Index: colors.py
===================================================================
--- colors.py	(revision 7446)
+++ colors.py	(working copy)
@@ -52,6 +52,7 @@
 import numpy as np
 from numpy import ma
 import matplotlib.cbook as cbook
+import types
 
 parts = np.__version__.split('.')
 NP_MAJOR, NP_MINOR = map(int, parts[:2])
@@ -393,7 +394,7 @@
 
 colorConverter = ColorConverter()
 
-def makeMappingArray(N, data):
+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.
@@ -407,9 +408,18 @@
     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 can be passed.
+
     The function returns an array "result" where ``result[x*(N-1)]``
     gives the closest value for values of x between 0 and 1.
     """
+
+    if type(data) is types.FunctionType:
+        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:
@@ -431,7 +441,7 @@
     # begin generation of lookup table
     x = x * (N-1)
     lut = np.zeros((N,), np.float)
-    xind = np.arange(float(N))
+    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]))
@@ -580,7 +590,7 @@
     primary color, with the 0-1 domain divided into any number of
     segments.
     """
-    def __init__(self, name, segmentdata, N=256):
+    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
@@ -627,26 +637,43 @@
                                  # 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._lut[:-3, 1] = makeMappingArray(self.N, self._segmentdata['green'])
-        self._lut[:-3, 2] = makeMappingArray(self.N, self._segmentdata['blue'])
+        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):
+    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=1
         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.
         """
 
         ncolors = len(colors)
-        vals = np.linspace(0., 1., ncolors)
+        if len(colors[0]) == 2:
+            vals = [v for v, col in colors]
+            colors = [col for v, col in colors]
+        else:
+            vals = np.linspace(0., 1., ncolors)
 
         cdict = dict(red=[], green=[], blue=[])
         for val, color in zip(vals, colors):
@@ -655,7 +682,7 @@
             cdict['green'].append((val, g, g))
             cdict['blue'].append((val, b, b))
 
-        return LinearSegmentedColormap(name, cdict, N)
+        return LinearSegmentedColormap(name, cdict, N, gamma)
 
 class ListedColormap(Colormap):
     """Colormap object generated from a list of colors.
Index: cm.py
===================================================================
--- cm.py	(revision 7446)
+++ cm.py	(working copy)
@@ -6,6 +6,8 @@
 """
 
 import os
+import types
+import copy
 
 import numpy as np
 from numpy import ma
@@ -14,7 +16,6 @@
 import matplotlib.cbook as cbook
 from matplotlib._cm import datad
 
-
 cmap_d = dict()
 
 # reverse all the colormaps.
@@ -23,7 +24,10 @@
 def revcmap(data):
     data_r = {}
     for key, val in data.iteritems():
-        valnew = [(1.0-a, b, c) for a, b, c in reversed(val)]
+        if type(val) is types.FunctionType:
+            valnew = lambda x: 1 - val(x)
+        else:
+            valnew = [(1.0-a, b, c) for a, b, c in reversed(val)]
         data_r[key] = valnew
     return data_r
 
@@ -33,12 +37,20 @@
 
 for cmapname in _cmapnames:
     cmapname_r = cmapname+'_r'
-    cmapdat_r = revcmap(datad[cmapname])
-    datad[cmapname_r] = cmapdat_r
-    cmap_d[cmapname] = colors.LinearSegmentedColormap(
-                            cmapname, datad[cmapname], LUTSIZE)
-    cmap_d[cmapname_r] = colors.LinearSegmentedColormap(
-                            cmapname_r, cmapdat_r, LUTSIZE)
+    if type(datad[cmapname]) is types.DictType:
+        datad[cmapname_r] = revcmap(datad[cmapname])
+        cmap_d[cmapname] = colors.LinearSegmentedColormap(
+                                cmapname, datad[cmapname], LUTSIZE)
+        cmap_d[cmapname_r] = colors.LinearSegmentedColormap(
+                                cmapname_r, datad[cmapname_r], LUTSIZE)
+    elif type(datad[cmapname]) in (types.TupleType, types.ListType):
+        datad[cmapname] = list(datad[cmapname])
+        datad[cmapname_r] = copy.copy(datad[cmapname])
+        datad[cmapname_r].reverse()
+        cmap_d[cmapname] = colors.LinearSegmentedColormap.from_list(
+                                cmapname, datad[cmapname], LUTSIZE)
+        cmap_d[cmapname_r] = colors.LinearSegmentedColormap.from_list(
+                                cmapname_r, datad[cmapname_r], LUTSIZE)
 
 locals().update(cmap_d)
 
