demo/quickstart.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e8a8110d",
   "metadata": {},
   "source": [
    "# MEI Datajoint-Free Demo\n",
    "\n",
    "Demo notebook to create MEIs from pretrained example model. Does not require connection to datajoint. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bcdd1b85",
   "metadata": {},
   "outputs": [],
   "source": [
    "# only need to run once at the beginning\n",
    "import sys\n",
    "sys.path.append('../')\n",
    "!pip3 install -q git+https://github.com/sinzlab/nnvision.git@model_builder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "initial_id",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-07T15:56:07.668936Z",
     "start_time": "2024-03-07T15:56:06.130978Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import torch\n",
    "from mei.methods import gradient_ascent\n",
    "from mei.modules import ConstrainedOutputModel\n",
    "import urllib\n",
    "\n",
    "import datajoint as dj\n",
    "# some hackity hack to supress datajoint promts (ignore)\n",
    "dj.config[\"database.host\"] = \"{host_address}\"\n",
    "dj.config[\"database.user\"] = \"{user}\"\n",
    "dj.config[\"database.password\"] = \"{password}\"\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from nnfabrik.builder import get_model\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6fe653f499b93fb",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### Download monkey V4 example model files\n",
    "\n",
    "Download pretrained model for monkey V4 from https://github.com/sinzlab/nnvision and define function to load the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6d6bb40169d29930",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-07T16:03:42.532931Z",
     "start_time": "2024-03-07T16:03:42.525407Z"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# method for loading example model\n",
    "def get_v4_model():\n",
    "\n",
    "    # download example model\n",
    "    os.makedirs('./pretrained_models', exist_ok=True)\n",
    "    url = 'https://github.com/sinzlab/nnvision/raw/model_builder/nnvision/data/model_weights/v4_task_driven/task_driven_ensemble_model_01.pth.tar'\n",
    "    filepath = './pretrained_models/task_driven_ensemble_model_01.pth.tar'\n",
    "    if not os.path.exists(filepath):\n",
    "        urllib.request.urlretrieve(url, filepath)\n",
    "\n",
    "    model_fn = 'nnvision.models.ptrmodels.task_core_gauss_readout'\n",
    "    model_config =  {'input_channels': 1,\n",
    "    'model_name': 'resnet50_l2_eps0_1',\n",
    "    'layer_name': 'layer3.0',\n",
    "    'pretrained': False,\n",
    "    'bias': False,\n",
    "    'final_batchnorm': True,\n",
    "    'final_nonlinearity': True,\n",
    "    'momentum': 0.1,\n",
    "    'fine_tune': False,\n",
    "    'init_mu_range': 0.4,\n",
    "    'init_sigma_range': 0.6,\n",
    "    'readout_bias': True,\n",
    "    'gamma_readout': 3.0,\n",
    "    'gauss_type': 'isotropic',\n",
    "    'elu_offset': -1,\n",
    "                    }\n",
    "\n",
    "    data_info = {\n",
    "        \"all_sessions\": {\n",
    "            \"input_dimensions\": torch.Size([64, 1, 100, 100]),\n",
    "            \"input_channels\": 1,\n",
    "            \"output_dimension\": 1244,\n",
    "            \"img_mean\": 124.54466,\n",
    "            \"img_std\": 70.28,\n",
    "        },\n",
    "    }\n",
    "\n",
    "    filename = os.path.join('./pretrained_models/task_driven_ensemble_model_01.pth.tar')\n",
    "    state_dict = torch.load(filename, map_location='cpu')\n",
    "\n",
    "    v4_data_task_sota = get_model(\n",
    "        model_fn, model_config, seed=10, data_info=data_info, state_dict=state_dict,\n",
    "    )\n",
    "\n",
    "    return v4_data_task_sota, data_info"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ad6c4f0009366eb",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### Load the model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3ad59882797cc0a8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-07T16:03:44.293525Z",
     "start_time": "2024-03-07T16:03:43.879304Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Leon\\miniconda3\\envs\\mei\\lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n",
      "c:\\Users\\Leon\\miniconda3\\envs\\mei\\lib\\site-packages\\transformers\\utils\\generic.py:441: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\n",
      "  _torch_pytree._register_pytree_node(\n",
      "c:\\Users\\Leon\\miniconda3\\envs\\mei\\lib\\site-packages\\transformers\\utils\\generic.py:309: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\n",
      "  _torch_pytree._register_pytree_node(\n",
      "[2024-03-08 10:58:03,167][INFO]: Connecting {user}@{host_address}:3306\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "datajoint connection not established, skipping model imports from nnfabrik tables\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Leon\\miniconda3\\envs\\mei\\lib\\site-packages\\torchvision\\models\\_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Leon\\miniconda3\\envs\\mei\\lib\\site-packages\\torchvision\\models\\_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=None`.\n",
      "  warnings.warn(msg)\n"
     ]
    }
   ],
   "source": [
