 have a mute tab option on in the browser. Very handy. My live can't hear it. Sorry about this. So many so much noise. Just check I'm using the right. I'm using the right microphone. I think so. Yeah, becoming louder when I move it towards me. Yeah, must be there for a microphone. Okay, that's annoying. So that's the music stuff. If you wanted to do more of it, might mute it for now, just using it for a mute side. Yes, I can minimize it because then the video stops working. Keep it. So now there's no music. That's better. Clear. Okay, so we already started sharing the code. Just want to ask control I copilot to remove any comments. It's doing it just make sure it's not removing anything else. We might need, right, that looks like she's removed the comments. So this is what the current application looks like. We're moving through this synthetic EEG. We will improve on it later, but it has this EMG components in it. The higher frequency, they just come through the signal as you scroll. I don't know why this, it's using a lot of my CPU as well. Not dropping any frames. That's good. Just trying to figure out just give this a sec. This silly window works. Yeah, that shit. Right, so I don't know why he does this heavy, heavy loading, but anyway, the idea is that we have this artifact to risk. Clear manually sometimes. It doesn't work at all. A Python code is coming next. So don't have to generate anything. Just wait for further code, but to make sure that you read this code. Yep, yep. Your JavaScript code for fetching, displaying, and analyzing EEG data is well structured and covers essential functionalities for an EEG data visualization and analysis tool. Here are a few key points. Fetch EEG data function. This function correctly handles fetching data from the server, error handling, and checks for correct data format. Good use of async await for asynchronous calls. Display EEG data function sets up the chart, JS Canvas and configures it for displaying EEG data. The dynamic scaling of the y-axis based on the maximum data point is a thoughtful touch. Display artifact risk function, a simple, effective way to display the calculated risk. Using to fixed to ensures the displayed value is easy to read. Analyze and display EEG function. This function sends EEG data to the server for analysis and then displays the results. The use of post request with JSON content is appropriate here. Scroll event listener, the implementation of a scroll event to load and display different segments of EEG data is a great feature. It helps in handling large data sets efficiently. Error handling and logging. Your code includes error handling and logging, which is crucial for debugging and ensuring a smooth user experience. A few suggestions for improvement. Caching responses. If the EEG data doesn't change frequently, consider caching the responses to reduce server load. Optimization for loud data sets. If you're handling very large EEG data sets, you might need additional optimizations to ensure performance. User feedback. When there's an error or data loading, consider providing visual feedback to the user. Overall, your JavaScript code is well written and seems to fit well into the functionality of your project. Looking forward to the Python code. With caching responses, how does that work and how do I change the code? Can we come back to this data? I would like to make other changes first, but first I'll share the Python code, the actual flask application. Caching responses in a web application like yours can significantly improve performance, especially when dealing with large data sets or frequent requests for the same data. Here's a basic overview of how it works and what changes you can make. What is caching? Caching temporarily stores copies of data to serve future requests quickly. In your case, it could store EEG data so the repeated requests don't have to refetch or reprocess the same data. In memory caching, store data in server memory. It's fast but limited by the server's memory capacity. Database caching, store data in a database, useful for larger data sets. Client-side caching, store data in the user's browser. This is useful for data that doesn't change often and doesn't need to be reloaded with each page visit. Decide where to cache, server, client, or database. Server-side, use a Python library like Flask caching. Implement caching logic, server-side, use a Python library like Flask caching. Client-side, modify your JavaScript to store and retrieve data from the browser's local storage. Client-side, modify your JavaScript to store and retrieve data from the browser's local storage. Example for server-side caching with Flask caching. Install Flask caching, pip install Flask caching. Configure caching in your Flask app. Decorate your root functions with a cached.cached, timeout equal, fuzzy logic setup. You've correctly set up fuzzy variables, membership functions, and rules. This setup will allow your application to evaluate EEG data based on amplitude and frequency to determine the artifact risk. EEG data generation and processing. Generate underscore synthetic underscore EEG. Generate synthetic EEG data with controlled EMG artifacts. This is great for testing your application. Pre-process underscore EEG underscore data normalizes the EEG data and extracts amplitude and frequency. Okay, for instance, let's say there's two... So we have the EEG data. Let me make sure the EEG data variable is only 10 seconds of data that is currently visible, that is loaded based on the scroller position. Also, this variable has too many data points after the decimal point. Can we reduce that? Yep. To ensure that the EEG underscore data variable represents only 10 seconds of data based on the scroll position and to reduce the number of decimal points in the data points, you can make the following adjustments to your Python and JavaScript code. Limiting data points. Modify. Modify the generate underscore synthetic underscore EEG function to generate data corresponding to 10 seconds. If your sampling rate, FS, is 250 Hertz, 10 seconds of data would mean 2,500 data points, 250 samples per second asterisk 10 seconds. To reduce the decimal points, you can round the data points. Use NP, round EEG underscore signal, decimals equals N, where N is the number of decimal points you want. Handling scroll position. In. In your JavaScript code, modify the scroll event listener to calculate the correct segment of the EEG data based on the scroll position. Since each data point represents one FS seconds for a 10 second window at 250 Hertz, you would need 2,500 data points. Your Python code will generate and send EEG data limited to 10 seconds and with fewer decimal points. Your JavaScript code will correctly load