As usual in the gnuplot-palettes repository they are accompanied by line style definitions using the palette colors.

# viridis
set style line  1 lt 1 lc rgb '#440154' # dark purple
set style line  2 lt 1 lc rgb '#472c7a' # purple
set style line  3 lt 1 lc rgb '#3b518b' # blue
set style line  4 lt 1 lc rgb '#2c718e' # blue
set style line  5 lt 1 lc rgb '#21908d' # blue-green
set style line  6 lt 1 lc rgb '#27ad81' # green
set style line  7 lt 1 lc rgb '#5cc863' # green
set style line  8 lt 1 lc rgb '#aadc32' # lime green
set style line  9 lt 1 lc rgb '#fde725' # yellow

Fig. 1 Photoluminescence yield plotted with the viridis colormap from Matplotlib (code to produce this figure, viridis.pal, data)

# plasma
set style line  1 lt 1 lc rgb '#0c0887' # blue
set style line  2 lt 1 lc rgb '#4b03a1' # purple-blue
set style line  3 lt 1 lc rgb '#7d03a8' # purple
set style line  4 lt 1 lc rgb '#a82296' # purple
set style line  5 lt 1 lc rgb '#cb4679' # magenta
set style line  6 lt 1 lc rgb '#e56b5d' # red
set style line  7 lt 1 lc rgb '#f89441' # orange
set style line  8 lt 1 lc rgb '#fdc328' # orange
set style line  9 lt 1 lc rgb '#f0f921' # yellow

Fig. 2 Photoluminescence yield plotted with the plasma colormap from Matplotlib (code to produce this figure, plasma.pal, data)

# magma
set style line  1 lt 1 lc rgb '#000004' # black
set style line  2 lt 1 lc rgb '#1c1044' # dark blue
set style line  3 lt 1 lc rgb '#4f127b' # dark purple
set style line  4 lt 1 lc rgb '#812581' # purple
set style line  5 lt 1 lc rgb '#b5367a' # magenta
set style line  6 lt 1 lc rgb '#e55964' # light red
set style line  7 lt 1 lc rgb '#fb8761' # orange
set style line  8 lt 1 lc rgb '#fec287' # light orange
set style line  9 lt 1 lc rgb '#fbfdbf' # light yellow

Fig. 3 Photoluminescence yield plotted with the magma colormap from Matplotlib (code to produce this figure, magma.pal, data)

# inferno
set style line  1 lt 1 lc rgb '#000004' # black
set style line  2 lt 1 lc rgb '#1f0c48' # dark purple
set style line  3 lt 1 lc rgb '#550f6d' # dark purple
set style line  4 lt 1 lc rgb '#88226a' # purple
set style line  5 lt 1 lc rgb '#a83655' # red-magenta
set style line  6 lt 1 lc rgb '#e35933' # red
set style line  7 lt 1 lc rgb '#f9950a' # orange
set style line  8 lt 1 lc rgb '#f8c932' # yellow-orange
set style line  9 lt 1 lc rgb '#fcffa4' # light yellow

Fig. 4 Photoluminescence yield plotted with the inferno colormap from Matplotlib (code to produce this figure, inferno.pal, data)

The changes in the default colormap address some of the points that were criticized of jet by Moreland and corrected by his colormap.

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

A colormap similar to the original is stored in the parula.pal file, which is also part of the gnuplot-palettes repository on github. An example application of the colormap is presented in Fig. 1.

In order to apply the colormap you can simply load the file.

load 'parula.pal'

The parula.pal file also includes definitions of line styles. The first line styles (1-9) corresponds to the colors of the parula palette, the line styles 11-17 correspond to the new Matlab line colors, see Fig. 2.

Fig. 2 Bessel functions from order zero up to six plotted with the new default Matlab line colors. (code to produce this figure, parula.pal, data)

set style line 11 lt 1 lc rgb '#0072bd' # blue
set style line 12 lt 1 lc rgb '#d95319' # orange
set style line 13 lt 1 lc rgb '#edb120' # yellow
set style line 14 lt 1 lc rgb '#7e2f8e' # purple
set style line 15 lt 1 lc rgb '#77ac30' # green
set style line 16 lt 1 lc rgb '#4dbeee' # light-blue
set style line 17 lt 1 lc rgb '#a2142f' # red

If you want to use only the palette and not the line colors, you should remove them from the parula.pal file.

Fig. 1 Two different distributions of measured angles. (code to produce this figure, hist.fct, data)

In Fig. 1 you see two different distributions of measured angles. They were both given as one dimensional data and plotted with a defined macro that is doing the histogram calculation. The macro is defined in an additional file hist.fct and loaded before the plotting command.

binwidth = 4
binstart = -100
load 'hist.fct'
plot 'histogram.txt' i 0 @hist ls 1,\
     ''              i 1 @hist ls 2

The content of hist.fct, including the definition of @hist looks like this

# set width of single bins in histogram
set boxwidth 0.9*binwidth
# set fill style of bins
set style fill solid 0.5
# define macro for plotting the histogram
hist = 'u (binwidth*(floor(($1-binstart)/binwidth)+0.5)+binstart):(1.0) smooth freq w boxes'

For a detailed discussion on why @hist calculates a histogram you should have a look at this discussion and the documentation about the smooth freq which basically counts points with the same x-value. The other settings in the file define the width of a single bin plotted as a box and its fill style.

