@header@
 
 
matplotlib.cm		index
/home/jdhunter/dev/lib64/python2.5/site-packages/matplotlib/cm.py

This module contains the instantiations of color mapping classes

 
Modules
       
matplotlib.cbook
matplotlib.colors
numpy.ma
matplotlib
numpy

 
Classes
       
ScalarMappable

 
class ScalarMappable
    This is a mixin class to support scalar -> RGBA mapping.  Handles
normalization and colormapping
 
  Methods defined here:
__init__(self, norm=None, cmap=None)
*norm* is an instance of :class:`colors.Normalize` or one of
its subclasses, used to map luminance to 0-1. *cmap* is a
:mod:`cm` colormap instance, for example :data:`cm.jet`
add_checker(self, checker)
Add an entry to a dictionary of boolean flags
that are set to True when the mappable is changed.
autoscale(self)
Autoscale the scalar limits on the norm instance using the
current array
autoscale_None(self)
Autoscale the scalar limits on the norm instance using the
current array, changing only limits that are None
changed(self)
Call this whenever the mappable is changed to notify all the
callbackSM listeners to the 'changed' signal
check_update(self, checker)
If mappable has changed since the last check,
return True; else return False
get_array(self)
Return the array
get_clim(self)
return the min, max of the color limits for image scaling
get_cmap(self)
return the colormap
set_array(self, A)
Set the image array from numpy array *A*
set_clim(self, vmin=None, vmax=None)
set the norm limits for image scaling; if *vmin* is a length2
sequence, interpret it as ``(vmin, vmax)`` which is used to
support setp
 
ACCEPTS: a length 2 sequence of floats
set_cmap(self, cmap)
set the colormap for luminance data
 
ACCEPTS: a colormap
set_colorbar(self, im, ax)
set the colorbar image and axes associated with mappable
set_norm(self, norm)
set the normalization instance
to_rgba(self, x, alpha=1.0, bytes=False)
Return a normalized rgba array corresponding to *x*. If *x*
is already an rgb array, insert *alpha*; if it is already
rgba, return it unchanged. If *bytes* is True, return rgba as
4 uint8s instead of 4 floats.

 
Functions
       
get_cmap(name=None, lut=None)
Get a colormap instance, defaulting to rc values if *name* is None

