palette « Gnuplotting

May 21st, 2013 | No Comments

As you may have noted, gnuplot and Matlab have different default color maps. Designing such a default map is not easy, because they should handle a lot of different things (Moreland, 2009):
– The map yields images that are aesthetically pleasing
– The map has a maximal perceptual resolution
– Interference with the shading of 3D surfaces is minimal
– The map is not sensitive to vision deficiencies
– The order of the colors should be intuitively the same for all people
– The perceptual interpolation matches the underlying scalars of the map

In his paper Moreland developed a new default color map that was mentioned already in a user comment. In Fig. 1 the map is used to replot the photoluminescence yield plotted in an earlier entry.

Fig. 1 Photoluminescence yield plotted with the default color map after Moreland, 2009 (code to produce this figure, data)

To use the default color map proposed by Moreland, just download default.plt and store it to a path, that is available to gnuplot. For example store it under /home/username/.gnuplotting/default.plt and add the following line to your .gnuplot file.

set loadpath "/home/username/.gnuplotting"

After that you can just load the palette before your plot command via

load 'default.plt'

Fig. 2 Photoluminescence yield plotted with gnuplots default color palette (code to produce this figure, data)

In Fig. 2 the same plot is shown using the default color map from gnuplot, which is a little bit dark in my opinion.

Fig. 3 Photoluminescence yield plotted with Matlabs default color palette (code to produce this figure, data)

Figure 3 shows the jet color map from Matlab, which is a classical rainbow map with all its pros and cons.

Tags: colormap, configuration, data, image, load, palette

December 1st, 2012 | 3 Comments

In an earlier entry we created a vector field from measured data. Now we will visualize functions with the vector plotting style. As functions we have two 1/r potentials which define the amplitude of the vectors, as can be seen in Fig. 1. It is often better to indicate the amplitude by a color instead of by the length of the single vectors, especially if there are big changes. For the exact functions have a look into the corresponding file.

Fig. 1 Vector field of two sources with the opposite charge. The color indicates the amplitude. (code to produce this figure)

By analogy to the data vector field we have again a dx, and dy function for the length of the vectors.

dx(x,y) = scaling*ex(x,y)/enorm(x,y)
dy(x,y) = scaling*ey(x,y)/enorm(x,y)

Now we need a trick, because we have to fill the u 1:2:3:4 for the vector style with our function data. The four columns are then x,y,dx,dy of the vectors, where dx, dy are the lengths of the vector in x and y direction. Here the special filename ++ is a big help. It gives us x and y points as a first and second column, which we could address by $1 and $2. The number of points for the two axes are handled by the samples and isosamples command.

set samples 17    # x-axis
set isosamples 15 # y-axis
plot '++' u ($1-dx($1,$2)/2.0):($2-dy($1,$2)/2.0):\
    (dx($1,$2)):(dy($1,$2)):(v($1,$2)) \
    with vectors head size 0.08,20,60 filled lc palette

To place the vector at the center of the single x, y points, we move the starting point of the vectors to x-dx/2, y-dy/2.

Tags: ++, functions, isosamples, palette, samples, special-filenames, vectors

October 17th, 2012 | No Comments

Some data could be nicely visualized by representing them as arrows. For example, assume that we have done an experiment where we played something to a subject through three loudspeakers and the subject should name the direction where he or she perceived it. In Fig. 1 we show the named direction by the direction of the arrows. The color of the arrow indicates the deviation from the desired direction. A white and not visible arrow means no deviation and a dark red one a deviation of 40° or more.

