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<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN"

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<title>Colormaps in Matplotlib — Matplotlib 2.2.0 documentation</title>

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<div id="unreleased-message"> You are reading an old version of the documentation (v2.2.0). For the latest version see <a href="https://matplotlib.org/stable/tutorials/colors/colormaps.html">https://matplotlib.org/stable/tutorials/colors/colormaps.html</a></div>

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<h3><a href="../../contents.html">Table Of Contents</a></h3>

<ul>

<li><a class="reference internal" href="#">Colormaps in Matplotlib</a><ul>

<li><a class="reference internal" href="#overview">Overview</a></li>

<li><a class="reference internal" href="#classes-of-colormaps">Classes of colormaps</a><ul>

<li><a class="reference internal" href="#sequential">Sequential</a></li>

<li><a class="reference internal" href="#sequential2">Sequential2</a></li>

<li><a class="reference internal" href="#diverging">Diverging</a></li>

<li><a class="reference internal" href="#qualitative">Qualitative</a></li>

<li><a class="reference internal" href="#miscellaneous">Miscellaneous</a></li>

</ul>

</li>

<li><a class="reference internal" href="#lightness-of-matplotlib-colormaps">Lightness of matplotlib colormaps</a></li>

<li><a class="reference internal" href="#grayscale-conversion">Grayscale conversion</a></li>

<li><a class="reference internal" href="#color-vision-deficiencies">Color vision deficiencies</a></li>

<li><a class="reference internal" href="#references">References</a></li>

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<li>Previous: <a href="colormapnorms.html" title="previous chapter">Colormap Normalization</a></li>

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<h1>Colormaps in Matplotlib<a class="headerlink" href="#colormaps-in-matplotlib" title="Permalink to this headline">¶</a></h1>

How (and why) to choose a particular colormap.

<h2>Overview<a class="headerlink" href="#overview" title="Permalink to this headline">¶</a></h2>

The idea behind choosing a good colormap is to find a good representation in 3D

colorspace for your data set. The best colormap for any given data set depends

on many things including:

<li>Whether representing form or metric data (<a class="reference internal" href="#ware" id="id1">[Ware]</a>)</li>

<li>Your knowledge of the data set (e.g., is there a critical value

from which the other values deviate?)</li>

<li>If there is an intuitive color scheme for the parameter you are plotting</li>

<li>If there is a standard in the field the audience may be expecting</li>

</ul>

For many applications, a perceptually uniform colormap is the best

choice — one in which equal steps in data are perceived as equal

steps in the color space. Researchers have found that the human brain

perceives changes in the lightness parameter as changes in the data

much better than, for example, changes in hue. Therefore, colormaps

which have monotonically increasing lightness through the colormap

will be better interpreted by the viewer. A wonderful example of

perceptually uniform colormaps is <a class="reference internal" href="#colorcet" id="id2">[colorcet]</a>.

Color can be represented in 3D space in various ways. One way to represent color

is using CIELAB. In CIELAB, color space is represented by lightness,

<img src="../../_images/mathmpl/math-374b544f7d.png" style="position: relative; bottom: -3px"/>; red-green, <img src="../../_images/mathmpl/math-d85c40ef59.png" style="position: relative; bottom: -3px"/>; and yellow-blue, <img src="../../_images/mathmpl/math-15b4f113ce.png" style="position: relative; bottom: -3px"/>. The lightness

parameter <img src="../../_images/mathmpl/math-374b544f7d.png" style="position: relative; bottom: -3px"/> can then be used to learn more about how the matplotlib

colormaps will be perceived by viewers.

An excellent starting resource for learning about human perception of colormaps

is from <a class="reference internal" href="#ibm" id="id3">[IBM]</a>.