and display the data segment based on the scroll position, ensuring it always represents a 10 second window. Rounding to two decimal points. In your JavaScript code, modify the scroll event listener to calculate the correct segment of the EEG data based on the scroll position. Since each data point represents one FS seconds for a 10 second window at 250 Hertz, you would need 2,500 data points. Yeah, let's double check that in JavaScript. We already have that. It's one of the changes. We had a constant being outside the if loop makes sense. Still dead. Right, the numbers are changing. CPU is doing extra, extra hours. Checking the microphone quickly. Yeah, sometimes you have to default some settings for it to work properly. Clear, clear. Okay, this variable is always the same value. It gives the same value. Why? Yep. If your fuzzy logic system in the Flask application is always returning the same artifact underscore risk value regardless of the input, there are a few potential causes to investigate. Static input values ensure that the input values for amplitude and frequency are actually varying and are correctly passed to the fuzzy logic system. If these inputs are not changing or are not correctly fed into the system, the output will remain constant. Fuzzy logic rules and membership functions review your fuzzy logic rules and membership functions. Sometimes if the rules are too rigid or the membership functions are not appropriately defined, the system might not be sensitive enough to changes in input. Data preprocessing, the way you preprocess the EEG data can significantly impact the input values for the fuzzy system. Verify that the preprocess underscore E underscore data function calculates the amplitude and average underscore frequency correctly and that these values are representative of the input EEG data. Rounding or data resolution issues, if the data is being rounded or truncated too aggressively, this might lead to loss of variability in the input values, causing the fuzzy system to receive similar inputs for different datasets. To debug this, you can add logging or print statements in your Flask application to check the values of amplitude underscore val and frequency underscore val just before they're input into the fuzzy logic system. This will help you verify if the inputs are varying as expected. Additionally, examine the outputs of the preprocess underscore EEG underscore data function to ensure they're accurate and reflect the characteristics of the EEG data. If these aspects are all working correctly and you're still facing issues, you may need to revisit the design of your fuzzy logic system, perhaps adjusting the membership functions or the rules to better accommodate the range and nature of your input data. I need to print the chute, printing both of them. That wasn't just working before. Four prints, no, three prints. Comment this now, actually showing the data. Why? It's weird. This line, the print out there, but causing trouble. It's causing trouble. Okay, whether we have an EEG print, I'm sure. Reneverage frequency. Why is it working now? Straight away. Frequency is changing. Amplitude is changing. Amplitude is an array. Is it meant to be this way? And frequency is one number. It should meant to be. I'm wondering if it will pick up on any problems. Given the output you've provided, it seems like your fuzzy logic system is not responding as expected to the variations in the amplitude and average frequency values. Here are some potential reasons and solutions to consider. Range and scaling of inputs ensure that the range of inputs, amplitude and average underscore frequency matches the universe of discourse defined in your fuzzy variables. If the input values fall outside the defined range, the fuzzy logic system may not respond correctly. Also, check if the inputs need to be scaled or normalized to match the expected range. Membership function overlap. Inspect your membership functions. If the overlap between the membership functions, like low, medium, high, is not sufficient or not appropriately defined, it could lead to a situation where most inputs are classified similarly, leading to similar outputs. Tweaking these functions to ensure proper coverage and overlap might help. Okay. Can we display everything on the front panel? Want to be able to see exactly how the input variables are changing and how the defuzzification and fuzzification method are working? Defuzzification and fuzzy rules are changing with the scrolling of the synthetic EEG. To display how the input variables are changing and provide insights into the defuzzification and fuzzy rules as you scroll through the synthetic EEG data, you need to modify both your front-end, JavaScript and back-end Python Flask code. Here's a general approach. Displaying input variables. Modify your... You need to modify the back-end to return additional information about the fuzzy logic process, like activated rules, membership values, etc. Once you have this data, create functions in JavaScript to dynamically update this information on the web page as you scroll through the EEG data. Returning additional fuzzy logic information. Modify. Adjust your generate underscore EEG or analyze underscore EEG endpoint to include this additional information in the response. With this setup, as you scroll through the EEG data, your web page will display how the input variables change and provide insights into the fuzzy logic process. This will give you a clearer understanding of how the inputs are influencing the fuzzy logic systems output. Yeah, we need more modifications. We would like the whole membership function to be displayed. And ideally, the input values, the activation to be displayed on top of these charts. Displaying the entire membership function along with the activation levels based on input values in real time is a sophisticated feature, requiring a combination of front-end and back-end modifications. To achieve this, you'll need to send the membership function data and activation levels from your flask back-end to your JavaScript front-end and then visualize them using a charting library. Here's a broad approach. Modify the analysis function. Adjust your analyze underscore EEG function to return details of the membership functions and their activation levels. This will involve extracting the membership function shapes and the degree to which each part is activated based on the input values. Serialize membership function data, membership functions and their activation levels are inherently graphical. You'll need to convert them into a format that can be easily sent over HTTP and understood by your front-end, like JSON. Choose a charting library. Use a JavaScript charting library capable of handling