 
Data
        Accent = <matplotlib.colors.LinearSegmentedColormap instance at 0x132c248>
Accent_r = <matplotlib.colors.LinearSegmentedColormap instance at 0x134b638>
Blues = <matplotlib.colors.LinearSegmentedColormap instance at 0x132c290>
Blues_r = <matplotlib.colors.LinearSegmentedColormap instance at 0x13c5cf8>
BrBG = <matplotlib.colors.LinearSegmentedColormap instance at 0x132c2d8>
BrBG_r = <matplotlib.colors.LinearSegmentedColormap instance at 0x13a1bd8>
BuGn = <matplotlib.colors.LinearSegmentedColormap instance at 0x132c320>
BuGn_r = <matplotlib.colors.LinearSegmentedColormap instance at 0x1364998>
BuPu = <matplotlib.colors.LinearSegmentedColormap instance at 0x132c368>
BuPu_r = <matplotlib.colors.LinearSegmentedColormap instance at 0x13643f8>
Dark2 = <matplotlib.colors.LinearSegmentedColormap instance at 0x132c3b0>
Dark2_r = <matplotlib.colors.LinearSegmentedColormap instance at 0x1364518>
GnBu = <matplotlib.colors.LinearSegmentedColormap instance at 0x132c3f8>
GnBu_r = <matplotlib.colors.LinearSegmentedColormap instance at 0x13c53f8>
Greens = <matplotlib.colors.LinearSegmentedColormap instance at 0x132c440>
Greens_r = <matplotlib.colors.LinearSegmentedColormap instance at 0x13a11b8>
Greys = <matplotlib.colors.LinearSegmentedColormap instance at 0x132c488>
Greys_r = <matplotlib.colors.LinearSegmentedColormap instance at 0x13c5638>
LUTSIZE = 256
OrRd = <matplotlib.colors.LinearSegmentedColormap instance at 0x132c518>
OrRd_r = <matplotlib.colors.LinearSegmentedColormap instance at 0x134b758>
Oranges = <matplotlib.colors.LinearSegmentedColormap instance at 0x132c4d0>
Oranges_r = <matplotlib.colors.LinearSegmentedColormap instance at 0x1364758>
PRGn = <matplotlib.colors.LinearSegmentedColormap instance at 0x132c680>
PRGn_r = <matplotlib.colors.LinearSegmentedColormap instance at 0x13a13f8>
Paired = <matplotlib.colors.LinearSegmentedColormap instance at 0x132c560>
Paired_r = <matplotlib.colors.LinearSegmentedColormap instance at 0x13a1878>
Pastel1 = <matplotlib.colors.LinearSegmentedColormap instance at 0x132c5a8>
Pastel1_r = <matplotlib.colors.LinearSegmentedColormap instance at 0x13c51b8>
Pastel2 = <matplotlib.colors.LinearSegmentedColormap instance at 0x132c5f0>
Pastel2_r = <matplotlib.colors.LinearSegmentedColormap instance at 0x13a1f38>
PiYG = <matplotlib.colors.LinearSegmentedColormap instance at 0x132c638>
PiYG_r = <matplotlib.colors.LinearSegmentedColormap instance at 0x1364bd8>
PuBu = <matplotlib.colors.LinearSegmentedColormap instance at 0x132c6c8>
PuBuGn = <matplotlib.colors.LinearSegmentedColormap instance at 0x132c710>
PuBuGn_r = <matplotlib.colors.LinearSegmentedColormap instance at 0x13c5e18>
PuBu_r = <matplotlib.colors.LinearSegmentedColormap instance at 0x134bbd8>
PuOr = <matplotlib.colors.LinearSegmentedColormap instance at 0x132c758>
PuOr_r = <matplotlib.colors.LinearSegmentedColormap instance at 0x13c5998>
PuRd = <matplotlib.colors.LinearSegmentedColormap instance at 0x132c7a0>
PuRd_r = <matplotlib.colors.LinearSegmentedColormap instance at 0x13c5ab8>
Purples = <matplotlib.colors.LinearSegmentedColormap instance at 0x132c7e8>
Purples_r = <matplotlib.colors.LinearSegmentedColormap instance at 0x13a1cf8>
RdBu = <matplotlib.colors.LinearSegmentedColormap instance at 0x132c830>
RdBu_r = <matplotlib.colors.LinearSegmentedColormap instance at 0x134b878>
RdGy = <matplotlib.colors.LinearSegmentedColormap instance at 0x132c878>
RdGy_r = <matplotlib.colors.LinearSegmentedColormap instance at 0x13c5758>
RdPu = <matplotlib.colors.LinearSegmentedColormap instance at 0x132c8c0>
RdPu_r = <matplotlib.colors.LinearSegmentedColormap instance at 0x13a1098>
RdYlBu = <matplotlib.colors.LinearSegmentedColormap instance at 0x132c908>