</div>

<h2>Classes of colormaps<a class="headerlink" href="#classes-of-colormaps" title="Permalink to this headline">¶</a></h2>

Colormaps are often split into several categories based on their function (see,

e.g., <a class="reference internal" href="#moreland" id="id4">[Moreland]</a>):

<li>Sequential: change in lightness and often saturation of color

incrementally, often using a single hue; should be used for

representing information that has ordering.</li>

<li>Diverging: change in lightness and possibly saturation of two

different colors that meet in the middle at an unsaturated color;

should be used when the information being plotted has a critical

middle value, such as topography or when the data deviates around

zero.</li>

<li>Qualitative: often are miscellaneous colors; should be used to

represent information which does not have ordering or

relationships.</li>

</ol>

<div class="highlight-python notranslate"><div class="highlight"><pre># sphinx_gallery_thumbnail_number = 2

import numpy as np

import matplotlib as mpl

import matplotlib.pyplot as plt

from matplotlib import cm

from colorspacious import cspace_converter

from collections import OrderedDict

cmaps = OrderedDict()

</pre></div>

</div>

<h3>Sequential<a class="headerlink" href="#sequential" title="Permalink to this headline">¶</a></h3>

For the Sequential plots, the lightness value increases monotonically through

the colormaps. This is good. Some of the <img src="../../_images/mathmpl/math-374b544f7d.png" style="position: relative; bottom: -3px"/> values in the colormaps

span from 0 to 100 (binary and the other grayscale), and others start around

<img src="../../_images/mathmpl/math-93b9261171.png" style="position: relative; bottom: -3px"/>. Those that have a smaller range of <img src="../../_images/mathmpl/math-374b544f7d.png" style="position: relative; bottom: -3px"/> will accordingly

have a smaller perceptual range. Note also that the <img src="../../_images/mathmpl/math-374b544f7d.png" style="position: relative; bottom: -3px"/> function varies

amongst the colormaps: some are approximately linear in <img src="../../_images/mathmpl/math-374b544f7d.png" style="position: relative; bottom: -3px"/> and others

are more curved.

<div class="highlight-python notranslate"><div class="highlight"><pre>cmaps['Perceptually Uniform Sequential'] = [

'viridis', 'plasma', 'inferno', 'magma', 'cividis']

cmaps['Sequential'] = [

'Greys', 'Purples', 'Blues', 'Greens', 'Oranges', 'Reds',

'YlOrBr', 'YlOrRd', 'OrRd', 'PuRd', 'RdPu', 'BuPu',

'GnBu', 'PuBu', 'YlGnBu', 'PuBuGn', 'BuGn', 'YlGn']

</pre></div>

</div>

<h3>Sequential2<a class="headerlink" href="#sequential2" title="Permalink to this headline">¶</a></h3>

Many of the <img src="../../_images/mathmpl/math-374b544f7d.png" style="position: relative; bottom: -3px"/> values from the Sequential2 plots are monotonically

increasing, but some (autumn, cool, spring, and winter) plateau or even go both

up and down in <img src="../../_images/mathmpl/math-374b544f7d.png" style="position: relative; bottom: -3px"/> space. Others (afmhot, copper, gist_heat, and hot)

have kinks in the <img src="../../_images/mathmpl/math-374b544f7d.png" style="position: relative; bottom: -3px"/> functions. Data that is being represented in a

region of the colormap that is at a plateau or kink will lead to a perception of

banding of the data in those values in the colormap (see <a class="reference internal" href="#mycarta-banding" id="id5">[mycarta-banding]</a> for

an excellent example of this).

<div class="highlight-python notranslate"><div class="highlight"><pre>cmaps['Sequential (2)'] = [

'binary', 'gist_yarg', 'gist_gray', 'gray', 'bone', 'pink',

'spring', 'summer', 'autumn', 'winter', 'cool', 'Wistia',

'hot', 'afmhot', 'gist_heat', 'copper']

</pre></div>

</div>

<h3>Diverging<a class="headerlink" href="#diverging" title="Permalink to this headline">¶</a></h3>

For the Diverging maps, we want to have monotonically increasing <img src="../../_images/mathmpl/math-374b544f7d.png" style="position: relative; bottom: -3px"/>

values up to a maximum, which should be close to <img src="../../_images/mathmpl/math-3199ca3512.png" style="position: relative; bottom: -3px"/>, followed by

monotonically decreasing <img src="../../_images/mathmpl/math-374b544f7d.png" style="position: relative; bottom: -3px"/> values. We are looking for approximately

equal minimum <img src="../../_images/mathmpl/math-374b544f7d.png" style="position: relative; bottom: -3px"/> values at opposite ends of the colormap. By these

measures, BrBG and RdBu are good options. coolwarm is a good option, but it

doesn’t span a wide range of <img src="../../_images/mathmpl/math-374b544f7d.png" style="position: relative; bottom: -3px"/> values (see grayscale section below).

