 be alive maybe maybe not this way here let's share this one make them quickly just in case someone wants to join in this one should be okay just read this while we post the detecting envelope shapes like squares trapezoids and other geometrical figures in time series data can be approached using both fuzzy logic and neural networks here are examples of how each method might be applied fuzzy logic approach define fuzzy rules that describe the characteristics of each shape for instance a square wave might be defined by its period amplitude and sharp transitions a trapezoid might be characterized by its asymmetrical rise and full times implementation create a set of fuzzy rules to capture these characteristics for instance a rule might state that if the rise time of a signal is short and the amplitude is within a certain range then the signal might be a square wave advantages easier to understand than interpret good for situations where precise definitions of shapes are known and can be described in rule-based logic neural networks approach use a neural network such as a convolutional neural network CNN to learn the characteristics of different shapes from a labeled data set the data set should include various examples of each shape under different conditions and noise levels implementation train the neural network on this data set allowing it to learn the features that distinguish each shape once trained the network can classify new unseen data into one of the known shapes advantages highly effective in complex noisy environments and learn subtle distinctions between shapes that might be difficult to capture with rule-based logic for both methods it's crucial to have a good representation of the time series data this might involve pre-processing steps like normalization noise reduction or feature extraction to make the characteristics of each shape more distinguishable so we be doing neural nets and fuzzy logic today some basic examples we had them running on a website quite a while back have to bring them bring them back at this all the descriptions for them these are the only two tools that are not actually working on the website by those those PHP files loading forever right so they have some description in them which we will use there's some old all the videos and describing how they used to work so we'll be bringing them back they used to be lab view and now we doing it all in Python there's this two tools to just a reminder what they used to be so this one just having this noisy side of so it and it's doing prediction of what the signal will look like essentially future future prediction and this one does detection so it has this various shapes of essentially what could be time series envelopes of a single and that's detecting the shape and yes this can add a noise onto it so it shows you how worse it gets with increased amount of noise so it's a pretty the detection is obviously easier than prediction generally just all of this should we get up copato check it in the open AI I mean get up compartment we're using a chip anyway okay let's start with something so we use that the the last tool that we have inside this the EG into music conversion and just use that there's a template sketch the names news to be called rename this in to fuzzy logic we already have that the HTML we just need to extract the text out of it that was this much nothing much interesting in it logic to the info yeah we use the same structure for the file we don't we currently you won't use any real dad of this one yeah we'll keep it as a flask application we deploy it hopefully when it does something useful mainly yes we have those shapes those shapes square left trapezoid and we'll try to do detection of those essentially have the input and deploy on a purchase server that quickly so there's some organizational stuff let one take two can delete that rename this to fuzzy logic so the page will remain the same yes this is without the this is with noise reduced quickly pop this into a GPT or decide updating stop that remove those so it's clear that it's a it's a flask application waveform shapes the FLS is tasked with identifying various waveforms like square left right trapezoids left right triangles and hexagons noise impact as the noise amplitude varies the detection error of the FLS also changes interestingly medium noise levels result in more misidentifications than higher noise levels this could be due to the way noise interacts with the specific features the FLS uses to identify shapes data points usage the FLS utilizes only six data points from top and bottom scanners to identified waveforms this limited data input might be a factor in the performance variability with noise right when we don't mind you improving it the origin it was lab you example and if you does all this industrial engineering manufacturing stuff so it was an example of a different boxes different shapes on a conveyor belt and a bunch of limited number of sensors detecting the shape of the box don't have to do it that way we can improve on it let's try the image thing so if we had all it is reduced go up to 4k or we don't need 4k 1080p should be more than enough give us an image pop this image into GPT-4 this is what the application front panel should look like based on the screenshot you've provided it appears to be the front panel of a software application designed to analyze waveforms using fuzzy logic the interface has a section for selecting the input wave shape and adjusting the noise amplitude which suggests the application is set up to test the robustness of waveform identification under different noise conditions