Developing Algorithm for SDP images and Creating a data set.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Minor Project review 1",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/devamsheth21/Bearing-Fault-Detection-using-Deep-Learning-approach/blob/main/Developing%20Algorithm%20for%20SDP%20images%20and%20Creating%20a%20data%20set.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IJrq-QmSPuC6",
        "outputId": "d6459ab9-8ca5-4574-a29a-ec83c020ce65"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dcP_fft2MXH3"
      },
      "source": [
        "import scipy.io\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "!wget http://csegroups.case.edu/sites/default/files/bearingdatacenter/files/Datafiles/98.mat?raw=true\n",
        "# rename the file\n",
        "!mv 98.mat\\?raw\\=true data.mat\n",
        "# update scipy\n",
        "mat = scipy.io.loadmat('data.mat')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pHSL_-gB8pQs",
        "outputId": "efe4d8c3-2353-4b31-9b43-13f4ee49d074"
      },
      "source": [
        "print(mat)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{'__header__': b'MATLAB 5.0 MAT-file, Platform: PCWIN, Created on: Mon Jan 31 15:28:20 2000', '__version__': '1.0', '__globals__': [], 'X097_DE_time': array([[ 0.05319692],\n",
            "       [ 0.08866154],\n",
            "       [ 0.09971815],\n",
            "       ...,\n",
            "       [-0.03463015],\n",
            "       [ 0.01668923],\n",
            "       [ 0.04693846]]), 'X097_FE_time': array([[0.14566727],\n",
            "       [0.09779636],\n",
            "       [0.05485636],\n",
            "       ...,\n",
            "       [0.14053091],\n",
            "       [0.09553636],\n",
            "       [0.09019455]]), 'X097RPM': array([[1796]], dtype=uint16)}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "bVtYNTai89Sn",
        "outputId": "3fbfbead-72cf-432d-f5a6-7184a11d6839"
      },
      "source": [
        "# visualization of data 97.mat\n",
        "c1 =np.array( mat[\"X097_DE_time\"])\n",
        "c2 = np.array(mat[\"X097_FE_time\"])\n",
        "c1 = c1[:1600]\n",
        "c1max = np.max(c1)\n",
        "c1min = np.min(c1)\n",
        "c1mean = np.mean(c1)\n",
        "# c1 = (c1 - c1mean)/max(abs(c1 - c1mean))\n",
        "import matplotlib.pyplot as plt\n",
        "plt.plot(range(0,1600),c1[:1600])\n",
        "# print(c1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f3f587e5750>]"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "id": "MtaQvkcQSDvt",
        "outputId": "471e5727-a84e-4729-94bc-9f46a451c040"
      },
      "source": [
        "# visualization of data SDP 97.mat\n",
        "z =35\n",
        "t =2\n",
        "for i in range(0,7):\n",
        "      r = ((c1 - c1min)/(c1max - c1min))\n",
        "      r = r[:len(c1)-t]\n",
        "      theta = (60*i + (((c1-c1min)/(c1max-c1min))*z))\n",
        "      phi = (60*i - (((c1-c1min)/(c1max-c1min))*z))\n",
        "      theta = theta[t:]\n",
        "      phi = phi[t:]\n",
        "      plt.polar(theta*(np.pi/180),r ,'.', c='blue')\n",
        "      plt.polar(phi*(np.pi/180),r ,'.', c='red')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L7UZ5Xjy-smN"
      },
      "source": [
        "import scipy.io\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "x=97\n",
        "!wget http://csegroups.case.edu/sites/default/files/bearingdatacenter/files/Datafiles/97.mat?raw=true -O data.mat\n",
        "# rename the file\n",
        "# !mv \"\" + x +\".mat\\?raw\\=true data.mat\"\n",
        "# update scipy\n",
        "mat = scipy.io.loadmat('data.mat')\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "fRrLzWvQXoGw",
        "outputId": "65bb078a-8ab3-4563-9acb-3ad434ca7ade"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.plot(range(0,len(c1)),c1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f11af3d2f10>]"
            ]
          },
          "execution_count": 72,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZagMmcgc7WnV",
        "outputId": "bc1a810d-5cc6-407b-9dae-c1bf5b339270"
      },
      "source": [
        "\n",
        "# c1max = np.max(c1)\n",
        "# c1min = np.min(c1)\n",
        "# c1mean = np.mean(c1)\n",
        "# print(c1max,c1min,c1mean)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0.35923569230769226 -0.3269003076923077 0.012260601185032428\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uti84eHyhclc"
      },
      "source": [
        "# Developing Algorithm for creating SDP images"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KexwVpgKpr3f"
      },
      "source": [
        "    # for i in range(0,7):\n",
        "    #     r = ((x - xmin)/(xmax - xmin))\n",
        "    #     r = r[:len(x)-t]\n",
        "    #     theta = (60*i + (((x-xmin)/(xmax-xmin))*z))\n",
        "    #     phi = (60*i - (((x-xmin)/(xmax-xmin))*z))\n",
        "    #     theta = theta[t:]\n",
        "    #     phi = phi[t:]\n",
        "    #     plt.polar(theta*(np.pi/180),r ,'.', c='blue')\n",
        "    #     plt.polar(phi*(np.pi/180),r ,'.', c='red')\n",
        "    # plt.show()\n",
        "  # sdp(c1)\n",
        "# print(x)\n",
        "# xmax = np.max(x)\n",
        "# xmin = np.min(x)\n",
        "# xmean = np.mean(x)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vkrPQ4d79FOm",
        "outputId": "c84d76a5-9ddb-48bd-c5f2-b1130a4b8f3d"
      },
      "source": [
        "cslice = c1[:2002]\n",
        "print(len(cslice))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2002\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rRLS3x9TDQwk"
      },
      "source": [
        "xmax = np.max(cslice)\n",
        "xmin = np.min(cslice)\n",
        "xmean = np.mean(cslice)\n",
        "x = cslice"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RGMkVRvLDsfb",
        "outputId": "a6e70bf3-da9b-4e21-8527-b0ebd1c6e2ee"
      },
      "source": [
        "print(xmax,xmin,xmean)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2.8617008982035927 -3.0476891217564868 0.006175293568706744\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0eeP5BU-C2Ms"
      },
      "source": [
        "t = 2\n",
        "z = 35\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "V-V8h92ggq_J",
        "outputId": "64d4f5a2-bb20-488e-87b5-548439754e89"
      },
      "source": [
        "c1 =np.array( mat[\"X144_DE_time\"])\n",
        "c2 = np.array(mat[\"X144_FE_time\"])\n",
        "temp = ((c1-c1min)/(c1max-c1min))*z\n",
        "th0 = 0 + (temp)\n",
        "th60 = 60 + temp\n",
        "th120 = 120 + temp\n",
        "th180 = 180 + temp\n",
        "th270 = 270 +temp\n",
        "r = ((c1 - c1min)/(c1max - c1min))\n",
        "r = r[:len(c1)-t]\n",
        "th0 = th0[t:]\n",
        "\n",
        "th60 = th60[t:]\n",
        "th120 = th120[t:]\n",
        "th180 = th180[t:]\n",
        "th270 = th270[t:]\n",
        "\n",
        "plt.polar(th0*(np.pi/180),r,\".\",c=\"blue\")\n",
        "plt.polar(th60*(2*np.pi/180),r,\".\",c=\"blue\")\n",
        "plt.polar(th0*(2*np.pi/180),r,\".\",c=\"blue\")\n",
        "plt.grid(False)\n",
        "plt.axis('off')\n",
        "# theta = (60*i + (((c1-c1min)/(c1max-c1min))*z))\n",
        "# phi = (60*i - (((c1-c1min)/(c1max-c1min))*z))\n",
        "# theta = theta[t:]\n",
        "# phi = phi[t:]\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(0.0, 6.283185307179586, 0.0, 1.05)"
            ]
          },
          "execution_count": 118,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FR7yjPgUCgb-",
        "outputId": "23ae715b-6716-484f-fc99-057a9287c70a"
      },
      "source": [
        "print(len(c1))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "122281\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "id": "ii8cubpUC_jZ",
        "outputId": "bb0024a0-b70d-45e0-e247-81022006b9ee"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "r = []\n",
        "theta = []\n",
        "phi = []\n",
        "angle = 1\n",
        "fig  = plt.figure()\n",
        "for i in range(0,2000):\n",
        "  xi = x[i]\n",
        "  r.append(((xi-xmin)/(xmax-xmin)))\n",
        "  theta.append(angle*60 + (((x[i+t]-xmin)/(xmax-xmin))*z))\n",
        "  phi.append(angle*60 - (((x[i+t]-xmin)/(xmax-xmin))*z))\n",
        "  \n",
        "  # fig.add_subplot(projection='polar')\n",
        "  plt.polar(theta,r,'*', c='blue')\n",
        "plt.show() \n",
        "\n",
        "  "
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303
        },
        "id": "PXHf892y_qGJ",
        "outputId": "04fef535-5da9-4c65-f4e3-60cd8663b860"
      },
      "source": [
        "plt.polar()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[]"
            ]
          },
          "execution_count": 24,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        },
        "id": "qmBvz2ewGwqF",
        "outputId": "806b1cff-e012-4f1d-80e5-75572e484c86"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "\n",
        "fig = plt.figure()\n",
        "ax = fig.add_subplot(111, projection='polar')\n",
        "bx = fig.add_subplot(111, projection='polar')\n",
        "c = ax.scatter(theta, r,c='blue')\n",
        "d = ax.scatter(phi,r ,c='red')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:8: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
            "  \n"
          ]
        },
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NpbhI3j37u4P",
        "outputId": "53dfa48f-0ef6-4232-e253-f215a446754e"
      },
      "source": [
        "print(np.min(rs))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1.409316294846968e-06\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "id": "8li7szSwj0lP",
        "outputId": "4b1a35ac-772f-4b53-f01d-6d716568b64a"
      },
      "source": [
        "\n",
        "for i in range(0,7):\n",
        "    r = ((c1 - c1min)/(c1max - c1min))\n",
        "    r = r[:len(c1)-t]\n",
        "    theta = (60*i + (((c1-c1min)/(c1max-c1min))*z))\n",
        "    phi = (60*i - (((c1-c1min)/(c1max-c1min))*z))\n",
        "    theta = theta[t:]\n",
        "    phi = phi[t:]\n",
        "    plt.polar(theta*(np.pi/180),r ,'.', c='blue')\n",
        "    plt.polar(phi*(np.pi/180),r ,'.', c='red')\n",
        "        # print(r)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WAggMOUAe9q9"
      },
      "source": [
        "# Creating SDP of sin waves\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 131
        },
        "id": "xyhkKlgDwbV9",
        "outputId": "16ba7563-bcf3-4a74-ff9c-4f389a883715"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "time = np.arange(0,10,1/2400)\n",
        "amp = np.sin(2*np.pi*time*50)\n",
        "fig = plt.figure()\n",
        "fig.set_size_inches(12,6)\n",
        "plt.subplots_adjust(hspace=1)\n",
        "\n",
        "plt.subplot(311)\n",
        "# plt.plot(x, sampled_f1)\n",
        "plt.plot(time,amp)\n",
        "plt.xlim(0, 1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(0.0, 1.0)"
            ]
          },
          "execution_count": 61,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAskAAABhCAYAAAAk5TxHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOy9eZAk13kn9nt1H11d1dfMdPfgPoiLEiUhuJQBkDqoFbgKiV7ZckgRsr0OrWVHWI7dcKzDcmjDdlhhWwB3JUrUraWu1Wp5iiREUqQIzuDgARIDECQwA8yBc7p7+qjuqq77zOc/sl5WVuY7vpcDzIyw+UUwiJmpV9+XX37v937fkVmMc45YYoklllhiiSWWWGKJZSqJq21ALLHEEkssscQSSyyxXGsSk+RYYoklllhiiSWWWGIJSEySY4klllhiiSWWWGKJJSAxSY4lllhiiSWWWGKJJZaAxCQ5llhiiSWWWGKJJZZYAhKT5FhiiSWWWGKJJZZYYgnIm0KSGWN/yhjbZYy98GZ8XyyxxBJLLLHEEksssVxNYW/Ge5IZY+8F0ALwl5zze0yfX15e5jfeeONl640lllhiiSWWWGKJJRadPPPMM1XO+YrtutSboZxz/gRj7Ebq52+88UacOnUKo7GD3WYfa5W8lb6tehdHSlmkkvRCeLs/Qn/kYLGYIa/hnGOj1sXxhTwYY+R1e80+5rIp5DNJ8prh2MF+a4Bj5Rx5DQBs1rs4Np9DMkG3r9kbYjTmWLD0xWa9i+MLBSv7dhs9zOfTyKXpvhiMHNQ6Axydt/PFRq2DtXIeCQtfHHaHAIByPk1e4zgcW4f2vthp9LBQyCCTosdtbzhGozvEkQi+WK/YxW29M0AywVDK0X0xdji2Gz2sW+7h7cMeluYySFvs4e5gjFZ/hJVS1krXxYOO9R4+aA+QTSVQzNIhMiqeXTrsYmXODs86gxG6gzGW5ui+iIpn1VYfxYw9nlVbfayW7bH9qCWetfojDEfOlcGzZg/zOXs8O2hHw/bV+ZwVnjV6Q3AHKBfeejzbbfRQLqSRTdF90R+NcdiJhmfW2N4ZgiWAeQs8cxyOSxHxbLEYAdt7Qxwp2fkiKp5lUgnMWeDZ2OHYafQi4dnyXNYK2zuDETqDMZbfQjxjjL1O/nKfXNWZ5F/9m+dx/0Mn8PRrB+Q133plH/c9dAK/9hn6ZMfY4fjPf+/r+NF/8xgO2gPyuo9+7VU88PBJfOzpi+Q1lw67+JEPncTP/8lTsKnS/8uPPYf7HzqB5zcOyWseP7eH+37jBH7982fIa4ZjBz/1O1/DT/zW42j2huR1v3viAu5/6CQ++51N8prX99t474dO4p/92bfJawDgf/yrZ/DAQydxbqdJXvPl09u4/6GTePjLZ8lresMxHvzwE3jww0+gNxyT133o78/i/odO4ksvbJPXnN9p4oGHTuJ/+PenyGsA4L/7s6fx3g+dxOv7bfKaz35nE/c/dBIfOXGBvKbZG+L9v/kEfup3vobh2CGv+/XPn8F9v3ECj53dJa95fuMQ9z90Av/iY98hr+Gc4xf+5Cn8yIdO4tJhl7zuY99+Aw88fBL/7slXyWtq7QF+7N8+hg/+3tcxduh7+F9/9gXc99AJPPXKPnnNqdcOcN9vnMD/9unnyWsch+Nnf/8b+NF/8xiqrT553Z9/4zU88PBJ/NW33iCv2W308KMfegw/90ffsMKzf/XJ7+L+h07iO2/UyGu+dr6K+x46gf/zETq2j8YOfvojX8OP/+bjXsJLkT94/GXc/9BJfOqZDfKaiwcdvO/hx/Bff/Rb5DUA8D/99bN44OETePFSg7zmqy/u4L7fOIH/7+9eJK/pj8b4wIefxE/81uPoDEbkdb/16Dnc/9BJfOF7l8hrXt5r4f6HT+Kf/4Udnv3zvziF+x8+iVf2WuQ1f/vdLdz/0El8+NFz5DXt/gg/8VuP4wMffhL9ER3b/98vvoj7fuMETry0Q15zZquBBx4+gV/562fJazjn+MV/9y287+HHsFHrkNd98tRFPPDwSfzh46+Q1xx2hnj/bz6On/nI1zCywPb/43Munn3jQpW85tk3arj/oZP4Xz/5XfIazjl+7g+/iR/90GPYbfTI6/7qqdfxwMMn8RffeI28JopcMZLMGPtlxtgpxtipvb099IZjfOqZDTgc+Jtn6UD16Wc3wDnwiWcukoP/uYt1nN9t4bA7xFfO0InNxyfk+BOn6CT5i89voz0Y47sTnRRp9Ib4wvOXMHI4/uY7dF8IgP/40xfJh/m3Xz3AGwcdVFsDnDy7R9b18VP2vvj89y6hN3Tw1CsHuHhAA4LdZg8nXtrFYOzgc8/RCfknT2149lEP86+dr+LSYQ+XDnt48jwNCDjnXlx86hm6Lz733BYGYwcnz+5ht0kDgosHHXzzlX30hg7+9rtbZF2fnNj1cYvk7rGze6i2+njjoINvvUJLWseO3xf0uP3MdzYxcji++Pw2mdhc2G3huYt1tAdjq8P8ExHi9itndlDvDCc6aSRvMHLw8VMXwTnwaQtffPrZTTjcxTVqovbC1iFe2m6i0Rvhy6ft8eyTFr74uxe20eyP8MJmAy9eoiWt7f4In3tuC2OH4zMWSbWH7U9vkA/zU6/X8Gq1jYP2wIrYfCICtn/h+UvoDsd4+jVXJ0UO2gN85cwOhmOOz0bAs48/Tcezb7y8j816F7vNPp44Z4HtIi4s8OyR57YwGDl48nyVnLRu1bt48nwVg5GDR6zwbOILi3v1xLk97Db72Kx38c2XaUmr48Mz4X+KfO65TQzHHH9/Zgc1YhHu1Wobp16voTu0wzNhl80e/upLOzhoD/BKtY1nXqfh2XDsTM5SS2x/dhNjh+Ozz22RE7Uzlxo4vdVAsz/Clyzw7BPemU+3L4pcMZLMOf9jzvm9nPN7V1ZWZqqEL2zSM+znJ5/l3D04KfLC5qHvv2m6OoMRLkyy3TNbDTJoz+qiVYXPbE1tOm3hC/H93eGYXGl83mfTaaJ99c4AG7Wup5MK2v6qONUXp32+sIkL8f0H7QG2idno8xHu1Xaj53Uj7OLW5/ct2rooccs59/y+We+SQXtG1xbNF6/vt9GdEDvqNQV1UatrUfw3GjveZy/stcig/XwEv5/fbUJsixci+oLaOfnehr19veHYS9pfutQkdwuejxAXL2379zC9MyZ0DcYOmYRG2SON3hCv7btJ+5mtBhxigSEKXpze8uOtPV40eiMPe03yQoS42G32sNvsT9bQsf2FTfvriopnYt1Oo4+9Jq1zEgUvNmpdNPsuRlBjHZjdj2ci4BkVLxyHe3a9Um2j1Y+AZ0Rdr+y1MRzzyRr7PQwAL23T8CwKZxqMHA9nzu00rboFtnLVxi0EMXzw7mM4u00D7f5ojPM7TXzgnmMA6MF/eusQi8UM3n3j4gxo6eSlbffQe/DuY+iP6KB9eusQ77t9Bbl0wsI+93MfuOcYzlyigXarP8Kr1XYEXzSwXsnjnetl8pozPvtsQPv0pUO8/86jSCaYta6fvPsoec1+q4/tRm/qCyIAn95q4NYjc7j1yBzdf5tTX2w3etgntrtPbzXwk3cfBTCbFJnWJBMM77/zKE5fosXtRq2LRm/k+YIK2qe3GnjnehnrlXykuH2VCNqOw3HmUiNS3ObSCbzv9hXyHn612kZ/5ODBu4+Bczpon946xLtvXMRiMUPW5ffFeSJoD8cOzm5HwbMGyvk0fvjmJZwh2ndup4mxw/GBe45hMHbIBYbTWw08cNsyipmkVdwCri9evNQkdbm6gzFe2WtF8sXR+SzedV2FfK9e9NnX6o/wBrHLdWargR+/4wjSSTqenfadc6e3aCS03hlgs96N5Iublou441gpUtxWWwOPMFPW/eO7joIxO/sYA37irqPkuBVFiSh4dufqPG5YKlj4wv3cB+45hosHXVKXi3OO01uHePBuca/ohbFMMoEfu+MIec1r+210BmPPF9QCw+mtBn7w+gpWStlIvnh5r03qco0djpe2o2H7XDaF+29dJq85v9vEcOzi2cjhOL9DH9+xlTfrFXD/EcA3AbyDMbbBGPsl05qXtpsoZpL4x3cfxWDs4DUCCX1lr42Rw/HgPceQSydwlnjond1u4s7VEu5cLeHsdpMEVOK7f/YH1z17TTIcO3h5r4171udx+9GShX0NLM9l8MBtK2j1R9ism0moqDr99PevIZlgVrqEL6ik4SXPF8cn32Fe1+6PcPGgi3ddV8bNy0UrXccX8nj3TUuotvokEirs+ac/4N6rs8SK3NmdBu44VsIdx0o4u0PbnOK7PV2E69pv9VFt9fHum5ZwfCFv5Yubl4t413VlXDzook0goWcD98pG13SPEH2x3UQywfDT378GgFYJ3ax30eqP8MBtK1iey1jpuv1oCfesz+PlvTYpqX4psIcp94pzHsILqn25dAIPTkD7lT0znr1WbWMwdvATdx3FXDZltYfvOFbCnavzOLtDw7OXgnuEoGvscFzYbeKutXm841hppkJs0lUppPGj7ziC7nBMGrU6v9uEw4Gf+r5VpJPMMm7nXV9Q/bdjv0d6wzFe22/jncfLuGVlzipuV8s5/PAtS6h1hqRKqLiOD75rHYzR7hXgXpeHZxZxC0zjguKLw84Q240efuiGBdywWKBj53YTNy0V8YPXL2DrsEcioeGzh67rzokvbGKJMdfvAA3Pths9NHoj3HfrEo7OZ6103XpkDvesl/FqlUZCo2L72Sh7ZLuJTDKBf/LO1QkOmEno6/tt9IYOfuyOIyjn0+R79dK2G7d3rc3j/E6LlFRHiduo8qaQZM75L3DOVznnac75cc75R01rtupdrC/kccOS+0TtBoEYbk4qmDcsFXF8oeD92biu3sX1iwVct1hAezAmbc7NWhfJBMM/umnJ+w6TbB/2MHa4p4uyRnz3dYsFXL9YIOsS137LyhyOzedIazjn2KxNdVVbfdLm3Kh1Ucgk8f3Xlcn2bU0+I3SRfVHr4HpLX4jK9p2r81gqZkiV7rHDcane83RdqvdIm3Oj1sVSMYM7V+fdP1Pu1eQzQtcm8UENf9wCU59SdH3/dWUUMknSHukNx6i2+tO4rXVJxEu8WeWWlTn3zwRdfl9E3SNjh2P70DxSI777H920hGSCkew77A7RHowjxK37doAbloren00iYueGpQKOL+TJHRoRF9cv5tEbOtgnjNRs1rpgDHjPLXQ82232MBzzadwSfbFRm41bGzy7abmItUo+El7UOkNSIrlZ6yKTSuCHblgg27dV74JzWPvCj7eAHV6841gJK3NZbNbNeOE43BcXBWw3eqREcrPWRTmfxj3rZe/PJtmY2OPHC4oEzzkSnk2++53rZZRyKZKuwcjBTrM33cMWeHa0lMNtRy3wrBY456z3cAGcwwrP7r1hAZlkgqSr2RvisDv08IK8hyf87KZlF88o2DSL7Xm6L3x4MZi8FYeyBvDhGVFXFLlq4xbbjR6OlfPe60VsDti1So4MpC4BGGCtnPde60K94cfmcygX0pgnbs6pfa6uzXqXNDqxWetirZLHWiXn/ZmuK+fqsiAA65Wp3ylAtVV37VsuZpFJJWjEdfIZocuGGEbxBWPA0Xl6XOw2exg5fKIrj5HDSQ/UCfuOzufAmB2Q2sStu67jxRJAP2AzqQSWi1msVfLk++va5+qySSTX/ffK4tDzfEHwnyAA/ril6prPpVAupMmJpMCG9Uoea+U8qq0BKZEMxa2VL6Z4YZLBaPqaOVvsPFLKYj6XxiIxkfTbt1bJkxPJrXp3Bm9t8Ez4nYIXzd4Qjd5oxu+UeN+YxNJCIY18mpZI+rFdxC2VeK3b3qvJZ1bLdLyotvsYjBzPPseCeK1V8jhSyrqJJIGQR4lbvy5bbE8lGFZKWbKu7cMeOJ+ePf0RPZFcq+SwVrbAmOA5Z1GsssWLjVoXxUwSlUIaqxUanm3V3RgQcVG3SCQFRlPtm8GLMs0Xo7GD7clr5tYnvqDys+W5DOZzaayUaIlkVLlqJPnSYQ+r8zkcKeWQSjDyYS4IwHoUArBAJ4YC3ABgfaFgpWt9Ah4DwuZ031HZi0Rc53MplHJprC/QAjK4od3voQNpIsHIQOX5YsH1e6M3Mr5yThAA4T+/zSb7jpRcAm8bF8I+/9+Z1q1X8sikEjhSylpVd8V17TbdA00nggDY2ifiNpFgLkkmPHnuB1Jbv68v5FHKuYmk7X48Xslj67BnTCT32wMMRs5MXJDv1eTdr7Zxa4sXIi5EIkldk0owHCnlyAmNnwBEsQ8AeY8Ifx33JZKmcQE/AThWniSSRF3FTBLlvItnFFwSn7HFCzfpz4ExNtFlh+3HF9xEstHVkw1BAPyJJEnXoUsAculkNF9Yx0UOqWQCx+ZzRF0+7KzQEsnuYIyD9gDH/XhGwqYujpXd92a7e5hecRX2+W3W6jp08SKfSWKxmLE6UwV2im6yTuqdIbrDMdYX8jheoXdbRPedMYa1MnUPd2bsE99D0uVLJKlrEgw4Vp4WQEyJ5G6zj/GkWLVeseuYzuIZ/dVxtnJVSDLn7gvqRfAfK+fIjlkr5yZkLYf9tnlzeuCxEKV66gLbOjlrm602UHT5CUAuncTyXIYMHkLHWiWH7YZ5c8rIkO0Bu1ah3asgAQDcxEgnO40pASjn0yhkkmTQntqXn7RG9b7YlB6wel2c84Df6STUIwCVPDh3r1UnwldulccukfTHrS0BoCZP4kdEhC5y1dpHANaIiWSUfQUIIBX2Wfpige6L3nCM/fYA6xUXm9bKdLwQGLhWyeOwOzQ+/CgjAHRiaLuH7ZMnPwGwSSSFfYy5vthpmscF5HERAS8Ie3iz3pshAO7f6df5CUApl0aJmEhu1nszBGCTgGdSX5CuaxbbSbF02EM2lcBSMUPGdmHLWiXnJpJJWkdShu2UNeLzVLxwJuN3axHwolJIo5hNkRPJadEk5yWSVOxcs/SFONOOW+BFfzT2ulXufqTysx6OzueQTia8jmSjp8ezWby1SCR9eEZN+qPKVSHJQ8cB5/CcslrOGTcZ4BIL8atFxyYtEVNLSbwO7Nh8DkvFDDLJBC4ZCArnfKIrP9GVI71WbLvRw0LB/TWm1YmdFGIIwPt1uWNEX2w3ep6OY+U8xg43zvKIa1gt5zx9Jl2CxBwT9s3nSW287cO+18Kj+sK7V2W3yuP6nVBd88XFajmHDmFz7hxOdYm124ZDpdl3fxVotWwft0d91wQQfHE4vVfJBMORUpam67CHY/OTuJ13qzymqvW2LwZXib7Yb7kEQOwRqi+2D3szsS7+jmLfajmHXDqJhUI6El64VVg92dhu9JFOMiwVM7641ftix4tbH15Y7uFVoi/8eFHOu1Uee120PbzT6KGUS6GYTVnfKw8vynkidvZn9jDnML5pIYhnjJnjduxw7Db7nn2r88Q9fNjzfkXM84UBm/z2if+n6vL2yHxu8guk+i6cHy+oeNsZjNDsjXA0sEdMsn3Y8zCavEcE3s67Xa6j5Sw5bqd7OIfD7hDdgb4w5vc71Re1zgCDsYPVCOecF0sR8CKTSmCpSPTFYX8mlnYIhbGdQze5W57LkvewIPl+vLDHW3u8KOXSmMumiLr6M7ooa6LK1SHJkwqBOFSW57KkmaGD9gBLxexkjfsTpPttPZCK98QuzmXAGMPSXAb7Lb2uVn+E4ZhjafIzp8tzWdQ7Q2Nlo9Yeej97LX5e0WSfeOfu0tx0ncm+qS5Xx8pkrYkkC18sTH4+s5xPG+2rd6b+A4Dlkus/E9modQbTNcIXBvuEL/w+rJJ8MZiuKWVoujoDpJMMpWwKpWwKmVTC6PeaxD7KvXLj1s4XtU5AV4moqzOYxtLEF6ZfmTxoD1DKuT5YLGbAGIx+P5jYt2Tri054j1Spe9hC19jhqHene2R5LoORw40t8lp7gIWCixXTPWz2HxDwBQHPpHhB3CNLRR+eGXR1B2P0hs6ML5r9kbELJ41by3u1Mpch72HbPeLHi2SCYbGQQdXgi8PuEJxjBi8O2gPjyM9BZ+C7pkncRsELyjk3s4fpeJFgmHTgUihkksY9Eo7bDOltQrXOYAaXXPuIeBHpnAv63RyDhUxyklC7sWHEdu+cm5ypJWLc+n0xR/SFxO8mXZxz1Gd0ZeDw6fms1NUZeD6g4lmt7SZkfrww7Xsg4jkX2iNmPBuMHLT6oxld3eHY6lcmbeQqkWQXkESm4m4Y802od4aoTH6TfpkKVJ0BUgmXDAGYkGQTMXSDJKjL9MMMtUlAAtObTt2cCz5dVKBaCNhH0VXKprzfVF8mJAyigiF0rcxlMRg73kvX9fZNA59in9jwCz5QNPli7HAcdoc+XTQgqHcGqEzIEGMMKwRC7vmiOPX7YXdorNTWJrqAKXE1HeZeQlPwH7B6XwgyFN4j5kRI6EklE1gomEFRAKmnq+TaZ0qe6hHiYhqDdF80JmTIi9sSkZD77HMP2kQkvKA8oT2zR0RyR4hbxoD5PF2XDGMAc/Lkj1tq8iTbI1Q8q0TAi3zaJUNUXbUAxizPZT0MMekS92rpcuLWYJ8gQ0FfUM65SiGDRIKRdU3jdmpfezA2VmpnsZ1K4u33iCBDIb8b43Z6HiQSDItFAp4FzrnluSwO2n1j8jS7h2mJZBAvVkpmPGv2Rxg5XKKLcs65enLpJErZVCS8oCQ0/rhdoZ5zkrgwx+1gssYOL6LKVSHJ4tfrRGa4NJdBrTPU/qqd43Bp1ka54YIMiXXUypA/uwGAPUJlQ4CvqNSSq7s+IKi29ZXa3nCMzmDs6VqyyLDFGrGOck0AsGh7QLSn4DE3qdSa7DsQGazfFwY9je4QDvf5r0ivQgk9U13290p8l3ZdZ6pL/L+50j0Em1SGALfqUG0SqyGhA9asayYuipTkaXaPLBUzGI7NldqDtqzyYt7DuXQC+YxLhijdoIOQfZM9Ymjh1zoDj+CJajK1MuTHi2ZPX6nlnE90BewjdEAq+TSSHhki+KI9SwzJeOHDW69SS7APmN3DpkrtcOyg2RuF4sKMTdMqo9BFre5O9zC94yd0iUotvXOXJtsnupiLQRJKSFoFqaHqUp1zNF3uZyv5NBKMUp0MJv1m+4JFExu8ED4HXGzaM2DnQTAuim6lVuCcUld7qktUNilduHSSYU4U7ooRzh4invnjFqBhZzCRXJrLojMwV2r93RYb+0q5aeEuCrZT8SKqXBWSPHa41xoCpkB1oAnIRm+WDFErtW6Q+DcM4dCTBAlFl58MuesoN3yWDC0Xs14GrdMD+AgAkbgGyRClvRbaMDYkdLKGMYblIuEAC5KhorlSG94wxMpLexgCUupoTAgINL7gnE90+Su1aVKS4SdDS3PmSm2IDBWJcdseYDFwwJKJYeAw11VqBRkKVWoJSat/X5Gqp4qEhqQrkEiaqyFyvNAlT0EytFBI0yq17WBCY65CRcULPxkS66jt04rvMB85HA3Nm23C/qN34Wb2MKEKFSKGRGJT6wTwguCLIBlansuiZRhzER2a6R62Oedm44JeMbTDC/+Z6lZqzV24EBkqmiu1soSLat9CAC9sx4SmyZNalzfSNdGVSycxl02RC0iicLcUMREHKB3J4B42+yJKYUx0MYWu8uTsomBTEG+p9tl2dqLK1akkT9oGXmtIAJUm2wsGSSaVwHwuRQDtYWDDZLDX0pONWjBIBFBpbl6QDLnXRTvMy/k0Ur5MCtCDdjBIStkUMskEabZzhgwV7avqS4Tq5HDsoOEjQ+46WvAHkwxAn83XAsRwoWi2D5jNej37LDPsZQLxag/GGIyd2USNQPIOOoNQQjMcc+0DiUoyRPB7sMNAJaEVC6Ca2jet1C4Vs+bqbtC+YgaN3kj708+quKW0hWfwopghVUNmRroIxCZIhrwxlwgdkP2WvlIbxItlatU6SoGhPfBm/IV9Jl21QAepkEkhn06SfDGDMRYdEJuq+tiZzIPO6DJjZ4gMFc14cRDYI5VCBglGq/oHz7m3Atu7gzG6w3EIm2y6EkKXw4G6ZsxlmvSLSi2tYijTRS6MFei+EPPtCwFdtH01u8ZUqVXFLSVRWwxgp7Ej2Xbn26cjXWZfBOPWG3OxTWiKbudJ90DidGbazhdR5apVkmc2GWGWJxgkYh2ltREMSFOlNliRE/bpgktGhpZLtPZksEoG6G94MEjctrA5+INkiPJA4pQMTWeSAT2JFzNXM74gjjME7QOgfaVOMOtNW1RqQ9UGwwOJQTLkVU819gUrmu46wmEuqZ4CBqAK6JrLppBNJUgVfL+uFSKJ95Mhin1BYuiuMz9sFdzDYj/qKrVBvFgsuDO1expfeGQoeMBS5piLvpGuEuFeBQ4VgE42gnvEWKkNVqHEvKChMhQiQyVaddK/hoIXQTIkbLQlXiulrPGBxOBIF2We0RvpCvjd9KqvIBmKghdJi0ptUJdpzKXWHsx2MQkd0+BIl1hnm9DY4IW4rnwmiSLxgcSgLsrZk08nvS6mTdwG/U7CC+mZr9M1G7eiy2juYg5Ce9hYNJmMqCZ98+2A3hfyc46GF0FsN425TDv9wTGXtxFJHo357PwUofISDBKAXqkNtidNumqdAZIJhvmcS4YolVpZkJCrDYFDGTBsTskBS6nUysZBAD3ZOOgMJmTLBY8Fwr2SJTSkllJwwxAqtbVO+IA1+SI43y50DSYVcJ0uPxmiVGplQOq28G2ru5Tq5KwuMVOr2yPB+XZgWqnVjbmo4lZ3WAYTGncd4WErScXQ1UXHC0qlNjjfPrXPnDwFcQkwEQAFXlDIkGQP6/FidqSLUqmVkSFKpVbWlQCoHQY77JRVkgEzXvhHukSlVhu3EvsoldogGYqKF6axuOB8u9AlxgGUujrBka5JLBHsC+qi+CKYfALERNKiIxmcbxe6TJXa8Hw7oVjVke1h2h6RnXMkvLCo1IqH/WaxiVapnZ1vN/tCFbek8bEABrq66IR8+kDi22jcYux7ShOgtbyCQeKu029OjwxZA9Vwpk1GeXWcnAxljJVaVYatrarLDliDfTIyRN2cfp+nkwlUCvpXx8nJkPnVcSEyRNqcs9UGQDwIoakM9WRkiEby/Nc0R3h1XHC+HXCBynq+nTALHiRDgGLPRkIAACAASURBVDkupASFMFMbnG8XtmqrDbLkKcKhQsKLABmi6JKRoaWi+dVxofl2QnKnwgvdGhkZonWeZskQRZeMDFFeHefGrcwXdnhhOmDlZIh2mPv1JCdkQ9fNUOGtqVKrrCRbFxj09yo43+6uoXUk/Xoor45TJjSWZIhUtQ6MdAF0PAuOPRp1BebbvQcSrc982luSFgJjTCb7gvPt7jr9HgmOIgr7TK+OC8+32xSrAthOeiBWgheGPeyfbxfrKK9XjCJXaSbZmbkJ87lJpZZCNizeSOCRIWkLXx9c/htH0SXsq0iAytQW9usSfjHNZ4fIkKGqLs16KS2lznAGfF1del/INudyMWus1LoJQ3jDmK4rm0ogn56SIVP1VNbenT6Ep4uL6SsIgekDiboZueB8O+D6XfdAophvrwRa8a59el3lIBky3KvgiIZrHzF58vlCPJBI0RVsr+keSJTNt1Mezgwmn4B5D8viVrw6TnePgx0Q8UCitq0uwYvlOf18tkeGJIeKCTv91+Sui44X2sMyQLwWxEwtYcSgEkgKafZZ4kVb4gvDXLx0jxSzxkptrTOLF6TqaWCky7OPMJbg1yWe89HGbSBhEDZGwQvTA4khbCe0yIPz7YAZ24Pz7cI+ii7/NU0fSLQ85+YyOGj3lZVa6Xw7MW79b+lyddHOOVu8CHZAxAOJlLHHIF7oeIxqvh3Qx23Qvqmut9G4RXAmmTGGhWJa+x7iWnuATCqBgq8ytDh5+4Hq1XGyIBGHmWnmJXgTFotZo33+7wemQKAiybKH/dJJ94FE7cNqndmH/YBpZUNFNrS+MFyX7FAxjWj4v5+iayTIkG+N+05n5lWLpbomWa8fPBaLGf01SQjAouFeietaDPhicU6vS1aRE9+hyua9+XaffQueffr2afDQM8etvBLvt12qS3LALhYzpIcs/Yf5YsF9IFH13m3ZfDs5bkPJXVb7Bh1ZRVN8h+m6/LrEA4kmXf6RLsC9rmZfPebizXTL4tZgnyzRNd1f9/vt/C6r1FYKGb0vOmEyJPaVCs9k8+1L1D0S3MOmuJWOdOn3iCOZby9kUsilE0ZsWpDgmTXeioeeNb4IdiXc79Bje5RzrjcMk6HK5BkBii9m7CvQ4nbB8syXEi+q3wP70eHu6JZMZPPtokBjwgtrvI16zrUj7BFJ4W6xmEF3qH7vtmykS/yAiQljwnGrt+9y5KqQZA6EbviCAUhF+3kGPAppcA7li+CDL3P3/7cuSILgJnTp7QtnsN4Bq9DVkZAhwAyKNUl1d6GQQX/koKvI5usSXywSfBFs+wOYJDRq8A2+LB0wH+aiIuP3O2MMCwU9Ca1LwG2hmEG9O1Rm8zJiSAHSugS03bjV+0JGhgC1L2QVinQygZIheZLZt1g0xW2YDC1E9AUlbv3z7TO6FOuC70oF3DGXdJKZ8SJ4rwyJuLdHJAeslgx1h9a6xFs0/HgmrlGVPMla3SaM8XRJ4pbkC4sDVkaG3O8w44WMDI0cdfIkO2BNsST+LVwAMcet+Jyny5A8yUa6hL06El9rh7F9sei+d1s1tidrdXvYTjhTZ+1LG4hXeKRrwRAXsrhNJhgqefOZGoqlCRlSJU91ia5p3OofbpXuYS0GDkMjXUZsl9iXn3SeTPtxIdDdNsZthHPO/TEbud9NeyQ40mXSJSsglfPuqzBNZ6p/pAswJ0+XI1eFJAOQZwLa7EF+4wD1TZBlvflMEvl0MpIubVY+qQyVLMiQLEg8XabKlYQM+b9TpWtmzGUyd2Wvy1AZag+syZBs7o+qS5bQ6JIn2eiOKXly59vlB5g2ljqDMBky6JIRAKFLXwEYShOG3tAxZvM2yVNvOEZ7MA4fKgXDHgnM/QEWcRuo1JpInuyAXSzokydZRc4Ut83eKPSMBWBOnmQjXSZiI5tvF8mTqfoXjiU9QZGNdJmIoYwAiD+bKldKbDfhRaDzlErokycZXrhkSH+vgiNdpoRBhrfCXmMXMxAX5HPOd4/F6IrKf7IupmefYQ9HJkMSbDKdqSEyVExjOObKt1PJqrumWJKNdLm6zHErK1bpdMnwVthr2wFZKFxG8qSwT9bFdNfpEwbZSBf5nJMkT7aVZFPydDly9UhyIQwE5spQOJMC1BmijAyJdSpd3sMxkkylpWmFHkzWJBL+ypD7HdE2jCFIgv7zNqeqqh7eMF4r1ECGZLr0rdDBTBVZXJOwXXVNgORQIZChoC5jZSNC8uSRIVv7JPdq2rZWxK0qeSqYkydr4jXR5SdDXvKkrO6Gq4wALaGRkUnATLwqkQ6wcFxoO09td6RrhgyRiWv4HlOSp1n79AmDLpHUVYYOOoOZ+XZXlzl5Co50UYmhFC+MrW7L5ElywLpje2q/CzIkw6Z6R/3Ev2yky0jiJXgr7DUlNLLzwP03Ol5kUgmUsurkSZAh2TlnPnssyZCkonm5upTnXDuMF6bkSTbSJXSZkicZLgnbZXIgGZkCiIUxSaIL6JOT4Hy7KXmSjcR59hkTGrkvzMlTBC4o8YUuebocuXYqySSyYXnAKiu16kyloaoMmVqhWvv04BbanIYDVjY/RQ3ISj4IBOoMcdp+DuvStUKlWe/kGusKXygThiiVZJMvOuH5drFOea8kYwlijW6OVJ71pme+Myi6ipyJDEWpyAXJkGmOVBe3dU02r4tbZaKrOGB1xGs0dnDYlVSSCXskONJlaoUqq2SkhEZun2qPyKrqQpfqUO4MxhiMZJUhmi/8Ilqh6ljSJE8RK3JKvFAc5jriVZeMTQBu3OrmSKVxa8T28PgdQEwYVHihIeTBkS53XUZ7XonPBNd0h2PlQ3jyOWY9xqiw00gMNbrU2DT03jokRCRPpjEmma56R/0GE/k5R+UkkqRasUb2ytIZXZrCWPBhPy95MmK7hJ+ZCiCW3W1l0q/hgrK3dPm/Q4UXlyNXjSSH51wzODS0QpVkSLM5M8kEigEypMsQZVVG/591wRW8caZWqI4YquzjnCtahubNGSRDni5DhUJ5gGmy+eA1meZIlWSomFYGvkeGbFteEjIkdEVpGQJAvau+x2r/GXwhO2AVPleSIWPCEJ6ndXWpW9C6Doi2FaqJW9MBFiJDmj0im2/326ur/gX3MCAOCHu8MLVCZYUCwEyGSgEy5B7mll0JU7dFYp/XCjUmkmFd9c5Q23lSkiHNHgmOdLm61HhR1+AtYEgYAvZ5yVMUMqS4Jt1Il9/+sH3h+XZXt3rkR4XtZl+E8dZLngx7REqGFHoEGbI+5yTjKkKXzUiXsNfhUP5QjzRuTcmTao9oiKFuvt3VpYvbsC8WdHihSBgWiu67pnXJk3WiK5lvF7pMZ4+Sn2mS8ahyDVWS08ps3kSGdA9ALRTTIfDQZW3KICEcYMEgMerSVMlUT4V2h2P0R471AasmQ2r7dBm2+51qHwZ1meZIlWRI0wpVkSES0Ct8YTOiIexz/11dUVImT5q4lZOhtLnVbRu3knEQV5f6XunmLd3vpCdPXitUo6uYSSKXDpOhKPPtfvtlulSHiqlKpkqeZDGoIkOmVujB5CX//pEuoTvKfLvKvqkuyR7RdHZU1cnFgvtDPW0Jninn26OSIY19upEuvS45XmiJl4YMNRTJk2qkyzTyIxuzctep90jkc06ia5o8qf2uJENtefKkS8SFHSpdsnNY97C5Mm4peBHATlPnSTbfLnRFmW/32x/WpdnDpnPOAi9U8+3eQ3gabJIW7jQ8wVSsUu2Rv3rqdenfU+SqkWRZawiQX6T3G+mSgNTNkSqBnkKGVEGiJUOSA9agK8GA+Zx8Hk8WkKqs1zRHKpsTdnVduc1p0lWQkiF1K1TZMvSAXtc+VRBDU/JkMUcq5tulB5iBeFXyEjJUVCdPuq4EYDhUbBMG5ZyweoxkSoZmfeHNkWoTXVV1V94KNQGplgyp4tZADG2SJ0GGgvvR1AqVJRmubkIH5M1KnggHWGikSxODqvl2SvKkwnbrZ0CKeryQPQMC6JMn2Xy7q0s9dqYaSzAmT53wbKywzzrp15xzqpEuo67OAPM5WRczrUyedCNd/n+X6ZLihS55ipDoDiWvLPV0GarWwfl2oVuVPOm6aX77Zdclw3bdQ3hR8EI1355KJlA2JE/yhMa9V9LkyRvpkj97o9ojD33pJenfU+RNIcmMsQcZY2cZYxcYY79q+nwqwaTVXUB+kaqKplinI0PSING0QtXEUE0Apg/7WVaSJ2tklSG/LTPXJHk/KECbI1UFpOqpUFM2L7OvPxqj1R/JK3K6qrWSAKiBQJUweMmTJfGiJE82c66q+XZPl6Y9qQJflS7VoWJ6pU6kuG3LQVs3zqCabwfMFTkpiS+qW6FTvFA9PKomQ8pui8Y+2Xy7LnlSVRnddXpdKoKiaoWa2pMyXYIMqeyzrgzpfKGIW2PyZIhbefKknkkG5L5QdTHF95jwVjYCIeyXrQHCeDt9CE+NF9IupgZv1eMW6nNONdJF0aXaV8L+oKjm2+dzKSQTzJ546ZInBZ7p8EI13w4gYtxqkidF3Ap7ddcVJaGRzbfrkifV2QjosV2d0KQxGDnoyJInzUgXIMeY0eTXOaPKZZNkxlgSwO8B+ACAuwD8AmPsLt2aZIAUAnpiqAoSwA0UazKkASoVIa/k1RtakCGlfbr2s6V9qmqDUZfqgC2onwpVkaGKptKtI0MLunEBxaGsq6KoyBDgXpdWlwJIlclTRz3fDqgSGg0Z0rVCFUBf0elSJDTJBHOzectKcqWgaYW2B5gP/CyoX7eWDEmvy7BHFPdKrUuOF/lMEtmUfI7USIZ07WcZGSIkNKr9qEueVHGr1iWfb9clT4IMqeyzJUNe3OrwVnpd+vEilS5T8hRO7tTEUDXSJXTZzrdHxYuKplug1GVInmQjXTq81cWt+5Yku86ddg8ryJAY29MlDCq/65In2UiXjoTq49ZcSQ5KRbOHVeNj2VQSxUxS6gvvYT/LhEE5367ZI0Zstxzp0p5zihjUJU+6X8WkyJtRSX43gAuc81c45wMAHwPwQd2CVCKslkRcbQ8wRZDoWqG1zlBKhnSv1FE9ECJ02dqnA4/L0qXN5uXZsowM6VqhqhlIoUs9kK+e+wNUxNCQzUvWjB0uffOB+z0a8j8ZpwmCBwlILbN52YvjXft0QKr2xaKiw9AdyOfb3e9Rt0Jrqvl2jX2yHwWZ6tKMM0TQFQUvDjVkSN8KVcWSeQ8rOzvKPayepdfpkpEhXfKk3cNFdfJU7wyVIxqAfo+oK/iqh/BUCY2+ai0jQ/n0JHnS4a2iba3G26GymwbYEy8VtrtdTIMu6R6Rz7eLURl5LMkTLmAy8qNMJPV7WJqcaPFCrqs/ks+3i+9RJU+yH0Zy9WiSO10BpKh5qDgCXmj3iKJq3eyNQr/s59fVViRPdcV4oK7bErnT31bN0puxPTjS5SZPaUXVXx6XVHkzSPI6gIu+P29M/m5GGGO/zBg7xRg7NR6Fjda1NvQVOXmQjCe/hKWqaLq65MEvI0PuOrku1YiGWKN6pU5NMcesa4WadMns6w7G6A2dSOMMMj26Vqiu2uA+hS/P5t0qmWxzmuezbVqhYr5drkudMMjeawu42fycYo5URza07UnlmJAmbjvy+XZATbxkL9731hgOc9n9Fdn8mzlioBzDMeCFbL4dUFdR9ECvboWqgF6XPOn2sJ4MqefbXV2yVm34/e0zujQJlypp1c2RasmQRWUIUO9h00gXoK7IyfQwxpTJk6oSL2xu9EYYyZInVdzqiFcEMqQb6dLihcI+3Ryp6mE/8XfKOVLNaIywJaRLdDHz8nssJ9byEQ1Xl378SebzQiaJjCJ50ic0pi6mXfKkGukC1Em/ruOse+uEatZflzyZE105nunGx5S62vKRLp0u3buuKXLFHtzjnP8x5/xezvm9t60uhP5d90od1WA9AOUDUDoypKvIqYIEUGeIpkxKqUuxOXWtUNXDfoCaeGk3jCFDlF2T0KWbmVZtGGUrVHGA6R6qiUKGtCTecMDKrsn9LnllQzcmpGqFuk8K60cMdOAmI0Oq9p+JoIjvlemSkQbvDSaKuPVfw4wuRSt0MHLQVJEhzQ/1qBIaQD1rbSJDgHoP65InW7xYKMrf50oiQwripcMzma7LwguJLl0rVPhdSoYU9mnn2zV4ocJbQF1smeKF+hyRtXPV42O65GmoJkOGPazrEirxQoXtCuKl07VYyEjnSFWvLBVrADmJ0ZEh1Zmvmm8H/MmT4uxR4JnyTDVg+2F3GEqeVO9v93+Pyj7ZSJe4LvsCkp7wyq5JJE9SvNBgp0h0g8mTdr5di7fyrgSgTiQv993JbwZJ3gRwne/Pxyd/Zy06IMinkzO/kS5E1QrVz/3pWxvKA1YxR6o9YBW6BBmSPZWs+2lG8SSzlAwpWqEmgiKzT/yd+oCVz5GKA1b1Jg2ZLo8MSXR5rVALgiJ0RWldARripUsYNMRQ54vgdTX7I4wU8+26V+poExrFPOMUSNVkSNVq1OrS7BGZL1St0LoXS5YVOW1CIyfxqvl7wDxHqt0jikNFNtIFqFuhuj2srULp7FPMdkbBC0GGVJVapa6OO98uJUOThCuYPOlmIPUPg8nxFoiIFwpduvn2XFo9RyreKKLsYmoqhlFa+LJ9D6jnzkl4EdClemUpAJRE8iS9LvnoDjCZBdeMQOjwQuVDHV6o4tbVRU+epm/p0iRPCryImtBo8UKhS7tHFOecbKQLcP0nS560JN7QJVTFhSqhUXVsqfJmkOSnAdzGGLuJMZYB8PMAHonyRUogUMw0AepWqK4dYmqF6jeMriJHb4V6ZEhT5VG1QpVBopgjNY1oAJr2pHbDWG5OhS7d/JmuFSpGY2RiTJ6iEC9dh0EBvukkw1xWAh4KsqHzn7YVarRPkjxp5y0jHiqqFn5ngJJkvh1QVzZ0Ixq6OdIDxdwfoG6FRiFDupEuV5c+blWVISCMZzpfaJMnxUiX+12qhMaMF8F77JEhJV6odenwduzw0JPppA6IqnOnIoaG5EmLF4Hr8rqYlhUvU9KvS56sOyDaM1VNDHVdTJkuXUKTSEzmSKMk4rLkibCH1WeqfYFB18UU1zCjR4O30+RJVd1Vn3PaUTqL5Ek30uXqUhfudCNdMl06X2iTJyM/uwZnkjnnIwC/AuDLAF4E8AnO+eko36UkXh35O34BNRDogF4/R6o5VDTjDKmEnAypWqE6oPd02QaJYXPKMkTTHKn15mxryJAiQ9SN02h1KR7eATTJk8bvquTJI0OaDFZXZZSRIVXypGvv6nUNlXtENUeqrcipKkOT+XalLk0r1Bi3ij2sbIXqkicNXshaoVGSJ91Il9BlT4bkCYMubk1zpNpDRdIK1ZIhRUVO1+oG9MmTjgyJa5jRpcEL7RypriKnSp4mXUwtGQpiu6bKCOirf6Y9EkqeNHGrmiPVvbJUfJeuK6HqYsp01TQdJJMuZdJfMCRPFpVkMd+urqor5lwNBSRxDX7RdZwBdbFF3wFJoz0Yoz9SdJ4sCmO6kS5XlzpuVfaZ+VnY79rkqaP3RU2KZ0OkJDFLlTdlJplz/kXO+e2c81s45/9P1O9ZKMh/LlF3wCozFQMJlf186djh7tOdmhsumyMVWa+uMmR9qCgqG9pxEOXmVCcMqjlS72E/TbWh3g1n86qZJvea5CTUSAwVZMPUVgfCWaTuYTUveWor2mQaXfJZLU3cKh5I1HVAhK4oZAiQJE/tCRmSzIOK5ElZ0bS0jxK3wYRBN98OQBq37jqzrmArVDfSJQ54JUGJUknWEADAriIn/j7oC918u1ijaoWqRrpUc6Sq99p663R7xOALNbaH41Y1R+o97KeJ20ZPkjxp9pVq/lk3ryquSzULbkoYlHgh8YWXPAX8p3tlqbBbTjb0c8yAXQfEtVs95mLSFd4jakIukqcg8dLNtwtdyq6EMem3LIDodBlIqMzvqpEukTyF8dZwr1RFQs0ensYtvdMv/j64R3Tz7WLN2OFoBJKnuoZYU+Sq/eKeTHRzYTrSAEiIl+Ewlx1gje5Q+doUYZ+wZ0aXJkhUrVBTkESqJCsqXrW2/GdBPV2SVqiRDCmyed0MpKoiZyRDqoTBQACEPcE1uXRCSoZcXeGKnC7JEH8vbYUaqjXiM7O6zAds8FD2yJAhkQzpmoCH7L3l3hxplA6IYo7UdKgoK4YWydP0YT+7bovu0FO1QnUjGq7d6laomXjRyZBKl26+XaxR6VJV1lStUBoZoj+w6/+ucFKtJ+QyXRQyxPn0VYCz9qlmY+XEVVfdFbqsR7qUxZahcqRrqkvRTdP4rz9y0B2GkyfdeSo+I9WlxQtLMqQ6Rzry97cDvuTJooAEuD6SdZ5IcatIaLQkNGCf7mE/QO931UiXqvOkm28Xdh+0VcmTuhIv7JnRRTjzlfPtxmJLOBFS4RlFrimSXCmkla1Q2wPWTIbCm9MI9AVFK1QTJKlkAvM5GfHSB0mlmA61Qr02WQQCUMmnpWQIgPSX+iiVePHdM+s0BCCflrdCzYQ83Ao1kSHVy9kP2kOlHldXeHOaEhpdpVFlX1nRCjX6XTIXZp5vV+nSg4dMl+lQqRTSylaobaJrOswrEvt08+3+71IdKiqRkVATGVoopJVzpGripY5bHRmS7REjGdJUrVX3NzF5qNgaLwryViilIifDGNVIl19XcA2gi1tVIqke6cqlkyhESJ5kP55jmm/XFUBUI11TXW/SOaeZby/lUkgw+w6I+wNTgS6miQxpurOqawJcXwR1mfdIWpo86ebbKwr/UfDCdqRL9eMvqh/qmNGlwAvVvaoUVMmT5o0TCryta0a6hH2qUUTdSBcgxwvVuA9FrimSLGuFDic/KWgiKOE5XD0ZklWSdT/U4f/7UJtC81oSQF4hNx3mslZoezDGcKwmQ+oheTX4inWqOWbb4Nc9BCGy+ZAvJn9WzjVJsvl615z1uvaF35ig84WspWRqQ6n8rvpREEDdCq113Pn2krYyNEs26pr32oprEt8d1KWLW1k2b+yAKKs88h88AIBiJolMMtwKNZEhWUXO+4EZQ8IQ9oX+UJHpqpv2iKQVKn7MRn0QyQ/YekdPhqRxq/mxHffvVboMB6wkYfDwwtB58rdCde9vF3oAGbab49Y2oVE9L1E36dJgp05Xqz+amSNtmObbFWTI5AvtOWcstoRb5LrkSVZg8MiQoosp2up+PJvGrbqDBKiKVfo9bLtHlA+ba+bbRfIkS+BVD/sJXTJu4bcjKCq8NcZtBF/I8IJz7mGTTObzaWXypJpvFzYoR2Ms8aLeUb+xgyLXFEmWAUHdsGFEKzQ8I2cgQ1IgNQWJgmxoqmSAmxXJqow6MiRrhZqqNSXFHKmujSe+L0rL0G+Tp6ujfthvqiuclZeyKWRSajIEzCZP3oiGKXmSdAtMCY167o8OpLqfBfXrCrVCO+r5dqFrEMjmde+1FXoA2eykPm5lrVDj3JrEF93BGN3hWOk/98dpJAmDoTIkS54oc3+ufXQCIHTZtv1lYy6mka50MoH5XMqqKwHIW6Hm6m6YxJN0ybotbT0ZkuFFzRC3InmSzXaa4tZ+pEtd/TMlT7I9oppvd3WF/W5qdU+TJwleGLA99FyGATtlxMv0sJ/QZTPSJXSNHI5mf5o8UcZBAEXSH8E+99/oeKF7ZamnSxKDpuquSJ4GoymemUe6FM/5mBIGxR52v5OOF6aRrmSCTX6y3C6hEXjrxzPj2GNBje1vm3ELWYZoIiji32xmDIWuYCvUPGKgIUOWlWQTGZIdsKZ2g+qp0ANDwiB7pQ5liB+YBdLecIzOQE2GprokSYbh/vptAsxkSDlHakxowtk8+VDx6Wr09GTI1SUfZzB1QIK6TIfKfG6SzUeoyIXfRDLUz7dLEl1TlVHYLtNlIijBVmjUDgiFDMkq3RQyNBO3Bvs8XTK8MBCAYCvU+LCfJG5NI12uLvk8o44MycYFTIm4MnkixG0oeSKToaku0cU07pEQiTd3Ft3Phfewap1InmxGY8T3qUdjDG+M8tnX6o/cLqY18TKNdKmxXaXL6zxJHvS17pgSR5L812WqxAvbo8St//v9es32RSiMSTiT6mE/QB+3piKhzXNcQlcoeTIWq8IJg/jZdp19JrmmSLKsLbzfMt8EGRCIJ7RNuvxZ0b53wxXzP5I50sNJZcikKxgk+y19diOrJIv/NuqSEgA9UAXnSA/apof9NMTV0r59g30yYkjSJQECo65iOtQKPWj3kU1p5ttlcUuwT0aG3Lg1x4WfyJt0ea3QYGVI8+J4177wHOlBu4+yZr5dBqSUuJVV5A7afdIesfG7rBU6GOlHusT3BZMn0x6WVeRIe1jii/22fmZat0cqinVe8uTT1ZyQIdN1yboSuriVJU+mt4MAiopcy6QrPEd6MDlHKnk6GaoZzgOhK4y3ff0elvjiLcOLQga9oYOub2xvv61+fztgiCVt3IbnSPfbfeM12epSJU/7BLyod4cYO7PVyVJWP9IF2N8rWfK0b9ojukRSkzyVAsmT6ZWlQpdqX+m6mIACb3XYFDFugYDfDVxwLptCOslmrqs7dH/Zr/z2qyT7CUAfALA8l1WukxKv1gDLc3oC4Ory34Q+CpkkChk5eMjmSKf2mcHNTzb22wPtNckqG9WWq2tF54tAQDqO+6QwRZc/uKqTTE9FhmSv1BFBbNIV3JzuvdJdU3h04oAQF0Fd/dEYzd7IoEuSPBnsk82RUnyhSp6WS7prCj8wuT+JC926YMLQ6I0wGDvauJXNkZrvlSZuS5YJjUGXbI5U+MI0OiE7iJY19smSp/12X+9zKdAT8EJSkSP73eeLaruPTCqhHOmSzZHaxK0fz6qmeyUhQxRsD1bkOOeotgdGDATCeFHOp5UjXbLkqUrxhSIRt8Vbkt8DydNo7KDeHRp0yfFiqZhVkiHZHOnUF3bFqsvCC0MM+nW1+yP0ho4BL8LJU7VFWsImzgAAIABJREFU28Pye6XbwxIS3+obrwkIcxIAWLLoPLlcw3AeSJOnPhFvI+CFJKnWn3NyvBC/RiwT73W2lvaZ5JoiybI5UnGRSxabszd034lpunEhXe2BVs9Ul3+T0cAt2Ardb/WxZEsA2gRfBIhXozfEyOFaXbKqtWufWo/slTrVyaGnWyebI91v63XJkqe9llvpNlXIZZUrmt9nEwYdIHqtUBm4WSZP1VYfywaCB4QrG9mUuk0GhInXlKwRErUA6OgAW9YK9fZwUe/3YKV7vzUwxK08OakU0srKEBAmXlXvILJPnnS+kM2RRiFeg5H7Giit/6RkaIBlzUiXp0uScGn3SFGWPPX1CZesCkXA9oUAtouZTVu8qLbM2B4kXvttgi8KGTQDc6T7Bl2ytjAluQvuYfcBXj1ZU+GFzj7ZHCkFL2RzpCZdqnMulWCYz8uTOyC8h6exZHvO6fdwPpNEPp2UFsZMumRJPyVua4G4LWVTyof9gHCxxbPPEi9Me8RLnqTnHL1YNRaFO8qIZQAvFovqh/08XRJs1+0Rk1xTJFk2R7rf6iPBKC18CZm0nAurTjJsnQTnSEkkXpGB6eyTvY90v9VHPq2udAPhOVJSBUDR2jD6IqDLy9q0m3N2jpREhqRjOG4bL2UgQ7IKgOlQBsJAoLNvqstfxaPp8idPg5GDRm9kBBwgHLfLc+rKkKsrLW2TUXwx22HQVxtkrdB9SvIUmCNt9s2Vblm3Zb/d1+4roetAMmZF2SPhRFLtC9kcqYhBmzlSsd6UfAIR4rYQPFTM2KkkXpp9L0ueqi13BlJV6Rb2SfFWl9DIEt1WX4tLgI54mfeImCPlnHv70WzfbAzO59QPLwtdtsRQWpEjxEVwjpRUoAnMkY7GDmodWqIbjltzchfEJYp9gAQvKIWxCGeqP3nqDEboDse0pD9K4c42bqV7WB+3XvIkiwsD4fUnT7XOAA6nFqsCia4J24MJAwEvTHJNkWQgDATVtps9qNr+QLgVSsl6Ze/FNI1ouLoCAUkZBwlkiL3hGM3+CCuadoPsITxTJuraNztHalMxDAa/rh3i1zW1j0aGgKnfG1336VidfbL3kZqSDCCcPFWj+oIQF+EDbFIZsngIz2v7a+yTzZHS4zY8lmCq7gLh5MnoiyCQttxKt2oG0tU12wq1AfogyTO11oKtUCu8aPvIUJQDbDKvqqt0B1uhVnFrUTEEwnOkAs902BTcI8Oxg3pnaOw8hZInIhnyz5F6eEuwz7ZLGEyeouCF+xPB+uRONkdqItZCV5QRDSA8OmF9zhHHmIDpHql13NfarWh0yeZITSMari55sUqXCMnHGQi6JOdcKsGUz+u4a2aTp/2oySchLoIdU0r1VJ48Uc7UdOhMNVW6FwuzyRMt+ZQkT+2+FpcADT8zrNPJNUeSgw/wVJuE6m6gFVolkLXp+5Vnb7i5khy2jxkq3cHWBiX7Err8T7rukSoAs63QaoRqCCD8bpe1VVt95NIJFAxtf2CaIe4R2yGylhIlYfC/UodSAQgCFed8ostckQv6YqGQ1la6o8StbI60SowL//tI94gzhsD0lTpe259wmPvBbY9U6Z7VRRmBEMlT3ZJshBJx4miM3z5RJTJVJ4PjDC7GmOMWmOIFJW5F8hTew3bEq9p0/9v0UBIw1UUZYxLfGfKFaQ8Hkqe9pgXZCM7hEnTVA2QynWSYz2k6dwHiVW2a4xaQnHME+1TJk00Xk1LpFrrqgbHCUi6FbEqD7ao9bOo8SUgeBW8PfcmT1R6e6BqNHRwYKt0AFHirT+6CvtgjJFwieZLp0ol4O9V0DaHbEkieOoMROgN9pdvVFe48UZJPYPo+f0qBRiRPtti5oPTF22TcAhC//jbbhqKABzC94ZS5v2Ar1HvATfPwDiA59AwPuIlrAvxZpRk8xHUFMz1dVi7WzOgizCcVM0k3mw9UuknEVZKV68AjOHdO2TAAJFUocwUgit+n9rm+aHTdp/1Nm6yi8IVOgr9sRZ2fqgTeu02tqs9m8+bKUPCHeqZkyL7lZbuHKdVdsS448kPR5b7FwfHWZAyV7mAr1KtcmfAiRAzNh3Kwak2J20SgFSoq3Sb7KoHkad/wgJt7TbOz1tMHrWwTXUoVLxAXhM5dPpNELp2YIUO1jv4BNwDSOVzdA26ufbO/tkkZLZLrouPF9Jyb+EKDnfP5NBib4pmodFPwIkjWdA+dAWG8oFRPha5QpZswMuVw973jri7a8zDA1Bei0k055+pBPDOcV8GHuakPkEl1EeK2Oxx7yROp0h3CGHMiLnSFk0/iOSfidqJT9yA3Y0yii5bQ1DsD73W21VYfc4ZKt0muOZIsa4VSHAOEA9KmFeo94EaoJPuzeVqFIpp9slYopULh11UlPOAWzObJlaFAK9RtP9Ps8zanBfHyJ097hGpIsBVKecAtmM1XCYeyq8t+NCZEAIjzU/64pVa6g7PW4vVlukp3sBVKqe66umazecqcsCxuAcIB5muFUh5wc9eEk2rjA24q4krpPAXGXCitbr99ZLzwtUJbxEr3YiGD4Zij5WuFkuM2hGeWrVDKcw+Kw1yX3AFi1tr1BRXPFosZNHu+5IkyThOsJBPJ0KJk5pfq9wMfdqaT+gfcxNsAbBIuoWt2bI82WjRrH1XXNG6neEbU5dvDpkq3lzxZFJCEruCD3FT7QsUgi5GfscNx0DEnDDK8MD3gVp4kTweBqr85biXElZDQANM9TO62+BK1aaXbzBMc7vI5Yd/lPLQHXIMkOdwKpc1PAdPRiarhVW4zuoIEgNDaAGY3pymwgk+F7nmVF3or1HF4JOJVbfWxaHjALaiLvmFmW6HVpv7NDIAvobHV5YsL8So3SoUCmG2Fmirdnq7O7IamVLyCrVBKy3DGPsKr3IDZyouodJv3SLhqbbIvmDxRXuUGuNdV7wymyVPTomIY0KV7hy4wW/GivMpN2OfqmuKFyeeh5MkCL2xnpkNx29K/ym2qKxMia3TCO20Lm+wrZVNIJWTJE70Vyjmf6LJNnsyVbldXBqHOIvUw910XtVsVjFvayI/rC+8BN2IBxI9Npkq30BXGW3Pc+pMn0ihiwL69pv051578VDl1j/j9TnnNl594idEiSiW54Uueqk1zVV2F7ebkLj1TrDK9yk3YF9RlwvZg8mSDF7X20EueKNgZKhJOXuWmq3S7uqbJE7USL8MLky9Mcs2RZP9ToZRXuQGyVqg5ExW6bCsAsooSKSB9rVB6ZWj6VCjlVW7imoBpK5TqCz8Zole6w8FvWhN8pQ6l0h20z6YyBPjIUJuWVfqJF7XSvSgh/6ZDOZg8USrdQIAMESvd4XEGcwUgqIta6fa3Qm1muoHZcQbTA27CvlCiS6h0A4G4JfrCZqTL1TVthVIr3cFuC6XSDcwmT9SKYeh5CQJxdR/Ck+GF+R6L5InyKjdA3m0hYbs/bokVzWDyRNElkpcgttMq3RM865hf5QZIOqaEiqbQFSRD9HNOjJGYdXnJk88+06vchC7bSneoak3Es5m4pcaFb6SGWukOJ0/mV7kJ+0Jxa5s8Uc+5YmZm3wM0XwzGDtqDMbnSLdvDpkq3q8s+uZPixWXMIwPXIEkWWcleq4/dBjETLWSQYMBeowcA2CVkeoALFLuTbHdnstb09KSwb7fZA+ccOw2qrox3PbvNHoqESvfyXBYjx/1ZxR3hC6N9GU8HANc+wpOdy6WpL8Ra03WJf99t9jAaO6i2BkRdmamuRg9LRXOle6WURbM/QncwpvtibtYXu40ezb65rFcFIceF8EWjh+5gjEZP//YSwE2eFouzMbhSMleGluey2G8PMBo70z1Ctm+iq0n3hWdf09IXzT7qnSGGY25ck8+4r3/07Gv06Hu40QfnfBq3Bl3+uAVcn5B90ZjGbYKZyZDwxV6zP+0gGXSV82mkEmxmP9ru4R0idvrj1v1/e+zcbfaQTSW0D7gJ+xzuHv7UPSwOuChxsRf0hQVeOA7HXjMadlYK5kr3cimL7nCMZm9I3sMrvlgCpnhhtG/mXlHPkeke6Y/GOGibsZ0xFjpTqXh20BlgMHLo9vkwBoiIZ7bY3uyhOfnREtOaTMr94THbPbwyl8Veqw/H4fZxO/n8ns0eaczuERMJ9eNFtdV3315isE+82nAmLsjYPuUxALAylzOsmfUF1e86ueZI8lolDwDYqnexWe8CANYX8to1qWQCR+dz2Ky7Dt2sd7G+UCDp2mv20R+NsTVZK/SrZH3y75v1HuqdIbrDsdE+8b1bh+71bNa65DWA64st4QuDfYVMCpVC2vv8Vr1rXOPqymH70D0cNmtdJBhwrKwPyKl9Pew2+xg7HOsVgt/Lec++TQv7xOepvlgsZpBNJax1rVdyXuxt1bvIphLGbNTvC3GfKfc4qIvmizzGDsdusz/dI4Z1x8o5JJirw3E4LtV7xBjMzcRSpZBG0dD2l+5h4nV5ug7pe6Q7HKPeGXr7/7hh3arvXvVHY+w2+7S49e/heg9H53PGSvcUL3xxa7AvmWA4Vs7Nxi0plvI4aA/QHYzJuvz3qtEbotkfkeM2uK9MZGit7NsjXlzo/Z5LJ7E8l/H8vkX0xVolh+2Gm7wLXatkPOui2upjMHZwnIhNni9q9D3s6ur59ojeF/P5FIqZZCS82Kp3wbmL7ekkM5IUf9xuH/Zm/k6vK2d99qxX8uDcJU+bNRperJSySCWYd122vnDt66GUTRnb/jP3qka7V0Fdm2S8zWMwcrDfHkTaw8Oxg+0GTdd6JT8TSyulrHame1ZXj8zPGGNYK/vPObovGr0Rmr0h2Rf+uO0MRqh1hiRdOrnmSPL6hAz5iaGJuIrP+AmAIFX6Ne5ntg9d0C7n09on3AE300snWYAAUHTlPSK+ddglXZP/hluRjXJ+hgBQdQ3GDqqtPpkACBI9e6/Mvlj3+6JO88X0gKWTZMaYp6vRG6LZG5FjqdkbodEbuhuaQADWK2H7hM0mXX7QtkkY/LpMCU3al0hW2y4BoB4qu75EknJNUeN2fSHvI0M9YtzOJk8UAjA3ORi3fASAiheX6m4iSY5baaJLxwtBAGixNImLQ9fvc9mUsbq7JBLJw5413op7u0m8VzJf0Pyex2bdJbzbDeoemSaSW/Uulueyxla3SCQ3fQSAik3+PWwTtza+YIx5eCEIAM3vOXQGYxx2XbKxWs4bW91yEm+PZzZx4ccLky/8iWStM0Rv6JD9vt8eoDccY/My9jDtnJstgFDPA7+uZILhqKESmksnsVR0E8mdRg8Op+5hN5EcO9yak0TlZ2KtTSJ5aYJNuXTCOJZZzqdRyCQDifjbjCQLx2zUphvGVAEAplmRIACUCoDIMMTmpDgzkWBYLeexWeuSKwDCvoP2AJ3BiEyGhH2CkKeTDEcIrYP1Bdc+mwpAsOJFCfxcOomVUnYmYTBV8YR9240ehmOHXt31+WKjNiEAhlk3sW7DoooX0kWsXM3nU5jLptxYqlnomsStTQXg+MLsvVopmQkAMAUqarXG/5ntwx65A3Kk5CaSNtXTqX09dAYjHLQHRPvcvSf8TiEA7rr8LImn+F0kku0+OW4Fdm1O4hagHSrHJ/bZEQDXF1s+PDMldyKR3KzZx8U0kbTbwwI7Eww4Nk/E9loH2xMCYBO3ni8I99dLJGt2cbG+4CaSg5FjUT2djdtcOmEc3RG6/PuKgrfiMxu1Ltk+fyJphWcL7h4ejR1cInaD/HGxVe9isZgxjiIC02KLrX3A1O+UNctzGWRSCWs8W5/gbW84xl6zT17jt+/YfM44iijs2az3vOITNQbHDvcq+BTOdKycA2PARoRzZKvexWF30q2y4SS1CHhW73idxbcdSS5kUlgopL0gWZ7LkAnApcMuLh4IYm15E2pdUnbo6srNkKFVYvUUAF7ebeOgPSAdeguFNHLphGff0fmcHQGwOJSD2TwlMRHr/ASA4ndR5XnxUgO9oeO1v3VydF5Ueab2mTYM4FZ5/ASAah8wjQuKL9wqT86zL8Fcmym6ekMHL15qYOxwK/tEIrlGvFdBYmizR2x0JSZVHuE/SgVA6DpoD/DybhsAvboLTIHUKm59cWFT5bl40MGlwy5p37vjAlnPvkohTSIAaxU3kXx9P6IvajT7hK6NmSoe3RevVdvYa/ZJuuZzs4nkUSIBCGGMbReuRt8ja+KAtcQLzoGz2000+yPSvRLjAmI/rpXNBMCzr2aPt4APO4lxEcQLU7dKrBmMHZy51MBwzEl+9yeSNnt4al8HAHEPl2eJFxXbRSK5Ue8ik0wYX60ITMcFzu+0ANCLfcK+DRs8K7uJpOcLC7y4eNAh+yKdTOBoaZpIlrIplHJmbBcdydeqLp7ZcCaBTRRcErpm+dnbjCQDwC0rczi/08T53SZuWZkjriliOOb4+zPbAIBbj5jXrVfyyKQSOL3VwKvVNm4hrBH2nZvYVymkSU9Piuv4/PNbM3/WCWPM1bXbwrmdJuma3O8uotUf4fHzewBovrhxqYgEA757sY6LtY6VrnM7TZzfaWK1nDPOq7pr3O/+wvOXyPalkwncuDTVRbbvSBHVVh/fevXA1UXw+y3L7me+/eoBqq2+hS/cuD2308SNS0XjuIprn70vCpkUVss5T5dN3F6sdfDdi3UkGHDTcpFs3xPn99Dqj6x8cW6niXO7LdyyMkciAME9cutKybhmsZhBpZCOFBevVts4vdVAJpUgVeSEfX9/ZgfDMSfFkruuiHO7TVzYadHXHCli7HB86TQdz1bLeeTTSbx4qYGX9yx0rRRxYaeJs9tNlLIpUrcqyh528aw4iQsbPJtDb+jg5Nldsq7rFgtIJRi+t3GI1w/s8Oz8TgvndlpYKWWN86rCPsAXtwRdyQTDTcvFSHu41hnimy/ve/aaROzzZ16v4dJhz2KPTM65nRauXywY51WFfcA0LijXlUsncXwh7+mysW+z3sWzb9TBGHATwRc3T+z7+stVHHaHEc65Fm5eKZKKVVHiYj6fwvJcBmd3mriwa+OLIl7f7+D5jQbSSYbrFwnYPrHvxEu76I8cK13nd11fkOP2yBw4B774Ah0vjpSymMumcHa7gQuTc4Ska2UOF3ZbeGm7gUImiVVCsUonl0WSGWM/xxg7zRhzGGP3XpYlPrlrbR6ntxo4c6mBu9bmyWsA4NPPbKCYSeL6RfMIRCqZwB3HSvj897YwGDu4a5Wuq9kb4StndnDX6jyJANx2dA7JBMOnn9kAANxNva7VeTz3Rg0XdltW9gGuL5aKGdKhl88kcfPKHD7znU1wDrqu1XnsNPp48nyVvOaO1ZJnn/gOity5No+nX6vhtf2OhX1lT9d6JY8yoaJZLqSxXsnj088K+8pEXfN4bb+Dp1+r4U6L+yvsA6a+oax78nwVO42+VVxwDnzmO5u4eWUOecOr5gAXqJaKmem9sriuC7stPPdGjWzf3b64TSYYbjtKI153rc7j0Rd30OiNrOwbjB18/ntbuONYiVTRvH6xgGImae+LtXm8eKmBF7YOLeybxm0uncBNyzTidcdqCV94/hL6I8fKvvZgjC+f3sadazQ8u/XIHNJJZr2H71qbx/c2DnFuOxqelfNpUnUyl07i1iNz+Nxzmxg73ArP9tsDPH5ul45nx0pgzI9nRLxYm8ep12t4pdq2sg8APv3sBo7N50jvgC3l0rhhqYC/edbyXq3OY6PWxVOv7NMxOoBnNrq++fI+Nutda1/8zbMbuHGpaHyeCHAr+EdK2Uj2vbzXwrMWeOaP2wQD7jhmXscYw52r83js7C4O2gMrvBg5HI98dxO3HikZ364CuGM4pVwKn4qA7S9tN/H8pg2eTX2RSSZIhDeRYLhztYQvvbCNzmBshWe9oYMvPn8Jd67OkxIarR2XtRp4AcDPAnjiMr9nRu5anUdn8lLxO4kBeduREtJJhmprgDssHHPX6rz37kjbzVltDcj25dJJ3Loy570vkVK5Atwb3uiNMHI4Wdcdx+bB2NQ+yqEHBHxhmZzst+m+mM+lcf1iwfthBeorWu5anffeIUnVFcU+sU744k4icb173f3+g/aAHEsrJff6q60BrlvMY57QuhL2iXc424K2TdwyxmZ88Q4C0AtdI4ej0RuRdQnQrrYGuGWlSP4pUX/cUnXd7fcF8Zpc0HZ1pRIMtx2hJzS9oYPOYEy27+aVIjKpBKqtAd5xbF77s/dBXba+EKSu2qLHbSaVwG1HSqi2BihkkrhxyVy5Eva1+iMMxnRsf8fREhIenpUi4RkdL6a+oK4pZlO4aamI6uRXLI/O0/Ds7rX5ybt3be6Vfw/T4k+ssz7nImDnYjGD1XIO1dYAa+Wc975giq79iNhu7Qsfnt1hocvh7ruSqfatlnOoFNKotga4cblIKkoE7YviC+r9FQWG/fYAyQTD7UeJeLY2j8HIQatPx/ablovIpV08u+3oHKnLCrwZ/IweFyq5LJLMOX+Rc372sq0IyP23LU//+9ZlzSenkkkl8J6blwAA771thazrvbe7ny3n0+Ry/t1rZe/QEutputxreeD2ZTLQP+C7lv/sliXSmmI2hXddV5nRSbPP1bVWzpGH3X/gugXvv9/3Dntf2Nj3Pp+v333zImnNYjGD2ycVSTv73M/edmSO/Is99944tckqBieffZ9VLE0/+wPXL2g+ORX/fbXSNbHvXddVSNUaAPjhm6exSr3HjLHL8kUywXDPGq2Kd/PyHCqTroLdHnY/+56bl0jVGiAanqWTCW+/v+82+z08l02RD707V6eVpyh4cd+ty+SixP1+PLuVhmf5TNLbW1Hu1UopixuWzJ1FAPi+49P4iRKD7719JRK2v4eIZ+VC2iMBUey7abmII8T28w/dEBHbxR6OgLdBvTo54ruvUfDsnvV50jgNAI9bAO75TZGoePY+X1y8c52GZzcuFbx3BEfZw/fesEAuStx3y/T7HyDiWTLBcP+t0bE9l3a7/hR5x7GS94NcNuewUjjnl/0/AI8BuNfwmV8GcArAqeuvv56b5PPf3eJf/N6W8XN+eWO/zT/y1XO83R+S14zHDv+TJ17mz71Rs9L17Vf3+Z9//VXuOA55Tb0z4L/96Dm+Ve9Y6frMsxv8K6e3rda8vNvkv3viPO8ORuQ1w9GY/9HjF/gLm3UrXV+/sMf/6qnXrHxx0OrzD3/lHN9pdK10ffzpN/hjZ3et1pzdbvDfP3mB94dj8pr+cMx//+QFfna7YaXrsbO7/ONPv2G1ZrfR4x/+yjm+3+qT1ziOw//DU6/zr5/fs9J1evOQ/+FjF/hwRPdFdzDiv3viPL+w27TS9eiZbf6ZZzes1lyqd/lvP3qO1zsD8hrHcfhffONV/u1X9610PfdGjf/JEy/z8Zget+3+kH/kq+f469W2la6/e36L/+13N63WvLHf5r/z6Dne6tnh2UeffIU/+/qBla5Tr+3zP/vaK1Z7uNF18WyjZodnn3tuk3/5hUtWa17da/HfPXGed/p0PBuNHf7Hj7/Mn9+ww7Nvvlzlf/lNOzyrtfv8tx89x3cO7fDsU6cu8hMv7litOb/T4L974jzvDem+GIzG/A8eu8BfvHRopevJc3v8Y99+3WrNXrPHf/vRc7za7Fmt+4/fep0/ec4Oz85sHfI/eOwCH1jgWW/o4tn5HTs8O/HiDv/UqYtWa7YPJ3jWtsOzv/zma/ybL1etdH3vYp3/8eMv85EFnnX6I/6Rr57jr+61rHR96YVL/HPP2eHZRq3Df+fRc7zRtfPFn37tFX7qNTs8e+b1A/7RJ1+ZwXYAp3gEfsv45De4VcIYexTAMck//Rrn/HOTzzwG4F9xzk9RiPm9997LT50ifTSWWGKJJZZYYoklllgiC2PsGc659bNzxt4p5/z90UyKJZZYYoklllhiiSWWf5hCGzB8k+WZZ55pMcbe9FnmWP7ByzKA6tU2IpZrTuK4iEUmcVzEIpM4LmKRyTuiLLoskswY+6cAPgJgBcAXGGPPcc5/krD0bJSydyxvb2GMnYrjIpagxHERi0ziuIhFJnFcxCITxlikGd/LIsmc888A+MzlfEcsscQSSyyxxBJLLLFca3JN/uJeLLHEEkssscQSSyyxXE25WiT5j6+S3liubYnjIhaZxHERi0ziuIhFJnFcxCKTSHFhfAVcLLHEEkssscQSSyyx/Kcm8bhFLLHEEkssscQSSyyxBOQtJcmMsQcZY2cZYxcYY78q+fcsY+zjk3//FmPsxrfSnliuDSHExf/CGDvDGPseY+yrjLEbroadsVxZMcWF73P/BWOMM8biJ9jf5kKJCcbYfzXBi9OMsb++0jbGcuWFcIZczxg7yRj7zuQc+SdXw85Yrqwwxv6UMbbLGHtB8e+MMfY7k7j5HmPsB03f+ZaRZMZYEsDvAfgAgLsA/AJj7K7Ax34JQI1zfiuA3wLw0FtlTyzXhhDj4jtwf+b8+wB8CsDDV9bKWK60EOMCjLESgH8B4FtX1sJYrrRQYoIxdhuA/x3AfZzzuwH8yytuaCxXVIhY8a8BfIJz/gMAfh7A719ZK2O5SvLnAB7U/PsHANw2+d8vA/gD0xe+lZXkdwO4wDl/hXM+APAxAB8MfOaDAP5i8t+fAvDjjDH2FtoUy9UXY1xwzk9yzjuTPz4F4PgVtjGWKy8UvACAX4ebTPeupHGxXBWhxMR/D+D3OOc1AOCc715hG2O58kKJCw5gfvLfZQBbV9C+WK6ScM6fAHCg+cgHAfwld+UpABXG2KruO99KkrwO4KLvzxuTv5N+hnM+AnAIYOkttCmWqy+UuPDLLwH4u7fUoliuBTHGxaQ1dh3n/AtX0rBYrppQsOJ2ALczxr7OGHuKMaarIsXy9hBKXPxfAH6RMbYB4IsA/ucrY1os17jY8o+r87PUscRCEcbYLwK4F8D7rrYtsVxdYYwlAPwmgH92lU2J5dqSFNzW6Y/A7Tg9wRh7J+e8flWtiuXjmeH7AAACCElEQVRqyy8A+HPO+b9ljP0wgH/PGLuHc+5cbcNi+Yclb2UleRPAdb4/H5/8nfQzjLEU3LbI/ltoUyxXXyhxAcbY+wH8GoCf4Zz3r5BtsVw9McVFCcA9AB5jjL0G4D0AHokf3ntbCwUrNgA8wjkfcs5fBXAOLmmO5e0rlLj4JQCfAADO+TcB5AAsXxHrYrmWhcQ//PJWkuSnAdzGGLuJMZaBOzz/SOAzjwD4byf//V8COMHjFze/3cUYF4yxHwDwR3AJcjxj+J+GaOOCc37IOV/mnN/IOb8R7qz6z3DOT10dc2O5AkI5Qz4Lt4oMxtgy3PGLV66kkbFccaHExRsAfhwAGGN3wiXJe1fUyliuRXkEwH8zecvFewAccs4v6Ra8ZeMWnPMRY+xXAHwZQBLAn3LOTzPG/m8ApzjnjwD4KNw2yAW4w9Y//1bZE8u1IcS4+BDw/7d3hzYVxlAYht8KHJKZEAhmgBEIQbMGCQMgmIBFUCiGwJEi/muo4TeE5PI8AzQVR3xtz0k7rZ4Pc5zvc87LP9s0v25nXfCP7KyJl+p8jPFafVZ3c06vkUdsZ13cVo9jjJu2Ib4rF3DHb4zx1HZoPjv0o99XJ1Vzzoe2/vSL6q36qK5/XFPdAADAd37cAwCAhZAMAAALIRkAABZCMgAALIRkAABYCMkAALAQkgEAYCEkAwDA4gtLtZXGmfYd8AAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "id": "DMk9VP3Mw0AK",
        "outputId": "ee089d01-31cf-4849-86e4-d3442f7fdae0"
      },
      "source": [
        "# SDP OF SIN WAVE\n",
        "for i in range(1,7):\n",
        "    rs = (amp - np.min(amp))/(np.max(amp)- np.min(amp))\n",
        "    th = (60*i + ((amp - np.min(amp))/(np.max(amp)- np.min(amp))*35))\n",
        "    ph = (60*i - ((amp - np.min(amp))/(np.max(amp)- np.min(amp))*35))\n",
        "    th = th[2:]\n",
        "    ph = ph[2:]\n",
        "    rs = rs[:len(rs)-2]\n",
        "    plt.polar(th*(np.pi/180),rs,'.', c='blue')\n",
        "    plt.polar(ph*(np.pi/180),rs,'.', c='red')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i1NYDiMyfMe3"
      },
      "source": [
        "Downloading data set\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2qvkVPDERWJk"
      },
      "source": [
        "linklst = [\"https://engineering.case.edu/sites/default/files/105.mat,https://engineering.case.edu/sites/default/files/106.mat\",https://engineering.case.edu/sites/default/files/107.mat,https://engineering.case.edu/sites/default/files/108.mat]\n",
        "for i in linklst : \n",
        "\n",
        "      !wget i\n",
        "      # update scipy\n",
        "      mat = scipy.io.loadmat('105.mat')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fTXFTojGsvqz",
        "outputId": "57e17476-d09e-479e-f413-95526ab0eb75"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WaO3eHiOfWlk"
      },
      "source": [
        "#Making Multiple Directories\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xXWMrXfQliLU"
      },
      "source": [
        "#Making Multiple Directories\n",
        "import os\n",
        "\n",
        "ztlist =[(39, 3), (41, 6), (39, 5), (17, 2), (17, 3), (29, 4), (46, 6), (14, 4), (43, 5), (31, 4), (10, 7), (12, 3), (42, 2), (56, 8), (49, 2), (59, 3), (49, 4),(51, 4), (49, 10), (47, 8), (15, 7), (13, 4), (34, 2), (29, 3), (40, 4), (12, 8)]\n",
        "\n",
        "for items in ztlist:\n",
        "  z=items[0]\n",
        "  t=items[1]\n",
        "  name= \"z=\"+str(z)+', t='+str(t)\n",
        "  path = os.path.join('/content/drive/MyDrive/MP(Inner-Outer)/48kpart2', name)\n",
        "  os.mkdir(path)\n",
        "  a = '/content/drive/MyDrive/MP(Inner-Outer)/48kpart2/' + name\n",
        "  path = os.path.join(a, \"Inner\")\n",
        "  os.mkdir(path)\n",
        "  path = os.path.join(a, \"Outer\")\n",
        "  os.mkdir(path)\n",
        "  \n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LpzPRrLhfb5o"
      },
      "source": [
        "## RANDOM WALK"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BBOH-ydteRVl",
        "outputId": "4a0fda12-8ba3-42d3-859a-0c106dd38e28"
      },
      "source": [
        "import random\n",
        "import itertools\n",
        "from itertools import permutations\n",
        " \n",
        "# initialize lists\n",
        "zvalues = [i for i in range(10,61)]\n",
        "tvalues =[i for i in range(2,11)]\n",
        " \n",
        "# create empty list to store the\n",
        "# combinations\n",
        "combinations = [(z,t) for z in zvalues for t in tvalues]\n",
        "choice = [random.choice(combinations) for _ in range(40)]\n",
        "print(len(choice))\n",
        "print(choice)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "40\n",
            "[(39, 3), (41, 6), (39, 5), (17, 2), (17, 3), (29, 4), (46, 6), (14, 4), (43, 5), (31, 4), (10, 7), (12, 3), (42, 2), (56, 8), (49, 2), (59, 3), (49, 4), (51, 4), (49, 10), (47, 8), (15, 7), (13, 4), (34, 2), (29, 3), (40, 4), (12, 8), (59, 10), (58, 3), (54, 9), (26, 8), (33, 4), (16, 7), (48, 10), (28, 2), (30, 8), (27, 3), (51, 2), (54, 10), (57, 2), (59, 9)]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QyIT9w-ej4mx"
      },
      "source": [
        "## VALUES TAKEN FROM RANDOM WALK ARE :\n",
        "\n",
        "(39, 3), (41, 6), (39, 5), (17, 2), (17, 3), (29, 4), (46, 6), (14, 4), (43, 5), (31, 4), (10, 7), (12, 3), (42, 2), (56, 8), (49, 2), (59, 3), (49, 4), (51, 4), (49, 10), (47, 8), (15, 7), (13, 4), (34, 2), (29, 3), (40, 4), (12, 8)\n",
        "\n",
        "Currently generated images from : \n",
        "\n",
        "ztlist = [[35,2],[30,2],[40,2],[30,9],[50,2],[30,7],[35,9],[35,7],[60,2],[40,7],[40,9],[60,1]]"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "DCekNsPXAq_-",
        "outputId": "6c70dee1-4154-4fd0-b691-6b32246bff5c"
      },
      "source": [
        "# int(len(c1)/1600 + 1\n",
        "#Devam 1 run Outer \n",
        "import scipy.io\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "!wget https://engineering.case.edu/sites/default/files/135.mat\n",
        "!wget https://engineering.case.edu/sites/default/files/136.mat\n",
        "!wget https://engineering.case.edu/sites/default/files/137.mat\n",
        "!wget https://engineering.case.edu/sites/default/files/138.mat\n",
        "!wget https://engineering.case.edu/sites/default/files/148.mat\n",
        "!wget https://engineering.case.edu/sites/default/files/149.mat\n",
        "!wget https://engineering.case.edu/sites/default/files/150.mat\n",
        "!wget https://engineering.case.edu/sites/default/files/151.mat\n",
        "!wget https://engineering.case.edu/sites/default/files/164.mat\n",
        "!wget https://engineering.case.edu/sites/default/files/163.mat\n",
        "!wget https://engineering.case.edu/sites/default/files/162.mat\n",
        "!wget https://engineering.case.edu/sites/default/files/161.mat\n",
        "#for OR @3 48k nlist = [148,149,150,151]\n",
        "nlist = [135,136,137,138,161, 162,163, 164,148,149,150,151]\n",
        "# nlist = [109,110,111,112]\n",
        "# ztlist = [[35,2],[30,2],[40,2],[30,9],[50,2],[30,7],[35,9],[35,7],[60,2],[40,7],[40,9],[60,1]]\n",
        "\n",
        "ztlist =[(39, 3), (41, 6), (39, 5), (17, 2), (17, 3), (29, 4), (46, 6), (14, 4)]\n",
        "# , (43, 5), (31, 4), (10, 7), (12, 3), (42, 2), (56, 8), (49, 2), (59, 3), (49, 4),...\n",
        "        #  (51, 4), (49, 10), (47, 8), (15, 7), (13, 4), (34, 2), (29, 3), (40, 4), (12, 8)] \n",
        "for item in ztlist: \n",
        "  for x in nlist:\n",
        "    matstr = str(x) + \".mat\"\n",
        "    # update scipy\n",
        "    mat = scipy.io.loadmat(matstr)\n",
        "    def sdp(c1):\n",
        "      z = item[0]\n",
        "      t = item[1]\n",
        "      c1max = np.max(c1)\n",
        "      c1min = np.min(c1)\n",
        "      c1mean = np.mean(c1)\n",
        "      #c1 = (c1 - c1mean )/ max(abs(c1- c1mean)) \"MENTIONED IN PAPER BUT NOT TAKEN AS IT IS NOT REQUIRED\" \n",
        "      for i in range(0,7):\n",
        "          r = ((c1 - c1min)/(c1max - c1min))\n",
        "          r = r[:len(c1)-t]\n",
        "          theta = (60*i + (((c1-c1min)/(c1max-c1min))*z))\n",
        "          phi = (60*i - (((c1-c1min)/(c1max-c1min))*z))\n",
        "          theta = theta[t:]\n",
        "          phi = phi[t:]\n",
        "          plt.polar(theta*(np.pi/180),r ,'.', c='blue')\n",
        "          plt.polar(phi*(np.pi/180),r ,'.', c='red')\n",
        "          plt.grid(False)\n",
        "          plt.axis('off')\n",
        "\n",
        "    c1 =np.array( mat[\"X\" + str(x) + \"_DE_time\"])\n",
        "    c1max = np.max(c1)\n",
        "    c1min = np.min(c1)\n",
        "    c1mean = np.mean(c1)\n",
        "    for j in range( int(len(c1)/1600)):\n",
        "        x = c1[j*1600:j*1600 + 1600]\n",
        "        sdp(x)\n",
        "        # /content/drive/MyDrive/MP(Inner-Outer)/48k part 2\n",
        "        plt.savefig(\"/content/drive/MyDrive/MP(Inner-Outer)/48kpart2/\" + \"z=\" + str(item[0])+\", \"+\"t=\" + str(item[1]) + \"/\"+\"Inner/\"  +\"48kIR_\" + str(item[0]) + \",\" + str(item[1]) + \"_\" + str(j))\n",
        "        plt.clf()\n",
        "\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "--2021-11-28 05:50:57--  https://engineering.case.edu/sites/default/files/135.mat\n",
            "Resolving engineering.case.edu (engineering.case.edu)... 129.22.104.251\n",
            "Connecting to engineering.case.edu (engineering.case.edu)|129.22.104.251|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 3896944 (3.7M)\n",
            "Saving to: ‘135.mat.2’\n",
            "\n",
            "135.mat.2           100%[===================>]   3.72M  9.05MB/s    in 0.4s    \n",
            "\n",
            "2021-11-28 05:50:58 (9.05 MB/s) - ‘135.mat.2’ saved [3896944/3896944]\n",
            "\n",
            "--2021-11-28 05:50:58--  https://engineering.case.edu/sites/default/files/136.mat\n",
            "Resolving engineering.case.edu (engineering.case.edu)... 129.22.104.251\n",
            "Connecting to engineering.case.edu (engineering.case.edu)|129.22.104.251|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 7789200 (7.4M)\n",
            "Saving to: ‘136.mat.2’\n",
            "\n",
            "136.mat.2           100%[===================>]   7.43M  19.6MB/s    in 0.4s    \n",
            "\n",
            "2021-11-28 05:50:59 (19.6 MB/s) - ‘136.mat.2’ saved [7789200/7789200]\n",
            "\n",
            "--2021-11-28 05:50:59--  https://engineering.case.edu/sites/default/files/137.mat\n",
            "Resolving engineering.case.edu (engineering.case.edu)... 129.22.104.251\n",
            "Connecting to engineering.case.edu (engineering.case.edu)|129.22.104.251|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 7789200 (7.4M)\n",
            "Saving to: ‘137.mat.2’\n",
            "\n",
            "137.mat.2           100%[===================>]   7.43M  16.2MB/s    in 0.5s    \n",
            "\n",
            "2021-11-28 05:51:00 (16.2 MB/s) - ‘137.mat.2’ saved [7789200/7789200]\n",
            "\n",
            "--2021-11-28 05:51:00--  https://engineering.case.edu/sites/default/files/138.mat\n",
            "Resolving engineering.case.edu (engineering.case.edu)... 129.22.104.251\n",
            "Connecting to engineering.case.edu (engineering.case.edu)|129.22.104.251|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 7807760 (7.4M)\n",
            "Saving to: ‘138.mat.2’\n",
            "\n",
            "138.mat.2           100%[===================>]   7.45M  15.6MB/s    in 0.5s    \n",
            "\n",
            "2021-11-28 05:51:00 (15.6 MB/s) - ‘138.mat.2’ saved [7807760/7807760]\n",
            "\n",
            "--2021-11-28 05:51:00--  https://engineering.case.edu/sites/default/files/148.mat\n",
            "Resolving engineering.case.edu (engineering.case.edu)... 129.22.104.251\n",
            "Connecting to engineering.case.edu (engineering.case.edu)|129.22.104.251|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 1993968 (1.9M)\n",
            "Saving to: ‘148.mat.2’\n",
            "\n",
            "148.mat.2           100%[===================>]   1.90M  6.12MB/s    in 0.3s    \n",
            "\n",
            "2021-11-28 05:51:01 (6.12 MB/s) - ‘148.mat.2’ saved [1993968/1993968]\n",
            "\n",
            "--2021-11-28 05:51:01--  https://engineering.case.edu/sites/default/files/149.mat\n",
            "Resolving engineering.case.edu (engineering.case.edu)... 129.22.104.251\n",
            "Connecting to engineering.case.edu (engineering.case.edu)|129.22.104.251|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 7761344 (7.4M)\n",
            "Saving to: ‘149.mat.2’\n",
            "\n",
            "149.mat.2           100%[===================>]   7.40M  21.5MB/s    in 0.3s    \n",
            "\n",
            "2021-11-28 05:51:02 (21.5 MB/s) - ‘149.mat.2’ saved [7761344/7761344]\n",
            "\n",
            "--2021-11-28 05:51:02--  https://engineering.case.edu/sites/default/files/150.mat\n",
            "Resolving engineering.case.edu (engineering.case.edu)... 129.22.104.251\n",
            "Connecting to engineering.case.edu (engineering.case.edu)|129.22.104.251|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 7770624 (7.4M)\n",
            "Saving to: ‘150.mat.2’\n",
            "\n",
            "150.mat.2           100%[===================>]   7.41M  20.0MB/s    in 0.4s    \n",
            "\n",
            "2021-11-28 05:51:02 (20.0 MB/s) - ‘150.mat.2’ saved [7770624/7770624]\n",
            "\n",
            "--2021-11-28 05:51:02--  https://engineering.case.edu/sites/default/files/151.mat\n",
            "Resolving engineering.case.edu (engineering.case.edu)... 129.22.104.251\n",
            "Connecting to engineering.case.edu (engineering.case.edu)|129.22.104.251|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 7779920 (7.4M)\n",
            "Saving to: ‘151.mat.2’\n",
            "\n",
            "151.mat.2           100%[===================>]   7.42M  19.9MB/s    in 0.4s    \n",
            "\n",
            "2021-11-28 05:51:03 (19.9 MB/s) - ‘151.mat.2’ saved [7779920/7779920]\n",
            "\n",
            "--2021-11-28 05:51:03--  https://engineering.case.edu/sites/default/files/164.mat\n",
            "Resolving engineering.case.edu (engineering.case.edu)... 129.22.104.251\n",
            "Connecting to engineering.case.edu (engineering.case.edu)|129.22.104.251|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 7752064 (7.4M)\n",
            "Saving to: ‘164.mat.2’\n",
            "\n",
            "164.mat.2           100%[===================>]   7.39M  18.3MB/s    in 0.4s    \n",
            "\n",
            "2021-11-28 05:51:04 (18.3 MB/s) - ‘164.mat.2’ saved [7752064/7752064]\n",
            "\n",
            "--2021-11-28 05:51:04--  https://engineering.case.edu/sites/default/files/163.mat\n",
            "Resolving engineering.case.edu (engineering.case.edu)... 129.22.104.251\n",
            "Connecting to engineering.case.edu (engineering.case.edu)|129.22.104.251|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 7733504 (7.4M)\n",
            "Saving to: ‘163.mat.1’\n",
            "\n",
            "163.mat.1           100%[===================>]   7.38M  18.7MB/s    in 0.4s    \n",
            "\n",
            "2021-11-28 05:51:04 (18.7 MB/s) - ‘163.mat.1’ saved [7733504/7733504]\n",
            "\n",
            "--2021-11-28 05:51:05--  https://engineering.case.edu/sites/default/files/162.mat\n",
            "Resolving engineering.case.edu (engineering.case.edu)... 129.22.104.251\n",
            "Connecting to engineering.case.edu (engineering.case.edu)|129.22.104.251|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 7724208 (7.4M)\n",
            "Saving to: ‘162.mat.2’\n",
            "\n",
            "162.mat.2           100%[===================>]   7.37M  17.3MB/s    in 0.4s    \n",
            "\n",
            "2021-11-28 05:51:05 (17.3 MB/s) - ‘162.mat.2’ saved [7724208/7724208]\n",
            "\n",
            "--2021-11-28 05:51:05--  https://engineering.case.edu/sites/default/files/161.mat\n",
            "Resolving engineering.case.edu (engineering.case.edu)... 129.22.104.251\n",
            "Connecting to engineering.case.edu (engineering.case.edu)|129.22.104.251|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 2079840 (2.0M)\n",
            "Saving to: ‘161.mat.2’\n",
            "\n",
            "161.mat.2           100%[===================>]   1.98M  5.99MB/s    in 0.3s    \n",
            "\n",
            "2021-11-28 05:51:06 (5.99 MB/s) - ‘161.mat.2’ saved [2079840/2079840]\n",
            "\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Lkc16ndkcfok",
        "outputId": "7090cfeb-7cb5-4c15-c37c-9ca9ab6137e6"
      },
      "source": [
        "# int(len(c1)/1600 + 1\n",
        "#Devam 2 run Inner \n",
        "import scipy.io\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "!wget https://engineering.case.edu/sites/default/files/109.mat\n",
        "!wget https://engineering.case.edu/sites/default/files/110.mat\n",
        "!wget https://engineering.case.edu/sites/default/files/111.mat\n",
        "!wget https://engineering.case.edu/sites/default/files/112.mat\n",
        "# !wget https://engineering.case.edu/sites/default/files/135.mat\n",
        "# !wget https://engineering.case.edu/sites/default/files/136.mat\n",
        "# !wget https://engineering.case.edu/sites/default/files/137.mat\n",
        "# !wget https://engineering.case.edu/sites/default/files/138.mat\n",
        "# !wget https://engineering.case.edu/sites/default/files/148.mat\n",
        "# !wget https://engineering.case.edu/sites/default/files/149.mat\n",
        "# !wget https://engineering.case.edu/sites/default/files/150.mat\n",
        "# !wget https://engineering.case.edu/sites/default/files/151.mat\n",
        "# !wget https://engineering.case.edu/sites/default/files/164.mat\n",
        "# !wget https://engineering.case.edu/sites/default/files/163.mat\n",
        "# !wget https://engineering.case.edu/sites/default/files/162.mat\n",
        "# !wget https://engineering.case.edu/sites/default/files/161.mat\n",
        "#for OR @3 48k nlist = [148,149,150,151]\n",
        "# nlist = [135,136,137,138,161, 162,163, 164]\n",
        "nlist = [109,110,111,112]\n",
        "# ztlist = [[35,2],[30,2],[40,2],[30,9],[50,2],[30,7],[35,9],[35,7],[60,2],[40,7],[40,9],[60,1]]\n",
        "\n",
        "ztlist =[(39, 3), (41, 6), (39, 5), (17, 2), (17, 3), (29, 4), (46, 6), (14, 4)]\n",
        "# [, (41, 6), (39, 5), (17, 2), (17, 3), (29, 4), (46, 6), (49, 2), (59, 3), (49, 4),...\n",
        "        #  (51, 4), (49, 10), (47, 8), (15, 7), (13, 4), (34, 2), (29, 3), (40, 4), (12, 8)] \n",
        "for item in ztlist: \n",
        "  for x in nlist:\n",
        "    matstr = str(x) + \".mat\"\n",
        "    # update scipy\n",
        "    mat = scipy.io.loadmat(matstr)\n",
        "    def sdp(c1):\n",
        "      z = item[0]\n",
        "      t = item[1]\n",
        "      c1max = np.max(c1)\n",
        "      c1min = np.min(c1)\n",
        "      c1mean = np.mean(c1)\n",
        "      #c1 = (c1 - c1mean )/ max(abs(c1- c1mean)) \"MENTIONED IN PAPER BUT NOT TAKEN AS IT IS NOT REQUIRED\" \n",
        "      for i in range(0,7):\n",
        "          r = ((c1 - c1min)/(c1max - c1min))\n",
        "          r = r[:len(c1)-t]\n",
        "          theta = (60*i + (((c1-c1min)/(c1max-c1min))*z))\n",
        "          phi = (60*i - (((c1-c1min)/(c1max-c1min))*z))\n",
        "          theta = theta[t:]\n",
        "          phi = phi[t:]\n",
        "          plt.polar(theta*(np.pi/180),r ,'.', c='blue')\n",
        "          plt.polar(phi*(np.pi/180),r ,'.', c='red')\n",
        "          plt.grid(False)\n",
        "          plt.axis('off')\n",
        "\n",
        "    c1 =np.array( mat[\"X\" + str(x) + \"_DE_time\"])\n",
        "    c1max = np.max(c1)\n",
        "    c1min = np.min(c1)\n",
        "    c1mean = np.mean(c1)\n",
        "    for j in range( int(len(c1)/1600)):\n",
        "        x = c1[j*1600:j*1600 + 1600]\n",
        "        sdp(x)\n",
        "        # /content/drive/MyDrive/MP(Inner-Outer)/48k part 2\n",
        "        plt.savefig(\"/content/drive/MyDrive/MP(Inner-Outer)/48k part 2/\" + \"z=\" + str(item[0])+\", \"+\"t=\" + str(item[1]) + \"/\"+\"Inner/\"  +\"48kIR_\" + str(item[0]) + \",\" + str(item[1]) + \"_\" + str(j))\n",
        "        plt.clf()\n",
        "\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "--2021-11-14 09:49:18--  https://engineering.case.edu/sites/default/files/109.mat\n",
            "Resolving engineering.case.edu (engineering.case.edu)... 129.22.104.251\n",
            "Connecting to engineering.case.edu (engineering.case.edu)|129.22.104.251|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 3903344 (3.7M)\n",
            "Saving to: ‘109.mat.1’\n",
            "\n",
            "109.mat.1           100%[===================>]   3.72M  7.19MB/s    in 0.5s    \n",
            "\n",
            "2021-11-14 09:49:19 (7.19 MB/s) - ‘109.mat.1’ saved [3903344/3903344]\n",
            "\n",
            "--2021-11-14 09:49:19--  https://engineering.case.edu/sites/default/files/110.mat\n",
            "Resolving engineering.case.edu (engineering.case.edu)... 129.22.104.251\n",
            "Connecting to engineering.case.edu (engineering.case.edu)|129.22.104.251|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 7779920 (7.4M)\n",
            "Saving to: ‘110.mat.1’\n",
            "\n",
            "110.mat.1           100%[===================>]   7.42M  12.5MB/s    in 0.6s    \n",
            "\n",
            "2021-11-14 09:49:20 (12.5 MB/s) - ‘110.mat.1’ saved [7779920/7779920]\n",
            "\n",
            "--2021-11-14 09:49:20--  https://engineering.case.edu/sites/default/files/111.mat\n",
            "Resolving engineering.case.edu (engineering.case.edu)... 129.22.104.251\n",
            "Connecting to engineering.case.edu (engineering.case.edu)|129.22.104.251|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 7770624 (7.4M)\n",
            "Saving to: ‘111.mat.1’\n",
            "\n",
            "111.mat.1           100%[===================>]   7.41M  11.2MB/s    in 0.7s    \n",
            "\n",
            "2021-11-14 09:49:21 (11.2 MB/s) - ‘111.mat.1’ saved [7770624/7770624]\n",
