Tutorials/Wireless/mmwave Paam Mobile: data_process_mobile_paam.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "machine_shape": "hm"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2HugC8Y_phW7"
      },
      "outputs": [],
      "source": []
    },
    {
      "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": "c1abc856-7c4c-4ce9-a86b-b93cc83e75f6"
      },
      "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": "3ede689f-818f-493c-caa9-ae649cc41bba"
      },
      "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[31m3.5 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/nrdz/test.bin'\n",
        "f = np.fromfile(open(file), dtype=np.complex64)"
      ],
      "metadata": {
        "id": "wWfdmKrtuMNI"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\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": 5,
      "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\"] = (20, 8)\n",
        "#plt.ylim([-100,-90])\n",
        "# plt.xlim([33,48])\n",
        "plt.grid()\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "vjQgrOA2_AwZ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 744
        },
        "outputId": "c32f7baf-b964-467e-9860-3d440c5cb062"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x800 with 1 Axes>"
            ],
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AdUddvIlYUVgWFuyvz//Sr1xgNqpXmzLbXj53VMkfxWHB/vr6rwOty40ms9o0CeQJfgAAALiUrVMt9H/1e53JdNzQ0sUWWUeZKf3gsyTd9+52HXr5NocdC/ar9kyvZ86ckSQNHTq00jbDhg2TJBUVFSk1NdXO0gAAnmD90fNO23f7sECn7dsVSgKmyxmzC6wB0+WOn8+pMmAqUSxp/5lMDZuzSZHPfqMl25w7gX1BUZH1dYrJM+YwAYCKlIRPjuix6ch9AQAAADVVMkdebFzF9yIu58iA6UoqGm4arlXtkMlkMkmSwsIq74rWpEkT6+vs7Gw7ygIA4MrevOe6Wj1eREjlk9HXJRZdeiLoyz2nnXaM1CyCJQBwV0aTWQs2JspoMl+5MQAAAOqdmOjW6tchjB72qJZqh0wWy6XhkQwGg03tAQDuJTLMuZOJ16aOESGa0L+dU4/RItRfIQHe6t4qVB89fKNTj1Xbpizb77R9t24Y4LR9AwDsY+uTqQAAAKhf6GEPW1R7TiYAgGeI/Ut/9X11rfKvPMKaQwzp1NSp+58+sqtu6drCOvljZLNgDf/XRhlzCuzar5+X9IeerfTsiCiPfVPlzMdBbu3eQgs3O3dIPgBAzZQ8kcqTqQAAAKgNjRv4aOH43rqYW6CHFse5uhw4mM0h065du3T+/JXn16hOu4EDB1a5HgDgeGHB/lo64UZN/eKAftepqRb9eEp/7N1Kn+5OVlGx5O9zaXzbRg28Zcotkq1D3bYI9VemuUDtGgeq/9XNNKkWJnbvHRmmdU8Otn7+6cS+emXVIQ29pple/TZehYWqdqjWv0OYurQM1aQ6+sROowZeyswr1tThnXT6Qk6lczlJkl/1OifXyKRBHfRz8kX9mJSuvw65ynkHAgDYrOTJVAAAAMAR/L0kcwX3XRoG+Ojd+3urd+RvU/A8MeQqzV1/3Pq5r7dBr93VTV1bNdLzK37WoV8uKj2HiZfqEptDpj/96U9Vri8ZTq867QoLuVgAwBVKhzLTR3aVJE0Z3lmxccka2jlc6w6nKSa6tcKC/WU0ma3LV+47Kxmkkd1bavryA9qffEG5hZK/t+Tn46037+mpoV2au/DMLukYEaJFD/aRJI3rFylJ2p1k1FOx+xXZJFA/Jp1XXqEU6Oelto0a6HBatkIDfPTeZW98atu746P1+CdxKioy6LnbovTG90d1wYYZLRv4eCl20k3qGBFiXfbynT3U/tlvKmz/h+ta2V1zZcKC/bV0Yl+n7R8AAAAAALiHb54YqD++vV3nsy/Nz9wpPEhvjY0uc3+ixJRbojTllqgK9/PRhBtlNJl1w8trVSgpwFvKK/ptvUHiQVY3ZFPIxDxLAOC5Sj/VXPpNQOnlU4ZfY13+1rhoxcYlW8Mod9c7Mkwbnh5i/bwkPHOn+od2aa74F2+zfn5jh6b6+/IDSs8x61xWvv51dw/1bNtYi7ed0JmMXO1ISleLEH/Fnc7QsKhwvXpn9wrPZdotnTRrzVHr5/7eBo3p3UaTb+5UK+cFAAAAAAA8V8eIEK2ZMtAh91nCgv31ycQbrdMiSLK+duWDwahctUOm559/3pl1AADqmLo+1E5dqL9jRIiWTepXbnnpsK86Jg65WjHXt9XibSckgzS+b3u3CdYAAAAAAEDd58j7LJdPi1D6NdwPIRMAAPVAWLC/zeEUAAAALnHHXvAAAADuwMvVBQAA4O6MJrMWbEyU0WR2dSkO4WnnAwAA4GyxccnalmhUbFyyq0sBAABwKzbNyQQAQH1UclNBktsPsVcdnnY+AAAAzhYT3brMRwAAAFxCyAQAwBV42k0FTzsfAAAAZ6sL83kCAAC4AiETAABX4Gk3FTztfAAAAAAAAOAazMkEAAAAAAAAAAAAmxEyAQCcwmgya8HGRBlNZleXAgBwAX4PAAAAAIDnI2QCADdmNJk1Y/kBDX5tvXYnGV1djk1i45K1LdGo2LhkV5cCAHABfg8AAAAAgOdjTiYAcCMJqVl6fsXPimwapLBgPx06m6nvDqVJkmIWbNcN7Rupe5vGCvTz1vi+7RUW7O/iiisXE91akjS0c7gWbExUTHTrcvUaTWbFxiVXuK6uMJrMWrzthGSQ2/+fVIcn/J8AcA8lvwdKPgIAAAAAPA8hEwC4kT+9v1OnLuRpa2LFvZZ2nMjQjhMZkqS31iWoQFL7Jg30zv3Xq2NESO0VaoNXvonX+qPn9Y9vD+tfd3dX0jmT5q4/XqbNhz+e0FeP3VQnQo0l25I08+tD+mPvVvr6YIoKCouUnW+RJH2w7YRu795Sx9NM+jEpXYM7NdXrY3q63XmVDpIklXkd87+tSjLm6qMfT+hURp7bX18A3FdYsL8mDurg6jIAAAAAeJCE1Cy9suqQpo+IkiTF/G+LMvKKy7Vr16SBIhoG6NVR3bin4WSETADgRk5dyKt224JfP55Iz9