<div class="highlight-python notranslate"><div class="highlight"><pre>cmaps['Diverging'] = [

'PiYG', 'PRGn', 'BrBG', 'PuOr', 'RdGy', 'RdBu',

'RdYlBu', 'RdYlGn', 'Spectral', 'coolwarm', 'bwr', 'seismic']

</pre></div>

</div>

<h3>Qualitative<a class="headerlink" href="#qualitative" title="Permalink to this headline">¶</a></h3>

Qualitative colormaps are not aimed at being perceptual maps, but looking at the

lightness parameter can verify that for us. The <img src="../../_images/mathmpl/math-374b544f7d.png" style="position: relative; bottom: -3px"/> values move all over

the place throughout the colormap, and are clearly not monotonically increasing.

These would not be good options for use as perceptual colormaps.

<div class="highlight-python notranslate"><div class="highlight"><pre>cmaps['Qualitative'] = ['Pastel1', 'Pastel2', 'Paired', 'Accent',

'Dark2', 'Set1', 'Set2', 'Set3',

'tab10', 'tab20', 'tab20b', 'tab20c']

</pre></div>

</div>

<h3>Miscellaneous<a class="headerlink" href="#miscellaneous" title="Permalink to this headline">¶</a></h3>

Some of the miscellaneous colormaps have particular uses for which

they have been created. For example, gist_earth, ocean, and terrain

all seem to be created for plotting topography (green/brown) and water

depths (blue) together. We would expect to see a divergence in these

colormaps, then, but multiple kinks may not be ideal, such as in

gist_earth and terrain. CMRmap was created to convert well to

grayscale, though it does appear to have some small kinks in

<img src="../../_images/mathmpl/math-374b544f7d.png" style="position: relative; bottom: -3px"/>. cubehelix was created to vary smoothly in both lightness

and hue, but appears to have a small hump in the green hue area.

The often-used jet colormap is included in this set of colormaps. We can see

that the <img src="../../_images/mathmpl/math-374b544f7d.png" style="position: relative; bottom: -3px"/> values vary widely throughout the colormap, making it a

poor choice for representing data for viewers to see perceptually. See an

extension on this idea at <a class="reference internal" href="#mycarta-jet" id="id6">[mycarta-jet]</a>.

<div class="highlight-python notranslate"><div class="highlight"><pre>cmaps['Miscellaneous'] = [

'flag', 'prism', 'ocean', 'gist_earth', 'terrain', 'gist_stern',

'gnuplot', 'gnuplot2', 'CMRmap', 'cubehelix', 'brg', 'hsv',

'gist_rainbow', 'rainbow', 'jet', 'nipy_spectral', 'gist_ncar']

</pre></div>

</div>

First, we’ll show the range of each colormap. Note that some seem

to change more “quickly” than others.

<div class="highlight-python notranslate"><div class="highlight"><pre>nrows = max(len(cmap_list) for cmap_category, cmap_list in cmaps.items())

gradient = <a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.linspace.html#numpy.linspace" title="View documentation for numpy.linspace">np.linspace</a>(0, 1, 256)

gradient = <a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.vstack.html#numpy.vstack" title="View documentation for numpy.vstack">np.vstack</a>((gradient, gradient))

def plot_color_gradients(cmap_category, cmap_list, nrows):

fig, axes = <a href="../../api/_as_gen/matplotlib.pyplot.subplots.html#matplotlib.pyplot.subplots" title="View documentation for matplotlib.pyplot.subplots">plt.subplots</a>(nrows=nrows)

fig.subplots_adjust(top=0.95, bottom=0.01, left=0.2, right=0.99)

axes[0].set_title(cmap_category + ' colormaps', fontsize=14)

for ax, name in zip(axes, cmap_list):

ax.imshow(gradient, aspect='auto', cmap=plt.get_cmap(name))

pos = list(ax.get_position().bounds)

x_text = pos[0] - 0.01

y_text = pos[1] + pos[3]/2.

