 live so late today, let's go, let's just talk into myself, hopefully someone true and seen, let's start in a sec, just doing a sound check and the likes, yeah there's a bit in the beginning that just went amazing, that's okay, correct next, just using a JGPS spellchecker and the likes, making suggestions for a text improvement, just write up the topics for today as well, let's start with just a few household, okay first of all, let me fix that video footage, the webcam webcam media part, now we have another one called something, clone digital twin just open it in the planning folder, just need to see which one was the last modified avatar this one here, from the terminal, it's this one here, hello, hello, hello, how are you doing some thumbnails, what are you do stuff, do one of those, the mouth, yeah it's dark in the room, so it's not sure if I turn on the lights, yeah it seems to be working better when the light is on, again it's the code for it, I wish we had the text to speech, that wasn't working very well, after that I have managed extension and the JGPG, yeah the voice control thing, try to turn it on, I have to restart there, yeah now it has that voice, it's working 1, 2, 1, 2, okay it is working, delete, clear, it's actually clearing, okay, so that's pretty nice, pretty neat, clear, clear, clear, clear, okay, clear, 1, 2, 1, 2, clear, you need to say it in more authoritative version, is it? I can understand my English, I have to learn English so that the bot understands me better, clear, right it's actually working fine, clear, I'll be saying clear a lot, clear, correct text, the live streams will be turned private or unlisted after the stream ends, so don't forget to subscribe to the YouTube channel, send, send, okay, what's the command to send those things, a cross extension version description, slide setting though, okay that's just weird, details, explanations, side settings, right, no wait, there was a command for sending the prompt for essentially hitting enter, developer mode, no, manage extension, and this is it, pretty sure there was some commands for it, yeah, sorry it's on the page itself, yep that's actually sending the thing, I should have a voice message but when I say clear, yes, you're allowed, okay, how about stop listening, correct this text, live streams, correct this text, last room return private, what list unlisted after this stream, so don't forget to subscribe to YouTube channel, can't it make sense, does it? Okay, don't forget to subscribe to YouTube, remove the stream, don't forget to subscribe, okay this one, I subscribe a little more after the stream ends, so don't forget to subscribe on YouTube, well that's the same thing as an improved text, improve text, send, send, send, forget the command, it's not send, it's yep, run to spend YouTube, last room will be taken down, once this stream concludes, add the following text, don't worry, the streams will be edited and uploaded again as videos, as standalone videos, don't worry though, the streams will be edited and uploaded again, as standalone videos, yeah, remember to subscribe on YouTube, as last streams will be taken down, once the stream concludes, don't worry though, the streams will be edited and uploaded again, as standalone videos, very deep, a image, I don't forgot about this guy, the link of the current stream, copy, yeah remember to subscribe on YouTube, as last streams will be taken down, once the stream concludes, don't worry though, the streams will be edited and uploaded again as standalone videos, it's suggesting me to post in the music industry format, no, totally LinkedIn suggestions, the automated AI stuff doesn't really work, can I say that, no, okay let's try, start a new chat, okay let's add this guy to the stream, before I forget, select window, yes, okay now we have them both, okay now I can really hear the CPU going, an extra mile, Python doing 4.3% CPU balance, if I move from performance to balance, it doesn't clear, clear, it's only if I really, the other thing is using this post inspector and it tells me there is no image, it gives me this message, but I do have an image, so why is it not showing, yep, said not showing, close this avatar, it's working okay, try refreshing the page, clearing your browser cache, or restarting the app, network issues, a slow or unstable internet connection can interrupt the image upload process, LinkedIn server issues, occasionally the problem might be on LinkedIn's end, especially if their servers are experiencing high traffic or technical difficulties, LinkedIn inspector tool error, the inspector tool itself might have a bug or issue causing it to incorrectly report that there's no image, to resolve this issue you can try the following steps, check LinkedIn's image requirements, make sure your image adheres to LinkedIn's specifications for size and format, reupload the image, attempt to reupload the image to see if it was a one-time uploading issue, try a different browser or device, if you're using a web browser, try a different one, or use the LinkedIn mobile app instead, check your internet connection, ensure you have a stable internet connection, wait and retry, if LinkedIn is experiencing technical issues, wait for some time and then try again, contact LinkedIn support, if none of these steps work, consider reaching out to LinkedIn's support team for assistance, all right those seem just generic advice, clear, clear, this seem to be fail to start speech recognition, it's because I'm using that, and using that, okay let's jump into development, okay so back on barnicales.com, we are working on this HEG Spectrogram, it's not in production yet, that