text: """
  A module for converting numbers or color arguments to *RGB* or *RGBA*
  
  *RGB* and *RGBA* are sequences of, respectively, 3 or 4 floats in the
  range 0-1.
  
  This module includes functions and classes for color specification
  conversions, and for mapping numbers to colors in a 1-D array of
  colors called a colormap. Colormapping typically involves two steps:
  a data array is first mapped onto the range 0-1 using an instance
  of :class:`Normalize` or of a subclass; then this number in the 0-1
  range is mapped to a color using an instance of a subclass of
  :class:`Colormap`.  Two are provided here:
  :class:`LinearSegmentedColormap`, which is used to generate all
  the built-in colormap instances, but is also useful for making
  custom colormaps, and :class:`ListedColormap`, which is used for
  generating a custom colormap from a list of color specifications.
  
  The module also provides a single instance, *colorConverter*, of the
  :class:`ColorConverter` class providing methods for converting single
  color specifications or sequences of them to *RGB* or *RGBA*.
  
  Commands which take color arguments can use several formats to specify
  the colors.  For the basic builtin colors, you can use a single letter
  
      - b  : blue
      - g  : green
      - r  : red
      - c  : cyan
      - m  : magenta
      - y  : yellow
      - k  : black
      - w  : white
  
  Gray shades can be given as a string encoding a float in the 0-1
  range, e.g.::
  
      color = '0.75'
  
  For a greater range of colors, you have two options.  You can specify
  the color using an html hex string, as in::
  
        color = '#eeefff'
  
  or you can pass an *R* , *G* , *B* tuple, where each of *R* , *G* , *B*
  are in the range [0,1].
  
  Finally, legal html names for colors, like 'red', 'burlywood' and
  'chartreuse' are supported.
  """
  import re
  import numpy as np
  from numpy import ma
  import matplotlib.cbook as cbook
  
  parts = np.__version__.split('.')
  NP_MAJOR, NP_MINOR = map(int, parts[:2])
  # true if clip supports the out kwarg
  NP_CLIP_OUT = NP_MAJOR>=1 and NP_MINOR>=2
  
  cnames = {
      'aliceblue'            : '#F0F8FF',
      'antiquewhite'         : '#FAEBD7',
      'aqua'                 : '#00FFFF',
      'aquamarine'           : '#7FFFD4',
      'azure'                : '#F0FFFF',
      'beige'                : '#F5F5DC',
      'bisque'               : '#FFE4C4',
      'black'                : '#000000',
      'blanchedalmond'       : '#FFEBCD',
      'blue'                 : '#0000FF',
      'blueviolet'           : '#8A2BE2',
      'brown'                : '#A52A2A',
      'burlywood'            : '#DEB887',
      'cadetblue'            : '#5F9EA0',
      'chartreuse'           : '#7FFF00',
      'chocolate'            : '#D2691E',
      'coral'                : '#FF7F50',
      'cornflowerblue'       : '#6495ED',
      'cornsilk'             : '#FFF8DC',
      'crimson'              : '#DC143C',
      'cyan'                 : '#00FFFF',
      'darkblue'             : '#00008B',
      'darkcyan'             : '#008B8B',
      'darkgoldenrod'        : '#B8860B',
      'darkgray'             : '#A9A9A9',
      'darkgreen'            : '#006400',
      'darkkhaki'            : '#BDB76B',
      'darkmagenta'          : '#8B008B',
      'darkolivegreen'       : '#556B2F',
      'darkorange'           : '#FF8C00',
      'darkorchid'           : '#9932CC',
      'darkred'              : '#8B0000',
      'darksalmon'           : '#E9967A',
      'darkseagreen'         : '#8FBC8F',
      'darkslateblue'        : '#483D8B',
      'darkslategray'        : '#2F4F4F',
      'darkturquoise'        : '#00CED1',
      'darkviolet'           : '#9400D3',
      'deeppink'             : '#FF1493',
      'deepskyblue'          : '#00BFFF',
      'dimgray'              : '#696969',
      'dodgerblue'           : '#1E90FF',
      'firebrick'            : '#B22222',
      'floralwhite'          : '#FFFAF0',
      'forestgreen'          : '#228B22',
      'fuchsia'              : '#FF00FF',
      'gainsboro'            : '#DCDCDC',
      'ghostwhite'           : '#F8F8FF',
      'gold'                 : '#FFD700',
      'goldenrod'            : '#DAA520',
      'gray'                 : '#808080',
      'green'                : '#008000',
      'greenyellow'          : '#ADFF2F',
      'honeydew'             : '#F0FFF0',
      'hotpink'              : '#FF69B4',
      'indianred'            : '#CD5C5C',
      'indigo'               : '#4B0082',
      'ivory'                : '#FFFFF0',
      'khaki'                : '#F0E68C',
      'lavender'             : '#E6E6FA',
      'lavenderblush'        : '#FFF0F5',
      'lawngreen'            : '#7CFC00',
      'lemonchiffon'         : '#FFFACD',
      'lightblue'            : '#ADD8E6',
      'lightcoral'           : '#F08080',
      'lightcyan'            : '#E0FFFF',
      'lightgoldenrodyellow' : '#FAFAD2',
      'lightgreen'           : '#90EE90',
      'lightgrey'            : '#D3D3D3',
      'lightpink'            : '#FFB6C1',
      'lightsalmon'          : '#FFA07A',
      'lightseagreen'        : '#20B2AA',
      'lightskyblue'         : '#87CEFA',
      'lightslategray'       : '#778899',
      'lightsteelblue'       : '#B0C4DE',
      'lightyellow'          : '#FFFFE0',
      'lime'                 : '#00FF00',
      'limegreen'            : '#32CD32',
      'linen'                : '#FAF0E6',
      'magenta'              : '#FF00FF',
      'maroon'               : '#800000',
      'mediumaquamarine'     : '#66CDAA',
      'mediumblue'           : '#0000CD',
      'mediumorchid'         : '#BA55D3',
      'mediumpurple'         : '#9370DB',
      'mediumseagreen'       : '#3CB371',
      'mediumslateblue'      : '#7B68EE',
      'mediumspringgreen'    : '#00FA9A',
      'mediumturquoise'      : '#48D1CC',
      'mediumvioletred'      : '#C71585',
      'midnightblue'         : '#191970',
      'mintcream'            : '#F5FFFA',
      'mistyrose'            : '#FFE4E1',
      'moccasin'             : '#FFE4B5',
      'navajowhite'          : '#FFDEAD',
      'navy'                 : '#000080',
      'oldlace'              : '#FDF5E6',
      'olive'                : '#808000',
      'olivedrab'            : '#6B8E23',
      'orange'               : '#FFA500',
      'orangered'            : '#FF4500',
      'orchid'               : '#DA70D6',
      'palegoldenrod'        : '#EEE8AA',
      'palegreen'            : '#98FB98',
      'palevioletred'        : '#AFEEEE',
      'papayawhip'           : '#FFEFD5',
      'peachpuff'            : '#FFDAB9',
      'peru'                 : '#CD853F',
      'pink'                 : '#FFC0CB',
      'plum'                 : '#DDA0DD',
      'powderblue'           : '#B0E0E6',
      'purple'               : '#800080',
      'red'                  : '#FF0000',
      'rosybrown'            : '#BC8F8F',
      'royalblue'            : '#4169E1',
      'saddlebrown'          : '#8B4513',
      'salmon'               : '#FA8072',
      'sandybrown'           : '#FAA460',
      'seagreen'             : '#2E8B57',
      'seashell'             : '#FFF5EE',
      'sienna'               : '#A0522D',
      'silver'               : '#C0C0C0',
      'skyblue'              : '#87CEEB',
      'slateblue'            : '#6A5ACD',
      'slategray'            : '#708090',
      'snow'                 : '#FFFAFA',
      'springgreen'          : '#00FF7F',
      'steelblue'            : '#4682B4',
      'tan'                  : '#D2B48C',
      'teal'                 : '#008080',
      'thistle'              : '#D8BFD8',
      'tomato'               : '#FF6347',
      'turquoise'            : '#40E0D0',
      'violet'               : '#EE82EE',
      'wheat'                : '#F5DEB3',
      'white'                : '#FFFFFF',
      'whitesmoke'           : '#F5F5F5',
      'yellow'               : '#FFFF00',
      'yellowgreen'          : '#9ACD32',
      }
  
