miykael/keras_example_on_structural_MRI_data.ipynb

## keras_example_on_structural_MRI_data.ipynb
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# `Keras`\n",
    "\n",
    "[Keras](https://keras.io/) is a high-level neural networks API, written in Python and capable of running on top of [TensorFlow](https://www.tensorflow.org/), [CNTK](https://docs.microsoft.com/de-de/cognitive-toolkit/), or [Theano](http://www.deeplearning.net/software/theano/). It was developed with a focus on enabling fast experimentation. *Being able to go from idea to result with the least possible delay is key to doing good research.*\n",
    "\n",
    "**Note 1:** This is not an introduction to deep neural networks as this would explode the scope of this notebook. But we want to show you how you can implement a convoluted neural network to classify neuroimages, in our case fMRI images.  \n",
    "**Note 2:** We want to thank [Anisha Keshavan](https://github.com/akeshavan) as a lot of the content in this notebook is coming from here [introduction notebook](http://nbviewer.jupyter.org/github/brainhack101/IntroDL/blob/master/IntroToKeras.ipynb) about Keras."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nilearn import plotting\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import nibabel as nb\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set(style=\"darkgrid\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load machine learning dataset\n",
    "\n",
    "Let's again load the dataset we prepared in the machine learning preparation notebook, plus the anatomical template image (we will need this for visualization)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "anat = nb.load('/templates/MNI152_T1_1mm.nii.gz')\n",
    "func = nb.load('/home/neuro/notebooks/data/dataset_ML.nii.gz')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nilearn.image import mean_img\n",
    "from nilearn.plotting import plot_anat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x165.6 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_anat(mean_img(func), cmap='magma', colorbar=False, display_mode='x', vmax=2, annotate=False,\n",
    "          cut_coords=range(0, 49, 12), title='Mean value of machine learning dataset');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As a reminder, the shape of our machine learning dataset is as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(40, 51, 41, 384)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "func.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Specifying labels and chunks\n",
    "\n",
    "As in the `nilearn` and `PyMVPA` notebook, we need some chunks and label variables to train the neural network. The labels are important, so that we can predict what we want to classify. And the chunks are just an eady way to make sure that the training and test dataset are split in an equal/balanced way.\n",
    "\n",
    "So, as before, we specify again which volumes of the dataset were recorded during eyes **closed** resting state and which ones were recorded during eyes **open** resting state recording.\n",
    "\n",
    "From the [Machine Learning Preparation](machine_learning_preparation.ipynb) notebook, we know that we have a total of 384 volumes in our `dataset_ML.nii.gz` file and that it's always 4 volumes of the condition `eyes closed`, followed by 4 volumes of the condition `eyes open`, etc. Therefore our labels should be as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['closed', 'closed', 'closed', 'closed', 'open', 'open', 'open',\n",
       "       'open', 'closed', 'closed', 'closed', 'closed', 'open', 'open',\n",
       "       'open', 'open', 'closed', 'closed', 'closed', 'closed'],\n",
       "      dtype='<U6')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels = np.ravel([[['closed'] * 4, ['open'] * 4] for i in range(48)])\n",
    "labels[:20]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***Second***, the `chunks` variable should not switch between subjects. So, as before, we can again specify 6 chunks of 64 volumes (8 subjects), each:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chunks = np.ravel([[i] * 64 for i in range(6)])\n",
    "chunks[:150]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Keras - 2D Example\n",
    "\n",
    "Convoluted neural networks are very powerful (as you will see), but the computation power to train the models can be incredibly demanding. For this reason, it's sometimes recommended to try to reduce the input space if possible.\n",
    "\n",
    "In our case, we could try to not train the neural network only on one very thin slab (a few slices) of the brain. So, instead of taking the data matrix of the whole brain, we just take 3 slices in the region that we think is most likely to be predictive for the question at hand.\n",
    "\n",
    "We know (or suspect) that the regions with the most predictive power are probably somewhere around the eyes and in the visual cortex. So let's try to specify a few slices that cover those regions.\n",
    "\n",
    "So, let's try to just take a few slices around the eyes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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WLVuWcD9//vOfZfbs2XLRRRfJtddeK3l5ebJ161b57ne/22Z+gUSd5PbqXETaPL8zz3McRyKRiAQCAdm2bVu7E611dvK19va/q9fc2SbNH7N22vmDUEVFhUydOlVGjBght912m3Tv3l0CgYBcdNFFbd7z7tbNnrwfc9+33HKLPPbYY3LVVVfJYYcdJrm5ufLiiy/KnXfeKeFwWJKTkztVz7W1tRKNRmX+/Pkyf/78Ntt2pV0CAADgwKOz3o7zzz9fbrrppoSTqK1evVoqKyvliSeesFJd6Qm/RMTqYO5UVVWVMM2ayFdptNqbhG7btm3WHcD9IT8/X/r379/uZGGZmZl7/fW++OILef/99+XRRx+V448/Pv7/5ubmNuXasmXLXn/9ztj5GeyMPdZ69+7dpX2deuqpMnXq1Dbrds6f8Pzzz8vIkSPjceIiIm+88Ua7+9vVxIh7W3FxcZu7+yJftfF9JS8vT4YPH27Vx047fxD629/+Js3NzbJ8+XLJyMgQEZHW1tY2E9V5wfPPPy+XXXZZfJ4IEZGVK1da23SmnnNzc8Xn88kPf/hD+da3vtVm2878uAUAAADvOOg769u3b29zF7uqqkrq6+sT3qXceWd2Z8dA5KuJnTZs2CBDhw5ts/+33norPhS+oqJC3nvvPTnvvPMSlmnkyJHy+9//XtavXx8fDlxZWSmrV6/erZRy+m55V4wdO1aef/556d+//37JKd1e3W7cuFH+9a9/yaBBg+L/GzdunDz33HNyzTXXJJyob18pLy+XHj16yIYNG+SCCy7Yo32NGzdOPvnkExk2bFjCjnYoFGozgeCKFSv26HX3lhEjRsiKFSuksrIyPkR7zZo1sm3btn32muPGjZNbbrlFSkpK2h2BIvJVnfn9fmv4+XPPPRcfVu4loVDIau/RaFSeeeYZa5vO1HNGRoaMGjVK1q5dK3Pnzt0/hQcAAMA+c9B31k844QQ55ZRTZPz48VJUVCQbN26U++67T9LT0+Xcc89t9zmjRo2SzMxMmTdvnsyaNUsqKytl6dKl0qNHjzbbFhQUyOzZs+OzwS9ZskSKioo67Kyfd955cu+998q0adPk6quvlqSkJLnjjjukoKBApk2b1uX3WF5eLitXrpTjjjtOMjMzpby8XLKysjr13BkzZsjTTz8t5513nlx22WXSo0cP2bZtm/zf//2fjB49Oh47vbf07dtXevbsKTfeeKPMmzdPGhsbZcmSJW3qdu7cuXL66afLOeecI9OnT5f8/Hz54IMPJD8/X6ZMmbJXy6T5/X5ZuHChzJ49WxoaGuT444+XlJQUWb9+vbz44ovywAMPdPqHjauuukrOOOMMueiii2Ty5MlSUFAgmzdvln/84x9y3nnnyTHHHCPHHnusXHfddXLnnXfKyJEj5eWXX5ZVq1bt0/fYWZMnT5Y777xTLr74Yrnqqqvi8deFhYXi9++bKTEmTZokjzzyiEyaNEm+973vSWlpqdTU1Mg777wjxcXFMn36dBk7dqxEo1G56qqrZMqUKfKf//xH7rvvvjbhDV5w7LHHykMPPSSHHHKI5OXlycMPPxyPz9+ps/W8YMECmTx5svh8PjnjjDMkMzNTKioqZOXKlXLNNddIeXn5/n57AAAA2E0H/QRzc+fOlQ0bNsjChQtl6tSpcvvtt8uAAQPkhRdesCa5MhUXF8v9998v27Ztk8suu0x++ctfyq233iqHHnpom2179eolCxYskKVLl8qsWbMkKytLfvvb33Z4Nzg1NTU+xP7qq6+WOXPmSElJiTz11FOSn5/f5ff44x//WNLT0+Wiiy6S0047rUsTTRUUFMhzzz0nffv2leuvv16mTp0qN998s9TX11t3uveW1NRU+eUvfynJyckyY8YMWbx4sVx55ZVy9NFHW9v17dtXVqxYIQUFBTJv3jy5/PLL5YUXXpCSkpK9Xqb2nHXWWfLrX/9aPvjgA5kxY4ZcccUV8vDDD8thhx0Wj3nujPLycnnuueckLS1NrrnmGrnwwgtl6dKlEggE4vHXF154ocyYMUN+9atfyRVXXCEVFRVyzz337KN31jXp6eny6KOPSlpamsycOVOWLl0qCxYskJycnE7/INRVaWlp8tRTT8n48eNlyZIlMnXqVFm4cKF88cUX8ZR8gwYNkjvuuEPWrFkjl1xyiaxYsULuv/9+yc7O3idl2hM33XSTHHXUUXLdddfJD3/4QxkwYIBceeWV1jadrefRo0fLH/7wB6murpYf/OAHcskll8gvfvELCQaDnZ70DwAAAN7g+/9pzr5x2ptM6asMRQD2pfXr18uxxx4rixcvlsmTJx/o4nxj7c163t9zHQAAABxsgsFgl59z0A+DB7Bn7r77bunevbv06tUrfte/sLBQTjvttANdtG8U6hkAAODgQmcdwB7x+Xzys5/9TLZs2SKBQEBGjx4tCxcu9OSQ868z6hkAAODgwjB4ADjIMQweAABg39qdYfAH/QRzAAAAAAB4DZ11AAAAAAA8hs46AAAAAAAec1B11jdv3nygiwAAnsJ5EQAAwJsOqtnge/bseaCLAAAAAADALh1Ud9YBAAAAAPg6oLMOAAAAAIDH0FkHAAAAAMBj6KwDAAAAAOAxdNYBAAAAAPAYOusAAAAAAHgMnXUAAAAAADyGzjoAAAAAAB5DZx0AAAAAAI+hsw4AAAAAgMfQWQcAAAAAwGPorAMAAAAA4DF01gEAAAAA8Bg66wAAAAAAeAyddQAAAAAAPIbOOgAAAAAAHkNnHQAAAAAAj6GzDgAAAACAx9BZBwAAAADAY+isAwAAAADgMXTWAQAAAADwGDrrAAAAAAB4DJ11AAAAAAA8hs46AAAAAAAeQ2cdAAAAAACPobMOAAAAAIDH0FkHAAAAAMBj6KwDAAAAAOAxdNYBAAAAAPAYOusAAAAAAHgMnXUAAAAAADyGzjoAAAAAAB5DZx0AAAAAAI+hsw4AAAAAgMfQWQcAAAAAwGPorAMAAAAA4DF01gEAAAAA8Bg66wAAAAAAeAyddQAAAAAAPIbOOgAAAAAAHkNnHQAAAAAAj0k+0AXYd5I6veWfRv/IWi7Nbog/7lFcZ63LPjRqLScPKYo/9iXbrxnbvMNarlsdtpYrKvLij7c2Z1jrtodTEpY3K9kuQ5lRXhGRYA/3dTOCMWtd4PRB1rKTnxN/3Pq7N611VR/aZUhJcfdVcEK6tc7XL2gtx0YNs18ny30d/7rPrXXyov26Te80xh9vXJdrrWuN2r8vtUTdOg8k2fWSmxmylrPz3WUn6rPWRSP2fv1JjiQSyLJfJ6XAKN8O+3kVn9vlr6jPspbrIm4dT/zXrQlfc1+JvfQTd6Gx2VrnbKy2ls3PZd3afGtdfUvAfq7Y9dsQcU81LTG7rgN+u43q9aYM1fYL09zPtP/Q7da6tCl2G5QkowxPr7FWvfL3Xtby542p7tPstyKlGfZxPLhblbXs97ltYH21/flvaLKPm00hs17UCymtRjWlqCrSrTXdaL/5KXb9Zqo6jBlP1p9bWB0nZhkLU1utdfkpEbu8jv3cZKNemqL2ufLUf94ue9vDw6+zljOS3XpIU20uqsraZJxndPtMT9L1adeDuX1rB21ZRMTcU5LP/hQzU+z9hlrdOtsaSrPWbQ3bX+VVLe7rRlXjGJprt98xh2xyy5Bsb7xuk32c/19VnrW8JeTWW0m6XS+901usZbNO1zXZ3y31kcRtX5dft/0+mW57LgjYbVDXqfkqun026+8W47PTx0yuauu6DZjngMNfuVv2t4V9F8Yf91KfSy/1uUSMeqiP2MelPh9oZv3qY0jXvbk+YhdJYh28jt62vtX+nBqMqtfn6rwUuww5xrlQ76cqbD+51q4maW4136s+Vu19FblfH9Ir3W47Ab/9XLPM+jugKarr1H2cqvZjnt9ERLKNNpmhro06OjdnqradnWafL/Jz7euE4t89Il9Xdw7+sbXcN8t+rwMKaq3l9DS3UYTUNXp1o/3dXh1OlUSa1fdfdYu7XK2uRbeF9HnKfZyi2nuBesm8gPu5JqvjMabOq7qt+Y3FXNW2clKiCbdNUa/jV8tm2xtSWGOtKxlk97caKtw63l6TKR1xjP2GWu3vw3UN9nO/cwCutU2bNm2RYLB7l57DnXUAAAAAADzmG3xnvfNOe3OxtfzoiPnxxz2b7DveowKbrOXs4vr441jI/rWpxr55J2+vL7GWNza7P4PpX7lS1K+macay/g1a/6LdUOfuN+9s+05IdNwx1nLS6rfjj1e/0s1at6nZvnszrMi901qYa9dLbMRQu1BJdtPyf7ku/tj39kfWutAn9siAdWvdW9Wf12db6/RdV/NOSveMJmtdjfqls67JfT8ZqfbP5o6qQ/MOfkytE/sGrqRWuL9gh1rsX1vrwh3fcdZ3H/Y3/4k3xB9HH/2+tS5Wb9dRazjx3Y/mqP15bwnZ77sy5P5yrN+x/nXYvPOg76JltNq/SNcbd+yj79o7Ghb82Fr293Tbkh4BkaLunmYZdxiLVVs5sk+ltZx3st1GnQZ3+9x/b7HWhd63R6CYd9Z3qDs5flUvmUYV67sqEVWpLcbbqVN3kJrVL+gdtUB9XkpLSvxLvb5boH9RN5f3xZ10rSBg3+3MC7gVXJpv/4IfVncUa5vdc0e1Oob1MavP1eb7TFd3XJP8iWs7TW2bqu6sZ0bNu+X2Zxjp4E6ZvoNYF7GP1cqqHEnkszYjgezXMb/ywuquYKoagZBrnKt1WwmrZbP9ZqtBZnqkSJ5RTznqrqA+hvRxbpfBrqcm4+5Msv4+Vncq9ec66u/7/2666cbPbow//sVhC6x15nEgYtdJld++Nae/+3T9me1Qj0Apy7OPMZ/RJrerO12V6lrD/J7foY5NPdIi2Sii/rxb1bZ1xl3LkGqv+g6+vnve0XlS39EPGqMZxvXcZq0rKmq0lpvq3fPL59vtkSyVIfvzMOs7TR1f+s6b2Uaz1IgTfa4JGCNHUtQokkx1t7noiUflm6K6xf7gdqhzo0+d6wsOdUfy+ZLtesnbaI84SNvinlfrWuzPMV2dP1L87uvGxP6+aWxVo0DNkXCqUTbbu5WwcVe+zXWsoo8rc+tQijpfq2PHHBmjvyP0fs1riJZYobWuRV3fNRrX0xWqL6aPbVNE9ROqWjo/0tqruLMOAAAAAIDH0FkHAAAAAMBj6KwDAAAAAOAxxKy3Y9o7t8Qf3zPEni0y70s7xqI05M4WGVGzOL5TYc/292G9HbdizoCrY560bGNWUx0L2qzibPqVu7Hb0YlTO9xvLNgj/vjTenv2+lr1fgaaMyxm2/Hgkmq/NyfVjkHz+d19OdvqrXW1G+znbjZiU/TstDpWM+B312eqeHEdW2rGsYRVbExH8TwpSYnjHEVEmozX1fuJOnrmWvt1z3rrtg73vT8lTbPjLNdOuMJarm505z/Y3Gx//p832nFWOhbMjG3VMUy67ecYQezFqXbd6/hg0xcqDjLwFzuAK9jdbd81NXac7qZmuw2abUfP9JzV1y6Tb2CZvRxx4wPTGj+11vVcZ8crflTvtvUdLR3PX+D3ufWiY/l16zX35O8w2tKufx2jnq1mgc1XceCmxqg+puz1Oo5sX/u2OrY+OXlG/HGP4XZ8YaxJzfD+hRvXW11ZbK3T8zHo9mu2bz27s44tN+ci0TH2ut2Z9VenZrttUHHpZgyhLl+NOq+/vtWNk9Xl0/GSNWpeBf0Zm3JS7I1z0914z4BfZVXx2bHxW8NGlg91zOcH7OeaZa5VWSnaztSvgjoN+vsi24h/T1UxphkqBnjQX+9LuN8Dbeb7N1nLX57xXWs5r9i9XqjaYp9D11arOW9U+zCX9PFdr+Kt0436zEu3j78MNdfADmMm7UjM/q6Jqe9U81Pb1XWUGWur5whpUKe2VvW1b+46RQXH59nNTopTjfeaY7/X3GPs95Ob6y5n/mWztS5TXW82GtljIqoe9JwL5rGg54TIVfMW5Prc+Ovuve3rs+wHH5dvqhuMuR1ERFaOudpa7nGoXReBYe650pdpX+OmHGJvm/qeO8dT1UY1R8MO+3yXbs5F4NfZRvR8JGZ8uLVKQolPb23mZGhu1dfHOruA+zr6uNf3ec3rMv0doV98fnvxAAAZm0lEQVTXvL6rVrHkH9fa2XPMWdsfGGrPvdHYQaaEH3zwU/mm4c46AAAAAAAeQ2cdAAAAAACPYRj8Lly5i+EU758wK/5YDxWpUsOyQx0M29DDWvXwQvO5OpVJphpClnbmQLdM7RfbKIS7Lz0MUA/+Nofbxz7faq1zTuy4KTm5xvAW9eZCYbueGo0hnjrtkJn2QcQeMpSmUo60STuT5I5zy1DDwFrUsFIz5VqjWqeHlJnDf2vVsJ6GqE6bZJfppv4L44/T1XsrCujhUO77y1BD83X6wd3xwugfWcvvb9epbYzhpir0QqfBaZOezagG9TbbbNvawTBifVz4zCGy6vNep9P+GZ/T9pA9hK1aHVPm8O+8VDtFiz9TtfWt1fZy2D0eHTUurU06M+PdpyWp1FEqXsDcVaoahqlTFpn1lJNs70e3M53KzXpNNbR1u3Gs6vQtelhakxpCbabjum3gQmtdsRranGcsZxpDkE/Zg5RvA/5yv1vWvlOsda3Ndlkrqt3zVY06jzeqY1qf1816aVEn0SY11Nb83NKT7DaYF7DH1qYb57odrR3/zt4j1a2zAnUeicT05+Lua2OzPl/Z+9XHX8A8rlUz0mkqTeaQeBGRAWqoe4GRykt/jzap927WYY80+1g9rJf9PZVV4r4hnypeqMre7+ZKN1QmSQ1Nzcm2y7950kXWspnKTYfH1Tfa554dxrnIHEo+dtXPZF9ITbM/1JRst6xJ29Q1gDqnVqvPwrzm0cNdN6jQIpMO+eiuPjczRVyeSmFYmpH4eNNhG/oaxm+FN3U8Zr6ugxCPNNXYA+pwbDLC3apq7SHQhSoUw1fsnmsy+9ZY6w6N2t8ttTXukPl1NfaQYf2dbCpW9Vve095v/mi3vP4R/a110dn32DuL2p+Hr8oNBZWttda68D/tYf2Vn7jDv3WYxPCXl7VT8v3rhDeWWMuRb19oLfvKjNDWsBrv3WifEwI93DotiNpphXVKuPe3FMUf62sRfQxmWd/nKuWwarNho6mFoh0Pe9fN3dxcn9v19YadwtFep78zOgpb0SEApunv3ZRwnYh9Lb1YXV/o0EvzWkq/b10mfZZINT6eNqFT6lorL8VIn6j6J1dsfFi6gjvrAAAAAAB4DJ11AAAAAAA8hs46AAAAAAAeQ8x6Fz19xLXWcmusIeG2wXQ7Tijg1zHsiX8r0Sl0zHiwLJVOqc9gO/4oOv47Cfer+b/cEH+cm2LHVOh48Qojpdqhr9mvWTDwNWvZKetlLfu+3Bh/3PSBnb6qsq6ntby9xW2WOhZazwuwzYih1et0PImZfqdJxXfp55px6iEVo14Zsp+7vsmtp+0h+zWbdP4XJSPZfW5Wil2GZvW6PdLc9X5f4hRau6tJvZ6uPzPET8fMJ6Xa27akJI4H1DFMOi7djLvSsUa1Kp4rpYPY3TwVB51slFnHKem91BlxpttUmrq+W+y27+9up2yJVbrnhLqPVeqrZjte1Yw/y1Hpf6QlcR027uLjN5qVVLUkTrMiYsdc67hTHXPdYlRpY6tu6/ZyOKrq3wh0y1GBbeF0FadnRIrF7NPmXvHpR0XWcqpK8VVtzFmhz0Ep6rjICOg4X/dxk4pnT/Gr991BSkM9v0VVzK0j/fnnqDrKynR3XJJpx0umqJjZeiPFUyRmx9dGHftz0e0jwzgVZqlzQn3ELlS2MfdDXradzipJtUnHWExXadNq1H7N9Jcbz7zUWpd/pF3fSUMOdV9Dxdc6a+x44fQqN7Y7Wb23zHw77js5S5XfKHK4VqXVU9/7EeO827of0hvW1dvnoPDnbn1uqbNTSuk5K/R3gvk90KTeV72KHzfPM01R+zOsbrG/U812pufCKFRzMAzKca8ndqiY+g3N9km1wShThoovbVXXAAUqMNf8/ttVDK957bStMcNal79yu7Wc+9na+ONYs10mn5rHJN2Yb0DPf6KZ5/miDPscUHC83Qacs46LP452s1MOd4mKZ08ttK8LezhuKtPUdXv/GmZP6dRtTZ9WWMtZhZsSPje6xa5js1EE7K8byWqy+wZp290Thp5Dpl7NG1JshPrrOZz0nA3mXEF5AT3PjZ4PSF0v+81jxS5Dmjpfm+k1A+pg0N9reUYa6kz9naFSG3dkySA7rXasg+tGfR1Ta0w3oFPYRVU9JKl6SjNOBOnqRBBNTTwvmT5HdBV31gEAAAAA8Bg66wAAAAAAeAzD4LtID10PGcM2eubaQ+KL1XKdStnSbAznC6nhH81RlRrLeN18lXYs0GP3x4nG3vg4/jjFX2ytS1VDXZqNMjQ22Gk38t7bYC371tkpcyKf18Ufr/u0wFr3eYM9TGxb2H2dZpV77sbPbpTOurn/woTrknfxM5WZ2qFOZeioi9j1Um2k6mpWQ3+jjj3+plUlk/GH3bEx2Un2kL1IzG4DST630H7p/HChzmqN6eE+dlnNdDo6RUiKXw//STw0WGtRQy07Gi2UrNqkOSRZpxLTdhipYsLqNfVwMnMYlQ5HaKy0Pxd/xg57OdtdH8i0h/p1y7ZDQMw6LQjYw+3XN9ntYeb7iVOX/KDUbutmTdila29oe8xYp4bVqXHPTTH3/bQ4XRvG6DNK1dhqv7ckn30OM9Mqpfr3fluvUENTe6Tbw7IzjWHxQfu03SZkpm3qFyfhtjrUpN4It9BpHvWQxuYOhszrIb1BY9hrsMhuAbm97RNa83b3dev/U2Kt29Fqv3kdsmKmAdTH33aVkjPmuEOstzfZbT1NDXXPDLjn1FSV8kanPTJ1O8IeM5o0pLe9Qarb7nxFduqr5CI7nCWvzm0T6jQuPnW+iNmjWiUWSXwuSsuwy5huhGQ1t+yDmA+lVoX1ZBuFT1apkwrUtYZuz1HHbTsBFeKRoYZwm3vW6Wd1yJ0ZAqLbekma/Y8e2e51VroKM9qm2uB24xjT1wB6aLtOQWWWQx9v+rnmttUt9rluw9Y8a7kl7JY/P2gPpU7NU9cLSe7nMUCqrHVFO+wQljQj5V2v4XbbltNOsRYdY+i7f8N6e9uw3bidHvYweSfLTXEoSfZ3Y2zoYGs5dYsbepIv28RrfOpsXrPV/p7wvenWow79C9fbDSYadtt4cprdQFpVaJoZ0qCHmPtUSKHZbsPqO0Jfl5napElTX6s6RZyZoVane81U7d8czq6v0XSdminM9Gt2JQVxm+9W43JE9xtqw3YZ6ozrmqi65ukoDa6IiN84JWaok4gOY4oZ58eOroE7gzvrAAAAAAB4DJ11AAAAAAA8hs46AAAAAAAeQ8x6F01dc4u1bKZ6SEmxYzhzS+xYnwwV/Nxc78ZTNTTZMeBNKs4pasQDqkwC0rLZjoHTGaA6sv11N3ZDx4/oOBVTY0iVr8qOi/TtsMsUNkLYt6u4sh0RHavpPr5pbedj1LXr/tP5517UfYG13GykXHuqyv7MT8/7kbXcUSiKjlEPS+I435gKzktqsj+PjGT3cNXxgHuDTlGVr+IXM5ISxxrptD26dGZqpmRf4hgmEZGA8bp624hj7zlsvG6rWtcmbt54rNNv6WUzbiwvYLdlf4pdL9FGFdDqdz9jHdeWkxOSRCIq3un0LsRv3bV+948Tsz23OPZnEeuwddv0trrtm/wqPc32kB1Al5PiLqf6935b1/HUAX/i+RlS9Lpd7NuMP9RpH/XrZBs7i6nZGvTcAuYxp+MPe6fbbfSQoJuGLPcou8TJc55ov+Ai8syR1yRcJ9I2ntJc1nOcRNT5pNqIx07x25ceueq70zwWdGxiR/x59veS09tOC+r0cmPyfdV2zG9StR3XmxF142sdFQwasZ8q9VvVHAxGXSSl2seUPn/k5bmx8TnRxOeHvSWg5geIGnWdplIY6m11TKY5145OY6TnIgkbx0LbtKB6jp7E2/bK0Gn/jLlU1Gte/O7N1vJP+rpze4TV8aVj1HXsaldSL5lzoOg0vFVh+1ov0OjWcfJWu77TstR3j3E6ySq0rydTAnpuB2NdqR3PLjvqrEV/2E0f5/t0nb1tWE3aE7MrzumfI4k4RfY8SP7CbLdMwTq9+QG3LWwfx9n1dr21GO1dt4cUdR3Tal4TqSkDHNUmzDk7eqs5LfQ8WboP0hFzLhv9naHp9m9un6rW6dS9BYHEKQX1taL5ffqtN5Z2XKgO7FDNsjpsfu+qVMJq2YxT31WMuk7l1mjMS6XnPoo6ap4p49rFnHNqd3BnHQAAAAAAj6GzDgAAAACAxzAMfg/VtLhVWKVSZ+R0V8PgS+zhFOmt7jiOnHp721Ct/dGYqdIirbufysj/5Tpr2Uwjoge86mVzOGKaHhqcrdJZdbfrwr/FHZZ53Ot3dK6w+9FvtiROi6V1JQODXw1tTVa/j4UlYjy2NUXtIYl1Le7nnrarMU274VI1ZPCPR1xrLZvDEbPUcEldHj3816wzPbRdp/ow25keSqmZz9Qp1vw+e1iaWf4MPdwzYrffdGOYl5kaSEQkI6hSk6j0HY3r3Me1VVnWuvqQPQSyzhh2d+xrP5MDIWrUoh7KrtuvXjbp5+rlqLifR0Tsz8ZMHyciUmeExmQk7/22rofHPnX4/1jLZsiHHoarh2Xr9uzvIHWbHhpu0qEYAb8OgzHX2c8dnK/Ss40xUpR1IWTmrLdus5YXGsOGRUQKA3r4cuLhhCl6yLxxTOlhoA3qO81c1nX4nX/d2l7Rv9q20h4inbIlcXoon1rntKphxEbuIqfGHnMZ3mGfL8IhO9QgPd3dPjXXbtt6NGSk3q2nlsZ9f0k24m/3WMtvH/f9hNuqEaBtQvDMIa06jEcPf+3wO0Atm6mhigL2ubok1x5TbJZp9D/ulI7cYKR+vbL3j611eYGOwwCzjTLp8gZUW88y0p62DbHRQ3KNUC59bRez33veI7+NP669ZKq1TodXZJa6j33d7aHqTsS+fvPVGuePHXZ6UUePC96DHFROSY/4Y//nWzvY8sCY/PYia1mHBZnnotw0+4pNp5Q0h8VnZql0y1kqVWWVuy//drtMNeGuBLbazPC4cwrt77g09b2Qqa5jzHS8HZ3LRUTSje/LnFT7vUaieqj43vk+19835kh33UT1V6DPOGnsahh8g0pfGzFCBcPqGj3Jp6+D/cZj2SPcWQcAAAAAwGPorAMAAAAA4DF01gEAAAAA8Bhi1vfQpNVu/JyZxk1EJKjSY6QNtmNPfAVuXHeKitkMfGnHIKZtcuOIomEVW/WtEmu5o9CI2FP/sJYrmoLxxzr9lk6nU5rlxu72Pk7FrJ85xlpOGjizg1J8vaWooEMzNjemgvx0uoaISo2VJEYqENEpLuznJu/nn9Z0ewgY5dHprNJUih8d02fGk+/quWacelTFoes4SFO2Sp1SmmXH3g0Y7MaoqkxB0lKn6jrNSN2Vb79mtFmlKFMxZgWPPB5//Pq4Oda6Y1b9vJ2SH1jdUt04ejNloYhIKJb4bKLbelTHu6vPKtbBb8M6FtakU5jtbzo9i34XzTpFlRGXruPz9NtMNWL/dBxgWMW3GyHUkpNs13WqOleHP26KP67dZKfK7Ipe6XaZ8lV6KDPOXn9MOvbP3Daitu0ojlHPGdCRbR/Y77XYv9Za9meuc8ugUp46Kqum35xeQgU2+pJVHLKq/+RA4kYbUymHzDj1uvrd/6z2hoYWO/Zex+G2nYPB3+5jkbZzu5jfCfrTbjNfjvEP/fnrtLEVjSotWSfds+Gn1vL3SuwYdj2/RDTViFfW8eHqO80sc6o6rvMCdqxzit+Yy0PFrBf89reSSCyirgN72K+TPKDQXQgW2U9OV+3MTMeWptpAUuL5Z7rK6eamcvPnZ+zBng6Mplb3WA012vWSnWKfT7KNmPakZvuzSc1Vc+YY6R3z0u15N3Y1D0Nn/aHKjsefWGDPSaSym0nACPTOTrGP7bwUlXrOuPbKU6kVo+q8UP7nBztX4F3olqZbolumxlYdO25vGzJSceqYdRWiLslqgyQjRbBfXQPpa3jzGmlPY/W5sw4AAAAAgMfQWQcAAAAAwGMYBr8XnfDGEmv53fT/tpZzY03WsowdGX8Y611qrfJvqbSWM155K/7YabbH0UXPnNDpMn75d3v4U70x7Kq3Gr4y5oTN1nLyd450yzt0qLUuKfnETpfh6y4rxR7+1BI1h7rYw2Iijj2mxnHsQy7FGFQW8Nn7zUmxt800Ulhl7ocjd+qaW6zlR0fMjz8OqaFNGUmJ06SJ2EPn/LtM3eYu65QhAb89fMxMwVacYw9773GkfZwkXeC2UadHD2td6hfrrGXfZnfIvP/EG2R3eXHYu9Yt3f0sa1T+wJRWPex137T1jGSdRsZ93ZyUPRl42TnnrraHCJptPUsNeRbVfhta9fBft+x6KLhO8WQeJ0kqVZROW9hiDMvNVmWqarSHlK5bkxt/XNm8+6l/pr9np7SsnzHFXt7qjhVfu7XAWmcOGRWxw19S1XDBVjXk2KyJU/95e6fLu6nWTlFV84Y9NDXZGJKcpMNxVIqwjEz3/BHIVMP/M1RbV2nf/Eab9aeq4dSt6txoDvn27/+Yj1F/vzv++OUxP7TWRVQYkh7KWW2kro2odfq40d8RJj1A1Dzv62Gqm5vstv5lk50Oc3fdV2EPi7+o+wJrudU4Hh11nyutg9RWST67HqKOCrlKcttoV4YIt4Ttzya1WdVv1F12Uu06cvLy7G0D7tB3nz5phcMJtxUR8YVDxut0HMbhq652t92DFHD7i05laR4f+vyml6vDbp2n1qtUZ9X2uaYl5n6W4ej+uYf6x2o7BeZpeT+ylqMx9/1EHbutJas4wowk93PfW8P2d6Vnmkof7HfLWBux67BKhQ6bUcd6iHw4qo9XFXJmXLvoNJxpKs2smf5uT7Mtc2cdAAAAAACPobMOAAAAAIDH0FkHAAAAAMBjiFnfh4a/vMxaruo+zVrOO1HFsBuc7j3t5bGj3MfZ2Z0ug//dd63l97fbcYXFqW5c3rGz7Pi+2JTL7GXjccpBFKOudUu343fMmJcWFf8SidnbtsbsQ86Mh9EpIjJVeowM46l7GP6yW6a948aw/3KYHc8Xs8PY2qRRM1OuhXaRjs2M+dXxfjrtW4/8evfxBDs21/n2CfbrdOsuiSQfdmXCdd90hUY4o04X2KjSWTUb8bbhaMcx1ikq9s5s6wH1OulJ6rnGYji6/1u72dZ/Pew6a52OO9fzN5gp11QmPMlUKdfM+Rt06jYtYMXx2vtZW2+nrzJj9ma+b8ed74ns+5+wlpt/eH78cVGDPefJFhVHb8bq6nDVU7oQl96Rhkhyh8tmzLU+l2Qm2409K+R+NybVqNRcfh13rmIVjfNfeqM9d0aHad32ML3PnqqN2CfykDqGdTqzpqhZn/a+9HN9xkwEqf6O45UzjWNB31GqV7HB+yr0+Tdb7ONmdunC+ONalX4vR8WqFhnH53Gv37H3CyciW6rt68BwKGQtd8+ujT9OSVlnrfO3qLSF+bntPv5qY9UmU9QcGGZMe7LqUuigXuO870u2rwO+Dr71xtL44zfHz7bWNam0e41GO90WtusspL7TzNSll757854Wc7f8qXbxAXnd3VWcas+lkGecgKpVCsq0JN3VdStcz9PjtEm/plI1m2kZ1XVMmrqOMc9NrXt4nuLOOgAAAAAAHkNnHQAAAAAAj/EFg0Hv50/YDZs2bTrQRQAAAAAAQILBYJefw511AAAAAAA8hs46AAAAAAAeQ2cdAAAAAACPobMOAAAAAIDH0FkHAAAAAMBj6KwDAAAAAOAxdNYBAAAAAPAYOusAAAAAAHgMnXUAAAAAADyGzjoAAAAAAB5DZx0AAAAAAI+hsw4AAAAAgMfQWQcAAAAAwGPorAMAAAAA4DF01gEAAAAA8Bg66wAAAAAAeAyddQAAAAAAPIbOOgAAAAAAHkNnHQAAAAAAj6GzDgAAAACAx9BZBwAAAADAY+isAwAAAADgMXTWAQAAAADwGDrrAAAAAAB4DJ11AAAAAAA8hs46AAAAAAAeQ2cdAAAAAACPobMOAAAAAIDH0FkHAAAAAMBj6KwDAAAAAOAxdNYBAAAAAPAYOusAAAAAAHgMnXUAAAAAADyGzjoAAAAAAB5DZx0AAAAAAI+hsw4AAAAAgMfQWQcAAAAAwGPorAMAAAAA4DF01gEAAAAA8Bg66wAAAAAAeAyddQAAAAAAPIbOOgAAAAAAHkNnHQAAAAAAj6GzDgAAAACAx9BZBwAAAADAY+isAwAAAADgMXTWAQAAAADwGDrrAAAAAAB4DJ11AAAAAAA8hs46AAAAAAAeQ2cdAAAAAACPobMOAAAAAIDH0FkHAAAAAMBj6KwDAAAAAOAxdNYBAAAAAPAYOusAAAAAAHgMnXUAAAAAADyGzjoAAAAAAB5DZx0AAAAAAI+hsw4AAAAAgMfQWQcAAAAAwGPorAMAAAAA4DF01gEAAAAA8JjkA12AfSUYDB7oIgAAAAAAsFu4sw4AAAAAgMfQWQcAAAAAwGPorAMAAAAA4DF01gEAAAAA8Bg66wAAAAAAeAyddQAAAAAAPIbOOgAAAAAAHkNnHQAAAAAAj6GzDgAAAACAx9BZBwAAAADAY+isAwAAAADgMXTWAQAAAADwGDrrAAAAAAB4DJ11AAAAAAA8hs46AAAAAAAeQ2cdAAAAAACPobMOAAAAAIDH0FkHAAAAAMBj6KwDAAAAAOAxdNYBAAAAAPAYOusAAAAAAHgMnXUAAAAAADyGzjoAAAAAAB5DZx0AAAAAAI+hsw4AAAAAgMfQWQcAAAAAwGP+HxwPkJSQbkEuAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 936x165.6 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_anat(mean_img(func).slicer[...,5:-25], cmap='magma', colorbar=False,\n",
    "          display_mode='x', vmax=2, annotate=False, cut_coords=range(0, 49, 12),\n",
    "          title='Slab of the machine learning mean image');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hmm.. That doesn't seem to work. We want to cover the eyes and the visual cortex. Like this we're too for down in the back of the head (at the Cerebellum). One solution to this is to rotate the volume.\n",
    "\n",
    "So let's do that:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Rotation parameters\n",
    "phi = 0.35\n",
    "cos = np.cos(phi)\n",
    "sin = np.sin(phi)\n",
    "\n",
    "# Compute rotation matrix around x-axis\n",
    "rotation_affine = np.array([[1, 0, 0, 0],\n",
    "                            [0, cos, -sin, 0],\n",
    "                            [0, sin, cos, 0],\n",
    "                            [0, 0, 0, 1]])\n",
    "new_affine = rotation_affine.dot(func.affine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pad functional image with zeros to prevent clipping during rotation\n",
    "pad_size = 5\n",
    "data_pad = np.pad(func.get_fdata(), pad_size,\n",
    "                  'constant')[..., pad_size:-pad_size]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Rotate and resample image to new orientation\n",
    "from nilearn.image import resample_img\n",
    "new_img = nb.Nifti1Image(data_pad, new_affine)\n",
    "img_rot = resample_img(new_img, func.affine, interpolation='continuous')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Delete zero-only rows and columns\n",
    "from nilearn.image import crop_img\n",
    "img_crop = crop_img(img_rot)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check if the rotation worked."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x165.6 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_anat(mean_img(img_crop), cmap='magma', colorbar=False, display_mode='x', vmax=2, annotate=False,\n",
    "          cut_coords=range(-20, 30, 12), title='Rotated machine learning dataset');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Perfect! And which slab should we take? Let's try the slices 20 to 22."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x165.6 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from nilearn.plotting import plot_stat_map\n",
    "img_slab = img_crop.slicer[..., 20:23, :]\n",
    "plot_stat_map(mean_img(img_slab), cmap='magma', bg_img=mean_img(img_crop), colorbar=False,\n",
    "              display_mode='x', vmax=2, annotate=False, cut_coords=range(-20, 30, 12),\n",
    "              title='Slab of rotated machine learning dataset');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Perfect, the slab seems to contain exactly what we want. Now that the data is ready we can continue with the actual machine learning part."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Split data into a training and testing set\n",
    "\n",
    "First things first, we need to define a training and testing set. This is *really* important, because we need to make sure that our model can generalize to new, unseen data. Here, we randomly shuffle our data, and reserve 80% of it for our training data, and the remaining 20% for testing.\n",
    "\n",
    "So let's first get the data in the right structure for keras. For this, we need to swap some of the dimensions of our data matrix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(384, 40, 66, 3)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = np.rollaxis(img_slab.get_fdata(), 3, 0)\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, the goal is to have in the first dimension, the different volumes, and than the volume itself. Keep in mind, that the last dimension (here of size 3), are considered as `channels` in the keras model that we will be using below."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note:** To make this notebook reproducible, i.e. always leading to the \"same\" results. Let's set a seed point for the random split of the dataset. This should only be done for teaching purposes, but not for real research as randomness and chance are a crucial part of machine learning."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from numpy.random import seed\n",
    "seed(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As a next step, let's create a index list that we can use to split the data and labels into training and test sets:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(307, 40, 66, 3) (77, 40, 66, 3)\n"
     ]
    }
   ],
   "source": [
    "# Create list of indices and shuffle them\n",
    "N = data.shape[0]\n",
    "indices = np.arange(N)\n",
    "np.random.shuffle(indices)\n",
    "\n",
    "#  Cut the dataset at 80% to create the training and test set\n",
    "N_80p = int(0.8 * N)\n",
    "indices_train = indices[:N_80p]\n",
    "indices_test = indices[N_80p:]\n",
    "\n",
    "# Split the data into training and test sets\n",
    "X_train = data[indices_train, ...]\n",
    "X_test = data[indices_test, ...]\n",
    "\n",
    "print(X_train.shape, X_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create outcome variable\n",
    "\n",
    "We need to define a variable that holds the outcome variable (1 or 0) that indicates whether or not the resting-state images were recorded with eyes opened or closed. Luckily we have this information already stored in the `labels` variable above. So let's split these labels in training and test set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train = labels[indices_train] == 'open'\n",
    "y_test = labels[indices_test] == 'open'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need to reformat the shape of our outcome variables, `y_train` and `y_test`, because Keras needs the labels as a 2D array. Keras provides a function to do this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from keras.utils import to_categorical\n",
    "y_train = to_categorical(y_train)\n",
    "y_test = to_categorical(y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And now we're good to go."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating a Sequential Model\n",
    "\n",
    "Now comes the fun and tricky part. We need to specify the structure of our convoluted neural network. As a quick reminder, a convoluted neural network consists of some covolution layers, pooling layers, some flattening layers and some full connect layers:\n",
    "\n",
    "<img src=\"data/deep_neural_networks.png\"/>\n",
    "\n",
    "Taken from: https://www.mathworks.com/videos/introduction-to-deep-learning-what-are-convolutional-neural-networks--1489512765771.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So as a first step, let's import all modules that we need to create the keras model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from keras.models import Sequential\n",
    "\n",
    "from keras.layers import Dense, Flatten, Dropout\n",
    "from keras.layers import Conv2D, MaxPooling2D, BatchNormalization\n",
    "\n",
    "from keras.optimizers import Adam, SGD\n",
    "\n",
    "from keras import backend as K"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As a next step, we should specify some of the model parameters that we want to be identical throughout the model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get shape of input data\n",
    "data_shape = tuple(X_train.shape[1:])\n",
    "\n",
    "# Specify shape of convolution kernel\n",
    "kernel_size = (3, 3)\n",
    "\n",
    "# Specify number of output categories\n",
    "n_classes = 2\n",
    "\n",
    "# Specify number of filters per layer\n",
    "filters = 16"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now comes the big part... the model, i.e. the structure of the neural network! We want to make clear that we're no experts in deep neural networks and therefore, the model below might not necessarily be a good model. But we chose it as it can be rather quickly estimated and has rather few parameters to estimate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_1 (Conv2D)            (None, 38, 64, 16)        448       \n",
      "_________________________________________________________________\n",
      "batch_normalization_1 (Batch (None, 38, 64, 16)        64        \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 19, 32, 16)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 17, 30, 32)        4640      \n",
      "_________________________________________________________________\n",
      "batch_normalization_2 (Batch (None, 17, 30, 32)        128       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 8, 15, 32)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 6, 13, 64)         18496     \n",
      "_________________________________________________________________\n",
      "batch_normalization_3 (Batch (None, 6, 13, 64)         256       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2 (None, 3, 6, 64)          0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 1152)              0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 256)               295168    \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 256)               0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 512)               131584    \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 2)                 1026      \n",
      "=================================================================\n",
      "Total params: 451,810\n",
      "Trainable params: 451,586\n",
      "Non-trainable params: 224\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "K.clear_session()\n",
    "model = Sequential()\n",
    "\n",
    "model.add(Conv2D(filters, kernel_size, activation='relu', input_shape=data_shape))\n",
    "model.add(BatchNormalization())\n",
    "model.add(MaxPooling2D())\n",
    "\n",
    "model.add(Conv2D(filters * 2, kernel_size, activation='relu'))\n",
    "model.add(BatchNormalization())\n",
    "model.add(MaxPooling2D())\n",
    "\n",
    "model.add(Conv2D(filters * 4, kernel_size, activation='relu'))\n",
    "model.add(BatchNormalization())\n",
    "model.add(MaxPooling2D())\n",
    "\n",
    "model.add(Flatten())\n",
    "\n",
    "model.add(Dense(256, activation='relu'))\n",
    "model.add(Dropout(0.5))\n",
    "\n",
    "model.add(Dense(512, activation='relu'))\n",
    "model.add(Dropout(0.5))\n",
    "\n",
    "model.add(Dense(n_classes, activation='softmax'))\n",
    "\n",
    "# optimizer\n",
    "learning_rate = 1e-5\n",
    "adam = Adam(lr=learning_rate)\n",
    "sgd = SGD(lr=learning_rate)\n",
    "\n",
    "model.compile(loss='categorical_crossentropy',\n",
    "              optimizer=adam, # swap out for sgd \n",
    "              metrics=['accuracy'])\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's what our model looks like! Cool!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fitting the Model\n",
    "\n",
    "The next step is now of course to fit our model to the training data. In our case we have two parameters that we can work with:\n",
    "\n",
    "*First*: How many iterations of the model fitting should be computed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "nEpochs = 100  # Increase this value for better results (i.e., more training)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Second*: How many elements (volumes) should be considered at once for the updating of the weights?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 16   # Increasing this value might speed up fitting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So let's test the model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "307/307 [==============================] - 2s 6ms/step - loss: 1.5855 - acc: 0.5277\n",
      "Epoch 2/100\n",
      "307/307 [==============================] - 1s 3ms/step - loss: 1.2631 - acc: 0.5440\n",
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      "Epoch 6/100\n",
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      "Epoch 7/100\n",
      "307/307 [==============================] - 1s 3ms/step - loss: 0.9952 - acc: 0.5375\n",
      "Epoch 8/100\n",
      "307/307 [==============================] - 1s 3ms/step - loss: 1.1032 - acc: 0.5277\n",
      "Epoch 9/100\n",
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      "Epoch 10/100\n",
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      "Epoch 84/100\n",
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      "Epoch 85/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "307/307 [==============================] - 1s 3ms/step - loss: 0.4244 - acc: 0.8306\n",
      "Epoch 86/100\n",
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      "Epoch 89/100\n",
      "307/307 [==============================] - 1s 4ms/step - loss: 0.4434 - acc: 0.7785\n",
      "Epoch 90/100\n",
      "307/307 [==============================] - 1s 3ms/step - loss: 0.4176 - acc: 0.8143\n",
      "Epoch 91/100\n",
      "307/307 [==============================] - 1s 4ms/step - loss: 0.4696 - acc: 0.7948\n",
      "Epoch 92/100\n",
      "307/307 [==============================] - 1s 3ms/step - loss: 0.3912 - acc: 0.8208\n",
      "Epoch 93/100\n",
      "307/307 [==============================] - 1s 3ms/step - loss: 0.3748 - acc: 0.8339\n",
      "Epoch 94/100\n",
      "307/307 [==============================] - 1s 3ms/step - loss: 0.4474 - acc: 0.8046\n",
      "Epoch 95/100\n",
      "307/307 [==============================] - 1s 3ms/step - loss: 0.4125 - acc: 0.7980\n",
      "Epoch 96/100\n",
      "307/307 [==============================] - 1s 3ms/step - loss: 0.4065 - acc: 0.8111\n",