Fig. 1 Vector field showing localization results. The arrows are pointing towards the direction the subject had named. The color indicates the deviation from the desired direction. (code to produce this figure, set_loudspeakers.gnu, data)

In gnuplot the with vectors command enables the arrows in the plot. It requires four parameters, x, y, dx, dy, where dx and dy controls the endpoint of the arrow as offset values to x,y. In our example the direction is stored as an angle, hence the following functions do the conversion to dx,dy. 0.1 defines the length of the arrows.

xf(phi) = 0.1*cos(phi/180.0*pi+pi/2)
yf(phi) = 0.1*sin(phi/180.0*pi+pi/2)

An optional fifth parameter controls the color of the vector together with the lc palette setting. The arrows start at x-dx,y-dy and point to x+dx,y+dy.

plot 'localization_data.txt' \
    u ($1-xf($3)):($2-yf($3)):(2*xf($3)):(2*yf($3)):4 \
    with vectors head size 0.1,20,60 filled lc palette

Tags: data, head, linecolor, palette, vectors

July 16th, 2012 | 10 Comments

Sometimes it can be helpful to visualize a third dimension by the color of the line in the plot. For example in Fig. 1 you see a logarithmic sweep going from 0 Hz to 100 Hz. Here the frequency is decoded by the color of the line.

Fig. 1 A logarithmic sweep ranging from 0 Hz to 100 Hz and decoding the frequency with the line color (code to produce this figure, data)

This can be easily achieved by adding a lc palette to the plot command, which uses the values specified in the third row of the data file.

plot 'logarithmic_sweep.txt' u 1:2:3 w l lc palette

The palette can be defined as shown in the Multiple lines with different colors entry. But it can be set in a more easy way, by only setting the start and end color and calculating the colors in between. Therefore, we are picking the two hue values in GIMP (the H entry in Fig. 2 and Fig. 3) for the starting and ending color.

Fig. 2 Picking the HSV value corresponding to the given color of #09ad00.

Fig. 3 Picking the HSV value corresponding to the given color of #0025ad.

These colors are then used to specify the palette in HSV mode. The S and V values can also directly be seen in GIMP.

# start value for H
h1 = 117/360.0
# end value for H
h2 = 227/360.0
# creating the palette by specifying H,S,V
set palette model HSV functions (1-gray)*(h2-h1)+h1,1,0.68

Tags: HSV, lines, palette

June 10th, 2012 | 4 Comments

In one off the last entries we defined a color palette similar to the default one in Matlab. Now we will use a color palette with only a few discrete colors, as shown in Fig. 1. This can be useful if we want to see all values from a measurement lying above a given threshold.

Fig. 1 Photoluminescence yield plotted with a palette with discrete colors (code to produce this figure, data)

The trick is to set maxcolors to the number of colors you want in your plot. In addition, the colors to use can be specified by the defined command. Note, that the absolute values you specify in the palette definition were automatically scaled to your min and max values (0 and 18 in this case).

set palette maxcolors 3
set palette defined ( 0 '#000fff',\
                      1 '#90ff70',\
                      2 '#ee0000')

Tags: colormap, maxcolors, palette, rgb

February 20th, 2012 | No Comments

Most of you will probably know the problem of visualizing more than two dimensions of data. In the past we have seen some solutions to this problem by using color maps, or pseudo 3D plots. Here is another solution which will just plot a bunch of lines, but varying their individual colors.

Fig. 1 Plot of interaural time differences for different frequency channels, indicated by different colors (code to produce this figure, data)

For this we first define the colors we want to use. Here we create a transition from blue to green by varying the hue in equal steps. The values can be easily calculated with GIMP or any other tool that comes with a color chooser.

set style line 2  lc rgb '#0025ad' lt 1 lw 1.5 # --- blue
set style line 3  lc rgb '#0042ad' lt 1 lw 1.5 #      .
set style line 4  lc rgb '#0060ad' lt 1 lw 1.5 #      .
set style line 5  lc rgb '#007cad' lt 1 lw 1.5 #      .
set style line 6  lc rgb '#0099ad' lt 1 lw 1.5 #      .
set style line 7  lc rgb '#00ada4' lt 1 lw 1.5 #      .
set style line 8  lc rgb '#00ad88' lt 1 lw 1.5 #      .
set style line 9  lc rgb '#00ad6b' lt 1 lw 1.5 #      .
set style line 10 lc rgb '#00ad4e' lt 1 lw 1.5 #      .
set style line 11 lc rgb '#00ad31' lt 1 lw 1.5 #      .
set style line 12 lc rgb '#00ad14' lt 1 lw 1.5 #      .
set style line 13 lc rgb '#09ad00' lt 1 lw 1.5 # --- green