  
  # add british equivs
  for k, v in cnames.items():
      if k.find('gray')>=0:
          k = k.replace('gray', 'grey')
          cnames[k] = v
  
  def is_color_like(c):
      'Return *True* if *c* can be converted to *RGB*'
      try:
          colorConverter.to_rgb(c)
          return True
      except ValueError:
          return False
  
  
  def rgb2hex(rgb):
      'Given an rgb or rgba sequence of 0-1 floats, return the hex string'
      return '#%02x%02x%02x' % tuple([round(val*255) for val in rgb[:3]])
  
  hexColorPattern = re.compile("\A#[a-fA-F0-9]{6}\Z")
  
  def hex2color(s):
      """
      Take a hex string *s* and return the corresponding rgb 3-tuple
      Example: #efefef -> (0.93725, 0.93725, 0.93725)
      """
      if not isinstance(s, basestring):
          raise TypeError('hex2color requires a string argument')
      if hexColorPattern.match(s) is None:
          raise ValueError('invalid hex color string "%s"' % s)
      return tuple([int(n, 16)/255.0 for n in (s[1:3], s[3:5], s[5:7])])
  
  class ColorConverter:
      """
      Provides methods for converting color specifications to *RGB* or *RGBA*
  
      Caching is used for more efficient conversion upon repeated calls
      with the same argument.
  
      Ordinarily only the single instance instantiated in this module,
      *colorConverter*, is needed.
      """
      colors = {
          'b' : (0.0, 0.0, 1.0),
          'g' : (0.0, 0.5, 0.0),
          'r' : (1.0, 0.0, 0.0),
          'c' : (0.0, 0.75, 0.75),
          'm' : (0.75, 0, 0.75),
          'y' : (0.75, 0.75, 0),
          'k' : (0.0, 0.0, 0.0),
          'w' : (1.0, 1.0, 1.0),
          }
  
      cache = {}
      def to_rgb(self, arg):
          """
          Returns an *RGB* tuple of three floats from 0-1.
  
