tessellation | build functional , reactive and well structured | Frontend Framework library

I am learning tessellation now. Trying to pass the colour via tesselation shaders. Like I found here, the most consistent answer: Passing data through tessellation shaders to the fragment shader
So far consulting also these sources, but did not find the problem in my code yet:
https://www.khronos.org/opengl/wiki/Tessellation_Control_Shader
https://www.khronos.org/opengl/wiki/Tessellation_Evaluation_Shader
As failing to pass for complex shapes, I narrowed problem down to simple basic triangle. Here is the problem. Only the last, third color is taken from the triangle data passed to shaders, see the TessTriangleRainbow::points in the C++ code snippet for tessellated triangle. No matter that I do, the first ad second color is ignored:

Note, exactly the same code and data but not tessellated looks ok:

Vertex shader for tessellated triangle:

I wish to calculate the nearest-neighbour distances (NNDs) of a point pattern by year with spatstat but get an error, although I cannot see any difference with the example provided in the documentation. What's the right way to do it?

I would like to know how Delaunay triangulation can be done to find the connectivity of the cells formed by voronoi tessellation

The following is the code that I'm using to generate voronoi cells.

I wrote a small tessellation program. I can write to a SSBO (checked output using RenderDoc) but reading the data back right away in the same shader (TCS) does not seem to work. If I set the tessellation levels directly, I can see that my code works:

In the main of the Tessellation Control shader:

I want to make a world map with a voronoi tessellation using the spherical nature of the world (not a projection of it), similar to this using D3.js, but with R.

As I understand ("Goodbye flat Earth, welcome S2 spherical geometry") the sf package is now fully based on the s2 package and should perform as I needed. But I don't think that I am getting the results as expected. A reproducible example:

So I am able to draw curved lines using tessellation shaders , but the lines coming out are very thin and jaggy(aliased). I was confused as how can I process these new points( isoline ), from tessellation eval shader to fragment shader and make it thicker and aliased.

I know their are multiple ways like using geometry shader and even vertex shader to calculate adjacent vertices and create polyline. But my goal isn't creating polyline, just a constant thickness and anti-aliased lines(edges). For which I think fragment shader is enough.

Kindly advise what will be the best and fastest way to achieve this and how can I pass "isolines" data from tessellation shader to fragment shader and manipulate their. Any small code which shows transfer of data from TES to FS would be really helpful. And pardon me, since I am beginner, so many of my assumptions above might be incorrect.

Vertex Shader: This is a simple pass through shader so I am not adding here.

Tessellation Eval Shader:

I'm trying to add a SCNGeometryTessellator to some SceneKit geometry that uses a custom SCNProgram. My geometry renders fine normally, but as soon as I add the SCNGeometryTessellator, I see this error:

I’am trying to classify bivariate point patterns into groups using spatstat. The patterns are derived from the whole slide images of lymph nodes with cancer. I’ve trained a neural network to recognize cells of three types (cancer “LP”, immune cells “bcell” and all other cells). I do not wish to analyse all other cells but use them to construct a polygonal window in the shape of the lymph node. Thus, the patterns to be analysed are immune cells and cancer cells in polygonal windows. Each pattern can have several 10k cancer cells and up to 2mio immune cells. The patterns are of the type “Small World Model” as there is no possibility of points laying outside the window.

My classification should be based on the position of the cancer cells in relation to the immune cells. E.g. most cancer cells are laying on the “islands” of immune cells but in some cases cancer cells are (seemingly) uniformly dispersed and there are only a few immune cells. In addition, the patterns are not always uniform across the node. As I’m rather new to spatial statistics I developed a simple and crude method to classify the patterns. Here in short:

1. I calculated a kernel density of the immune cells with sigma=80 because this looked “nice” for me. Den<-density(split(cells)$"bcell",sigma=80,window= cells$window) (Should I have used e.g. sigma=bw.scott instead?)
2. Then I created a tessellation image by dividing density range in 3 parts (here again, I experimented with the breaks to get some “good looking results”).

Background: We have ~140 million polygons split into 5 indices (region-[1-5]) with 2 shards each. It was loaded with ES 7.10. The field containing the polygon is named 'shape' and is mapped as a geo_shape field.

Here's an indexed example: