04_Pick_Random_Samples.ipynb

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  "nbformat": 4,
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  "metadata": {
    "colab": {
      "name": "04_Pick Random Samples.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyN8pe0LJnikrb8oQAq6MSbZ",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Seyed-Ali-Ahmadi/Building-Damage-Assessment/blob/master/04_Pick_Random_Samples.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JgnQQwmB_fPH"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pIoB9pGz_og-",
        "outputId": "3d22774c-6471-4c00-e63c-5d38e7a69053",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 249
        }
      },
      "source": [
        "H = 39\n",
        "W = 67\n",
        "n_samples = int(0.5*(H*W)/10)\n",
        "# Uniformly distributed\n",
        "rows = np.random.randint(0, H, n_samples)\n",
        "cols = np.random.randint(0, W, n_samples)\n",
        "\n",
        "print(n_samples)\n",
        "\n",
        "plt.figure(figsize=(10, 5))\n",
        "plt.subplot(121), plt.plot(cols, rows, '.k'), plt.gca().invert_yaxis(), plt.gca().set_aspect('equal', 'box')\n",
        "\n",
        "img = np.zeros((H, W))\n",
        "for i in range(n_samples):\n",
        "  img[rows[i], cols[i]] = 1\n",
        "\n",
        "plt.subplot(122), plt.imshow(img, cmap='gray')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "130\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(<matplotlib.axes._subplots.AxesSubplot at 0x7f7bf0b9c320>,\n",
              " <matplotlib.image.AxesImage at 0x7f7bf0a57e48>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 121
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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fHwpMRGRW42fhxfF1SnmZTJSRzXqo4zGQFLOt/HAWHjVV9Lu1qWPOPMBlDKhefJ7a3yRsQFFTsQHljfzLGIjIBSJyn4g8KiKPiMhHg/fPFpF7ReTx4N/XlB01NRen9lMZeP4ioqqkGQP1IoCPq+p+AFcA+LCI7AdwFMCqql4MYDV47RSXpm67FEvVysgrx+1QSWp7/iIix+WYynsHgEMAjgPYG7y3F8DxcX9rchkDl6bauxRL1crMa1OWhfAZHFvGoMj5Cw2YWu5qXMw/k8U08hyWaRaeiOwDcBmA+wGcq6rPBr96