotlib.ticker as ticker
  import matplotlib.cm as cm
  import matplotlib.colors as colors
  import matplotlib.collections as collections
  import matplotlib.font_manager as font_manager
  import matplotlib.text as text
  import matplotlib.cbook as cbook
  import matplotlib.mlab as mlab
  
  # Import needed for adding manual selection capability to clabel
  from matplotlib.blocking_input import BlockingContourLabeler
  
  # We can't use a single line collection for contour because a line
  # collection can have only a single line style, and we want to be able to have
  # dashed negative contours, for example, and solid positive contours.
  # We could use a single polygon collection for filled contours, but it
  # seems better to keep line and filled contours similar, with one collection
  # per level.
  
  
  class ContourLabeler:
      '''Mixin to provide labelling capability to ContourSet'''
  
      def clabel(self, *args, **kwargs):
          """
          call signature::
  
            clabel(cs, **kwargs)
  
          adds labels to line contours in *cs*, where *cs* is a
          :class:`~matplotlib.contour.ContourSet` object returned by
          contour.
  
          ::
  
            clabel(cs, v, **kwargs)
  
          only labels contours listed in *v*.
  
          Optional keyword arguments:
  
            *fontsize*:
              See http://matplotlib.sf.net/fonts.html
  
            *colors*:
              - if *None*, the color of each label matches the color of
                the corresponding contour
  
              - if one string color, e.g. *colors* = 'r' or *colors* =
                'red', all labels will be plotted in this color
  
              - if a tuple of matplotlib color args (string, float, rgb, etc),
                different labels will be plotted in different colors in the order
                specified
  
            *inline*:
              controls whether the underlying contour is removed or
              not. Default is *True*.
  
            *inline_spacing*:
              space in pixels to leave on each side of label when
              placing inline.  Defaults to 5.  This spacing will be
              exact for labels at locations where the contour is
              straight, less so for labels on curved contours.
  
            *fmt*:
              a format string for the label. Default is '%1.3f'
              Alternatively, this can be a dictionary matching contour
              levels with arbitrary strings to use for each contour level
              (i.e., fmt[level]=string)
  
            *manual*:
              if *True*, contour labels will be placed manually using
              mouse clicks.  Click the first button near a contour to
              add a label, click the second button (or potentially both
              mouse buttons at once) to finish adding labels.  The third
              button can be used to remove the last label added, but
              only if labels are not inline.  Alternatively, the keyboard
              can be used to select label locations (enter to end label
              placement, delete or backspace act like the third mouse button,
              and any other key will select a label location).
  
          .. plot:: mpl_examples/pylab_examples/contour_demo.py
          """
  
          """
          NOTES on how this all works:
  
          clabel basically takes the input arguments and uses them to
          add a list of "label specific" attributes to the ContourSet
          object.  These attributes are all of the form label* and names
          should be fairly self explanatory.
  
          Once these attributes are set, clabel passes control to the
          labels method (case of automatic label placement) or
          BlockingContourLabeler (case of manual label placement).
          """
  
          fontsize = kwargs.get('fontsize', None)
          inline = kwargs.get('inline', 1)
          inline_spacing = kwargs.get('inline_spacing', 5)
          self.labelFmt = kwargs.get('fmt', '%1.3f')
          _colors = kwargs.get('colors', None)
  
          # Detect if manual selection is desired and remove from argument list
          self.labelManual=kwargs.get('manual',False)
  
          if len(args) == 0:
              levels = self.levels
              indices = range(len(self.levels))
          elif len(args) == 1:
              levlabs = list(args[0])
              indices, levels = [], []
              for i, lev in enumerate(self.levels):
                  if lev in levlabs:
                      indices.append(i)
                      levels.append(lev)
              if len(levels) < len(levlabs):
                  msg = "Specified levels " + str(levlabs)
                  msg += "\n don't match available levels "
                  msg += str(self.levels)
                  raise ValueError(msg)
          else:
              raise TypeError("Illegal arguments to clabel, see help(clabel)")
          self.labelLevelList = levels
          self.labelIndiceList = indices
  
          self.labelFontProps = font_manager.FontProperties()
          if fontsize == None:
              font_size = int(self.labelFontProps.get_size_in_points())
          else:
              if type(fontsize) not in [int, float, str]:
                  raise TypeError("Font size must be an integer number.")
                  # Can't it be floating point, as indicated in line above?
              else:
                  if type(fontsize) == str:
                      font_size = int(self.labelFontProps.get_size_in_points())
                  else:
                      self.labelFontProps.set_size(fontsize)
                      font_size = fontsize
          self.labelFontSizeList = [font_size] * len(levels)
  
          if _colors == None:
              self.labelMappable = self
              self.labelCValueList = np.take(self.cvalues, self.labelIndiceList)
          else:
              cmap = colors.ListedColormap(_colors, N=len(self.labelLevelList))
              self.labelCValueList = range(len(self.labelLevelList))
              self.labelMappable = cm.ScalarMappable(cmap = cmap,
                                                     norm = colors.NoNorm())
  