      "Epoch 97/100\n",
      "307/307 [==============================] - 1s 3ms/step - loss: 0.3909 - acc: 0.8371\n",
      "Epoch 98/100\n",
      "307/307 [==============================] - 1s 3ms/step - loss: 0.3758 - acc: 0.8371\n",
      "Epoch 99/100\n",
      "307/307 [==============================] - 1s 3ms/step - loss: 0.3849 - acc: 0.8241\n",
      "Epoch 100/100\n",
      "307/307 [==============================] - 1s 3ms/step - loss: 0.3933 - acc: 0.8371\n",
      "CPU times: user 6min 45s, sys: 47.8 s, total: 7min 33s\n",
      "Wall time: 1min 50s\n"
     ]
    }
   ],
   "source": [
    "%time fit = model.fit(X_train, y_train, epochs=nEpochs, batch_size=batch_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Performance during model fitting\n",
    "\n",
    "Let's take a look at the loss and accuracy values during the different epochs:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10, 4))\n",
    "epoch = np.arange(nEpochs) + 1\n",
    "fontsize = 16\n",
    "plt.plot(epoch, fit.history['acc'], marker=\"o\", linewidth=2,\n",
    "         color=\"steelblue\", label=\"accuracy\")\n",
    "plt.plot(epoch, fit.history['loss'], marker=\"o\", linewidth=2,\n",
    "         color=\"orange\", label=\"loss\")\n",
    "plt.xlabel('epoch', fontsize=fontsize)\n",
    "plt.xticks(fontsize=fontsize)\n",
    "plt.yticks(fontsize=fontsize)\n",
    "plt.legend(frameon=False, fontsize=16);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Great, it seems that accuracy is constantly increasing and the loss is continuing to drop. But how well is our model doing on the test data?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluating the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "77/77 [==============================] - 0s 2ms/step\n",
      "Loss in Test set:      0.45\n",
      "Accuracy in Test set:  79.22\n"
     ]
    }
   ],
   "source": [
    "evaluation = model.evaluate(X_test, y_test)\n",
    "print('Loss in Test set:      %.02f' % (evaluation[0]))\n",
    "print('Accuracy in Test set:  %.02f' % (evaluation[1] * 100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Run more model iterations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Not bad for just a few iterations. But let's see what we reach if we iterate a few hundred times more?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "nEpochs = 200"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/200\n",
      "307/307 [==============================] - 1s 3ms/step - loss: 0.3716 - acc: 0.8339\n",
      "Epoch 2/200\n",
      "307/307 [==============================] - 1s 3ms/step - loss: 0.3503 - acc: 0.8534\n",
      "Epoch 3/200\n",
      "307/307 [==============================] - 1s 4ms/step - loss: 0.3671 - acc: 0.8436\n",
      "Epoch 4/200\n",
      "307/307 [==============================] - 1s 3ms/step - loss: 0.4289 - acc: 0.7883\n",
      "Epoch 5/200\n",
      "307/307 [==============================] - 1s 3ms/step - loss: 0.4037 - acc: 0.8176\n",
      "Epoch 6/200\n",
      "307/307 [==============================] - 1s 3ms/step - loss: 0.3380 - acc: 0.8697\n",
      "Epoch 7/200\n",
      "307/307 [==============================] - 1s 3ms/step - loss: 0.3713 - acc: 0.8502\n",
      "Epoch 8/200\n",
      "307/307 [==============================] - 1s 3ms/step - loss: 0.3594 - acc: 0.8339\n",
      "Epoch 9/200\n",
      "307/307 [==============================] - 1s 3ms/step - loss: 0.3413 - acc: 0.8567\n",
      "Epoch 10/200\n",
      "307/307 [==============================] - 1s 3ms/step - loss: 0.3504 - acc: 0.8274\n",
      "Epoch 11/200\n",
      "307/307 [==============================] - 1s 3ms/step - loss: 0.3146 - acc: 0.8762\n",
      "Epoch 12/200\n",
      "307/307 [==============================] - 1s 3ms/step - loss: 0.3528 - acc: 0.8404\n",
      "Epoch 13/200\n",
      "307/307 [==============================] - 1s 4ms/step - loss: 0.3434 - acc: 0.8632\n",
      "Epoch 14/200\n",
      "307/307 [==============================] - 1s 4ms/step - loss: 0.3373 - acc: 0.8632\n",
      "Epoch 15/200\n",
      "307/307 [==============================] - 1s 3ms/step - loss: 0.3298 - acc: 0.8795\n",
      "Epoch 16/200\n",
      "307/307 [==============================] - 1s 4ms/step - loss: 0.3361 - acc: 0.8534\n",
      "Epoch 17/200\n",
      "307/307 [==============================] - 1s 3ms/step - loss: 0.3062 - acc: 0.8925\n",
      "Epoch 18/200\n",
      "307/307 [==============================] - 1s 4ms/step - loss: 0.3287 - acc: 0.8502\n",
      "Epoch 19/200\n",
      "307/307 [==============================] - 1s 3ms/step - loss: 0.2904 - acc: 0.8893\n",
      "Epoch 20/200\n",
      "307/307 [==============================] - 1s 3ms/step - loss: 0.3349 - acc: 0.8730\n",
      "Epoch 21/200\n",
      "307/307 [==============================] - 1s 3ms/step - loss: 0.3390 - acc: 0.8534\n",
      "Epoch 22/200\n",
      "307/307 [==============================] - 1s 3ms/step - loss: 0.2682 - acc: 0.9055\n",
      "Epoch 23/200\n",
      "307/307 [==============================] - 1s 4ms/step - loss: 0.3077 - acc: 0.8827\n",
      "Epoch 24/200\n",
      "307/307 [==============================] - 1s 4ms/step - loss: 0.3004 - acc: 0.8664\n",
      "Epoch 25/200\n",
      "307/307 [==============================] - 1s 3ms/step - loss: 0.2678 - acc: 0.9023\n",
      "Epoch 26/200\n",
      "307/307 [==============================] - 1s 4ms/step - loss: 0.2856 - acc: 0.9023\n",
      "Epoch 27/200\n",
      "307/307 [==============================] - 1s 3ms/step - loss: 0.3172 - acc: 0.8632\n",
      "Epoch 28/200\n",
      "307/307 [==============================] - 1s 3ms/step - loss: 0.3399 - acc: 0.8599\n",
      "Epoch 29/200\n",
      "307/307 [==============================] - 1s 3ms/step - loss: 0.2580 - acc: 0.8958\n",
      "Epoch 30/200\n",
      "307/307 [==============================] - 1s 4ms/step - loss: 0.3317 - acc: 0.8567\n",
      "Epoch 31/200\n",
      "307/307 [==============================] - 1s 4ms/step - loss: 0.3184 - acc: 0.8664\n",
      "Epoch 32/200\n",
      "307/307 [==============================] - 1s 4ms/step - loss: 0.2741 - acc: 0.8925\n",
      "Epoch 33/200\n",
      "307/307 [==============================] - 1s 4ms/step - loss: 0.2966 - acc: 0.8632\n",
      "Epoch 34/200\n",
      "307/307 [==============================] - 1s 4ms/step - loss: 0.2955 - acc: 0.8893\n",
      "Epoch 35/200\n",
      "307/307 [==============================] - 1s 4ms/step - loss: 0.2520 - acc: 0.9186\n",
      "Epoch 36/200\n",
      "307/307 [==============================] - 1s 4ms/step - loss: 0.2619 - acc: 0.9121\n",
      "Epoch 37/200\n",
      "307/307 [==============================] - 1s 4ms/step - loss: 0.2450 - acc: 0.9153\n",
      "Epoch 38/200\n",
      "307/307 [==============================] - 1s 4ms/step - loss: 0.2580 - acc: 0.9121\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Epoch 195/200\n",
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      "Epoch 196/200\n",
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      "Epoch 197/200\n",
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      "Epoch 198/200\n",
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      "Epoch 199/200\n",
      "307/307 [==============================] - 1s 3ms/step - loss: 0.0319 - acc: 0.9935\n",
      "Epoch 200/200\n",
      "307/307 [==============================] - 1s 3ms/step - loss: 0.0467 - acc: 0.9902\n",
      "CPU times: user 13min 8s, sys: 1min 33s, total: 14min 41s\n",
      "Wall time: 3min 28s\n"
     ]
    }
   ],
   "source": [
    "%time fit = model.fit(X_train, y_train, epochs=nEpochs, batch_size=batch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10, 4))\n",
    "epoch = np.arange(nEpochs) + 1\n",
    "fontsize = 16\n",
    "plt.plot(epoch, fit.history['acc'], marker=\"o\", linewidth=2,\n",
    "         color=\"steelblue\", label=\"accuracy\")\n",
    "plt.plot(epoch, fit.history['loss'], marker=\"o\", linewidth=2,\n",
    "         color=\"orange\", label=\"loss\")\n",
    "plt.xlabel('epoch', fontsize=fontsize)\n",
    "plt.xticks(fontsize=fontsize)\n",
    "plt.yticks(fontsize=fontsize)\n",
    "plt.legend(frameon=False, fontsize=16);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Wow, much better! At least on the training data. What about the test data?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "77/77 [==============================] - 0s 963us/step\n",
      "Loss in Test set:      0.23\n",
      "Accuracy in Test set:  90.91\n"
     ]
    }
   ],
   "source": [
    "evaluation = model.evaluate(X_test, y_test)\n",
    "print('Loss in Test set:      %.02f' % (evaluation[0]))\n",
    "print('Accuracy in Test set:  %.02f' % (evaluation[1] * 100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's amazing. But keep in mind that overfitting is a constant problem. We try to prevent this with the Dropout layers and by using `relu` activation function. Setting up the right model for convoluted neural networks can be a very tricky!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analyze prediction values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What are the predicted values of the test set?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2.0705704e-01, 7.9294300e-01],\n",
       "       [1.0280789e-02, 9.8971927e-01],\n",
       "       [3.1622759e-01, 6.8377239e-01],\n",
       "       [9.8177367e-01, 1.8226389e-02],\n",
       "       [8.1285499e-02, 9.1871452e-01],\n",
       "       [9.8433137e-01, 1.5668590e-02],\n",
       "       [8.7444633e-01, 1.2555365e-01],\n",
       "       [2.9071409e-04, 9.9970931e-01],\n",
       "       [9.1210592e-01, 8.7894119e-02],\n",
       "       [9.1404474e-01, 8.5955262e-02]], dtype=float32)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = model.predict(X_test)\n",
    "y_pred[:10,:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, those values can be between 0 and 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(6, 4))\n",
    "fontsize = 16\n",
    "plt.hist(y_pred[:,0], bins=16, label='eyes closed')\n",
    "plt.hist(y_pred[:,1], bins=16, label='eyes open');\n",
    "plt.xticks(fontsize=fontsize)\n",
    "plt.yticks(fontsize=fontsize)\n",
    "plt.legend(frameon=False, fontsize=16);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The more both distributions are distributed around chance level, the weaker your model is."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note:** Keep in mind that we trained the whole model only on one split of test and training data. Ideally you would repeat this process many times, so that your results become less dependent on what kind of split you did."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing Hidden Layers\n",
    "\n",
    "Finally, as a cool additional feature: We can now visualize the individual filters of the hidden layers. So let's get to it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Aggregate the layers\n",
    "layer_dict = dict([(layer.name, layer) for layer in model.layers])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Specify a function that visualized the layers\n",
    "def show_activation(layer_name):\n",
    "    \n",
    "    layer_output = layer_dict[layer_name].output\n",
    "\n",
    "    fn = K.function([model.input], [layer_output])\n",
    "    \n",
    "    inp = X_train[0:1]\n",
    "    \n",
    "    this_hidden = fn([inp])[0]\n",
    "    \n",
    "    # plot the activations, 8 filters per row\n",
    "    plt.figure(figsize=(16,8))\n",
    "    nFilters = this_hidden.shape[-1]\n",
    "    nColumn = 8 if nFilters >= 8 else nFilters\n",
    "    for i in range(nFilters):\n",
    "        plt.subplot(nFilters / nColumn, nColumn, i+1)\n",
    "        plt.imshow(this_hidden[0,:,:,i], cmap='magma', interpolation='nearest')\n",
    "        plt.axis('off')\n",
    "    \n",
    "    return "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can plot the filters of the hidden layers:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JY7rklI6UHgkMkPnozWwnovr4ZGUXnjJAlmZuYrT8BLGItEVfdDO2L+3Y8Ip4as8yn2bfk76GUkPmvZY9GfQBAMvsVERoObI3O3Vz2z7ecYuYhqzpnu8EY++G7Pt5tPiXp7wzWFzteNI3mH9vfZkbALjBuJ0VKVkzN2Vl3wIQNSCiUl4emXQ5pPZES/XuYA3bkLWYbsgxxZbLYVXlYLnZxX5X3v2g34+mjh9hHIsofUg7+r7PEf1ocE9jTaHvb7PuYolqo3+a+USQHjC/jVLxNYGhUm6cwPMa6pxOsIdNxVdTb02pp9eJWnLdemuCeETGYza2bJ7DaI5zcWRczvGYT11J0uwN/rPJF2PwUfublGr7Lth9XQj86ZaP8cHdshZuz74vcDiZRi54bnEkScqMrqdYmpF9bg/LWWeKbTFsFznoPwaA4zUx1HcTRg/LfUnTOKLtmhe8ObdAW6jiGyJEiBAhQoQIESJEiBAhfmLxExBBNWjT7rLJTby75y2AiNx8oyIWfEzPsMET9dfuSJQDSkXrOLvp1sWrnPFy5DXxAN1d/mtMQ2gqQ8ntGIi3eYW3gqOqvmKLGtN18ZBUG0cC77RpxBiIXoGDRFHGa89QbwklwjAS9KW2AnCjcXMQrTsTTTQWGTxJ6asdHc0n1mBqErUZKz0aiBJoWpREVER9tkZfydO1rwOnR4TOBZfDu9iOVA0kryKCvP+oHw/+v9lcxvW94hE3NdirxAYzEY3xmnjNc1GdGaUUcE/j3kAhdom3gqImHnqLKFEVDV1qZnH9dl0/i7GafHdFyuBE1QuUYp+tzWBr4glcb/XTcOW4m/oirEjI39+64xPckBXa347qP+EqL+JVmZ9mxJdaY+XGKKmo3FOPsTp4tnFnL32m0M4Olu8lHhEVzN7oRgqOKAbnzRWM1yWa2q7T+3y4HO3YVpoz9ASrMy8DYMAb4qqseL5v7HZYo+i7Gj6TDenLR6pRjipq7kzDI2bK55jRofICAeuh6niB0q/nd0Qkqo6Po8STyraHTieCGjG0QOG36rVYHpcxH9E1DlbFG7/DvSegrMUigwG1JRHtJW1JH224c7jq3UaNDBld2nTaORio9EXMNLomc0e/tRFbzQlTzeco1Q6qpzk3ynAYQf3xELXaNa4vjGCdpkV4ff6/AfCN2d89p+9czohNNrmJt2bfDMCWHHxpRProyVGnUyFz7VW5dzLNMQBm6weD/9aaw8xXrZwfUWlTd3XNEkoYkE6s4Qrr5RR0FSn1UqTVPP+s/3AwVlaxjdcNyPx3c3eFvz8qY/Sh+kGqirVUd+doee05RCeiojc1ezpgpaxI3ETNl8jg8eIDAeMhFRsibsr5G26BQqVd3/rSjkU493ZsM8Pmz/sfXvVx3rxMFsF/HM6ypyDrzbcLv8dCEQtdT7Ax+/rg9zWqdviPnHsoKPrtfJrzfNqmZfYErB7Xa+C6Ja7IvQOAsjZDApnb+7x+Dmt7ABgu3B2cKxYZDMQEPa/JurzcR9LPsbP4JQBS8WXUmrJXOhs9sM1cWJLZHuydTk6FuXgRmzPhx0l/WZ9/A9eZIk40mDSoO21lZoLo6HjNIa6E/0otN6hnm7Q0ZtV+p+jYAV234TmM6jLXbTVW0KsowU+XikE0dNiZo6jPUFFU/q1cE3y/6rXotWTvVXEcjmgyht/Rt47fOvp5AGrNYwF91NSiNFWbZa1lwd64YU+RikmKVn90C6P1pwBIR4dw5rH/NKX0rWsmhpoHRgs/YHG1YycqvzQnFSSaXomWK/3uZuu1zPgiMnf2efX8kE1uoljde8b/a8omeKFinWvyr2fWlnml1pxccI1MxlYF+9aJ4mPMnyvbtGPdSJxh3F688biIdzMdWu8aNdm9r38bT07LQ/7j9CeDkiSV1hiNiLy4l+u3cV1aDL0tTi9PNmTh3ef9kB5zLQDrM3fRQDqdRZSYn1JX8hjyZFLfmEozpQsN9cn4Tqbq0nHujL+JXd5Buj3ZyB5pfTeY5JPRVWz0rwXgazOf5COrPwrAJ85goM43Ti2zB0PRJautSXLRlYBQfNuGq+2UqDXH1LViRCyhYvwkqAhHrQH6EmK8D3mr2dH4BgBr43dwlco1ihs6Y3WZJFYmfbqi8rnuEhiZjxdncBQ/P2F0k1AKkjZ24GgY0rppqTylFWkzKGEy1ySgDRsa9MT0wEC9M9nLjKLP3Nrn8MNp+dLBks+VKodyfoFkTYuwKncnADkvTRRRF83Hb2Fbl0wkk3UvWIBi5jq+U5aF3XUrVOoyeecjq2g5MunVjbmgDwgWnxKihhlsBNdmXsGgNwTApnSKtWm5z6mWwfFJ2dBEdZ+mot/PNAkW5zM9UanlBW3t+QSqv4auM9cUqzQyr/5M2tKJmxpqfablEcj1J/UIMaOdm+pzTV7GS7zwKp7IyueZ6j7yCckvzxsrGFDPM2IeperJwh7TM5iqb6WMPlJxGY8tv7MAt6gTR/qiGLyXJ5f1/0aYRu6Cz4Evy/wSdwxIm3/j3FIXLwv6szcCcKt5BxtkyHH3aPN5N1CW2UMiKtS1sjbDibn7znq8q3I9QVI0boyJo3h5PBnMUrf3+/REbeZaMg4G401642JMPjHzMrb3iPG55soZPvUdySn8ynCSpUk5w8bGSh60ZeO7xNhCRpMHujbbxbV5VTqjYXC4LPPD0WoNFxmvqVw3MV+M2JyfZlYTw65mlaia0jeE3ryY5lS5l3Ri7UlpOh9YJtT167tafPwZWQ++U1g43zoRXUFGGQiF+mFW+vL5GZ7km8WvAbA29xp0VWpksvRo8N1TcwpXp24HwMNjrLmT62LilN/R0DlQfwCAWPzljFZEgflVuQ8z50u/aK+LAK/M/ToNzw5+71b7tKTZx0uibwLgqD9GQRNj9djcd066D12tgfMd+33pqxgvPtS+cxZXOwo0zJNyB2/PikZK1DMDA3Io4c9LT+ko2eejBlG1Vnk+jDRkbSk1qviarHsZP4UqLEFBK5NSyvXxqE632iu9tDdHQu3qr/F7uWc8hofsVddmYrTUsvRA/RDb09IuMcPkiCx1HCn73B4X+v1UrIiuSJau5uCpe12p9zKs0pWKiRkSvqylE94hXJX+MlneEQRTqvVjQXrTdZHXcpg96g3pLJ51smNk9WVu4ERB5sNrcu/hxTl5f5+Z/MI5Bw7OB2czTgG2ZX8GgKcLn31B5z009y+8KvdhAL5b/6sFj6k2jgTPtjr/2iBnfVvunewsiMPCdUtnKPl28doxpPiGCBEiRIgQIUKECBEiRIhFgUVP8X1x9oNEVaB3WTzBU4pOFPMTDOjiYT3qjxFRaqw5UhzX5ZgNrKamPHlP+w9ypSY1v1J6hCd8VdfUHme5dR0Ay1lCypRrHXKmeFFaQt7buzx6onKeK/qnWXKjjXG90DerX9nPHz4gxc9v6akyUpf7eM/uzwSiDUOJa7lKl+Tif5r5HZbnXgHAW7IvYktWPA+fOdYptjzdbHJPWTwd3alNvC0ntZf2FqvcW/ozAEwjE4TMFxJAeD5cavrLmvzrA1r0sbl7eGOXeHRWZyyS6usJEw6VpDuWbI+IkqMzNEgoOugT5enAq2dhMqKS/g0sUr4ILbTpvQAJoqSUR3bUmyOJ0FoGIkmWpwxu7xW6xA1rRvnTx1cH12vXXd075/BDVzy3/3zF1Xz6oHgL76k/Rh/iFdwW7+fZmrggG1otqAca9aPEkXvptxIUbPFYl6iz3FIRRkNnsinvZSge5f6GCBeIB2sxCkEYbMyLF/z6yHqWJqUtViQ9Cna7NinMyiNRc3xSSmE3F+nUcSu0/IC6pGkarqLotjwPFXQmqutBvdMlCZ3BuByTND1UdyBmeGzKFYlHZHxWmhHmGlF1bZOiLc/U8DTWp8UrPVqP8dScRBQenakwo8n46fKzDESEkujOmxZtz6fmKbqvZgQK0SXbxVH3vSIdYaImx/zAeYjxkgiIXGqBnf9bKL5fueYjvHXHJy7qNdbl3wjAgbl/PqfjL/VYzKW2sswUls+A30cFmcv2ut+nWBEBvVh0gLXxOwDo83v5fvVvAaF6tqn6EbMrWKtqzROYZlYdU8RQ1NqomQ/EcZ6vAH1bcXVD+i5epKirb1zWou7KWP7nYYMvTf32gt9te+hXRW/iyogwa5YmDbrVlN5w4bmCzBtfL30+ECRbnnsFXQjtOOdnmNFkPh5xdyl1SzjXyNulaUdt3ufOvfzGqo/zuiFh1PzXZ2exkDZ6qvyVgF5nml1sSr0agGH7CYrV54Lz5FOi1jlXeSagYcesrlPqM8q1ezPX0W1I++S9Xu7oFlr0RN3jlYMu94zJve+uzjGmCxPtzuRV3Dkg/SBj2bxmx1fVc+pBBEbTInSrVKeE0cPVmrCL1mUjNFT6xpOl2SDl6t7q355U9aDNmAOIKop4g8opSsSLpR0Xvpc7sx9iSVz2gv1xnaFEO21FI2mqOu4Vjf1FGbMD8QhqKSET0dlXEubBcW2UQU/2oR4+47pEnS0/iuXLGrbM6GJFWj7HjU7t8KQJuYgfpCiVbYOtOelDT85myUdkHMV0j7ISKbx7TOdtK2Tx/sRzdrD3PqYPc40p+9yoobEkoSKrPoFq8FjNY7wh3z2kHQoYDau1IZalLHWMw3PeMAC7577MuUTeLnU7GkaaXxwSxexSy+PuxoMAjBYevGD3sRAss+ecROyGcnecxC5oz7fz6fsRqy8YU1fk3sGuwhde0L20lbq/f+P7WNUt+6Off7CXbxc+edqxpzIHzoT/MCq+GiaZpAwG12vxe2tEMv+rx2sc0mUyzvkDwQDdFOvlyZZMjvvK/04mLgvbMmMbeV+UxJ71HqSlaEr56CqOF6WzLcveylqVv9qgRUWTxWGjOcRzjkgqz3AiUAtc5q/nxV1dAW30Qz93DD0rq+f4v7d44/1iAL1lsIcNKTFIfvHAU2QRFbMoMU74Espfz7U850uear++nhclVgJQtT2+PPNHACzJ3MBvDEkR9Q2pBiklHf+loyn+clSO8bxKsNk4VynwSzXos0lRdk1bg4yXRQH3yvSbuTEl9I+y7dMX7+QWHiyLETGijwaKtluNFWQi8v5/UN8XULJXaH2MekIb69YytNQzHdL3MqjKAqWIMaVyU33cgAbc1GQBeGe/HHd1rkbBVpNoo/M8CcPnmyfkvEMJC1PNxnuKVerIxD+qH2GsJrkXW+J3kVWUlxH9RJAjNV59mmsTQot73UA3WzMykX/5WOdaSxIGX5i7B4CJ4o9YXIuwYCh3B9uQshIbsjGWqoW3K+Ix1WznmXRoTA2XYOG1PVAVf6i7MvmD9Pd2Pmqh5ZBWTqKkpRNVx8cMLaAuJU2Iq5IzgzGbFckaa1fKxJ55RTeoklP2/hKV43KCQiHBiZL0m+9PpQIDeqruUrClHZOGydKUXGRVyqdXLeAlR+dQRfrieM0jp3Jh16f9wJiuu7BjWp5nvz3JU2Upgn6uUuyhgXr+SERXUGseu6DnvDn7AX7vCnmXNz30x+f0nUs1Ftt6CPn4apYgaSsePs/VZe6ot8aCY6NWD10JOUbDYKIixtqpJVfa87TnO5SD/OnLR588vfzW86O9Bg5mbw7mqFGmebrwOXXEYird1dHWANiSfzsAb+pew+45mY/ua/5bkCuvYdKffREArt8M1sap0pNELCn1Mp/mnkttpVIX432+Gm4ytirI4Y1YaZq2UKGz8RUM6VcA0OvnyZlRnvZkr9XnDXIQWbuXaJt495DQqtuKoQuhrcp7ZfrNvLJLKJI3djf5fw+I42BH4W8W/J6upwLV9DNv1hdvDup1ufcC4OByR1YcBGvTPjtm2irz87QUbJ+KLWtGy/OYU1ojeT0erCsj7hwRtWeJEeG4LsbdBtZQVnTaKCaOUrae0QqUtCl1fIpN+kpyau/UHdO5QgVE1qUr7CzIPsXxNdYkxVDeX4kFtMonZ3yercu5ivoMEy3R2Wg5ZSKmfDcfWcWQJ47967JdgWEd0w1u6pPxeHNPmQen5PjvT9Qx1L7mW4U/4PIbqJ091ur8awH4wzVb+PAhmQOPVh5coGTkxcG5Gno/1jW0CIYyPs8nsLUwTp7LzoRQxTdEiBAhQoQIESJEiBAhQvzEYlFgU0olAAAgAElEQVS6232cQJFwWxf80l6htXanNrHBux4AC4OiinZGDY21iDjArQO/yFhNPJAH/ONsTIrnprtxJ0dc8Qa5vs2wLx6nqdpeNsXFc9jwCSg1u9wjrFcKwHhwqCk0z5o5zc7xIq9OSiL53V/t5yvH5DtPto5Q1oT+9PhUnocnxP6fax3hdd1SB+sLs18NkqHHaSf9wySPUtXEg5P2u8glRfF1tn6AL54QenCGBGtSnZpi7RpGw4W7L1gR5QuNWyOvA+DuyudJxiRqemVsgEykLZzjMVoV70vBtumyJBptOEuJ6eL5KzsOPSr0dmN0PTUl4boybVKeFW/QsHYCR6nwRklha/I+mr7JRkuuO9mqUUYitLbWREfn0xPioV4zvoY3LFPRtpYW0GRanhYIHZVsD+XwPAkb/U2k40Kz6CfPdb3iAT5cXseTLalnm4ktZU7VXB2udoGi/t7Y6weF1sdqbkAPXmxo08VeZLwIXXkdu6PQF+28EF0TL3HT1QIKbtaCktLLaPgdil0uAhNKmbnktKgq+l/Dt0n48g6qthfUN265MNds04B9ynbHY9cXyzI0Jh77LbtbrJYhTzKSYbIq/eN4Lc7Bikx3k3WfE6riuaFpeHQUgA+XpN88VJijT4m0rM3EAsrv4UaJVFPaztLi3NjTUs/p4PqqL47FgwhHiIuPCx09Bfhh8c/5wLPvueDnvRBoOsII0dDJKDbJc9qTJ72HtuJjozXB6AKUXNPI4bgSPUvFV3OzJWuP63vsykkNvFJzJFBd9b3GRffupxPrWBaVdJs7M+u4vkuu1/A0vnpMxuu3FqCYtdFeA0cK9zNhSqR4MHUdJ0fbLre4Tvv6J0cc1mmyf1me8Pj7GVmT2tFTgJdkP4illFC/W/wjdFWFIBlfQUJFUCfnRVCbdgFXqajKe5Hrzhd5mR9xnbInmdZ2BsdLNEfu8YiZDyjFTxc+ywfPIfDSjs7sKPwNI74Iea1NvYSVughz7Tjl+Pb60hVfF9AX/0v//8PfTUgN8pNVpBenSNKK/Cu5Oi50XEMjoO9W7GigUh/RNfaWhMmXNiKklVpvzrCYqaq9ie+xOiVrTKOcIWnIuuV4Pq5ibU16JfKajP2YbgS1v/FypNScMKtP8Yj7CMtrwhDUaxp7C0K7feuKZLC2FmyNR5vC/Bup+uyvyr76geIfB5Txpdq2YC+XiBvc1xRhq+Olh9iWkWMsXdZvgAP+fupjwoKcbqYZUuk5Q/EoT9UXk6in3JeuJ3hrXtIl/vGYy+uy8s5+f+6rl/BOLs78OpS7g9sssRUecZ45hSr/40PTDHz/4ogkLTIDVQZMIrqcNRn12XDpS8sAyGlDLFUS2pmIzpTKNXu4fpirrJUAOB5MKVnobnoCqkRvzORfJ76hzt/L5pzk0VWZY9KXBb+iF7Do5C/u9verY6bpi0mHvUrbSiym0/Rkc/1LB/YGap4prZslvlBGI4bGsKLPlGsHuHadHP/fr7ueyYJIWMctm199TDbBD7X+JbhuTSszW35a3ogWw41Lx72hO8lIVc7TdDuTFcgEDudOKbwUiFh93FMV7ns+sZY3pCUXal+5Qk9MrIjrenT2q3Iyph7BUvv6w3aJZYjR1/Bt8lHZcG3IQsNVhkZD1GMBSpUURZV3tJF17NNEZSzqRykrCmedVmB0LNe7OOEUgnzWIjXuHlW5hU6d5QmZsA1NC0qd7G1Osj0lC1DaShA328aTT7optKcxu8poTfpQoeVwR1LodVVnDZMN2UAdLNd4uCL0pSH6yVhC4Xlxr87jo3L+8XOkMV0aaEEem47GFV3ynjKWz5zKO627Gk01R1UdggXZ98FWn3VNIyOPStrySfbIeY5XDWYbql97Bo5abU1dC/JX2wq+IOduqfFnaBrTDYeqI/dRcyJMNK3g2BM1+X7Z9oN80bLtBbnpN/YmiRuWOhfMtVSZo2JXUGooZnRyalNalPlZEe1rNdyOQdqkSaSdx3fBaDQhLiUGc7eyxhC15lM305cTUhpE1gPbr/FA+dOAGJDt/E1DjxBV64GlJyg2hRbYaM0E/dHzW5impL/ErS7yEenHFdtloiRrj+tWgxxU74ybp/nzlMZQ7nbW+LJeD+sHODr37QW/NX+9apdYiZk5ejx55z+7ZgZDOby+NtzDCGOnnaMvcwNrNDFoD/lPnKRQ26YwF+3jp3zrcs+p7Xmis6F7ff43qbvyft+3+y8wFQVvMHcrs4puvVd74qQcuDYNtlI/RDIi7+ynez/KTEuMomd4mJRSUZ0qPY6lyu74eKfRu9uY7+T2cehJS+6o6zvYWvOsT5WMrTrJ+M2pHNQXmXdxQ4+spWXHZ5e/sFJpQ5XucmMreUv3fwdEFfrvxD+C553qgL/c7Xg6Mn4v4yq95Jb+KJlILPhfXCn07pq1aSDHmK4e7FXXxHIMWbKXefmgzmBM3vcTsTgPTwltNqGbrHCFTntCPw7tXFM3RkRRV1N6hKbSS6ghG6vjmlC1fTz6NaGJFx2DYbU2TtY9ZpqyHm7ORdld+WFw329VZYt+fl2BHbMyRyyNN7ly7jUAHC57gTL/4ZLNQW0XAFkGuKE3oZ4dlJQEDdefpxGygLf/kqIzd2USq3l0Rgzzm3rS/N7wX1+SO8iltp7kiFoImhZjMHsj0bZDghTPFcXIPJc0iA8O3BIEVr60/8zGaZuW77qVk4zl9t/PtJe5WMYphBTfECFChAgRIkSIECFChAixSLCoIqhteuNdqZ9hUpxGfGr8cd7bczsg6mRtT8CRsk1VeR3RYKwpUcxD9l4iygOZ8NMMxCWS0XThVWmhbH2r9FdMGeKZ7DFWM6OJmMAybz1JRfE9pg+T9cXrGCNJtyeftw9YHKv4HKmIh6uf5exsfBOAsjnKoPlK+Y6hByp1APcqB/BtQxaGqmv1gUdTPFCVmkap2CAfWy6eZ9+He02poTraqpJQ93TP9BTXpIXO8961FQ7tEurvCPfj+5fbG3U6NidfE9RsGki8MvCGrEokOax4nxXbZKYp7bg8ZWGrKNeaSA/9qnbYMiyGK+2IWcensq/YpKG8hWsjfexXoToXnz5/MDiuoSIOHh6Oek+H3GmqeoHb41vUeaGqqMNDyWQg5jNZ94LI2yHnUQ4oAYs7oq/n7avEA9kfbfKUos6sTsbYr5xae6pVVijhjkLLCYo8H+VpbjUkInn7EoMHxsQD9dUTZQp+2+O/eDzEEauX1ZqqN2jpJJQK4fwapzHdD2qWur4WvD8gqDVbsiVyCpA1vaAtB+IadUcOcm2Nhivt4OoEddt0rTP2y7Yd0K6XxCPkologxJSLiFIiwOGyH5xLg0CQouG59KmI/Jqky4v6xXu/bGMRU0V1d92X54lZpRJehayipDfdaED3NXVIGXLOnqjNc2X5romJowQvQlx8nKpqeCEwWniQzWtvl19mLuipzxPS/+KRJRiGzCkzlb0nedDbNQgtI0nLExphpTm+IAXa82roWvs8u7lXRWDq9ixRFVlt+B6uW553/YXmJD9QkbSMBAYWx3SpZXpqfcs24tGlQR3VWa0YrBHVxhGmeByAbd8785toK0zmjGXsasg1Tq7L10GlMXLeBe4vDk6PNvTHLe6tiwCN73uYRjz4X1tdudwao10bXtejQb3sWGSQ6/VbAeiO6jxkS/R7ovBYoMYMZxMcWhi51Fau1G8D4IHyp5nznjnr8fOjpx9Z/fGA0pkwPNrP/MnjB86oiP2a3C8DsDbTYbD9wt5OzcbF0XYLI50QKmvMT9Adkyjj41MOqzMypsZqHgNK9TZp6Qxowh6zdJ0eLar+rjGgVACfmPFZp95Dd9RnTUr676FKA6vdB9DxVAQyopkBDbjleTzryuBx7Sa3RN7Ahqx8P2MRVEyYbsITszK2LQwmNImqj8zp3BWX+u5F46W8dEDR7B2Droh8vmc8zlhN2nQwaTCktrlR3SBZlAit58MT09JmlqazIdep717TXpj42cWFvM9Nxm3ENGm7a/ItmofOnYa8Of82nisKA9L3vRfUV0+Oni48x2aTa+lnDdO+MGH2lb//ggTkPnbor9j/CknhqDof5xOHF66pvFCEVNdT58ACu3i2xyIyULUgrHxTn8mRijTUcOFuHvYlR3QgFkPtN9mUs9g5K79YfpT1Kjdzrb89aOKnauMcKXc2qBFdJok35P4rRx3ZdUxxnPWeGCmj+jhRT9R2Z91jDDelk65M3MSLu4WSpGtwqFLjwdoXAdie+E/0JkQBMUoKTW3Yn6qN00de3YnBFydFVv/+h2/l15bIxP+Prz/El3f8AgC/deJBni3IYOmJwVKVavpo4Qgx5Jdev5fpukwMv7nLZ0oXw1rkqRfFLgroqCnWtBIRS+hHm60lgVG/JB5hQ04mgzUpj+GafJ5tdgZnX9wI8hirjk/Vkec+VgFLKekO++PElAx9s+XgKcP/OGMMINc1NJ39iuKy1FvNLDIp21qTjayjqbiodcdjc16GQ0SHHTPKgHbsgKoyPHVLUJB8xLyViiPOgp2FFP82I5vAVfoSplVul4PLw2XxTOhojGuSj9qtreSEI8d8+0QSUxlqaT2KOa9EzuXPs1FGYOoaUipXpjeuk7OUAelD2pTPKdOjHJSZMQKKr4Yo3IIsWu3c3pqrEdfllwJGQBOyPR9bORFivk5RWahxU8dQRm/LNehXi/lAXIzhtuJu2dEZrauc4ZYXODwMXWNFuj3dmUHe8+6SwaqkrLDVZyLsUEbpQ5Odt98dIzC+MxGNpiv/Ga25zDblH4OJePDMDs4ClLQQFwsjhfv5qa7fBOAbs7+74DHpxDrKtQMv6LzfmL7wua3nD+lcteaxgBJ76sahXXKl/RM6xlwbbepl0y5Qb44E5xbVcE47VleGa7UxTtOeUv852chqU0YdZ5bhedeeD8PI0JXcEPz+YLVd/P2Fb1Y9lVu5/xzywyTlZbHk9Z+sdrk0J6k+99afDvLCbsz+Io7aBzWo4akc//FiR6/C82rUlUNe1xNBukLcjJLVJEXkBG7QLqn4mjMa8AtBw6RcH+Z77h+d9bi+zA0BrXpF/pWBQ+JY2eWTx0Q35EwpR5oWIREVp2fETPPNwp8A4M810HXZ7yzN3MRkTTbwp5c2utxrYwdDUcldrFKiOyr7x2xEp6rWNEPrqNd3RQ00de/LUzpDcWnfp2Y7DtiG6/P1UXlvq2OZwCGa0E2mXWn3vNdLVKn7HtdGyTmy19zrPUTGFOd8Hyto+DaeKsH40r4Ku0syHxyraCSVk6PldfrkjaklvFSVEfrMQY8vHpH17c7BFKuTsn/LRGCqIc/wzeJzXKXKz8SMjqP4YHMWU427Me0Yewpyr6u91Uxw7n3x4sInl5K9+/XZbqZV2bs/PFA+25dOw565f7ggd7Myf9dJKRFtVeEXR7Zh6fDZ8b8Dzl6po702VBvDtOealj3Jym995rzu6dwM4Ys3BkOKb4gQIUKECBEiRIgQIUKEWBRYRBFUP0jK35JpsGtOIkmvzn04oGV+v/k4y3xRt41Ucxz3xaOb8zN0RcWjs3O2wQhCZxmkh8+P/646u8/GvAgjtaizUtWP2xi5mu81HwZgqrKbkahE3jYZtzGTkAz9K83VrEzKPXzpWJXvFf+IFXmh8u6o/QvpWMdjpQeePcgp8ZuN+Tcx6Im38Dme5C/GhS7zoS98N6ADZBIb+Kei1ESdmzoSePtjkUHiEYnUzRgZbOcqAK7O5DlaFo+W7UwvWKz3cqHt4RmuPsyGpNBFVqYNHp+RZ607PuNKwbVsayxRjKaEqQVKreP1Jt1ReX/76nPscYS2kvdWsUHVrR2gN4g+TvpFxpWolY/LkPIkx3SDTZ6qxRqxmGlJl9c9nSXJCIeqQoXT0SjZ8r9C02PCkb+7uHxeqSpu1a7kqty7AZjRRvnOiCpsbpfIIonke/yDHK7KvUbNbCBg4fl2QNcqMIKji5fyuD9HS0UReozVisoVvMlzf+kXAaYh0cQ4mYDmrAETjY5fq12kO23qgRhSy+vUPrV0AvEk1+94Xg+2IKmUE0ydQHAqahiklIrvWKNOQpc+kDB1WuqkmYgRMCk0IGn42L58f6qpBZF4TROxMrmPDu24ZBPUPh2eLbO/KF7lTbkUilWOoXk8U1MRo7rU2QUR7JpTdXWbWp20J57r2WZXkHIwoXXobiHODcnYKv778ncA8NH9C1OQzob7m/9y1v+/0OgpQNbPveDvXAp4Silzfv1Xw8gEaR6WkQrmmkSkG0fNx9XGOFFD6IUrzeuZjst3TxTuW/A6lh5ngyYKrHpC4wel/wOcLophmbI+aZp+xpqBntdgprJbfa4teMz51D49N1w8EY/zRVt4BDhJVXNH7WvYKurYm95Gt74SgDMRDj2vxn3FPwTgMXtd0Afmq96eHD19fvE9H+e0yKemtoqW1UUyKmurpum0I5llp3OHX5r6bV6e/TV5hkiMB+wfAJwk8oTvBf14rnIyfTihopDT9X1nSF1aPAKCEasvoNrG/BTjii6UMHWWJOTd9MY0kiotxtQ0dhVlb3G44dJE9nDH2Y2u9jK3W7ewNCLjdK5ls9OXGusnSg+zPCt07huMqxixZaxsM1cG61yhuYVp77A657Ncq93CHX1qP1aLMVyTa5RtL4icRnSDV+VFRblk++wsSGT1qHaIhCvR7M8eh1116afxSA89luyfJ5q7mbCFoZa1llJyJNLdtIuqf0DC6qFfl327jUuXLorVl0/Lt71H1wOK7WPGbWxLyR76ock/veh3ELVkDM1X0j5W6OQ1dKWv4sWRbQBsyes03bNHTtuYT7VfCMK2PJ3ur+spfE/25y9cTfjc6qCeDxaRgQpNle9StM2AmhczDJ72JKclRoYulddZtl1yvmwaV8VT7CvKZDfCNFco9bp81CDvCqVptvw0Yy1RGEtbg2zvkgnA9WFDQ4y+Eft+uhIykAwMer15kuFlGWxXZNPs8rdTd2WTGovkKTUkb3AkbjGryYbVx+NYSzYJE+5zjPlybSkxo6i86W1UW7Kg11pTRE15nkxkKdWGGCqN1gSO6jgtM8tOTf6+r5BluvxU8O5cd+FF/9Kjs3gMJbeT92TQTzV8upWqnaZJ0WiQXOLRKsHf24bhFeksA3GZSNZlutlcFufCgVqZw7pMwJtZz9KkGDDZZg8rPZm8V2VMds/JO5t162xMynst2R6vGmpT3hIcKsPrVAJFwdZ4dlYG5i7vIK4un2N+MlgkfWAAMUqPtH7EvyuDuCe6noKjckc9yMSXA2C7VRxfFiDf92i0hNbbsOcoqI1iw56hJyUUcweblv3C6CUXE+2i1gk/Q6+y3DIWgdKyofk4ftsA9JlVhmvVIVDr1RAad/uYmZYc4/qdUj6TNTcwVjdlYaLRPmecmFp4G64fGMOGBmuVyvf6VAvb1zhclQuWbALadsl2ArpwVDNoufIMNdelotqlptU45MlYPjDn0UAW/bLbWTgieopjauPccAo4rvQt04iRjohzynQ2k0L6d1RPoSv61OXcG1+Kwt8XCtXGkfMyTNtol+46G9oqt+dCd3xx9oNUtOp538/FRJvC25O+hpZaM029oxgaMdPkIlIiLa31UlaO3ILzLBNF2ZxM8AgLbfKTsVUMxoWymPW7GdPEiB2vP3NSblWbhqlr5lnzG9vpHsnYsuBv89+/aXbRkxSH43Tl2VOymdrU3MVnYJ4/ZN5Kx5czUuyopQZ5sngk1P6lZk8zZ5/eV+c7JqBTouVsTpjN+bcBcKz+yPNuYheCabaVPCu0HOlzESNJuw+1qw4A9Ga2s9MXSvLk1KMsBB/nJCr6fLTL5kT0FNO15x/XlxMte5JpR/RM7oq/OlijIjo8Ni06BEnDDMrJDNcrQam7TbFehpKy7pVafYGTttTymWzIftb1/cDQW5LZzocGrwUkRWajI20y3fADOvFV1komm7JvfduKGFtzFfaXZI/zdMHkQFGMHNv3grxTy7OIFWVfc8AdR2VBkfBT2Kp0n4nBuoRQ0ie8/Uw0xdkUMZKBM6zmztBoyVpqu9UgT7rsjTDVUmWfsjfOc3BdXieDhhbcwZsHu4KSeX81IgYinNyvz4Tbs7/CXqSfvz75Mm7tk+e7f0LnM2P/67TjP7DsYzxekjlzj31vMG7nz6+z5afxE1LaaXXS5n8femE55GfCmebqszkG26kIo6UfncG5ePFyUEOKb4gQIUKECBEiRIgQIUKEWBRYRBFULRBkaLg6v7BOLPUTtTh7j0rUa8gbpDsukZIlCZ3HZiQKcqReCdTNanqJ/ao+aq1ZYbWxHYBX97+ayYYcv0/bg9JY4bPTj3Gs2FEbfElEBIx2OkcDz7PW3EjFlnvQNPgv+bt4qiBupsPGPrpNqU01Wn+KIhJJG4hfyRFNqCstp4zttr3xBqaKnlVbk3hKhdbzGriK8tJ0iwt6NHzfCyijCaOblor2lmr7FqXK3XRrP/+pX2gKB4sOa7Py0telXCIqQtkbs1BOPUq2z4AvtNJteZ9ni+JR1IC3LG+qzxG+fkIijv1xjUElMjDbMlidlPe3JT/NfWPiESw7sYBe88ysxudHRRjktsxSliUhooR6xutQcuRGdN0IImn9/lIaurRd0asxp4sYla5ZmIb0MxeberuOm9fC1IWzbJkdgRLbrdJUdXE9r0XLl8+JaD/1tpiF0Ydlirez2Slxe9mR8pNBJHNrtsmUqv05UtdZEpP3lzY9cmnxHHq+RlXVBbU0n6Qpf7c9HRU4py8GFRXcszSDFSnlAc5WOVGXaELSNFFiz3gQeIk1Ot89VrOwfRhVjr2ZhstcS77ULhou1+j44hrzRCFsmpRV/VzHb+Kp2sK2V8ef5xls09F8OtQ0zzdpKkGsMbMTlZhtHsJ2Ln89Yh83iHQlY0vOi+Z66XDxaEJtvBChGBeXbj/7/AdeRkyXd9CfFQpuTM8yUn4MgEZtlFJt3/N8e+HoRX98Kys9Edo4rB8kpWjO6ehQEHnbkn87y5RK+h52Mly4+6RzLM+9AgCHBjU1t/m+i6fU1DUthmlIv+xObqSi0hrmiz5ZZg+eorSJkvDioHT+eHADEZNV5vWkMqLk/mDxT4L3sSx1I12KuTWjjzKNsHRiVp6Sqol6qirzQpHI+VHW9w5+jANV2a/sOSV6eq6pQSmVxmTpcXoNoXeOObuDyO/W7Fs4WBcl7ZnK3qBOq9yH3J+mGedEU6woccpac5gzKUdfbrSfW9cjwd5uW5fB6qQ8n483Tz0aamrtuqE7TU9U2nqupXFLj0RZs5EWP5wWplexJfuiNpYie9hb+1zeeKvM4Z++dx22Cte6vjCMAKqOw6as7CGmWhA1HUYbqt54xQnS5Vzfw9dkvh3TjtHlyJ60mxyTmmJc6OPk/HZd9sM0PVnTWm4lWBubbhlDtamGHiiJe34LUz2/oUfpz1yn3osbzAmXD35wL2184sS3+PqVtwW/nyly2p7b5s95k/oEE3MiMvfXxR/x1/P0vNoidanYMso1WX+eKs3xaPEvn/cuvzDx2+rn8x56UXEioB5f+nG3iAxUP6BpvXPn7zD79p8BoGcyzZ6nRCVrD/Aq7cMA2J5FzhQDIRcxeKwuk/Gb85t5ab/sVt//3BSH60I1uSH1n4moSfO+qUd4/8vEgNl46Gp+TW04TxTu40tTv33anQ1z8gKcqK7g+tgbAE5S3TLNLqJK2r3pV0hqQlUpchxX0XQtMx9QHxqtaSxF601ElwQTXTqyZB5HfSoozA5C+wHoMlYois1iQ6cTF6t7uaFLJq89c7C3IJ9XJU12zhrB0e3cwIrtszothsSaVB1TSbDP2jpTTXlnOcthRUrlMZY8buuVCX7z0BQ9L5YTvesTg2yXV88HbjqIXZO/7zg2wLtU7s9I3WF9psyTSrU1Y2ns0/cAsF2/iic9oWTP6lMk1SZtUh8NSg8NWldQRybsgnM8cDRErexJeTPtz7ZTDdpR0/SAPuv7XkDhqXlzi6pckO0IXeeQ9gw367cAcP2ycf55v1CYS62OMrap+UH+ZsnRaSil27Tp4ytaW8HWmWzKZ9uDnqj0laVxH0uXzz2JOmPKwXRjd5WvHZcJ3vEgo7jFaUtjst42VjWqDoHic9rSqTpy3KhXJO6Ls8DwdGx7Ht3Xl2M8zaOhFl5Ds7BVeRjPt9E0eaA2TRvAdVu4SqFX16wgv8/znaAdPd/B9xeHh6Ht6FrcxiksNhrnMW0X6/1rLvdtPC/+XJXCeXja4nO2GALFSukUarf0Y8vMk4zJunKD8Uq25mRsLU/6PDolY2Om1eTabvn7u7NbGG3Id9enBnj174hB5d5xG8bDsiGzv9vD7N53AvCvB5bxr8dbrMnIOltqebTcznqgKkOx35pmZ+3rADheje6YqIBKiQa5j5ZbxnPbSrXxM+attnMjz0RlN4xMoKi9GJy47Q38cW8nU6XHg7/nEmK4rvXWsRPRohife+j0E5wjfnnoneRVlsGRis8DxT8O/vcLQx8DJJ3imTlpnwcauxjwhIb9w+KfMz9VJ5+6klL9KABb029g55woMBtGhs05Sb05VHuQpi0OhoiZCeZC04jRn70eEMPVcc9uoGpajKZadzSMs6QoXF4VX1+tCRGzJ5hbvztW4XO3yjv42qEhliXlHnfPddb0a/I2aVOe6dlSjILdLg+j0x+VOfA71SYF5SD/1LYYmzeLhfJn96/nrV8Q6v7/d2WBgbw4Hb743HLG6jJOu6MG+Yi8k4mGxh/vyQfG5HPOCDelZO1+vDLOkZb0s6iZwVDreMaIYDvitNDRKSljteVWiOpKh8EpBFTeqJXFVmlZlplE1+V5PKeFrcav73tUbXmGfGQVWkDcvNy5xDrtdafamqTmyL3/6ZaP8cHdv7XgN051xsHZVXxvSL8LIKj+AO3xdf44leJ/sRGLSMrB6UraFx8hxTdEiBAhQoQIEay2RRQAACAASURBVCJEiBAhQiwKLKIIakfZ6j3976VRk6K0G94VYeUTrwJgrLKDjFLGrTse2Yh4jQotl5dkxAO5Ie3yzVHxAP+PlevZmhdKrO0VqLTku1Xn/XS/RMSJpveaDH9FaFLuSz+C/aG/AaBV6Nju331mBd8akWvNNFtkLYvVGfn9Nzf+Op852KFjtNXU/q3yVaq6eJ/uTPw0O32Jzo3Wngw8S4aRCFQPXa9BKiZKv+XWGG5Am9A6HmBcPOWM0zEC9bjFB3k3y3Mv5aee+N8AfHLjx3j9cvGifflIP3NNpXxnaLS9of1xPRDOuXciESj6Xt/tBTXC7pmIBpG6XERn44BElCNxB01RW142AG//OaHy6suGKHxVPD9xw2XTUmmTiUNLeWgqF3iQr+7yeachNJS5FlxVEXGtff4wy7ReADy/h2q7XXyduCb9bMY7GBRXT1r9zCkqlk01oOxCR3QI38E02vSXCLpSqjW1aEAfXRwQ7+JI4QFetE684HvGetmSUR5TLcGkqgM61TLIq/qoGdOjqDzDEU8jo7ykWzI1togTlky0RdxSVFzHZEbRelddMcfGlyhqpR7hyi8IJc1uGWgqyjoxm2a6Lu+74eoYmk/NbSv/WvTEJHSw3F4a+NkrthfUz605PjUlQqH7OkldouIOTaqe9A/HbQRMB3QCr6+m6UEqwslvysakU8O2HX09VfH00uLyU+F+UuF4TWb0xVRbWsbH8tztJFV97SFvkIemZO54tlghostck0ttPkkZdUX+5QBcq13NHl9oZt8r/y0ndFFYf4O5Jjj2/trfsST+8wCsS2v82t7fASCbXM/RLyjxJMel8V0RqmsVdDxV+3tFoklUN/n7uX8FIGZkmK6KwmdvcktAqTO1GLYjkaaZSgktLWOl6RRxFTXesrpIpTbKs6NTrMn1Tk19eT4RsMVGD25HIeZHI/a87P08OS1Rq48efZypsrRdT/oa8uZKQNKHpLYhgBvU/S25Tb5X7NQr/fb1wjC745YDvO/vpV3XZ/VAff4vtma4/o1K0O9nX4v35xLJPvLQAA9PyLxbPfZutkSWsE8JON6SXcI/l4Q+XqMc0Byv0a9hTKU4dMXXUrGEJmo71SCCmo4MUmkrlc5T/T1Tm/h+E11LqaecLxh4agrA5W5TVT99Hr36XatixGKyrmxON7h7XNao7pge7FmeLeoMKMrYUzMeOxST54oui6Vxeb53roriK8G9tcuOsneP7GEPljxuH5A16apPLcPbKOPjQw/Oi7RHLPzDQpl3jxZ4/O5eluZkTE2UcuwutgULl3AzbwUgYcJjMzKuhqJRdCVEatsOTU0YRYP6FsZ8YTjWWzOgmA4Ney5gDWroaGrvZxiJYJ30fS+IsrasGsYC6+flwHyRpF8ZejcpS/rbq5ZN8MHdF0ag7aD/2I/1/YVw5ujp+afJ6HrijCyV+Xg+xsqFhub7/mUb6dq8jiqKk/Jynd9/J9obXgyAseY9fHD5xwF47WCNvzkoA9TzO8bg0qRBSdW42N7l8S/H5Ty3DljcNaRKziwtUivKYj48neNIRQbhzUvHgwX2D3cNkVK39L7NI6TSQuMYm8wwrTbQnz2UoGK7HFUb2agfJaE2pjf3poKSGscrLsMt6fCPVr8U0Dt1PUI+IQtHXM8zXHwgeAcppXTYsOewbZW7g4NhyK4+YmbQNXmG7tg6RitPyPs6D07/fNrwjwvtlAmnzbv/s82/xi8/J9SGf9j2Pl77sqMA/OD7Q4ET4UTVJmnKZNAb01mdlnY8WNaIKB/BRN1jQ7bjMNickXa5Zf0Jdh+VyXtvKcmPVFWQv/5vx6k8KwvFH/xwbZDHuCXrMa6UZndMtxjxZ7giJpL2UUML+tPBUiPIWVyXjfDUnEzeNi41TQbxrDYeUD91zaSo6HWFyh4MlU9kmalgwjaNWKD+artVopZM6jEzS90Wukx3bN1ZCpMvjIvXjhptgsXP9P4mv3GFbCjXXTlDZLW03fi9PvcPS27SdEunJyKLljlP3bc7YpNSlKZ1AzMk/3/27jTMrqu8E/1/j2eealKpSvNgSZYHeQbbgMHGGBzGQBI6kDQhgQTS3HR3kpuQkOc+6dDJ0xk63Zd0SELgkgSSQGIw82QwgyfwJMuSNc+qKtV45nEP98O7zjpVtkoq2ZZqC/6/L6oqnWGfvc/ee71rvetdBQnwYyMGzJS8X3WPh9Qmea/2mIfkL1wlm1Cpw7/9tt4Wzar5v/sPInxGGmut3SVMH0pirCiNo7pn40RdztWqb2J6XkpxN9vwWMVHS/X0nPZqmFUdScXwFJpqeQU/aOl0w27nAyDl87up+JbpwlFp9nl3rb4hz7QOoNqQRuBS0wpfrOO44JpqxPX7v2fkQ/jbsTOnLdFzZZKbsTomHVZLXYj9Ql5Tn//ruPrnpcz/u9gMwz3jdvVldsBV86eLjcNnuR4+tyFpGK5uQD+fhtSFvab2mlsH75KOgA1/sA43vVYanT8q/Y3+/0L6Kt2Yz9qjulL8S607dUfpcfMQXmLL9bIVBHgmOAoAGA6GdeD6ruHf151zf3X8FfjaS78pr7PxFP71aenY/+LJJu6vf0pts4mEO4BiTea/SsO1t5/jqnL5/GOyufAWHCx+CYB8z1xH7sv5xHpdY6HRntGrDZztuth97vwli55PRfKLdT6eetN/BAA8OTaE69ZIMH5ooh+fOS73pLjVu/dkHWCsLr9sygLfGZc2xFV9caxMyN/XJHqd1F88ZetKv7+87SRW/ZYcr4c/OANf3WMHkg1U1RSog5U0NmWkveKHBmZbMXx7Uu6HriltKUDaz+WOnDOOaWJVSo7vM8UmZkO5v9WNKixIe3PGOIkpVVE5hK+nudhmAnFXOsw6fk2vQuAHTT0P2bFTSDh96r0SehrN/BT3s3nxj2Ovk2RD4fUAgI9s3gZH1SO5/UMOzLv/+Lxe97LCWwEA++f+bdHHRGkpyGcbyFyL6crjZ/y/cwelSwuMn89xZIovERERERERRUI0xtoh0Xk2uQUA8OA9fXjXh2VR4FtzH9CT/fdXE0iqiqyzLU8vbPxPc99HUq0/OtfahndskB6SjZki/ny3pO899mgLs5BRrtEwja+9RVIH2zUb5Yr09Pxb+X6sDa8EAOx9cAir0456zQBttS7rioSJQ80ijgfS25C1R3X634/G9qIvJqOj15lXo2JI+kvC7UdcrSNWbp5ApSk9j1OdXQhVT5Tj9Okqhwm3H163pzGoIQi6I292L53CMOH7F2JR8xfOVL3273/6v+HuvKQc3bhyEid3So/ibNvBiar0EqYdC/mY7D8vBPaoQo7j9Q5eMaxG2DoGVH0lvHKogbwrv2S2m7j3AXnNPcWmLthz4BtJ/L97pUrSd2p7sQVSZXm8ZmJcpZD58FFAFtOqXK5jmLq66z5jH9YGG9TrBjBVj1sDTcwa0kPaCqtoq3QzAyaaasQbht3rObRSOtUpZmVRVWtrhgh0sYx6e0an9fro6McvvxBxV0aX676PPXPyvRs4WoOviqEenilAFSeEHwKlTjcNFkhb3aIrDuKWqhY4m0XttBzTwtEWVq+RkePESsDMy3cmuXUQxb+SNdbGJnLY8GVJuW9M2ZiclhGVtm/BD9Ri4tV1OFh1oQp6o+aFmG0G6ucO4pZsUyFm6fVRO0GoKzbPmTOYDWRkwjRMxFW2Qj2c1Wm6luHA09UJO710X1h6Dbjnpv0uf/r9/FGKCz16WnrX25H7xNJGGi8FOXcN+oLB5d6MF8ww7EVTtzJJKU50tgJaFzqlKwzb+j0sKw3b6lU+n1Hpwc9du0+ux7aVU2mjgOeVsKAX3+ieo/FIFEcCAMPoTQUKw7Ye9cre/vdnrDBdbhzF6qxkkgXwUW/JiOKX23+K7khsJrkZgRpBnfAq2FOSc/B4ope2/YmJP8IbC5ISfP+tX8Of75dj+cATX9OF8Gwrs6CKsqSuGuiRfZtNbsFwTNpI+9v/jqxaSWCs8cSCkfBUTKZrmYajs0wq3mEsZaTlTBXQo7qe893538Z77pef37jaxQOPrwMga5MOq69yrQOo2xBqXq+ib9M3EbfkHrMxHWBDSkYWH5pJIWbJYxwTuK4g956/3r0KH56U43W0lsSD0/LcPWXoVSyKmMOAIW2i9ZkYwhCYbsq+q3oe7G4hP4Q4AGkDx/00js2pYoKw0DLk/YpGr3zsdGMfPDXyaZuJXtFPK6UzHWwjpotFtr1ZXYXbsVN6hQoHCf3z8um1sY5XZC3i1/3wi7gh914AwI/u/pszPutszjZyCkgl7b+LZAaTnOOz6lr7bKaZRF9KUskXG2G9kG2dCASoveH2Kx2ZK9MKTNziykXwaLOCvzotqSOvdO/CIV8u0i2jgc2q6lzDn0MDcuI+hRjeHK4DAJyoJXF1n7z+xKkBFFVwsSmWx+4DcrOYasXwwYMSuL4l8wpMq8btU95hPFyWeYwbwh16eZH7S8eQMYcwasj2zYXjaIWS1tAX24iVqkT/Y9gJD3Kip90VuqJvMTyig0/HSqOlgpx2Z1KXcreNGGpq/lsQVHW6kmtlUIhL4LQ22ILTjsxVWY7qWmfTvTC9NPd+OCpo/OS+UZ3aMtnw9PzhoYSJ1Un5+9GagZQqx2qZDj57Sm6Ybx7JIakCnk8ddfG4+rybnl6NKyVrAm9a7eLGAXn8544P4pGqPCYTFlBXS4dUgxBNNafCg486qogHchfJI4Uy5P/iRhpHTZlHasFBEnLBn8ZxrArkZG0adUyZcoGvtMfRVPN1LDOpU0LnV1kuN0/olNG406vkbBqOThn1ItKQejYTBo7W5VJxeM8ana6Uc0KozHo4BmAZKgAMDRyqqQq4AZCyu3NsQ7QC+T48Nl3AF0/KOZGyQ9yijt26kSmEYbd8voGPfEMaQEeqoa4I2l3QHAAaXoiG56PmScOnHnTQUY2gDjwEqmPpeAdIq3k9rmFhypjTr5E0pXOrFVbR8NVC415NzxM2YM6rSplAK5COJ8Mw4aglhUxYOnANAk+fs8vr4lVJzH3in/G7G2Qqxh8f/sOL8p4XkqlTGqOnkJaA5FWxu3HEl/T0p2tfWJAS2XVt9ueRDOUa9/3y3ywI1pZS2fls1XEBwPcXX04pFV+vpzI02tO6MwcAqqoqLOAjo6a8pN0VmKpKrYa2V0HClc5lCaJ6gY3ZPedMtzdPHL3UOdOMz6sUCnh+twr38s5bDENfB6mvyf0mvjEuxyXh9CHpyLUwCDsoN6U90vGKqKnjW22N67TK4dwt8NX0krdm78K/FO8FgAXzjucHvB++7A/w6Sn5/dUPf2rBNuXTUmuhWH36OdtrW3Ls+lJb9NSiemcKJ+uSmplJbtLXxbZXgWMPqO2eRlV1wlcxdoYOhrNLdDtGF11mJjq+Xv0Y3jv8fgBAxQNO1uS6vyZt6krzrmUgrS4nl2c7eFylzR6vhrgsJz9/6WQbb10j58f2bBunmvL3mwYC7KvI39M28Jb3yPd9VcrCWE3OzTWJFHw1U6/cbOgUXadmIuNYsFV6t22YmFD3rmlzDHFIYGnCxBHsBABsxfXIh9LeOdA+pNssufhq3Unb8suwrN55V+/Id9Q0HV1zw+zEEbPlvjq/8zZu5NBGd2Bluar4yrXEMFx9zfjt9X+AT819/4K943IHp2dKmxfd/X/mgZEgqOuU7MWmY1xIURmuISIiIiIiop9wERhB7fWg7OrI5P2VqduxKSu9i6eaFt6avRsAcKLWQixUvUyxFbh1SHqGspM/hU/P/B8AQCk8jE8feR8A4LHwUXRCGa28CjdjMJQR16lWG+99WkZEj7V/iO3OHQCApG2g6UvvykAwhKYpPT0Hw8dRrksaYL11DNN2H4bT1+jtnq5KSqJtJjFpys+uk8Fm6xZ5vv8QxqqSSnBT7j04Blljc6K0cJ2zRltVpLUzsFSarIeFvdhNNcKzz/yhHomNFkOnlO31v4+tiTcAAE7UQjxVk88Xg4stSemdnWmGyDpyHAfjwMdOP61exURCVZMrdfK6QuymrIX2nKwF1goCfGJGer1uLr4UFU967NoBMGpI73spbOo1xY6FTyJvyndgbbAWJ8wTcEMZJTttzKJmyCheIyzpNVjz4Qp01Ei4bcRgqqp7A2G/TgENnAC+310Ps6PTdw2jN6qWcAf0aFutvbAXqzviug47sMc6v97mC8mcl5KWVClHfW6AOZXKO9PqVcEbioWIqSq7xZaJFfFQP2ZnUb7LG1IWVsTVfgIwLh1zmG4EeKYkKfCbJvNYk5Tv+8mGrQtclVqB7iXuBKYeMQUAPwzRmlctN5h3TekWMOvAR1JVS54OqrDVsTPDBBKQa03DqMM35Q07Zk2/hmGYyFqS3VDFhC4EEYQdnZ6dD1egbshoUnctuOV29nUEX3zPd+TUsrJnHYlbDq2worMtoqY7UvatsKPXDn+27miWEZpIqntJIb110QXoFzp7pVXb7tOF/mZr+/Qoi2tn0WyP6cJMhfgGxFRFVgC6Eq9U1u1mJORRrksxnkrjmF4XPAg8BOq7a1mpBd+PZEzW5dsYu1VXsq8bZUy35XXK9UNIqYKDAFBtdKvBLvdau4Ge/rEll0RDbU4Y+rrgXql+UE+RCcM22moaz0jyOhwtfweA3IdGDMnkWZMy0Jg8+z3jc5Pj2F381Bn/70wjp10xR+6n1daELoQzHLtSr6s51nhCH6PLMq/Bsaasqxl3C+ddJK5bWDEI6mi0pQqtZWV0YZWlVBhdDp43i8+WZW3M1/uvxipV0Mg1gZqniuyZJjqqEOdUy8awKoZkGgZG1c9PzPr46phaczjnYlxlm5XbQJ+6lx6utNAfk3tYt+gVABxuVPCj5r8DADYkX4FXpiXL7kC5gSm/qu+HHjxUTJUhFDYQV+dm3ShjEJL554UBfHWebHZfgYNtaV81/TLytpxT7aCGal2yx8KwhXx6OwAgZQ+i7km72jBsXW02k9yMdWqFhOnweGRWnwhDH4nYKgDAY7NV3J2SdPq/LX7nBbzq8x8VNs30c6qUv1i62ZlNM7GgCrCp0rO3Z9+McV9il2en8qbV6ioD+ctwaO7eC7J9i4lGS0rJuvJleaaYw56inCRrEknsqspJdco8jLtS1+nH//NxufFsTmXwD1f+KgDgNw7dh3vn/kSem78TWVNuZnvCx3W5c9NwMOhI6uBa90ZMG3JB/P4ssNaVhnI1qOH2xA4AwOFaDQ8bku67vfDzmPT3621YGW7CpCkBZ9srIqWWisk7a9BRjde56lN41/DvAwC+0rgPw4a89/rc1QsW8O2WLN8Rfz+qjtyQdxX/Uf9/CB+rLNkmJ3Rw2n9o3t57ccpiv5gcM6ErxZ30evNbBu0kjlTk72EY4kfTcsFan3Fxg70NAHCgPY2aKftg50wbxbbctBteiKStgj6YGGzKhfWoN4PxMQlGCmYCcVP2x81DOTw5I4HupF/ALTG5sffHTbTmVuptckMH05ATt9IZw0qVYl4xZtGG3BzXBltw3JS0uOOl7yHhyhy1TGx0wUmfiss2Fay1+mYeWD52mJcDAH5g3I+0KSkXPjpYEUjAPWBkdKAbBd05J+N+GcWOBPxZB3opIGde/kWpY+j03dWJXlBU7tgoqTbKk0ULDU8aaE0/xFxLPqtrmphtyvfhac/EnqIcO8kCV3O/kxaGVTHdpg8cUu1VPwx1yjEAtAND3x/q8DGngsbBMI9qd743bJw0JIU7hQKqodxU56dYe35DLwc14F6GQijHK7A8qExhFOx1yKt5in1hBgfUnB3fb+s5cMuZoRYijHTlwK4LGZyuLdwFADg297Xzel7TK6LkRmeZmTNZLDiNOcN6mbL5VWEzyc06PViCXDnPBjJX6+VM2qhjurEPgCwPcaYlDTxvFlPl536fPN9GIrYKri0pgkmjgGYox7bRntaVUA0jhoGMXF/bfhVb7bcDAPqNDA4bRwEAQ8FKbEjI6xgA9jTkWOz3H8DV1qsAADf3ZfGDGUlZHPd3I+/KdXSrfRtGLbmPPxk+fcb5ncvFsaVB+J3yEbx5QO4T05WdOvXXMEw9F9SysrjSlmlPnaCDQ+o8OVm8D93FTU4EN5xxio9pJrFKzV/d3/7eeW+nARstT/atp/4FgHF/p55nWGsega32cyNRft772bbyuCH98wCwoD10ffrn8VhV5tS6bj5SU5nmz82eKj8GABjof42uklvtBHpVgNlmgJM1ub99ZXYSlzvS7qj7Pp6Ylb+vS8WRdeXxNQ+YU8tBzHZa8NWgzFxYQ7Mp7aC6F8fKpGzDvto0Xh7/OQDALQNJdGPXMEzgUNXAAUPaq52woZdC2xJeiTnI96mFJpKhfC9nzSm0VHvn0Ny9SKu5zJ7fwOGK1IVJxFbpjqR2ZxJZW4KfTeF2nHDkeuGYCb1fLnfuQDqUm3dgrIJvLfccVGHA0G3u7SvS+J3rZNv/9nMv5FWf/w3/hsw7kYbspwPG02iqdOzJ8iMvZIMAQKfc5xJrMZyUe8Bs5whaHbnWbDBGcV1Krp//37wANRFbhXf1vwwAcO/MkUVe3cSFijuY4ktERERERESREKERVAMni/cBAP7w0Agus2Q0NWVbiKlJ5S9zr9EFUo5U2lgdl5GxYsvHVEt6g/9o7atx3/jtAIDpThMbk/KYg7UqJlwZQQ3go6F6j2JIIBVKL6AJA095koa0w9mA4zUZQZo2ZnBV7HUAgMvjBRytr9AdJY+070VcpcK0O5NotGXCeBjfjN3VL+hP98XaVwAAf7nxLnz8iLxuv53A7bn/AgC4r/QXejHtN6zox8OTqjqtlUVajcq+If0W3Dwon//3jn3pWfsvKiOnvR4k24zBC3q/r1P7yTYNZNUipyaAhC1fw4PlNtakpYfwtuFBPDQlo1NZ18SUyocqdjrIOvJ92OUdRTqUnrw1dgF1X3p2j4ZjWBdIr949EzM4AUld+OnMK3Fdn/RwfmvcR5+VQN6V9254AayOFIwoO2uRDno9imuCdQCAcXMcvzQgqSpjyevw+aqkgrT8sh6peufA+9BQQ3p+EGKNWlh3f7mFrCPf0URQwLAq8HVLX59e/+zeU5VnVfE9e6rdhdZRo94ePNTVoGgnMND0u0WMeo+Vwyx/n2lbiKuU4LwToKNGViebIezux/Ohqy7HbQMdNSrbDkK4qgvYD6TiLgCk5j2m5gErk+r7YwAT9QDd0aC8a+vnzLRNZFSPpI9Ap/i6pomkL72IK+IuTEN6ifvjJrpf16+WDiEdqO+WWUC/Wlz9aC2Piinn77CZwbBay7XYDjDWlutIIbFBFfhYflEdOX0+6xou1atz/xUA8M3Sn5/3yGlXqfYM7kq8GQBw7lJCy8+ADVcV2hlMbUegCsONFXujZ5X6AT3iZRgu4q5kBayzrkMV0lu/OrgMebU29HiwF01VAGUpKZaeX0Q+uQGjloyOHqjfr0dgbSuPZExeN+OO6ArotdZp7Ddk+stKZzvGWlKsZW/9s9jly+vknFUo+TLSscLZignIaMCDM1IIDZBpE1tNGTG8eziP4zU5ke1aTKccL/86sKFOh3YQw7/PdEccff33MJT1iwFgOHMdThvyuQ2Yeq1FHx2UfdkHKWsQU/PeYV1B2inNoIzLcTUA4EAsgXJ933luqQdPVfhNxdfpYlTyOnItzCQ3o9WR7822cBuOGF9Xn2Hhfu5uUybs1xlhBmz88sjvAgA+X/3agpHTTfmfAgC8bXgA+0+sAbCwAFSUzD/vTjcClNu9dlhC3exycQMJT+5pa51VUAOfcE0LGUe+p98abwJqVknMsDCUkHtVzk1ivCH7M2ckYal7rBeEeLQiqd1brDW4ekgevz7l40BVVZ83DfQ5Lta15f42Z5RxlaqufKJRxwSkrTuE9Xra2V2x2/CMylY44QwtGBX/9DUfBAAcqdl4eEpS0vdgL26wpd1065CJvxiT6047qOENhd8EALxulY1dRdnuByvlyBSDlPuP7KumF+Lvnl67rNvzSOmvl/S4Nfk7cbz4jXM+rpst89rE6/FPX5Ipdf511+KLL/kWAODR2Wug6mxhZ6mMLYnsc16j0TqJj05KrOEvuo7phUvZNsIwXLYktMUWP95QeD1en5E5nv0xYPec7JhWECCjGvkGDOxrqnmk2KXnZKwzB3HCl0ZZFimsS8rcholGGyNJSW84VW9hnyEX7IFgRA+rzxhFrAgl0MjaLr7TlsZN0yvhzsTPyOPjNo7VGjqvf1Mmid0VCXYTcPCMIRfSijehg43ZypMYzN4AALjOvA3v3yx/v6xQxC89IheWOFy8fEiCooemGngylPRd10jip3MvAQAUW4FOmT3mT+Ox0icBPL8b78VaxHp7QVJ31oWjWJ+R/R8zDagVfFDtQC9Vcrrho9iRz7IxHdfl2OPzqrZON3w9F7Hkt+Crk2OFk8QBT+Z2OqGDK5KSknqy3sSMWu7nZbmVuhLs/kodSdNBX0y2fbrVwbRqNFWNMkqQdE0PTYxAlj9agQKOGDK3ZjPW4hdVJf9HZlzsyMv+fPMdR/A/vyDLNzxTDLAtP6+kuWo0fXru33CLIwtE/++XFPF7j8oN7ninhH2BNNaWNlfs4hzHfPoKvMqV7b2iz4HKXEIQAll1g01aoaTXAih2DKj+BzhmiJq6Obsm9HNjZoiplvxS7gDj6krpmAbSKne46YeIqycMJw2orCfMtUKV/iuP90PpDJDP0Aucj1SbqKq5XSECDKvUwzmvCVd91uv7E3jFoDS+r159Gt89LB1jlY6Jqba8SandS2eea/fea1XKwHBcNqodGBhryIP+z+RXl7wIedeLdRzPdi7+OPMmJBVwYNOHzzqv7nxcm/9lAMDjxY8t6fEX65o6XzcFzzBMNNsSULh2BusSNwMAjjYeRK25WGqWei/Y2FZ4GwCgLxjUy6PtKn/2vOf+GbDhOHIP7Xhl5FKbAAB3ehmfqAAAIABJREFUJd6o07X2dk5jUi1x0QnrqLak4/hs29n9nFtjr8LO6ufU60/r4OdY8dt6vuNQ9ia8Oi51JRpegC+UJc1ZOmvO3el3YY9j7142lL0RgMxl7877KqSvQsaWIGK2dUjXM2i0xmGo51p2dkHHU3dKyUjiGhxQc8T6Mlfi7Tm5Zv/Vyf+O8+nANs0khjLXYlJt0+LfAQPXq3Pk+tQwPnpqfqVSNcffjJ3x+SP5l2MHpF3zleL/0H+POcP4+g3/AQDw7ck0/vCgzGvPJrc8K8he7uMon6+Q3q6r8N+ReJtum2QdW3e0djvjAWC25WMkKc+teb1tP1xpwVedFGtSCVRUb+xYu4qVjgyyHPAmsVqtBpGwLUy2Zb9uy6QxkpT3GquHKKo5OG0/RCFmoeHL77tbp1Ex5HszGqzBcVOCz3XBJqxNpPS2drdqvObh2gH53K8YLCPlSvD51/v6sDqllt7zgWNV+W75AbBfVYkdQB7v2iDPNQ1gWg0gPTgV4oG2tJEPz30FS/leXqzravca80KnBHTndV6o+aSLySQ3L1qZvTsQ9l+32JhpS+P7nU9++Jyv6dgDvUrdZ6gSLyxcqOPIFF8iIiIiIiKKhEh2t2fCPgyqQiQnayE2ZmUzfzRT0yOoR5sVnFKTv5teEQeC7wIAJtyVeuHg/nA7yqo36Y6RmB7VmW1Z6PjdNL0c5lQxGB8dTBiSMNPw8kjZkgLlWmkcDySd4unGHObCY1hnXAsAuCqfwNvXyDb9P7t9Xf211ppYsEbbq2N3AgB2d07iI/sldfXVI0NY5UjPw9+/9TD+5rsy8jaOSV3hNxlbC6cgPY3jzRYeC+4HALwh9RrEQykM9YPS/z7fXXyB9Xo3D1SlMvMN/e9BTS2amU0aUDVxELOAPvUt9AITJmSfPVOpYpVK4T5ZayOl0oBvGrL0qz84CQwl5O+zTR/pUB4fg42n6nIcE4hjjSm9jkcqbewOVcEAs4mVwXpUGvJ+N/VnsEONdj42148HZnPqtRw9Wr6tEMcRVS/i3rk/wUP7bwIAfGD4Nbhzq6R0/tm9m3XlWdMAdqoiCJVOBzU10p131+qO33IzBk/1ujqw9ShIlLQ6RRy2ZUR5qDGKbbJr4IXAUEzOr1ZgwFTroKZDYFqNjqZsAxW1P8IQSKmR81Qc2JGX/VHxLH182z4wpdYibvkBTNV7d6oWwjLkNaudAJ1Wb3Q97ZgYjMv/JSzAVdWEdxRiqPtyITlaNXCiKhtiwdQjq4/PGJhuSgbFvso65FS1aMuATn9J2b1+essAXDWin3VCpG15/NZcCd85LaP2a80dmDZ2qc8cjXSm0fwrceoFVSeMNntYCu0kYqtwz/W/AwB4y6N/8oJesz/MveDtutC6vf3Z5BYMpa/Uf58LJdOjNa/AzWJCeNgz98/PexvmF4wJ4S3oae8W5/i2+S1cY0ixjRhiWAep/DlrTiEWl8yGs42gdqfOTMVP6IqUc9VpHJ37ynMeO1l+BA9ZQ2rbzGeluS/nupoGumMCcXcFbrZfCQAo+y08kZaLjWGYmG3JMU27K3G6/Kh6ro+kGiltexVd9dYyk9imRovnMImbcu8BANw9NIiPTt2vn9tNGz779Uiua2tyt8FFEhPnGD3Pp7ejYchjTtV7WVzDuVt1++XZo6eZpLRxxorfwxieW7ypP3UZPnpArsf7O72iSN01YHuWe31Uua832tM6PftB7zv4wPBrAACn6r11u9tBiIIqgJRzLRwsyXNbQaBXjdicjaOtMnOaXohr+6VNeauVxdOqvuQ6VZAPABqej6wlbc2mH+KYGqwbb7QRqPZEI+yg6JkYTcj+3GgN4bFA7uNPB9/DjYZ8b7bk47oA4d5igP64fEdXpmxdcOm7U1l8dUKuJb+6wceD07J9SduAGqDFYMICIOddqdPBk0W52RfbIW7slwfVOgFWqqKQh5e4py+k+deu7rU07o68oIJcF2Pk9INqzfE/eNsBvPsTUrV5stXEN/HnZ3z8faW/kH9/uPDv89cuPpP1mdtwuPyts26LAeOCnY2RTPFV/wsAeOeKD+KKglrOI4Q+YY5WQ11q+6HSaWTUUhEp08VuyGLftWAaKRWceGELQ5ATYwz74Bjy+E3B5Sir5P9Rq4CHfAl0u4vTAoBlOHqx4bn6IQymtmMlJHXJCE18/z2q4u4jg7j1oc8AAFqdCb04bsLtR60plYI9v4gNBUm98dGBpebXXmVsx8+ulQtDn9vBO5+Ri/dk+RHclPs1AMDt/YOYa8vhekl/gF8/IO+1lAXXn+3ipDGFiKkS1dtTPwVfzYt6WWaNTsN0TENfyGtegEJMjnXD6y0/c6jSxtFAGj3z99mVsZVYq1bAfnSmgT5Hgs2mH2B63hI8fWoZlzAEDodSjTkXFmDCgKm2tc9K4HevkO/Bg9M5PDgpN47/a2sdMUt+/su9WfzT5J/K81OXYbUtaejXx9eirkrK/8jfhfcNS6Xl8bqBh4vSOIrB0SlaTbRx+6Ckwe2e6+h5utWgjcd8mVsgaYrLncbU/XscI7mXAgBel7wVl8s0NpiQZWcAIGP7aKkU36N1R3dATDZClFW6Utwy9WcdTZkouN1y+0C5I8+daIS6om/MMhGid9PuhN3g0UBD3dhd00TMNPV8n4RtYEdBnnPX2nE8MyX7+btTCd1BdbwaYK4t++2UP4e8WrC8z4lha172QdYJoaadYijm4UhNvnMPnG4jq/KLVyYtvZxOzglxuilv8O3TVTzYlHOzWyXwXJjiuzx+bvD38C9TZ051ujr/iwCAncVPLum1Lk4q2pmXMRjIXKuXvZqfXt4NTABgMHM14qYE3ePVxxdtlCymO38VhqmXz+p4c7AtCTA9vwQDFkbzrwAAjIaX4fKEqv7tmjpNfroZ4GuN+wFIZfruvThAZ8Gc2cX0liRp4ULUXrhwx7F37LLJLXhdSuaUPukdwN45WSZkIHM1srbUnPDRwfE5aRzOn68dd0cQqG10rBRijhzTzeZLUTElmnl2h4Nlyfyys1fMVh1vycsQIoCnOvEXu4b96uiHFqT1doPP22JvwZfVnNJUfKV+HdNw0OpIB2y3UvFzt8DGLbn3qc/v47H6ZwGcLb1wcRfnfOylNxqGi5dlpa22Lp7BoAr0BuLAVTnpGNhfjeFoVfbzSBJ4arb3/e3WOThUbqKptr2JNlY5cuwGExb2lHvBT96SczthmzjVkrZLAy09Ze2UeRKxMIFVkLnfpbCpBzM2Ft6Ip94uj2vO2fjvD0qQ80ypCUd1Ch/AMWxWbebJoIKOIZ0Em5whPO5JuvVN7hZc3y+PL3UMHCir6sOtDmz1Om9cbeF7k/KZDQA7m5LS/3jx77GUjoaLcRxzqW243n4tAODb5Y9EYL762Q3nZL79/z36SvzluFzvn0+9hZH8ywEAk5WnFj0n465aomaRoH2p9SSY4ktERERERESXrMh2t29VRRuyjqmrd040Qsy1eqMoB5oyOrXO7teFVb7auB++St/rBA3Yqgd5AGvQgfSKbAh36HUQH2r+OwYSUgRnndGPAVN6kk75O9HxpFdqReIKHJ6TNaAuL7wdQ8EKvZ3XF7JQS6pix2AJW5+SFI+dxU/iitQb5HXNQRxypPdhf+M+VH3pDXTNNAYh1VwfDx/HmwMppLRtaAavPy7rn33ZcFA3pNfsY9OP4ybrNgDAtzo2cq5UuKvUD0RsvcNur5iBllp7dnftS3hpUtbq+kHlFG5JSy/xoXITfWrx6S05C88UuyNjBk7V5VgfDSbRNqQXtoU6RlVV3boX4KFp+fsJ8zjGO5LiOxwO4saCGimo+xhvyWhq3HBwldpnpgFszlooqY6ybbkADU9Oh/e+/iD+0w2yVtmTH/F16uoVBQuva/0GACDjOPihJ5P9v9E8iQHI6/ZhGI+o0de0Y6LPTOm98r22VHV2rTTiU5Lu1oGPB2tS2XBT5g6YwfxTcrnTmEQYNjFZlZTVJ7AJliGj4i8dCGCrfZOwfDR8OY5pO9TFkCzT0KPi5XaAhhpp3jPnw+jVC9Ejq50w0NV9LcPQa8llXUMXWwrDUPfyhqH0IKfVaPtwwsBbr5BUwcLPrMToCSmkdu33O/jc3rXqdU001FqrGT+Jlur9O95poj4jo6lJy8JVKvd8wAUso1eE6VRTvk8zLQeVtKRZrUqamJbLDupowfOjkdo7X7eYyrmK5vwkWWz01ICNNw3I92XnmTuWl8mZrwnPXlxdPzps6jQ2wzDRCmXtcPMsI+3dxevjTh8qDZm6YJquTq21jRhWmHLTGwwHMBqXEc1VKQtHKj4uy8m55QXAPXN7AQB7Jz6jXz+d2IhcTK6Xk9W9+nU7fk1/RxvtqUVT5c63cFN0hOiOUpbrB/GZ5v8CALwp/59wx2qpkPpgZRwHWpLFtVhmlGXGYIaO/r1bSfew+6geUM6nr1hQLMxUlYwNu2/e6HdvBD2T3KzXN824I7jeuAnbC/KcJ2ebKLjyfp/80AR+6kPS1pDRU/k8t+f+Mxw1gr8Lu5BXxbHiVhaVtmSPNVpTi47SaIaNkiGjrFPhEbQ70V6LGPB1lphjp7A3fBgAsBZ34MmifH8HnDgmGtIOPd3wsSGjMh2aQEpl/sy1fDxV6q5L2oGn7kkZJHVV+qfKZcypIkfrjBGdUZQzLaTU8c0ZcXjq71uwAacwg5uH5L1nW3G8b4N8z37uT0yEa6UNFr/vMdxyQBpChhHX9+6x2SxSKltoPfKYass9rdzp6Kk3u1rj2BHK6+ScUH+ex9unUIMcx4lja/UKDge8SXTQen67+gIq1fbjPvTWlR7NS/p9VKfFlJpHAQCPzwRYFci59twVq89tKRkr50p3Di9gOzXCKb4ik9yM3xqV+UXlDnSwujoV4rNjcoG9PNGvU/Z2h/uxWuW4nzCPYUYtG2MbMdimNCYTRgEDahmSFOI69bKOFi5Pyol00wBQU8tp5J0A5Y6ceP84NoG7B1bqUuFrEh6aKrXxqaKFD90qeezf3rsaHz8kG/vN6sexOSMB5+pgFA97XwYgSxl0Gw+mlcSGrMwJcBBDoO40HbSQVsvgtIwG0mpeVH+YQ1GlJi+1PPV8y5GOtrUglZBjYQLDocynuLoviZy617YC6IAxaQNHKr35m8chNzkHMVSN4rzXkTTqPcbjaPhyQRy1rsQtqXUAgJmmj9Oqo+HKTBbXq7kQx+sWVsYDTKi0zAcm6/h6SfL3v3zjb+HOB9Wc4Vd/Hv90RG7I5XaIjVk51p88fQBjHQnaqs0xOJZKMXeHUFKNuh2pn8ZNWdm+feU6Hul8Se0vE3FbLfcASy+50G9vwKm6LG7dXZ7hXC5WhbtuqmAhvRW3u1I5c0e/jRVx2Z8pK0DF6yVkdMLuouMGJlWsNl73Ue7I9rYDH3FT3i9AqOfNZJxeleAw7FVAHEkaGFTzXeu+gZJKCW76QM6B/r9N6QYuG5Sb+NA1bVhvkmWBvM/8EJ/+inQ+PTZr4lhVvmhDcQdH6yrgNIo6Db1lNLBedYTs6Evq8328HmJXVb5nNaOObCjpjSvslG4wPBk+heOl++WzLbExfeFTfC19nbtQy7tcKN3rxt65z5zjkWf3ruHfBwB8YuKPFvz9TOmPO/K/hGFIeurXin+6pNdfjiq+Z3kF9e/827sFU90DHSur00TnByq2lUeg0tuW8t3dUHg9blDLymzO2XhouoofdeT+Vm2eWjTIfEufLDHygH8/Tpceeu7WG3F0ly+42Ol2F6uKb1IF6bclfha7VfX/43Pf0ufnmvydOFH8NgBpH6zOSlrfyfKDCwK97nISrpXCZPkx/b6uusf4QRuZxCr12XpLQhRre7E6dxsA4FrzWlzTL0HOeD3Er2wq4oFpef6puqGvf/uKPj49r1OnmyL4MvtWfL70cQAyvan7dy9oYbYm6aDzt3lt4a5FUxK7ne1bMq/F7rlPnfExS3Hxzkc5pqaZ0OfMlfl34rUF6WwpuIAqhQLbkMq3gEw16VapP1LrnWtj5gTMUO57yTCNqinXJTu04agaHWutAXRb7q5lIqXqIlyeN3BZWs6Xum9iX8XBsLpHD8Q83HXrUQBA8VgMzZY0vNa9tw+H/1qOzeePrcAPTss9cNwvwVSdGQUjhT2GdHiUOidRcKSN7aODTcHlsn0wMaXaZqfC3XqaXLM9h3xCHu8aaXhqAOlk8duISoovAKxQ05jOdD36cZeMrV20zdm9vtRaE4uk2rOKLxEREREREf2Yi/AIaq8HuLvW2ce3bsOxuozkvGvnh/Uam1fa63Q1tFP+HHxDfi4ZUzik1gXLpbZho3MLAGBf8z7YaoL5kLsNeVUdLTQCxEKVsudkdYpfwwdON2Q3PVmbxKw5gSOl++T5mR1wVZGHP1hzI97xGhlB/dmPrcV9TZngb1sJlOtHAQB3596PUiBDSk/59+k04kZ7CoMZ6YmerR2A50vP82DmahQbkpIXc3JwLEkZ3WG+Ek/4sg3PZxHr5eztf/fK38c/TH0UAHBt6m1424gUsnp8OsArhuW4VzwDO2d7ld+6a9vWzDKGA0mp8RDgpCkj5C4SGA2kl9gxLDyFRwAATb+MbWoB95fk+zCgaobsKfr4butHiBmS0jne3ImhuPQEvnvwGuxRqcYNL8CuUHqAb09ux1dq8rqT9afRl5TUCtdIo9SRQhK2GcNMdTcA6a1+Sfbd8vrmEcy15Tj6QVsfR9dKoeVL2l3eWYPTdTmWF7u4DrC042gYLjbkpZjAlcY2vEM6ibGr5KLP7a4BF2CiaennzKiMnslGgOlWbzSkWx05DENYKmXIMUyd4msa0AWJEraBUTnNsDHV1sXSKh210HbQzXbw4avR24ztY3Vazq+2b+FkXYpC/MNhoODKZ51s9rbnFKYxrRJlyu2TsFVxrYw9jARklK2NBoqeVEi1DAcpS64duXAQgRrxOentRKkuUwiiM4JKd+V/CwCwOhnH3439t7M+9u78b+tCJd0KiOcSlRHUZy/k3i1eE7My8NRoZNYewXRDrmv11jFdCCOf2ICGJ2mV5dqBBaPthkojvFgjmufuub8wLl52kVznXKcfG9K3AViYJWCaSb0tYdheQvqzgXRCskRMw4ZpygiZa6ZQa8v+m5/en05sxO1xmUpVDzrI29L2ub/zTYyYl+PqmEw/2tM6jT0N+T7VW8fQl5EigHfF79ajqe8Z+RD+Vp1TrjOEzWnJGNtX/rIekXesrJ7yczFc/PPRgm1LhpvnzeLnBn8PALA2bekR6NUJHwer3fW1Q1jqRna82lthoC9YgQTkWMwZc/AM+Rw+Ojo9thM2kDYkw8MJXRRCyfwbcVPYrFLsLQOYbgFPliQFPGfGUVPVkC2YeJ26oZ6sGbhzWEY7Hy8m8NFJKfVa7ozprMOCtRahur+dqD2MvPqeWYYDP5QR18twHfZBnlvvTOvU8zAMkE/KdzdvrUbRl/vnUtcKv9jZftnkFr3t7xt5D/5qTNqqF/MaFCXGvJmgZ86+unAjqBEOUPWjYNty8v3llvfj135dLnAf+PAwxuu9D/yg/30AwIixFYlQohAHNk6Zx9WrmGiGkipxJXagpMqtP1j5OBKuNDJvjr0FRciF/8nav+sL63DmOlRUHvY251VYaeV0VVkbFg77cqLZRgxrDbl4180q9lW+CkAuVt2Fch/zv4GXqGphc2F1QXpuLrUNgFS7K9Ul8AqCGgxD3WjsPsRd2RcZZwTTdcmZlxzxc1d8nW85Lt7dL3Eqvh7/Y9M7AAAn6wY+MSNpTLfYL0NfTF4r5xo4oPJ9N2Vd7C7JBfQHrXvwUvdNAKTq6smOHNOqUcGoqlaXsCwUPbmQHzCeRh8kcM2HWcTVviyFdVSNMrIqZXrSHNNzczy0kFJp1ekwi+OGVIWOGWk9FxmQNEAA6BgtHG/JhdnzGzqwiVkZtNUi3p7fRKg+fxB4MFV6aza+Go2OBN9ZdxXmmnLclzpPcDkaxd1qnu8Y/HWdWvTyIQ8VT26MM20T3VPTMaF/Hq8HSKm5orPNQM9pSZq9pXxKQRMJVaV5Rbw3H0ZeS35ZlTJ0RdB2AFQ6wIxamqYdhJhpy/emz3GxSd2s1yQD7C2r+TFzFew1JBUuCAOkTLnRJ8MsSpAy/FV/UqfDOWZCb0O9M72gKqXrSIrvoHMZLHUhn+jsQakmjY2lNuYvboDauxm3fu8/IPbhT78o7/3jZDT/SmwOrwYA3F/6yyU9JyoB6nz59BW4zpLpCkfMAxiryfc+7vahWJX7RzK2CtfEX6+fUzWkw8yBq+9tMo1BOtVqzaNY+vz4bkfV+Vfb7abczdUPnaVxeH73vaW42KmhQq5NL839Kt6xWlJc3/90rxMlGVuLdcmbAQABfBypytyxZwd8awt3AZAqzYEKRjKJNSjWZC7w/OtRzBmGr34fzdyITqimOtT2wzBMnQLe7szowGtb+m68sV+CjfF6gH8tSrXg7hId3ddNxmRqS8evwQ9UUOXXz6tGxlKrgy5mOc7HbqdKtTmGl6XeCQBow8emhHRwrkiYyKvVBw+WAxRictx/OFfS81fvTr4aJxtyj9ln7MJoKB1MA2YaR0Nph9aNMmJq5Yr14Wo9BSqGuB5kaRktePBRVksnjgTr4arzsQ0fk6a8lg0H17sbAQBfqH0eo45c93bPfRqWqtC9IXuHnv4y2zmCIJCfQwRIOjLAYBtxFNXcyJZXgqU6tILQw2C6mwYcR92XKQXTlScQpRTfhedjb7t+YYVMDXm4vRf75/7t3O9xzoCuJ+YMo6AGOlJmf28ftw6hXN93zvcCpKMJWHgOnr8zV4fv/R8wkn8ZJtSyVws73s/23B6m+BIREREREdEl6xIYQT2zybe9E8dOy2jiiXoSf32go/9vnyHFa2wjhncPylqVn508iRuTUm3MMYFptdbiA94jGA2lIuF6tw9tterwY+ETGAwlxaVh1PUImW0mkLGH9XuVOifQaEtK1Ib0bRgKpPrr08F3MVeVXstt+Tdjb+le/ZyhzLUAgH5zra50VuqcRKBSJTLOCOq+vGa1OQbXll4s20ro6otrrGtxzJPejFprQvd2LrWHcrl7+9/U9zsAgMG4oyu49sdNvVbl0YqPFUnp7cu70OtqdgLgsowco/snQt0DWfdCTLdk//lhiP5Yr8rhlEorbYYd1CA9k2vsPmQdC+WOvPCYV8aAKem+D3hfxxZT0sEfqXwS63O3AwAq/gQmy4/o1+2mxbl2RqeEtDoTushD3CnAMqUX0fObaHsyMhGGHmD0+oaG0/IdTRoFHFU940tNJ1me4yg9anF3Je5K/wIA4GfWmsg5si2uGaDYkf0/2bJxvCaPtwzo3uPpFlDpyHEfihuYUx37lXaAht+tDGwgo74QDa9XGdgwgJKq5j3TbiFlOYhb8ria56OieuxlAWm1WDp8zJrSkzztH8a0SsMGAvSntwOATtcFgGZQ1sWr5o+gtv3avOMY6BHUPme9HoEveSd0oaylrjW5nCm+f7hZFv3+3WdeDse+40XZjgvt8sLbF6z12C26c8/sH5/x8YuNxthW/oyVRUfzr8Sp4v3qt6hmpZyfoexNSFhyz6x5k2oEAxjIXIMhWyrZj3V2odWR/eFYKV2Nut46DtuSUbQgbC/oQc8m5bm2lUCtOYEBlQmUN0ZwuP5d/X/PZ73u5bAcx7G7tmsqPqqr858s3ofutfZVud/ACVOmH8x5R/Xx2jP3z3r/j8Suho9eOuiaQEZWvl/9OHw1jcS2C4u2EbopxFvit6Np1PT5lUluxkZXqs7vLP4D7sxJJfsJYwo7i/+gnt07R2y7D+szt8lrhnkc9aTtVKw+o9fqNc24HuENw/kVzy3EHLkOx92Cvq+eq5LomSzn+bi18DM6XftXRj6kiwAmbAMb5ZaBpm/gWFX+fqLWwsasZP7lXeAJNS9mLmhghS2ZC0MJG9OqwlLN99Dqpn8jhIVu5XsT23Nyvzpe9TDmlfV9KQFHZwiOWgWUfXmPlbEk7inJWs9ynh5Un9nRI+6p+Hrk4+sAAF7Y1KPiMSunU3xbflm3YT2/qds+eXetbrdacDDVknbxUkcIL/5xXDgi+M4Vkqr9j6d7xcEWu2/M5zpDC9px3UKTC7/vz6bavOltC6pwv1AXdnrGT3SK70LdiothGOA9K/8zAODdG0tYmZcL8A/HVuAb49I4tgzgYEUCkm+UP4I7su8HAIwmEnq5i53+QTTVSXuLcw1U2xi7O8d1+mefmcLxUFJp6kYFVxpb4Kvd9s36P6MvIUP0U7Xd81JuDORVw7fSOL7oAtndYMY0bF1S3YABQ53cpuHq3P/Ar5+xkRVzhuGoi9hSh/mXtzG18ALQrfh3d/IVGEnKDflfZ/bijrTceEvtACsScuKGIdBUB+lItYVXrZR0lpmWgSdm5VivjMcWvNv+hlxIrskW0FbnUcYxJNBp9+YWx9S8jwP4ka6GuDp3G2Zbsk8X37eLn6Dzv69BUFN/XXjKdb8D2cQaFFVq6MWeuwg8v/PRVEF9PrUJr03cDQB46aCJ9Sm5gdlGiEfn5IbZCqSKISDnZsxSS7egt0fKHQNTTfnNC4C+mDzBn7fLkjZQVNfZ8ZqHThggbsn3oxMEqPmyT2bCMtqG3AwqxizmOtLAK9UPLti/3c+QcAf1ovetTqmXymv29ksQePDVHPIQgV6+wbUz+oYchB7qrdPq8WeuYvpsUZmDen3+VwAAs8bEgnT2qNhYeCMA6NoC56Obmu4H9XPeqH924IPY5Uua/fxA+GyW+1xciu78wY3mTbpCZ8toYRLSKJ2/7EDcHUHclWtTu1PR50EQeAsqPnYbPzdm3400Emiit28fa9wD4PkFGMtlOTv9JKlNLVOmUvcAIOkMYFot9RUEddyU+zUAUsF/VV46UIuto7oz+xrzVdgVPgAACzpVFzOcu1VPdSj7Y0hZg3BVCumV5mYvT6bBAAAgAElEQVTMeHItnDKnUAmlo2/+POeFn8RGf0ZSXVP20KLVehf34qRtL/f5OJyT2he1zmlYai5nv7MRQ6pWxl1D/cg6qnq9HWJ/Rc7H/hgwIbsbTT/U1XoTtoGq6tR9qjaNrDo+eTuG/rjc/07V23jJoLzXyXqIk7U2BlRn/XDSxDG1MkLTD3AokHNyZ/GT89op3rPaHs9N0Z+fwmqYLkLV0QDD1PtJpsdIu9U04/DVVCfDsOCq9s5S5yMv7/l4McOjXjvSsrKLxgznyzBcpOIy2GZbiRc18D0fTPElIiIiIiKiS9YlN4K6mO5E5uv6DZ0KeKphQHUs4ZuTRfiqd2LATONVK6XX95kicLCmKuY6CaRV9RU/BHY3pKcwjzQ66rnrEmnMtTycCmW089Hi3y3Yjm4Pdb01Gele4+XuXVzM5sJbAADbjcuQVKNiIylLj6BNN33c15KCWNvC63HHsIx+BSHw5Ix8pqxrwjLkS5ByDDS8XnrNupT8fLpp4J65vZgLpKJcqXEMTbWguOsMIuGq3uT6oYu+Dt/5iM5xtOA6ss82pW/H2/qlsMOVuTaSlvSkNgMTLV/Or3Zg6GM61bZgG73L0HSrV6wg0SsGrNePs4zeunKdACi2Al1AqdT2Md2REc5T5knMqFGwmeruJY9KL4cojKCaZnrBiO9vrpPU3z87+ocveLteiBty7wUAHMdTkV6jLjrn4ovnyrwUeukP+7EXMqWk2h5HEMhnnT+SmkluxlDs8uc1uh0lUckuMgwXK3MvASD3upwlI2PTfh3fK/0vADLKfXlSVjkoGVM4VZOiVtn46jOOnJpmGq4to2Xz2ydX538RyVDupQfDH2LIugx3ZmUE9+vlA2fJIliOkaalidL56DpSNKrdmdQ/D6WuxHXGDQCkEGRBJX6N10O9PureUlNXEx90ExhSN8Tj1Tb6VUHJrGuiXz232O4VDBxMmJhsBDilCi71u3HMtSWz6eul/wlTTV2xzPiSp6Ish6icj5aV1dsymLk60veiKPqJSPE94+vA1nP63pD/LyioE3coYUJNb8QDczNoGXJyNo0aXpeTBvTNA229VMWhmqVTEDNOiG+NyeOfNp7S81ELYRbfLP05HFsql7l2RufmF5tHl1x9dblF6eJ9Jvn0FbqaXDbI62O3xhzQcyc2pdJQq5CgL9ZLDd1XqWJlTNJfLs9bKKnpyadqvp7v+qi/G4fmvqTfbyh7PeoduUjXmqfOMU8gOqJ5HHvziK5PvhXXZGXeW1/MQL9aiqYTSjovIFV+h9TyPxk7QNzqXZIcFbhaBnBKLV0z2TT0/FXXNFBqByir3O1pv46Dhsytmyg/vuQU2+UWhQB1Mdfnf+U5HXEXSza5ZclzlZZbNM9FOl/ROo69ALCbhrk6eysagdSuyJmjurJoLrUNpZpUZjbNpO6Qu7zwduwrf1l+zrwRLUMClvkVSQ3Dha1ePx0fgWulkDDlun107isv8DMsj2gdR+E6Q4g7sl/rrdPYkZFlfq5JDqOp6p+caNYw7MqUrZl2E46at5uxbWzIynasSoZQs9TghcCKmNz/DlRt3DMtbVAHMSTCOMqGzOPdXf6cngsaInjRUkgvtCgeRzp/TPElIiIiIiKiS9aPxQjqYl6X/23s6JOhmfF6gPGmjLy1Qw+jcRlha/uhTgdNOyYeqUv1TQcu4qGkQHTQwV7vfgCAZcbgBy0023PqM5jwfOmpPFdVryi5lHql+jI7dPVJ24jBUutkBvB1AakwDPR6YR2jrY9dCi42ZeTnZypV7Ap/AADwww78oIWOJ8fONG3UmqfUa10ao6dAlI+jnFOO3a8X6d5hvBxZu1tNrreuaRBCF3lYnzEwpHqDbQOw1AhqKzAw2VLrrLaA8bo85lizinHzBKY7UuSl3DiqCzI8n7UXl0sUR1C7xaPmj0LfmvsAHijLwuXnm/qeT1+BN6VlDeNPnv6zRc+z/zj8+/hSTUZtpiuPn/d2L5fonot0PqJ7HOX6N5p/OdaFUoDxgdJH9N8vK7wZB4pfkIeGgS6oGHdH9Lqkpfrhc46cXUpZC2cT3eMobLtPV1Q2YGOLmt60BRvgmt2pMAFKqijfjDGDQigFhla5aT3iuiXfW7Gg2A6xqyIjprPmFMY6u1BvSSXZIPR05eRLJbMIiO5x7Fbejjl9aLROvmiv++PqJzbFd6lyqvy9aTh4Vexu/feiJ4HrUfMQTHWxP1L+NnyVIjOYuQYNdSGpNceRjK1AUy0tcykFpfNF9aQ/N0uXqg/DDkxTUmHyqU06iN0aXoOqWk5mT+dbsNVcCz9ood6WecWZ+Cq0vQrqrXH1WpdOUDrfpXQcTTONdFyWehqMbdUdDSZMDAeyZE/GjMFR6frGvHWzq14HMypVqWTMoORLZ0LDm0WjPQPPK6lHXjpB6XxRDFAXem41x8Uek0luWLCkyNbCzwAA9s3dg5G8LFcRwtcVY2/NfQDX5vL4l+LXASyt6mgUXUrnIi3uUjqOudQ2bHRkSbTHix8742MSsVX655+khvSldBznp2QDvcr+YeghZku7Zl3yZoyq++SEMYl0KCnZdaOKY56sOuAHLV19vu2V4dpZvWzUpRSUzndpHMf5977D6m+XZlvkQmGKLxEREREREV2ylnUElYiIiIiIiKiLI6hEREREREQUCQxQiYiIiIiIKBIYoBIREREREVEkMEAlIiIiIiKiSGCASkRERERERJHAAJWIiIiIiIgigQEqERERERERRQIDVCIiIiIiIooEBqhEREREREQUCQxQiYiIiIiIKBIYoBIREREREVEkMEAlIiIiIiKiSGCASkRERERERJHAAJWIiIiIiIgigQEqERERERERRQIDVCIiIiIiIooEBqhEREREREQUCQxQiYiIiIiIKBIYoBIREREREVEkMEAlIiIiIiKiSGCASkRERERERJHAAJWIiIiIiIgigQEqERERERERRQIDVCIiIiIiIooEBqhEREREREQUCQxQiYiIiIiIKBIYoBIREREREVEkMEAlIiIiIiKiSGCASkRERERERJHAAJWIiIiIiIgigQEqERERERERRQIDVCIiIiIiIooEBqhEREREREQUCQxQiYiIiIiIKBIYoBIREREREVEkMEAlIiIiIiKiSGCASkRERERERJHAAJWIiIiIiIgigQEqERERERERRQIDVCIiIiIiIooEBqhEREREREQUCQxQiYiIiIiIKBIYoBIREREREVEkMEAlIiIiIiKiSGCASkRERERERJHAAJWIiIiIiIgigQEqERERERERRQIDVCIiIiIiIooEBqhEREREREQUCQxQiYiIiIiIKBIYoBIREREREVEkMEAlIiIiIiKiSGCASkRERERERJHAAJWIiIiIiIgigQEqERERERERRQIDVCIiIiIiIooEBqhEREREREQUCQxQiYiIiIiIKBIYoBIREREREVEkMEAlIiIiIiKiSGCASkRERERERJHAAJWIiIiIiIgigQEqERERERERRQIDVCIiIiIiIooEBqhEREREREQUCQxQiYiIiIiIKBIYoBIREREREVEkMEAlIiIiIiKiSGCASkRERERERJHAAJWIiIiIiIgigQEqERERERERRQIDVCIiIiIiIooEBqhEREREREQUCQxQiYiIiIiIKBIYoBIREREREVEkMEAlIiIiIiKiSGCASkRERERERJHAAJWIiIiIiIgigQEqERERERERRQIDVCIiIiIiIooEBqhEREREREQUCQxQiYiIiIiIKBIYoBIREREREVEkMEAlIiIiIiKiSGCASkRERERERJHAAJWIiIiIiIgigQEqERERERERRQIDVCIiIiIiIooEBqhEREREREQUCQxQiYiIiIiIKBIYoBIREREREVEkMEAlIiIiIiKiSGCASkRERERERJHAAJWIiIiIiIgigQEqERERERERRQIDVCIiIiIiIooEBqhEREREREQUCQxQiYiIiIiIKBIYoBIREREREVEkMEAlIiIiIiKiSGCASkRERERERJHAAJWIiIiIiIgigQEqERERERERRQIDVCIiIiIiIooEBqhEREREREQUCQxQiYiIiIiIKBIYoBIREREREVEkMEAlIiIiIiKiSGCASkRERERERJHAAJWIiIiIiIgigQEqERERERERRQIDVCIiIiIiIooEBqhEREREREQUCQxQiYiIiIiIKBIYoBIREREREVEkMEAlIiIiIiKiSGCASkRERERERJHAAJWIiIiIiIgigQEqERERERERRQIDVCIiIiIiIooEBqhEREREREQUCQxQiYiIiIiIKBIYoBIREREREVEkMEAlIiIiIiKiSGCASkRERERERJHAAJWIiIiIiIgigQEqERERERERRQIDVCIiIiIiIooEBqhEREREREQUCQxQiYiIiIiIKBIYoBIREREREVEkMEAlIiIiIiL6/9l77wA5r+s++3nL9LIz27AAFr0R7EUURZGiRFGyZMoqbpIlxZblxHH97LjGjiUln2zL/hzbsRMn7rJlW46rbKowkiiKokSKvQEkAaJ3LLbO7PSZt+SPc+fdXRAgFsACO9R3nj/Iwexb7rzl3nvO+d1zlJ5ADVRFURRFURRFURSlJ1ADVVEURVEURVEURekJ1EBVFEVRFEVRFEVRegI1UBVFURRFURRFUZSeQA1URVEURVEURVEUpSdQA1VRFEVRFEVRFEXpCdRAVRRFURRFURRFUXoCNVAVRVEURVEURVGUnkANVEVRFEVRFEVRFKUnUANVURRFURRFURRF6QnUQFUURVEURVEURVF6AjVQFUVRFEVRFEVRlJ5ADVRFURRFURRFURSlJ1ADVVEURVEURVEURekJ1EBVFEVRFEVRFEVRegI1UBVFURRFURRFUZSeQA1URVEURVEURVEUpSdQA1VRFEVRFEVRFEXpCdRAVRRFURRFURRFUXoCNVAVRVEURVEURVGUnkANVEVRFEVRFEVRFKUnUANVURRFURRFURRF6QnUQFUURVEURVEURVF6AjVQFUVRFEVRFEVRlJ5ADVRFURRFURRFURSlJ1ADVVEURVEURVEURekJ1EBVFEVRFEVRFEVRegI1UBVFURRFURRFUZSeQA1URVEURVEURVEUpSdQA1VRFEVRFEVRFEXpCdRAVRRFURRFURRFUXoCNVAVRVEURVEURVGUnkANVEVRFEVRFEVRFKUnUANVURRFURRFURRF6QnUQFUURVEURVEURVF6AjVQFUVRFEVRFEVRlJ5ADVRFURRFURRFURSlJ1ADVVEURVEURVEURekJ1EBVFEVRFEVRFEVRegI1UBVFURRFURRFUZSeQA1URVEURVEURVEUpSdQA1VRFEVRFEVRFEXpCdRAVRRFURRFURRFUXoCNVAVRVEURVEURVGUnkANVEVRFEVRFEVRFKUnUANVURRFURRFURRF6QnUQFUURVEURVEURVF6AjVQFUVRFEVRFEVRlJ7AXc6T23Z2SY7jOGk8b3pJjrVYNhXfDcCDb+hn9LN/Me8vlvzXitG1/y3LJgyDy9q+cxEE1SU7lmUt1WNkAaH57FDMXgXAnfF3UEw4AAynbL4ydQqAQ+HTvM55MwDftdZlT0W2OVjxceQ2UEzYVDthdIYVKbkn98+c5Gj4PK+17wBgJmjwgv81AGYbh/D92TO0qfcIQ2/JjrV093EO1ykwmL0agDfF38jrh+X6jzctZlpyXZt+SCEu369IQakt+040Q6Zb8vsStk0mJtskHGj7ss2LtTKn7GOkwxwADavGsfoTctzOBEFQX/LfdClYqvu4tO+iUMxew78feg8A442AR1p7ADhSf4x663C03VXFDwLwwsynz3n0VYU7OFH6+nm2pXffQ+j9d1FZHL15Hx3AX6JjnT83Fv4dAB1a7Cz9tflWx8aLPCrd62fbWdKJFQA0OzPkkqMAXOPcSQwzr7H3kiIPwFRwmCDsmLbNzS9nm0dptk/MO4NLyNx1iMeGAfCD5rw5Tm/T+/fx8nN34RcBeMHaweGZL5pvv/XeR42gKoqiKIqiKIqiKD2BFYbhspncFxJBtSyxqYOgQTK+EmCBx+hy8+ydP8kbH78fgJvct/PV8n8DwLKS2HYcgCBoL1v7zkZvRVDnR0jk82jhzbw1eSsAQymbHTMNAGKWTTEeA2B9zmZtWjyHnQB2lGRfL4DxhngXh1MxDtRqsg0+dw33RWf88niJWwoFALb3BfztYdluzB5jb+U+2cebpJcjOL3qXUzERgC4I/1B3jmaBqDUthhvyjVseCHrsvIur0n75FyJDhyuxzhS697HkLGG/L58zMG15fu4DSlXPpfbIU0/oM9EYKebAU96L8n5/KNM1XYB9Ly3uHciqHPP+hXF9wLwvoEreGhC+ov7y78bbZlLbyHmZADYZN/ClC398IGZz5316OuKbwfgeOXxBaqXq4ofXFTktZfpxXfRsuKEYe+NP71Mb91Hx/x/+aKnp5NKSHSv0Tq2zC15ZXrrPs47lhEuSmRT+tu1hbeyLbwGgKF4krov93tdNk7LlzEz41ocqsq85pngeRxkHhTgsy7YKNvYcZ4IHgag0jpOzM1Qqj5/lt+UlHaEzSX7bZeCXr2PF0rMHTTzyosnGV9FNiFzrcnK00tyzEvFhdzHV5WBGoZNHEckDr044VxfvJsPDbwGgIFEyH/Y9TsABEEd1+0H5CZ15Rhh6EUG9+WWAC+/gTo3EbYsMeRfl/9hvnuVXKeJpsXRqnTS3zEaknTk+vz9IZvRjJzv3aurfPmUPEN7yx5lTzrvhOUwFpQBeGP/EBUj8f1m9QhXx2VwLcRt9lUatIz8ZSSeIW8kpKcaHWYCMYiP2Qc4Vn0MgHZnfF67pe3LTS913sn4KgCuSL+d94/Idb5zuEy1I8edbsfYW5V7/fyMz6a8TL625zxemJVtvjZZwjeTsUE7S804d67uy0XnOVxpR8ZqzLZp+j7tYO79KYci6x23T+CYyUApOMFk7UWAy74cYDEsn4E69zxbVoJthXcB8OOrtvHpYzKIJoiTsxIAfKH0W9H2tp3mZ9b8PAB/PP5pqo395zxbwci9y7U9Cwwny0qeNlE618S89+RMvfIuOk6+J8fHVwu9ch9fDQzlbwZgYvaJZW7Jy+md+3hmefatfT/BG/oHAGj6RMuSVqZgxnSNjgWvH5B+0Q8tPnNUjNK9jVlatAAo21PcHNsGwEjK4dmSzO3GrQmGwkEayMF2+Q+e0Vi1rOS8wE/vLYnpnfu4uDHnlr4fA+D51hepNQ++7O8/s+5j/N4RGUfP5hxIJUZf5gAqZq8FoN4ap9UZe9k+ufQWUsbOGJ997JztvNyoxFdRFEVRFEVRFEV51dLDEdS5iEjX027b2SWN/F1KvPG/xx1+3zm3+8k1HwXgD47+6pKduyvBabbGsIzM+PQI7fJGUOc8UY6T53XZHwDgqkyRraLApeZZHKpIm6dbHilXIio7vUOUQpESXhneSAn5HcetXQywDoBi2E9gjr85led4Q6Khz4UP8ebEW+S8FtzfepCZ+j4AtmbeylWxtbJP3uFgRTyeoxmHA7Pi+fla574zeopdp8CK3A0A1L0pXFuiTQk7H23TCet0TFTW8xvR9wk3zyr7SgBK1ikm6iJJleQz55YW94p3MZfewvcVvg+A7xxtEbPl3iXsgN0Vkfj+85EmB+wDAPh0GArkOS3bUxypi8cv5qZZGRep00AwTBq5lsOJJIdaEhXfEz7GZuu1AAQEnLIP0wwkYpSzh0mGIjsdCgfxTD9StkpMcgSA2fYxKvW98363SJ0SsX7SCUki0fYqkVfZttzo/QlCDz8Qz7UftLHNNUvFB0m74g2vdsZotKfkOJ3xRV2/yx9BNcnccMDs8+19P8UbR+RaPDbe4TPTv7Ho45xvNHNr8XvYM/NPC77rjgdBUCWX3gJApb4vOrbrFPD80hnODbn0ZgDWJF7Drpl/NC3yoqQgYRjQ8aZe1lbLiuMaVU4i1kcqZhQcC97zxXnOl/tdvFB1kWUlcWy57wuv74UifbVtp1414/V8lvs+vhr52OaP8fF9H7+k53hr388BcF/5dxa1/XLfR2teDtJuoqJEbITXpL8HgLuG+rmmT1RfXmAx3pL3Zl26Q9KR+cdMO4YXSj/3XMnh0Rl5P6ftCeJhCoBMmOb2fum3CnHYVQrMMUMGUw5m9QunGj6PtWX5y96Zf12QPKmrYsulNjBb3zPvNzgL2v/KnC2R11z/aVlJXEf6+cXKXJf7Pp7nGbi78AsATIRlnij/MQD59DZ+brXYA/9l32+dNXKaTsgcdkvqTewomSUvlr1AbeQ6Bb6z8OMAfHb2kwuiqbf1/SQAbxzo5xMHLux9FAVOzfzrzAqmkb7b2cyNADxU/u+LOu63kMR3oXHqOrJOcGkGz8uP/+c/ivNv/+iSHf8HVnyEvzr1a9G/+3PXAzBTeb7HDNQ52V7MHQTg27I/xB1mUjzRhLQ51Esljy82/gWAemuCVFwm/0m3EMkXPjzyEUbS0vs+PlXFN8/N+lQ2MkpP2GPUmJHP1SeJuyIVXZG8mvHWi9F12Zq4k3woz2PGilMJxQj5xuwf8+OrpcM5WG3xVPgNAEr1gwTmhcsmV9EXG41+5VRLjB8LG9eRQSSY93K2OqWoHbnYKuKWWaPpHWWmIhKc+etTetlA7T5rHyi8k3eukk53ohVjZ1mO9ex0nSeCrwAwXXk2eudjTjYyBouxdawIxDng4BCa+1iw0sRsub8lv8kBSyS6J6tPR8YjQCYxQj4m8uJ8OETTks7VwmYgkMyIdatK3aoA0AqrzHri5Kg2T5COD8lvSWyiEcizEoZB1A4LO8qY6IVzRmkQesRtMYYHnY1YRpCyq3wPQdDt4BfXvV5eA9WKJh6WneTfDP0HALYXbH75pQufZHafhaK7jv0z95z3/t1J0tnWTL5cBiy4ToGB7HYAElaOI6Uvn/e5X5nFZVBdznfx5cb7K5NJblggP5u/rrBr1L+Sc+XVPia/Esvdp75aebuZmH+x9F+XuSXCct5HWfvd7TN8hvO3APCe7LexOmPWnaZ9NmRknnKgmuJ+Y2cUEzbXFGTcqHgWh8xU7XC1zdFwAoAJDpKzZNxaFaxmZULmEGnXptaRcavpB/hhSNKRcakThNR8GccmrBJjiCE6Xtmx4D3uLtVptk9ERrbtpBfh+HKwLGPQnqUPt+1sdF8Wu/b11fA+3t73UwDc1l/g/zvYDTbNjf139f0sN/XL3Od/jZ17Ocz83AF3F36Re+ctrZnPYO5GAjNPma48u+BvHxz+FQA+Pf7r5/djFsGVxfefd54WlfgqiqIoiqIoiqIor1p6NoKqmQcXz+mZGuekcg1sIzdd/gjqnMwjERvhLRmR9d40mORhkyF00pqKZJg1b4KB2CYArgi3sSEnv6PaCfls7fOAZA7dEpdI7KPeDvpDyWa2NTbMibZEsHYEX2OjI4kctieGmTUFNB3LYl0uRqkl12Wy1WFlSiI4e2oVjtji4Xpf8QZ+5iaJNJycyvOvRyWSe/94mXH7JAAvlf6VtYU3m19pEyMZfe5G4dYE65i15Hfu8x4h60pkL6DDKiTBQTbM8HTn/wAwW3+JXo+gjvTdzof65XffOtDmS2OSwGFfpc6YJdGXsWA3aUeu2WiwmXVx0XA3/YBmIPdijGmq1pwHN2UkusPhILa5Bh4B/a5c17hjMd4S7+tAPMnarEPNJMIab/iRvHvWa9Mfk+fmO0YthhLyjvzm7g6rE3KORzo7oshnJ5yTXluWzXAgcpsc6SjRRN2qRs9ow59h0BVp6XCwigzy/DxQ/yv8wCS2WKTc8vJEUOdkva4rEbCfX/vjJI2w4T/vXSqJ3vnXbDw9oncm3ln8j7zIboCzRmgdJz/PQ+8tahyZ6y9rnPld690Iajdb9pmSZnTJJDfI/+PDjM8+br7trQRTvcSrIWKjnJvluI+2LZHMMGhHstgtxe/iLRmp6Z6PWdQ8effK7YBSW7aJWTZHApG8JsMUG5KisIrbFlUTEXVti7GWjFGH7P34SDR0MFzNjBlvm+EsnolMxqw0rWCWq6zbAMjZiSj5Y9Nq0DFj2slg94KkOvOXCnQjvymnyGRD5MFB2MHz5Rxh6EXqsPlzynRiHYmYjPVxJ0O1Jf1Toz1x3nPPV9v72I1Ad/zqgvH/fCXqF8PDt/8Mtz3036J/j/TdDsBY+aElOf5HNn2MxyZljn3fvMz+Sz1X7a3eszuxWJTeXely+iQsGRODoNE6vhzNOY05I6ubwfM29276YmLMPDPVImk6jbXWCNtdk2U3Y+Oa+H65HfDZinSgrpWg2hR55pH0DvJtKUVTDcexjJRlZ6fJCCJTu8m+i9GkyGxH0jbFoszGhxI+GzJVHEteqFPNBLetOxK1+sC4GB7bRg+QGpTncexImtUpGSxuKvTx+0f/XNrk5FkTbAXge1f3kXHlmKWORSeQ3/9SOWA0I4NO0/8uum6hFSkYN0qXmVbAvob8fjFQuwKH3ikxAHOd3Yf638ymrFyPhybjVDvSzpXJFBuc9QAMJDdgxlf2ltscaYvMtkOHk5Y4ARrBDHFjJLhWgrYlmQTTYZZhW67ZaCbGyrRcy4wbsiopxuCW/CwD2TrHSmZQDSxSrtwvxw5ZUZB1h/mhFuVTYuCO7amRbkv7toTbeMkW6XCRlaw0z82bV6RYlZTj1Hybsaa042ClyHhTnAuWA1lHnt3BjEOpLT80H4zS8uV3Vuq9kk11br2m6xb49yOSadAP4ROH/mSJz3Xm59W20yTMGs9G6xhrC98GwNrwCp5pfS4ytmzbXZDB8Ne3fgyAX9kzZ0D3ZbZTNiWE5rMmfztXhdLPbC8k+bPJzwCctdQCnNtZZ2H1pDmXSozSbJ3dMO0yXw5/bsP03OttLVwdoxXlNGw7PS8LrsVdfT8LwG0DWVqmSzww63HMk18u1tMAACAASURBVFwKJ+2D1HyR7I7YVzAcimQ3Zjk4lvTXQQgVT961uG1HVQdCAjyzDGnGGicdyvjXH44wbIlh2A59Zq06fWZ9+XAqht2Q41YCh7wjztvrEmt40CnK995YJBUd6budAVuctE2qkcFZa47NZf0NA1xHjPJ20KDb97e9MkPp7dG1qdtifI/kb+RkWZxkvV7e5kI5W9nLcWtpSssshk8dmAv+fXzLx/jYRTmfXx4o+aW37uF375c5733lkPnzi6VEJb6KoiiKoiiKoihKT9BDEt+XZ+0F+Prrf5Y7vjk/hKycTjK+aoHXprey+IpnJZ1YS8wVT9tAfHOUwfU1uUGmjMy25nlszYtXb1s+YNes+E8OzLY5HM4l7Lg+KRKKFxoTtC3xwnmWx6pAIjA+AWtTIuH8Zms3R+uPAnBL8nuirK7j9gl8OtyRkIyxzzcm2JoQufCvXDvN/3pJPn+++lSUUGfE3c6EL1l/w9Bnky3yl4Ph09wZvwuAK4tulDWv4cNTk9K+J8NvMGhLMe0t1hr2BxLd7lgtbkuKJ6rU9vn87F8AXcneuYu0X275Sza1idfE3wnALf15TDlSJpsh4w2RHMVtm5WmVu3KlESPAQ41qpFkt0E7+tymw5Q99/x2n421bpGUCaNPtto0TaKiCnVOWnIf1oRXMGhn2V6Q58YCEuay3b2yzFhDoud/uNejityLPeFjDDgiezzV2c1gTKLlg8FKNiZE+pp2beqetDsXszlZl3NXgzaDMfFIByFR/dWbBuNR+//L/t8mCLpy4d5JktRNeHFd4QdYZ0mk+J6Z31yS8/YKfZntDMTlfjrEKPvynl1MXbjFRgwv97tYzF7LTHXHkp3zfOg+SxcaSe3K3fbauzg0c+8Zz3CudyceG150luzz4dUmKVTOzOW+jxYutokmvjX3I1xblLFn/2wnGutKXouj9lFAopIrzDKSLClC87xXqFOxJVmfQ4xiMBCdY9I2UllmGQhlnLwqsYJVaRn0/BDWpOU4A3Gfmm9zvCFjaNaVeqsAXgi3DcgYlXJ8/uW4zMUfKB/j2dInAelfuskHrw6vZVVaxlgvCJk1aqkpr0HMzFNesnYuSFC3qfhuOU44zAsNWbrUbI3N6zNeHdnRz4eFUfSX/w0Wv/Tk5r4fAYgyAZ8Po4W7OFb6mvnX2TPxTtVkycxiMyp3+Y8bPsZfz8jxT5QeZs5++1aV+IbBgsHuI5tE1nXHNy9t6vJvBSr1vyLmviX694ICv+Fy3mKLeExkKx2/RjIuMpLvyN3A/lkxFtoBDCZNptZKwDMlkUa+UHYZSsikPyCkZEnHPBiujtaR1u1qtMYzHqbYZYk0ZbZ9jGmz7qIcHo8ylp6wj0VZXffM/BOZ5Ab+IlrL5pDv+2kAPv5cP383IZnYLCtJNrUGgLXOKHfkzOTXggdqYiSNzzzGP/AUAOGkj2WZ0ij5G2ibdN1e0KAYk7WwbStgJBQDIQhDvm+dXIu063Nkp3TqF9IpXUq68svh5JXclBc50apUyLPTXYl0m7jJuNsOAk7W5F0+Vp3LHDjgpiKDrhV0ohIyQ06WKVM6yDXfARzxZsh70qmfsMcoI89A3Z9itiFy7GPeV4nHhngulAyya8Kt9JmsyA9POHy99meAdMDdAtrvyb49ynT4YpjluL9TjtV6nEfKXVl8GGWaTsT6qDXl+6HcdXx3UqSpuZgVrX1t+rDaTAzW9r2J6ZbIl0WqvZzMSW+G++T5i4Ux7il9axmmXcq1XWeU/n5rIfd0uYxTmG+Yzq3PXV+8mxut67h+QJZvfGF8gsfKf/iyfW/p+zFGkuLkWe/cxK5A+tRvVv5s3uRu4URnfmbRLjEnw03pnwBgl//gK8q4FeVSE+KxLf8OADZkkuTlNWAo6TLRlPdlbTrFdYkrADhR8xlry/PuYlOIyXzH6djRfL9uVzlly3zOwqaNbJ8iH2WrP96s41jikM/GLE40un2+w6qUx+aszC/KHZcDNWnUSMInZUrZPDCR5amSyI6xmMvi7VVoORLISDgOm/Ny3CtzbY40pK3fOJWhY8b064Ib6C+I0TzJ4WhuNkiBDek3ALCr9Y8XdG17gcWU4zqbcTr/b91s9efi2rTMnaftd9MfjJxzTvjdA7/MP09JabhjpfsjJ+Jr+n6Eff4jwMIx4/XOG/hyTPKodLxJ8mnJhRKE3jmzDH/iCyu4/UOyvO5dTz7KXJhzaZejqcRXURRFURRFURRF6Ql6QOLbDQ3b37KLpi833chPx5ucJytYDomvg2XFzPmb3Fj4dwAMhH0csiVj5/pgA3lXPEpjXoWXgm8CkHdXcw2S+e5EMINrvEEbEwXuWCGevEM1m31l8Uw2A5+O+Y0JyyEbk6jpZKvFCeuU/GYroIMkFnCJ0aTKeuO9f9/aDDtm5Lhfrr2AY843EqyKssIetw/x+rgkYBlMOhyuSGRwTTbGhzaKJCcV6/Dne8UDeaLmc7hdNlfCiY6TIc62Prkvkw2fuCPn7Yvb/F1ZakudKj9C70h8La4sfh8A2+313Dos2z0zFURZBVt4rEtIIqGpdpsDltzfYjDEoCXfh0DGJBVKuTbrc3P+sccnTXbBsEO/qR3bCYMoKlvyWnTMdahaNaYtiaQkyWLj8NqEZHy+pmjxknEGP10fo2xJkqT1wQYyjjyLh4Jx1ttyj5qBz0hSorbvGvW4flikLrsn+3muLN7SZ6c87q2L53c4cSVXI8kfVmVijJrETe0Axptyf//w2Cei7Nmv5FGdz6WW+FpWnFR8JQCr0zexd+YzF32uSyWxPF9sO80tuQ8DcFtxgIdn5J4/Uv6f59y3L7OdN8VFtXB2uXNvZfHt1ps9ve5dL5NPb+PmmESXNmUyfGriTwHJdLk0Y9HZ79FclubFnUfv47cGl2ts7Eb7M8kNvC0t4+S6XIxOIN9PNgNmOybjbiIe1W4fqwdMtkTqWQ1bkQx4yppi1pLkSQ4xksjzW6ccnTVOmhYmukk2+uyFLdJW0bTMZku4kUJcxr183GZrfq7lT0/JfOmB1qPYZq6Rs4Y43nlOjuU32B5/izlfjJYlc6fV1pzk+Jv+/bQ8SQSYivXTZ68GYDubybhyzOsHHL46JuP7Q63PnDM6dzqvJonv5WJjUZZZrQo2RNUDvlT+7Ys65qrCHQAkrT4OzHzujNtkUzLPKv3qG3jXr8kcSmq0Xpq56rLeLcuyCYNuVrLeylT6aqarJ7dw5z0UlytYPj9baF9UwmND7js4ZqSUN2Teyg1JMfRqnTByUdyaK1JqfwcAlQ40TDr2rDcYrXXc2TpJ+4TITTf32dR9+X17rH3EjLS2FB7nmtZro3ZkTYa7VBgnaQzmathiS3oTG42RtLcCX6xJxzzR2s2NsW8HYNaqEgtln8FgFRtyTtS+ii+Dy839Fte8RSQfj9w7xKemZb3FZGUnPzEqxctty8LYoeybbfGvs7Iudlt4TZTd7fuLa8lXxIg4tahrfXmIuQNcZcuazVUZl10luWNp1+aWrAyeIbImEyAXS9LfFrmIH4ZRFsKYZbMxL11OZt56mJoH44iBX7GnCXyRVNetetTfZclQtMVwTQUxUqEYj4N2lpXpGGtM8fOGDy/W5F5U7TLpUIxjnyCSoYT43DggnfpsB67Iy+95zcpx9k5KltlPvNSkZSYEV6WGeFf2vbJvCKsy8gzEbThQkX2fa5yKBu5Cdju1psiRF2ugXhrmJk+p+ErqrcMAOOlbluToG7NvYvfMPyzJsVKJ0YVLE86DIKhHxugj5XNsfBrl2i7uOackODjH3y8vXXn7q4lsfAWHTNbuJ8t7XrEszisRcwfPsl7q7PMHKR/UCyxcc/c6R5YK3IsaqK9e5u7nW1LvY1tB5gqTzZBDNen745aDb5znB5qzPN+S72NhjNWO5Dzo+C5pW/a9NreBZ8syDu0OH6Vty/Ze2CJjyxjj0YrKvgwkt0RZdb2wyaQnS4/aXoXZxBijbXGoruz0EzOO05M1jz3+XJWH7v4hwTypvMWmrAQ7hlNOtGwnZltRj5gKi1R9kYlWGoeJGcd7h4BVmbjZPiRnnMP5xOrzNlAvLecu5bcYXFful+fNXPSxFkPXgDyAxWDuhpf9/ZrC97Oz9NeveIyn3vj/cNOD/yP694nS18953o5nlqydarEua5ZjlZxX2OPiUImvoiiKoiiKoiiK0hMsawR1oexUI6hLjmVz+X0QId1wfy45ih+ILKRNnde7dwJwstGOPHkAGVe8WEdqsKMscpEx+wS5QKQqHatDoRsFJUU21s3u67G9IJG05sx69tuSkeyK8CbWZSTa1vADppuSeKkBbM1IxC8fT5J14XBVvF3/VPkcK12RFF8be1sUaZ0Frk6L1/KGfos9cijGG35UcPm+Z+HDz4un8bWZ72Ni9glACl4fq4m0Z3M+zj1l8UzOl1eu6dvKEe8ZAB44uYLA6r33YFXuNSTNPWp4ITOmqJtlQdqV69QOQpom4t3yQ5q+vNvNwI+kzSnX4WRdvs/ELGZN3dDJVouVlnggC0EOxzyzo7FhTDk4ZtodKuZZmrAmWY1E0a8fiBO3JRIKcLjq07RETpQN+0iG8hwMuCnyJr3ys81TFOJrAah6Fr9xVCL7P39gP0EgB2q0pyimRc4y3Hwj/YmYabfNl0oiX97XeADbPCfrU6/nWSQ7Y6n6fJRoYnkJo4QMzc5E9O1SRT1ngqNzCTUuUuq7uOjp4qS2F8qqwh3MNA6cR3suP/n0tmVJvOW6/Xje9AXvf7L8OLbJKD+/eD2INBKg1jwYJUPy/HqUiMRx8tyWFQn3IWsXp2qS6OP0KOz7Bv8TAF9t3xf1wUKvVLGdqxeYTqw10jjl1U5XQh63bQ7MSpSxP+mwvU8SFwVhyGRTxrqk51IPJSI6Y0/QNAqbLDnWmnrt1xYCRtNSc3R06i084YnCoxwcx8FEaL19dEwCxlL7MNm4JH90rSTltihl4m6OEWsrRTN3Gg9neaki40AtnKJtJO9xO0vNk/7bc1rRO7glfRc3D8qY+YHth3nmmJzjUwdcHmjfL7/TWce2lCgBDrqPMIAkSdqcT0bVDPbNQsUkSuw9LqxvWF24k+OlB6J/n61v7PZtheR6Ss1DgPRzS8lk5emXfTfF4bNuP5wXBdW1316GB1/+d8uKL8gy3E2QuSJzLRMNqRn/9CMruLZgrt0lHCqXV5AdenNlUAIt/L1UuEY24vtVLNuUAwgvtUzt5YV6Z6o7ognymsRrSDtiuHaCgK+ZSVY6nMtGd627kU1p6ez7W5vY3C+dsR/AhOngv9S8n5dqYoBcb99JsyyGbguP67gWgDHKTLXEcK35HrYxeDYmClG5kPGGxXDK5em6THLWO6/hzrx0zNOtgGMNOcdxXuDFKSmD8H8amxk38pers+/h+sIPAXAieIGfHnkbAK4NVetDAOyc/Ue+WP0rAJrTJ7AsaVM6sY7BlEhgn+/cTyEuqeb9MGSitXuxF/yS023vqmBTVDi84c89R14Q8pxxKPTZycgQTTsOo1m5d0kj7QGodEJeqsigONYeY8wMvJ7fYHvszQAkiDMST0f7dM/bCj1KlngH1rCSollX0/Rhb9nnYFsms1uSRbbFxWiqeT7NQAyaWa9N0jUS8PZh/umYXP9BJ822UD5XnfVMxmSgPsbTuMaJ8hxPMTH9grSjc4ruoJaIjZBKimOiwSzNYE5f2vEWTsIvL3OyJdtkHfT9WRwnH31eCmSd9OXk7MZp91080H74vA247gBcaZ9ka0rKRT3X+tS8LexXPPflJOHOLSK7GFn0+XIhxmkxey0lU8YgDJv4fnPB30DGCNeRZ/R3tn+U0ZTMAx6fivE/TvwRAIOZKziE9BUbwyvxM9KHJ6yb2RpKRtSM47LbE8niVPWF827rpWW+jFD6jndkP8g/tj6xbC1SLhaZy7huH9tyshyo5LWYsmQMWFMbZDglY1QnCGn60n8cDSepWjJWpcN8tHyoTYeYWcdU7kDJ2AedIGSFKbuWsQtMhmJ4pJwiyYz0BbOt43RMWbOmVyIwy7ry8VES4VwgIEuKMbNWdaZxgGxC+r2VbKbqSrtjYYKEceLfEF/HV0+KU/hYfV20hOdkp0zM5DbJBUXWGnnrevvdnPTlOJ+vPMOKQJzAm5MFysYon23PyYpfzcw3Tl+JrjH67twH+LsFTrOLY0WfZNCdqOw849r6V5Lr/uiwzFXtlVPc3vdTADxU/u9R7poPD/8Ykybr9Gemf4OOmS8cKX2Z0YKMjwPpBp/c2S2lF3CpAmEq8VUURVEURVEURVF6gmVPaRUY2Z6ydMzVaHLgkkdOX07C1D5tdcai89+R2UghLh7CrOuwuXlFtH1XBrsx57AqJW66/dUY6zLy+YpcA8t4nu2Dd/Foaw8ApxgnCORcbxoskDPBOtca5KBxKj1UOUXMyGIOtcq8riDevnXZkBdLUqMLoBzW2TcrbsvD4TjjlqlxWnoiqvl3Ver93Nkv9by29jn8zaREU8dnH+NXZh8DJImHH3QjBXZUt891+ymk1gPg2Alm2uJZS7oFMoiU+Yh1DD+YX8B5eZOzxEy0pmhlaPpy/YsJmz6j3QlD2OhIhDzjSrIjgJgNKbNufrIZYnYl7lgUXYmWON4qYmbfk9Zusoi8aUU8xXqTiCoETMlbWn6ShCfd1Wg6wYacZc4V0vAdptqy/8lmg1lTK65uVQkscw0t8JoiURqIb6ZlrnPNj/GSLbKVQ6V7F/z+ektSVaUTK2h3uolZwigztmXZtD2J6rpuAtdEnOXaLGe/1pUtWQuipTfk3gfAk6U/Peue3d92McmdpEj4/Re8/4XQLS5/IfSlRMEwPvsYz9X3LlWTLgn19lyCoF6VIXdJuQVmzlCQ3sLlRkeygz6SKEd1a3953x9HyoONhbfxxvT3AzDOJIc6jwPwnDVGX0wiSnFSHDe1kce9fbR8eRd7bU7RrcMt44h8fi7Ys4wtUi4Oi66iIpNYwYpAFDtH7aMkQ5MxmpCSWQqTdC2GkzIHOVa3aIbyjA+FIyTM3OSwfYD9Vdm3L57hWE32PeXVOGlL1DQkiOqED4arORbKexN3c1RMne75dYJPzD5Goi9LMyyYVjusCmTZiptOkDXfZ8MM6TAdtftA8yEA7vWOc4t5T/0Qjpt52u7wUcp1aZOXbtLxJQlTzSoxFcgSiU6nhp+Q7cvtIi2T6ClYoJRcmDTs8nPuJSNnT8p2droZdt+Sup5Pjkvyvr+d+PUzbptOrIuSF56Lm/t+BGBBPdR0YgXVxpmzk7+7+EuAZKdfWxAZ9pHSlzlalTnRH/3Xfh4q/2q0vefLcWpewEkzbxjM3chsU8aZdlDnWOlrAEzVryFxGeany26g9op06lsR6wyy20vHXEdTSIvufqIyV0LgZL3DTEsG54GETdKktPVC2G4y393S36IVyPf3Hg/4zKysNVoTbI066SOVh7kq+y4Abk6vZtRkbx1OBLxQls87Zmc5YstEc5DVbIzJGtLBpMNXynKc5w5/CttO804jcUiEMY6G0hGlwhQbjFzYLsTIWzIAXZnLkDCG1/889SUmq7J20XHybMxLR94frGBX56uAGDBxRwadTtCIXvQg9CLJXBWXRko+u04qWlfSC9gmJXtAGEkthpNWlH13shlEXdRMK4wkSqMZi475Q6kdRPc6H7d4IZAMftPhYa5EZCob3Du5c6QrCQ45Zez7JyZbNM2AVnATrM3I4LwhZ5G05Xk7WreYafmsSIpxeLLZYMyUMEqRJxvKWp5ZawbftHZVsCFap/po5wt0PBk8V/TdGk0mvaAerS917URUdLzZniEwk+4g9PB8Oc54Z3eUuVdY/nVvFk7kXEkn1vHhUZF0PXnmGuPA0mQdvhzG6WDuxjOuvbmQ0jfjxrm0kPmTp97J4tt1iCw13aUY4RkMypdtixs9VwBXFt8PwP7qAxTTm7FNRtCzycxCPB5p/jPAgslZuzMelV6Z9U9w3+w9Udu6JZIa7VPMhHPF5rvF6KU9c1Larmy7PU+avJjfdkkw14OQqPxUOsy/wg7Kq4VCfB3xUJ7BvmAgWk7kE+KbtPGOZUc5HJzQjZaOpMME+21xVByefZDZjBiXh0vraBsna9xOc7wm0lDLsqMlCB4+TROIiNkpXFOazbbTUeWGMPQ4WL6fXEqy4hfjG0iFMjfLhQP4iAHZokN3JG9ZLeKuZL7vc9ewn0MAPFU+QdmsoWx1StG7ND47zZQthnIwr4yI66Tx4q2o3Z4p7xeE89eiLvcYeW7b43yNU5jLsPsnZynVMp/FGqcwZ5g6Tp5ae9y07+zzxdGMzF/eY/0S/zo9V0LtL8Z+DYCHW9+zYPtuCZ4vN79MuSHtOn0szaelLOOJRpJj9o55f7k0Y6RKfBVFURRFURRFUZSeYHkjqJYdOVFOzxylXDjWMt/WrtSqW1sL4P7mP/NDg+Jp35ILeWxSvFdPtw9RCSWz3N9OJ2iFVXOMMsOuJK/ZnMrzC2tEEvxS5Tq+NiYhttm2z5Qrv3X/LKxMi5fy41c57KveCECpYxEzjvWjdYtrEyIP+/7tH+XqfJORtJzvs8f6eWpSonCPhg+SsMSLGNDhREcipU+WB5iyxaOUdgbIGWlgrXWKKc9k/3TKUZTDD9oEcfEq+n478jCGoRfdI8fJkoqJ7DhlFyOPWLsHkt55vnhxx5lhqi2RxdpUkbtHpe1r0hY7pHwpMy2PspE2TzSTFOKyTdy2uLooN+CGQpXt+S0A7Jq9IorEZl1453rxHs/UUuyZlWs/kIgz2xFv80jSp2IctJ0gZHVKLtDrB+ucaKQ4WpcI0FdOxkiZ6ESHFrOWNDAkYMZ8ToZpJqyj5jc2cYzU2A9a0bPr+22ScZFeB34nipSGoRd5Gi3s6BlPu/0kMyKZmqjuiOr/zsntl5d66zB/c/TCs7AuBUuZgfZM0VOAdmdqSY6//N79M/PmzA/yYONvgYVyvsUwWriLU1Wp9dzxZqLvHSdzXomz5kdPAXaXJVJwR+7f8VD1Lxf1zJ8tcjBd2Wk+zXnkw7BNo33KfF7YMYZnuU8XWmf1UhCG8yM10i98/o6Q0c8uT3uUi2XumSt3juG4oraq2CVGAoncN8MOY4H0t5s6w0w0ZW7bslpkjLT2uH0ikuyuz9/JWEMiUhPNJ6IkYpudWxnJiKrgRuu6qPb6A9U/x3VElptMbSQdkwQ3tuVGidQSdp6qd4pmW9rhxVo4RiG0IuxnGhnrZu0SsXnJlBImmV4znCW0RAGWtPPUHZOVOPDwzVzdmh/jmjd/D0KPqnkHa9YEjZZEIiXJoHIhpMzcNZdYzXXW7QDc1/ids27/P4+JfNdx8gzmZD48f9ycNnPWLl21o4V9VhVSd/z+030eabtv3l8uzXjZAxJfQY3TpcNxu9k6z6xNv9S0OiVz/tmogPFNsXfwy689BEAq12H6ITFUmF3Ptj5ZF3G8HuKZecnNAyGHatL5rU6HXDcsHdx3/HicH7xXftdf7tzAcaNI/I1bjzHyNpP1d6zObR+VsgTOU09Dx0xqxqbwds51kN6Mz67nZA3rimTA9QNiqGzsvJW1Zv1rzbO455Ts06bDNbZIHJqBz464NLbSOEyrI9nrss7wXNHr0ItkspZj45lMe5YVp9OVw1g2CVvul2slLkO25cXjGWPtpeb9/NyorAe7tq/N7RuOAHBgrJ8TDWn7SDpOyhh6AIWY/I7RVIeMK7/VBq4rykT4/d82iV+Ta7z7hSHWfacZOJ+ZYKsvDotW2aXvb34QAKvVxH5RpERUaoTG2Kp/c4rM/iIJk+q/M5JkbVWcGRZS8gZkDc10S9pR8lpsDaSkUDyVZqIjMqtqa2yBU6Urt7YtN5L7Wpa7YJvumho/7JA0HbaFvUDutFycbki8tk+k7l+f+gox9y3ndax8WpxFizEwryp+kCtsyeCYizlReR8L+MfZrwGLKwp+YVzckpGFktHe4yX7RX5/m2Qs/pGdv3Ze+x4vP0wYNl/2/cVmde5ObL5W/r2LOo5pzRm/nWv36ctW5m/fm06FrrFtWcnoWq18m8Po10WuebnXaysXTzfD/UBsEw1Tmmw4XMHdq2QcOl63KLXEoCu3fSomJ8FqaxDPjPHTYYUcYmTmnQSdpMwPas2DjLrXAfDzG/tJ2OIofboUY/+s9KXvsH+UbQUZb8fqAYdMKb0XnW/gWCZbfesQ/YlNZF3JvbAy3EzcGKgNOhy3ZDwtsJprE1LB4FSzybQlbar647QteV7TzkDkSK+3JyIZsetkaZt142EYRM5bwiAaA11rLpO/ZSXO2ActF+mEBBnO5jB7d/GX+Ib3RQCmK89e1HnOR857Jro5BxqtY9zHmZalLKS7bGNF7nq8eevyu9n8q60zO/FaixgPRtNJTjVS5l9z5bOWug9Wia+iKIqiKIqiKIrSE/RMBBXOz0uvnJ0gWF4PVdNkmry78IvcvVq8eSuSHvftk8X6QQgzJmA+3erw9KQpYu3YrMmKh29duknDFy/lzhloB+Lhu+MvS6zsF79KwydKWvQPe9Zw/Fnx4tw+2GbVAyI7S8c62JZ4dUrNBMcbEtm5YWiKk5UMn9wvXs52EPKQyRQZJ83xsacAyCVXMWhvBEQmOuuJ/LRGm5N1kUsEQYtCcj0ARVbSSMyYbcajiKgftKMMla5JnASQT63FNR7PVliNoqy9gGXJxb098d380DbJErjm/Wn+8DfE63jfiQ7ZmEQwhlMOlXnqu04g9yjnOuyrmWy9DYs3DsmzuaUEsxNyf/9sf4Ff+JeTAHz12GauK8h1GmukuPXf/CUAnmdTrcr2QQj1thyz7a9gVznL7oq0dcd0k4NGvuuELn2heJ+n7YmobRnyVGy5RxOdPZH8KCQgazzJNnZUW86ynChqGoRtwkDuqeukowi5ayXn7vUyKRfORNdb+obsD/H7R0Ty85WhD5z3M2LcAwAAIABJREFUcc6nT949+zleiLyw586UuJS4TiHy3J+pPty56NXIaZdDM/fyIzP3nnvDM9BLkYsL53QPvRkAejrZorQ5DJvYRunxY7++kmOlP1vORikXQfdd2j9zD/vNd1+99eeYakv/cbIem5e93mZjXOYNDW9OIVVggOsHZPxYn/ZYO/U6AHaF13Bzv8xL3n7dXqozMj8YThb4ZkyOc3W+zfVrDgEQ+Ba7x0Tie7j+7Rypyzvx+ESTybBCBhk3U1YM3zyLrbDBADKOO2GMF03G+gQJYpZExhwrFo1vYRjQ7iZwDIMoguoH7XnJFIm+DyBSlbWp4Jnke46dxPN7px86V1Tz7lGXe3YuPnJ6tgz2f3vN+3nPk795hj3On8XWv44b9WLcykZJqmBOMXM25UxX4v1KZGM2M83uEpNLl4x1WQ3UMPRxHdHje35JDdMlIuhOrHHmMghecroPqR2tE7q39FvcO2850hXF9wLwusRWDjXk5Ri3T3G9IwbgxrzDEZMC+2f3nOBA/UEA8sk1bK/eAkClU+Qd5nj/6cMHGfrEMwCUqs/z/St+BYD1uSo//LS0YX5KbtcpROujErER3pH7MC+GIu8MCKL1FFPeAWxbXo28vZKRQCQyU1aJo7YYah4dHFuMpJG+WwnNBGks3EPbGCiN1nFss04km1xNJrkakLWO3X07fg3Pkc7DYW5A6AW6EpFrimm+cVwGya/9gsXfl2QNXCrWz1XBbQA0/RQDCbPu1IHdJbmP3xhvUTedY5YkK5KmHMxjm3hmWp6Zz1S+wsFvypqKDVmLWwbnBrktn5d1OTPV+RnjnCgbputkySZXkTSdarl5NNoqmxjBt9cD0KJOLhSJa82e5XhLnhvPb5CI9ZljpWi2xXDt+LUom2F3O5ABtvuc2LaL022HlYi+Dwkj4355VIdzcpvArCOeL798YebTl/TsqfgQ1UZ38Ls8hkO3PE5/ZttZsvKee9+lyGB8qfnJNR/lD46Ko+EXN3yMv5n5GnApJdNCzB2MsqcXwjxvWSHv22wHppryrgcheGFIv/Eanmr4PO7JezvV3kcxLhlE+8Ihnit9aolaNv/56lVjda5d3f79VKMHkgwoF8FcH/vTaz8GwMrsON84JIbl/eVj9JsM8psyWQKT0Tcbs6l58jkXm5ubzXo2bxoWr/3WXBqTZoPff2gL69LyPH/ge4+Q+6psc+UfXIVVEuer/9rXMvKUcZY/sovK0zLevnRwiGdmBjhSl/PUvLlSMbEgw6gZMyc7TWYs6a+TxEkiTpSalSBu5L625eKYZzcRK0bLX4J5ORkCv0L33bOs9BnnMv4yB08Wy/cO/CcAPnHsybNscWbH69nk+lsLZQrZqwGZq3b5qbUf4ze/S+agE/syTFbken9yf4F/rUq5n/HqTu658Ye496TMNT49cw8f6P9BAD5b+xITs09Ex+s6wHKpNdHcZH2wlRd5ZN42co6+9ObT5lXCROUZziXZHatfHkeuSnwVRVEURVEURVGUnmDZwzW9kuXyWwkr8theTuZ7WuY8S8N5iXz+QP/bePOweM+agcc/HRZP43pnE+Mmw91DkzVWJ+T7bdZavm1EkoFkXBhvyPFn2vDR5yQK9+KO/ZE36o6+n+Yj14lU898+logip4nYyLykRQF+IFHBVGKQl8IjFANJkmRjUbUk0YDjxpgKRbhT8o/SsiUiOlZ7lm2ZtwGwJlyNlxZP5VR7XxQx8/xGlDjHdQukE+b4Vox20C0k79ExUVbbipEwmfL6wiGmTP3WXiBmpMhxG14yEtrNefjh+AcB8W6NNeReO5bFZLObMCgk6cg1H04kuWEgHR2nK3saa9qcrIs3t+3XOBITycrt6S18+aREOj964K8WSHC6nr9ErJ+mWeBvuy55dxVpxFudT43gmOLnPh06zEWnJ5D6qLXOBJmYRMUDt0M7EG9woz0ZJfiyrLkMva6TIuZ2Mxh2Iu9xGAaRrDdOmpjJyOi6fQRB22yzHMnfwnmf5jydv7xRPP2/ceDjrCu+HYDDM188ryNnkhuoNQ++4jYrk9ext7H/Fbc5F2dKVuQ4+bPKkrrRz8nqC+d9LttOLjhGL9JNNveZyoNR0fWxesAwpi4di4mgWlhmOcF8uW8yvipKvNQKLD59TPrRsj3F7pl/AKQe4Iwlkq5rEiv55IQoEOKkWBPI0o0OPofsPZRqR8zZ7AUe+lpTZPxjRlUCkmxm6aTHvRY5FbpjQxj60Xzn4zdUuOery9kq5cKx6MZ2riy+l/etlT7p2cl+npuSMW2cfaS4BoDj9RbDSZl3ZGMW403ZptIJGWtIpGplKs5rB6XPSzohlY58v2+2ycmsvLOHP7WBO4dlDuH99UMce9YkZCpPMm2WvMx0VjFh6s1PtmCiEXC4IftMWdN0LBmP0mGWbEfGtBoNiibzvU/AhL8PgHpnkoGkJLO0ceZUQRCNb1h2JOu1rFg0HoZhEEXw4m4Ox5PPjfYpwp7JZTa/1vUcP7DiI1F28MOnzjY+nruvse00t+d+GIDfezG/IHL6gSFR+6UcKB2Wa/N7O1fz+0d/G+iORfIMvK3v53jvzn9mc+KNAFzp3MEnx34LePn8oruspd6aiGq4PpcN8OclSVqVl/rzY9Vnzthu18mfs/7rFQWX/ZPS719c6qdXxgrD5XtcpKiwZu9darph/jBowlkkoxeyPutsWKedozuBOlL6cvTdT675KDlT7+VwxcfYL0y02hyy5BEfDEb46BXysn7hZIrVZh6TdUP+5JhMemY4SR9iXNSsEm8zKdirnYDP1aSwe7tT4T35fyNtaFc4aophrwm2ksFIa/E5ah+mYAzUjtWhYDrppBWjaWTK+6ydDCDpvQ91HmdFXLK/bmMTO5GSCKfqOxhOi3wjTppTLZkkt70KybhZB2Bn8EwmP8eKMVOXCXw6PsSKhBwzGWbY35CJphhm586MFi5httjT72NXknJ36t10AmnD1f0uU6av84OQtllSM97o4NrS3tWZGNvysv3OGViTke9tC74wJhO0k/ZhBs264s2Jfl47KA/ECyX4kxO/GrVhffFuANYGW4gZx4tPQNUSI7FqlWXwnCcGsUP53LFaC9LnVywpQdIJGwwia5EnOULJFCC3LDtyLjhOPBpgJSuvPA9B6OHa4iCJ2ano+5XOVdQs+W2HynMyn8UaPUt1H+ffQws3MvASsRGq//mt0u6P/PU5j5NLb6FS7x1nyVJw4Rl6zzyROZ1L+S52uaL43shoXLJz4fK2ws8A8BNbbHaU5bn/6L7fZn2fZJqd7hxcMMFa2FYx8C/tGtfLJ99d6vt4ea6PcjqX8n3szrHemvtR7lopE5UXSwFTLRkPakGbBjK39fDYFJc1ojPtFgftQwAUgyHyJovvdcVMZLjtr7Q5ZTLop0ngmrHtxoE0I0nZ6K9PjOGZd2FF2I9nMkXvsp6kaZx4A+5GcmE/gfmbT4eWJUtV2syNS33hUBTUSIQJJm1xRB2pPEwmKWVz4naGWbN8ptWZjq6Hbceha5Qyl8U3CJokYzK3irnpqPSe5zfPafyczuXoV5eKXHpLNBd5pSWLPzb6UQD+8NjcXOeV1pa6ToFkXBz31UU4f08/1vyllNF3bj+ed2Gl5/77VR/lr45LkODJ0p9xqcZHlfgqiqIoiqIoiqIoPcGyS3y7LDYzlbJ4QsJLmF/rdOaifadqO8w3cxGcRytjrLQkmpi0Hcod+T5m2awLJJq1z97L505Igeqr+gJKHfGf/PmxkzxX+hsA8unNrIltBWAkHObrNSk2vD5czZvj7wSg5DQ52BZP0dpYH7dkXgtAJ4SDFQn/DccTpFqbOWVJUpxsmCWIsi2G7LWkoH3NmyAdk2QEA/HNHDYL1yeT+6JsoX7QJm48obmwn4rxHFqWTdwWGc3V1utpGHlNliSdjCQX2s2TDAYrAUgQ49C8WqLLTbUh7+NBp8SsLdfz1PggCdNtDMWTWOa2Z1yHVRnxwrYDeHRiLjPz4apc1xdqM+wNZUF/moHIs3uyVWfHjFynph9yd+EXAYjbNpMmyU/aipNx5LyFuIMfigd7pjXA0XCSpomoOqGLb8l96dCiFIo3OG0XiZlshqEVMBXKb0tY2SgDsx92omRXnaCBY+q3pewiSUui6/mwSNWS7ITJMIttfHz5MBtlDD4VX0GzM3X+F3ypsezIsdnxq5x6/NzdfTfKmIuvXLYI6lLUjDudZHwV6cQwcHH17JaHuQjuWOfFxe1hEpydXaFkcVvfTwAwbp/gwfr/BuDRXYUoUnp34RcpxuQd+NvSfWc91+WJDPamfPd8+cGRjwDwTPPoEiaKUi43bjf5oRtja1besdlOnCCU9yXWsUkF8rkZegwkZJy4YSDNkxMyf5kOmsSNbNa24KRZLvNE8BhjVaki0J/ZxuucNwMw0wr57IzMd3ZXPkcyJhG16eQW6r5EJeNOlpWuKLJOebtoOGU2hqKEatEmYcbAuhWLIn1Vq0QtkPFqhbUpSia4InsdEzVRg4VxP6oF3k2CCZBwh6LPLa88t7QlaEeJJovxDdQcyaI/dQFLMC4dS5Nh/q6+n+X+8u8CUKnvZUWfSGhfqZro/y79y8u+eyX7x/NLVBsvXw6ZTW2i3pKlE/PVWqdnSj7TUsqE23fBEdQjNYuGNV8ddmnqoC6vgRrOpdxW4/TCOfGdH2LVv8wNdsu97q0vJenLSa2LMmo+Vf4U7yr8LAAnvFmSRmq7KpHiQFOMi6PVR9gRrJejhXk2G5noG/tWkQp/DIA18T6uKXZLini85EnHl4utJ+nISxLzUhQTYsDsrlb48pQs9nEsl4QjaxVHm1cwYOVYgRgVFVrY5iUrUaPfyHoTsSxvSF4BwJF6g7uGRFL8gxurPFuScxypWbxjpUimy50YXzhxJwAvVmcjY+41Axl2TEunscfaxxXI2o5rglvY3ifG2Ww74IlW75S5sE2mYQ+P/bUHAJhIruFq6/UAjLXrrE1J2zOuxeGKtP2YVyaPyGBTjstoRga2a3NFrkXk3zHbwii+OVn3mTVa4YCQgYRsn4/ZFDwxDNt+GMmMTzbakTMhaTvkgyzZUNrRos2kWSvXCRskjBQrRZ5VgciV9tgvsDmU9UEjsQzdZIqDSYe4+TzbCSPJlR+CM8/Ts7cq2X19Atan5PgDCZuO2X7nVJKgufzrGcNwbvANgir/9dk1597HOJReKSvsuYqbXyyLPe75ZN9ttk9wdVryf0+z0EDNpjYBi5NOLQ/z1hSHi5tQnXsMCHm4/Acv+3b+OHxv6bcih0UyMRL9zbLiC44fc0W+eL7Svf/fME/a9o8lyaA98x9fT/z/Xa4GKReHRbszDsAj/sNsmHwTAGEILd/IacOQprnvaTvGcEoGkEIs4MqivFOTzQzHTR6GI1WfmbY4z0PLj4zPjDPEtMmRMFYtUTPl0YrpzZGDd6q5N5LQphND2Map1C2Tdtw9BBBl5wVx5HYsOV81GI8kvp7lcbT1KCAS1dWFO6N9TtQlE+xg7jqK7npAyrSlzfp4i/WR0TtReyFa95imL1Lo94Dbdh5L4/S6v/y75NIyn7s29rYz9qunc7blEufLYHIbhxsvHy+HslcuGMMzScmgPj9/xBuS38NAXuZ4nx7/9QX739In8+3Hyn94xvO+frDNV0qJM/5tKVGJr6IoiqIoiqIoitITLG8EdZ4ETblw1n/+/yz4txUVV26DyWh6+bCixfTN9om5by0Xz0TMr80V6dartoBbi+KB8/huyibRzMPVCjsrEqkactNRxGxFyuHhcYlExiybN8RvBuB4q8YhkwypEI6wxh8yxwy4My6Rk2LC4ZGmREns0GZzPhlF5b5U28VkS/YfTGxlwCTwKYb9vFgXr+VBnuPZqngFH3/+St4zKO1LOPC7u8UT1fA9vtn5OwDekXkv+4yn9UvTZW7NipR3g3UVbxwW7+p4K81hU/86mbIZbG0DOGem1MtBpyPyjyPsoJCSWrUFexXDrkSuHAuGU+Lj8gMYSBq5UqsPc1mp+R5PzEpkxcamaEmkc0UyHiVV6k/aUbTyYLXFhCcRsWI7FW0zknLJmpDrZNPmZEMiOAnbZm0sG0VBD9XrnEK80kVWMhDIs5WzE1RMRt+KN0baESl5zfNIO9LugYTUdZR2NDliScTo+4e3cKQmDXyyMk7NFvHO3X1buNNkpj7WiLNjRtoXhB6phNS8vVRRxsURLPhX2USpLzZzale6tdycb9bdJ0t/esbvuxKpVwM/PPS9POnK8ze/tu2lohtRb7SOEY+JRNrz6zgmw/do7nUMhvKsn+36nouhvPTh8+v5fSsRzovUdKNKdt/lHpeVpSOMEl8FYcDzJYlUTlKiL5Q5wSw14mbulSbG18dFYdXvJok7MpaU2h1OYZYY+Rk65l3bEF7LutwbAInElroZc0lSMVHQhp2I0oa1KNOfkQjeSmt7lK3etmIknL5IvmvbDrlQxsMp61gU7eyzV88tVQkKUZZ6gOMlUU5ZuGwvfi8A1XCKfTOfld+WXMMVjiTfm7AmOdCUJVCWZTOUuhKArdY6XgwlM3Bgorq9QjfZ1cUmDe0uh3mYpVkWM5S/eVH94aGZe8/4/ebwhgWZ3efPJ0f6pOb8rUMp/vPej59x/7NFTrvsq8Z5XUb6/WcXqIcXl1BwsSz7TOPca2WUc9GVm3TJp8WYKFWfX1Bi5XIx3zDtclX+uxlJyb3OxSzG6t3MbyGvycnEfktugIfHRdrS8oPIOHEsi+O+vPj/PPt/2zvzKDuu+s5/by1v33vX1tYuW0KSZcsWGIwxYANZHDIEBjJhDhiGw2EOxpkMh8MwMyEhyzDkJDMJZCNhC+DgAGYNxCzGYyO8y5ska22p3eq9X799qeXOH79b9V63uqVWq9VdbX6ff9TdqldVr25V3fu7v+/9/o7id3K0fvNlaRuHi/SaHmxIf01oj8zhqKDtx+wj2CrogazXssjJLnVcFwcLo5CCzmOr3I73bbgWAHB9tuoHWF8ZiODpOrmV9WAznq98BwBwVXwf/nrsh3SM4iP+fXx7+i783tq3AwCqNtAskPwtpGt+gHW8YOGeAdr/U+4h3NW3V32fKu7Jt7eTJ3BYmfVX3uDUlTb2a9RhZk3TLzBuuRJVVXQ8bgikQvT9SpZATc1AZEMmrAYFtA3YiKpg8HitiGviJLdOmgJHpun5L8uWHbouBGoOffehqkTdpZ+rsunLsXVHYMpqIKKCppwRwY7odQCATUlgokHb5RsS3Wp90EjtamyIkTzl2pyELWmbxyZcPNygNX47tW24s4dKeER0iak6HduAgbVq8qLuSPzbCA1UBsuWX1onG9qI4fKTl3q5rwAzO4qfN44CAH41cxe+k/9fi95rcKWw5+OVZ7nQWhsv0G1fSxRUDuan0GskF/npyxs8hNRxM9GNqKnrackaDtc8t/bFrenaIsgj4DfWvAFV9d541DqCwQpJDbfHXr/K12t611z3+8b3f+ricnsmuEjVT7nSgqXueVdITCin+JhMIKf8J0KahjEVmFXtBvpUtYCuSAj1Gj1TVTT8ZTEhTcfZJkl2O/UYtiTp7yM1GzGHPlsVyda6UyOJsKD9TOCMH3j2G9cjJZMY0YYAAAV3CBOqhIyEi7RJy5i2YzNSpufvoGFSOwAAGMQvfFm/hI06WuX2PNZEr0WXTud31D7jS403JG/COzv3AADihsR9R/5FfWJp1n0uFYsNTBPRzXP2gxcKLD+qyrz977OfuehyiMudrLupI40HC3P/30iBJhH+p/r3YmzO3gEAOJn/lv+3D95xHHd/eYv6TeJKrUFliS/DMAzDMAzDMAwTCAKQQW2Xoy5frbOXEm/r/Cj+eeKP/d9LtbMreDYz8TIYz07/E55T9fXemP4grslQ5snUgJNqEqtsSVRsytodcg9jrXWVv5+6pOmgV5m34tt5mrn6Sn4SFZuyxzUrj5iqNXZCP460ypTGzZejE1QDKmWaGLJGAQCj8iSq1rifYdaNW/DQKN1/j46beMsGmhHamxNY36SM2aMTNYyr2qCnncdwZ+evAgDuvm07JvIkFfnxcARfG2pla3bGyYTpodoJPDBF0udycxj1Js20rk0dQMlWRbnLUYRFy8ggKEyWj+BomgyGms0qepuUoT+Q7EWfMn+IGa2i43FDQ8WmZ/hUYxppQRlURzqwVWq6P5zE2SrJTH9QfRrnSo8CADKxTVinU0Y57BiI66bap456U81UQ2JYo2spoKGhldHtknHPDfFuP5M72QD6onS8dTHg8Qn6edIdwHCVth+rCdy+ltr9vVsqeNmkckCsC5xVSidHSujKrjiKEMqg8/5/pQmUBLX1y7ATuQjdS4MTD0DTIou72EtMe+3PY3maxU5l3jtDrrmUNZGDQHuNuYW4FHrvqJg2n+wyOOtQDpb+0S/+fn3mvZcoq535PVIxWk5Qa04uyODIk0L3RHbBVAZVQ9MPor2/jkc2+q7Y5woHFyQlP1j4tPpX4LnXkjlHzVqHP3runQCA+6b+dM7PaVoM61KkjpmovYAdUZIabta7ce9kqz8MYh3SqYbt15leKsMUZjmhZ6lYH8RYjIwad4c2+MqhgtNARUlzI1oUnSrDOSUrGFMS2kY9gjIos+oIGyHV9+tCoFun7KsjJR4pk4JrUD6HtEaySlc6vklSztwITY2dx5pH0Bkil+B+dCMR0mE1qG78tBz0M5xSuhBKCZV3a4g4dOy6I9Hr0jH6I2/DWJTeC2fsJ3BKyUkjoR68tfPD6jyAshqzhUQC6ShVZNght2NvhrLMo3UT27JvAQCcKHy/bWnG0spBF8fiYo75VETzZT47k/vw2QlS3C3kXavrKTjOhXyA58bry9pNHWfzAdU//s3w/5nzGLNroLdnTj1qwwJXJbyDCLRynS5eQhJfbUmL8P6y0h6cAmi76fRllfbORojQjAFid4oktN2REJR6F49Oln255qu644gaNEjMNXejrhzxmo7EmEMBxUHnYXSAfl4nt6Cp0c9rolkMSnrwHWn5+8zKDDrCJL+daDQglK17WEsgE16DnRo5eN7cY+B9v0byF3NHBkLJaorfHcH/fZikDGnDhGbT+d1k3IYHpqhjuvfHeehK/hKXdRyt/QAAYLt1NEHrNvrQB0ujF/aB6CvxY/F1AMCZ/A/wly4F37/b93pMO4NtV3Dl2q4d1y3jVJ6kzbnkXvQZ1wMAYoZAXinzByuA5dL5akL4kqG1sZwvA67XLBRcGiSeak76pWFc6WB7itYJ98u1qEqvYzdQcWhRqAaBsGq7qhTIuDQB4QgH6+UG7EpTB3vH2gpe/W5qC9GRgP2MKib9oy48PEqdvi5MDMlJdWyJL52lwXafkcCAmvAoaXl0KtffKExfxhUTIWR1GvBWnBhGXeqoDmmHMFake0nKJhynZcW/ksg5OouNehd+e+v7AAB3H/7D8/5/sYRNul4Na+SSP5tN7Iap0TM3VnzkotLckNl93tIGj/bi5x6aFpt3zap3jPsrX7rk815uXLcKR92L18Z68URh8cFXeyH5zuQ+AMBW7eX+u3O2G6V3/eYasHhU6qcvY/28xO6ffhEA0Jvah+ECSXxDZjciqtRX+zm7bhXblRu3E209b+3BKQCkYuRgWagcWeR5LRWtQfARdwBvz7wZAPDXHKCuWmy37pdu8SYxAaA3HIWpBjkhTcC06OezVtF3z7XcDtRVqQ4TIUQ06t+6o7q/vKnYdHGkQetUS7Vz0KM0/qjaUzDUsqKSMwJXlX6JGh1wlAfDkJxERyONGCgxsEHbjUPWvXTezjR2Rt+mjhfCczUay+TsJDq9EjqmjnNNGp+vNffATNH7Oayl0FB9fd11IJWBRFxkMW7RM/a0/gS+OUjlVm7sBPZoFDQPmr9AreG9h1c6OBW40mMsrx+72bwd2TC17z+oyhYXYjHB6fdu+DB+8yl6f37iZGtt6Tt7PoYvjX0KAPUTn56jf2xnl3kbDratpb0q+yYAM9e7pj7+Ohx5+Sn1m4bWdWSJL8MwDMMwDMMwDPMSZMUlvh4XmuFmFoeYMUO0XHMR7TX7ZhpfjRaojtbnCwdxTZaMhLZq/dieUTVRoy5qDp3nWM2CpbK/IU1DRKp6k3I3xjXK0BxzjyKskWnAenEDrjZJynLIGoCtDH7SMolO5S67MRnDRJ1mAV/Z1YuTFQMvKnfWki3w7n8k6eo3i19AIkyOu39y1e24pYsycqdLMdgWzX4edH+KmE6GTsPlJ/Ga+O8AACZlEbGwJ5+sYVhQVnbALfoT6Ke1JHStVUNqrPgUAOBE/HX+PoOG53ZnalE8KUmOe3CigJcbJKtLGgbCSleSDQsUVdOXLInJBv0yKSbhiJZiwpvpjYo0wjLq/z2jMpQp00DFpvuhO2qgO+JJiEOI6Sl1XBdVR2BSHS+iO5AFaqOBb7j41LPUpnVb4rpOVed2+ACeF8/QOdWPIxemzKdlb/bdDMtyEllQltaFgUfrVFjbNGLoFlfT9qKOapNmngvOAPJ6K3NkGh1qnytdG9LL2rTMKe6d/GP8zLpR/X3pZFZN6+Lftd01sV16mS8/gx3ZtwIAxvDIRaW582VP5+PCfQvdFx2x7Riaw+AtGFK0Fk/VSc0gcAc6EuSUOVl6BkLJyhcj2Z4okanXBK6cuddcM/Gz8c59vHwYnUkyWenWt+E1CXqOfxI+gcP5r9LfUzfiBfGs/1kv89uZ3IdJlZWMR9ajNEedwJWD+qLD+a8i7X4AANUVXlm3b2ax2HbeNwmLyDuwP0PLiSqW9J3lJxuuL7nMyhzCkobdXaEIRpr0zNpwcc6mrNloSfdVEg3R8P1nctEt/hKgrLkepqQxxBhOwJF0sA6tH11KXTSujcOWSWyIUkZ0qhECkqTo+vWuPpwq0fjqSxOfQU+CnjWJLZhSYzDN0VDX6HlMyIw/1pJw8ZQko6RAJvvAAAAXFUlEQVSE6EBK0neuoeg7vOvCRFEtySnZJjRlRmnorX5+5Wl/p1+aeVMuuRcNi5Rv7YoR0+j0+3wBwzes+sbUn1z22V6MX3n0k/7PIbMbB2I0xv7i6Cfm3P7W9N34SeHP/d83ZX8NALBOS8/Ybr9GS64G0HpvW5+5H9vT5BwtRvW2+twvMRdfL3Bql/pysHp5tNadSf8BWe4zgDqD+RixSQqyUV+PoQrd3D+fKvkvZhs2ejVv7aiBrjpJvI5pz8OSdG9k9PUouTRQPSSfQ1eDXOletJ/0pc3jRgZGgRxod6RjOF4mWWk6lEDdAb5Vps6lv7IXjxfJKbIruQfv6bydtjMt7NpMAfHO/GY81KDgsyrzKNo0mG1YIxgCbaOJ1vUuVl+YIUnz2qURK6LaGPX/7jkCPlEdRr658uVl5sIbOI4WDvpywHY2pXQMVeianyg20XS9guUuaioQTSCFvKCAriqn4aq2zmKNH6weFycQcUmKm6inYAmKPJ1qB5oOXb+EqcHxby0NU3UXp9Wa3qcmU+g6Tet9xus2bqY5C6yJOjhcJHlUQVbRkPR96lYeYw4NZie140iq8jC2U8WwQW3RlOXWALIBlGokww6bGTTVoMJ1qrCNjH89bCfY6zrHlMzoHV3/DV8Z/6OLbL0wPNfnC9EePM2WpZ6p/nzOz3hrZlzMLd81jU44qr+4UL8RCSn35fOCULqZvJIKQce7F5/AN/Dxje8GAHx2dBMKLrl1ehOBQcBzrvyDJ/dCJmiAaxozA9SeNEkB28/bsicwUaJ7ZVo/DUPSEoA7+7bghSjdD8O1Jo4qKZoDC09MK4m2tCE0mvQs1076EyHBoDUI9qSX9+//TfTnqFbDum9/bkXOarnYk/mPAIC16MH3pz95ka1XA9Ivy1ISBZwoUgCWNAwULOrTSrKBCY3GKRERR04jr4yRZhUV5