Index: _cm.py
===================================================================
--- _cm.py	(revision 7446)
+++ _cm.py	(working copy)
@@ -4,6 +4,8 @@
 
 """
 
+import numpy as np
+
 _binary_data = {
     'red'  :  ((0., 1., 1.), (1., 0., 0.)),
     'green':  ((0., 1., 1.), (1., 0., 0.)),
@@ -32,96 +34,115 @@
                 'green': ((0., 0., 0.),(1.0, 0.7812, 0.7812)),
                 'blue':  ((0., 0., 0.),(1.0, 0.4975, 0.4975))}
 
-_flag_data = {'red':   ((0., 1., 1.),(0.015873, 1.000000, 1.000000),
-                        (0.031746, 0.000000, 0.000000),(0.047619, 0.000000, 0.000000),
-                        (0.063492, 1.000000, 1.000000),(0.079365, 1.000000, 1.000000),
-                        (0.095238, 0.000000, 0.000000),(0.111111, 0.000000, 0.000000),
-                        (0.126984, 1.000000, 1.000000),(0.142857, 1.000000, 1.000000),
-                        (0.158730, 0.000000, 0.000000),(0.174603, 0.000000, 0.000000),
-                        (0.190476, 1.000000, 1.000000),(0.206349, 1.000000, 1.000000),
-                        (0.222222, 0.000000, 0.000000),(0.238095, 0.000000, 0.000000),
-                        (0.253968, 1.000000, 1.000000),(0.269841, 1.000000, 1.000000),
-                        (0.285714, 0.000000, 0.000000),(0.301587, 0.000000, 0.000000),
-                        (0.317460, 1.000000, 1.000000),(0.333333, 1.000000, 1.000000),
-                        (0.349206, 0.000000, 0.000000),(0.365079, 0.000000, 0.000000),
-                        (0.380952, 1.000000, 1.000000),(0.396825, 1.000000, 1.000000),
-                        (0.412698, 0.000000, 0.000000),(0.428571, 0.000000, 0.000000),
-                        (0.444444, 1.000000, 1.000000),(0.460317, 1.000000, 1.000000),
-                        (0.476190, 0.000000, 0.000000),(0.492063, 0.000000, 0.000000),
-                        (0.507937, 1.000000, 1.000000),(0.523810, 1.000000, 1.000000),
-                        (0.539683, 0.000000, 0.000000),(0.555556, 0.000000, 0.000000),
-                        (0.571429, 1.000000, 1.000000),(0.587302, 1.000000, 1.000000),
-                        (0.603175, 0.000000, 0.000000),(0.619048, 0.000000, 0.000000),
-                        (0.634921, 1.000000, 1.000000),(0.650794, 1.000000, 1.000000),
-                        (0.666667, 0.000000, 0.000000),(0.682540, 0.000000, 0.000000),
-                        (0.698413, 1.000000, 1.000000),(0.714286, 1.000000, 1.000000),
-                        (0.730159, 0.000000, 0.000000),(0.746032, 0.000000, 0.000000),
-                        (0.761905, 1.000000, 1.000000),(0.777778, 1.000000, 1.000000),
-                        (0.793651, 0.000000, 0.000000),(0.809524, 0.000000, 0.000000),
-                        (0.825397, 1.000000, 1.000000),(0.841270, 1.000000, 1.000000),
-                        (0.857143, 0.000000, 0.000000),(0.873016, 0.000000, 0.000000),
-                        (0.888889, 1.000000, 1.000000),(0.904762, 1.000000, 1.000000),
-                        (0.920635, 0.000000, 0.000000),(0.936508, 0.000000, 0.000000),
-                        (0.952381, 1.000000, 1.000000),(0.968254, 1.000000, 1.000000),
-                        (0.984127, 0.000000, 0.000000),(1.0, 0., 0.)),
-              'green': ((0., 0., 0.),(0.015873, 1.000000, 1.000000),
-                        (0.031746, 0.000000, 0.000000),(0.063492, 0.000000, 0.000000),
-                        (0.079365, 1.000000, 1.000000),(0.095238, 0.000000, 0.000000),
-                        (0.126984, 0.000000, 0.000000),(0.142857, 1.000000, 1.000000),
-                        (0.158730, 0.000000, 0.000000),(0.190476, 0.000000, 0.000000),
-                        (0.206349, 1.000000, 1.000000),(0.222222, 0.000000, 0.000000),
-                        (0.253968, 0.000000, 0.000000),(0.269841, 1.000000, 1.000000),
-                        (0.285714, 0.000000, 0.000000),(0.317460, 0.000000, 0.000000),
-                        (0.333333, 1.000000, 1.000000),(0.349206, 0.000000, 0.000000),
-                        (0.380952, 0.000000, 0.000000),(0.396825, 1.000000, 1.000000),
-                        (0.412698, 0.000000, 0.000000),(0.444444, 0.000000, 0.000000),