old LabVIEW, a php page sitting there, that is not working, now we do have a new one, we close all this for stack, we open index HTML app style, we delete this style, copy, and we have the JSON script, JavaScript should be running, we are not doing virtual environment, that's what the page currently looks like, yeah that's the spectrum working, this is the whole seizure, different channels, refresh, it's default showing you 10 seconds, this can be corrected there, the time that should be 10 seconds as well, this is actually the time in the file, it's a very long file, changing logarithmic to linear scale, okay okay obviously log scale is bad because you can see more details in the lower frequency component, now we still have the wavelet noise, okay now when you switch this button, the original becomes the dominant thing, but it should also change the HEG Spectrogram, I'm pretty sure we did this last time, but we haven't finished, question with GPT is should we find that shed and continue from where we left off or should we start a new one, probably starting a new one, yes better, yeah let's do that, what woulda, at the bottom of each script we have this to not just read the code, don't generate anything, well that's because nothing kind of fits, the whole thing doesn't fit into, probably in the beginning, sorry copy, in the beginning as well, no actually, let's leave it, just clean it up quickly, yeah to leave that frequency scale is not being used, not being used, yeah we know what's the 400 request with that, okay so how chagivity can help, let's see how we just put them in the tabs in the same order as we, it's a flask application designed for analyzing and visualizing EEG, electroencephalogram data, the key features of this application include data loading and pre-processing, it loads EEG data from a text file and pre-processes it for analysis, this includes options for detrending the data and handling, okay okay, butter with bandpass filter, okay, prevents a butter with bandpass filter for filtering EEG data within specified frequency bands, delta, theta, alpha, theta, this doesn't work this, waver denoising and transformation, applies waver denoising and transformation to the EEG data with options for choosing the waver type and the level of composition, signal analysis and visualization, it's a bit annoying, I haven't figured out how to use this properly yet, but didn't I just say confirm reading, sorry I'm giving it a bit of an attitude, sorry apologies, I'm apologizing to you, not to the board, let's see the last line, just curious how on Earth, yes I saw the last line, it's gonna read it, yes? I saw the last line of your message, confirm reading the code and do not generate anything else yet, I confirm that I've read and understood the code you provided, if you have any specific questions about it or need further assistance, please let me know, okay why were we generating stuff though and let's actually delete that, so it doesn't get confused later, the HTML, so remove the spectrum, what, right, I can delete that, this would have to be rewritten, right, so let's pop that one in as well, it keeps explaining stuff, HTML code you provided, it appears to be a web page for an EEG, electroencephalogram, data visualization application, the page includes various elements such as a header with navigation, a main content area with controls for EEG, okay maybe, plot containers, dedicated, okay maybe we need it as a separate prompt, I have read through the HTML code you provided, if you have any specific questions or need further assistance related to this code, please let me know, okay that's more like it, I will leave that prompt there just so it doesn't do it again, clean it up a bit, so we removed those bits, yeah I didn't realize it had a placeholder and a sneaky little thing, yeah I don't have a play button, should be good enough, that's a good response, no. I have read through the JavaScript code you provided, this script is designed for an EEG data visualization web application, it handles user interactions with various controls on the web page such as sliders and toggles to update EEG signal visualizations in real time, the script makes use of the plotly library for rendering graphs and communicates with a flashback end to fetch EEG data based on user selected parameters, key aspects of the script include initialization, sets up variables and DOM element references for various controls like the file scroller, channel selector, window size input, etc. API interaction constructs API requests of fetch data based on user inputs using parameters such as channel number, window size, the noising options and others, data visualization utilizes plotly to plot EEG data, it handles both raw signal visualization and the generation of spectrograms, event listeners, sets up event listeners for different user interface elements to trigger data fetching and visualization updates based on user input. Adaptive endpoint selection determines the API endpoint based on whether the application is running in a production environment or locally. User interface updates dynamically updates the interface elements like the window size label and fetches new data when user interaction occurs. If you have any specific questions about this code or need further assistance, feel free to ask. Yes, what the problem was in one of the things as far as I'm concerned, it's ready for production, but let's hold our forces for a bit and we'll get a couple of things. By the way, we also should have an info all fixed. It should have all a prompts. So we should actually be describing them here instead of straight into GPT. I mean, GPT