Learning matplotlib

I've been doing some calculations today on the structure of some heavy transactinide nuclei.  This has involved running a code (ev8, from W. Ryssens et al, Comput. Phys. Commun. 187 175 (2015)) and manipulating the output data, which I would like to plot in the kind of sector plot that nuclear physicists use when plotting the potential energy surfaces of nuclei using Hill–Wheeler coordinates.

Here's an example of such a plot, which I've taken from the arxiv paper 1809.04406 (also by W. Ryssens et al., though the al is slightly different to the first paper above):

I have all the necessary data and have mapped it to the right sort of coordinates, but I am struggling to get matplotlib to encase it in the right circular sector.  So far, I've got it looking like this:

Any suggestions on how to mask this into a sector, would be most welcome.  I'd quite like to add a labelled axis along the gamma=60º line, too, if you have any ideas.  And add in those grey axis lines.  I can handle changing all the colours and font sizes that I need to do.

I should probably be posting this to stackexchange, right?

edit: This blog post might prove helpful, so I link to it for my future reference: http://blog.rtwilson.com/producing-polar-contour-plots-with-matplotlib/

Another Google Scholar Purge

Google Scholar has again gone through a process of forgetting or purging a number of citations to my papers.  This time last week it said I had 1,589 citations to my work.  Today, 1,170 (both figures down from 2,130 in January last year).  Google Scholar is fairly opaque in how it does things, and I don't know what records it deleted and why, but it seems unfortunately very unreliable.   As a record of my papers, it's useful.  For citations?  It's becoming increasingly less so.  Anyone else noticed the same with them?

After noticing (some years ago) strangely fluctuating numbers coming from Google Scholar I started periodically recording the number of citations it reported for me on a spreadsheet.  Hence I can present the graph attached here.  The latest downward line is smaller than the one I noticed mid-way through last year, but still takes the number of citations back to that of around 5 years ago!

Nuclear Spot The Difference #6 revisited

Regular readers of this blog will no doubt remember a previous spot-the-difference in which the understandable confusion between Art Malik and Jim Al-Khalili was explored.  With the recent acquisition of a beard, it has been claimed that nuclear physicist Jim now looks like comedian Alexei Sayle.  I present the following pictures to allow the reader to judge

Sayle		Al-Khalili

Here is Jim from his pre-nuclear days, singing "Ullo John, got a new motor?"

Relativity makes mercury liquid

The link between the theory of General Relativity and the orbit of the planet Mercury is pretty well-know.  What I learned recently is that it is a consequence of Special Relativity that the element mercury is liquid at room temperature.  

In a paper by Krista G. Steenbergen, Elke Pahl, and Peter Schwerdtfeger (J. Phys. Chem. Lett. 8, 1407 (2018)) the authors perform calculations of bulk mercury using both relativistic and non-relativistic quantum mechanics, and show that the use of relativity lowers the melting point of mercury by 160 K.  The relativistic calculations give a melting point close to nature (within 10K).  They are sophisticated quantum mechanical calculations in which bulk many-body effects are taken into account.  You can't simulate melting and freezing by considering the interaction of two or three atoms, but need at least tens of them to get a realistic picture of bulk matter.  That's not easy to calculate, and the authors have made a convincing case that they have done so, and that the dominant effect in causing mercury's unusually low melting point comes from relativity.

The picture below is from the paper, showing the result of a simulation with a disordered liquid phase on the left, becoming an ordered solid on the right.