Xot7bowAu3OqeoGrrj35t1JvO3eTiKLdLkZfsrbJuckafol9dKkib0b6fpI7vWSo22emXlQS3celKStGRn+eGfMnILtWTHKevn64+e1wtfHdS/x0bXWo3VMX9Doj7dfUpLtp9UkK+XDqdla94PR+Xn7aX0nEJJ0qmMS9fiifRcTf50r5qFBGj6iCjemAEAAAAAcBlGBakdS7YlacaKeEnS+iPnqmx7Mj1XJ9NzNWzOJk27pZMmDrm6Nkqsl5iTCQA8QJa5/BMbrlY6YLLFwq0n3W6S+ITULHX9f99YAyZbrDiQ4oSK7LN4W5Iy84p0+kKuDqdlS5JM5mJrwHS5g2eztP7IOY1ZsLU2ywQAAAAAoE5gPlLn251ktAZMtpq15qiDq0Fp1Q6ZNmzY4LCDnj17VqNGjXLY/gAAnmX+hkRXl1DGfe/ukCnf1VU4Tk0zyfScIscWAgAAAACAB4iJbq1+HcKYj9SJpizb5+oSUIlqh0xDhw7VpEmTlJmZadcB//vf/6pLly5auXKlXfsBAHiuH5POu7qEMn6pYa8sT+RuvcwAAAAAAHC1kvlIGSrPefx9Da4uAZWodshksVi0cOFCdenSRStWrLD5QPHx8erfv7/++te/KjMzUwYDFwUAoGIXKxm2Da5X3a7/RpNZCzYmEkoBAAAAAAC7mfIYXcVdVTtkGjVqlCwWi86ePavRo0frnnvuUVpa2hW3y8/P13PPPadevXpp+/btslgsuuaaa7R+/Xq7CgcAeK45Y3q4ugSH8bRHKqrb9Z/xqAEAAAAAgKO0bBTg6hJQiWqHTF988YWWLVumiIgIWSwWxcbGKioqSh988EGl22zatEndu3fXq6++qvz8fPn4+GjGjBnat2+fBgwY4JATAAB4nt6RYa4uwWEsri7Awarb9Z/xqFHf0ZsPAAAAABznn3d5zgPJnqbaIZMkxcTE6NChQ7r//vtlsVh04cIF/elPf9Itt9yiEydOWNtdvHhREyZM0JAhQ3Ts2DFZLBbdeOON2rNnj1588UX5+fk5+jwAoF7zcXUBHs7Xpt+WkBiPGqA3HwAAAAA4TseIEFeXgErYfNusUaNGWrRokdasWaPIyEhZLBatXbtW3bp105tvvqlPP/1UUVFReu+992SxWBQcHKx58+Zp69atuvbaa51xDgDgMUoPrRbgLXkZpFu6hKtlw8pv1HsZpAXjo51fnI2ah3pOuNC5eWiNt/UjoALqJXrzAQAAAIB78PW0uQzcTI0ffr/55pt14MABTZ8+XfPmzVN2draefPJJSZLFcmlwoJEjR+qtt95Sq1atHFMtAHi4u3q10pc/ndWoni31+pie1uUJqVl6ZdUhjbuhreasPaqT6TkacnUzRYYHa3zf9m7ZW2TJQzfosaVxSjqfo8Iii0qmZ/TzlvKLpHZNGuiqZsHq2DRQC7eetG43oX871xRchTn39NSwOZvKLfc2SFEtQxTo56PmIf5afSBFBaXGx/P38dJb915Xi5VWT592jbTzZEal630MUlSLUB1LMymvsNi6fHjnprVQHeAZSnrzAQAAAACcJ8TfWx2bBumnM5nqd1UTtWjUQNsSjSoutigly6wGvl76z/+5370ZT2LXCEuBgYGaM2eOCgoK9NZbb0m6FDAZDAa9+uqrmjp1qkOKBID6YtqIKF0dEVLuyfeOESFa9GAfSdLQLs1dUZrNOkaE6NspgyX9FpJNHxElSdbXJV2dcwst+nxPsu7q1VrTR3Z1UcWV6xgRorgZwzTn+6PaknBek4d21Ff7filzDkaTWde2aqShncO1ct9ZySC3DQD/d19vPf7RHm1LStdNHZooqnmINei7qUMTzf2/XgoL9tfuJKP+9MFumQuLNSa6tV4a3c3FlQMAAAAAgPrq5Tu6aMaKePl6