examples/paper-examples/3-particle_sizing.ipynb

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          ]
        }
      ],
      "source": [
        "%matplotlib inline\n",
        "!pip install deeptrack"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "<!--<badge>--><a href=\"https://colab.research.google.com/github/softmatterlab/DeepTrack-2.0/blob/develop/examples/paper-examples/3-particle_sizing.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a><!--</badge>-->"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Example 3. Single particle sizing\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## 1. Setup\n",
        "\n",
        "Imports the objects needed for this example."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "execution": {
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          "iopub.status.idle": "2022-06-30T10:16:36.119547Z",
          "shell.execute_reply": "2022-06-30T10:16:36.119547Z"
        }
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\backend\\_config.py:11: UserWarning: cupy not installed. GPU-accelerated simulations will not be possible\n",
            "  warnings.warn(\n",
            "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\backend\\_config.py:25: UserWarning: cupy not installed, CPU acceleration not enabled\n",
            "  warnings.warn(\"cupy not installed, CPU acceleration not enabled\")\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Downloading ParticleSizing...\n",
            "Downloading file: 370.0\tMB\r\n",
            "Download Complete!\n",
            "Extracting ParticleSizing...\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import deeptrack as dt\n",
        "\n",
        "from deeptrack.extras import datasets\n",
        "datasets.load('ParticleSizing')\n",
        "experimental_data = np.load('datasets/ParticleSizing/sizing_150nm_227nm_PSL.npy', allow_pickle=True)\n",
        "\n",
        "import scipy.io as IO\n",
        "\n",
        "IMAGE_SIZE = 64"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## 2. Defining the dataset\n",
        "\n",
        "### 2.1 Defining the training set\n",
        "\n",
        "The training set consists of simulated 64 by 64 pixel images, containing a single particle each. The particles are simulated as spheres with a radius between 100nm and 400nm, and a refractive index between 1.37 and 1.67. Its position in the camera plane is constrained to be within within two pixels of the image center, and is sampled with a normal distribution with standard deviation of 2 pixel units along the axis normal to the camera plane. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-06-30T10:16:36.123048Z",
          "iopub.status.busy": "2022-06-30T10:16:36.122548Z",
          "iopub.status.idle": "2022-06-30T10:16:36.126049Z",
          "shell.execute_reply": "2022-06-30T10:16:36.126049Z"
        }
      },
      "outputs": [],
      "source": [
        "particle = dt.MieSphere(\n",
        "    position=lambda: (IMAGE_SIZE / 2, IMAGE_SIZE / 2) + np.random.rand(2) * 4 - 2,\n",
        "    z=lambda: np.random.randn() * 2,\n",
        "    radius=lambda:1e-7 + 3e-7 * np.random.rand() ,\n",
        "    refractive_index=lambda: np.random.rand() * 0.3 + 1.37,\n",
        "    L=8,\n",
        "    position_unit=\"pixel\",\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "We use a single wavelength brightifeld feature to calculate the field at the camera plane. We aberrate the image with a random coma, which was observed in the experimental data. Moreover, the pupil is cropped at high frequencies."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "execution": {
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          "shell.execute_reply": "2022-06-30T10:16:36.134547Z"
        }
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:171: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 23.133858267716537 should be at most 0.5\n",
            "To fix, set magnification to 47.0, and downsample the resulting image with dt.AveragePooling((47.0, 47.0, 1))\n",
            "\n",
            "  warnings.warn(\n"
          ]
        }
      ],
      "source": [
        "HC = dt.HorizontalComa(coefficient=lambda c1: c1, c1=0 + np.random.randn() * 0.5)\n",
        "VC = dt.VerticalComa(coefficient=lambda c2:c2, c2=0 + np.random.randn() * 0.5)\n",
        "\n",
        "def crop(pupil_radius):\n",
        "    def inner(image):\n",
        "        x = np.arange(image.shape[0]) - image.shape[0] / 2\n",
        "        y = np.arange(image.shape[1]) - image.shape[1] / 2\n",
        "        X, Y = np.meshgrid(x, y)\n",
        "        image[X ** 2 + Y ** 2 > pupil_radius ** 2] = 0\n",
        "        return image\n",
        "    return inner\n",
        "CROP = dt.Lambda(crop, pupil_radius=23)\n",
        "\n",
        "optics = dt.Brightfield(\n",
        "    NA=1.3,\n",
        "    resolution=1.13e-6,\n",
        "    wavelength=635e-9,\n",
        "    aperature_angle=53.7 * 2 * np.pi / 360,\n",
        "    polarization_angle=lambda: np.random.rand() * 2 * np.pi,\n",
        "    magnification=10,\n",
        "    output_region=(0, 0, IMAGE_SIZE, IMAGE_SIZE),\n",
        "    padding=(64,) * 4,\n",
        "    return_field=True,\n",
        "    pupil= HC >> VC >> CROP\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "The noise is simulated as gaussian distributed noise, with independent real and imaginary parts. The amplitude of the noise is determined by the dummy property `level`, which is explicitly shared between the two features. Finally, the real and imaginary parts of the field are separated into two layers, and the plane wave is subtracted."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-06-30T10:16:36.137550Z",
          "iopub.status.busy": "2022-06-30T10:16:36.137049Z",
          "iopub.status.idle": "2022-06-30T10:16:36.141549Z",
          "shell.execute_reply": "2022-06-30T10:16:36.141549Z"
        }
      },
      "outputs": [],
      "source": [
        "real_noise = dt.Gaussian(\n",
        "    mu=0, \n",
        "    sigma=lambda: np.random.rand() * 0.02,\n",
        ")\n",
        "\n",
        "noise = real_noise >> dt.Gaussian(\n",
        "    mu=0, \n",
        "    sigma=lambda real_sigma: real_sigma * 0.03j,\n",
        "    real_sigma=real_noise.sigma\n",
        ")\n",
        "\n",
        "def func():\n",
        "    def inner(image):\n",
        "        image = image - 1\n",
        "        output = np.zeros((*image.shape[:2], 2))\n",
        "        output[..., 0:1] = np.real(image)\n",
        "        output[..., 1:2] = np.imag(image)\n",
        "        return output\n",
        "    return inner\n",
        "\n",
        "\n",
        "complex_to_float = dt.Lambda(func)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "We now define how these objects combine. Note that the noise is added inside the optics. This means that it will have the same PSF as the sample, which is what is observed. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-06-30T10:16:36.144048Z",
          "iopub.status.busy": "2022-06-30T10:16:36.144048Z",
          "iopub.status.idle": "2022-06-30T10:16:36.146550Z",
          "shell.execute_reply": "2022-06-30T10:16:36.146550Z"
        }
      },
      "outputs": [],
      "source": [
        "dataset = optics(particle >> noise) >> complex_to_float"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### 2.2 Defining the training label\n",
        "\n",
        "We extract the parameters we want the network to learn. We also rescale them such that they have a similar size."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-06-30T10:16:36.149050Z",
          "iopub.status.busy": "2022-06-30T10:16:36.149050Z",
          "iopub.status.idle": "2022-06-30T10:16:36.152050Z",
          "shell.execute_reply": "2022-06-30T10:16:36.152050Z"
        }
      },
      "outputs": [],
      "source": [
        "def get_label(image):\n",
        "    px = np.array(image.get_property(\"position\")) - IMAGE_SIZE / 2\n",
        "    z = image.get_property(\"z\") * 0.1\n",
        "    r = image.get_property(\"radius\") / 1e-6\n",
        "    n = image.get_property(\"refractive_index\") - 1.33\n",
        "    return np.array([r * 10, n * 10])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# 2.3 Visualizing the dataset\n",
        "\n",
        "We resolve and show 16 images"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-06-30T10:16:36.154548Z",
          "iopub.status.busy": "2022-06-30T10:16:36.154548Z",
          "iopub.status.idle": "2022-06-30T10:16:38.134548Z",
          "shell.execute_reply": "2022-06-30T10:16:38.134548Z"
        }
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:272: RuntimeWarning: invalid value encountered in sqrt\n",
            "  * np.sqrt(1 - (NA / refractive_index_medium) ** 2 * RHO),\n"
          ]
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 4 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 4 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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JrDoWcxemjitl6r3L4p9NshFT3IjlNrFzUhaMrsosDD8HKa7D8v3G3MMxl2IVKa7WJouWt1novM9fema2Ffg0sAq42d1vKB0/HvgscB5wCLjM3R8Pjr8JeBi41t3/r14GWcEkNCzoe+h2jHEVUm77Wemr2GrbDMS2mZUp7Zr00dZdl9Iu1YVa972vGkuXrtpZnK2qgxaiFiIT+nAjmtkq4CbgXcA5wPvN7JxSsyuAw+5+NvAp4MbS8U8Cf9v6BoUQWZOzG1HGlhCZ0FPM1gXAnLs/5u4vAbcxqFEVso1BrSqAO4GLrPjJa2bvAb4P7OviHoUQ+ZKzsTUVC1GnTnPG9le5B1NchzGqXGSx7ZDU4psprs4uC6+GNHFJhu9DVdZfzB0c6zO1QGrMjbd69eqh7ct91nUppn4+m7gUu56G74ENwJPB63ngwlgbdz9qZs8B683sCINCoRcD/76PwU0DXbppUl02KWPpUlfLfXeZgdj2nJTzU8Mo6n4f27onU92LdV2PVdetO5YuxziKWTOg6jAVxpYQoj0Nheo0M9sbvN7hg3IHXXAt8Cl3/3GXPxCEEHmSs7ElN6IQK5sFdz8/+CsbWvuBs4LXG4t9Q9uY2WrgJAaB8hcCf2pmjwN/CPyRmV3V/S0IIcRwzGyrmT1qZnNmdswaqGZ2vJndXhy/f3FpLzO72MweMLNvFf++IzjnvGL/nJn935bwa3IqZrbK1mxKYdCUDL5UUt2DbVyHVev+jcNd2CVVY4y5+1Km58vXCl15secduv6OHj26tB26FMvjiu2PZUamZGVC3L3ZpZuoqu+efhXuAbaY2WYGRtV24AOlNos1q74GXArc54PB/MvFBmZ2LfBjd/9MH4McF8OecdsstC6LlTbJOGwyrr50qseM2lr7u6BtZmSM2LNP1dsULa6bPdkFfWhYkOBzMYMQiD1mtsvdHw6aLSX4mNl2Bgk+lwELwG+5+1Nm9jbgbgYhEwB/Dvxb4H5gN4OF7iuTgDSzJUQm9BEg7+5HgasYCM0jwB3uvs/MrjOzdxfNbmEQozUHfBQ45tejEEKMYpoSfNz9QXd/qti/D1hXzIKdCZzo7l8vflR+FnjPqIFMxcyWEKI9fc0KuPtuBr/ewn3XBNtHgPeOuMa1vQxOCJENPWhY4wQfBjNbi/w28E13f9HMNhTXCa+5gRHI2BIiE3IOLhVC5E8DDeszwQcAMzuXgWvxkjbXmZixFYtvKR9LIYzbKZNSKb5JzFZKnFXVwtcpi2KHVMVipMQU1Y19K9Mkriw8FsZQxeKZyvcROxZuh3Fdsfitcv+pi1fHSF1sfFibPlOnZWyNj9Q4rRix71Cf1dG7HEvdz2rb59WWtjFbKedXldqIaXTbGLsmsVV1n3dV+67juRp8Fhbc/fyK43USfOZLCT6Y2UbgC8CH3f17QfuNI655DIrZEiIDmsRryTgTQkwLPenXUoKPma1lkOCzq9QmXJR+KcHHzE4Gvghc7e7/EIzzAPC8mf1KkYX4YRIWspexJUQmyNgSQswyU5bgcxVwNnCNmT1U/P1ccez3gJuBOeB7JCxHNhVuxFTCKcuqRYdj1HUdlqd+U8o6pJZ+CIlNJacudt12UeyQFDdClQs0xd0Ya1N+RikLQ6e4FCFeFqKJSzHmBk15H6tS6Nsi42kypLpZmrjr6vY56bGkjGsSLsVUN2LKs2jyjGIlcUJSn0td12Ff7sU+6GMMTRN83P164PrINfcCb6szDgXIC5EJ0yCWQgjRlJw1TMaWEJmQs1AJIfInZw0bu7G1+DBTq3K3ncqNudjqVoMvv4650lKzDMOxxCqlp7o3U6boU593ihswdMmV3XWxDMQmFfNT+g+JuRTL42yyYHTsWimuzpDy+9OVG1ExWONh8Rl3mQFYt++q7ao+6roRq+4xJXShy+zJvp53k2fURL9SQgxCqnSibrZrl/SZmZi7hmlmS4hMyFmohBD5k7OGydgSIhNyFiohRP7krGETcyNW0SQLLqW/0OWTMo6yWynsP9xOcT+Vp4vDsaS4EVOzEdtS91pVGZvhOGPPq7xgdEjMPRuS4lIsjyXlHqsySVMWrE51G3S5oG/OQpUDXWYdpvTRJFM4NQwhJWu6CU2Kh8Zo4tKsm6VZlRXf9hlXjXMUTQrSNnFNd03OGqaZLSEyIWehEkLkT84aJmNLiAzIPbhUCJE3uWvY1BtbTYqXhtTNlKlyH1UV4BxG6OJq4kaMUX4OMVdcanZdSKz/JutHxrIDY+Mt7687/phLcdg4hxFbi7FqHNPk+stZqGaJNq7h8nuYol+prq+642pbSLmvcVWR8ryq7qtN8dCq/y9i+5tkgiZWTh85jqpxdZlJWoecNUzL9QghhBBC9MhIY8vMzjKzL5vZw2a2z8w+Uuw/1czuMbPvFv+e0v9whRAxRq0jNuwvd6RfQswOOetXihvxKPDv3P2bZnYC8ICZ3QP8DnCvu99gZlczWLzxY6MuVjfbLSULpCobJiVTJuYerMpGjBFzHZZdXCmZkbFsvKpxpbg6q6aLY88r5uosF/VMyaxMJVYUNfZcUoswpqxtWDX1Hvvs1c1MLLdL+XxVMWviMyY61a8+aOLWitHEJZdS7LkqGzEkZd3TKndbG1dn1bGUeyy/Tjk/9TtXt6BsakHsmGZ1mRU6Tl3JWcNGzmy5+wF3/2ax/SMGK2dvALYBO4tmO4H39DRGIUQCmtk6FumXELNDzvpVK0DezDYBvwTcD5zh7geKQ08DZ0TOuRK4EqprKgkhmjOL4jNu2upX25lHIUSc3DUs2foxs9cDnwf+0N2fL00zupkNfUruvgPYAbBu3bp8n6QQEyZnoWpLF/q1du1aPWAheiRnDUsytsxsDQOh+it3/y/F7mfM7Ex3P2BmZwIHuxpUit8/FgNUjiFKiYeK9VG1yHJISpxW6rhi1dVj2+Vxto3ZCondS2y7fO1YbERViYYYsfitWMxT+b5ii0fHYjFi91HuP2TSZSByFqo29K1fTUoZNClFULePJnE/qTFiKbG0MS1NrWyfQupnPiW2svw6phOpzyg8ljIr2iRmK2V/VT/TRs4alpKNaMAtwCPu/sng0C7g8mL7cuCu7ocnhEhFMVvHIv0SYnbIWb9SZrZ+DfgQ8C0ze6jY90fADcAdZnYF8ATwvl5GKIQYySyKz5iQfgkxA+SuYSONLXf/KhCbd7yo2+EM6GsqOdZH6qLSYT+xEgcx12F5jLH+Q3fZmjVrhu4vT0k3qRQfu1aMsP/wfl966aVl7cL7D4+llsSoO5bYvTdZPDp8T6oq3sdc2G1XOGi7oHjOQtWUvvSrbdXzJm6pLscSK1+QWtYgJfQi5lJsW5IiJHV1h5jOlcM7YuOM6X2T72/Kc6l6RrEwkLafnZT9fWtMzhqm9EAhMiFnoRJC5E/OGiZjS4hMyFmohBD5k7OGTYWxlbqAZ0oGYlU2T92FQcvEpoxTXIfl64bT2qG7MMV1WDXGtlXbY/2kuAdg+bMIj7344otL2y+//PLQMaa6FFPcpmW3QcqXOPZ+pWYjxto0qTjdhJyFapYYdxhE6nVjLqe6q2xAmh7Evidl111ITL9Sx5Xi3gzblEMEUv6/CKn6vycl3CDWX6obMaTt9z8lS73vTMacNWwqjC0hRDtyDy4VQuRN7homY0uITMhZqIQQ+ZOzhk2FsdVk0d8mhd2aZCCGxArexcZVlQ1T13UYy4SEtIKjqdk8KYVUqzIj6xJzKULcrZjiwihfK/YsUxaornIj1l2UuvxZ71Jc+hIqM9sKfBpYBdzs7jeUjh8PfBY4DzgEXObuj5vZBRTV1xlkBF7r7l/oZZBjpu2Cz6n61WbR4io3Yl0XXVknUtyFqYtax8IwUkIiqgqkxrK8qwpEp2QwhlRlI6YUTG6yEHVKce/YOEZdu06bYddui4wtIcTU04dQmdkq4CbgYmAe2GNmu9z94aDZFcBhdz/bzLYDNwKXAd8Gznf3o0WV9n80s//m7vXrfQghsidnY6t5gSYhxErgAmDO3R9z95eA24BtpTbbgJ3F9p3ARWZm7v6TwLB6DZCvkgohRAUTm9lqsx4WtF/DK2XKtspdF5vuDollHJZfx9xyYR9h30eOHFl2rdAVFxYSjbnowudV5UYMx/ia17xm5HZ5/CnuhXAs5QKpsWn48LnEMouqXMMpLpCqrKTYuOq6FLump1+FG4Ang9fzwIWxNsUs1nPAemDBzC4EbgXeDHxoJcxqpWR1NSlG2TYTLPa9S8mIq3IjhsTCHarWU43pasrahGVibsRQy9auXRu9VkxDUlyK5fuquwZryrq2ZVIKnFbpV12XYhOXZB1yntmSG1GIDGiRyXOame0NXu9w9x3R1jVx9/uBc83s54GdZva37n5k1HlCiJWFshGFEDNBQ6FacPfzK47vB84KXm8s9g1rM29mq4GTGATKh2N7xMx+DLwN2IsQQpSQsTVmUjIQmxSKjLm4Uqb9y8di7cLrxlxy5WMx12HoVvvJT36ytB0WCIXlbsXQdRjLRqy6x9jUe2ydwzKhWzG8x3DqPqQqmydlPcW22ZAxqjIIUwg/B6mfz7ZC05NQ7QG2mNlmBkbVduADpTa7gMuBrwGXAve5uxfnPFm4Ft8MvBV4vI9BjovFz0LbbMSuxlGnvxT9irmymqwZG2pRbBvqFzKtahNqQ3gvYZ9VISChfoX6F8tSrNKvFLftOMJhqqgKKal7fhfkbGwpQF6ITFichq/zl3DNo8BVwN3AI8Ad7r7PzK4zs3cXzW4B1pvZHPBR4Opi/68zyEB8CPgC8HvuvtDtXQshcqFr/YJB6Roze9TM5szs6iHHjzez24vj95vZpmL/ejP7spn92Mw+UzrnK8U1Hyr+fm7UOKZyZksIUZ++fhW6+25gd2nfNcH2EeC9Q877HPC5XgYlhMiOrjWsZemaI8AfMwh9eNuQy3/Q3ZNDIjSzJUQGNJnVynnKXggxW/SkX21K17zg7l9lYHS1ZmIzW30vaDmsn7pVl8s++JRFplOqFpfbhdcKYwvCOK2f/vSnS9tVpR9SxhUrowDLY7NiZSRSF4x+7Wtfu7SdEstWVWoj9r6E55SfcUgsjbvJwt0pqfJ106u7QMZTftSNyUmNAYr1EYvTquo7Fk8Zi9OqWpEh1IPY4tGxkhIQjy1NWfGj3E+4nRK/VRWzFdOW2HuUOq7c6EHDWpWuGXHtvzCzV4DPA9f7iMHLjShEJsjYEkLMMg00rNfSNRV80N33m9kJDIytDzFYsiyKjC0hMkHGlhBilmmgYWMpXTNknPuLf39kZn/NwF2Zj7EVmyKPVeuuIjYV28StlOJGrCpREE53h2UdQndhuF2eOo+VWAjTmGPlJcplJF544YWR/VeVRYjdf0pJiPJ9hdeKlYGIuRTLz7vLBX1TPm9t07CbIGNrfPTlykm9bt0q9VXtuiz3EHPdhZTLwITa8LrXvW7o/lAzqlbT+NGPfrS0/eMf/3hou3LpiZBYuEGsbFDV80opYdTE7RuSEsZQ5ZLski70pwcNa1y6JnbBwiA72d0XzGwN8JvAl0YNZKaMLSHEcBTwLoSYZfrQsCIGa7F0zSrg1sXSNcBed9/FoHTN54rSNc8yMMgAMLPHgROBtWb2HuAS4Ang7sLQWsXA0PqPo8YiY0uITJCxJYSYZfrQsKala4pjmyKXPa/uOKYiG7FJ5dqU65avXbef1Gn4lGnl8rjCaeUwgyac7g5dfOGUfHka/oQTTljaPvXUU5e2TznllKXtcEo+nIYPp90BfvCDHwzdDtuF4yo/0zBr5/jjjx865thir1UZmzGXRMoKA8PGuci4s3zK44jdVxNkbPXPsM9I1UK/XfbXpGp8ynVTsw5DYu77FNfh61//+mXHQp067bTTlrZPPvnkpe2Yfj333HPLrhVq1jPPPLO0/eyzzy5th1neZZdiqG2xrOnU0IGUVUpSXYpdalNKpnTqqipdk7OGjbQ+zOw1ZvYNM/tHM9tnZv97sX9zUW11rqi+Onw9FiHEWFCdrWORfgkxO+SsXylTPS8C73D3XwB+EdhqZr/CoMrqp9z9bOAwgyqsQogJIWNrKNIvIWaEnPVrpBuxiMpfTOtYU/w58A5ejerfCVwL/Hn3Q3yVWHZglXuwTSHAJtk8sayVMrEMntiCqeF1Q7chwBvf+Mal7Te/+c1L2xs3blzaPvHEE4f2F067Azz++ONL2+F9heeEWT7lbMZY8dXQDZCS8VNuFxJ7j1ILnNYl1SWZMj0vxssk9KuuC2Zcruw2GbllUhaPDr/PYYHj9evXL7vWWWe9mpn/pje9aWn7DW94w9J26HoMNXJhYXndySeeeGJpO/yexjQ2DOEoH4sVcg61pSocZtwFabtk1gyZWSApiMnMVtlgMdmDwD3A94Af+mCRWhhUZd3QywiFECNpMqu1UgRV+iXE9JO7fiX9/Hf3V4BfNLOTgS8Ab03twMyuBK6EbmcbhBDLmTXxGRdd6VdVrTwhRHty1rBa1o+7/9DMvgz8KnCyma0ufh0Oq8q6eM4OYAfAunXrvKvMwyZvSt2p3Kq1rmKZJqmFMWNuxNh6gKEbLszegeXT8Oecc87S9pYtW5a2wyyfsI/9+5e/bWEGYegGDF2Hoauwam2y2DplYWZS1bNL+azEPgfj+tJOkzhM01imkbb6tXbt2lYPuEmG1zhcjE30K+byiq0nGBYrPf3005ddKwx9eOtb3zp0f5iZGOrKgQMHll0r1JZQv55//vml7VDLym7EFF2OhSu0LWRc9ZmY1hCFrjUnZw1LyUY8vfhFiJmtAy4GHgG+zKDaKgyqr97V0xiFEAnkPg3fBOmXELNDzvqVMrN1JrDTzFYxMM7ucPe/MbOHgdvM7HrgQQZVWIUQE2LWxGdMSL+EmBFy1rCUbMT/DvzSkP2PMVh8caqYxJp0TfoIXZKxdf/Cewmnx8PMQlietbN58+al7Z//+Z8f2ibMsikXGAyn2J966qml7fn5+aXtQ4deXaOzXBQwZeo9NiVelc0TyyyKZUKVSV3nMoVpFIRZ/KU3DrrWr8VnXKUzs/A+tHVFtXEjlsMgzjzzzKXtTZs2LW2HYRBhsebQ9RdmOcLytV3DoqahloUZjGF7iBdrTdWZkDbPeBKfoRRXZaqrs2n/s/DdaYoi1oXIhJyFSgiRPzlrmIwtITIhZ6ESQuRPzhomY0uITMhZqIQQ+ZOzhsnYKtFlbE/VdWP9hB+22CLNYRkIWB53FaZIh5WaY6UfygtRn3TSSUvbYZxFWBKiqt5QSmXpcaQxp1Z9z4mchWraaPKs61aTnyaqSj+ExFbTCOO31q1bt+ycUL9C/Qlju2IxW2X9CuNZw3iumH6lPu+2361pfV9TGKeu5KxhMraEyIDcg0uFEHmTu4bJ2BIiE3IWKiFE/uSsYVkYW7FyCbNISpXpsossdAuGU+xhBeVwO2xfXkg6PD9MfY6Vbqga/yTfi5XgNiyTs1CtFMrv4SzrWap+hdoSWwg6tjJFWb9i5WbahofM8vvQJ6ogn04WxpYQIm+hEkLkT84aJmNLiEzIWaiEEPmTs4aN3dhanM6dJjdPVRXzrihfN8yIiW3HqrGXqx4/++yzS9vhwqxhZk94Tji9/k//9E/LrhWef/jw4aXt0A0ZW5AblmdNhvcSW7g7pDzVn5LxlHLdlUDuwaXTQt0K8l2+J9O0GHEsXCAcY/h9ji1qD8v1K6z6HmZDxxaPLi9EffDgwaXtH/7wh0vbP/nJT5a2y6tehIR6Ule/Ukk5f1xZktNE7ho2PRaPEEIIIUSGyI0oRCbk/KtQCJE/OWtY9sZW3WKaVS7FmIsx1kc4jV4uBBq+jrneQndfOHUeTo/D8kVWw0J+YaZOWOw0dEmWp+Hn5uaGHguLB4bjKt9XuGB2uB3eY8ztULVYdyybKOV9hOXvXUpB2bZMeiHZLjGzrcCngVXAze5+Q+n48cBngfOAQ8Bl7v64mV0M3ACsBV4C/ld3v6+XQU6QvgqcltulZCo36T9FI6u+T7FxhTrz05/+dGk7dBsC7N+/f2k7LD4auh5POOGEpe1QC0O3I8D3v//9pe2nn356afv5558fen75OabocmqoSV138qRdw9OAjC0hxNTTh1CZ2SrgJuBiYB7YY2a73P3hoNkVwGF3P9vMtgM3ApcBC8BvuftTZvY24G5gQ+eDFEJkQc7GlmK2hMiExQDTOn8JXADMuftj7v4ScBuwrdRmG7Cz2L4TuMjMzN0fdPfFadd9wLpiFkwIIY6hB/2aGiY2sxVzt0G7jMAmb0Bsurw8rZvi/gpdbOF6YGXCewzbhduxbMTyemDhNHzY7tChQ0vboXsxHGOYcQjLXZLhFH2YzRMSugph+bqNKespxgqnQlo2YtvMxC4/L5OkhficZmZ7g9c73H1H8HoD8GTweh64sHSNpTbuftTMngPWM5jZWuS3gW+6+4tkRvkz0Jc7ustsxLZuxNj3LuZGDPUj1CVY7q4LMwUXFl79+IS6El637JIM9Svcfu6554b2UZVNXdeNmLp+ZAqp2a5d9TcNzKIBVQe5EYXIhIZCteDu53c9lhAzO5eBa/GSPvsRQsw2MraEEFNPT0K1HzgreL2x2DeszbyZrQZOYhAoj5ltBL4AfNjdv9fHAIUQeZCzsaWYLSEyoaeYrT3AFjPbbGZrge3ArlKbXcDlxfalwH3u7mZ2MvBF4Gp3/4du7lIIkSuK2ZoSYrEBVcTKB6TEhaXGbIXXjcUsVJWRCGMDwjinWOmHMCUalsc2hLERP/jBD4b2EY63HIsVxjaEsWHhvYRxZWEsWPl12C6MeYg9u7APODaGaxjhc2ySkp2yv0vaLohbRR/jL2KwrmKQSbgKuNXd95nZdcBed98F3AJ8zszmgGcZGGQAVwFnA9eY2TXFvkvc/SAzSpvq3ynvT7lNlzGBKWUkUj9DsUrrsfjVsAxNuXRN2C5c6SIs3RCLZS1Xow9jUMN4rlDnqkryxErXxGJOQ6pWwKgbf5pK25jTlP2p1+pCf2bNgKrDTBlbQojh9PlLz913A7tL+64Jto8A7x1y3vXA9b0MSgiRFbM4W1UHGVtCZELOQiWEyJ+cNWymjK2UlP9UYlPqqW6p2PRprLp6VXmL0MUXTpeHLsWwj/JCqmF15tDdGFZNDvsLx1i+Vnh+2C4cY5iGHS4WWz4Wjj98XjH3aNmNGHO5xd6jqvcr5uqt66rsky7T+cV008T90lfph/C7kaqxKW7E2Pcs1CtY/r0P3YihGy/sL2wf6gcsdxeG4RbhOeF4y+V5wj5jYRAhVatcxDQnZBxlZKquOy2laxbpQ8NarICxnkHdwH8B/KW7XxWccx7wl8A6BrP+H/ERg0/+X8TMVpnZg2b2N8XrzWZ2v5nNmdntRfCsEGJC9BQgnwXSLyGmn671K1gB413AOcD7zeycUrOlFTCATzEoUwNwBPhj4N8PufSfA/8W2FL8bR01ljo/2T8CPBK8vhH4VDHAw8WAhRATQsZWJdIvIaacHvSrzQoYL7j7VxkYXUuY2ZnAie7+9WI267PAe0YNJMmNWNTK+V+ATwAftcHc4zuADxRNdgLXMrD2kuhySrxJn7H+q6bOw6nksstr2P4qF1folguPha63GOXnFboCw/5jroKU6W1YPqUejit0Fb7+9a9fdk54LLaodqwyfls3YurnKHbd1MWuU9yKqc+4K1ag8ZRMl/rVRqu6ztwa1UeTFTBStsvXirnYYvdY/p6F7r4wazH2rMPzy2EAoYbEsg5D7S2vgBHqXIq2VI2lr8zj1PcoxiTclSn0pGFdrYBRbj9fuubINV9TY7b+DPgPwOLS6+uBH7r74ic7qTMhRH/I2IryZ0i/hJh6GmjYqOXGpoaRxpaZ/SZw0N0fMLO31+3AzK4EroTlvyiEEKJvutSvlFpLQoixMmq5sVYrYFRcc+OIax5DivXza8C7zex/Bl4DnMggsv9kM1td/DqMdlZYmTsA1q1bV9tsDadyY0VJq6ZSY8diGThVxKboY31UFTUNCY3Q8LqxKe2y6IdT7zG3XMytVb73WKZOON0eFi4tT8PHiqfGMohCF2jVtHvs/lPdiG2m3qvcMSmMa3pfM1tD6Uy/1q5du5Rw1Pa9GocrJ9WNGJKSmTjs9SIxgzSWjVx+HdOpJu7N2KLSoa6VsxFji0/HMitjYx82tmFjjOlXk4WoY6zwbMSlFTAYfMe382r4wCKLK2B8jWAFjIoxHjCz583sV4D7gQ8D/8+ogYwMPnH3j7v7RnffVAz0Pnf/IPDlYmAUA71r1LWEEP2hAPljkX4JMTt0rV/Fj6nFFTAeAe7wYgUMM3t30ewWYH2xAsZHgasXzzezx4FPAr9jZvNBJuPvATcDc8D3gL8dNZY2fr2PAbeZ2fXAg8WAhRATYiUYTx0i/RJiyuhDw7zhChjFsU2R/XuBt9UZRy1jy92/Anyl2H6MQVplI6rceClrGKa49MqvY33G3GpV2YixaeVY1l+5eGiMmEsxVuAP4uuGxTITw+2qewyvG8vgqXKPhv2H9x8rZNrWjVhFbOo/9uVOLWg7roKno1gpM1Vt6FK/Stdd9jpl7bm271VKNneV6y8lDCI11CL2HQi/p6kFUlOypqueXayfmHuwHEccu5eYxle5EVPG1cR12CQDMTaWFNr2V6efnDVMEetCZELOQiWEyJ+cNUzGlhCZkLNQCSHyJ2cNG6uxFU4TVk2ph8dSC03G9semQMPp31iBvqpMvbqZMilr8JWJFT5NzQCsuzZXuZ/YNHwVMddhLGOy6rnEsolS1kOsygyKuUqarLkY6zPWR9/T8KJfunrGMf1qe/1Ud1+sn9jnNpXYd6Uqazi2hmwT91XMXRfrv+q73aXrMGU7pCocJmV/qnsyRurzTnFn99HvLKKZLSEyIWehEkLkT84aJmNLiAzIPbhUCJE3uWuYjC0hMiFnoRJC5E/OGjYVxlbZ713X3xxLHYa0xadT2pRfp8Qzpfr2U8YbKwlRHkvbUgRtquSXX8dKPMQW8S4/x1jMVsrCt+XnnVLuIfZZq0pVj9EkxqQtOQvVtNHX+9ukcnhq/GpIlWYO66/cd4qepZTtKb8edymVqv8vUuK0mpShSCn9UBWzVfd73uUKGH1rTM4aNhXGlhCiPTkLlRAif3LWMBlbQmRCzkIlhMifnDVsYsZWlbuubjpp6kKbbcpAlI+Fbq3QLZbiUoS0FOdY1fNyH1XlKroiNqVe5UaMTb2nlpeIuQ5T0qVT3QOxKf2Yq7JMl+Ue2rhQcg8unRaGPeMq/apqN2x/k/cwtb8Ud12KS7HcLqX0TeqC8SFNvg8p7s0qnWhTwb483rpV46v6qOvKa1vuYRJakruGaWZLiEzIWaiEEPmTs4bJ2BIiE3IWKiFE/uSsYWM1tsxsaXqz6qHGFkCNTctWLVxddyq4aho65iKMuRRTqxOnLGpdNfXdpGpzjFg/se2yG7HudHss4xDquw6rsj9TXC1Nqkyn9BHrr86xrvoX7RimX1UrYISkZENX0aX7qI1LsWpcKXqbukB2XZdgk3GlZq/HqNLYugtOp7oRY/QVQjLOPnPWsPHm2QohhBBCrDDkRhQiE3L+VSiEyJ+cNWxixlZfU55VLp+UrJmYC7N8LNZPzNVYNSVeNwOmnNnYxF2YQl33QJnY1HlqsdKUe4llbFZlGaVM74+ruGJX713umTyzStvM6roLEFdpVEo2ZOxzn5odl7KdWmSzy4y6lO1R41wkNdygbtZhqq6mjLFJ2EhK/32HQeSsYZrZEiITchYqIUT+5KxhitkSIhMWfxnW+UvBzLaa2aNmNmdmVw85fryZ3V4cv9/MNhX715vZl83sx2b2mW7vVgiRG33o17Qw9pmtxanWJpkusUyVqnW+2mSBlN11qWvnLVJVGDN0n5X7qTPG1PNj12pC1TONFQYNt8Pn1aQga5gB2SRjM2WdsipS+0y57rSvjWhmq4CbgIuBeWCPme1y94eDZlcAh939bDPbDtwIXAYcAf4YeFvxN/MMe7+6/D51WdQ01cUf2x9+N6qKmtYdSw0jf2SbVHdbKjFtq1ugtGosXbo3664jPOzabVA2Yjqa2RIiA5rMaiUK2wXAnLs/5u4vAbcB20pttgE7i+07gYvMzNz9BXf/KgOjSwghovQ1Mz8tKGZLiEzoSXw2AE8Gr+eBC2Nt3P2omT0HrAcW+hiQECJPZs2AqsPYja26046x6eqUQp7l80OXVcz1VvVmp6yBGJvWrXIp1s1aqXLBNpmuj40ltr/K9Ra755T9ZWIu3dg6jWH71EysmEsxpKrwYV0Xbuq4mtBQqE4zs73B6x3uvqOjIa0I+iwmmRL6ENufWvy4SUZbzMXYNrsuZX9bUt36qZqXQl0tL997X5+xtv9fhHQxRhlbQoipp6FQLbj7+RXH9wNnBa83FvuGtZk3s9XAScChJoMRQqxcVryxZWaPAz8CXgGOuvv5ZnYqcDuwCXgceJ+7H+5nmEKIUfQkVHuALWa2mYFRtR34QKnNLuBy4GvApcB9PkWqKf0SYjaYItnonDoB8v/a3X8x+BV8NXCvu28B7i1eCyEmQF8B8u5+FLgKuBt4BLjD3feZ2XVm9u6i2S3AejObAz5KoAWFofNJ4HfMbN7Mzun2zpORfgkxxShAPs424O3F9k7gK8DHRp206NetKtcQI1bdvapqcqyfLuO3YjFIVX7+8JxYWm9IrMp81ZhTYgOqKqWnxC9UnV93Iemq9PLweafEGaTGksVIfcax/V1WxU6lL/Fx993A7tK+a4LtI8B7I+du6mVQ7WmlXyFNnntKKn/Ta8fOjelnSvmA1DinJvFbbUtfpI5zWJsqnUh5FlX31SaWrUlc2ThKP/SpX6CZLQAH/l8ze8DMriz2neHuB4rtp4EzOh+dECKZ3H8ZtkD6JcQMkLN+pc5s/bq77zeznwPuMbPvhAfd3c1s6J0X4nYlwJo1a1oNVggRZ9bEZ4x0ol8pM6JCiObkrGFJxpa77y/+PWhmX2BQ6PAZMzvT3Q+Y2ZnAwci5O4AdAOvWrVt6kk2mPGPTtzGXIsRdXqkV6GNjCQndkKkLGMdcjDFBbzvGttPVsbFUTcPH+o+5N8tjjJV1SLmXKrdtzCUao2pR6xRx6Guh8DI5C1UbutKvtWvXtnrAKW7mMm2rqMfapbgx2658kOpui42xTd9V7dreV6pLsI3rMFVXm3w+egw3mIprTCsj/6cxs9eZ2QmL28AlwLd5NQOJ4t+7+hqkEKKaJi7EnIVtEemXELNB7vqVMg1zBvBVM/tH4BvAF93974AbgIvN7LvAbxSvhRBimpB+CbGCMbOtZvaomc2Z2TFZx2Z2vJndXhy/38w2Bcc+Xux/1MzeGex/3My+ZWYPlYpCRxnpRnT3x4BfGLL/EHBRSifDqHLlxLJmYu6nkCZuvBhNsv5iY2zrXqybTVemiRuxSbtYnzGXYFUF9pQs1dRnlOI6jGV8VlWQj/WRsr9M219qs/ZLbxz0pV9t6dKtn9rHOD4fdd2LZSahUyk0ySxs4zpMdSPWHe+00/VYzWwVcBNwMYOlxvaY2S53fzhodgVw2N3PNrPtwI3AZTYoU7MdOBd4I/AlM3uLuy/+p/Wv3T15STItRC1EJuQ+DS+EyJse9OsCYM7dH3P3l4DbGJR9CdnGoPwLwJ3ARTawbrcBt7n7i+7+fWCuuF4jZGwJkQkytoQQs0wP+rUBeDJ4PV/sG9rGB0WcnwPWjzh3WDmZSqZibcTyFGndgqcxt2P5WOri1Sn9hNTNOkkdV0jbjLa+XFzl/bFnFHPLNZnujmWYVj3HFNdhzAXcxChJXWw7RG7E6aePZ1x1zbrf2/Baqe662Hbb4p9N6Ou6qaToXBM3YkiK67BtlneXjPN9aHAPp5Vipnb4IIO4b44pJ+Puf191wlQYW0KIdmimSggxyzTUsAV/dQmuYewHzgpebyz2DWszb2argZOAQ1Xn+vByMpXGltyIQmSC3IhCiFmmB/3aA2wxs81mtpZBwPuuUpuwDMylwH0+uPguYHuRrbgZ2AJ8o6KcTCVTMbPVJNMkNt1dzm4L3TltXHdlUlyKVe1jhVhjY0x1D8SI3UuqGzBG1dR5W3dhbFq9iRsxJDauqszIlDE2cfXGnlcTZDxNhvJ7nepyj50fO6dtKEBKHyn7U6/VNtyh7bWa3Ffdc9q6gJsUlE0hVW+7/Ex1oT9da5i7HzWzq4C7gVXAre6+z8yuA/a6+y7gFuBzZjYHPMvAIKNodwfwMHAU+H13f8XMzgC+UDyj1cBf+6CcTCVTYWwJIdojY0sIMcv0FBO5G9hd2ndNsH0EeG/k3E8AnyjtG1pOZhQytoTIBBlbQohZJmcNm0pjK+aaSSl2Wp7+DF1DMZdiSFUmZMxN1MR1ljLN28TtEBtvExdVk3PqugtTp87brG0I9V2HqVPvTdyYfaAYrNkl9bM2DpdiKimftS7dpm1dmrH9fX5n+nIXNsl+j40rtn9cGdTla+WsYVNpbAkh6pOzUAkh8idnDZOxJUQm5CxUQoj8yVnDZGwJkQk5C5UQIn9y1rCxG1uLD7NJ3FFISvxW+VgYnxOLtUktD1FVtT6lTczXnhInlRob1LU/vQ0p8QDl+2pT8b9q8ei6cVpVKxz0lXY/6WuJ5qTEWbUtJdCkLEJKrM44Po9VGh1rl9Kmaf91SY2Xm2Q1/NTPR5dj7OIec9YwzWwJkQG5B5cKIfImdw2TsSVEJuQsVEKI/MlZw2bK2KpbEqKKmLsuVtm9TIrrsWp6PjbmlCn91HGN44ObOkUdEntGVddKcSeHlO+9reswdqzu1Hnbiv1iOmhbvqCJ667udavo0qXYVmdSvoNNxjKOqu9V57Ypz5HqHm2rX2J8zJSxJYSIk/OvQiFE/uSsYTK2hMiEnIVKCJE/OWvYxLIRQ9pOfXdZKb0q0y3VxTisfdUUcywbcdJVopv0mZKp11cF/NiC3mXqTr2H1eur2qX016eY5CxUs0Rd12HbKuJdXjekL51p4sZrQpPn0qUbMUab6vup54d0+Uz71picNUwzW0JkQO6ZPEKIvMldw2RsCZEJOQuVECJ/ctawsRtbw9w7Zddd3SnbqvaxLLSU8aW6FJsQG3NKNuEkPpCTcGmmZBo2WUg6JMV12OQeU12aXZKzUE0bqa6wuq6stgsupxZ4jtFkXNO0WHaXLtU2z6vcf136KrzaZ59dMA1j6AvNbAmRCTkLlRAif3LWsKR1X8zsZDO708y+Y2aPmNmvmtmpZnaPmX23+PeUvgcrhIizGPNQ5y8FM9tqZo+a2ZyZXT3k+PFmdntx/H4z2xQc+3ix/1Eze2d3d5uO9EuI2aAP/ZoWUme2Pg38nbtfamZrgdcCfwTc6+43FAJ8NfCxJoMou1li7rsm07qhO6juGoSpxUP7mi6fpmzEGKnrNKY879QMnNRCpiFdFliN9Vn389UlfYmPma0CbgIuBuaBPWa2y90fDppdARx297PNbDtwI3CZmZ0DbAfOBd4IfMnM3uLu9Xz77elVv8q0eR/auqWqXGddFg+N0Vc2YSpdZmPW7a+LdnWZdKZh1/3PmgFVh5H/U5rZScC/Am4BcPeX3P2HwDZgZ9FsJ/CefoYohEihp5mtC4A5d3/M3V8CbmPw3Q8JteBO4CIbqPA24DZ3f9Hdvw/MFdcbG9IvIWaHnGe2UqYlNgM/AP7CzB40s5vN7HXAGe5+oGjzNHDGsJPN7Eoz22tme48ePdrNqIUQx9DQ2Dpt8ftZ/F1ZuuwG4Mng9Xyxb2gbdz8KPAesTzy3bzrTLy2tJES/5GxspbgRVwO/DPyBu99vZp9mMOW+hLu7mQ29c3ffAewAWLdunQf7ox2mZASmTl+muI/aFkKNUeVqrDv9muqumyZS3H1dut6aFEtt4joM6dK92fY9big+C+5+fquOp5vO9Gvt2rVe9xl3WQCzbqZgeX+XGZAxJpH114Ym9576vOue3/b/tCZMm8EybePpkhR1nwfm3f3+4vWdDMTrGTM7E6D492A/QxRCpNCTG3E/cFbwemOxb2gbM1sNnAQcSjy3b6RfQswIOc9sjTS23P1p4Ekz++fFrouAh4FdwOXFvsuBu3oZoRBiJE0MrUSx2gNsMbPNRXD5dgbf/ZBQCy4F7iumgHYB24tsxc3AFuAbndxwItIvIWaDnvRrakjNRvwD4K8KsX0M+DcMDLU7zOwK4Angff0MUQiRQh/i4+5Hzewq4G5gFXCru+8zs+uAve6+i0Hw+efMbA54loFBRtHuDgbGzVHg9338mYgg/RJiJpg1A6oOScaWuz8EDIvruKjT0bza39J2LH6rbWxAbLvLGKIuS0c0GUvbuLS2tI3NahPbULUqQd1rVY2rbpxWVX9tYzH6Eip33w3sLu27Jtg+Arw3cu4ngE/0MrBExq1fpb6XtmM60+QzkXp+7JwuSSkdMQsxV03OmcU4rWkmZ2Nr9qKuhRBCCCFmCC3XI0Qm5PyrUAiRPzlr2MSMrSZVi0OXTduU/dhYQsLq81DfFVY11R87P7YQdZN7rLsIdypN3q+216pbAT71/BTKY0wZc5MyFG3JWaimmdTSHm1dirHrpo6rTaX4JtdqW5k+5fy2n/lxuSEnWd6iTJtn1mcYBOStYZrZEiIDZjE7RwghFsldw2RsCZEJOQuVECJ/ctawsRpbR44cWfjOd77zArAwzn6njNNYufe/ku8d6t3/m+tePGehmgZefvnlhaeeeuoJVvbneCXfO6zs++9VvyBvDRurseXup5vZXs97eZBKVvL9r+R7h/7vP2ehmgbc/XRY2Z/jlXzvsLLvfxz3nrOGqfSDEJmQewVmIUTe9KFfZrbVzB41szkzu3rI8ePN7Pbi+P1mtik49vFi/6Nm9s7Uaw5DMVtCZICMJyHELNOHhpnZKuAm4GIG66TuMbNd7v5w0OwK4LC7n21m24EbgcvM7BwGq2GcC7wR+JKZvaU4Z9Q1j2ESM1s7JtDnNLGS738l3zv0fP+a2RobK/lzvJLvHVb2/fd+7z3o1wXAnLs/5u4vAbcB20pttgE7i+07gYtsUMdiG3Cbu7/o7t8H5orrpVzzGMY+s+XuK/nDuqLvfyXfO/R//zKexsNK/hyv5HuHlX3/47j3HjRsA/Bk8HoeuDDWxgdrwT4HrC/2f7107oZie9Q1j0FuRCEyQcaWEGKWaaBhp5nZ3uD1jmk1iGVsCZEJMraEELNMAw1bGJEhuR84K3i9sdg3rM28ma0GTgIOjTh31DWPYawxW00i+GcVMzvLzL5sZg+b2T4z+0ix/1Qzu8fMvlv8e8qkx9oXZrbKzB40s78pXm8usj3miuyPtZMeY1+Y2clmdqeZfcfMHjGzX+3zvW8SryXjrB4rSb9AGgYrV8PGrV/QW8zpHmBL8b6tZRDwvqvUZhdwebF9KXCfDy6+C9heZCtuBrYA30i85jGMzdgKsgLeBZwDvL+I9s+Vo8C/c/dzgF8Bfr+436uBe919C3Bv8TpXPgI8Ery+EfiUu58NHGaQBZIrnwb+zt3fCvwCg+fQ63svY6s/VqB+gTQMVq6GjV2/oPsAeXc/ClwF3F3cwx3uvs/MrjOzdxfNbgHWm9kc8NHF+3L3fcAdwMPA3wG/7+6vxK45aiw2LsE1s18FrnX3dxavPw7g7v/nWAYwYczsLuAzxd/b3f2AmZ0JfMXd//lkR9c9ZraRQYbHJxh8gH8L+AHwhiIIcdnnISfM7CTgIeCfefAFM7NH6em9X7NmjZ966qm1zzt48OADI6bhBdIvkIaxQjRsEvoFzTRslvRrnG7EYVkBGyJts6IokvZLwP3AGe5+oDj0NHDGpMbVM38G/AfgZ8Xr9cAPi18FkPf7v5mBKP9F4YK42cxeR8/vvWa2emXF6hdIw4rXK0XDJqJf0E9R02lBFeR7xsxeD3we+EN3fz48VvxqmK1PTAJm9pvAQXd/YNJjmRCrgV8G/tzdfwl4gdKUe67vvcgPadiKQ/rVA+M0tlKyArLCzNYwEKm/cvf/Uux+ppiCpfj34KTG1yO/BrzbzB5nUPDtHQxiAE4usj0g7/d/Hph39/uL13cyEK9e33vNbPXKitMvkIatUA2biH6BZra6olEE/6xiZsYg8O4Rd/9kcCjMfLgcuGvcY+sbd/+4u290900M3uf73P2DwJcZZHtApvcO4O5PA0+a2WI8w0UMgix7e++bGFqzJlYTZkXpF0jDVqqGTUK/in6z1q+x1dkqAgoXI/hXAbd6QgT/DPNrwIeAb5nZQ8W+PwJuAO4wsyuAJ4D3TWZ4E+FjwG1mdj3wIAMhz5U/AP6q+I/5MeDfMPhx09t7P2viM0usQP0CadgwVoqGjV2/IG8NG1s2ohCiP1avXu0nnnhi7fMOHz48M9k8Qoh8aaJhs6RfqiAvRCboh5MQYpbJWcNkbAmRCTkLlRAif3LWMBlbQmTALAaMCiHEIrlrmIwtITIhZ6ESQuRPzhomY0uITMhZqIQQ+ZOzhsnYEiITchYqIUT+5KxhWq5HiEwYd1FAMzvVzO4xs+8W/54SaXd50ea7ZnZ5sP8TZvakmf241UCEEFmQc1FTGVtCZMCEKshfDdzr7luAeymtnwYDgwz4E+BC4ALgTwKj7L8V+4QQK5zcK8jL2BIiEyYgVtuAncX2TuA9Q9q8E7jH3Z9198PAPcDWYrxfd/cDbQchhMiDnI0txWwJkQkTEJ8zAmPpaeCMIW02AE8Gr+eLfUIIsYxZM6DqIGNLiExoKFSnmdne4PUOd9+x+MLMvgS8Ych5/1upbzezfJVSCNE7MraEEFNPQ6FaqFpbzN1/I3bMzJ4xszPd/YCZnQkcHNJsP/D24PVG4CtNBiqEyJucjS3FbAmRARMKkN8FLGYXXg7cNaTN3cAlZnZKERh/SbFPCCGWUIC8EEIM5wbgYjP7LvAbxWvM7HwzuxnA3Z8F/g9gT/F3XbEPM/tTM5sHXmtm82Z27QTuQQghesdmzToUQhyLmfmqVatqn/fKK688UOVGFEKIcdBEw2ZJvxSzJUQm6IeTEGKWyVnDZGwJkQk5C5UQIn9y1jAZW0JkQs5CJYTIn5w1TMaWEHlwN3Bag/MWuh6IEEI0oImGzYx+KUBeCCGEEKJHVPpBCCGEEKJHZGwJIYQQQvSIjC0hhBBCiB6RsSWEEEII0SMytoQQQggheuT/B5UIe4ZJX8afAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 720x360 with 4 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 4 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 4 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 4 