@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 0xeebb90>
Accent_r = <matplotlib.colors.LinearSegmentedColormap instance at 0xf53f80>
Blues = <matplotlib.colors.LinearSegmentedColormap instance at 0xeebbd8>
Blues_r = <matplotlib.colors.LinearSegmentedColormap instance at 0xfeb680>
BrBG = <matplotlib.colors.LinearSegmentedColormap instance at 0xeebc20>
BrBG_r = <matplotlib.colors.LinearSegmentedColormap instance at 0xfd2560>
BuGn = <matplotlib.colors.LinearSegmentedColormap instance at 0xeebc68>
BuGn_r = <matplotlib.colors.LinearSegmentedColormap instance at 0xf72320>
BuPu = <matplotlib.colors.LinearSegmentedColormap instance at 0xeebcb0>
BuPu_r = <matplotlib.colors.LinearSegmentedColormap instance at 0xf57d40>
Dark2 = <matplotlib.colors.LinearSegmentedColormap instance at 0xeebcf8>
Dark2_r = <matplotlib.colors.LinearSegmentedColormap instance at 0xf57e60>
GnBu = <matplotlib.colors.LinearSegmentedColormap instance at 0xeebd40>
GnBu_r = <matplotlib.colors.LinearSegmentedColormap instance at 0xfd2d40>
Greens = <matplotlib.colors.LinearSegmentedColormap instance at 0xeebd88>
Greens_r = <matplotlib.colors.LinearSegmentedColormap instance at 0xf72b00>
Greys = <matplotlib.colors.LinearSegmentedColormap instance at 0xeebdd0>
Greys_r = <matplotlib.colors.LinearSegmentedColormap instance at 0xfd2f80>
LUTSIZE = 256
OrRd = <matplotlib.colors.LinearSegmentedColormap instance at 0xeebe60>
OrRd_r = <matplotlib.colors.LinearSegmentedColormap instance at 0xf570e0>
Oranges = <matplotlib.colors.LinearSegmentedColormap instance at 0xeebe18>
Oranges_r = <matplotlib.colors.LinearSegmentedColormap instance at 0xf720e0>
PRGn = <matplotlib.colors.LinearSegmentedColormap instance at 0xeebfc8>
PRGn_r = <matplotlib.colors.LinearSegmentedColormap instance at 0xf72d40>
Paired = <matplotlib.colors.LinearSegmentedColormap instance at 0xeebea8>
Paired_r = <matplotlib.colors.LinearSegmentedColormap instance at 0xfd2200>
Pastel1 = <matplotlib.colors.LinearSegmentedColormap instance at 0xeebef0>
Pastel1_r = <matplotlib.colors.LinearSegmentedColormap instance at 0xfd2b00>
Pastel2 = <matplotlib.colors.LinearSegmentedColormap instance at 0xeebf38>
Pastel2_r = <matplotlib.colors.LinearSegmentedColormap instance at 0xfd28c0>
PiYG = <matplotlib.colors.LinearSegmentedColormap instance at 0xeebf80>
PiYG_r = <matplotlib.colors.LinearSegmentedColormap instance at 0xf72560>
PuBu = <matplotlib.colors.LinearSegmentedColormap instance at 0xf53050>
PuBuGn = <matplotlib.colors.LinearSegmentedColormap instance at 0xf53098>
PuBuGn_r = <matplotlib.colors.LinearSegmentedColormap instance at 0xfeb7a0>
PuBu_r = <matplotlib.colors.LinearSegmentedColormap instance at 0xf57560>
PuOr = <matplotlib.colors.LinearSegmentedColormap instance at 0xf530e0>
PuOr_r = <matplotlib.colors.LinearSegmentedColormap instance at 0xfeb320>
PuRd = <matplotlib.colors.LinearSegmentedColormap instance at 0xf53128>
PuRd_r = <matplotlib.colors.LinearSegmentedColormap instance at 0xfeb440>
Purples = <matplotlib.colors.LinearSegmentedColormap instance at 0xf53170>
Purples_r = <matplotlib.colors.LinearSegmentedColormap instance at 0xfd2680>
RdBu = <matplotlib.colors.LinearSegmentedColormap instance at 0xf531b8>
RdBu_r = <matplotlib.colors.LinearSegmentedColormap instance at 0xf57200>
RdGy = <matplotlib.colors.LinearSegmentedColormap instance at 0xf53200>
RdGy_r = <matplotlib.colors.LinearSegmentedColormap instance at 0xfeb0e0>
RdPu = <matplotlib.colors.LinearSegmentedColormap instance at 0xf53248>
RdPu_r = <matplotlib.colors.LinearSegmentedColormap instance at 0xf729e0>
RdYlBu = <matplotlib.colors.LinearSegmentedColormap instance at 0xf53290>
RdYlBu_r = <matplotlib.colors.LinearSegmentedColormap instance at 0xfd20e0>
RdYlGn = <matplotlib.colors.LinearSegmentedColormap instance at 0xf532d8>
RdYlGn_r = <matplotlib.colors.LinearSegmentedColormap instance at 0xf53b00>
Reds = <matplotlib.colors.LinearSegmentedColormap instance at 0xf53320>
Reds_r = <matplotlib.colors.LinearSegmentedColormap instance at 0xf727a0>
Set1 = <matplotlib.colors.LinearSegmentedColormap instance at 0xf53368>
Set1_r = <matplotlib.colors.LinearSegmentedColormap instance at 0xf57440>
Set2 = <matplotlib.colors.LinearSegmentedColormap instance at 0xf533b0>
Set2_r = <matplotlib.colors.LinearSegmentedColormap instance at 0xf53c20>