          #self.labelTexts = []   # Initialized in ContourSet.__init__
          #self.labelCValues = [] # same
          self.labelXYs = []
  
          if self.labelManual:
              print 'Select label locations manually using first mouse button.'
              print 'End manual selection with second mouse button.'
              if not inline:
                  print 'Remove last label by clicking third mouse button.'
  
              blocking_contour_labeler = BlockingContourLabeler(self)
              blocking_contour_labeler(inline,inline_spacing)
          else:
              self.labels(inline,inline_spacing)
  
          # Hold on to some old attribute names.  These are depricated and will
          # be removed in the near future (sometime after 2008-08-01), but keeping
          # for now for backwards compatibility
          self.cl = self.labelTexts
          self.cl_xy = self.labelXYs
          self.cl_cvalues = self.labelCValues
  
          self.labelTextsList =  cbook.silent_list('text.Text', self.labelTexts)
          return self.labelTextsList
  
  
      def print_label(self, linecontour,labelwidth):
          "if contours are too short, don't plot a label"
          lcsize = len(linecontour)
          if lcsize > 10 * labelwidth:
              return 1
  
          xmax = np.amax(linecontour[:,0])
          xmin = np.amin(linecontour[:,0])
          ymax = np.amax(linecontour[:,1])
          ymin = np.amin(linecontour[:,1])
  
          lw = labelwidth
          if (xmax - xmin) > 1.2* lw or (ymax - ymin) > 1.2 * lw:
              return 1
          else:
              return 0
  
      def too_close(self, x,y, lw):
          "if there's a label already nearby, find a better place"
          if self.labelXYs != []:
              dist = [np.sqrt((x-loc[0]) ** 2 + (y-loc[1]) ** 2)
                      for loc in self.labelXYs]
              for d in dist:
                  if d < 1.2*lw:
                      return 1
                  else: return 0
          else: return 0
  
      def get_label_coords(self, distances, XX, YY, ysize, lw):
          """ labels are ploted at a location with the smallest
          dispersion of the contour from a straight line
          unless there's another label nearby, in which case
          the second best place on the contour is picked up
          if there's no good place a label isplotted at the
          beginning of the contour
          """
  
          hysize = int(ysize/2)
          adist = np.argsort(distances)
  
          for ind in adist:
              x, y = XX[ind][hysize], YY[ind][hysize]
              if self.too_close(x,y, lw):
                  continue
              else:
                  return x,y, ind
  
          ind = adist[0]
          x, y = XX[ind][hysize], YY[ind][hysize]
          return x,y, ind
  
      def get_label_width(self, lev, fmt, fsize):
          "get the width of the label in points"
          if cbook.is_string_like(lev):
              lw = (len(lev)) * fsize
          else:
              lw = (len(self.get_text(lev,fmt))) * fsize
  
          return lw
  
      def get_real_label_width( self, lev, fmt, fsize ):
          """
          This computes actual onscreen label width.
          This uses some black magic to determine onscreen extent of non-drawn
          label.  This magic may not be very robust.
          """
          # Find middle of axes
          xx = np.mean( np.asarray(self.ax.axis()).reshape(2,2), axis=1 )
  
          # Temporarily create text object
          t = text.Text( xx[0], xx[1] )
          self.set_label_props( t, self.get_text(lev,fmt), 'k' )
  
          # Some black magic to get onscreen extent
          # NOTE: This will only work for already drawn figures, as the canvas
          # does not have a renderer otherwise.  This is the reason this function
          # can't be integrated into the rest of the code.
          bbox = t.get_window_extent(renderer=self.ax.figure.canvas.renderer)
  
          # difference in pixel extent of image
          lw = np.diff(bbox.corners()[0::2,0])[0]
  
          return lw
  
      def set_label_props(self, label, text, color):
          "set the label properties - color, fontsize, text"
          label.set_text(text)
          label.set_color(color)
          label.set_fontproperties(self.labelFontProps)
          label.set_clip_box(self.ax.bbox)
  
      def get_text(self, lev, fmt):
          "get the text of the label"
          if cbook.is_string_like(lev):
              return lev
          else:
              if isinstance(fmt,dict):
                  return fmt[lev]
              else:
                  return fmt%lev
  
      def locate_label(self, linecontour, labelwidth):
          """find a good place to plot a label (relatively flat
          part of the contour) and the angle of rotation for the
          text object
          """
  
          nsize= len(linecontour)
          if labelwidth > 1:
              xsize = int(np.ceil(nsize/labelwidth))
          else:
              xsize = 1
          if xsize == 1:
              ysize = nsize
          else:
              ysize = labelwidth
  