          *arg* can be an *RGB* or *RGBA* sequence or a string in any of
          several forms:
  
              1) a letter from the set 'rgbcmykw'
              2) a hex color string, like '#00FFFF'
              3) a standard name, like 'aqua'
              4) a float, like '0.4', indicating gray on a 0-1 scale
  
          if *arg* is *RGBA*, the *A* will simply be discarded.
          """
          try: return self.cache[arg]
          except KeyError: pass
          except TypeError: # could be unhashable rgb seq
              arg = tuple(arg)
              try: return self.cache[arg]
              except KeyError: pass
              except TypeError:
                  raise ValueError(
                        'to_rgb: arg "%s" is unhashable even inside a tuple'
                                      % (str(arg),))
  
          try:
              if cbook.is_string_like(arg):
                  argl = arg.lower()
                  color = self.colors.get(argl, None)
                  if color is None:
                      str1 = cnames.get(argl, argl)
                      if str1.startswith('#'):
                          color = hex2color(str1)
                      else:
                          fl = float(argl)
                          if fl < 0 or fl > 1:
                              raise ValueError(
                                     'gray (string) must be in range 0-1')
                          color = tuple([fl]*3)
              elif cbook.iterable(arg):
                  if len(arg) > 4 or len(arg) < 3:
                      raise ValueError(
                             'sequence length is %d; must be 3 or 4'%len(arg))
                  color = tuple(arg[:3])
                  if [x for x in color if (float(x) < 0) or  (x > 1)]:
                      # This will raise TypeError if x is not a number.
                      raise ValueError('number in rbg sequence outside 0-1 range')
              else:
                  raise ValueError('cannot convert argument to rgb sequence')
  
              self.cache[arg] = color
  
          except (KeyError, ValueError, TypeError), exc:
              raise ValueError('to_rgb: Invalid rgb arg "%s"\n%s' % (str(arg), exc))
              # Error messages could be improved by handling TypeError
              # separately; but this should be rare and not too hard
              # for the user to figure out as-is.
          return color
  
      def to_rgba(self, arg, alpha=None):
          """
          Returns an *RGBA* tuple of four floats from 0-1.
  
          For acceptable values of *arg*, see :meth:`to_rgb`.
          In addition, if *arg* is "none" (case-insensitive),
          then (0,0,0,0) will be returned.
          If *arg* is an *RGBA* sequence and *alpha* is not *None*,
          *alpha* will replace the original *A*.
          """
          try:
              if arg.lower() == 'none':
                  return (0.0, 0.0, 0.0, 0.0)
          except AttributeError:
              pass
  
          try:
              if not cbook.is_string_like(arg) and cbook.iterable(arg):
                  if len(arg) == 4:
                      if [x for x in arg if (float(x) < 0) or  (x > 1)]:
                          # This will raise TypeError if x is not a number.
                          raise ValueError('number in rbga sequence outside 0-1 range')
                      if alpha is None:
                          return tuple(arg)
                      if alpha < 0.0 or alpha > 1.0:
                          raise ValueError("alpha must be in range 0-1")
                      return arg[0], arg[1], arg[2], alpha
                  r,g,b = arg[:3]
                  if [x for x in (r,g,b) if (float(x) < 0) or  (x > 1)]:
                      raise ValueError('number in rbg sequence outside 0-1 range')
              else:
                  r,g,b = self.to_rgb(arg)
              if alpha is None:
                  alpha = 1.0
              return r,g,b,alpha
          except (TypeError, ValueError), exc:
              raise ValueError('to_rgba: Invalid rgba arg "%s"\n%s' % (str(arg), exc))
  
      def to_rgba_array(self, c, alpha=None):
          """
          Returns a numpy array of *RGBA* tuples.
  
          Accepts a single mpl color spec or a sequence of specs.
  
          Special case to handle "no color": if *c* is "none" (case-insensitive),
          then an empty array will be returned.  Same for an empty list.
          """
          try:
              nc = len(c)
          except TypeError:
              raise ValueError(
                  "Cannot convert argument type %s to rgba array" % type(c))
          try:
              if nc == 0 or c.lower() == 'none':
                  return np.zeros((0,4), dtype=np.float)
          except AttributeError:
              pass
          try:
              # Single value? Put it in an array with a single row.
              return np.array([self.to_rgba(c, alpha)], dtype=np.float)
          except ValueError:
              if isinstance(c, np.ndarray):
                  if c.ndim != 2 and c.dtype.kind not in 'SU':
                      raise ValueError("Color array must be two-dimensional")
                  if (c.ndim == 2 and c.shape[1] == 4 and c.dtype.kind == 'f'):
                      if (c.ravel() > 1).any() or (c.ravel() < 0).any():
                          raise ValueError(
                              "number in rgba sequence is outside 0-1 range")
                      result = np.asarray(c, np.float)
                      if alpha is not None:
                          if alpha > 1 or alpha < 0:
                              raise ValueError("alpha must be in 0-1 range")
                          result[:,3] = alpha
                      return result
                      # This alpha operation above is new, and depends
                      # on higher levels to refrain from setting alpha
                      # to values other than None unless there is
                      # intent to override any existing alpha values.
  
              # It must be some other sequence of color specs.
              result = np.zeros((nc, 4), dtype=np.float)
              for i, cc in enumerate(c):
                  result[i] = self.to_rgba(cc, alpha)
              return result
  
  colorConverter = ColorConverter()
  
  def makeMappingArray(N, data, gamma=1.0):
      """Create an *N* -element 1-d lookup table
  
      *data* represented by a list of x,y0,y1 mapping correspondences.
      Each element in this list represents how a value between 0 and 1
      (inclusive) represented by x is mapped to a corresponding value
      between 0 and 1 (inclusive). The two values of y are to allow
      for discontinuous mapping functions (say as might be found in a
      sawtooth) where y0 represents the value of y for values of x
      <= to that given, and y1 is the value to be used for x > than
      that given). The list must start with x=0, end with x=1, and
      all values of x must be in increasing order. Values between
      the given mapping points are determined by simple linear interpolation.
  
      Alternatively, data can be a function mapping values between 0 - 1
      to 0 - 1.
  
