@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 a colors.normalize instance to map luminance to 0-1
cmap is a cm colormap instance
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 0xeecb90>
Accent_r = <matplotlib.colors.LinearSegmentedColormap instance at 0xf54f80>
Blues = <matplotlib.colors.LinearSegmentedColormap instance at 0xeecbd8>
Blues_r = <matplotlib.colors.LinearSegmentedColormap instance at 0xfeb680>
BrBG = <matplotlib.colors.LinearSegmentedColormap instance at 0xeecc20>
BrBG_r = <matplotlib.colors.LinearSegmentedColormap instance at 0xfd2560>
BuGn = <matplotlib.colors.LinearSegmentedColormap instance at 0xeecc68>
BuGn_r = <matplotlib.colors.LinearSegmentedColormap instance at 0xf73320>
BuPu = <matplotlib.colors.LinearSegmentedColormap instance at 0xeeccb0>
BuPu_r = <matplotlib.colors.LinearSegmentedColormap instance at 0xf58d40>
Dark2 = <matplotlib.colors.LinearSegmentedColormap instance at 0xeeccf8>
Dark2_r = <matplotlib.colors.LinearSegmentedColormap instance at 0xf58e60>
GnBu = <matplotlib.colors.LinearSegmentedColormap instance at 0xeecd40>
GnBu_r = <matplotlib.colors.LinearSegmentedColormap instance at 0xfd2d40>
Greens = <matplotlib.colors.LinearSegmentedColormap instance at 0xeecd88>
Greens_r = <matplotlib.colors.LinearSegmentedColormap instance at 0xf73b00>
Greys = <matplotlib.colors.LinearSegmentedColormap instance at 0xeecdd0>
Greys_r = <matplotlib.colors.LinearSegmentedColormap instance at 0xfd2f80>
LUTSIZE = 256
OrRd = <matplotlib.colors.LinearSegmentedColormap instance at 0xeece60>
OrRd_r = <matplotlib.colors.LinearSegmentedColormap instance at 0xf580e0>
Oranges = <matplotlib.colors.LinearSegmentedColormap instance at 0xeece18>
Oranges_r = <matplotlib.colors.LinearSegmentedColormap instance at 0xf730e0>
PRGn = <matplotlib.colors.LinearSegmentedColormap instance at 0xeecfc8>
PRGn_r = <matplotlib.colors.LinearSegmentedColormap instance at 0xf73d40>
Paired = <matplotlib.colors.LinearSegmentedColormap instance at 0xeecea8>
Paired_r = <matplotlib.colors.LinearSegmentedColormap instance at 0xfd2200>
Pastel1 = <matplotlib.colors.LinearSegmentedColormap instance at 0xeecef0>
Pastel1_r = <matplotlib.colors.LinearSegmentedColormap instance at 0xfd2b00>
Pastel2 = <matplotlib.colors.LinearSegmentedColormap instance at 0xeecf38>
Pastel2_r = <matplotlib.colors.LinearSegmentedColormap instance at 0xfd28c0>
PiYG = <matplotlib.colors.LinearSegmentedColormap instance at 0xeecf80>
PiYG_r = <matplotlib.colors.LinearSegmentedColormap instance at 0xf73560>
PuBu = <matplotlib.colors.LinearSegmentedColormap instance at 0xf54050>
PuBuGn = <matplotlib.colors.LinearSegmentedColormap instance at 0xf54098>
PuBuGn_r = <matplotlib.colors.LinearSegmentedColormap instance at 0xfeb7a0>
PuBu_r = <matplotlib.colors.LinearSegmentedColormap instance at 0xf58560>
PuOr = <matplotlib.colors.LinearSegmentedColormap instance at 0xf540e0>
PuOr_r = <matplotlib.colors.LinearSegmentedColormap instance at 0xfeb320>
PuRd = <matplotlib.colors.LinearSegmentedColormap instance at 0xf54128>
PuRd_r = <matplotlib.colors.LinearSegmentedColormap instance at 0xfeb440>
Purples = <matplotlib.colors.LinearSegmentedColormap instance at 0xf54170>
Purples_r = <matplotlib.colors.LinearSegmentedColormap instance at 0xfd2680>
RdBu = <matplotlib.colors.LinearSegmentedColormap instance at 0xf541b8>
RdBu_r = <matplotlib.colors.LinearSegmentedColormap instance at 0xf58200>
RdGy = <matplotlib.colors.LinearSegmentedColormap instance at 0xf54200>
RdGy_r = <matplotlib.colors.LinearSegmentedColormap instance at 0xfeb0e0>
RdPu = <matplotlib.colors.LinearSegmentedColormap instance at 0xf54248>
RdPu_r = <matplotlib.colors.LinearSegmentedColormap instance at 0xf739e0>
RdYlBu = <matplotlib.colors.LinearSegmentedColormap instance at 0xf54290>
RdYlBu_r = <matplotlib.colors.LinearSegmentedColormap instance at 0xfd20e0>
RdYlGn = <matplotlib.colors.LinearSegmentedColormap instance at 0xf542d8>
RdYlGn_r = <matplotlib.colors.LinearSegmentedColormap instance at 0xf54b00>
Reds = <matplotlib.colors.LinearSegmentedColormap instance at 0xf54320>
Reds_r = <matplotlib.colors.LinearSegmentedColormap instance at 0xf737a0>
Set1 = <matplotlib.colors.LinearSegmentedColormap instance at 0xf54368>
Set1_r = <matplotlib.colors.LinearSegmentedColormap instance at 0xf58440>
Set2 = <matplotlib.colors.LinearSegmentedColormap instance at 0xf543b0>