          XX = np.resize(linecontour[:,0],(xsize, ysize))
          YY = np.resize(linecontour[:,1],(xsize, ysize))
          #I might have fouled up the following:
          yfirst = YY[:,0].reshape(xsize, 1)
          ylast = YY[:,-1].reshape(xsize, 1)
          xfirst = XX[:,0].reshape(xsize, 1)
          xlast = XX[:,-1].reshape(xsize, 1)
          s = (yfirst-YY) * (xlast-xfirst) - (xfirst-XX) * (ylast-yfirst)
          L = np.sqrt((xlast-xfirst)**2+(ylast-yfirst)**2).ravel()
          dist = np.add.reduce(([(abs(s)[i]/L[i]) for i in range(xsize)]),-1)
          x,y,ind = self.get_label_coords(dist, XX, YY, ysize, labelwidth)
          #print 'ind, x, y', ind, x, y
  
          # There must be a more efficient way...
          lc = [tuple(l) for l in linecontour]
          dind = lc.index((x,y))
          #print 'dind', dind
          #dind = list(linecontour).index((x,y))
  
          return x, y, dind
  
      def calc_label_rot_and_inline( self, slc, ind, lw, lc=None, spacing=5 ):
          """
          This function calculates the appropriate label rotation given
          the linecontour coordinates in screen units, the index of the
          label location and the label width.
  
          It will also break contour and calculate inlining if *lc* is
          not empty (lc defaults to the empty list if None).  *spacing*
          is the space around the label in pixels to leave empty.
  
          Do both of these tasks at once to avoid calling mlab.path_length
          multiple times, which is relatively costly.
  
          The method used here involves calculating the path length
          along the contour in pixel coordinates and then looking
          approximately label width / 2 away from central point to
          determine rotation and then to break contour if desired.
          """
  
          if lc is None: lc = []
          # Half the label width
          hlw = lw/2.0
  
          # Check if closed and, if so, rotate contour so label is at edge
          closed = mlab.is_closed_polygon(slc)
          if closed:
              slc = np.r_[ slc[ind:-1], slc[:ind+1] ]
  
              if len(lc): # Rotate lc also if not empty
                  lc = np.r_[ lc[ind:-1], lc[:ind+1] ]
  
              ind = 0
  
          # Path length in pixel space
          pl = mlab.path_length(slc)
          pl = pl-pl[ind]
  
          # Use linear interpolation to get points around label
          xi = np.array( [ -hlw, hlw ] )
          if closed: # Look at end also for closed contours
              dp = np.array([pl[-1],0])
          else:
              dp = np.zeros_like(xi)
  
          ll = mlab.less_simple_linear_interpolation( pl, slc, dp+xi,
                                                       extrap=True )
  
          # get vector in pixel space coordinates from one point to other
          dd = np.diff( ll, axis=0 ).ravel()
  
          # Get angle of vector - must be calculated in pixel space for
          # text rotation to work correctly
          if np.all(dd==0): # Must deal with case of zero length label
              rotation = 0.0
          else:
              rotation = np.arctan2(dd[1], dd[0]) * 180.0 / np.pi
  
          # Fix angle so text is never upside-down
          if rotation > 90:
              rotation = rotation - 180.0
          if rotation < -90:
              rotation = 180.0 + rotation
  
          # Break contour if desired
          nlc = []
          if len(lc):
              # Expand range by spacing
              xi = dp + xi + np.array([-spacing,spacing])
  
              # Get indices near points of interest
              I = mlab.less_simple_linear_interpolation(
                  pl, np.arange(len(pl)), xi, extrap=False )
  
              # If those indices aren't beyond contour edge, find x,y
              if (not np.isnan(I[0])) and int(I[0])<>I[0]:
                  xy1 = mlab.less_simple_linear_interpolation(
                      pl, lc, [ xi[0] ] )
  
              if (not np.isnan(I[1])) and int(I[1])<>I[1]:
                  xy2 = mlab.less_simple_linear_interpolation(
                      pl, lc, [ xi[1] ] )
  
              # Make integer
              I = [ np.floor(I[0]), np.ceil(I[1]) ]
  
              # Actually break contours
              if closed:
                  # This will remove contour if shorter than label
                  if np.all(~np.isnan(I)):
                      nlc.append( np.r_[ xy2, lc[I[1]:I[0]+1], xy1 ] )
              else:
                  # These will remove pieces of contour if they have length zero
                  if not np.isnan(I[0]):
                      nlc.append( np.r_[ lc[:I[0]+1], xy1 ] )
                  if not np.isnan(I[1]):
                      nlc.append( np.r_[ xy2, lc[I[1]:] ] )
  
