Hari-Writes

Using WSL2 for iverilog, gtkwave & C++

2026-07-24T00:00:00+00:00

The following blog was written and summarized with the help of Gemini AI.

Windows WSL2 Digital Design & C++ Setup Guide</a></h1>
A complete, modern guide to setting up a high-performance, lightweight Linux development sandbox on Windows using WSL2 (Debian). This guide details the complete configuration for native C/C++ compilation (`gcc</code>, g++</code>, make</code>), cross-OS Git synchronization, line-ending normalization, an HDL simulation suite (iverilog</code>, gtkwave</code>).`
1. Why WSL2 Debian over MSYS2 or Ubuntu?</a></h2> When configuring a build environment on Windows, developers typically evaluate three avenues: MSYS2 / MinGW-w64: Runs natively on Windows using the Universal C Runtime (UCRT). While excellent for standalone Windows binaries, it lacks true POSIX compliance, forcing hardware description tools or embedded toolchains to rely on Windows ports that can be brittle or perform poorly.</li> WSL2 Ubuntu: The default choice, but heavily bundled with system background services, Snap packages, and unnecessary telemetry that consumes memory and system overhead.</li> WSL2 Debian: The optimal lightweight solution. Ubuntu is derived from Debian, meaning Debian uses the identical, robust apt</code> package manager. However, Debian starts as a clean slate, using minimal base memory, running no bloated background daemons, and leaving maximum hardware resources available for heavy compiler passes, cross-compilation, and simulation loops.</li> </ul> 2. WSL2 Debian Core Installation</a></h2> If you ran a baseline wsl --install</code> and see no default Linux distributions when running wsl -l -v</code>, follow these steps to deploy a clean Debian environment. Step 1: Provision the Distribution</a></h3> Open a PowerShell or Windows Command Prompt with Administrator privileges and check available online distributions: wsl --list --online</code></pre> Install the core Debian distribution package: wsl --install -d Debian</code></pre>Step 2: Initialize Linux Environment</a></h3> A new console window will automatically initialize the environment. </li> Provide a Linux Username and Password when prompted (this establishes your local administrative credentials for sudo</code>). </li> Verify your running environment version from a fresh PowerShell terminal: wsl --list --verbose</code></pre> Note: Under the VERSION</code> column, verify that it displays 2</code>. WSL2 uses a real, optimized Linux kernel running within a highly efficient hypervisor. </li> </ol> Understanding Storage Allocations (The 1TB Illusion)</a></h3> When installing packages, the package manager may print a disk utilization metric similar to: Space needed: 366 MB / 1,026 GB available</code></pre> Do not be alarmed if you only have a 256GB or 512GB host SSD. WSL2 provisions virtual environments inside a Dynamically Expanding Virtual Hard Disk (ext4.vhdx</code>). Linux reads the maximum internal boundaries of the volume (capped at 1TB by default), but on your host Windows partition, the file only occupies the exact number of bytes used by your files (typically ~1.5GB to 2.5GB for a base setup). It will never overwrite or affect external physical disk partitions, including native Linux dual-boot setups. 3. Installing C/C++ Build Essentials</a></h2> Log into your fresh Debian terminal interface and execute a system package registry synchronization followed by a core utility installation. # Update the local package metadata lists sudo apt update # Execute pending security or architectural upgrades sudo apt upgrade # Install the consolidated compilation package sudo apt install build-essential</code></pre>Verification</a></h3> Ensure the tools are successfully linked to your runtime PATH by querying their respective versions: gcc --version g++ --version make --version</code></pre> 4. Cross-OS Git & File System Configuration</a></h2> Running a Windows-native Git executable (git.exe</code>) inside a Linux directory structure introduces performance