 Okay, we should be live now. Let's check the sound, the video and everything. Okay, it's working okay. Go over what we are doing. For this, just an intro, these streams are for developing BarneyCals.com. There's a bunch of tools on the main page that you can go check out. Last one is still the EEG2 music conversion development. This is what it sounds like. There is a seizure in it, so you can hear what the epileptic seizures sound like by scrolling through this sample data. This data is from 16 channels. 0 to 15. Yeah, channel 12 was more. The sound it produces is a bit more cheery to the beginning of the file and make the window size 100. This will be the sound of the whole waveform. This is the seizure in it, so this is normal EEG before the seizure, then the seizure, and this quiet, which is normal for epileptic seizures, quiet EEG after the seizure. If you go back to, you can select your window time, whatever you like. You can either select the volume manually or do a auto volume selection. You can also do an auto duration that will play longer tunes. If you select the longer a window size and show the tunes. Yeah, so let me restart this for a sec. So when you load the page, it loads with a window size of 10 seconds by default. And default auto volume, you can actually select it yourself. If you had the auto volume turned on, so before seizure, it will be quiet. Seizure will become louder and louder. The end is much quieter as well, because as I showed you before, it's normally a very quiet EEG at the end of the seizure. In channel 12, you have a much higher amplitude seizure in it, so the sound will be louder as well. Channel 7, the seizure onset is more gradual, and this is the after the seizure, very quiet EEG. We should be right, but so another tool, we have this bunch of tools at the bottom of the page that currently do not work, phasologic and neural networks. And what we decided to do is take this ECG game, sorry for the ads, and adjust it and make essentially an algorithm abort that will play this game. So I explained it before, happy to explain it again, but let's just dive into coding and then we can zoom out if needed. Someone is asking questions that will be very nice. So we have this HTML, it has some explanation at the bottom, currently just explaining how to play the game, it has nothing This HTML code is for a web page titled, Cardio Quest ECG Waveform Scoring Game. It's structured to provide an interactive game experience focused on identifying abnormal ECG patterns. Key elements include, we also have this Python code and two JavaScripts, so we'll get there. Well, how about actually we'll do the waveform first because we not changing it today. So this is waveform JS. The waveforms, JS script is designed for generating electrocardiogram, ECG, waveforms, both normal and abnormal, that are used in your Cardio Quest ECG scoring game. Here's a breakdown of its key functionalities. Initial setup, it creates an array T values representing time points and defines parameters like new, sigma and amplitude for the waveforms. Gaussian function utilizes a Gaussian function to model the R wave, a critical component of the QRS complex in ECGs. Waveform generation, generate full waveform function produces a complete ECG waveform with optional variations for the QRS complex. It includes the P wave, QRS complex, modifiable, S wave and T wave. The waveform is adjusted vertically on the canvas 300. Okay, now we also have this game logic, just three format document, that's okay. The game underscore logic, JS script is the core of your Cardio Quest ECG waveform scoring game. It's well structured and integrates several key functionalities. Canvas setup, initializes the canvas for drawing waveforms and sets colors and fonts. Game variables, manages score, game speed, waveform data, an X coordinate for waveform scrolling. Draw waveform, draws the ECG waveform on the canvas. Draw score, displays the player's score. Clear canvas, clears the canvas for redrawing. Draw counters, displays hits, false alarms, correct rejections, and misses. Click handling, detects clicks on the canvas and updates the score based on the type of waveform clicked. Scoring logic, manages scoring with hits, false alarms, correct rejections, and misses. Scrolls the current waveform across the canvas. Generates a new waveform once the current one scrolls off. Waveform analysis, sends waveform data to a backend server for analysis. Displays analysis results on the web page. Game loop, a continuous loop that updates the game state, including drawing the waveform, updating scores, and managing. Okay, can we focus on the fuzzy logic design? I'll share the last piece of code, which is the Python Flask application. And then we will go from there. A current problem is that the features of the fuzzy algorithm are not displayed on the front page. Let's do this one quickly. Your Python Flask application integrates fuzzy logic for analyzing ECG waveforms. It's a crucial part of your system, processing the waveform data and applying fuzzy logic to classify the waveforms as normal or abnormal. Let's address the issue of fuzzy algorithm features not being displayed on the front page. Dataflow, the application receives waveform data, processes it, applies fuzzy logic, and sends back the analysis results. The front end end should display these results. Fuzzy logic setup. You've defined antecedents, input variables, for frequency, amplitude, and peaks, and a consequent output variable for abnormality. Membership functions and