on the right side it displays the output wave which is the detected shape along with statistical data like percent detection error and percent unknown indicating the performance of the fuzzy logic system in real time the graph on the left seems to show the selected waveform in this case a left triangle with a shaded area representing the amplitude over time the graph on the right shows the performance metrics where the red line likely represents the percentage of detection error and the blue line represents the percentage of times the waveform was not identified at all unknown this type of application could be useful for demonstrating the principles of fuzzy logic in signal processing and for educational purposes such as in A also save this image and drop downs as expected now we have the actual HTML in the template folder start using a GitHub code in a sec just need the template leave all the header stuff we need that as well from container it's not really a waveform analysis but something we'll get into the fuzzy logic in a sec have a CSS for it and let's do this document this quickly the drop-down menus less than select greater than are used to select the input and output waveform types the range slider less than input type equals range allows for the adjustment of the noise amplitude placeholder divs with the class graph away you would integrate the plotting library like potley the displayed waveform and the detection error graphs the CSS and JavaScript files are referenced but not included here you would need to create in the handle the styling and functionality such as responding to user input and updating the graphs this HTML template is a starting point and with backend logic to process the data generate the graphs and update the statistics in real time write the flask location template is the home analysis goes into this simple flask creation we don't need that let's run this quickly only one run this running surprisingly okay it's a good start but the JavaScript over name it doesn't mean HS is suggesting to have in it creating a main JS file for your flask app involves writing JavaScript code to handle user interactions send Ajax requests the flask backend and update the web page dynamically with results below is an example of what main JS file might look like missing missing quite a bit some papers that the chiptip plug-in this is playing cold scholar AI bound time series prediction of driving motion scenarios using yeah I don't know about that interval type 2 fuzzy aggregation approach combining multiple neural networks clustering prediction time series it's a lot of robotics time series prediction and anomaly detection the reference spiking it works check this links legit at all a cheat or torque it's a legit website seem to load something it's a legit it's conference conference right this what the Google scholar gave obviously forecasting financial time series it's not quite the topic but I'm sure this will get a lot of views yeah I'm interested in how they did the comparison between the two how do they even measure performance we can do it with past data just sticking to prediction quickly template for the jail script we add eventlessness to the input waveform selection and the noise amplitude slider when the user changes the selection or the sliders value the analyze waveform function is called the analyze waveform function gathers the input data and sends it to the flask backend via a post request turn analyze route which you will need to implement in your app.py the server is expected to process this data and return JSON with the output wave detection error an unknown percentage which are then used to update the page remember this is a basic example depending on the actual processing and data structure you use you may need to adapt the JavaScript code accordingly also to make this work you need to set up the corresponding back-end route in flask to handle the post request and perform the analysis. I'm having trouble finding the style CSS for how this should be two of those that the application it's a bunch of errors it's the wrong bolder name now this one's okay log is okay script.js gets static.com main.js it's loading forever from the CD and the loading plotly it's still running what's it doing yeah plotly loading forever why yeah let it rest for a while I don't know what's loading for so long but let it be can you write the HTML description for this web application yep so if I write description for this application in HTML to remove some of the descriptions there's no data source because it's generated get rid of this section right so the problem is that we do not get an error. Let's have a template somewhere yeah I can update the graphs here how to update the graphs using charts and charts.js and we're using plotly. Using plotly instead of charges how do I add the charts but how many should be just the one isn't it so we're ready including plotly yes it is a great tool thank you input graphups graph it's right so that two of them let's make sure they actually the noise output is important and noise so the JavaScript should take this noise output and generate a waveform out of it it should have a basic shape based on the input window how do we make sure we have the input chart to be the waveform leave the noise oh come on we have noise amplitude embedded add it to the signal envelope it's two things one by one and the signal being plotted now is this to even know this is JavaScript but and yes ideally how to modify this file with the recent changes 8 input graph that creates plotly graph in the input graph division don't have an input graph division