Plotting Antenna Pattern
For my pymininec project (a re-implementation of the original Mininec  in Python, see the pymininec README for a description on how I was able to reverse-engineer 40 year old Basic code), I had needed a method to graphically display the results. So at first I had included a visualization tool for antenna pattern in pymininec.
But after I had added a quick & dirty parser for the output of
it turned out, the program is useful on its own. So I made it a
standalone program and named it plot-antenna.
NEC (numerical electromagnetics code) was originally developed at the
Lawrence Livermore National Laboratory in Fortran. Up to version 2 this
is free software, later versions (they're at version 5 of NEC now)
are non-free. Neoklis Kyriazis translated the Fortran version of NEC-2
to C and called it
nec2c. He did some optimizations, e.g. include the
separate program SOMNEC (for computing a Sommerfeld/Norton ground model)
nec2c and got rid of many compile-time limits of
the original Fortran code. The C version
nec2c is one of a small number
of open source antenna modelling programs. It is a command-line utility
that compiles an antenna description into an output text file with a lot
of tables. There are other programs like xnecview that can display
antenna pattern from this output.
Note that I didn't find an official web-page for
nec2c – it is
included in Debian and Ubuntu (Ubuntu also is searching for the
official upstream version) and it seems it was originally hosted on
Google Code. There is a version on github with a link to another
version on github. The software seems to be unmaintained these days
– not much of a problem since NEC version 2 doesn't get any updates
for some decades now.
Now back to plot-antenna: Originally the program only had a parser for output of pymininec (and of the original Mininec  program in Basic). I later added a parser for NEC. The current parsers are quick & dirty implementations and may not be very robust – but they do work for me.
The first version used Matplotlib for the graphic display and this is still supported. Originally I had implemented azimuth and elevation pattern and a 3d display.
Not to mention zooming: This works fine in plotly (using the scroll wheel of the mouse in the graphics above) and doesn't seem to be implemented at all in Matplotlib.
In the plotly version we display the plots for all frequencies in a single plot. For the 3D view all except the currently-selected frequency are hidden. You can select the frequency for which the plot should be displayed in the legend on the right side.
The best feature in plotly is the live-display of the 3D-coordinates of the surface on mouse-over. In the current implementation this displays azimuth and elevation angles as well as the absolute and relative gains at the selected point on the antenna pattern.
On the other hand I needed several workarounds in plotly:
There is a bug in hover when displaying coordinates
plotly does not support menus for 3D surfaces, I had to put an additional (empty, with one white dot) graphics into the same display for showing the menu. The menu is used when an antenna pattern is computed for a set of frequencies. In that case the frequencies can be selected in the menu on the right side.
The antenna pattern in the example above is taken from an article by
L.B. Cebik . When you compare the pattern in that article to the 3D
pattern above (or you look at the azimuth/elevation diagrams in the
description of plot-antenna), you'll notice that the plotly 3D view is
slighty distorted (the lobe is too long). I've not yet found out if this
is a problem of plotly or my use of it. I do try to get an equal aspect
ratio in all dimensions by setting
range to the same value but as you can see the aspect ratio does not
When we compare this to an azimuth diagram created with plotly:
the dimensions look ok. Notice that also in the 2D diagram you can display the angle (azimuth in this case) on mouse-over. When using Matplotlib, this is also available but the cursor does not snap to the drawn pattern. So with Matplotlib you always have to guess if the cursor is now exactly at the correct position. Also note that, again, you can select the frequency to display in the frequency legend on the right side. But this time you can display the plots for several frequencies at the same time (which does not make much sense in an opaque 3D plot).