-                        (0.460317, 1.000000, 1.000000),(0.476190, 0.000000, 0.000000),
-                        (0.507937, 0.000000, 0.000000),(0.523810, 1.000000, 1.000000),
-                        (0.539683, 0.000000, 0.000000),(0.571429, 0.000000, 0.000000),
-                        (0.587302, 1.000000, 1.000000),(0.603175, 0.000000, 0.000000),
-                        (0.634921, 0.000000, 0.000000),(0.650794, 1.000000, 1.000000),
-                        (0.666667, 0.000000, 0.000000),(0.698413, 0.000000, 0.000000),
-                        (0.714286, 1.000000, 1.000000),(0.730159, 0.000000, 0.000000),
-                        (0.761905, 0.000000, 0.000000),(0.777778, 1.000000, 1.000000),
-                        (0.793651, 0.000000, 0.000000),(0.825397, 0.000000, 0.000000),
-                        (0.841270, 1.000000, 1.000000),(0.857143, 0.000000, 0.000000),
-                        (0.888889, 0.000000, 0.000000),(0.904762, 1.000000, 1.000000),
-                        (0.920635, 0.000000, 0.000000),(0.952381, 0.000000, 0.000000),
-                        (0.968254, 1.000000, 1.000000),(0.984127, 0.000000, 0.000000),
-                        (1.0, 0., 0.)),
-              'blue':  ((0., 0., 0.),(0.015873, 1.000000, 1.000000),
-                        (0.031746, 1.000000, 1.000000),(0.047619, 0.000000, 0.000000),
-                        (0.063492, 0.000000, 0.000000),(0.079365, 1.000000, 1.000000),
-                        (0.095238, 1.000000, 1.000000),(0.111111, 0.000000, 0.000000),
-                        (0.126984, 0.000000, 0.000000),(0.142857, 1.000000, 1.000000),
-                        (0.158730, 1.000000, 1.000000),(0.174603, 0.000000, 0.000000),
-                        (0.190476, 0.000000, 0.000000),(0.206349, 1.000000, 1.000000),
-                        (0.222222, 1.000000, 1.000000),(0.238095, 0.000000, 0.000000),
-                        (0.253968, 0.000000, 0.000000),(0.269841, 1.000000, 1.000000),
-                        (0.285714, 1.000000, 1.000000),(0.301587, 0.000000, 0.000000),
-                        (0.317460, 0.000000, 0.000000),(0.333333, 1.000000, 1.000000),
-                        (0.349206, 1.000000, 1.000000),(0.365079, 0.000000, 0.000000),
-                        (0.380952, 0.000000, 0.000000),(0.396825, 1.000000, 1.000000),
-                        (0.412698, 1.000000, 1.000000),(0.428571, 0.000000, 0.000000),
-                        (0.444444, 0.000000, 0.000000),(0.460317, 1.000000, 1.000000),
-                        (0.476190, 1.000000, 1.000000),(0.492063, 0.000000, 0.000000),
-                        (0.507937, 0.000000, 0.000000),(0.523810, 1.000000, 1.000000),
-                        (0.539683, 1.000000, 1.000000),(0.555556, 0.000000, 0.000000),
-                        (0.571429, 0.000000, 0.000000),(0.587302, 1.000000, 1.000000),
-                        (0.603175, 1.000000, 1.000000),(0.619048, 0.000000, 0.000000),
-                        (0.634921, 0.000000, 0.000000),(0.650794, 1.000000, 1.000000),
-                        (0.666667, 1.000000, 1.000000),(0.682540, 0.000000, 0.000000),
-                        (0.698413, 0.000000, 0.000000),(0.714286, 1.000000, 1.000000),
-                        (0.730159, 1.000000, 1.000000),(0.746032, 0.000000, 0.000000),
-                        (0.761905, 0.000000, 0.000000),(0.777778, 1.000000, 1.000000),
-                        (0.793651, 1.000000, 1.000000),(0.809524, 0.000000, 0.000000),
-                        (0.825397, 0.000000, 0.000000),(0.841270, 1.000000, 1.000000),
-                        (0.857143, 1.000000, 1.000000),(0.873016, 0.000000, 0.000000),
-                        (0.888889, 0.000000, 0.000000),(0.904762, 1.000000, 1.000000),
-                        (0.920635, 1.000000, 1.000000),(0.936508, 0.000000, 0.000000),
-                        (0.952381, 0.000000, 0.000000),(0.968254, 1.000000, 1.000000),
-                        (0.984127, 1.000000, 1.000000),(1.0, 0., 0.))}
+_flag_data = {
+        'red': lambda x: 0.75 * np.sin((x * 31.5 + 0.25) * np.pi) + 0.5,
+        'green': lambda x: np.sin(x * 31.5 * np.pi),
+        'blue': lambda x: 0.75 * np.sin((x * 31.5 - 0.25) * np.pi) + 0.5,
+}
 
