Tutorials/Wireless/mmwave Paam Mobile: sb1_read_GRC_data.ipynb

{
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  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "uCLKuQvA6X_v"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import os\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import math"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive', force_remount=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BDzNyRlh6tP4",
        "outputId": "b4445aa6-bf3f-479f-9b1d-7da372e5301c"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install control\n",
        "import control"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SLOZcDlL8d49",
        "outputId": "6f4bb1fb-4f1c-4ce8-f577-8b1d92710ded"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting control\n",
            "  Downloading control-0.9.4-py3-none-any.whl (455 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m455.1/455.1 kB\u001b[0m \u001b[31m4.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from control) (1.23.5)\n",
            "Requirement already satisfied: scipy>=1.3 in /usr/local/lib/python3.10/dist-packages (from control) (1.11.4)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from control) (3.7.1)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->control) (1.2.0)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib->control) (0.12.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->control) (4.47.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->control) (1.4.5)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->control) (23.2)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->control) (9.4.0)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->control) (3.1.1)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib->control) (2.8.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib->control) (1.16.0)\n",
            "Installing collected packages: control\n",
            "Successfully installed control-0.9.4\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "file = '/content/drive/MyDrive/28G Measurements/JCAS/NRDZ/aa_data/jan3_2024/test.bin'\n",
        "f = np.fromfile(open(file), dtype=np.complex64)\n",
        "\n",
        "# avg across full cycle of tone (function of how many samples you have per cycle)\n",
        "averages = []\n",
        "for i in range(len(f[::10000])):\n",
        "  averages.append(np.mean(\n",
        "      (np.abs((f[i*10000:(i+1)*10000])))**2 # in scale of power\n",
        "  ))\n",
        "samples = averages\n",
        "\n",
        "samples_dB = control.mag2db(samples)"
      ],
      "metadata": {
        "id": "8C81LeMN_AwY"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(np.divide(range(0,len(samples_dB)), 250),(samples_dB),s=0.2)\n",
        "plt.rcParams.update({'font.size': 20})\n",
        "plt.xlabel(\"Time\")\n",
        "# plt.xticks(range(0,90, 5))\n",
        "plt.ylabel(\"Relative RX Power (dB)\")\n",
        "plt.rcParams[\"figure.figsize\"] = (6, 16)\n",
        "plt.ylim([-125,-40])\n",
        "# plt.xlim([33,48])\n",
        "plt.grid()\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "vjQgrOA2_AwZ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 909
        },
        "outputId": "437269f3-9dd1-4bd3-834e-7a97fcd90cb2"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x1000 with 1 Axes>"
            ],
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          },
          "metadata": {}
        }
      ]
    }
  ]
}