MVSEC_data_and_graph.ipynb

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  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.4"
    },
    "colab": {
      "name": "MVSEC_data and graph.ipynb",
      "provenance": []
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ueDU1oH0slXI",
        "outputId": "f3d83dba-d9fb-4385-81e3-70d2fbd17be4"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mOQydYDQsR7z",
        "outputId": "c6e2d893-e9e3-49d3-f027-13993c537b42"
      },
      "source": [
        "import h5py\n",
        "import numpy as np\n",
        "file_name = \"/content/drive/MyDrive/hdf5/outdoor_day/outdoor_day1_data.hdf5\"\n",
        "\n",
        "f = h5py.File(file_name, \"r\")\n",
        "for key in f.keys():\n",
        "    print(key) #Names of the groups in HDF5 file."
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "davis\n",
            "velodyne\n",
            "visensor\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iOfvlU7bsR77",
        "outputId": "42b2fe9f-d47e-4db3-db76-16ba67489f53"
      },
      "source": [
        "#Get the HDF5 group\n",
        "group = f['davis']\n",
        "\n",
        "#Checkout what keys are inside that group.\n",
        "for key in group.keys():\n",
        "    print(key)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "left\n",
            "right\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7WM8XqoLsR77"
      },
      "source": [
        "subgroup = group['left']\n",
        "required = subgroup['events']"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AYYg5TyZsR78"
      },
      "source": [
        "x = []\n",
        "y = []\n",
        "t = []\n",
        "p = []\n",
        "for i in required[:1000]:\n",
        "    x.append(i[0])\n",
        "    y.append(i[1])\n",
        "    t.append(i[2])\n",
        "    p.append(i[3])"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pZXlVmvKsR78"
      },
      "source": [
        "x = np.asarray(x)\n",
        "y = np.asarray(y)\n",
        "t = np.asarray(t)\n",
        "p = np.asarray(p)"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Nt8H9jUIsR78",
        "outputId": "fc082f24-e8a1-48b3-859a-0c3fa3f936a3"
      },
      "source": [
        "len(x), len(y), len(t), len(p)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1000, 1000, 1000, 1000)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w3PDy3btsR79"
      },
      "source": [
        "np.save('x_1000',x)\n",
        "np.save('y_1000',y)\n",
        "np.save('t_1000',t)\n",
        "np.save('p_1000',p)"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c16Zi3C6sR79"
      },
      "source": [
        "np.save('event_5000',np.asarray(required[4000:5000]))"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mA0HdvIesR79"
      },
      "source": [
        "event = np.load('/content/drive/MyDrive/MVSEC data/events/event_1000.npy')"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CbGuy14nsR7-",
        "outputId": "19569389-78d7-46c7-bf05-30b2bdd8eb9e"
      },
      "source": [
        "event"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 1.4300000e+02,  5.4000000e+01,  1.5061179e+09,  1.0000000e+00],\n",
              "       [ 3.0000000e+00,  1.2900000e+02,  1.5061179e+09, -1.0000000e+00],\n",
              "       [ 1.0600000e+02,  5.3000000e+01,  1.5061179e+09,  1.0000000e+00],\n",
              "       ...,\n",
              "       [ 3.3800000e+02,  2.2100000e+02,  1.5061179e+09,  1.0000000e+00],\n",
              "       [ 2.2900000e+02,  1.8400000e+02,  1.5061179e+09,  1.0000000e+00],\n",
              "       [ 1.2000000e+02,  2.0500000e+02,  1.5061179e+09,  1.0000000e+00]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "cCtyZC5nuBAU",
        "outputId": "50393c6a-ffd4-4af5-bbd7-7b60360b910b"
      },
      "source": [
        "import numpy as np\r\n",
        "import math\r\n",
        "import scipy.io\r\n",
        "import random\r\n",
        "import networkx\r\n",
        "\r\n",
        "M = 8\r\n",
        "R = 150\r\n",
        "class Event:\r\n",
        "    def __init__(self, x, y, t, p):\r\n",
        "        self.x = x\r\n",
        "        self.y = y\r\n",
        "        self.t = t\r\n",
        "        self.p = p\r\n",
        "\r\n",
        "def isWithin(event1, event2, radius, alpha=1, beta=0.000005):\r\n",
        "    x_i = event1.x\r\n",
        "    x_j = event2.x\r\n",
        "    y_i = event1.y\r\n",
        "    y_j = event2.y\r\n",
        "    t_i = event1.t/10000\r\n",
        "    t_j = event2.t/10000\r\n",
        "    spatial = (x_i - x_j)*(x_i - x_j) + (y_i - y_j)*(y_i - y_j)\r\n",
        "    temporal = (t_i - t_j)*(t_i - t_j)\r\n",
        "    if (alpha * spatial) + (beta * temporal * 100000000) >= 0:\r\n",
        "      print(math.sqrt(alpha * spatial + beta * temporal))\r\n",
        "      return math.sqrt(alpha * spatial + beta * temporal) <= radius\r\n",
        "\r\n",
        "\r\n",
        "def main():\r\n",
        "    data = np.load('/content/drive/MyDrive/MVSEC data/events/event_1000.npy')  # first 1000 events\r\n",
        "    event = []\r\n",
        "    for each in data:\r\n",
        "        event.append(Event(each[0], each[1], each[2], each[3]))\r\n",
        "\r\n",
        "    # shuffling the list and selecting the first M events i.e., random sampling\r\n",
        "    np.random.seed(53)\r\n",
        "    np.random.shuffle(event)\r\n",
        "    selected_event = event[:M]\r\n",
        "\r\n",
        "    for e in selected_event:\r\n",
        "      print(e.x, e.y, e.t, e.p)\r\n",
        "    \r\n",
        "    adMat = np.zeros([M, M], dtype=int)\r\n",
        "    print('Initial\\n', adMat)\r\n",
        "    for row in range(M):\r\n",
        "        for col in adMat[row]:\r\n",
        "            if row == col:\r\n",
        "                continue \r\n",
        "            if isWithin(selected_event[row], selected_event[col], R):\r\n",
        "                adMat[row][col] = 1\r\n",
        "                adMat[col][row] = 1\r\n",
        "    print('Final\\n', adMat)\r\n",
        "    G = networkx.from_numpy_matrix(np.array(adMat))\r\n",
        "    networkx.draw(G, with_labels=True)\r\n",
        "        \r\n",
        "if __name__ == \"__main__\":\r\n",
        "    main()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
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            "40.0 210.0 1506117897.2218544 1.0\n",
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            "257.0 158.0 1506117897.2897274 1.0\n",
            "146.0 73.0 1506117897.3918524 1.0\n",
            "Initial\n",
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            "Final\n",
            " [[0 0 1 1 1 1 0 1]\n",
            " [0 0 0 0 0 0 0 0]\n",
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          "name": "stdout"
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        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IYa35RzVsR7-"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}