    "model, data_info = get_v4_model()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c788d169aea0d573",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### Create single cell model\n",
    "Here you select the index of the cell for which you'd like to optimize the MEI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "373b3ddfb37d0ae8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-07T16:04:12.277919Z",
     "start_time": "2024-03-07T16:04:12.271129Z"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "cell_index = 403\n",
    "single_cell_model = ConstrainedOutputModel(model, cell_index)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1efd3e0f9df5180",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### Configurations\n",
    "Configurations for the MEI optimization algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "99bd11cab7d2a9bf",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-07T16:04:19.415414Z",
     "start_time": "2024-03-07T16:04:19.409919Z"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "seed = 8\n",
    "\n",
    "# learning rate\n",
    "lr = 1\n",
    "\n",
    "# number of iterations for which the MEI is optimized\n",
    "num_iterations = 1000 \n",
    "\n",
    "# interval with which the objective will be evaluated\n",
    "interval = 10\n",
    "\n",
    "# shape of the MEI\n",
    "input_shape = (1,) + data_info[\"all_sessions\"]['input_dimensions'][1:] \n",
    "\n",
    "device='cpu'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a44d83e2fbe5dbcd",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-07T16:04:19.765675Z",
     "start_time": "2024-03-07T16:04:19.761910Z"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "method_config = dict(\n",
    "    initial=dict(path=\"mei.initial.RandomNormal\"),\n",
    "    optimizer=dict(path=\"torch.optim.SGD\", kwargs=dict(lr=lr)),\n",
    "    stopper=dict(path=\"mei.stoppers.NumIterations\", kwargs=dict(num_iterations=num_iterations)),\n",
    "    objectives=[dict(path=\"mei.objectives.EvaluationObjective\", kwargs=dict(interval=interval))],\n",
    "    device=device,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf698daaa94cd4be",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### Optimize MEI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1fee3d12d9400fb7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-07T16:05:23.041844Z",
     "start_time": "2024-03-07T16:04:52.570212Z"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "mei, final_evaluation, tracker_log = gradient_ascent(model=single_cell_model, config=method_config, seed=seed, shape=input_shape)   "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c381e644273682fe",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### Visualize MEI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "cd9ad93678e77d68",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-07T16:05:23.122178Z",
     "start_time": "2024-03-07T16:05:23.043003Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 1, 100, 100])\n",
      "tensor(38.2673)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2d74e0aa7c0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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L9jvbUX0bXHbNa3WsZuOfZdGHxXbL0j7w/eZztL70M2Q+ulKmT74fer7kw8qy8WbvA+vK53DpvoY4ynzSfC7PcTxpWWyHUrvpeHM6BmOMMS8Z/AEyxhjTCv4AGWOMaYW+9QFNnTq11sJVW6SeT7050/dLITWy/S/Ua9UOlkPdVHVV6q9Mu0ubt2zZ0vVaom2R+cYimvYzNAp1YOr/qrVTE6a/KPPfEb032z8U0Vkf9fvQ35KFBSntA1LNPvMZjnVvLyHxtU3pM6EvQPunlGaefhHtg8yHGNEcQ6X9XVq/bAyMRbZPLgsXRF9r5mtiO7E+vaRjYN2zNOEcE1qfkk1ZaCS2E/13+rsxPDycPkfbieOUY4LvsNqk/V7aG1lfN66rjDHGmL2MP0DGGGNaoW8luIGBgXq6p9PA0pJOnRJyOSunl5TOdHrMaSrLUqmjFKFbn8PpO8N8ZCFmKM9RntBlmrQpm84TSii8VutHeYJTdpWPKPVRLtJrWVf2B1Gpg9IM66N9kEUJJ+wrSpWUg7U/SpGntd0YVigLP5XJoxGdY1zLYjupbEObaH8mV/M9K4UwUmm5lyynzLzK9y7LIMoxzedq3/IcJUVtC9qQRZMuZf3lc7Q/OBZZHx2LbKdMHqNNHOPs926/gxzf3fAMyBhjTCv4A2SMMaYV/AEyxhjTCn3rA3ruuedq/Vf1RGrRY923h/HqkHtQ7TrzBfDazOcT0fQ5UBPmc+jj0pBApRDnqu3SF0O9XzXkUlgPatGaDZPaM5dpqv5cSjOg51nXkg9Il9izDdnGmf8uywabhfuP6AxppDaXMkRquzHbaJbNk88s+QbUjpIPRW1i+9Onou1IvwHhdgi1ueTnVB8Wxxr7Xfsr811EdI5NhWG5WJb6TbJMsRHN8ZWlv4jIM6bSl5wtly75wpXZs2c3jkvjluOgVzwDMsYY0wr+ABljjGkFf4CMMca0Qt/6gJQslEcWFr6UuoG6r95LnZSasZZd8g1oudSES2FwVNulzpvtIaJN9D2pHaV9Gjyv95ZSTSvZXouIpt6cpeuO6OyfTIvOdHg+J0txzXJK6SW0HUvpu7XdGHIp89XQ58C+oo9Ln8P+4Puh/oBewutwTLCuGzZs6PocQpuykEVZP3NcltJUa39xX1a2r4btwudkvzEk89+xP2ij/q7QZ5WFMCrtK+P5br85Jd9RXf64rjLGGGP2Mv4AGWOMaQV/gIwxxrRC3/qAqqqq9VLVb0t7YbI4XyRLx5CFXOe9mU4d0fR10OdT2oOjUMfO9iPQv8Jj1bwznTqinLY6s1G1YGraWdpttgtTEmR7e9iG2Z6ikuat/glq/9yjsnnz5sZxNp6y1Ov0DdBXme11K/lftC8z315E0zdAe7P9UuxnXjs0NNQ41jrw2synVYrjp+OJ7c++zNqR/iGWpe9/qT/0/aAvhv3O52rbcP8Xx7HayHeSKeu139l3pfrouHUsOGOMMS8Z/AEyxhjTCn0rwU2YMKGeUuv0krITj3U6zOlkKdOnUppCZlIGj9UmSgzZ0tGIZv1KoepVcigtM1VK024eUzpTaH8mmWSSHOtWkl4VjolM5sxC60c0l7uyTSnjUCbUupeWMWvZfE4WXodjovR+6L2UfEohpRS+O9qmpZQQWciZUpZWheOSbapSE5cps91mzZrVOOYSbyVL49LLVolSGJtsaXUJ7Q+OgSyMT5a+IyIPp6X3jvd99QzIGGNMK/gDZIwxphV6+gCtXr06TjjhhJgxY0bMmTMnzjzzzFi3bl3jmu3bt8fy5ctj1qxZsf/++8dZZ50VIyMje9VoY4wxL3168gFdf/31sXz58jjhhBPiueeei49//OPxZ3/2Z3HXXXfVOui5554bP/jBD+Laa6+NwcHBWLFiRbztbW+Ln/3sZz0ZNnHixFoPVj0xC1Ee0dQ+qVeWUkKrj4ghTrI0ECV/UeZHoJZeSu+tcHkl2yK7VnX4bElwRGeba1lsY2rGem0WLp/Xsr15L+ujfUffBjVvPc/nULPXa1nX0jJgbUcuuaUfJEvZQfv1uBQKiT4JbUfaxDGv9WE70X+XLUXO3rOIZltwTPD90D5gP/P95lJlhf4V+mPUpjlz5jTOsZ20Phz/fH91zPBapqmgTXov/Tp89/WYfZVt9WCblvpDx2K2FaIbPX2Arrvuusbx1772tZgzZ06sXbs2/viP/zieeOKJ+OpXvxpXX311vOlNb4qIiCuvvDJe85rXxC9+8Ys46aSTOsrcsWNHoyMy558xxph9hxfkA9rzfwJ7gieuXbs2du3aFUuXLq2vWbRoUSxYsCBuvPHGMctYvXp1DA4O1v/Nnz//hZhkjDHmJcLz/gCNjo7GypUr45RTTomjjz46IiI2bdoUU6ZM6cggODQ0FJs2bRqznFWrVsUTTzxR/8dIucYYY/ZNnvc+oOXLl8edd94ZN9xwwwsyYOrUqR0aZURzH5Cep7aYrWkvhcjJ9uSU0gEo1EWp5aqmX9oHRNTm0v6cbnpsRGe76Z4V2s/9LPRJ6Hn6THhvpkVne6DYd/RBZGFKaAO1dG0blkMfiu4P4bVZP5NSyovMj8gxoz6IUuganlcfCm1gffQ8pXH2R7YPKNtbNdZ5hWNEx2rJfvUJlVKmcGxqv5f2wuh+I/7PN310+hz6rEohr/Q865ONA5ab7X1jO3CvG/13Om51/GfvQsPOcV0FVqxYEd///vfjJz/5SRx66KH1vw8PD8fOnTs7jB4ZGYnh4eHn8yhjjDH7KD19gKqqihUrVsS3v/3t+PGPfxwLFy5snD/uuONi8uTJsWbNmvrf1q1bF+vXr48lS5bsHYuNMcbsE/QkwS1fvjyuvvrq+O53vxszZsyo/TqDg4Mxffr0GBwcjPe+971x3nnnxUEHHRQzZ86MD33oQ7FkyZIxV8Bl7N69u55i6xSYEgmnuDr1LC155jJNnRJzOp9FhWW5nH7qFLgUJoZlZRlGsyytJQlOn8PpO++lzVkom2ypdRZFmPZnGUMjOqWCLAwI66dLTVnXLNQIr83s570cE2w3tSlb9h7RHItZKKqIznZU+Yv3ZjIg24XjSevDumZbGFg2y80kd/Yrj3vJ2sq20HtL0b1VdmP7Z7J5Fql8rOcqlO9oUyaxZ+Gbst/TiM7+yMJyjYeePkBXXHFFRET8yZ/8SePfr7zyynjXu94VERGf//znY+LEiXHWWWfFjh074rTTTosvfvGLL8hIY4wx+x49fYDGk+Nh2rRpcfnll8fll1/+vI0yxhiz7+NYcMYYY1qhb9MxKFlWTWqfmR+EYSay0CPUhLMsoLQp002psZZmlbrMuZfQL9RqGXpEdfpSuoUsnAt9ZTzWJaosh0s61WbawHZjGBlti1JGUbWDbUiyvqNPK8uuyjbOlrbz2mzJNtuUYzHzK9BfkdUvSzFC6Fsthc/SdzZbXt8r6scpjQmOA/WV9bLkmX2VZZLN/JglSqkz9Jg2ZL9P2ZL4iM4xr2RpHbrhGZAxxphW8AfIGGNMK/gDZIwxphX61gf03HPP1Tqi6pLUWLlHQvVO6tT0G1BrVw0zSzkc0dSxs/DyPN+rD0j1Z5ZLHTvjscceaxxn4TdKPgjV+KnnU29mWByFYXy0rqVwOlkaDvZdlrKgFBpJ24L6N/diZD4UjlPWT/2RpRAzWj+WU0rnrbCfsz1EHOMsN0tJUPK3qB1sJx6r/4Jjgv2hfs+SL+nRRx/tem8voYXoaxoaGmoc95IbLQvjU/I1ZeXQp5X5ObN9iBHN+up4Gc+K6QjPgIwxxrSEP0DGGGNaoW8luEmTJtVTXZ0GcpqtUWsjmlPRUnZISlpZSArKVJm0lN3L6S+n99lySkbapVykskgmJUU026YUqoPSk0oOfA7lF21z2p9FKC7JjVyuqzJCFiE9otkWlC4yCYLtwnbLlu6XwjWpPFwKs6LlZplVIzr7R49LoYS0n3luTw6wPajESxu43SELd0Rov5ZVylCr7VaSklgf7a+SbK7jmlI366b9zDFAmTNbQv9CwmVxzGQhfwj7Q8tSOY5SZDc8AzLGGNMK/gAZY4xpBX+AjDHGtELf+oCqqqq1VtWqGVKGSzFV7+RSRWqd1E1VJ6a+XFoGrDAUiUJtlPosNVatAzX7LBwK/QbU5dUOtkMWbiOiqT+XfE3a5qx7po9Td6dunYV3Yb9TW9dQQtT+6X9Rm7Pl6GOhNtHeTEsvoWOT7cQ2Zt9q37Gu3Kag9S1lpM2Wtpd8sVqHLE0F7WDd6RfR5eu0l+VyzGcZRenP0/qUwlrp+8Bxmb3PPF9KJ6Hji0vmM78z+4Z1JZmPdDx4BmSMMaYV/AEyxhjTCv4AGWOMaYW+9QEpqgtTo8z8ONTsqXlzL49qodSIqe3qc6l90l+kum9pjwTDrmQpIqhrq3+MoUWyfUs8l4W5oR0ln1YG/Xnqmynt22BbZL4BtpP6fbZu3do4l+3hyrT/iDzlAverZXo5y6H/ZcuWLV1tKKV412P6v7LwLqVU0wp9DKX9LfrcLIV1RPN9Ybtk4YLYLrw36w+Oiaw+HLe8NtsbRhvps9bxVErNwrKyc3rMd4VkoZGyZ3bDMyBjjDGt4A+QMcaYVvAHyBhjTCv0rQ9oYGCg1huz9K5Z/Cdq2ryWGqzq3Lw3i1tGLZ1+Hd0LwP1F9AllMdDob8nSL9PnQC1d2zSLiTeWzepLo28gi1FF/Zg+OfV/lWKc0c+mNvI5tFHbsZf9FNS4Mx9cRHOfEJ/D8aXH9MlxH0eWjoHP4ZjP0lTTD5Kl/qZvQ/uH7cR+zvyEHNPsZ21jtiH7OYuHxrpmvyM8x2Mdbxw/2b4s1rXkd9YxznbhtXqez+GxjqdSOgzeq22u5Yx3T5BnQMYYY1rBHyBjjDGt0LcS3IQJE2o5J8tUSilAz1M24PQxW8LKaznd12spuXHaqtPjkhSTyTyUEbLpMKfAlAayjK68lxKKSgGl0PpZeBEuOdd7eS7LnkobWZ8svEjWhjymDEi5MVvyTJuyZeYsh3KLym60n+2USVo8l4U3yrY78Fq2SykFiZZF+xnu6PHHH6//Zn9k76wu8R/LBi7LzrJ7ZqGG+D6TLDQV240Sr9pE6ZVjRuuXpdmIaNZ906ZNjXN8v/l+dAs3lWVobdg5rquMMcaYvYw/QMYYY1rBHyBjjDGt0Lc+oIkTJ9aaomqupZAT6o8ppWbOloNSy83C61BjzUJs0AeUhYEfq2yltORWydJsU+NmG1Mf1+tpXxa2h0ur+Ry9t5TuOkudXQqdkqW4yJa2096S70zbiXWnj0ttos8tC+dSCrmU+aVIFt6olApEn8v3jO3CbQsaWojvM99RTX9NH0r2jpZCFPE5+juSpVqPaL5L9GHx/dZ24xhgv/PdysJ/sc21bejH4TjWcFRMyVEKP6VtoeO05Aur7x/XVcYYY8xexh8gY4wxreAPkDHGmFboWx/Qrl27av1R9eVSOB3Vral1UjPOoIbJ0Da6ryAL4xHR1G55LX0D9LcoWTgdni+lH8/sJdlzSns8tJ3oy8i09VLKZN6bhROhvyILG8821msfe+yxxjmG3mH9dB8H7aW/Qm2mH4dtqveW9ltwH43WIUsxEpGHU8n2rPAc7d+4cWPjWNuR/grusVObeS3fJbWf44fH9L9k/pZsD1QphJf2He3Nxh6vL+11Uxv5G5ml+8j2E0V01k+fo/aN97fWMyBjjDGt4A+QMcaYVuhbCU6jYevUrpQJUKUnhrLIIjVH5JIDp8u6HJTP4RRdJSzKWaWlmFk2WJItw+a0W6fSlP0YtoRyhZbFcik5aJuzfbNwQaVls5nsyX7msV5bir6s59lOtCkbX7yWbdrtvojO/tCyOH74HC4LVhvZxhxfWRgoyo/Zu0M5hs/NlsXz3ixiOlGJju8o25j9kUlIlP6y8ZQtY6b9bGP+jmi7ZcuhSel3LluaT8mN8nCWpXU8eAZkjDGmFfwBMsYY0wr+ABljjGmFvvUBPfnkk7XOqf6WUpgP9bFQj82WE0c0NX4+h1qoaukslxq3aqMlrTlbbkwbsrAfWQoF2pyFtYnI/Uc8ly21phZNG9UOas28l8/Vvi6F8dH+oM+BOrz2T+bbKz2H5WYZUTl+2HdZ2KHSeMqew2P187CduDxa34fZs2c3zmVhYgh9NZmvjOd4r7Y5fXtZGK6IfLl39htUCn2kzy2leaCPSMd4L+O2tAxb76Ufk/5Hnu+WNbfkr96DZ0DGGGNawR8gY4wxreAPkDHGmFboWx/QzJkzaz+A6p2lvReZb4PaJ/0Men2WtjaiqTeXQoLoc7OQOBF52JUs7FBEU8ulL4a6e6b7su6sX7a/gpqx+u/o92CYGG03aty8NkttTt8f/RVZSusslQZ1do6nzFdG/1EWQod+Tfad9m1pL0/mn8zG2lhlKxwT2u8cA2wXhvzXdqINbIssbAztfeSRR7rawGPWR20qpVBRGLKLfadjnO9DKeyNwnefz9HfGY49/j5p/TjGs76KaI43vTfbW6R4BmSMMaYV/AEyxhjTCn0rwT3zzDO1TKFTa8pmnBLq1LqUXZTLmPX6UkRllQZKoS5U5qHEwOm8ZieMaMotpeypOh3mOcoIahNlG5XNIjrro21BKYn3an1Ly36z7KMlSVSn/OwPyncqV/QSkoU2ZOVGNJcxc8k56679Q3mIfamSItuBkiLbMYuSnEluPMdyVcLieOG1PK9lM1sq5dOM7L0j2bJlQnmOaN89/vjjjXOZ9Mq+K0mg+t7R3kySphyfyfOUTzM3BcvScTresDyeARljjGkFf4CMMca0gj9AxhhjWqFvfUCTJ0+uNVDVQum3oYavGit1Xurw9Ouo/l9K+5DpnXyu2shzXBKZZUEshXNRfZa6NXVf1Zt5bsuWLY1j+nWUkq8sCx/CvlObqYeXQiNpH5SWgKpN1NIzG+nzIdTLtWz6lrJUFL0sOS9l4GT9tG3oa6L+r+OC13Lcat1pA/sj89uyXI5NbQuWw/Gk71IpxQjfu8yXTPTaUhgrHXulUGFZOgbWh/Zrf5SWe+txyf/L30HtWy2HdemGZ0DGGGNawR8gY4wxreAPkDHGmFboWx/Q7t27a+1S9USmIM5CjVD/puZKLTTbS5KFz6cey3TFqmtT16XGnenj9OvQT6LXPvroo6lNqoEznE7JJtWFMz05otmmbH/65LIQJ/TNZHtWGA6F/aPHpf7I9udwz0e2v4XPycYifXBZXekbK4X412OO8YMPPrhxnO03yt4HkvkFI3LfZeYX4bjNwtOwriyXfqrM/8V+ztKtZGFvSr9PtDHrD9qk7ZSFM4rIU0TQJvrgtawszFA3PAMyxhjTCv4AGWOMaQV/gIwxxrRC3/qAdB+Q6pnUk3tJF51ptxFNPbOX1MbUOzM9