+_prism_data = {
+        'red': lambda x: 0.75 * np.sin((x * 20.9 + 0.25) * np.pi) + 0.67,
+        'green': lambda x: 0.75 * np.sin((x * 20.9 - 0.25) * np.pi) + 0.33,
+        'blue': lambda x: -1.1 * np.sin((x * 20.9) * np.pi),
+}
+
+_bwr_data = ((0.0, 0.0, 1.0), (1.0, 1.0, 1.0), (1.0, 0.0, 0.0))
+_brg_data = ((0.0, 0.0, 1.0), (1.0, 0.0, 0.0), (0.0, 1.0, 0.0))
+
+# Gnuplot palette functions
+gfunc = {
+        0: lambda x: 0,
+        1: lambda x: 0.5,
+        2: lambda x: 1,
+        3: lambda x: x,
+        4: lambda x: x**2,
+        5: lambda x: x**3,
+        6: lambda x: x**4,
+        7: lambda x: np.sqrt(x),
+        8: lambda x: np.sqrt(np.sqrt(x)),
+        9: lambda x: np.sin(x * np.pi / 2),
+        10: lambda x: np.cos(x * np.pi / 2),
+        11: lambda x: np.abs(x - 0.5),
+        12: lambda x: (2 * x - 1)**2,
+        13: lambda x: np.sin(x * np.pi),
+        14: lambda x: np.abs(np.cos(x * np.pi)),
+        15: lambda x: np.sin(x * 2 * np.pi),
+        16: lambda x: np.cos(x * 2 * np.pi),
+        17: lambda x: np.abs(np.sin(x * 2 * np.pi)),
+        18: lambda x: np.abs(np.cos(x * 2 * np.pi)),
+        19: lambda x: np.abs(np.sin(x * 4 * np.pi)),
+        20: lambda x: np.abs(np.cos(x * 4 * np.pi)),
+        21: lambda x: 3 * x,
+        22: lambda x: 3 * x - 1,
+        23: lambda x: 3 * x - 2,
+        24: lambda x: np.abs(3 * x - 1),
+        25: lambda x: np.abs(3 * x - 2),
+        26: lambda x: (3 * x - 1) / 2,
+        27: lambda x: (3 * x - 2) / 2,
+        28: lambda x: np.abs((3 * x  - 1) / 2),
+        29: lambda x: np.abs((3 * x  - 2) / 2),
+        30: lambda x: x / 0.32 - 0.78125,
+        31: lambda x: 2 * x - 0.84,
+        32: lambda x: gfunc32(x),
+        33: lambda x: np.abs(2 * x - 0.5),
+        34: lambda x: 2 * x,
+        35: lambda x: 2 * x - 0.5,
+        36: lambda x: 2 * x - 1.
+}
+
+def gfunc32(x):
+    ret = np.zeros(len(x))
+    m = (x < 0.25)
+    ret[m] = 4 * x[m]
+    m = (x >= 0.25) & (x < 0.92)
+    ret[m] = -2 * x[m] + 1.84
+    m = (x >= 0.92)
+    ret[m] = x[m] / 0.08 - 11.5
+    return ret
+
+_gnuplot_data = {
+        'red': gfunc[7],
+        'green': gfunc[5],
+        'blue': gfunc[15],
+}
+
+_gnuplot2_data = {
+        'red': gfunc[30],
+        'green': gfunc[31],
+        'blue': gfunc[32],
+}
+
+_ocean_data = {
+        'red': gfunc[23],
+        'green': gfunc[28],
+        'blue': gfunc[3],
+}
+
+_afmhot_data = {
+        'red': gfunc[34],
+        'green': gfunc[35],
+        'blue': gfunc[36],
+}
+
+_rainbow_data = {
+        'red': gfunc[33],
+        'green': gfunc[13],
+        'blue': gfunc[10],
+}
+
+_seismic_data = (
+        (0.0, 0.0, 0.3), (0.0, 0.0, 1.0),
+        (1.0, 1.0, 1.0), (1.0, 0.0, 0.0),
+        (0.5, 0.0, 0.0))
+
+_terrain_data = (
+        (0.00, (0.2, 0.2, 0.6)),
+        (0.15, (0.0, 0.6, 1.0)),
+        (0.25, (0.0, 0.8, 0.4)),
+        (0.50, (1.0, 1.0, 0.6)),
+        (0.75, (0.5, 0.36, 0.33)),
+        (1.00, (1.0, 1.0, 1.0)))
+
 _gray_data =  {'red':   ((0., 0, 0), (1., 1, 1)),
                'green': ((0., 0, 0), (1., 1, 1)),
                'blue':  ((0., 0, 0), (1., 1, 1))}
@@ -249,77 +270,6 @@
                         (0.952381, 0.951711, 0.951711),(0.968254, 0.968075, 0.968075),
                         (0.984127, 0.984167, 0.984167),(1.0, 1.0, 1.0))}
 