Set2_r = <matplotlib.colors.LinearSegmentedColormap instance at 0xf54c20>
Set3 = <matplotlib.colors.LinearSegmentedColormap instance at 0xf543f8>
Set3_r = <matplotlib.colors.LinearSegmentedColormap instance at 0xf58680>
Spectral = <matplotlib.colors.LinearSegmentedColormap instance at 0xf54440>
Spectral_r = <matplotlib.colors.LinearSegmentedColormap instance at 0xf548c0>
YlGn = <matplotlib.colors.LinearSegmentedColormap instance at 0xf54488>
YlGnBu = <matplotlib.colors.LinearSegmentedColormap instance at 0xf544d0>
YlGnBu_r = <matplotlib.colors.LinearSegmentedColormap instance at 0xf73f80>
YlGn_r = <matplotlib.colors.LinearSegmentedColormap instance at 0xfd2e60>
YlOrBr = <matplotlib.colors.LinearSegmentedColormap instance at 0xf54518>
YlOrBr_r = <matplotlib.colors.LinearSegmentedColormap instance at 0xf73680>
YlOrRd = <matplotlib.colors.LinearSegmentedColormap instance at 0xf54560>
YlOrRd_r = <matplotlib.colors.LinearSegmentedColormap instance at 0xfeb200>
autumn = <matplotlib.colors.LinearSegmentedColormap instance at 0xe51320>
autumn_r = <matplotlib.colors.LinearSegmentedColormap instance at 0xf58320>
binary = <matplotlib.colors.LinearSegmentedColormap instance at 0xe513b0>
binary_r = <matplotlib.colors.LinearSegmentedColormap instance at 0xf589e0>
bone = <matplotlib.colors.LinearSegmentedColormap instance at 0xe51368>
bone_r = <matplotlib.colors.LinearSegmentedColormap instance at 0xfeb9e0>
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 0xe513f8>
cool_r = <matplotlib.colors.LinearSegmentedColormap instance at 0xfd27a0>
copper = <matplotlib.colors.LinearSegmentedColormap instance at 0xe51440>
copper_r = <matplotlib.colors.LinearSegmentedColormap instance at 0xf549e0>
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 0xe51488>
flag_r = <matplotlib.colors.LinearSegmentedColormap instance at 0xfd2320>
gist_earth = <matplotlib.colors.LinearSegmentedColormap instance at 0xf545a8>
gist_earth_r = <matplotlib.colors.LinearSegmentedColormap instance at 0xfeb8c0>
gist_gray = <matplotlib.colors.LinearSegmentedColormap instance at 0xf545f0>
gist_gray_r = <matplotlib.colors.LinearSegmentedColormap instance at 0xfeb560>
gist_heat = <matplotlib.colors.LinearSegmentedColormap instance at 0xf54638>
gist_heat_r = <matplotlib.colors.LinearSegmentedColormap instance at 0xf73e60>
gist_ncar = <matplotlib.colors.LinearSegmentedColormap instance at 0xf54680>
gist_ncar_r = <matplotlib.colors.LinearSegmentedColormap instance at 0xf73c20>
gist_rainbow = <matplotlib.colors.LinearSegmentedColormap instance at 0xf546c8>
gist_rainbow_r = <matplotlib.colors.LinearSegmentedColormap instance at 0xf587a0>
gist_stern = <matplotlib.colors.LinearSegmentedColormap instance at 0xf54710>
gist_stern_r = <matplotlib.colors.LinearSegmentedColormap instance at 0xfd2c20>
gist_yarg = <matplotlib.colors.LinearSegmentedColormap instance at 0xf54758>
gist_yarg_r = <matplotlib.colors.LinearSegmentedColormap instance at 0xf73200>
gray = <matplotlib.colors.LinearSegmentedColormap instance at 0xe514d0>
gray_r = <matplotlib.colors.LinearSegmentedColormap instance at 0xfd29e0>
hot = <matplotlib.colors.LinearSegmentedColormap instance at 0xe51518>
hot_r = <matplotlib.colors.LinearSegmentedColormap instance at 0xf73440>
hsv = <matplotlib.colors.LinearSegmentedColormap instance at 0xe51560>
hsv_r = <matplotlib.colors.LinearSegmentedColormap instance at 0xfd2440>
jet = <matplotlib.colors.LinearSegmentedColormap instance at 0xe515a8>
jet_r = <matplotlib.colors.LinearSegmentedColormap instance at 0xf58c20>
pink = <matplotlib.colors.LinearSegmentedColormap instance at 0xe515f0>
pink_r = <matplotlib.colors.LinearSegmentedColormap instance at 0xf588c0>
prism = <matplotlib.colors.LinearSegmentedColormap instance at 0xe51638>
prism_r = <matplotlib.colors.LinearSegmentedColormap instance at 0xf58f80>
spectral = <matplotlib.colors.LinearSegmentedColormap instance at 0xe51758>
spectral_r = <matplotlib.colors.LinearSegmentedColormap instance at 0xf738c0>
spring = <matplotlib.colors.LinearSegmentedColormap instance at 0xe51680>
spring_r = <matplotlib.colors.LinearSegmentedColormap instance at 0xf54e60>
summer = <matplotlib.colors.LinearSegmentedColormap instance at 0xe516c8>
summer_r = <matplotlib.colors.LinearSegmentedColormap instance at 0xf54d40>
winter = <matplotlib.colors.LinearSegmentedColormap instance at 0xe51710>
winter_r = <matplotlib.colors.LinearSegmentedColormap instance at 0xf58b00>
@footer@