GfRaTDeN6tXG1SVBdoZMSUlJmjBhgtavXy+DwWDtwSRJL774ory9vfW3v/1NXl6MFQQA1eGpT76XDskklXktSVNu7qQ2TQLdelipsGB/vVwqZLn8jUzp/7spw6+p1dpsFRbsr6UT+5ZZVlG41zsyTOufGqzYuGS3/r8BAAAAAACeb1y/SI3rF+nqMnCZGqU/FotFb7zxhrp166b169fLYrGoXbt2WrlypZ555hl5e3srNzdXU6dO1Q033KB9+/Y5um4AgAcpCWjcsddPfcf/DQAAAAAAACpjc8h04MAB3XDDDXrmmWeUk5Mjg8GgKVOm6ODBg7rttts0e/Zs7dixQz179pTFYtGePXt0/fXX69lnn5XZbHbGOQAAAABwAaPJrAUbE5WQmqUFGxO1Lj5FQ9/YoN1JRleXVqWSuncnGTVm/jYN+9d69XhhjdbFp7i6NAAAAKDOKXl/bTRx/78+qvZwefn5+XrxxRf12muvqbCwUBaLRd26ddM777yj66+/vkzb6667Trt27dLrr7+uF154QXl5eXrttde0fPlyLViwQIMHD3b0eQAAAABuw2gyKzYuWdFtG+m/GxI17oa2WrLjVJm53OqaknMqGT4zNi5ZOfmF+un0RX178BcdTslSQVGxCoulmAXb1Sk8SI2C/PTqqG5udc5Gk1l3/GeLzmTkycsgFf824rce/3iv4l+61XXF1UDp/5fLe50aTWbN35CoQymZmjz0asWdyrBek3X5WgQAAIB7iY1L1rbESw+aeeI0EKhatUOmHj166OjRo7JYLPL399eMGTM0depU+fhUvAtvb29NnTpVd955pyZMmKBNmzbp2LFjGjp0qP70pz/p9ddfV8OGDR12IgAAAIC7iI1L1hdxpzVr9WF5G6SNR87Jy8ugyZ/uVbOQAD07/GpJ0qKtSbqrd7s6MSTlnO+P6vM9yTqWmqXT6Tn6+ZdM+Xt7KaegQLkF5dsfTcuWlK0/vb9Lm6b+rtbrrcz8DYk6k5EnqWzAJEk5BcUymswKC/avMrxxJ5MW79auUxmavfqwRnZvrr8O7aT3t53QD4fTlJNfoIzcIknSkdRMFRVfGvr8Qk6hfj57UaufGOjW5wYAAIC6oeRBNOZzrp+qHTIdOXJEknTTTTdp4cKFuuaa6k1qfvXVV2vDhg16++23NXXqVF28eFHvvfeeVq1apTNnztSsagAAAMCNdWwWpCNp2ZKkol+DjOJii34+myUpS3uSzuvFaOnNtUf12Z6zCgnwkUVS73ZNNMkN50HbnWTUkh2nJEmxe357D29S0RW3PXUh12l11cSOKwzlN+Af69SqUaDOXMxRYZH07cEUvRbTXesOp7ll4LTrVIYkySJpxf4Urdhf8ZB/57LKJoFpWflavO2Epgyv3t91AAAAQGVK5nNG/VTtOZlCQ0P1v//9T5s2bap2wFTan//8Z8XHx+uOO+6QxWJRSgrjnQMAUBpjGAOeY+bK+AqXW379l1dULOlSAHUsLVt7Tl3U3lMXtXBzkp769Kdaq7O6Hnh/h6tLcJjj57OqXJ9TYNGxc9nKybcov8iivaczNGzOJs374Zjmb0ispSprhzE739UlAAAAAKjjqh0y/fzzz5o4caJdB2vRooW+/PJLffrppwoPD7drXwAAeJqSMYxj45JdXQoAOxkMNd92/bHzjivEQUxmy5Ub1RE1PReTuUjfxXvWg3I/HE5zdQkAAAAA6rhqh0ytWrVy2EHvvvtuHTp0yGH7AwDAE8REt1a/DmGMYQx4gNTMPFeX4DbCg/1cXUIZ3nZsezLdvYb+s9fZi1yngKcwmsx65et4jXtnuxJSs8osn/PdEc35/oi1tzy95wEAgCNVe04mR2vUqJGrDg0AgFtiDGPAcwy+upnWHKKXiCSZfx0a0