-_prism_data = {'red':   ((0., 1., 1.),(0.031746, 1.000000, 1.000000),
-                         (0.047619, 0.000000, 0.000000),(0.063492, 0.000000, 0.000000),
-                         (0.079365, 0.666667, 0.666667),(0.095238, 1.000000, 1.000000),
-                         (0.126984, 1.000000, 1.000000),(0.142857, 0.000000, 0.000000),
-                         (0.158730, 0.000000, 0.000000),(0.174603, 0.666667, 0.666667),
-                         (0.190476, 1.000000, 1.000000),(0.222222, 1.000000, 1.000000),
-                         (0.238095, 0.000000, 0.000000),(0.253968, 0.000000, 0.000000),
-                         (0.269841, 0.666667, 0.666667),(0.285714, 1.000000, 1.000000),
-                         (0.317460, 1.000000, 1.000000),(0.333333, 0.000000, 0.000000),
-                         (0.349206, 0.000000, 0.000000),(0.365079, 0.666667, 0.666667),
-                         (0.380952, 1.000000, 1.000000),(0.412698, 1.000000, 1.000000),
-                         (0.428571, 0.000000, 0.000000),(0.444444, 0.000000, 0.000000),
-                         (0.460317, 0.666667, 0.666667),(0.476190, 1.000000, 1.000000),
-                         (0.507937, 1.000000, 1.000000),(0.523810, 0.000000, 0.000000),
-                         (0.539683, 0.000000, 0.000000),(0.555556, 0.666667, 0.666667),
-                         (0.571429, 1.000000, 1.000000),(0.603175, 1.000000, 1.000000),
-                         (0.619048, 0.000000, 0.000000),(0.634921, 0.000000, 0.000000),
-                         (0.650794, 0.666667, 0.666667),(0.666667, 1.000000, 1.000000),
-                         (0.698413, 1.000000, 1.000000),(0.714286, 0.000000, 0.000000),
-                         (0.730159, 0.000000, 0.000000),(0.746032, 0.666667, 0.666667),
-                         (0.761905, 1.000000, 1.000000),(0.793651, 1.000000, 1.000000),
-                         (0.809524, 0.000000, 0.000000),(0.825397, 0.000000, 0.000000),
-                         (0.841270, 0.666667, 0.666667),(0.857143, 1.000000, 1.000000),
-                         (0.888889, 1.000000, 1.000000),(0.904762, 0.000000, 0.000000),
-                         (0.920635, 0.000000, 0.000000),(0.936508, 0.666667, 0.666667),
-                         (0.952381, 1.000000, 1.000000),(0.984127, 1.000000, 1.000000),
-                         (1.0, 0.0, 0.0)),
-               'green': ((0., 0., 0.),(0.031746, 1.000000, 1.000000),
-                         (0.047619, 1.000000, 1.000000),(0.063492, 0.000000, 0.000000),
-                         (0.095238, 0.000000, 0.000000),(0.126984, 1.000000, 1.000000),
-                         (0.142857, 1.000000, 1.000000),(0.158730, 0.000000, 0.000000),
-                         (0.190476, 0.000000, 0.000000),(0.222222, 1.000000, 1.000000),
-                         (0.238095, 1.000000, 1.000000),(0.253968, 0.000000, 0.000000),
-                         (0.285714, 0.000000, 0.000000),(0.317460, 1.000000, 1.000000),
-                         (0.333333, 1.000000, 1.000000),(0.349206, 0.000000, 0.000000),
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-
 _spring_data = {'red':   ((0., 1., 1.),(1.0, 1.0, 1.0)),
                 'green': ((0., 0., 0.),(1.0, 1.0, 1.0)),
                 'blue':  ((0., 1., 1.),(1.0, 0.0, 0.0))}
@@ -1708,9 +1658,7 @@
 0.94509804248809814), (0.99159663915634155, 0.95686274766921997,
 0.95686274766921997), (0.99579828977584839, 0.97254902124404907,
 0.97254902124404907), (1.0, 0.9843137264251709, 0.9843137264251709)],
-'green': [(0.0, 0.0, 0.0), (0.0042016808874905109, 0.0, 0.0),
-(0.0084033617749810219, 0.0, 0.0), (0.012605042196810246, 0.0, 0.0),
-(0.016806723549962044, 0.0, 0.0), (0.021008403971791267, 0.0, 0.0),