mbp1SZ/Vskv7EXSNPvekZPuNSjHB2I7aFrQ/06JpA/0iWfrrXuKJ/e53v2ucy2LDlfZ7abmldNFz5szp+lz6ltgWei3HAH1N2l/Z/rSIzj05ajP1fLa5+oRYLttf/WocLyX/l7ZFlqYiounD4hjPfgsy3x7LjWiOxSzlC+3gOfq0dAyxHbKYixHNcZHFuYtojluONY5bHQfsZ/4Oss3VL6XnnI7BGGNMX+MPkDHGmFboWwlu4sSJ9XQwW+bIaatOpTmt5jS8lHlSofyiU2tOu7NMrLSBy6Mpt+j1XLbMsBn6XC7D5tS6NIXP7tUpPeUiync6RS9JMSpDUX6gDZRb5s+fP+Yzx0LbiW3KMaESFvuVkhvHpt7LdmG/a9kcI3zOyMhIV3uzDJwRzTZm6oMslQCfQzLJJQuXFdFsmyxNRUTzvWM/c4xkaRRK4ylbmsx79behlKE2C63F9yHbkpGFWOK1bO8HH3ywcZxlOC5te9Hz+s6WxssePAMyxhjTCv4AGWOMaYUX9AG65JJLYsKECbFy5cr637Zv3x7Lly+PWbNmxf777x9nnXVWQzIwxhhjIl6AD+iWW26Jf/u3f4vXve51jX8/99xz4wc/+EFce+21MTg4GCtWrIi3ve1t8bOf/aw3wwYGaj1VNcxSWH7VYKlXUt/MlqxSY81SQLOcTPOmTaWlyRpu58ADD2yco2avWi59DAzbo0sv6bvgtdSXtT6ZHh6RhzR57LHHGsdaP7b3Qw891LVclpX5BXltKW27Li9esGBB4xyXpG7cuLFxrGOIWvqmTZsax1loJIYW0udyKTVTOdCXpmF+2K+su7Yj3zP2j9pPPwifw2P1w5VSpGdLhmmj9nsp1QHRscr+IJk/NdvqQdhXHDNqE99Rtpu+H6W66730F3F8ER1v2daUbjyvGdBTTz0Vy5Yti6985SuNH40nnngivvrVr8bnPve5eNOb3hTHHXdcXHnllfHzn/88fvGLX4xZ1o4dO2Lbtm2N/4wxxuz7PK8P0PLly+P000+PpUuXNv597dq1sWvXrsa/L1q0KBYsWBA33njjmGWtXr06BgcH6/90NZMxxph9l54/QNdcc03cdtttsXr16o5zmzZtiilTpnQsDx4aGuqQHPawatWqeOKJJ+r/NmzY0KtJxhhjXoL05APasGFDnHPOOfGjH/0oDZ3fC1OnTh1TQ92+fXutTaoWXdrLo2XxWvoCqFWrZk9/S7Y/hDopNW7VWKkJ049AvTkLb5GFNCntz8nalPot/Ul6nn6cTFufPXt245h1V62a/i3q2EwdoHVnf7Du2f4cPlf9L9yPc9tttzWOqZ+rz+iuu+5qnMtCLnF/Dn1nOk7Zd1u3bm0cs421bLYp7dd+p0+UPgdtf/oyWC5/O7Qt+H5ke+pYN+7pytIxlELF6HMznwnL4niiS4G/QUqWToJ20KbM50I/M/uDY0jJwnDRRj33oviA1q5dG5s3b443vOENMTAwEAMDA3H99dfHZZddFgMDAzE0NBQ7d+7seAlGRkZieHi4l0cZY4zZx+lpBvTmN785fv3rXzf+7d3vfncsWrQoPvaxj8X8+fNj8uTJsWbNmjjrrLMiImLdunWxfv36WLJkyd6z2hhjzEuenj5AM2bMiKOPPrrxb694xSti1qxZ9b+/973vjfPOOy8OOuigmDlzZnzoQx+KJUuWxEknndSTYaOjo/V0Vae12XLDiOZ0M5N4xkKnjZzFUe5SmYE+rywLIikthdXpMqe/3F+l9eU0m/brcykLUN7KZDYu+6X8pVP09evXN85lUZMp43ApMuuXhR7hmFEZisu7Kb0eccQR9d+U0dhur371qxvHv/3tb+u/2f58jrYbxw9lQZX2KNdx/ND3qn1LmYTjNIvmvXnz5saxLhlmu3Dcst+1vmwnyr8qUWcSFeEzS+GB9DyXPDOEjkIJLotEXQqnw/pkUdBZHw1TVNpqoHJ8KdxXKRtBr+z1WHCf//znY+LEiXHWWWfFjh074rTTTosvfvGLe/sxxhhjXuK84A/QT3/608bxtGnT4vLLL4/LL7/8hRZtjDFmH8ax4IwxxrRC36Zj2LFjR61Vqv5M3ZpaqJ6nP4I6aaYDU7fOlkCXwlUoJc00C3tTytKq2jv9Bgzrodo67Wcb01+hdlAf52pH1ZS5zDQLp18Kb8S2UJuzcP8REY888kj9d2l59/333x/deO1rX9s4Zhurfn744Yc3zv3mN79pHB922GH132wX+uBuueWW+u/jjz++cW7evHmNY4439aEwXQH7MkulkW3DyJYaR+QZRukD0r6KaI6LUkZR9dWUtjBw+4OeZ92zZeZsQz5Hr83ORXTWJ8uay7I0LFQpG6w+h2OC71mWPkbLcToGY4wxfY0/QMYYY1rBHyBjjDGt0Lc+oOnTp48ZIqK0l0f1/lLYniyVQymEvOrA9FfQF6A6KsvhngLWT/X0kparWnWWfiGiqeFTP2Y67wzq/dxHQL1cyXxa3N/C/RRZfTIfA8vKQvpENMfIK1/5ysY5pme47777GscPPPBA/fcvf/nLxrlXvepVjWP18xx55JGNc0NDQ43jn/zkJ/Xf3JvEdqPvTOtDX1OW7p5+kCw8Dfucz6GN+t5xPHFsqo+C7zNt6hYmJqLsS87qnoXa4t7DLH0Mz/H9pv9O24I2ZL4athN9ovr7xbrSRv6u6PVqf8kPWD97XFcZY4wxexl/gIwxxrRC30pwVVXVU1BdcsipXTYVpRxEuSuL8MtpKkPOaJRbTlu5BJrT8m7PjOisn9a9tLQxC5FDVJaiZKhZM8c6r9NwLtvMQuRkkcsj8oyKjCqcRULmc7J+ZvtzjKg8wWcyx9Udd9zROP7Wt75V/812ogSnS90ZBopZhxctWlT/zdiMvJfLZrXNWVfKj1rfLBRVRPM9ZPiWUmRkbeOSRK3LpfmOZpJ6KRJ+JlNxjGThskq/T/o+9JKplDZyPGVLqymbcVuFthvbm/dyjHQbt6UssnvwDMgYY0wr+ANkjDGmFfwBMsYY0wp96wNSVMMvZQLU8/RHlMJxqBadLcflc6jVUl/Olo5zCXcW+p3l9JJiIQvxw7Ae1IHp09LllvQF0Eb1RWV6eERTN+a1bH/6OtjX3eyNyNM+ZH4QTa8Q0Wm/hj+JiEZ6eYb0YX00rA+Xd7NuOkbon2N9OA50vPEc66PPYbvwONsCQP8KfUT6/pTGbRa2h/ar/4vjhdC/l6VjyHxApSXP2j9879jPbGOFfk22W+Yv5hjR953l8veJy9l1O0QWSqsbngEZY4xpBX+AjDHGtII/QMYYY1qhb31A27dvr3VY1UqpqVKzVB048/FE5Gvnqc9mmmov+1toA9fLU19WO0ppwlWrZjnU3X/3u9/VfzOsDdNf07ehmjH7IwtdX0rVnIXToc8k25tRShusYW9K/i/tH15LG7g3Q31nLDfz1TCN9uLFixvHN998c1ebOKbZxhpmib4x9o/6RdjPrI+e5znuK+MeNR23pVQa6hPimCilElCy9N28l+3EPWnaxqw7x57Wp7Svj3VX6BujjWpTyf+V+bAIf3O6hS/LbFc8AzLGGNMK/gAZY4xpBX+AjDHGtELf+oAmT55c64ga5yvb1xDR1IWps1Mz5r1ZnKbM50Cdl8eqx1JjpQ3cj5Dtb8nirjEOHp+jmnGWjjii03+kfcA2pU9CtfQsfhttpIbMmGYM8a9lU6NnX2b7svhc9SvQp8B2+qM/+qPGsWriP/jBDxrnmHJB2/iNb3xj4xz9ClmK+jlz5jSOabOmQuD+Fo4RHavZXqqIiJGRka720jfDdB86jumzov9I+6c0JnSPVBYnLqJzzKhNbEPGUtPxxLFVioOn8P3gb4WWnfVVRLN+tJf7+hS2f2nvnrZTFsuxG54BGWOMaQV/gIwxxrRC30pwkyZNqmWvLMRDthST02FKcpQR9F7KE9n0mNNSomWVMjMy/IbKGZR8VPaIiBgeHq7/pqzGKbrWtbTEmfKLTu+5hDtLJ8G6sT7aX5z6l5Z7a9mUJik5qE1c8pyFiSmlOmAInTPOOKP++6STTmqco7SkfUAZ7YYbbmgcb968uf771FNPbZyj3EK5S98B9jPHvEpYHE+8V2Hf8P3geFKb2O9sU+1Llkv5V8uivVlGXZbNscd20nGcZTGlTYR9laVNYLkc8yoblrY/aLmldsnScuhvijOiGmOM6Wv8ATLGGNMK/gAZY4xphb71AekybNUTqUUzbIz6OrIUCrw2Il8uzaWYagf15VLIH4V+BYbXz+pDf0W30OgRuXbLcukzydqNz6GGrLp8tqQ8ounvKvmAqE13Kyeis37ad/PmzWucu/vuuxvHaj99F4RjUzX82bNnN8498sgjjWP1+9x2222Nc1yufvLJJ9d/00dC31KWXj0blxHNvuRSatZHxyLHAO/N0jVzTGvYpIjmuOAYYH26hYmJ6HzP2B/ZEu7MX1Qae/oO0Ca+Z/Q16e8gfSy8V89n7wqh34nL09l3Wnf1wzoltzHGmL7GHyBjjDGt4A+QMcaYVuhbH9CECRNqvVT9F1l4+Yim7ku/Af0V2d4A6tb0oajGmqVfiMjX/mdh1COaGit1VfoGaIfCPVCZDygLuR7R9AGV/F9ad/qAqNmr7k4fHP0K2d4r6vv032ndqf3Tp3LvvffWf7/61a9unKM+zr5UTZzjiagd7A/uIVLfAPcxsa5MfaBjKNuPQ5vZ3nxOFoqffp0svEsplYaOzVKoLR2bHHscT9n7zeew3XQc89ps/2C2DzGi833nPieFvwVZigX6qXQ8lfzX4x1P3gdkjDGmr/EHyBhjTCv0rQS3a9eueqquslRpea5Ouzn1zKawEc1pYxb1NSKP/MopbhY5W6MTR3Qum9V7OR3Osi2WQnVoW3C6XHoOp+nd7I1oylCUF/lclQLmzp3bOEepj9Krtlsp9IiWxfYmKqFwSTDHHm1SuaK05DmLGk6ZUMcMl80yIjTHpvYlJR/2nY4hjh/2RxYh/be//W3jmO2mY5HtlL1LmbQd0ZTDSjI566e/Hb1EU8/CVkU0+5nPZH14XuvDMcIxrs8pSZV6TNmP9aHUp/XLQqZ1wzMgY4wxreAPkDHGmFbwB8gYY0wr9K0PSFF9uaRFZ74ZavgMe5+FvaG2Tr1c4RJV1bF5jvazflp3avbUl1Xfpx+Ex9m1JX+F6rtczkrNOFt+TD8P66OwnRgKRjV+6thsU/U1UUvvZbk60zFwWbbq5aWwMdlSZC5j1nbKMnlGdPaP2lzyoaj/jqk0WJ/DDjus/pu+MI4B1k/frdI2Cw2dxOX2WZgYnmO/Z5mWS32nY5P9wTGi1/aatkL7gL6YLOxYljmZzymlHGE7qU3arwwj1A3PgIwxxrSCP0DGGGNawR8gY4wxrdC3PqAdO3bUOqbqidRN6RtQffPggw9unKOOTQ08CzGThdSgX4f7jTKfA7VbavaHHHJI/Tf1ZbaFatPZPg0+l5o2r2VZ3dLwjnUt/RcK25++ASVLnRGR71mhb0l9cg8++GDjHPcvqP1MgU4tPdtvxDbmWNRxwXbJQiMxncSvfvWrxjH9CPpObNy4sXFuaGio63P4LvE5DzzwQP03xwTfB55XXw7bn2Nc05GzXNZV04rwWvpQeD4bT+zLzN/CUEnqU2Fd6aPmeR1ffEc5xrO0FRx7Ok6Z3qaU0kbvdSgeY4wxLxn8ATLGGNMKfSvB7b///vW0uJeowjplZJiV0pJnns/QezklpxSg0hmnv5xms34q32WSIeEySE7DtaxeQu1ENNuJbca6a9ls7yysB+vK57B+WlYWrTiiKatR3lq/fn3X5zJSNiWTTMrMbIho9o8uaR7ruVkoIUYrnj9/fuNYlzVTduLY1DZlOx1xxBGNY7WDMjLlIZ5Xea8Uikffj9L7q3IS35VSJl+VREtbGrTvOC4ZKknHNSXnUubS7L3kMnPtr1IYIu1njkvayLprH2QR6rvhGZAxxphW8AfIGGNMK/gDZIwxphX61gf07LPP1pqn6sDUL6mLqtZL/ZVhPah3qv5MfZlkITX4HL2Wvg3awFAYmeadLcOmvk8NXLVcllNqY9V6qftSs1dKy0y1fllIorGeq8dsQ9ZPl83TX3TkkUc2jtW3QXtZV/at6vLU91k/7Wf23f333984Vj8DfUBsJ122HNH0Bxx66KGNc1yWrT4hlst71c/DFCMcT/RXaDvy3iwVAvsuC8FEXwb9IuxLrW+WQTSi6eOlTSTziZbQcZyFDorIfU30lenYpB+Q/rvMB+Rl2MYYY14y+ANkjDGmFfwBMsYY0wp96wMaGBioNU/VPkvhaFST5D4T7rGhX0R1U2rE1PdVN2U59E+oL4r6MY8ZCiPzNVGbVn2cujvR+jGUUMknpMfcd0J9mX4fhX4RbQu2YWlfkF7PdqGunfkG2P46ZtguDKtETT/zodC3oXXfsGFD4xxtVL2fIXL4HIbT176jj5TPydKVMz3JQw89VP/NVBkkC2vF/Tj0h6mfh+2dpZ0vhZsiOp74fmR+Ho7TzB+Z+Z0i8nTefCdpk9rBNuXvoB6X0qtkoXmyfYnd8AzIGGNMK/gDZIwxphX8ATLGGNMKfesDmjRpUq3zqzZKXZS+ANUhqUlS38/2t2RptSOa/iX6BqilZ76NLA01r2dd6Q/T+pT0cdVySzZkqahpEzVktqPC/VJqY8mPwPpoX2fpriOa/i+OAfrktG9ZDtuF2nrmK6CPUcumb4zjOIvFR78nx636buivyPaOPfzww41zbGMdE1n6i4jO9yOLIcay1Ea2P/fUaV15bcn/ov3OMcFU4OrnpL1ZuhLaxHuzcczfHPa72pilU4lo1p3vK9/n7L3Td8kpuY0xxvQ1/gAZY4xphb6V4Hbv3j3mUmJOUymh6BLPLMNmRL48t4TKF5wOM7OkTo85xc2kjIjmFJhTabZFFuYjC6FBG3gvJSG9nuceeeSR6AZlAi4hzpbNlpa6Z+GOMjsokWTt1qtUqRJpliU3ormcnSFlKEdqaB62IduJUp++E1xmzfdF68vQO5RmshBYJTkmyxzLumuGUfYV3y0dB6XfDbYTl5kr7B8dQ2xD/jZk40kzuEZ09m0mZ2fbEviOcpzq2OM5tjHfLf291THAenfDMyBjjDGt4A+QMcaYVuj5A/TQQw/FO9/5zpg1a1ZMnz49jjnmmLj11lvr81VVxYUXXhhz586N6dOnx9KlS+Pee+/dq0YbY4x56dOTD+jxxx+PU045Jf70T/80/ud//icOPvjguPfeexua72c/+9m47LLL4qqrroqFCxfGBRdcEKeddlrcddddxZSzyujoaK2X6nJW+m3Gum+svyM6ly1Tl8/SDFAjVg2ZWi41b9WTSymsqeGrxkrtmXo5bexmb0RTq87SaPNaXk+bGHpEl4My1D77UpfRslz2ZeavoP5Mf4X62UpL6NV+9jN9KOw7He9Mi8A2V78Ul/lmy5q5lJ31yXwZbH/eqz4J+iaztvjlL3/ZOHfUUUc1julX0PeFvxEcT5mPlz6SXpa207ehfp2SD0ufy/eM76i2Mc9lIYp4PX9H+Fz15bBufK62ceZLisi3wWTpVLrR0wfoM5/5TMyfPz+uvPLK+t8WLlzYMPbSSy+NT3ziE3HGGWdERMTXv/71GBoaiu985zvxjne8o6PMHTt2NAYSPxLGGGP2TXqS4L73ve/F8ccfH29/+9tjzpw5ceyxx8ZXvvKV+vz9998fmzZtiqVLl9b/Njg4GIsXL44bb7xxzDJXr14dg4OD9X/z589/nlUxxhjzUqKnD9B9990XV1xxRRx55JHxwx/+MD7wgQ/Ehz/84bjqqqsi4v+WSHIZ8tDQUGP5pLJq1ap44okn6v8YCdgYY8y+SU8S3OjoaBx//PHx6U9/OiIijj322LjzzjvjS1/6Upx99tnPy4CpU6eO6buoqqrWH1WLZvh/oton17RzfwJ1U9VyuT+EenOWvpu6qer9WQqFsc5rfUtr8tV+6smUNtXPUErvm6VnKIW1176lz2FkZKRxrP+TwjHB+nCPhJZdCumvOjZtov9F606/FP0vw8PDjeM77rij/rsUpkf3u/zud79rnKON2jYcA1nYnojmGCml3dCxWNqHpWGu6O+ijXyH1cdCe4n2AfuK7aQ28v2lT4j3Zim5+c5qu9F+3puFCmO78LyOGfq7Mt8M+4q/I1lKBb533MvXzb/NNupGTzOguXPnxmtf+9rGv73mNa+J9evXR8T/vYD8YRkZGel4OY0xxry86ekDdMopp8S6desa/3bPPffEYYcdFhG/X5AwPDwca9asqc9v27YtbrrppliyZMleMNcYY8y+Qk8S3Lnnnhsnn3xyfPrTn46//uu/jptvvjm+/OUvx5e//OWI+P3UbuXKlfGpT30qjjzyyHoZ9rx58+LMM8/sybBdu3bVU/csqnCWLZL3MdQFpRmdlpekJV2WSv8Wp61qE59JGYdTV5UvOP1lWVkEX7aFTpcpE1CKoYSix1yeS/uz5awMs6LtxLpS1smiDLNd2JdZ9OUsurqGwBnrWvov1cZXvvKVjXOUj1TK4NJp9scRRxwxpn1jXUspU9uC9lOG0nHMa9mXKuvwmZR/KZ/q8nxKS7RJ25TZdjmedJzy2mwMROTLpSnfaZvyWo5jheOU/c52VJmtJInqeY5/jr1MbmS5PK/jQsuhjN+Nnj5AJ5xwQnz729+OVatWxUUXXRQLFy6MSy+9NJYtW1Zf89GPfjSefvrpeN/73hdbt26NU089Na677rqe9gAZY4zZ9+k5GOlb3/rWeOtb39r1/IQJE+Kiiy6Kiy666AUZZowxZt/GseCMMca0Qt+mY5g5c2atQaufh3pmpnlTJy2lKMjC+LMs1WO5wu+hhx5qHOt5atGlMP1aP2rn1Na1PtSPueRWj0s+Hy7xVJ2eGjF9Qmoj25/lqj+MPh761WijSryl7JdqM9uJ127cuLFruRwT1NZ1U7VGDKENEc3+oF+Q4Wh0IdDRRx/d1d6I3KeVpeiIyMNecczvWQkb0dmGLId+Eh0Hmb20mbJ+5kOhvXwP6bPQ59KmzH62IcvVtuFqYY7xLA0Kx202Fun7zrKn8r1ifehz1/P6frwoy7CNMcaYvYU/QMYYY1rBHyBjjDGt0Lc+IA3Fo2S+mIimD4XnNNx/RKc2rRomr2V4F9VKS+vsu62VH8uGLJQH9dcsnUSWvrd0nu2W+YRK+6VUC+Y+DWrTmvaZz3z44Ycbxxr6JaLZTryXenkWRolo/aids6+ysDj067zqVa9qHKs/gHu26HtSO9gu3G/E81lqE7aFjnn6I1iu2sRYkKw7/SL6XL7zPM72l2Shqkq+GR5ryB+G8MrSlWRhbiKa42nOnDmNcyWftf62cfxnY5PvM/tdy6X/jmOa57uF2sr86YpnQMYYY1rBHyBjjDGt0LcS3K5du+opqU6POX2kXKHXlqbvWaRaLnHOlj1y+suotvpcTsk5PaakpVIZbcjkuyw0R0S+pJPT52xpLM9RZtMl6ZRiiNadEiHlCuaN0lA9bONMDij1h0LJk8tos2X+PPfDH/6wcTxv3rz6b8q9WSTnLKJ7RGcWWl3SzaXuXMasMk+WFTei+b7QBi7N57JytZF15/uQZQXNpCZub+A7yneJfT1emziest+rUpRqPkf7i/J1toyc5WbbErLtARGdcrDaqG1cyiK7B8+AjDHGtII/QMYYY1rBHyBjjDGt0Lc+oGnTptV6qerPDFeRhcWgfpmFkI9oasq8N/OhcNk1dVLVtbMwGBGdy7/Vh1LScnXpaCn6uNa1tPyT2rrq41u2bGmc4/JQXZJL7Z/PVR8Ey6ENTM+g2jX1+yydBMl8KLSfvrMstErJT/jggw/Wf3P88FjLpR+Hx5kfoZQJV9uJ44k2aT+zX9lO9PNky3ezZf4cp1lqitI7mi3/5pjIfDeZDzGi+d5lYW0iOttCn0v7+Xulvw20n8/R49IWBvrduv02l7Y37MEzIGOMMa3gD5AxxphW8AfIGGNMK/StD2jr1q21Hqn7CEqhaxRqrNRN6SvQsrgngtquaqOlUDyaspsaPW2iT0X31dCHRW1XNVieo42qGbMNaRP9bHo9fXIMc6++Du4HyfxU1KKpcZNe0qlnZdEPouVyTw33YtAnp+fpN2Bf6r2ltNq6J4pjuLQvSLV5vh8cBzpW2WbU+PU5WbrxsZ6jsO/YprqPhr6kzF/B340sxQjPc5xmvhqWQ5vUT8vfgtIeHL2XNtBGrW9pD5fey/eO45TjS8ef/k6MNyW3Z0DGGGNawR8gY4wxreAPkDHGmFboWx/QtGnTam1StUXqpJmvoBRbidqolk2dOttzwGupeatOWkp1QBtV56YmTH1Z9VpeS71ZbaaWnoXLp820IYuTlaVF4L3UqdlX9D2p9s79IFndS3s81GamGWB9mIpdxwF9EPSHqU0sl6kb1MdCHxD3GxG1g2MxS7+c+RAjmvZzjxb7ku+LlsWxx/7QmHPsV7ab+uBKvwW0UccX40JmKcc5LrPxVEo9QV9g5jvLfFysK8vR95+/R6wPfUL6Dut44fvaDc+AjDHGtII/QMYYY1qhbyW43bt311NQnZZzWsrpsE61s2XKEXmIEJ7LwmZQBtHlkhHNaSrt5fEjjzzSOFaZoZSlVZdTUiLJwqzQfk7nS6kdFD5X607JLQsJwvYuhUbS6ymnMKyPLhWnDVkYHJZLaYOSlqafYLlExxuX1NJ+heF/KK/wHdDxVupXtYP9QalGrz344IMb5yhJsy20fqVtCVo/jmnWPUvjwneJ40vfLUp9bCdt0yw0WEQuTZWyqWp9SuGytN85nmi/jgO2C8cIl9gr2bveDc+AjDHGtII/QMYYY1rBHyBjjDGt0Lc+oP3226/2TehySuqiPFbNkjopNeMsZAuXGzIMi2q5XPqa+S+onfNe6s0ayiZLQRDR1G+5dJT2a93ZDvR/ZfSSyqHU/tly3CyFQkTT5uHh4cY5+tW0LI4f2qTHrCttZN9pmm2OCWrkmZ+TY0TTQtCHSJ8P/Vb6XPpF2O9aVsnnoOXyHNNYcNxqevXSc/TeUnoVtYlL6DkmsvA0bKcsjXtpGbzaWEp3nfkNeS/7PYM+Xx2bpW0J2dLwLI15NzwDMsYY0wr+ABljjGkFf4CMMca0Qt/6gHbs2FHrnKotcq0/17jrHg9NZRDR6UOhvqz6JrV1phnQ8/QbcL+RHmdpmyM6tWjVz6m/ck2+6s8sl/eqP4PP5LVZ6nLqvvRtaFnZ/gmeL/n6OA7Uf8E0CdyXtWDBgvrvjRs3ps/RulMPJ/RTqZ5eCiGVpXXm2FN/Ef0g7Etq+to/pXBH2T4gojaz/Zl+/JBDDmkc33vvvfXf2bsT0awPfVZsU30/OC459tjm6vcphQfS8/S1cq+ePqf0nvG5mY+U6O8Mwz7RV9mLTzRLd+99QMYYY14y+ANkjDGmFfpWgquqqp7q6nSO02xOh3WKS+mFkhWnxypJZNPUiObUlNNWLsVUmYTLQRlqJAuDw5AslLRUliqF+NFrKQVkEgOPKfFQEtW2KYXX0Tal/ZSaslBJ7PdDDz20caxtzvambKt1ZXuz3Vj3LEQLx6LaX5KLdCxmkZnHOq/jmn2XLS/mtdm7RMmHEhyjhquNHCMce/qOsq6UhzQUEqW8LINoRLONOSb4fmS/T3xOlhG1tAxbjynXZVsAOJ7YFiqZZlL9WHTbspFF7lY8AzLGGNMK/gAZY4xpBX+AjDHGtELf+oAGBgZqTVr1Tmq1XEqqod9LodCpX1JHVbLnlsJXqLZO/wS1Ut6ryzq5jDwLmcPnUDM+8MADu56jhs9w+tlyZGrIWQidLCMktecszQPPq/4d0emTU38FNfrMN8Bnsm5ZXdnP9GllS2FJtgSXPp8spD/7iuO/W7bLse5V/x3fB9rLumvG1wceeKBxjmNT+4N1o59Tz9Ne1od+KoXL4LOQM9ny+ojm+MrSkUR0vh/6HpaWYWfpSbLfII5x/sawHbWsXkJ47cEzIGOMMa3gD5AxxphW8AfIGGNMK/StD2jXrl21nqrhaBjGg3t5lFKqAJLtZaC2nvkReK9qrCUf0Jw5cxrHupeBmjefq74O2ss9RKrl0ob7778/MtRXQM2bewwyvw417mzvQJYSPaLp06LvL9uXRX8RdWz1K7AN2f6klzBE2hb0NzIciur03I9Dfx37Q8umDfQV6H6pXkLXcIxzTGtfRTT7h3Wnb0bbnL4m9p36ajKf4Vg263NYLsde9lvAvsv239GvwzbvZe+e3puFiIpo9mW2r3IsuqWBKIVu2oNnQMYYY1rBHyBjjDGt0LcS3IQJE+qprmb65FQ6CwVDSacUtkSn1pySU0JRGYrP4ZRXl3FSfmB9ssjHXP5J+TGL3EwZSiUItgOlAIYiyUJ3cIltFj6E03u1iXVj3SltqM3MwMm+UxlXx1ZExMMPP9w41r5l2CRKVmzzbFk/0fFGCTeLqMx24DMzmYr2cuxp2SyXY0SlTI5/vktZFmNKhuw7LYvvEm3SMZ+Fk6INPC5ts9BxwPHPdsvkLta1VJbCcaxjphRyScc4z5XcGHq9/vZm2VwVz4CMMca0gj9AxhhjWsEfIGOMMa3Qtz4gDcWjOiR1UWrG6lOhnsl7qY2qvlnKIpiFKc+WOWahgyI69XO1g5km6XtSG7kMMtOteY5LX7dt29Y4Vn2Xy07p09IluLSfOrz6J7LMqmM9V303Bx98cOMc+1KfQ78O+0PHUCn7K9tcfWtZmJ6I5tijDRxr2nf0D/F9yLJs0l6OpyxLq24P4LWlzLFZOBe2McvSNs3SqUQ061oKXcO6Z5ljWZb2La/Nsv4SlpuF6uHScGZa1jFfep+17mzvUrgmtVnrWkrjUN8/rquMMcaYvYw/QMYYY1rBHyBjjDGt0Lc+IA3Fo3pmybeR+YCoY1MvVy2avoG5c+c2jtUO6rHUfVW7LYWq594G9ZvQfvpBtKxSCI1Mt6aeTC1aU1zTr8Y9H0opXLvuO6G+z1Tm2b6mUvpr1bzpb6Hmrfsr6EsqhfTXtmG5HHtaH7bThg0bGsfZnhvWlXu8VJvnc7inSCntodO2YTmlfSg6vthOHMdqM/0RHDN6beZHi+gct3o+C6cTkfv3Mj9bKbwU+yd7p7N9QPydyHx9tKmUulxt0veO72A3PAMyxhjTCv4AGWOMaQV/gIwxxrRC3/qAqqqq/Tuqz1K7pdao56lnZvtOIppabxYyPqKp+2ZpBCK6hyyP6LSfe2XUV1BK95uFQOdzVBOn/dR96a9Qv0nJho0bN9Z/s++oraueTF8Gr6XvSf0KbCf6edRG+hw4JnTPBPVvjgm2m6ZTp/+IMefUn1FK3aApIji2WFf2u7Ybxzifq/3OujP9tbbjwoULG+fuvvvuxjHHiLYTn5OlrOcYoA9OU9iXfIj0r2T7pbK4cuwPtrGOzZJ/hahfhzawrCytO99ZbWP61Tie+H7o+NI2LKW+2YNnQMYYY1rBHyBjjDGt0LcSnKJTO4aRyLJqlqQlyjoqIzCkP6emKk9QCuB0XuWXbLnnWOhUmnJRJtWUQtdkYWIYDoXSgJ5XmYM2RDTrx+dQbpw/f379NyUe3st2y0LKsy1UHmCqCUpWWh/2s0pHEZ39oTIPJR9KGyofPfjgg41zlO/0uWwnZm1ligVdrss2zTK8lsL26HPZrxxPXDKsbc6+4tJqfS7fsyyMFduJsjKlM+33kgSn44nlcsyohJWF7IrIxzzfSdqYpVvJssHy95SSG8e4jlvtK9a7G54BGWOMaQV/gIwxxrRCTx+g3bt3xwUXXBALFy6M6dOnxxFHHBGf/OQnG9O2qqriwgsvjLlz58b06dNj6dKlce+99+51w40xxry06ckH9JnPfCauuOKKuOqqq+Koo46KW2+9Nd797nfH4OBgfPjDH46IiM9+9rNx2WWXxVVXXRULFy6MCy64IE477bS46667Uo2ZTJo0qdZ71VdDPZmapD6DGiu1UOqdem8p9H6WQpl6v2rE9JGwXPqlVAfOlghHNPXYbKkl7Sj5Nui30iXEbKdsGXkpJIvWne3E52TpGOgfov6faekMQ3TYYYd1tZf+ile96lVdy2L704+gdedzWHftd44Jpkng+FI/A9+dLHSVhl+K6Bzz2sYsl0vq6V9Vn1CWpj0iX0Kf1ZX2sj9ok44RPidLKV7y6+j5UogfHut7yjblc7Vsvg9Zumz2HY853nQc0/81Hnr6AP385z+PM844I04//fSIiDj88MPjm9/8Ztx8880R8fuOuPTSS+MTn/hEnHHGGRER8fWvfz2GhobiO9/5TrzjHe/oKHPHjh2NQcUfVWOMMfsmPUlwJ598cqxZsybuueeeiIi444474oYbboi3vOUtERFx//33x6ZNm2Lp0qX1PYODg7F48eK48cYbxyxz9erVMTg4WP+nK6GMMcbsu/Q0Azr//PNj27ZtsWjRopg0aVLs3r07Lr744li2bFlERGzatCkiOncdDw0N1efIqlWr4rzzzquPt23b5o+QMca8DOjpA/Stb30rvvGNb8TVV18dRx11VNx+++2xcuXKmDdvXpx99tnPy4CpU6eOGaZ/586dtX6qGit1RmqsqpNSZ6dGzPP6nCxMDM/T78Fr1c9A/wrvZTgRvZfacxbmIwvFEdHUdrnfgH4Q+mNUT6f2zH01ma9peHi4caz/k8IxQZ9WtreBPjhNXxDR3NNF7f/www9vHGc+B+6xoU9R7+VzmN5DfTel0E7aFuxn7i/K9q/RXpal44Llsj7az1n6C5ZLO/je0QeRhbLhc9R3yXL4HmZ7uvhOcizq70qWAj2i2U60t7TXSutb2sOV7a0i+q6V0pxne5V0T+B49wH19AH6yEc+Eueff37tyznmmGPigQceiNWrV8fZZ59d/6CMjIw0XrCRkZF4/etf38ujjDHG7OP05AN65plnOr7okyZNqv+vZeHChTE8PBxr1qypz2/bti1uuummWLJkyV4w1xhjzL5CTzOgv/iLv4iLL744FixYEEcddVT88pe/jM997nPxnve8JyJ+P/1cuXJlfOpTn4ojjzyyXoY9b968OPPMM3sybMqUKfV0VaUBTnE5ZdfpJKeB/Hhyaq3TSUpLLEtlKE5xOZVWGylvMXQKZREtu7SMXClJM1o/LvOl5FZaHtrNXlLKtsj+UVj3OXPmNI5VDmNfHX300Y3jBx54oP6bUakXLFjQONY2zsL0RHTKUgcffHDXa7m0XaWNUrZOHeMcA5TcKNWoPMl+Z1lZdltKMWpztmWB10Y03w++z5lsnkV/p40cW3y/2U7aX9lWj4g8PBCfq2OEY6IU4VrfAb6DXD2cZR/mc/Xe0vjhvTpmOJ7GQ08foC984QtxwQUXxAc/+MHYvHlzzJs3L/7hH/4hLrzwwvqaj370o/H000/H+973vti6dWuceuqpcd111/W0B8gYY8y+T08foBkzZsSll14al156addrJkyYEBdddFFcdNFFL9Q2Y4wx+zCOBWeMMaYV+jYdw+TJk2u9Mcs4mIUHz5ZDR3SmElD9k74m6tZZRlT6lrKlvDym5qpLWjNNnveWtHRdpUjtme2U+dJ4LeujdlCz53JpXVbOc3yOZlrlvYTpDbTfOUaopavmzS0AXG5MH4reS/9Q5nejn5DjSZfuczk3l/XTx6i+QS4jp19N3y3u4+NePX1fslQZEZ1jRN9pjkWWpefZ3llKBV7Lfuf7ri4DvjuZH5rn+PukvzHZb8pYz9X6lcZeL0vbNRQS37PM5xPR/G1QvxNt74ZnQMYYY1rBHyBjjDGt4A+QMcaYVuhbH9D27dtr7Vj1zixcSERTw6QOSQ2fWrTqm1wPT2062zNEzTXTx2l/lnIh06lZdqazRzRDblCjz9JURDR9OfQbMJRHtjeAPiHtZ/YVtWnanIXi4b1adinthrYbUx2wn2nzli1bul5LbV21dLZLlsaC5TLdNftD+5J+M7WX93L8ZykK+EzC8aXjmufYd1o/9nMW4od9w36mX0THRSltQrdnjmVTlg6jlLJD/Xnc15ftTeLvBseTtiN/f0rhguhL6xXPgIwxxrSCP0DGGGNaoW8luIkTJ9ZTUJWhKMFxmqpTwiysTUTn8mKVgDg95tRTp6qctnLanYUPKWUcVOkgy7TKslkuU2Ro3XltSa7Q53LZb5Y1lPZS5ny+YYcimkuKsyjhEREbNmyo/z7mmGMa5/jcbOkryWRD1p3o+SyCckRTas1kp4hOiUSlGl6byXXsV0rJWVickhSubVySXnXcZtJwRB7aqfTeZVH1KWFlGY+z6NEluY7Sn1KS67TsLEp7RDNkFJfmsz7sj26/g6XxvgfPgIwxxrSCP0DGGGNawR8gY4wxrdC3PqBJkybVOqdqi/TjZGHuucSWui/DlOi91LizLIilpdSqGWd6fkRn/bQsat6Z3k+bqO/rMk6Geiktw1Ztms+hNq3PZTlZllP655ixMvPfZSk6Iprhaximh2NGtXXWjeF16OvQ8cXnsN10bJay/up44rmSj1Ez1tL+LLUDQ/Gw3GwJPX2X7LvMfzRW/rFu5WRhfErvKM+rzyvz6Ubk/kmOiSxcTbY0n+fZ/jzWa/me8X3X5felTMpc5t+t7vYBGWOM6Wv8ATLGGNMK/gAZY4xphb71AY2OjtZavmrE1CizdNH0p/DabN09dWnqtXottdtMj6X9LJfHamMWmj6i6W8pPWd4eLj+m2E9aD+fo2XTB0F9WSntc9D+Ke33yjR87tdh6gbVrQ855JDGuV5SZbM+vFd9HxwjHJtav9J+NYV7qbhHhTbT76PomIhojplDDz20cY5jRG3m3jC+d/Sz9ZJGRNuptBdJn8v9LTNnzmwcZ2kUaAPro/dyTGThv0qpGzhmFD4nyzidha2ijRzDmW+Mz9Vx6nQMxhhj+hp/gIwxxrSCP0DGGGNaoW99QFVV1Vp9tiafOqleSz28FDpc9UzqydSttexMz49o6qG0IUvdULKJ9+oxz9E3o3tuuJafOm+23yjbXxTRbBv6bdhualMpBlXWl2wn6uPqN2EK7ix+YObj4bURzf1GpVTy2u8cA2wLtZljjcf0H2ncr1KacB23pVTyuk+Lqe7pq2Hf6bjguNV9SxHNupfioemYoQ2l9BhZSgmOY723lEpDn0t72e88r/cy3iHHokJ/TLZ3jD4rvg+0Sd933SNUit1Ylzeuq4wxxpi9jD9AxhhjWqFvJbhucOqfLRvkElVmtFQ5IqIpV2RpBXie11La0GlrKXxIlgaC9cmmx6VQ+ypfUJ5jXTmF1zpQjiB6L+UILpfW+mWh9CM621zlFi4DZv1UkqN0kS3/Zt9Q2qPN2nelrLl6nvIKx4zKSRz/WTidiGa/l5Y8Z+2Uhfin/az7Qw891Dim7KOwL9XGrG4RzXHMZdeUXtkWWgfKaqy72sT+YLgvfS6fmYW8imhKfXwOJUZ9H9hOmZxdCu2UZUTVvqJ93fAMyBhjTCv4A2SMMaYV/AEyxhjTCn3rAxoYGKj1eNWQqfOSLCV3KVWzarClsCtqB30D1FFVu6Wuy3Kz0CrU0nmtHjP0COujZXH5J9uYNmv9SuFEMl8ZUX25tEyWWrSWzXahD0X9GSU/jtav1P60OQu7wjGibc5zPFYbeY5tTN+f+kGzfo3I37tsuXrmI4no9PloH9D/SLRs+jLod9BySzawf/S95L18l/Q8faK0SevH9ua1WVnZVg/aWAolpP2cpSYfqyxtRy7jHw+eARljjGkFf4CMMca0gj9AxhhjWqFvfUCTJ0+udf8s3Dl9A3ptyd+S+Tqox/I56kcopYDOwulQL6eNep5pqWljFnqEqE3Uk+nroF8ha2OWpe1GzT5r/8xnFdGpN6u2Tr8OtXa1P0srENHsL5bD+tBGrTv7lamN9VrawHt1HJT8LVn4fMLxpP3DcrmHS30FfFdKKSOyEEysu/Yd259102M+s5Q2XGF/cMyojXz3M/8w7eW9WWqNUroDbadeQm2Vfve4n0rrp+9KlmZd8QzIGGNMK/gDZIwxphX6VoJ79NFH6ym2hszJQoAQTmlLkV11ysvpcTaVLkUK1qWwpeyEmUyVSTxjla2w7lmoDrYbl6hqWVlU54hcKsiWjrJuXA7Kftf6sO+ysEoluSsLe8N2y8KhDA0NNc5R8tH60SY+R9s8k4Yj8ijuhH2pMhXLzcLnsA3Zd5kEVFoyrDItbeC9Wh+2KaO2s431t4HvJO2nDK1ky/zZ3hxfrJ+e5zuaZVIeb2TqiM4x/HxDn5VCadXXjdsyY4wxZi/iD5AxxphW8AfIGGNMK/StD+iAAw6o9Wv1oVB/pQauWii151I2UtVcmVIh03Kpm2bhK0rLZlkf1YlZLvX9bMkz9Wa1mTpvyf+ium9puXe2lDfzS1H7px7O+qn+zHvpv1CbaX+ml7O9+Ry2sYbiZ93Z5loW/WhMHaBZQnktfQPU4vW5pdAp2jasWzYWS76yUhqI7F71/ZXCQGXhjegL47GWXcpcqmOGvqXs96rk72K/69L90rJy/c3kbxffnSxUWMnv3G2LSWmZ+B48AzLGGNMK/gAZY4xpBX+AjDHGtELf+oCeeuqpWltVHbi0R0I12CyMfUQejp7aLctSDZznqK1rqtpsD8RY6PW8llq0arv0YTH0SxY+hHtsSqGGlGwvUpbumselvSPZnptSOBo9z2vZl9pO1NKZ6oA6vN7L+mShbGhD1nccE6UQ/3o9bSrtlVEy3xLrRuiT0/qUQuRoX5Z8lzpOOaYzPw5hXTMfL+veS3qPLI1LRPN3hKm+s/2DHOPZ+0tK+3n0fC+pV+r7x22JMcYYsxfxB8gYY0wr9K0Et99++9Xymk7RGeaD8pBOPUuRszmV1mWcWfgWPpflZNJeaTkxUdknWw7N55ZkELWJkiFt4pRdl4eW7lWJlBIDJR6VQUpT/2wZcymbqsogpb7LlrZzjGTLspmNl/ZrlGEuJ6YMlWUQpQ1ZBstSGB+9NwsHRDuyjJsRnW2u44tjms/RccFrSRa1nbImn6PXs9+zjLXZ+I9oSoEcP3zPKKPrO802zaKGk8yNwXL4HvJ3pVsILC/DNsYY09f4A2SMMaYV/AEyxhjTCn3rAxodHa11WNVjM39ERFNXpVbLpZjUZ3VZLZevUjPWcCjUavlc1VypEWe+JV5PzZ7XZhkTM7285AfhkmetXynbotpRCjevNmblRHS2m2re9EFkKRZKfhy1eXBwsHEuy4xJG3mO/hZtU/ZzZmM21sY6n0F/ntpYav/MN8M25XN0vJXC3mT+Oy6Tz5bQl1KoZKGdslBCWUbdiOYY4vvA8USb1Sb6gLJUM6Xl3r2kk+DvrdY365tueAZkjDGmFfwBMsYY0wr+ABljjGmFvvUBDQwM1Nq3avalfTOquY43LexY13PfBvV/1ULpS8r05VKIiiw9A7Vo6r56zHOZBs79OAzzkYWrYbnUtdVnV/LNKNT+qVtTL8/sZZurJk79m32pUNfOwgFFNPf2sD/oU8z8j5mfLQuJw2t5XNrzkV1LP5VS8plkfjb6eFl3rR/bn8/J/CAslzbp7whtov9O0T4fy6YsJXrp/dbfnFI/6ztAXyvL7eU3k/VRf562S+ZXUjwDMsYY0wr+ABljjGkFf4CMMca0Qt/6gLZv315roqpnZnp+RFN7LKUCJnqeGncWe4k+B/pFVHNlOdSxqbWrPkubeKzPoU3UrbVtGN+Jexfo21BNmddm+jKhX0f9PrSffcdyVafnPpPMxlIaZG2nUizBbF8TfVqZX4115XhSm0t7PIiOA/Z79n4cdNBBjWPWXduU44VjnHXX/uI5vsNZ37EtdEyU0tnTp9JL32X7gLLfINrPvstiMvJ3MIs/mdnLskrpPUqp2XvFMyBjjDGt4A+QMcaYVuhbCW769On11F2nqqUpXxYmhtNjlpWFJc9CnHD6m4Ui4bSbNmQSXWkZs9aXNtF+lYu4DJuSCZeh6jQ8y4Aa0ZS/uEQ1y7zKdiiFp1EbuZSUspouge5lyXYpS26WNZc2ZWMkC6Uf0Wx/Skmsa5aKghld2e9ZubRR3x2OadqQhfEhHJtaFsdlFiqJY2/Lli2pjdl2j2y5N23gONZ2Y71LoXj0XtrLvsxSUWRbQWgD+53omNH6lMZwfc+4rjLGGGP2Mv4AGWOMaYW+k+D2yEi6O1qndpwaZ9NhyhyUW7LVUaVouVkGzmz6WcpYma3qK+0wz+xnuboaijvvsxVBfE6pPnpvSXLIJLhS9susnSgJZSuEKNtquZQueC3Rscq6c+ypzaVI5to/tIF9mb0DWV1L13KM6720gWRZTlluVp/S6q4sk3KpnTIJLovCQfuz35hS1uWsbNqQ1acUiSKztxS5RW3WftxjT+kdmVCVrvgD8+CDD8b8+fPbNsMYY8wLZMOGDXHooYd2Pd93H6DR0dF4+OGHo6qqWLBgQWzYsKHDgWj+j23btsX8+fPdTgXcTuPD7TQ+3E45VVXFk08+GfPmzUsXmfSdBDdx4sQ49NBD69UXM2fOdAePA7fT+HA7jQ+30/hwO3WHKwLHwosQjDHGtII/QMYYY1qhbz9AU6dOjX/6p3/q2GxmmridxofbaXy4ncaH22nv0HeLEIwxxrw86NsZkDHGmH0bf4CMMca0gj9AxhhjWsEfIGOMMa3gD5AxxphW6NsP0OWXXx6HH354TJs2LRYvXhw333xz2ya1xurVq+OEE06IGTNmxJw5c+LMM8+MdevWNa7Zvn17LF++PGbNmhX7779/nHXWWTEyMtKSxf3BJZdcEhMmTIiVK1fW/+Z2+j0PPfRQvPOd74xZs2bF9OnT45hjjolbb721Pl9VVVx44YUxd+7cmD59eixdujTuvffeFi3+w7N79+644IILYuHChTF9+vQ44ogj4pOf/GQjwKbb6QVS9SHXXHNNNWXKlOrf//3fq9/85jfV3//931cHHHBANTIy0rZprXDaaadVV155ZXXnnXdWt99+e/Xnf/7n1YIFC6qnnnqqvub9739/NX/+/GrNmjXVrbfeWp100knVySef3KLV7XLzzTdXhx9+ePW6172uOuecc+p/dztV1WOPPVYddthh1bve9a7qpptuqu67777qhz/8YfXb3/62vuaSSy6pBgcHq+985zvVHXfcUf3lX/5ltXDhwurZZ59t0fI/LBdffHE1a9as6vvf/351//33V9dee221//77V//6r/9aX+N2emH05QfoxBNPrJYvX14