complex visualizations, like chart, JS or D3, JS. Visualize membership functions and activations. Once you receive the membership function data and activations from your back-end, use your chosen library to plot these on your web page. You'll plot the membership functions as they are and overlay the activation levels on top. This approach requires careful coordination between your back-end and front-end, especially in terms of data formatting and visualization. It's a complex task, but will provide a highly interactive and informative interface for your EEG analysis application. Can you write the prompts or GitHub co-pilot? Certainly. To create prompts for GitHub co-pilot, you want to clearly describe the programming task you need assistance with. GitHub co-pilot is designed to understand and generate code based on the context provided in the prompt. Here are some example prompts that could be adapted for your EEG data analysis and visualization project. Prompts for extracting membership function data bash copy code hash GitHub co-pilot. Hash Python, function to extract and serialize fuzzy variable membership functions. Hash I need a function in Python using the scikit-fuzzy library that extracts the membership function data. Hash from a fuzzy variable and returns it in a serializable format, like a list of dictionaries. Hash each dictionary should contain points defining the shape of the membership function. Prompts for back-end endpoint to send membership function data. Prompts for front-end JavaScript code to visualize membership functions. Prompts for interactive EEG data scrolling and analysis. Each of these prompts is tailored to specific parts of your project, clearly outlining the task in the context. You can adjust these prompts based on the specific requirements and libraries you are using. Here what it does just pop this serialize membership function at least membership decrease least fuzzy variable. Where am I returning chase? This is not it. I have to do the whole clear there. So in Python select the whole code some large levels to make sure you have to add the membership functions. It's actually generated. Then the second romp this send the membership function data you have to select the whole Python code I don't know. Right. So we have another route. So we have to check generate EEG analyze EEG and we have this one as well. Should be working. Okay. Let's just check that it's still running quickly. Yep. Still running. That's okay. And JavaScript. Now in JavaScript select the whole code to visualize the membership functions functions fuzzy variable. Yeah. We need to visualize all those. Yeah. Every time you ask it's something it will generate a function. That's okay. It's just using yeah. Using charge. Yes. We currently have we're using plot data. No plot data is using that. Yeah. Pretty sure we're using this already. Async function is constant. Can stay together. Stay event listener. This membership membership function can go here. We need to prompt for interactivity data scrolling. This show will have to change the HTML is currently this one is not being used. Right. That's why we need this code. We're modifying an existing function Tringify async Analyze in display a plot that is not working. Analyze in display. Right. We need to also how about the prompt for changing the HTML code Yep. Creating a prompt for GitHub Copilot to assist with modifying HTML code involves clearly specifying the changes you want to make to your existing HTML structure. Here's an example prompt that you can adapt for updating your EEG data analysis and visualization web page. This prompt specifies the task of adding new elements to an existing HTML page for visualizing additional data. It's essential to be clear about the elements you want to add and their intended functionality. Adjust the prompt based on your specific requirements so the exact nature of the changes you wish to implement. Right, I'm first on the section G-Fuzzy logic analysis we have the EEG chart new section so we can replace this section. Okay, we've got a lot of stuff. Yeah, I like to combine alpha trace frequency amplitude getting in here It's a JavaScript issue, isn't it? It's the const a plot data evasion level membership function meant to be multiple membership functions. Yeah, that plot is not correct. The plots are not displaying anything. Is there anything wrong with the code? Even when I move the scroller am I finishing soon? Making much progress. Based on the JavaScript code you've provided the implementation seems mostly correct but there are a few areas where issues might arise. Fetching membership function data the function analyze and display EEG fetches membership function data via get underscore membership underscore functions. Ensure this endpoint is correctly implemented in your flash back end and is returning the expected format of data. If there's an issue with the back end response the charts might not display correctly. Data format and parsing the plot membership functions function expects data in a specific format an array of x y pairs for each membership function. Make sure the data received from the back end matches this expectation. The canvas elements amplitude chart frequency chart artifact risk chart exist in your html with the correct tids. Okay, can you generate prompts for github co-pilot? Certainly. To address the issue of the charts not displaying when scrolling through the EEG data here are some example prompts you can use with github co-pilot for debugging and enhancing your code prompt for debugging chart display issues graphql copycode hash github co-pilot hash javascript debugging chart js display issues in a web application hash i have a web application using chart js to display EEG data and membership functions hash the charts are supposed to update as I scroll through the EEG data but they're not displaying anything hash I need help with debugging this issue the data is fetched asynchronously from a flask back end hash could you provide a checklist or code snippets to diagnose common issues with chart js in this scenario prompt for back end data validation prompt for front end data parsing and validation prompt for enhancing scroll event handling using these prompts github co-pilot can assist in generating code snippets debugging steps or general advice on how to tackle the specific issues you're facing with your EEG data visualization application remember to adapt the prompts to fit the exact technologies and libraries you're using in your project we're also using control control i for the whole thing what that was a two-generic prompt but at the low and low EEG data and membership functions okay this function is not being called is it okay might just continue next time