+'green': [(0.0, 0.0, 0.0),
 (0.025210084393620491, 0.0, 0.0), (0.029411764815449715, 0.0, 0.0),
 (0.033613447099924088, 0.011764706112444401, 0.011764706112444401),
 (0.037815127521753311, 0.023529412224888802, 0.023529412224888802),
@@ -1942,10 +1890,7 @@
 0.94509804248809814, 0.94509804248809814), (0.99159663915634155,
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-(0.96638655662536621, 1.0, 1.0), (0.97058820724487305, 1.0, 1.0),
-(0.97478991746902466, 1.0, 1.0), (0.97899156808853149, 1.0, 1.0),
-(0.98319327831268311, 1.0, 1.0), (0.98739492893218994, 1.0, 1.0),
-(0.99159663915634155, 1.0, 1.0), (0.99579828977584839, 1.0, 1.0), (1.0,
-1.0, 1.0)]}
+
+_gist_gray_data = {
+        'red': gfunc[3],
+        'green': gfunc[3],
+        'blue': gfunc[3],
+}
+
+_gist_heat_data = {
+        'red': lambda x: 1.5 * x,
+        'green': lambda x: 2 * x - 1,
+        'blue': lambda x: 4 * x - 3,
+}
+
 _gist_ncar_data = {'blue': [(0.0, 0.50196081399917603,
 0.50196081399917603), (0.0050505050458014011, 0.45098039507865906,
 0.45098039507865906), (0.010101010091602802, 0.40392157435417175,
@@ -3445,14 +2157,6 @@
 0.81176471710205078), (0.10101009905338287, 0.90588235855102539,
 0.90588235855102539), (0.10606060922145844, 1.0, 1.0),
 (0.1111111119389534, 1.0, 1.0), (0.11616161465644836, 1.0, 1.0),
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 (0.20202019810676575, 1.0, 1.0), (0.20707070827484131, 1.0, 1.0),
 (0.21212121844291687, 0.99215686321258545, 0.99215686321258545),
 (0.21717171370983124, 0.95686274766921997, 0.95686274766921997),
@@ -3476,15 +2180,6 @@
 (0.30808082222938538, 0.10588235408067703, 0.10588235408067703),
 (0.31313130259513855, 0.047058824449777603, 0.047058824449777603),
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 (0.41919192671775818, 0.0, 0.0), (0.42424243688583374,
 0.0039215688593685627, 0.0039215688593685627), (0.42929291725158691,
 0.027450980618596077, 0.027450980618596077), (0.43434342741966248,
@@ -3507,10 +2202,6 @@
 0.062745101749897003), (0.52020204067230225, 0.039215687662363052,
 0.039215687662363052), (0.52525252103805542, 0.015686275437474251,
 0.015686275437474251), (0.53030300140380859, 0.0, 0.0),
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 (0.57575756311416626, 0.0, 0.0), (0.58080810308456421, 0.0, 0.0),
 (0.58585858345031738, 0.0039215688593685627, 0.0039215688593685627),
 (0.59090906381607056, 0.0078431377187371254, 0.0078431377187371254),
@@ -3533,10 +2224,6 @@
 (0.67676764726638794, 0.011764706112444401, 0.011764706112444401),
 (0.68181818723678589, 0.0039215688593685627, 0.0039215688593685627),
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 (0.7373737096786499, 0.0, 0.0), (0.74242424964904785,
 0.031372550874948502, 0.031372550874948502), (0.74747473001480103,
 0.12941177189350128, 0.12941177189350128), (0.75252526998519897,
@@ -3550,9 +2237,6 @@
 0.89411765336990356, 0.89411765336990356), (0.79292929172515869,
 0.98823529481887817, 0.98823529481887817), (0.79797977209091187, 1.0,
 1.0), (0.80303031206130981, 1.0, 1.0), (0.80808079242706299, 1.0, 1.0),
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 (0.84343433380126953, 1.0, 1.0), (0.84848487377166748,
 0.99607843160629272, 0.99607843160629272), (0.85353535413742065,
 0.98823529481887817, 0.98823529481887817), (0.85858583450317383,
@@ -3668,15 +2352,7 @@
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-0.019607843831181526, 0.019607843831181526), (0.98739492893218994,
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-0.011764706112444401, 0.011764706112444401), (0.99579828977584839,
-0.0078431377187371254, 0.0078431377187371254), (1.0,
-0.0039215688593685627, 0.0039215688593685627)]}
 