WdQ1lqSzoybRVFQ8KNBQ7dyjS+LFDQViGZkEh0GHWu92+WX4YtoOlJKSppuRNAdN7GXVKaoOSZe0UF9667tx/EPB2np0kDtnTgFcq/PypTviP+UfBw73Wv98yprdI/m7TPQBfWl68U2TKiEwenpHyJs0ncbc57DoRht0xi5GsNQrt92q4RNMPDGqpcm9Z0qHZrz75Y9gQ2Z2wAA50qPLtkEdSS0Zs7qGADwWx0fBQC8aBdwVlA7/t22/fihqjDwYAG4Jf0hAMBD5c/DdqgdD7kz+7vRGo2D/qU+s2zl7CUTAPDjn63HQ6N077aC06WHJb4MwzAMwzAMwzBMIBDSW+G8AnhyL8JFK5saHLe91QnNoAkIQMydJF9K504xzzEWwie2/Q9fZtsXE6ioJEzFkn6WLGoIlC365ee1U760pSpas45r3HWIK9OA02IQ65UTXWc4jKba0TFnCEMOScLKjRGsjx/A6eJPAADxSB80NSvYZW7DmHXU//k9PeS2+Idnvjyve5s3a9aJDTjWUMWthYZNoVcAAFy4GHaeBwA02wphG3oUFVVntSu+E9ONATq/2kksJBO9lCZjC21HLxO1PfZ6xJT0+t+vzeE4GQTC1ASy1BQYq0tM1Wl2suG6OO1QBrUhatgEyng3XQfToGtiQPdng23YcATNznXJjJ+JndDGMOHQAv2aPQUBzZfaum4dMSXP3hC5wb8PTmsDOFOmul/zmfhoWgzJKJlu6VoYhqD7LKf3+46JVZlHyaKZTMdt+i7QcbMHtsreTZSehKZcTheqBFmqdrxwG55/P70p82GY6jt8Kz+3W+qVYraLqaFTBtqb4QWAHdm34mj+axfdVzxCRjiJcO+8GcTFu6YuTLa0Es+il5GqihKO57+hPhtZtj7U0DPQVLbSdsqIR9b6qp2wkcJbU28AAPREBR4YoxeEDg33F/7M38fHt1KW9ZODX4JpUIbn/Daie3d/+j8hA3rn/Kj4aaTjW/wtmhbtf7Zc9lJdfJenHVuSwp3Z38Yzf0lprnd8KHme6SGzOJb7eTT0DHqSlHHcJ26Ao4bWe3MRHJ2mvuukew59IBXWBKZRUyZJk+4ZX32VFesRc5WrrqjCFa3snqHkwTVR8ZejbMcmXNdJz2BHWCJl0PZ5i5RGGVMZGjkClqr5PVYX2JshdVIuZOHeQeqvpAS+XyF1TUSkkHbVUhVh4Yz9OACgUD3V6vfCfb7UlfpVoa5XGLoyWwobaX97y65csone8rXjhcdb6zKvxYvTP17AMRZvXPcrGXJHtqXrVzD4SfM76n1I4/o9mf+AbTqNa9qzm/3ZN+DE+O/RsT/xOYR+/8vqfEL+NpFQt1/bNmz2ztkWt6bvxoMVUhPOt9Tm/ev+Ow6V6P8OFj7T9j9LO1YNUIAKtKfZvfWLF0ptM3PjS5qkHfgAFSCXMQC4OqPhVIlux66IwLlq637w7NuFEIiqBR3dUQ35Bm0/Ubf9Nat5t+Y70Q24YzAlPejD4gSGCg8DAKKhHmSi/ahYFNisC+3zA9/T1sE5HR9706/0ixzHwv0w1FrJYvUFdKX2AyDZ8ViTPltv5uGqhzIW7vL3Yzt1/7O2U4ftkNX82sR+jFRpbSQNsoIZoOpq/eee5FvxoksB/1jxEbw+/V8AANd3xBFV7u1lG3ix3JKAjDfppd0RiqA/QRvZLjDVoLYrWg7CmqbOByhb9NkBOQwT9KIdwSkUmy/6+3SlDVPZ8ieNXuRU4JsX5zBaoes53ztE0xIIGfR9LKeMRGRt63u2rRP22sh26pBqYKkJE2GT1mukjDWouXl1LZ7wr2WwAlR/K7TfU8kYrSVpt5YPOrM715vTdwEA1oQSuGeJJMstghqg6tDUPfqF3Xfjv57+GQBgM/bi58XPqnMK3mTvmszN0EDv5Ds7b8LHT1B73Zr+IJ6wyQF9oY67Xl+naaE258vLWwe1EgPiT11NsuW7Dt0EM/yGJTv+SrA5e8cFnZ6Xi+V/HgUiIZoc1bUwXh8jl9yKY6EKWsazI5ZB3Cvv0nAwVKf+YVA7g3HrGACg37gOGzXaz7BToHWoABIygbxGS1lG7SOo2xQYdkS2YqO7AwCwIZLAVUna/0hVomi5mLaUB4Qs+kFtViTwqm6SCPdEXJyt0t//avTbqDepH4uEskga5MyekT2YAJUvnKgcQdOe9q+LN0Hlus22caUOXfXJYSMLx6Xv0LDGcaklXVbivXolS10upoTMfMFkOzen78Knr1NLpYopfOwkTdQdLXwHNyffAwB4oPAX/vaJ6OZ5Ey7t5enmO1ZR0ITgoekvYCHXazHtyBJfhmEYhmEYhmEYJhAELIOqaMv8BXEGOMgIEfKzz5oWm7cO6kpkUNvb+2LHf1fvx5AJ0/zJdMNFTv0c1gFVVhPjNRdjdfqux8QJrHWpSPS4Nu7LR6/JhDGoMngN18VJh8yJDOg4aT+CNSbV0XtFdBMGKjSbGdEM/MIh6e948THfFMhzugSovtVc8gcBY0FmMXNBn/Vmohb2WK5EBtUjEd3s319hM+3Pkgqho9ukGd0DoR3IqrZLmvCdDUdq0nc2LDRdVCzaz6AzhauUA24qpCGkt+rBRdTPlgsM1ymjqUGgiBr6VFbXkRKTKmvZQAOTGmVOvTquc37veWYLW9IYbc73kBAh/5oZegKWmlWe7V69EJY3gzoTT55+dvrf/L91JvdhovS0+u3KzSa3k4iSm/J8s7qzaXcE9n6+Kv2aC7b1Qml/jwY3g9oiGduK6b+jzNs//1kSBYsetD8dehxn8j9YsvNaCmLhftwWJ4fJH5S/2KZuENiQIVfwkcohaOoapKP9SGgki6zLov+OdKSFquU5ZmoIqYxNn74TEUkKmmP2gyjXyTxqoVmL5W5HTUv4/WHhXW9H7O/vBACYxuuW7DyWg5BJbdQb34v1kupezq6lu5ysRAbVY13mVtyg3QAAOCxP+jW114WS6Fbyosm6g7EG9StHxCE0XLo/U/oaxCT1Z2FE0CFJpVNG3a9gIOH6Si3LriIRpv2v0XYioz6rQ4MDF6eUcY6NOrpASyHyGEafpPdtn57GwzZJVyfLRyBVrXLDyCAXJ3VNt9ji128913gaTbt03rdv2kX/sxAGDJ3eyZ7KCFj4u72dlXivXuoSnSvLhfsfr9+s1M7g3utI4lu0dLz7GVKmtGdfDT2DeISy852hbRiuktlT+7KIWLi/LeM9M2vrjZWuy7wLj0///YLOz+MlIPFtp1UexRscLiTNHVSW64Y39Iy/hosCVO+mmJksD47EV0cmQWU7GtY06g1q312Zt2OTILllKqTjjWvoNq07Akm1puInowYeKg8AACIyhmvj1EFarvSLZHdEBKrqEpwqNTAsKai8JtyDhuNiSpWN6Q5H/LWqM13LBIRaiwhpLyD4nPmweu0u3SbkjIfYafuEcqeNbfTXcyy0jMZKDoqB1rpB00igK0olL2zUUWzQoLAvsgfbJK0T25WNYG2MrsFkQ0A1EY4VHF/Ka0sXuTDJ/zYldXSFafvxRmt98mTdxdkaPUcxzUQ2ZPqTGc8UijihSk8sZL3I5TH7xewNUDQIQYOQhQarKxegzt25ZBK7cJVBA6xnivfM+d4Kmd1z3qftVvsXImzSoKphjSzoM4aRQ07Jkaeqx2HbefU/csY2hiq30i7r1rSEvw7KdZs0CYrzJyW8tay90d04lf9e2zbBlNu3fQLpOE0KvTn57/DOjTSBU7RMPDRBEy2fGviDeT99pREwcE2WJI9bsB4PNEkCOp+Utzf9SjjK3bxhFxFWMnxNGP6yiYZd9B1UXddGyCT37rjZgy2gScUXtRMXLGszFyvRju2TZPcfoEFmX7yK9z1O9+xKBnqXimHk8Jbs+wEAa2M6fjFNfe6gOIaYoP6iJMcxNP2A+oT0+5FYuMdv693iVfhp6W8ALG7ctNLPoyf3vSb2JnSDqhBsS0XhqiF3sdmapH26PoKMpPs3pYcx5NC7ba2e9Ze8DFh55DVakhSVcYxIkgS70oapkVz3WhyAod5zQ3ISpgwhqpbGFEUZeUHv67TsQlnQOHGk8SyK1RPqvNsnI3XEwjTRn41uQlmNvduDU9e1/efRcev+Nde0EHT1Ho6YWX+b1RKgtpgp9/V8DHbpt+LJOr3Dqo0zuCn9nwHMfk7b+9bFyIbVOqk5PvfJHbRu/y9GHsB+QT4nJbeBfVm6h+Z71wsR8p2We+K7MdWg9ihVj/uBbi68GcNlSsZY9kRrDCtdrFGO613YiHFQlYOh6QcX9N1Y4sswDMMwDMMwDMOsWgKcQfVYve6+7a6UcxsXtb7bchOcDOoC9g8DyRjN7rjSxk2hNwMgh96JhpKdiFG8o4eyH6YmMVanqckdSQfn6kpS0wAOT9NM7GltAA2UMVR6FMBMtzIBAxElmbHs8gxX0aCx0hnUhe2X7v0tmTchpmaJJ3DWd8mNI+u77YY0Hbuy9Pe9GQthjV5Pw3UDw/WW+cPj1WEAwBhOQxcmxqrk+uk51J3PlTU+uFxWUuK7EH4j9xEAwH1TF3f67Urtx3jxsYtuZxo0k3uhzKmAgY3ZNwKgOntSWm3/u2Jd15wE8Vl8V+/HcFufks/XDDwyTtfvu8XPYV3iRgCYZWhz+c+JJ40/kHovTJUZ3J1K4bvlJwDgkjOay83KZ2yovXLJPfjKNSR5ft33bkDj978OAIj/1T1Ldn4vZYLyPAoRQmdyDwBgn3g11sWoP8yENEw3qa1rtkRPjMYpxaaLF6t19VmBkiQ1RFrEMC5JYeUIxzd/HNXOIATKcu0SWxE11NKopoWIrqM3aqh9AU8VKTN72HkQ0xXKwAZ9XB2UdlwMUZWBTkXWX1J96quyb8Jb0tcDAH44fRrPejWeAdx3/UfwyFTLtfm5PPWD3yx9HYXKUbVVsPpG4CUn8Z2LmcHqXGUJVpr2QVcrNW4jaMnqoAeoydjWOV1Fe9OvxB4cADBzTUYDZXS7VCIkigjSSl7SFw3hRKWstrEwKOilfHb6fsx+iL01NI5bv2SXtZUiiC9vISL++pNcfKu/TqxUPe6XqOmO7UJY0DYhRNEvKUBtSBumksfGNMOXarsSOOTSy3fSPuU7Dc4uKbFaCU6AOreUtX2JwkICyyvD5bmzXmmC+CwaRg5rkyTV/kDPDZik+TxoAhir0eD4e9UHUFVu5qnQOkzVSO4337M13zp7XU9hU+p12G/Qko1cRMNPy1QO6vn8PQjyBFE7wWnH1mTB3sy78f719O787NlxPFb428s/uQsgYCAcUut+V2kVheC0Y/t+WsFqv7YXG3Vy94+bml+poGTbmJQko81r46hK6uu2yJchpiZ/4rqBpkvbH8Yxf/85twdZ1a92hEJImhoGK/TQP4tDatwDBPk9OpsgtuNsPOmvZVdQqZ/2/35nH1Wo+MrU5y8wed6iP0s+AjUnj2J9EACwNr4fadmFN+To+f/p5CRygtbbj8lC27vgpdc/BitqYhiGYRiGYRiGYX5pWWUZ1JkETZrQbvRBssaW0VPQCHoGdcb+F+CMq2mxGUYKXjb0YmZDnlFLNNyJYnUAwNJemyvNaphdbMczoWmfZTSMHLJKwm27DbhKxllpjMJxqE0X+qwbRg6OU1afuXQ33ZUiOBnUGXtT/7a6iO7UjRgrPrKEx2jh9QdhM9M22xzsWeF2VsOz+Nr07wIAbu1J4LEJej6arotNSZLVa0JgWLmRDdiTcARl8G7PrcG+LD2Xa6J1lC06v5+NR/FCgf5+1D2LdW6fb9Lyo8oXV6WpYbDasWW8Fo+QS/3Xdv8WLJeu8X0vGvj8yCcu8xgvTYLVjm37UrL3bHIXtmpkOmOJ1tKFDpnGiDIzcoWLhEumUb1aGuvipjof4IWi51Zv+zUpzzpP+g6scbML07Uzq15lFNR2bMdTc2bjW5GvkvHQjfF34Onmv6r/j2K6/Nx5n7sl/SHoKj64PpeA8unEF6d+iKnKCwCAjalbcar4IySj9PwnjB6/Dnyx+sIV+T5Xgl8Cie+FoGBwuQel7a65QQ9K21lNAepFjq7+nf82bncIdNzW/RE1s8gr97rVIumdzWp4eS8Er40ct3qBZ/h8l1xNi0DXIpdV4iUIBDNAbed8R8FEdDMqSoa00OvuSbwbzTF/0uma7NsxbD2PfPkZtdXqCUrbWW3Polde6HrtOow6NMA1YWBrnNaJn6pUkNYpcH11r+H3bGnDRcmm/u1fz9VRVxNKOjT8rPL5OctvrSaC344CQj2PH9n0UdzSRWsUR+ohfPUMPYfX5qJQ1Uzw58P3tT1bvzwEvx1bxML92BCjNeFhGUUe5LGgCQN1VX4mrnWgpuS+jrT8QLTanGwr8ab5buX15tiq7Q/bWU3t2I6mxbA5TZLdipzwKxtUG6P+NrPH4Z5fx69nPoQdGZJzf2v6KEaswxAqplitzzJLfBmGYRiGYRiGYZhVy4pmUBmGYRiGYRiGYRjGgzOoDMMwDMMwDMMwTCDgAJVhGIZhGIZhGIYJBBygMgzDMAzDMAzDMIGAA1SGYRiGYRiGYRgmEHCAyjAMwzAMwzAMwwQCDlAZhmEYhmEYhmGYQMABKsMwDMMwDMMwDBMIOEBlGIZhGIZhGIZhAgEHqAzDMAzDMAzDMEwg4ACVYRiGYRiGYRiGCQQcoDIMwzAMwzAMwzCBgANUhmEYhmEYhmEYJhBwgMowDMMwDMMwDMMEAg5QGYZhGIZhGIZhmEDAASrDMAzDMAzDMAwTCDhAZRiGYRiGYRiGYQIBB6gMwzAMwzAMwzBMIOAAlWEYhmEYhmEYhgkEHKAyDMMwDMMwDMMwgYADVIZhGIZhGIZhGCYQcIDKMAzDMAzDMAzDBAIOUBmGYRiGYRiGYZhAwAEqwzAMwzAMwzAMEwg4QGUYhmEYhmEYhmECAQeoDMMwDMMwDMMwTCDgAJVhGIZhGIZhGIYJBP8f9D2e7DrYPR0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1152x576 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_activation('conv2d_1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 32 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_activation('conv2d_2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 64 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_activation('conv2d_3')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion of 2D example\n",
    "\n",
    "The classification of the training set get's incredible high, while the validation set also reaches a reasonable accuracy level above 80. Nonetheless, by only investigating a slab of our fMRI dataset, we might have missed out on some important additional paramters.\n",
    "\n",
    "An alternative solution might be to use 3D convoluted neural networks. But keep in mind that they will have even more parameters and probably take much longer to fit the model to the training data. Having said so, let's get to it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Keras - 3D Example\n",
    "\n",
    "We could take the full brain for the 3D convoluted neural network. But the more voxels we include the longer the model takes to fit to the training data. Therefor it makes sense again to reduce the whole brain volume to a slab around the regions that we're interested in. But for this case, let's not just keep 3 slices, but a bit more:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x165.6 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from nilearn.plotting import plot_stat_map\n",
    "img_slab = img_crop.slicer[..., 15:27, :]\n",
    "plot_stat_map(mean_img(img_slab), cmap='magma', bg_img=mean_img(img_crop), colorbar=False,\n",
    "              display_mode='x', vmax=2, annotate=False, cut_coords=range(-20, 30, 12),\n",
    "              title='Slab of rotated machine learning dataset');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This should do the trick. Now we just have to extract the data from this slab:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(384, 40, 66, 12, 1)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = np.rollaxis(img_slab.get_fdata(), 3, 0)[...,None]\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note:** We added another dimension to our array with `[...,None]`. This is needed for Keras and Tensorflow as this represents the channel dimension."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Split data into a training and testing set\n",
    "\n",
    "The splitting of the data into training and test set is exactly the same as before."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(307, 40, 66, 12, 1) (77, 40, 66, 12, 1)\n"
     ]
    }
   ],
   "source": [
    "# Create list of indices and shuffle them\n",
    "N = data.shape[0]\n",
    "indices = np.arange(N)\n",
    "np.random.shuffle(indices)\n",
    "\n",
    "#  Cut the dataset at 80% to create the training and test set\n",
    "N_80p = int(0.8 * N)\n",
    "indices_train = indices[:N_80p]\n",
    "indices_test = indices[N_80p:]\n",
    "\n",
    "# Split the data into training and test sets\n",
    "X_train = data[indices_train, ...]\n",
    "X_test = data[indices_test, ...]\n",
    "\n",
    "print(X_train.shape, X_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create outcome variable\n",
    "\n",
    "Also here, everything is the same as in the 2D example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train = labels[indices_train] == 'open'\n",
    "y_test = labels[indices_test] == 'open'\n",
    "\n",
    "from keras.utils import to_categorical\n",
    "y_train = to_categorical(y_train)\n",
    "y_test = to_categorical(y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating a Sequential Model\n",
    "\n",
    "So, first, let's again import the necessary modules. Notice that we swithced `Conv2D` and `MaxPooling2D` with their 3-dimensional counter part."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from keras.models import Sequential\n",
    "\n",
    "from keras.layers import Dense, Flatten, Dropout\n",
    "from keras.layers import Conv3D, MaxPooling3D, BatchNormalization\n",
    "\n",
    "from keras.optimizers import Adam, SGD\n",
    "\n",
    "from keras import backend as K"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we need again to identify the important model parameters. In this case we need to extend the kernel size to 3 dimension:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get shape of input data\n",
    "data_shape = tuple(X_train.shape[1:])\n",
    "\n",
    "# Specify shape of convolution kernel\n",
    "kernel_size = (3, 3, 2)\n",
    "\n",
    "# Specify number of output categories\n",
    "n_classes = 2\n",
    "\n",
    "# Specify number of filters per layer\n",
    "filters = 16  # For better results, increase this value to 8"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And we're good to go and can setup our model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv3d_1 (Conv3D)            (None, 13, 22, 6, 16)     304       \n",
      "_________________________________________________________________\n",
      "batch_normalization_1 (Batch (None, 13, 22, 6, 16)     64        \n",
      "_________________________________________________________________\n",
      "max_pooling3d_1 (MaxPooling3 (None, 6, 11, 3, 16)      0         \n",
      "_________________________________________________________________\n",
      "conv3d_2 (Conv3D)            (None, 4, 9, 2, 32)       9248      \n",
      "_________________________________________________________________\n",
      "batch_normalization_2 (Batch (None, 4, 9, 2, 32)       128       \n",
      "_________________________________________________________________\n",
      "max_pooling3d_2 (MaxPooling3 (None, 2, 4, 2, 32)       0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 1024)              525312    \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 1024)              0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 2048)              2099200   \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 2048)              0         \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 2)                 4098      \n",
      "=================================================================\n",
      "Total params: 2,638,354\n",
      "Trainable params: 2,638,258\n",
      "Non-trainable params: 96\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "K.clear_session()\n",
    "model = Sequential()\n",
    "\n",
    "model.add(Conv3D(filters, kernel_size, activation='relu', input_shape=data_shape,\n",
    "                 strides=(3, 3, 2)))\n",
    "model.add(BatchNormalization())\n",
    "model.add(MaxPooling3D())\n",
    "\n",
    "model.add(Conv3D(filters * 2, kernel_size, activation='relu'))\n",
    "model.add(BatchNormalization())\n",
    "model.add(MaxPooling3D(pool_size=(2, 2, 1)))\n",
    "\n",
    "model.add(Flatten())\n",
    "\n",
    "model.add(Dense(1024, activation='relu'))\n",
    "model.add(Dropout(0.5))\n",
    "\n",
    "model.add(Dense(2048, activation='relu'))\n",
    "model.add(Dropout(0.5))\n",
    "\n",
    "model.add(Dense(n_classes, activation='softmax'))\n",
    "\n",
    "# optimizer\n",
    "learning_rate = 1e-5\n",
    "adam = Adam(lr=learning_rate)\n",
    "sgd = SGD(lr=learning_rate)\n",
    "\n",
    "model.compile(loss='categorical_crossentropy',\n",
    "              optimizer=adam, # swap out for sgd \n",
    "              metrics=['accuracy'])\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fitting the Model\n",
    "\n",
    "As before we need to specify how many epochs we want to run and what the batch size is. As you will see, the fitting of the 3D convoluted model takes much much longer than the one for the 2D model. Therefore we recommend to drastically decrease the number of epochs (at least for this workshop example)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "nEpochs = 5  # Increase this value for better results (i.e., more training)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 16   # Increasing this value might speed up fitting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And we're good to go!\n",
    "\n",
    "***Please set `run_3D_convnet` to `True` if you want to fit the model and run the rest of the analysis.***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "run_3D_convnet = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 3 µs, sys: 0 ns, total: 3 µs\n",
      "Wall time: 7.15 µs\n"
     ]
    }
   ],
   "source": [
    "%time \n",
    "if run_3D_convnet:\n",
    "    fit = model.fit(X_train, y_train, epochs=nEpochs, batch_size=batch_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Performance during model fitting\n",
    "\n",
    "Let's take a look at the loss and accuracy values during the different epochs:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "if run_3D_convnet:\n",
    "    fig = plt.figure(figsize=(10, 4))\n",
    "    epoch = np.arange(nEpochs) + 1\n",
    "    fontsize = 16\n",
    "    plt.plot(epoch, fit.history['acc'], marker=\"o\", linewidth=2,\n",
    "             color=\"steelblue\", label=\"accuracy\")\n",
    "    plt.plot(epoch, fit.history['loss'], marker=\"o\", linewidth=2,\n",
    "             color=\"orange\", label=\"loss\")\n",
    "    plt.xlabel('epoch', fontsize=fontsize)\n",
    "    plt.xticks(fontsize=fontsize)\n",
    "    plt.yticks(fontsize=fontsize)\n",
    "    plt.legend(frameon=False, fontsize=16);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluating the model\n",
    "\n",
    "How about the performance on the test data?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "if run_3D_convnet:\n",
    "    evaluation = model.evaluate(X_test, y_test)\n",
    "    print('Loss in Test set:      %.02f' % (evaluation[0]))\n",
    "    print('Accuracy in Test set:  %.02f' % (evaluation[1] * 100))"
   ]
  }
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