F3cGNlEW5PSXV2GW/Cr9iOHQP20Lj5FT3++X//vtiilZuW75bxsu5OMenTpXqVnm1Xw64/bse/s0B/7tNH4vu017fP9+u7X30f7T2eoe+tG2pRwTknncnTeZNb027q4sPqKGU1mxcYlu+XXGwAAlOeSkOmTTz7RrFmztG/fPlccHgAAAHCqtk2DXF2C2/jX3T1cXUIZZ+i9Y1XoOaMgAg5xPM0kSYp+cY0yC34b93Tysv2SpO0J57XooRtcUltlJi6JkzG7oMyy1Cyz5q9P0LwfElRc6vt8/dHzWn/0tyFZP4877ZYh0yNLdmvHiQy9uylRadkFirmuhYw5hZo+IkodI0JcXR4AN0MwDbherYVMxcXF+vDDDzV79mwdPXq0tg4LAAAA1LpAX3sGZfMsQ7s0d3UJZbx+d3fFLNju6jLcwsCOTV1dAuASRpNZi7edkAxSsyA/vfRNvFo1DpQxM0cvRkvmSjpgrj92XkaT2a1uYl4eMJWo7BxKS88pdHA19vtyz2ntOJEhSUr79dxi9/4iSUo6Z9KGZ37nqtIAuKmSuY0lMTII4CI1CpmKiooUFxen06dPy9fXV+3bt1f37t0rbfvuu+9q9uzZOnnypCTJYrHIz8+9xmYHAAAAHGV8v/aa+0OCq8tABXpHhrm6BLfRo00jV5cAuMSc749qyY5TZZYdP58jf+8rd+9b/OMJTbn5GmeVVu/97bP9la47kZ7rdiEfANcrmdOYuY0B17F5FO433nhD4eHh6tu3r8aMGaPRo0fruuuuU1RUlL7++usybb/66it17txZf/nLX3Ty5ElZLBb5+/vrkUceoTcTAAAAPFZYsL/tb7Q9UIemga4uoUL9I5u4ugS3ML5fe1eXALjEJztPXblRJUqG1INzFF8h55u96lDtFAKgziiZ25gAGnAdm/72feKJJ/TMM88oIyNDFoulzL8jR47ozjvv1DfffCNJeuyxx3TnnXfq+PHjslgsCgoK0lNPPaWkpCT95z//Udu2bZ1yQgCA+sNoMmvBxkQZTWZXlwIA5TRs4JLpT53C34bR/wyS/L2kNo0baMF9vZ1Wkz08aV6myKYNarwtN2NQX9kzH9nGY+ev3AhOs3LfGVeXAAD1EvdfUJVq/+W7Y8cOzZs3TwaDQRaLRTfddJO6desmPz8/HTp0SGvXrlVhYaGefPJJ7dy5U2+99ZYkKSQkRE888YSmTJmixo0bO+1EAAD1D2MvA3BnkU2DdeF0hqRLwUt172k2cMPpnDpFhOrA2cxqtW3RMEB3926t8X3bu22I8frd3TVl2T41auBrHTJu9YFfZMypeG4Td7bwvus19p0dSs2q+A/+yq49L4NTywI81i1dIlxdQhnhwX5KM+W7uoxak1fk6goA1CajyazPd5+Ue/3krR+MJrNi45IVE91aYcH+io1L1oYj57T9uFGv393Dbd/nwzWqHTK9++67lzbw8dHnn3+u22+/vcz63bt369Zbb9WxY8f0yiuvSJKGDx+u999/X82bu9dkvwAAz8DYywDc2T9jumvikjiZC4rl5+OlwqJinbqQe8Xtolo2rIXqbHPjVWFlQiYvSaENvJWVV6TGgb7KLbx01++a8FD9M6a7OkaEuKjS6ukdGabNU8tOHj/l5k669c3NSqvi6cymQe7XO61jRIhWPTFAv3tjgy7mFpZZF+BjULOQAJ2u4Lpz16EMAXf37IgoV5dQxtIJN2rYnE2uLqPWBLjfj2EADmQ0mTV/Q6L2n8nQ1eEhOpqWpfMXc/XENVJ6dr4iGvmWa7942wnJILd+wKkyCalZemXVIU0fEeV2759LP9Q7tHO41h1K1bG0TF3MLdILXx3Uv8dGu7hCuJNq/3revn27DIb/3959x1dRpX8c/9500ogkVBNaIHQRCUgVUEBBEVFEellFbIiuP2y4gu4qIlhZBQQEUQHpIsWlCAgBKQGREnoxAWmhpPf5/ZHNbEIKuSTh3tx83q9XXk7mnDnzTHxyubnPnDMWjRgxIleBSZJCQ0P17rvv6vnnn5dhGGrWrJlWrlwpZ2c7vBUTAOAQstZeBgB7VKeyjxaMaG3+4dvjjmp6ddFeHTgbI6dsc0vcnCQXZykhVfIr56IPeze1XdD5eKZjsLYcvaCI8/Gq5OOuuU/drdu83LQoPCrzj85DF8y7HEsrf2939WxWTbO3nlRaulQrwFMnLyWY/6dcnCyaOtA+l//z93bXzMGh+r9Ff8jVyaJTl+Ll7uasXnferiFtaqrPV9t0OT5zltZdQeVV3tNNY+zsg3KgtLC317mifCh55+2+xRjJrfFFfz7UBBzZyO93a+vJy5Kk7SevSJLcnTPfjX295YTeeKiJ2Tc6LllPfbNTeyOvydXFSQnJ6RrzUMNbH/R1ss8AuhKforHLD6hqeQ+di0nSS/fVVfifV822nv8OU3xqus5dS9Lql+6xdeg5eLk6KezYJYVU9FS3z35Vasb/2pbvO6cXz8faXWEMtlPoIlNkZKQkqXPnzvn2uf/++yVJFotFL774IgUmAAAAlGn+3u56uWs98/vpQ1ro/xbu1ckLMZLi1byGnyb3b6E5207pp71/qUfTqnb5x5q/t7u6Nq6q5PScMWYV+u0x5pvxTIdgebo6m3fDnrwYp7/N2SkZFj0RGqTQWv62DjFfobX8tXF0p1xLm0jSVwOb6+UFe1XR271UzDQD7FXXBpVsHUKefD2cFWPlOnKVfNw0c1jLEoqo5NxZnccwAI4sq8CUl/nbT2vPmVg93KSqJq07ovIerjp9OXO2dnJahsJPX7lVYeYrOi5ZL87bo6griboUm6zle8/mWNJ4y7HMmUHjVx+Ss0VK/+/dTIfPxdoi3AKNW3FQaRnS9LDTeba/OG+3Vr3U4RZHBXtV6CJTTEzm8hg1a9bMt0/2tgYNuDMOAAAAyM7f212THm+qb8OOS0lH9UmfZvL3dtfg1jXl6eZi18t/loYYi+r6oqC/t7s2vNLJLNqUBnnN8s1reUCgLHOWZO2jffy9XDX+sTtKIpwie7jp7Vq460/5eborOMAr14e0bWr5ae+ZGMWnZKiCp6vqVPbW+480sbtZWZJU3sNZ15LS5STJYpHuqRugv64m6tCFeFX2yZxBy0oGQNmUlCHtOHlFO/47w+lqQs5lgi/FJ9kirBye/XaXdpy+KkmaseVkgc9kTc/WmJF/N5tJu0FQB8/F3ZpAUCoUushkGIYsFotcXPI/xMnJydz29vYuWmQAAACAA/L3dtfz99bVqlVHVcHLzdxn7x+alYYYS0JZvW7AkX01uLme/S5cKdk+QLNI8vd0k5SoAE83nYlNlUXSi51q64+zsRrTvYFdFmWkzGfKBVXwNGcwvrV0nxbvjlL9Kr7yLeeqNsH+mjwgNNcsR3u0+Nm2uZ5PMmzWDv0VmyxvD9dSU/AHcOtFXrZtkWnXyWizwCSpwAIT4Gh4ZCIAAAAAACgzjl2MV2AFL6VnGAq8rZxCa96mwa1rytfdSatWrdKS59tq2d5zdl+QyXJ9MTyr6HT9M/NKQ8G8TmUfzbpuGb8x3RuYhafS8P8DgG3Yuqjz2pJ9No4AsB2ri0x//fVXoWYpFaZf9erVrT09AAAAAADATevdPFAJyWnm89eyChepqamSpApebqWiIJOf7AUlR3gGW16FJwCwN33uul3j/3PE1mHgOnk9rxTFz+oiU9euXQtst1gshe6XlpZWYB8AAGAfeGMGAAAcxfXPXwMAoKi+2HTC1iEgD4vCo7T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}