degradation and cross-system friction. The correct design paradigm is to utilize a native Linux Git instance but securely pipe configurations and permissions. Step 1: Install Native Linux Git</a></h3> sudo apt update && sudo apt install git -y</code></pre>Step 2: Neutralize Shared Partition Permission Errors</a></h3> When opening an existing repository located on a shared cross-OS partition (e.g., an NTFS or exFAT drive mounted inside WSL under /mnt/</code>), running git diff</code> will often output a catastrophic permission mode shift: old mode 100644 new mode 100755</code></pre> This occurs because Windows file systems do not map native Linux execution attributes (chmod +x</code>) natively, making every file look executable to the WSL subsystem. Force Git to ignore file mode changes globally: git config --global core.fileMode false</code></pre>Step 3: Absolute Line-Ending Normalization (CRLF vs LF)</a></h3> Windows environments parse newlines as Carriage Return + Line Feed (), while Linux requires standard Line Feed (). If unmanaged, modifying a file on Windows will corrupt the entire Git change log by flagging every single line as modified. Configure Git within WSL to use the Input Filter. This translates CRLF</code> to LF</code> instantly on incoming commits, but ensures checking out repositories retains LF</code>: git config --global core.autocrlf input</code></pre>Step 4: Credential Sharing & Identity Syncing</a></h3> If you wish to retain Git on Windows alongside WSL, you can instruct your Linux environment to borrow your Windows secure identity vault so you never re-authenticate: git config --global credential.helper "/mnt/c/Program\ Files/Git/mingw64/bin/git-credential-manager.exe"</code></pre> Finally, configure your development signature profile: git config --global user.name "Your Global Handle" git config --global user.email "your.email@example.com"</code></pre> 5. Integrating VS Code with WSL</a></h2> Visual Studio Code provides a split architecture for headless or virtualized development. The user interface runs natively on your Windows desktop GPU, while a background node compiler backend agent executes inside the Debian container environment. Launch VS Code natively within Windows. </li> Access the Extension Marketplace (Ctrl + Shift + X</code>). </li> Install the official WSL Extension published by Microsoft. </li> To open any project directory, open your WSL Debian terminal, navigate to the targeted project folder, and enter: code .</code></pre></li> VS Code will load instantly. Look at the lower-left status accent bar; it will explicitly display WSL: Debian</code>, confirming that all terminal instances, linters, and compiler lookups are executing natively through your lightweight Linux layer. </li> </ol> Mandating LF in VS Code</a></h3> Ensure VS Code forces Line Feed standard natively for any newly generated text assets: Open global Settings via Ctrl + ,</code>.</li> Search for the term: eol</code></li> Locate the parameter Files: Eol and alter the variable selection from auto</code> to .</li> </ol> 6. Hardware Description Language (HDL) Simulation Setup</a></h2> For digital design and verification engineering workflows, deploy the ultra-fast Icarus Verilog compiler and GTKWave visualization engine inside your Debian workspace. sudo apt update sudo apt install iverilog gtkwave</code></pre>Running GUI Frameworks Natively via WSLg</a></h3> WSL2 supports native graphical acceleration (WSLg) automatically out of the box. Running the command gtkwave</code> inside Debian will render the complete Linux graphical client application layout window seamlessly right onto your Windows desktop canvas. Because launching a GUI application blocks input streams inside active CLI terminals, always attach a background processing ampersand (&</code>) flag when invoking graphical entities: gtkwave design_dump.vcd &</code></pre> This hands system interactive control back to your shell prompt immediately while the visualization application runs asynchronously in the background.

