 Right, so this tool is just called Spectrum, and we were ready to remember how many sessions worked on it. Spectrum, yeah, over here, we have this info file, which is a description and some prompts from maybe it was working kind of okay already, let's run it. So this is a Flask application is indicated by this line here. It's running locally, it's not in production yet, and that's what it looks like. It still has the, well not still, I mean I might leave it there, the feature for wavelet noise removal, this chart actually shows both. Right, and that's what we were trying to do last time without much success is also changing what the EEG Spectrogram shows when you toggle this switch. We have a prompt for it here at the end. We can't program, I can't program. If anyone can, please do let me know. Happy to collaborate. They won't need to program anything. I need to either use a GitHub core pilot, which I had a mixed success with, and I had a better success with GPT4. Let's start on our chat, start popping stuff in. So obviously, we have the Python code, we have also this JavaScript CSS and index HTML. So let's start from index HTML and it has at the bottom confirm ring, the code to generate anything else. Sometimes this gets ignored. Let's put some exclamation marks if that prompt, let's see what it says. Right, so it's read through the text, then that's great. Maybe exclamation marks did help. Some of the times it just ignored me. Why this has a template? This has a template. Don't like it. It's kind of obvious that there's previous code there. Yeah, don't like this one. Otherwise it looks okay. So this one actually never being used can remove it. Right, let's pop it in. As again, confirm reading the code and do not generate anything else. Exclamation mark read the code didn't generate much else. That's great. We also have this Python code. We definitely don't need that. It's just for testing field field spectrogram are not being used might use them later. Right, we have these two lines I've read through the flask Python code you provided. It's designed for a web application that visualizes EEG electroencephalography data. The code includes routes for loading and processing EEG data applying wavelet denoising, generating spectrograms and calculating metrics like signal to noise ratio SNR and mean squared error MSE. Yeah, that's great. So yeah, we need assistance with this wavelet denoise on when the wavelet denoise is turned on the spectrogram should display the spectrum of the EEG signal after noise reduction. This means the spectrogram will show the frequencies present in the signal after the noise components have been minimized or removed by the wavelet denoising process. Actually didn't listen should listen to what the pot says. Let's listen to it again spectrogram underscore data route to account for wavelet denoise when wavelet denoising is enabled apply the apply underscore wavelet underscore denoising function to the EEG data before generating the spectrogram when wavelet denoising is disabled use the original EEG data for the spectrogram. Right, so spectrogram that is in a Python script of a scope Python spectrogram right, we already have wavelet denoise there. Yeah, we also want to take levels. Do we have levels? Yes, we did have levels. That's for the levels up there. Yeah, the changing you can tell the blue on the denoise thing essentially how detailed the denoising function is the wavelet transform is that's fine. Yeah, we want to take those as well. Right, they're not yet being used. That's okay. Start sample on window size. Okay, EEG data and start sample and sample channel if wavelet denoise with changing EEG data, changing EEG data, generate a spectrogram this line expected to be the same. Yeah, we have some parameters that we later can make inputs as well. So we can play a round wave. Right, and this one remains the same. That's preparing response and then turning it into a chase and and instead of reading that, let's best way to just test it because it's not actually changing. We save, it's safe. Yeah, because it's in debugging mode. So any changes or make should take effect immediately. There's just not much change between noise denoise. And to expect this to change just slightly, but it doesn't. Yeah, to expect changes there, it's read there. Ensure that the front end sends the wavelet denoise, wavelet transform and levels parameters in a request to spectrogram underscore data update the spectrogram visualization based on the response. Okay, so we need to adjust the front end as well. Can you just do that? For the front end adjustments to work with the wavelet denoising feature in your EEG data visualization application, you need to modify the JavaScript that interacts with the flash back end. Here are the key steps to ensure the front end correctly sends requests and updates the spectrogram based on the wavelet denoise setting. Modify the JavaScript functions that send requests the spectrogram underscore data endpoint to include the wavelet denoise, wavelet transform and levels parameters. This is crucial for the back end to know whether to apply wavelet denoising and with which parameters. Okay. In the function that fetches and displays the spectrogram include the logic to send the state of the wavelet denoising toggle and the selected wavelet transform and levels. Right, so we're really doing that. Do we need those constants? Yes, it has a placeholder in there, so it's not the full function. Have to be careful with those things. This line. Right, so we're taking wavelet denoise transform and levels into account as well, so we can comment that one out. The more changes out of endlessness to the wavelet denoise toggle and other related controls like wavelet type and level selectors to update the spectrogram whenever these settings are changed. We already have this line. Yes, do I still need to do that? Okay, why do I need another function? Why do I need another function? Do I have to add another function? If