RdYlBu_r = <matplotlib.colors.LinearSegmentedColormap instance at 0x13a1758>
RdYlGn = <matplotlib.colors.LinearSegmentedColormap instance at 0x132c950>
RdYlGn_r = <matplotlib.colors.LinearSegmentedColormap instance at 0x134b1b8>
Reds = <matplotlib.colors.LinearSegmentedColormap instance at 0x132c998>
Reds_r = <matplotlib.colors.LinearSegmentedColormap instance at 0x1364e18>
Set1 = <matplotlib.colors.LinearSegmentedColormap instance at 0x132c9e0>
Set1_r = <matplotlib.colors.LinearSegmentedColormap instance at 0x134bab8>
Set2 = <matplotlib.colors.LinearSegmentedColormap instance at 0x132ca28>
Set2_r = <matplotlib.colors.LinearSegmentedColormap instance at 0x134b2d8>
Set3 = <matplotlib.colors.LinearSegmentedColormap instance at 0x132ca70>
Set3_r = <matplotlib.colors.LinearSegmentedColormap instance at 0x134bcf8>
Spectral = <matplotlib.colors.LinearSegmentedColormap instance at 0x132cab8>
Spectral_r = <matplotlib.colors.LinearSegmentedColormap instance at 0x132cf38>
YlGn = <matplotlib.colors.LinearSegmentedColormap instance at 0x132cb00>
YlGnBu = <matplotlib.colors.LinearSegmentedColormap instance at 0x132cb48>
YlGnBu_r = <matplotlib.colors.LinearSegmentedColormap instance at 0x13a1638>
YlGn_r = <matplotlib.colors.LinearSegmentedColormap instance at 0x13c5518>
YlOrBr = <matplotlib.colors.LinearSegmentedColormap instance at 0x132cb90>
YlOrBr_r = <matplotlib.colors.LinearSegmentedColormap instance at 0x1364cf8>
YlOrRd = <matplotlib.colors.LinearSegmentedColormap instance at 0x132cbd8>
YlOrRd_r = <matplotlib.colors.LinearSegmentedColormap instance at 0x13c5878>
autumn = <matplotlib.colors.LinearSegmentedColormap instance at 0x1270998>
autumn_r = <matplotlib.colors.LinearSegmentedColormap instance at 0x134b998>
binary = <matplotlib.colors.LinearSegmentedColormap instance at 0x1270a28>
binary_r = <matplotlib.colors.LinearSegmentedColormap instance at 0x1364098>
bone = <matplotlib.colors.LinearSegmentedColormap instance at 0x12709e0>
bone_r = <matplotlib.colors.LinearSegmentedColormap instance at 0x141e098>
cmapdat_r = {'blue': [(0.0, 1.0, 1.0), (0.63492100000000007, 0.44444400000000001, 0.44444400000000001), (1.0, 0.0, 0.0)], 'green': [(0.0, 1.0, 1.0), (0.25396799999999997, 0.77777799999999997, 0.77777799999999997), (0.63492100000000007, 0.31944400000000001, 0.31944400000000001), (1.0, 0.0, 0.0)], 'red': [(0.0, 1.0, 1.0), (0.25396799999999997, 0.65277799999999997, 0.65277799999999997), (1.0, 0.0, 0.0)]}
cmapname = 'bone'
cmapname_r = 'bone_r'
cmapnames = ['Spectral', 'copper', 'RdYlGn', 'Set2', 'summer', 'spring', 'Accent', 'OrRd', 'RdBu', 'autumn', 'Set1', 'PuBu', 'Set3', 'gist_rainbow', 'pink', 'binary', 'winter', 'jet', 'BuPu', 'Dark2', ...]
cool = <matplotlib.colors.LinearSegmentedColormap instance at 0x1270a70>
cool_r = <matplotlib.colors.LinearSegmentedColormap instance at 0x13a1e18>
copper = <matplotlib.colors.LinearSegmentedColormap instance at 0x1270ab8>
copper_r = <matplotlib.colors.LinearSegmentedColormap instance at 0x134b098>
datad = {'Accent': {'blue': [(0.0, 0.49803921580314636, 0.49803921580314636), (0.14285714285714285, 0.83137255907058716, 0.83137255907058716), (0.2857142857142857, 0.52549022436141968, 0.52549022436141968), (0.42857142857142855, 0.60000002384185791, 0.60000002384185791), (0.5714285714285714, 0.69019609689712524, 0.69019609689712524), (0.7142857142857143, 0.49803921580314636, 0.49803921580314636), (0.8571428571428571, 0.090196080505847931, 0.090196080505847931), (1.0, 0.40000000596046448, 0.40000000596046448)], 'green': [(0.0, 0.78823530673980713, 0.78823530673980713), (0.14285714285714285, 0.68235296010971069, 0.68235296010971069), (0.2857142857142857, 0.75294119119644165, 