+_gist_rainbow_data = (
+        (0.000, (1.00, 0.00, 0.16)),
+        (0.030, (1.00, 0.00, 0.00)),
+        (0.215, (1.00, 1.00, 0.00)),
+        (0.400, (0.00, 1.00, 0.00)),
+        (0.586, (0.00, 1.00, 1.00)),
+        (0.770, (0.00, 0.00, 1.00)),
+        (0.954, (1.00, 0.00, 1.00)),
+        (1.000, (1.00, 0.00, 0.75))
+)
+
+_gist_stern_data = {
+        'red': (
+            (0.000, 0.000, 0.000), (0.0547, 1.000, 1.000),
+            (0.250, 0.027, 0.027), (0.2500, 0.250, 0.250),
+            (1.000, 1.000, 1.000)),
+        'green': lambda x: x,
+        'blue': (
+            (0.000, 0.000, 0.000), (0.500, 1.000, 1.000),
+            (0.735, 0.000, 0.000), (1.000, 1.000, 1.000))
+}
+
+_gist_yarg_data = {
+        'red': lambda x: 1 - x,
+        'green': lambda x: 1 - x,
+        'blue': lambda x: 1 - x,
+}
+
 datad = {
+    'afmhot': _afmhot_data,
     'autumn': _autumn_data,
     'bone':   _bone_data,
-    'binary':   _binary_data,
+    'binary': _binary_data,
+    'bwr':    _bwr_data,
+    'brg':    _brg_data,
     'cool':   _cool_data,
     'copper': _copper_data,
     'flag':   _flag_data,
+    'gnuplot': _gnuplot_data,
+    'gnuplot2': _gnuplot2_data,
     'gray' :  _gray_data,
     'hot':    _hot_data,
     'hsv':    _hsv_data,
     'jet' :   _jet_data,
+    'ocean':  _ocean_data,
     'pink':   _pink_data,
     'prism':  _prism_data,
+    'rainbow': _rainbow_data,
+    'seismic': _seismic_data,
     'spring': _spring_data,
     'summer': _summer_data,
+    'terrain': _terrain_data,
     'winter': _winter_data,
     'spectral': _spectral_data
     }
@@ -5874,7 +2624,3 @@
 datad['gist_stern']=_gist_stern_data
 datad['gist_yarg']=_gist_yarg_data
 
-
-
-
-