you're asking whether you need to add another function in your JavaScript code for handling the wavelet denoise feature in the EEG data visualization application, it depends on how your current code is structured and what functionality you want to achieve. Based on the previous discussion, here's a quick assessment. It's not that we're sick to edit this. Do I must have this function? Yeah, because we're really doing this kind of thing. Does it? Yeah, it consists of easy debugging. That's fine. How many times do we do that? I'm just trying to understand the structure of the code. Your application is relatively simple and you don't anticipate the need for more complex update logic in the future directly calling fetch and display spectrogram in each event listener is a viable option. Where is the fetch and display, fetch and display spectrogram, false caller, yes. So that will be replaced, wouldn't if I keep this one, do I need to replace the new function then I would assume so. No, what? No, you don't need to replace all instances of fetch and display spectrogram channel start second window size with update spectrogram. The function fetch and display spectrogram is specifically designed to fetch and display the spectrogram based on the provided parameters channel start second window size. It's used in various parts of your script where these specific parameters are known and need to be passed directly. The update spectrogram function, on the other hand, is more of a convenience function for event listeners. It's designed to be called when there's a change in any of the controls that affect the spectrogram, like the wavelet denoise toggle, wavelet transform selector or level selector. This function retrieves the current values of these controls along with the channel start second and window size and then calls fetch and display spectrogram with these values. Well, right, because also it be updated here, but then I'll just take why not just take the original or denoise signal in raw EEG and turn it into spectrogram. Yeah, there's like a weird logic to the whole, to the whole thing. My original, it's because my original prompt is not great. Using the original or denoise signal from the raw EEG signal chart to generate the spectrogram is indeed a viable and logical approach. This method would ensure consistency between the displayed raw signal and its corresponding spectrogram. Here's why this approach can be beneficial and how it can be implemented. Consistency, the spectrogram directly corresponds to the raw signal being displayed, ensuring that any changes or noise reduction applied to the raw signal are accurately reflected in the spectrogram. Efficiency, reduces the need to process the data twice for the raw signal and the spectrogram separately. This can be more efficient, especially for large datasets or complex wavelet transformations. Simplicity simplifies the data flow in your application. You process the data once and use it for both the raw signal plot and the spectrogram, making the system easier to maintain and debug. Okay, we'll have to make quite a few changes. Just save what we got so far. Old style, old school version control. Yeah, I wouldn't be able to use a GitHub co-pilot for this. That's too complicated. So we need to do something in the backend. So this is real EEG. If you were wondering, this is recorded from the surface of the brain recorded from an implant can fetch the data from EEG.org. If you like, think pretty sure anyone can get the data just requires a simple login. And yes, they will have a viewer on the website as well. But that's nothing as good as this one. Obviously, I'm biased. So don't listen to that. Right. So actually, it's working now. Yeah, there's just not much difference going on. You can see it's slightly changing. You can only see it with very small window size. I have to check. That's actually working. Okay. And yes, some, well, it's definitely changing. It just, it just has my new difference there. There's some other channels. Increasing the levels should, well, anyway, the interface is working. So that's a good sign. This working in like a page, page refresh. Right. So that's the EEG beats this 100 seconds of EEG has a seizure in it. So if you look at say 18 seconds or so, you can scroll through it and it shows you the spectrum. I wish there was like, yeah, in previously, I had like a play button so it could just keep scrolling through the file while I'm taking a break. You have to be careful with auto scaling here as well. Yeah, it's still a bit buggy to be careful with numbers auto scaling like here, because this file has a seizure in it, EEG with a seizure 16 channel EEG can select your channel. I don't know what's happening with this one from memory channel 10 had a nice seizure in it. And just saying those spectrogram plots have to be careful with the colors, the numbers, the scale auto scaling, because during seizure, just refresh the page will go to default 10 second default. So you have those 1500. That's what's going on during baseline. And then when you have the seizure, so this is the epicenter of the seizure. See this number going up to a 4000 5000. So yeah, just have to be careful with this kind of auto scaling stuff. So you can differentiate between baseline EEG and something else like seizure in this case. Yes, this up and running. You can check it out yourself. Let me know what you think just happened there like a jumpy business. Yeah, I have to keep testing. But yeah, this file has a seizure in it. Yeah, there's some sort of weird delays that don't not get on this on when running locally as much as smooth scroll and everything. But yeah, the server might be overloaded with me in live streaming. So I might finish, finish that, finish the live stream. Hopefully, that tool will be more responsive for you. And we will go from there. Don't forget to subscribe, like, comment, requests, complaints. Check the sites, provide your feedback. And I'll see you next time. Bye.