              # The current implementation removes contours completely
              # covered by labels.  Uncomment line below to keep
              # original contour if this is the preferred behavoir.
              #if not len(nlc): nlc = [ lc ]
  
          return (rotation,nlc)
  
  
      def add_label(self,x,y,rotation,lev,cvalue):
          dx,dy = self.ax.transData.inverted().transform_point((x,y))
          t = text.Text(dx, dy, rotation = rotation,
                        horizontalalignment='center',
                        verticalalignment='center')
  
          color = self.labelMappable.to_rgba(cvalue,alpha=self.alpha)
  
          _text = self.get_text(lev,self.labelFmt)
          self.set_label_props(t, _text, color)
          self.labelTexts.append(t)
          self.labelCValues.append(cvalue)
          self.labelXYs.append((x,y))
  
          # Add label to plot here - useful for manual mode label selection
          self.ax.add_artist(t)
  
      def pop_label(self,index=-1):
          '''Defaults to removing last label, but any index can be supplied'''
          self.labelCValues.pop(index)
          t = self.labelTexts.pop(index)
          t.remove()
  
      def labels(self, inline, inline_spacing):
          trans = self.ax.transData # A bit of shorthand
  
          for icon, lev, fsize, cvalue in zip(
              self.labelIndiceList, self.labelLevelList, self.labelFontSizeList,
              self.labelCValueList ):
  
              con = self.collections[icon]
              lw = self.get_label_width(lev, self.labelFmt, fsize)
              additions = []
              paths = con.get_paths()
              for segNum, linepath in enumerate(paths):
                  lc = linepath.vertices # Line contour
                  slc0 = trans.transform(lc) # Line contour in screen coords
  
                  # For closed polygons, add extra point to avoid division by
                  # zero in print_label and locate_label.  Other than these
                  # functions, this is not necessary and should probably be
                  # eventually removed.
                  if mlab.is_closed_polygon( lc ):
                      slc = np.r_[ slc0, slc0[1:2,:] ]
                  else:
                      slc = slc0
  
                  if self.print_label(slc,lw): # Check if long enough for a label
                      x,y,ind  = self.locate_label(slc, lw)
  
                      if inline: lcarg = lc
                      else: lcarg = None
                      rotation,new=self.calc_label_rot_and_inline(
                          slc0, ind, lw, lcarg,
                          inline_spacing )
  
                      # Actually add the label
                      self.add_label(x,y,rotation,lev,cvalue)
  
                      # If inline, add new contours
                      if inline:
                          for n in new:
                              # Add path if not empty or single point
                              if len(n)>1: additions.append( path.Path(n) )
                  else: # If not adding label, keep old path
                      additions.append(linepath)
  
              # After looping over all segments on a contour, remove old
              # paths and add new ones if inlining
              if inline:
                  del paths[:]
                  paths.extend(additions)
  
  class ContourSet(cm.ScalarMappable, ContourLabeler):
      """
      Create and store a set of contour lines or filled regions.
  
      User-callable method: clabel
  
      Useful attributes:
        ax:
          the axes object in which the contours are drawn
        collections:
          a silent_list of LineCollections or PolyCollections
        levels:
          contour levels
        layers:
          same as levels for line contours; half-way between
          levels for filled contours.  See _process_colors method.
      """
  
  
      def __init__(self, ax, *args, **kwargs):
          """
          Draw contour lines or filled regions, depending on
          whether keyword arg 'filled' is False (default) or True.
  
          The first argument of the initializer must be an axes
          object.  The remaining arguments and keyword arguments
          are described in ContourSet.contour_doc.
  
          """
          self.ax = ax
          self.levels = kwargs.get('levels', None)
          self.filled = kwargs.get('filled', False)
          self.linewidths = kwargs.get('linewidths', None)
          self.linestyles = kwargs.get('linestyles', 'solid')
  
          self.alpha = kwargs.get('alpha', 1.0)
          self.origin = kwargs.get('origin', None)
          self.extent = kwargs.get('extent', None)
          cmap = kwargs.get('cmap', None)
          self.colors = kwargs.get('colors', None)
          norm = kwargs.get('norm', None)
          self.extend = kwargs.get('extend', 'neither')
          self.antialiased = kwargs.get('antialiased', True)
          self.nchunk = kwargs.get('nchunk', 0)
          self.locator = kwargs.get('locator', None)
          if (isinstance(norm, colors.LogNorm)
                  or isinstance(self.locator, ticker.LogLocator)):
              self.logscale = True
              if norm is None:
                  norm = colors.LogNorm()
              if self.extend is not 'neither':
                  raise ValueError('extend kwarg does not work yet with log scale')
          else:
              self.logscale = False
  