f7969u5o3b161evXqFq3qHzZv3lxFRHX99ddXVVVVW7durSZPnlxde+219TX/+7//W0VEdeONN7ZlZms8+eST1ZFHHln96Ec/qt74xjfWHyC30+/52Mc+Vp166qldz4+OjlbDw8PVv/zLv9T/tnXr1mrq1KnVN7/5zT+EiX3B6aefXr3nPe9p/Nvb3va2atmyZVVVuZ32Bn0nwe3cuTPWrl0bS5curf9t4sSJsXTp0rjxxhtbtKx/2JOm+KCDDoqIiLVr18auXbsabbZo0aJYsGDBy7LNli9fHqeffnqjPSLcTnv43ve+F8cff3y8/e1vjzlz5sSxxx4bX/nKV+rz999/f2zatKnRToODg7F48eKXVTudfPLJsWbNmrjnnnsiIuKOO+6IG264Id7ylrdEhNtpb9B30bAfffTR2L17dwwNDTX+fWhoKO6+++6WrOofRkdHY+XKlXHKKafE0UcfHRERmzZtiilTpsQBBxzQuHZoaCg2bdrUgpXtcc0118Rtt90Wt9xyS8c5t9Pvue++++KKK66I8847Lz7+8Y/HLbfcEh/+8IdjypQpcfbZZ9dtMdY7+HJqp/PPPz+2bdsWixYtikmTJsXu3bvj4osvjmXLlkVEuJ32An33ATI5y5cvjzvvvDNuuOGGtk3pOzZs2BDnnHNO/OhHP4pp06a1bU7fMjo6Gscff3x8+tOfjoiIY489Nu6888740pe+FGeffXbL1vUP3/rWt+Ib3/hGXH311XHUUUfF7bffHitXrox58+a5nfYSfSfBzZ49OyZNmtSxMmlkZCSGh4dbsqo/WLFiRXz/+9+Pn/zkJ40sg8PDw7Fz587YunVr4/qXW5utXbs2Nm/eHG94wxtiYGAgBgYG4vrrr4/LLrssBgYGYmhoyO0UEXPnzo3Xvva1jX97zWteE+vXr4+IqNvi5f4OfuQjH4nzzz8/3vGOd8QxxxwTf/u3fxvnnnturF69OiLcTnuDvvsATZkyJY477rhYs2ZN/W+jo6OxZs2aWLJkSYuWtUdVVbFixYr49re/HT/+8Y9j4cKFjfPHHXdcTJ48udFm69ati/Xr17+s2uzNb35z/PrXv47bb7+9/u/444+PZcuW1X+7nSJOOeWUjmX899xzTxx22GEREbFw4cIYHh5utNO2bdvipptuelm10zPPPNORzXPSpEkxOjoaEW6nvULbqyDG4pprrqmmTp1afe1rX6vuuuuu6n3ve191wAEHVJs2bWrbtFb4wAc+UA0ODlY//elPq40bN9b/PfPMM/U173//+6sFCxZUP/7xj6tbb721WrJkSbVkyZIWre4PdBVcVbmdqur3S9QHBgaqiy++uLr33nurb3zjG9V+++1X/cd//Ed9zSWXXFIdcMAB1Xe/+93qV7/6VXXGGWe87JYXn3322dUhhxxSL8P+r//6r2r27NnVRz/60foat9MLoy8/QFVVVV/4wheqBQsWVFOmTKlOPPHE6he/+EXbJrVGRIz535VXXllf8+yzz1Yf/OAHqwMPPLDab7/9qr/6q7+qNm7c2J7RfQI/QG6n3/Pf//3f1dFHH11NnTq1WrRoUfXlL3+5cX50dLS64IILqqGhoWrq1KnVm9/85mrdunUtWdsO27Ztq84555xqwYIF1bRp06pXvvKV1T/+4z9WO3bsqK9xO70wnA/IGGNMK/SdD8gYY8zLA3+AjDHGtII/QMYYY1rBHyBjjDGt4A+QMcaYVvAHyBhjTCv4A2SMMaYV/AEyxhjTCv4AGWOMaQV/gIwxxrSCP0DGGGNa4f8Br8Lb+fsTeI8AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(mei.shape)\n",
    "print(torch.max(torch.abs(mei)))\n",
    "plt.imshow(mei[0,0], cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c75ff7e7f87ecd0d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-07T16:05:23.204491Z",
     "start_time": "2024-03-07T16:05:23.130293Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'evaluation')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(\n",
    "    tracker_log[\"mei.objectives.EvaluationObjective\"][\"times\"],\n",
    "    tracker_log[\"mei.objectives.EvaluationObjective\"][\"values\"],\n",
    ")\n",
    "plt.gca().set_xlabel(\"# iteration\")\n",
    "plt.gca().set_ylabel(\"evaluation\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35bfb1984459a87",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### Constraints \n",
    "So far, we have not introduced contraints into MEI optimization. \n",
    "This results in implausible MEIs with high pixel values and high contrast and a forever increasing activation. We need to constrain the overall norm of the MEI and also clip the range of pixel values to the range of pixel values which were in the training data. \n",
    "The norm is then established empirically such, that ideally, value clipping is not necessary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b5f6177b20a27a2f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-07T16:05:42.671870Z",
     "start_time": "2024-03-07T16:05:42.665066Z"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "p = 2\n",
    "norm_value = 10\n",
    "max_pixel_value = 2.2606\n",
    "min_pixel_value = -1.75047\n",
    "\n",
    "method_config['postprocessing'] = dict(path=\"mei.postprocessing.PNormConstraintAndClip\", \n",
    "                                       kwargs=dict(p=p, norm_value=norm_value, max_pixel_value=max_pixel_value, min_pixel_value=min_pixel_value))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "672fc86fa133b0bd",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-07T16:06:13.763653Z",
     "start_time": "2024-03-07T16:05:43.252737Z"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "mei, final_evaluation, tracker_log = gradient_ascent(model=single_cell_model, config=method_config, seed=seed, shape=input_shape)   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "28778da38e6c03ad",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-07T16:06:13.847668Z",
     "start_time": "2024-03-07T16:06:13.763989Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 1, 100, 100])\n",
      "tensor(2.0273)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(mei.shape)\n",
    "max_value = torch.max(torch.abs(mei))\n",
    "print(max_value)\n",
    "plt.imshow(mei[0,0], cmap='gray', vmin=min_pixel_value, vmax=max_pixel_value)\n",
    "plt.colorbar()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "52008e68bb87ea9c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-07T16:06:13.918082Z",
     "start_time": "2024-03-07T16:06:13.852678Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'evaluation')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(\n",
    "    tracker_log[\"mei.objectives.EvaluationObjective\"][\"times\"],\n",
    "    tracker_log[\"mei.objectives.EvaluationObjective\"][\"values\"],\n",
    ")\n",
    "plt.gca().set_xlabel(\"# iteration\")\n",
    "plt.gca().set_ylabel(\"evaluation\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.18"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}