 You're just checking this and let's jump straight into it. This indicates the level of decomposition you want in the wavelet transform. The following modifications to your code will integrate wavelet processing. This is just one of many thresholding techniques you can use for denoising. You might want to adjust this or offer multiple options. The wavelet used for decomposition, wavelet transform, is directly obtained from the client request. You should ensure that this is a valid wavelet type supported by PyWT, e.g. DB1, DB2, R, etc. Consider adding some error handling or validation for this. The level parameter is also directly obtained from the client request. Again, you should ensure that this is a valid value. Too large a value might result in an error or undesired behavior. If you wish to use wavelet coefficients, further analyses or visualizations, you can extend the code accordingly. The above example just applies to denoising. We also have the outputs. Make sure it's the correct MID. I want to do some error checking first. Let's pop this. Okay, we do get an error. This is not the type of error I was expecting. Do we get it? Even if I remove this? No. Can we print out this list? Why copilot doesn't want to work? Is it doing something? We can just do that, can you? This is the list. Okay, we need to adjust this dropdown. We actually have names. Let's pop it back into GPT. The list you've provided appears to be the wavelet names supported by PYWT, and the log entry suggests that a request was made with an unsupported wavelet transform value, UWT. Are any of them legit? No. Given this, it seems that the validation code is working correctly when provided with an unsupported wavelet, UWT, the server responded with a 400 bad request. That's fine. Here's a modified version of the earlier code to achieve that. How do we update this HTML bit? Can we update this? Wait a second. Should we correct responses? UWT? We want to update this with the violet wavelets. We need to clear that. Run this. This is the violet wavelets. Shall you understand the context? Okay, updating the value, this value. It's not a valid value. Okay, let's try this. It should be updated. It's not giving in there or now. Okay, we might add more later. Should we do it now? Because we'll forget later, wouldn't we? Okay, give me the DB3. Yeah, there's good feeling of them. Okay, let's just add four. That should be enough. Okay, you know what to do, don't you? It's the same two. Yes. Right, and it's enough. A biotogonal command-kit-up co-pilot. Why aren't you doing it by yourself? It shouldn't be. How should it? Ah, there is no 1.2, so thank you for that. 1.4 and yeah, there's 1.1, 1.3, 1.5, and 2.2. Okay, we'll leave it there for now. Oops, add this. We need another input 4. Let me check. Let's make this one default HTML. Okay, how do we do this? Give me something. Yeah, kit-up co-pilot. It's either I don't know how to use it or it's absolutely hopeless. One sec, what are we doing? I missed this one. Well, problem can be make the default option spell. Yeah, kit-up co-pilot can learn from chgbt how to modify code. Yeah, just need to add the word selected there. So, now we have db1 as default. Now, obviously this should change the signal. Okay, this stopped working. Okay, what's up? What's error before fix? Can you fix this? The error message indicates that the variable eGunDiscordator is being used before it has been assigned any value. Specifically, the line. Save this file as well. What was the last change? So, this line is doing what? Is attempting to pass eGunDiscordator to the pywt. Yeah, what do I have to do? What do I have to do? So, line 87 it's not indicating any problem. It starts the thing. It's working okay. When I hit Wavelet in Noise I get this error only when selecting Wavelet in Noise is on and this is the error. Can you give me more specific answer if Wavelet in Noise is over there? What's the problem? Yeah, it doesn't have an else function. Let's try a co-pilot that's doing something. Let's do some manual printing coefficients. Why? Okay, error handling is good. Let's do some more error handling. It should help try. Okay, so instead of Okay, now the app is having an error. Don't have else yet. Okay, so this is running okay. Why is this not sending this? What am I doing wrong? What am I doing wrong? What am I doing wrong? If Wavelet in Noise is just be reverse multi-level discrete Wavelet transform. Yeah, this doesn't make any sense. Now to fix the code. Sorry, no super long prompts. I'll probably get a timeout soon. Just trying to understand what's going on. Okay, so we have the we have this error. Okay, so we have the error. So, we have the error. We have the error. We have this. Right, so this should come first. Yeah, I don't want any comments like that to look obvious that that's the continuation of the code. Where is if do we have if the trend? We do. Okay, now I'm really confused. Yeah, let's just check in finance. Should go eG data and the trend if necessary then removing check in finance and then weather denoising rest of the code. Okay, let's try this. So we're starting the thing. It's refresh. This is working. Okay, that's working. And it's actually doing something. So it's removing some noise. This is actually only useful up to a very limited point. Let's reduce the window size. Yeah, so you can see the noise reduction there. So yeah, the 4 does know something about eG. It's a particularly noisy channel. This is the trending. This filter orders only. Yeah, we need restructure. The whole thing could actually just let's try this again. Yeah, I want to know how to so on the larger screen ideally everything will be set from left to right on a smaller screen on a tablet of phone. Yeah, it's obviously will have to go top to bottom. But otherwise it's actually working. It's actually doing something useful. So next thing we'll be patching it up a bit and publishing on the side. So it will go instead of this one here, we'll probably put it up top there. So it will eventually look at least as good as this one, if not a much should be much better. So hopefully you'll be able to test it yourself. What's it going? Yes, this levels in HTML should be limited levels value should have a mean and max like that ID would zero just be the same as having it off and default about 2 Yeah, there's not much difference between the different types. Now the other thing is when I change the frequency spectrum is updating as well. It's good. It's changing by by the role Yeah, the question is this is now with no errors Now that the immediate issue is resolved, I would recommend periodically reviewing and refactoring the code for performance, maintainability, unreadability. Moreover, always consider adding more comprehensive error handling and logging mechanisms to make it easier to diagnose and fix potential issues in the future. So the thing is so we did that the effect the noising is not very clear. Let's see what it says maybe definitely knows way more than I do. Okay process Okay Okay That looks, sounds, very interesting. Can you give me the details? Certainly, the thresholding method you use in Wavled denoising can significantly affect the results. The method you are currently using is known as the universal threshold. Adjusting it or using other methods can give different results. Here are some ways to play around with the thresholding. You can adjust the multiplier, currently 0.5, in the formula to see if a different value gives better results. Should I have it as an input? The PYWT threshold function has a mode argument that allows you to choose between soft thresholding, default and hard thresholding. Soft thresholding shrinks coefficients that are smaller than the threshold towards 0, while hard thresholding sets coefficients smaller than the threshold to 0. Another commonly used threshold, especially for signals with Gaussian wide noise, is the Minimax threshold. This threshold can be estimated as... Sure, shrink is a method that aims to minimise mean squared error, MSE. It involves a bit more computation, but can sometimes give better results than the universal threshold, especially for signals with non-Gasian noise. Bayes shrink is another adaptive thresholding method that's based on a Bayesian framework. The main idea is to estimate the threshold based on the variances of the signal and noise. It requires additional computation, but can be more robust in certain situations. It's often beneficial to experiment with different thresholds and thresholding methods to find what works best for your specific dataset and application. Visualising and comparing the denoised signals with different thresholds can be very useful. Also, remember that it's not always about achieving the cleanest signal, but rather preserving the important features of the signal while reducing the noise. Oh, you're so smart. Okay, let's add a multiplier. We might continue this next time, but yes, we made quite a bit of progress, but denosing actually works. I assume this number should be between 0 and 1. Yeah, Github compiles as 1 or 2, but yeah, it's definitely the original thing was 0.5.