          if self.origin is not None: assert(self.origin in
                                              ['lower', 'upper', 'image'])
          if self.extent is not None: assert(len(self.extent) == 4)
          if cmap is not None: assert(isinstance(cmap, colors.Colormap))
          if self.colors is not None and cmap is not None:
              raise ValueError('Either colors or cmap must be None')
          if self.origin == 'image': self.origin = mpl.rcParams['image.origin']
          x, y, z = self._contour_args(*args)        # also sets self.levels,
                                                     #  self.layers
          if self.colors is not None:
              cmap = colors.ListedColormap(self.colors, N=len(self.layers))
          if self.filled:
              self.collections = cbook.silent_list('collections.PolyCollection')
          else:
              self.collections = cbook.silent_list('collections.LineCollection')
          # label lists must be initialized here
          self.labelTexts = []
          self.labelCValues = []
  
          kw = {'cmap': cmap}
          if norm is not None:
              kw['norm'] = norm
          cm.ScalarMappable.__init__(self, **kw) # sets self.cmap;
          self._process_colors()
          _mask = ma.getmask(z)
          if _mask is ma.nomask:
              _mask = None
  
          if self.filled:
              if self.linewidths is not None:
                  warnings.warn('linewidths is ignored by contourf')
              C = _cntr.Cntr(x, y, z.filled(), _mask)
              lowers = self._levels[:-1]
              uppers = self._levels[1:]
              for level, level_upper in zip(lowers, uppers):
                  nlist = C.trace(level, level_upper, points = 0,
                          nchunk = self.nchunk)
                  col = collections.PolyCollection(nlist,
                                       antialiaseds = (self.antialiased,),
                                       edgecolors= 'none',
                                       alpha=self.alpha)
                  self.ax.add_collection(col)
                  self.collections.append(col)
  
          else:
              tlinewidths = self._process_linewidths()
              self.tlinewidths = tlinewidths
              tlinestyles = self._process_linestyles()
              C = _cntr.Cntr(x, y, z.filled(), _mask)
              for level, width, lstyle in zip(self.levels, tlinewidths, tlinestyles):
                  nlist = C.trace(level, points = 0)
                  col = collections.LineCollection(nlist,
                                       linewidths = width,
                                       linestyle = lstyle,
                                       alpha=self.alpha)
  
                  if level < 0.0 and self.monochrome:
                      ls = mpl.rcParams['contour.negative_linestyle']
                      col.set_linestyle(ls)
                  col.set_label('_nolegend_')
                  self.ax.add_collection(col, False)
                  self.collections.append(col)
          self.changed() # set the colors
          x0 = ma.minimum(x)
          x1 = ma.maximum(x)
          y0 = ma.minimum(y)
          y1 = ma.maximum(y)
          self.ax.update_datalim([(x0,y0), (x1,y1)])
          self.ax.autoscale_view()
  
      def changed(self):
          tcolors = [ (tuple(rgba),) for rgba in
                                  self.to_rgba(self.cvalues, alpha=self.alpha)]
          self.tcolors = tcolors
          for color, collection in zip(tcolors, self.collections):
              collection.set_alpha(self.alpha)
              collection.set_color(color)
          for label, cv in zip(self.labelTexts, self.labelCValues):
              label.set_alpha(self.alpha)
              label.set_color(self.labelMappable.to_rgba(cv))
          # add label colors
          cm.ScalarMappable.changed(self)
  
  
      def _autolev(self, z, N):
          '''
          Select contour levels to span the data.
  
          We need two more levels for filled contours than for
          line contours, because for the latter we need to specify
          the lower and upper boundary of each range. For example,
          a single contour boundary, say at z = 0, requires only
          one contour line, but two filled regions, and therefore
          three levels to provide boundaries for both regions.
          '''
          if self.locator is None:
              if self.logscale:
                  self.locator = ticker.LogLocator()
              else:
                  self.locator = ticker.MaxNLocator(N+1)
          self.locator.create_dummy_axis()
          zmax = self.zmax
          zmin = self.zmin
          self.locator.set_bounds(zmin, zmax)
          lev = self.locator()
          zmargin = (zmax - zmin) * 0.000001 # so z < (zmax + zmargin)
          if zmax >= lev[-1]:
              lev[-1] += zmargin
          if zmin <= lev[0]:
              if self.logscale:
                  lev[0] = 0.99 * zmin
              else:
                  lev[0] -= zmargin
          self._auto = True
          if self.filled:
              return lev
          return lev[1:-1]
  
