IsoSurface | Infinite terrain library based on IsoSurface generation

My goal is to display a line graph and an average line on the same plot. This is for a Django project that displays a list of scores from a database. This database is pretty big (over 100k). The app shows an average line for each page (page only containing 20 results). I had this working using Scatter objects instead of Figure objects.

I am trying to define a function in 3D cylindrical coorindates in Matlab, and then to convert it to 3D cartesian for plotting purposes.

For example, if my function depends only on the radial coordinate r (let's say linearly for simplicity), I can plot a 3D isosurface at the value f = 70 like the following:

I'd like to plot a convergence process of the MLE algorithm with the plotly library.

Requirements:

• the points have to be colored colored in the colors of the clusters, and change accordingly each iteration
• the centroids of the clusters should be plotted on each iteration.

A plot of a single iteration may be produced by Code 1, with the desired output shown in Figure 1:

Code 1

I am attempting to combine a 3D surface plot as shown here and a 3D isosurface plot as shown here in Plotly.

I do not wish to combine them through subplots. The goal is to be able to plot topography with a surface plot and another variable through an isosurface plot, but have them on the same figure.

Is there any way to do this with Plotly?

I am trying to plot a 3-subplot PlotLy graph. From top to bottom: Candlesticks, normal line graph, normal line graph.

Here is my code:

I have done a graph with the exact code posted here. It is almost exactly what I want but I would like the 2D histogram slices to be filled and not just a line. Therefore, I add the parameter surfaceaxis=0 as I found in this example. The image I get is the following:

I see that the code kind of "tries" to do what I want but not quite. I tried other options like go.Surface or go.Isosurface but didn't work.

My code:

I want to plot the isosurface of a specific %-contour in a 3d kernel density estimate. Then, I want to know which points are within that 3d shape.

I'll show I approach the 2d situation to illustrate my problem (code imitated from R - How to find points within specific Contour and How to plot a contour line showing where 95% of values fall within, in R and in ggplot2).

I have created a program that is able to predict given a set positions of shape nx3 , an it a isovalue of shape nx1 (n is the # of samples). Now I want to be able to visualize this and save it as a mesh, I have read that a way to find the vertices and faces of the isosurface is by using an algorithm called marching cubes, that I found it is in python skimage.measure. However I can't seem to understand how to utilize given my data, to give it to the measure.marching_cubes_lewiner, since it only says that accepts volumetric data (M,N,P).

I've tried to reshape my it into a 3d array but I have no idea how it should look like. I tried to do it as grid and failed. I feel it shouldn't be hard but I'm new to meshes and grids.

I have a 3D scalar field mesh given in non-cartesian coordinate system. After coordinate transformation back to conventional cartesian coordinates mlab.contour3d displays wrong result, while mlab.points3d works as expected. How can I view isosurfaces of given mesh in different coordinate systems?

This is my code