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Support for unstructured grid with a vertical dimension #15
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Hey @dennissergeev, Stoked to hear that you're excited about Prior to Apologies for the poor quality GIF, it doesn't really do the colormap justice... but I think you get the idea of the direction I'm headed in with that kind of capability. So time-series support is definitely in the pipeline, perhaps along with There are some fundamentals that I'm keen to bank first for With regards to the vertical I've not quite thought that far ahead to be honest ( I was mulling over geodesic ribbon trajectory slices through unstructured meshes to show vertical levels in the planar, but I've not ventured too far into this space other than daydreaming about it... however I am motivated by concrete use cases though 😉 So if you can be more specific and give me some examples that would really help 👍 |
Thanks for the speedy response @bjlittle!
Looks awesome! I would love to use this once you're happy with the prototype.
I agree that's the way to go. I'm mostly using
Fair enough. I can try to help with some "good first-time issues"...
I can of course just plot some slices of the vertical coordinate, but eventually I would like to create plots with the vertical dimension, i.e. planet's radius, such as vertical cross-sections, isosurfaces, wind vectors. As an example, here's what I made with PyVista a while ago: https://dennissergeev.github.io/exoconvection-apj-2020/ If this kind of visualisation can be combined with the time animation as in your gif above, that would be even more awesome. |
@dennissergeev Awesome, I've come across that visualization before, super cool, congrats! Okay, I can see what you're aiming for, and that all makes sense, thanks. I'm guessing your Trappist-1e 3D viz could also be projected e.g., say the sphere to Plate Carree, but still have the 3D vertical convection layers on top, thus taking advantage of a 2D planar but viewed in 3D, if you get my meaning 🤔 I think that your original 3D viz is perhaps currently possible in ParaView. I know that @tinyendian (Wolfgang Hayek @ NIWA) might be able to comment or point you to what's possible there too - if you're interested? |
Thanks @bjlittle!
Yes, certainly. Visualising it both in a planar projection or as a 3D sphere would be great! I guess that's why you're envisaging CRS support - to have an API similar to
That would be useful to know, so any tips from Wolfgang would be appreciated. P.S. Great logo by the way! |
Hi @dennissergeev and @bjlittle, visualisations such as Trappist-1e (which looks great btw!!) can definitely be done with ParaView, including time-dependence of the model data, spherical projection, moving cameras, interactive web publication, ... I haven't used PyVista before, but, looking at the examples, it seems to use a similar philosophy as ParaView, providing a simplifying layer on top of VTK. ParaView comes with a few perks, such as a powerful GUI, and a client-server mode, which is useful for very large models that require an HPC backend to handle. Its Python scripting capabilities are comprehensive and integrate nicely with the GUI, but the price to pay is that the scripts look somewhat obscure compared to PyVista, the latter seems a bit more Pythonic. Very happy to help, if you'd like to give ParaView a try (it can read CF-netCDF and LFRic output), or if you have any questions. Btw, I worked in the exoplanet group in Exeter 10 years ago as a postdoc, great to see the work that you guys are doing there! And say hi to Nathan for me please 🙂. |
hi @tinyendian, Thanks! Yes, the exoplanet group is growing stronger, so expect more cool 3D visualisations of exoplanet simulations 😄! I actually used ParaView a little bit in the past, though only as a GUI. Now I prefer using PyVista because of its pythonic API and integration with Jupyter, but in the meantime if I wanted to give ParaView another go and visualise LFRic output, where should I start? |
Hi @dennissergeev, the easiest way to start with ParaView is a conda installation of the LFRic reader plugin, which will install the latest ParaView GUI as a dependency,
If you have a ParaView build on your system already (along with the required build tools and dependencies), you can also easily build the reader yourself, see the instructions on https://github.com/niwa/lfric_reader. Note that Jupyter integration was recently added to ParaView, too, https://blog.kitware.com/paraview-jupyter-notebook/, but I haven't tried this out yet. With regards to Python scripting, I agree that PyVista coding looks a lot nicer and straightforward. ParaView really has its strengths when it comes to interactive visualisation (have a look at, e.g., linked cameras for comparing two models), and handling very large datasets (hundreds of millions of cells) efficiently. Maybe there will be ways to integrate Iris/PyVista and ParaView a bit in the future, e.g., via automatically generated ParaView scripts (ParaView can be fully controlled by Python scripting). Looking forward to seeing more exoplanet simulations 😍! |
Thanks very much @tinyendian! I'll give it a go. |
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Hey @dennissergeev, Just wanted to let you know that I want to get my ducks-in-a-row to schedule time for supporting this issue. The first step in this journey will be to extend the capability of the I'm guessing that you've got a tonne of archived data that you'd be keen for Cheers 😄 |
Hey @bjlittle! This is exciting to hear! Happy to help test / develop things, just ping me. The mesh is C48 and it has 38 vertical levels. Currently, the output netCDF file does not store the level height values (only level numbers), but in this simulation I used the (/ &