      def _initialize_x_y(self, z):
          '''
          Return X, Y arrays such that contour(Z) will match imshow(Z)
          if origin is not None.
          The center of pixel Z[i,j] depends on origin:
          if origin is None, x = j, y = i;
          if origin is 'lower', x = j + 0.5, y = i + 0.5;
          if origin is 'upper', x = j + 0.5, y = Nrows - i - 0.5
          If extent is not None, x and y will be scaled to match,
          as in imshow.
          If origin is None and extent is not None, then extent
          will give the minimum and maximum values of x and y.
          '''
          if z.ndim != 2:
              raise TypeError("Input must be a 2D array.")
          else:
              Ny, Nx = z.shape
          if self.origin is None:  # Not for image-matching.
              if self.extent is None:
                  return np.meshgrid(np.arange(Nx), np.arange(Ny))
              else:
                  x0,x1,y0,y1 = self.extent
                  x = np.linspace(x0, x1, Nx)
                  y = np.linspace(y0, y1, Ny)
                  return np.meshgrid(x, y)
          # Match image behavior:
          if self.extent is None:
              x0,x1,y0,y1 = (0, Nx, 0, Ny)
          else:
              x0,x1,y0,y1 = self.extent
          dx = float(x1 - x0)/Nx
          dy = float(y1 - y0)/Ny
          x = x0 + (np.arange(Nx) + 0.5) * dx
          y = y0 + (np.arange(Ny) + 0.5) * dy
          if self.origin == 'upper':
              y = y[::-1]
          return np.meshgrid(x,y)
  
      def _check_xyz(self, args):
          '''
          For functions like contour, check that the dimensions
          of the input arrays match; if x and y are 1D, convert
          them to 2D using meshgrid.
  
          Possible change: I think we should make and use an ArgumentError
          Exception class (here and elsewhere).
          '''
          # We can strip away the x and y units
          x = self.ax.convert_xunits( args[0] )
          y = self.ax.convert_yunits( args[1] )
  
          x = np.asarray(x, dtype=np.float64)
          y = np.asarray(y, dtype=np.float64)
          z = ma.asarray(args[2], dtype=np.float64)
          if z.ndim != 2:
              raise TypeError("Input z must be a 2D array.")
          else: Ny, Nx = z.shape
          if x.shape == z.shape and y.shape == z.shape:
              return x,y,z
          if x.ndim != 1 or y.ndim != 1:
              raise TypeError("Inputs x and y must be 1D or 2D.")
          nx, = x.shape
          ny, = y.shape
          if nx != Nx or ny != Ny:
              raise TypeError("Length of x must be number of columns in z,\n" +
                              "and length of y must be number of rows.")
          x,y = np.meshgrid(x,y)
          return x,y,z
  
  
  
      def _contour_args(self, *args):
          if self.filled: fn = 'contourf'
          else:           fn = 'contour'
          Nargs = len(args)
          if Nargs <= 2:
              z = ma.asarray(args[0], dtype=np.float64)
              x, y = self._initialize_x_y(z)
          elif Nargs <=4:
              x,y,z = self._check_xyz(args[:3])
          else:
              raise TypeError("Too many arguments to %s; see help(%s)" % (fn,fn))
          self.zmax = ma.maximum(z)
          self.zmin = ma.minimum(z)
          if self.logscale and self.zmin <= 0:
              z = ma.masked_where(z <= 0, z)
              warnings.warn('Log scale: values of z <=0 have been masked')
              self.zmin = z.min()
          self._auto = False
          if self.levels is None:
              if Nargs == 1 or Nargs == 3:
                  lev = self._autolev(z, 7)
              else:   # 2 or 4 args
                  level_arg = args[-1]
                  try:
                      if type(level_arg) == int:
                          lev = self._autolev(z, level_arg)
                      else:
                          lev = np.asarray(level_arg).astype(np.float64)
                  except:
                      raise TypeError(
                          "Last %s arg must give levels; see help(%s)" % (fn,fn))
              if self.filled and len(lev) < 2:
                  raise ValueError("Filled contours require at least 2 levels.")
              # Workaround for cntr.c bug wrt masked interior regions:
              #if filled:
              #    z = ma.masked_array(z.filled(-1e38))
              # It's not clear this is any better than the original bug.
              self.levels = lev
          #if self._auto and self.extend in ('both', 'min', 'max'):
          #    raise TypeError("Auto level selection is inconsistent "
          #                             + "with use of 'extend' kwarg")
          self._levels = list(self.levels)
          if self.extend in ('both', 'min'):
              self._levels.insert(0, min(self.levels[0],self.zmin) - 1)
          if self.extend in ('both', 'max'):
              self._levels.append(max(self.levels[-1],self.zmax) + 1)
          self._levels = np.asarray(self._levels)
          self.vmin = np.amin(self.levels)  # alternative would be self.layers
          self.vmax = np.amax(self.levels)
          if self.extend in ('both', 'min'):
              self.vmin = 2 * self.levels[0] - self.levels[1]
          if self.extend in ('both', 'max'):
              self.vmax = 2 * self.levels[-1] - self.levels[-2]
          self.layers = self._levels # contour: a line is a thin layer
          if self.filled:
              self.layers = 0.5 * (self._levels[:-1] + self._levels[1:])
              if self.extend in ('both', 'min'):
                  self.layers[0] = 0.5 * (self.vmin + self._levels[1])
              if self.extend in ('both', 'max'):
                  self.layers[-1] = 0.5 * (self.vmax + self._levels[-2])
  
          return (x, y, z)
  
      def _process_colors(self):
          """
          Color argument processing for contouring.
  