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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6Eck2H/3mIhHMTStl5NrSJjt+ZLl37jpe+3PX6EUAetukpJGMQMy82DUSs6mpEfCfvVelIPf3tc+/S/VzjZW2d2kngO3V9HYAn+qnOUKItsiRvhHSMCHWGCXo14ojXST/GhOH060k9wF4B4D3ALiO5KUAvgHgTUM2UgixMssqQkMjDRNiOShBwyLRi7/irLqgzQkj0T+ZtjQ+X5tEdF3OF43Ma3r9bZIxRug6XJwzrXh4ZsNootaISS+3LmL+yd0Hr/1e26OmpC7PcZl/+Q1N3xo2JNFC2pFvM/c9LcKk2LUQd2R5tI3R79EzpXlmx5wuRcx46T3yIlLntXPesbz9c2ZH77q8Zzqk+04pGqYyQEKMhBIESwgxXkrQsP68eIUQq4p8uoQQy8wQ+kVyG8m7SO4l+awkyCRfSfJWkodJvqG27hmSt1X/dvZxjQsd6SLZOG1EJDIxOiTfdGg091CbRjJGh9SHTj63UltSmkZV5uhqbmtjUvTw3glvSD5qzvXa6EUb1df1Gcko+sfMZs8r9w73WU8zJfKu55J1Rs3688hF3TZN/Bn9ZvvUQu9YuehtT3+8JN11M14kmtDTxVw7I38Hc+bc3Dsyb3kuonOtp4EiuQ7AhwBcCGAfgN0kd5rZHclm3wTwNgC/NecQ3zOzF/fZJpkXhRgJGrkSQiwzA2jY+QD2mtk9AEDyWkyqUcw6XWZ2b7Wuv19IGfTTWoiRMJR5sePw/HZOikrfTXJ7fV8hhJgygH6dBuC+ZH5ftSzKJpJ7SP49ydc32M9lTZgX6zevS72yrpF9Q5kUc0PHkaH3puaA+j7ekHiuLX3+6oi0P1p3rmv0YoSuEYeRYfs27fIYykery/A8yRMxSc9wHgADcEu176HeG7pgcmbhrnVhUzzd8Mw/bWqZeufLuT1EzF1e26NJPBdBVJdzLgJTcn97ItGi6fb1RKsp3r1vk6g08rckfV71mox9/41ocbytJPck8ztsktC4L37czPaTfD6Az5D8spl9rcsBZV4UYiQMZF7sMjz/GgA3mtnBav2NALYB+OshGiqEWG5aaNgBMzsvs34/gDOS+dOrZdH27K/+v4fkzQBeAqBTp0vmRSFGwkDmxS7D812H9oUQBTGAfu0GcA7Js0huAHAJJtUoVoTkFpIbq+mtAF6B5MdmWzTSJcRIaDnSNfTwvBBChOh7tN7MDpO8HMANANYBuNrMbid5FYA9ZraT5MsAfBLAFgCvJfn7ZvYiAD8F4E+rEfznAHhPza2iFQvvdE3t0VG/Im+7aLZjz+adC4Nd6dw5oqHUTdNE5F7GyLV4bayfO5cqoWm7uvhR1ffvGuLelGgakpSIL+LQGZ1bMOTw/H5Myu+k+97cpHFrla6pINqkCokUYG7j+xnRrDbfUzTrvdd+71hR2hTijuhyujz1caq30fPR8nStjT9x04LodSJpeVJy/td9MISLhJntArCrtuzKZHo3JtpU3+9/APhHfbdH5kUhRkAb0+LQw/OY/Lq8qBqm3wLgomqZEEIcwVDR12sNmReFGAkD/UpsPTxvZgdJvguTjhsAXDV1qhdCiDrL2pFqwsI7XV3MK5FM4vXjRwoad6XPbMsRU0F9SDeSYdjbvittzItRs0XEJNDmI+2aBX6tZo4fSrDaDs9X664GcPUgDVsFpvc4moKlK96xPT2Ifo/eN9imSHXEjJia1+ptjJhN29BGf5reF2/f3LG8+5KSM+Ol50/vXSSVBOCbRCPvcZ/uJE2PPxY00iXESChBsIQQ46UEDVOnS4iRUIJgCSHGSwkatvBO17ybGo2IiERn5KJR0nVNo/zq7YpmbvaIDOt6UYn1+xV5UbsO/Xqmzuhwc9PKAvPm59EmO3+b/T2896tN5FgXltmxdBmJ3uum2dqj52xqFqq3xTOdRcxowJF65BV9Tk1n3vbz5ocmWsEkvRee9qemuujfHu9Y6T3K/X3yTIrp+9EmO30kWjxX3L0rpWiYRrqEGAklCJYQYryUoGHqdAkxEkoQLCHEeClBwxbe6ZoOT0aT5TVNMllf3iZZoEe00Ok86sPFkXZFE+d5bYmYNnJt99qYS6jntTliequ3JVJk1htezz3ryH2Nmka97ZomHeyDEgRrtZk+1z7vdfRdaaoHuSLVnnkxkvi3fv6mJsU2pvc2LhzePcolk/Yio3NFn73l6XzE1Ji7xki0ajQ63zOheibNnHZ3jQSvU4KGrc3YdyGEEEKIkSHzohAjoYRfiUKI8VKChi2002Vms+HIXDRLm6Hk9Bwp3lCsFw0YMW3m1rWJOPTaGI188u6XF1mTu79Nk7N2TdTqnbvetuhzidDUzNy1PlvKULXLSon8WWa61jKM4JmO6usiLga579yLtPNMVJ4u1dviTXc1L3panHP78L4nL6FpnaY1LXPX6J0z4vZRf3ZehKpnaozqSlctK0XDNNIlxEgoQbCEEOOlBA1b8ecWyTNIfpbkHSRvJ/nr1fITSd5I8u7q/y3DN1cI4VFKwdgmSL+EWB5K0K/ISNdhAP/ezG4l+SMAbiF5I4C3AbjJzN5D8goAVwB4+0oHmw5b5pK/RUxJ3ja5Ic6ISTFHF5Ni/XojZrjIkHRuXRvzYkrX4flITbWu0aVdzTQR03IuIWBTU+eQiSCXVYQGplf9mpKLOGxqxmtDpC5p/dvIJfKcEqmdWJ/3opS9NtbNnpGoyug9jbhteNGW9flIxGXU1Lhhw4bZdKSGcJ1IVHg0krGpTvT53q5ECRq24l8sM7vfzG6tpr8L4E4ApwG4GMA11WbXAHj9QG0UQgTQSNezkX4JsTyUoF+NfLpIngngJQC+AOAUM7u/WvUAgFP6bZoQIsoyi9CikH4JsXYpRcPCnS6SxwL4BIDfMLNHa8PrRnLu3SJ5GYDLgPzwqRCiGyUIVlv60K+jjjpqEU0VolhK0LBQp4vkUZgI1l+a2X+uFj9I8lQzu5/kqQAemrevme0AsAMANmzYYFNbc+7mdk0TEVnX58P1jhXNup8S8c/I+XR5fhtR3ycv+3A0C7OXuTmSnTqXTsHLTu9lRM4VK4+Qez+iWaS7nmc1jzUm+tKvo48+2uuY5c4d2q4L3ntf/5Hr+RJ5/k4536cUzy/J06W6ZkR8t/r03cylK0qv8+mnn5673LsXUf+uFC9rfa4QdzQ1hEebYtgefWtOCRoWiV4kgI8AuNPM3pes2glgezW9HcCn+m+eECKKfLqejfRLiOWhBP2KjHS9AsCvAvgyyduqZb8L4D0AriN5KYBvAHjTIC0UQqzIMovQwEi/hFgCStGwFTtdZvbfAHjj4hc0PWHkpkaG5KOFNrtkSK/T1IwYNaFGhue9Ifj6dt5w81BFZuvLPTOkl4Ihl83au3+R4rO5exTJvNyn+TtKHxmdxZH0rV8RImkiPDN+9BlGTIq5jPRNTYq5tBjpeTwzYjqda5fXxkhql3nz887puSfk2pyaGj2zY12/mpobcyk9PJ2LpLjIZbf3jpsSdd/pQxdL0DBlpBdiJJQgWEKI8VKChqnTJcRIKEGwhBDjpQQNW3ina94QZM4UtNK+dbqaaKIZ2j2TgPfSRLPI58yIHp6JrOu98NoSNRV40UttzJ7eMHYkCjS3zjPNRj/+rsPril4cH01Niim5AvLeOdpUm/BM/J65KqfR6bedptXI6URK+t565rqukYHedF2XPfeOSJRf7tl9//vfn7tPNDu99zfCc7Pxnm9u/7UQiV2Chg1T6l4IsVDaRC5GBY7kNpJ3kdxblcypr99I8uPV+i9USUhB8iiS15D8Msk7Sf5Ov1cthBgLpURfq9MlhHAhuQ7AhwD8EoBzAfwKyXNrm10K4JCZnQ3g/QDeWy1/I4CNZvaPALwUwL+adsiEEKJEFmpeJBkapo0UAY0M1UfPEcUzKUZMALkirxGTYm6o3Ys+amPGS/GGtD1zQn1dpMBuuk39l4tnUvDMgLmIVu9ZRMyD9XZ554xEYeUYKrq2I+cD2Gtm9wAAyWsxqVt4R7LNxQDeWU1fD+CDVX4sA3AMyfUAjgbwNIBHh2jkMhNNEJzSphh0RL+i5vpI4lPvO6ubu5566qnZdGqGS6c9s2cuqjJtS1pwOp3etGnTEfun6yL6lVJ/VmmUY3rNkQSw0QjPSKRs/R7V7793ztVgWUevmqCRLiFGwkDD86cBuC+Z31ctm7uNmR0G8B0AJ2HSAXscwP0Avgngj8zsYLerFEKMlRLMi4peFGIktBShrST3JPM7bFL6pg/OB/AMgOcB2ALgv5L8u+momRBCpCxrR6oJayJ6MRfZF1ne5dw5cvUSI0PynkmuPh8Zhk/PVze7ecn62pgUI+aFtL3pcDwAbNy4ce60V2MsN6QeSVzqmRRzZoemy3MmH+/ZrQYtz3/AzM7LrN8P4Ixk/vRq2bxt9lWmxOMBPAzgLQA+bWbfB/AQyf8O4DwAS9vpmt7jNtGxHrlIs6bRsdH31tOWiJYBMTeC9FipRj355JNHHCud90yNbcyLaVs8k2JdSzdv3jx3O0/zcrrstTldHonwrq/zNCfijpFb5+la9F1X9GIMmReFGAEDRi/uBnAOybNIbgBwCSZ1C1PSOoZvAPAZmxz8mwBeDQAkjwHwcwC+2sPlCiFGRinRizIvCjEShhAhMztM8nIANwBYB+BqM7ud5FUA9pjZTkwKSn+M5F4ABzHpmAGTqMePkrwdk1I8HzWzL/XeSCHEKFjWjlQTFt7pmg6n5obd20RktG0H0C5RoTfE7bU9l4QvErGYDrWnQ/X1dZ65LZroz4uS9JL71Zd7Zot0qD6NeIzeI28YPDe87x0rgmfOrOO9O12ja9swlGCZ2S4Au2rLrkymn8QkPUR9v8fmLR8D6b2Ovlvee5szl0WeaddElt45cqZ/z10gxTMpPvHEE0ds973vfW/uPl6UXa7t3r1Mz59O1/XL+1aPPvro2XR6X1Itq7tapHrk1bH0TL655x6p25vTwmjE90rnaNLmKOp0CSGWhhIESwgxXkrQMHW6hBgJJQiWEGK8lKBh6nQJMQKW2bFUCCFK0bCFZ6SfZ//PZd9N6dOPy1ue87/xbOGR8+UypKdEfKpy9novNNpL5VC/p6lPxeOPPz53Og3rzmXH93zdcr4iKZH3wHsnogVyI+9UNOQ68n4MKSolCNZqE0kZ0ZRoRvpoGH/kWE19uqKFodPvP9WS1G8rna5vl+JlkfcKNtePlfpupctTn7Joio70GlNdjVbnSNvs+b1G/aM8/fRSUeR8kyN+XLnluTQ9bShBwzTSJcRIKEGwhBDjpQQNU54uIUZCKXluhBDjZAj9IrmN5F0k95K8Ys76V5K8leRhkm+ordtO8u7q3/b6vm1YqpEuz3wTDa+NZBJvE47cFS/tQdSkmJoLjz322Nn0iSeeOJvesmXLbDrNuly/jtSM+PDDD8+mv/3tb8+mDx06NJuumwq8AtBege+cCcNLvxFJH5EzW/aZRT5ipomaorqarNSJWh7apBHp+n5434p3jmgBZk+/UpNeziUhPU+amuG4446bTR9//PGz6dS8V7+OVL8OHjw4dzrdpq5fKek1p6ZDLxt/tOqIZ15MyVVDSYm8E1GXGY+oXvaRGqdvDSO5DpN8gRdiUjd2N8mdZnZHstk3AbwNwG/V9j0RwDswqaJhAG6p9j2EDixVp0sIMR+NXAkhlpmBNOx8AHutqvdK8loAFwOYdbrM7N5qXb3X+BoAN5rZwWr9jQC2AfjrLg1Sp0uIkaBOlxBimRlAw04DcF8yvw/Ayzvse1rXBi2005X2ZKND5d5DiGYMT+kz4siLconiFUNNTYpepvn6MHZqUvzRH/3R2fQZZ/ywTvFpp/3wXUmH6us88sgjs+l9+/bNPadnQqjPe9OpOTQdtm8TxZoO1UcLs6ZEonfq+67Vzs1abdeYmH6HTSsctCUS9etFNXoZ3QHfRO8Vr859W+mxvMoZqWbUj5WaFE8++eTZdKpZz3ve82bTqX7V3/nUjHjffT/8e3nvvffOpu+///7Z9GOPPXbE/qm5MdUmLxI8F1Hq3UsvsjBn/m3jWtMXOS2MREI2ocX1bCW5J5nfYWY7OjdkQFbsdJHcBOBzADZW219vZu8geRaAawGcBOAWAL9qZvNjf4UQYhWQfgkxag6Y2XmZ9fsBnJHMn14ti7AfwKtq+97cpHHziHRNnwLwajP7GQAvBrCN5M8BeC+A95vZ2QAOAbi0a2OEEO1R9OJcpF9CLAkD6NduAOeQPIvkBgCXANgZbM4NAC4iuYXkFgAXVcs6seJIl02ubDoGe1T1zwC8GsBbquXXAHgngD9Z6XjT4UgvGm3O+Z+1b255jnS4NxINUh9K9dbl9lnpfEDziMV0eBsATjjhhNl0alJ84QtfOJt+/vOfP5t+7nOf67bloYcemk0fc8wxs+nUVPDoo4/Opr/73e8esb9nUkin2xR29Yi+B9GC3/POnXsPovustLwPCulENaJv/WpK04iu3LvmmfvauGpE3pVcMfqU9Hv2kjun56snEU3NhalJ8ad+6qdm02efffZsOjVB1nnwwQdn06nbRdqWXPRiqr9pctU0+jLdJtXiXALZ9G9Pvcj2lJx5sW8z3pQ2bjopaz05qpkdJnk5Jp2ldQCuNrPbSV4FYI+Z7ST5MgCfBLAFwGtJ/r6ZvcjMDpJ8FyYdNwC4aupU34WQT1cVdnkLgLMxCb/8GoBHzGz6JvfiYCaEaEdBI1eNkX4JsfYZSsPMbBeAXbVlVybTuzExHc7b92oAV/fZnlCny8yeAfBikidg0iN8YX6PH0LyMgCXAYtzPhWiRNTpmk9f+lUPYBFC9EsJGtZIRczsEZKfBfDzAE4gub76teg6p1WRBDsAYOPGjTYdJs3VhvLWRZJi1okMf0aH55seK2dC9RIKelGNaYe1bl5ME5+m0YupSfHcc8+dTZ966qlz2w4A3/rWt2bTqakgTY6aRv+ky4Ejh+69umLeM809B29IPZrYNvK8vASG9XN7bW4aIQn0k1BwXrvEs+mqX0cffbQly2fb5PSrdqwV25irbedFL7YxN/VZG9SLJva+k7p5MU2CmmrTT/zET8ymf/qnf3o2nUYy1u9XGnGdtiWNanzggQfmLgeOdI/wIjE9LctFX0ciQXPvVPTvXYSmz7hrpH7bc42VFb9WkidXvxBB8mhMMrveCeCzAKYp87cD+NRAbRRCBJAj/bORfgmxPJSgX5GRrlMBXFP5RTwHwHVm9jck7wBwLcl3A/gigI8M2E4hxAosqwgNjPRLiCWhBA2LRC9+CcBL5iy/B5MU+63IRWNEhr6jZqVIzb5oLcBIBIk3/JpLdheJ0EzPV/ctSWspppGMW7dunU2nZseceTEdOk/NiGmEUZrMsG4q8O53G9Oul3iwTZRg06F+z9SY26dNdFk0meVKLPMvvyEZSr9WOOdsOmKOyblaRN7b3P5euyLLu+Ldh7p+efUWU/1KNSuN0M6dM3WVSHUxjcqu65d3LM9VIiVnGu4aid3UvJiLPI3sH41q7DNRaykaJs9QIUZCCYIlhBgvJWiYOl1CjIQSBEsIMV5K0DB1uoQYCSUIlhBivJSgYatW8Lq+PCXihxW1cXv7R3yy6tt0zWQeaaN3rFw7mvo7RWnzATTN1h71PYjs4/nA1ecj2elzoeBN04VEU0Z0zWM3lGCR3AbgA5hkdP6wmb2ntn4jgD8H8FIADwN4s5ndW637XwD8KYDjAPwAwMvM7EksOUNlCM9927ntVtp+SJp+89H7FfHbjfpuRvzhotsNde/b+ONFiPqUerT5+9oWdbqEEEvBUE6oVdTfhzBJtbAPwG6SO83sjmSzSwEcMrOzSV6CSV3DN5NcD+AvMCkm/T9JngRgfg0UIUTRlOJI399PNCHEqjJQnq7zAew1s3vM7GkA1wK4uLbNxZjULwSA6wFcwMlP6IsAfMnM/mfVvodtkh1eCCGehfJ09QzJhQ9/t8kC7dGluGc0/UQkzLgespwWZk0LUKfZltNC1rlM+Wm25ocffng2/Z3vfGc2nRaJrRfl9kx8keloRvrIUH/dVBfZv6s51ctuH32/Fp1pOshpAO5L5vcBeLm3jU0KzH4HwEkAfhKAkbwBwMkArjWzPxyikYumT5PiMtA1U75XCBs4Uk9SnUmrXaTpH3KakW6X7v/oo4/OplO9zFUTSFNbeNM5l4A+/9Y1TT2SczNpo00e0dQSbY43VmReFGIktBSsrST3JPM7bFL6pg/WA/jHAF4G4AkAN5G8xcxu6un4QogRoU6XEGLsHDCz8zLr9wNIs1HOq1M43WZf5cd1PCYO9fsAfM7MDgAAyV0AfhaAOl1CiCJZeKcrMizfZciyz55ym0z5bYZrveF5r3jsU089dcT+6dD5gw8+OJv++te/Prdd6bB7/X6lZsh77rlnNp2aHdPzpYVg66TX4g3JR82LXiSRN7xfX56ev6lpaMhfX9Fs4hEGauduAOeQPAuTztUlAN5S22YnJvULP49JPcPPmNnUrPjbJDcDeBrALwB4/xCNXDS5CLholYR5RItne5G9uXdgKHNXxCSXmhTTQtLAkS4Rqc6k+pOeI9Wv+jWl6772ta/NPe5jjz02m667aqS6sWHDhrnTUS1p+nesjck6Er0ddW1pmjW/fiyZF2NopEuIETCUY2nlo3U5gBswSRlxtZndTvIqAHvMbCcmdQs/RnIvgIOYdMxgZodIvg+TjpsB2GVmf9t7I4UQS88yO8c3QZ0uIUbCgDX0dgHYVVt2ZTL9JIA3Ovv+BSZpI4QQIos6XQMyVPTPaiQH9IaRcwkUPRNZOnTtFVmtm/QeeeSR2fT+/T90t0nvRTqEnxaVrQ+vp8fat2/fbDqNCkojjOqmgvS60mKy3vB8V7zh8Vz0YlNyZr82Ra6HogTBWm2m71WbJMqeHuSem6cnEc3J0dWs7X1rnnnRi2QEjtQmT79Sl4a0eHWdVL9SzUqPm5oX69ee6lRaiHvTpk2z6VTXcvexaZHqnNtEJOK7TTLaSCR4zkyq5KjN0UiXECOhBMESQoyXEjRMnS4hRkIJgiWEGC8laNjCk6N2rS/nHbfNui5EhvfTc9eHXtP7kE6nQ9eeOaGeXDAdnk9JkwAeOHBgNp0Om9df8scff3zuPul0uk19/9SksHHjxtl0JPonN3TtfYzekHibupl9D5VH6EtkSnFCXW2m33Q0KaanP9FnlZri0mN5y1NyUZWeWcyLhIxGRab6lU57UdnAkclRU51JXRfS5cccc4zbllSbUlPjoUOH5p6vfo9SM2Kqk6mWee4Rdc1IdTqiX7lIwsh7FH2nIrUju763bShFwzTSJcRIKEGwhBDjpQQNU6dLiJFQgmAJIcZLCRqmTpcQI6EEwRJCjJcSNGzhna6prbjrzY36anl+DF1TVkRs6VE/iHTaSxmRHquepiH13Ur9JdLlqU9DLmVDmo7iiSeemDudnqPu25L6cbQJs05JzxPxsfIy3efo6scVDc2OnL8rJQjWapL6pHbNSN8mvYiXgsDzyaoTKVLtna/uh+Wlv0inUz+o1L+p/p2lmpP6W6U6l6apyelXuk+qf17ljLSNALB58+bZtOfT5elX3dc2d//mHavN3yfvHG38u1YjzU2dEjRMI