why it keeps jumping around between the JavaScript and Python pretty sure we have no points don't thank you now morey it's a pleasure having someone live so it feels like I'm actually talking to a person try to create an application looks like this we had it running before on the side that was lab view that's not running anymore it's basically selecting a shape of a signal adding noise to it and there's a detection algorithm in this case fuzzy logic algorithm that is used to detect the original signal and obviously we've increased noise it becomes harder to detect the signal currently we are replicating this scene why then having a few problems and if this shell a script code they need to data Python script as well we're still kind of in a template stage right so we have yes we are doing flash application and the next HTML have a method that will be called using a post the HTML request called analyze and it will give you the result and yes the debug is true as this actual signal coming from we have this template ask the same question how to update considering we should have the data actually coming a from the back end from a Python of late wolf fix this for us we have event no elements getting elements from the HTML data output wave signal well what we currently have doesn't work so we might as well replace it the era went away the era went away this by the way not the eG this will be more basic don't tell me we have 56 errors this will be like a fake eG it's still time serious data loading forever so selecting as if I mean later we can add the other shapes to it as well that might be more eG relevant like a spike can have an envelope of a spike or slow wave eG and this particular tool is using fuzzy logic to try and detect this signal currently we have to update this HTML because we actually need the charts in it actually using github copal which meant the gpt4 okay no I meant with the charts we need the charts visible we have charts and now it's the id output wave input wave now the input wave is already there now that's that's the drop-down menu we actually need the chart as well have noise output yet the input graph input graph so we do have the input graph and output graph don't see them where are they okay so they are empty now the one thing that the github copal cannot do as opposed to gpt4 is hold a larger context for more files the question is if we supposedly in theory we should be able to do it using this workspace we ask questions here it should take look at the all the open files in theory do we the input and output graphs are not displaying anything how do we fix this so it's collecting workspace structure detecting which workspace information to collect supposedly it's looking it's looking at multiple files hopefully but before this stuff was giving us an error by the time it takes for this to do this detection it feels like I'll get another error because it's essentially github copal doesn't know where to look in the project while while we wait just a question following up on the message on LinkedIn I plan to convert the eg files to audio for my dissertation research I hope you're willing to help my best recalculation yeah happy to help but I won't be doing your dissertation for you so any specific questions are more than welcome yes getting this and cutting there while deciding what workspace information to collect length I guess the issue is length so it's too much too too many lines of code for essentially github copal to hold this information and I know for a fact that gpt4 interface can handle all like essentially more lines of code so let's try input graph obviously we need the JavaScript for this yeah so it's actually select all just with the input graphs and not display anything how do we fix that and now it's actually looking at the 77 lines of the JavaScript code plotly libraries correctly imported yeah plotly libraries loading forever to know why check if it's imported check if the data is there I suspect there's no data noisy signal update input graph log noisy signal to console let's lock this quickly see if it's not just generate signal is not defined generate signal defined now understand this main JS 42 22 42 22 which one is it to generate signals not if I'm not having that the label by the linda for us it's a template isn't it to replace this yeah this should be a function it's all template templates so far format document signal the error should go away can something wrong with the loading time and where's the network now those those errors actually okay yeah it's loading the plotly okay yeah so they do have the data you do have some noisy data looks like it's overlaid on top of a sinusoid yeah we can add noise just great and the output is not doing anything because we don't have that functionality yet yeah there's a lot of problems where do we log you can remove this logging maybe it will run faster quickly fix not I don't know if you can fix it I don't remember having this issue before the CDN seem to be loading forever loading forever see what it's suggesting anything forever it's eventually loading but it's way too long it's meant to be a mini version of it as well check your internet actually I'm dropping frames in my stream okay not dropping them anymore but I really drop like thousand thousand frames that's not cool right different CDN actually one of this one's working yeah I'll try it when they're not streaming supposedly taking too much bandwidth internet connection to while streaming and by the way yes it is affecting my server as well so the barnicales.com a website might be slow okay I have a problem is obviously the output