rules are well set up. Data processing, the app calculates dominant frequency, peak count, and amplitude from the waveform data normalizes these values and feeds them into the fuzzy logic system. Result generation, the fuzzy logic system computes the abnormality level and membership values for each input feature. Front end integration, ensure that the front end, your JavaScript code, correctly handles the JSON response from the Flask app. The response includes the fuzzy logic analysis result and the membership values. Troubleshooting display issue. Check if the response from the Flask app is correctly structured and includes all necessary data. In your JavaScript, ensure that the display analysis result function correctly accesses and displays the response data. Verify that the HTML elements, like raw frequency, normalized frequency, etc., are correctly named and can be accessed by the JavaScript. Use browser developer tools to debug any issues with receiving or processing the response on the front end. Debugging, use print statements or logging in both the Flask app and JavaScript to trace the flow of data and identify where the breakdown occurs. Addressing these points should help you resolve the issue of displaying fuzzy logic features on your website. If the problem persists, it might be necessary to delve deeper into the specifics of your front end code and how it interacts with the back end. The error message you're encountering, float, argument must be a string or a real number, not state per simulation, suggests that there is a problem with the way the membership values are being extracted and converted to floats in the fuzzy underscore logic underscore analysis function of your Flask application. This issue likely arises from these lines. The membership underscore value property is not a straightforward numerical value, but a state per simulation object in the context of a control system simulation. To resolve this, you should access the membership values after the compute method is called using the input method of the control system simulation object. Here's how you can fix it. This adjustment should correctly convert the fuzzy set membership values into floats and resolve the error. Ensure to test the updated code to confirm that it functions as expected. The error message 127.0.0.1, the 1st of February, 2024, 14 hours, 15 minutes and 29 seconds post analyze underscore waveform HTTP 1.1 500 indicates that your Flask server is hitting a server side error when handling the post request to analyze. Since you mentioned that there are values in the terminal, but they're not shown on the front page, it seems the server is successfully processing the data or something goes wrong afterward, possibly in the fuzzy underscore logic underscore analysis function or when forming the response. This issue is causing the server to return a 500 internal server. Detailed error logging. Add more detailed logging in your Flask app, especially in the analyze underscore waveform function and the fuzzy underscore logic underscore analysis function to capture the exact point of failure. This could help in identifying a specific line or operation causing the error. Check fuzzy logic analysis. Ensure that all operations within the fuzzy underscore logic underscore analysis function are correctly handling the data. Pay special attention to any operations that could potentially raise exceptions. I'm thinking we changed the fuzzy logic analysis to make the numbers correct, but the analyze waveform haven't changed. Consistency in data types and formats. Check if the data types and formats expected by the new analysis logic match those produced by analyze underscore waveform. Any mismatch can lead to errors. Testing with new logic after updating analyze underscore waveform, test it thoroughly with various inputs to ensure it's working as expected with the new analysis logic. Analyze underscore waveform root in your Flask app looks well structured for handling waveform data analysis using fuzzy logic. Let's address the error that was occurring in your fuzzy underscore logic underscore analysis function. Receive and process data. The function successfully receives waveform data from the post request and processes it. Fuzzy logic analysis. You call fuzzy underscore logic underscore analysis with the process data. Ensure that the function correctly computes the fuzzy logic results and retrieves the membership values as discussed in the previous message. Response creation. The function prepares adjacent response containing the analysis result and the membership values for frequency, amplitude and peaks. It also includes the process data. Error handling. The try except block is a good practice to catch any errors that might occur during processing or analysis. Debugging. The print statement for debugging is useful. Once the error is resolved, consider removing or commenting out these print statements for production deployment. After adjusting the fuzzy underscore logic underscore analysis function to handle the state per simulation object correctly, test this route again. If the error persists or if a new error arises, review the stack trace for more detailed information about where and why the error is occurring. Ensure that all parts of the system, the front end JavaScript, the flask app and the fuzzy logic processing work seamlessly together for a smooth user experience. Guess the message. It's coming from fuzzy logic analysis, normality analysis, input. Didn't know you can actually get all the