can help us write the prompts. Okay, so there's a couple of issues with this code. First of all, when changing wait, let the noise input raw EEG signal chart changes, but the EEG spectrum does not. We would like to fix that thing about it. The issue you're encountering where changing the wavelet denoise input updates the raw EEG signal chart, but not the EEG spectrogram can be resolved by ensuring that the spectrogram is also updated when the wavelet denoise setting is changed. Here's a modification to your existing code to address this issue. Update the fetch and display spectrogram function ensure that this function takes into account the current state of the wavelet denoise setting and any other relevant settings that impact the spectrogram when it fetches and displays the spectrogram. This can be achieved by either passing these settings as parameters to the function or by having the function access them directly. Call fetch and display spectrogram on wavelet denoise change. Currently, it seems that only the raw signal chart fetch data and render is updated when the wavelet denoise setting is changed. You need also call fetch and display spectrogram to update the spectrogram accordingly. Here is an updated snippet of your code to illustrate this. In this updated snippet, when the wavelet denoise setting is changed, it not only calls fetch data and render to update the raw signal chart, but also calls fetch and display spectrogram with the current channel, start second, and window size to update the spectrogram. Make sure that your back-end flask API correctly handles the wavelet denoise setting for both the raw signal and the spectrogram data. This way, when the front-end requests new data with the updated settings, the back-end will provide the appropriate process data for both visualizations. Right, so let's check what we currently have. So we only have fetch and render. Yeah, that's what's changing the gchar. So replacing all data, we need to be intended. We can leave those there for a while, so we know what was changed. Check it now. Refresh the page. Spectrogram still doesn't seem to change the display spectrogram as well. So wavelet denoise, would you expect the wavelet denoise update the EEG spectrogram to show the noise signal? If the wavelet denoise setting is correctly implemented and integrated into both the raw EEG signal processing and the spectrogram generation in your back-end, flask API, then updating the wavelet denoise setting should indeed affect the EEG spectrogram. Here's what you can expect. Noise reduction in spectrogram, wavelet denoise is a technique used to reduce noise in signals. When applied to EEG data, it should ideally remove or reduce artifacts and noise, leading to a cleaner spectrogram. This means that after applying wavelet... David, giving me some generic responses. I apologize if my previous responses seem too generic. Let's address your specific issue regarding the wavelet denoise function and its effect on the EEG spectrogram in a more targeted way. Understanding wavelet denoise and spectrogram, asterisk. Wavelet denoise, this process aims to reduce noise in the EEG signal. When you apply wavelet denoising, it should ideally clean up the signal by removing irregularities and noise components. EEG spectrogram, the spectrogram visualizes the frequency content of the signal over time. When you apply denoising, it should reflect in the spectrogram by showing a clearer signal with reduced noise artifacts, especially in frequency bands where noise is more prevalent. When you enable the wavelet denoise function, it should not only update the raw EEG signal but also the EEG spectrogram. The spectrogram should show a clearer distinction in frequency bands with less noise or random fluctuations. Check the back end processing, ensure that the flask API is configured to apply the wavelet denoise setting to the data used for generating the spectrogram. The same denoise logic used for the raw signal should be applied to the data before generating the spectrogram. Front end API interaction, confirm that when the denoise setting is changed, the front end makes the appropriate API calls with the updated settings for both the raw signal and the spectrogram. In your JavaScript event listener for wavelet denoise, ensure that after calling fetch data and render, you also call a function like fetch and display spectrogram, or similar, that makes an API call to get the updated spectrogram data. Example snippet, JavaScript copycoded denoise, add event listener, change, function. Fetch data and render, updates the raw signal chart. Let channel equals channel selector, value. Let start second equals pass int, file scroller, value, 10. Let window size equals pass int, window size input, value, 10. Fetch and display spectrogram, channel, start second, window size, updates the spectrogram. Closing curly bracket closing parenthesis semicolon. Debugging. If the spectrogram doesn't update as expected, you may need to debug both the front end JavaScript and the flask API to ensure the denoise setting is correctly handled and applied. I hope this response is more specific to your needs. If there's anything more particular you'd like to address, please let me know. Okay, let's break it down. When wavelet denoise input turned on, the spectrogram should show the denoist signal. Spectrogram. When wavelet denoise input is off, the spectrogram should show the original spectrum of the signal. Thank you for clarifying. Based on your description, it seems you want the spectrogram to behave in the following manner with respect to the wavelet denoise input. 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. wavelet denoise off. When the wavelet denoise is turned off, the spectrogram should display the original, unaltered spectrum of the EEG signal. This will include all the frequencies present in the signal, including the noise. To achieve this in your application, you need to ensure the following. Backend processing. Your your flask API should be capable of processing the EEG data both with and without the wavelet denoising applied. When the wavelet denoise setting is enabled, the API should apply the denoising process to the EEG data before calculating the spectrogram. The front end should correctly send the state of the wavelet denoise toggle on, off, to the back end when requesting data for the spectrogram. The fetch and display spectrogram function should be modified to request and display the correct version of the spectrogram based on the denoise setting. Here is a suggestion for modifying the event listener for the wavelet denoise toggle in your JavaScript code. In this setup, when the state of the wavelet denoise toggle changes, it triggers both fetch data and render and fetch and display spectrogram, ensuring that both the raw EEG signal and the spectrogram reflect the current denoise setting. Ensure that both your back end and front end are properly coordinated in handling this setting. This setup will allow users to visualize the differences in the EEG signal's frequency content with and without noise reduction. Yes, changes to the back end may be necessary to ensure it correctly processes and responds to the wavelet denoise setting. Here's what you need to consider for the back end, typically a Flask API in this case. Handling wavelet denoise parameter, your Flask API should be able to receive and interpret the wavelet denoise setting from the front end requests. This involves, REG. Receiving the wavelet denoise toggle state, on, off, as part of the request. Adjusting the data processing pipeline based on this setting. Performing wavelet transformation. Thresholding or modifying wavelet coefficients to reduce noise. Reconstructing the signal from the modified coefficients. Generating the spectrogram. If wavelet denoise is on, generate the spectrogram from the denoise data. If wavelet denoise is off, generate the spectrogram from the original EEG data. Returning the correct data, ensure that the API sends back the appropriate data, either the original or denoised EEG signal, and the corresponding spectrogram, based on the wavelet denoise setting. Testing and validation, after making these changes, it's crucial to thoroughly test the API to ensure that it behaves as expected for different states of the wavelet denoise toggle. Documentation, comments, update any documentation or comments in your code to reflect these changes and to provide clarity on how the wavelet denoise feature is implemented and affects the data processing. Here's a simplified example of how you might handle this in your flask route. In this example, apply underscore wavelet underscore denoise and generate underscore spectrogram are hypothetical functions you'd implement to perform the respective tasks. The key is to conditionally apply the denoising based on the input from the front end and ensure that both the raw EEG data and its spectrogram are correctly processed. You're correct, and I apologize for the confusion. The hypothetical functions apply underscore wavelet underscore denoise and generate underscore spectrogram were mentioned as generic placeholders to illustrate the concept. In the context of your existing flask application, the actual implementation would depend on the details of your current setup, which you shared earlier. Given the details from your earlier code snippet, here's how you could modify your flask back end to handle the wavelet denoise setting for the spectrogram. Incorporate wavelet denoise into spectrogram generation, within Y. Within your spectrogram underscore data route, you should add logic to process the EEG data with wavelet denoising if the wavelet denoise parameter is set to true. Extract the wavelet denoise parameter from the request. Apply wavelet denoising to the EEG data segment if this parameter is true, before proceeding with spectrogram generation. Here is an updated version of your spectrogram underscore data route with these considerations. In this code, apply underscore wavelet underscore denoising is a function you would need to define based on your denoising logic. The key part here is that the denoising is applied conditionally based on the wavelet denoise parameter from the request. Please adjust this example to fit the specifics of your application and the actual implementation of your wavelet denoising process. Do I actually have applied wavelet denoising? I don't think so. Do I actually have applied wavelet denoising function? Do I actually need it as a new function or can I use existing code that already generates the denoise signal? If you already have existing code in your flask application that generates the denoised EEG signal, you can certainly reuse that code instead of creating a new function. The key is to integrate this existing denoising logic into the route that generates the spectrogram. Based on the code you've shared earlier, you're already processing the EEG data for denoising in the get underscore data route. You can apply a similar approach in your spectrogram underscore data route. Here's how you can modify the spectrogram underscore data route to use the existing denoising logic. Extract the relevant code, identify the part of your existing code in the get underscore data route that applies wavelet denoising. Reuse the