the output shouldn't be a selection it's definitely shouldn't be a drop-down menu it should be an output right so let's change that the output should not be an input so it should not be a drop-down menu it should just display the output place this code and not that the web display is a paragraph you can update the text of this paragraph this JavaScript code using in a text property okay I mean it could be an input only if we are training but we are not training at the moment and should we change it yes level 4 output yes we can change that this output wave let's get rid of that for a sec output wave selection statistic data yeah output data is not updated I need to know how to control output wave they need to update or output wave so in html we have that okay and in JavaScript update output data won't work but okay because we don't actually have the output data we also don't need this constant yeah so the output should be displayed as text to one work with it okay we actually need the input waves to work properly we're generating the functions and Python code or a JavaScript it's a key to generate a JavaScript I want to do as much as possible on the front end so my server is not overloaded it is the actual wave forms you know we can add this sign so it's as well the square bricks one of our triangle default is still son wave okay a case and left is that case sensitive must be some work square works and there is no output there is no output a do we have that don't have those functions do it no graph is updating I don't know what you're talking about a cofiga cofiga so we have the noise everything it doesn't work ends up being a sinusoid and there is no output there is no output the analyze doesn't do anything anymore doesn't yeah that's just a mock-up generate signal and noise analyze signal calculate detection error could lead to no unknown percentage sounds much better doesn't it I couldn't shoot just do it to begin with not dropping frames anymore in my stream but the JavaScript is loading forever put wave is empty detection error is always 0% unknown is always 0 oh really really in the context seems like once I'm displayed output wave detection error and unknown percentages or html check html control output wave you already have that detection gate output in JavaScript function we have this function it makes sense having an error in JavaScript and 67 that's where the error is from noise signal yeah those are just templates let's mark the actual code to calculate unknown percentage should be signal noise signal should be input and output and now it should be using fuzzy logic that's right okay item code should do a fuzzy logic we fight fuzzy logic library yeah that's not quite right just an example that's a good start to install this library we'll still let the globally longer quickly even though it was two hours and we haven't even started with the actually designing the fuzzy logic give me a number it's not noisy it's came to point five so is it scary something else yeah we need to change yes the one one good thing about lab view was not using it anymore many because of the cost of the license but it had that toolkit for designing fuzzy logic so this membership functions I'm not sure how we're gonna do it now essentially it's taking out the features from the signal one thing is for sure output wave could predict the text input wave so the values should be the same and simply return the input way yes we do want to perform the fuzzy logic no that would not do it main content your main content section includes the application description and placeholders for additional information about how it works getting started features key insights technical details and a closing note flask variables the use of flasks URL underscore for in script tags is correct and ensures that the policy or static files remain valid even if the structure of your application changes it gets references to various HTML elements that it needs to interact with it defines functions to update the output statistics and the input waveform graph it contains a generate signal function to create different types of waveforms based on the selection it defines an analyzed waveform function that generates generates a noisy signal based on the selected waveform and noise amplitude updates the input graph with the noisy signal sends the waveform data to the server for analysis via a post request it has event listeners set up for when the user changes the input waveform type or the noise amplitude it performs an initial analysis when the page loads here are some points for improvement and clarification the generate signal function includes cases for different types of waveforms but it seems incomplete you should ensure all waveform types in a select element have corresponding cases in this function the noisy signal generation uses a simple random function for noise depending on your requirements you might want to use a more sophisticated noise model the fetch calls sends data to the analyze endpoint which you need to implement on your server side in flask to accept the post request and return the analysis results the comment replace with your function to generate the signal suggests that you should implement a more robust signal generation function based on your application's needs make sure to handle errors appropriately on the server side and provide feedback to the user in case something goes wrong after fetching the analysis results you should update not only the statistics but also the output graph if needed this part of the code is indicated by the comment you would also call functions here to update the