diffuse | music player that connects to your cloud/distributed storage | Storage library

i create a simple scene with a cube and a floor for study propose.

i set the delegate to self, but I can't understand why my render stop print out my message after 12 frame.. my I know why it stop? should not running forever since my scene is in view?

here my custom scene:

Please consider the snippet below. It plots a set of spheres connected by some segments. The function to draw the smooth spheres comes from the discussion at

How to increase smoothness of spheres3d in rgl

What puzzles me is the following: when I zoom in/out the RGL plot, the spheres and the segments behave differently. In particular, if I zoom in, the segments look rather thin with respect to the spheres, whereas they look really wide when I zoom out.

Is there a way to correct this behavior, so that the proportion between the spheres and the segments is always respected regardless of the zoom level? Thanks a lot

I'm trying to make shaders for my game. In the fragment shaders, I don't want to set the color of the fragment but add to it. How do I do so? I'm new to this, so sorry for any mistakes.

This a jelly shader from unity asset store yet I cant figure out how to make it always active as it requires 2 vector3 (_ModelOrigin and a __ImpactOrigin). Any ideas on how to edit it is always active?

Thats how I use it right now :

My question is: How to forecast out of sample values with exogenous predictors using the Statsmodels state-space class TVRegression and the custom data provided in the example (see link below). I have spent several hours searching for examples of how to forecasts out-of-sample values when the regression model contains exogenous variables. I want to build a simple Dynamic Linear Model class. I found a class in Statsmodels, TVRegression (see here), [https://www.statsmodels.org/dev/examples/notebooks/generated/statespace_custom_models.html][1] that should solve my problem. The TVRegression class takes two exogenous predictors and a response variable as arguments. I copy and pasted the code and ran the example in the link above without a problem. However, I am unable to produce a simple out-of-sample forecast, even using the example data given. The TVRegression class is a child class of sm.tsa.statespace.MLEModel and thus should inherit all of the associated methods. One of the methods of sm.tsa.statespace.MLEModel is forecast() and according to the UserGuide I should be able to provide a simple step argument and get out of sample forecast: MLEResults.forecast(steps=1, **kwargs). The code used to generate the dependent :y and the independents(exogs) x_t;w_t

In the attempt to get diffuse lighting correct, I read several articles and tried to apply them as close as possible.

However, even if the transform of normal vectors seems close to be right, the lighting still slides slightly over the object (which should not be the case for a fixed light).

Note 1: I added bands based on the dot product to make the problem more apparent.
Note 2: This is not Sauron eye.

In the image two problems are apparent:

1. The normal is affected by the projection matrix: when the viewport is horizontal, the normals display an elliptic shading (as in the image). When the viewport is vertical (height>width), the ellipse is vertical.
2. The shading move over the surface when the camera is rotated around the object.This is not much visible with normal lighting, but get apparent when projecting patterns from the light source.

Code and attempts:

Unfortunately, a minimal working example get soon very large, so I will only post relevant code. If this is not enough, as me and I will try to publish somewhere the code.

In the drawing function, I have the following matrix creation:

I exported a default cube from Blender 3.0 to gltf+bin. I try to draw it in pure WebGL.

It is just a very simple example. You will see magic numbers in this example like:

I am using unity.

I used Triplanar to make the top of the cube as snow terrain and the rest of the sides as cliff terrain.

Here I have to insert a normal map.

However, if the normal map is applied, the image of the cliff face is covered with the image of the snow terrain.

The phenomenon is shown in the following image.

I am trying to add two images together.

When I do so in GIMP, using the 'Addition' layer-mode I get the following desired output.

Now, I am trying to execute this in Python, however I can not manage to get it working properly. According to the gimp documentation, as far as I understand the formula for addition is simply the addition of each pixel, set to a max of 255.

Equation for Addition mode: E = min((M+I), 255)

I try to apply this in Python but the output looks different. How can I get the same output in Python as the gimp addition layer blending mode?

My attempt: