 waveform detection using fuzzy logic, trying to detect these different waveforms. So we have those membership functions that as you change the input waveform, you get different activation. So we're looking at symmetry of the signal and sharpness, so both for square, you have high activation, triangle, yeah, so that doesn't make sense. Left trapezoid, right, left trapezoid is getting something, yeah, we need a bit of more noise and expect sharpness is low, square, so highly symmetrical, so that makes sense, and sharpness is low, that also makes sense, let's say for trapezoid, let's compare the two, it's only also highly symmetrical, it's pretty much the same, so based on what we have sharpness is, has low activation is high, so meaning it's not very sharp, it's same as for square, and that's why we're having trouble distinguishing between the two, expect the sharpness for square to be slightly higher, as you know we have GPT helping us out, suggesting printing the activation level sounds good, however, is there any option to plot the actual numbers on top of one of the charts, for example, can we overlay these values on top of the symmetry and sharpness membership functions as dots of different colors? Yes, you can definitely overlay the activation levels on your symmetry and sharpness membership function parts. Okay, can you check the Python code, we're already sending those, we're already doing it, can you find the relevant code, please check above you should have the Python code, let me know if you have trouble finding it generating very slowly again, doesn't make sense, do we, do we send this already, yes we did, I suspect we need to start a new conversation, because it forgot about the code, should we just update it here, we already have that code, here is the current code that we have, yes those should be these two plots, we want to overlay that on top of them, the code, it's a plot container, okay, right, it seemed to have access to the JavaScript, so we have the scatter plot, now we also adding these markers, symmetry activation trace, the centroid symmetry membership function, interactivation trace, we still have the one trace, let's do this one by one, get rid of that, interactivation trace, and we can format this out, and we're getting some sort of error, and when we get error, we'll play some music to relax, there's nothing indicating an error, must beat the names, let's see that symmetry activation law, do we send the activation law, it seems legit, the terminals seem okay, right, because the problem with JavaScript, because it is, JavaScript, I don't understand is it line 101 or 94, okay, I know, no, wait, I don't know, you don't have the fuzzy data, do we have the fuzzy data, it's actually 124 isn't it, 24, symmetry function, symmetry activation trace, must be an array, why, it is an array, why values are an array, yeah, I don't want to ask, get up, go past, because it won't have all the context, get this error around this code, let's inspect and correct it, why do you correct it, we'll just use the code, what so did I do, I need to add this to keep one as well, yes, looks like, okay, it just bombarded my graph, if maybe the original thing gave was correct, right, so it was correct, so I had to activate the symmetry activation level, so, right, actually moving around, this is good, what's the, let's just the red, symmetry activation level, what's the blue in the green, symmetry activation, and yes, we'll have to do more of this monitoring, yeah, we actually want to get rid of that, say, working well, right, not the questions, this red, green, blue, yeah, we need to sort it out later, might do it next time, so I'll leave you with some amazing music, yeah, if you can see maybe as well, you can, you can do a lot of TikTok videos, there, I'll add the instructions to it and wrap it up in a proper page, I'll see you later, this is what buy buy sounds, two heads.