Unipolar/UNPolar_RZ.ipynb.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"authorship_tag":"ABX9TyPHLopAgFdDH3cKzkSPGgiC"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","execution_count":1,"metadata":{"id":"r-6z75MvvcfR","executionInfo":{"status":"ok","timestamp":1710433389107,"user_tz":-210,"elapsed":3,"user":{"displayName":"Elham Jafari","userId":"09412467550778604481"}}},"outputs":[],"source":["# This code defines a function unipolar_rz_plot that generates a Unipolar Return-to-Zero (RZ) line coding plot for binary input data.\n","# The function extends each bit in the input binary data by 1000 samples to visualize the encoding.\n","# The main function main allows the user to input binary data as a string, converts it to a list of integers,\n","# and then plots the Unipolar RZ line coding for the input data."]},{"cell_type":"code","source":["import numpy as np\n","import matplotlib.pyplot as plt"],"metadata":{"id":"ld3q845ovwAa","executionInfo":{"status":"ok","timestamp":1710433397462,"user_tz":-210,"elapsed":517,"user":{"displayName":"Elham Jafari","userId":"09412467550778604481"}}},"execution_count":2,"outputs":[]},{"cell_type":"code","source":["def unipolarRZ(X):\n","    encoded_signal = []                                                     # Initialize an empty list to store the encoded signal\n","    for i in range(len(X)):                                                 # Iterate through each bit in the input data X\n","        encoded_signal.extend([X[i]] * 1000)                                # Extend each bit value for 1000 samples\n","\n","    t = np.linspace(0, len(X), len(encoded_signal), endpoint=False)         # Generate the time axis for the plot\n","    plt.figure(figsize=(8, 4))                                              # Create a plot to visualize the Unipolar RZ line coding\n","    plt.plot(t, encoded_signal, linewidth=2)\n","    plt.title('Unipolar RZ line coding')                                    # Set plot title, x-axis label, y-axis label, and grid\n","    plt.xlabel('Time')\n","    plt.ylabel('Amplitude')\n","    plt.grid(True)\n","    plt.ylim(-0.5, 1.5)                                                     # Set the y-axis limits for better visualization"],"metadata":{"id":"Zettml68v7Ps","executionInfo":{"status":"ok","timestamp":1710433408012,"user_tz":-210,"elapsed":452,"user":{"displayName":"Elham Jafari","userId":"09412467550778604481"}}},"execution_count":3,"outputs":[]},{"cell_type":"code","source":["# The main function prompts the user to enter a binary input sequence, converts it to a list of integers,\n","# and then plots the unipolar RZ line coding representation of the input sequence using Matplotlib.\n","\n","\n","\n","def main():\n","    print(\"\\n\\n***************************************************************************************\")\n","    X_str = input(\"---> Enter the binary data (e.g., 1011100): \")\n","    X = [int(x) for x in X_str]\n","    print(\"\\n\\n\")\n","    unipolarRZ(X)\n","\n","\n","if __name__ == \"__main__\":\n","    main()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":531},"id":"-3c4XWfCwFRs","executionInfo":{"status":"ok","timestamp":1710433416837,"user_tz":-210,"elapsed":6079,"user":{"displayName":"Elham Jafari","userId":"09412467550778604481"}},"outputId":"396d8de9-16c1-428d-817e-08a9b7d8052c"},"execution_count":4,"outputs":[{"output_type":"stream","name":"stdout","text":["\n","\n","***************************************************************************************\n","---> Enter the binary data (e.g., 1011100): 1011100\n","\n","\n","\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 800x400 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]}]}