      The function returns an array "result" where ``result[x*(N-1)]``
      gives the closest value for values of x between 0 and 1.
      """
  
      if callable(data):
          xind = np.linspace(0, 1, N)**gamma
          lut = np.clip(np.array(data(xind), dtype=np.float), 0, 1)
          return lut
  
      try:
          adata = np.array(data)
      except:
          raise TypeError("data must be convertable to an array")
      shape = adata.shape
      if len(shape) != 2 and shape[1] != 3:
          raise ValueError("data must be nx3 format")
  
      x  = adata[:,0]
      y0 = adata[:,1]
      y1 = adata[:,2]
  
      if x[0] != 0. or x[-1] != 1.0:
          raise ValueError(
             "data mapping points must start with x=0. and end with x=1")
      if np.sometrue(np.sort(x)-x):
          raise ValueError(
             "data mapping points must have x in increasing order")
      # begin generation of lookup table
      x = x * (N-1)
      lut = np.zeros((N,), np.float)
      xind = (N - 1) * np.linspace(0, 1, N)**gamma
      ind = np.searchsorted(x, xind)[1:-1]
  
      lut[1:-1] = ( ((xind[1:-1] - x[ind-1]) / (x[ind] - x[ind-1]))
                    * (y0[ind] - y1[ind-1]) + y1[ind-1])
      lut[0] = y1[0]
      lut[-1] = y0[-1]
      # ensure that the lut is confined to values between 0 and 1 by clipping it
      np.clip(lut, 0.0, 1.0)
      #lut = where(lut > 1., 1., lut)
      #lut = where(lut < 0., 0., lut)
      return lut
  
  
  class Colormap:
      """Base class for all scalar to rgb mappings
  
          Important methods:
  
              * :meth:`set_bad`
              * :meth:`set_under`
              * :meth:`set_over`
      """
      def __init__(self, name, N=256):
          """
          Public class attributes:
              :attr:`N` : number of rgb quantization levels
              :attr:`name` : name of colormap
  
          """
          self.name = name
          self.N = N
          self._rgba_bad = (0.0, 0.0, 0.0, 0.0) # If bad, don't paint anything.
          self._rgba_under = None
          self._rgba_over = None
          self._i_under = N
          self._i_over = N+1
          self._i_bad = N+2
          self._isinit = False
  
  
      def __call__(self, X, alpha=None, bytes=False):
          """
          *X* is either a scalar or an array (of any dimension).
          If scalar, a tuple of rgba values is returned, otherwise
          an array with the new shape = oldshape+(4,). If the X-values
          are integers, then they are used as indices into the array.
          If they are floating point, then they must be in the
          interval (0.0, 1.0).
          Alpha must be a scalar between 0 and 1, or None.
          If bytes is False, the rgba values will be floats on a
          0-1 scale; if True, they will be uint8, 0-255.
          """
  
          if not self._isinit: self._init()
          mask_bad = None
          if not cbook.iterable(X):
              vtype = 'scalar'
              xa = np.array([X])
          else:
              vtype = 'array'
              xma = ma.array(X, copy=False)
              mask_bad = xma.mask
              xa = xma.data.copy()   # Copy here to avoid side effects.
              del xma
              # masked values are substituted below; no need to fill them here
  
          if xa.dtype.char in np.typecodes['Float']:
              np.putmask(xa, xa==1.0, 0.9999999) #Treat 1.0 as slightly less than 1.
              # The following clip is fast, and prevents possible
              # conversion of large positive values to negative integers.
  
              xa *= self.N
              if NP_CLIP_OUT:
                  np.clip(xa, -1, self.N, out=xa)
              else:
                  xa = np.clip(xa, -1, self.N)
  
              # ensure that all 'under' values will still have negative
              # value after casting to int
              np.putmask(xa, xa<0.0, -1)
              xa = xa.astype(int)
          # Set the over-range indices before the under-range;
          # otherwise the under-range values get converted to over-range.
          np.putmask(xa, xa>self.N-1, self._i_over)
          np.putmask(xa, xa<0, self._i_under)
          if mask_bad is not None:
              if mask_bad.shape == xa.shape:
                  np.putmask(xa, mask_bad, self._i_bad)
              elif mask_bad:
                  xa.fill(self._i_bad)
          if bytes:
              lut = (self._lut * 255).astype(np.uint8)
          else:
              lut = self._lut.copy() # Don't let alpha modify original _lut.
  
          if alpha is not None:
              alpha = min(alpha, 1.0) # alpha must be between 0 and 1
              alpha = max(alpha, 0.0)
              if bytes:
                  alpha = int(alpha * 255)
              if (lut[-1] == 0).all():
                  lut[:-1, -1] = alpha
                  # All zeros is taken as a flag for the default bad
                  # color, which is no color--fully transparent.  We
                  # don't want to override this.
              else:
                  lut[:,-1] = alpha
                  # If the bad value is set to have a color, then we
                  # override its alpha just as for any other value.
  