11CjIBSnFCFEOOkFA1Tp0uIkVCCYAkhxksJGrYmOl25G+2ZfLoOheayxUdoGsJbv450nWcWi2SgBo4cUk+LYadD6mkodW5IOR2i9qa9orD1+aZh1rmQ6/rQ/TzamGzaZPbukoV+yIKxJQhWyUTMi22yj3vves48ln6P6bftuUqkWpD7tlLNSnXNMw9G08yk7UrdHtJpADj22GNn06mp0UttkyvknbY5kjIi6h4ReQ9ypsa1YEb0KEHDwr0NkutIfpHk31TzZ5H8Asm9JD9OcsNKxxBCDMd0eL7Jv1KQfgmx9ilBv5oM8fw6gDuT+fcCeL+ZnQ3gEIBL+2yYEKIZ6nRlkX4JscYpQb9C5kWSpwP4ZwD+AMBvcjI++WoAb6k2uQbAOwH8yUrHmt6oRWX/bmryiWa396bbmBe9c3gmudzL5kUJpcvbXKOXXbreRm+dZ87ImTAi0The5FXuGpu+e7ns9k2jf3LPbllFZK3Tp35Nib5DTU059XctYvrztsllzffMWl7EcN105rXFMzWm+pFrS7p/ek5P13KmM++4qamzbl6MRFx7JsW6CTTdLhK9GH1XIhofdY/o6mbjHVf4RH26/hjAbwP4kWr+JACPmNn0Ke8DcFq/TRNCRFnmX34L4I8h/RJiTVOKhq3YNSX5ywAeMrNb2pyA5GUk95Dc4/X2hRDdkXnx2fSpX5FgDiFEe0rQr8hI1ysAvI7kPwWwCcBxAD4A4ASS66tfi6cD2D9vZzPbAWAHAGzcuNGmN6pPk6Jn3qvPe0OpEVNffT5SPDaHZx7wCmHniLQleo2RZH25KJt0nfdc0s53bkg8EvHjXXsbM166PDfs3zShYc4U1KeZfVlFaGB606/NmzfP1a/6c/fcFSLF2aPvbZvoxYieeN9p/Qez15Z0eVTLvGjo1FwXSTQK+PfbK8Rdj772zKNeJHXaxrp50fue03N407l3Kp32TJjpdDSiNcKQrkBAGRq24h03s98xs9PN7EwAlwD4jJm9FcBnAbyh2mw7gE8N1kohxIpopOvZSL+EWB5K0K8unm9vx8QpdS8mPhIf6adJQog2qNPVCOmXEGuMEvSrUXJUM7sZwM3V9D0Azm96wunwpDdcWqfPiLD0WN4waXS4NWr6m9eO+vmbtiV37nSIOuJD1yYyr03iUW8YvE3ETde2NE0yGTUv5szckXZ1YZlFaFF01S8zm6tf9e+sSz3OaGRxvV3zpnP7pzqRfoPRuoIp3jcUMaPV8Ux/ufvt4Zk3I3UngeYmxbp+RdxGvHa1SVrtRXvX29X0nVyUrpSiYWsiI70QojslCJYQYryUoGHqdAkxEkoQLCHEeClBw1at0xUdEvfoalZqQ9Okcrnr8obuvaHvnEnNGyL3hvFz965rsk/PdBiJssm1s2mSyag5t+s70dX83Wc00FCCRXIbJhF/6wB82MzeU1u/EcCfA3gpgIcBvNnM7k3W/xiAOwC808z+aJBGLojpPY6auCLfYPR79N4VT0vr70NTnci9m+mx0wShkYjpnEtDxAwZfc8j32b9OabtT02HXkLU3Hvgabl3jW2i8CMJpKMuJGuBEjpda+uOCyFaM4QjPcl1AD4E4JcAnAvgV0ieW9vsUgCHbFJS5/2YlNhJeR+A/9L5AoUQo2YIR3qS20jeVdVZvWLO+o1V/dW9VT3WM6vlZ5L8Hsnbqn//dx/XKPOiECNgQCfU8wHsrRzPQfJaABdjMnI15WJMyugAwPUAPkiSZmYkXw/g6wAeH6JxQohxMISGJT8aL8Sk8sRukjvNLNWv2Y9Gkpdg8qPxzdW6r5nZi/tsk0a6hBgJA6WMOA3Afcn8vJI5s21skmz0OwBOInksJqkZfr/zxQkhRs8A+jX70WhmTwOY/mhMuRiT+qvA5EfjBWzqz9KAhY90TW9Um5D6SEh+NH1EG9+DpqkdcufoksIgd/6Ir0juWJGw9Nx1RXy3PD+EaGqGXEb8eW2sz3vTXkqR3P3y3p2mfhd90PLYW0nuSeZ32CQLex+8E8D7zeyxATVs1anf90hqGs+/KveuNS1+3SYFS3otqb9R/VieX1Obd7CeFX5Km2L2KRHNqqfCiBSz9vzmor5qnq+u13bAL0SeEtX7NkW2F8UA+jjvR+PLvW3M7DDJ72CSuw8AziL5RQCPAvgPZvZfuzZI5kUhRkJLwTpgZudl1u8HcEYyP69kznSbfSTXAzgeE4f6lwN4A8k/BHACgB+QfNLMPtimoUKIcdNCw4b80Xg/gB8zs4dJvhTA/0PyRWb2aJeDqtMlxEgYaBRtN4BzSJ6FSefqEgBvqW2zE5NSOp/HpLTOZ2zSmH8y3YDkOwE8pg6XEMKjhYYN9qOx0rCnqnbdQvJrAH4SwB50YE10unI32hv6bjPE7C2PFqyOhNdGzV3e0HfTbQB/uNobRo6Gu0fOnzPjRcyIOXNoJH1GFM+84L0fuXM0vS/p9tF735ShHOmr4fbLAdyAScqIq83sdpJXAdhjZjsxKaHzsaqkzkFMOmajZN57kXtXImbq6PvcNH1EtC3p+dPvLPeee++39/1HrzE9v5dKJ0fke/TS2tTnc9vNa2/dHOoVsPam26SyiRRRr7erT/Nin+bJgTSs9Y9GkicDOGhmz5B8PoBzANzTtUFrotMlhFi7mNkuALtqy65Mpp8E8MYVjvHOQRonhBAOHX80vhLAVSS/D+AHAP61mR3s2iZ1uoQYCUM66QshxNAMNFrf6kejmX0CwCf6bs/CO13T4cio+aav8+WOGzXjpUSGu9sMvXptyZnhPNNdm2zDTTOkR03DaVsipsb6Pl0z0kfXRbaPmAgXYVLMnVP0D8nZO+kVjK7TteC19914tDEJet+pF30379hTvHc9Z2r0XD361C/PdFd/dhETXyTTfH3eMzV658tFu3ttScmZPYcqeN2HqbEEDdNIlxAjoQTBEkKMlxI0TJ0uIUZCCYIlhBgvJWjYqnW6cma/LhGLuW0ipsaU+jZRc988oolWuyZ9jSRmzB03cl9yJswuRarrRMyLbQpGN21j3WQSGZ7vs5B1hKGiF8WRzHv2dbPSUO4R3ncXTY7qRfBFop/r1+0Vo24TYdmlAH3XZNi5tkQiPI866qjZdP2eeMldm0Z+zmvnvDZGpqP0GUkZpRQN00iXECOhBMESQoyXEjRMnS4hRkIJgiWEGC8laNjCO13TIUjPVAc0v/FdTVpdE2S2aYs3DD9ULSxvSD1nXowkMa23t2kS00iiUqB5vbE60QS4EdokpvToGm3a9fwiThq9mKOpWb/NN9/026q3KxdxGTmfF9nYNAlxfR8P71h9uxd4z9ernegtn3fsKV5kc/pMcvfL09g2CaS73te+6ziWoGEa6RJiBJTiDyGEGCelaJg6XUKMhBIESwgxXkrQsIV2ukiGapc1jeCLmvEi0YvRYdVITbU2NE2GCHQz47UxL+aSJkaGuL3ImGjETop3jlxUpRdVFB1ej97Leeeut6vP4fkSBGu1mT6jrrUAU6LPzTORe/USo23xTI1tatJGTGrRdnk60WeEdPRvh5cQNWrSiyRnzWmJd81dTYpefdxoAmuvjW0pQcM00iXESChBsIQQ46UEDQt1ukjeC+C7AJ4BcNjMziN5IoCPAzgTwL0A3mRmh4ZpphBiJUoQrDZIv4RYDkrQsCZ2sf/VzF5sZudV81cAuMnMzgFwUzUvhFgFpk6oTf8VhPRLiDVMKfrVxbx4MYBXVdPXALgZwNtX2mmeT0Tdpt80c3xKNEO6Z3/ucu6+idrIu6RpiBZjjvg31M8f8QnJFXn1/A28dnm+WvV2NSUXet81dUif/imr/b4uGa30q2vFhrbHrNM15YznY9QmtUpTXa3j3aMumerb4vmnec+0jR9X5Frqx21z/ik5XW1a4HuolEZTStCw6EiXAfh/Sd5C8rJq2Slmdn81/QCAU3pvnRAiTCm/FFsg/RJiCShBv6IjXf/YzPaTfC6AG0l+NV1pZkZy7h2oRO4ywK/XJYQQA9KLfm3YsGH4lgohRk2oF2Rm+6v/HyL5SQDnA3iQ5Klmdj/JUwE85Oy7A8AOANi0adMgXdOo+SYyjJ0bKu/Ss86ZqCLniBaZbmpqrHeEI+bGNmHSXjoGLyNzfZ0XwpwSDeNvakLJpdXwiIR419cpI/0w9KVfmzdvtjamoeRYs+muRYSbpkCp450/YtKvnz9aJHul5bntFl1APkcbc6xnxoumQepSwLqu6ZG2RM2IfZsbS9CwFZ8eyWNI/sh0GsBFAL4CYCeA7dVm2wF8aqhGCiFWRubFZyP9EmJ5KEG/IiNdpwD4ZNWjXQ/gr8zs0yR3A7iO5KUAvgHgTcM1UwiRY5lFaGCkX0IsAaVo2IqdLjO7B8DPzFn+MIALhmhUSptoGI+hssg3PXduXSRKL7euqamxfr6m0Zu5bTxzSmpGTIe+c8Pg6bG8rP1DfbDRa4y8k10LhOcoQbCa0qd+mdns3e3zueXwNKvNuxY5h/fNRk2N6X3xsuZHs9uneCbURRV29q4/Gv3tEXWVaPr3quvfm5ShIxZTStAwebYLMRJKECwhxHgpQcMWO9wjhBiMoXy6SG4jeRfJvSSflUSU5EaSH6/Wf4HkmdXyC6s0DV+u/n91v1cshBgT8ukagL6GM9sMK6f7NB1ibXP+aPRQ1+HeiAkiErlZ365Lcj/AT8La5liRJLddzSleG6PRi5GIxVzkaVeGECGS6wB8CMCFAPYB2E1yp5ndkWx2KYBDZnY2yUsAvBfAmwEcAPBaM/sWyZ8GcAOA03pv5AKZ3uP0Xckl5R26HUA8cab3fkSSq0ZN7F67cmZ4z12gTZRy5HuKfttdIyabRm92TZTc1TScEnFZadvOHMvakWqCRrqEGAFtRrmCAnc+gL1mdo+ZPQ3gWkyyuadcjElWdwC4HsAFJGlmXzSzb1XLbwdwNMmNPVyuEGJklBJ9LZ8uIUbCQCJ0GoD7kvl9AF7ubWNmh0l+B8BJmIx0TfnfANxqZk8N0UghxPKzrB2pJqxap6uNWSmyPHrONkPHQ0UPRYaFc8Pui4ja69PU6F1LzuzQtQ5aJPrIa2N0eL6r2bMrLd+DrST3JPM7bJIQtDdIvggTk+NFfR53NZje41yUb+TZt0n82SWSsb5dun+aIDmNLM6Z8SLJnaP61fQ770pOv5pGQ0e/8z7/jnlEv/+m5s1FReoC6nQJIZaIloJ1wMzOy6zfD+CMZP70atm8bfaRXA/geAAPAwDJ0wF8EsA/N7OvtWmgEKIMSuh0yadLiJEwkE/EbgDnkDyL5AYAl2CSzT0lze7+BgCfMTMjeQKAvwVwhZn9936uUggxVuTT1TNtblTT6Ip555wSSQIYOXd9nTcMnkvG6t2HyPJcxI03Ha316NVF7BJtWadphGWdyD5tTI1NTQtNzhlpS5eh+6FEqPLRuhyTyMN1AK42s9tJXgVgj5ntBPARAB8juRfAQUw6ZgBwOYCzAVxJ8spq2UVmNrfO4Vjo05TkvR8Rk2SbSFnP1NgmMjgl921Fat96yVWjmhFx26hvF6FN5GqfSZDb/B2L/E1tUzeza7TnMnekmiDzohAjYUDfvl0AdtWWXZlMPwngjXP2ezeAdw/SKCHE6FCnSwixNJQgWEKI8VKChqnTJcRIKEGwhBDjpQQNWxOdrjahrl1DiCNpD3K28Ejag6j9OxJ2nPMDiGRY93wPcv5h3jlytMki7dHUb6WNb59H1O8i4p+WO7e3XRtKEKzVpmklisj3HE0fEfk2vSLTuXN632bq35VLi9HUR7JNwetUy9poSZu29EnT9yb3LTdtc863r6te933/StAwRS8KIYQQQiyANTHSJYToRimRP0KIcVKKhi1Vp8sbCk2H0duEAHvDutE0Dx65IeXI8L5npqhvHxkKjpoNve08E0guu3RXc3A0zUVftDHtNg3/7hqi3uTYon+GyMid+7abVoLImasjWcaj32zE1cJrb5sM+pHlbY4VdQGJZM3PETHBtjEtt0mp1PRetKk60pYSNGypOl1CCJ8SBEsIMV5K0DB1uoQYCSUIlhBivJSgYWui05W70U2HT3PDvV5W466FqduYziJFm71h+3pUUiSCr6uprk0B1IgJo02xX49c1FgkqrTNO+GxqKiolBIEazUhOXt3cqY+z8TXtQh6ZJ80yi9n+k/xdDGXbT1dF4mSbvPNt7lfES2Ofud9RshH6LMiRvTeNTUpRqPd21KChil6UYgR0KbuYgkCJ4RYDobSL5LbSN5Fci/JK+as30jy49X6L5A8M1n3O9Xyu0i+po/rXBMjXUKI7qgTJYRYZvrWMJLrAHwIwIUA9gHYTXKnmd2RbHYpgENmdjbJSwC8F8CbSZ6LSR3ZFwF4HoC/I/mTZvYMOrDQThfJTkniuhZX9kyCntkwGvWR0mcboya9aILQeUSj6aIRL00TxebMDpEitZEormhbIgkfc+dsQ58io07X2qBLUt/ct93UxN6mXV5C1KiJyjNPdtXFPqNG2xSp7po0OnKsribFNkTMiKn5eGgG0LDzAew1s3sAgOS1AC4GkHa6Lgbwzmr6egAf5OTGXgzgWjN7CsDXSe6tjvf5Lg0KmRdJnkDyepJfJXknyZ8neSLJG0neXf2/pUtDhBDdkHlxPtIvIZaDAfTrNAD3JfP7qmVztzGzwwC+A+Ck4L6Nifp0fQDAp83shQB+BsCdAK4AcJOZnQPgpmpeCLFKqNPlIv0SYglooV9bSe5J/l222tewEiuaF0keD+CVAN4GAGb2NICnSV4M4FXVZtcAuBnA2wPHe9ayXESEZ0pqEz2YbucNMXdNaNrV9BQxO+RMZx5NzXN9EznP4cOHj5j3hri9zoKXmBGImRS8+92nCaHtdpHjFNSJCtO3fs0jGtHlvVNtzGhe9GBu/4gbxVB6kIu+9rZbS7S5R03NiF2Txg5Vjzjq4tP1/C017ICZnZdZvx/AGcn86dWyedvsI7kewPEAHg7u25jIXToLwLcBfJTkF0l+mOQxAE4xs/urbR4AcErXxggh2qORrrlIv4RYEgbQr90AziF5FskNmDjG76xtsxPA9mr6DQA+Y5OD7wRwSRXdeBaAcwD8f12vMdLpWg/gZwH8iZm9BMDjqA3FVw2cewdIXjYd+lukQ54QpaFO11x606/6SKwQol/61q/KR+tyADdg4lZwnZndTvIqkq+rNvsIgJMqR/nfRKUPZnY7gOswcbr/NIB/ax0jF4FY9OI+APvM7AvV/PVVox4keaqZ3U/yVAAPzdvZzHYA2AEAmzZt8oStvs/c6cg2uSHappF1dZqaAXLXEY26a0pTM2LuxY3c19xQe9NEhfVjpZ10b7jbe45tkil2Na00NSf0TSGdqKb0pl9HH320Td8Rz/QdJZqQ1Hu/vX2ibWnqHhE1W7ahi0mxzbmHMmG2MTO3aUsk+jrSjvq6pnU+c21pyxAaZma7AOyqLbsymX4SwBudff8AwB/02Z4V75iZPQDgPpIvqBZdgEnPLx2S2w7gU302TAjRDI10PRvplxDLQwn6Fc3T9e8A/GVlE70HwL/ApMN2HclLAXwDwJuGaaIQYiWWWYQWgPRLiDVOKRoW6nSZ2W0A5kUIXNBra4QQomekX0KItcLCywBNbcA5X5qIbbqNT5dHG7+gpm1sU9jZa0uuwG7Edyv3a6LPlBeRY3m+WvV1XQteNyX67CK/zIb0hxnqWKIfvHcysry+zku7ED2Wd1yPPlOl5M4XSefSVVdTchof0fU26RQi/sRtfL2CzuRHzLfx753SJl1RE0rQMBW8FmIkDOXTxTVWMFYIMU7k0yWEWBqGECGuwYKxQohxsqwdqSasWqcrl7Khadhyn1mUu4bzprQxS3lmxFy7cia6ecuHyqicO3ZTc+xK52lyjhxdU2l0bUvUHBRhIMFacwVj1wJR/YpoVrSSQuSd6lpkOiWqX562RFMbNDWR5dwOuqbi8UydXVNTeO+LN527R5F3p6vGt6kg0Ef6CHW6hBBLwYDD7fOKvr7c28bMDpNMC8b+fW3fzgVjhRDjY5lNhk1Qp0uIkdBSsLaS3JPM77BJQlAhhFgo6nT1zFNPPXXgH/7hHx4HcGCR511jbEW511/ytQPNrv/Hmx68pWAtXcHY1eLJJ5888JWvfOUbKPs9LvnagbKvf1D9AtTp6h0zO5nknhVEftSUfP0lXzsw/PUPJFizgrGYdJguAfCW2jbT7O6fR1IwluROAH9F8n2YONL3UjB2tTCzk4Gy3+OSrx0o+/oXce3qdAkhloYhBKvy0ZoWjF0H4GqrCsYC2GNmOzEpGPuxylH+ICYdM1TbTQvGHkZPBWOFEONEnS4hxFIwpBOqrbGCsUKI8SFH+uEo3Um35Osv+dqBga+/BMFaI5T8Hpd87UDZ1z/4tZegYSzhIoUYOxs2bLDnPve5jffbv3//LaX6qAgh1g5tNGwZ9UvmRSFGgn5ACSGWmRI0TJ0uIUZCCYIlhBgvJWjYQgter1Q4d0yQPIPkZ0neQfJ2kr9eLT+R5I0k767+37LabR0KkutIfpHk31TzZ1UFkfdWBZI3rHYbh4LkCSSvJ/lVkneS/Pkhn32bYtclCFyflKRfgDQMKFfDFq1fQDsNW0YW1ulKCuf+EoBzAfxKVRB3rBwG8O/N7FwAPwfg31bXewWAm8zsHAA3VfNj5dcB3JnMvxfA+83sbACHMCmUPFY+AODTZvZCAD+DyX0Y9NmXIlqrQYH6BUjDgHI1bOH6BTTXsGVkkSNds8K5ZvY0gGnh3FFiZveb2a3V9HcxeWlPw+Sar6k2uwbA61elgQND8nQA/wzAh6t5Ang1JgWRgXFf+/EAXolJ/iqY2dNm9ggKefYjpSj9AqRhpWqY9GtYFtnpmlc4t4jityTPBPASAF8AcIqZ3V+tegDAKavVroH5YwC/DWBa7v4kAI+Y2eFqfszP/ywA3wbw0co08WGSx2DgZ6+RrkEpVr8AaVg1X4qGrYp+ARrpEj1A8lgAnwDwG2b2aLrOJm/Ncr45GUj+MoCHzOyW1W7LKrEewM8C+BMzewmAx1Ebih/i2avTJYZAGlYcq6Jf1XFHr1+L7HSNqvhtBJJHYSJWf2lm/7la/CDJU6v1pwJ4aLXaNyCvAPA6kvdiYoZ5NSY+AidUBZGBcT//fQD2mdkXqvnrMRGxQZ+9Ol2DUpx+AdKwQjVsVfQLUKerb2aFc6uIj0swKZQ7Sir7/0cA3Glm70tWTYsDo/r/U4tu29CY2e+Y2elmdiYmz/kzZvZWAJ/FpCAyMNJrBwAzewDAfSRfUC26AJP6g4M9+zYdrmUVrVWiKP0CpGGlathq6Fd13iL0a2F5uswpnLuo868CrwDwqwC+TPK2atnvAngPgOtIXgrgGwDetDrNWxXeDuBaku8G8EVUjpoj5d8B+MvqD/Q9AP4FJj9yBnv2yypCy0CB+gVIw+ZRioYtXL+AMjRMZYCEGAHr16+34447rvF+hw4dWroyGkKI8dFGw5ZRv5SRXoiRoB9QQohlpgQNU6dLiJFQgmAJIcZLCRqmTpcQI2CZHUuFEKIUDVOnS4iRUIJgCSHGSwkapk6XECOhBMESQoyXEjRMnS4hRkIJgiWEGC8laJg6XUKMhBIESwgxXkrQMHW6hBgBpTihCiHGSSkapk6XECOhBMESQoyXEjRskbUXhRADsujaZSRPJHkjybur/7c4222vtrmb5PZq2WaSf0vyqyRvJ/meTo0RQiw9JdReVKdLCNGWKwDcZGbnALipmj8CkicCeAeAlwM4H8A7ks7ZH5nZCwG8BMArSP7SYpothBCrgzpdQoyERY90AbgYwDXV9DUAXj9nm9cAuNHMDprZIQA3AthmZk+Y2Werdj8N4FYAp3dtkBBieSlhpEs+XUKMhFUQoVPM7P5q+gEAp8zZ5jQA9yXz+6plM0ieAOC1AD4wQBuFEEvCsnakmqBOlxAjoMMvv60k9yTzO8xsx3SG5N8B+NE5+/1e7fxGsnEDSK4H8NcA/i8zu6fp/kKIcbDMo1dNUKdLiJHQUrAOmNl5mWP+oreO5IMkTzWz+0meCuChOZvtB/CqZP50ADcn8zsA3G1mf9yk0UKI8bHITlflb/pxAGcCuBfAmyoXiPp22wH8h2r23WZ2TbX8ZgCnAvhete4iM5ungUcgny4hRsIq+HTtBLC9mt4O4FNztrkBwEUkt1QO9BdVy0Dy3QCOB/AbXRsihFh+FqxfXQOBAOCtZvbi6t+KHS5AnS4hRsMqdLreA+BCkncD+MVqHiTPI/nhqk0HAbwLwO7q31VmdpDk6ZiYKM8FcCvJ20j+WtcGCSGWl2UJBOpyUpkXhRgJi/aHMLOHAVwwZ/keAL+WzF8N4OraNvsAcOg2CiGWhwVrWB+BQB8l+QyAT2BielzxAtTpEmIc3ABga4v9DvTdECGEaEEbDdu0ioFAbzWz/SR/BJNO168C+POVdlKnS4gRYGadhryFEGI1GULDhgwEMrP91f/fJflXmPh8rdjpkk+XEEIIIUqjdSAQyfUktwIAyaMA/DKAr0ROyhLyYgghhBBCTCF5EoDrAPwYgG9gkjLiIMnzAPxrM/u1art/CeB3q93+wMw+SvIYAJ8DcBSAdQD+DsBvmtkzK55XnS4hhBBCiOGReVEIIYQQYgGo0yWEEEIIsQDU6RJCCCGEWADqdAkhhBBCLAB1uoQQQgghFoA6XUIIIYQQC0CdLiGEEEKIBaBOlxBCCCHEAvj/Acm4ZTp5F93nAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 720x360 with 4 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 4 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "## NUMBER_OF_IMAGES = 16\n",