0.75294119119644165), (0.42857142857142855, 1.0, 1.0), (0.5714285714285714, 0.42352941632270813, 0.42352941632270813), (0.7142857142857143, 0.0078431377187371254, 0.0078431377187371254), (0.8571428571428571, 0.35686275362968445, 0.35686275362968445), (1.0, 0.40000000596046448, 0.40000000596046448)], 'red': [(0.0, 0.49803921580314636, 0.49803921580314636), (0.14285714285714285, 0.7450980544090271, 0.7450980544090271), (0.2857142857142857, 0.99215686321258545, 0.99215686321258545), (0.42857142857142855, 1.0, 1.0), (0.5714285714285714, 0.21960784494876862, 0.21960784494876862), (0.7142857142857143, 0.94117647409439087, 0.94117647409439087), (0.8571428571428571, 0.74901962280273438, 0.74901962280273438), (1.0, 0.40000000596046448, 0.40000000596046448)]}, 'Accent_r': {'blue': [(0.0, 0.40000000596046448, 0.40000000596046448), (0.1428571428571429, 0.090196080505847931, 0.090196080505847931), (0.2857142857142857, 0.49803921580314636, 0.49803921580314636), (0.4285714285714286, 0.69019609689712524, 0.69019609689712524), (0.5714285714285714, 0.60000002384185791, 0.60000002384185791), (0.7142857142857143, 0.52549022436141968, 0.52549022436141968), (0.85714285714285721, 0.83137255907058716, 0.83137255907058716), (1.0, 0.49803921580314636, 0.49803921580314636)], 'green': [(0.0, 0.40000000596046448, 0.40000000596046448), (0.1428571428571429, 0.35686275362968445, 0.35686275362968445), (0.2857142857142857, 0.0078431377187371254, 0.0078431377187371254), (0.4285714285714286, 0.42352941632270813, 0.42352941632270813), (0.5714285714285714, 1.0, 1.0), (0.7142857142857143, 0.75294119119644165, 0.75294119119644165), (0.85714285714285721, 0.68235296010971069, 0.68235296010971069), (1.0, 0.78823530673980713, 0.78823530673980713)], 'red': [(0.0, 0.40000000596046448, 0.40000000596046448), (0.1428571428571429, 0.74901962280273438, 0.74901962280273438), (0.2857142857142857, 0.94117647409439087, 0.94117647409439087), (0.4285714285714286, 0.21960784494876862, 0.21960784494876862), (0.5714285714285714, 1.0, 1.0), (0.7142857142857143, 0.99215686321258545, 0.99215686321258545), (0.85714285714285721, 0.7450980544090271, 0.7450980544090271), (1.0, 0.49803921580314636, 0.49803921580314636)]}, 'Blues': {'blue': [(0.0, 1.0, 1.0), (0.125, 0.9686274528503418, 0.9686274528503418), (0.25, 0.93725490570068359, 0.93725490570068359), (0.375, 0.88235294818878174, 0.88235294818878174), (0.5, 0.83921569585800171, 0.83921569585800171), (0.625, 0.7764706015586853, 0.7764706015586853), (0.75, 0.70980393886566162, 0.70980393886566162), (0.875, 0.61176472902297974, 0.61176472902297974), (1.0, 0.41960784792900085, 0.41960784792900085)], 'green': [(0.0, 0.9843137264251709, 0.9843137264251709), (0.125, 0.92156863212585449, 0.92156863212585449), (0.25, 0.85882353782653809, 0.85882353782653809), (0.375, 0.7921568751335144, 0.7921568751335144), (0.5, 0.68235296010971069, 0.68235296010971069), (0.625, 0.57254904508590698, 0.57254904508590698), (0.75, 0.44313725829124451, 0.44313725829124451), (0.875, 0.31764706969261169, 0.31764706969261169), (1.0, 0.18823529779911041, 0.18823529779911041)], 'red': [(0.0, 0.9686274528503418, 0.9686274528503418), (0.125, 0.87058824300765991, 0.87058824300765991), (0.25, 0.7764706015586853, 0.7764706015586853), (0.375, 0.61960786581039429, 0.61960786581039429), (0.5, 0.41960784792900085, 0.41960784792900085), (0.625, 0.25882354378700256, 0.25882354378700256), (0.75, 0.12941177189350128, 0.12941177189350128), (0.875, 0.031372550874948502, 0.031372550874948502), (1.0, 0.031372550874948502, 0.031372550874948502)]}, 'Blues_r': {'blue': [(0.0, 0.41960784792900085, 0.41960784792900085), (0.125, 0.61176472902297974, 0.61176472902297974), (0.25, 0.70980393886566162, 