          rgba = np.empty(shape=xa.shape+(4,), dtype=lut.dtype)
          lut.take(xa, axis=0, mode='clip', out=rgba)
                      #  twice as fast as lut[xa];
                      #  using the clip or wrap mode and providing an
                      #  output array speeds it up a little more.
          if vtype == 'scalar':
              rgba = tuple(rgba[0,:])
          return rgba
  
      def set_bad(self, color = 'k', alpha = None):
          '''Set color to be used for masked values.
          '''
          self._rgba_bad = colorConverter.to_rgba(color, alpha)
          if self._isinit: self._set_extremes()
  
      def set_under(self, color = 'k', alpha = None):
          '''Set color to be used for low out-of-range values.
             Requires norm.clip = False
          '''
          self._rgba_under = colorConverter.to_rgba(color, alpha)
          if self._isinit: self._set_extremes()
  
      def set_over(self, color = 'k', alpha = None):
          '''Set color to be used for high out-of-range values.
             Requires norm.clip = False
          '''
          self._rgba_over = colorConverter.to_rgba(color, alpha)
          if self._isinit: self._set_extremes()
  
      def _set_extremes(self):
          if self._rgba_under:
              self._lut[self._i_under] = self._rgba_under
          else:
              self._lut[self._i_under] = self._lut[0]
          if self._rgba_over:
              self._lut[self._i_over] = self._rgba_over
          else:
              self._lut[self._i_over] = self._lut[self.N-1]
          self._lut[self._i_bad] = self._rgba_bad
  
      def _init():
          '''Generate the lookup table, self._lut'''
          raise NotImplementedError("Abstract class only")
  
      def is_gray(self):
          if not self._isinit: self._init()
          return (np.alltrue(self._lut[:,0] == self._lut[:,1])
                      and np.alltrue(self._lut[:,0] == self._lut[:,2]))
  
  
  class LinearSegmentedColormap(Colormap):
      """Colormap objects based on lookup tables using linear segments.
  
      The lookup table is generated using linear interpolation for each
      primary color, with the 0-1 domain divided into any number of
      segments.
      """
      def __init__(self, name, segmentdata, N=256, gamma=1.0):
          """Create color map from linear mapping segments
  
          segmentdata argument is a dictionary with a red, green and blue
          entries. Each entry should be a list of *x*, *y0*, *y1* tuples,
          forming rows in a table.
  
          Example: suppose you want red to increase from 0 to 1 over
          the bottom half, green to do the same over the middle half,
          and blue over the top half.  Then you would use::
  
              cdict = {'red':   [(0.0,  0.0, 0.0),
                                 (0.5,  1.0, 1.0),
                                 (1.0,  1.0, 1.0)],
  
                       'green': [(0.0,  0.0, 0.0),
                                 (0.25, 0.0, 0.0),
                                 (0.75, 1.0, 1.0),
                                 (1.0,  1.0, 1.0)],
  
                       'blue':  [(0.0,  0.0, 0.0),
                                 (0.5,  0.0, 0.0),
                                 (1.0,  1.0, 1.0)]}
  
          Each row in the table for a given color is a sequence of
          *x*, *y0*, *y1* tuples.  In each sequence, *x* must increase
          monotonically from 0 to 1.  For any input value *z* falling
          between *x[i]* and *x[i+1]*, the output value of a given color
          will be linearly interpolated between *y1[i]* and *y0[i+1]*::
  
              row i:   x  y0  y1
                             /
                            /
              row i+1: x  y0  y1
  
          Hence y0 in the first row and y1 in the last row are never used.
  
  
          .. seealso::
  
              :meth:`LinearSegmentedColormap.from_list`
                 Static method; factory function for generating a
                 smoothly-varying LinearSegmentedColormap.
  
              :func:`makeMappingArray`
                 For information about making a mapping array.
          """
          self.monochrome = False  # True only if all colors in map are identical;
                                   # needed for contouring.
          Colormap.__init__(self, name, N)
          self._segmentdata = segmentdata
          self._gamma = gamma
  
      def _init(self):
          self._lut = np.ones((self.N + 3, 4), np.float)
          self._lut[:-3, 0] = makeMappingArray(self.N,
                  self._segmentdata['red'], self._gamma)
          self._lut[:-3, 1] = makeMappingArray(self.N,
                  self._segmentdata['green'], self._gamma)
          self._lut[:-3, 2] = makeMappingArray(self.N,
                  self._segmentdata['blue'], self._gamma)
          self._isinit = True
          self._set_extremes()
  
      def set_gamma(self, gamma):
          """
          Set a new gamma value and regenerate color map.
          """
          self._gamma = gamma
          self._init()
  
      @staticmethod
      def from_list(name, colors, N=256, gamma=1.0):
          """
          Make a linear segmented colormap with *name* from a sequence
          of *colors* which evenly transitions from colors[0] at val=0
          to colors[-1] at val=1.  *N* is the number of rgb quantization
          levels.
          Alternatively, a list of (value, color) tuples can be given
          to divide the range unevenly.
          """
  
          if not cbook.iterable(colors):
              raise ValueError('colors must be iterable')
  
          if cbook.iterable(colors[0]) and len(colors[0]) == 2 and \
                  not cbook.is_string_like(colors[0]):
              # List of value, color pairs
              vals, colors = zip(*colors)
          else:
              vals = np.linspace(0., 1., len(colors))
  
          cdict = dict(red=[], green=[], blue=[])
          for val, color in zip(vals, colors):
              r,g,b = colorConverter.to_rgb(color)
              cdict['red'].append((val, r, r))
              cdict['green'].append((val, g, g))
              cdict['blue'].append((val, b, b))
  
          return LinearSegmentedColormap(name, cdict, N, gamma)
  
  class ListedColormap(Colormap):
      """Colormap object generated from a list of colors.
  
      This may be most useful when indexing directly into a colormap,
      but it can also be used to generate special colormaps for ordinary
      mapping.
      """
      def __init__(self, colors, name = 'from_list', N = None):
          """
          Make a colormap from a list of colors.
  