fig.text(x_text, y_text, name, va='center', ha='right', fontsize=10)

# Turn off *all* ticks & spines, not just the ones with colormaps.

for ax in axes:

ax.set_axis_off()

for cmap_category, cmap_list in cmaps.items():

plot_color_gradients(cmap_category, cmap_list, nrows)

<a href="../../api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="View documentation for matplotlib.pyplot.show">plt.show</a>()

</pre></div>

</div>

</li>

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</ul>

</div>

<h2>Lightness of matplotlib colormaps<a class="headerlink" href="#lightness-of-matplotlib-colormaps" title="Permalink to this headline">¶</a></h2>

Here we examine the lightness values of the matplotlib colormaps.

Note that some documentation on the colormaps is available

(<a class="reference internal" href="#list-colormaps" id="id7">[list-colormaps]</a>).

<div class="highlight-python notranslate"><div class="highlight"><pre>mpl.rcParams.update({'font.size': 12})

# Number of colormap per subplot for particular cmap categories

_DSUBS = {'Perceptually Uniform Sequential': 5, 'Sequential': 6,

'Sequential (2)': 6, 'Diverging': 6, 'Qualitative': 4,

'Miscellaneous': 6}

# Spacing between the colormaps of a subplot

_DC = {'Perceptually Uniform Sequential': 1.4, 'Sequential': 0.7,

'Sequential (2)': 1.4, 'Diverging': 1.4, 'Qualitative': 1.4,

'Miscellaneous': 1.4}

# Indices to step through colormap

x = <a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.linspace.html#numpy.linspace" title="View documentation for numpy.linspace">np.linspace</a>(0.0, 1.0, 100)

# Do plot

# Do subplots so that colormaps have enough space.

# Default is 6 colormaps per subplot.

dsub = _DSUBS.get(cmap_category, 6)

nsubplots = int(<a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.ceil.html#numpy.ceil" title="View documentation for numpy.ceil">np.ceil</a>(len(cmap_list) / dsub))

# squeeze=False to handle similarly the case of a single subplot

figsize=(7, 2.6*nsubplots))

for i, ax in enumerate(axes.flat):

locs = [] # locations for text labels

for j, cmap in enumerate(cmap_list[i*dsub:(i+1)*dsub]):

# Get RGB values for colormap and convert the colormap in

# CAM02-UCS colorspace. lab[0, :, 0] is the lightness.

rgb = <a href="../../api/cm_api.html#matplotlib.cm.get_cmap" title="View documentation for matplotlib.cm.get_cmap">cm.get_cmap</a>(cmap)(x)[<a href="https://docs.scipy.org/doc/numpy/reference/arrays.indexing.html#numpy.newaxis" title="View documentation for numpy.newaxis">np.newaxis</a>, :, :3]

lab = cspace_converter("sRGB1", "CAM02-UCS")(rgb)

# Plot colormap L values. Do separately for each category

# so each plot can be pretty. To make scatter markers change

# color along plot:

# http://stackoverflow.com/questions/8202605/matplotlib-scatterplot-colour-as-a-function-of-a-third-variable

if cmap_category == 'Sequential':

# These colormaps all start at high lightness but we want them

# reversed to look nice in the plot, so reverse the order.

y_ = lab[0, ::-1, 0]

c_ = x[::-1]

else:

y_ = lab[0, :, 0]

c_ = x

dc = _DC.get(cmap_category, 1.4) # cmaps horizontal spacing

ax.scatter(x + j*dc, y_, c=c_, cmap=cmap, s=300, linewidths=0.0)

# Store locations for colormap labels

if cmap_category in ('Perceptually Uniform Sequential',

'Sequential'):

locs.append(x[-1] + j*dc)

elif cmap_category in ('Diverging', 'Qualitative',

'Miscellaneous', 'Sequential (2)'):

locs.append(x[int(x.size/2.)] + j*dc)

# Set up the axis limits:

# * the 1st subplot is used as a reference for the x-axis limits

# * lightness values goes from 0 to 100 (y-axis limits)

ax.set_xlim(axes[0, 0].get_xlim())

ax.set_ylim(0.0, 100.0)