Digital Image Processing Course Assignment implemented over Python 2023-11-12T00:00:00+00:00 We were given assignment for the Digital Image processing elective at my undergrad in semester 7. Instead of solving these numericals manually, I've written the following script to compute the required image matrices that were asked. Contains histogram equalization for contrast enhancement (1st ques) and DCT, Hadamard transform codes. </li> Finds the global threshold using iterative mean method </li> To run the program, you can pass in the registration number like python .\main.py <reg_no></code></pre></li> This code needs numpy</code> package for execution. </li> </ul> Code</a></h1> import sys import numpy as np import matplotlib.pyplot as plt IMG_DIM = (4,4) def print_dict(d): for key, value in d.items(): print(f"{key} \t {value}") def hist_equalize(img): """ Takes in an image numpy array and performs histogram equalization Returns the histogram equalized image array - Calculates the required bit for equalizing by itself """ max_val = np.max(img) if max_val>=8: no_of_bits = 4# find the number of bits required to represent else: no_of_bits = 3 max_val = int(2**no_of_bits) print(f"Using {int(no_of_bits)} bits for histogram equalization.\nHence the Max value is {max_val-1}.\n") img_dict = dict() # For calculating the number of occurences of each pixel values for i in range(max_val): # in order to account for pixel values that don't occur in the image img_dict[i] = 0 for i in range(IMG_DIM[0]*IMG_DIM[1]): # update the img_dict with pixel count in given image img_dict[img[i//4,i%4]] = np.count_nonzero(img==img[i//4,i%4]) print(f"Image and its distribution before constrast enhancement: \nPixel value\tCount") print_dict(img_dict) tab = dict() # Histogram equalization table dictionary cumm_sum = 0 for key, value in img_dict.items(): tab[key] = [value,0,0] # [occurence of that pixel intensity, cummulative sum, ceil(cummulative_sum*(max-1)/(no_of_pixels)) for key, value in tab.items(): cumm_sum+=value[0] tab[key][1] = cumm_sum tab[key][2] = np.ceil((cumm_sum*(max_val-1))/(IMG_DIM[0]*IMG_DIM[1])) print(f"\nHistogram equalization mapping function is:") print_dict(tab) hist_eq_img = np.zeros(img.shape) for key, value in tab.items(): # Mapping r to s (histogram equalization) hist_eq_img[img == np.float32(key)] = value[2] print(f"The histogram equalized image is: \n{hist_eq_img}") return hist_eq_img def dct(img): # assuming a square matrix img """ Performs Discrete Cosine transform on the input image array. Constructs kernel by itself - Assumes the input image array is a square matrix - Returns the DCT transform applied image """ def a(u): # coefficient if u==0: return (1/IMG_DIM[0])**(0.5) else: return (2/IMG_DIM[0])**(0.5) kernel = np.zeros(IMG_DIM) for u in range(IMG_DIM[0]): # kernel formation for x in range(IMG_DIM[1]): kernel[u,x] = np.float32(a(u)*np.cos(((2*x+1)*u*np.pi)/(2*IMG_DIM[0]))) print(f"The DCT kernel formed is: \n{kernel}\n") dct_img = np.matmul(kernel,np.matmul(img,np.transpose(kernel))) # applying the transform print(f"The DCT transformed matrix for the given image is:\n G = \n{dct_img}") return dct_img def hdt(img): """ Performs Discrete Cosine transform on the input image array, constructs kernel by itself - Assumes the input image array is a square matrix - Returns the DCT transform applied image """ h0_kernel = (0.5**(0.5))*np.array([[1,1],[1,-1]]) # H1 kernel of hadamard transform count = img.shape[0]/h0_kernel.shape[0] # used to find the number of kronekar product operations to be performed to obtain the kernel kernel = h0_kernel # intial kernel while count: kernel = np.kron(h0_kernel,kernel) count-=2 print(f"\nThe Hadamard Transform kernel is:\n{kernel}\n") hdt_img = np.matmul(kernel,np.matmul(img,np.transpose(kernel))) # technically you dont need to take the transpose print(f"The Hadamard transformed matrix for the given image is:\n G = \n{hdt_img}") return hdt_img def compute_thresh(img): """ Computes the global threshold for an image for segmentation - Computes the global threshold by finding the average of mean pixel values of two threshold levels - threshold = (u_l+u_g)/2 => threshold is updated over 40 iterations - Assumes 40 iterations of threshold value updation - Returns the global threshold value """ intial_threshold = np.mean(img) print(f"\nInitial threshold: {intial_threshold}") for iter in range(40): u_l, u_g = 0, 0 count_l, count_g = 0, 0 for i in range(img.shape[0]): for j in range(img.shape[1]): if img[i,j] > intial_threshold: count_g +=1 u_g = (u_g*(count_g-1) + img[i,j])/count_g else: count_l +=1 u_l = (u_l*(count_l-1) + img[i,j])/count_l intial_threshold = (u_l+u_g)/2 print(f"Thresholding value after 40 iterations of mean updation {intial_threshold}") return