 The project is a web application that demonstrates the use of fuzzy logic for detecting waveform shapes amidst varying noise levels. It's built with a flashback end and a front-end utilizing HTML, CSS, and JavaScript. Main features. Waveform analysis. Users can select different types of waveforms and adjust noise levels. The app analyzes these inputs using fuzzy logic to identify the waveform type. Interactive visualization. It provides real-time graphical representations of input and output waveforms, membership functions, roll activations, and aggregated outputs, enhancing the user's understanding of fuzzy logic operations. Educational tool. With its ability to demonstrate how noise affects waveform detection and the intricacies of fuzzy logic, it serves as a valuable educational tool in signal processing and fuzzy logic principles. Suggestions for improvement. User interface enhancements. Improve the user interface for better accessibility and user experience. This could include responsive design for different devices and more intuitive controls. Advanced analysis features. Introduce more complex waveform types and noise patterns for more comprehensive analysis capability. Performance optimization. Optimize the back-end calculations and data handling to enhance the responsiveness of the application, especially when dealing with complex waveforms and high noise levels. Tutorial or guide. Include an interactive tutorial or guide to help new users understand the principles of fuzzy logic and how to use the application effectively. Feedback mechanism. Implement a user feedback system to gather insights on user experience and areas for improvement. This project effectively combines web development with complex signal processing concepts, making it a unique educational tool. With enhancements, it could become an even more robust and user-friendly application. This project effectively combines web development with complex signal processing concepts, making it a unique educational tool. With enhancements, it could become an even more robust and user-friendly application. The Python Flask application in your project exhibits these three main features. Signal processing and noise addition. The Flask back-end includes functions like generate underscore signal and add underscore noise that create various types of waveforms and add noise to them, respectively. This feature is fundamental for simulating different waveform conditions and analyzing how noise impacts signal detection. Fuzzy logic analysis. The fuzzy underscore logic function in the application applies fuzzy logic principles to analyze the noisy signal. It uses membership functions and rules to classify the waveform type. This showcases the application's core functionality of demonstrating fuzzy logic in waveform detection. Data visualization and API integration. The application provides endpoints like, analyze, which process the user's input, waveform type and noise level, perform the analysis, and return the results in a format that can be visualized on the front end. This integration allows for dynamic, real-time updates of the waveform graphs and fuzzy logic analysis results on the web interface. These features made the Flask application a critical component of the overall project, enabling the interactive analysis and visualization of waveforms using fuzzy logic. Adjusting the fuzzy logic parameters and enhancing visualization in your Flask application involves both back-end modifications and front-end updates. Here are some steps to consider. Back-end, Flask application, adjustments. Customizable fuzzy logic parameters. Expose more parameters in your fuzzy underscore logic function, allowing users to modify membership function shapes, rule definitions, and defuzzification methods. Implement API endpoints to receive these parameters from the front end. Dynamic membership functions. Allow users to define their custom membership functions, e.g., triangular, trapezoidal, Gaussian. Provide an interface for users to input parameters for these functions, like start, peak, and endpoints. Extend rule base. Add functionality to create and modify fuzzy rules dynamically. Users could define new rules or adjust existing ones based on their analysis needs. Front-end, HTML, CSS, JavaScript, enhancements. Interactive control panel. Implement sliders, drop-downs, or input fields to adjust fuzzy logic parameters like membership function thresholds or rule weights. Use Ajax to send these parameters to the Flask back-end without needing to reload the page. Advanced visualization tools. Integrate more sophisticated charting libraries, e.g., D3, JS, Chart, JS, for detailed and interactive data visualizations. Provide graphs for individual membership functions, rule activations, and defuzzified results. Real-time data updates. Utilize web socket or long-pulling for real-time data updates in your graphs as users adjust the parameters. This will make the application more dynamic and engaging. Interactive membership function editor. Create a tool where users can visually adjust the shape of membership functions and see the immediate effect on the output. This can be a directable interface where users can move points to reshape the membership functions. Parameter presets and saving. Allow users to save and load presets of their fuzzy logic parameters. This feature would enable users to experiment with different settings without losing their configurations. Tutorial or guided walkthrough. Include an interactive tutorial or guided walkthrough in the UI to help new users understand how to adjust and interpret the fuzzy logic parameters. By implementing these back-end and front-end enhancements, you can make your fuzzy logic waveform detector more flexible, powerful, and user-friendly. These improvements will enable users to explore a broader range of scenarios and gain deeper insights into the behavior of fuzzy logic systems. Data limit. You may want to limit the amount of historical data you keep in the browser to avoid performance issues. Initial load. Decide how much historical data to load initially, e.g., last 10 changes. Styling and layout. Adjust the graph styling and layout to accommodate the dynamic nature of the data. By implementing these changes, your output graph will dynamically display the percent detection error and percent unknown values, updating with each change in the input parameters. To implement the functionality of tracing percent detection error and percent unknown over time in output graph, with updates occurring every time any of the inputs change, you need to make specific changes to your existing code. Below are the key modifications to add or ev- Additions, back-end, flask-app, changes. Add a di- Additions, back-end, flask-app, changes. Add a data structure, e.g., a list, store historical data of percent detection error and percent unknown. Modify the analyze endpoint to return this historical data along with the current analysis results. Front-end, JavaScript, changes. Implement a function to update the output graph with new data points. This function should use Plotly's methods to append new data to the existing graph. Modify the eventlessness for input changes to call the analysis function, which should fetch updated data and refresh the graph. Old graph initialization or update code. If you have existing code that re-initializes the output graph entirely on each input change, you may need to remove or modify this to instead update the existing graph. Sample code snippets, back-end, Python flask, add a global list to store the history. We still don't know which one is a better GitHub co-pilot or using GPT on the OpenAI.com. The jury is still out. Assume this is a new JavaScript function. We do have an update graph, don't we? Should be update, output, graph. Do we have an analyze waveform? Yes. I think we call that the actual data. So we have different types of data to name them properly. This is not actually happening. Let's see. I don't understand if that's a bit taking stuff from the JSON file. Would it write some code or still thinking about it? It's not quite clear what's being sent there to the chatbot, considering we have the context in two files. Make sure output graph is visible. Yeah, it's just not the GitHub co-part is not doing anything. Okay, that's obviously not it. Also, we're not doing anything with the signal. We're taking data from JSON. Okay, that's obviously won't do it. That's clearing the graph. Okay, signal map. No, it's not signal map. Yeah, so the output graph that is missing at the moment should be plotting detection error and present unknowns. Over time. Modify, analyze, okay. Detection error history. Yeah, we should have it already. Detection error history already. Detection error history. Yes. Need to update the output graph. Okay, can you actually do it? So we should be passing this as a JSON file. You might not need it over here. Yeah, I would like to clear it. It should be like a default option. Yeah, so this is writing a wrong code. It's not actually taking the correct data. How to update based on the previous response. This is update to the JavaScript. Mainly after this update output graph function. But this is more like it. Yeah, so we have two trends. Present detection error and present unknown. Yeah, data looks legit. And we're using plot lead plot. Actually make it to make sure we're actually using this function properly as well. Analyze, waveforms, the next function. Update output, yes. Data noise signal, yes. Graph. We're passing the entire data to this object. Don't think this is quite right. Why are we sending the whole data to the update output graph function? Shouldn't it be just the necessary components from the JSON file? Well, it's not a file, but you know what I mean. From the API. So in the DSNR we're only passing the necessary data to the update output graph file. In this case, the function needs both detection error history and unknown percentage history. Okay, but they're not in data. They make more sense. Yeah, update data, graph should not accept data. Yeah, this one looks more like it. So we're only passing the necessary data to the update output graph. Sounds legit. Does it actually work? Is it being kidding? Yeah, that error should have been an easy fix. There's some sort of naming differences. I should be able to handle this one quickly. Input wave declared twice. So how do we fix this input? All right, I have different names. I have different names. JSON, first error should go always. Second one first. What is the signals undefined? Why? We actually don't have a noisy signal. We should have only one signal. I mean, we're happy to check that. How was it? Do we work with this core pilot? Can you just ask over? So we're passing on the whole button code back end. Just ask if the server returns the expected noisy signal property in the response data question mark. Yes, the server does return the expected noisy signal property in the response data. The analyzed root result. I have this noisy signal somewhere. Noise to least. Noisy signal is undefined. The client said it could be due to the error in the generate signal. Some sort of weird problem. Let me know if there's any questions. I'm more than happy to go over the whole project