          *colors*
              a list of matplotlib color specifications,
              or an equivalent Nx3 floating point array (*N* rgb values)
          *name*
              a string to identify the colormap
          *N*
              the number of entries in the map.  The default is *None*,
              in which case there is one colormap entry for each
              element in the list of colors.  If::
  
                  N < len(colors)
  
              the list will be truncated at *N*.  If::
  
                  N > len(colors)
  
              the list will be extended by repetition.
          """
          self.colors = colors
          self.monochrome = False  # True only if all colors in map are identical;
                                   # needed for contouring.
          if N is None:
              N = len(self.colors)
          else:
              if cbook.is_string_like(self.colors):
                  self.colors = [self.colors] * N
                  self.monochrome = True
              elif cbook.iterable(self.colors):
                  self.colors = list(self.colors) # in case it was a tuple
                  if len(self.colors) == 1:
                      self.monochrome = True
                  if len(self.colors) < N:
                      self.colors = list(self.colors) * N
                  del(self.colors[N:])
              else:
                  try: gray = float(self.colors)
                  except TypeError: pass
                  else:  self.colors = [gray] * N
                  self.monochrome = True
          Colormap.__init__(self, name, N)
  
  
      def _init(self):
          rgb = np.array([colorConverter.to_rgb(c)
                      for c in self.colors], np.float)
          self._lut = np.zeros((self.N + 3, 4), np.float)
          self._lut[:-3, :-1] = rgb
          self._lut[:-3, -1] = 1
          self._isinit = True
          self._set_extremes()
  
  
  class Normalize:
      """
      Normalize a given value to the 0-1 range
      """
      def __init__(self, vmin=None, vmax=None, clip=False):
          """
          If *vmin* or *vmax* is not given, they are taken from the input's
          minimum and maximum value respectively.  If *clip* is *True* and
          the given value falls outside the range, the returned value
          will be 0 or 1, whichever is closer. Returns 0 if::
  
              vmin==vmax
  
          Works with scalars or arrays, including masked arrays.  If
          *clip* is *True*, masked values are set to 1; otherwise they
          remain masked.  Clipping silently defeats the purpose of setting
          the over, under, and masked colors in the colormap, so it is
          likely to lead to surprises; therefore the default is
          *clip* = *False*.
          """
          self.vmin = vmin
          self.vmax = vmax
          self.clip = clip
  
      @staticmethod
      def process_value(value):
          """
          Homogenize the input *value* for easy and efficient normalization.
  
          *value* can be a scalar or sequence.
  
          Returns *result*, *is_scalar*, where *result* is a
          masked array matching *value*.  Float dtypes are preserved;
          integer types with two bytes or smaller are converted to
          np.float32, and larger types are converted to np.float.
          Preserving float32 when possible, and using in-place operations,
          can greatly improve speed for large arrays.
  
          Experimental; we may want to add an option to force the
          use of float32.
          """
          if cbook.iterable(value):
              is_scalar = False
              result = ma.asarray(value)
              if result.dtype.kind == 'f':
                  if isinstance(value, np.ndarray):
                      result = result.copy()
              elif result.dtype.itemsize > 2:
                  result = result.astype(np.float)
              else:
                  result = result.astype(np.float32)
          else:
              is_scalar = True
              result = ma.array([value]).astype(np.float)
          return result, is_scalar
  
      def __call__(self, value, clip=None):
          if clip is None:
              clip = self.clip
  
          result, is_scalar = self.process_value(value)
  
          self.autoscale_None(result)
          vmin, vmax = self.vmin, self.vmax
          if vmin > vmax:
              raise ValueError("minvalue must be less than or equal to maxvalue")
          elif vmin == vmax:
              result.fill(0)   # Or should it be all masked?  Or 0.5?
          else:
              vmin = float(vmin)
              vmax = float(vmax)
              if clip:
                  mask = ma.getmask(result)
                  result = ma.array(np.clip(result.filled(vmax), vmin, vmax),
                                    mask=mask)
              # ma division is very slow; we can take a shortcut
              resdat = result.data
              resdat -= vmin
              resdat /= (vmax - vmin)
              result = np.ma.array(resdat, mask=result.mask, copy=False)
          if is_scalar:
              result = result[0]
          return result
  
      def inverse(self, value):
          if not self.scaled():
              raise ValueError("Not invertible until scaled")
          vmin = float(self.vmin)
          vmax = float(self.vmax)
  
          if cbook.iterable(value):
              val = ma.asarray(value)
              return vmin + val * (vmax - vmin)
          else:
              return vmin + value * (vmax - vmin)
  
      def autoscale(self, A):
          '''
          Set *vmin*, *vmax* to min, max of *A*.
          '''
          self.vmin = ma.min(A)
          self.vmax = ma.max(A)
  
      def autoscale_None(self, A):
          ' autoscale only None-valued vmin or vmax'
          if self.vmin is None:
              self.vmin = ma.min(A)
          if self.vmax is None:
              self.vmax = ma.max(A)
  
      def scaled(self):
          'return true if vmin and vmax set'
          return (self.vmin is not None and self.vmax is not None)
  
  class LogNorm(Normalize):
      """
      Normalize a given value to the 0-1 range on a log scale
      """
      def __call__(self, value, clip=None):
          if clip is None:
              clip = self.clip
  
          result, is_scalar = self.process_value(value)
  
          result = ma.masked_less_equal(result, 0, copy=False)
  