          Note that we base the color mapping on the contour levels,
          not on the actual range of the Z values.  This means we
          don't have to worry about bad values in Z, and we always have
          the full dynamic range available for the selected levels.
  
          The color is based on the midpoint of the layer, except for
          an extended end layers.
          """
          self.monochrome = self.cmap.monochrome
          if self.colors is not None:
              i0, i1 = 0, len(self.layers)
              if self.extend in ('both', 'min'):
                  i0 = -1
              if self.extend in ('both', 'max'):
                  i1 = i1 + 1
              self.cvalues = range(i0, i1)
              self.set_norm(colors.NoNorm())
          else:
              self.cvalues = self.layers
          if not self.norm.scaled():
              self.set_clim(self.vmin, self.vmax)
          if self.extend in ('both', 'max', 'min'):
              self.norm.clip = False
          self.set_array(self.layers)
          # self.tcolors are set by the "changed" method
  
      def _process_linewidths(self):
          linewidths = self.linewidths
          Nlev = len(self.levels)
          if linewidths is None:
              tlinewidths = [(mpl.rcParams['lines.linewidth'],)] *Nlev
          else:
              if cbook.iterable(linewidths) and len(linewidths) < Nlev:
                  linewidths = list(linewidths) * int(np.ceil(Nlev/len(linewidths)))
              elif not cbook.iterable(linewidths) and type(linewidths) in [int, float]:
                  linewidths = [linewidths] * Nlev
              tlinewidths = [(w,) for w in linewidths]
          return tlinewidths
  
      def _process_linestyles(self):
          linestyles = self.linestyles
          Nlev = len(self.levels)
          if linestyles is None:
              tlinestyles = ['solid'] * Nlev
          else:
              if cbook.is_string_like(linestyles):
                  tlinestyles = [linestyles] * Nlev
              elif cbook.iterable(linestyles) and len(linestyles) <= Nlev:
                  tlinestyles = list(linestyles) * int(np.ceil(Nlev/len(linestyles)))
          return tlinestyles
  
      def get_alpha(self):
          '''returns alpha to be applied to all ContourSet artists'''
          return self.alpha
  
      def set_alpha(self, alpha):
          '''sets alpha for all ContourSet artists'''
          self.alpha = alpha
          self.changed()
  
      contour_doc = """
          :func:`~matplotlib.pyplot.contour` and
          :func:`~matplotlib.pyplot.contourf` draw contour lines and
          filled contours, respectively.  Except as noted, function
          signatures and return values are the same for both versions.
  
          :func:`~matplotlib.pyplot.contourf` differs from the Matlab
          (TM) version in that it does not draw the polygon edges,
          because the contouring engine yields simply connected regions
          with branch cuts.  To draw the edges, add line contours with
          calls to :func:`~matplotlib.pyplot.contour`.
  
  
          call signatures::
  
            contour(Z)
  
          make a contour plot of an array *Z*. The level values are chosen
          automatically.
  
          ::
  
            contour(X,Y,Z)
  
          *X*, *Y* specify the (*x*, *y*) coordinates of the surface
  
          ::
  
            contour(Z,N)
            contour(X,Y,Z,N)
  
          contour *N* automatically-chosen levels.
  
          ::
  
            contour(Z,V)
            contour(X,Y,Z,V)
  
          draw contour lines at the values specified in sequence *V*
  
          ::
  
            contourf(..., V)
  
          fill the (len(*V*)-1) regions between the values in *V*
  
          ::
  
            contour(Z, **kwargs)
  
          Use keyword args to control colors, linewidth, origin, cmap ... see
          below for more details.
  
          *X*, *Y*, and *Z* must be arrays with the same dimensions.
  
          *Z* may be a masked array, but filled contouring may not
          handle internal masked regions correctly.
  
          ``C = contour(...)`` returns a
          :class:`~matplotlib.contour.ContourSet` object.
  
          Optional keyword arguments:
  
            *colors*: [ None | string | (mpl_colors) ]
              If *None*, the colormap specified by cmap will be used.
  
              If a string, like 'r' or 'red', all levels will be plotted in this
              color.
  
              If a tuple of matplotlib color args (string, float, rgb, etc),
              different levels will be plotted in different colors in the order
              specified.
  
            *alpha*: float
              The alpha blending value
  
            *cmap*: [ None | Colormap ]
              A cm :class:`~matplotlib.cm.Colormap` instance or
              *None*. If *cmap* is *None* and *colors* is *None*, a
              default Colormap is used.
  
            *norm*: [ None | Normalize ]
              A :class:`matplotlib.colors.Normalize` instance for
              scaling data values to colors. If *norm* is *None* and
              *colors* is *None*, the default linear scaling is used.
  
            *origin*: [ None | 'upper' | 'lower' | 'image' ]
              If *None*, the first value of *Z* will correspond to the
              lower left corner, location (0,0). If 'image', the rc
              value for ``image.origin`` will be used.
  
