Closed kaarelmand closed 4 years ago
It just occurred to me that REY (suggestion no. 1) could be better implemented simply as a switch to REE()
that inserts Y right after Dy.
Thanks for the feature requests @kaarelmand!
For the Pm broken-lines issue, the simple solution is to discard it from the index/x-axis when building the plot, and I have a quick fix for this ready to go. While it may be a literature standard, the inclusion of Pm in the index/x-axis label for the pyrolite.plot.spider.REE_v_radii
seems a bit disingenuous. While it makes sense to get the spacing right on a spider plot (like the one above), the REE_v_radii
plots are spaced by radii, and as such the position of the other elements will be unaffected by whether you include Pm or not.
On the spider plot side, for elements other than Pm it's informative to see where you're missing data, and the line-across-the-gap method would start to interfere with that (especially when it comes to larger datasets, and where the density methods come in). The added complexity is that plotting would then need to be done on a per-line basis (or at least per-missing-data-pattern basis). Let me know if this REE_v_radii
solution doesn't fit (i.e. you need the Pm label on the x-axis) and perhaps we can work something out which does.
As for the PAAS data, once you find it, it should fit within the current refcomp folder. Have a look at the other data files there, and me know if you have any questions about getting this in the right format. Have a look at McLennan (2001), this includes an updated PAAS composition and a few others which you might want to bring in too.
For REY, I think creating an indexing function akin to pyrolite.geochem.ind.REE
might be the way to go. This could be supplemented with a selection method on pyrolite.geochem.pyrochem
and a plotting method akin to pyrolite.plot.pyroplot.REE
.
Finally, let me know whether you'd like to work on any of these issues yourself (in which case feel free to ask any pyrolite/development questions), otherwise I'll have a crack.
McLennan, S.M. (2001) Relationships between the trace element composition of sedimentary rocks and upper continental crust. Geochemistry, Geophysics, Geosystems 2. doi.org/10.1029/2000GC000109.
Thanks for the thorough reply!
For the 1st and 2nd suggestions, I could certainly try my hand at submitting some PRs, though it would be my first official code contribution, so I might have some questions here or on gitter later, if you don't mind.
As for jumping the Pm gap, I agree that just skipping Pm usually works and if you can use the REE_vs_radii plot, the problem is already solved. I guess the issue is that I'm most often working as a contributor on someone else's paper and don't have too much creative control when making figures. These authors usually implore me to go with the most traditional plotting practices (for my field): a spider plot-like equally-spaced distribution of elements on the x-axis, and a line over Pm. Perhaps the ideal solution here would be to style the Pm gap line differently (e.g., dashed), so as to keep the REY pattern continuous, but highlight the lack of Pm data. I imagine this would likely introduce a lot of complexity for little gain; perhaps this issue could go on the back-burner then.
And to clarify, do you think a pyrolite.geochem.ind.REY
function would be better, or just an includeY
switch for the pyrolite.geochem.ind.REE
function (and the other REE methods), like the dropPm
switch?
Feel free to ask anything related to the issues here, but general discussion or development questions might be better directed to Gitter. Happy to help out wherever needed!
I'll have a bit more of a think about it, but I think an REY()
function could be less verbose (i.e. df.pyrochem.REY()
over df.pyrochem.REE(includeY=True)
. There might need to be some modifications of plotting functions to deal with Y.
For the Pm data - I'll think about this a bit more, but perhaps we can create a separate issue for this as it's likely to come up in other spider plot discussions.
Meanwhile, I discovered this hilarious paper (have a read of the abstract + graphical abstract for a good laugh), and feel that I should likely add this new European Shale as well.
And while I'm at it, I should round out my PR by adding some updated variants of PAAS, EUS, NASC, MUQ, more from Condie (1993) and others in the above paper.
It's ridiculous how similar they all are! Looks like we'll have to source some more hard rock reference compositions to keep up.
I'll keep an eye out and look to merge soon after you finish it up.
Afternoon @kaarelmand. I ended up adding the REY functionality - check it out on the develop branch and let me know if this is roughly what you were after. It passes basic tests, and follows the ordering from the spider plot you used above. While there isn't a plotting method for this one, you should be able to use df.pyrochem.REY.pyroplot.spider()
or similar to achieve that plot. If this is up to scratch I'll close the issue. Cheers!
For reference, it was added with 9582f1085c30d300156ebca72544338a4c6f5222 (you can check the commit for an overview of additions).
Closing this one, as this seems to have addressed the issue.
Is your feature request related to a problem? Please describe. While pyrolite is already a highly useful tool for working with REE data, some additional convenience features for common workflows in surface REE cycling would be really handy:
geochem.ind.REE()
, no such function exists for the REYs.2. Pyrolite is missing the most used reference composition for my field -- Post-Archaean Australian Shale (PAAS) [2]. This composition data is surprisingly hard to find online, so I'll try to get a hold of the book.Added in PR #37.Describe the solution you'd like
REY()
function identical toREE()
, except for the addition of Y between Dy and Ho.2. Add PAAS data to the reference composition database.Describe alternatives you've considered
2. I can store PAAS data in an array and feed that directly topyrochem.normalize_to()
.pyroplot
yet, especially given #27, but here is a StackOverflow discussion on this with matplotlib.Relevant References [1] Bau, M. and Dulski, P. (1996). Distribution of yttrium and rare-earth elements in the Penge and Kuruman iron-formations, Transvaal Supergroup, South Africa. Precambrian Research, Geology and Geochemistry of the Transvaal Supergroup 79, 37–55. https://doi.org/10.1016/0301-9268(95)00087-9
[2] Taylor, S.R. and McLennan, S.M. (1985). The Continental Crust; Its composition and evolution; an examination of the geochemical record preserved in sedimentary rocks. Blackwell, Oxford. 312.