responses that will go where you needed to go. Trying to access the normality analysis as if it were a dictionary but it's not the input option as an instance of the input acceptor class which does not support item as Simon used the view method instead. Another error is similar. It would be a different problem where the values are just not displayed. That doesn't look correct. Yeah, let's continue with GPT-4 instead. Can you regenerate the whole function? Sure, I can help you regenerate the fuzzy underscore logic underscore analysis function with the necessary adjustments. The revised function will process the data using fuzzy logic and correctly extract the membership values. Here's the updated function. In this revised version, the key changes in how the membership values are extracted after the compute method. Note that compute underscore membership underscore value is a pseudo method. You need to replace it with the actual method used in your fuzzy logic library, like psychic fuzzy, to extract the membership values correctly. The rest of the function remains similar to your original design, the finding fuzzy variables, setting up membership functions, creating rules and computing. Well, let's look to know that they compute, compute membership value. In this revised version, the key changes in how the membership values are extracted after the compute method. Note that compute underscore membership underscore value is a pseudo method. You need to replace it with the actual method used in your fuzzy logic library, like psychic fuzzy, to extract the membership values correctly. The rest of the function remains similar to your original design, the finding fuzzy variables, setting up membership functions, creating rules and computing the fuzzy logic result. Where is this compute a membership function value? It's a pseudo method. What does it actually mean? You're correct that the compute underscore membership underscore value is not an actual method in the psychic fuzzy library. My apologies for the confusion. In psychic fuzzy, you typically compute the fuzzy membership values before running the control system simulation. Here's how you can adjust the function to extract membership values correctly. In this version, the fuzz, in terp underscore membership function is used to calculate the degree of membership of each input value in each fuzzy set. This function is part of the psychic fuzzy library and is the correct method to obtain the membership values. So fuzz and terp is a legit method. It's getting different errors. In this version, the fuzz, in terp underscore membership function is used to calculate the degree of membership of each input value in each fuzzy set. This function is part of the psychic fuzzy library and is the correct method to obtain the membership values. Getting this error, let me explain. The error object of type in 64 is not json serializable suggests that there's an issue with converting numpy data types like in 64 to json format. In python, json serialization works seamlessly with native python data types, but not always with numpy data types. Here's how you can fix this issue. Convert numpy types to native python types. Before returning the data in the json response, convert any numpy data types to native python types. This can be done using python's native type functions like int and float. Apply the conversion in your fuzzy logic analysis function. You'll need to apply this conversion in the fuzzy underscore logic underscore analysis function, particularly where the membership values are calculated. Here is the adjusted part of your fuzzy underscore logic underscore analysis function. In this updated code, I've wrapped the results of fuzz. Interp underscore membership with float, which will convert the numpy float 64 or int 64 types into native python float or int types that are json serializable. This should resolve the serialization error you're encountering. Yeah, how about this one? Are you kidding? The error message you're seeing, crisp output cannot be calculated, likely because the system is too sparse, suggests that the fuzzy logic rules you set up in your system are not being activated by the input values. This typically happens when the input values do not sufficiently overlap with the defined membership functions, meaning no rules are triggered and the system cannot produce a crisp output. Here are some steps to troubleshoot and resolve this issue. Review membership functions, ensure that the membership functions for frequency, amplitude and peaks cover the entire range of possible input values. If there are gaps in the coverage, it's possible for inputs to fall into these gaps, leading to no rules being activated. Adjust membership functions, if your inputs are from zero or one, as in your processed data, make sure your membership functions account for these values. For instance, you might need to adjust the triangles or add new membership functions to cover these extremes. Check rules logic, review the logic of your fuzzy rules. Ensure that they're logically consistent with the membership functions and can be triggered by the range of input values you expect. Input range normalization, verify that the normalization of input values is correctly done. If the normalized values don't align well with your defined membership functions, this could lead to the issue you're facing. Default output handling, implement a mechanism to handle cases where no rules are fired. This could be a default output or a message indicating that the input values