thing. Students, software developers, anyone is more than welcome to make comments to recommend going over. Eventually everything ends up on bionicgales.com. Sorry for the ads. It does have ads. Close it for now. This will be one of the earlier tools that actually were working already. It kind of went down the priority list. But now we're bringing it back. This tool doesn't currently work. Only have the general structure for the application. And we do have some older videos of how it used to work. So essentially turn all this into GPT prompts. And turning it into a Python application. That video should work pretty well. It's a good explanation of what we are trying to do. To know why it's not quality as well. Definitely don't need the 4K. I used to do videos in 4K not realizing that they're actually not 4K. So that's what that tool meant to be doing. It meant to be able to select your input waveform, which we already have. Add noise on top of the signal. And there is a fuzzy logic algorithm behind this that will do detection based on a couple of points in the signal. Now we're actually changing console considering we're rebuilding the whole thing. We're also making a few changes. Where the fuzzy logic algorithm actually looking at two features. We might add more features later. One is the symmetry of the signal. And the other one is the sharpness. So how spiky or not spiky it is. So we're looking at those two. This is what this prototype does. If we manage to get it working again. Which we should be able to... It's complaining about the noisy signal not being passed to the front end from the server. A copart says that the data is being returned from the server. The noisy signal is undefined in the client's side. JavaScript could be due to an error in the generate signal or add noise functions. I don't think there is an error in the add noise functions. Why is this the only one that has a two-list conversion? Is it possible that the error is due to the fact that the noisy signal is the only one that is converted to list before transferred as JSON? Well, everything else works except noisy signal. So if anything, that one should be working correctly. It should be an issue. It's a good practice to ensure all non-pairs are converted to list over another JSON serial ismo. So now I'm confused at suggesting we should be turning all of them to list. We show the JSON file. Let's check the noisy data. Success data. Okay, whatever. Do that quickly. See the whole data in the console. JavaScript is being loaded. Loading forever. Hopefully it's just because I'm streaming at the moment. I'm having a bunch of errors. Input graphs stopped working as well. Get suggesting troubleshooting. That's fine. We can do that. Okay, but that's not enough. Should have that error message appearing now. I need like 20 seconds to load the JavaScript. Okay, I'm getting like different errors now. 32 whopping seconds to load a plotly JavaScript. That's crazy. Unbelievable. I don't know if it's related or not, but I'm also dropping frames in the live stream. So that make me think it's to do with my Internet collection. Yeah, and I got to save this JavaScript as well. So the changes are made. One note executing. Right, noisy signal data is missing. That noisy signal. And we have it in the Python code. Something with noisy signal. Converting it to least. Noisy signal converting to least. I'm getting stuck going in circles. It's a major try. Remove that to least conversion. I need that JavaScript locally somewhere. So it doesn't take 20 seconds to load. Up to a troubleshoot. So if we have the back end as this. Up to 8. This is taking the JavaScript as reference. Okay, I don't understand. Why don't you just take the code? Wait, can I try this again with doing workspace.js based on app.py. Selecting workspace structure and deciding which workspace information to collect. And this thing was always giving me an error. So I don't know if it just doesn't work on Linux. It's the problem of me using Microsoft product on Ubuntu Linux machine. It doesn't work, I would assume. So take a quick break while it's thinking. Your HTML code for the fuzzy logic waveform detector. Website looks well structured and includes various key elements such as meta tags for SEO. Links to star sheets, script tags for JavaScript, and structured content within the body. Here are a few observations and suggestions. Meta tags, good use of meta tags for SEO and sharing purposes. You might want to consider adding a meta tag for keywords related to your site. Linking star sheets, you have linked to star sheets both locally and remotely. Ensure that the paths are correct, especially for the local star sheet, style.css. Responsive design, the meta tag for viewport is correctly set, which is good for responsive design. Navigation, the navigation structure is clear. Make sure the paths, like. About, are correctly pointing to the respective pages. Interactive elements, you have interactive elements like dropdown menus and sliders, which are great for user engagement. Graphs, including graphs with plotly is a great choice for interactive data visualization. Content section, the content is informative and well structured with headings and paragraphs. This is good for user readability and SEO. Scripts, you're loading external scripts and have a script for delayed loading of additional scripts. Ensure that this delayed loading does not affect user experience or essential functionality. Cross origin resource sharing, cores, be cautious with the cross origin equals, anonymous attribute and script tags. It's used correctly, but make sure that the external resources support