            "\n",
            "--2021-11-14 09:49:21--  https://engineering.case.edu/sites/default/files/112.mat\n",
            "Resolving engineering.case.edu (engineering.case.edu)... 129.22.104.251\n",
            "Connecting to engineering.case.edu (engineering.case.edu)|129.22.104.251|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 7770624 (7.4M)\n",
            "Saving to: ‘112.mat.1’\n",
            "\n",
            "112.mat.1           100%[===================>]   7.41M  1.83MB/s    in 4.0s    \n",
            "\n",
            "2021-11-14 09:49:26 (1.83 MB/s) - ‘112.mat.1’ saved [7770624/7770624]\n",
            "\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "txD0QBSecnoR",
        "outputId": "f51cf5c4-18c3-4180-b9d7-79286cccf497"
      },
      "source": [
        "# int(len(c1)/1600 + 1\n",
        "#Shyam 1 Outer \n",
        "import scipy.io\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "!wget https://engineering.case.edu/sites/default/files/135.mat\n",
        "!wget https://engineering.case.edu/sites/default/files/136.mat\n",
        "!wget https://engineering.case.edu/sites/default/files/137.mat\n",
        "!wget https://engineering.case.edu/sites/default/files/138.mat\n",
        "!wget https://engineering.case.edu/sites/default/files/148.mat\n",
        "!wget https://engineering.case.edu/sites/default/files/149.mat\n",
        "!wget https://engineering.case.edu/sites/default/files/150.mat\n",
        "!wget https://engineering.case.edu/sites/default/files/151.mat\n",
        "!wget https://engineering.case.edu/sites/default/files/164.mat\n",
        "!wget https://engineering.case.edu/sites/default/files/163.mat\n",
        "!wget https://engineering.case.edu/sites/default/files/162.mat\n",
        "!wget https://engineering.case.edu/sites/default/files/161.mat\n",
        "#for OR @3 48k nlist = [148,149,150,151]\n",
        "nlist = [135,136,137,138,161,162,163, 164,148,149,150,151]\n",
        "# nlist = [109,110,111,112]\n",
        "# ztlist = [[35,2],[30,2],[40,2],[30,9],[50,2],[30,7],[35,9],[35,7],[60,2],[40,7],[40,9],[60,1]]\n",
        "\n",
        "ztlist =  [(43, 5), (31, 4), (10, 7), (12, 3), (42, 2), (56, 8), (49, 2), (59, 3), (49, 4)]\n",
        "# [(39, 3), (41, 6), (39, 5), (17, 2), (17, 3), (29, 4), (46, 6), (59, 3), (49, 4),...\n",
        "#  (51, 4), (49, 10), (47, 8), (15, 7), (13, 4), (34, 2), (29, 3), (40, 4), (12, 8)] \n",
        "for item in ztlist: \n",
        "  for x in nlist:\n",
        "    matstr = str(x) + \".mat\"\n",
        "    # update scipy\n",
        "    mat = scipy.io.loadmat(matstr)\n",
        "    def sdp(c1):\n",
        "      z = item[0]\n",
        "      t = item[1]\n",
        "      c1max = np.max(c1)\n",
        "      c1min = np.min(c1)\n",
        "      c1mean = np.mean(c1)\n",
        "      #c1 = (c1 - c1mean )/ max(abs(c1- c1mean)) \"MENTIONED IN PAPER BUT NOT TAKEN AS IT IS NOT REQUIRED\" \n",
        "      for i in range(0,7):\n",
        "          r = ((c1 - c1min)/(c1max - c1min))\n",
        "          r = r[:len(c1)-t]\n",
        "          theta = (60*i + (((c1-c1min)/(c1max-c1min))*z))\n",
        "          phi = (60*i - (((c1-c1min)/(c1max-c1min))*z))\n",
        "          theta = theta[t:]\n",
        "          phi = phi[t:]\n",
        "          plt.polar(theta*(np.pi/180),r ,'.', c='blue')\n",
        "          plt.polar(phi*(np.pi/180),r ,'.', c='red')\n",
        "          plt.grid(False)\n",
        "          plt.axis('off')\n",
        "\n",
        "    c1 =np.array( mat[\"X\" + str(x) + \"_DE_time\"])\n",
        "    c1max = np.max(c1)\n",
        "    c1min = np.min(c1)\n",
        "    c1mean = np.mean(c1)\n",
        "    for j in range( int(len(c1)/1600)):\n",
        "        x = c1[j*1600:j*1600 + 1600]\n",
        "        sdp(x)\n",
        "        # /content/drive/MyDrive/MP(Inner-Outer)/48kpart2\n",
        "        plt.savefig(\"/content/drive/MyDrive/MP(Inner-Outer)/48kpart2/\" + \"z=\" + str(item[0])+\", \"+\"t=\" + str(item[1]) + \"/\"+\"Outer/\"  +\"48kOR_\" + str(item[0]) + \",\" + str(item[1]) + \"_\" + str(j))\n",
        "        plt.clf()\n",
        "\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "--2021-11-27 13:48:46--  https://engineering.case.edu/sites/default/files/135.mat\n",
            "Resolving engineering.case.edu (engineering.case.edu)... 129.22.104.251\n",
            "Connecting to engineering.case.edu (engineering.case.edu)|129.22.104.251|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 3896944 (3.7M)\n",
            "Saving to: ‘135.mat’\n",
            "\n",
            "135.mat             100%[===================>]   3.72M  5.14MB/s    in 0.7s    \n",
            "\n",
            "2021-11-27 13:48:47 (5.14 MB/s) - ‘135.mat’ saved [3896944/3896944]\n",
            "\n",
            "--2021-11-27 13:48:47--  https://engineering.case.edu/sites/default/files/136.mat\n",
            "Resolving engineering.case.edu (engineering.case.edu)... 129.22.104.251\n",
            "Connecting to engineering.case.edu (engineering.case.edu)|129.22.104.251|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 7789200 (7.4M)\n",
            "Saving to: ‘136.mat’\n",
            "\n",
            "136.mat             100%[===================>]   7.43M  20.7MB/s    in 0.4s    \n",
            "\n",
            "2021-11-27 13:48:47 (20.7 MB/s) - ‘136.mat’ saved [7789200/7789200]\n",
            "\n",
            "--2021-11-27 13:48:47--  https://engineering.case.edu/sites/default/files/137.mat\n",
            "Resolving engineering.case.edu (engineering.case.edu)... 129.22.104.251\n",
            "Connecting to engineering.case.edu (engineering.case.edu)|129.22.104.251|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 7789200 (7.4M)\n",
            "Saving to: ‘137.mat’\n",
            "\n",
            "137.mat             100%[===================>]   7.43M  21.9MB/s    in 0.3s    \n",
            "\n",
            "2021-11-27 13:48:48 (21.9 MB/s) - ‘137.mat’ saved [7789200/7789200]\n",
            "\n",
            "--2021-11-27 13:48:48--  https://engineering.case.edu/sites/default/files/138.mat\n",
            "Resolving engineering.case.edu (engineering.case.edu)... 129.22.104.251\n",
            "Connecting to engineering.case.edu (engineering.case.edu)|129.22.104.251|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 7807760 (7.4M)\n",
            "Saving to: ‘138.mat’\n",
            "\n",
            "138.mat             100%[===================>]   7.45M  28.7MB/s    in 0.3s    \n",
            "\n",
            "2021-11-27 13:48:48 (28.7 MB/s) - ‘138.mat’ saved [7807760/7807760]\n",
            "\n",
            "--2021-11-27 13:48:49--  https://engineering.case.edu/sites/default/files/148.mat\n",
            "Resolving engineering.case.edu (engineering.case.edu)... 129.22.104.251\n",
            "Connecting to engineering.case.edu (engineering.case.edu)|129.22.104.251|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 1993968 (1.9M)\n",
            "Saving to: ‘148.mat’\n",
            "\n",
            "148.mat             100%[===================>]   1.90M  8.80MB/s    in 0.2s    \n",
            "\n",
            "2021-11-27 13:48:49 (8.80 MB/s) - ‘148.mat’ saved [1993968/1993968]\n",
            "\n",
            "--2021-11-27 13:48:49--  https://engineering.case.edu/sites/default/files/149.mat\n",
            "Resolving engineering.case.edu (engineering.case.edu)... 129.22.104.251\n",
            "Connecting to engineering.case.edu (engineering.case.edu)|129.22.104.251|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 7761344 (7.4M)\n",
            "Saving to: ‘149.mat’\n",
            "\n",
            "149.mat             100%[===================>]   7.40M  23.7MB/s    in 0.3s    \n",
            "\n",
            "2021-11-27 13:48:50 (23.7 MB/s) - ‘149.mat’ saved [7761344/7761344]\n",
            "\n",
            "--2021-11-27 13:48:50--  https://engineering.case.edu/sites/default/files/150.mat\n",
            "Resolving engineering.case.edu (engineering.case.edu)... 129.22.104.251\n",
            "Connecting to engineering.case.edu (engineering.case.edu)|129.22.104.251|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 7770624 (7.4M)\n",
            "Saving to: ‘150.mat’\n",
            "\n",
            "150.mat             100%[===================>]   7.41M  22.7MB/s    in 0.3s    \n",
            "\n",
            "2021-11-27 13:48:50 (22.7 MB/s) - ‘150.mat’ saved [7770624/7770624]\n",
            "\n",
            "--2021-11-27 13:48:50--  https://engineering.case.edu/sites/default/files/151.mat\n",
            "Resolving engineering.case.edu (engineering.case.edu)... 129.22.104.251\n",
            "Connecting to engineering.case.edu (engineering.case.edu)|129.22.104.251|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 7779920 (7.4M)\n",
            "Saving to: ‘151.mat’\n",
            "\n",
            "151.mat             100%[===================>]   7.42M  24.9MB/s    in 0.3s    \n",
            "\n",
            "2021-11-27 13:48:51 (24.9 MB/s) - ‘151.mat’ saved [7779920/7779920]\n",
            "\n",
            "--2021-11-27 13:48:51--  https://engineering.case.edu/sites/default/files/164.mat\n",
            "Resolving engineering.case.edu (engineering.case.edu)... 129.22.104.251\n",
            "Connecting to engineering.case.edu (engineering.case.edu)|129.22.104.251|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 7752064 (7.4M)\n",
            "Saving to: ‘164.mat’\n",
            "\n",
            "164.mat             100%[===================>]   7.39M  25.6MB/s    in 0.3s    \n",
            "\n",
            "2021-11-27 13:48:51 (25.6 MB/s) - ‘164.mat’ saved [7752064/7752064]\n",
            "\n",
            "--2021-11-27 13:48:51--  https://engineering.case.edu/sites/default/files/163.mat\n",
            "Resolving engineering.case.edu (engineering.case.edu)... 129.22.104.251\n",
            "Connecting to engineering.case.edu (engineering.case.edu)|129.22.104.251|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 7733504 (7.4M)\n",
            "Saving to: ‘163.mat’\n",
            "\n",
            "163.mat             100%[===================>]   7.38M  19.0MB/s    in 0.4s    \n",
            "\n",
            "2021-11-27 13:48:52 (19.0 MB/s) - ‘163.mat’ saved [7733504/7733504]\n",
            "\n",
            "--2021-11-27 13:48:52--  https://engineering.case.edu/sites/default/files/162.mat\n",
            "Resolving engineering.case.edu (engineering.case.edu)... 129.22.104.251\n",
            "Connecting to engineering.case.edu (engineering.case.edu)|129.22.104.251|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 7724208 (7.4M)\n",
            "Saving to: ‘162.mat’\n",
            "\n",
            "162.mat             100%[===================>]   7.37M  7.85MB/s    in 0.9s    \n",
            "\n",
            "2021-11-27 13:48:53 (7.85 MB/s) - ‘162.mat’ saved [7724208/7724208]\n",
            "\n",
            "--2021-11-27 13:48:53--  https://engineering.case.edu/sites/default/files/161.mat\n",
            "Resolving engineering.case.edu (engineering.case.edu)... 129.22.104.251\n",
            "Connecting to engineering.case.edu (engineering.case.edu)|129.22.104.251|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 2079840 (2.0M)\n",
            "Saving to: ‘161.mat’\n",
            "\n",
            "161.mat             100%[===================>]   1.98M  9.11MB/s    in 0.2s    \n",
            "\n",
            "2021-11-27 13:48:54 (9.11 MB/s) - ‘161.mat’ saved [2079840/2079840]\n",
            "\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J66UI670cryt",
        "outputId": "7090cfeb-7cb5-4c15-c37c-9ca9ab6137e6"
      },
      "source": [
        "# int(len(c1)/1600 + 1\n",
        "#Shyam 2 run Inner \n",
        "import scipy.io\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "!wget https://engineering.case.edu/sites/default/files/109.mat\n",
        "!wget https://engineering.case.edu/sites/default/files/110.mat\n",
        "!wget https://engineering.case.edu/sites/default/files/111.mat\n",
        "!wget https://engineering.case.edu/sites/default/files/112.mat\n",
        "# !wget https://engineering.case.edu/sites/default/files/135.mat\n",
        "# !wget https://engineering.case.edu/sites/default/files/136.mat\n",
        "# !wget https://engineering.case.edu/sites/default/files/137.mat\n",
        "# !wget https://engineering.case.edu/sites/default/files/138.mat\n",
        "# !wget https://engineering.case.edu/sites/default/files/148.mat\n",
        "# !wget https://engineering.case.edu/sites/default/files/149.mat\n",
        "# !wget https://engineering.case.edu/sites/default/files/150.mat\n",
        "# !wget https://engineering.case.edu/sites/default/files/151.mat\n",
        "# !wget https://engineering.case.edu/sites/default/files/164.mat\n",
        "# !wget https://engineering.case.edu/sites/default/files/163.mat\n",
        "# !wget https://engineering.case.edu/sites/default/files/162.mat\n",