graphs ensure you include appropriate error handling for network errors or server side issues lastly ensure the script is linked correctly to your HTML and that the server side processing script app.py is prepared to handle the requests and serve the necessary responses this script is client-side JavaScript and should be linked to your flask applications HTML template so that it can control the behavior of the web page in response to user inputs make sure to test this script thoroughly to ensure it behaves as expected this flask application serves as the back end for the waveform analysis tool it defines several routes and functions to process the waveform data received from the client side and returns the analysis results here's a breakdown of the applications components and some suggestions for improvement flask routes the the home route serves the index HTML template signal generation and analysis the generate underscore signal function should create a signal based on the input underscore wave parameter you will need to replace the placeholder code with the actual signal generation logic the add underscore noise function adds Gaussian noise to the signal based on the noise underscore amplitude provided fuzzy logic analysis the analyze underscore signal function is intended to perform the fuzzy logic analysis on the noisy signal the fuzzy underscore logic function is where you define your fuzzy membership functions and rules this function uses the SK fuzzy library to apply fuzzy logic operations on the noisy signal the example provided is very basic and should be tailored to match the specific fuzzy logic operations that are appropriate for application the calculate underscore detection underscore error and calculate underscore unknown underscore percentage functions are placeholders to calculate the error metrics you need to replace these with actual calculations that compare the original signal to the noisy signal and determine how well your fuzzy logic analysis is performing response the analyze route returns a jason response containing the original input wave type noise amplitude and the analysis results including the output wave detection error and unknown percentage suggestions for improvement implement the actual signal generation logic and generate underscore signal for different wave types the fuzzy underscore logic function currently only demonstrates how to use the SK fuzzy library and does not relate to the actual waveform analysis you need to define the fuzzy sets and rules that correlate to the specific features of the waveforms you're analyzing ensure the calculate underscore detection underscore error and calculate underscore unknown underscore percentage are properly implemented to reflect the accuracy of the fuzzy logic analysis error handling ad appropriate error handling throughout your application to manage and respond to any unexpected behavior or errors during the processing validation ensure that the input data from the client is validated before processing to prevent any potential issues or security vulnerabilities before deploying the application you need to test all these components to ensure they work together as expected and provide accurate results based on the input data to implement the generate underscore signal function for different wave types you will need to define how each waveform is generated let's assume that you want to create different wave forms with a fixed length of 100 data points here's how you might implement the generate underscore signal function for square left triangle right triangle and left trapezoid waveforms this generate underscore signal function defines the signal generation for several wave form types square a basic square wave oscillating between minus one and one left triangle a triangle wave that ramps up from minus one to one right triangle a triangle wave that ramps down from one to minus one left trapezoid a trapezoid wave with a ramp up sustained high level and sudden drop right trapezoid a trapezoid wave with a sustained high level a ramp down and a sustained low level hexagon a waveform with sections to create a hexagon shape each waveform is generated based on the time vector t which goes from zero to one over the length of the signal the np piecewise function is particularly useful for generating waveforms like the trapezoid and hexagon as it allows you to define different functions over different intervals of t remember to test each waveform to ensure they're being generated correctly you might need to adjust the parameters and conditions to match the exact shapes you want your labels should have left them as they were this one here oops control shifts he always forget so it's giving me the sinusoid you know why so it should have been okay yeah we need to want the fuzzy rules at moment just the template and the gpt4 interface on opening is so much better can actually hold on larger context fuzzy underscore logic for waveform analysis requires defining fuzzy sets and rules that are tailored to the specific characteristics of the waveforms you're trying to analyze you need to identify key features of each waveform that can be used to differentiate them let's go through a hypothetical setup for a fuzzy logic system that distinguishes between a square triangle and trapezoid wave based on features such as symmetry and peak sharpness firstly you would define the universe of discourse and membership functions for each feature for simplicity let's assume we're looking at two features symmetry and peak underscore sharpness here's an outline of what the fuzzy underscore logic function could look like the original