cores. Accessibility, consider adding all text for images and aria labels where necessary to improve accessibility. Footer, there doesn't appear to be a footer section. You might want to add one for completeness, including copyright information, links to privacy policy. Your JavaScript, main, JS file for the fuzzy logic application is well organized and follows good coding practices. Here are some key observations and potential areas for enhancement. Of endlessness, you've properly set up of endlessness for the DOM content loaded event and input changes. This is good for responsive UI updates. Fetching data, the use of fetch to communicate with the server for analyzing the waveform and getting fuzzy logic parameters is a modern approach. Make sure error handling is robust for network issues or server errors. Dynamic graph updates, the integration with plot leaf updating graphs dynamically based on user input is a great feature. This makes the UI interactive and user friendly. Function modularization, your functions are well modularized, update output, update input graph, update output graph, plot fuzzy logic parameters. This improves readability and maintainability of the code. Error logging, you're logging errors to the console which is good for debugging. Consider adding user friendly error messages in the UI for a better user experience. Code comments, you have some comments, but adding more descriptive comments, especially for complex logic, would be beneficial for future maintenance or for other developers who might work on this code. Data validation, ensure that the data received from the server is validated before using it in your functions to avoid runtime errors. Diffusification visualization, the visualization of the defusification process with a marker on the centroid is a nice touch for educational purposes. Performance optimization, depending on the size of the data and the frequency of updates, your flask application code for the fuzzy logic application is well organized and follows good Python coding practices. Here are some observations and potential areas for improvement. Rooting and rendering, your roots and rendering of the index, HTML page are correctly set up. Data handling in, analyze root. You are correctly receiving JSON data, pausing it and using it to generate and analyze signals. Ensure the keys in data.get, match the JSON body sent from the client. Signal generation and analysis. The functions generate underscore signal, add underscore noise, allies underscore signal and fuzzy underscore logic are well structured and seem to cover the necessary logic for signal processing and fuzzy analysis. It's good to see motorized functions for different aspects of signal processing. Fuzzy logic implementation. The fuzzy logic implementation appears comprehensive, utilizing membership functions, rule definitions and defusification. Ensure that the parameters for the membership functions and rules align with your application's requirements. Error handling. Good use of try except blocks in your, analyze root to handle exceptions. Returning a 500 status code with the error message is appropriate for unexpected server errors. History tracking. Tracking detection error and unknown percentage history is a good feature. However, keep in mind that these lists will grown definitely as the server runs, which might consume more memory over time. Consider implementing a mechanism to limit their size. Front end back end integration. Your back end is set up to provide the necessary data for the front end. Ensure that the data structure sent from the back end matches what the front end expects. Code comments and documentation. Consider adding more comments, especially for complex logic in the fuzzy underscore logic function, to improve code readability and maintainability. Performance considerations. For the calculate underscore symmetry underscore array and calculate underscore peak underscore sharpness underscore array functions, instead of optimizing. In your flask application, when you retrieve data from the JSON body of a post request using data.get, it's crucial to ensure that the keys you're using in the get, method match the keys in the JSON object sent from the client side. In your, analyze root, you have. Here are the key points to check. Consistent key names, ensure that the keys in good way value and noise amplitude value are exactly the same in the JSON object that your client side JavaScript sends in a request body. Any mismatch in key names will result in none being assigned to your Python variables. JavaScript client side, on the JavaScript side, when you make the post request with the JSON body, the keys in the JSON object must exactly match these. For example, handle missing data. If there's a possibility that the keys might not be present in every request, it's a good idea to handle these cases. For instance, you can provide default values when using get. Testing, test your application thoroughly to ensure that the data sent from the client is correctly received and processed by the server. This can be done through unit tests or by manually testing the application with various inputs. By ensuring consistency in key names between your client side JavaScript and flask server, you can reliably transfer data within your application. The functions generate underscore signal, add underscore noise, allies underscore signal, and fuzzy underscore logic are well structured and seem to cover the necessary logic for signal processing and fuzzy analysis. It's good to see motorized functions for different... The fuzzy logic implementation appears comprehensive, utilizing