0.0000000_r_def, 0.0005095_r_def, 0.0020380_r_def, 0.0045854_r_def, &
0.0081519_r_def, 0.0127373_r_def, 0.0183417_r_def, 0.0249651_r_def, &
0.0326074_r_def, 0.0412688_r_def, 0.0509491_r_def, 0.0616485_r_def, &
0.0733668_r_def, 0.0861040_r_def, 0.0998603_r_def, 0.1146356_r_def, &
0.1304298_r_def, 0.1472430_r_def, 0.1650752_r_def, 0.1839264_r_def, &
0.2037966_r_def, 0.2246857_r_def, 0.2465938_r_def, 0.2695209_r_def, &
0.2934670_r_def, 0.3184321_r_def, 0.3444162_r_def, 0.3714396_r_def, &
0.3998142_r_def, 0.4298913_r_def, 0.4620737_r_def, 0.4968308_r_def, &
0.5347160_r_def, 0.5763897_r_def, 0.6230643_r_def, 0.6772068_r_def, &
0.7443435_r_def, 0.8383348_r_def, 1.0000000_r_def /) Let me know if you need more info! |
@dennissergeev Starting to play in this space, so I just thought that I'd give you a minor update... I've been working on a use case with an oceanographer at the Met Office, where they have 50 depth levels (bathymetry) of ORCA ocean model data (on tri-polar curvilinear grid), which has been pre-processed through a canny edge filter to detect potential temperature gradients. Initially, they want to visualise the data as a point cloud, which is an ideal first use case. I was able to easily extend
votemper.mp4Next steps are to:
In hindsight, I should have rendered this example with Baby steps. |
votemper-coastlines.mp4 |
votemper-coastlines-widget.mp4 |
Sorry for my slow response, I was on holiday :) This looks amazing, many thanks for sharing! Is this functionality available in the latest release? (No rush, just wondering.) And since you've shown that point cloud rendering is possible, I guess extending this to vectors/arrows to show the flow velocity in 3D should be quite straightforward, right? |
I've put up a pull request to add @dennissergeev! 🎉 |
@all-contributors please add @mgrover1 for ideas and promotion |
I've put up a pull request to add @mgrover1! 🎉 |
@bjlittle - just found this one - subscribed immediately! Click for context, and potential good 1st target = WACCM-XContextThis sort of volume / isosurface rendering could be very interesting for space weather use-cases in Earth's ionosphere, thermosphere and below (where we have more-easily supportable geographic lat/lon coords, as well as more bonkers ones too!). These regions can be highly dynamic, with multi-scale spatiotemporal effects which ~require seeing ~4D representations to understand full picture - risk losing insights if you collapse data straight to 2D or lower.
There's some very nice videos here illustrating this sort of thing - look at all of those ripples as the solar storm hits on 17 Mar 2015 - the 4D synoptic view here invaluable - a lower-dimensional representation couldn't cut it!
From dim recollection I believe @eelcodoornbos generated these with Blender (?). In any case, using output from NCAR's WACCM-X model. WACCM-X: a good first target?Iff this is of interest, WACCM-X might be a good target for dipping geovista's toes in the space weather water (if you've not already?):
Later can see if Geovista's ugrid support might be an easy way around trickier things like geomagnetic coordinates in other models, ... There's WACCM-X output available from the NASA Community Coordinated Modeling Center (CCMC). E.g. for this "St Patrick's day storm", there's run "WACCMX-Weimer-01_2015-03-TP-01_102523_IT_1", and from there: Happy to put some effort into giving this a whirl if of interest! |
@all-contributors please add @edmundhenley-mo for ideas and userTesting |
I've put up a pull request to add @edmundhenley-mo! 🎉 |
@edmundhenley-mo Great! 🚀 Good news! I've finally managed to secure a deployment into a science team to work on this ... it's the only way I'm going to get the time to do it! I'll come knocking at your door afterwards for sure! 👍 |
Amazing 🤩 |
This is INCREDIBLE! Great work here @bjlittle 👍 |
This is all proof-of-concept, but it's looking quite encouraging. I'm currently working with some amazing Atmospheric Dispersion scientists with some gorgeous datasets, and I'll be looking to consolidate and bank this capability within I'll update the |
Great animations, thanks for uploading them! Looking forward to using this for my exoplanet simulations! |
Same dataset but with isosurfaces and an opacity linear transfer function for plume-like rendering ... The widgets won't be part of Also see YouTube animation ... |
Wow this is amazing!!! Isosurfaces are the main thing I'm waiting for in geovista, so thank you so much for working on this! |
@mo-benjaminevans Yes, indeed. Both #15 and #16 completely complement each other. Being able to visualize how winds influence plume behaviour is a critical part of your science. So just to confirm, you will be able to overlay 3D vectors on top of a 3D plume 👍 You'll be glad to hear that the plan is for both features to be available in the next release of |
This is quite exciting. |
Hi,
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Reposting this last bit so that it pings Julius: is this code open by chance? 😄 |
Thanks @banesullivan. |
Oh it totally did 🤦🏻♂️ I didn't see the first |
✨ Feature Request
Firstly, thanks very much for creating this library, I'm excited to use it in my research!
Secondly, would it be possible to generalise
geovista.Transform.from_unstructured()
for it to take in arrays with more than 2 dimensions, i.e. vertical levels (and possibly time)?Motivation
I am trying to visualise LFRic output (happy to eventually contribute to the gallery by the way), and while the
from_unstructured()
method works great for 2D arrays, I would like to plot something w.r.t. model height. Currently there's no obvious way to do this as that function accepts only longitudes and latitudes, throwing an error if I pass a full 3D array asdata
.This is probably related to the "isosurfaces support" to-do item.
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