logic in spectrogram underscore data, apply the same logic in the spectrogram underscore data route to denoise the data before generating the spectrogram. For example, if you have a segment in your get underscore data route like this, you can apply a similar logic in your spectrogram underscore data route. In this updated route, you apply the same denoising logic to the EEG data before generating the spectrogram. The function denoise underscore using underscore wavelet is an example. Replace it with the actual functional code snippet you use in the get underscore data route for denoising. This approach ensures consistency in how denoising is applied across different parts of your application. Remember to test the updated endpoint to ensure it behaves as expected with the wavelet denoise setting. All right, so this actually makes a bit of sense. So when we generate the spectrum data, are we already doing this? EEG data denoise wavelet denoise, okay, you need to, can you regenerate the whole spectrogram data function? Sorry, I won't be able to integrate that. I don't know, quite well enough. Absolutely. Let's recreate the spectrogram underscore data function in your Flask app, integrating the wavelet denoising logic that you already have in your existing get underscore data endpoint. I'll use the structure and variable names from your previous snippets as much as possible. Here's a restructured version of the spectrogram underscore data route. In this version of the spectrogram underscore data function, wavelet denoise parameter, the function checks if wavelet denoising is enabled using the wavelet denoise parameter from the request. Denoising logic, if denoising is enabled, it calls apply underscore wavelet underscore denoising, which should contain the logic you already use in your get underscore data route. Make sure to implement this function based on your existing denoising code. Spectrogram generation, it then generates the spectrogram using the potentially denoised EEG data. Response preparation, finally, it prepares and returns the spectrogram data in a JSON format. Replace apply underscore wavelet underscore denoising with your actual denoising function. If your denoising logic is complex or involves multiple steps, it's a good idea to encapsulate it in a function like this for better readability and reuse across your application. The pass statement in the apply underscore wavelet underscore denoising function is used as a placeholder to indicate where you should implement the actual wavelet denoising logic. In Python, pass is a null operation, nothing happens when it executes. It's often used in places where your code will eventually go, but you haven't written it yet. It's a way to write a function structure without filling in the details immediately, allowing the rest of your code to run without errors in the meantime. Since I don't have the specifics of your wavelet denoising implementation, I included pass to suggest that this is where you should add your existing denoising code. Here's how you might modify it with actual logic. In your final implementation, replace pass with the code that performs the actual wavelet denoising process. This implementation should align with how you've set up wavelet denoising in other parts of your application, ensuring consistency in data processing. Thank you for providing the specific wavelet denoising code you have. Since you already have this implementation, you can directly integrate it into the spectrogram underscore data function in your Flask application. You don't need a separate apply underscore wavelet underscore denoising function as I previously suggested. Instead, you can use your existing wavelet denoising logic directly within the spectrogram underscore data function. Here's how you can modify the spectrogram underscore data route with your existing wavelet denoising code. In this version of the spectrogram underscore data route, the existing wavelet denoising logic is directly used. When the wavelet denoise parameter is true, this logic is applied to the EEG data before generating the spectrogram. This should ensure that the generated spectrogram reflects the denoised EEG signal when wavelet denoising is enabled and the original signal when it is not. Be sure to test this implementation to confirm that it behaves as expected with your front end. Okay, this will be really odd because I'll have this code twice. You're right. Duplicating the same wavelet denoising code in multiple places isn't ideal due to reasons like maintainability and potential fairers. A better practice is to encapsulate this logic into a reusable function. You can then call this function wherever needed, reducing code duplication. Let's refactor your wavelet denoising logic into a separate function and use this function in both the get underscore data and spectrogram underscore data routes. Here's how you can define a reusable function for wavelet denoising. Now, in both your get underscore data and spectrogram underscore data routes, you can use this function. And similarly for get underscore data, this approach makes your code cleaner, easier to maintain and more consistent. If you need to update your wavelet denoising process in the future, you'll only have to make changes in one place. Apply underscore wavelet underscore denoising function and it will be reflected wherever the function is used. Okay, so assume functions can go in the beginning of the code. And I'm pretty sure I don't need this one. Just call it. So now we have this supply wavelet denoising now in spectrogram data. Hey, where is it? No, wait, let's do this one first and get data. Yeah, over here.