Hello all,

I am interested in efficiently showing a 3D structure using isocontours. My data is obtained in cylindrical coordinates, with an azimuthal decomposition q(x,r,\theta)=\hat{q}(x,r)e^{im\theta}. It is quite large (about 600k values raw), and I also know a lot about where I expect my surfaces to be spatially.

Right now, I followed the directions at this page : 3d isosurface plots in Python. This leads to a meshgrid to obtain a coarse structured grid of inhomogenous spacing in x, but regular in y and z. I project this Cartesian grid onto the \theta=0 plane, interpolate, then obtain \theta through the function `numpy.arctan2`

. It takes time and it produces very large plots with poor resolution. If I deviate from this process by introducing an inhomogeneous grid, I obtain a blank plot. I increase resolution, I get monsters my navigator cannot handle.

My question is this : if I know that my data is located around r=1 for instance, can I leverage that ? `Isocontour`

is not permissive when it comes to the mesh and its underlying requirements are unclear to me. It seems only capable to handle homogeneous meshesâ€¦

To illustrate my point, consider this data in the x-r plane :

I have to project it on a clearly inadequate mesh that looks something like this (intentionally coarsified for clarity) :

And it gives out something ugly :

Any ideas ?

Hi @hawkspar, Iâ€™m not really sure what exactly do you try to plot, maybe a extract of your data and some code which creates the current status of your figures.

In general I would recommend the forum search, specially @empet has a lot of great answers dealing with scientific data and 3d plots in general. Maybe this helps to get some insights concerning the â€śplotly wayâ€ť of plotting.

Thanks for the quick answer ! Iâ€™m plotting isocontours of modes right now. I have an application that generates my fields (scalars or vectors) in the x-r plane for a prescribed azimuthal wavenumber m. Therefore I am plotting numerical results that are smooth, but do not have an analytical expression. I want to plot these efficiently in 3D.

Iâ€™m not sure what youâ€™re asking for - you really think the 600k data would be useful and insightful ? I have already provided an x-r plane view of what a representative function looks like here.

I can give you pseudo-code, which will probably be clearer than a snippet from my application :

```
1. Compute functions in x-r plane with refined mesh
2. Create a coarse structured cartesian mesh that is homogeneous in y and z, not x
3. Project the cartesian mesh onto the x-r plane, interpolate function onto new mesh
4. Do the azimuthal decomposition - multiply by e^(i m arctan2(Z,Y))
5. If necessary, correct orientation - rewrite e_r and e_theta in cartesian coordinates
6. Use go.Isosurface for 2 surfaces at 10% of min/max of the real part of the function
```

For 1 see this representative image from the first post, for 2 see a representative mesh and a result is visible here. My current problem is with resolution, file size, and computation speed, in that order.

Google and a forum search did not yield much useful information. I feel like isocontours are much less used than most 3d plotting solutions, which is a shame.