Set3 = <matplotlib.colors.LinearSegmentedColormap instance at 0xf533f8>
Set3_r = <matplotlib.colors.LinearSegmentedColormap instance at 0xf57680>
Spectral = <matplotlib.colors.LinearSegmentedColormap instance at 0xf53440>
Spectral_r = <matplotlib.colors.LinearSegmentedColormap instance at 0xf538c0>
YlGn = <matplotlib.colors.LinearSegmentedColormap instance at 0xf53488>
YlGnBu = <matplotlib.colors.LinearSegmentedColormap instance at 0xf534d0>
YlGnBu_r = <matplotlib.colors.LinearSegmentedColormap instance at 0xf72f80>
YlGn_r = <matplotlib.colors.LinearSegmentedColormap instance at 0xfd2e60>
YlOrBr = <matplotlib.colors.LinearSegmentedColormap instance at 0xf53518>
YlOrBr_r = <matplotlib.colors.LinearSegmentedColormap instance at 0xf72680>
YlOrRd = <matplotlib.colors.LinearSegmentedColormap instance at 0xf53560>
YlOrRd_r = <matplotlib.colors.LinearSegmentedColormap instance at 0xfeb200>
autumn = <matplotlib.colors.LinearSegmentedColormap instance at 0xe43320>
autumn_r = <matplotlib.colors.LinearSegmentedColormap instance at 0xf57320>
binary = <matplotlib.colors.LinearSegmentedColormap instance at 0xe433b0>
binary_r = <matplotlib.colors.LinearSegmentedColormap instance at 0xf579e0>
bone = <matplotlib.colors.LinearSegmentedColormap instance at 0xe43368>
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 0xe433f8>
cool_r = <matplotlib.colors.LinearSegmentedColormap instance at 0xfd27a0>
copper = <matplotlib.colors.LinearSegmentedColormap instance at 0xe43440>
copper_r = <matplotlib.colors.LinearSegmentedColormap instance at 0xf539e0>
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 0xe43488>
flag_r = <matplotlib.colors.LinearSegmentedColormap instance at 0xfd2320>
gist_earth = <matplotlib.colors.LinearSegmentedColormap instance at 0xf535a8>
gist_earth_r = <matplotlib.colors.LinearSegmentedColormap instance at 0xfeb8c0>
gist_gray = <matplotlib.colors.LinearSegmentedColormap instance at 0xf535f0>
gist_gray_r = <matplotlib.colors.LinearSegmentedColormap instance at 0xfeb560>
gist_heat = <matplotlib.colors.LinearSegmentedColormap instance at 0xf53638>
gist_heat_r = <matplotlib.colors.LinearSegmentedColormap instance at 0xf72e60>
gist_ncar = <matplotlib.colors.LinearSegmentedColormap instance at 0xf53680>
gist_ncar_r = <matplotlib.colors.LinearSegmentedColormap instance at 0xf72c20>
gist_rainbow = <matplotlib.colors.LinearSegmentedColormap instance at 0xf536c8>
gist_rainbow_r = <matplotlib.colors.LinearSegmentedColormap instance at 0xf577a0>
gist_stern = <matplotlib.colors.LinearSegmentedColormap instance at 0xf53710>
gist_stern_r = <matplotlib.colors.LinearSegmentedColormap instance at 0xfd2c20>
gist_yarg = <matplotlib.colors.LinearSegmentedColormap instance at 0xf53758>
gist_yarg_r = <matplotlib.colors.LinearSegmentedColormap instance at 0xf72200>
gray = <matplotlib.colors.LinearSegmentedColormap instance at 0xe434d0>
gray_r = <matplotlib.colors.LinearSegmentedColormap instance at 0xfd29e0>
hot = <matplotlib.colors.LinearSegmentedColormap instance at 0xe43518>
hot_r = <matplotlib.colors.LinearSegmentedColormap instance at 0xf72440>
hsv = <matplotlib.colors.LinearSegmentedColormap instance at 0xe43560>
hsv_r = <matplotlib.colors.LinearSegmentedColormap instance at 0xfd2440>
jet = <matplotlib.colors.LinearSegmentedColormap instance at 0xe435a8>
jet_r = <matplotlib.colors.LinearSegmentedColormap instance at 0xf57c20>
pink = <matplotlib.colors.LinearSegmentedColormap instance at 0xe435f0>
pink_r = <matplotlib.colors.LinearSegmentedColormap instance at 0xf578c0>
prism = <matplotlib.colors.LinearSegmentedColormap instance at 0xe43638>
prism_r = <matplotlib.colors.LinearSegmentedColormap instance at 0xf57f80>
spectral = <matplotlib.colors.LinearSegmentedColormap instance at 0xe43758>
spectral_r = <matplotlib.colors.LinearSegmentedColormap instance at 0xf728c0>
spring = <matplotlib.colors.LinearSegmentedColormap instance at 0xe43680>
spring_r = <matplotlib.colors.LinearSegmentedColormap instance at 0xf53e60>
summer = <matplotlib.colors.LinearSegmentedColormap instance at 0xe436c8>
summer_r = <matplotlib.colors.LinearSegmentedColormap instance at 0xf53d40>
winter = <matplotlib.colors.LinearSegmentedColormap instance at 0xe43710>
winter_r = <matplotlib.colors.LinearSegmentedColormap instance at 0xf57b00>
@footer@