        "\n",
        "for i in range(8):\n",
        "    dataset.update()\n",
        "    image_of_particle = dataset.resolve()\n",
        "\n",
        "    plt.figure(figsize=(10,5))\n",
        "    plt.subplot(1,2,1)\n",
        "    plt.imshow(image_of_particle[:, :, 0], cmap=\"gray\")\n",
        "    plt.colorbar()\n",
        "    \n",
        "    plt.subplot(1,2,2)\n",
        "    plt.imshow(image_of_particle[:, :, 1], cmap=\"gray\")\n",
        "    plt.colorbar()\n",
        "    plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## 3. Defining the network\n",
        "\n",
        "The network used is a Convolutional network, with a the pixel error as loss."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-06-30T10:16:38.137547Z",
          "iopub.status.busy": "2022-06-30T10:16:38.137547Z",
          "iopub.status.idle": "2022-06-30T10:16:38.592050Z",
          "shell.execute_reply": "2022-06-30T10:16:38.591549Z"
        }
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"model\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_1 (InputLayer)        [(None, 64, 64, 2)]       0         \n",
            "                                                                 \n",
            " conv2d (Conv2D)             (None, 64, 64, 32)        608       \n",
            "                                                                 \n",
            " activation (Activation)     (None, 64, 64, 32)        0         \n",
            "                                                                 \n",
            " conv2d_1 (Conv2D)           (None, 64, 64, 32)        9248      \n",
            "                                                                 \n",
            " activation_1 (Activation)   (None, 64, 64, 32)        0         \n",
            "                                                                 \n",
            " spatial_dropout2d (SpatialD  (None, 64, 64, 32)       0         \n",
            " ropout2D)                                                       \n",
            "                                                                 \n",
            " max_pooling2d (MaxPooling2D  (None, 32, 32, 32)       0         \n",
            " )                                                               \n",
            "                                                                 \n",
            " conv2d_2 (Conv2D)           (None, 32, 32, 64)        18496     \n",
            "                                                                 \n",
            " activation_2 (Activation)   (None, 32, 32, 64)        0         \n",
            "                                                                 \n",
            " conv2d_3 (Conv2D)           (None, 32, 32, 64)        36928     \n",
            "                                                                 \n",
            " activation_3 (Activation)   (None, 32, 32, 64)        0         \n",
            "                                                                 \n",
            " spatial_dropout2d_1 (Spatia  (None, 32, 32, 64)       0         \n",
            " lDropout2D)                                                     \n",
            "                                                                 \n",
            " max_pooling2d_1 (MaxPooling  (None, 16, 16, 64)       0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " conv2d_4 (Conv2D)           (None, 16, 16, 128)       73856     \n",
            "                                                                 \n",
            " activation_4 (Activation)   (None, 16, 16, 128)       0         \n",
            "                                                                 \n",
            " conv2d_5 (Conv2D)           (None, 16, 16, 128)       147584    \n",
            "                                                                 \n",
            " activation_5 (Activation)   (None, 16, 16, 128)       0         \n",
            "                                                                 \n",
            " max_pooling2d_2 (MaxPooling  (None, 8, 8, 128)        0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 8192)              0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 64)                524352    \n",
            "                                                                 \n",
            " activation_6 (Activation)   (None, 64)                0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 64)                4160      \n",
            "                                                                 \n",
            " activation_7 (Activation)   (None, 64)                0         \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 2)                 130       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 815,362\n",
            "Trainable params: 815,362\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "import tensorflow.keras.backend as K\n",
        "import tensorflow.keras.optimizers as optimizers\n",
        "\n",
        "model = dt.models.Convolutional(\n",
        "    input_shape=(IMAGE_SIZE, IMAGE_SIZE, 2),\n",
        "    conv_layers_dimensions=(32, 64, 128),\n",
        "    dense_layers_dimensions=(64, 64),\n",
        "    steps_per_pooling=2,\n",
        "    number_of_outputs=2,\n",
        "    dropout=(.2, .2),\n",
        "    loss=\"mae\",\n",
        "    optimizer=\"adam\",\n",
        "    dense_block=dt.layers.DenseBlock(activation=\"relu\")\n",
        ")\n",
        "\n",
        "model.summary()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## 4. Training the network"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "We use the `ContinuousGenerator` to generate the images. It creates a new thread and generates images while the model is training. \n",
        "\n",
        "Set TRAIN_MODEL to True to train the model, otherwise a pretrained model is downloaded."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-06-30T10:16:38.605548Z",
          "iopub.status.busy": "2022-06-30T10:16:38.605047Z",
          "iopub.status.idle": "2022-06-30T10:20:40.661050Z",
          "shell.execute_reply": "2022-06-30T10:20:40.660553Z"
        },
        "scrolled": false
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Generating 2009 / 2000 samples before starting training\n",
            "Epoch 1/100\n",
            "31/31 [==============================] - 4s 18ms/step - loss: 0.8815 - val_loss: 0.5950\n",
            "Epoch 2/100\n",
            "31/31 [==============================] - 0s 11ms/step - loss: 0.5773 - val_loss: 0.5481\n",
            "Epoch 3/100\n",
            "31/31 [==============================] - 0s 11ms/step - loss: 0.5761 - val_loss: 0.5358\n",
            "Epoch 4/100\n",
            "31/31 [==============================] - 0s 11ms/step - loss: 0.5635 - val_loss: 0.5392\n",
            "Epoch 5/100\n",
            "31/31 [==============================] - 0s 11ms/step - loss: 0.5547 - val_loss: 0.5305\n",
            "Epoch 6/100\n",
            "31/31 [==============================] - 0s 11ms/step - loss: 0.5409 - val_loss: 0.5485\n",
            "Epoch 7/100\n",
            "31/31 [==============================] - 0s 11ms/step - loss: 0.5339 - val_loss: 0.4989\n",
            "Epoch 8/100\n",
            "31/31 [==============================] - 0s 11ms/step - loss: 0.5282 - val_loss: 0.4923\n",
            "Epoch 9/100\n",
            "31/31 [==============================] - 0s 11ms/step - loss: 0.5099 - val_loss: 0.5271\n",
            "Epoch 10/100\n",
            "31/31 [==============================] - 0s 11ms/step - loss: 0.5131 - val_loss: 0.5194\n",
            "Epoch 11/100\n",
            "31/31 [==============================] - 0s 11ms/step - loss: 0.5215 - val_loss: 0.5228\n",
            "Epoch 12/100\n",
            "31/31 [==============================] - 0s 11ms/step - loss: 0.5130 - val_loss: 0.4946\n",
            "Epoch 13/100\n",
            "31/31 [==============================] - 0s 11ms/step - loss: 0.5097 - val_loss: 0.5089\n",
            "Epoch 14/100\n",
            "31/31 [==============================] - 0s 11ms/step - loss: 0.5029 - val_loss: 0.5175\n",