          self.autoscale_None(result)
          vmin, vmax = self.vmin, self.vmax
          if vmin > vmax:
              raise ValueError("minvalue must be less than or equal to maxvalue")
          elif vmin<=0:
              raise ValueError("values must all be positive")
          elif vmin==vmax:
              result.fill(0)
          else:
              if clip:
                  mask = ma.getmask(result)
                  val = ma.array(np.clip(result.filled(vmax), vmin, vmax),
                                  mask=mask)
              #result = (ma.log(result)-np.log(vmin))/(np.log(vmax)-np.log(vmin))
              # in-place equivalent of above can be much faster
              resdat = result.data
              mask = result.mask
              if mask is np.ma.nomask:
                  mask = (resdat <= 0)
              else:
                  mask |= resdat <= 0
              np.putmask(resdat, mask, 1)
              np.log(resdat, resdat)
              resdat -= np.log(vmin)
              resdat /= (np.log(vmax) - np.log(vmin))
              result = np.ma.array(resdat, mask=mask, copy=False)
          if is_scalar:
              result = result[0]
          return result
  
      def inverse(self, value):
          if not self.scaled():
              raise ValueError("Not invertible until scaled")
          vmin, vmax = self.vmin, self.vmax
  
          if cbook.iterable(value):
              val = ma.asarray(value)
              return vmin * ma.power((vmax/vmin), val)
          else:
              return vmin * pow((vmax/vmin), value)
  
      def autoscale(self, A):
          '''
          Set *vmin*, *vmax* to min, max of *A*.
          '''
          A = ma.masked_less_equal(A, 0, copy=False)
          self.vmin = ma.min(A)
          self.vmax = ma.max(A)
  
      def autoscale_None(self, A):
          ' autoscale only None-valued vmin or vmax'
          if self.vmin is not None and self.vmax is not None:
              return
          A = ma.masked_less_equal(A, 0, copy=False)
          if self.vmin is None:
              self.vmin = ma.min(A)
          if self.vmax is None:
              self.vmax = ma.max(A)
  
  class BoundaryNorm(Normalize):
      '''
      Generate a colormap index based on discrete intervals.
  
      Unlike :class:`Normalize` or :class:`LogNorm`,
      :class:`BoundaryNorm` maps values to integers instead of to the
      interval 0-1.
  
      Mapping to the 0-1 interval could have been done via
      piece-wise linear interpolation, but using integers seems
      simpler, and reduces the number of conversions back and forth
      between integer and floating point.
      '''
      def __init__(self, boundaries, ncolors, clip=False):
          '''
          *boundaries*
              a monotonically increasing sequence
          *ncolors*
              number of colors in the colormap to be used
  
          If::
  
              b[i] <= v < b[i+1]
  
          then v is mapped to color j;
          as i varies from 0 to len(boundaries)-2,
          j goes from 0 to ncolors-1.
  
          Out-of-range values are mapped to -1 if low and ncolors
          if high; these are converted to valid indices by
          :meth:`Colormap.__call__` .
          '''
          self.clip = clip
          self.vmin = boundaries[0]
          self.vmax = boundaries[-1]
          self.boundaries = np.asarray(boundaries)
          self.N = len(self.boundaries)
          self.Ncmap = ncolors
          if self.N-1 == self.Ncmap:
              self._interp = False
          else:
              self._interp = True
  
      def __call__(self, x, clip=None):
          if clip is None:
              clip = self.clip
          x = ma.asarray(x)
          mask = ma.getmaskarray(x)
          xx = x.filled(self.vmax+1)
          if clip:
              np.clip(xx, self.vmin, self.vmax)
          iret = np.zeros(x.shape, dtype=np.int16)
          for i, b in enumerate(self.boundaries):
              iret[xx>=b] = i
          if self._interp:
              iret = (iret * (float(self.Ncmap-1)/(self.N-2))).astype(np.int16)
          iret[xx<self.vmin] = -1
          iret[xx>=self.vmax] = self.Ncmap
          ret = ma.array(iret, mask=mask)
          if ret.shape == () and not mask:
              ret = int(ret)  # assume python scalar
          return ret
  
      def inverse(self, value):
          return ValueError("BoundaryNorm is not invertible")
  
  
  class NoNorm(Normalize):
      '''
      Dummy replacement for Normalize, for the case where we
      want to use indices directly in a
      :class:`~matplotlib.cm.ScalarMappable` .
      '''
      def __call__(self, value, clip=None):
          return value
  
      def inverse(self, value):
          return value
  
  # compatibility with earlier class names that violated convention:
  normalize = Normalize
  no_norm = NoNorm
  
  def rgb_to_hsv(arr):
      """
      convert rgb values in a numpy array to hsv values
      input and output arrays should have shape (M,N,3)
      """
      out = np.zeros_like(arr)
      arr_max = arr.max(-1)
      ipos = arr_max > 0
      delta = arr.ptp(-1)
      s = np.zeros_like(delta)
      s[ipos] = delta[ipos] / arr_max[ipos]
      ipos = delta > 0
      # red is max
      idx = (arr[:,:,0] == arr_max) & ipos
      out[idx, 0] = (arr[idx, 1] - arr[idx, 2]) / delta[idx]
      # green is max
      idx = (arr[:,:,1] == arr_max) & ipos
      out[idx, 0] = 2. + (arr[idx, 2] - arr[idx, 0] ) / delta[idx]
      # blue is max
      idx = (arr[:,:,2] == arr_max) & ipos
      out[idx, 0] = 4. + (arr[idx, 0] - arr[idx, 1] ) / delta[idx]
      out[:,:,0] = (out[:,:,0]/6.0) % 1.0
      out[:,:,1] = s
      out[:,:,2] = arr_max
      return out
  