# Set up labels for colormaps

ax.xaxis.set_ticks_position('top')

ticker = <a href="../../api/ticker_api.html#matplotlib.ticker.FixedLocator" title="View documentation for matplotlib.ticker.FixedLocator">mpl.ticker.FixedLocator</a>(locs)

ax.xaxis.set_major_locator(ticker)

formatter = <a href="../../api/ticker_api.html#matplotlib.ticker.FixedFormatter" title="View documentation for matplotlib.ticker.FixedFormatter">mpl.ticker.FixedFormatter</a>(cmap_list[i*dsub:(i+1)*dsub])

ax.xaxis.set_major_formatter(formatter)

ax.xaxis.set_tick_params(rotation=50)

ax.set_xlabel(cmap_category + ' colormaps', fontsize=14)

fig.text(0.0, 0.55, 'Lightness $L^*$', fontsize=12,

transform=fig.transFigure, rotation=90)

fig.tight_layout(h_pad=0.0, pad=1.5)

</pre></div>

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</ul>

</div>

<h2>Grayscale conversion<a class="headerlink" href="#grayscale-conversion" title="Permalink to this headline">¶</a></h2>

It is important to pay attention to conversion to grayscale for color

plots, since they may be printed on black and white printers. If not

carefully considered, your readers may end up with indecipherable

plots because the grayscale changes unpredictably through the

colormap.

Conversion to grayscale is done in many different ways <a class="reference internal" href="#bw" id="id8">[bw]</a>. Some of the better

ones use a linear combination of the rgb values of a pixel, but weighted

according to how we perceive color intensity. A nonlinear method of conversion

to grayscale is to use the <img src="../../_images/mathmpl/math-374b544f7d.png" style="position: relative; bottom: -3px"/> values of the pixels. In general, similar

principles apply for this question as they do for presenting one’s information

perceptually; that is, if a colormap is chosen that is monotonically increasing

in <img src="../../_images/mathmpl/math-374b544f7d.png" style="position: relative; bottom: -3px"/> values, it will print in a reasonable manner to grayscale.

With this in mind, we see that the Sequential colormaps have reasonable

representations in grayscale. Some of the Sequential2 colormaps have decent

enough grayscale representations, though some (autumn, spring, summer, winter)

have very little grayscale change. If a colormap like this was used in a plot

and then the plot was printed to grayscale, a lot of the information may map to

the same gray values. The Diverging colormaps mostly vary from darker gray on

the outer edges to white in the middle. Some (PuOr and seismic) have noticeably

darker gray on one side than the other and therefore are not very symmetric.

coolwarm has little range of gray scale and would print to a more uniform plot,

losing a lot of detail. Note that overlaid, labeled contours could help

differentiate between one side of the colormap vs. the other since color cannot

be used once a plot is printed to grayscale. Many of the Qualitative and

Miscellaneous colormaps, such as Accent, hsv, and jet, change from darker to

lighter and back to darker gray throughout the colormap. This would make it

impossible for a viewer to interpret the information in a plot once it is

printed in grayscale.

# Indices to step through colormap.

wspace=0.05)

fig.suptitle(cmap_category + ' colormaps', fontsize=14, y=1.0, x=0.6)

# Get RGB values for colormap.

rgb = <a href="../../api/cm_api.html#matplotlib.cm.get_cmap" title="View documentation for matplotlib.cm.get_cmap">cm.get_cmap</a>(plt.get_cmap(name))(x)[<a href="https://docs.scipy.org/doc/numpy/reference/arrays.indexing.html#numpy.newaxis" title="View documentation for numpy.newaxis">np.newaxis</a>, :, :3]

# Get colormap in CAM02-UCS colorspace. We want the lightness.

L = lab[0, :, 0]

L = np.float32(<a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.vstack.html#numpy.vstack" title="View documentation for numpy.vstack">np.vstack</a>((L, L, L)))

ax[0].imshow(gradient, aspect='auto', cmap=plt.get_cmap(name))

ax[1].imshow(L, aspect='auto', cmap='binary_r', vmin=0., vmax=100.)

pos = list(ax[0].get_position().bounds)

# Turn off *all* ticks & spines, not just the ones with colormaps.

for ax in axes.flat:

ax.set_axis_off()

plot_color_gradients(cmap_category, cmap_list)

</pre></div>

</div>

</li>

</li>

</li>

</li>

</li>

</li>

</ul>

</div>

<h2>Color vision deficiencies<a class="headerlink" href="#color-vision-deficiencies" title="Permalink to this headline">¶</a></h2>

There is a lot of information available about color blindness (e.g.,

<a class="reference internal" href="#colorblindness" id="id9">[colorblindness]</a>). Additionally, there are tools available to convert images to

how they look for different types of color vision deficiencies (e.g.,

<a class="reference internal" href="#vischeck" id="id10">[vischeck]</a>).