0.70980393886566162), (0.375, 0.7764706015586853, 0.7764706015586853), (0.5, 0.83921569585800171, 0.83921569585800171), (0.625, 0.88235294818878174, 0.88235294818878174), (0.75, 0.93725490570068359, 0.93725490570068359), (0.875, 0.9686274528503418, 0.9686274528503418), (1.0, 1.0, 1.0)], 'green': [(0.0, 0.18823529779911041, 0.18823529779911041), (0.125, 0.31764706969261169, 0.31764706969261169), (0.25, 0.44313725829124451, 0.44313725829124451), (0.375, 0.57254904508590698, 0.57254904508590698), (0.5, 0.68235296010971069, 0.68235296010971069), (0.625, 0.7921568751335144, 0.7921568751335144), (0.75, 0.85882353782653809, 0.85882353782653809), (0.875, 0.92156863212585449, 0.92156863212585449), (1.0, 0.9843137264251709, 0.9843137264251709)], 'red': [(0.0, 0.031372550874948502, 0.031372550874948502), (0.125, 0.031372550874948502, 0.031372550874948502), (0.25, 0.12941177189350128, 0.12941177189350128), (0.375, 0.25882354378700256, 0.25882354378700256), (0.5, 0.41960784792900085, 0.41960784792900085), (0.625, 0.61960786581039429, 0.61960786581039429), (0.75, 0.7764706015586853, 0.7764706015586853), (0.875, 0.87058824300765991, 0.87058824300765991), (1.0, 0.9686274528503418, 0.9686274528503418)]}, 'BrBG': {'blue': [(0.0, 0.019607843831181526, 0.019607843831181526), (0.10000000000000001, 0.039215687662363052, 0.039215687662363052), (0.20000000000000001, 0.17647059261798859, 0.17647059261798859), (0.29999999999999999, 0.49019607901573181, 0.49019607901573181), (0.40000000000000002, 0.76470589637756348, 0.76470589637756348), (0.5, 0.96078431606292725, 0.96078431606292725), (0.59999999999999998, 0.89803922176361084, 0.89803922176361084), (0.69999999999999996, 0.75686275959014893, 0.75686275959014893), (0.80000000000000004, 0.56078433990478516, 0.56078433990478516), (0.90000000000000002, 0.36862745881080627, 0.36862745881080627), (1.0, 0.18823529779911041, 0.18823529779911041)], 'green': [(0.0, 0.18823529779911041, 0.18823529779911041), (0.10000000000000001, 0.31764706969261169, 0.31764706969261169), (0.20000000000000001, 0.5058823823928833, 0.5058823823928833), (0.29999999999999999, 0.7607843279838562, 0.7607843279838562), (0.40000000000000002, 0.90980392694473267, 0.90980392694473267), (0.5, 0.96078431606292725, 0.96078431606292725), (0.59999999999999998, 0.91764706373214722, 0.91764706373214722), (0.69999999999999996, 0.80392158031463623, 0.80392158031463623), (0.80000000000000004, 0.59215688705444336, 0.59215688705444336), (0.90000000000000002, 0.40000000596046448, 0.40000000596046448), (1.0, 0.23529411852359772, 0.23529411852359772)], 'red': [(0.0, 0.32941177487373352, 0.32941177487373352), (0.10000000000000001, 0.54901963472366333, 0.54901963472366333), (0.20000000000000001, 0.74901962280273438, 0.74901962280273438), (0.29999999999999999, 0.87450981140136719, 0.87450981140136719), (0.40000000000000002, 0.96470588445663452, 0.96470588445663452), (0.5, 0.96078431606292725, 0.96078431606292725), (0.59999999999999998, 0.78039216995239258, 0.78039216995239258), (0.69999999999999996, 0.50196081399917603, 0.50196081399917603), (0.80000000000000004, 0.20784313976764679, 0.20784313976764679), (0.90000000000000002, 0.0039215688593685627, 0.0039215688593685627), (1.0, 0.0, 0.0)]}, 'BrBG_r': {'blue': [(0.0, 0.18823529779911041, 0.18823529779911041), (0.099999999999999978, 0.36862745881080627, 0.36862745881080627), (0.19999999999999996, 0.56078433990478516, 0.56078433990478516), (0.30000000000000004, 0.75686275959014893, 0.75686275959014893), (0.40000000000000002, 0.89803922176361084, 0.89803922176361084), (0.5, 0.96078431606292725, 0.96078431606292725), (0.59999999999999998, 0.76470589637756348, 0.76470589637756348), (0.69999999999999996, 