RMS | Resturant Management System

I have a dataframe:

I'm not sure where I'm going wrong, but in any case, here's what I'm seeing:

Lets say I have a library that's defined something like this:

I was doing camera calibration with OpenCV and manage to get all the camera parameters, but now I am not sure if I did everything correctly.
Here is the image I used I used 6 points on the image (4 court corners and two in the middle where the net touches the court lines)
imgPts = [[577, 303], [1333, 303], [495, 517], [1422, 517], [366, 857], [1562, 857]]
Assuming the top left corner is the origin I constructed the corresponding world coordinates in meters (23.77m x 10.97m):
objPts = [[0, 0, 0], [10.97, 0, 0], [0, 11.8, 0], [10.97, 11.8, 0], [0, 23.77, 0], [10.97, 23.77, 0]]
Following is my code for obtaining the camera_matrix, rotation translation vectors:

I would like to create the following table

Where

I am trying to plot a simple logistic regression in R.

I am following this tutorial to conduct the logistic regression and calculate a P-value (https://mgimond.github.io/Stats-in-R/Logistic.html). I am trying to use ggplot2 and ggpmisc to plot the regression. I have been trying to use this guide (http://cran.nexr.com/web/packages/ggpmisc/vignettes/user-guide-1.html#stat_fit_glance) to stat_fit_glance to display a p-value

I'm trying to set the rms level of an AudioSegment in Pydub relative to another file, before overlaying the two. The method I'm trying to use involves setting the relative rms of the first file to be +4 dB more intense than the second- I know rms isn't modifiable, but dBFS is. I'm trying to modify it with apply_gain(), but printing the rms and the dBFS doesn't show any differences before and after calling that method.

At the moment, my code looks something like:

I am trying to extend RecognitionService to try out different Speech to Text services other than given by google. In order to check if SpeechRecognizer initializes correctly dummy implementations are given now. I get "RecognitionService: call for recognition service without RECORD_AUDIO permissions" when below check is done inside RecognitionService#checkPermissions().

I've made the following function which creates scalar profile plots from a scalar field.

(Note: I read a few other related questions on this site, but I don't think they apply to my specific circumstance; if they do, my apologies.)

Problem: I am trying to derive photometric parameters for a specific location on a planetary body. This is an extremely non-linear problem and the photometric parameters interact in non-linear ways. I've reduced the problem to 5 of the most important parameters, and I've talked with experts in the field who have told me that, yes, there is no way to analytically solve these equations and all they do is parameter-space sweeps. The idea is to have a broad parameter space first, then refine it as parameters are converged upon (this sentence is not yet written into the code snippet below).

Because I need to search through a 5D parameter space, I have written this as five nested for() loops. Each time the inner-most loop iterates, it checks the result against the actual data, determines if the RMS is less than the previous best, and saves those parameters if it is.

Question: Is there any way to speed this up? It is painfully slow, slower than the "Igor Pro" language I was programming in before (but Igor can't be called by Python, and all my other code is in Python). I can't think of a way to "Pythonize" things since it has quite a bit of math for each unique parameter value and because it does the testing with each iteration. The other "only" thing I can think of is to coarsen the parameter space further with each iteration so that, for example, it would not need to do 10^5 iterations below, but I run the risk of missing sensitive local minima if I do that.