        "# !wget https://engineering.case.edu/sites/default/files/161.mat\n",
        "#for OR @3 48k nlist = [148,149,150,151]\n",
        "# nlist = [135,136,137,138,161, 162,163, 164]\n",
        "nlist = [109,110,111,112]\n",
        "# ztlist = [[35,2],[30,2],[40,2],[30,9],[50,2],[30,7],[35,9],[35,7],[60,2],[40,7],[40,9],[60,1]]\n",
        "\n",
        "ztlist =[(43, 5), (31, 4), (10, 7), (12, 3), (42, 2), (56, 8), (49, 2), (59, 3), (49, 4)]\n",
        "# (39, 3), (41, 6), (39, 5), (17, 2), (17, 3), (29, 4), (46, 6), (14, 4), (43, 5), (31, 4), (10, 7), (12, 3), (42, 2), (56, 8), (49, 2), (59, 3), (49, 4),...\n",
        "        #  (51, 4), (49, 10), (47, 8), (15, 7), (13, 4), (34, 2), (29, 3), (40, 4), (12, 8)] \n",
        "for item in ztlist: \n",
        "  for x in nlist:\n",
        "    matstr = str(x) + \".mat\"\n",
        "    # update scipy\n",
        "    mat = scipy.io.loadmat(matstr)\n",
        "    def sdp(c1):\n",
        "      z = item[0]\n",
        "      t = item[1]\n",
        "      c1max = np.max(c1)\n",
        "      c1min = np.min(c1)\n",
        "      c1mean = np.mean(c1)\n",
        "      #c1 = (c1 - c1mean )/ max(abs(c1- c1mean)) \"MENTIONED IN PAPER BUT NOT TAKEN AS IT IS NOT REQUIRED\" \n",
        "      for i in range(0,7):\n",
        "          r = ((c1 - c1min)/(c1max - c1min))\n",
        "          r = r[:len(c1)-t]\n",
        "          theta = (60*i + (((c1-c1min)/(c1max-c1min))*z))\n",
        "          phi = (60*i - (((c1-c1min)/(c1max-c1min))*z))\n",
        "          theta = theta[t:]\n",
        "          phi = phi[t:]\n",
        "          plt.polar(theta*(np.pi/180),r ,'.', c='blue')\n",
        "          plt.polar(phi*(np.pi/180),r ,'.', c='red')\n",
        "          plt.grid(False)\n",
        "          plt.axis('off')\n",
        "\n",
        "    c1 =np.array( mat[\"X\" + str(x) + \"_DE_time\"])\n",
        "    c1max = np.max(c1)\n",
        "    c1min = np.min(c1)\n",
        "    c1mean = np.mean(c1)\n",
        "    for j in range( int(len(c1)/1600)):\n",
        "        x = c1[j*1600:j*1600 + 1600]\n",
        "        sdp(x)\n",
        "        # /content/drive/MyDrive/MP(Inner-Outer)/48kpart2/z=51, t=4\n",
        "        plt.savefig(\"/content/drive/MyDrive/MP(Inner-Outer)/48kpart2/\" + \"z=\" + str(item[0])+\", \"+\"t=\" + str(item[1]) + \"/\"+\"Inner/\"  +\"48kIR_\" + str(item[0]) + \",\" + str(item[1]) + \"_\" + str(j))\n",
        "        plt.clf()\n",
        "\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "--2021-11-14 09:49:18--  https://engineering.case.edu/sites/default/files/109.mat\n",
            "Resolving engineering.case.edu (engineering.case.edu)... 129.22.104.251\n",
            "Connecting to engineering.case.edu (engineering.case.edu)|129.22.104.251|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 3903344 (3.7M)\n",
            "Saving to: ‘109.mat.1’\n",
            "\n",
            "109.mat.1           100%[===================>]   3.72M  7.19MB/s    in 0.5s    \n",
            "\n",
            "2021-11-14 09:49:19 (7.19 MB/s) - ‘109.mat.1’ saved [3903344/3903344]\n",
            "\n",
            "--2021-11-14 09:49:19--  https://engineering.case.edu/sites/default/files/110.mat\n",
            "Resolving engineering.case.edu (engineering.case.edu)... 129.22.104.251\n",
            "Connecting to engineering.case.edu (engineering.case.edu)|129.22.104.251|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 7779920 (7.4M)\n",
            "Saving to: ‘110.mat.1’\n",
            "\n",
            "110.mat.1           100%[===================>]   7.42M  12.5MB/s    in 0.6s    \n",
            "\n",
            "2021-11-14 09:49:20 (12.5 MB/s) - ‘110.mat.1’ saved [7779920/7779920]\n",
            "\n",
            "--2021-11-14 09:49:20--  https://engineering.case.edu/sites/default/files/111.mat\n",
            "Resolving engineering.case.edu (engineering.case.edu)... 129.22.104.251\n",
            "Connecting to engineering.case.edu (engineering.case.edu)|129.22.104.251|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 7770624 (7.4M)\n",
            "Saving to: ‘111.mat.1’\n",
            "\n",
            "111.mat.1           100%[===================>]   7.41M  11.2MB/s    in 0.7s    \n",
            "\n",
            "2021-11-14 09:49:21 (11.2 MB/s) - ‘111.mat.1’ saved [7770624/7770624]\n",
            "\n",
            "--2021-11-14 09:49:21--  https://engineering.case.edu/sites/default/files/112.mat\n",
            "Resolving engineering.case.edu (engineering.case.edu)... 129.22.104.251\n",
            "Connecting to engineering.case.edu (engineering.case.edu)|129.22.104.251|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 7770624 (7.4M)\n",
            "Saving to: ‘112.mat.1’\n",
            "\n",
            "112.mat.1           100%[===================>]   7.41M  1.83MB/s    in 4.0s    \n",
            "\n",
            "2021-11-14 09:49:26 (1.83 MB/s) - ‘112.mat.1’ saved [7770624/7770624]\n",
            "\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N4Fu5Q8Mc4Bh",
        "outputId": "01bd7fcf-447e-460c-90f6-31607613c9bb"
      },
      "source": [
        "# int(len(c1)/1600 + 1\n",
        "#Pranav 1 run Outer \n",
        "import scipy.io\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "!wget https://engineering.case.edu/sites/default/files/135.mat\n",
        "!wget https://engineering.case.edu/sites/default/files/136.mat\n",
        "!wget https://engineering.case.edu/sites/default/files/137.mat\n",
        "!wget https://engineering.case.edu/sites/default/files/138.mat\n",
        "!wget https://engineering.case.edu/sites/default/files/148.mat\n",
        "!wget https://engineering.case.edu/sites/default/files/149.mat\n",
        "!wget https://engineering.case.edu/sites/default/files/150.mat\n",
        "!wget https://engineering.case.edu/sites/default/files/151.mat\n",
        "!wget https://engineering.case.edu/sites/default/files/164.mat\n",
        "!wget https://engineering.case.edu/sites/default/files/163.mat\n",
        "!wget https://engineering.case.edu/sites/default/files/162.mat\n",
        "!wget https://engineering.case.edu/sites/default/files/161.mat\n",
        "#for OR @3 48k nlist = [148,149,150,151]\n",
        "nlist = [135,136,137,138,161, 162,163, 164]\n",
        "# nlist = [109,110,111,112]\n",
        "# ztlist = [[35,2],[30,2],[40,2],[30,9],[50,2],[30,7],[35,9],[35,7],[60,2],[40,7],[40,9],[60,1]]\n",
        "\n",
        "ztlist =[(51, 4), (49, 10), (47, 8), (15, 7), (13, 4), (34, 2), (29, 3), (40, 4), (12, 8)]\n",
        "# (39, 3), (41, 6), (39, 5), (17, 2), (17, 3), (29, 4), (46, 6), (14, 4), (43, 5), (31, 4), (10, 7), (12, 3), (42, 2), (56, 8), (49, 2), (59, 3), (49, 4),...\n",
        "        #  (51, 4), (49, 10), (47, 8), (15, 7), (13, 4), (34, 2), (29, 3), (40, 4), (12, 8)] \n",
        "for item in ztlist: \n",
        "  for x in nlist:\n",
        "    matstr = str(x) + \".mat\"\n",
        "    # update scipy\n",
        "    mat = scipy.io.loadmat(matstr)\n",
        "    def sdp(c1):\n",
        "      z = item[0]\n",
        "      t = item[1]\n",
        "      c1max = np.max(c1)\n",
        "      c1min = np.min(c1)\n",
        "      c1mean = np.mean(c1)\n",
        "      #c1 = (c1 - c1mean )/ max(abs(c1- c1mean)) \"MENTIONED IN PAPER BUT NOT TAKEN AS IT IS NOT REQUIRED\" \n",
        "      for i in range(0,7):\n",
        "          r = ((c1 - c1min)/(c1max - c1min))\n",
        "          r = r[:len(c1)-t]\n",
        "          theta = (60*i + (((c1-c1min)/(c1max-c1min))*z))\n",
        "          phi = (60*i - (((c1-c1min)/(c1max-c1min))*z))\n",
        "          theta = theta[t:]\n",
        "          phi = phi[t:]\n",
        "          plt.polar(theta*(np.pi/180),r ,'.', c='blue')\n",
        "          plt.polar(phi*(np.pi/180),r ,'.', c='red')\n",
        "          plt.grid(False)\n",
        "          plt.axis('off')\n",
        "\n",
        "    c1 =np.array( mat[\"X\" + str(x) + \"_DE_time\"])\n",
        "    c1max = np.max(c1)\n",
        "    c1min = np.min(c1)\n",
        "    c1mean = np.mean(c1)\n",
        "    for j in range( int(len(c1)/1600)):\n",
        "        x = c1[j*1600:j*1600 + 1600]\n",
        "        sdp(x)\n",
        "        # /content/drive/MyDrive/MP(Inner-Outer)/48k part 2\n",
        "        plt.savefig(\"/content/drive/MyDrive/MP(Inner-Outer)/48kpart2/\" + \"z=\" + str(item[0])+\", \"+\"t=\" + str(item[1]) + \"/\"+\"Outer/\"  +\"48kOR_\" + str(item[0]) + \",\" + str(item[1]) + \"_\" + str(j))\n",
        "        plt.clf()\n",
        "\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "--2021-11-27 13:27:51--  https://engineering.case.edu/sites/default/files/135.mat\n",
            "Resolving engineering.case.edu (engineering.case.edu)... 129.22.104.251\n",
            "Connecting to engineering.case.edu (engineering.case.edu)|129.22.104.251|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 3896944 (3.7M)\n",
            "Saving to: ‘135.mat.3’\n",
            "\n",
            "135.mat.3           100%[===================>]   3.72M  4.33MB/s    in 0.9s    \n",
            "\n",
            "2021-11-27 13:27:52 (4.33 MB/s) - ‘135.mat.3’ saved [3896944/3896944]\n",
            "\n",
            "--2021-11-27 13:27:52--  https://engineering.case.edu/sites/default/files/136.mat\n",
            "Resolving engineering.case.edu (engineering.case.edu)... 129.22.104.251\n",
            "Connecting to engineering.case.edu (engineering.case.edu)|129.22.104.251|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 7789200 (7.4M)\n",
            "Saving to: ‘136.mat.3’\n",
            "\n",
            "136.mat.3           100%[===================>]   7.43M  6.97MB/s    in 1.1s    \n",
            "\n",
            "2021-11-27 13:27:54 (6.97 MB/s) - ‘136.mat.3’ saved [7789200/7789200]\n",
            "\n",
            "--2021-11-27 13:27:54--  https://engineering.case.edu/sites/default/files/137.mat\n",
            "Resolving engineering.case.edu (engineering.case.edu)... 129.22.104.251\n",
            "Connecting to engineering.case.edu (engineering.case.edu)|129.22.104.251|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 7789200 (7.4M)\n",
            "Saving to: ‘137.mat.3’\n",
            "\n",
            "137.mat.3           100%[===================>]   7.43M  15.0MB/s    in 0.5s    \n",
            "\n",
            "2021-11-27 13:27:54 (15.0 MB/s) - ‘137.mat.3’ saved [7789200/7789200]\n",
            "\n",
            "--2021-11-27 13:27:55--  https://engineering.case.edu/sites/default/files/138.mat\n",
            "Resolving engineering.case.edu (engineering.case.edu)... 129.22.104.251\n",
            "Connecting to engineering.case.edu (engineering.case.edu)|129.22.104.251|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 7807760 (7.4M)\n",
            "Saving to: ‘138.mat.3’\n",
            "\n",
            "138.mat.3           100%[===================>]   7.45M  12.1MB/s    in 0.6s    \n",
            "\n",
            "2021-11-27 13:27:55 (12.1 MB/s) - ‘138.mat.3’ saved [7807760/7807760]\n",
            "\n",
            "--2021-11-27 13:27:56--  https://engineering.case.edu/sites/default/files/148.mat\n",
            "Resolving engineering.case.edu (engineering.case.edu)... 129.22.104.251\n",
            "Connecting to engineering.case.edu (engineering.case.edu)|129.22.104.251|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 1993968 (1.9M)\n",
            "Saving to: ‘148.mat.3’\n",
            "\n",
            "148.mat.3           100%[===================>]   1.90M  5.09MB/s    in 0.4s    \n",
            "\n",
            "2021-11-27 13:27:56 (5.09 MB/s) - ‘148.mat.3’ saved [1993968/1993968]\n",
            "\n",
            "--2021-11-27 13:27:56--  https://engineering.case.edu/sites/default/files/149.mat\n",
            "Resolving engineering.case.edu (engineering.case.edu)... 129.22.104.251\n",
            "Connecting to engineering.case.edu (engineering.case.edu)|129.22.104.251|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 7761344 (7.4M)\n",
            "Saving to: ‘149.mat.3’\n",
            "\n",
            "149.mat.3           100%[===================>]   7.40M  14.8MB/s    in 0.5s    \n",
            "\n",
            "2021-11-27 13:27:57 (14.8 MB/s) - ‘149.mat.3’ saved [7761344/7761344]\n",
            "\n",
            "--2021-11-27 13:27:57--  https://engineering.case.edu/sites/default/files/150.mat\n",
            "Resolving engineering.case.edu (engineering.case.edu)... 129.22.104.251\n",
            "Connecting to engineering.case.edu (engineering.case.edu)|129.22.104.251|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 7770624 (7.4M)\n",
            "Saving to: ‘150.mat.3’\n",
            "\n",
            "150.mat.3           100%[===================>]   7.41M  14.4MB/s    in 0.5s    \n",
            "\n",
            "2021-11-27 13:27:58 (14.4 MB/s) - ‘150.mat.3’ saved [7770624/7770624]\n",
            "\n",
            "--2021-11-27 13:27:58--  https://engineering.case.edu/sites/default/files/151.mat\n",
            "Resolving engineering.case.edu (engineering.case.edu)... 129.22.104.251\n",