Then we plot our data with these colors and get Figure 1 as a result.

plot for [n=2:13] 'itd.txt' u 1:(column(n)*1000) w lines ls n

There the interaural time difference (ITD) between the right and left ear for different frequency channels ranging from 236 Hz to 1296 Hz is shown. As can be seen the ITD varies depending on the incident angle (azimuth angle) of the given sound.

Another possibility to indicate the frequency channels given by the different colors is to add a colorbox to the graph as shown in Figure 2.

Fig. 2 Plot of interaural time differences for different frequency channels, indicated by different colors as shown in the colorbox (code to produce this figure, data)

To achieve this we have to set the origin and size of the colorbox ourselves. Note, that the notation is not the same as for a rectangle object and uses only the screen coordinates which is a little bit nasty. In addition we have to define our own color palette, as has been discussed already in another colorbox entry. In a last step we add a second phantom plot to our plot command by plotting 1/0 using the image style in order to get the colorbox drawn onto the graph.

set colorbox user horizontal origin 0.32,0.385 size 0.18,0.035 front
set cbrange [236:1296]
set cbtics ('236 Hz' 236,'1296 Hz' 1296) offset 0,0.5 scale 0
set palette defined (\
    1  '#0025ad', \
    2  '#0042ad', \
    3  '#0060ad', \
    4  '#007cad', \
    5  '#0099ad', \
    6  '#00ada4', \
    7  '#00ad88', \
    8  '#00ad6b', \
    9  '#00ad4e', \
    10 '#00ad31', \
    11 '#00ad14', \
    12 '#09ad00' \
    )
plot for [n=2:13] 'itd.txt' u 1:(column(n)*1000) w lines ls n, \
   1/0 w image

Tags: colormap, data, for, label, lines, palette

January 5th, 2012 | 10 Comments

This time another colormap plot. If you are using Matlab or Octave you are probably be familiar with Matlabs nice default colormap jet.

Fig. 1 Photoluminescence yield plotted with the jet colormap from Matlab (code to produce this figure, data)

In Fig.1, you see a photoluminescence yield in a given region, and as you can see Gnuplot is able to apply the jet colormap from Matlab. This can be achieved by defining the palette as follows.

set palette defined ( 0 '#000090',\
                      1 '#000fff',\
                      2 '#0090ff',\
                      3 '#0fffee',\
                      4 '#90ff70',\
                      5 '#ffee00',\
                      6 '#ff7000',\
                      7 '#ee0000',\
                      8 '#7f0000')

The numbers 0..8 are automatically rescaled to 0..1, which means you can employ arbitrary numbers here, only their difference counts.

If you want to use this colormap regularly, you can store it in the Gnuplot config file as a macro.

# ~/.gnuplot
set macros
MATLAB = "defined (0  0.0 0.0 0.5, \
                   1  0.0 0.0 1.0, \
                   2  0.0 0.5 1.0, \
                   3  0.0 1.0 1.0, \
                   4  0.5 1.0 0.5, \
                   5  1.0 1.0 0.0, \
                   6  1.0 0.5 0.0, \
                   7  1.0 0.0 0.0, \
                   8  0.5 0.0 0.0 )"

Here we defined the colors directly as rgb values in the range of 0..1, which can be alternatively used a color definition.
In order to apply the colormap, we now can simple write

set palette @MATLAB

Tags: colormap, macros, Matlab, palette, rgb

Gnuplotting

Default color map

Vector field from function

Vector field from data file

Using a palette as line color

Defining a palette with discrete colors

Multiple lines with different colors

Matlab colorbar with Gnuplot

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