  def hsv_to_rgb(hsv):
      """
      convert hsv values in a numpy array to rgb values
      both input and output arrays have shape (M,N,3)
      """
      h = hsv[:,:,0]; s = hsv[:,:,1]; v = hsv[:,:,2]
      r = np.empty_like(h); g = np.empty_like(h); b = np.empty_like(h)
      i = (h*6.0).astype(np.int)
      f = (h*6.0) - i
      p = v*(1.0 - s)
      q = v*(1.0 - s*f)
      t = v*(1.0 - s*(1.0-f))
      idx = i%6 == 0
      r[idx] = v[idx]; g[idx] = t[idx]; b[idx] = p[idx]
      idx = i == 1
      r[idx] = q[idx]; g[idx] = v[idx]; b[idx] = p[idx]
      idx = i == 2
      r[idx] = p[idx]; g[idx] = v[idx]; b[idx] = t[idx]
      idx = i == 3
      r[idx] = p[idx]; g[idx] = q[idx]; b[idx] = v[idx]
      idx = i == 4
      r[idx] = t[idx]; g[idx] = p[idx]; b[idx] = v[idx]
      idx = i == 5
      r[idx] = v[idx]; g[idx] = p[idx]; b[idx] = q[idx]
      idx = s == 0
      r[idx] = v[idx]; g[idx] = v[idx]; b[idx] = v[idx]
      rgb = np.empty_like(hsv)
      rgb[:,:,0]=r; rgb[:,:,1]=g; rgb[:,:,2]=b
      return rgb
  
  class LightSource(object):
      """
      Create a light source coming from the specified azimuth and elevation.
      Angles are in degrees, with the azimuth measured
      clockwise from north and elevation up from the zero plane of the surface.
      The :meth:`shade` is used to produce rgb values for a shaded relief image
      given a data array.
      """
      def __init__(self,azdeg=315,altdeg=45,\
                   hsv_min_val=0,hsv_max_val=1,hsv_min_sat=1,hsv_max_sat=0):
         """
         Specify the azimuth (measured clockwise from south) and altitude
         (measured up from the plane of the surface) of the light source
         in degrees.
  
         The color of the resulting image will be darkened
         by moving the (s,v) values (in hsv colorspace) toward
         (hsv_min_sat, hsv_min_val) in the shaded regions, or
         lightened by sliding (s,v) toward
         (hsv_max_sat hsv_max_val) in regions that are illuminated.
         The default extremes are chose so that completely shaded points
         are nearly black (s = 1, v = 0) and completely illuminated points
         are nearly white (s = 0, v = 1).
         """
         self.azdeg = azdeg
         self.altdeg = altdeg
         self.hsv_min_val = hsv_min_val
         self.hsv_max_val = hsv_max_val
         self.hsv_min_sat = hsv_min_sat
         self.hsv_max_sat = hsv_max_sat
  
      def shade(self,data,cmap):
          """
          Take the input data array, convert to HSV values in the
          given colormap, then adjust those color values
          to given the impression of a shaded relief map with a
          specified light source.
          RGBA values are returned, which can then be used to
          plot the shaded image with imshow.
          """
  
          rgb0 = cmap((data-data.min())/(data.max()-data.min()))
          rgb1 = self.shade_rgb(rgb0, elevation=data)
          rgb0[:,:,0:3] = rgb1
          return rgb0
  
      def shade_rgb(self,rgb, elevation, fraction=1.):
          """
          Take the input RGB array (ny*nx*3) adjust their color values
          to given the impression of a shaded relief map with a
          specified light source using the elevation (ny*nx).
          A new RGB array ((ny*nx*3)) is returned.
          """
          # imagine an artificial sun placed at infinity in
          # some azimuth and elevation position illuminating our surface. The parts of
          # the surface that slope toward the sun should brighten while those sides
          # facing away should become darker.
          # convert alt, az to radians
          az = self.azdeg*np.pi/180.0
          alt = self.altdeg*np.pi/180.0
          # gradient in x and y directions
          dx, dy = np.gradient(elevation)
          slope = 0.5*np.pi - np.arctan(np.hypot(dx, dy))
          aspect = np.arctan2(dx, dy)
          intensity = np.sin(alt)*np.sin(slope) + np.cos(alt)*np.cos(slope)*np.cos(-az -\
                  aspect - 0.5*np.pi)
          # rescale to interval -1,1
          # +1 means maximum sun exposure and -1 means complete shade.
          intensity = (intensity - intensity.min())/(intensity.max() - intensity.min())
          intensity = (2.*intensity - 1.)*fraction
          # convert to rgb, then rgb to hsv
          #rgb = cmap((data-data.min())/(data.max()-data.min()))
          hsv = rgb_to_hsv(rgb[:,:,0:3])
          # modify hsv values to simulate illumination.
          hsv[:,:,1] = np.where(np.logical_and(np.abs(hsv[:,:,1])>1.e-10,intensity>0),\
                  (1.-intensity)*hsv[:,:,1]+intensity*self.hsv_max_sat, hsv[:,:,1])
          hsv[:,:,2] = np.where(intensity > 0, (1.-intensity)*hsv[:,:,2] +\
                  intensity*self.hsv_max_val, hsv[:,:,2])
          hsv[:,:,1] = np.where(np.logical_and(np.abs(hsv[:,:,1])>1.e-10,intensity<0),\
                  (1.+intensity)*hsv[:,:,1]-intensity*self.hsv_min_sat, hsv[:,:,1])
          hsv[:,:,2] = np.where(intensity < 0, (1.+intensity)*hsv[:,:,2] -\
                  intensity*self.hsv_min_val, hsv[:,:,2])
          hsv[:,:,1:] = np.where(hsv[:,:,1:]<0.,0,hsv[:,:,1:])
          hsv[:,:,1:] = np.where(hsv[:,:,1:]>1.,1,hsv[:,:,1:])
          # convert modified hsv back to rgb.
          return hsv_to_rgb(hsv)
  