            "Epoch 15/100\n",
            "31/31 [==============================] - 0s 11ms/step - loss: 0.4996 - val_loss: 0.5064\n",
            "Epoch 16/100\n",
            "31/31 [==============================] - 0s 11ms/step - loss: 0.5138 - val_loss: 0.4990\n",
            "Epoch 17/100\n",
            "31/31 [==============================] - 0s 11ms/step - loss: 0.4995 - val_loss: 0.4963\n",
            "Epoch 18/100\n",
            "31/31 [==============================] - 0s 11ms/step - loss: 0.4959 - val_loss: 0.4907\n",
            "Epoch 19/100\n",
            "31/31 [==============================] - 0s 11ms/step - loss: 0.4915 - val_loss: 0.4914\n",
            "Epoch 20/100\n",
            "31/31 [==============================] - 0s 11ms/step - loss: 0.4739 - val_loss: 0.4631\n",
            "Epoch 21/100\n",
            "31/31 [==============================] - 0s 11ms/step - loss: 0.4825 - val_loss: 0.4865\n",
            "Epoch 22/100\n",
            "31/31 [==============================] - 0s 11ms/step - loss: 0.4673 - val_loss: 0.4604\n",
            "Epoch 23/100\n",
            "31/31 [==============================] - 0s 11ms/step - loss: 0.4668 - val_loss: 0.4692\n",
            "Epoch 24/100\n",
            "31/31 [==============================] - 0s 11ms/step - loss: 0.4539 - val_loss: 0.4742\n",
            "Epoch 25/100\n",
            "31/31 [==============================] - 0s 11ms/step - loss: 0.4543 - val_loss: 0.4518\n",
            "Epoch 26/100\n",
            "31/31 [==============================] - 0s 11ms/step - loss: 0.4346 - val_loss: 0.5006\n",
            "Epoch 27/100\n",
            "31/31 [==============================] - 0s 11ms/step - loss: 0.4386 - val_loss: 0.4675\n",
            "Epoch 28/100\n",
            "31/31 [==============================] - 0s 11ms/step - loss: 0.4193 - val_loss: 0.4110\n",
            "Epoch 29/100\n",
            "31/31 [==============================] - 0s 11ms/step - loss: 0.3904 - val_loss: 0.4073\n",
            "Epoch 30/100\n",
            "31/31 [==============================] - 0s 11ms/step - loss: 0.3771 - val_loss: 0.4544\n",
            "Epoch 31/100\n",
            "31/31 [==============================] - 0s 11ms/step - loss: 0.4221 - val_loss: 0.3631\n",
            "Epoch 32/100\n",
            "31/31 [==============================] - 0s 11ms/step - loss: 0.3835 - val_loss: 0.4450\n",
            "Epoch 33/100\n",
            "31/31 [==============================] - 0s 11ms/step - loss: 0.3701 - val_loss: 0.3619\n",
            "Epoch 34/100\n",
            "31/31 [==============================] - 0s 11ms/step - loss: 0.3512 - val_loss: 0.5325\n",
            "Epoch 35/100\n",
            "31/31 [==============================] - 0s 11ms/step - loss: 0.3333 - val_loss: 0.3666\n",
            "Epoch 36/100\n",
            "31/31 [==============================] - 0s 11ms/step - loss: 0.3341 - val_loss: 0.3528\n",
            "Epoch 37/100\n",
            "31/31 [==============================] - 0s 10ms/step - loss: 0.3603 - val_loss: 0.3043\n",
            "Epoch 38/100\n",
            "31/31 [==============================] - 1s 12ms/step - loss: 0.3415 - val_loss: 0.3639\n",
            "Epoch 39/100\n",
            "31/31 [==============================] - 1s 14ms/step - loss: 0.3067 - val_loss: 0.3534\n",
            "Epoch 40/100\n",
            "31/31 [==============================] - 1s 13ms/step - loss: 0.3201 - val_loss: 0.3305\n",
            "Epoch 41/100\n",
            "31/31 [==============================] - 1s 13ms/step - loss: 0.3122 - val_loss: 0.2938\n",
            "Epoch 42/100\n",
            "31/31 [==============================] - 1s 14ms/step - loss: 0.3042 - val_loss: 0.4223\n",
            "Epoch 43/100\n",
            "31/31 [==============================] - 1s 16ms/step - loss: 0.3006 - val_loss: 0.3197\n",
            "Epoch 44/100\n",
            "31/31 [==============================] - 0s 10ms/step - loss: 0.2987 - val_loss: 0.3670\n",
            "Epoch 45/100\n",
            "31/31 [==============================] - 0s 10ms/step - loss: 0.2902 - val_loss: 0.2699\n",
            "Epoch 46/100\n",
            "31/31 [==============================] - 0s 11ms/step - loss: 0.2986 - val_loss: 0.3271\n",
            "Epoch 47/100\n",
            "31/31 [==============================] - 0s 11ms/step - loss: 0.2984 - val_loss: 0.3315\n",
            "Epoch 48/100\n",
            "31/31 [==============================] - 0s 11ms/step - loss: 0.2937 - val_loss: 0.3079\n",
            "Epoch 49/100\n",
            "31/31 [==============================] - 0s 11ms/step - loss: 0.2728 - val_loss: 0.4172\n",
            "Epoch 50/100\n",
            "31/31 [==============================] - 0s 10ms/step - loss: 0.2756 - val_loss: 0.2905\n",
            "Epoch 51/100\n",
            "31/31 [==============================] - 0s 11ms/step - loss: 0.2711 - val_loss: 0.3424\n",
            "Epoch 52/100\n",
            "31/31 [==============================] - 0s 11ms/step - loss: 0.2782 - val_loss: 0.2830\n",
            "Epoch 53/100\n",
            "31/31 [==============================] - 0s 11ms/step - loss: 0.2633 - val_loss: 0.2602\n",
            "Epoch 54/100\n",
            "31/31 [==============================] - 0s 11ms/step - loss: 0.2495 - val_loss: 0.2567\n",
            "Epoch 55/100\n",
            "31/31 [==============================] - 0s 11ms/step - loss: 0.2516 - val_loss: 0.2790\n",
            "Epoch 56/100\n",
            "31/31 [==============================] - 0s 11ms/step - loss: 0.2542 - val_loss: 0.3367\n",
            "Epoch 57/100\n",
            "31/31 [==============================] - 0s 11ms/step - loss: 0.2849 - val_loss: 0.3025\n",
            "Epoch 58/100\n",
            "31/31 [==============================] - 0s 11ms/step - loss: 0.2731 - val_loss: 0.3701\n",
            "Epoch 59/100\n",
            "31/31 [==============================] - 0s 11ms/step - loss: 0.2503 - val_loss: 0.2528\n",
            "Epoch 60/100\n",
            "31/31 [==============================] - 0s 11ms/step - loss: 0.2465 - val_loss: 0.2510\n",
            "Epoch 61/100\n",
            "31/31 [==============================] - 0s 11ms/step - loss: 0.2415 - val_loss: 0.3628\n",
            "Epoch 62/100\n",
            "31/31 [==============================] - 0s 11ms/step - loss: 0.2479 - val_loss: 0.4407\n",
            "Epoch 63/100\n",
            "31/31 [==============================] - 0s 10ms/step - loss: 0.2556 - val_loss: 0.3024\n",
            "Epoch 64/100\n",
            "31/31 [==============================] - 0s 11ms/step - loss: 0.2384 - val_loss: 0.3015\n",
            "Epoch 65/100\n",
            "31/31 [==============================] - 0s 11ms/step - loss: 0.2519 - val_loss: 0.2719\n",
            "Epoch 66/100\n",
            "31/31 [==============================] - 0s 11ms/step - loss: 0.2301 - val_loss: 0.2762\n",
            "Epoch 67/100\n",
            "31/31 [==============================] - 0s 12ms/step - loss: 0.2325 - val_loss: 0.2538\n",
            "Epoch 68/100\n",
            "31/31 [==============================] - 0s 11ms/step - loss: 0.2289 - val_loss: 0.2546\n",
            "Epoch 69/100\n",
            "31/31 [==============================] - 0s 11ms/step - loss: 0.2397 - val_loss: 0.3348\n",
            "Epoch 70/100\n",
            "31/31 [==============================] - 0s 11ms/step - loss: 0.2296 - val_loss: 0.2834\n",
            "Epoch 71/100\n",
            "31/31 [==============================] - 0s 11ms/step - loss: 0.2294 - val_loss: 0.2862\n",
            "Epoch 72/100\n",
            "31/31 [==============================] - 1s 18ms/step - loss: 0.2300 - val_loss: 0.3016\n",
            "Epoch 73/100\n",
            "31/31 [==============================] - 1s 13ms/step - loss: 0.2278 - val_loss: 0.2465\n",
            "Epoch 74/100\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 0.2184 - val_loss: 0.2455\n",
            "Epoch 75/100\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 0.2287 - val_loss: 0.2663\n",
            "Epoch 76/100\n",
            "31/31 [==============================] - 1s 15ms/step - loss: 0.2384 - val_loss: 0.2666\n",
            "Epoch 77/100\n",
            "31/31 [==============================] - 1s 17ms/step - loss: 0.2348 - val_loss: 0.2387\n",
            "Epoch 78/100\n",
            "31/31 [==============================] - 0s 10ms/step - loss: 0.2226 - val_loss: 0.2759\n",
            "Epoch 79/100\n",
            "31/31 [==============================] - 0s 11ms/step - loss: 0.2258 - val_loss: 0.2828\n",
            "Epoch 80/100\n",
            "31/31 [==============================] - 0s 11ms/step - loss: 0.2256 - val_loss: 0.3349\n",
            "Epoch 81/100\n",
            "31/31 [==============================] - 0s 11ms/step - loss: 0.2248 - val_loss: 0.2931\n",
            "Epoch 82/100\n",
            "31/31 [==============================] - 0s 11ms/step - loss: 0.2210 - val_loss: 0.2789\n",
            "Epoch 83/100\n",
            "31/31 [==============================] - 0s 11ms/step - loss: 0.2402 - val_loss: 0.2776\n",
            "Epoch 84/100\n",
            "31/31 [==============================] - 0s 10ms/step - loss: 0.2220 - val_loss: 0.2546\n",
            "Epoch 85/100\n",
            "31/31 [==============================] - 0s 10ms/step - loss: 0.2228 - val_loss: 0.2482\n",