              This keyword is not active if *X* and *Y* are specified in
              the call to contour.
  
            *extent*: [ None | (x0,x1,y0,y1) ]
  
              If *origin* is not *None*, then *extent* is interpreted as
              in :func:`matplotlib.pyplot.imshow`: it gives the outer
              pixel boundaries. In this case, the position of Z[0,0]
              is the center of the pixel, not a corner. If *origin* is
              *None*, then (*x0*, *y0*) is the position of Z[0,0], and
              (*x1*, *y1*) is the position of Z[-1,-1].
  
              This keyword is not active if *X* and *Y* are specified in
              the call to contour.
  
            *locator*: [ None | ticker.Locator subclass ]
              If *locator* is None, the default
              :class:`~matplotlib.ticker.MaxNLocator` is used. The
              locator is used to determine the contour levels if they
              are not given explicitly via the *V* argument.
  
            *extend*: [ 'neither' | 'both' | 'min' | 'max' ]
              Unless this is 'neither', contour levels are automatically
              added to one or both ends of the range so that all data
              are included. These added ranges are then mapped to the
              special colormap values which default to the ends of the
              colormap range, but can be set via
              :meth:`matplotlib.cm.Colormap.set_under` and
              :meth:`matplotlib.cm.Colormap.set_over` methods.
  
          contour-only keyword arguments:
  
            *linewidths*: [ None | number | tuple of numbers ]
              If *linewidths* is *None*, the default width in
              ``lines.linewidth`` in ``matplotlibrc`` is used.
  
              If a number, all levels will be plotted with this linewidth.
  
              If a tuple, different levels will be plotted with different
              linewidths in the order specified
  
            *linestyles*: [None | 'solid' | 'dashed' | 'dashdot' | 'dotted' ]
              If *linestyles* is *None*, the 'solid' is used.
  
              *linestyles* can also be an iterable of the above strings
              specifying a set of linestyles to be used. If this
              iterable is shorter than the number of contour levels
              it will be repeated as necessary.
  
              If contour is using a monochrome colormap and the contour
              level is less than 0, then the linestyle specified
              in ``contour.negative_linestyle`` in ``matplotlibrc``
              will be used.
  
          contourf-only keyword arguments:
  
            *antialiased*: [ True | False ]
              enable antialiasing
  
            *nchunk*: [ 0 | integer ]
              If 0, no subdivision of the domain. Specify a positive integer to
              divide the domain into subdomains of roughly *nchunk* by *nchunk*
              points. This may never actually be advantageous, so this option may
              be removed. Chunking introduces artifacts at the chunk boundaries
              unless *antialiased* is *False*.
  
          **Example:**
  
          .. plot:: mpl_examples/pylab_examples/contour_demo.py
          """
  
      def find_nearest_contour( self, x, y, indices=None, pixel=True ):
          """
          Finds contour that is closest to a point.  Defaults to
          measuring distance in pixels (screen space - useful for manual
          contour labeling), but this can be controlled via a keyword
          argument.
  
          Returns a tuple containing the contour, segment, index of
          segment, x & y of segment point and distance to minimum point.
  
          Call signature::
  
            conmin,segmin,imin,xmin,ymin,dmin = find_nearest_contour(
                       self, x, y, indices=None, pixel=True )
  
          Optional keyword arguments::
  
          *indices*:
             Indexes of contour levels to consider when looking for
             nearest point.  Defaults to using all levels.
  
          *pixel*:
             If *True*, measure distance in pixel space, if not, measure
             distance in axes space.  Defaults to *True*.
  
          """
  
          # This function uses a method that is probably quite
          # inefficient based on converting each contour segment to
          # pixel coordinates and then comparing the given point to
          # those coordinates for each contour.  This will probably be
          # quite slow for complex contours, but for normal use it works
          # sufficiently well that the time is not noticeable.
          # Nonetheless, improvements could probably be made.
  
          if indices==None:
              indices = range(len(self.levels))
  
          dmin = 1e10
          conmin = None
          segmin = None
          xmin = None
          ymin = None
  
          for icon in indices:
              con = self.collections[icon]
              paths = con.get_paths()
              for segNum, linepath in enumerate(paths):
                  lc = linepath.vertices
  
                  # transfer all data points to screen coordinates if desired
                  if pixel:
                      lc = self.ax.transData.transform(lc)
  
                  ds = (lc[:,0]-x)**2 + (lc[:,1]-y)**2
                  d = min( ds )
                  if d < dmin:
                      dmin = d
                      conmin = icon
                      segmin = segNum
                      imin = mpl.mlab.find( ds == d )[0]
                      xmin = lc[imin,0]
                      ymin = lc[imin,1]
  
          return (conmin,segmin,imin,xmin,ymin,dmin)
  
  