intial_threshold def image_segment(img, threshold): """ Segments the image for a given threshold - Returns a boolean ndarray of the thresholded binary image """ print(f"The segmented image for the given global thresold of {threshold} is\n{1*(img>=threshold)}") return 1*(img>=threshold) if __name__ == "__main__": if(len(sys.argv)==1): reg_no = input("Enter your registration number: ") else: reg_no = str(sys.argv[1]) img_no = reg_no+str(2*int(reg_no)) img_no = img_no[len(img_no)%IMG_DIM[0]:] print(f"Your matrix is formed from: {img_no}") img = list() for i in range(IMG_DIM[0]): temp = list() for j in range(IMG_DIM[1]): temp.append(float(img_no[IMG_DIM[0]*i+j])) img.append(temp) img = np.array(img) print(f"Your image is: \n{np.array2string(img)}") # 1. constrast enhancement print("\n\nQ1 Histogram equalization<explains the reasons why yourself>\n\n") hist_eq_img = hist_equalize(img) # 2. Bit plane slicing... do it yourself print("\n\nQ2 Bit plane slicing <do it yourself>\n") # 3.1 Discrete Cosine Transform print("\n\nQ3.1 DCT transform of equalized image <show internal matrices, this just prints the kernel and DCT op>\n") dct_img = dct(hist_eq_img) # 3.2 Discrete Hadamard Transform print("\n\nQ3.2 Hadamard transform of equalized image <show internal matrices, this just prints the kernel and DCT op>\n") had_img = hdt(hist_eq_img) # 4. Global threshold value and segmentation print("\n\nQ4 Thresholding and segmentation of equalized image <Write the theory for thresholding and segmentation (refer slides - global thresholding topic)>\n") # Global threshold computation threshold = compute_thresh(hist_eq_img) # Segment the image! seg_img = image_segment(hist_eq_img,threshold) # returns an binary numpy array # 5. Morphological process print("\n\nQ5 Morphological process (dilation and erosion, use a 3x3 structing element) <do it yourself>\n")</code></pre> Thanks to the Code reviewers</a></h1> Dhasharadh</li> Ananthakrishnan</li> Anusha</li> </ul> Pandas DF to HTML or JSON using Flask on RPi 2023-06-24T00:00:00+00:00 We will be using Flask to read a particular csv file (which is acting like a database here) and return that to a Front-end HTML route (don't panic, its a technical name for a specific URL) using Flask. Starting a virtual environment</a></h1> Before looking at the Flask code and you trying to run it in your side, Lets have a look at making virtual environments so that our Project is not subject to version changes (some functions that we use may deprecate down the line, so we use virtual environments to be on the safer side). I usually create a virtual environment whenever I do something in Flask. Steps to create a virtual environment Install virtualenv package using the pip command. This helps to create, manage and run virtual environments using simple command line interface. pip install virtualenv</code></pre></li> Now open terminal and navigate to your project folder mkdir iot_mini_project cd iot_mini_project</code></pre></li> Create a new virtual environment using virtualenv by specifying a name. I usually prefer to keep it env. virtualenv <your_env_name></code></pre></li> Now that you have created the virtual environment, you still haven't accessed it yet (by this I mean, you haven't set your intrepreter for python as the one in the virtualenv that was created, your'e still using your global python that's installed). Now to run the virtual environment, use In case if your'e using powershell .\env\Scripts\activate.ps1</code></pre> In case of Windows command prompt .\env\Scripts\activate</code></pre> In case of Linux (Raspbian OS) source venv/bin/activate</code></pre></li> You now have started the virtual environment, you can check if its so by looking at the present working directory in the terminal: Before starting the virtualenv C:\iot_mini_project></code></pre> After starting the virtualenv (your_env_name) C:\iot_mini_project></code></pre></li> Now you can install the necessary libraries, this would be installed in the virtual environment, and not in the global python install. For this example, we would requiring Flask and Pandas. pip install Flask pandas</code></pre></li> Once, your'e done with this, you can move on to the actual Flask code. </li> </ul> Code used</a></h1> Flask program</a></h2> Named ref_code.py (or anything that you wish to) Create a folder named "templates" (it is important you don't change the name of this folder!) and a python file with name of your liking. The folder structure of your project directory should look something like.. │ ref_code.py │ └───templates table_render.html</code></pre> Flask Code: # import the necessary libraries from flask import Flask, jsonify, render_template import pandas as pd """ We use jsonify to return a json format of our dataframe (could be used for plotting further in JS) render_template is used to return a HTML file when a particular URL is requested by the user """ app = Flask(__name__) # Creating the Flask application instance @app.route("/") # Home page route def homepage(): """ This is just a simple home page route this returns the HTML text Hello World when hit at this "/" root URL """ return "<h1>Hello world</h1>" @app.route("/api/get_table/") # this could be used for API access (but gives HTML data as response) def get_table_data(): """ Used for getting the dataframe as a HTML table content returns the dataframe as HTML when requested at the URL /api/get_table/ """ df = pd.read_csv("dataset.csv") return df.to_html() @app.route("/table_render/") # this could be used for rendering a static HTML page when this route is hit def table_render(): """ Renders the HTML content with the Table contents (check table_render.html for more info!) returns the dataframe as HTML when requested at the URL /table_render/ """ df = pd.read_csv("dataset.csv") return render_template("table_render.html",table=df.to_html()) @app.route("/api/get_table_json/") # this could be used for API access for plotting def get_table_json(): """ Used for getting the dataframe as a JSON content returns the dataframe as HTML when requested at the URL /api/get_table_json/ """ df = pd.read_csv("dataset.csv") return jsonify(df.to_json()) if __name__ == "__main__": # Used to run the application app.run(debug=True) # Keeping Debug as True provides Hot-reloading feature! # By default the app runs at port number 5000</code></pre>table_render.html file</a></h2> <!DOCTYPE html> <html lang="en"> <head> <meta charset="UTF-8"> <meta http-equiv="X-UA-Compatible" content="IE=edge"> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <title>Data Table</title> </head> <body> <h1>Hello - Checkout my dataset</h1> {{table|safe}}  </body> </html></code></pre>Running the Python file</a></h1> Just type python .\<your_app_name>.py</code></pre> Notice that the Application has started running in the Local Host IP (127.0.0.1:5000) at the port number 5000 If you open your browser and go to 127.0.0.1:5000/ You will be greeted with the "Hello World!" message! Congrats if its your first time running a Flask application! Homepage at 127.0.0.1:5000/ looks like </li> The HTML table which will be rendered on the page can be accessed at 127.0.0.1:5000/table_render/</code> </li> The HTML table can also be accessed as an API at 127.0.0.1:5000/api/get_table/</code> I hope you can check the difference between the previous two methods, The /table_render/</code> renders a HTML page with the Table content. So you can have other HTML content on that page with this Table. Meanwhile /api/get_table/</code> just returns the table in a HTML format! </li> In case if you want the table content in JSON format which would be very useful in case of plotting using JS libraries, then you can get the JSON data at 127.0.0.1:5000/api/get_table_json/</code>. Hit me up if you need some help with that as well! Looks cryptic ikr! </li> </ul> Further improvements</a></h1> Now this application runs in the local host meaning the application cant't be accessed by devices that are connected to the same network. To access the application from devices connected in the same network, just change the app.run(debug=True)</code> in the flask python file to app.run("0.0.0.0",debug=True)</code> Passing 0.0.0.0</code> as a parameter, would allow the Flask application to listen to URL requests from all the networks that it is connected to. Usually its the localhost and a network IP (when connected to a network) Use the same python .\<your_app_name>.py</code> to start your application. You can see the network IP like so.. (Im hiding it here for obvious reasons) If you connect your phone to the same network as your Raspbery Pi (when youre running the flask app in RPi), you can access the application at the link that is shown in the command line. An example of accessing the application from my phone! Reference codes</a></h1> Python Script</a></li> HTML template</a></li> </ul>

Hari-Writes

Using WSL2 for iverilog, gtkwave & C++

Step 2: Initialize Linux Environment</a></h3> A new console window will automatically initialize the environment.</p> </li> Provide a Linux Username</strong> and Password</strong> when prompted (this establishes your local administrative credentials for sudo</code>).</p> </li>

Mandating LF in VS Code</a></h3> Ensure VS Code forces Line Feed standard natively for any newly generated text assets:</p> Open global Settings via Ctrl + ,</code>.</li> Search for the term: eol</code></strong></li>

Digital Image Processing Course Assignment implemented over Python

Pandas DF to HTML or JSON using Flask on RPi