DsC5I/5mXkTWRWT95MmTWXZXiEtdQC7FUrUy89rpdNDtdjn2iUqR5/xFRDRK6gaUiLwSwAqAa1X1t+HfhVrqZ1DVZVWdUdWZPXv2FAo2C5e6gFyKpWpNyivVR97zV/gC0ECYRFQjqWbhicguAHcBuFtVPx+8dxzArKo+KyJ7Aayp6iVJ2zE9Cy88dRuA1WnpdZwWn1eT8hrV5LzHcWEWXlnnrzJn4UXPu67M0HI1LlOann+KlX8ZAxkcQV8F8GtVvTb0/ucAbKrqDSJyFMDZqvqJpG3ZWsaAU+LJBB5nZ7LdgCrz/FXlMgb84iZXhY/Nhh6X+ZcxAPBWAB8EcEBEHgzSuwDcAOCQiDwO4B3Bayc1aQwS2cPjzEm1P38RkZvGPgtPVX8AYFSz82C54VRjOC5neGfAxLgcduU0j43jjJL5cP4iIjc14mHCnU4Hq6urxho07MppJtPHGfnDVtcIuw5pHB4TozWiAQUUf9p9FnFdOfwybQaTxxkREdmTaR0oSodT+YmIiPzWmDtQJrErx28c30ZERGxAVYRdOX7i+DbyCce3EMfB5ccuPKIMuFQBEREBbEDVWr/fx+LiIvr9vu1QGmN2dhYTExMQEUxMTJQ2vo11SURUL+zCqyl2JdkzvOWd5jFIabAuiYjqh3egaopdSXasra1he3sbqort7e1Syp11SU2gqqcTNYPvdc4GVE1xqQQ7qih31iURUf2MfZhwmWw9TNhXVUynL2ObWbaR9FlXlwtwtdzL2maZsdh+mHCZqnyYcBPwobRuqnIWnid1PvocFr7FVnWanp5Wclev19PJyUlttVo6OTmpvV6v0m0kfbaMWJosb/mVXe4A1tXgOabKBECZ8qfIccHkSIr5P8s635lGnsPYhUenlTEWJ8s2kj7LcUHF5C0/ljtVRUROpzLFNHSNbSvps2XGVaVwvZRdN67WeVn1wll4DnClq2o4Fmc4GyzPWJws20j6bBmxNFmW8gsffyx3IqJ0OAbKMtemsHMMlD/SlF/c8QeAY6BicAyUm6LfYUXudmTdVvjz0c+WGRftVLRsk+otxshzGO9AWRbXZWKzsVDGI2iybCPps3wcTjFpyi/u+Ot2uyx3IqIx2ICyjF0mZBOPP0rL1TsqZcaRdVtJn3elfHxU5I5Tnr8fpTENKFe7gzqdDlZXV52MLcrVMvSBrbKt0/FHROSSRoyBcm2cUR2xDKvjc9lyDJQ/XL0DRTROwWN35DmsEcsYcGp2cSzD6rBsqQ5T3quc7l6lOpStr1wp96qO3UY0oPiojOKiZTg1NYXFxUX0+33bodVeWcdnv99nnRARGdKILjyA43fKMCzDqakpXHvttV52OdlS9Ph0tRuQXXjpsHusOixbezIuF+AqLmPAKfHFDctwcXHRqaUXfFD0+HRtOQwiIt81pgFF5eHUd/ewTuqtxlfnzuMCl/b4/mBiNqAq4nOXoetT330u+1Hi6qSJ5UBEZEpjxkCZ5Op4lCZg2Q+4Ug4cA0Wu4R2oerJ4Byr/MgYi8jIR+aGIPCQij4jIZ4L3LxKR+0XkCRH5loi0y466rjgt3R6W/QDLgYioWmmWMfg9gAOq+iYAlwK4UkSuAHAjgC+o6usB/AbANdWFWS9NXzYhOp3e5PT6ppf9EMuB0mjiGkl1WM+qifUyTrjOXCmfTF14IvJyAD8A8CEA3wHwh6r6ooh0AHxaVY8k/X1TuvCAZo7DAc7sOjp27JjxJQ+aWvZRLpQDu/Dcxu4sN7Fekhkun2LLGIhIC8AGgNcD+CKAJwGcUtUXg4+cAHDeiL+dBzAPABdeeGG2sGusqcsmRLuOVlZWjE+vb2rZR7EcBkMQAHwfwG4Mzne3qeqnROQiAN8EMIXBue2DqrplL1IiqptUK5Gr6raqXgrgfACXA3hD2h2o6rKqzqjqzJ49e3KGSXUR7Tqam5tjVxLZxCEIRFSJTMsYqOopEbkPQAfAWSIyEdyFOh/AM1UEOI4L3RT0krjp9G984xtZR2SFDu71/y54uStICuAAgL8O3v8qgE8D+JLp+Gxj19BO44a0mCov1ksyV8pnbANKRPYAeCFoPE0COITB1dt9AN6HwW3wqwHcUWWgcVyZqk07RbuO2JVENhUZgkBENEqaLry9AO4TkZ8AeADAvap6F4DrAHxMRJ7AYBzBzdWFGY9TtYlonCJDEERkXkTWRaQZs1+IKLWxd6BU9ScALot5/ykMTkbWZHl8Bbv6mqPquuaxVE95hiCo6jKAZcDPWXhElF+tH+WS9pEi7OprjqrrmsdSvdgagsBp6PXEeqIsUs3Cc1mn00G32038EmNXX3NUXdc8lmrH2SEIRFRvtb4DlRafVN8cVdc1j6V6cXkIAhHVW2MeJlzFuBWOhXFTuF4AsN4t4krk6bDLj8JMHg889sYaeQ5rTAOqbBwL4z7WkX1sQKXDLzEKYwPKKSPPYbUfA2ULx8K4j3VERERVYQMqpyJPu+/3+1hcXES/368uwJqosiyy1lFSLKwzIiLaQVWNpenpafVJr9fThYUF7fV6mf5mcnJSW62WTk5OZvpb35goi7R1lBQL6yw/AOtq8BxTZcLgETC1SjH1wVTDxHq0mkaewxoxC68qeR5REtet1NRxOSbKIm0dJcXCOiMioih24RlWpOvPNy6VRVIsLsVJRERu4Cw8C/JOg/dx+rxLeUpa/iApTj46ZjTOwrMren7nDKt6Yj1aNfocZnIMgW9joEziOBxzspR11fVS93oHx0DVOkXqkqkm5ZMUV8z/UabkNPIcxi68muCUfHOylDUfHUNE1EwcRO64YffN1NRUbR4hUucuJyDb41r46BgiombiGCiHRVfSPnbsGDY3N51umPiy+neWRiDHQI3GMVD1Fv5+4LibM7laPklxRb/zXYrbUSPPYbwD5bBo983m5ia63a7tsBL5MuU/yxIVeZazqCoWojIV+XJtwhe1q3kKx5W1HkzVmw/HB8dAOayO0+frGDMREVFWvANVQNVdK51OB6urq9a6b/Lkz3bMQL27vIiIqB44BionX8b6jFLX/NU1bl9xDFRz+dBF4wN24RU28hzGLrycfJ9eXtf81TVuorqLrpEjIjtSHcSs/VV7WevBVL3V8fiIYgMqRr/fx+LiIvr9/sjP+D7Wp6r8pSnbIupaL1WXS9XbJyJqHJMr+dZhJfKsq1AvLCzUbnXotMrOn6lVtetWLz6vZg6uRN6YFFP3tUs+5IGp9DTyHMZB5BFZpuH7Pr287PyZWuKgbvVSdbn4srQEZacGx5m40g1TJM8+5IHMYRdeRF27gOqAZRuv6nJhuRMRlY+z8GJwGnx1WLbxfF3NnLPw7GrinQwf8uxDHjwy8hzGBhQRVYYNKLua+EXsQ559yINH+CgXIiIfZPlybeIXb5V5NtWw8aXewuXlQ71EcQwUERERUUZsQBERERFlZLQLb2Nj41ci8nTKj58D4FdVxpMBY4nHWOIxlpe81uK+iYgqY3QQeRYisu7K4FPGEo+xxGMsfhKRkwCehv1G6SiMKxvGlZ6LMQFm4nqtqu6J+wUHkRMRpTA8ibraKGVc2TCu9FyMCbAfF8dAEREREWXkcgNq2XYAIYwlHmOJx1iIiDznbANKVZ058TOWeIwlHmPxnqtlyriyYVzpuRgTYDkuZweRExEREbnKyTtQInKliBwXkSdE5KjhfX9FRJ4XkYdD750tIveKyOPBv68xFMsFInKfiDwqIo+IyEdtxSMiLxORH4rIQ0Esnwnev0hE7g/q6lsi0q46lmC/LRH5sYjcZTOOYN8/F5GfisiDIrIevGejjs4SkdtE5L9E5DER6dg6domIfOdcA0pEWgC+COCdAPYDuEpE9hsM4RYAV0beOwpgVVUvBrAavDbhRQAfV9X9AK4A8OGgLGzE83sAB1T1TQAuBXCliFwB4EYAX1DV1wP4DYBrDMQCAB8F8Fjota04hv5cVS8NzQixUUf/AuB7qvoGAG/CoHxsHbtesnlxF4nDmQu90P6dueCLxOXUxV9MfM5cDIZicuKiMCYuty4SVdWpBKAD4O7Q6y6AruEY9gF4OPT6OIC9wc97ARy3VDZ3ADhkOx4ALwfwIwBvwWANjom4uqtw/+dj0Bg4AOAuAGIjjlA8PwdwTuQ9o3UE4NUA/htBt7ytOHxOAFoAngTwOgBtAA8B2G8plrcDeHPkPHUTgKPBz0cB3Gg4pr0A3hz8/CoAP8PgIth2XALglcHPuwDcj8EF6a0APhC8/68APmSpLj8G4OsA7gpeW49rxDnNaj0G+/0qgL8Pfm4DOMtmXM7dgQJwHoBfhF6fCN6z6VxVfTb4+TkA55oOQET2AbgMg//8VuIJrpQeBPA8gHsx+DI5paovBh8xVVfHAHwCwP8Fr6csxTGkAO4RkQ0RmQ/eM11HFwE4CeDfgqvZL4vIKyzE4bPLATyhqk+p6haAbwJ4j41AVPX7AH4defs9GHzBIPj3rwzH9Kyq/ij4+X8wuAN6ngNxqar+Lni5K0iKwQXYbbbiAgAROR/AXwD4cvBaXIhrBKv1KCKvxuDC4WYAUNUtVT1lMy4XG1BO00Ez1+jIexF5JYAVANeq6m9txaOq26p6KQZ3gC4H8AYT+w0TkXcDeF5VN0zvO8HbVPXNGHQ7f1hE3h7+paE6msDgjsSXVPUyAP+LSHedjWPXMy5e3IU501h24YIvEo8rF39Rrl0MDrlwURjl3EWiiw2oZwBcEHp9fvCeTb8Ukb0AEPz7vKkdi8guDBpPX1PV223HAwBBq/8+DLrKzhKR4Yr2JurqrQD+UkR+jsEdgAMYjP0xHcdpqvpM8O/zAL6NQePSdB2dAHBCVe8PXt+GQYPK6rFCdthsLLtywRfZr/WLvyhHLwaHXLgojHLuItHFBtQDAC4OBtK1AXwAwJ2WY7oTwNXBz1djMBapcsHt3JsBPKaqn7cZj4jsEZGzgp8nMRiL9RgGDan3mYpFVbuqer6q7sPg2PgPVf0b03EMicgrRORVw58BHAbwMAzXkao+B+AXInJJ8NZBAI+ajsNzLl7chVlvLLt4wRdm+eIvyrmLwSFHLgqjnLtIdK4BFdy6/AiAuzH4gr5VVR8xtX8R+QaAPoBLROSEiFwD4AYAh0TkcQDvCF6b8FYAHwRwIJgN8aCIvMtSPHsB3CciP8GgkXuvqt4F4DoAHxORJzC4/XyzgVji2IrjXAA/EJGHAPwQwHdU9XuwU0f/AOBrQR1dCmDBUhy+cvHiLsxqY9mlC75IXE5c/EW5djE45MpFYZSLF4lcSJOIKKXgAuYYBjPyvqKqn7UUxzcAzGLwNPpfAvgUgH/HYAbXhQCeBvB+VY0ONK8yprcB+E8AP8VLY3r+CYNxUDbj+jMMBhe3MLhpcKuq/rOIvA6DOz9nA/gxgL9V1d+biisS4yyAf1TVd9uOK9j/t4OXEwC+rqqfFZEpWKzHILZLMRhw3wbwFIC/Q1CnNuJiA4qIiIgoI+e68IiIiIhcxwYUERERUUZsQBERERFlxAYUERERUUZsQBERERFlxAYUERERUUZsQBERERFlxAYUERERUUb/D0wdxdnBDUW6AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 720x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bT0uMtFUCDBo",
        "outputId": "37952a69-44fc-41c5-ad54-bbe983880e3b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        }
      },
      "source": [
        "# Normally distributed\n",
        "rows = np.random.normal(H/2, 3, n_samples)#.astype(int)\n",
        "cols = np.random.normal(W/2, 3, n_samples)#.astype(int)\n",
        "plt.plot(cols, rows, 'ko')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f7bf9a708d0>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 82
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}