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.

Fig. 1 Mercator projection of the world (code to produce this figure, data)

In order to achieve the Mercator projection, we apply the following function.

set angles degrees
mercator(latitude) = log( tan(180/4.0 + latitude/2.0) )
set yrange [-3.1:3.1]
plot 'world_110m.txt' u 1:(mercator($2)) w filledcu ls 2

Fig. 2 Equirectangular projection of the world (code to produce this figure, data)

By just plotting the data as we have done for Fig. 2, we have the Equirectangular projection with constant spacing between the latitudes and meridians. The blue background color in the first two figures can be achieved directly with a terminal setting.

set terminal pngcairo size background '#c8ebff'

Fig. 3 Mapping of the Mercator projection (code to produce this figure)

In Fig. 3 the Mercator projection function is shown as an input-output-function of the latitude values. The placing of the latitude values on the y-axis can be easily done with a loop.

set ytics 0
do for [angle=-80:80:20] {
    set ytics add (sprintf('%.0f',angle) mercator(angle))
}

Fig. 1 A simple heat map (code to produce this figure, data)

Suppose we have the following data matrix, stored in heat_map_data.txt.

6 5 4 3 1 0
3 2 2 0 0 1
0 0 0 0 1 0
0 0 0 0 2 3
0 0 1 2 4 3
0 1 2 3 4 5

The normal way of plotting them would be with

plot 'heat_map_data.txt' matrix with image

But to be able to interpolate the data we have to use splot and pm3d instead.

set pm3d map
splot 'heat_map_data.txt' matrix

In Fig. 1 the result of plotting the data just with splot, without interpolation is shown. Note, that the result differs already from the plot command. The plot command would have created six points, whereas the splot command comes up with only five different regions for every axis.

Fig. 2 A heat map interpolated to use twice as much points on every axis (code to produce this figure, data)

Now if we want to double the number of visible points, we can tell pm3d easily to interpolate the data by the interpolate command.

set pm3d interpolate 2,2

The two numbers 2,2 are the number of additional points along the x- and y-axis.
The resulting plot can be found in Fig. 2.

Fig. 3 A heat map interpolated with an optimal number of points (code to produce this figure, data)

In addition to explicitly setting the number of points we can tell gnuplot to choose the correct number of interpolation points by itself, by setting them to 0.

set pm3d interpolate 0,0

Now gnuplot decides by itself how to interpolate, which leads to the result in Fig. 3.

Fig. 1 Filledcurves with transparency settings as on the gnuplot demo site (code to reproduce this figure)

The interesting part in the code looks like this.

set style fill transparent solid 0.5 noborder
plot d1(x) fs solid 1.0 lc rgb "forest-green" title 'µ= 0.5 σ=0.5', \
     d2(x) lc rgb "gold" title 'µ= 2.0 σ=1.0', \
     d3(x) lc rgb "red" title 'µ=-1.0 σ=2.0'

The set style command sets the fill style to 50% transparency, which is overwritten by the explicit fs option to the first plotting command in order to plot the green curve without transparency.

Fig. 2 Filledcurves with different transparency settings (code to reproduce this figure)

Now the question is how to plot filled curves with different transparency settings? The simple answer is, by just using this explicit fs plot argument. The result is shown in Fig.2 and can be reached with the following code. Now we apply a transparency of 75%, 50%, and 25% going from the green to the red curve.

set style fill noborder
plot d1(x) fs transparent solid 0.75 lc rgb "forest-green" \
        title 'µ= 0.5σ=0.5', \
     d2(x) fs transparent solid 0.50 lc rgb "gold" \
        title 'µ= 2.0 σ=1.0', \
     d3(x) fs transparent solid 0.25 lc rgb "red" \
        title 'µ=-1.0 σ=2.0'

Fig. 1 Plot of the world with the new data file and a resolution of 1:110m (code to produce this figure, data)

At the download page, three different resolutions of the data are available with a scale of 1:10m, 1:50m, or 1:110m. The data itself is stored as shape files on naturalearthdata.com. Hence I wrote a script that converts the data first to a csv file using the ogr2ogr program. Afterwards the data is shaped with sed into the form of the original world.dat file.
Here are the resulting files:

• 1:10m: world_10m.txt
• 1:50m: world_50m.txt
• 1:110m: world_110m.txt

Fig.1 shows the result for a resolution of 1:110m. Note that we have to use linetype instead of linestyle in gnuplot 4.6 in order to set the colors of the grid and coast line.

In order to compare the different resolutions of the coast line files, we plot only the part of the map where Denmark is located (which is completely missing in the original data).
Here is the example code for a scale of 1:110m.

set xrange [7.5:13]
set yrange [54.5:58]
plot 'world_110m.txt' with filledcurves ls 1, \
    '' with l ls 2

Fig. 2 A plot of Denmark at a resolution of 1:110m. (code to produce this figure, data)

Fig. 3 A plot of Denmark at a resolution of 1:50m. (code to produce this figure, data)

Fig. 4 A plot of Denmark at a resolution of 1:10m. (code to produce this figure, data)

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.

Fig. 1 An animated spiral trajectory (code to produce this figure, data)

In order to create the animation we have to produce a set of png images and create the resulting gif animation with GIMP as shown in the Animation I – gif entry. Therefor, we have to tell gnuplot at which point of the data it has to stop for each image. This can be achieved by the every option. The point at the end of the line is just one data point. Here the start point and end point for every are just the same.

do for [ii=1:99] {
    splot 'spiral.txt' every ::1::ii w l ls 1, \
          'spiral.txt' every ::ii::ii w p ls 1
}

The downward spiral is created by running the loop in the other direction.

do for [ii=99:1:-1] {
    splot 'spiral.txt' every ::1::ii w l ls 1, \
          'spiral.txt' every ::ii::ii w p ls 1
}

By the way, I don’t know why the antialiasing of the output png images is not working in this example. If you have any idea, feel free to tell me.

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