thing it was looking at three senses but this example is better because it's actually taking out the features in this function the extract underscore symmetry underscore feature and extract underscore peak underscore sharpness underscore feature functions would contain the actual logic to calculate the symmetry and peak sharpness of the signal these are just placeholders and need to be replaced with actual feature extraction logic the fuzzy sets are defined for low and high values of each feature the rules are then applied to these membership functions based on the logic you decide in this example we assume a square wave has high symmetry and low peak sharpness etc finally the aggregated membership function is defuzzified to make a crisp classification decision please note that the above is a simplified example an actual feature extraction and fuzzy logic implementation will require a deeper understanding of the signal characteristics and the appropriate fuzzy logic design you will need to adjust the membership function parameters and the rules based on the actual features of the waveforms you're analyzing oh this is so much better than what the github co-pilot gave that's for sure it's actually understands of the context of what is it we're trying to do still having some technical issues with the tool itself it's actually became worse the output to properly implement the calculate underscore detection underscore error and calculate underscore unknown underscore percentage functions you need to define a methodology to quantify the performance of your fuzzy logic analysis these functions should compare the output of your fuzzy logic system the classified waveform type against the expected results the actual waveform type and calculate relevant metrics here's an example of how these functions might be implemented the detection error can be measured as the percentage of times the fuzzy logic system misclassifies the waveform type the unknown percentage could be the percentage of waveforms that the system can classify confidently in these functions original underscore wave and classified underscore wave are lists containing the types of each waveform in the same order the calculate underscore detection underscore error function compares each pair of original and classified wave types and counts mismatches the calculate underscore unknown underscore percentage function counts how many times the classified wave is marked as unknown note ensure that the output of your fuzzy logic analysis the classified underscore wave list and the ground truth data the original underscore wave list are correctly aligned and have the same length this approach assumes that you have a labeled dataset or a mechanism to generate ground truth labels for your waveforms if your application doesn't provide such labels you may need to adjust this approach accordingly this output way should be text obviously you'll get a lot of errors let's clear this quickly don't trust the github while it is much as i trust gpt4 just the implementation of it the error message you're encountering indicates a mismatch in the lengths of the arrays past the fuzz the fuzz function in your fuzzy underscore logic method specifically the symmetry underscore universe array and the aggregated fuzzy membership function array need to have the same length here are the steps to troubleshoot and resolve this issue verify array lengths ensure that the symmetry underscore universe array and the aggregated array past the fuzz defuzz function have the same length this is crucial for the fuzzy logic library to perform defuzzification correctly check membership function definitions review how you have defined the fuzzy membership functions like mf underscore symmetry underscore low mf underscore symmetry underscore high etc and ensure they match the universe of discourse symmetry underscore universe and p underscore sharpness underscore universe each membership functions definition should use the respective universe array as its base review aggregation step when aggregating the rules to form the aggregated array make sure the operation doesn't inadvertently change the length of the array the aggregation should result in an array with the same length as the symmetry underscore universe and p underscore sharpness underscore universe add print statements before the fuzz defuzz line to print the lengths of the arrays for example python copy code print length of symmetry underscore universe len symmetry underscore universe print length of aggregated len aggregated inspect the arrays to see if their lengths indeed match example correction if the aggregated array is shorter or longer than the symmetry underscore universe you need to revise how it's being created make sure that all operations leading up to its creation maintain the array length consistent with that of the universe array ensure that the universe arrays symmetry underscore universe peak underscore sharpness underscore universe span the entire range of possible values for each feature make sure the membership functions mf underscore symmetry underscore low mf underscore symmetry underscore high etc are defined correctly over their respective universe arrays by carefully reviewing and adjusting these aspects of your code you should be able to resolve the length mismatch issue and ensure that your fuzzy logic analysis works correctly so the length of the symmetry universe is 100 problem with this array this problem of the array doesn't have a length in it it's actually an error let's check and see it's a float 64 anyway we'll have to troubleshoot it next time i'll see you later bye