Fig. 2 Two different distributions of measured angles. The bins of the histograms are shifted to be centered around 0°. (code to produce this figure, hist.fct, data)

It is important that the two values binwidth and binstart are defined before loading the hist.fct file. These define the width of the single bins and at what position the left border of a single bin should be positioned. For example, let us assume that we want to have the bins centered around 0° as shown in Fig. 2. This can be achieved by settings the binstart to half the binwidth:

binwidth = 4
binstart = -2
load 'hist.fct'
plot 'histogram.txt' i 0 @hist ls 1,\
     ''              i 1 @hist ls 2

Fig. 1 Examples of color maps from colorbrewer2.org ordered in three categories (code to produce this figure, data)

The diverging color maps are for data with extremes at both points of a neutral value, for example like the below and above sea level. The sequential color maps are for data ordered from one point to another and the qualitative color maps are for categorically-grouped data with now explicit ordering.
Thanks to Anna Schneider there is an easy way to include them (at least the ones with eight colors each) into gnuplot. Just go to her gnuplot-colorbrewer github site and download the color maps. Place them in the same path as your plotting file, or add the three pathes of the repository to your load pathes, for example by adding the following to your .gnuplot file.

set loadpath '~/git/gnuplot-colorbrewer/diverging' \
    '~/git/gnuplot-colorbrewer/qualitative' \
    '~/git/gnuplot-colorbrewer/sequential'

Fig. 2 Photoluminescence yield plotted with the YlGnBu color map from colorbrewer2.org (code to produce this figure, data)

After this you can pick the right color map for you on colorbrewer2.org, keep its name and load it before your plot command. For example in Fig. 2 we are plotting again the photoluminescence yield with the sequential color map named YlGnBu. First we load the color map, then switch the two poles of the color map by setting the palette to negative, and finally plotting the data.

load 'YlGnBu.plt'
set palette negative
plot 'matlab_colormap.txt' u ($1/3.0):($2/3.0):($3/1000.0) matrix with image

Fig. 3 Eight lines plotted with the Paired color map from colorbrewer2.org (code to produce this figure)

The nice thing of the palettes coming with gnuplot-colorbrewer is that they also include the corresponding line colors. In Fig. 3 you see the Paired qualitative color map in action with lines.

load 'Paired.plt'
plot for [ii=1:8] f(x,ii) ls ii lw 2

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.

We create the png files in analogy to the gif example, hence we will discuss only the generation of the movie here. In order to compose a avi file from the png files we are using Mencoder. Gnuplot is able to directly start Mencoder by its system command.

# Create movie with mencoder
ENCODER = system('which mencoder');
if (strlen(ENCODER)==0) print '=== mencoder not found ==='; exit
CMD = 'mencoder mf://animation/*.png -mf fps=25:type=png -ovc lavc -lavcopts vcodec=mpeg4:mbd=2:trell -oac copy -o huygen.avi'
system(CMD)

The first two lines check if Mencoder is available and quit gnuplot if not. The Mencoder command itselfs gets the directory containing the png files mf://animation/*.png, frames per second and input type-mf fps=25:type=png, video -ovc and audio -oac options, and finally of course the output file -o huygen.avi.

In order to generate a webm video file for a web site, ffmpeg can be used to convert the video.

$ ffmpeg -i huygen.avi huygen.webm

Fig. 1 Animation of Bessel function (code to produce this figure, loop)

Fig. 1 shows a gif animation of a Bessel function of first kind and zeroth order. Gnuplot has a gif output terminal, but the result will be smoother if we first create png files with Gnuplot and then the animated gif file with another program. In order to create a set of png files we have to loop through the time value t and different output files.

# initializing values for the loop and start the loop
t = 0
end_time = 1
system('mkdir -p animation')
load 'bessel.plt'

The above code sets a start value for the running time variable t and the end_time variable at which the looping should be stopped. Then a folder for the output png files is created and the loop file bessel.plt is loaded. The content of this loop file is shown below.

# bessel loop
t = t + 0.02
outfile = sprintf('animation/bessel%03.0f.png',50*t)
set output outfile
splot u*sin(v),u*cos(v),bessel(u,t) w pm3d ls 1
if(t<end_time) reread;

Here we update the time t, create a file name with a different number and plot out Bessel function. Finally the last line checks if our time t is smaller than the end_time variable, and executes the loop again if this is the case.

After we have created the png files in the animation folder, we start GIMP and load all the files as layers (File > Open as Layers). After this we save all layers together as an animated gif file.