did not trigger any rules. Debugging, temporarily add more print statements in your Flask app to display the normalized input values and the degree of membership in each category right before the compute method is called. This can help you understand how the inputs are interacting with the membership functions. Certainly, let's focus on addressing why the raw and normalized values aren't being displayed on the front end. This issue typically involves ensuring the data is being correctly passed from the back end to the front end and then properly rendered in your HTML. Here are steps to troubleshoot and resolve this. Check JSON response from back end. Ensure that your Flask back end is correctly including the raw and normalized values in the JSON response. Use print statements in the Flask app to confirm that the data is being structured correctly before it's sent. Examine front end JavaScript code. In your front end JavaScript, ensure that the function responsible for handling the response, display analysis result or similar, is correctly parsing the JSON and extracting the values. Again, use console logs to debug. HTML element ids, verify that the ids in your HTML match those used in your JavaScript. For instance, if you're trying to display the values in elements with ids like raw frequency, ensure these ids are exactly the same in both your HTML and JavaScript. There is a thank you young slug. My microphone was muted. That helps a lot. That's why you need people in your joining your live. Sorry for that. Hopefully you can hear now. So just do a quick recap. We have this game you can access it on the site on Bionicures.com or free. You can play it. It's teaching you how to process ECG data, how to review ECG data. Still getting errors. Let's close this for a sec. So when you start the game, your score is zero. If you click on normal AG, it gives you a false alarm. And that task is to find abnormal AG, EEG waveforms. This one is normal, gave me a correct rejection. This is normal again. I expect another correct rejection. My score is 10. Normal again, correct rejection, score will be 20. I don't know why, but there is a normal again. So I don't have to do anything. This is abnormal. It's missing a bit in the beginning. So I clicked on it, got one hit. So the idea is you have to keep increasing the greens and reducing or not increasing the reds. So you need more hits, correct rejections. This is obviously abnormal. So I'm clicking on it, getting hit. Every time you get plus 100 points, it becomes faster just to give you more of a challenge. This looks normal. Anyway, what we're doing now is trying to replace me playing the game with a port, with a machine learning algorithm, a simple one at this stage. It's normal. And here at the bottom of the page, we see the features that we're looking at. We're looking at frequency peaks. Let's see if the peaks are even working okay. Don't think they, right. So they are changing. That's good. Yeah, we don't want to normalize the number of peaks. The peaks should be okay. So we're still working on this working progress. I don't know why the frequency is always zero. Should not be zero. We'll check this in a sec. We have, okay, we are live streaming now. So we like you to do it quickly because we don't have much time left. The frequency seem to be always zero. How do we fix that in the code? I need an actual code specific to what to the code I shared with you before. Also the number of peaks, if we're assuming that the number of peaks, peaks, not peaks, if we are assuming that a specific ECG should have a certain number of positive and negative peaks should have three positive peaks and one negative, then we don't have to normalize these numbers as well. So we need to adjust the code accordingly. Understood. Let's address these issues quickly. Review frequency calculation. You're using the FFT fast Fourier transform to determine the dominant frequency. The NP dot abs frequencies are max approach finds the index of the maximum value, which may not be the correct way to find the dominant frequency. You should calculate the frequency from the index Python copycode hash assuming your sampling rate is defined, e.g. samples per second. Sampling underscore rate equals hash to find this based on your data. Frequencies equals NP dot FFT FFT for Q, LEN NP underscore data D equals one sampling underscore rate. Dominant underscore frequency underscore index equals NP dot abs NP dot FFT dot FFT NP underscore data are max. Dominant underscore frequency equals abs frequencies dominant underscore frequency underscore index. Let's say okay, I should check for zero data. Okay. Ensure your input data Wait for underscore data. It's not a flat line. Clear. More clear works. I'm using co-pilot github co-pilot. Yes, it can. So it's better at coding, obviously. Which one is better to use for this project? GitHub co-pilot or chat GPT for coding? Chat, chat, chat, chat GPT. Anyway, the both can do the task but Choosing between github co-pilot and GPT, like chat GPT, for coding in your project depends on your specific needs and the context of your work. GitHub co-pilot, asterisk integration. Okay, just do it quietly. Yeah, obviously integration is better. Context to where? Yeah, so that's not quite clear. Language support, yeah. Both of them support the same language. A GPT can hold more context, that's for sure. So this page will hold the context for the whole conversation. Okay, we might have to continue it next time, maybe later today. So I'll see you in a bit. Bye.