membership functions, rule definitions, and defuzzification. Tracking detection error and unknown percentage history is a good feature. However, keep in mind that these lists will grow indefinitely as the server runs, which might consume more memory over time. Consider implementing a mechanism to limit their size. The errors you're encountering in your main. JS file for the fuzzy logic application suggests a few specific issues. Error fetching fuzzy logic parameters, HTTP error 404. This error occurs because the browser is attempting to fetch data from a root, get fuzzy logic parameters that doesn't exist in your flask app. You need to define this route in your flask application and ensure it returns the expected fuzzy logic parameters. This error suggests that the response from your analyze route does not contain the noisy signal data. Ensure that the flask backend is correctly calculating and including noisy signal in the response JSON. You need to verify a few key points in your flask application. Check the add underscore noise function. Make sure the add underscore noise function is correctly implemented to add noise to the signal. It should take the original signal and noise amplitude as inputs and return a noisy signal. Verify data flow in analyze route. In your analyze route, ensure that add underscore noise is being called with the correct parameters and its output is assigned to noisy underscore signal. Confirm that noisy underscore signal is being included in the result dictionary that is sent back as JSON. Enable noisy signal in JSON response. In the result dictionary inside the analyze route, uncomment or ensure this line is active, noisy signal, noisy underscore signal, tallest. Since noisy underscore signal is a numpy array, convert it to list before sending it in JSON as JSON doesn't support numpy array format directly. Here's an example snippet for your flask code with the relevant parts for clarity. Python, copy-coded add dot route, analyze methods equals, post, def analyze, try, data equals request dot get underscore JSON, Input underscore wave underscore value equals data dot get, Input wave, noise underscore amplitude underscore value equals data dot get, noise amplitude. Signal equals generate underscore signal, Input underscore wave underscore value, noisy underscore signal equals add underscore null. Debugging, after making these changes, test your analyze route using a tool like postman or through your front end. You can also add print statements or use a debugger to check the values inside the flask app before they're sent in the response. By following these steps, you should be able to ensure that noisy signal is correctly calculated and included in your flask backend's JSON response. Error, tag error, cannot read properties of undefined, reading, map. This error is likely due to attempting to access a property of an undefined object in the update-output graph function. It seems like either detection error history or unknown percentage history, or both, are not properly passed to this function, or they're not in the format expected, an array that can be mapped over. You need to ensure that these arrays are being sent correctly from the server and received properly in the front end. Define the missing route, create a Get fuzzy logic parameters route in your flask application that returns the necessary data. To define the missing, Get fuzzy logic parameters route in your flask application, you need to create a new route that returns the necessary data for fuzzy logic parameters. This data should match what your front end JavaScript expects to receive for rendering the graphs and other elements related to fuzzy logic. You're seeing the differences between co-pilot and GPT-4. What happens if I use a GPT-4 suggestion and try to complete it? Doesn't quite work. To complete the Get underscore fuzzy, underscore logic, underscore parameters function in your flask application, you need to define the data structure that represents the fuzzy logic parameters required by your front end. This data typically includes membership functions, rule activations, and other parameters relevant to your fuzzy logic system. Here's an example of how you might implement this function. This one, how's it different from co-pilot problem is that we're really defining those values and why do we have to do it again? I always had better luck with GPT-4, not this time. It's only trouble to having trouble integrating, having trouble integrating. Get fuzzy logic parameters root in your flask application is needed if your front end JavaScript code requires certain data related to fuzzy logic parameters to function correctly. The necessity and purpose of this root depend on the specific requirements of your application. Here are a few reasons why such a root might be necessary. Initialization of front end visualization if your application's front end includes visualizations or interactive elements that require initial fuzzy logic parameters like membership functions, rule sets, etc. This root can provide the necessary data to initialize those components. Separation of concerns keeping the logic for calculating fuzzy logic parameters on the server, flask app, separates the concerns between back end and front end. The back end handles data processing and logic while the front end focuses on presentation and user interaction. Dynamic parameter adjustment if your application allows users to view or interact with different fuzzy logic setups for example, adjusting membership functions or rules. Yet fuzzy logic parameters root can provide a way to fetch and update these parameters dynamically. Consistency