            "Epoch 86/100\n",
            "31/31 [==============================] - 0s 11ms/step - loss: 0.2112 - val_loss: 0.2354\n",
            "Epoch 87/100\n",
            "31/31 [==============================] - 0s 11ms/step - loss: 0.2068 - val_loss: 0.2480\n",
            "Epoch 88/100\n",
            "31/31 [==============================] - 0s 11ms/step - loss: 0.2376 - val_loss: 0.2814\n",
            "Epoch 89/100\n",
            "31/31 [==============================] - 0s 10ms/step - loss: 0.2102 - val_loss: 0.2668\n",
            "Epoch 90/100\n",
            "31/31 [==============================] - 0s 10ms/step - loss: 0.2125 - val_loss: 0.2338\n",
            "Epoch 91/100\n",
            "31/31 [==============================] - 0s 11ms/step - loss: 0.2132 - val_loss: 0.2378\n",
            "Epoch 92/100\n",
            "31/31 [==============================] - 0s 11ms/step - loss: 0.2103 - val_loss: 0.2466\n",
            "Epoch 93/100\n",
            "31/31 [==============================] - 0s 10ms/step - loss: 0.2177 - val_loss: 0.3040\n",
            "Epoch 94/100\n",
            "31/31 [==============================] - 0s 10ms/step - loss: 0.2221 - val_loss: 0.2353\n",
            "Epoch 95/100\n",
            "31/31 [==============================] - 0s 10ms/step - loss: 0.2076 - val_loss: 0.2572\n",
            "Epoch 96/100\n",
            "31/31 [==============================] - 0s 11ms/step - loss: 0.2213 - val_loss: 0.2525\n",
            "Epoch 97/100\n",
            "31/31 [==============================] - 0s 10ms/step - loss: 0.2065 - val_loss: 0.2394\n",
            "Epoch 98/100\n",
            "31/31 [==============================] - 0s 10ms/step - loss: 0.1994 - val_loss: 0.2730\n",
            "Epoch 99/100\n",
            "31/31 [==============================] - 0s 10ms/step - loss: 0.1980 - val_loss: 0.2792\n",
            "Epoch 100/100\n",
            "31/31 [==============================] - 0s 11ms/step - loss: 0.2008 - val_loss: 0.2288\n"
          ]
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "TRAIN_MODEL = True\n",
        "\n",
        "validation_set_size = 256\n",
        "validation_set = [dataset.update().resolve() for _ in range(validation_set_size)]\n",
        "validation_labels = [get_label(image) for image in validation_set]\n",
        "\n",
        "if TRAIN_MODEL:\n",
        "    generator = dt.generators.ContinuousGenerator(\n",
        "        dataset & (dataset >> get_label),\n",
        "        min_data_size=int(2e3),\n",
        "        max_data_size=int(2e4),\n",
        "        batch_size=64,\n",
        "        max_epochs_per_sample=33\n",
        "    )\n",
        "\n",
        "    with generator:\n",
        "        h = model.fit(\n",
        "            generator,\n",
        "            validation_data=(\n",
        "                np.array(validation_set), \n",
        "                np.array(validation_labels)\n",
        "            ),\n",
        "            epochs=100,\n",
        "        )\n",
        "    \n",
        "    plt.plot(h.history[\"val_loss\"])\n",
        "    \n",
        "else:\n",
        "    model_path = datasets.load_model(\"ParticleSizing\")\n",
        "    model.load_weights(model_path)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## 5. Evaluating the network\n",
        "\n",
        "We show the the error as a function of some properties."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-06-30T10:20:40.663553Z",
          "iopub.status.busy": "2022-06-30T10:20:40.663553Z",
          "iopub.status.idle": "2022-06-30T10:20:41.642550Z",
          "shell.execute_reply": "2022-06-30T10:20:41.642077Z"
        }
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "8/8 [==============================] - 0s 2ms/step\n"
          ]
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "properties = [\"radius\", \"refractive_index\", \"sigma\"]\n",
        "\n",
        "validation_prediction = model.predict(np.array(validation_set))\n",
        "\n",
        "validation_errors = [\n",
        "    np.abs(validation_prediction[:, idx] - np.array(validation_labels)[:, idx]) for idx in range(2)\n",
        "]\n",
        "\n",
        "error_names = [\"radius\", \"refractive index\"]\n",
        "\n",
        "for property_name in properties:\n",
        "    property_values = np.array([image.get_property(property_name) for image in validation_set])\n",
        "    if property_values.ndim == 1:\n",
        "        property_values = np.expand_dims(property_values, axis=-1)\n",
        "    \n",
        "    for col in range(property_values.shape[1]):\n",
        "        values = property_values[:, col]\n",
        "\n",
        "        plt.subplot(1, property_values.shape[1], col + 1)\n",
        "        for validation_error in validation_errors:\n",
        "            plt.scatter(values, validation_error, alpha=1)\n",
        "        plt.xlim([np.min(values), np.max(values)])\n",
        "        plt.ylim([np.min(validation_error), np.max(validation_error)])\n",
        "        plt.yscale(\"log\")\n",
        "        plt.ylabel(\"Absolute error\")\n",
        "        plt.xlabel(\"{0}[{1}]\".format(property_name, col))\n",
        "        \n",
        "    plt.legend(error_names)\n",
        "\n",
        "    \n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "We estimate the size of particles using experimentally captured images. The particles were traced, so we display the average size and refractive index measured by the model over the trace."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-06-30T10:20:41.645049Z",
          "iopub.status.busy": "2022-06-30T10:20:41.645049Z",
          "iopub.status.idle": "2022-06-30T10:20:47.928050Z",
          "shell.execute_reply": "2022-06-30T10:20:47.927582Z"
        }
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
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            "3/3 [==============================] - 0s 2ms/step\n",
            "3/3 [==============================] - 0s 2ms/step\n",
            "1/1 [==============================] - 0s 13ms/step\n",
            "1/1 [==============================] - 0s 12ms/step\n",
            "2/2 [==============================] - 0s 3ms/step\n",
            "2/2 [==============================] - 0s 3ms/step\n",
            "2/2 [==============================] - 0s 3ms/step\n",
            "2/2 [==============================] - 0s 3ms/step\n",
            "1/1 [==============================] - 0s 13ms/step\n",
            "7/7 [==============================] - 0s 3ms/step\n",
            "1/1 [==============================] - 0s 13ms/step\n",
            "9/9 [==============================] - 0s 3ms/step\n",
            "1/1 [==============================] - 0s 12ms/step\n",
            "1/1 [==============================] - 0s 14ms/step\n",
            "9/9 [==============================] - 0s 12ms/step\n",
            "1/1 [==============================] - 0s 13ms/step\n",
            "4/4 [==============================] - 0s 3ms/step\n",
            "2/2 [==============================] - 0s 2ms/step\n",
            "1/1 [==============================] - 0s 13ms/step\n",
            "1/1 [==============================] - 0s 12ms/step\n",
            "5/5 [==============================] - 0s 3ms/step\n",
            "3/3 [==============================] - 0s 3ms/step\n",
            "5/5 [==============================] - 0s 3ms/step\n",
            "1/1 [==============================] - 0s 13ms/step\n",
            "1/1 [==============================] - 0s 13ms/step\n",
            "9/9 [==============================] - 0s 3ms/step\n",
            "1/1 [==============================] - 0s 13ms/step\n",
            "1/1 [==============================] - 0s 13ms/step\n",
            "4/4 [==============================] - 0s 3ms/step\n",
            "2/2 [==============================] - 0s 3ms/step\n",
            "1/1 [==============================] - 0s 13ms/step\n",
            "1/1 [==============================] - 0s 13ms/step\n",
            "6/6 [==============================] - 0s 3ms/step\n",
            "2.9173846\n"
          ]
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "predictions = np.array([\n",
        "        np.mean(\n",
        "            model.predict(\n",
        "                np.array(experimental_data[idx])\n",
        "            ),\n",
        "            axis=0\n",
        "        ) for idx in range(len(experimental_data))\n",
        "])\n",
        "\n",
        "print(np.mean(predictions))\n",
        "plt.scatter([],[], c='b', alpha=1)\n",
        "plt.scatter(0.227 * 1000, 1.58, marker='x', s=200, c='r')\n",
        "plt.scatter(0.15 * 1000, 1.58, marker='x', s=200, c='g')\n",
        "\n",
        "plt.scatter(predictions[:, 0] * 100, predictions[:, 1] / 10 + 1.33, c='m', alpha=0.1)\n",
        "plt.scatter(0.227 * 1000, 1.58, marker='x', s=200, c='r')\n",
        "plt.scatter(0.15 * 1000, 1.58, marker='x', s=200, c='g')\n",
        "\n",
        "\n",
        "plt.legend([\"Model prediction\", \"150nm PSL\", \"227nm PSL\"])\n",
        "# plt.axis([100, 300, 1.4, 1.7])\n",
        "plt.xlabel('Radius (nm)')\n",
        "plt.ylabel('Refractive index')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": []
    }
  ],
  "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": "3.10.5"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 2
}