The most common form of color vision deficiency involves differentiating between

red and green. Thus, avoiding colormaps with both red and green will avoid many

problems in general.

</div>

<h2>References<a class="headerlink" href="#references" title="Permalink to this headline">¶</a></h2>

<tr><td class="label"><a class="fn-backref" href="#id2">[colorcet]</a></td><td><a class="reference external" href="https://github.com/bokeh/colorcet">https://github.com/bokeh/colorcet</a></td></tr>

</tbody>

</table>

<tr><td class="label"><a class="fn-backref" href="#id1">[Ware]</a></td><td><a class="reference external" href="http://ccom.unh.edu/sites/default/files/publications/Ware_1988_CGA_Color_sequences_univariate_maps.pdf">http://ccom.unh.edu/sites/default/files/publications/Ware_1988_CGA_Color_sequences_univariate_maps.pdf</a></td></tr>

</tbody>

</table>

<tr><td class="label"><a class="fn-backref" href="#id4">[Moreland]</a></td><td><a class="reference external" href="http://www.kennethmoreland.com/color-maps/ColorMapsExpanded.pdf">http://www.kennethmoreland.com/color-maps/ColorMapsExpanded.pdf</a></td></tr>

</tbody>

</table>

<tr><td class="label"><a class="fn-backref" href="#id7">[list-colormaps]</a></td><td><a class="reference external" href="https://gist.github.com/endolith/2719900#id7">https://gist.github.com/endolith/2719900#id7</a></td></tr>

</tbody>

</table>

<tr><td class="label"><a class="fn-backref" href="#id5">[mycarta-banding]</a></td><td><a class="reference external" href="https://mycarta.wordpress.com/2012/10/14/the-rainbow-is-deadlong-live-the-rainbow-part-4-cie-lab-heated-body/">https://mycarta.wordpress.com/2012/10/14/the-rainbow-is-deadlong-live-the-rainbow-part-4-cie-lab-heated-body/</a></td></tr>

</tbody>

</table>

<tr><td class="label"><a class="fn-backref" href="#id6">[mycarta-jet]</a></td><td><a class="reference external" href="https://mycarta.wordpress.com/2012/10/06/the-rainbow-is-deadlong-live-the-rainbow-part-3/">https://mycarta.wordpress.com/2012/10/06/the-rainbow-is-deadlong-live-the-rainbow-part-3/</a></td></tr>

</tbody>

</table>

<tr><td class="label"><a class="fn-backref" href="#id8">[bw]</a></td><td><a class="reference external" href="http://www.tannerhelland.com/3643/grayscale-image-algorithm-vb6/">http://www.tannerhelland.com/3643/grayscale-image-algorithm-vb6/</a></td></tr>

</tbody>

</table>

<tr><td class="label"><a class="fn-backref" href="#id9">[colorblindness]</a></td><td><a class="reference external" href="http://www.color-blindness.com/">http://www.color-blindness.com/</a></td></tr>

</tbody>

</table>

<tr><td class="label"><a class="fn-backref" href="#id10">[vischeck]</a></td><td><a class="reference external" href="http://www.vischeck.com/vischeck/">http://www.vischeck.com/vischeck/</a></td></tr>

</tbody>

</table>

<tr><td class="label"><a class="fn-backref" href="#id3">[IBM]</a></td><td><a class="reference external" href="http://www.research.ibm.com/people/l/lloydt/color/color.HTM">http://www.research.ibm.com/people/l/lloydt/color/color.HTM</a></td></tr>

</tbody>

</table>

Total running time of the script: ( 0 minutes 2.044 seconds)

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