and centralization having a dedicated root for fetching fuzzy logic parameters ensures that there is a single source of truth for this data. It helps maintain consistency, especially if the same set of parameters is used in your application. Efficiency in data handling by fetching fuzzy logic parameters only when needed you can reduce the initial load time of your application and make the front end more efficient especially if the parameters are complex or large in size. Debugging and maintenance, a separate root for fuzzy logic parameters can simplify debugging and maintenance. You can modify or troubleshoot the fuzzy logic aspects of your application without impacting other functionalities. In conclusion, the yet fuzzy logic parameters may not be necessary data regarding the fuzzy logic component of your application. This setup is useful for applications where fuzzy logic plays a crucial role in the user interface or data processing. However, if your front end does not require preloaded fuzzy logic parameters or if these parameters are static and can be included directly in the front end code, then this root might not be necessary. If you determine that the yet fuzzy logic parameters root is not necessary for your application, it's perfectly fine to omit it. This decision should be based on the specific application. Here are a few considerations to confirm your decision. Static parameters. If the fuzzy logic parameters are static and do not change, or if they are already embedded within your front end code, then a separate root to fetch these parameters might be redundant. All in one request. If all required data, including fuzzy logic parameters, is being sent in a single request, like in your Analyze root, and there's no need for additional, separate calls to fetch parameters, then the separate root is not necessary. Simplicity and efficiency, eliminating unnecessary roots can simplify your application's architecture and improve efficiency, as it reduces the number of HTTP requests and potential points of failure. Front end independence. If your front end is designed to operate independently without the need for fetching additional data from the back end, this aligns well with a streamlined approach where a root like, yet fuzzy logic parameters is not needed. Less server load. Few roots mean less load on the server, especially for roots that might not be essential for the core functionality of your application. Maintaining an application becomes easier when there are fewer components and roots, especially when they're not crucial for the application's functionality. In summary, if it's obviously obvious that we don't need an extra a function there. If we get there, we should not get that error. You need to solve the fact that noise signal data is missing. Try corepile. Back to corepile. Keep jumping back and forth between corepile and github and github4. Server is not returning noise as signal might be returning it, but it's not defined. Check any can we do this the server side don't expect exceptions can try commenting out the to list method. Yeah, try that already and signal re-noise is signal loading forever, that's another problem. Okay, we're getting a 504 post analyze. I'm from the back end didn't notice this one before there's a try and accept block that catches all exceptions the exact issue is the end accept to actually print the error good idea print the error the flask application is not actually running have to restart it control f5 okay I think now we're getting closer I don't remember this whatsoever a non-type some non-type there is some probably error message that you're trying to call a lower method non-object that's the line in question it's a good suggestion because yes we need more travel shooting, it's possible that the input wave is none I think it was possible all this that is what's happening I need to keep travel shooting so the input wave is empty why? generate signal function ensure it's always present try quest get chaser and input wave value it's the same for example should value keep doing the same should be getting them twice now okay so I do keep this one down it was working before that's why just need to sort it out input wave value should always be present that's right I didn't think there isn't even an option for it not to be present okay we already have that we're going in circles now you are going in circles you already gave me that response can you try something else yes the input wave value is none but why does it happen to begin with decline site might not be sending input wave to the request lost one we could use more deeper again printing the whole so we have input wave square noise amplitude 0.1 okay it's something with the name it's because we changed I think an hour ago almost two hours ago understand the context yeah the input wave value okay we have the signal the noisy signal the output clear console noise signal is definitely there we are getting closer that's working now noise signal same okay get noisy signal data is missing that the probe so now we are labeling json javascript or python code let's try javascript okay what did it say the issue might be that the noisy signal data is not in the expected format the update input graph function expects noisy signal to be an array that's not an array is it not an array looks like an array but it could be a list I don't even know the difference really it does contain numeric values and and that white console that won't work it's giving an error a break I don't know if there's any questions no actually you might finish up because we're going in circles see you next time bye