            "Connecting to engineering.case.edu (engineering.case.edu)|129.22.104.251|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 7779920 (7.4M)\n",
            "Saving to: ‘151.mat.3’\n",
            "\n",
            "151.mat.3           100%[===================>]   7.42M  13.0MB/s    in 0.6s    \n",
            "\n",
            "2021-11-27 13:27:59 (13.0 MB/s) - ‘151.mat.3’ saved [7779920/7779920]\n",
            "\n",
            "--2021-11-27 13:27:59--  https://engineering.case.edu/sites/default/files/164.mat\n",
            "Resolving engineering.case.edu (engineering.case.edu)... 129.22.104.251\n",
            "Connecting to engineering.case.edu (engineering.case.edu)|129.22.104.251|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 7752064 (7.4M)\n",
            "Saving to: ‘164.mat.3’\n",
            "\n",
            "164.mat.3           100%[===================>]   7.39M  5.49MB/s    in 1.3s    \n",
            "\n",
            "2021-11-27 13:28:01 (5.49 MB/s) - ‘164.mat.3’ saved [7752064/7752064]\n",
            "\n",
            "--2021-11-27 13:28:01--  https://engineering.case.edu/sites/default/files/163.mat\n",
            "Resolving engineering.case.edu (engineering.case.edu)... 129.22.104.251\n",
            "Connecting to engineering.case.edu (engineering.case.edu)|129.22.104.251|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 7733504 (7.4M)\n",
            "Saving to: ‘163.mat.3’\n",
            "\n",
            "163.mat.3           100%[===================>]   7.38M  9.95MB/s    in 0.7s    \n",
            "\n",
            "2021-11-27 13:28:02 (9.95 MB/s) - ‘163.mat.3’ saved [7733504/7733504]\n",
            "\n",
            "--2021-11-27 13:28:02--  https://engineering.case.edu/sites/default/files/162.mat\n",
            "Resolving engineering.case.edu (engineering.case.edu)... 129.22.104.251\n",
            "Connecting to engineering.case.edu (engineering.case.edu)|129.22.104.251|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 7724208 (7.4M)\n",
            "Saving to: ‘162.mat.3’\n",
            "\n",
            "162.mat.3           100%[===================>]   7.37M  13.4MB/s    in 0.6s    \n",
            "\n",
            "2021-11-27 13:28:03 (13.4 MB/s) - ‘162.mat.3’ saved [7724208/7724208]\n",
            "\n",
            "--2021-11-27 13:28:03--  https://engineering.case.edu/sites/default/files/161.mat\n",
            "Resolving engineering.case.edu (engineering.case.edu)... 129.22.104.251\n",
            "Connecting to engineering.case.edu (engineering.case.edu)|129.22.104.251|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 2079840 (2.0M)\n",
            "Saving to: ‘161.mat.3’\n",
            "\n",
            "161.mat.3           100%[===================>]   1.98M  5.39MB/s    in 0.4s    \n",
            "\n",
            "2021-11-27 13:28:04 (5.39 MB/s) - ‘161.mat.3’ saved [2079840/2079840]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 837
        },
        "id": "aQKTc3lmc6UC",
        "outputId": "e81eb598-3ac5-469e-a333-18c1691508b3"
      },
      "source": [
        "# int(len(c1)/1600 + 1\n",
        "#Pranav 2 run Inner \n",
        "import scipy.io\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "!wget https://engineering.case.edu/sites/default/files/109.mat\n",
        "!wget https://engineering.case.edu/sites/default/files/110.mat\n",
        "!wget https://engineering.case.edu/sites/default/files/111.mat\n",
        "!wget https://engineering.case.edu/sites/default/files/112.mat\n",
        "# !wget https://engineering.case.edu/sites/default/files/148.mat\n",
        "# !wget https://engineering.case.edu/sites/default/files/149.mat\n",
        "# !wget https://engineering.case.edu/sites/default/files/150.mat\n",
        "# !wget https://engineering.case.edu/sites/default/files/151.mat\n",
        "# !wget https://engineering.case.edu/sites/default/files/164.mat\n",
        "# !wget https://engineering.case.edu/sites/default/files/163.mat\n",
        "# !wget https://engineering.case.edu/sites/default/files/162.mat\n",
        "# !wget https://engineering.case.edu/sites/default/files/161.mat\n",
        "#for OR @3 48k nlist = [148,149,150,151]\n",
        "# nlist = [135,136,137,138,161, 162,163, 164]\n",
        "nlist = [109,110,111,112]\n",
        "# ztlist = [[35,2],[30,2],[40,2],[30,9],[50,2],[30,7],[35,9],[35,7],[60,2],[40,7],[40,9],[60,1]]\n",
        "\n",
        "ztlist =[(51, 4), (49, 10), (47, 8), (15, 7), (13, 4), (34, 2), (29, 3), (40, 4), (12, 8)]\n",
        "        #  (39, 3), (41, 6), (39, 5), (17, 2), (17, 3), (29, 4), (46, 6), (14, 4), (43, 5), (31, 4), (10, 7), (12, 3), (42, 2), (56, 8), (49, 2), (59, 3), (49, 4),...\n",
        "        #  (51, 4), (49, 10), (47, 8), (15, 7), (13, 4), (34, 2), (29, 3), (40, 4), (12, 8)] \n",
        "for item in ztlist: \n",
        "  for x in nlist:\n",
        "    matstr = str(x) + \".mat\"\n",
        "    # update scipy\n",
        "    mat = scipy.io.loadmat(matstr)\n",
        "    def sdp(c1):\n",
        "      z = item[0]\n",
        "      t = item[1]\n",
        "      c1max = np.max(c1)\n",
        "      c1min = np.min(c1)\n",
        "      c1mean = np.mean(c1)\n",
        "      #c1 = (c1 - c1mean )/ max(abs(c1- c1mean)) \"MENTIONED IN PAPER BUT NOT TAKEN AS IT IS NOT REQUIRED\" \n",
        "      for i in range(0,7):\n",
        "          r = ((c1 - c1min)/(c1max - c1min))\n",
        "          r = r[:len(c1)-t]\n",
        "          theta = (60*i + (((c1-c1min)/(c1max-c1min))*z))\n",
        "          phi = (60*i - (((c1-c1min)/(c1max-c1min))*z))\n",
        "          theta = theta[t:]\n",
        "          phi = phi[t:]\n",
        "          plt.polar(theta*(np.pi/180),r ,'.', c='blue')\n",
        "          plt.polar(phi*(np.pi/180),r ,'.', c='red')\n",
        "          plt.grid(False)\n",
        "          plt.axis('off')\n",
        "\n",
        "    c1 =np.array( mat[\"X\" + str(x) + \"_DE_time\"])\n",
        "    c1max = np.max(c1)\n",
        "    c1min = np.min(c1)\n",
        "    c1mean = np.mean(c1)\n",
        "    for j in range( int(len(c1)/1600)):\n",
        "        x = c1[j*1600:j*1600 + 1600]\n",
        "        sdp(x)\n",
        "        # /content/drive/MyDrive/MP(Inner-Outer)/48kpart2/z=51, t=4\n",
        "        plt.savefig(\"/content/drive/MyDrive/MP(Inner-Outer)/48kpart2/\" + \"z=\" + str(item[0])+\", \"+\"t=\" + str(item[1]) + \"/\"+\"Inner/\"  +\"48kIR_\" + str(item[0]) + \",\" + str(item[1]) + \"_\" + str(j))\n",
        "        plt.clf()\n",
        "\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "--2021-11-27 17:46:37--  https://engineering.case.edu/sites/default/files/109.mat\n",
            "Resolving engineering.case.edu (engineering.case.edu)... 129.22.104.251\n",
            "Connecting to engineering.case.edu (engineering.case.edu)|129.22.104.251|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 3903344 (3.7M)\n",
            "Saving to: ‘109.mat.1’\n",
            "\n",
            "109.mat.1           100%[===================>]   3.72M  9.32MB/s    in 0.4s    \n",
            "\n",
            "2021-11-27 17:46:38 (9.32 MB/s) - ‘109.mat.1’ saved [3903344/3903344]\n",
            "\n",
            "--2021-11-27 17:46:38--  https://engineering.case.edu/sites/default/files/110.mat\n",
            "Resolving engineering.case.edu (engineering.case.edu)... 129.22.104.251\n",
            "Connecting to engineering.case.edu (engineering.case.edu)|129.22.104.251|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 7779920 (7.4M)\n",
            "Saving to: ‘110.mat.1’\n",
            "\n",
            "110.mat.1           100%[===================>]   7.42M  14.6MB/s    in 0.5s    \n",
            "\n",
            "2021-11-27 17:46:39 (14.6 MB/s) - ‘110.mat.1’ saved [7779920/7779920]\n",
            "\n",
            "--2021-11-27 17:46:39--  https://engineering.case.edu/sites/default/files/111.mat\n",
            "Resolving engineering.case.edu (engineering.case.edu)... 129.22.104.251\n",
            "Connecting to engineering.case.edu (engineering.case.edu)|129.22.104.251|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 7770624 (7.4M)\n",
            "Saving to: ‘111.mat.1’\n",
            "\n",
            "111.mat.1           100%[===================>]   7.41M  13.7MB/s    in 0.5s    \n",
            "\n",
            "2021-11-27 17:46:40 (13.7 MB/s) - ‘111.mat.1’ saved [7770624/7770624]\n",
            "\n",
            "--2021-11-27 17:46:40--  https://engineering.case.edu/sites/default/files/112.mat\n",
            "Resolving engineering.case.edu (engineering.case.edu)... 129.22.104.251\n",
            "Connecting to engineering.case.edu (engineering.case.edu)|129.22.104.251|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 7770624 (7.4M)\n",
            "Saving to: ‘112.mat.1’\n",
            "\n",
            "112.mat.1           100%[===================>]   7.41M  13.3MB/s    in 0.6s    \n",
            "\n",
            "2021-11-27 17:46:41 (13.3 MB/s) - ‘112.mat.1’ saved [7770624/7770624]\n",
            "\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 217
        },
        "id": "3xeW1dhoafsx",
        "outputId": "0bec6784-5e78-4512-9ec8-cc0532b5bd47"
      },
      "source": [
        "import scipy.io\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "!wget http://csegroups.case.edu/sites/default/files/bearingdatacenter/files/Datafiles/147.mat?raw=true\n",
        "# rename the file\n",
        "!mv 147.mat\\?raw\\=true data.mat\n",
        "# update scipy\n",
        "mat = scipy.io.loadmat('data.mat')\n",
        "def sdp(c1):\n",
        "  z = 35\n",
        "  t = 2\n",
        "  c1max = np.max(c1)\n",
        "  c1min = np.min(c1)\n",
        "  c1mean = np.mean(c1)\n",
        "  #c1 = (c1 - c1mean )/ max(abs(c1- c1mean))\n",
        "  for i in range(0,7):\n",
        "      r = ((c1 - c1min)/(c1max - c1min))\n",
        "      r = r[:len(c1)-t]\n",
        "      theta = (60*i + (((c1-c1min)/(c1max-c1min))*z))\n",
        "      phi = (60*i - (((c1-c1min)/(c1max-c1min))*z))\n",
        "      theta = theta[t:]\n",
        "      phi = phi[t:]\n",
        "      plt.polar(theta*(np.pi/180),r ,'.', c='blue')\n",
        "      plt.polar(phi*(np.pi/180),r ,'.', c='red')\n",
        "      plt.grid(False)\n",
        "      plt.axis('off')\n",
        "c1 =np.array( mat[\"X147_DE_time\"])\n",
        "c2 = np.array(mat[\"X147_FE_time\"])\n",
        "c1max = np.max(c1)\n",
        "c1min = np.min(c1)\n",
        "c1mean = np.mean(c1)\n",
        "for j in range( int(len(c1)/1600)):\n",
        "    x = c1[j*1600:j*1600 + 1600]\n",
        "    sdp(x)\n",
        "    plt.savefig()\n",
        "    plt.clf()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "--2021-10-26 11:47:44--  https://engineering.case.edu/sites/default/files/105.mat\n",
            "Resolving engineering.case.edu (engineering.case.edu)... 129.22.104.251\n",
            "Connecting to engineering.case.edu (engineering.case.edu)|129.22.104.251|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 2910768 (2.8M)\n",
            "Saving to: ‘105.mat.5’\n",
            "\n",
            "105.mat.5           100%[===================>]   2.78M  6.26MB/s    in 0.4s    \n",
            "\n",
            "2021-10-26 11:47:44 (6.26 MB/s) - ‘105.mat.5’ saved [2910768/2910768]\n",
            "\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2HDmFra_8QOb",
        "outputId": "5836802a-6f24-436c-a4e0-97afd1c6a75c"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z4oM1w2y2aXm",
        "outputId": "9c8b8ce0-d38f-42d9-a146-2f67d75832c5"
      },
      "source": [
        "import cv2\n",
        "import numpy as np\n",
        "import matplotlib as plt\n",
        "from google.colab.patches import cv2_imshow\n",
        "\n",
        "path_a = \"/content/B007_00.png\"\n",
        "path_b = \"/content/B007_01.png\"\n",
        "path_c = \"/content/Normal_0_0.png\"\n",
        "a = cv2.imread(path)\n",
        "b = cv2.imread(path_b)\n",
        "c = cv2.imread(path_c)\n",
        "\n",
        "#a = cv2.cvtColor(a, cv2.COLOR_BGR2GRAY)\n",
        "#b = cv2.cvtColor(b, cv2.COLOR_BGR2GRAY)\n",
        "\n",
        "a_mean = np.mean(a)\n",
        "b_mean = np.mean(b)\n",
        "\n",
        "print(a_mean,b_mean)\n",
        "#cv2_imshow(a)\n",
        "\n",
        "\n",
        "#methods = ['cv2.TM_CCOEFF', 'cv2.TM_CCOEFF_NORMED', 'cv2.TM_CCORR','cv2.TM_CCORR_NORMED','cv2.TM_SQDIFF', 'cv2.TM_SQDIFF_NORMED']\n",
        "#for i in (methods):\n",
        "result[t] = cv2.matchTemplate(c,a,cv2.TM_CCOEFF_NORMED)\n",
        "    #print (\"Method {}  : Result{}\") .format(method[i],result[t])\n",
        "print(result)    "
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "233.38124785665295 233.92004511745543\n",
            "{0: array([[0.53325176]], dtype=float32)}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HMo2roQE4Bfk",
        "outputId": "b03664c2-fe2f-44b9-cffd-e07b0104ff99"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    }
  ]
}