0.49019607901573181, 0.49019607901573181), (0.80000000000000004, 0.17647059261798859, 0.17647059261798859), (0.90000000000000002, 0.039215687662363052, 0.039215687662363052), (1.0, 0.019607843831181526, 0.019607843831181526)], 'green': [(0.0, 0.23529411852359772, 0.23529411852359772), (0.099999999999999978, 0.40000000596046448, 0.40000000596046448), (0.19999999999999996, 0.59215688705444336, 0.59215688705444336), (0.30000000000000004, 0.80392158031463623, 0.80392158031463623), (0.40000000000000002, 0.91764706373214722, 0.91764706373214722), (0.5, 0.96078431606292725, 0.96078431606292725), (0.59999999999999998, 0.90980392694473267, 0.90980392694473267), (0.69999999999999996, 0.7607843279838562, 0.7607843279838562), (0.80000000000000004, 0.5058823823928833, 0.5058823823928833), (0.90000000000000002, 0.31764706969261169, 0.31764706969261169), (1.0, 0.18823529779911041, 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flag = <matplotlib.colors.LinearSegmentedColormap instance at 0x1270b00>
flag_r = <matplotlib.colors.LinearSegmentedColormap instance at 0x13a1998>
gist_earth = <matplotlib.colors.LinearSegmentedColormap instance at 0x132cc20>
gist_earth_r = <matplotlib.colors.LinearSegmentedColormap instance at 0x13c5f38>
gist_gray = <matplotlib.colors.LinearSegmentedColormap instance at 0x132cc68>
gist_gray_r = <matplotlib.colors.LinearSegmentedColormap instance at 0x13c5bd8>
gist_heat = <matplotlib.colors.LinearSegmentedColormap instance at 0x132ccb0>
gist_heat_r = <matplotlib.colors.LinearSegmentedColormap instance at 0x13a1518>
gist_ncar = <matplotlib.colors.LinearSegmentedColormap instance at 0x132ccf8>
gist_ncar_r = <matplotlib.colors.LinearSegmentedColormap instance at 0x13a12d8>
gist_rainbow = <matplotlib.colors.LinearSegmentedColormap instance at 0x132cd40>
gist_rainbow_r = <matplotlib.colors.LinearSegmentedColormap instance at 0x134be18>
gist_stern = <matplotlib.colors.LinearSegmentedColormap instance at 0x132cd88>
gist_stern_r = <matplotlib.colors.LinearSegmentedColormap instance at 0x13c52d8>
gist_yarg = <matplotlib.colors.LinearSegmentedColormap instance at 0x132cdd0>
gist_yarg_r = <matplotlib.colors.LinearSegmentedColormap instance at 0x1364878>
gray = <matplotlib.colors.LinearSegmentedColormap instance at 0x1270b48>
gray_r = <matplotlib.colors.LinearSegmentedColormap instance at 0x13c5098>
hot = <matplotlib.colors.LinearSegmentedColormap instance at 0x1270b90>
hot_r = <matplotlib.colors.LinearSegmentedColormap instance at 0x1364ab8>
hsv = <matplotlib.colors.LinearSegmentedColormap instance at 0x1270bd8>
hsv_r = <matplotlib.colors.LinearSegmentedColormap instance at 0x13a1ab8>
jet = <matplotlib.colors.LinearSegmentedColormap instance at 0x1270c20>
jet_r = <matplotlib.colors.LinearSegmentedColormap instance at 0x13642d8>
pink = <matplotlib.colors.LinearSegmentedColormap instance at 0x1270c68>
pink_r = <matplotlib.colors.LinearSegmentedColormap instance at 0x134bf38>
prism = <matplotlib.colors.LinearSegmentedColormap instance at 0x1270cb0>
prism_r = <matplotlib.colors.LinearSegmentedColormap instance at 0x1364638>
spectral = <matplotlib.colors.LinearSegmentedColormap instance at 0x1270dd0>
spectral_r = <matplotlib.colors.LinearSegmentedColormap instance at 0x1364f38>
spring = <matplotlib.colors.LinearSegmentedColormap instance at 0x1270cf8>
spring_r = <matplotlib.colors.LinearSegmentedColormap instance at 0x134b518>
summer = <matplotlib.colors.LinearSegmentedColormap instance at 0x1270d40>
summer_r = <matplotlib.colors.LinearSegmentedColormap instance at 0x134b3f8>
winter = <matplotlib.colors.LinearSegmentedColormap instance at 0x1270d88>
winter_r = <matplotlib.colors.LinearSegmentedColormap instance at 0x13641b8>
@footer@